Upload folder using huggingface_hub

.claude/settings.local.json

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+{
+  "permissions": {
+    "allow": [
+      "Bash(pip list *)",
+      "Bash(pip install *)",
+      "Bash(python chatbot/pipeline/run_pipeline.py)",
+      "Bash(python3 chatbot/pipeline/run_pipeline.py)",
+      "Bash(python3 -c \"import anthropic; print\\(anthropic.__version__\\)\")",
+      "Bash(python3 chatbot/test_retrieval.py)",
+      "Read(//home/jawoo/.config/**)",
+      "Read(//home/jawoo/.claude/**)",
+      "Bash(python3 -c ' *)",
+      "Bash(grep -v \"^Loading weights\\\\|UserWarning\\\\|return torch\\\\|onnx\\\\|^$\")",
+      "Bash(python3 *)",
+      "Bash(streamlit run *)",
+      "Bash(curl -s http://localhost:8501)",
+      "Bash(pkill -f \"streamlit run\")"
+    ]
+  }
+}

.streamlit/config.toml

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+[browser]
+gatherUsageStats = false
+
+[theme]
+primaryColor = "#2e7d32"
+backgroundColor = "#ffffff"
+secondaryBackgroundColor = "#f5f5f5"
+textColor = "#1a1a1a"

Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Install system deps for chromadb / sentence-transformers
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "chatbot/app.py", \
+     "--server.port=7860", \
+     "--server.address=0.0.0.0", \
+     "--server.headless=true"]

README.md

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 ---
-title: Maize Research Assistant
-emoji: 👁
-colorFrom: blue
-colorTo: indigo
+title: KALRO Maize Research Assistant
+emoji: 🌽
+colorFrom: green
+colorTo: green
 sdk: docker
 pinned: false
+license: cc-by-4.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# KALRO Maize Research Assistant
+
+A research chatbot for Kenya Agricultural and Livestock Research Organization (KALRO), grounded in 16 research papers and training manuals on Kenya maize production.
+
+## Knowledge Base
+
+- Hybrid breeding and combining ability (KALRO Muguga inbred lines)
+- Genotype × environment interaction and stability analysis
+- Water harvesting technologies for ASAL (zai pits, ngolo pits, tied ridges)
+- Maize Lethal Necrosis (MLN): epidemiology, resistance screening, seed transmission
+- Genetic gains in the Kenya Hybrid Maize Program (1999–2020)
+- Farmer preferences for stress-tolerant variety traits
+- Agronomic factors and the smallholder yield gap
+- KCEP-CRAL and KCSAP extension programmes
+
+## How It Works
+
+Questions are answered using a two-layer retrieval system:
+
+1. **Wiki layer** — structured summaries of each paper (primary source)
+2. **PDF layer** — raw extracted text from all 16 documents (fallback for detail)
+
+Retrieval uses `all-MiniLM-L6-v2` embeddings stored in a local Chroma vector database. Generation uses Claude Sonnet via the Anthropic API.
+
+## Setup (self-hosting)
+
+Set `ANTHROPIC_API_KEY` as a Space secret (Settings → Variables and secrets).
+
+To rebuild the knowledge base from new documents:
+
+```bash
+python3 chatbot/pipeline/run_pipeline.py
+```

chatbot/app.py

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+"""
+KALRO Maize Research Chatbot — Streamlit UI
+Run: streamlit run chatbot/app.py
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from dotenv import load_dotenv
+load_dotenv(Path(__file__).parent.parent / ".env")  # no-op on HF Spaces (secrets via env vars)
+
+import streamlit as st
+from chatbot.chat import Chatbot
+from chatbot.retriever import Retriever
+
+# ── page config ────────────────────────────────────────────────────────────────
+
+st.set_page_config(
+    page_title="KALRO Maize Research Assistant",
+    page_icon="🌽",
+    layout="wide",
+)
+
+# ── shared retriever (loaded once per server, shared across all user sessions) ─
+
+@st.cache_resource(show_spinner="Building knowledge base — first run only, ~30s...")
+def _load_retriever() -> Retriever:
+    db_path = Path(__file__).parent / "db" / "chroma.sqlite3"
+    if not db_path.exists():
+        embed_script = Path(__file__).parent / "pipeline" / "embed.py"
+        subprocess.run([sys.executable, str(embed_script)], check=True)
+    return Retriever()
+
+# ── session state ──────────────────────────────────────────────────────────────
+
+if "chatbot" not in st.session_state:
+    st.session_state.chatbot = Chatbot(retriever=_load_retriever())
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []     # list of {role, content, meta}
+
+
+# ── sidebar ────────────────────────────────────────────────────────────────────
+
+with st.sidebar:
+    st.markdown("## 🌽 KALRO Maize Research Assistant")
+    st.markdown(
+        "Ask questions about maize production, varieties, diseases, "
+        "soil & water management, and extension in Kenya."
+    )
+    st.divider()
+
+    if st.button("New conversation", use_container_width=True):
+        st.session_state.chatbot.reset()
+        st.session_state.messages = []
+        st.rerun()
+
+    st.divider()
+    st.markdown("**Knowledge base**")
+    st.markdown("- 16 research papers & manuals\n- Wiki: 163 structured chunks\n- PDFs: 1,013 raw text chunks")
+    st.markdown("**Model:** Claude Sonnet 4.6")
+    st.markdown("**Embedding:** all-MiniLM-L6-v2")
+
+    # Show sources for the last answer
+    if st.session_state.messages:
+        last = st.session_state.messages[-1]
+        if last["role"] == "assistant" and last.get("meta"):
+            meta = last["meta"]
+            st.divider()
+            st.markdown("**Sources used (last answer)**")
+            pdf_flag = meta.get("pdf_fallback_used", False)
+            if pdf_flag:
+                st.warning("PDF fallback triggered — answer draws from raw document text")
+            for chunk in meta.get("chunks_used", []):
+                tag = "📄 Wiki" if chunk.layer == "wiki" else "📑 PDF"
+                dist = f"{chunk.distance:.2f}"
+                st.markdown(f"{tag} `{dist}` {chunk.citation()}")
+
+
+# ── main chat area ─────────────────────────────────────────────────────────────
+
+st.markdown("## 🌽 KALRO Maize Research Assistant")
+st.caption("Answers grounded in KALRO research papers, field trials, and training manuals.")
+
+# Render conversation history
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+        if msg["role"] == "assistant" and msg.get("meta"):
+            meta = msg["meta"]
+            with st.expander("Sources", expanded=False):
+                for chunk in meta.get("chunks_used", []):
+                    tag = "📄 Wiki" if chunk.layer == "wiki" else "📑 PDF"
+                    st.markdown(f"- {tag} **{chunk.citation()}** (dist: {chunk.distance:.2f})")
+
+# Chat input
+if prompt := st.chat_input("Ask about Kenya maize research..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        with st.spinner("Searching knowledge base..."):
+            response = st.session_state.chatbot.ask(prompt)
+
+        st.markdown(response.answer)
+
+        with st.expander("Sources", expanded=False):
+            if response.pdf_fallback_used:
+                st.warning("PDF fallback triggered — wiki confidence was low; answer also draws from raw document text.")
+            for chunk in response.chunks_used:
+                tag = "📄 Wiki" if chunk.layer == "wiki" else "📑 PDF"
+                st.markdown(f"- {tag} **{chunk.citation()}** (dist: {chunk.distance:.2f})")
+
+    st.session_state.messages.append({
+        "role": "assistant",
+        "content": response.answer,
+        "meta": {
+            "pdf_fallback_used": response.pdf_fallback_used,
+            "chunks_used": response.chunks_used,
+        },
+    })

chatbot/chat.py

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+"""
+KALRO Maize Research Chatbot — Claude API + tiered retrieval.
+
+Usage:
+    from chatbot.chat import Chatbot
+    bot = Chatbot()
+    response = bot.ask("What water harvesting technologies work best in semi-arid Kenya?")
+    print(response.answer)
+    print(response.citations)
+"""
+
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import anthropic
+from dotenv import load_dotenv
+
+load_dotenv(Path(__file__).parent.parent / ".env")
+
+from chatbot.retriever import Retriever, Chunk
+
+MODEL = "claude-sonnet-4-6"
+MAX_CONTEXT_CHUNKS = 8          # max chunks to include in the prompt
+MAX_HISTORY_TURNS = 6           # conversation turns to keep in memory
+
+SYSTEM_PROMPT = """You are a research assistant for KALRO (Kenya Agricultural and Livestock Research Organization), \
+specialising in maize production in Kenya. You answer questions based on a curated knowledge base of research papers, \
+field trials, and training manuals.
+
+## How to use the provided context
+
+You will receive context chunks labelled [WIKI] or [PDF].
+- [WIKI] chunks come from structured, reviewed wiki summaries — treat these as your primary source.
+- [PDF] chunks come directly from raw research documents — use them for detail or verbatim data \
+  not captured in the wiki.
+
+## Answering rules
+
+1. Base your answer on the provided context. Do not invent facts or statistics.
+2. At the end of your answer, list the sources you used under a "**Sources**" heading, \
+   using the citation labels provided with each chunk. One bullet per source.
+3. If the context is insufficient to answer fully, say so clearly and state what is and is not covered.
+4. Keep answers concise and practical — the audience is agricultural researchers and extension officers.
+5. Use plain English. Spell out abbreviations on first use.
+"""
+
+
+@dataclass
+class ChatResponse:
+    answer: str
+    citations: list[str]
+    pdf_fallback_used: bool
+    chunks_used: list[Chunk] = field(default_factory=list)
+
+
+def _build_context_block(chunks: list[Chunk]) -> str:
+    parts = []
+    for i, chunk in enumerate(chunks, 1):
+        layer_tag = "[WIKI]" if chunk.layer == "wiki" else "[PDF]"
+        citation = chunk.citation()
+        parts.append(f"{layer_tag} [{i}] {citation}\n{chunk.text}")
+    return "\n\n---\n\n".join(parts)
+
+
+class Chatbot:
+    def __init__(self, retriever: Retriever | None = None):
+        self._retriever = retriever or Retriever()
+        self._client = anthropic.Anthropic()   # reads ANTHROPIC_API_KEY from env
+        self._history: list[dict] = []
+
+    def ask(self, question: str) -> ChatResponse:
+        all_chunks, pdf_fallback = self._retriever.retrieve(question)
+        top_chunks = all_chunks[:MAX_CONTEXT_CHUNKS]
+
+        context_block = _build_context_block(top_chunks)
+        user_message = f"""## Context\n\n{context_block}\n\n---\n\n## Question\n\n{question}"""
+
+        messages = self._history[-MAX_HISTORY_TURNS * 2:] + [
+            {"role": "user", "content": user_message}
+        ]
+
+        response = self._client.messages.create(
+            model=MODEL,
+            max_tokens=1024,
+            system=SYSTEM_PROMPT,
+            messages=messages,
+        )
+
+        answer = response.content[0].text
+
+        self._history.append({"role": "user", "content": user_message})
+        self._history.append({"role": "assistant", "content": answer})
+
+        citations = [c.citation() for c in top_chunks]
+
+        return ChatResponse(
+            answer=answer,
+            citations=citations,
+            pdf_fallback_used=pdf_fallback,
+            chunks_used=top_chunks,
+        )
+
+    def reset(self):
+        self._history.clear()

chatbot/extracted/5918316f944c1ded652dc9bd8559f216401b.json

@@ -0,0 +1,67 @@
+{
+  "filename": "5918316f944c1ded652dc9bd8559f216401b.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/5918316f944c1ded652dc9bd8559f216401b.pdf",
+  "page_count": 15,
+  "pages": [
+    {
+      "page": 1,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n1 \n \n \n \n \n \n \nAn Evaluation of Stakeholder Involvement in the Development of Communication \nPlans Used in the Diffusion of Improved Maize Varieties among Farmers in the Semi-\nArid Lower Eastern Kenya \n \n \nEmily Keles Muli, Prof. Maurice Sakwa, PhD and Dr.  Masaya H. Chakava, PhD \n \n \n \n \n \n"
+    },
+    {
+      "page": 2,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n62 \n \nAn Evaluation of Stakeholder Involvement in \nthe Development of Communication Plans Used \nin the Diffusion of Improved Maize Varieties \namong Farmers in the Semi-Arid Lower \nEastern Kenya \n1*Emily Keles Muli \nPhD Student, Mass Communication \nJomo Kenyatta University of Agriculture and \nTechnology \n2Prof. Maurice Sakwa, PhD \nJomo Kenyatta University of Agriculture and \nTechnology \n3Dr.  Masaya H. Chakava, PhD \nJomo Kenyatta University of Agriculture and \nTechnology \n \n \nArticle History \nReceived 13th August 2024 \nReceived in Revised Form 17th September 2024 \nAccepted 25th October 2024 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nAbstract \nPurpose: While it is acknowledged that increased \nadoption of agricultural innovations is today hinged \non increased and proper application of participatory \ncommunication \napproaches, \nthe \nadoption \nof \nimproved maize varieties in the semi-arid Lower \nEastern Kenya has remained low at less than 30 \npercent despite the application of the approach. \nProper application of the approach requires adequate \ninvolvement of stakeholders in communication \nactivities that include the assessment of the \ncommunication needs and the formulation of the \ncommunication strategies at all stages of the \ndevelopment and diffusion of an innovation. The \npurpose of this study was therefore, to assess the \nlevel of stakeholder involvement in the development \nof communication plans used in the diffusion of \nimproved maize varieties among farmers in the \nsemi- arid region of Lower Eastern Kenya \nMethodology: Qualitative data was collected from \nfarmers, \nagricultural \nscientists, \nagricultural \nextension officers and documents. It was analysed \nusing a thematic analysis method in accordance with \napriori themes and sub-themes developed by the \nresearcher from the literature, principles of \nparticipatory communication approaches and the \nconcerns of the research questions.  \nFindings: The study revealed a critical gap in \nstakeholder involvement during the development of \nthe communication plans which could hinder the \nformulation of an effective communication strategy. \nUnique Contribution to Theory, Practice and \nPolicy: In order to improve the adoption rates of \nimproved maize varieties in the semi-arid Lower \nEastern Kenya, stakeholders should adequately be \ninvolved in the development of communication \nplans to enable them to uncover their information \ndeficits and identify their existing communication \nnetworks necessary for sustained adoption of the \nmaize seeds. This involvement facilitates free and \nopen dialogue which leads to conscientization in \nwhich individuals and communities develop a \ncritical understanding of their social reality through \nreflection and action; ownership where participants \naccept the initiative and become active participants \nand; praxis (practice) in which the imitative is fully \naccepted and used.     \nKeywords: Participatory Communication, \nStakeholder Involvement, Development, \nCommunication Plans, Improved Maize Varieties \n©2024 by the Authors. This Article is an open access \narticle distributed under the terms and conditions of the \nCreative Commons Attribution (CC BY) license \n(http://creativecommons.org/licenses/by/4.0\n"
+    },
+    {
+      "page": 3,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n63 \n \nINTRODUCTION \nMany agricultural innovations that could benefit farmers fail to achieve sustainable adoption \nrates due to inadequate involvement of stakeholders in developing communication plans used \nin the diffusion of the innovations. Engaging all the stakeholders during this crucial activity in \nthe development and diffusion of an innovation would help in identifying the community’s \nspecific information needs and leverage existing, relevant communication networks within the \ncommunity, thereby enhancing the diffusion of these innovations. Food and Agriculture \nOrganization (FAO) emphasises that achieving sustainable adoption of innovations is today \nhinged more on the people involved in their use than in the innovations themselves (Rizzo, et \nal. 2024). This shift in perspective underscores the pivotal role of participatory communication \napproach which is rooted in dialogue fostering equitable sharing and exchange of information, \nknowledge, experiences, and perceptions among stakeholders. The aim of participatory \ncommunication approach is to make communication and dialogue an integral part of the \ndevelopment process unlike in earlier linear models in which communication was seen as a \ntool to persuade people to adopt new practices (Tufte and Mefalopulos, 2009). The emphasis \nin the linear models was the transfer of information on innovations from the source to the \nreceiver (Servaes, 2021). The role of the receiver for whom the innovations were developed \nwas totally disregarded in the communication process, continually resulting in failure of \ninnovations that were otherwise sound and beneficial (Lundy, 2005). \nParticipatory communication approach holds that true development is from within and \ntherefore advocates for the involvement of all the stakeholders in the search for solutions \nthrough dialogic interaction throughout a project life cycle. Participatory communication in \nagricultural research allows people who are traditionally the researched to have a voice by \nletting them play a key role in making decisions on communication goals during the research \nactivities. Key to this process, according to Freire (1972), is free and open dialogue which leads \nto conscientization in which individuals and communities develop a critical understanding of \ntheir social reality through reflection and action; ownership where participants accept the \ninitiative and become active participants and; praxis (practice) in which the imitative is fully \naccepted and used.                  \nAccording to Tufte and Mefalopulos (2009), stakeholders often have very different visions and \ndefinitions of participation in development. Therefore, it is important for development \npractitioners to be clear on their conceptual approach to participation. According to Balit, \n(2007) the 9th UN Roundtable in Rome, (2004), on “Communication and Sustainable \nDevelopment adopted the vision of participatory communication that emphasizes that \ncommunication for development must be about people and the processes needed to facilitate \ntheir sharing of knowledge and perceptions in order to effect positive developmental change \nand should be based on dialogue, following the two-way, horizontal model and not the \ntraditional one-way, vertical model of sender message-channel-receiver. Communication for \ndevelopment must also give voice to those most affected by the development issue(s) at stake, \nallowing them to participate directly in defining and implementing solutions and identifying \ndevelopment directions. It recognizes that reality is largely socially constructed implying that \nthere can be different realities or different perceptions for the same situation according to \nspecific groups’ perceptions and needs. Thus, the role of development and communication is \nnot to “impose” the correct reality, but rather to foster dialogue, to facilitate mutual \nunderstanding among different perspectives. It is also contextual and uses a number of tools, \ntechniques and media, to facilitate mutual understanding, define and bridge differences of \n"
+    },
+    {
+      "page": 4,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n64 \n \nperceptions. These tools and techniques are most effective when used at the beginning of \ndevelopment initiatives.  \nInternational confirmation of the participatory communication approach was given at the 2004 \nplenary session of the 9th UN Roundtable on Communication for Sustainable Development in \nRome requiring communication to be at the centre of development initiatives (The \nCommunication Initiative, 2007). Thus, there was a shift in the trend of the communication \napproach to development initiatives at the global level, from a one-way to a two-way \nparticipatory approach. The international shift in the approach to development communication \nalso influenced the approach to the communication of agricultural innovations in Kenya where \nthe main challenge was the adoption rates of innovations (Mbithi, 1972; De Groote, 2005; Bett, \net al2017). The National Agricultural Research System Policy, (NARSP) acknowledged the \nshift to participatory methods which employed a bottom–up strategy, relating to stakeholders \nin simple and effective terms, and communicating and working in partnership (NARSP, 2012). \nIn line with this policy, the Kenya Agricultural and Livestock Research Organization, KALRO, \nemployed the participatory approach to raise the adoption rates of its agricultural innovations \nincluding improved maize varieties among farmers in the semi-arid Lower Eastern Kenya, a \nregion characterised by erratic rain patterns.  According to Oakdel (2023) improved maize \nvarieties for the semi-arid regions are known for their high yield, early maturity, and resistance \nto pests and diseases. Adoption rates for the improved maize varieties have however, remained \nlow at below 30 per cent despite the application of participatory communication and its \nacknowledged technical excellence (Mbithi, 1972; De Groote, 2005; Bett, et al, 2017). Yet \nexamples of positive results have been reported wherever participatory communication has \nbeen applied (Kadiyala et al, 2021; Miraftab 2004). According to Van de Fliert (2010), where \nparticipatory communication approach is applied, low adoption rates of innovations can only \nbe attributed to inappropriate use of the approach. The purpose of this study was therefore, to \nassess the level of stakeholder involvement in the development of communication plans used \nin the diffusion of improved maize varieties among farmers in the semi- arid region of Lower \nEastern Kenya.   \nTheoretical Review \nThis study is premised on Paulo Freire’s Theory of dialogical action (1974) which is seen by \nmany researchers and practitioners as the foundation of participatory communication \napproaches to diffusion and adoption of innovations (Mefalopulos, 2008, Cornish and Dunn, \n2009). According to Tufte and Mefalopulos (2009), participatory communication strategy \noffers a specific perspective on how to articulate social processes, decision-making processes, \nand any change processes. Proponents of the participatory communication approach use the \nconcepts of the dialogical action theory as a communicative tool of involvement of all \nstakeholders in the diffusion of innovations. \nFreire’s Theory of Dialogical Action \nFreire’s (1968) Theory of the Dialogical Action is essential for understanding the critical role \nof giving the voice to those who have traditionally been excluded from contexts of dialogue \nand participation (del Mar Ramis, 2018). According to Freire (1970) dialogical \ncommunication, can be considered as a tool to develop an individual’s capacity for reflection \nabout their own living conditions in terms of which they willingly and actively participate and \nbecome enthusiastic supporters of the change process. \n"
+    },
+    {
+      "page": 5,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n65 \n \nIn participatory communication approach, the theory of dialogical action is useful in bringing \nabout the involvement of local participants or beneficiaries, while also creating opportunities \nfor them to shape their own processes through critical reflection. It allows the sharing of \ninformation, perceptions and opinions among the various stakeholders. According to Tufte and \nMefalopulos (2009) participatory communication is not just the exchange of information and \nexperiences: it is also the exploration and generation of new knowledge aimed at addressing \nsituations that need to be improved (Tufte and Mefalopulos, 2009).   \nFollowing Freire’s (1970) concepts, diffusion of agricultural innovations is seen as involving \na process of information sharing and dialogue between innovators and farmers for whom the \ninnovations are developed. Through dialogue, and the reflection that it entails, farmers increase \nthe scope of their perception, becoming aware of situations and conditions in their lives of \nwhich they were previously not aware. \nThe key concepts of Freire’s theory of dialogic action are: \nDialogue: Free and open dialogue is the main concept of participatory communication.  \nDialogue allows the sharing of information, perceptions and opinions among the various \nstakeholders, thereby facilitates their empowerment. It is not just the exchange of information \nand experiences: it is also the exploration and generation of new knowledge aimed at \naddressing situations that need to be improved.  \nConscientization: Another key concept in Freire’s approach is conscientization, ways in which \nindividuals and communities develop a critical understanding of their social reality through \nreflection and action. This involves examining and acting on the root causes of oppression as \nexperienced in the here and now. Conscientization should be learned through teaching based \non dialogue and communication; a dialogue that should be between participants engaged in \ncritical thinking. The process of developing a critical awareness of one’s social reality through \nreflection and action is fundamental because it is the process of changing the reality.   \nOwnership: Freire noted that, without dialogue, people accept content in a passive way and \nthey rarely reflect on them as validity of the knowledge. Dialogue enables partners in an \ninitiative to become deliberate, goal-seeking participants and therefore owners of an initiative.  \nPraxis: Finally, there is praxis which is the act of engaging, applying and exercising, the new \nideas. It is the culmination of dialogue, conscientization and ownership of an initiative by a \ncommunity. \nMETHODOLOGY \nExplanatory research design was used for this study. The target population for the study were \nthe farmers who belong to farmer groups in the maize growing zones in the Kenya Cereal \nEnhancement Programme - Climate Resilient Agriculture Livelihoods (KCEP-CRAL) \nprogramme in Lower Eastern Kenya - Machakos, Makueni and Kitui counties and the \nagricultural extension officers in those counties. Purposive sampling was applied. Farmer focus \ngroup discussion (FGD) participants were drawn from the individual members of the farmer \ngroups. In total there were 12 FGDs. Extension officers totaling eleven in the selected areas \nwere also purposively selected and interviewed. Farmer FGD participants and scientists and \nAEO interviewees totaled 125. Table 1 below shows total number of farmers FGD participants \nand AEOs interviewees and their areas covered. The study employed qualitative data collection \nmethods. Data was collected using FGDs, in-depth interviews and documents analysis. Data \n"
+    },
+    {
+      "page": 6,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n66 \n \nwas analyzed thematically. Three themes were developed to study stakeholder involvement in \nthe development of communication plans. The themes are:  \ni) Communication needs identification, \nii) Communication plans formulation, and \niii) Extent of dialogue. \nParticipant Coding \nParticipants were sequentially assigned numbers with abbreviations depending on their \noccupations for professionals, thus. AS01-03 for Agricultural scientists and AEO 01 – 11 for \nAgricultural Extension Officers. Farmer participants were identified with letters FGD (for \nfocus group discussion participants) followed by numbering denoting the specific focus group. \nSequel numbering followed the gender identification (M for men or W for women), for instance \nFGD 6, M01. \nTable 1: AEOs and Agricultural Scientists Interviewees and Farmer Focus Group \nDiscussion Participants by Location and Gender \nSub county \nNo of FCDs \nNo. of participants \nTotal no. of participants \n \n \nMen \nWomen \n \nMakueni \n6 \n15 \n40 \n55 \nYatta \n4 \n13 \n24 \n37 \nMbooni \n2 \n8 \n11 \n19 \nSubtotal \n12 \n36 \n75 \n111 \nAEOs \n-- \n-- \n-- \n11 \nAS \n-- \n-- \n-- \n3 \nTotal \n12 \n36 \n75 \n125 \nRESULTS \nThe following are findings of the analysis of the qualitative data on level of stakeholder \ninvolvement in the development of communication plans used in the diffusion of improved \nmaize varieties among farmers in the semi- arid region of Lower Eastern Kenya. \nCommunication Needs Identification \nThe study sought to understand the extent to which stakeholders were involved in the \nidentification of the communication needs in relation to maize farming in the semi-arid Lower \nEastern Kenya during the introduction of improved maize varieties. In participatory \ncommunication model, there are no senders and receivers of information; instead, the \ncommunication process is transactional and the players are all regarded as communicators.  In \nthe approach, identification of information needs - ideally through participatory rural \ncommunication appraisal (PRCA) - is the first step in the formulation of a communication \nstrategy. PRCA is a communication methodology that utilizes visualization techniques and \nparticipatory exercises to open dialogue and generate information for the design of effective \ncommunication programs for development purposes. It involves stakeholders in joint \ninvestigations not only of the Problems and the Needs, but also of the Opportunities and \npossible Solutions (NOPS), facilitating the required common understanding needed to address \nboth structural as well as communication issues. Identification of needs establishes what the \ncommunity already knows, what the information deficits are as well as existing communication \nmethods that could be utilized for effective communication. According to Hawkins and Van \n"
+    },
+    {
+      "page": 7,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n67 \n \nden Ban (1999) ownership of a project is greatly influenced by the stakeholders’ involvement \nin the identification of communication needs. Hawkins and Van den Ban (1999) state that by \nactively participating in the communication needs identification process, farmers are able to \nconvey their objectives.  \nFindings from this study indicate that although a majority of the farmer participants in focus \ngroup discussions (FGD) were given information about improved maize seed varieties, the \nagricultural scientists did not undertake any communication activities to uncover the existing \ncommunication networks in the community and to understand farmer information needs in \nrelation to maize seeds. The methods through which the farmers came to learn about the maize \nseeds confirm this as illustrated by some of the typical answers from farmers throughout the \nresearch area. \n          “My son came from school with a message that there was a planned chief’s baraza. It \nwas at the baraza that I learned of the introduction of the new maize variety and where \nthe demonstration of the new seeds would be held” – (FGD 01, W02)  \n“We were invited to demos at the station where we learnt about the seeds. And later, \nduring field days we were explained about the advantages of the new seed varieties and \ngiven samples to plant” – (FGD 06, W 05) \nThis lack of involvement extended even to the agricultural extension officers who are a key \nlink between the farmers and the researchers. An agricultural extension officer stated: \n             “I come into contact with agricultural scientists only when they have something they \nwant to introduce to farmers. They will tell me: We have these seeds which we want \nyou to distribute to farmers. They will send the seeds I distribute. Other times they will \nask me if I have a farmer’s group which they can use to try a new seed. Sometimes \nthey will call me when there is a planned demo to organise farmers to attend. Most \ntimes they will go direct to farmers if they have worked with them before”– (AEO, O1 \nInterviews) \nAnother agricultural extension officer described how the communication process has changed \nwith the dawn of devolved system of government in Kenya in 2013. The participant described \nhow regular meetings used to be held involving farmer representatives, ministry of agriculture \nofficials and researchers during which stakeholders would express their needs and share \nknowledge.  However, according to the participant, the outcomes of these meetings were not \nshared.  \n            “I remember we used to hold monthly meetings .... I remember every meeting was \nattended by at least three farmer representatives, researchers, extension workers and \nrepresentative from the ministry of agriculture. Discussions were very free. And the \nresearchers took notes. What I can’t tell you is what the researchers did with the notes \nthey took.” – (AEO, O5 Interviews) \nSimilar views were expressed by yet another agricultural extension officer who described a \nscenario when before devolution, they used to hold monthly meetings with farmers, researchers \nand extension workers in which they shared information.  \n            “There was even a research extension liaison officer at Kalro-Katumani.... Every \nseason, breeders invited extension workers and farmers so that everyone was kept \ninformed. For example, before they introduction of QPM (quality Protein Maize) in \nMakueni in 2013, the extension workers and farmers met with the breeders and had \n"
+    },
+    {
+      "page": 8,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n68 \n \ndiscussions. In my view, the maize variety they introduced was liked by farmers though \nit later went out of the market – (AEO, 07 Interviews) \nThese findings show that farmers and extension officers, key stakeholders in agriculture, were \nnot involved at this crucial stage of communication needs assessment as required in \nparticipatory communication. Using Pretty (2006), the degree of involvement of stakeholders \nin communication needs assessment could best be described as passive participation where the \napplication of participatory communication is least applied and in which primary stakeholders \nof a project participate by being informed about what is going to happen or has already \nhappened. The failure to involve key stakeholders in agricultural sector is an indication that \nthere were no prior efforts to establish farmer communication needs and existing \ncommunication channels before undertaking the improved maize diffusion process. The \nfindings are similar to those obtained by other researchers such as Kaliba, Verkuiji and \nMwangi, (2009); Musembi, (1998) who also found that farmers were not involved in the \nidentification of their communication needs. In a similar research Kamau, (2007) looked at \npractices of agricultural scientists while carrying out research and found that the research cycle \nis usually dictated by production factors and available funds. Kamau (2007) concluded that \nparticipation in research often thinly disguises the deeply institutionalized value system \ninherent in the research process for quantitative results. This exclusion of stakeholders in \ncommunication activities would seem to defeat the purpose of participatory communication \nwhich was introduced to facilitate the inclusion of farmers’ voice in the research process, and \nhence improve on the appropriateness of the farming innovations. According to FAO and GTZ \n(2008), achieving sustainable agricultural development is less based on material inputs such as \nseeds and fertilizer than on the people involved in their use. This focus on human resources \ncalls for increased knowledge and information sharing about agricultural production, as well \nas on appropriate communication methodologies, channels and tools. \nOn paper however, National Agricultural Research System Policy 2012 states that the current \nprocedure for all research in Kenya starts with a participatory diagnosis phase, followed by \nparticipatory prioritization, planning, implementation, evaluation and scaling-out. All these \nphases require participation of all stakeholders from the beginning to the end. Similarly, \nKALRO in its 2017 – 2021 Strategic Plan aims for continous engagement in technology \ndissemination activities and to develop and promote use of participatory research methods in \nthe organization.  \nDesigning Communication Strategy \nCommunication for farming innovations is based on the premise that successful farming \npractices calls for the conscious and active participation of all the stakeholders at every stage \nof the development process. Communication for agricultural development can thus be \ndescribed as the planned and systematic use of communication, through defined \ncommunication channels, to collect and exchange information among all those concerned to \nformulate a communication plan (Bessette, 2006). This study therefore, also sought to know \nwhether all the key stakeholders were involved in designing the communication strategy used \nin the diffusion of improved maize seed varieties. Discussions with farmers and extension \nworkers in the semi-arid Lower Eastern Kenya however, showed that neither the farmers nor \nthe agricultural extension officers were involved in the communication strategy formulation. \nParticipants in a focus group discussion explained to the point at which they got involved in \none project: \n"
+    },
+    {
+      "page": 9,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n69 \n \n“The first time we met the researcher was when she came to request us to give her \nplots for planting her seeds for demonstration. Our role was to do mother and baby \ndemos and then we evaluate. We have been attending demos at the Kambi Mawe \n(research station). I believe that’s how the breeder got our contacts.”  – (FGD 04, \nM03) \nThe same pattern of operation was described by members of other focus groups discussions \n(FGDs). According to one of the participants: \n“The researcher informed us that he needed to demonstrate a seed variety. He asked \nus to identify a farm from among our members where he could do the demo and we \ndid.” – (FGD 07, M02) \nAn agricultural extension officer confirmed the process of involvement thus:  \n              “I don’t know whether the scientists have a communication strategy in place. Many \ntimes, I see the promoters of a project come to our offices to get contacts of farmer \ngroups who can attend their events.” – (AEO 2 Interviews) \nLack of involvement of farmers in the planning of communication activities leads to low \nadoption of agricultural innovations. FAO and GTZ (2008) state that in many countries low \nagricultural production has been attributed, among other factors, to poor linkages between \nResearch-Advisory Service-Farmers and to ineffective technology delivery systems, including \npoor information packaging, inadequate communication systems and poor methodologies. It is \nnotable that KALRO policy does not emphasise the need for proper formulation of \ncommunication plans to be used in diffusion of innovations and additionally, there are no \ndepartments for communication at the research institute level. Additionally, the lack of \ninvolvement of agricultural extension officers who are key stakeholders in agricultural \ndevelopment and diffusion processes leads to distrust and antagonism between the agricultural \nextension officers and the researchers which further affects perceptions and adoption of \ninnovations. Asked about what their role in designing the communication strategy was, an \nagricultural extension officer said: \n“Once scientists have worked with a farmer group, they go straight to them when they \nhave an innovation to introduce or test with farmers. Since they have their contacts, \nthey apparently don’t see the need to involve government officers in planning their \nactivities.” - (AEO 6 Interviews) \nThe attitude of agricultural scientists on the need to involve the other stakeholders in \ncommunication strategy formulation was expressed by one scientist who told this researcher: \nResearch focuses on solving a certain known or expected problem which a farmer may \noverlook. What research is doing is to bring solutions based on data accumulation and \nanalysis. Discussing issues with farmers at the initial stages would therefore, be of no \nbenefit – (AS03) \nFrom the quotations above, it is clear that farmers are incorporated into the research activities \nlong after the conception of the project. These comments from both farmers, agricultural \nextension officers and agricultural scientists show the lack of involvement of key stakeholders \nin the semi-arid Lower Eastern Kenya in communication plans formulation. Using Pretty \n(2006), the degree of involvement of stakeholders in communication plans formulation could \nbest be described as passive participation. The stakeholders were not involved in decision \nmaking but were only informed about the planned activities. The findings are also supported \n"
+    },
+    {
+      "page": 10,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n70 \n \nby other studies such as those of De Groote, et al (2002) and Wekesa, et al (2003) which \nexamined factors affecting adoption of improved maize in Eastern Africa and particularly in \nKilifi in Kenya. Without regard to other stakeholders, agricultural scientists decide the \ncommunication activities they want to undertake as well as the roles the farmers and extension \nofficers should play in the activities. In the participatory communication approach, \ncommunication plans should spring from the community’s perceptions of their communication \nneeds and problems and should therefore be developed with their active participation \n(Anyaegbunam Mefalopulos and Moetsabi, 2004).  \nExtent of Dialogue in Communication Activities \nParticipatory communication emphasizes the role of dialogue in the implementation of \ndevelopment projects. According to Freire (1970), consensus in the implementation of a project \nis arrived at as a result of dialogic action – conversation or shared dialogue to explore the \nmeaning of something. Dialogical actions promote understanding, consensus, praxis (practice) \nwhile non-dialogic actions or monologues, distort communication, lead to lack of consensus \nand lack of adoption. According to Freire (1974), through dialogue and the reflection, people \nincrease the scope of their perception, becoming aware of situations and conditions in their \nlives which they were not previously aware of. And it is through “knowing” the world through \nthis dialogue, action and reflection that the conditions for transformation and empowerment \nare enabled ending in praxis” or practice of the new information.  \nThe narrations in the previous sections of this report show that maize farmers in Lower Eastern \nKenya and agricultural extension officers were not involved during communication needs \nidentification and neither were they involved in the formulation of the communication strategy. \nIt therefore means that there was no dialogue between the farmers and the agricultural scientists \nin the formulation of the communication strategy. For a majority of the farmer participants in \nthe focus group discussions, the first time they came face to face with agricultural scientists \nwas during demos at the station, activities that were decided on by the agricultural scientists: \n “The first time we had contact with agricultural scientists was during demos at the \nstation. That is the first time we spoke with them.”- (FGD 09, M01) \nSimilarly, during the implementation stage activities, up bottom, non-dialogic communication \nactivities still persisted as indicated in one of the statements quoted earlier:  \n“During the demos and field days, members of the group are explained about the \nadvantages of the new crop and given samples to plant” - (FGD 06, W05) \nHowever, findings from this study in the semi-arid Lower Eastern Kenya established that there \nwas an element of dialogue at evaluation stage in the development of the maize seeds during \non station and field days where seed varieties had been planted: \n“We had discussions with the scientists when we were evaluating the maize varieties. \nFarmers were open about their opinions. Sometimes we had different opinions and the \nagricultural scientists had their own.”- (FGD 06, M04) \nHowever, farmers and agricultural officers stated that decisions such as where or at whose \nfarm the demos would be held were often made by the scientists. This is often a unitary \ndecision with no effort at consensus. According to an extension officer: \n"
+    },
+    {
+      "page": 11,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n71 \n \n“Agricultural scientists often identify leaders of farmer groups and approach them with \nthe need for demos. The researcher is the one with the criteria for the selection of the \nlocation. It has to be convenient” - (AEO, 08 Interviews) \nThe result of lack of dialogue is failure to arrive at consensus which is necessary for creating \nconditions for transformation that enables the practice of the new information or idea (Freire, \n1974). A good illustration of communication activities that do not promote dialogue and \nconsensus in the case of the improved maize is a story narrated to this researcher by an \nagricultural extension officer: \n             “In one of the on-station demonstration workshops, the researcher planted five \nvarieties of improved maize seeds including the traditional maize seeds (Machakos \nWhite maize variety) commonly known as Kikamba or kinyanya. But the improved \nseeds did very poorly while the local seeds did very well. The farmers pointed this to \nthe breeder. Instead of trying to figure out the explanation with farmers, the \nresearcher decided to shift focus and make soil management the agenda of the demo”– \n(AEO, 07 Interviews) \nThe consequence of this lack of dialogue is that many farmers and agricultural extension \nofficers in Lower Eastern Kenya are confused and cannot tell what maize seeds come from \nKALRO or other sources. An example is a conversation this researcher held with two \nagricultural extension officers, one of whom had participated in the introduction of improved \nmaize varieties developed by KALRO in collaboration with CIMMYT. \n“I participated with Kalro scientists in the evaluation of …varieties at Kiboko Research \nStation (a substation of KALRO-Katumani Research Centre). It was very good and \nfarmers loved it. I don’t know what happened to it. I don’t see it anymore.” (AEO, 03 \nInterviews) \n“It’s still in the market. One of the varieties was given the name ……” (AEO,5 \nInterviews)  \nThat is strange. I know … seed. But how am I expected to link what we evaluated at \nKiboko to the … in the market? (AEO, 03 Interviews) \nWhere there is no consensus, there is a plurality of solutions (Freire 1974).  This is the situation \nin Lower Eastern Kenya where currently many seed varieties have been developed by private \ncompanies that are involved in aggressive marketing efforts. This researcher counted not less \nthan 20 improved varieties, with all kinds of names, all in competition for farmers’ attention. \nAs one farmer sardonically put it during one of the focus group discussions:  \n“Siku hizi mbegu zinachanganyika. Tumechanganyikiwa” (These days seeds are mixed \nup and we are mixed up, we don’t know which seeds to trust). It is survival of the loudest \nin promoting, not necessarily the best suited – (FGD 06, M02) \nAnother consequence of lack of dialogue and consensus is that eenthusiasm also wanes. \nAnother extension officer described the enthusiasm he witnessed when he was involved in an \nevaluation of a maize variety he described as promising to be good for the area he was working \nin but the farmers did not get a chance to give or get feedback. \n            “We planted demos with five seed varieties. Since then, I have not seen the breeders \nand the maize varieties we evaluated. I see like currently maize is not being promoted \n.... It’s like it has been relegated.” – (AEO, 04 Interviews)                        \n"
+    },
+    {
+      "page": 12,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n72 \n \nThe findings above, show that the level of dialogue among stakeholders in the diffusion of \nimproved maize in Lower Eastern Kenya was not optimal. The findings are similar to those of \nother studies such as Schroeder, et al (2013) and Ouma and De Groote, (2011) which show \nlack of involvement of farmers in the introduction of hybrid maize innovations. Tufte and \nMefalopulos (2009) advocate actively engaging stakeholders in open dialogue to generate \ninformation for the design of effective communication programs and activities from the early \nstages of the research and design of interventions of a development project through to its \nevaluation. It is at the early stage that the development problem is accurately defined. All \nrelevant stakeholders should be involved in dialogue in this process to share their perceptions \nof the problem, existing community knowledge and relevant contextual information. \nStakeholder Involvement in the Development of Communication Plans in the Diffusion \nof the Improved Maize Varieties \nThis study aimed to assess the extent of stakeholder involvement in the development of \ncommunication plans used in the diffusion of improved maize varieties in the semi-arid Lower \nEastern Kenya. In this study, a qualitative method was employed to collect data from farmers, \nagricultural extension officers, and scientists involved in implementing participatory \ncommunication during the improved maize diffusion process. Overall, the involvement of \nstakeholders in communication activities for the development of the communication plan was \nminimal or lacking in most instances and could therefore, not lead to conscientization and \nownership which in Freire’s (1974) Dialogic Action are critical stages in social change as \nreceiver communities became aware of their social reality and thus become partners in search \nfor solutions.  \nStakeholders were excluded at all the stages of the development of communication plans where \nthe communication needs and social networks would have been identified empowering them \nto become partners in the diffusion process of the improved maize in Lower Eastern Kenya. \nThe results revealed a lack of meaningful engagement from key stakeholders—both farmers \nand agricultural extension officers. Insufficient stakeholder involvement in the formulation of \ncommunication planning hinders effective dialogue, preventing the identification of farmer \ninformation needs and consensus-building. Comments from farmers, agricultural extension \nofficers, and scientists highlighted this deficiency. Previous research has demonstrated that \nproperly implemented participatory communication fosters dialogue, facilitates the sharing of \nperceptions and knowledge, and builds consensus among stakeholders, ultimately increasing \nownership and the eventual increase in the adoption of agricultural innovations. \nSUMMARY, CONCLUSIONS AND RECOMMENDATIONS \nSummary  \nThe study identified a significant gap in stakeholder involvement during the development of \nthe communication plans used during the development and diffusion of improved maize seed \nvarieties in the semi-arid Lower Eastern Kenya. At the critical stage of the identification of \ninformation needs for the development of the communication plans which is essential to help \nidentify farmers’ needs, understand their prior knowledge, and prioritize key aspects, there was \ninsufficient engagement which could hinder the formulation of an effective communication \nstrategy. \nDiscussions with farmers and extension workers in the study area also revealed that neither \ngroup was involved in the communication strategy formulation. Studies indicate that low \nagricultural production and ineffective technology delivery systems, including poor \n"
+    },
+    {
+      "page": 13,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n73 \n \ninformation packaging, inadequate communication systems, and suboptimal methodologies, \ngreatly affect the adoption and diffusion of agricultural innovations. \nEffective innovation diffusion also relies on dialogue among all stakeholders, which promotes \nunderstanding, consensus, adoption, and diffusion. A lack of dialogic communication leads to \na lack of consensus and adoption. This study found that there was a lack of dialogue among \nstakeholders during the diffusion process of the improved maize varieties in the semi-arid \nLower Eastern Kenya. \nConclusion \nIn conclusion, this study aimed to assess stakeholder involvement in the development of \ncommunication plans used in the diffusion of improved maize varieties in the semi-arid Lower \nEastern Kenya. The findings align with prior research by De Groote, Doss, Lyimo, Mwangi, \nand Alemu (2002) and Wekesa, Mwangi, Verkuijl, and De Groote (2003), which also \nhighlighted a lack of stakeholder engagement in the adoption process of improved maize in \nEastern Africa, particularly in Kilifi, Kenya. While agricultural scientists may have their own \nreasons for not involving stakeholders in communication plan development, participatory \ncommunication researchers consistently emphasize that communication plans should be rooted \nin the community’s perceptions of their communication needs, opportunities and challenges. \nIgnoring these community perspectives may lead to the failure of development initiatives \n(Anyaegbunam, Mefalopulos, and Moetsabi, 2004). \nRecommendations \nBased on the conclusions of this study, here are the researcher’s recommendations: \n1. Agricultural research scientists should actively involve stakeholders in the development of \ncommunication plans. The goal is to engage stakeholders in identifying their communication \nneeds and opportunities. By doing so, researchers can uncover farmer information deficits, \nunderstand farmers’ prior knowledge, and discover their preferred communication methods. \nThis will facilitate the designing of effective communication plans. \n2. The study also recommends fostering continuous dialogue between farmers, agricultural \nextension workers, and scientists throughout the communication activities in the diffusion of \ninnovations. By doing so, researchers can ensure that communication materials resonate with \nfarmers’ needs, preferences, and local context. \n \n \n \n \n \n \n \n \n \n"
+    },
+    {
+      "page": 14,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n74 \n \nREFERENCES \nAli, A. C., & Sonderling, S. (2017). Factors affecting participatory communication for \ndevelopment: the case of a local development organization in Ethiopia. Journal \nKomunikasi: Malaysian Journal of Communication, 33(1), 80-97. \nAnyaegbunam, C., Mefalopulos, P., & Moetsabi, T. (2004). Participatory Rural \nCommunication Appraisal Starting with the People: A Handbook. Food & Agriculture \nOrg. \nBessette G. (2020), Participatory Development Communication and Natural Resources \nManagement. Handbook of Communication for Development and Social Change, \n2020 ISBN: 978-981-15-2013-6 \nBalit, S. (2007). Communication for isolated and marginalized groups. Communication And \nSustainable Development, 101. \nBessette G. (2004). Involving the Community: A Guide to Participatory Development \nCommunication. Ottawa: IDRC Books.  \ndel Mar Ramis, M., Gandia, V., Bellot-Arcís, C., Cibrian, R., Peredes-Gallardo, V., & \nGandia, J. L. (2018). Evaluation of Invisalign treatment using the Peer Assessment \nRating (PAR) index. Evaluation, 2(1), 13-19. \nFreire, Paulo., (2006).  Pedagogy of the Oppressed, 30th Anniversary Ed. New York: \nContinuum \nKamau G. (2007). Researching with Farmers: A study of KARI participatory research practices \nin context, PhD Thesis, Wageningen University \nLundy, M. M., Gottret, M. V., & Ashby, J. A. (2005). Learning alliances: An approach for \nbuilding multistakeholder innovation systems. ILAC Brief. \nMaharani Dita Novita. 2002. An Evaluation of the Participatory Communication Approach for \nthe Development of the Kotagede Area: A Case Study of the Rekompak-JRF Heritage \nExtension Programme in Yogyakarta, Indonesia. A Thesis presented to the Faculty of \nthe Graduate School Ateneo de Manila UniversityManila University. \nMefalopulos P., 2003 Theory and Practice of Participatory Communication:  The case of the \nFAO Project “Communication for Development in Southern Africa” PhD Dissertation, \nUniversity of Texas,  \nMelkote, S. and Steeves, H. Leslie., 2001. Communication for Development in the Third \nWorld: Theory and Practice for Empowerment (2nded.). New Delhi: Sage. \nMozammel and Schechter.,2005. Communication for Development and Social Change, Sage \nPublication. \nNaidoo, L,.2010. The Participatory Development Communication Approach of Thusong \nService Centres in Tshwane. M.A. Thesis, Northwest University, Potchefstroom, South \nAfrica \nNgumbo, L. N. (2015). Participatory communication in Poverty Reduction in Kenya: A study \nof Murang’a County (Doctoral dissertation, University of Nairobi). \n"
+    },
+    {
+      "page": 15,
+      "text": "International Journal of Communication and Public Relation \nISSN 2520-7989 (Online)   \n \n \n  \nVol.9, Issue 4, No.5, pp 62 - 75, 2024                                                                                                                                  \n                                                                                                                                          www.iprjb.org                             \n75 \n \nRizzo, G., Migliore, G., Schifani, G. et al. Key factors influencing farmers’ adoption of \nsustainable innovations: a systematic literature review and research agenda. Org. Agr. \n14, 57–84 (2024). https://doi.org/10.1007/s13165-023-00440-7 \nServaes, J., & Malikhao, P. (2014). The role and place of Communication for Sustainable \nSocial Change (CSSC). International Social Science Journal, 65. \nTufte, T. & Mefatopulos, P, (2009. Participatory Communication: A Practical Guide, World \nBank Working Paper No. 170. Washington DC: The World Bank. \n \n \n"
+    }
+  ]
+}

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+  "filename": "861533390-KALRO-KCEP-Maize-Extension-and-Training-Manual-04042016.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/861533390-KALRO-KCEP-Maize-Extension-and-Training-Manual-04042016.pdf",
+  "page_count": 18,
+  "pages": [
+    {
+      "page": 1,
+      "text": " \n \n \n \n \n \n \n \n \nTHE KENYA CEREAL ENHANCEMENT PROGRAMME (KCEP) \n \nADAPTATION AND DISSEMINATION OF AVAILABLE TECHNOLOGIES FOR \nSMALLHOLDER ADOPTION \n \n \n \n \n \n \n \n \nKALRO-KCEP MAIZE TRAINING AND EXTENSION MANUAL \n \n \n \nAPRIL, 2016 \n \n"
+    },
+    {
+      "page": 2,
+      "text": "\t\r  \nii\r  \n \nTable of content \nTraining Objectives\t\r  .....................................................................................................................................\t\r  iii\t\r  \nOutcomes\t\r  .....................................................................................................................................................\t\r  iii\t\r  \nAcknowledgements\t\r  ......................................................................................................................................\t\r  iv\t\r  \n1\t\r  \nIntroduction/ Background\t\r  ......................................................................................................................\t\r  1\t\r  \n1.1\t\r  \nEcological requirement\t\r  ...................................................................................................................\t\r  1\t\r  \n1.1.1\t\r  \nAltitude\t\r  ....................................................................................................................................\t\r  1\t\r  \n1.1.2\t\r  \nRainfall\t\r  ....................................................................................................................................\t\r  1\t\r  \n1.1.3\t\r  \nSoil type\t\r  ...................................................................................................................................\t\r  1\t\r  \n1.1.4\t\r  \nTemperature\t\r  .............................................................................................................................\t\r  1\t\r  \n2\t\r  \nKey Operations\t\r  ......................................................................................................................................\t\r  1\t\r  \n2.1\t\r  \nPre-field operations\t\r  ........................................................................................................................\t\r  1\t\r  \n2.1.1\t\r  \nVarietal Selection\t\r  ....................................................................................................................\t\r  1\t\r  \n2.1.2\t\r  \nSeed selection and treatment\t\r  ...................................................................................................\t\r  2\t\r  \n2.1.3\t\r  \nTesting for germination\t\r  ...........................................................................................................\t\r  2\t\r  \n2.1.4\t\r  \nSite selection\t\r  ............................................................................................................................\t\r  2\t\r  \n2.2\t\r  \nField Operations\t\r  .............................................................................................................................\t\r  4\t\r  \n2.2.1\t\r  \nLand preparation\t\r  ......................................................................................................................\t\r  4\t\r  \n2.2.2\t\r  \nSoil fertility management\t\r  ........................................................................................................\t\r  4\t\r  \n2.2.3\t\r  \nPlanting\t\r  ....................................................................................................................................\t\r  4\t\r  \n2.2.4\t\r  \nWeeding\t\r  ...................................................................................................................................\t\r  5\t\r  \n2.2.5\t\r  \nCrop rotation\t\r  ............................................................................................................................\t\r  5\t\r  \n2.2.6\t\r  \nControlling pests and diseases\t\r  .................................................................................................\t\r  5\t\r  \n2.2.7\t\r  \nHarvesting\t\r  ................................................................................................................................\t\r  9\t\r  \n2.3\t\r  \nPostharvest handling\t\r  .......................................................................................................................\t\r  9\t\r  \n2.3.1\t\r  \nSeed drying\t\r  ..............................................................................................................................\t\r  9\t\r  \n2.3.2\t\r  \nShelling\t\r  ....................................................................................................................................\t\r  9\t\r  \n2.3.3\t\r  \nGrain and seed dressing and storage\t\r  ........................................................................................\t\r  9\t\r  \n2.3.4\t\r  \nProper storage\t\r  ..........................................................................................................................\t\r  9\t\r  \n2.4\t\r  \nMaize storage pests\t\r  .......................................................................................................................\t\r  10\t\r  \n2.5\t\r  \nMould and aflatoxin control\t\r  .........................................................................................................\t\r  11\t\r  \n2.6\t\r  \nUtilization and value addition\t\r  .......................................................................................................\t\r  11\t\r  \n3\t\r  \nPractical\t\r  ...............................................................................................................................................\t\r  12\t\r  \n3.1\t\r  \nHow to determine the moisture content of maize\t\r  .........................................................................\t\r  12\t\r  \n4\t\r  \nTake home messages\t\r  ...........................................................................................................................\t\r  12\t\r  \n5\t\r  \nFurther reading\t\r  ....................................................................................................................................\t\r  13\t\r  \n"
+    },
+    {
+      "page": 3,
+      "text": "\t\r  \niii\t\r  \nOverview \nThe purpose of this module is to train extension officers, lead farmers and service providers on \nmaize production along the value chain, with enhancement of gender participation for improved \nlivelihoods. \n \nTraining Objectives \n• To train the extension officers and other stakeholders to enhance the productivity on   maize. \n• To enhance and empower the capacity of farmers on the maize value chain for food security. \n• To increase commercialization of maize. \n• To promote gender inclusion and participation in maize production along the value chain. \nOutcomes \n1. Extension officers and other stakeholders trained on maize production. \n2. Capacity of farmers for food security along the maize value chain enhanced. \n3. Commercialization of maize enhanced. \n4. Gender inclusion and participation in maize along the value chain enhanced. \n"
+    },
+    {
+      "page": 4,
+      "text": "\t\r  \niv\r  \n \nAcknowledgements \nThe authors of this manual acknowledge the support from European Union (EU) through the \nInternational Fund for Agricultural Development (IFAD) and the Kenya Cereals Enhancement \nprogram (KCEP) of the Ministry of Agriculture, Livestock and Fisheries MoALF in \ncollaboration with the Kenya Agricultural and Livestock Research Organization (KALRO)-\nKCEP, Nairobi, Kenya. \n"
+    },
+    {
+      "page": 5,
+      "text": "\t\r  \nv\t\r  \n \n \nAbbreviation/Acronym \nAC     \n Acre \nAEZ    \nAgro-ecological-zones \nCAN \n Calcium ammonium nitrate \nDAP   \nDi-ammonium phosphate \nFYM   \nFarmyard manure \nHa      \nHectare \nKALRO   Kenya Agricultural and Livestock Research Organization \nKCEP   \nKenya Cereal Enhancement Programme \nKSC   \nKenya Seed Company \nNPK   \nNitrogen, Phosphorus, Potassium \nUH    \nUpper Highlands \nLH    \n Lower Highlands \nUM   \nUpper Midlands \nWSC   \nWestern Seed Company  \nGDP  \nKenya’s Gross Domestic Product \n°C   \nDegrees Celcius \n \n"
+    },
+    {
+      "page": 6,
+      "text": "\t\r  \n1\r  \n \n1 \nIntroduction/ Background \nMaize is a staple food and contributes to about 65% of daily per capita cereal consumption. It \nserves as subsistence and a commercial crop, and grows on an estimated 1.4 million hectares. \nMaize accounts for more than 20% of the total agricultural production and 25% of agricultural \nemployment. It is an important source of carbohydrate, protein, iron, vitamin B, and minerals. \nMaize products include baked, roasted and boiled fresh maize on the cob, porridges, pastes, beer, \nstarch, oil and livestock feed from by-products of fresh and dry maize grain. The stalk provides \nan important source of livestock feeds as crop residue and silage.  \n1.1 \nEcological requirement \nMaize grows across a range of agro-ecological zones and most varieties differ in maturity, \nresistance/tolerance to pests and diseases.  \n1.1.1 Altitude \nMaize is grown at all altitudes ranging from sea level at the Coast to 2,200 m above sea level. \nHowever, if planted in very low or high altitudes poor yields will be realised. \n1.1.2 Rainfall \nRainfall requirements vary with different varieties. In general maize will do well in areas that \nreceive 600-900mm of rainfall. The rainfall should be evenly distributed during the growing \nperiod. For higher yields, the crop should receive enough rainfall during the first five weeks after \nsowing and at flowering time. Otherwise any drought at flowering time will interfere with \npollination and drastically lower the yields. Dry weather conditions are required during \nharvesting. \n1.1.3 Soil type \nSoils should be well drained, well aerated, deep and warm. The best soils for growing maize are \nsilt loam with sufficient nutrients and a pH range of 5.0-7.0. Water logged soils should be \navoided because if maize stands in water for more than two days it wither and die off. Soils \nshould be routinely analyzed. \n1.1.4 Temperature \nThe temperature requirements of maize vary with varieties. Cold conditions at high altitude will \nextend life cycle, whereas high temperatures will expose the crop to much respiration and lower \nthe yields. The optimum temperature for good yields will be 30°C. \n2 \nKey Operations \n2.1 \nPre-field operations \n2.1.1 Varietal Selection \nThe choice of appropriate maize varieties for a given location is very important because every \nvariety has extensively been tested and recommended based on climatic conditions, soil type, \nyield potential, resistance to pest and diseases or maturity period among others  (Table 1).  Some \n"
+    },
+    {
+      "page": 7,
+      "text": "\t\r  \n2\r  \ntolerant and resistant varieties include Hybrid 6218 is tolerant to leaf blight, Hybrid 6213 and \n6210 are tolerant to grey leaf spot.  \n2.1.2 Seed selection and treatment  \nFarmers are advised to use certified seeds and practice best agronomic practices for best yields.  \n2.1.3 Testing for germination  \nWhile the germination percentage (%) of seeds is supposed to be indicated on every sold packet, \nfarmers often get non-germinating seeds and this results in disappointment after planting. \nTherefore it is advisable to conduct a simple germination test. Counting a 100 seeds of a selected \nmaize variety and put between moist old newspapers does this. After four days of incubation, the \ngerminated seeds are counted and divided by the total seeds and expressed as a percentage. For \nexample, if out of the 100 seeds 85 seeds germinate, germination percent will be 85%. A \ngermination percentage of 85-100% is considered good for the maize seed. In case germination \nrate is lower, you may increase sowing density correspondingly to ensure appropriate plant \ndensity (for example plant 2-3 seeds) per hole. \n \n2.1.4 Site selection \nTo ensure high maize yields, select highly productive land suitable that is suitable for maize \nproduction. For example, avoid steeply sloping land, or near a swamp, very sandy soils, and \nareas with shallow surface soil and places with couch grass.  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
+    },
+    {
+      "page": 8,
+      "text": "\t\r  \n3\r  \n \n \n \nTable 1: Maize varieties five KCEP implementing counties in Western Region \nVarieties \nSource \n AEZ where grown  \nKCEP sites \nMaturity \n(Days) \nYield \npotential 90 \nkg bags/acre \nH 6213 \nKenya Seed Company \nHighlands \nTrans \nNzoia, \nNandi, Nakuru, \nKakamega \nBungoma \n160-210  \n50 \nH6210 \nKenya Seed Company \nHighlands \nTrans \nNzoia, \nNandi \nNakuru, \nKakamega \nBungoma \n160-210 \n45 \nH629 \nKenya Seed Company \nHighlands \nTrans \nNzoia, \nNandi \nKakamega and  \nBungoma \n160-210 \n43 \nKH600-23A \nAgricultural \nDevelopment \nCorporation (ADC) \nHighlands \n \n140-175 \n56 \nH614D \nKenya Seed Company \nHighlands \nNandi, Nakuru \nand Bungoma \n160-210 \n33 \nH627 \nKenya Seed Company \nHighlands \nBungoma \n150-210 \n47 \n \nWH509 \nWestern \nSeed \nCompany \nMoist Mid-Altitude \nBungoma \n120-180 \n30-35 \nWH505 \nWestern \nSeed \nCompany \nMoist Mid- altitudes \nBungoma \n120-180 \n30-35 \nH516 \nKenya Seed Company \n \nMoist Mid altitude \nBungoma \n \n20 \nPHB30G19 \nPioneer \nSeed \nCompany \n \nMoist Mid-altitude \nNakuru \n90-120 \n30 \nH517 \nKenya Seed Company \nMoist Mid-altitude \n \nBungoma \n120-130 \n32 \nH624 \nKenya Seed Company \nHighlands \n \nNakuru \n140-180 \n32 \nH6218 \nKenya Seed Company \nHighlands \nTrans \nNzoia, \nNandi, Nakuru \nand  \nKakamega \n150-200 \n56 \nPANNAR \n(691) \nPannar \nSeed \nCompany \nHighlands \nKakamega \n150-200 \n25 \nWH507 \nWestern \nSeed \nCompany \n \nMoist Mid-altitude \nBungoma \n120-130 \n32 \nSIMBA61 \nSeed \nCompany \n(SeedCo) \n \nMoist Mid-altitude \nBungoma \n120-130 \n32 \n"
+    },
+    {
+      "page": 9,
+      "text": "\t\r  \n4\r  \n2.2 \nField Operations \n2.2.1 Land preparation \nA maize farm can be prepared using Conventional CA methods. Conservation Agriculture \nincludes spraying with recommended herbicides and crop residue management. Conventional \nmethods include hands, us of an ox-plough or a tractor. It’s important to prepare the land early \nenough to allow weeds to dry and decompose.  \n \nTable 2: Land preparation practices/technologies employed in five KCEP implementing Counties in \nWestern Region \nCounty/ Sub-county \nLand preparation practices/technologies \nConventional land \npreparation:  \nConventional \nland \npreparation-  \nConservation \nAgriculture \n1. Bungoma County (Tongaren and \nSirisia sub counties); \n2. Kakamega County (Lugari and \nLukuyani sub-counties); \n3. Nandi \nCounty \n(Mosop \nand \nChesumei sub-counties); \n4. Trans Nzoia County (Kwanza and \nCherangani sub-counties); \n5. Nakuru County (Njoro and Molo \nsub- counties) \nPlough twice and harrow \nonce or plough once but \nat least 3 months before \nthe anticipated time of \nplanting + 2 harrows just \nbefore planting \nOne plough  \n(disc or \n mould board) + 1 \nharrow \nUse Glyphosate \n based herbicide at 2 \nl/acre during fallow \nand 1.2 l/acre at \nleast  2 weeks before  \nplanting. \n. \n2.2.2  Soil fertility management \nSoil fertility can be managed through several strategies including, organic fertilizers/manures, \nand inorganic or chemical fertilizers.  \n \nOrganic fertilizers/manures \nFarmyard manure (FYM) can be applied either alone at a rate of 4 tonnes per acre. When FYM is \nused in combination with inorganic fertilizers half the recommended rates should be applied.  \n \nInorganic/chemical fertilizers \nFertilizer recommendations are based on different agro-ecological zones and soil types. The \nmain fertilizers used are Di-ammonium phosphate (DAP) at 50kg/acre (50kg bag/acre) (applied \nat planting), Calcium ammonium phosphate (CAN) at 92kg/acre (2 bags of 50kg each) top-\ndressed when the plants are knee-high. However, farmers should follow the recommendations \nagreed during the KCEP validation workshop until further notice.  \n \nManagement of acid soils \nLiming is recommended for acid soils. However, other fertilizers with a liming effect like MEA \nMazao (100 kg/acre), Mavuno basal (100 kg/acre) and Mavuno for topdressing (75 kg/acre) are \nrecommended for acidic soils.  \n2.2.3 Planting \nPlanting time  \n"
+    },
+    {
+      "page": 10,
+      "text": "\t\r  \n5\r  \nThe time of planting is a very critical step in maize production. Planting should be done within \nthe first two weeks of the onset of rains. Early planted maize allows the germinating seed to \nbenefit from nitrogen flux effect, warm soil temperatures and good aeration and escape from \ninsect pests and diseases. Maize planting depth ranges from 2.5-5 cm.  \n \nSpacing and plant population per hectare\t\r  \nThe recommended spacing and planting density of maize for different zones is as shown in Table \n3.  \n\t\r  \nTable 3: Recommended spacing and planting density of maize for different zones \nRegion \nSpacing \nDensity (plants per ha) \nHighland \n75x25cm 1 plant/hill (pure stand) \n75 x 50cm 2 plants/hill (intercrop) \n53,333 \n53,333 \nMedium \n75 x 30cm 1 plant/hill (pure stand) \n75 x 60cm 2 plants/hill (intercrop) \n44,444 \n44,444 \nDry land \n90 x 30cm 1 plant/hill (pure stand) \n90x 60cm 2 plants/hill (intercrop) \n37,850 \n37,850 \n. \n2.2.4 Weeding  \nWeeds reduce maize yields by competing for moisture, nutrients, space and light. Weeds are also \nan alternative host to pests and diseases. The most critical stage of weed competition in the life \nof a maize plant is during the first four to six weeks after emergence of the crop. The most \ncommon practice in weed management are hand weeding and use of herbicides. Some of the \nrecommended practices include: hand weeding that should be done at least three weeks after \nemergence of the plants followed by a second weeding at knee high. Other recommended \napproaches include the use of a dense legume cover crop to suppress weeds for example lablab \n(Lablab purpureus), velvet bean (Mucuna pruriens) or sunhemp (Crotalaria juncea) and \ndesmodium. Farmers can regularly scout their fields and uproot Striga weed early enough before \nthe seeds are produced. \n2.2.5 Crop rotation \nCrop rotation is highly recommended to reduce build-up of maize diseases and insect pests. \nRotation is mainly done with beans, cowpeas and peas. Rotating of maize with other cereal crops \nlike sorghum and millet should be avoided, especially in case of Maize Lethal Necrosis Disease \n(MLND).  \n2.2.6 Controlling pests and diseases \nDiseases and pests are largely responsible for the low maize yields, however, the incidence and \nseverity vary between seasons. Integrated pest and disease management is recommended. Some \nexamples of common maize pests and diseases and their control measures are shown in Tables 4 \nand 5, respectively. \n"
+    },
+    {
+      "page": 11,
+      "text": "\t\r  \n6\r  \nPests \n \nTable 4: Field pests, destructive stage, damaging symptoms and control \nPest \nSymptoms \nControl \nFIELD PESTS: \n \n \n \nMaize stem borers \n \n \n \n \n \nCaterpillars feed on young plants (a)and cause  dead \nhearts (b). They later burrow into stems (c and d) \naffecting water and nutrient flow. \n• \nIntercrop with non-host crops. \n• \nIntercrop with green leaf \ndesmodium. \n• \nPlant trap crops around the \nplot. \n• \nBiological control with \npredators and parasites. \n• \nUse recommended \ninsecticides. \nMaize leafhoppers  \n \n \n• \nSlender hoppers with two small black spots \nbetween eyes and hop away on disturbance  \n• \nHoppers feed on maize plants and transmit maize \nstreak virus. Plants turn chlorotic and streaked \nand damage may be up to 100% \n• \nPlant away from grassland or a \nprevious irrigated crop.  \n• \nPlant early to reduce risk of \nvirus transmission. \n• \nKeep the fields free from grass \nweeds. \n• \nRemove residues of cereal \ncrops. \n• \nUse \nrecommended \nMSV \nresistant maize varieties. \n• \nCatch them with sticky green \ntraps \nMaize Aphids  \n \n• \nSmall, 1 to 4 mm long, soft-bodied dark green to \nbluish-green in color with two long antenna. \n• \nFeed on young leaves in dry periods. \n• \nMottling stunted growth and dieback. Sooty \nblack mold becomes evident. \n• \nMixed cropping, trap crops. \n• \nUse predators and parasites like \nladybirds and hover fly.  \n• \nSprays with recommended \nChemical in severe infestations.  \nBollworms \n \n  \n \n• \nPolyphagous pest that damages many crops. \n• \nBollworms feed on leaves, buds, growing points, \nflowers and fruit.  \n• \nDamage reduces leaf area, which slows plant \ngrowth. Also feeds on flowers and fruit causes \nthe main damage. \n• \nIntercrop maize with other \ncrops in heavy infestations. \n• \nSpray with approved \ninsecticides \nB\t\r  \nC\t\r  \nD\r  \nA\t\r  \n"
+    },
+    {
+      "page": 12,
+      "text": "\t\r  \n7\r  \nCutworms \n \n• \nThese are caterpillars that are found in the soil \nand damage young seedlings.  \n• \nThey cut off the plant at or below the ground \nlevel. \n• \nSeed dressing with approved \nseed dressers. \n• \nSpread bait on the ground. \n \nDiseases \n \nTable 5: Selected maize diseases, symptoms and control strategies \nDisease \nSymptoms \nPrevention/control \nDowny mildew \n \n \n \nLeaves and leaf sheaths have white \nand yellow stripes, stunting of the \nwhole plant, which produces no \nyield. \n• \nEarly planting.  \n• \nCrop rotation. \n• \nResistant varieties \nNorthern Corn (Turcicum)  \nLeaf Blight \n \nA chlorotic “halo” develops into a \nnecrotic lesion that grows into \nmature cigar-shaped lesions about \n2 cm wide and 15 cm long. The \nsymptoms appear first on lower \nleaves and increase in size and \nnumber as the plant develops.  \n• \nPlanting resistant varieties. \n• \n Crop rotation.  \n• \nFungicides in the early stages \nof the disease. \n \nSouthern leaf blight  \nLight brown leaf spots appear with \na brown margin, of up to 25 mm \nlong and 2-6 mm wide. The spots \nare at first restricted by the leaf \nveins, but later leaves dry out and \ndie prematurely. Survival in soil \noccurs for up to 12 months. \n• \nUse disease-free seed or \ntreated seed with fungicides. \n• \nDestroy crop residues and \nvolunteer plants. \n• \nPractice crop rotation. \n• \nUse tolerant/resistant varieties. \nGray leaf spot (GLS) \n \n \nMature lesions, about 5cm long \nand 0.3 cm wide start from the \nlower leaves and increase in \nnumber usually after silking. The \ncolour changes from light to gray \nlesions, which may grow together \nand kill the entire leaves.  \n• \nObserve field hygiene; \n• \nUse resistant/tolerant \nvarieties; \n• \nEarly planting; \n• \nProper tillage; \n• \nCrop rotation for at least 2 \nyears    with non -host crops \nlike potato, beans or pea; \n• \nUse recommended rates of \nfertilizers. \nCommon rust  \n \nThe disease is characterized by \nelongate raised bumps (pustules) \nscattered or clustered together on \nboth leaf surfaces that are red to \ndark \nbrown \nin \ncolor. \nThe \nsymptoms are mainly observed in \nthe mid and upper canopy of the \ncrop, especially during tasseling. \nLater the epidermis is ruptured and \nthe lesions turn black and spores \nare released as the plant matures. \n• \nUse resistant varieties. \n• \nFoliar application of \nfungicides. \n• \nCultural practices. \n"
+    },
+    {
+      "page": 13,
+      "text": "\t\r  \n8\r  \nCommon smut \n \nThe fungus attacks all parts of the \nplant that is leaves, stalks, tassels \nand ears even below the soil \nsurface. It gains entry through \nwounds or thin walled cells of \nactively growing maize. Galls that \nreplace individual kernels and are \ncovered with white membranes \ncharacterize common smut. Later, \nthe galls break open releasing black \nmasses of spores that will infect \nmaize in the following season. \n• \nObserve field hygiene \n• \nPlant resistant varieties \n• \nSpray with fungicides \n• \nAvoid injuring the plants. \nHead Smut  \n \nThe fungus penetrates the seedlings \nand grows inside the plant without \nshowing \nsymptoms, \nuntil \nthe \ntasseling and silking stage.  \n \n• \nPlant resistant varieties.  \n• \nPlant early, when temperatures \nare unfavorable for spore \ngermination.  \n• \nTreat seeds with systemic \nfungicides.  \n• \nMaintain balanced soil \nfertility.  \n• \nWhere feasible, remove and \nburn smutted tassels and ears \nas they emerge to reduce \ninoculum spreading.  \n• \nCrop rotation. \nMaize lethal necrosis disease  \n \n \n \nThese include chlorotic mottle on \nthe leaves, usually starting from the \nbase of the young leaves in the \nwhorl and extending upwards \ntoward the leaf tips, mild to severe \nleaf mottling, dwarfing and \npremature aging. Necrosis of the \nyoung leaves in the whorl before \nexpansion leading to a “dead \nheart”. Lesions sometimes \nassociated with unfurled leaves \nappear on the stem. Tassels with no \npollen and finally there is poor \ngrain filing.  \n \n• \nIntegrated pest and disease \nmanagement. \n• \nUse of certified seeds. \n• \nObserve field hygiene. \n• \nManage the hosts and vectors. \n• \nAvoid \nvolunteering \ncrops/weeds in the farm. \n• \nObserve closed seasons. \n• \nCop rotate with non-cereal \ncrops. \n \nMaize streak virus \nNarrow white to yellowish streaks \non the leaves whorls. The central \nleaves die, resulting in a dry, \nwithered parallel to the mid rib \n'dead-heart' symptom. \n \n \n• \nUse of resistant varieties. \n• \nPlant early in the season \n• \nEradicate grass weeds. \n• \nControl vectors. \n \n"
+    },
+    {
+      "page": 14,
+      "text": "\t\r  \n9\r  \n \n2.2.7 Harvesting \nGreen maize \nMaize that is to be eaten green is ready for harvest when the grain hardens or when the silky \nflowering at the top of the maize cob turns black.  \n \nDried maize \nThe recommended moisture content for dry maize is 13%.   \n2.3 \nPostharvest handling  \n2.3.1 Seed drying \nMaize can be dried on cob or after shelling. Both shelled and unshelled maize can be dried in the \nsun on a cemented floor, mats, and tarpaulins, raised structure like cribs or specially constructed \ndrying sheds. Maize should not be dried on bare ground because the grains will pick up moisture, \ndirt and insects. In case of open air-drying, the grain should be protected from rain, night dew, \ndomestic animals and birds. Maize can also be dried in mobile motorized driers, which are now \naccessible to farmers and can dry up to 37 bags in two hours.  The recommended moisture \ncontent for dry maize is below 13%. \n \n                   \n  \n \n \n \n \nFigure 2: Methods for drying maize. \n \n2.3.2 Shelling \nShelling should be done immediately after drying the cobs. Sort the cobs to remove diseased or \nrotten grains. Shelling can be done using a hand-held Sheller or motorized Sheller. Shelling of \nthe grains should be done carefully, so that the grains do not get damaged.  After shelling, the \ngrains should be cleaned by removing any dirt and foreign matter, small and damaged seeds. \n2.3.3 Grain and seed dressing and storage \nBefore storage, and consuming maize treat the grains with Super actellic @ 50g per bag/90kg. \nStore maize as dry as possible in air-tight bins or drum or well-secured gunny bags.  \n2.3.4 Proper storage \nThe dried shelled grains should be stored in metallic silos or packed in Hermatic bags (Agro-Z \nbags). Place the silos or bags on pallets in a clean and well-ventilated store.  \nMaize drying on bare ground \n(not recommended \nMaize \ndrying \non \ntarpaulin \nor \nmobile \ndrier \n(Recommended) \n"
+    },
+    {
+      "page": 15,
+      "text": "\t\r  \n10\t\r  \n \n \n \nFigure 3: Maize storage methods \n2.4 \nMaize storage pests \nSeveral post-harvest pests affect maize and some selected pests of economic importance are \nshown in Table 6 below: \n \nTable 6: Storage pests, symptoms, damages caused and control strategies \n \nPest \nSymptoms \nDamage \nControl \n \n \nLarger \nGrain \nBorer (LGB) \nA beetle like insect \nthat is a serious \npest of maize. \n \n \nLGB will attack maize on \nthe cob, both before and \nafter harvest. \n \n \nAdults bore into maize husks, cobs \nproducing large quantities of grain \ndust as they tunnel.  \n• \nEarly harvesting, drying and \nstorage in cleaned and \ndusted store and bags. \n• \nUse plant extracts such as \nNeem, pyrethrum and castor \nseed. \n• \nUse recommended \nchemicals.  \nMaize weevils \nThe \nadults \nare \nsmall (2.5 to 4.0 \nmm long), black \nwith a long, narrow \nsnout. \n \nFemale lays eggs inside the \ngrain. \n \n \n \nLarva (grub) lives inside the grain \nhollowing it out leaving circular \nholes on the surface of the grain \n• \nEarly harvesting, drying and \nstorage \nin \ncleaned \nand \ndusted store and bags. \n• \nRecommended \nChemicals \ncan also be used. \nRodents \n(Rats and mice) \nFeed on maize grain.  \n \n• \nUse rat guard when building \nthe in the granaries. \n• \nUse traps \n• \nUse baits. \n \nPoor\t\r  Maize\t\r  storage\t\r  condition\t\r  \nSky\t\r   plastic\t\r   and\t\r   small\t\r  \nmetal\t\r  silos\t\r  \t\r  \nLarge-­‐scale\t\r  metal\t\r  silos\t\r  \n"
+    },
+    {
+      "page": 16,
+      "text": "\t\r  \n11\t\r  \n2.5 \nMould and aflatoxin control \nAflatoxin in maize is caused by moulds (Aspergillus species). Cooking or heating cannot destroy \naflatoxins. The only way is to dry maize grain quickly after harvest to moisture levels of between \n12 and 15% and ensure good ventilation.  \n \n2.6 \nUtilization and value addition \n1. The grains are ground to produce maize flour and it is also consumed as a food grain \nfresh, ground, boiled or mixed with other foods. \n2. The stalks, leaves, and other remains from the maize cobs are used to feed domestic \nanimals especially dairy cattle. \n3. The stalks and cobs are used to provide domestic fuel particularly in the rural areas. They \nare also used as organic manure. \n4. The grains are used in manufacture of corn oil and animal feeds hence it is a vital raw \nmaterial for industrialization. \n"
+    },
+    {
+      "page": 17,
+      "text": "\t\r  \n12\t\r  \n \n3 \nPractical  \n3.1 \nHow to determine the moisture content of maize \nMoisture content can be determined by using a moisture meter. Where farmers have no moisture \nmeter, they can use the biting grain method or the salt test.  \nSalt test \nPut maize grains into a 750ml glass bottle (which should be about one third full). Add 2–3 \ntablespoons (20–30g) of dry salt and shake vigorously for 1 minute to mix the salt and grain, and \nthen leave for 15 minutes. If the salt sticks to the side of the bottle, the moisture content of the \ngrain is above 15% and is not safe for storage. If the salt does not stick to the bottle, the moisture \ncontent is below 15% and the grain is safe for storage. \n \n4 \nTake home messages \n1. Always plant certified seeds. \n2. Carry out timely land preparation. \n3. Always plant early. \n4. Do timely weeding. \n5. Always control pests and diseases. \n6. Harvest at physiological maturity. \n7. Dry to 13% moisture content. \n8. Treat grains before storage. \n9. Store in a clean dry place. \n"
+    },
+    {
+      "page": 18,
+      "text": "\t\r  \n13\t\r  \n \n5 \nFurther reading  \nAcland, J.D. (1971). East African Crops. Food and Agriculture Organization of the United \nNations, Longman Group Limited.  \nDe Groote, H., Owuor, G., Ouma, J., Muhammad, L. and Danda, K. (2005). The Maize \nGreen Revolution in Kenya Revisited. E-Journal of Agricultural and Development \nEconomics, Vol. 2, No. 1, 2005, pp. 32 – 49. \nFAO, (2010). Food Security Statistics. Http://www.fao.org/economic/ess/food/security/-\nstatistics/en/ (accessed 30th December 2010). \nIITA. (1982). Maize Production Manual Series No. 8. Vol. 1, Chapters 1 – 9.  \nKiiya W, Ndungu F, Onyango R, Lunzalu E, and Mulati J. (2005). Maize Varieties, soil \nfertility, improvement and appropriate agronomy practices.  \nMoA-Agricultural Information Resource Centre. (2011). Field Crops Technical Handbook, \n3rd Edition Vol. 1. Nairobi. \n  \n"
+    }
+  ]
+}

chatbot/extracted/Agronomic Performance of Single Crosses of Maize.json

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+  "filename": "Agronomic Performance of Single Crosses of Maize.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/Agronomic Performance of Single Crosses of Maize.pdf",
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+  "pages": [
+    {
+      "page": 1,
+      "text": "_____________________________________________________________________________________________________ \n \n*Corresponding author: E-mail: njokafm@yahoo.co.uk, njokafm@gmail.com; \n \nJournal of Applied Life Sciences International \n \n12(1): 1-8, 2017; Article no.JALSI.30932 \nISSN: 2394-1103 \n \n \n \n \nAgronomic Performance of Single Crosses of Maize \nin Kiambu and Embu Counties of Kenya \n \nFredrick M. Njoka1* and Jeniffer M. Kariuki2 \n \n1Department of Agricultural Resource Management, University of Embu, P.O.Box 6-60100,  \nEmbu, Kenya. \n2Department of Crop Sciences, Kenyatta University, P.O.Box 43844, Nairobi, Kenya. \n \nAuthors’ contributions \n \nThis work was carried out in collaboration between both authors. Author FMN designed the study, \nperformed the statistical analysis and wrote the first draft of the manuscript. Author JMK managed the \nanalyses of the study. Both authors read and approved the final manuscript. \n \nArticle Information \n \nDOI: 10.9734/JALSI/2017/30932 \nEditor(s): \n(1) Shahira M. Ezzat, Department of Pharmacognosy, Faculty of Pharmacy, Cairo University, Egypt. \n(2) J. Rodolfo Rendón Villalobos, Department of Technological Development, National Polytechnic Institute, México. \nReviewers: \n(1) Abdulwahab Shaibu, Bayero University, Kano, Nigeria. \n(2) Ahmed Medhat Mohamed Al-Naggar, Cairo University, Egypt. \n(3) Evellyn Giselly de Oliveira Couto, University of São Paulo, Brazil. \n(4) Aamir Raina, Aligarh Muslim University, India. \n(5) S. Gandhi Doss, Central Sericultural Research & Training Institute, India. \n(6) B. A. Monpara, Junagadh Agricultural University, India. \nComplete Peer review History: http://www.sciencedomain.org/review-history/19489 \n \n \n \nReceived 9th December 2016 \nAccepted 7th June 2017 \nPublished 12th June 2017 \n \n \nABSTRACT \n \nAims: This study was conducted to determine the agronomic performance of respective maize \nsingle crosses in different environments in varying soil and climatic zones in Kenya.  \nMethodology: The trials were conducted at experimental stations of Kenya Agricultural and \nLivestock Research Organization (KALRO), Muguga South and KALRO Embu in Kiambu and \nEmbu counties of Kenya respectively. The germplasm used in this study were 36 single crosses \namong 18 inbred lines of maize. The experiment was laid out in a 6 x 6 lattice randomized complete \nblock design (RCBD) with two replications. Agronomic performance was measured by collecting \nand analyzing data on plant height, ear height, disease scores of maize streak virus and gray leaf \nspot and grain yield. Data was subjected to analysis of variance (ANOVA) using Genstat 12 \nprogram for individual single crosses. Mean separation was done using Tukey’s comparison \nmethod at 5% significance level. \nOriginal Research Article \n"
+    },
+    {
+      "page": 2,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n2 \n \nResults: The best performing crosses in Muguga were also the best in Embu on grain yield \nproduction with exception of cross MUL 516 x MUL508 which had a mean grain yield of 11.9 t/ha in \nMuguga but produced 2.7 t/ha in Embu which was attributed to other factors other than genetic \nmakeup. Variation in yield showed a diverse genetic background of genotypes studied under these \nconditions. The grain yield ranged between 1.01 t/ha (MUL533 x MUL513) to 11.9 t/ha (MUL 516 x \nMUL 508) both in Muguga). The best performing cross for grain yield in Muguga was MUL 516 x \nMUL 508) while in Embu the best performing cross for grain yield was MUL541 x POPA. Data on \ndisease scores where natural infestation was visually scored showed that majority of the crosses \nhad a score of one confirming their near immunity status.  \nConclusions: For grain yield improvement crosses MUL508 x MUL688, POPA x MUL14, MUL513 \nx MUL114 and MUL513 x CN244 can further be evaluated and eventually released to farmers as \nthey indicated promising relationship with yield potential compared to other crosses. Further \nresearch on agronomic performance of the crosses can be done not only in the research sites but \nalso in other regions of Kenya. \n \n \nKeywords: Genotypes; mean performance; significant difference. \n \n1. INTRODUCTION \n \nMaize (Zea mays L.) is the world’s most widely \ngrown cereal and it is the primary staple food for \nmajority of population in many developing \ncountries [1]. It is a major source of food in Sub- \nSaharan Africa and it is grown by both small and \nlarge scale farmers [2]. Maize is an important \nsource of carbohydrate, protein, iron, vitamin B, \nminerals, livestock fodder and it is used in \nindustries for starch and oil extraction [3]. In \nKenya maize production is divided into six agro-\necological zones based on elevation and climate. \nThese regions include: the lowland tropics \ncomprising of the coastal strip and adjoining \ninland area, the dry mid attitude, the dry \ntransitional zones in the South East, the \nhighlands tropics, the moist transitional zone to \nthe East and West of the highland tropics, the \nmoist mid altitude zone around Lake Victoria [4]. \nThe moist transitional zones are the most \nimportant maize production zones followed by \nthe highland tropics. Maize yield variability is \nextremely high in Sub-Saharan Africa (SSA) than \nother regions of the world, as maize production is \nprimarily rain fed. Between 2005 and 2008, for \nexample the average maize yield in SSA was \nestimated at 1.4 t/ha which is very low as \ncompared to 2.5 to 3.9 in developing countries \n[5]. Between 2003 and 2005 the World Food \nProgram spent USS1.5 billion to alleviate food \nshortage due to drought and food failure in SSA \nalone [6]. With maize occupying such central \nposition in Kenya’s diet and farm production \nactivities, it’s imperative that ways and means of \nimproving maize productivity be sought [7]. One \nway of increasing maize production is by \nidentifying maize varieties that perform well in \ndifferent agro-climatic zones of Kenya. The main \nobjective \nof \nthis \nstudy \nwas \nto \nevaluate \nperformance of single cross hybrids in varying \nclimatic and soil conditions of Kenya.  \n \n2. MATERIALS AND METHODS \n \n2.1 Study Area \n \nThe experiment was undertaken in KALRO \nMuguga and KALRO Embu in Kiambu and Embu \ncounties respectively as shown in Fig. 1. \n \n2.2 Planting \nMaterials \nand \nField \nManagement \n \nThe experiment was laid out in a 6 x 6 lattice \nrandomized complete block design (RBCD) with \ntwo replications. The plots were ploughed before \nthe onset of rains and harrowed to produce a \nmedium tilth for maize. The plots consisted of 3 \nrows of 11 hills each at a spacing of 75 cm inter \nrow and 25 cm between hills. Planting at Muguga \nwas done on 10/4/2012 and in Embu on \n14/4/2012 at the onset of the long rains. A \ncompound fertilizer di-ammonium phosphate \n(DAP) was applied at the recommended rate of \n80 kg P2O5 per hectare during planting time.  \nTwo seeds were planted per hill but later thinned \nto leave one plant per hill. The plot area \nmeasured 5.55 m2 (0.75 m x 3 rows) x (0.3 x 11 \nplants) and had a population of 33 plants, giving \na total population of 53333 plants per ha-1. Hand \nweeding was done twice during the growth \nperiod; first two weeks after emergence and the \nsecond weeding four weeks after the first \nweeding. \nTop \ndressing \nwas \ndone \nusing \nnitrogenous fertilizer, Calcium Ammonium Nitrate \n(CAN 21%N) at the rate of 80 kg N per hectare \n"
+    },
+    {
+      "page": 3,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n3 \nafter thinning the plants. Maize stalk borer \n(Busseola \nfusca) \nwas \ncontrolled \nusing      \nBulldock ® (Beta-cyfluthrin) applied on the funnel \nof each plant at the rate of 6 kg per ha-1. \nHarvesting \nwas \ndone \non \n9/10/2012 \nat       \nKALRO Muguga and 10/10/2012 at KALRO \nEmbu. \n \n \nThe germplasm used in this study were 18 inbred \nlines and their respective single crosses derived \nfrom KALRO Muguga. The entries were used \nboth as the maternal parents in one cross as well \nthe paternal parents in the reciprocal cross \n(Table 1). \n \n2.3 Data Collection \n \nData was collected during growth period and \nafter attainment of physiological maturity. Data \nwas recorded on 12 randomly picked plants from \neach row. The pre harvest data included \nmeasurement of plant height, ear height and \nvisually scoring for gray leaf spot (GLS) and \nmaize streak virus (MSV). The post- harvest data \ncollected was grain yield per hectare. \n \n \n \nFig. 1. Study sites of KALRO Muguga and KALRO Embu \nArrows designate the two study areas Source: [8] \n \nTable 1. Inbred lines used in the study as parents of single crosses \n \nEntry \nSingle crosses \nEntry \nSingle crosses \n1. \n \nMUL 508 X MUL 516 \n2. \n \nPOP A X MUL 141 \n3. \n \nMUL 508 X MUL 521 \n4. \n \nPOP A X MUL 536 \n5.\nMUL 508 X MUL141\n6.\nPOP A X MUL 541\n7. \n \nMUL 508 X MUL 541 \n8. \n \nPOP A X MUL 688 \n9. \n \nMUL 508 X MUL 688 \n10. \n \nMUL 513 X MUL 531\n11. \n \nMUL 508 X CN 244 \n12. \n \nMUL 513 X MUL 533\n13. \n \nPOP A X  MUL 511 \n14. \n \nMUL 513 X MUL 114\n15. \n \nPOP A X MUL 521 \n16. \n \nMUL 513 X CN 244 \n17. \n \nPOP A X MUL 114 \n18. \n \nMUL 513 X MUL 516\n"
+    },
+    {
+      "page": 4,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n4 \n \n2.4 Data Analyses \n \nData collected was subjected to analysis of \nvariance (ANOVA) using Genstat 12 software. \nMean separation was done using Tukey’s \ncomparison method at 5% significance level.  \nThe data from the two environments, KALRO \nMuguga and KALRO Embu was analyzed \nseparately. \nAgronomic \nperformance \nwas \nanalyzed by determining coefficient of variation \n(CV) for plant height, ear height, maize streak \nvirus and gray leaf spot and grain yield (GY). \nGenotypes with low CV, low disease scores and \nhigh grain yield (GY) were considered most \ndesirable. \n \n3. RESULTS AND DISCUSSION \n \n3.1 Mean Performance of Crosses on \nDifferent \nMorphological \nTraits \nin \nKALRO Embu \n \nThe analysis of variance (ANOVA), showing the \nmean squares of plant height (PH), ear height \n(EH), disease scores of maize streak virus (MSV) \ndisease, gray leaf spot (GLS), and grain yield \n(GY) for Embu are shown in Table 2. The \ncrosses showed a highly significant difference \n(p=.001) for plant height and ear height. They \nalso showed a significant difference (p=.05) on \ngrain yield.  \n \nThe data (Table 3) showed that the mean ear \nheight for the crosses ranged between 53 cm for \nentry 30 (MUL533x MUL513) to 134 cm for entry \n18 (MUL141 x POPA). Most of the crosses \nshowed resistance to MSV with a mean score of \n1. However crosses: MUL516 x MUL508, POPA \nx MUL141, MUL513 x MUL531, MUL 513 x MUL \n516 (entry 2, 19, 28, 36) respectively had MSV \nscore of 2 and above, crosses MUL531 x \nMUL513 (entry 28) had a score of 4 which \nindicated infection. The mean plant height for the \ncrosses in Embu was 185 cm, ear height       \n99.5 cm, disease scores for MSV and GLS were \n1 and 1.77, respectively, while mean grain yield \nwas 4.14 t/ha (Table 3). Test cross POPA x MUL \n521 (entry 15) had the highest plant height     \n(236 cm) in Embu while the lowest was 119 cm \nfor MUL533 x MUL513 (entry30) (Table 3). \n \n3.2 Mean Performance of Crosses in \nKALRO Muguga \n \nThe analysis of variance (ANOVA) for plant \nheight, ear height, disease scores for MSV and \nGLS and grain yield for Muguga are shown in \nTable 4. The crosses showed significant (p=.05) \ndifference for plant height, ear height and GLS, \nwhile there was no significant difference on               \nMSV and grain yield on the crosses in Muguga \n(Table 4). \n \nData on the mean plant height in Muguga ranged \nbetween 148±4.00 cm for MUL 533 x MUL 513 \n(entry 30) to 278±3.00 cm for POPA x MUL541 \n(entry 23) (Table 5). The  cross which had the \nlowest mean  plant height  MUL533 x MUL 513 \n(entry 30) also had the lowest mean grain yield of \n1.01±0.4742 t/ha, the cross also had low mean  \near height (45±6 cm) and a GLS score of \n3.25±0.25 (Table 5). Cross MUL516 x MUL508 \n(entry \n2) \nhad \na \nmean \nplant \nheight \nof         \n178±1.5 cm, mean ear height of 55±2 cm and the \nhighest grain yield of 11.9±10.84 t/ha (Table 5). \nThe second best cross in grain yield was POPA x \nMUL541 (entry 23) which had grain yield of \n10.08±0.83 t/ha; this test cross also had the \nsecond highest mean ear height of 123±1.00 cm \n(Table 5). Cross POPA x MUL 141(entry 19) had \nthe \nhighest \nmean \near \nheight \nof                  \n125±2.5 cm and was the third best in mean grain \nyield of 8.65±0.33 t/ha. Crosses CN244 x \nMUL508 (entry 12) and POPA x MUL511 (entry \n13) had equal mean plant heights but different \nmean ear heights and different mean grain yields \n(Table 5).  \n \n \nTable 2. Analysis of variance of crosses for different morphological traits in KALRO Embu \n \nSource of variation \nDf \nPH (cm) \nEH (cm) \nMSV \nGLS \nGY (t/ha)\nReplication \n1 \n6290.7 \n660.1 \n10.889 \n27.5035 \n3.19 \nGenotype \n35 \n2047.7** \n1221.8** \n2.2 \n0.5527 \n4.02* \nError \n35 \n175.8 \n169 \n1.203 \n0.5035 \n1.74 \nOverall mean \n \n185 \n99.5 \n1.00 \n1.44 \n2.68 \nCV% \n \n7.1 \n4.3 \n5.5 \n49.4 \n7.2 \n*,** Significant at (p=.05), and (p=.001) respectively, PH-plant height, EH-ear height, MSV-maize streak virus, \nGLS-grey leaf spot, GY-grain yield, CV%-Coefficient of variation \n \n \n"
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+    {
+      "page": 5,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n5 \n \nTable 3. Mean performance (± standard error) of crosses on different morphological   \ntraits in Embu \n \nEntry \n Crosses \nPH (cm) \nEH (cm) \nMSV \nGLS \nGY (t/ha) \n1 \nMUL508XMUL516 \n157±3.0 \n71±0.5 \n2.5±1.5 \n2.5±1.0 \n2.8±0.1 \n2 \nMUL516XMUL508 \n143±11.0 \n70±11.0 \n2±1.0 \n2.5±1.0 \n2.7±0.7 \n3 \nMUL508XMUL521 \n135±19.5 \n60±6.0 \n1±0.0 \n1.25±0.25 \n2.0±0.7 \n4 \nMUL521XMUL508 \n151±14.5 \n75±2.5 \n0.5±0.5 \n2.5±1.5 \n2.3±0.9 \n5 \nMUL508XMUL141 \n195±10.0 \n99±5.0 \n1.5±0.5 \n2±0.5 \n4.2±0.7 \n6 \nMUL141XMUL508 \n197±27.0 \n105±11.0 \n0.5±0.5 \n1.75±0.75 \n4.6±1.7 \n7 \nMUL508XMUL541 \n157±21.0 \n72±8.0 \n1±0.0 \n2±1.0 \n2.6±1.2 \n8 \nMUL541XMUL508 \n153±17.5 \n78±7.5 \n1±0.0 \n2.25±1.25 \n2.3±1.0 \n9 \nMUL508XMUL688 \n223±8.0 \n116±3.5 \n1.5±1.5 \n1±0.0 \n5.3±1.0 \n10 \nMUL688XMUL508 \n221±0.5 \n121±7.5 \n1±1.0 \n2±1.0 \n4.1±0.6 \n11 \nMUL508XCN244 \n162±15 \n71±4.0 \n1.5±1.5 \n1±0.0 \n3.2±0.8 \n12 \nCN244XMUL508 \n210±0.5 \n123±0.5 \n0.5±0.5 \n1±0.25 \n4.8±0.7 \n13 \nPOPAXMUL511 \n209±12.0 \n121±11.5 \n1±0.0 \n1±0.0 \n3.2±0.0 \n14 \nMUL511XPOPA \n198±5.0 \n102±3.0 \n1±0.0 \n2±0.5 \n5.1±0.6 \n15 \nPOPAXMUL521 \n236±7.0 \n132±5.5 \n1±0.0 \n2.3±0.75 \n6.7±0.3 \n16 \nMUL521XPOPA \n210±13.0 \n123±1.0 \n0.5±0.5 \n1±0.0 \n4.3±0.2 \n17 \nPOPAXMUL114 \n186±2.5 \n94±6.0 \n1±1.0 \n2.25±0.75 \n5.3±0.3 \n18 \nMUL114XPOPA \n221±8.0 \n134±3.5 \n1±1.0 \n2.25±0.75 \n5.8±0.1 \n19 \nPOPAXMUL141 \n192±24.5 \n123±15.5 \n2±0.0 \n1±0.0 \n3.6±0.9 \n20 \nMUL141XPOPA \n212±2.5 \n131±5.0 \n0.5±0.5 \n1.25±0.25 \n5.6±0.8 \n21 \nPOPAXMUL536 \n219±23.5 \n124±20.5 \n0.5±0.5 \n1.75±0.75 \n4.5±2.4 \n22 \nMUL536XPOPA \n186±17.0 \n101±6.0 \n1±0.0 \n1.75±0.75 \n5.4±1.2 \n23 \nPOPAXMUL541 \n214±4.0 \n125±1.5 \n1±0.0 \n1.25±0.25 \n6.8±0.2 \n24 \nMUL541XPOPA \n223±4.0 \n129±2.5 \n1±1.0 \n2.25±0.75 \n6.9±0.5 \n25 \nMUL688XPOPA \n223±12.0 \n132±8.5 \n1±0.0 \n2.25±1.25 \n5.0±0.3 \n26 \nPOPAXMUL688 \n195±7.0 \n122±4.5 \n1±0.0 \n2.25±1.25 \n3.6±0.9 \n27 \nMUL513XMUL531 \n151±22.0 \n83±8.5 \n2±2.0 \n1.75±0.75 \n2.3±1.0 \n28 \nMUL531XMUL513 \n153±1.5 \n73±2.0 \n4±1.0 \n2±1.0 \n4.0±1.3 \n29 \nMUL513XMUL533 \n140±5.5 \n66±6.0 \n1±1.0 \n1±0.0 \n2.3±0.3 \n30 \nMUL533XMUL513 \n119±10.0 \n53±5.5 \n0.5±0.5 \n2.5±1.5 \n1.7±0.8 \n31 \nMUL513XMUL114 \n192±5.0 \n126±3.6 \n2±2.0 \n1±0.0 \n4.2±1.4 \n32 \nMUL114XMUL513 \n200±17.5 \n100±10.5 \n1±0.0 \n1.5±0.5 \n5.6±0.9 \n33 \nMUL513XCN244 \n207±3.0 \n111±0.0 \n3.5±0.5 \n1.25±0.25 \n4.6±0.7 \n34 \nCN244XMUL513 \n186±5.5 \n90±3.5 \n0.5±0.5 \n1.5±0.5 \n3.6±1.4 \n35 \nMUL516XMUL513 \n146±22.5 \n76±13.0 \n2.5±1.5 \n2±1.0 \n3.1±0.8 \n36 \nMUL513XMUL516 \n144±3.5 \n76±0.5 \n2±0.0 \n2.25±1.25 \n4.9±1.7 \n \nMean \n185±13.26 \n99.5±13 \n1±1.097 \n1.77±0.7096 4.14±1.319 \n \nL.S.D 5% \n26.92 \n26.39 \n2.2 \n1.4405 \n2.678 \nPH-plant height, EH-ear height, MSV-maize streak virus, GLS-grey leave spot, GY-grain yield, L.S.D % –Least \nSignificant Difference (5%) \n \nTable 4. Analysis of variance of crosses for different morphological traits in Muguga \n \nSV \nDf\nPH (cm)\nEH (cm)\nMSV\nGLS\nGY (t/ha)\nReplication \n1 \n19 \n32 \n0.0868 \n0 \n1.488 \nGenotype \n35 \n2175.9** \n796.8** \n0.1725 \n0.2865** \n11.536 \nError \n35 \n362.4 \n115.7 \n0.2225 \n0 \n8.215 \nOverall mean  \n \n214 \n79.9 \n1.16 \n1.94 \n4.84 \nCV% \n \n0.3 \n1.2 \n4.2 \n0 \n4.2 \n** Significant at (p=.05), PH- Plant height, EH- Ear height, MSV-Maize streak virus, GLS- Grey leaf spot, GY- \nGrain yield, CV%-Coefficient of variation \n \n"
+    },
+    {
+      "page": 6,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n6 \n \nTable 5. Mean performance (± standard error) of crosses for different morphological traits in \nKALRO Muguga \n \nEntry\nCrosses\nPH (cm)\nEH (cm)\nMSV\nGLS\nGY (t/ha)\n1 \nMUL508XMUL516 \n156±16.0 \n42±5.5 \n1±0.00 \n3.5±0.50 \n1.78±0.1901 \n2 \nMUL516XMUL508 \n178±1.5 \n55±2.0 \n1±0.00 \n3±0.00 \n11.9±10.8447 \n3 \nMUL508XMUL521 \n181±1.5 \n58±5.5 \n1±0.00 \n3±0.00 \n1.95±0.3049 \n4 \nMUL521XMUL508 \n160±7.5 \n54±6.0 \n2±1.00 \n3.25±0.25 \n1.84±0.0886 \n5 \nMUL508XMUL141 \n221±14.5 \n79±5.5 \n1.25±0.25 \n2.25±0.25 \n3.49±1.1328 \n6 \nMUL141XMUL508 \n242±6.0 \n92±0.5 \n1.25±0.25 \n2.75±0.25 \n5.17±0.0279 \n7 \nMUL508XMUL541 \n212±6.5 \n78±12.5 \n1.25±0.5 \n2.5±0.50 \n3.24±0.0854 \n8 \nMUL541XMUL508 \n220±10.5 \n76±3.5 \n1±0.00 \n2.5±0.50 \n5.71±1.0585 \n9 \nMUL508XMUL688 \n274±2.5 \n87±4.5 \n1±0.00 \n2±0.00 \n5.64±0.3859 \n10 \nMUL688XMUL508 \n233±15.5 \n87±3.5 \n1±0.00 \n2.75±0.25 \n4.66±0.8067 \n11 \nMUL508XCN244 \n211±5.0 \n68±10.0 \n2±0.00 \n2.25±0.25 \n3.84±02879 \n12 \nCN244XMUL508 \n238±17.5 \n84±10.5 \n1±1.00 \n2.25±0.25 \n3.99±0.7511 \n13 \nPOPAXMUL511 \n238±21.5 \n101±6.0 \n1±0.00 \n1.5±0.00 \n7.01±0.9207 \n14 \nMUL511XPOPA \n220±4 \n81±5.0 \n1±0.00 \n2±0.00 \n5.7±0.0413 \n15 \nPOPAXMUL521 \n234±13.0 \n83±3.5 \n1±0.00 \n2.5±0.50 \n5.64±0.8723 \n16 \nMUL521XPOPA \n233±28.5 \n95±2.8 \n1±0.00 \n2±0.00 \n5.07±1.6994 \n17 \nPOPAXMUL114 \n180±6.0 \n66±12.0 \n1.75±0.75 \n2.5±0.00 \n3.94±0.2226 \n18 \nMUL114XPOPA \n227±17.5 \n95±3.0 \n1±0.00 \n2.75±0.25 \n5.38±1.0128 \n19 \nPOPAXMUL141 \n260±3.5 \n125±7.0 \n1±0.00 \n1.75±0.25 \n8.65±0.3338 \n20 \nMUL141XPOPA \n250±18.5 \n117±8.5 \n1±0.00 \n1.75±0.25 \n6.75±2.6645 \n21 \nPOPAXMUL536 \n249±34.0 \n108±23.5 \n1±0.00 \n2±0.50 \n7.79±1.9045 \n22 \nMUL536XPOPA \n189±9.5 \n74±5.5 \n1.75±0.75 \n2.5±0.00 \n3.42±0.6465 \n23 \nPOPAXMUL541 \n278±3.0 \n123±1.0 \n1±0.00 \n1.5±0.00 \n10.08±0.8281 \n24 \nMUL541XPOPA \n232±11.0 \n96±5.5 \n1±0.00 \n1.5±0.00 \n6.23±0.6035 \n25 \nMUL688XPOPA \n227±15.5 \n86±3.5 \n1±0.00 \n2.25±0.25 \n5.77±1.039 \n26 \nPOPAXMUL688 \n209±4.5 \n86±1.0 \n1±0.00 \n2.5±0.00 \n4.75±0.472 \n27 \nMUL513XMUL531 \n197±15.5 \n67±9.0 \n1±0.00 \n2.5±0.50 \n2.95±0.1149 \n28 \nMUL531XMUL513 \n182±1.5 \n72±2.0 \n1±0.00 \n3±0.50 \n1.82±0.1047 \n29 \nMUL513XMUL533 \n197±7.5 \n65±1.5 \n1.5±0.50 \n2.5±0.00 \n3.42±0.5265 \n30 \nMUL533XMUL513 \n148±4.00 \n45±6.0 \n1±0.00 \n3.25±0.25 \n1.01±0.4742 \n31 \nMUL513XMUL114 \n241±20.5 \n86±12.0 \n1.5±0.5 \n1.75±0.25 \n6.05±0.8808 \n32 \nMUL114XMUL513 \n240±6.5 \n84±1.0 \n1.25±0.25 \n2±0.50 \n5.75±0.6533 \n33 \nMUL513XCN244 \n217±8.0 \n80±2.5 \n1±0.00 \n2.5±0.00 \n4.28±0.712 \n34 \nCN244XMUL513 \n186±10.5 \n70±11.0 \n1.25±0.25 \n2.5±0.50 \n4±0.8857 \n35 \nMUL516XMUL513 \n163±12.0 \n49±1.0 \n1±0.00 \n3.25±0.70 \n2.82±0.5103 \n36 \nMUL513XMUL516 \n187±13.0 \n71±11.0 \n1±0.00 \n2.5±1.00 \n2.83±0.6653 \nMean \n214±19.04 \n79.9±10.76\n1.16±0.4 \n1.944±0.32 \n4.84±2.866 \nL.S.D 5% \n38.64 \n21.84 \n0.9576 \n0.6645 \n5.819 \nPH-plant height, EH-ear height, MSV-maize streak virus disease, GLS-grey leaf spot, GY-grain yield, L.S.D- Least \nSignificant Difference (5%) \n \nThe height of the main ear is a very important \ncharacteristic for breeding maize, the higher it is \nthe more ears can develop from the nodes below, \nhowever if it is too high the weight of the ear may \nbend the stalk or even break it. Although low ear \nheight is unfavorable for yield and makes \nharvesting difficult, it does protect the stalk from \nexcessive weight [9]. The study revealed that \ncrosses from inbred line POPA had the highest \nplant height, ear height and also the highest grain \nyield in Embu (Table 3). According to Viola et al. \n[10], maize displays an orderly sequence of \ndevelopment of yield components namely: ear \nheight, plant height, ears per plant and grain \nweight. This explains why indirect selection can \nbe used by searching for improved yield \ncomponents. The crosses in Embu and Muguga \nshowed significant difference on plant height, ear \nheight and grain yield (P≤ 0.05). The results \nrevealed that   entry 23 in Muguga (POPA X MUL \n541) had the highest plant height (278±3.00 cm) \n(Table 5). The contrast was observed with entry \n30 (MUL 533 X MUL 513) having plant height of \n(119±4.0 cm) in Embu which was the lowest and \n"
+    },
+    {
+      "page": 7,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n7 \n \nalso \nrecorded \nthe \nlowest \ngrain \nyield \nof \n(1.675±0.47 t/h) (Table 4). The possible reason \nfor the observed differences among the crosses \non yield and yield components was variation in \ngenetic makeup. Different hybrids have also been \nevaluated for morphological and agronomic traits, \nshowing significant variation [11].  \n \nGrain yield being a complex trait is influenced by \nvarious environmental factors including biotic and \nabiotic factors. There is also interplay of various \nmorphological characteristics that influence final \nyield. The best performing crosses in Muguga \nwere also the best in Embu on grain yield \nproduction with exception of cross MUL 516 x \nMUL508  which had a mean grain yield of 11.9 \nt/ha in Muguga but produced 2.7 t/ha in Embu \nwhich was attributed to other factors other than \ngenetic makeup. Variation in yield showed a \ndiverse genetic background of genotypes studied \nunder these conditions. The grain yield ranged \nbetween 1.01t/ha entry 30 (MUL533 x MUL513) \nto 11.9 t/ha entry 2 (MUL 516 x MUL 508) both in \nMuguga (Table 5). The best cross for grain yield \nin Muguga was MUL 516 x MUL 508 (entry 2) \n(Table 5) while in Embu the best cross for grain \nyield was MUL541 x POPA (entry 24) (Table 3) \nindicating the crosses were unstable attributed to \nenvironmental factors. \n \nData on disease scores where natural infestation \nwas visually scored on a scale of 1-5 showed   \nthat among the evaluated crosses majority \nshowed an MSV average score of 1.16 in KALRO \nMuguga while in KALRO Embu the highest MSV \naverage scores of 4.00 were recorded. These \nobservations could be attributed to high incidence \nof leaf hoppers (Cicadulina spp.) which transmit \nmaize \nstreak \ndisease \nin \nEmbu. \nCrosses \nMUL531xMUL513 and MUL513 x CN244 had the \nhighest \nmean \nMSV \nscores \n4.0 \nand \n3.5 \nrespectively.  \n \n4. CONCLUSION \n \nIn Embu inbred line POPA produced the best \ngrain yields when crossed with MUL541 and \nMUL521. Its high grain yield was also witnessed \nin Muguga where on average its performance \nwas superior to other inbred lines. Cross MUL \n516 x MUL 508 had the highest overall mean \ngrain yield (11.9 t/ha) but had a GLS score of 3 \ncompared to the second best in mean grain yield \nPOPA x MUL541 10.1t/ha. These findings \nshowed that POPAX MUL541 though a good \nyielding cross was affected by GLS disease \nthough at a late stage when the ears had already \ndeveloped. Crosses with high plant height had \nhigher mean grain yield than those with low plant \nheight, while crosses with low ear height had low \nmean grain yield and vice versa. In order to \ndevelop promising genotypes, it is essential to \nknow the different traits particularly those \nassociated with grain yield which is the ultimate \nobjective in any breeding program.  \n \n \nThe present findings are useful to breeders in \nselecting the potential parental materials for \nmaize improvement programs in mid altitude \nagro-ecological zones in Kenya. For grain yield \nimprovement crosses MUL508 x MUL688, POPA \nx MUL141, MUL513 x MUL114 and MUL513 x \nCN244 can further be evaluated and eventually \nreleased to farmers as they indicated promising \nrelationship with yield potential compared to other \ncrosses.  Inbred line MUL 513 can further be \nevaluated for grain yield improvement with all the \nother inbred lines which had high grain yields. \nInbred lines MUL 508, POP A and MUL513 can \nbe used in improving other genotypes on disease \nresistant trait. There is also need for further study \nof the inbred lines used in this study for stability \nand adaptability in other counties of Kenya. \n \nCOMPETING INTERESTS \n \nAuthors have declared that no competing \ninterests exist. \n \nREFERENCES \n \n1. \nOfori E, Kyei-Baffour N. Agro-meteorology \nand maize production; 2006.  \nAvailable:http://www.wmo.ch/pages/prog/w\ncp/agm/gamp/documents/chap13c-draft  \n(Accessed on 3/10/2015) \n2. \nAnami S, De Block M, Machuka J, Van \nLysebettens M. Molecular improvement of \ntropical maize for drought stress tolerance \nin Sub-Saharan Africa. Critical Reviews in \nPlant Sciences. 2009;28(1-2):16-35. \n3. \nKenya agricultural and livestock research \norganization. Strategic plan implementation \nframework. \nK.A.L.R.O, \nKenya. \n2009-\n2014;15. \n4. \nCorbett JD. Classifying maize production \nzones in Kenya through multivariate cluster \nanalysis. \nIn: \nMaize \ntechnology \ndevelopment \nand \ntransfer: \nA \nGIS \napplication for research planning in Kenya, \nHassan \nRM(Ed.). \nCAB \nInternational, \nWallingford UK.  2005;15-25. \n5. \nSmale M, Byrerlee D, Jane T. Maize \nrevolutions in Sub-Saharan Africa. World \n"
+    },
+    {
+      "page": 8,
+      "text": " \n \n \n \nNjoka and Kariuki; JALSI, 12(1): 1-8, 2017; Article no.JALSI.30932 \n \n \n \n8 \n \nBank Policy Research Working Paper. \n2011;56(59):34. \n6. \nEdmeades G. Drought tolerance in maize. \nAn emerging reality. Companion Document \nto Excecutive Summary ISAAA Briefs. \n2008;39. \n7. \nGOK. Increasing value in agriculture. \nKenya Vision 2030. Government of Kenya. \n2007;6-7. \n8. \nDe Groote H, Owuor G, Doss C, Ouma J, \nMuhammed L, Danda K. The maize green \nrevolution in Kenya revisited- Electronic \nJournal of Agriculture and Development \nEconomic. 2005;2(1):32-49. \nAvailable:www.Fao.org/es/esa/JADE \n(Accessed 24/4/2013) \n9. \nZsubori Z, Gyenes Z, Hegyi, Illes O, Pok I, \nRacz F, Szoke C. Inheritance of plant and \near height in maize (Zea mays L.) Maydica. \n2002; 28:1-5. \n10. \nViola G, Ganesh M, Reddy SS, Kummar \nCVS. Study of heritability and genetic \nadvances in elite baby corn (Zea mays) \nLines. Progressive Agriculture Introduction \nto Regression Test Automation Software. \n2003;3(2):127-128. \nAvailable:www.operativesoft.com  \n(Accessed: 21/6/2013) \n11. \nIhsan H, Khalil IH, Rehman H, Igbal M. \nGenotypic \nvariability for morphological \ntraits among exotic maize hybrids. Sarhad \nJ. Agric.  2005;21(4):599-602. \n_________________________________________________________________________________ \n© 2017 Njoka and Kariuki; This is an Open Access article distributed under the terms of the Creative Commons Attribution \nLicense (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n \n \n \nPeer-review history: \nThe peer review history for this paper can be accessed here: \nhttp://sciencedomain.org/review-history/19489 \n"
+    }
+  ]
+}

chatbot/extracted/In_Situ_Water_Harvesting_Technologies_an.json

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+  "filename": "In_Situ_Water_Harvesting_Technologies_an.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/In_Situ_Water_Harvesting_Technologies_an.pdf",
+  "page_count": 20,
+  "pages": [
+    {
+      "page": 1,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n1 \nIN-SITU WATER HARVESTING TECHNOLOGIES AND FERTILIZER \nRATES INCREASE MAIZE AND BEAN YIELDS IN THE SEMI-ARID \nKATUMANI, KENYA † \n \n[LAS TECNOLOGÍAS DE COSECHA DE AGUA IN SITU Y LAS TASA DE \nFERTILIZANTE AUMENTAN LOS RENDIMIENTOS DE MAÍZ Y \nFRIJOLES EN EL SEMIÁRIDO KATUMANI, KENIA] \n \nKelvin M. Wafula1*, Nancy N. Karanja1, George N. Karuku1  \nand Anthony O. Esilaba2 \n \n1 Department of Land Resource Management and Agricultural Technology, \nUniversity of Nairobi, P.O Box 29053-00625 Nairobi, Kenya. \n2 Kenya Agricultural and Livestock Research Organization (KALRO) Headquarters. \nP.O Box 57811-00200 Nairobi, Kenya. Email: wafulakelvin2019@gmail.com \n*Corresponding author \n \n \nSUMMARY \nBackground: Crop production in the arid and semi-arid lands (ASALs) is constrained by erratic rainfall and poor soil \nfertility. Therefore, climate smart agriculture mechanisms such as in-situ rainwater harvesting technologies and \nrecommended fertilizer rates would be vital for ensuring food security. Objective: To evaluate selected in-situ water \nharvesting technologies and fertilizer rates on soil water content and yield of maize and beans at KALRO Katumani \nResearch Center in Machakos County, Kenya during the 2019 and 2020 short and long rain seasons, respectively. \nMethodology: The experiment was established in a randomized complete block design with a split-split plot \narrangement, replicated three times, with in-situ water harvesting technologies comprising of zai pits, ngolo pits, \ncontour furrows and conventional tillage, as the main plots, whereas the split plots were varying rates of fertilizer \ninputs: Di-ammonium phosphate (DAP), goat manure and control. The split-split plots comprised of maize and beans \ncropping systems. Soil moisture content was assessed at 4, 8, 12 and 16 weeks after emergence, whilst nutrient uptake, \nuse efficiency and crop yields at physiological maturity. Data was subjected to analysis of variance. Results: Soil \nmoisture, maize and beans yields, nutrient uptake and use efficiency were significantly (p ≤ 0.05) increased by in-situ \nwater harvesting technologies and fertilizer inputs. Highest soil moisture content was recorded under zai and ngolo \npits and lowest in conventional tillage treatments. Ngolo pits recorded higher maize and beans grain yield. Application \nof DAP fertilizer increased maize and beans grain yield compared to control. Intercropping maize and beans increased \ngrain yield significantly (p ≤ 0.05) compared to sole maize and sole beans. Implications. There is need for promoting \na combination of in-situ rainwater harvesting technologies especially ngolo and zai pits with application of DAP+ \nmanure in semi-arid areas where water is scarce coupled with poor soil fertility. Conclusion: Ngolo and zai pits \nincreased soil water retention capacity while application of DAP fertilizer led to increased crop yield and the study \ntherefore recommends their adoption within the study area and extrapolation to areas of similar conditions.  \nKey words: in-situ water harvesting; ngolo pits; zai pits; nutrients uptake; use efficiency.  \n \nRESUMEN \nAntecedentes: La producción de cultivos en las tierras áridas y semiáridas (ASAL) se ve limitada por la irregularidad \nde las lluvias y la escasa fertilidad del suelo. Por lo tanto, los mecanismos de agricultura climáticamente inteligente, \ncomo las tecnologías de recolección de agua de lluvia in situ y las tasas de fertilizante recomendadas, serían vitales \npara garantizar la seguridad alimentaria. Objetivo: Evaluar tecnologías seleccionadas de recolección de agua in situ y \ntasas de fertilizantes sobre el contenido de agua del suelo y el rendimiento de maíz y frijoles en el Centro de \nInvestigación KALRO Katumani en el condado de Machakos, Kenia, durante las temporadas de lluvia corta y larga de \n2019 y 2020, respectivamente. Metodología: El experimento se estableció en un diseño de bloques completos al azar \ncon un arreglo de parcelas divididas y divididas, replicado tres veces, con tecnologías de recolección de agua in situ \nque comprenden pozos zai, pozos ngolo, surcos de contorno y labranza convencional, como las parcelas principales. \nmientras que en las parcelas divididas se variaron las tasas de aportes de fertilizantes: Fosfato diamónico (FDA), \nestiércol caprino y testigo. Las parcelas divididas fueron constituidas por los sistemas de cultivo de maíz y frijol. El \ncontenido de humedad del suelo se evaluó a las 4, 8, 12 y 16 semanas después de la emergencia, mientras que la \n \n† Submitted February 17, 2022 – Accepted June 27, 2022. http://doi.org/10.56369/tsaes.4247 \n \n Copyright © the authors. Work licensed under a CC-BY 4.0 License.  https://creativecommons.org/licenses/by/4.0/ \nISSN: 1870-0462. \n"
+    },
+    {
+      "page": 2,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n2 \nabsorción de nutrientes, la eficiencia de uso y el rendimiento de los cultivos en la madurez fisiológica. Los datos se \nsometieron a análisis de varianza. Resultados: La humedad del suelo, los rendimientos de maíz y frijol, la absorción \nde nutrientes y la eficiencia del uso aumentaron significativamente (p ≤ 0.05) con las tecnologías de recolección de \nagua in situ y los aportes de fertilizantes. El contenido de humedad del suelo más alto se registró en pozos zai y ngolo \ny el más bajo en tratamientos de labranza convencional. Los pozos de ngolo registraron un mayor rendimiento de grano \nde maíz y frijol. La aplicación de fertilizante DAP aumentó el rendimiento de grano de maíz y frijol en comparación \ncon el control. El cultivo intercalado de maíz y frijol incrementó el rendimiento de grano (p ≤ 0.05) en comparación \ncon el maíz único y el frijol único. Implicaciones. Es necesario promover una combinación de tecnologías de \nrecolección de agua de lluvia in situ, especialmente pozos ngolo y zai con la aplicación de estiércol DAP+ en áreas \nsemiáridas donde el agua escasea y la fertilidad del suelo es deficiente. Conclusión: Los pozos ngolo y zai aumentaron \nla capacidad de retención de agua del suelo, mientras que la aplicación de fertilizante DAP condujo a un aumento del \nrendimiento de los cultivos y, por lo tanto, el estudio recomienda su adopción dentro del área de estudio y la \nextrapolación a áreas de condiciones similares. \nPalabras clave: captación de agua in situ; pozos de ngolo; pozos zai; absorción de nutrientes; eficiencia de uso. \n \n \nINTRODUCTION \n \nRainfed agriculture is the primary source of \nlivelihoods for majority of farmers in sub-Saharan \nAfrica (SSA) (Mechiche-alami and Abdi, 2020). \nUnfortunately, this sector has been marred with a \nmyriad of challenges including, but not limited to low \nand poor rainfall distribution, water scarcity, poor soil \nfertility, high evapotranspiration rates and high \nnutrient losses through erosion and runoff (Vanlauwe \net al., 2017; Mzezewa et al., 2011; Yazar and Ali, \n2016; Gikonyo et al., 2022). These challenges have led \nto low yields, leaving majority of household’s food \ninsecure (Rockström et al., 2003; Mutekwa, 2009). \n \nThe reduction in yields from farmers’ fields \ndemonstrate the need for appropriate agricultural \nproduction technologies, innovations and management \npractices (TIMPS) that are climate smart and geared \ntowards conservation of the little water received in the \nASALs, for the farmers to realize increased food \nproduction (Nyang’au et al., 2021; Ngetich et al., \n2014, Zougamore et al., 2014; Karuku, 2018; Gikonyo \net al., 2022). \n \nAmong the proposed technologies that have been \neffective in increasing crop production are the in-situ \nrainwater harvesting technologies such as zai pits, \nfurrow-ridges, tied ridges, earth and stone bunds and \nmulch ripping (Abubaker et al., 2014; Biazin et al., \n2012). These are simple and more affordable \ntechnologies that trap and hold rain water where it falls \nlong enough, increasing time for infiltration, delaying \nthe occurrence of severe water stress, thus buffering \ncrops against damage resulting from water deficits \n(Bayala et al., 2012; Dile et al., 2013; Mudatenguha et \nal., 2014; Nyamadzawo et al., 2013). Another unique \ntechnique is the use of the ngolo cultivation \ntechnology, which has been practiced by the Matengo \ncommunity in Tanzania. This system is characterized \nby combination of anti-erosion and soil fertility \nmaintenance technique of pits and ridges on steep \nslopes (Kato, 2001). \n \nStudies have shown that in-situ water harvesting \ntechnologies increase crop yields. For instance, JICA \n(1998) reported that maize grain yield increased by 1.3 \ntimes in ngolo pits plots compared to those under \nconventional tillage. A similar experiment at Mt \nKilimanjaro indicated 2.3 times higher maize grain \nyield in ngolo pits compared to those under \nconventional tillage and 3 times more compared to \nthose under bench terraces. In Ethiopia, Cofie and \nAmede (2015) reported increased potato and bean \nyields by 500% and 250%, respectively, as well a 300-\n700 % increase in crop water productivity in farms \nwith zai pits compared to those without. In Mali, \nMalesu et al. (2006), found out that maize yields under \nzai pit increased by a factor of 10 compared to \nconventional tillage.  \n \nIn as much as these technologies have shown an \nincrease in crop yields, their effectiveness is inefficient \nunless supplemented with soil fertility amendments. \nCombining water harvesting technologies with \nfertilizer inputs create a synergy that increases water \nand nutrient use efficiency, hence increasing yield \n(Winterbottom et al., 2013). Miriti et al. (2007) \nobserved that tied ridges in combination with \nintegrated nutrient management had the potential to \nimprove crop production in semi-arid eastern Keya.  \nNjeru et al. (2015) reported that integration of organic \nand inorganic inputs under various water harvesting \ntechnologies could be considered as an alternative \noption towards food security for semi-arid areas under \nthe changing climatic conditions.  \n \nIn order to address these challenges of soil fertility \ndecline, water scarcity and low economic returns, a \ntrial was established in Katumani, Machakos County \nwith the aim of addressing the effects of in-situ water \nharvesting technologies with combined fertilizer \ninputs on soil moisture content, nutrient uptake, use \nefficiency and yield of maize and beans.  \n \n \n"
+    },
+    {
+      "page": 3,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n3 \nMATERIALS AND METHODS \n \nStudy site \n \nThe experiment was conducted at Katumani Research \nStation of Kenya Agricultural and Livestock Research \nOrganization (KALRO) in Machakos County (Figure \n1), 80 km south-east of Nairobi, amid the short and \nlong rain seasons of 2019 and 2020, respectively.  The \nstation lies between latitudes 1°35′ S and longitude \n37°14′ E, at an elevation of 1575 meters above sea \nlevel. The area falls under agro-climatic zone IV \n(Jaetzold et al., 2006).  \n \nKatumani experiences a bimodal rainfall pattern with \nthe long rains commencing in March and ends in May \nwhereas the short rains occur in November and taper \noff in January (Recha et al., 2012). The site’s average \nannual rainfall ranges between 450-600 mm (Jaetzold \net al., 2006). The mean maximum and minimum \ntemperature are 24.6 and 13.7 0C, respectively. The \nmean potential evaporation ranges from 1820mm to \n1840mm with an estimated evapotranspiration (ETo) of \n1239 mm per year (Gicheru and Ita, 1987).  \n \nThe predominant soil types are Ferralo-Chromic \nLuvisols (WRB 2015), having high sand and low clay \ncontent, and exhibiting high bulk density (Karuku and \nMochoge, 2016; Karuma et al., 2014; Mbayaki and \nKaruku. 2021a and b; Mbayaki and Karuku 2022)). \nThese soils have low nitrogen mineralization potential, \nwith a pH of 6.3 (Kwena et al., 2018; Karuku and \nMochoge, 2018). Crops grown in the area include \nmaize (Zea mays, beans (Phaseolus vulgaris), millet \n(Pennisetum glaucum), sorghum (Sorghum bicolor), \ngreen grams (Vigna radiata), pigeon peas (Cajanus \ncajan) cowpeas (Vigna unguiculata) and dolichos \nlablab (Lablab purpureus) and mangoes (Mangifera \nindica). \n \nExperimental Design \n \nThe experiment was laid out in a split-split plot design \nwith individual treatments arranged in a randomized \ncomplete block design (RCBD) and replicated three \ntimes.  \n \nTreatments \n \ni. \nIn-situ water harvesting technologies namely; \nzai pits, ngolo pits, contour furrows and \nconventional tillage as the main plots. \nii. \nFertilizer types and rates; 100 kg/ha Di-\nammonium phosphate (DAP), 50 kg /ha DAP \n+ 2.5 t/ha goat manure, 5 t/ha goat manure as \nthe split plots and a control (no input). \niii. \nCropping systems; sole maize, sole beans and \nmaize-bean intercrop as the split-split plots. \n \nThe goat manure used had an alkaline pH (>7.0) with \na total nitrogen (TN) content of 2.1%, while organic \ncarbon was 6.4 and 7.4%, phosphorus levels were 785 \nand 730 ppm, while potassium levels were 17.5 and \n14.7 cmol/kg, in the manure used during the 2019 SR \nand 2020 LR seasons, respectively.  \n \n \n \n \nFigure 1. Study site in Machakos county, Kenya.  \n"
+    },
+    {
+      "page": 4,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n4 \nThe blocks measured 15 cm in length and a width of \n23 cm. The spacing between main plots was 2 m path, \nbetween split plots was 1 m while between the split-\nsplit plots was 0.5 m. In each of the split-split plots, \nthere were 3 zai pits under the zai pit technology and \nthree ngolo pits under the ngolo technology. The test \ncrops were maize (Katumani KDV4 variety) and beans \n(KATB1 variety). These varieties were selected due to \ntheir good adaptability, early maturation and yield \nhighly under semi-arid conditions.  \n \nAgronomic practices  \n \nLand preparation \n \nLand preparation and installation of the rain water \nharvesting structures was done on 16th October of 2019 \nbefore the onset of the short rains. Zai pits were \nconstructed by digging a hole measuring 1.5 m × 1.5 \nm to a depth of 30 cm using a hand hoe (Figure 2a). \nThe top 0-15 cm soil was piled on one side, and that \nfrom 15-30 cm piled on the lower side of the pits to \ntrap water in case of runoff, leaving a pit (zai) at the \ncenter. The top 0-15 cm dug out soil was then mixed \nwith the fertilizer and manure treatments and returned \nto half-fill the pit before planting.  \n \nNgolo pits: During the construction of ngolo pits, \ndried pigeon peas residues were collected, cut into \nsmaller pieces and then spread on the four sides of \nsquares measuring 1.5 m × 1.5 m (Figure 2b). Soil \nfrom the center of the pit was heaped evenly on the \nplant residues, leaving a pit at the center (ngolo) as \ndescribed by Kato et al. (2001). Maize and beans seeds \nwere planted on the heaped soils while the ngolo pits \nwas left bare to collect rain water. \n \nContour furrows were prepared by digging 0.3 m \ndeep trenches and planting was done in the furrows. \nThe conventional tillage system involved preparation \nof land using hand hoes; which is the farmers practice \nin the study area. \n \nCrop husbandry \n \nSowing was done at the onset of the rains on 19th \nOctober2019 and 20th April 2020 for the SR and LR \nseasons, respectively.  Short rain season (SR) \ncommenced in October 2019-February 2020, whereas \nlong rain season (LR) from April 2020-August 2020. \nMaize was planted at a spacing of 75 cm between the \nrows and 30 cm within rows, while beans were planted \nat a spacing of 45 cm between rows and 15 cm within \nrows. Two maize and three bean seeds were planted \nper hill, and then thinned two weeks after planting to \none maize and two beans per hill, giving a population \ndensity of 44,444 maize and 296,296 bean plants per \nhectare, respectively. Plants were randomly tagged for \naccuracy and ease of monitoring growth and data \ncollection. \n \nWeeding was done using a hand hoe at the emergence \nof weeds. At 4 and 8 weeks after emergence. To \ncontrol black cut worms (Agrotis ipsilon), (Duduthrin \n(Lambdacyhalothrin 17.5g/L) pesticide was sprayed, \nwhile corn leaf aphids (Rhopalosiphum maidis) were \ncontrolled by using Thunder (Imidacloprid 100g/L + \nBeta-cyfluthrin 45g/L) and Marshal (35 percent \nCarbosulfan). During the growing season, pesticides \nwere sprayed four times at 14-day intervals.  \n \nBeans and maize were harvested at physiological \nmaturity (2 and 4 months), respectively. \n \n \n \nA) Zai pits \nB) Ngolo pit \nFigure 2. These rain water harvesting structures (pits) were left on the land for the preceding long rain season. \n \n \n"
+    },
+    {
+      "page": 5,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n5 \nInstallation of access tubes \n \nThe polyvinyl chloride (PVC) access pipe, 100 cm \nlong and 5cm in diameter, with a watertight lid at the \nbottom, were manually inserted in the auger holes, in \nthe middle of each plot for soil moisture measurement. \nIn the zai pits, the pipes were placed in between the \nmaize crops along the rows. In ngolo pits, pipes were \nplaced on top of the ridges, between the crops, whereas \nin contour furrows, they were placed inside the \nfurrows. Pipes were placed between the maize crops \nalong the rows in in the middle of the conventional \ntillage. \n \nData collection \n \nWeather data \n \nDaily weather data on rainfall (mm), maximum and \nminimum temperature (°C) was obtained from the \nmeteorological weather station located at the KALRO-\nKatumani meteorological station.  \n \nSoil moisture content \n \nSoil moisture was measured at 4, 8, 12 and 16 weeks \nafter planting (WAP) non-destructively using a \ncalibrated Neutron 503DR Hydro probe. This was \ncalibrated using the gravimetric water content (g/100 g \nsoil) by plotting a graph of neutron counts against \ngravimetric water content.  A line of best fit was \ndeveloped with, \n \n𝑦= 𝑚𝑥 × 𝑐                                                              (1) \n \nWhere;   \ny = gravimetric water content, m = the gradient, x = \nneutron counts and c = y intercept.  \n \nAll the neutron probe readings were converted into \ngravimetric by multiplying with m (gradient of the line \nof best fit). Finally, the gravimetric water readings \nwere converted into volumetric using (Eqn 2) \n \n𝛳= 𝜔𝜌𝑏÷ 𝜌𝑤                                                        (2)     \n \nPlant tissue sampling and analysis \n \nBeans and maize plant tissue samples were collected \n65 and 120 days after sowing when crops attained \nphysiological maturity. Five (5) randomly selected and \ntagged maize plants were cut at the base with a \nmachete and separated into grains and biomass, \nwhereas ten (10) bean plants were uprooted by hand. \nGrains were threshed manually and their weights \nrecorded using a weighing balance (± 0.05g precision). \nThree (3) maize Stover and five (5) bean straws from \nthe harvested batch were chopped into smaller pieces. \nA subsample of grains and biomass were put in \nrespective khaki bags, and dried in the oven at 70 0C \nfor 24 hours to a constant weight, while beans were sun \ndried for 3 days to attain a moisture content of 12.5%.  \n \nThe dried samples were ground using a Willey Mill \nand passed through a 2 mm sieve for analysis of N, P \nand K contents using standard procedures as shown in \nTable 1. \n \nNutrient uptake \n \nThe nutrients (N, P and K) uptake was calculated as a \nproduct of nutrient concentration in grains or straw and \nthe yield (Eqn 3). \n \n𝑁𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑢𝑝𝑡𝑎𝑘𝑒 (𝑘𝑔 ℎ𝑎−1) =\n𝑛𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 ×\n𝑡𝑜𝑡𝑎𝑙 𝑑𝑟𝑦 𝑚𝑎𝑡𝑡𝑒𝑟 𝑦𝑖𝑒𝑙𝑑                                           (3) \n \n \nTable 1. Laboratory procedures. \nParameter \nMethod \nReferences \nTotal \nnitrogen \nModified \nmicro-\nKjeldahl method \nBremner,1996 \nAvailable \nphosphorus \nExtracted \nby \nMehlich-1, \nthen \nmeasured using a \nUV \nspectrophotometer \nMurphy \nand \nRiley, 1962 \nPotassium \nFlame photometer \nBarnes et al., \n1945 \n \n \nNutrient use efficiency \n \nNutrient use efficiency was computed using the \nformula as described by Brentrup and Palliere (2010) \n(Eqn 4). \n \nNutrient use effiiciency =\n 𝑌𝑖𝑒𝑙𝑑 𝑖𝑛 𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑑 𝑝𝑙𝑜𝑡𝑠 −𝑦𝑖𝑒𝑙𝑑 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑝𝑙𝑜𝑡𝑠 \n𝑡ℎ𝑒 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑓𝑒𝑟𝑡𝑖𝑙𝑧𝑒𝑟 𝑎𝑝𝑝𝑙𝑖𝑒𝑑\n                 (4) \n \nBiomass and grain yield \n \nFinal biomass and grain yield were obtained from \nplants harvested from the net plot measuring 2.25 m2 \nafter discarding the border rows and end of plants of \neach row. The collected subsamples were oven dried at \n70 0C for 48 hours. Dry maize and beans grain and \nStover/straw were computed using (Eqn 5). \n \n𝐺𝑟𝑎𝑖𝑛/𝑏𝑖𝑜𝑚𝑎𝑠𝑠 𝑦𝑖𝑒𝑙𝑑 (𝑘𝑔 ℎ𝑎−1) =\nGrain dry yield (kg) ×10,000 m2 \ntotal area of the plots \n                                   (5) \n \n \n \n"
+    },
+    {
+      "page": 6,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n6 \nStatistical analysis \n \nEffect of different treatments on soil moisture, yield, \nnutrient uptake and use efficiency was determined in a \ntwo-way ANOVA with the aid of GenStat 15th edition \n(Lane and Payne, 1997). Mean separation was done \nusing Fisher’s protected Least Significant Difference \n(LSD) at 5% significance level. \n \nRESULTS AND DISCUSSIONS \n \nSoil physical and chemical properties  \n \nThe soil physical and chemical properties are \npresented in Tables 2 and 3. \n \nThe soil had a sandy clay loam (SCL) textural class, \nwith percentage sand decreasing down the soil profile, \nwhereas clay content increased. Kwena et al. (2018) \nobtained similar findings in the textural class and this \nmay have an implication to water holding and rain \nwater holding issues. Similarly, the diffusivity may be \nlower and hence availability of nutrients is limited and \nhence may affect the nutrient use efficiency.  \n \nThe bulk density was 1.4 g/cm3 at the upper horizon \nand it decreased down the soil profile. The high bulk \ndensity at the top horizon could have formed due to \ncompaction caused by previous shallow ploughing \nwhich created an impervious layer and a hard pan. \nDigging of ngolo and zai pits as well as construction of \nthe contour furrows helped in breaking the surface \ncrust, hence improving water infiltration (Danjuma \nand Mohammed, 2015). This could be the probable \nreason for higher soil moisture obtained at deeper \nhorizons under the rain water harvesting technologies, \nas the technologies collected water and retained it for \nperiod of time. \n \nThe average soil pH was 6.25, within the range \nbetween 5.0 -7.0, required for effective growth of \nmaize and beans (FAO, 2012). Soil pH plays a pivotal \nrole in the chemical characterization of the soil. In \nmost arid and semi-arid areas, a mixture of minerals \nexists each with different zero point of charge (ZPC) \nsimilar to this study site. A soil is composed of so many \nconstituents that the ZPC value of the soil is \ndetermined and/or affected by their physico-chemical \nproperties and eventually its efficiency in crop \nproduction (Bennett et al., 2019). \n \nThe % OC ranged from 0.6 to 1.2 %, hence low, \naccording to London (2014). Soil organic matter is a \nkey attribute of soil quality that impacts soil \naggregation, resulting in increased infiltration, \nmovement of water in the soil and available water \ncapacity. Soils with organic matter content ≤ 3% are \nconsidered not suitable for crop production, because \nthe ideal organic matter content is ≥ 6% (USDA, \n1997), hence there in need for addition of fertilizers \nand manure in order to increase crop production. \n \nThe TN ranged between 0.08 to 0.1%, hence regarded \nas low (London, 2014). The low TN could be attributed \nto the low soil organic carbon, mainly as a result of \nlack of crop residue plough back. Available \nphosphorus content ranged between 15 to 23.4 ppm, \nrated at medium in relation to the threshold value of 25 \nppm (Brennan et al., 2013; Fairhurst, 2012). \nExchangeable potassium (K) concentration ranged \nbetween 0.9 to 1.7 cmol/kg.  \n \nClimatic data  \n \nMonthly climatic data during crops’ growing season \nare shown in Table 4. \n \nIn the 2019 SR, most rainfall was recorded at crop \nplanting (I) and vegetative development stages (II); \n219.9 and 211.5 mm, respectively (Figure 3). On the \nother hand, minimal rainfall was recorded at \ntasseling/silking stage; 57.9 mm. Low rainfall \nespecially at tasseling/silking stage could result to \nwater stress and therefore affect grain filling process \nand eventually, yield. However, this was not the case \nin the 2019 short rain season, probably because of the \npresence of the in-situ water harvesting technologies \nwhich could have stored enough soil moisture and \navailed it to crops for uptake.   \n \n \n \nTable 2. Soil physical properties of the experimental site. \nDepth \nPb (g/cm3) \nPorosity % \nSand % \nClay % \nSilt % \nKsat cm/hr \nTextural class \n0-15 \n1.4 \n0.47 \n74 \n24 \n2 \n19.6 \nSCL \n15-30 \n1.2 \n0.55 \n70 \n28 \n2 \n43.5 \nSCL \n30-45 \n1.2 \n0.55 \n68 \n30 \n2 \n36.1 \nSCL \n45-60 \n1.2 \n0.55 \n66 \n32 \n2 \n32.8 \nSCL \n60-75 \n1.2 \n0.55 \n64 \n32 \n4 \n37.4 \nSCL \n75-90 \n1.3 \n0.51 \n62 \n34 \n4 \n9.1 \nSCL \nAverage \n1.25 \n0.53 \n69 \n29 \n3 \n29.8 \nSCL \nLegend: Pb- Bulk density, SCL- Sandy clay loam. \n \n"
+    },
+    {
+      "page": 7,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n7 \nTable 3. Soil chemical properties. \nParameters \n0- 15 \ncm \n15-30 \ncm \n30-60 \ncm \n75-90 \ncm \npH (H2O) \n6.6 \n6.5 \n6.1 \n5.80 \nOrganic carbon \n(OC) (%) \n1.2 \n1.3 \n0.9 \n0.55 \nTotal Nitrogen \n(TN) (%) \n0.1 \n0.1 \n0.1 \n0.08 \nPhosphorus (P) \n(ppm) \n23.4 \n25.1 \n23.9 \n15.00 \nPotassium (K) \n(cmol/kg) \n1.7 \n1.9 \n1.8 \n0.90 \n \n \nIn the 2020 LR, minimal rainfall was recorded at crop \nvegetative development, tasseling/silking and at \nmaturity; 12, 6.3 and 5 mm, respectively. The highest \nrainfall experienced was at initiation (I); 140.8 mm. \n \nOn average, the two cropping seasons recorded low \nrainfall, though higher in short rain season than in the \nlong rain season (Figure 3). Low rainfall in the 2020 \nLR season would imply that crop yields would be \nlower as uptake of water and nutrients by plant roots \nwould be difficult as water is held at high tension \nmeaning more energy expended in water uptake that \ncould go to yield production.  \n \nReference evapotranspiration was higher during the \n2020 LR season compared to the 2019 SR season, with \nhigher values recorded at tasseling/silking (III) and \nmaturation (IV) stages compared to values recorded at \nvegetative/development (II) and at initiation (I) stages \n(Figure 3). \n \nThe increase in the ETo values coincided with \ntasseling and silking stages where the rate of \ntranspiration because at this stage, the plants are fully \ndeveloped. If not managed well, the crops might wilt \ndue to higher water loss because in this period, the \nwater demand is the greatest and therefore, a strict \ncontrol of water supply is quite necessary (Farias et al., \n2017). \n \nThe average maximum and minimum temperature \nrecorded in the 2019 SR was 25 and 15.1 0C, \nrespectively. The hottest months were February and \nMarch, corresponding to crop maturation stages with a \nmean temperature of 26.2 0C. In the 2020 LR, the \naverage maximum air temperature was 24.4 and \nminimum was 12.7 0C, with May and June as the \nhottest months with maximum mean temperature of \n24.8 and minimum temperature of 13 0C. \n \n \nFigure 3. Rainfall, reference evapotranspiration (ETo), minimum (Tmin) and maximum (Tmax) temperature recorded \nfor different maize development stages namely; initiation (I), vegetative and development (II), tasseling/silking (III) \nmaturation (IV). \n \n \n5\n10\n15\n20\n25\n30\n0\n50\n100\n150\n200\n250\nI\nII\nIII\nIV\nI\nII\nIII\n1V\n2019 SR\n2020 LR\nTemperature (°C)\nRainfall and ETo (mm)\nRainfall (mm)\nETo (mm)\nTmax (°C)\nTmin (°C)\n"
+    },
+    {
+      "page": 8,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n8 \nInfluence of in-situ water harvesting technologies, \nfertilizer inputs and cropping systems on soil \nmoisture retention \n \nTable 4 presents soil moisture content (cm3 /cm3) at \ndifferent sampling times. Soil moisture recorded at 4 \nweeks after planting (WAP) showed significant (p ≤ \n0.05) differences among the in-situ rain water \nharvesting technologies. Ngolo pits recorded 22.87 \ncm3/cm3 moisture, significantly higher than contour \nfurrows and conventional tillage which recorded 19.45 \nand 16.42 cm3 cm-3, respectively. The higher and \nsignificant soil moisture observed in ngolo pits \ncompared to the other technologies could be attributed \nto the increased water retention as well as the mulching \neffect resulting from the buried crop residues during \nconstruction. These findings are consistent with those \nof Malley (2005) who found out that the buried \nresidues in the ngolo pits, helped in improving soil \nfertility status, conserved soil moisture and led to \nincreased maize yield. \n \nA similar trend was observed in the soil moisture \nrecorded at 8 WAP (tasselling and silking stage).  \n \nAt this stage, crops had fully developed and the ground \ncover was sufficient to reduce the direct impact of solar \nradiation which helped in reducing soil evaporation \nrate (Qi et al., 2011). Well established crop cover can \nalso increase water infiltration and reduce runoff (Yu \net al., 2016). This could have been attributed to the \nreduced ETo as reported earlier.  \n \nSimilarly, cropping systems had a significant (p ≤ \n0.05) effect on soil moisture between cropping at crop \nvegetative \ndevelopment \nstage \n(4 \nWAP) \nand \ntasselling/silking stages (8 WAP). Sole plots of maize \nand beans recorded; 18.01 and 18.97 cm3/cm3 \nmoisture, respectively at 4 WAP and 11.08 and 12.36 \ncm3/cm3 moisture, respectively at 8WAP compared to \nintercrop. \n \nComparison of soil moisture between seasons and \ntreatments showed that 2019 SR season was higher \ncompared to the 2020 LR. This could be attributed to \nthe differences in the amount of rainfall received, with \nthe 2020 LR season, receiving only 309 mm the entire \ngrowing season, which is below the maize and beans \nwater requirement of 500-800 and 300-500 mm, \nrespectively (FAO, 2012; Abideen, 2014). This was a \nlimiting factor in contrast to the 2019 SR season where \nthe total rainfall received was 1078 mm. This implied \nthat crops did not suffer water stress during the 2019 \nseason, hence the higher yields recorded. \n \n \nTable 4. Influence of in-situ water harvesting technologies, fertilizer inputs and cropping systems on soil water \ncontent in cm3/cm3 at the top 0-20 cm depth. \n  \n2019 SR \n2020 LR \nTreatments \n4 WAP \n8 WAP \n12 WAP \n16 WAP \n4 WAP \n8 WAP \n12 WAP \n16 WAP \nWater harvesting technologies (T) \n  \nNgolo pits \n22.87a \n14.75a \n17.21a \n16.78a \n14.97a \n13.40ab \n14.76a \n14.21a \nZai pits \n20.15ab \n13.14ab \n17.12a \n16.71a \n13.82a \n13.78a \n15.49a \n15.16a \nContour furrows \n19.45b \n11.22b \n15.43a \n16.20a \n13.72a \n12.24b \n14.50a \n14.82a \nConventional tillage \n16.42c \n10.91c \n14.74a \n16.10a \n13.64a \n11.58b \n13.41a \n11.02a \nFertilizer inputs (I) \n  \nDAP \n18.80a \n11.15a \n17.30a \n17.10a \n14.17a \n12.41a \n14.75a \n13.77a \n½ DAP + ½ Manure \n19.30a \n12.15a \n15.97ab \n16.53a \n14.22a \n12.49a \n14.86a \n13.72a \nManure \n17.99a \n11.05a \n15.77b \n16.25a \n13.99a \n12.48a \n14.29a \n14.39a \ncontrol \n16.34a \n11.01a \n15.47b \n16.00a \n13.85a \n12.22a \n14.26a \n13.34a \nCropping systems (CS) \n  \nSole beans  \n18.97a \n12.36a \n16.68a \n16.61a \n14.93a \n12.17a \n14.42a \n13.32a \nSole maize \n18.01a \n11.08ab \n15.91a \n16.47a \n13.91a \n12.56a \n14.45a \n13.00a \nIntercrop \n16.44b \n10.57b \n15.79a \n16.34a \n13.33a \n12.49a \n14.75a \n15.10a \nSummary p-values \n  \n  \n \n  \n  \n  \n  \n  \nT \n0.002 \n0.008 \n0.172 \n0.940 \n0.046 \n0.006 \n0.507 \n0.346 \nI \n0.157 \n0.311 \n0.014 \n0.474 \n0.549 \n0.710 \n0.824 \n0.671 \nCS \n0.037 \n0.029 \n0.288 \n0.814 \n0.105 \n0.239 \n0.836 \n0.006 \nI × CS \n0.054 \n0.558 \n0.151 \n0.082 \n0.624 \n0.929 \n0.975 \n0.623 \nT × CS \n0.389 \n0.306 \n0.546 \n0.188 \n0.091 \n0.904 \n0.281 \n0.944 \nT × I × CS \n0.038 \n0.406 \n0.656 \n0.629 \n0.207 \n0.434 \n0.349 \n0.614 \nLegend: DAP fertilizer (100 kg/ha), half rate DAP + half rate goat manure (50 kg /ha+2.5 t/ha), Manure (5 t/ha), WAP-\nWeeks after planting *Means followed by the different letter down the column differ significantly at p ≤ 0.05 \n \n"
+    },
+    {
+      "page": 9,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n9 \nThe difference in soil moisture content at different \ngrowth stages could be attributed to the amount of \nrainfall, soil evaporation, transpiration and crop water \nuptake (Mujdeci et al., 2010).  For instance, low soil \nmoisture was recorded at tasselling stage (8 WAP) and \nsilking (12 WAP) compared to vegetative development \nstage (4 WAP). Tasselling and silking are the critical \nstages where crops water requirement is high and \ntherefore takes up a lot of water from the soil. Moisture \nstress and nutrient deficiencies occurring at these \nstages could greatly reduce the number of kernels per \nrow, resulting in shorter ears and lower yield potential \n(Admasu et al., 2017). \n \nFormation of pits during the construction of ngolo and \nzai technologies allowed more storage of rain water \nand time for infiltration, thus the reason for higher soil \nmoisture content. One proven attribute of ngolo pits is \nsoil entrapment in the pits, which helps in reducing \nrunoff \nwhilst \nencouraging \ninfiltration \nand \nsedimentation.  \n \nAmede et al. (2011), Milkias et al. (2018) and \nGebreegziabher et al. (2009), recorded higher soil \nmoisture content in zai pits and tied ridges probably \ndue to increased water retention, infiltration and \nreduced run-off. Fatondji et al. (2006), while working \non psammentic paleustalf soils in Niger reported \nsimilar findings with zai pits retaining significantly \nmore soil water than conventional tillage.  \n \nThe low soil water content recorded in intercrop \nsystem compared to monocrops could be due to the \nhigh population density per plot, which could have \nresulted in higher water extraction from the soil. These \nfindings are in conformity with those of Karuma et al. \n(2014), while working on Alfisols and Acrisols soil \ntypes in the semi-arid area of Mwala in Machakos \ncounty.  \n \nBeans provided soil cover during the vegetive and \ndevelopment stages, a probable reason for higher soil \nmoisture in bean plots. Steiner (2002) reported that \ncropping systems that offer surface cover promote soil \nwater conservation by reducing evaporation and \nincreasing infiltration rate. \n \nMaize grain and biomass yields \n \nTable 5 presents the interactive effects of water \nharvesting technologies, fertilizer inputs and cropping \nsystems on maize grain and Stover yields. \n \nIn-situ water harvesting technologies, fertilizer inputs \nand cropping systems significantly (p ≤ 0.05) \nimproved maize grain yield during the 2019 SR \nseason. Highest maize grain yield (4.5 t/ha) was \nobtained in ngolo pits, which was 28.5, 44 and 68.6 % \nhigher than zai pits, contour furrows and conventional \ntillage, respectively (Table 3). While there was no \nsignificant difference between maize yields in zai pits \nand contour furrows, mean separation indicated that \nzai pits had 21.7% more yield than contour furrows. \n \nApplication of fertilizer and manure resulted in a \nsignificant (p ≤ 0.05) maize yield increase with \nfollowing trend; DAP ≥ DAP + 2.5 t/ha manure ≥ 5 \nt/ha of manure ≥ control treatments. Significant \ndifferences (p < 0.001) in maize grain yield were also \nobserved between cropping systems, where maize \nyield was higher in the intercrop compared to sole \nmaize. This could be attributed to the increased water \nand nutrient use efficiency and the complementarity \nbetween the two crops as alluded by Hauggaard-\nNielsen et al. (2008) and Buhk et al. (2017). \n \nSimilar trend was observed in stover production, with \nin-situ rain water harvesting technologies having a \nsignificant (p ≤ 0.05) effect. Highest stover of 7.43 t/ha \nwas recorded in ngolo pits, which was significantly \ndifferent from stover obtained from contour furrows \nand conventional tillage which recorded the least \nstover of 4.39 and 3.16 t/ha, respectively. Application \nof DAP fertilizer gave the highest stover yield with \ncontrol plots yielding the lowest stover.  \n \nIn the 2020 LR season, the effect of fertilizer inputs \nwas significant (p < 0.001) in influencing maize grain \nand stover yields. The highest mean yields were \nrecorded in plots treated with DAP alone and in \ncombination with manure at half rates, with the lowest \nin the control. This could therefore imply that addition \nof organic and inorganic amendments to the soil \nimproved the chemical properties that enhanced \navailability of nutrients and their uptake as alluded by \nRuganzu et al. (2015). \n \nMoisture content plays a pivotal role in crop’s \nphysiological development from germination to \nmaturity \nas \nit \ncontrols \ncrop’s \nphenological, \nphysiological and morphological characteristics (Khan \net al., 2001). When there is soil water scarcity, the \nnumber of grains per plant and yield per unit area also \ndeclines (Saberina, 2010). This is because the \nbiochemical processes occurring in the plant are \naffected and the crops tend to hasten their maturity and \ncan end up wilting. Higher grain and stover yields \nobserved in ngolo and zai pit technologies could be \nattributed to the nutrient and moisture availability. \nCrop roots absorb these available resources, resulting \nin increased growth and improved grain yield. \n \n \n \n"
+    },
+    {
+      "page": 10,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n10 \nTable 5. Maize Stover and grain yields as affected by in-situ water harvesting technologies, fertilizer inputs and \ncropping systems. \nTreatments \n2019 SR \n2020 LR \nStover \nGrains              \nStover  \nGrains           \n \nt/ha \nWater harvesting technologies (T) \nNgolo pits  \n7.43a \n4.52a \n4.21a \n1.55a \nZai pits  \n5.98ab \n3.23b \n2.65a \n1.06a \nContour furrows  \n4.39bc \n2.53b \n3.47a \n0.88a \nConventional tillage \n3.16c \n1.42c \n2.24a \n0.61a \nS.E. \n0.528 \n0.248 \n0.527 \n0.303 \nLSD ≤ 5% \n1.827 \n0.574 \n2.5824 \n1.048 \nFertilizer inputs (I) \nDAP \n7.20a \n3.67a \n4.81a \n1.54a \n½ DAP + ½ Manure \n5.66b \n3.49a \n3.49b \n1.25a \nManure \n5.09b \n2.53b \n2.51c \n0.87b \nControl \n3.01c \n2.02c \n1.74d \n0.44c \nS.E. \n0.233 \n0.145 \n0.179 \n0.121 \nLSD ≤ 5% \n0.681 \n0.323 \n0.522 \n0.353 \nCropping system (CS) \nSole maize \n5.01b \n2.88b \n2.97b \n0.97a \nMaize-bean intercrop \n5.36a \n3.21a \n3.31a \n1.08a \nS.E. \n0.118 \n0.069 \n0.081 \n0.051 \nLSD ≤ 5% \n0.339 \n0.132 \n0.234 \n0.146 \nSummary of p-values \nT \n0.006 \n<.001 \n0.134 \n0.234 \nI \n<.001 \n<.001 \n<.001 \n<.001 \nCS \n<.001 \n<.001 \n0.024 \n0.124 \nT×CS \n0.685 \n0.330 \n0.151 \n0.500 \nT×I \n0.008 \n0.058 \n0.090 \n0.050 \nI×CS \n0.659 \n0.659 \n0.017 \n0.045 \nT×I×CS \n0.439 \n0.467 \n0.477 \n0.997 \nLegend: DAP fertilizer (100 kg/ha), half rate DAP + half rate goat manure (50 kg/ha+2.5 t/ha), Manure (5 t/ ha1), \n*Means followed by the different letter down the column differ significantly at p ≤ 0.05 \n \n \nKumar et al. (2000) observed that availability of \nmoisture in the soil during crop growth stages resulted \nin \nbetter \ncrop \ngrowth \nand \nimproved \nyield. \nMudatenguha et al. (2014) linked the significant \nincrease in maize yield under zai pits compared to \nconventional tillage to, the ability of zai pits to collect, \nstore and avail soil moisture to the crop roots during \ngrowth.  \n \nJICA (1998) attributed the higher maize yields \nrecorded under ngolo pits to improved soil fertility \nstatus, which could have resulted from decomposition \nof the buried crop residues during the construction of \npits.  \n \nAccording to Kato (2001), darker soil rich in organic \nmatter is formed in deeper layers under ngolo pits, \nwhen buried residues are mixed into the deep soils, \nwhich provide conditions favorable for high crop \nyields (Kato, 2001).  \n \nThese results are consistent with Wouterse (2017) who \nreported that zai pit technology was an intervention \nused by smallholder farmers to increase agricultural \nproduction through improving rainwater capture, \nreducing runoff, reducing water evaporation from the \nsoil increasing water infiltration. Biazin et al. (2012), \nDanjuma et al. (2012) and Kar et al. (2013) also \nreported that rain water harvesting technologies in \ncombination with the use of inorganic and organic \ninputs increases nutrients in the soil, thereby \nimproving crop productivity. \n \nThe significant increase in grain and biomass yield in \nresponse to the application of DAP fertilizer alone and \nmixture of DAP and manure at half rates could be \nattributed to increased nutrient and soil moisture \navailability, which could have facilitated the uptake of \nnutrients by plant roots, translating to high yield. Soil \nmoisture has an impact on the forms, solubility, and \naccessibility of plant nutrients required for crop growth \n(Ampofo, 2006). Increased yield following fertilizer \n"
+    },
+    {
+      "page": 11,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n11 \nand manure application could be attributed to the \nimproved fertility status of the soil, as alluded by Patel \net al. (2013). This result signified the more prominent \nroles played by DAP fertilizer and manure in \nenhancing growth of crops and thus yield. This is due \nto the fact that mineral fertilizer provides nutrients that \nare easily soluble in soil solution, whereas organic \nmanures help to improve soil health and health, \nthereby improving nutrient availability and making \nnutrients readily available to crops (Aziz et al., 2010; \nAyuke et al., 2004; Bationo, 2004). \n \nUnder cropping systems, grain and stover yields from \nintercropping systems outperformed those from \nmonocrop.  This implies that intercropping is more \nefficient than mono-cropping at utilizing soil water and \nnutrients, which could be attributed to intercrop \ncomplementarity \nand \nsynergist \neffects. \nThis \ncontradicts the findings of Belel et al. (2014), who \nobtained lower yields in intercropping systems due to \ncompetition for moisture, nutrients and light. \n \nLower maize grain and stover yields were recorded in \n2020 long rains in all the treatments, which could be \nattributed to the low amounts of rainfall received \nduring the season (Figure 1). Given that maize requires \n500-800 mm of water in the entire growing season \n(FAO, 2012), the rainfall amount recorded in this \nseason was inadequate to meet the crop’s seasonal \nwater requirement. This might have resulted in water \nstress conditions, which might have resulted in reduced \nnutrient uptake, growth and yield (Khondaker et al., \n2013).  \nBean grain yield \n \nThe interactive effects of in-situ water harvesting \ntechnologies, fertilizer inputs and cropping systems on \nbean yields is shown in Figure 4. \n \nBean yield was significantly (p ≤ 0.05) affected by in-\nsitu water harvesting technologies, fertilizer inputs and \ncropping systems. A significant (p ≤ 0.05) interaction \nbetween water harvesting technologies × fertilizer \ninputs × cropping system was observed. Higher grain \nyield of 1.64 t/ha was obtained in bean-maize \nintercropping system under ngolo pits following the \napplication of 100 kg/ha DAP fertilizer, whereas \nlowest yield of 0.44 t/ha was obtained from control \nplots of sole beans under conventional tillage during \nthe 2019 SR (Figure 4). \n \nDuring the 2020 LR, rainfall distribution was poor, \nwith \nprolonged \ndrier \nconditions \nexperienced \nthroughout the growing season, and this greatly \naffected beans, resulting to crop failure. \n \nThese results show that combination of in-situ water \nharvesting technologies; ngolo pits and DAP fertilizer \nfavored beans growth through provision of water and \nnutrients for uptake, and thus improved yield. Under \nthe different cropping system, higher yield was \nobserved under the intercrop system. This could be \nattributed to the complementarity and synergist effects \nbetween intercrops.\n \n \n \nFigure 4. Bean grain yield as affected by in-situ water harvesting technologies, fertilizer inputs and cropping systems. \n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\nBeans\nIntercrop\nBeans\nIntercrop\nBeans\nIntercrop\nBeans\nIntercrop\nNgolo pits\nZai pits\nContour Furrows\nConventional Tillage\nGrain yield (t ha-1)\nTechnologies\nDAP\nDAP+ Manure\nManure\nCTRL\n"
+    },
+    {
+      "page": 12,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n12 \nDrought experienced during the 2020 LR especially at \nthe crop flowering stage could have resulted in \nsignificant reduction in crop growth and hence low dry \nmatter production. The drying of leaves signifies a \nreduction in photosynthesis the pathways, hence low \nleaf development and reduced light interception. This \nin turn results to a significant reduction in yields \n(Emam et al., 2010). Similar findings were presented \nby Rezene et al. (2013) who reported that drought \nstress at the pre-flowering resulted to a reduction in \nseed quality, lowered the number of pods per bean \nplant, ultimately leading to a reduction in yields. \n \nEffect of in-situ water harvesting technologies, \nfertilizer inputs and cropping systems on nutrient \nuptake \n \nThe interactive effect of water harvesting technologies, \nfertilizer inputs and cropping systems on nutrient \nuptake is shown in Table 6. \nIn-situ water harvesting technologies, fertilizer inputs \nand cropping systems had significant (p ≤ 0.05) effects \non grain N, P and K uptake.  In the SR, the highest \ngrain N, P and K uptake by maize grain (67.7, 48.2 and \n24.9 kg/ha, respectively) recorded in ngolo pits were \nsignificantly different from zai pit, contour furrows \nand conventional tillage. The lowest uptake by grain \nwere exhibited in conventional tillage (Table 6).  \n \nHighest N, P and K content was recorded following \napplication of DAP fertilizer, with control plots \nexhibiting the lowest grain N, P and K contents. \nApplication of 100 kg/ha DAP fertilizer recorded 31.8, \n29.6 and 31.6% higher N, P and K, respectively than \napplication of 5 t/ha manure and 56.3, 53.2 and 54.5% \nhigher N, P and K uptake, respectively than control \nplots. \n \n \nTable 6. Nutrient uptake in maize grain as affected by water harvesting technologies, fertilizer inputs and \ncropping systems \n \nTreatments \n2019 short rain (SR) \n2020 long rain (LR) \nGrains uptake (Kg/ha) \nGrains uptake (Kg/ha) \nN  \nP \nK \nN \nP \nK \nWater harvesting technologies (T) \nNgolo pits \n67.7a \n48.2a \n24.9a \n23.2a \n20.1a \n12.1a \nZai pits \n43.1b \n40.7ab \n16.2b \n15.9a \n19.0a \n10.9a \nContour furrows \n38.9b \n35.2b \n12.5b \n11.2a \n11.7a \n10.0a \nConventional tillage \n19.5c \n25.7c \n9.5c \n11.2a \n11.3a \n7.4a \nS.E. \n3.180 \n2.07 \n0.913 \n3.432 \n2.131 \n1.011 \nLSD ≤ 5% \n11.003 \n10.62 \n3.527 \n11.878 \n7.374 \n 5.119 \nFertilizer inputs (I) \nDAP \n55.4a \n42.5a \n20.9a \n20.9a \n19.9a \n13.3a \n½ DAP + ½ Manure \n51.7a \n37.1a \n18.5b \n18.3ab \n17.6a \n10.4a \nManure \n37.8b \n29.9b \n12.5c \n14.3b \n15.6a \n7.6ab \ncontrol \n24.2c \n19.9b \n9.5d \n8.0c \n9.1b \n4.3b \nS.E. \n3.531 \n2.81 \n0.887 \n1.505 \n1.697 \n0.988 \nLSD ≤ 5% \n10.307 \n11.13 \n2.076 \n4.394 \n4.953 \n 4.729 \nCropping systems (CS) \nSole maize \n38.8b \n43.2b \n14.2b \n14.5a \n15.1a \n12.2a \nMaize-bean intercrop \n45.8a \n56.5a \n16.6a \n16.2a \n16.0a \n12.8a \nS.E. \n1.264 \n1.71 \n0.137 \n0.823 \n0.556 \n0.412 \nLSD ≤ 5% \n3.64 \n4.92 \n0.711 \n2.371 \n1.603 \n0.945 \nSummary p-values \n  \n  \n  \n  \n  \n  \nT \n<.001 \n<.001 \n<.001 \n0.136 \n0.048 \n0.079 \nI \n<.001 \n<.001 \n<.001 \n<.001 \n0.001 \n0.034 \nCS \n<.001 \n<.001 \n<.001 \n0.155 \n0.238 \n0.122 \nT × I \n0.196 \n0.084 \n0.008 \n0.019 \n0.260 \n0.312 \nT × CS \n0.473 \n0.469 \n0.056 \n0.243 \n0.038 \n0.541 \nI × CS \n0.364 \n0.200 \n0.728 \n0.870 \n0.719 \n0.119 \nT ×I × CS \n0.264 \n0.633 \n0.124 \n0.979 \n0.742 \n0.674 \nLegend: DAP fertilizer (100 kg/ha), half rate DAP + half rate goat manure (50 kg/ha+2.5 t/ha), Goat manure (5 t/ ha). \n*Means followed by the different letter down the column differ significantly at p ≤ 0.05. \n \n"
+    },
+    {
+      "page": 13,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n13 \nCropping systems significantly (p ≤ 0.05) affected \ngrain uptake, with higher N, P and K uptake recorded \nin intercropped plots than in sole maize plots. \n \nDuring the LR, in-situ water harvesting technologies \nand cropping systems did not significantly influence N, \nP and K uptake, however the N, P and K grain contents \ndiffered significantly (p ≤ 0.05) with fertilizer inputs. \nApplication of 100 kg ha-1 DAP fertilizer led to \nsignificantly higher N, P and K content than N, P and \nK contents recorded in control plots. \n \nNitrogen, phosphorus and potassium values were \nsignificantly higher in ngolo as compared to zai pits, \ncontour furrows and conventional tillage, probably due \nto availability of soil moisture and better root growth \nthat favored nutrient uptake. Water is critical in \ndetermining a plant's ability to absorb nutrients from \nthe soil, because, soil water content influences nutrient \nmovement from the soil, to the roots and to the \naboveground part of the plants (Rani et al., 2020; Li et \nal., 2009). Outarra et al. (2006) reported a positive \ncorrelation between soil moisture and N, P and K \nuptake due to improved soil moisture status which \nincreases the availability of nutrients. Similar findings \nwere reported by Dougbedji (2002) who found that zai \npits \nimproved \nnitrogen \nuptake \ncompared \nto \nconventional tillage on psammentic paleustalf soils in \nNiger. \n \nThe higher uptake of N, P and K in ngolo pits \ncompared to contour furrows and conventional tillage \ncould be attributed to the improved soil health status \ndue to the decomposition of the buried crop residues. \nMalley (2005) reported that incidences where soil \nfertility status is improved, then nutrients are readily \navailable to crops, hence an increased uptake. These \nfindings corroborate with those of Pasley et al. (2019), \nwho reported higher N, P and K uptake as a result of \nincreased fertility status. \n \nThe results show that adding DAP and manure had a \npositive response to N, P and K uptake and it was high \nin plots treated with 100 kg ha-1 DAP followed by \nmixture of 50 kg ha-1 DAP + 2.5 t ha-1 manure. This \nmight be due to the increased supply of all nutrients \ndirectly though organic and inorganic sources to crops. \nThis proposition is consistent with that of Haile et al. \n(2012), who reported that N, p and K uptake by wheat \ncrop was significantly increased when the highest dose \nof N fertilizer was applied. Similar findings were \nreported by Malo and Ghosh (2019) who reported \nhighest uptake of N, P and K by rice following the \napplication of inorganic and organic fertilizers. \n \nIt was also noted that uptake of N, P and K increased \nunder combined use of in-situ water harvesting \ntechnologies and fertilizer inputs. This could be \nattributed to the conserved soil moisture which might \nhave helped in dissolving the soil nutrients from the \napplied DAP fertilizer, making them easily available \nfor plant uptake.  \n \nDuring dry season, soils become dry and therefore, \nplants experiences difficulty absorbing nutrients, \nbecause most nutrients are in elemental forms rather \nthan ionic forms, resulting in low uptake and hence \nnutrient levels may be lower than normal (Liu et al., \n2013; Jones et al., 2011). This could explain why there \nwas higher nutrient uptake in the 2019 short rains as \ncompared to 2020 long rains. These findings are \nconsistent with the findings of Ademba et al. (2014), \nwho reported that N, P, and K uptake varied seasonally \ndue to variation in rainfall patterns. Thus, weather \nconditions have a significant impact on a plant's ability \nto absorb nutrients, with low uptake occurring during \nseasons with insufficient rainfall (Ibrahim et al., 2011; \nSigunga et al., 2002).  \n \nEffect of in-situ water harvesting technologies, \nfertilizer inputs and cropping systems on nitrogen \nand phosphorus use efficiency \n \nTable 7 presents the effects of water harvesting \ntechnologies, fertilizer inputs and cropping systems on \nN and P use efficiency. \n \nNitrogen and phosphorus use efficiency showed \nsignificant (p ≤ 0.05) response to the main effects of \nin-situ water harvesting technologies and fertilizer \ninputs but not with cropping systems in the 2019 SR \nseason. Nitrogen and phosphorus use efficiency were \n30.12 and 38.3 kg/ha under ngolo pits, significantly \nhigher than 12.4 and 16.9 kg/ha N and P use efficiency \nunder conventional tillage in the 2019 SR season \n(Table 7). No significant difference in N and p use \nefficiency was recorded between ngolo, zai pits and \ncontour furrows. \n \nApplying DAP fertilizer at 100 kg/ha, led to an \nincrease in N and P use efficiency, whereas application \nof 5 t/ha manure resulted in lower N use efficiency. \nNitrogen and phosphorus use efficiency under DAP \nalone and mixture of DAP + manure showed an \naverage of 57.4 and 41.9 % increase, respectively over \nmanure alone in the 2019 SR season.   \n \nIn the 2020 LR, neither in-situ water harvesting \ntechnologies nor cropping systems were significant in \ninfluencing N and P use efficiency. However, fertilizer \ninputs had a significant (p≤ 0.05) on N and P use \nefficiency. The highest values of N and P use \nefficiency at 39.1 and 40.1 kg/ha, respectively were \nobtained following application of 100 kg ha-1 DAP \nfertilizer, and lowest N and P use efficiency values \nrecorded in plots treated with 5 t/ha manure. \nCombination of DAP and manure applied at half rates  \n \n"
+    },
+    {
+      "page": 14,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n14 \nTable 7. Nitrogen and phosphorus agronomic use efficiency in maize cropping system under in-situ water \nharvesting technologies, fertilizer inputs and cropping systems. \nTreatments \n2019 SR \n2020 LR \nNUE \nPUE \nNUE \nPUE \nWater harvesting technologies (T) \nNgolo pits  \n30.16a \n38.27a \n21.11a \n26.42a \nZai pits  \n24.39a \n34.18a \n12.04a \n14.55a \nContour furrows  \n25.89a \n32.60a \n10.80a \n12.05a \nConventional tillage  \n12.44b \n16.92b \n10.04a \n13.14a \nS.E. \n2.93 \n3.70 \n2.13 \n1.93 \nLSD ≤ 5% \n10.14 \n12.8 \n8.708 \n13.06 \nFertilizer inputs (I) \nDAP \n39.09a \n40.05a \n22.97a \n24.47a \n½ DAP + ½ Manure \n23.93b \n35.90a \n15.39b \n17.98a \nManure \n16.64c \n23.28b \n13.20c \n10.92b \nControl \n- \n- \n- \n- \nS.E. \n2.19 \n2.95 \n1.93 \n1.23 \nLSD ≤ 5% \n6.55 \n8.84 \n4.941 \n7.006 \nCropping system (CS) \nSole maize \n22.5a \n29.4a \n13.28a \n16.86a \nMaize-bean intercrop \n23.9a \n30.1a \n14.43a \n17.72a \nS.E. \n1.96 \n2.53 \n1.01 \n1.44 \nLSD 5% \n5.72 \n7.39 \n2.464 \n3.182 \nSummary p-values \n  \n  \n  \n  \nT \n0.024 \n0.014 \n0.100 \n0.131 \nI \n<.001 \n<.001 \n<.001 \n0.008 \nCS \n0.613 \n0.845 \n0.347 \n0.585 \nT × CS \n0.214 \n0.230 \n0.150 \n0.524 \nT × I \n0.345 \n0.368 \n0.406 \n0.365 \nI × CS \n0.705 \n0.916 \n0.639 \n0.817 \nT × I × CS \n0.894 \n0.968 \n0.579 \n0.856 \nLegend: DAP fertilizer (100 kg/ha), half rate DAP + half rate goat manure (50 kg/ha+2.5 t /ha), Goat manure (5 t ha). \nNUE-Nitrogen use efficiency, PUE- Phosphorus use efficiency *Means followed by the different letter down the \ncolumn differ significantly at p ≤ 0.05. \n \n \nled to 14.2% increase in N use efficiency compared to \nwhen manure was applied at 5 t/ha. \n \nThe higher N and P agronomic use efficiencies denoted \nby yields under ngolo and zai pits than in conventional \ntillage is a probable indication that there was better \nutilization of nutrients and water in the two \ntechnologies. Crops under these technologies could \nhave benefited from the conserved water and available \nnutrients at the root zone, which resulted in faster \ngrowth, higher nutrient uptake, enhanced utilization \nand yield. Availability of moisture directly influences \nthe ability of crops to take up nutrients from the soil, \nand in turn their utilization efficiency. Dougbedji \n(2002) \nreported \nsimilar \nfindings, \nwhere \nthe \nconcentration of N in pearl millet grain was higher \nunder zai pits compared to conventional tillage. \n \nIn line with the present finding, Shaheen et al. (2012) \nreported that the efficiency of plants to absorb nutrients \nand the capacity of the soil to supply them are reduced \nunder low soil moisture condition, and therefore in \nagreement with this study’s findings.  \n \nThe beneficial effect of fertilizers in enhancing \nnutrient use efficiency of crops could be attributed to \nthe rapid early growth, which contributes significantly \nto dry matter accumulation and hence higher use \nefficiency (Kugedera et al., 2019). This could probably \nbe the reason for increased N and P use efficiency \nfollowing application of 100 kg/ha DAP and mixture \nof 50 kg/ha + 2.5 t/ha manure. Higher uptake and use \nefficiency contribute to better us of applied nutrients \nand reduce losses from the soil (Oo et al., 2010). \n \nCONCLUSIONS \n \nZai and ngolo pits recorded consistently higher soil \nmoisture content at all the sampling times across the \ntwo seasons compared to conventional tillage.  \n \n"
+    },
+    {
+      "page": 15,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n15 \nGrain and biomass yield as well as nutrient uptake and \nuse efficiency from zai and ngolo pits were higher than \nthose from contour furrows and conventional tillage. \nIt was noted that grain and biomass yield in plots \ntreated with 100 kg/ha DAP fertilizer was not \nsignificantly different from those obtained from plots \ntreated with a mixture of 50 kg/ha DAP + 2.5 t/ha goat \nmanure. Farmers can therefore apply a mixture of \nmineral fertilizer and animal manure at half rates to \nobtain optimal yield. \n \nCereals and legumes are recommended to be grown \nunder ngolo or zai pits. This is due to the ability of the \ntwo technologies to store water that will be available \nto crops and to cushion crops against droughts that are \npredicted to become more frequent and severe as a \nresult of climate change. \n \nFunding: This work was fully funded by Kenya \nAgricultural \nLivestock \nResearch \nOrganization \n(KALRO) through the Kenya Cereal Enhancement \nProgram-Climate \nResilient \nand \nAgricultural \nLivelihoods (KCEP-CRAL) project. \n \nConflict of interest: The authors confirm that there are \nno known conflicts of interest associated with this \npublication. \n \nCompliance with ethical standards do not apply: \nDo not apply. No human participants or animals were \nused in the studies undertaken in this article by any of \nthe authors. \n \n \nData availability: Data is available with Kelvin \nWafula \nMukhebi \n(wafulakelvin2019@gmail.com) \nupon reasonable request. \n \nAuthor contribution statement (CRediT): Kelvin. \nM. Wafula- conceptualization, data curation, formal \nanalysis, \nmethodology, \ninvestigation, \nproject \nadministration, visualization, resources, software, \nwriting original draft. Nancy N. Karanja, George N. \nKaruku and Anthony O. Esilaba- supervision, \nvalidation, writing- review and editing.  Anthony O. \nEsilaba- funding. \n \nREFERENCES \n \nAbideen, Z.U., 2014. Comparison of crop water \nrequirements of maize varieties under \nirrigated condition in semi-arid environment. \nJournal of Environment and Earth Science. \n4(9), \npp. \n1-3. \nhttps://www.iiste.org/Journals/index.php/JE\nES/article/view/11836  \n \nAbubaker, B.M.A., En, Y.S., Cheng, S.G. and Alhadi, \nM., 2014. Impact of different water \nharvesting techniques on soil moisture \ncontent and yield components of sorghum. \nPakistan Journal of Agricultural Sciences, \n51, pp- 779-788.  \n \nAdemba, J.S., Kwach, J.K., Ngari, S.M., Esilaba, A.O. \nand Kidula, N.L., 2014. Evaluation of organic \nand inorganic amendments on nutrient \nuptake, phosphorus use efficiency and yield \nof maize in Kisii County, Kenya. African \nJournal of Agricultural Research, 9(20), \npp.1571-1578. \nhttps://doi.org/10.5897/AJAR2012.709.  \n \nAdmasu, R., Tadesse, M. and Shimbir, T., 2017. Effect \nof growth stage moisture stress on maize (Zea \nmays) yield and water use Eeficiency at West \nWellaga, Ethiopia. Journal of Biology, \nAgriculture and Healthcare, 8(23), pp. 98-\n103. \nhttps://www.iiste.org/Journals/index.php/JB\nAH/article/view/40192/41338  \n \nAmede, T., Menza, M. and Awlachew, S.B., 2011. Zai \nimproves nutrient and water productivity in \nthe \nEthiopian \nhighlands. \nExperimental \nAgriculture, \n47(S1), \npp. \n7-20.  \nhttps://doi.org/10.1017/S0014479710000803  \n \nAmpofo, E.A., 2006. Soil moisture dynamics in coastal \nsavanna soils in the tropics under different \nsoil management practices. Hydrological \nSciences Journal, 51(6), pp. 1194-1202. \nhttps://doi.org/10.1623/hysj.51.6.1194 \n \nAziz, T., Ullah, S., Sattar, A., Nasim, M., Farooq, M. \nand Khan, M.M., 2010. Nutrient availability \nand maize (Zea mays) growth in soil amended \nwith organic manures. International Journal \nof Agriculture and Biology. 12(4), pp. 621-\n624.  \n \nBationo, A., Fairhurst, T., Giller, K. E., Kelly, V., \nLunduka, R., Mando, A., and Zingore, S. \n2012. Africa Soil Health Consortium: Hand \nbook \nfor \nIntegrated \nSoil \nFertility \nManagement. CAB International. ISBN (e-\nbook), \n978(1), \n78064. \nhttps://publications.cta.int/media/publication\ns/downloads/1853_PDF.pdf  \n \nBayala, J., Sileshi, G.W., Coe, R., Kalinganire, A., \nTchoundjeu, Z., Sinclair, F. and Garrity, D., \n2012. Cereal yield response to conservation \nagriculture practices in drylands of West \nAfrica: a quantitative synthesis. Journal of \nArid \nEnvironments, \n78, \npp. \n13-25. \nhttps://doi.org/10.1016/j.jaridenv.2011.10.011 \n \n"
+    },
+    {
+      "page": 16,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n16 \nBelel, M.D., Halim, R.A., Rafii, M.Y. and Saud, H.M., \n2014. Intercropping of corn with some \nselected legumes for improved forage \nproduction: A review. Journal of Agricultural \nScience, \n6(3), \np. \n48. \nhttps://doi.org/10.5539/jas.v6n3p48.  \n \nBennett, J.A. and Klironomos, J., 2019. Mechanisms \nof plant–soil feedback: interactions among \nbiotic and abiotic drivers. New Phytologist, \n222(1), \npp. \n91-96. \nhttps://doi.org/10.1111/nph.15603.  \n \nBiazin, B., Sterk, G., Temesgen, M., Abdulkedir, A. \nand Stroosnijder, L., 2012. Rainwater \nharvesting and management in rainfed \nagricultural systems in sub-Saharan Africa–a \nreview. Physics and Chemistry of the Earth, \nParts \nA/B/C, \n47, \npp. \n139-151. \nhttp://dx.doi.org/10.1016/j.pce.2011.08.015.  \n \nBrentrup, F. and Pallière, C., 2010, March. Nitrogen \nuse efficiency as an agro-environmental \nindicator. In Proceedings of the OECD \nWorkshop on Agri-environmental Indicators, \nMarch (pp. 23-26).  \n \nBuhk, C., Alt, M., Steinbauer, M.J., Beierkuhnlein, C., \nWarren, S.D. and Jentsch, A., 2017. \nHomogenizing and diversifying effects of \nintensive agricultural land-use on plant \nspecies beta diversity in Central Europe: A \ncall to adapt our conservation measures. \nScience of the Total Environment, 576, pp. \n225-233. \nhttps://doi.org/10.1016/j.agwat.2020.106434. \n \nChaudhari, P.R., Ahire, D.V., Ahire, V.D., Chkravarty, \nM. and Maity, S., 2013. Soil bulk density as \nrelated to soil texture, organic matter content \nand available total nutrients of Coimbatore \nsoil. International Journal of Scientific and \nResearch Publications, 3(2), pp. 1-8.  \n \nCofie, O. and Amede, T., 2015. Water management for \nsustainable agricultural intensification and \nsmallholder resilience in sub-Saharan Africa. \nWater Resources and Rural Development, 6, \npp. \n3-11. \nhttps://doi.org/10.1016/j.wrr.2015.10.001. \n \nDile, Y.T., Karlberg, L., Temesgen, M. and \nRockström, J., 2013. The role of water \nharvesting to achieve sustainable agricultural \nintensification and resilience against water \nrelated shocks in sub-Saharan Africa. \nAgriculture, Ecosystems & Environment, \n181, \npp. \n69-79. \nhttps://dx.doi.org/10.1016/j.agee.2013.09.014.  \nDougbedji, \nF., \n2002. \nOrganic \namendment \ndecomposition, nutrient release and nutrient \nuptake by millet (Pennisetum glaucum (L.) R. \nBr.) in a traditional land rehabilitation \ntechnique (zai) in the Sahel (Vol. 1). Cuvillier \nVerlag. \n \nEmam, Y., Shekoofa, A., Salehi, F. and Jalali, A.H., \n2010. Water stress effects on two common \nbean cultivars with contrasting growth habits. \nAmerican-Eurasian Journal of Agricultural \n& Environmental Sciences, 9(5), pp. 495-499. \nhttps://doi.org/10.1080/03650340.2010.530256.  \n \nEsilaba, A.O., Njiru, E., Ruto, R., Omondi, S.P., \nKwena, K.M., Thuranira, E.G., Mwangi, J.A. \nand Simiyu, A.W., 2020. Enhancing Soil \nProductivity and Water and Nutrient Use \nEfficiencies \nin \nIntegrated \nCropping-\nLivestock Production Systems in Kenya. \nIAEA Tecdoc Series, p. 56. https://www-\npub.iaea.org/MTCD/Publications/PDF/TE-\n1924web.pdf.  \n \nFairhurst, T., 2012. Handbook for integrated soil \nfertility management. CTA/CABI. \n \nFAO. 2012. Crop Evapotranspiration (Guidelines for \ncomputing \ncrop \nwater \nrequirement). \nIrrigation and Drainage. 56, p. 163 \n \nFarias, V.D.D.S., Lima, M.J.A.D., Nunes, H.G.G.C., \nSousa, D.D.P. and Souza, P.J.D.O.P., 2017. \nWater Demand, Crop Coefficient \nand \nUncoupling Factor of Cowpea in The Eastern \nAmazon1. Revista Caatinga, 30, pp.190-200. \nhttps://www.redalyc.org/pdf/2371/23714856\n9021.pdf  \n \nFatondji, D., Martius, C., Bielders, C.L., Vlek, P.L., \nBationo, A. and Gerard, B., 2006. Effect of \nplanting technique and amendment type on \npearl millet yield, nutrient uptake, and water \nuse on degraded land in Niger. Nutrient \nCycling in Agroecosystems, 76(2), pp. 203-\n217. \nhttps://link.springer.com/article/10.1007/s10\n705-005-6209-9  \n \nGebreegziabher, T., Nyssen, J., Govaerts, B., Getnet, \nF., Behailu, M., Haile, M. and Deckers, J., \n2009. Contour furrows for in situ soil and \nwater \nconservation, \nTigray, \nNorthern \nEthiopia. Soil and Tillage Research, 103(2), \npp. \n257-264. \nhttps://doi.org/10.1016/j.still.2008.05.021  \n \nGicheru, P.T. and Ita, B.N., 1987. Detailed soil survey \nof the Katumani National Dryland Farming \n"
+    },
+    {
+      "page": 17,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n17 \nResearch Station Farms (Machakos District). \nRepublic of Kenya, Ministry of Agriculture, \nNational Agricultural Laboratories, Kenya \nSoil \nSurvey. \nhttps://books.google.co.ke/books/about/Detai\nled_Soil_Survey_of_the_Katumani_Nat.htm\nl?id=GnoiHAAACAAJ&redir_esc=y  \n \nGikonyo N.W., Busienei, J.R., Gathiaka, J.K; Karuku, \nG.N. 2022. Analysis of Household savings \nand adoption of climate Smart Agricultural \nTechnologies. Evidence from smallholder \nfarmers in Nyando Basin, Kenya. Heliyon, \n8(6), \npp. \nE09692. \nhttps://doi.org/10.1016/j.heliyon.2022.e09692.  \n \nHaile, D., Nigussie, D. and Ayana, A., 2012. Nitrogen \nuse efficiency of bread wheat: Effects of \nnitrogen rate and time of application. Journal \nof Soil Science and Plant Nutrition, 12(3), \npp.389-410. \nhttp://dx.doi.org/10.4067/S0718-\n95162012005000002     \n \nHauggaard-Nielsen, H., Jørnsgaard, B., Kinane, J. and \nJensen, E.S., 2008. Grain legume–cereal \nintercropping: The practical application of \ndiversity, competition and facilitation in \narable \nand \norganic \ncropping \nsystems. \nRenewable Agriculture and Food Systems, \n23(1), \npp.3-12. \nhttp://dx.doi.org/10.1017/s17421705070020\n25. \n \nIbrahim, A.A., Ati, F.Q. and Adebayo, A.A., 2011. \nEffect of climate on the growth and yield of \nsorghum (sorghum bicolor) in Wailo, \nGanjuwa local government area, Bauchi state. \nResearch Journal of Environmental and \nEarth Sciences, 3(5), pp.469-472.  \n \nJaetzold, R., 2010. Farm Management Handbook of \nKenya: Volume II: Natural Conditions and \nFarm Management Information; Annex: \nAtlas of Agro-Ecological Zones, Soils and \nFertilising by Group of Districts; Subpart A2: \nNyanza Province Homa Bay and Migori \nCounty. https://d-nb.info/109742894X/34  \n \nJones, C., Olson-Rutz, K. and Dinkins, C., 2011. \nNutrient uptake timing by crops. Montana, \nUSA: Montana State University. \n \nKaruku G. N. 2018. Soil and Water Conservation \nMeasures and Challenges in Kenya; a \nReview. International Journal of Agronomy \nand Agricultural Research, 12(6), pp. 116-\n145.  \n \nKaruku, G.N. and Mochoge, B.O., 2018. Nitrogen \nmineralization potential (No) in three Kenyan \nsoils, nitisols, ferralsols and luvisols. Journal \nof Agricultural Science, 10(4), p. 201. \nhttps://doi.org/10.5539/jas.v10n4p69.  \n \nKaruma, A., Mtakwa, P., Amuri, N., Gachene, C.K. \nand Gicheru, P., 2014. Enhancing soil water \ncontent for increased food production in \nsemi-arid areas of Kenya results from an on-\nfarm trial in Mwala district, Kenya. Journal \nof Agricultural Science, 6(4),  pp. 125-134. \nhttp://dx.doi.org/10.5539/jas.v6n4p125.  \n \nKathuli, P. and Itabari, J.K., 2015. In situ soil moisture \nconservation: Utilization and management of \nrainwater for crop production. In Adapting \nAfrican Agriculture to Climate Change (pp. \n127-142). \nSpringer, \nCham. \nhttps://doi.org/10.1007/978-3-319-13000-\n2_11.  \n \nKato, M., 2001. Intensive cultivation and environment \nuse among the Matengo in Tanzania. African \nStudy \nMonographs, \n22(2), \npp.73-92. \nhttps://doi.org/10.14989/68204.  \n \nKhondaker, Z.H., Khan, T.H., Rahman, S. and Islam, \nA., 2013. Effect of soil moisture stress on the \navailability and uptake of N, P and K by \nwheat plants and growth, yield and quality of \nwheat [in Bangladesh]. Bangladesh Journal \nof \nSoil \nScience \n(Bangladesh). \nhttps://agris.fao.org/agris-\nsearch/search.do?recordID=BD8625190  \n \nKugedera, A.T., Kokerai, L.K. and Chimbwanda, F., \n2018. Effects of in-situ rainwater harvesting \nand integrated nutrient management options \non Sorghum production. Global Scientific \nJournals, 6(12), pp. 415-427 \n \nKwena, K.M., Ayuke, F.O., Karuku, G.N. and Esilaba, \nA.O., 2018. No rain but bumper harvest: the \nmagic of pigeon pea in semi-arid Kenya. \nInternational \nJournal \nof \nAgricultural \nResources, Governance and Ecology, 14(2), \npp.181-203. \nhttps://doi.org/10.1504/IJARGE.2018.10015\n157.   \n \nLandon, J.R., 2014. Booker tropical soil manual: a \nhandbook for soil survey and agricultural land \nevaluation in the tropics and subtropics. \nRoutledge. \nhttps://doi.org/10.4324/9781315846842 . \n \nLi, S.X., Wang, Z.H., Malhi, S.S., Li, S.Q., Gao, Y.J. \nand Tian, X.H., 2009. Nutrient and water \n"
+    },
+    {
+      "page": 18,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n18 \nmanagement effects on crop production, and \nnutrient and water use efficiency in dryland \nareas of China. Advances in agronomy, 102, \npp.223-265. https://doi.org/10.1016/S0065-\n2113(09)01007-4.  \n \nLiu, X., Fan, Y., Long, J., Wei, R., Kjelgren, R., Gong, \nC. and Zhao, J., 2013. Effects of soil water \nand nitrogen availability on photosynthesis \nand water use efficiency of Robinia \npseudoacacia \nseedlings. \nJournal \nof \nEnvironmental Sciences, 25(3), pp.585-595. \nhttps://doi.org/10.1016/S1001-\n0742(12)60081-3 \n \nMalesu, \nM.M., \n2006. \nRainwater \nharvesting \ninnovations in response to water scarcity: The \nLare experience (No. 5). World Agroforestry \nCentre. \nhttps://agris.fao.org/agris-\nsearch/search.do?recordID=SO2007100028  \n \nMalley, Z.J.U., Kayombo, B., Willcocks, T.J. and \nMtakwa, P.W., 2004. Ngoro: an indigenous, \nsustainable and profitable soil, water and \nnutrient conservation system in Tanzania for \nsloping land. Soil and Tillage Research, \n77(1), \npp.47-58. \nhttps://doi.org/10.1016/j.still.2003.10.003  \n \nMalo, M. and Ghosh, A., 2018. Studies on different \nagrometeorological indices and thermal use \nefficiencies of rice in New Alluvial Zone of \nWest Bengal. Bulletin on Environmental \nPharmacology and Life Sciences, 7(6), pp.72-\n78. \n \nMbayaki, C.W. and George N. Karuku, G.N. 2021a. \nGrowth and Yield of Sweet Potato (Ipomoea \nBatatas L.) Monocrops Versus Intercrops in \nthe Semi-Arid Katumani, Kenya. Tropical \nand Subtropical Agroecosystems 24(3), pp. \n#99. \nhttps://www.revista.ccba.uady.mx/ojs/index.\nphp/TSA/article/view/3489/1686   \n \nMbayaki, C.W. and George N. Karuku, G.N. 2021b. \nPredicting impact of climate change on water \nrequirements for directly sown rain-fed sweet \npotato in the semi-arid Katumani region, \nKenya. \nTropical \nand \nSubtropical \nAgroecosystems \n24(2), \npp.#61. \nhttps://www.revista.ccba.uady.mx/ojs/index.\nphp/TSA/article/view/3574/1617    \n \nMbayaki, C.W. and George N. Karuku, G.N. 2022. \nSoil hydraulic properties of a chromic Luvisol \nin \nKatumani, \nKenya. \nTropical \nand \nSubtropical Agroecosystems 25 (3) (2022): \n#094. https://doi.org/10.56369/tsaes.4200  \n \nMechiche-Alami, \nA. \nand \nAbdi, \nA.M., \n2020. \nAgricultural productivity in relation to \nclimate and cropland management in West \nAfrica. Scientific reports, 10(1), pp.1-10. \nhttps://doi.org/10.1038/s41598-020-59943-y  \n \nMilkias, A., Tadesse, T. and Zeleke, H., 2018. \nEvaluating the effects of in-situ rainwater \nharvesting techniques on soil moisture \nconservation and grain yield of maize (Zea \nmays L.) in Fedis district, Eastern Hararghe, \nEthiopia. Turkish Journal of Agriculture-\nFood Science and Technology, 6(9), pp. \n1129-1133. https://doi.org/10.24925/turjaf.v \n6i9.1129-1133.1839.  \n \nMiriti, J.M., Kironchi, G., Esilaba, A.O., Heng, L.K., \nGachene, C.K.K. and Mwangi, D.M., 2012. \nYield and water use efficiencies of maize and \ncowpea as affected by tillage and cropping \nsystems \nin \nsemi-arid \nEastern \nKenya. \nAgricultural \nWater \nManagement, \n115, \npp.148-155. \nhttps://doi.org/10.1016/j.agwat.2012.09.002. \n \nMucheru-Muna, Monicah., Mugendi, Daniel., Pypers, \nPieter, Mugwe, Jayne., Kung'u, J., Vanlauwe, \nBenard. and Merckx, R., 2014. Enhancing \nmaize productivity and profitability using \norganic inputs and mineral fertilizer in central \nKenya \nsmall-hold \nfarms. \nExperimental \nAgriculture, \n50(2), \npp.250-269. \nhttps://doi.org/10.1017/S0014479713000525\n. \n \nMudatenguha, F., Anena, J., Kiptum, C.K. and \nMashingaidze, A.B., 2014. In Situ Rain \nWater Harvesting Techniques Increase Maize \nGrowth and Grain Yield in a Semi-Arid \nAgro-Ecology \nof \nNyagatare \nRwanda. \nInternational Journal of Agriculture and \nBiology, 16(5), pp. 996-1000.  \n \nMutekwa, V.T., 2009. Climate change impacts and \nadaptation in the agricultural sector: The case \nof smallholder farmers in Zimbabwe. Journal \nof Sustainable Development in Africa, 11(2), \npp. 237-256.  \n \nMzezewa, J., Gwata, E.T. and Van Rensburg, L.D., \n2011. Yield and seasonal water productivity \nof sunflower as affected by tillage and \ncropping systems under dryland conditions in \nthe Limpopo Province of South Africa. \nAgricultural Water Management, 98(10), pp. \n1641-1648. \nhttps://doi.org/10.1016/j.agwat.2011.06.003. \n \n"
+    },
+    {
+      "page": 19,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n19 \nNjeru, P.N.M., Mugwe, J., Maina, I., Mucheru-Muna, \nM., Mugendi, D., Lekasi, J.K., Kimani, S.K., \nMiriti, J., Oeba, V.O., Esilaba, A.O. and \nMutuma, E., 2015. Integrating Farmers and \nScientific Methods for Evaluating Climate \nChange Adaptation Options in Embu County. \nIn Adapting African Agriculture to Climate \nChange (pp. 185-197). Springer, Cham. \nhttps://doi.org/10.1007/978-3-319-13000-2.  \n \nNkuna, T.R. and Odiyo, J.O., 2016. The relationship \nbetween temperature and rainfall variability \nin the Levubu sub-catchment, South Africa. \nInternational Journal of Environmental \nScience, 1, pp. 66-75. \n \nNyamadzawo, G., Wuta, M., Nyamangara, J. and \nGumbo, \nD., \n2013. \nOpportunities \nfor \noptimization of in-field water harvesting to \ncope with changing climate in semi-arid \nsmallholder farming areas of Zimbabwe. \nSpringerPlus, \n2(1), \npp.1-9. \nhttps://doi.org/10.1186/2193-1801-2-100.  \n \nNyang'au, J.O., Mohamed, J.H., Mango, N., Makate, \nC. and Wangeci, A.N., 2021. Smallholder \nfarmers’ perception of climate change and \nadoption \nof \nclimate \nsmart \nagriculture \npractices in Masaba South Sub-County, Kisii, \nKenya. \nHeliyon, \n7(4), \np. \ne06789. \nhttp://doi:10.1016/j.heliyon.2021.e06789. \n \nOo, A.N., Banterng, P., Polthanee, A. and Trelo-Ges, \nV., 2010. The effect of different fertilizers \nmanagement strategies on growth and yield of \nupland black glutinous rice and soil property. \nAsian Journal of Plant Sciences, 9(7), p. 414. \nhttps://doi.org/10.3923/ajps.2010.414.422. \n \nOsunbitan, J.A., Oyedele, D.J. and Adekalu, K.O., \n2005. Tillage effects on bulk density, \nhydraulic conductivity and strength of a \nloamy sand soil in southwestern Nigeria. Soil \nand Tillage Research, 82(1), pp. 57-64. \nhttps://doi.org/10.1016/j.still.2004.05.007. \n \nOuattara, K., Ouattara, B., Assa, A. and Sédogo, P.M., \n2006. Long-term effect of ploughing, and \norganic matter input on soil moisture \ncharacteristics of a Ferric Lixisol in Burkina \nFaso. Soil and Tillage Research, 88(1-2), pp. \n217-224. \nhttps://agris.fao.org/agris-\nsearch/search. \n \nPasley, H.R., Cairns, J.E., Camberato, J.J. and Vyn, \nT.J., 2019. Nitrogen fertilizer rate increases \nplant uptake and soil availability of essential \nnutrients in continuous maize production in \nKenya and Zimbabwe. Nutrient Cycling in \nAgroecosystems, \n115(3), \npp. \n373-389. \nhttps://doi.org/10.1007/s10705-019-10016-1.  \n \nRani, P., Batra, V.K., Bhatia, A.K. and Sain, V., 2020. \nEffect of water deficit and fertigation on \nnutrients uptake and soil fertility of drip \nirrigated onion (Allium cepa L.) in semi-arid \nregion of India. Journal of Plant Nutrition, \n44(6), \npp.765-772. \nhttps://doi.org/10.1080/01904167.2020.1860\n220. \n \nRecha, J., Kinyangi, J. and Omondi, H., 2013. Climate \nrelated risk and opportunities for agricultural \nadaption and mitigation in semi-arid Eastern \nKenya. CCAFS East Africa Program Project \nReport. \nCopenhagen: CGIAR Research \nProgram on Climate Change, Agriculture \nand Food Security. \n \nRezene, Y., Gebeyehu, S. and Zelleke, H., 2013. \nMorpho-physiological response to post-\nflowering drought stress in small red seeded \ncommon bean (Phaseolus vulgaris L.) \ngenotypes. Journal of Plant Studies, 2(1), p. \n42.  https://doi.org/10.5539/jps.v2n1p42 \n \nRockström, J. and Falkenmark, M., 2015. Agriculture: \nIncrease water harvesting in Africa. Nature \nNews, \n519(7543), \np. \n283. \nhttps://doi.org/10.1038/519283a.  \n \nRockström, J., Barron, J. and Fox, P., 2003. Water \nproductivity \nin \nrain-fed \nagriculture: \nchallenges and opportunities for smallholder \nfarmers \nin \ndrought-prone \ntropical \nagroecosystems. \nWater \nProductivity \nin \nAgriculture: Limits and Opportunities for \nImprovement, \n85199(669), \np. \n8.   \nhttps://doi.org/10.1079/9780851996691.014\n5. \n \nSchoonover, J.E. and Crim, J.F., 2015. An introduction \nto soil concepts and the role of soils in \nwatershed \nmanagement. \nJournal \nof \nContemporary Water Research & Education, \n154(1), \npp. \n21-47. \nhttps://doi.org/10.1111/j.1936-\n704X.2015.03186.x \n \nSigunga, D.O., Janssen, B.H. and Oenema, O., 2002. \nEffects of improved drainage and nitrogen \nsource on yields, nutrient uptake and \nutilization efficiencies by maize (Zea mays \nL.) on Vertisols in sub-humid environments. \nNutrient Cycling in Agroecosystems, 62(3), \npp. \n263-275. \nhttps://doi.org/10.1023/A:1021242918555  \n \n"
+    },
+    {
+      "page": 20,
+      "text": "Tropical and Subtropical Agroecosystems 25 (2022): #110                                                                                                              Wafula et al., 2022 \n20 \nSvendsen, M., Ewing, M. and Msangi, S., 2009. \nMeasuring irrigation performance in Africa \n(No. 894). Washington DC: International \nFood Policy Research Institute (IFPRI). \n \nTrenberth, K.E., 2011. Changes in precipitation with \nclimate change. Climate Research, 47(1-2), \npp. \n123-138. \nhttps://doi.org/10.3354/cr00953. \n \nVanlauwe, B., AbdelGadir, A.H., Adewopo, J., Adjei-\nNsiah, S., Ampadu-Boakye, T., Asare, R., \nBaijukya, F., Baars, E., Bekunda, M., Coyne, \nD. and Dianda, M., 2017. Looking back and \nmoving forward: 50 years of soil and soil \nfertility management research in sub-Saharan \nAfrica. International Journal of Agricultural \nSustainability, \n15(6), \npp. \n613-631. \nhttps://doi.org/10.1080/14735903.2017.1393\n038  \n \nVanlauwe, B., Bationo, A., Chianu, J., Giller, K.E., \nMerckx, R., Mokwunye, U., Ohiokpehai, O., \nPypers, P., Tabo, R., Shepherd, K.D. and \nSmaling, E.M.A., 2010. Integrated soil \nfertility management: operational definition \nand consequences for implementation and \ndissemination. Outlook on agriculture, 39(1), \npp. \n17-24. \nhttps://doi.org/10.5367/00000001079116999\n8.  \n \nWouterse, F., 2017. Empowerment, climate change \nadaptation, and agricultural production: \nevidence from Niger. Climatic Change, \n145(3), \npp. \n367-382. \nhttps://doi.org/10.1007/s10584-017-2096-8.  \n \nYazar, A. and Ali, A., 2016. Water harvesting in dry \nenvironments. In Innovations in Dryland \nAgriculture (pp. 49-98). Springer, Cham. \nhttps://doi.org/10.1007/978-3-319-47928-\n6_3  \n \nYu, Y., Loiskandl, W., Kaul, H.P., Himmelbauer, M., \nWei, W., Chen, L. and Bodner, G., 2016. \nEstimation \nof \nrunoff \nmitigation \nby \nmorphologically different cover crop root \nsystems. Journal of Hydrology, 538, pp. 667-\n676. \nhttps://doi.org/10.1016/j.jhydrol.2016.04.060\n. \n \nZhang, S., Grip, H. and Lövdahl, L., 2006. Effect of \nsoil compaction on hydraulic properties of \ntwo loess soils in China. Soil and Tillage \nResearch, \n90(1-2), \npp.117-125. \nhttps://doi.org/10.1016/j.still.2005.08.012.  \n \nZougmoré, R., Jalloh, A. and Tioro, A., 2014. Climate-\nsmart soil water and nutrient management \noptions in semiarid West Africa: a review of \nevidence and analysis of stone bunds and zaï \ntechniques. Agriculture & Food Security, \n3(1), pp. 1-8. https://doi.org/10.1186/2048-\n7010-3-16.  \n  \n \n \n \n"
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+      "page": 1,
+      "text": "A critical analysis of planting dates of maize for rainfall variability adaptation for enhanced food \nsecurity at Ol joro Orok; Kenya \n \nJ.W. Onyango1, JM Miriti2, A.O. Esilaba3 Phillip Sagero4 \n1National Agricultural Research Laboratories, P.O. Box 14733-00800 Nairobi \nMobile: +254726879012, E-mail: joabwamari@yahoo.com \n2National Agricultural Research Centre, Muguga South \n3Kenya Agricultural and Livestock Research Organisation \n4Kenya meteorological Department \n \nABSTRACT \nAn analysis of an experiment; which was part of a larger experiment to test the viability of \nvarious climate adaptation strategies in east and southern Africa, was done to determine the \neffect of planting dates on maize grain productivity in a cool environment in Kenya. Trials were \ncarried out in 2012 and 2013 seasons at Ol Joro Orok representing a cool-wet environment in \nKenya. The trial consisted of two planting dates namely early planting (EP) at rainfall onset and \nlate planting (LP) two weeks later. In order to maintain nutrient levels 20 kg ha-1 of nitrogen (N) \nha-1 were applied at planting and a further 20 kg ha-1 as top dressing respectively. Comparative \nanalysis between the planting dates was done of air dried-grain harvested at physiological \nmaturity separated from the cobs. In the long rains 2012 the difference of the yields between the \ntwo planting dates was 8.7% while in the 2013 rains it was 23.6%. The differences in yields \nbetween the planting dates in 2012 were not significant but they were significantly different at p \n< 0.05 in the 2013 rains. In 2013 EP ensured 172% (71.4 mm) more rain availability compared to \nLP between the silking to maturity phases while in 2012 the equivalent differences were only \n0.4% (2.2 mm). Whereas adequate rainfall distribution and amounts is critical for maize crop \nproduction; the eventual grain yields is actually determined by comparing these rainfall aspects \n"
+    },
+    {
+      "page": 2,
+      "text": "during the grain filling/forming phase. Analysis of such rainfall schedules can enable the farming \ncommunity to sustainably grow maize by instituting appropriate timing of water applications.   \nKEY WORDS: Planting date, Total rainfall amounts, Rainfall distribution, Maize phenology \n \nINTRODUCTION \nHigh temporal and spatial variability of climatic factors and especially rainfall amounts and \ndistribution are among the most important factors affecting rain-fed crop agriculture. Koli (1970) \nworking in Ghana for example recommends late sowing of the Mais de Socorro variety \nexplaining that this is suitable only if intercropped with other crops in order to protect the soil \nfrom erosion. Casini (2012) also explains that late planting and early harvest alone can be \npotentially useful methods of averting pre-harvest infestation of maize by S. cereallela. A higher \nyielding variety maize variety may therefore not be necessary to compensate for potential loss of \nyield resulting from a shortened growing season. Koli (1970) observed that 85 and 45% of grain \nyield reduction for respective Perla Pandino and Bayo Blando maize varieties occurred as the \ndate of sowing was delayed by two weeks relative to the optimal date. It is for example not \nunusual for a relatively late sown crop to out-yield the control crop sown within what would be \nconsidered to be the optimum period probably due to relatively higher amounts of rainfall (Green \net al., 1985). Ngetich et al (2011) while studying the effects of planting dates in central Kenya \nobserved no significant effect on maize stover during the long rains season of 2010 concluding \ntherefore that this was caused by rainfall patterns and amounts. In order therefore to optimize on \nyield under variable climatic conditions; planting at the appropriate time to fit with limited \nmultiyear, multi-location replications, and crop maturity length and growing season is critical \n(Saseendran et al, 2005). Tollenaar & Bruulsema (1988) reiterates that the time from silking to \nphysiological maturity in maize lengthened with delay in planting dates. Appropriate planting \ndates should be chosen to ensure a favorable climate during a critical growth stage, such as at \nflowering, rather than to ensure an optimal climate early in the crop’s growth (William et al, \n2010). Earlier planting have been explained to place the tasseling and silking period ahead of the \ngreatest risk of moisture stress and drought damage (Otegui & Melon, 1997).  \n"
+    },
+    {
+      "page": 3,
+      "text": "In practice, recommended dates are normally drawn up from the results of long-running series of \nagronomic experiments, which can give mean planting dates for highest yield together with \nrealistic estimates of expected yield penalties for each week of delay in planting (Lauer et al., \n1999). However, in accepting such guidelines, several reservations must be appreciated in \naddition to the fact that use of the recommended date is not a guarantee of highest yield for that \nseason (Oktem, 2000). In South Africa these dates should be scheduled such that the growth \nstages of maize most sensitive to heat and water stress do not coincide with mid-summer \ndroughts (ARC-GCI, 2002; Du Toit et al., 2002). The objective of this study therefore \nhighlighted some salient rainfall aspects affecting planting dates as key farm management \npractice with direct impacts on maize productivity in a cool wet area of central Kenya. \nMATERIALS AND METHODS \n2.1 \nStudy site \nThe study was conducted at the Oljororok KALRO experimental station (0.04 S and 36.35 E) \nfrom April 2012 to February 2014. The site lies at an elevation of 2400 m and its agro-ecological \nzone is described as Upper Highland sub-zone 2-3 (Pyrethrum, wheat and barley Zone) with \naverage annual rainfall and temperatures of 820-990 mm and 14.0 oC, respectively (Jaetzold et \nal, 2006).  Though the general rainfall patterns in Kenya are bimodal type where the long rains \ncrop season fall between March and May and the short between October and December, the Ol \nJoro orok site receives rainfall throughout the year. This study was conducted between March \n2013 and February 2014 and the rainfall distribution, mean monthly rainfall and temperatures at \nthe site during the study period are presented in figure 1 and table 1. The soil is classified as \nAndo luvic phaeozem which is a deep dark brown soil, well drained with a clay loam texture \n(Jaetzold et al., 2006). At sowing, the initial soil chemicals properties are presented in Table 2.   \n \nTable 1. Weather conditions at Oljororok during the study period  \nSeason \nLR2012 \nLR2013 \nSowing date \nEarly  \nLate \nEarly  \nLate \nRainfall (mm) \n945 \n899 \n777 \n799 \nTmax (oC) \n 20.9 \n20.8 \n21.2 \n21.1  \nTmin (oC) \n8.2 \n8.1 \n7.2 \n7.3  \n"
+    },
+    {
+      "page": 4,
+      "text": " \n \nFigure 1. Rainfall and temperatures at the study site during LR2012 and LR2013 seasons \n \nTable 2. Initial soil chemicals properties at the Oljororok KARI experimental station \nSoil depth (cm) \npH \n% N \n% C \nP ppm \nK ppm \n0-20 \n5.35 \n0.25 \n2.88 \n11.18 \n877.99 \n20-40 \n5.50 \n0.23 \n2.34 \n1.08 \n1013.07 \n \n \n \nRainfall (mm) \nLR2012 \nRainfall (mm) \nDays from sowing \nLR2013 \n"
+    },
+    {
+      "page": 5,
+      "text": "2.2 \nTreatments \nThe effects of early planting dates (EP) and late planting (LP) as treatments on performance of \nmaize (DK8031 variety) were evaluated. The treatments which were replicated thrice in a \nComplete Randomized Block Design consisted of two planting dates the latter being one week \nafter the first. Sowing was done at a plant spacing of 75 cm by 30 cm in plots measuring 9 by 6 \nm. All plots received 40 kg N/ha as CAN, split into two doses and no phosphorus fertilizer was \napplied. During the LR2012 season, early planting was done on 18th April 2012 while the and \nlate planting treatments was on 28th April 2012. Early and late planting during the LR2013 were \ndone on 2nd May 2013 and 14th May 2013, respectively. Grain maize crop yields were measured \nat maturity and expressed in Kg ha-1. Analysis of variance was carried out using Genstat Release \n12.1 (PC/Windows Vista); Copyright 2009, VSN International Ltd.  \n \nRESULTS \nEffect of planting dates  \nThe difference of days between early and late planting during LR2012 season was 10, while in \nLR2013 was 12 days. The germination of maize in early planting was over 90% while there was \nvery poor germination in late planting crops especially in the LR2013 season. Gapping was done \nusing thinned plant seedlings within each plot at thinning. In the first month after germination, \nmaize seedlings leaves turned purple in colour which was an indication of phosphorus deficiency \n(Plate 2). The phosphorus deficiency however disappeared with time as the crop grew and the \nmaize plants were able to extract phosphorus from the soil.  \n \n        \n  \n  \n"
+    },
+    {
+      "page": 6,
+      "text": "Maize plants in seed priming trial  \nFertilized maize plants in crop variety trial \n \nThe yields of maize were significantly (P≤0.05) affected by seasons with the yields of late \nplanted crop in 2013 being the lowest. While maize yields in 2013 were altogether lower than \n2012, late planting particularly reduced the establishment and yields of maize. See table 3. \n  \nTable 3. Effect of planting on maize yield \nFactor \nSeason \nMeans \nLR2012 \nLR2013 \nPP \nGrain \nTDM \nPP \nGrain \nTDM \nGrain \nTDM \nPlanting date  \nEarly \n66667 \n6591 \n14273 \n38519 \n3082 \n7564 \n4837 \n10919 \nLate \n59136 \n5396 \n11640 \n11482 \n788 \n2406 \n3092 \n7023 \nLSD(0.05) \nNS \nNS \nNS \n12031 \n118 \n1078 \n1038 \n932 \n(%) \n  \n4.5 \n8.7 \n20.5 \n 6.6 \n    18.7 \n      10.1 \n     23.6 \n        10 \n \n \nDISCUSSION \nThe comparative rainfall amounts received between sowing and emergence was only 13.4mm \nmore; that is 12.3% in 2012 within a difference of a day and this could not have contributed \nsignificantly to the yield differences at this early crop phase. Up to 115.4 mm (14.4%) more rain \nwas received in 2012 than in 2013 over the developmental stage of emergence to silking \nensuring a relatively better crop in the 2012 year. Long-term simulations also revealed longer \nmaturity length hybrids lose yield faster than short maturity length hybrids with planting delay. \nDuring the short rains season of2009 (SR09), there was 27% increase in the stover of dry planted \nmaize while wet planting effect was not significant. Ngetich et al (2011) for example showed \nthat, relative to late planting, dry planting increased maize stover yields by 53% (significance \np=0.05) during the long rains season of 2009 (LR09), followed by wet planting that led to a 19% \nincrease in central Kenya. \n \n \n"
+    },
+    {
+      "page": 7,
+      "text": " \nTable 1: A comparison of rainfall aspects in 2012 and 2013 at Nyahururu \nPlanting \ndate \nYear \n2012 \n2013 \nPhase \nEmergence \nSilking Maturity \nTotal \nEmergence \nSilking Maturity \nTotal \nEarly \nNo of \ndays \n13 \n168 \n11 \n192 \n12 \n167 \n13 \n192 \nRain \n(mm) \n109.3 \n802 \n55.9 \n967.2 \n95.9 \n686.6 \n112.8 \n895.3 \nLate \nNo of \ndays \n13 \n168 \n12 \n193 \n12 \n167 \n13 \n192 \nRain \n(mm) \n160.3 \n566.7 \n53.7 \n780.7 \n1.4 \n793.4 \n112.8 \n907.6 \n \n \n \n \n \n6354 \n \n \n \n2901 \n \n \n \n \n \n6075 \n \n \n \n400 \n \nIn the early planted maize the year 2012 had 7.4% more rain than there was in 2013 while the \nrainfall amounts occurred over the same period of 192 days. This 56.1mm deficiency of rainfall \nin 2013 compared to 2012 occurred over the two crop phases of sowing to emergence (13.4mm) \nand emergence to silking (115.4mm) when the crop was developing. Despite 66.9mm more rain \nfalling between silking to maturity in 2013 compared to 2012, the amount could not compensate \nfor the relatively poorer development performance period hence the lower yields. In the late \nplanted maize, although 13.9% (126.9mm) more rain occurred in 2013 compared to 2012 it was \nconcentrated more between the later crop phases of emergence to silking (226.7mm) and silking \nto maturity (59.1mm). The sowing to emergence period suffered a serious deficit of 99.1% \n(1.4mm) compared to 2012 the excess probably causing more harm to the already low \npopulations that emerged. A scenario in 2012 whereby better moisture supply period from \nestablishment to silking in 2012 compared to 2013 is therefore envisaged. Ngetich et al (2011) \nalso observed a similar scenario whereby no grain yields due to early rainfall cessation whereas \ndry planting increased grain yields by 77% compared to late planting. The effect of wet planting \n"
+    },
+    {
+      "page": 8,
+      "text": "was negligible (significance p=0.05) while dry planting increased grain yields by 26% compared \nto wet planting which led to 24% decrease probably due to the rainfall pattern during the onset of \nthe season. \n \nFigure 2: A comparison of rainfall at various phases at Ol Joro orok in 2012  \n \n \nFigure 2: A comparison of rainfall at various phases at Ol Joro orok in 2013 \n \n0\n200\n400\n600\n800\n1000\n1200\nS-E\nE-S\nS-M\nTotal\nRainfall (mm) \nPhenological phases  \nearly\nlate\n0\n200\n400\n600\n800\n1000\n1200\nS-E\nE-S\nS-M\nTotal\nRain (mm) \nPhenological phases \nearly\nlate\n"
+    },
+    {
+      "page": 9,
+      "text": "ACKNOWLEDGEMENTS \n \nThis study was supported by the Federal Ministry for Economic Cooperation and Development \nof Germany through the International Crops Research Institute for the Semi-Arid Tropics \n(ICRISAT) and Kenya Agricultural and Livestock Research Organisation (KALRO).  \n \nREFERENCES \nARC-GCI, 2002., Maize information guide. ARC-Grain Crop Institute, Potchefstroom, South \nAfrica. Pp. 31-38. \n \nCASINI P. Maize production as affected by sowing date, plant \ndensity and row spacing in the Bolivian Amazon, Journal of Agriculture and Environment for \nInternational Development – JAEID, 2012, 106 (2): 75-84 \n \nDu Toit et al., 2002) DU TOIT, A.S., PRINSLOO, M.A., DURAND, W. & KIKER, G., 2002. \nVulnerability of maize production to climate change and adaptation in South Africa, Combined \nCongress: South African Society of Crop Protection and South African Society of Horticultural \nScience, Pietermaritsburg (SA). \n \nGREEN, T.S., ENDER, M. & MOCK, J.J., 1985. Effect of sowing dates on maize yields. Agric. \nSci. 20, 51-63. \n \nHans Kgasago, Effect of planting dates and densities on yield and yield components of short and \nultra-short growth period maize (Zea mays L.). Submitted in compliance with the requirements \nfor the degree M. Inst. Agrar: Agronomy. In the Faculty of Natural and Agricultural Sciences \nDepartment of Plant Production and Soil Science University of Pretoria, October 2006  \n \nKOLI, S. EThe optimum planting date for maize in Ghana, (1970). Ghana journal of agricultural \nscience 3, 73-81. Accra Ghana University press \n \n"
+    },
+    {
+      "page": 10,
+      "text": "Ngetich, K. F.; Mugwe, J. N.; Mucheru-Muna, M.; Shisanya, C.A.; Mugendi, D.N.; Diels, J. \n2011. Effect of Planting Dates on Maize Yield under Rainfed Conditions in the Central \nHighlands of Kenya, Department of Agricultural Resource management, Kenyatta \nUniversity: http://ir-library.ku.ac.ke/handle/123456789/8341,URI: http://ir-\nlibrary.ku.ac.ke/handle/123456789/8341 RP-Department of Agricultural Resources Management \n(ARM) [51] \nOKTEM, A. 2000., Determination of sowing dates of corn (Zea mays L. saccharata Sturt) under \ndryland conditions. Turkish Journal of Agriculture and Forestry. 21, 65-71. \n \nOTEGUI, M.E. & MELON, S., 1997. Kernel set and flower synchrony within the ear of maize: I. \nSowing date effects. Crop Sci. 37, 441-447. \n \nSaseendran S. Anapalli, L. Ma, D. C. Nielsen, M. F. Vigil, and L. R. Ahuja, Simulating Planting \nDate Effects on Corn Production Using RZWQM and CERES-Maize Models. Agron. J. 97:58–\n71 (2005).  \nTollenaar & Bruulsema (1988) TOLLENAAR, M. & BRUULSEMA, T.W., 1998. Effects of \ntemperature on rate and duration of kernel dry matter accumulation of maize. Can. J. Plant Sci. \n68, 935- 940.  \n \nWilliam J. Sacks1 *, Delphine Deryng2 , Jonathan A. Foley3 and Navin Ramankutty2, Crop \nplanting dates: an analysis of global patterns, Global Ecology and Biogeography, (Global Ecol. \nBiogeogr.) (2010) 19, 607–620  \n \nP.A. Weston, Influence of planting date, harvest date and maize hybrid on preharvest infestation \nof maize by Sitotroga Cerealla. Proceedings of 6th international working conference on stored \nproduct infestation,Vol 1. Community Research Service , Kentucky State University, Frankfort, \nKY 40601 USA \n \n"
+    }
+  ]
+}

chatbot/extracted/Maize production systems farmers perception and current status of maize lethal necrosis in selected counties in Kenya.json

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+  "filename": "Maize production systems  farmers  perception and current status of maize lethal necrosis in selected counties in Kenya.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/Maize production systems  farmers  perception and current status of maize lethal necrosis in selected counties in Kenya.pdf",
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+    {
+      "page": 1,
+      "text": "All Life\nISSN: 2689-5293 (Print) 2689-5307 (Online) Journal homepage: www.tandfonline.com/journals/tfls21\nMaize production systems, farmers’ perception\nand current status of maize lethal necrosis in\nselected counties in Kenya\nFaith Njeru, Samuel Mwaura, Paul Mbogo Kusolwa & Gerald Misinzo\nTo cite this article: Faith Njeru, Samuel Mwaura, Paul Mbogo Kusolwa & Gerald Misinzo (2022)\nMaize production systems, farmers’ perception and current status of maize lethal necrosis in\nselected counties in Kenya, All Life, 15:1, 692-705, DOI: 10.1080/26895293.2022.2085815\nTo link to this article:  https://doi.org/10.1080/26895293.2022.2085815\n© 2022 The Author(s). Published by Informa\nUK Limited, trading as Taylor & Francis\nGroup\nView supplementary material \nPublished online: 14 Jun 2022.\nSubmit your article to this journal \nArticle views: 6966\nView related articles \nView Crossmark data\nCiting articles: 8 View citing articles \nFull Terms & Conditions of access and use can be found at\nhttps://www.tandfonline.com/action/journalInformation?journalCode=tfls21\n"
+    },
+    {
+      "page": 2,
+      "text": "ALL LIFE\n2022, VOL. 15, NO. 1, 692–705\nhttps://doi.org/10.1080/26895293.2022.2085815\nMaize production systems, farmers’ perception and current status of maize lethal\nnecrosis in selected counties in Kenya\nFaith Njeru\na, Samuel Mwaurab, Paul Mbogo Kusolwac and Gerald Misinzo\nd\naDepartment of Veterinary Microbiology, Parasitology and Biotechnology, Southern African Centre for Infectious Disease Surveillance (SACIDS),\nSokoine University of Agriculture, Morogoro, Tanzania; bKenya Agricultural and Livestock Research Organization (KALRO), Food Crops Research\nCentre, Njoro; cDepartment of Crop Science and Horticulture, College of Agriculture, Sokoine University of Agriculture, Morogoro, Tanzania;\ndSACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania\nABSTRACT\nMaize Lethal Necrosis (MLN) is a disease of maize reported in Kenya in 2012 that results in yield losses\nof up to 100%. The epidemiology of MLN is complex as the disease is caused by the synergistic\ninteraction of 2 viruses (Maize chlorotic mottle virus (MCMV) and a potyvirus). In addition, multiple\nreservoirs and transmission pathways exist for the spread of MLN. The current study was conducted\nto understand farmers’ maize production practices, their understanding of MLN, and the status of\nMLN in Kenya. Therefore, a survey of 406 randomly selected farmers was conducted in Bomet, Narok,\nKirinyaga, Embu, and Nakuru. To confirm the presence of MLN, maize leaf samples were collected\nfrom 18 fields and tested for MCMV and SCMV by molecular techniques. MLN Symptoms observed\nincluded chlorotic mottle on leaves, necrosis, and premature plant death. MCMV and SCMV were\ndetected in all the maize growing regions at varying levels of incidence, and severity. Sequence\nanalysis of the partial coat protein genes of randomly selected positive samples of the two viruses\nshowed little variability within the studied isolates and those retrieved from the GenBank. The results\nindicated that MLN is still prevalent in Kenya with farmers’ planting susceptible varieties.\nARTICLE HISTORY\nReceived 6 September 2021\nAccepted 23 April 2022\nKEYWORDS\nMLN; farmers’ perception;\nmaize; genetic diversity\nIntroduction\nMaize (Zea mays L.) is one of the most important cereal\ncrops in Africa, covering 40 million hectares which are\nmainly in smallholder settings and producing about\n81 million tonnes (FAOSTAT 2019). In Kenya, maize\nis not only a significant contributor to food security\nand nutrition, it is also a source of employment and\nincome to millions of subsistence farmers. Currently,\nmaize is cultivated on 2.196 million hectares of land\nin the country, engaging more than 3 million small-\nholder farmers and with an annual production of 3.897\nmillion tonnes (FAOSTAT 2019).\nHowever, maize production in Kenya is around\n1.77 t/ha (FAOSTAT 2019), this is far below the achiev-\nable potential of 6 t/ha when maize is cultivated under\ngood agronomic and management conditions, use of\nthe right quality of fertilizers and use of improved\nmaize hybrids adaptable to the agro-ecological zones\n(Odendo et al. 2001).\nCONTACT Faith Njeru\nFaith.njeru@sacids.org\nwanjikunjeru2012@gmail.com\nDepartment of Veterinary Microbiology, Parasitology and\nBiotechnology, Southern African Centre for Infectious Disease Surveillance (SACIDS), Sokoine University of Agriculture, P.O Box 3297, Chuo kikuu, SUA, Morogoro,\nTanzania\nSupplemental data for this article can be accessed here. https://doi.org/10.1080/26895293.2022.2085815\nMaize Lethal Necrosis (MLN), caused by the syn-\nergistic interaction between Maize chlorotic mottle\nvirus (MCMV) and Sugarcane Mosaic Virus (SCMV)\nis one of the major biotic constraints that severely\naffects maize production in Kenya (Miano 2014).\nMaize yield losses to MLN have been reported to range\nfrom 30%-100% depending on the variety, stage of\ndisease infection and prevailing environmental con-\nditions (Mahuku et al. 2015). MLN infected plants\n(Figure 1) show a wide range of symptoms which\ninclude chlorotic mottle on the leaves, mild to severe\nmottling, dwarfing, premature aging of the plants,\nnecrosis developing from leaf margins to midrib,\nnecrosis of young leaves in the whorl leading to a ‘dead\nheart’ symptom and drying up of whole plant (Miano\n2014).\nMLN is transmitted from one field or plant\nto another via vectors where MCMV has been\nshown to be transmitted by chrysomelid beetles\n© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group\nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,\ndistribution, and reproduction in any medium, provided the original work is properly cited.\n"
+    },
+    {
+      "page": 3,
+      "text": "ALL LIFE\n693\nFigure 1. Maize plants infected with MLN, showing the various\nsymptoms observed. Photo taken by Faith Njeru at farmers’ field\nin Kenya.\n(Nault et al. 1978) and thrips (Cabanas et al. 2013)\nwhile aphids have been reported to spread SCMV\n(Louie 1980). MLN causing viruses can also be spread\nbetween cropping cycles by farmers planting contam-\ninated maize seeds (Jensen et al. 1991), or through\ninfected maize debris (Kinyungu et al. 2019). Contin-\nuous maize production has been associated with MLN\noutbreaks (Redinbaugh and Stewart 2018). Crop rota-\ntion has been reported to significantly reduce the inci-\ndence of MCMV, contributing to higher maize yields\n(Hutchens 1978). Results from epidemiological stud-\nies on MLN suggest that farmer’s maize production\npractices have a significant effect on the incidence of\nMLN.\nTo develop MLN management practices that are\neffective, efficient and easily adaptable to the small-\nscale farmer, there is a need to understand farmers’\nknowledge in relation to the different aspects of the\ndisease. Understanding the role of knowledge in farm-\ners’ practices is also an important starting point for\ndeveloping a management strategy that fits the context\nof the smallholder farmer maize production practices.\nIn Kenya, studies have been done to understand the\nviruses associated with MLN (Wamaitha et al. 2018)\nand their geographical distribution (Mwatuni et al.\n2020). However, no studies have been conducted to get\ninsight into farmers’ knowledge and their role in MLN\ncontrol. Hence, the present study focuses on under-\nstanding farmers’ know-how on scientific knowledge\nand recommended management practices on MLN\nand also on their local knowledge of the disease man-\nagement. The study will also look at the limiting factors\nfor the adoption of recommended practices and farm-\ners’ perception on the use of mobile phones for disease\nmonitoring and information sharing. The findings of\nthe study are relevant to the design of MLN disease\nmanagement practices in the context of smallholder\nfarmer in Kenya.\nMaterials and methods\nStudy area\nThe study was conducted covering 3 agro-ecological\nzones: Highland tropics (HT), Moist transitional\n(MT), and Moist Mid attitude (MM). Five counties\nwere purposively selected from these zones target-\ning those with a high incidence of MLN (40–70%)\nfrom previous survey (Mwatuni et al. 2020). The selec-\ntion of the counties also considered that the areas\nhave high maize production. Sampled counties were\nBomet,Nakuru,Kirinyaga, Embu, and Narok.\nSelection of respondents\nThe study population comprised maize farmers in the\ntarget counties, from which a representative sample\nwas obtained. The sample size was obtained using\nCochran’s sample size formula, the desired confidence\nlevel was set at 95% and a desired precision of 5%. The\ndesired proportion of attributes was set at 50% (max-\nimum variability). This gave a sample of 385 respon-\ndents from the population, which was adjusted to 482\nto mitigate for 20% non-response rate. The probability\nproportion to size method based on the total number\nof maize farmers in the counties was used to determine\nthe distribution of the sample size across the coun-\nties. Within each target sub-county, the respondents\nwere randomly selected based on a sampling frame\ndeveloped with the help of the Agricultural extension\nofficers based in the region.\nSample size calculation:\nFormula n◦= Z2pq/ e2\nn0 is the sample size, Z2 is the abscissa of the normal\ncurve that cuts off an area a at the tails (1 – a equals the\ndesired confidence level, e.g. 95%) 1, e is the desired\nlevel of precision, p is the estimated proportion of an\nattribute that is present in the population, and q is 1-\np. Final sample size = effective sample size/ (1- non-\nresponse rate anticipated).\n"
+    },
+    {
+      "page": 4,
+      "text": "694\nF. NJERU ET AL.\nData collection\nIn the present study, a semi-structured questionnaire\nwas developed and pre-tested in Mutithi sub-county,\nKirinyaga to improve the questions set as per the study\nobjectives. The questions set aimed at understanding\nfarmers’ knowledge on MLN, their perceptions and the\nrole of knowledge in MLN control practices. The ques-\ntions asked included but not limited to, personal and\nhousehold characteristics of the farmer; maize produc-\ntion practices(varieties grown, yield obtained, external\ninputs used, key diseases and management practices,\nand chemical use); farmer knowledge on MLN and\nthe methods practised to control the disease. In addi-\ntion, farmers were questioned on the type of informa-\ntion they accessed regarding maize farming and the\nmedia platform they mostly used to access agricultural\ninformation.\nThe questionnaire was administered through face to\nface interviews conducted in the local language of the\ncommunity during the period from March–May 2021.\nThe respondents targeted were either the household\nhead, spouse or the member responsible for making\nfarming decisions. Informed consent was sought from\nthe respondents and the data handled in accordance\nwith the General Data Protection Regulation (GDPR).\nFarmers were also provided with coloured pho-\ntographs (taken at MLN CIMMYT Naivasha screening\nfacility), at different stages of MLN severity to deter-\nmine their ability to diagnose the disease.\nMLN test sample collection\nMaize leaf samples (84) were randomly sampled from\nthe farmers’ fieldswhich were close either to the main\nor rural roads in Kirinyaga, Bomet and Narok coun-\nties. The collected maize leaf samples were preserved\nin silica gel and transported to Kenya Agricultural\nand Livestock Research Organization (KALRO) Njoro\nfor molecular analysis. In each selected field, maize\nplants were selected following the staggered ‘X’ pat-\ntern and evaluated for disease severity. MLN severity\nwas scored on a rating scale of 1–9 where 1 clean\nplant with no symptoms and 9 severely affected plant\n(https://mln.cimmyt.org/mln-scoring/mln-hybrid-sc\noring-scale/).\nData analysis\nThe survey questionnaire collected data on farmers’\nmaize production systems and their perceptions on\nMLN. The farmers’ response to the questions was\nrecorded in the Kobo Toolbox platform which enables\nthe data to be received in real time, where data can\nbe easily managed and checked for any mistakes. On\ncompletion of the field survey, the final datasets were\ndownloaded from the server as Excel (XLS) files and\nused for further analysis. The survey data were anal-\nysed using descriptive statistics (percentage, frequency\nand mean) to present findings in summaries and tables\nafter the data were encoded. Pearson Chi-square test\nwas used to determine whether there was significant\ndifference in maize production practices and farmers’\nknowledge of MLN management among farmers of\ndifferent categories, and study counties. Disease inci-\ndence and disease prevalence were determined by the\npercentage of the plants showing MLN symptoms in\nindividual farms and the percentage of farms in a\ncounty with MLN symptoms, respectively.\nTotal nucleic acid extraction\nTotal nucleic acid (deoxyribonucleic acid and ribonu-\ncleic acid) were extracted from the 84 maize leaf sam-\nples collected from the farmers’ field in Kirinyaga,\nNarok and Bomet Counties using a modified cetyl-\ntrimetyl-amoniumbromide (CTAB) protocol where\n0.4 g of maize leaf tissue was ground in 2 ml of extrac-\ntion buffer using a mortar and pestle (Semagn 2014).\nThe RNA pellet was suspended in 50 μL of deionized\nwater.\nA Nano-Drop spectrophotometer was used to mea-\nsure the RNA concentration at maximum absorbance\nof 260 nm, and the purity was assessed by measuring\nthe 260/280 and 260/230 absorbance ratios.\ncDNA synthesis and RT-PCR\ncDNA was prepared from 1 μg of RNA following the\nNEB #M0253 first Strand cDNA Synthesis Standard\nProtocol (New England BioLabs, Ipswich, USA) as per\nthe instruction manual. Subsequently, the synthesized\ncDNA was used as a template for polymerase chain\nreaction (PCR) using MCMV and SCMV-specific\nprimers. MCMV-specific primers used were MCMV\nF 5′- AACATTCACAGCAGACACC -3′ and MCMV\nR 5′- GATAGCCACAATGAATCGTCC-3′ whereas\nSCMV specific primers were SCMV F 5′-TCTACTGA\nGCGATACATGCC-3′ and SCMV R 5′-CGTGTGTTT\nGAACCACGAAC-3′ to produce an amplicon of 259\nand 169 bp in length, respectively.\n"
+    },
+    {
+      "page": 5,
+      "text": "ALL LIFE\n695\nThe PCR conditions for MCMV reaction were 94°C\nfor 2 min, followed by 35 cycles of 94°C for 30 s, 50°C\nfor 30 s, and 72°C for 1 min, with a final extension at\n72°C for 7 min. The PCR conditions for SCMV reac-\ntion were similar to those of MCMV but with the\nannealing temperature being set at 60��C. The PCR\nproduct was analysed on 2% agarose gel in TBE buffer\n(70 V, 75 min) and visualized under UV light.\nSanger sequencing\nTen samples were selected for Sanger sequencing based\non the geographical location and the quality of cDNA.\nThe amplified PCR product was sent to Inqababiotec\nfor sequencing. Both forward and reverse sequencing\nwas performed on an automated DNA sequencer (ABI\n3500XL Genetic Analyzer) using the specific MCMV\nand SCMV F and R primers. The sequences were\nviewed in FinchTV and ambiguous and contaminated\nsequences were removed. Both forward and reverse\nsequences were merged and aligned in Bioeditto get\nthe consensus sequence for use in further analysis.\nThe partial coat protein gene sequences of 6 samples\nin this study were registered in GenBank and the acces-\nsion numbers for MCMV (OL461943, OL461944,\nand OL461945); SCMV (OL461946, OL461947, and\nOL461948) were provided. The 6 isolates were anal-\nysed by BLASTN to determine the sequence identity\nand similarity. Phylogenetic trees were constructed for\nthe coat protein genes isolates determined in this study\nand those retrieved from GeneBank. The phylogenetic\ntree was constructed in MegaX where the sequences\nwere aligned in Clustal Omega and saved in a Fastafor-\nmat, the best model for phylogenetic tree construction\nwas identified as Jukes-Cantor model for both MCMV\nand SCMV data. Bootsrap analysis with 1000 repli-\ncates was performed to evaluate the significance of the\ninterior branches.\nResults and discussion\nSocio-demographic profile of the interviewed\nfarmers\nOf the 406 farmers who were interviewed in the\npresent study, there was almost equal representa-\ntion by both males (56.4%) and females (43.6%)\n(Table 1). Statistically, there was a significant asso-\nciation between the gender of the farmer and the\ncounty where they were from, with Embu (23.6%) and\nKirinyaga (36.7%) counties reporting a high number of\nfemales (Pearson Chi-Square = 49.869, p = 3.845e-\n10). This is in contrast to most survey studies done\nin Africa which report a higher representation of\nthe males than the females citing the engagement of\nfemales in domestic activities which limit their mobil-\nity and crucial opportunities for interaction with other\nstakeholders(Mudde et al. 2017). Therefore, efforts to\npromote MLN disease management strategies should\nbe structured in such a way that they are easily acces-\nsible to both males and females.\nThe average age was 44.1 years, with many farm-\ners (44.4%) aged between 36 and 45 years. This age\nrepresents a young population of maize farmers, pre-\nsenting great prospect of introducing new MLN man-\nagement practices as young people easily adapt to new\nways of doing things. The majority of the respondents\n(95%) had formal education, either primary school\n(grade 1–8), secondary school (grade 9–12) or uni-\nversity. Only 5% of the farmers were illiterate. Bomet-\ncounty had the highest number of university graduates\n(17.9%) while Narok had none. Hence it is easier for\nextension officers to relay relevant MLN control infor-\nmation to farmers. The average household size of the\nfarmers was 5 individuals and 4 people as dependants\n(mostly school-going children). Therefore, less fam-\nily labour to work in the farm is available unless the\nfarmer hires workers. Hence MLN control measures\ndevised should be less labour intensive for them to be\neasily adaptable to the farmer.\nMaize production systems\nThe study indicated that maize production in the study\narea is small scale, and the average field size was 1.58\nacres (0.64 hectares) (Table 2). However, there was\na statistically significant difference in the means of\nthe land owned with the county F(4, 401) = 63.25,\np = 2e-16. Respondents from Narok had on average\n3.41 acres under maize cultivation while Embu county\nhad the least area under maize cultivation, 0.42 acres\non average. Age of the respondents had no statistically\nsignificant effect on the average land owned, how-\never, gender had a significant effect F(1, 404) = 58.99,\np = 1.21e-13. This is in contrast with a report by\nKansiime et al. (2019) where older farmers owned\nlarger parcels of land. Male farmers owned on aver-\nage 1.1 more acres of land compared to female farmers.\n"
+    },
+    {
+      "page": 6,
+      "text": "696\nF. NJERU ET AL.\nTable 1. Profile of sample farmers.\nSample counties\nBomet\nNarok\nEmbu\nKirinyaga\nNakuru\nOverall\nn = 106\nn = 50\nn = 61\nn = 103\nn = 86\nn = 406\nFemale\n16.9\n8.5\n23.2\n36.7\n14.7\n43.6\nMale\n33.2\n15.3\n8.7\n16.6\n26.2\n56.4\nAverage household size\n5.8\n5.8\n5.6\n4.8\n5.0\n5.4\nAverage Dependants\n4.1\n4.9\n3.8\n4.0\n3.6\n4.0\nAverage Age of household head\n41.1\n41.4\n48.9\n46.1\n43.7\n44.1\nNote: Survey data May–June 2021.\nTable 2. Maize production practices for 2021.\nBomet\nNarok\nEmbu\nKirinyaga\nNakuru\noverall\nAverage area under maize cultivation (acres)\n1.84\n3.41\n0.42\n0.67\n2.12\n1.58\nmaize yield (t/ha in good season)\n2.49\n6.86\n1.56\n2.03\n4.98\n3.30\nmaize yield (t/ha in 2020/2021 season)\n1.22\n1.42\n0.77\n0.94\n2.96\n1.48\nAverage cost of inputs (Ksh)\norganic fertilizer\n3376\n10,506\n2387\n4434\n10,626\n6065\npesticides\n1150\n3500\n1207\n2604\n1236\n2014\nweed management (per cent)\n(n = 102)\n(n = 50)\n(n = 61)\n(n = 98)\n(n = 86)\n(n = 397)\nmanual weeding\n100\n92\n85\n57\n73\n80\nuse of herbicides\n0\n2\n5\n2\n13\n4\nuse both methods\n0\n6\n10\n41\n14\n15\npest control methods (percent)\npesticides\n88\n93\n79\n77\n66\n75\nBiological/ cultural methods\n13\n7\n18\n6\n26\n15\npractice irrigation (percent)\n0\n2\n34\n60\n1\n21\nplough (percent)\n(n = 104)\n(n = 50)\n(n = 61)\n(n = 98)\n(n = 86)\n(n = 399)\nTractor\n2\n100\n0\n8\n60\n28\nhand plough with jembe\n4\n0\n100\n11\n33\n26\nanimal plough\n4\n0\n0\n67\n3\n18\nMaize variety grown\nn = 119\nn = 34\nn = 59\nn = 94\nn = 77\nn = 383\nImproved maize variety (percent)\n82\n56\n98\n88\n97\n87\nOPV (percent)\n18\n44\n2\n12\n3\n13\nOther constraints to maize production\nn = 5\nn = 25\nn = 34\nn = 78\nn = 69\nn = 211\nPoor market prices\n20\n72\n38\n41\n41\n91\nHigh cost of inputs\n20\n12\n44\n37\n17\n21\nWeevils\n38\n12\nA study done in Zambia also reported male farm-\ners owning larger parcels of land compared to female\nfarmers (Kansiime et al. 2019).\nIn the present study, farmers reported maize pro-\nduction at 3.3 t/ha when the season is favourable\n(timely rains and no biotic stresses). However, the\nfarmers noted that it is a while since these yields\nwere obtained as the agricultural sector has been hit\nby many biotic and abiotic stresses. Estimated maize\nproduction for the 2020/2021 cropping season was\n1.48 t/ha. Majority of farmers (77.3%) obtained an\naverage yield of less than 2 t/ha. Respondents from\nNakuru county (9.6%), reported an average yield of\n4.41 t/ha with these farmers reporting MLN incidence\nrates of less than 10% in their farms. These farms\nwere the ones located in Molo area at an altitude of\n2411.02 m above sea level (GPS data). Previous studies\nhave reported low MLN incidence at higher altitude\nareas due to unfavourable weather for insect survival\n(Guadie et al. 2019).\nFarmers spent approximately Ksh. 6065 on organic\nfertilizer (47.8 Kgs of fertilizer per acre), for the whole\nseason (Table 2). Farmers cited low use of fertiliz-\ners to be due to lack of capital and inaccessibility to\ngovernment subsidised fertilizers. Use of herbicides\nfor weed control in combination with manual weed-\ning was also common among farmers in Kirinyaga\n(41%). Use of biological and cultural methods for\n"
+    },
+    {
+      "page": 7,
+      "text": "ALL LIFE\n697\npest control was common in Nakuru (26%). Farm-\ners in Nakuru(altitude of 2411.02 m above sea level,\nGPS data)reported having minimal pest damage say-\ning when it rains, the pests are washed off. Other bio-\nlogical control methods reported included the use of\nash, soil, tobacco, and ariel (detergent). In addition,\nsome farmers reported to being against the use of pes-\nticides especially when growing maize for their own\nuse while others cited lack of money to buy the pes-\nticides. However, some farmers were of the opinion\nthat there has been a change in the pests affecting the\nmaize crop, as the cultural control methods were very\neffective (eliminating the pests by 90–100%, according\nto farmers’ observation) 5–10 years ago, but they were\nnoting these methods becoming less effective.\nMaize production in the study area was mainly rain-\nfed, with irrigation being practiced in Kirinyaga (60%)\nand Embu (34%). Most respondents (73.5%) reported\nto have noticed the impact of climate change on farm-\ning reporting less and delayed rains, changes in rain-\nfall patterns making it hard for the farmers to predict\nplanting times. Therefore, with the changes in weather\npatterns being noticeable, there should be emphasis to\neducate the farmers on the use of irrigation, and the\nuse of drought tolerant varieties.\nThe majority respondents (87%) planted improved\nmaize varieties (Table 2) with maize varieties planted\nbeing different per county. This is supported by the\nresults of the ANOVA test showing statistically signifi-\ncant differences between the maize variety planted and\nthe county F(4,401) = 28.01, p = 2e-16. In Kirinyaga\ncounty, DUMA 43 (52%) was the most planted hybrid\nselected for its drought tolerant and fast maturing\ntraits followed by Pioneer 3253 (31%) selected for its\nfavourable markets quality traits as it is sold as green\nmaize. Other hybrids planted in Kirinyaga county\nincluded DK777, Pannar, Sungura, babycorn, DK8031\nand DK9089. In Nakuru county and Narok, H6213 is\nthe most planted while in Bomet, DK777 and H614\nhybrid varieties in Embu county. These hybrids are\nselected for their adaptability to the agro-ecological\nzones. However, only a selected few of the hybrids are\nplanted per county and there is a need to introduce and\ndiversify maize varieties planted per region.\nRespondents in Narok reported that the OPV (Sir-\nare) was more tolerant to MLN compared to the hybrid\nvarieties. The average maize production of hybrids in\nNarok was 2.1 and 1.75 t/ha for the OPV. Therefore,\nthough the OPVs were perceived to be more disease\ntolerant, their yield was still low. Hence, the OPVs have\nunexploited genetic diversity for novel traits that can\nbe adopted into breeding programmes.\nThough pests and diseases are considered to be a\nmajor constraint to maize production, in the present\nstudy, most farmers (91%) reported poor market prices\nas the main drawback to maize farming (Table 2).\nAppropriate policies and regulations should be put in\nplace to make maize farming economically attractive\nto the farmers. Weevils was reported to be a prob-\nlem in Nakuru county. Resistance to pests and dis-\neases is considered favourable to the farmers if the\ntrait is combined with other traits such as high yield-\ning, favourable traits to the market which are more\nfavourable to the farmer. Breeders breeding for biotic\nand abiotic stresses should also take into account farm-\ners’ preferences.\nFarmers’ knowledge of MLN and if they have\nobserved it in their farms\nDuring the survey, farmers were shown photos of\nMLN infected maize plants. The majority of the farm-\ners (80%) could identify MLN, though given different\nnames in the different counties (Table 3). The sever-\nity of MLN is noted in the present study as 93%\nof the farmers reported to having observed MLN in\ntheir farms. Statistically, there was no difference in the\nknowledge of MLN and the age or gender of the farmer.\nHowever, there was a statistically significant difference\nin the means of the farmers that observed MLN and the\ncounty, F (4,392) = 4.22, p = 0.00231. Fewer farmers\nin Kirinyaga reported having observed MLN com-\npared to farmers in Bomet and Narok. Farmers (78%)\nalso reported MLN as the most problematic maize\ndisease they have observed. Other diseases observed\nincluded common rust, ear rots, leaf blight, smut.\nCommon rust was reported as a major problem by 30%\nof farmers in Nakuru (data not shown). Farmers also\nindicated the unpredictability of MLN, with farmers in\nBomet noting a reduction in incidence and severity of\nMLN in 2018 and then a resurgence in 2019.\nAcross all the study areas, more than 70% of\nthe respondents reported that MLN is higher dur-\ning the short rains (sunny season) and off-season\nplantings (Table 3). Similarly, Regassa et al. (2020)\nreported relatively high MLN incidence during the\noff-season plantings which support our findings.\n"
+    },
+    {
+      "page": 8,
+      "text": "698\nF. NJERU ET AL.\nTable 3. Percent of farmers knowing and having observed MLN in their farms.\nBomet\nNarok\nEmbu\nKirinyaga\nNakuru\nOverall\nn = 106\nn = 50\nn = 61\nn = 101\nn = 86\nn = 404\nKnow MLN-yes (percent)\n45\n90\n97\n99\n84\n80\nObserved MLN in the farm (percent)\n99\n98\n93\n86\n90\n93\nSeason when MLN incidence is high\n(n = 102)\n(n = 49)\n(n = 55)\n(n = 93)\n(n = 82)\n(n = 381)\nShort rains\n72\n98\n71\n74\n87\n79\nlong rains\n5\n0\n16\n14\n1\n7\nall seasons maize is planted\n2\n2\n13\n10\n5\n6\ndo not know\n22\n0\n0\n2\n7\n8\nEnvironment plays a critical role in disease develop-\nment and the dry and hot conditions during off season\nand short rains/sunny season would be a favourable\nenvironment for reproduction and movement of vec-\ntors to transmit MLN causing viruses. A few of the\nfarmers (6%) reported MLN being high any season\nthat maize is planted. This could be attributed to\nmaybe late plantings in long rains which have been\nreported to contribute to high MLN incidence or\ndelayed rains (which simulate sunny season in the long\nrains) (Jumbo et al. 2015).\nYellowing was reported by 60% of the farmers as the\nmost common symptom associated with MLN. Few\nfarmers could identify the early onset MLN symp-\ntoms (chlorotic mottle on leaves), making it difficult\nfor the farmer to control the disease early before it\nspreads. Premature plant death and male sterility were\nnot identified by farmers as MLN symptoms. There-\nfore, yellowing was the prominent MLN symptoms\nrecognized by the farmers who had limited know-how\nof other symptoms associated with MLN.\nAbout 46.3% of the farmers reported to observe\nMLN when the maize was at the pre-flowering stage\nand 36.5% at the vegetative stage. Other prominent\nstages were post-flowering stage (7.3%), 4–5 leaf stage\n(5.3%) and flowering stage (2.2%). About 2% of the\nfarmers noted that MLN can affect the maize plant at\nany growth stage. The results of this study are con-\ncurrent with previous studies reporting that MLN can\naffect maize plants at all growth stages (Beyene et al.\n2017).\nFarmers’ perception on MLN causal pathogens and\nspreading mechanisms\nConcerning the causal agent of MLN, 42.3% of the\nrespondents did not answer this question. However, of\nthose who responded (80.7%) did not know the causal\nagent of MLN, 8.5% of the farmers mentioned envi-\nronmental factors and 2% poor seed. However, 13% of\nthe farmers (majority being from Nakuru and Bomet)\nmentioned viruses as the causal agent of MLN.\nThough most of the farmers (84.2%) did not know\nhow MLN is spread from one area to another and\nfrom one farm to another, 15% of the respondents\nidentified insects, wind, contaminated seed, infected\ndebris/soil and contaminated farm tools as possible\nmechanisms of MLN distribution. Statistically, there\nwas a significant association between the farmers who\nhad some idea on how MLN is spread and their edu-\ncation level (Pearson Chi-Square = 190.85, p = <\n0.001). In addition, there was a significant association\nbetween gender and the respondents who answered\nthe question on MLN spread mechanisms (Pearson\nChi-Square = 67.471, p = < 0.001) with more men\n(70%) responding to the question compared to 30% of\nthe women.\nMLN causing pathogens can be spread from one\nfarm to another by insects and wind, therefore the\npractice of neighbouring farmers could have an effect\non an otherwise MLN free farm. The lack of knowledge\non MLN spreading mechanism limits how the farm-\ners view the importance of concerted effort in MLN\ncontrol.\nMaize yield losses to MLN\nAbout 31% of the respondents estimated the maize\nyield loss to MLN at 50–70% (Table 4). Statistically,\nthere was a significant association between farmers’\nestimation of yield loss to MLN and their location/\ncounty (Pearson Chi-Square = 384.11, p = 2.2e-16).\nNakuru was least affected by MLN, with 43% of farm-\ners reporting less than 10% maize yield losses to MLN\n(Table 4). The results of this study show that farmers\ncould appreciate the potential magnitude of yield loss\n"
+    },
+    {
+      "page": 9,
+      "text": "ALL LIFE\n699\nTable 4. Perceived yield loss due to MLN in different counties of\nKenya.\nBomet\nEmbu\nKirinyaga\nNakuru\nNarok\nOverall\n(n = 53) (n = 49) (n = 88) (n = 84) (n = 48) (n = 322)\n0–10%\n0\n8\n43\n13\n11–25%\n2\n8\n11\n12\n8\n25–40%\n2\n4\n5\n25\n8\n10\n40–50%\n8\n2\n3\n0\n2\n3\n50–70%\n85\n10\n24\n6\n52\n31\n70–90%\n2\n12\n16\n10\n33\n14\n90–100%\n2\n63\n33\n5\n4\n21\ndue to MLN where experimental studies have shown\nyield reductions of up to 70% in highly susceptible\nhybrids (Uyemoto et al. 1980).\nFurther analysis of the data showed that there was a\nsignificant association between estimated maize yield\nlosses and the cropping system practiced (monocrop-\nping, intercropping or both) (Pearson Chi-Square =\n97.524, p =\n<0.01) and whether the farmer prac-\ntised crop rotation (Pearson Chi-Square = 79.388,\np = <0.01). A study by Regassa et al.(2020) reported\na significant association between cropping systems,\ncrop rotation and MLN incidence. In addition, level\nof weeds had a positive correlation with MLN sever-\nity scores (Figure 2). Findings of this study confirmed\nthe reports of Gudero Mengesha et al. (2019), that\nMLN severity was high in plots with high weed levels.\nPearson correlation between frequency of weeding and\nMLN scores is −0.34, meaning that the two variables\nvary in opposite directions. This means that the more\nyou weed, there will be low MLN severity scores, since\na higher frequency of weeding reduces the number of\nweeds on the farm.\nMLN control measures\nIn the present study, farmers’ management of MLN\nwas limited with 74.8% reporting to not controlling\nthe disease. These farmers reported that MLN spreads\nvery fast, making any control attempt not effective.\nOther reasons given for not controlling MLN included:\nexpensive, not knowing how to control the disease,\nnot knowing MLN is a disease that needed to be con-\ntrolled, not being economically viable.\nThe control method that most farmers thought\nwas not economically viable was roguing, the pref-\nerence being to let the maize crop grow to cut the\nstalks for the animals or leave them as manure. In\naddition, some farmers also reported that roguing\nFigure 2. Correlation between level of weeds in the farm, num-\nber of times of weeding per season and mean MLN scores of\nrandomly selected maize plants in the farm (MLN mean scores\nof 25 maize plants in each farm. Correlogram plotted in R stu-\ndio: https://www.R-project.org/. (More intense colours for more\nextreme correlations)).\nwas not effective as MLN control method. Similarly,\nMudde et al.(2017) reported that 40% of the farm-\ners practising roguing to control MLN said the prac-\ntice was ineffective. When the data were extrapolated\nby county and gender, there was a statistically sig-\nnificant association between farmers’ management of\nMLN and their geographical location (Pearson Chi-\nSquare = 384.11, p = 2.2e-16) and gender (Pearson\nChi-Square = 11.585, p = 0.00305). A Tukey post-\nhoc test revealed that more men than women tried to\ncontrol MLN (p = 0.0006962). Hence there should be\nmore emphasis to reach and educate more women on\nMLN control methods.\nOf the farmers who tried to control MLN, crop\nrotation and roguing were the most used methods.\nGenerally, most farmers practised crop rotation for\n3–6 months (one season interval) which is ineffective\nfor disease management. A few of the farmers used a\ncombination of methods which included: crop rota-\ntion, roguing, vector control and use of clean seeds\n(Figure 3). In addition to the use of resistant varieties,\ndecontamination of farm tools was also used though\nto a smaller scale.\nSource of information on MLN management\nThe majority of the farmers mentioned having\nreceived information on MLN management. The main\n"
+    },
+    {
+      "page": 10,
+      "text": "700\nF. NJERU ET AL.\nFigure 3. Methods used to control MLN by farmers in the study counties.\nsource of information (41.6%) was fellow farmers. Sta-\ntistically, there was a significant association between\nthe farmers who received information on MLN and\nthe education level (Pearson Chi-Square = 512.12,\np > 0.01). Furthermore, 56.4% of the farmers who got\naccess to information had secondary education. Farm-\ners acknowledged receiving information from multiple\nsources which included: extension workers, field visits,\nradio, television and agricultural offices. Few farmers\n(8%) relied on mobile phones as a source of infor-\nmation on MLN. This seems to indicate that though\nfellow farmers are the main source of information on\nMLN management, other platforms are also gaining\ntraction.\nFarmers’ use of mobile phones and\ncommunity-based system\nMost of the farmers (96.78%) in the study area owned\na mobile phone, of whom 78.5% had a smartphone.\nThese results are consistent with previous studies\nthat have reported mobile phone ownership of up\nto 95.1% among the Kenyan adult population (Krell\net al. 2021). Most of the farmers (59.2%) reported that\nusing their mobile phones for communicating with\nfamily. In addition 24.9% of the farmers used their\nmobile phones to coordinate farm activities and mar-\nketing of the maize crop. Only 9.5% of the farmers\nused their mobile phones to get information on maize\nmanagement while 15.9% accessed social media plat-\nforms (Facebook, WhatsApp, Twitter). Statistically,\nthere was a significant association between education\nlevel and farmers use of mobile phones (Pearson\nChi-Square = 278.07, p > 1.434e-14). More farmers\n(60%) whose education level was above secondary\nlevel used mobile phones for coordination of farm\nactivities and getting information on maize manage-\nment.\nWhen farmers were asked about Mbeguchoice\n(www.mbeguchoice.com), an online platform devel-\noped to assist farmers in buying the best seeds for\ntheir agro-ecological zone, none of the farmers knew\nabout it. A study by Wyche and Steinfield (2016) also\nreported the lack of awareness by farmers on how\nthey can use their mobile phones to gain access to\nagricultural information, market information.\nAll of the farmers also reported lack of a comm-\nunity-based system to discuss issues on maize pro-\nduction citing the lack of monetary gain in the sec-\ntor. However, this is contradictory as maize is an\nimportant crop which can be used in several applica-\ntions including use as for animal feed and in industry\n(fuel production). The lack of information leads to\nfarmers in the study areas planting maize for their\nown use (50%) or selling through middlemen (23%)\n(Data not shown) leading to lack of gain from maize\ncultivation.\nMLN disease severity, incidence and prevalence\nAll the fields evaluated in Kirinyaga, Bomet, Nakuru\nand Narok had plants infected with MLN disease.\n"
+    },
+    {
+      "page": 11,
+      "text": "ALL LIFE\n701\nFigure 4. Map showing severity (A) and incidence (B) of MLN in 4 study counties.\nTable 5. MLN severity analysis, incidence and prevalence. The\ndifference in the mean level of severity among the different coun-\nties was statistically not significant at α = 0.05 (F-Value = 0.863,\nP-value = 0.4446).\nCounty\nMean severity\nIncidence\nPrevalence\nBomet\n4.928\n96.8\n100\nKirinyaga\n4.412\n92.2\n100\nNarok\n4.23\n90.67\n100\nNakuru\n3.93\n79.68\n100\nNarok region had the highest MLN incidence, fol-\nlowed by Bomet, whereas the lowest incidence was\nnoted in Nakuru (Figure 4). BometCounty registered\nthe highest disease symptom severity 4.928 while\nNakuru registered the lowest 3.93 on the 1−9 MLN\ndisease symptom severity scale.\nThe samples collected from the farmers field were\nat different growth stages including 4–5 leaf stage,\nvegetative, pre-flowering and flowering stage. Statisti-\ncally, there was no significant difference in MLN mean\nscores and the maize growth stage (f (3) = 2.044,\np = 0.154). In addition, maize crop existing at differ-\nent growth stages in the field simultaneously due to\nfarmers’ practise of planting maize at different times\nfacilitates easy transmission of MLN from older to\nyounger plants by the insect vectors and leads to con-\ntinuous MLN disease infection (Table 5).\nResults of RT-PCR\nMCMV and SCMV were detected by RT-PCR in 69\nand 73 pooled leaf samples respectively out of the 84\nmaize leaf samples. Double infection of MCMV and\nSCMV was reported in 60 maize leaf samples. Inter-\nestingly in Kirinyaga, 3 asymptomatic maize leaf sam-\nples collected tested positive for MCMV with 1 of the\nsamples testing positive for both MCMV and SCMV\n(Figure 5 and Table 6).\nSequence comparisons of partial coat protein region\nof SCMV\nBlast analysis of the aligned SCMV sample sequences\n(Kirinyaga, Bomet and Narok) identified the organ-\nism as SCMV. The SCMV isolates used in this\nstudy shared 99-100% nucleotide sequence identity\namong themselves. Analysis of the sample sequences\nin BLASTn, using the default parameters revealed\nnucleotide identity of 96.45% to 100% with SCMV\nsequences deposited in the GenBank. A high similar-\nity was noted between East African isolates (Tanza-\nnia, Rwanda and Ethiopia) and previously deposited\nKenyan isolates.\nTo further understand the genetic relationships\namong the global SCMV isolates, the 3 SCMV\nsequences from this study and genomes from Gen-\nBank representing different parts of the world were\nused on a phylogenetic analysis. Phylogenetic anal-\nysis of the partial gene sequences showed that\nthey all belong to a single monophyletic clade of\nSCMV (Figure 6). Previous studies have also reported\nthat SCMV sequences are phylogenetically diverse\nand tend to cluster together by geographical origin\n(Mahuku et al. 2015).\n"
+    },
+    {
+      "page": 12,
+      "text": "702\nF. NJERU ET AL.\nFigure 5. Results of MCMV and SCMV on a 2% agarose gel: total RNA was extracted from the samples collected from farmers’ field, cDNA\nwas then prepared followed by RT-PCR for MCMV and SCMV using specific primers for these viruses.\nTable 6. MLN viruses testing results using RT-PCR for MCMV and SCMV.\nMCMV\nSCMV\nMLN\nCounty\nNo. of samples\ntested\nPositive\nPercent\npositive\nPositive\nPercent\npositive\nPositive\nPercent\npositive\nKirinyaga\n25\n11\n40\n20\n80\n8\n32\nBomet\n25\n24\n96\n19\n76\n18\n72\nNarok\n34\n34\n100\n34\n100\n34\n100\nOverall\n84\n69\n82\n73\n87\n60\n72\nSequence comparisons of the partial coat protein\nregion of MCMV\nThe 3 MCMV isolates used in the analysis shared\n98.84% to 100% nucleotide identity with other sequences\nin the GenBank. The highest identity was observed\nwith other East African isolates. The MCMV iso-\nlates used in the study were aligned in Clustal\nOmega among themselves using the default set\nparameters, the nucleotide identity identified by\nSequence Manipulation Suite (Ident and Sim) was\n100%. The results from the present study sup-\nport results from previous studies which report low\ngenetic variability of MCMVv(Guadie et al. 2019)\n(Figure 7).\nFigure 6. Phylogenetic relationships among SCMV coat protein genes of isolates determined in this study (SCMVKirinyaga, SCMVBomet,\nSCMVNarok)andthoseretrievedfromGenBank(accessionnumberandgeographicregionisgiven).Theevolutionaryhistorywasinferred\nby using the Maximum Likelihood method and Jukes-Cantor model. Evolutionary analyses were conducted in MEGA X.\n"
+    },
+    {
+      "page": 13,
+      "text": "ALL LIFE\n703\nFigure 7. Phylogenetic tree reconstructed based on coat protein genes of isolates determined in these study (OL461945, OL461943,\nOL461944) and those retrieved from the GenBank: The evolutionary history was inferred using the Maximum Likelihood method based\non the Jukes-Cantor model at 1000 bootstraps. GenBank accession numbers and country of origin are indicated.\nConclusions\nThe present study has shown that MLN, caused by\nthe combination of MCMV and SCMV which were\npresent in all the study regions, is widely distributed\nand still a major problem to maize production in\nKenya. From the results of the study, farmers are still\nplanting maize varieties which are susceptible to MLN.\nThis is evident from the symptoms observed on the\nfarms. In addition, the varieties being planted had\nbeen screened for their tolerance to MLN, and the\nresults of the study showed that they were suscepti-\nble to MLN (Semagn et al. 2014). Since breeding for\ntolerant varieties is an economically and environmen-\ntally friendly way to control plant viral diseases, efforts\nshould be made to release MLN tolerant varieties to the\nfarmers.\nThe findings of the study showed that, though most\nfarmers knew MLN, their understanding of MLN was\nlimited in regard to its causal agents, spread mecha-\nnisms and visible symptoms on infected maize plants.\nTherefore, the lack of a better understanding of MLN,\nlimited the farmers to adopt effective management\npractices. For example, the farmers uprooting maize\nplants with visible symptoms, threw them at the farm\nside or in ditches. Studies have shown that MCMV can\nbe transmitted through MLN infected maize residues\n(Kinyungu et al. 2019). Therefore, improper disposal\nof MLN infected plants can lead to spreading of the dis-\nease instead of controlling it. In addition, the duration\nof crop rotation practised (3–6 months) is insufficient\nto lead to effective MLN management. Some farmers\nreported to practising crop rotation by changing the\nvariety of maize planted. There should be sufficient\nefforts to introduce new plant varieties favourable to\nthe farmer and the consumer to minimize overdepen-\ndence on maize.\nMost farmers owned a mobile phone, meaning that\nthere is an opportunity to use the platform for infor-\nmation sharing on MLN disease, its management and\nalso on other aspects of maize production such as\naccess to tolerant seed varieties and marketing. How-\never, before the use of the mobile-based platform is\nadopted, sufficient study should be conducted to find\nout the most acceptable, affordable and adaptable way\nfor the farmers to adopt the new technologies.\nFrom the results, MLN can attack maize crop at\nany stage of growth as the farmers reported to observ-\ning the disease from when the maize plant was at the\n4–5 leaf stage. The presence of MLN (MCMV and\nSCMV) was also confirmed by the results of RT-PCR\nand sequencing analysis. This corroborates with find-\nings from previous studies that MLN attack crops at\nany stage of growth (Frank et al. 2016).\nAsymptomatic plants also tested positive for\nMCMV. Therefore, there should be deployment of field\nrapid test to be able to detect MCMV early on for\nimplementation of proper control measures.\nTherefore, the present study depicts the complexity\nof MLN and its significant effect on maize production,\nfarmers’ income and food security. Maize production\nis mainly small scale and MLN mitigation measures\nshould be adaptable to be incorporated in the farm-\ners’ daily practices. Farmers should be sensitized on\ndiversification of their cropping systems with research\nbeing done to identify alternative crops attractive to\nthe farmers, the different agro-ecological zones and the\n"
+    },
+    {
+      "page": 14,
+      "text": "704\nF. NJERU ET AL.\nmarket. In addition, the government should invest in\nestablishing irrigation schemes as most of the maize\nfarming was rainfed with farmers citing unpredictabil-\nity of the weather as constraint to maize farming.\nAuthor contributions statement\nFaith Njeru was involved in concept conceptualization,\nstudy design, data acquisition, data analysis, and writ-\ning of the manuscript. Samuel Mwaura was involved\nin data acquisition, molecular work and design of the\nmolecular experiments. Paul Kusolwa contributed to\nsupervision of the project, review of the paper and\nchange in its write up. Gerald Misinzo contributed to\nsupervision and guidance for the project. All authors\nagree to the work done and for its publication.\nAcknowledgements\nI, Faith Njeruacknowledge Kenya Agricultural and Livestock\nResearch Organization (KALRO), Food Crops Research Cen-\ntre, Njoro where the molecular work was conducted. I acknowl-\nedge the farmers who contributed to the study by giving their\nviews on MLN and allowing us to collect samples from their\nfields.\nDisclosure statement\nNo potential conflict of interest was reported by the author(s).\nFunding\nThis manuscript is part of a Ph.D. project (Application of\nNanobody as a diagnostic tool against Maize Lethal Necrosis)\nfunded by the Partnership for Skills in Applied Sciences, Engi-\nneering and Technology (PASET), Regional Scholarship and\nInnovation Fund, (RSIF).\nData availability statement\nThe authors confirm that the data supporting the findings of\nthis study are available in the manuscript, and within the arti-\ncles referenced and their supplementary materials. The raw\ndata used for analysis is found at: njeru, faith (2021), ‘Maize\nproduction systems, farmers’ perception and current status of\nMaize Lethal Necrosis in selected counties in Kenya’, Mende-\nley Data, V2: https://doi.org/10.17632/n5cpfr4536.2. The raw\ndata that has been deposited include the MLN survey data,\nthe QGIS raw data, the sequencing raw data and the unpro-\ncessed RT-PCR gel images which are in the folder named\nnew folder.The partial coat protein gene sequences of 6 sam-\nples in this study were registered in GenBank(https://www.\nncbi.nlm.nih.gov) and the accession numbers for MCMV\n(OL461943, OL461944, and OL461945); SCMV (OL461946,\nOL461947, and OL461948) were provided.\nEthics approval and consent to participate\ndeclaration\nThe project which is part of the Ph.D. project was approved\nby the Board College of Veterinary Medicine and Biomed-\nical Sciences, Sokoine University of Agriculture under the\nreference number: SUA/PVM/D/2020/0005/03, and by the\nNational Commission for Science, Technology & Innovation\n(NACOSTI) under the reference number: 742958.Farmers\nwere explained to the purpose of the study and the reason for\nthe survey, and asked if they were willing to participate and they\nwould respond verbally with Yes/No.\nSupplementary data\nThe full version of the questionnaire is provided as a\nseparate attachment.\nORCID\nFaith Njeru\nhttp://orcid.org/0000-0003-3945-8740\nGerald Misinzo\nhttp://orcid.org/0000-0003-1827-6403\nReferences\nBeyene Y, Gowda M, Stephen LMS, Olsen M, Oikeh SO, Juma\nC, ... Prasanna BM. 2017. Genetic analysis of tropical maize\ninbred lines for resistance to maize lethal necrosis disease.\nEuphytica. 213(9):1–13. doi:10.1007/s10681-017-2012-3.\nCabanas D, Watanabe S, Higashi CHV, Bressan A. 2013. Dis-\nsecting the mode of maize chlorotic mottle virus trans-\nmission (Tombusviridae: Machlomovirus) by Franklin-\niella williamsi (Thysanoptera: Thripidae). J Econ Entomol.\n106(1):16–24. doi:10.1603/EC12056.\nFAO. 2019. FAOSTAT. Food and Agriculture Organization of\nthe United Nations, Rome, Italy. https://www.fao.org/faostat/\nen/#data.\nFrank K, Robert G, Brian EI. 2016. Status of maize lethal necro-\nsis in eastern Uganda. Afr J Agric Res. 11(8):652–660. doi:\n10.5897/ajar2015.10616.\nGuadie D, Knierim D, Winter S, Tesfaye K, Abraham A.\n2019. Survey for the identification and geographical dis-\ntribution of viruses and virus diseases of maize (Zea\nmays L.) in Ethiopia. Eur J Plant Pathol. 153(1):271–281.\ndoi:10.1007/s10658-018-1568-7.\nGudero Mengesha G, Kedir Mohammed B, Sultan Salo K.\n2019. Management of maize lethal necrosis disease through\nhand weeding and insecticide applications at Arba Minch\nin Southern Ethiopia. Cogent Food Agric. 5(1):1705746.\ndoi:10.1080/23311932.2019.1705746.\nHutchens NJ. 1978. LD2668T41981H87.pdf. Kansas State Uni-\nversity.\nJensen SG, Wysong DS, Ball EM, Higley PM. 1991. Seed\ntransmission of maize chlorotic mottle virus. Plant Dis.\n75:497–498.\n"
+    },
+    {
+      "page": 15,
+      "text": "ALL LIFE\n705\nJumbo B, Makumbi D, Kimunye JN, Mahuku GS. 2015.\nIntegration of maize Lethal Necrosis disease manage-\nment in crop-livestock intensification to enhance pro-\nductivity of smallholder agricultural production ... , 2.\nhttps://cgspace.cgiar.org/bitstream/handle/10568/58478/esa\n_mln_poster_mar2015.pdf?sequence = 1&isAllowed = y.\nKansiime MK, Mugambi I, Rwomushana I, Lamontagne-\ngodwin J, Rware H, Phiri NA, ... Day R. 2019. Farmer\nperception of fall armyworm (Spodoptera frugiderda J.\nE. Smith) and farm-level management practices in Zam-\nbia. Pest Manage Sci. 75(May): 2840–2850. doi:10.1002/ps.\n5504.\nKinyungu TN, Muthomi JW, Subramanian S, Miano DW,\nOlubayo FM, Maobe MA. 2019. Role of maize residues in\ntransmission of maize chlorotic mottle virus and effect on\nyield. Int J Biosci. 6655:338–349.\nKrell NT, Giroux SA, Guido Z, Hannah C, Lopus SE, Cay-\nlor KK, Evans TP. 2021. Smallholder farmers’ use of mobile\nphone services in central Kenya. Clim Dev. 13(3):215–227.\ndoi:10.1080/17565529.2020.1748847.\nLouie R. 1980. Sugarcane mosaic virus in Kenya. Plant Dis.\n64(10):944. doi:10.1094/PD-64-944.\nMahuku G, Lockhart BE, Wanjala B, Jones MW, Kimunye JN,\nStewart LR, ... Redinbaugh MG. 2015. Maize Lethal Necro-\nsis (MLN), an emerging threat to maize-based food security\nin Sub-Saharan Africa. Phytopathology®. 105(7):956–965.\ndoi:10.1094/PHYTO-12-14-0367-FI.\nMiano DW. 2014. A real threat to food security in the Eastern\nand Central Africa. University of Nairobi Digital Repository.\n1973(Mcmv). http://hdl.handle.net/11295/79053.\nMudde B, Olubayo F, Miano D, Kilalo D. 2017. Farmer knowl-\nedge, perceptions and management of maize lethal necrosis\ndisease in selected agro-ecological zones of Uganda. Afr J\nRural Dev. 2(September):247–261.\nMwatuni FM, Nyende AB, Njuguna J, Zhonguo X, Machuka E,\nStomeo F. 2020. Occurrence, genetic diversity, and recom-\nbination of maize lethal necrosis disease-causing viruses in\nKenya. Virus Res. 286(January):198081. doi:10.1016/j.virusr\nes.2020.198081.\nNault LR, Styer WE, Coffey ME, Gordon DT, Negi LS, N\nCL. 1978. Transmission of maize chlorotic mottle virus\nby chrysomelid beetles. Phytopathology. 68(7):1071–1074.\nhttp://www.apsnet.org/publications/phytopathology/backiss\nues/Documents/1978Articles/Phyto68n07_1071.pdf.\nOdendo M, De Groote H, Odongo OM. 2001. Assessment of\nfarmers’ preferences and constraints to maize production in\nmoist midaltitude zone of Western Kenya. Paper presented at\nthe 5th International Conference of the African Crop Science\nSociety, p. 21–26.\nRedinbaugh MG, Stewart LR. 2018. Maize lethal necrosis:\nan emerging, synergistic viral disease. Annu Rev Virol.\n5(1):301–322.\ndoi:10.1146/annurev-virology-092917-04\n3413.\nRegassa B, Abraham A, Fininsa C, Wegary D, Wolde-Hawariat\nY. 2020. Distribution of maize lethal necrosis epidemics and\nits association with cropping systems and cultural prac-\ntices in Ethiopia. Crop Prot. 134(November 2019):105151.\ndoi:10.1016/j.cropro.2020.105151.\nSemagn K. 2014. Leaf tissue sampling and DNA extraction\nprotocols BT – molecular plant taxonomy: methods and\nprotocols. In: P. Besse, editor. Totowa, NJ: Humana Press;\np. 53–67. https://doi.org/10.1007/978-1-62703-767-9_3.\nSemagn K, Beyene Y, Babu R, Nair S, Gowda M, Das\nB, ... Worku M. 2014. Molecular breeding for developing\nstress resilient maize for Sub-Saharan Africa. Crop Sci.\n55(February 2015): 1449–1459. doi:10.2135/cropsci2014.09.\n0646.\nUyemoto J, Bockelman D, Claflin L. 1980. Severe outbreak of\nCorn Lethal Necrosis Disease.PDF.\nWamaitha MJ, Nigam D, Maina S, Stomeo F, Wangai A, Nju-\nguna JN, ... Garcia-Ruiz H. 2018. Metagenomic analysis of\nviruses associated with maize lethal necrosis in Kenya. Virol\nJ. 15(1):90. doi:10.1186/s12985-018-0999-2.\nWyche S, Steinfield C. 2016. Why don’t farmers use cell phones\nto access market prices? Technology affordances and barriers\nto market information services adoption in rural Kenya. Inf\nTechnol Dev. 22(2):320–333. doi:10.1080/02681102.2015.10\n48184.\n"
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chatbot/extracted/Multilocation_Evaluation_for_yield_and_Y.json

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+  "filename": "Multilocation_Evaluation_for_yield_and_Y.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/Multilocation_Evaluation_for_yield_and_Y.pdf",
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+  "pages": [
+    {
+      "page": 1,
+      "text": "  © 2020, IJSRBS All Rights Reserved                                                                                                                                 72 \nInternational Journal of Scientific Research in ___________________________   Research Paper .  \nBiological Sciences  \n \nVol.7, Issue.1, pp.72-78, February  (2020)                                                                      E-ISSN: 2347-7520 \n \nMultilocation Evaluation for yield and Yield Related Traits in Three-\nWay Cross Maize Hybrids in Kenya \n \nBenard Masila1*, Charles Langat2 \n \n1,2Department of Crop Breeding, Kenya Agricultural and Livestock Research Organization (KALRO), Nairobi, Kenya   \n \n*Corresponding Author:   benardmasila@gmail.com,   Tel.: +254-723-007-691 \n \nAvailable online at: www.isroset.org \nReceived: 05/Feb/2020, Accepted: 20/Feb/2020, Online: 28/Feb/2020 \nAbstract— Maize (Zea mays L.) is the third most important crop after wheat and rice worldwide. It is the main staple food \nin Kenya. The objective of this study was to evaluate genotype by environment interactions and yield stability of twenty \nthree-way cross hybrids at four locations in Kenya evaluated in two seasons. The experiment was conducted in an alpha \nlattice design (Incomplete Randomized Block Design) with three replications. There was significant variation for grain \nyield among the genotypes, locations and their interaction. Stability analysis was evaluated using the joint regression, \nadditive main effects and multiplicative interactions (AMMI) and GGE biplot methods. The environmental and genotypic \nmeans ranged from 2.72 to 7.67 and 2.39 to 5.56 respectively. The regression coefficient (βi) and deviation from regression \n(s2di) values of these genotypes ranged from 0.55 to 1.64 and 0.02 to 0.59 respectively. There were also significant \ndifferences for genotypes, environments and genotype by environment interaction for the AMMI analysis of variance. The \ntotal proportion of variation contributed by genotypes, environments and genotype by environment interaction was 8.82%, \n76.03% and 9.17% respectively. When considering the Pi, βi, S2 di and the AMMI biplot analysis, the most stable genotype \nin the high yielding category in this study considering all stability parameters was WE-CMT-TWC-1001 (G1) followed by \nWE-CMT-TWC-1003 (G3) and WE-CMT-TWC-1020 (G20). The best genotype with both high mean yield and high \nstability was WE-CMT-TWC-1003 (G3). The genotypes identified could be utilized as reference for genotype evaluation \nand tested further for selection. \n \nKeywords— Multilocation trial, grain yield, three way cross maize hybrids \n  \nI.  INTRODUCTION  \n \nMaize (Zea mays  L.) is the main staple food for many \nnations and third most important crop after wheat and rice \nworldwide [1,2]. It is the main staple food in Kenya \nestimated to contribute to about 68% of daily per capita \ncereal consumption, 35% of total dietary energy \nconsumption and 32% of total protein consumption thereby \nindicating that Kenya’s national food security is strongly \nlinked to production of adequate quantities of maize to \nmeet an increasing domestic demand [3]. \n \nIn plant breeding, the process of identifying genotypes \nwith high yield potential and yield stability across \nenvironments is a fundamental activity [4]. Identification \nof stable genotypes by plant breeders is usually difficult \ndue to the presence of genotype by environment interaction \n(GEI) [5]. GEI causes the relative ranking of genotype \nperformance to change across environments and thereby \naffecting breeding progress [6]. Due to GEI effect, \ngenotypes with wide adaptation are rarely identified [7]. \n \nVarious stability analyses using the GEI have been \ndeveloped in order to identify genotypes with good yield \nperformance \nand \nyield \nstability \nacross \ndifferent \nenvironments [8]. These stability analysis methods include, \nadditive main effects and multiplicative interaction model \n(AMMI), principal component analysis (PCA), linear \nregression analysis, analysis of variance (ANOVA) and \nGGE biplot analysis [9]. The ANOVA describes the main \neffects with no information on individual genotypes and \nlocations, which are elements of the interaction and the \nAMMI uses the principal component analysis to explain \ngenotype performance by incorporating the use of \nANOVA and PCA [10]. The AMMI explains the main \neffects of genotypes, environments and their interaction by \ncombining the additive components in a single model for \nthe main effects of genotype and environment and \nmultiplicative components of their interaction [11]. \n \nThe linear regression analysis proposed by Eberhart and \nRussell, [12] classifies variation in genotype performance \ninto predictable (regression) and unpredictable (deviation \nfrom regression) evaluating yield and stability respectively \n[13].  ccording to this model, a relatively lo er value of \nregression coefficient (βi) around 1 (βi  1)  ill mean a \ngenotype is less responsive to environment and therefore \nmore adaptive [4]. Deviation from regression (s2di) if \nsignificantly different from zero will mean the genotype is \nless stable across environments and if not significantly \ndifferent, the genotype is stable [12]. If the phenotypic \nindex (pi) is negative, the genotype has a low grain yield \nand if phenotypic index is positive, the genotype has a high \ngrain yield [14]. The environmental index (Ij) reflects the \n"
+    },
+    {
+      "page": 2,
+      "text": "  Int. J. Sci. Res. in Biological Sciences                                                                                             Vol.7, Issue.1, Feb 2020 \n  © 2020, IJSRBS All Rights Reserved                                                                                                                                 73 \nsuitability of an environment to hybrid maize production. \nNegative environmental index reflects a poor environment \nwhile positive environmental index reflects a favorable \nenvironment for the hybrid maize production [15]. \n \nThe GGE (genotype plus genotype-by-environment) biplot \nanalysis combines the genotype and genotype by \nenvironment effects in genotype evaluation [6]. It uses \ngraphic axes to identify candidate genotypes in the mega \nenvironments (groups of environments sharing the same \ntest genotypes) [16]. The GGE biplot also incorporates \nANOVA and PCA by classifying genotypes and genotype \nby environment interaction sum of squares together by use \nof the PCA method [17]. \n \nBecause of increasingly importance of maize production in \nKenya there is need to improve its production and thereby \nimprove food security. This will be achieved by growing \nhigh yielding and stable maize varieties. Therefore, this \nstudy aims to evaluate the yield performance and yield \nstability of three way cross maize hybrids across four \nenvironments in Kenya using AMMI, GGE biplot and joint \nregression methods. \n \nII. \nMETHODOLOGY \n \nGermplasm: \nThe experimental materials used in the study were 20 three \nway cross hybrids and four commercial local check \nvarieties (Table 1). The 20 three way crosses were obtained \nfrom the Kenya Agricultural and Livestock Research \nOrganization (KALRO) breeding program. \n \nExperimental sites: \nThe experiments were conducted at KALRO Kakamega, \nKALRO Katumani, KALRO Kiboko and KALRO Kitui in \nKenya. The agro-climatic descriptions of the four \nexperimental sites are presented on table (Table 2). \n \nExperimental design: \nThe 20 three way cross hybrids together with four local \nchecks were planted in an Alpha Lattice Design \n(Incomplete Randomized Block Design) with three \nreplications in all the locations for two seasons. Each \nhybrid was sown in two row plot of 5.0 m. Two seeds were \nplanted in each hill and thinning was later done to one \nplant per hill. Plant spacing was 0.75 m between rows and \n0.25 m between hills. \n \nA recommended application of fertilizer for nitrogen (60 \nkg N ha-1) and phosphate (60 kg P2O5) was applied for \neach location to ensure healthy and vigorous plants. The \nexperimental sites were also kept free of weeds by hand \nweeding throughout the growth cycle of the plants. \nSupplemental irrigation was done when necessary. \n \nData collection and analysis: \nData on yield and yield related characters was collected \naccording to the standard protocols provided by CIMMYT \n[19]. \nAnalysis of variance for every location was done for grain \nyield and yield related characters using the SAS computer \nprogram [20]. Bartlett’s test  as used to evaluate the \nhomogeneity of error variances before the combined \nanalysis of variance across environments.  \n \nThe stability analysis for genotype by environment \ninteraction was estimated using the AMMI model [21,22]. \nIn this model, the contribution of every genotype and every \nenvironment to the genotype by environment interaction is \nestimated using  the GGE biplot whereby genotype mean \nyields and environmental means are plotted against the \nfirst interaction principal component axes scores (IPCA1). \nAMMI analysis computational program is supplied by \nDurate and Zimmermann [22]. \n \nThe regression model stability parameters, regression \ncoefficient (βi) and deviation from regression (S2di) were \ncalculated according to the method proposed by Eberhart \nand Russell [12]. The t-test was used to test the significant \ndifferences among the βi values and unity  hile the F-test \nwas used to test significance of the S2di values. \n \nIII. \nRESULTS  \n \nANOVA for grain yield (t ha-1) and yield related traits \nThe analysis of variance for grain yield within locations \nshowed significant differences for genotypes (Table 3). \nThe analysis of variance also showed significant \ndifferences for grain yield (GY), number of plants \nharvested (NP), number of ears harvested (NE), grain \nmoisture content percentage (MOIST) and ear aspect (EA) \nacross genotypes, locations and their interaction (Table 4). \n \nEberhart and Russell joint regression model \nThe environmental and genotypic means ranged from 2.72 \nto 7.67 and 2.39 to 5.56 respectively. Twelve genotypes \nhad a higher grain yield (positive phenotypic index) and \nalso twelve genotypes had a lower grain yield (negative \nphenotypic index). Kiboko (-0.78), Katumani (-1.08) and \nKakamega (-1.54) were poor environments for hybrid \nmaize production while Kitui (3.41) was the best for \nhybrid maize production. The regression coefficient (βi) \nand deviation from regression (s2di) values of these \ngenotypes ranged from 0.55 to 1.64 and 0.02 to 0.59 thus \nshowing that these genotypes responded differently to \ndifferent environment (Table 4). \n \nAccording to the joint regression model, the most stable \ngenotypes as indicated by the lowest (s2di) values were \nWE-CMT-TWC-1017 with a genotype mean of 4.35 which \nwas ranked eleventh with a phenotypic index (pi) of 0.09, \nregression coefficient (βi) of 1.03 and deviation from \nregression (s2di) of 0.02 then followed by WE-CMT-TWC-\n1008 with genotype mean of 4.16 (ranked fourteenth) with \npi value of -0.10, βi value of 0.81 and s2di value of 0.05 \nand WE-CMT-TWC-1003 with genotype mean of 5.27 \n(ranked second) with pi value of 1.01, βi value of 0.80 and \ns2di value of 0.06. The most unstable genotype as indicated \nby the highest s2di value was WE-CMT-TWC-1007 with a \n"
+    },
+    {
+      "page": 3,
+      "text": "  Int. J. Sci. Res. in Biological Sciences                                                                                             Vol.7, Issue.1, Feb 2020 \n  © 2020, IJSRBS All Rights Reserved                                                                                                                                 74 \nmean of 3.93 (ranked eighteenth) with pi value of -0.34, βi \nvalue of 1.64 and s2di value of 0.59. The genotype was \nclassified as the most unstable because its s2di value was \nsignificantly different from zero and bi value was \nsignificantly different from 1 as compared to the rest \n(Table 5).  \n \nAdditive main effects and multiplicative interaction \n(AMMI) analysis \nThe combined analysis of variance (ANOVA) according to \nthe AMMI 2 model indicated that there were highly \nsignificant \ndifferences \n(p≤0.01) \nfor \ngenotypes, \nenvironments and the interaction of genotype by \nenvironment (Table 4). The IPCA were ordered according \nto decreasing importance [8]. All genotypes showed highly \nsignificant differences for the first IPCA scores (Table 6).   \nThe total variation explained (%) was 8.82% for \ngenotypes, 76.03% for environments and 9.17% for \ngenotype by environment interaction and the two IPCA \naxes explained 88.4% of the genotype by environment \ninteraction. The first IPCA captured 66.02% of the total \ninteraction sums of squares in 36% of the interaction \ndegrees of freedom and the second IPCA captured 22.38% \nof the interaction sum of squares in 33% of the interaction \ndegrees of freedom (Table 6).  \n  \nThe analysis of the GGE biplot \nThe GGE biplot gives a visual expression of the \nrelationship between the first principle component analysis \n(IPCA) and the means of the genotypes and environments. \nThe IPCA scores of genotypes indicate the stability or \nadaptation of the genotypes to the environments. The \ngreater the IPCA score, whether negative or positive (as it \nis a relative value) the more specifically adapted is a \ngenotype to certain environments. The more the IPCA \nscores are close to zero, the more adapted or stable the \ngenotype is across all the environments sampled. The \nenvironment scores from AMMI analysis relating to \ninteraction also have a meaningful interpretation in that, \nenvironments \nwith \nlarge \nIPCA \nscores \nare \nmore \ndiscriminating of genotypes while environments with \nIPCA scores near zero show little interaction across \ngenotypes and low discrimination among genotypes [8]. \n \nFrom the biplot analysis, environments are categorized into \nfour parts i.e Quadrants I (top left) and IV (Bottom left) as \nlower yielding environments and Quadrants II (top right) \nand III (bottom right) as the high yielding environments. \nTherefore, the high yielding environment as indicated by \nthe biplot analysis \nis \nKitui and lower \nyielding \nenvironments are Kakamega, Katumani and Kiboko. Kitui \nshowed more discrimination for genotypes as compared to \nKakamega, Katumani and Kiboko (Figure 1). \n \nThe genotypes categorized under favorable environments \nconsidering the IPCA 1 scores with above average means \nwere G1 (WE-CMT-TWC-1001), G3 (WE-CMT-TWC-\n1003), G18 (WE-CMT-TWC-1018), G20 (WE-CMT-\nTWC-1020) and G14 (WE-CMT-TWC-1014). Among \nthem, G18 (WE-CMT-TWC-1018) was considered more \nstable with IPCA values close to zero. The genotypes \ncategorized \nunder \nlow \nyielding \nenvironments \nare \ncategorized into the upper and lower left quadrants of the \nbiplot. G21 (PH3253 (Local check)) was categorized as the \nmost unstable genotype according to the AMMI model. \nGenotypes that are close to environment indicates their \nbetter adaptation to that environment, therefore G7 (WE-\nCMT-TWC-1007) was the best adapted to Kiboko and \nKatumani while G24 (WH505 (Local check)) was best \nadapted to Kakamega. \n \nSince IPCA2 also played an important role (22.38%) of \nexplaining the genotype by environment interaction, \nIPCA1 scores were plotted against IPCA2 scores to further \nexplain the adaptation (figure 2). G1 (WE-CMT-TWC-\n1001), G21 (PH3253 (Local check)) G24 (WH505 (Local \ncheck)), G10 (WE-CMT-TWC-1010) and G7 (WE-CMT-\nTWC-1007) were categorized as unstable genotypes. G9 \n(WE-CMT-TWC-1009), \nG12 \n(WE-CMT-TWC-1012), \nG15 \n(WE-CMT-TWC-1015), \nG18 \n(WE-CMT-TWC-\n1018), and G19 (WE-CMT-TWC-1019) were categorized \nas moderately stable genotypes. G17 (WE-CMT-TWC-\n1017) was categorized as the most stable according to the \nmodel.  \n \nWhen considering the regression model, AMMI analysis \nand the GGE biplot, the most stable and ideal genotypes in \nthe high yielding category in this study were WE-CMT-\nTWC-1001 (G1), WE-CMT-TWC-1003 (G3) and WE-\nCMT-TWC-1020 (G20). The best genotype with both high \nmean yield and high stability was WE-CMT-TWC-1003 \n(G3). The genotypes can therefore be recommended as \nreference for genotype evaluation and tested further for \nselection. \n \nIV. DISCUSSION \n \nGenotype performance \nSignificant mean squares for grain yield from the analysis \nof variance for the four locations indicated that the mean \nyield of genotypes differed from location to location due to \nenvironmental diversity. Similarly, there were also \nsignificant \ndifferences \namong \nthe \ngenotypes \nthus \nindicating that the genotypes differed in their yield \npotential across locations. The presence of significant \ngenotype by location interaction indicated the differential \nin performance of genotypes across environments, \ntherefore, genotypes performed well in one environment \nand performed poorly in another environment. Similar \nresults have been reported whereby a change in \nenvironment cause genotype by environment interaction on \nmaize [23,16]. \n \nThe highly significant differences (p≤0.01) in genotypes, \nlocations and genotype by location interaction for grain \nyield indicated the need to develop varieties that are \nadapted to particular environmental conditions and \nvarieties that are exceptional in their stability across \nenvironments [24]. \n"
+    },
+    {
+      "page": 4,
+      "text": "  Int. J. Sci. Res. in Biological Sciences                                                                                             Vol.7, Issue.1, Feb 2020 \n  © 2020, IJSRBS All Rights Reserved                                                                                                                                 75 \nThe significant mean squares for locations and genotypes \nfor days to tasseling, days to silking, plant height, ear \nheight, number of ears, moisture, grain texture and husk \ncover indicate that the genetic expressions of these traits \nwere affected by environmental conditions at the four \nlocations [25]. \n \nStability analysis \nThe genotype by environment interaction for grain yield \nwas significant hence showing that the stability parameters \n(βi and s²di) estimated by linear response to change in \nenvironment were not the same for all genotypes across \nenvironments. These findings are in agreement with \ndifferent authors in their study on yield stability of maize \ngenotypes \n[26,27]. \nThey \nreported \nthat \ngenotypes, \nenvironments and genotype by environment interactions \nhad significant effect on yield of maize genotypes.  \n \nThe AMMI analysis of variance for all the genotypes \nindicated that there were large sum of squares and highly \nsignificant mean squares for environment hence indicating \nthat the environments were diverse with large differences \namong the environmental means causing most of the \nvariation in the grain yield. These results are in agreement \nwith the findings of [28,17] who declared significant all \nthe genotypes, environment and genotype by environment \neffects in the ANOVA of AMMI. Previous research also \nreported that environment contributed the largest portion of \nthe total variance whereby 80% and above of total sum of \nsquare variance is contributed by environment while 10% \nis contributed by genotype and environment interaction \n[5]. \n \nGenotype \nperformance \nand \nstability \nacross \nenvironments \nA good genotype must have both high mean yield \nperformance and also be stable for selection for broad \nadaptation [29]. Therefore GGE biplot, regression \ncoefficient (βi) and deviation from regression (s2di) were \nused to determine the mean performance and stability of \ngenotypes for grain yield because of the significant \ninteraction for grain yield alone.  \n \nAccording to the joint regression model, a stable variety \nhas βi values close or equal to unity (1) and s2di values \nclose or equal to zero (0) [30]. This method has been \nwidely used but it has some difficulties that can be realized \nfrom the analysis of the results (Table 4) whereby the \ngenotype PH3253 (Local check) was the most stable when \nconsidering s2di values but unstable when considering the \nβi values. This makes it difficult on using the method alone \nto recommend high yielding and stable varieties for future \nproduction. Similar results were reported by different \nauthors [26,31]. \n \nThe biplot showed the pattern of variability of genotypes, \nenvironments and their interaction, however, different \nscaling methods for biplot puts different weight on means \nand stability thereby causing the choice of scaling method \nto affect the ranking of the genotypes in relation to mean \nperformance and stability [32,33].  \n \nContrary to this, genotype PH3253 (Local check) was high \nyielding but unstable. This fact can cause a serious \nchallenge to plant breeders in variety selection because the \nhighest yielding genotypes may not be preferred by \nfarmers due to their instability across environments. This \nfinding is also in agreement with [34] who reported that \nhigh interaction caused difficulties in selection of high \nyielding genotypes due to their inconsistency to perform \nacross different environments.  \n \nV. CONCLUSION AND FUTURE SCOPE  \n \nFrom the study conducted, yield performance of maize was \nhighly affected by environmental change. There is also \nneed to test the maize hybrids for more environments to \npromote breeding efficiency for genotype stability across \nenvironments. The most preferred genotype was WE-\nCMT-TWC-1003 (G3), followed by WE-CMT-TWC-\n10020 (G20) and WE-CMT-TWC-1001 (G1). These \nhybrids need to be tested further and thereafter be \ncommercially released in order to increase maize \nproduction in Kenya in order to increase food security.   \nFigures and Tables \nA  \nTable 1: Description of maize genotypes used in the study \nGenotype No. \nGenotype Code \nSource \n1 \nWE-CMT-TWC-1001 \nKALRO \n2 \nWE-CMT-TWC-1002 \nKALRO \n3 \nWE-CMT-TWC-1003 \nKALRO \n4 \nWE-CMT-TWC-1004 \nKALRO \n5 \nWE-CMT-TWC-1005 \nKALRO \n6 \nWE-CMT-TWC-1006 \nKALRO \n7 \nWE-CMT-TWC-1007 \nKALRO \n8 \nWE-CMT-TWC-1008 \nKALRO \n9 \nWE-CMT-TWC-1009 \nKALRO \n10 \nWE-CMT-TWC-1010 \nKALRO \n11 \nWE-CMT-TWC-1011 \nKALRO \n12 \nWE-CMT-TWC-1012 \nKALRO \n13 \nWE-CMT-TWC-1013 \nKALRO \n14 \nWE-CMT-TWC-1014 \nKALRO \n15 \nWE-CMT-TWC-1015 \nKALRO \n16 \nWE-CMT-TWC-1016 \nKALRO \n17 \nWE-CMT-TWC-1017 \nKALRO \n18 \nWE-CMT-TWC-1018 \nKALRO \n19 \nWE-CMT-TWC-1019 \nKALRO \n20 \nWE-CMT-TWC-1020 \nKALRO \n21 \nPH3253 (Local check) \nPIONNER \n22 \nDK8031 (Local check) \nMONSANTO \n23 \nWE1101 (Local check) \nAATF/KALRO \n24 \nWH505 (Local check) \nWSCO \nKALRO: Kenya Agricultural and Livestock Research Organization \nAATF: African Agriculture Technology Foundation \nWSCO: Western Seed Company \n \nTable 2. Geographical and Climatic data for four sites (locations) used in the study \nSite \nGeographic \nLocation \n \nMean \nannual \nRainfall \nTemperature \n(ºC) \n \nAgro-ecology and soil type \n \nSource \n \nLongitude \nLatitude \nAltitude \nMin \nMax \n"
+    },
+    {
+      "page": 5,
+      "text": "  Int. J. Sci. Res. in Biological Sciences                                                                                             Vol.7, Issue.1, Feb 2020 \n  © 2020, IJSRBS All Rights Reserved                                                                                                                                 76 \n(mm) \nKatumani \n37˚32' E \n1˚35' S \n1580 \n582 \n13.9 \n24.7 \n Semi arid with Loamy sand soil \n[18]. \nKiboko \n37˚75' E \n2˚15' S \n993 \n548 \n17.0 \n30.6 \nSemi arid with ferrasols to ferric luvisol \nsoils \n[6]. \nKakamega \n34˚45' E \n0˚16' N \n1585 \n1995 \n13.0 \n28.6 \nSub humid with basaltic loam soil \n[6]. \nKitui \n38˚1'E \n1˚ 22' S \n1100 \n775 \n14.0 \n34.0 \nArid to semi arid with red sandy soil \n[1]. \n \nTable 3. Mean square from ANOVA and percentage of variance components for grain yield (tha-1) evaluated for the genotypes in each \nlocation (averaged over two seasons) \n \n \nKakamega \n \nKatumani \n \nKiboko \n \nKitui \nSource  \ndf \nMS \nSS \n%SS \n \nMS \nSS \n%SS \n \nMS \nSS \n%SS \n \nMS \nSS \n%SS \nBloc/Rep \n3 \n1.44** \n4.32 \n10.67 \n \n0.09ns \n0.27 \n0.93 \n \n1.59* \n4.76 \n13.28 \n \n4.00* \n12.00 \n9.00 \nGenotype \n23 \n1.32** \n30.35 \n74.94 \n \n1.03** \n23.63 \n81.04 \n \n1.00* \n23.10 \n64.41 \n \n4.11** \n94.63 \n70.95 \nError \n21 \n0.28 \n5.83 \n14.40 \n \n0.26 \n5.26 \n18.04 \n \n0.38 \n8.00 \n22.31 \n \n1.27 \n26.75 \n20.06 \nTotal \n47 \n \n40.51 \n \n \n \n29.15 \n \n \n \n35.86 \n \n \n \n133.38 \n \nCV% \n \n19.38 \n \n \n \n15.72 \n \n \n \n17.74 \n \n \n \n14.71 \n \n \ndf = degrees of freedom, MS = mean squares, SS = sum of squares, %SS = percentage sum of squares \n \nTable 4. Mean square from ANOVA for yield (tha-1) and yield related traits evaluated for the genotypes across four locations (averaged \nover two seasons) \nSource \ndf \nGY \nNP \nNE \nMOIST \nEA \nReplication \n1 \n3.30 \n70.08 \n27.00 \n2.90 \n0.00 \nGenotype \n23 \n3.82** \n205.17** \n268.41** \n17.99** \n0.93** \nLocation \n3 \n252.65** \n1456.06** \n1788.85** \n268.82** \n19.37** \nGen ×  Loc \n69 \n1.32** \n21.19* \n30.52* \n7.28** \n0.49* \nError \n95 \n0.59 \n12.75 \n20.31 \n4.24 \n0.32 \nS.E (Mean) \n \n0.77 \n3.57 \n4.51 \n2.06 \n0.56 \nL.S.D (0.05) \n \n1.53 \n7.09 \n8.95 \n4.09 \n1.12 \nCV% \n \n18.10 \n9.80 \n12.10 \n10.30 \n20.40 \nMean \n \n4.26 \n36.30 \n37.35 \n20.01 \n2.76 \ndf = degrees of freedom, GY =  grain yield, NP = number of plants, NE = Number of ears, MOIST = moisture content, EA =  ear aspect  *and** = \nSignificant at 5% (p ≤ 0.05) and 1% (p ≤ 0.01) respectively \n \nTable 5. Joint regression stability analysis for grain yield (tha-1) across four locations (averaged over two seasons) \nGenotype \nPedigree \nLocations \nAcross \n \nP index  \n \n \n \n \n \nKakamega \nKatumani \nKiboko \nKitui \nEnvrmnts \nRank \n(Pi) \nβi \ns²di \nRank \n1 \nWE-CMT-TWC-1001 \n3.88 \n4.61 \n4.11 \n9.67 \n5.56 \n1 \n1.30 \n0.83 \n0.08 \n5 \n2 \nWE-CMT-TWC-1002 \n2.11 \n2.32 \n3.21 \n6.67 \n3.57 \n22 \n-0.69 \n1.08 \n0.09 \n7 \n3 \nWE-CMT-TWC-1003 \n3.52 \n4.14 \n3.90 \n9.54 \n5.27 \n2 \n1.01 \n0.80 \n0.06 \n3 \n4 \nWE-CMT-TWC-1004 \n2.79 \n3.73 \n4.12 \n7.52 \n4.54 \n9 \n0.28 \n1.10 \n0.11 \n10 \n5 \nWE-CMT-TWC-1005 \n2.26 \n4.11 \n3.69 \n6.94 \n4.25 \n13 \n-0.01 \n1.11 \n0.26 \n22 \n6 \nWE-CMT-TWC-1006 \n3.44 \n3.30 \n2.56 \n9.93 \n4.81 \n5 \n0.55 \n0.65 \n0.11 \n11 \n7 \nWE-CMT-TWC-1007 \n2.74 \n3.12 \n4.37 \n5.49 \n3.93 \n18 \n-0.34 \n1.64 \n0.59 \n24 \n8 \nWE-CMT-TWC-1008 \n2.59 \n2.55 \n3.14 \n8.35 \n4.16 \n14 \n-0.10 \n0.81 \n0.05 \n2 \n9 \nWE-CMT-TWC-1009 \n1.96 \n3.35 \n3.92 \n6.98 \n4.05 \n16 \n-0.21 \n1.05 \n0.20 \n20 \n10 \nWE-CMT-TWC-1010 \n3.19 \n3.56 \n4.56 \n7.12 \n4.61 \n8 \n0.35 \n1.27 \n0.19 \n19 \n11 \nWE-CMT-TWC-1011 \n2.85 \n3.50 \n3.20 \n6.62 \n4.04 \n17 \n-0.22 \n1.31 \n0.12 \n12 \n12 \nWE-CMT-TWC-1012 \n2.71 \n3.35 \n3.99 \n7.53 \n4.39 \n10 \n0.13 \n1.06 \n0.08 \n6 \n13 \nWE-CMT-TWC-1013 \n2.81 \n3.33 \n2.90 \n6.12 \n3.79 \n19 \n-0.47 \n1.44 \n0.17 \n17 \n14 \nWE-CMT-TWC-1014 \n4.04 \n3.26 \n2.97 \n9.86 \n5.03 \n4 \n0.77 \n0.68 \n0.14 \n14 \n15 \nWE-CMT-TWC-1015 \n1.59 \n3.43 \n3.41 \n7.91 \n4.08 \n15 \n-0.18 \n0.83 \n0.12 \n13 \n16 \nWE-CMT-TWC-1016 \n2.58 \n3.64 \n4.38 \n6.72 \n4.33 \n12 \n0.07 \n1.25 \n0.27 \n23 \n17 \nWE-CMT-TWC-1017 \n2.93 \n3.22 \n3.58 \n7.66 \n4.35 \n11 \n0.09 \n1.03 \n0.02 \n1 \n18 \nWE-CMT-TWC-1018 \n2.71 \n3.18 \n4.49 \n8.64 \n4.75 \n6 \n0.49 \n0.84 \n0.10 \n8 \n19 \nWE-CMT-TWC-1019 \n2.27 \n2.18 \n3.59 \n6.90 \n3.73 \n21 \n-0.53 \n1.02 \n0.16 \n16 \n20 \nWE-CMT-TWC-1020 \n4.30 \n3.88 \n3.46 \n8.79 \n5.11 \n3 \n0.85 \n0.89 \n0.18 \n18 \n21 \nPH3253 (Local check) \n2.83 \n2.21 \n2.72 \n10.78 \n4.63 \n7 \n0.37 \n0.55 \n0.07 \n4 \n22 \nDK8031 (Local check) \n3.32 \n2.30 \n2.69 \n6.72 \n3.76 \n20 \n-0.51 \n1.08 \n0.25 \n21 \n23 \nWE1101 (Local check) \n1.58 \n2.38 \n2.81 \n6.10 \n3.22 \n23 \n-1.04 \n1.15 \n0.10 \n9 \n24 \nWH505 (Local check) \n0.31 \n1.85 \n1.78 \n5.63 \n2.39 \n24 \n-1.87 \n0.99 \n0.14 \n15 \n \nMean \n2.72 \n3.18 \n3.48 \n7.67 \n4.26 \n \n \n \n \n \n \nEnv. Index (Ij) \n-1.54 \n-1.08 \n-0.78 \n3.41 \n \n \n \n \n \n \n \nLSD (0.05) \n \n \n \n \n1.53 \n \n \n \n \n \ns²di, βi and Pi = deviation from regression, regression coefficient and phenotypic index respectively \n \n \n"
+    },
+    {
+      "page": 6,
+      "text": "  Int. J. Sci. Res. in Biological Sciences                                                                                             Vol.7, Issue.1, Feb 2020 \n  © 2020, IJSRBS All Rights Reserved                                                                                                                                 77 \nTable 6. Analysis of variance (ANOVA) based on the AMMI model for grain yield (t ha-1) for the genotypes across four environments \n(averaged over two seasons) \nSource \n df \n SS \n MS \nTotal variation \nexplained (%) \nG×E explained \n(%) \nCumulative \n(%) \nTotal \n191 \n996.80 \n5.22 \n \n \n \nEnvironments (E) \n3 \n757.90 \n252.65** \n76.03 \n \n \nBlocks within (E) \n4 \n6.10 \n1.54* \n \n \n \nGenotypes (G) \n23 \n87.90 \n3.82** \n8.82 \n \n \nGen × Env (G×E) \n69 \n91.40 \n1.32** \n9.17 \n \n \nIPCA 1 \n25 \n70.40 \n2.81** \n \n66.02 \n \nIPCA 2 \n23 \n13.40 \n0.58 \n \n22.38 \n88.4 \nResidual \n92 \n53.40 \n0.58 \n \n \n \ndf = degrees of freedom, MS = mean squares, SS = sum of squares, *and** = Significant at 5% (p ≤ 0.05) and 1% (p ≤ 0.01) respectively \n \n \n \nFigure 1. Biplot of interaction principal components analysis \n(PCA) axis 1 mean yield (tha-1) for the genotypes grown in four \nenvironments. The vertical line represents the grand mean of the \nexperiment while the horizontal line is PCA axis 1=0. \n \n \n \nFigure 2. Biplot of interaction principal components analysis \n(PCA) axis 1 versus axis 2 for grain yield (tha-1) for the \ngenotypes grown in four environments \n \nACKNOWLEDGMENT  \n \nThe author acknowledge the Centre Director, KALRO - \nAMRI, (Kenya Agricultural and Livestock Research \nOrganization - Agricultural Mechanization Research \nInstitute) for hosting research, providing the germplasm \nand research facilities.  The author thank KALRO-\nAMRI Kiboko and Katumani staff for assisting in \nmanaging field experiments and data collection. \n \nREFERENCES \n \n[1] \nB. Mbuvi, M. M imali, M. Githiri, “Estimation of General \nand Specific Combining Ability of Maize Inbred Lines Using \nSingle Cross Testers for Earliness”, W J Agric Res, Vol.6, \nNo.2, pp.37–48, 2019. \n[2] \nR. U.  ziz, S. Gaher al, “Egg parasitism caused by \nTrichogramma spp. Against maize stem borer, Chilo partellus”, \nIntl J Scientific Res in Biol Science, Vol.4, No.5, pp.14–17, \n2017. \n[3] \nD. Chepkesis, H. Parzies, C. Schroeder, T. Onyango K’Oloo, \nR. Nar Bahadur, N. Jick, D. Gemenet, “Potentials of Hybrid \nMaize Varieties for Small-Holder Farmers in Kenya: A Review \nBased on Swot Analysis”, Afric J Fd, Agric, Nutr and Dvpt, \nVol.13, No.2, pp.7562–7568, 2013. \n[4] \nD. Djurovic, M. Madic, N. Bokan, V. Stevovic, D. Tomic, S. \nTanaskovic, “Stability Parameters for Grain Yield and its \nComponent Traits in Maize Hybrids of Different FAO Maturity \nGroups”, J Cntrl Eurpn Agric, Vol.15, No.4, pp.199–212, \n2014.   \n[5] \nY. Beyene, S. Mugo, T. Tefera, J. Gethi, J. Gakunga, S.  \nAjanga, H. Karaya, R. Musila, W. Muasya, R. Tende, S. \nNjoka, “Yield stability of stem borer resistant maize hybrids \nevaluated in regional trials in east Africa”, Afric J Plt Sci, \nVol.6, No.2, pp.77–83, 2012. \n[6] \nF. Nzuve, S. Githiri, D. M. Mukunya, J. Gethi, “Analysis of \nGenotype x Environment Interaction for Grain Yield in Maize \nHybrids”, J Agric Sci, Vol.5, No.11, pp.75-85, 2013. \n[7] \nM. Kwabena Osei, B. Annor, J. Adjebeng- Danquah, A. \nDanquah, E. Danquah, E. Blay, H. Adu-Dapaah, “Genotype × \nEnvironment Interaction: A Prerequisite for Tomato Variety \nDevelopment”, In S. Tatu Nyaku & A. Danquah (Eds.), Recent \nAdvances in Tomato Breeding and Production, IntechOpen, \nLondon UK, pp.72-91, 2019. \n[8] \nE. Tena, F. Goshu, H. Mohamad, M. Tesfa, D. Tesfaye, A. \nSeife, “Genotype × environment interaction by AMMI and \nGGE-biplot analysis for sugar yield in three crop cycles of \nsugarcane (Saccharum officinirum L.) clones in Ethiopia”, \nCogent Fd and Agric, Vol.5, No.1, 2019. \n[9] \nJ. Adjebeng-Danquah, J. Manu-Aduening, V. E. Gracen, I. K. \n sante, S. K. Offei, “AMMI Stability Analysis and Estimation \nof Genetic Parameters for Growth and Yield Components in \n"
+    },
+    {
+      "page": 7,
+      "text": "  Int. J. Sci. Res. in Biological Sciences                                                                                             Vol.7, Issue.1, Feb 2020 \n  © 2020, IJSRBS All Rights Reserved                                                                                                                                 78 \nCassava in the Forest and Guinea Savannah Ecologies of \nGhana”, Intrnl J Agrmy, PP.1-10, 2017. \n[10] B. Mitrovic, D. Stanisavljevic, S. Treskic, M. Stojaković, M. \nIvanovic, \nG. \nBekavac, \nM. \nRajković, \n“Evaluation \nof \nExperimental Maize Hybrids Tested in Multi-Location Trials \nUsing AMMI and GGE Biplot Analyses”, Turk J Fld Crps, \nVol.17, No.1, pp.35–40, 2012. \n[11] G. V. Miranda, L. V. de Souza, L. J. M. Guimarães, H. \nNamorato, L. R. Oliveira, M. O. Soares, “Multivariate \nanalyses of genotype x environment interaction of popcorn”, \nPesquisa Agropecuária Brasileira, Vol.44, No.1, pp.45–50, \n2009.  \n[12] S.  . Eberhart, W.  . Russell, “Stability Parameters for \nComparing Varieties”. Crp Sci, Vol.6, No.1, pp.36–40, 1966.   \n[13]  . Morsy, W. Fares, S. Ragheb, M. Ibrahim, “Stability \nAnalysis of some Soybean Genotypes using a Simplified \nStatistical Model”, J Plnt Prod, Vol.6, No.12, pp.1975–1990, \n2015. \n[14] D. Bassa, F. Gurmu, H. Mohammed, “Comparison of \nUnivariate and Multivariate Models to Analyze Stability of \nCommon Bean (Phaseolus vulgaris L.) Genotypes in \nEthiopia”, Agrotechnology, Vol.8, No.2, pp.1-7, 2019. \n[15] M. Matin, A. Aminul Islam, M. Khaleque Mian, J. Ahmed, M. \n miruzzaman, “Stability analysis for yield and yield \ncontributing characters in hybrid maize (Zea mays L.)”, Afric \nJ Agric Res, Vol.12, No.37, pp.2795–2806, 2017. \n[16] T. R. A. de Oliveira, H. W. L. de Carvalho, G. H. F. Oliveira, \nE. F. N. Costa, G. de A. Gravina, R. D. dos Santos, J. L. S. de. \nCarvalho Filho, “Hybrid maize selection through GGE biplot \nanalysis”, Bragantia, Vol.78, No.2, pp.166–174, 2019. \n[17] C. Singh, A. Gupta, V. Gupta, P. Kumar, R. Sendhil, B. Tyagi, \nG. Singh, R. Chatrath, G. Singh, “Genotype x environment \ninteraction analysis of multi-environment wheat trials in India \nusing AMMI and GGE biplot models”, Crp Brdng and Appl \nBiotech, Vol.19, No.3, pp.309–318, 2019. \n[18] T. Tefera, S. Mugo, Y. Beyene, H. Karaya, J. Gakunga, G. \nDemissie, “Postharvest Insect Pest and Foliar Disease \nResistance and Agronomic Performance of New Maize \nHybrids in East Africa”, Intrnl J Plnt Brdng and Gntics, Vol.7, \nNo.2, pp.92–104, 2013. \n[19] C. Magorokosho, B. Vivek, J. MacRobert, “Characterization \nof Maize Germplasm Grown in Eastern and Southern Africa. \nResults of the 2008 Regional Trials Coordinated by CIMMYT”, \nHarare, Zimbabwe, pp.1-64 2009. \n[20] S S, “S S ® 9.4 for Windo s”, SAS Institute Inc, Cary, N.C, \nUSA, 2013. \n[21] A. Bisawas, U. Sarker, B. Banik, M. Rohman, M. Talukder, \n“Genotype x environment interaction for grain yield of maize \n(Zea mays L.) inbreds under salinity stress”, Bngldsh J Agric \nRes, Vol.39, No.2, pp.293–301, 2014. \n[22] J. B. Duarte, M. J. Zimmermann, “Selection of locations for \ncommon \nbean \n(Phaseolus \nvulgaris \nL.) \ngermoplasm \nevaluation”, Revista Brasileira de Genética, Vol.14, No.3, \npp.765–770, 1991. \n[23] H. Mafouasson, V. Gracen, M. Yeboah, G. Ntsomboh-\nNtsefong, \nL. \nTandzi, \nC. \nMuteng a, \n“Genotype-by-\nEnvironment Interaction and Yield Stability of Maize Single \nCross Hybrids Developed from Tropical Inbred Lines”, J \nAgrnmy, Vol.8, No.62, pp.1-17, 2018. \n[24] S. Ngailo, H. Shimelis, J. Sibiya, K. Mtunda, J. Mashilo, \n“Genotype-by-environment interaction of newly-developed \nsweet potato genotypes for storage root yield, yield-related \ntraits and resistance to sweet potato virus disease”, Heliyon, \nVol.5, No.3, pp.1-23, 2019. \n[25] E. K. Mageto, D. Makumbi, K. Njoroge, R. Nyankanga, \n“Genetic analysis of early-maturing maize (Zea mays L.) \nInbred lines under stress and non-stress conditions”, J Crp \nImprvmnt, Vol.31, No.4, pp.560–588, 2017. \n[26] W.  nley, H. Zeleke, Y. Dessalegn, “Genotype X environment \ninteraction of maize (Zea mays L.) across North Western \nEthiopia”, J Plnt Brdng and Crp Sci, Vol.5, No.9, pp.171–181, \n2013. \n[27] J. Crossa, “From Genotype × Environment Interaction to Gene \n× Environment Interaction”, Currnt Gnmcs, Vol.13, No.3, \npp.225–244, 2012. \n[28] L. C. I. da Silveira, V. Kist, T. O. M. de Paula, M. H. P. \nBarbosa, L.  . Peternelli, E. Daros, “AMMI analysis to \nevaluate the adaptability and phenotypic stability of sugarcane \ngenotypes”, Scientia Agricola, Vol.70, No.1, pp.27–32, 2013. \n[29] F. E. Belo , J. R. Seebauer, “Management and environmental \nfactor contributions to maize yield”, MDPI Books, pp.1-189, \n2019. \n[30] T. Welu Gebremedhin, “Adaptation of food barley (Hordeum \nvulgare L.) genotypes”, J Agric Sci, Vol.60, No.2, pp.227–235, \n2015. \n[31] P. C. Rodrigues, D. G. S. Pereira, J. T. Mexia, “A comparison \nbetween Joint Regression Analysis and the Additive Main and \nMultiplicative Interaction model: The robustness with \nincreasing amounts of missing data”, Scientia Agricola, \nVol.68, No.6, pp.679–686, 2011. \n[32] D. Bustos-Korts, I. Romagosa, G. Borràs-Gelonch, A. M. \nCasas, G. A. Slafer, F. van Eeuwijk, “Genotype by \nEnvironment Interaction and  daptation”, In R. A. Meyers \n(Ed.), Encyclopedia of Sustainability Science and Technology, \nSpringer New York, New York, pp.1–44, 2018. \n[33] W. Yan, M. S. Kang, B. Ma, S. Woods, P. L. Cornelius, “GGE \nBiplot vs. AMMI Analysis of Genotype-by-Environment Data”, \nCrp Sci, Vol.47, No.2, pp.643–653, 2007. \n[34]  G. A. Owusu, D. Nyadanu, P. Owusu-Mensah, R. Adu \n moah, S.  missah, F. C. Danso, “Determining the effect of \ngenotype × environment interactions on grain yield and \nstability of hybrid maize cultivars under multiple environments \nin Ghana”, Ecol Gntcs and Gnmcs, Vol.9, pp.7–15, 2018. \n \n \n \n"
+    }
+  ]
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chatbot/extracted/TOASJ-12-215.json

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+  "filename": "TOASJ-12-215.pdf",
+  "path": "/home/jawoo/Claude/Kenya Maize/raw/papers/TOASJ-12-215.pdf",
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+      "text": "Send Orders for Reprints to reprints@benthamscience.ae\n215\n1874-3315/18\n2018  Bentham Open\nThe Open Agriculture Journal\nContent list available at: www.benthamopen.com/TOASJ/\nDOI: 10.2174/1874331501812010215, 2018, 12, 215-226\nRESEARCH ARTICLE\nResponse  of  Selected  Maize  Inbred  Germplasm  to  Maize  Lethal\nNecrosis Disease and Its Causative Viruses (Sugarcane Mosaic Virus\nand Maize Chlorotic Mottle Virus) in Kenya\nJames Karanja\n1,*, John Derera\n2, Augustine Gubba\n2, Stephen Mugo\n3 and Ann Wangai\n1\n1Kenya Agricultural and Livestock Research Organization, National Agriculture Research Laboratories P. O Box\n14733-00800, Kabete, Nairobi\n2University of Kwa Zulu Natal, Pietermaritzburg Campus, Private Bag X01, Scottsville 3209, South Africa\n3International Maize and Wheat Improvement Centre (CIMMYT)-Kenya, ICRAF House, United Nations Avenue-Gigiri,\nand P.O Box 1041-00621, Nairobi, Kenya\nReceived: April 12, 2018\nRevised: July 12, 2018\nAccepted: August 7, 2018\nAbstract:\nBackground:\nMaize lethal necrosis (MLN) disease continues to reduce the productivity of maize drastically threatening food security in the\naffected regions. It continues to cause yield loss of 30–100 percent in farmers’ fields, depending on the time of infestation which is\nvalued at $198 million in Kenya. This has not only threatened regional trade, but also seed industry. It has been reported in the major\nmaize belts of Uasin Gishu, Trans-Nzoia, Bomet, Narok and Nandi Counties. MLN is caused by the synergistic interaction between\nSugarcane Mosaic Virus (SCMV) and Maize Chlorotic Mottle Virus (MCMV). The disease has then spread to other Eastern and\nCentral African countries with devastating food security and economic consequences.\nObjectives:\nThis study highlights result after screening selected maize inbred lines for resistance to MLN, SCMV and MCMV in identifying\npromising lines for integration into the breeding program for MLN resistance.\nMethods:\nSixty-five (65) maize genotypes were artificially inoculated using virus strains collected from Bomet County in Kenya at 3-4 leaf\nstage. Data on disease severity and incidence, AUDPC and flowering were recorded.\nResults:\nFrom the result, the inbred lines had significant differences for SCMV, MCMV and MLN reactions. Based on Area Under Disease\nProgress Curve (AUDPC) score and ELISA analysis, genotypes MLN001 and MLN006 have the lowest score of 270, whereas OH28\nhad a maximum at 1259 under MCMV. Genotypes MLN042 and MLN041 were identified as the most promising sources of resistant\nagainst SCMV. However, no genotype was identified to have acceptable levels of tolerance to MLN, but MLN001 and MLN013\nwere  identified  as  the  best  performers  under  MLN.  This  study  also  validated  the  presence  of  MLN  tolerance  in  MLN013\n(CKDHL120312) and MLN001 (CKDHL120918) as earlier reported by CIMMYT. These tolerant genotypes are now serving as\ndonors in the introgression of the tolerance into the Kenyan adapted maize backgrounds and development of improved MLN tolerant\nvarieties. This will go a long way in restoring and ensuring sustainable maize productivity in improving the livelihoods of the\nsmallholder farmers who form 75% of the major maize producers in Kenya.\n* Address correspondence to this author at the Kenya Agricultural and Livestock Research Organization, National Agriculture Research Laboratories\nP. O Box 14733-00800, Kabete, Nairobi; Tel: +254721811561; E-mail: jakakah79@gmail.com\n"
+    },
+    {
+      "page": 2,
+      "text": "216   The Open Agriculture Journal, 2018, Volume 12\nKaranja et al.\nConclusion:\nThe identified inbred lines would be recommended for use in varietal development, MLN management and to enhance maize\nproductivity, in the MLN endemic regions and further research in understanding the mode of gene action for MLN tolerance.\nKeywords: Maize, MCMV, SCMV MLN, Threatening food, AUDPC.\n1. INTRODUCTION\nMaize accounts for > 20% of total agricultural production, and 25% of agricultural employment in Kenya [1]. Thus,\nKenya’s national food security is strongly linked to the production of adequate quantities of maize to meet an increasing\ndomestic demand [2, 3]. The total land area under maize in Kenya is about 1.5 million ha, with 70-80% of maize being\nproduced by small-scale farmers with an average on-farm production of 1.5-2.6 tons per ha.\nThe major biotic causes of stress in maize include Striga a parasitic weed, insect pest, diseases mainly northern corn\nleaf blight, maize lethal necrosis (MLN), Maize streak virus (MSV), and common leaf rust, gray leaf spot (GLS), stalk\nand ear rot.\nMLN is a new disease in Kenya with its first incidences noticed in 2012 [4 - 6]. Earlier the disease was observed in\nKansas, USA, in 1978 Niblett and Claflin where it was identified as corn lethal necrosis (CLN) disease [7]. Both MLN\nand CLN are caused by double infection of maize plants by maize chlorotic mottle virus (MCMV) (Machlomovirus:\nTombusviridae)  in  combination  with  any  of  the  cereal  viruses  in  the  Potyviridae  group,  Sugarcane  mosaic  virus\n(SCMV) (Potyvirus: Potyviridae), Maize dwarf mosaic virus (MDMV) or Wheat streak mosaic virus (WSMV). MCMV\nis transmitted by vectors such as Thrips (Frankliniellawilliamsi Hood) and beetles [8] while SCMV is transmitted by\nAphids [9]. Transmission and spread of MCMV have also been reported to be through seeds from infected plants [10] at\na rate of 0.0003% which can translate into a high number of infected plants resulting in epidemics. MCMV is a threat\non its own and may cause significant yield loss even in the absence of the other viruses.\nFig. (1). Distribution and losses affiliated by MLN in Kenya.\nSource: De Groote et al,. 2016\nSince its first reports in Bomet county in 2011, the disease has spread into other areas [5] (Fig. 1). Yield losses of up\nto 100% which is an estimated grain loss of 126, 000 metric tonnes valued at $52 million have been reported in Kenya\n[2] while Castillo-loayza [11] reported yield losses of up to 59% due to MCMV in Peru. A survey conducted in the\nmaize growing regions of Kenya in 2013/2014 indicated that 60% of the 2,467 randomly selected samples were positive\n"
+    },
+    {
+      "page": 3,
+      "text": "Response of Selected Maize Inbred Germplasm to Maize\nThe Open Agriculture Journal, 2018, Volume 12   217\nfor MCMV with more than 40% of these being infected with MCMV alone. In DRC, MCMV was detected in 40 to\n80% of symptomatic samples collected from the Beni, Lubero, and Rutshuru territories of North Kivu Province in 2013\n[12, 13]. Other than seed, MCMV has also been reported to be transmitted through soil with 70% of emerging plants\nfound to be infected [12].\nDepending on the maize variety, a number of viruses infecting the plant, part of the plant infected, time of infection\nand prevailing environmental conditions, infected plants show a wide range of symptoms [14]. Common symptoms\ninclude; chlorotic mottle on the leaves usually starting from the base of the young leaves in the whorl and extending\nupwards towards the leaf tips, mild to severe leaf mottling, dwarfing and premature aging of the plants, necrosis of\nyoung leaves in the whorl before expansion leading to a “dead heart” and drying up of the whole plant. Severely\naffected plants form small cobs with little or no grain set. The entire plant can frequently be killed before flowering [5,\n15 - 17] (Fig. 2).\nFig. (2). Disease symptoms of MLN.\nPoor agricultural practices like leaving infected maize crop residues in the field, relay maize planting and maize\nmonoculture aid in inoculum build-up which increases the disease transmission season after season. In addition, poor\ncrop rotation and lack of proper weed management practices has increased maize susceptibility and also aided in\noffering alternative hosts to the vectors where the weeds are susceptible to the MCMV and SCMV. The management of\nMLN disease could be achieved by integrating cultural methods, use of a chemical such as seed dressing and vector\ncontrol with host resistance breeding. However, use of chemicals is uneconomical and environmentally unfriendly,\nespecially among the resource-constrained smallholder maize farmers.\nOther measures include; effective monitoring, rigorous implementation of the maize-free period (at least 3 months),\ntimely planting, and use of certified, MCMV-free seed and crop rotation with non-cereal crops [18]. However, these\n \n \n \n \n \n Dead heart  \nPremature drying \nhusk \nMosaic and mottling \nPremature drying leaf \nsheath \nShorten internodes \nSterile tassel \na \nb \nc \nd \ne \nf \n"
+    },
+    {
+      "page": 4,
+      "text": "218   The Open Agriculture Journal, 2018, Volume 12\nKaranja et al.\napproaches are difficult to implement in Bomet, Kisii and Narok counties where relay planting of maize is a common\npractice due to frequent rainfalls. Therefore, the most effective control method for MLN, SCMV and MCMV, is the use\nof resistant maize genotypes.\nDevelopment  of  virus-resistant  maize  germplasm  and  hybrids  is  the  most  cost-effective  and  environmentally\nfriendly approach to control disease [19, 20]. The utilization of disease nurseries and trials in the screen houses and field\ninfestation is the easiest approach to screen for MLN resistance in maize.\nThe objective of this study was to identify sources of resistance to MCMV, SCMV and MLN in a set of selected\nmaize genotypes that can support further research for genetic analysis and development of durable resistant maize lines\nand hybrids with good agronomic performance under MLN.\n2. MATERIALS AND METHODS\n2.1. Host plants\nSeeds  of  65  selected  maize  genotypes  were  obtained  from  the  Kenya  Agricultural  and  Livestock  Research\nOrganization (KALRO), International Maize and Wheat Improvement Center (CIMMYT) [21] and USDA, ARS Corn,\nSoybean and Wheat Quality Research Unit (CSWQRU) in Wooster, Ohio. The germplasm was selected based on\nprevious studies and data on resistant to other diseases such as maize streak virus, grey leaf spot, Turcicum leaf blight,\ncommon leaf rust among others and/or which from previous screening showed indications of resistance or tolerance to\nMLN. Four yellow maize lines; P405, OH28, N211 and KS23-6 were used as checks where; PA405 is resistant to\nSCMV but susceptible to MCMV and MLN, OH28 (CI.112-1 X Oh920) X (I11.A x I11.B) which was released in 1943\n[22]. and it is susceptible to Maize dwarf mosaic virus (MDMV), SCMV, Wheat streak mosaic virus (WSMV), Maize\nchlorotic dwarf virus (MCDV), Maize mosaic virus (MMV), and Maize fine streak virus (MFSV) [23 - 25, 14]was used\nas a susceptible check while N211 and KS23-6 were used as tolerant checks for both MCMV and MLN [12]. In the\nsecond and third screening, germplasm that had a severity score higher than OH28 in MCMV and MLN were removed,\nleaving only thirty genotypes. Plastic pots measuring 30 cm in diameter filled with autoclaved (heat-sterilized) silt-\nloam soil and manure mix (6:1, v/v) were arranged in a randomized complete block design of three replications. Seeds\nwere sown at a rate of five seeds per pot, giving a total of 15 plants per genotype. Di-Ammonium Phosphate (DAP) and\nCalcium Ammonium Nitrate (CAN) were applied to give 60kgN and 60kgP2O5 ha\n-1 at planting and at 8 weeks after\nplanting (WAP) respectively.\n2.2. Virus isolates/ Inoculum preparation and inoculation\nMCMV and SCMV isolates used in this study were originally collected from Bomet County in South Rift valley in\nKenya and have been maintained at KALRO-Kabete, National Agricultural Research Laboratories (NARL) by serial\npassage on to susceptible maize hybrid H614 in separate greenhouses. Virus strain identity was verified at each passage\ntime inoculum was prepared for a test by also inoculating susceptible maize germplasm OH28 and H614, Sorghum\nbicolor (L.) Moench cv. Atlas and Sart. Atlas and Sart sorghums are resistant to MCMV while Sart is susceptible to\nMDMV and SCMV but resistant to WSMV [16]. As in other crops, it is very difficult to diagnose virus diseases in\nmaize-based solely on symptoms, as these vary significantly based on plant genotype, time of infection, environmental\nconditions and the potential for multiple infections. Therefore, the serological assay, ELISA, was used to check the\nVirus purity, inoculation and disease assessment for MCMV and SCMV.\nAt 3 to 4 leaf stage (10 days after planting) all plants were mechanically inoculated twice within a 1-week interval\nby rubbing the 2 youngest leaves [26]. Virus inoculum was prepared from freshly harvested infected symptomatic\nyoung plants (infected 10-15 days prior to the main inoculation). Before inoculation, leaves were homogenated in 0.01\nM phosphate buffer (pH 7.0) in 1:8 dilutions and 0.6% of 22µm carborundum were added prior to inoculation. The\ninoculum was kept in ice during inoculation time. The plants were watered daily until all the plants flowered.\n2.3. Symptoms identification/rating\nDisease incidences, severity and 50% days to anthesis (pollen shade) were recorded. Plants were diagnosed for virus\nsymptoms from 7 days post-inoculation (dpi) every 2 days interval up to 56 days, using a 1 to 5 scale, where; 1= no\nsymptoms, 3= mild symptoms and 5= Severe chlorosis (dieback of the plant) (Fig. 3). Included in the diagnoses were\nnotations of whether symptoms were local lesions on inoculated leaves or systemic infections that were limited or\ngeneral and consisted of mosaics, mottles, or flecks and streaks.\n"
+    },
+    {
+      "page": 5,
+      "text": "Response of Selected Maize Inbred Germplasm to Maize\nThe Open Agriculture Journal, 2018, Volume 12   219\nFig. (3). Disease severity rating scale for SCMV, MCMV and MLN in maize.\nDouble-antibody  sandwich  ELISA  (DAS-ELISA)  analysis  was  carried  out  to  confirm  virus  identity  prior  to\ninoculation and non-symptomatic plants. 500 mg of tissue were taken from the tip of 6\nth and 7\nth leaf and homogenized in\n2.5 ml of 1x extraction buffer; PBST, pH 7.2 (137 mM NaCl, 3 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4), containing\n2% BSA and 0.05% Tween 20. The ground samples were placed at –20°C for long-term storage. The samples were\nthawed to room temperature before loading plates. Plates were coated with a 1:1000 dilution of virus-specific IgG\n(1mg/ml) in carbonate coating buffer, pH 9.6, and the secondary antibody; alkaline phosphatase conjugated IgG (1\nmg/ml, Sigma) at a 1:5000 dilution, both of which proved to be optimal in a previous evaluation. After incubation of the\nplates containing a primary antibody for 1 hour at 37°C, the wells were washed with 200 µl of PBST, 200 µl of the\ndiluted samples were added and plates were incubated at 37°C for 1 hour. Washing was done as above and secondary\nantibody was added, plates were incubated for 1 hour at 37°C. Finally, 200 µl of a 0.6 mg/ml solution of p-nitrophenyl\nphosphate  (Sigma)  was  added  to  each  well  and  color  development  was  allowed  to  continue  for  1  hour  at  room\ntemperature. ELISA Nunc (Inter Med., Denmark) microtiter plates were used and absorbance was recorded at 405 nm\nin an MR700 Dynatech spectrophotometer (Dynatech, UK). Blank, buffer, positive control from the primary inoculum\nand health sample were used on all plates as controls. The readings were carried out at 60 minutes after addition of the\nsubstrate. In cases of doubt, the reading overnight was compared to the reading at one hour. A sample was judged as\npositive when the reading was greater than three times the absorbance value of a healthy (negative) plant extract\nreaction.\n3. STATISTICAL ANALYSIS\nAnalysis of variance for each trial and combined analysis across years was performed using Multi-environment\nTrial analysis with R; version 5.0, GenStat 15\nth edition and Breeding management system software, version 3.0.9 to\ndetermine the level of significant difference between genotypes. Area Under the Disease Progress Curve (AUDPC)\nwhich  is  a  better  indicator  of  disease  expression  over  time  was  calculated  on  a  single  plant  basis  by  trapezoidal\nintegration over the whole observation period as follows:\nAUDPC= ∑i[ (DSi + DSi-1) x (ti-ti-1)]/2\nWhere “i” ={6, 8, 10, 12, 14, 16, 18, 20, 22, 31, 38, 45, 56} are the days of observation, “DS” is the disease score\nusing the above severity score of 1 to 5 and “t” represents the number of days post-inoculation [27, 28]. Genotypic\ncorrelations (Rg) between treatments were estimated according to Coopre et al., 1996 as:\nRg = Rp(12)/(H1 x H2)\n1/2\nIn which Rp(12) is the phenotypic correlation between the traits measured in treatments 1 and 2, H1 and H2 are the\nbroad-sense heritability for the trait measured in screen houses 1 and 2, respectively.\n4. RESULTS\nInfection was observed in all the inoculated plants but the maize lines differed significantly (P <0.001) for the\nresistance to SCMV, MCMV and MLN. Disease severity differed significantly with plant growth with first disease\nsymptoms in susceptible genotypes observed at 5 to 6 days post-inoculation (dpi) for SCMV, 10 to12 days for MLN\nand 14 to 15 days for MCMV. Disease severity was rated up to 56 dpi a time at which most of the lines had attained\n50% flowering. There was significant genetic variation (p≤0.001) among the genotypes at 14, 35 and 56 dpi for disease\nseverity, mean disease score and area under disease progress curve (AUDPC) (Figs. 4, 5, 6 and Table 1). Only 17% of\nthe total germplasm screened under SCMV had a mean score < 2.5. Two maize genotypes; MLN041 and MLN 042 had\n \n"
+    },
+    {
+      "page": 6,
+      "text": "220   The Open Agriculture Journal, 2018, Volume 12\nKaranja et al.\nthe lowest AUDPC value of 286 compared to mean AUDPC value of 1338.4 for the susceptible germplasm Table 1.\nFig. (4). SCMV disease severity progress for the top performing germplasm (bottom) compared to the susceptible (top) obtained by\nplotting disease severity scores against the number of days post inoculation at 14 (early), 35 (middle) and 56 days (late).\nFig. (5). MCMV disease severity progress for the resistant germplasm (bottom), tolerant (middle) and susceptible (top) obtained by\nplotting disease severity scores against the number of days post inoculation at 14 (early), 35 (middle) and 56 days (late).\n \n0.0\n1.0\n2.0\n3.0\n4.0\n5.0\n6.0\n14dpi\n35dpi\n56dpi\nMean Score\nSCMV disease severity (scale 1-5) \nDays post inoculation \nAtlas\nMLN042\nMLN041\nMLN013\nPA405\nMLN028\nMLN019\nMLN020\nMLN056\nCML444\nCML312\nCML442\nCML 539\nOH28\n0\n1\n2\n3\n4\n5\n6\n14dpi\n35dpi\n56dpi\nmean\nMCMV disease severity (scale 1-5) \nDays post inoculation \nMLN001\nMLN006\nAtlas\nMLN012\nMLN016\nMLN008\nMLN007\nN211\nMLN009\nMLN050\nKS23-6\nCML312\nCML444\nCML 539\nCML442\nPA405\nOH28\n"
+    },
+    {
+      "page": 7,
+      "text": "Response of Selected Maize Inbred Germplasm to Maize\nThe Open Agriculture Journal, 2018, Volume 12   221\nFig. (6). MLN disease severity progress for the tolerant germplasm (middle)and susceptible (top) obtained by plotting disease\nseverity scores against the number of days post inoculation at 14 (early), 35 (middle) and 56 days (late).\nTable 1. Mean performance of 10 lowest in disease severity from among 65 maize germplasm and 5 susceptible checks\nevaluated under SCMV, MCMV and MLN in 2015 P≤ 0.001.\nMCMV\nSCMV\nMLN\nPedigree AUDPC\nDays to 50%\nAnthesis\nDays to 50%\nSilking\nPedigree\nAUDPC\nDays to 50%\nAnthesis\nDays to 50%\nSilking\nPedigree\nAUDPC\nMLN001\n270 a\n71.33 abcdefgh 73.67 abcdefghijk\nAtlas\n286 a\n68 bcdefghijkl\n73.67 defghijklm\nAtlas\n196 a\nMLN006\n270 a\n59.67 abc\n62 abc\nMLN042\n286 a\n66 abcdefghij\n69 abcdefghij\nMLN001\n471.3 ab\nAtlas\n270 a\n70 abcdefgh\n74.33 abcdefghijk MLN041\n288 a\n69 bcdefghijklm\n70.67 bcdefghijklm MLN013\n518 abc\nMLN012\n286 a\n75 bcdefgh\n77 abcdefghijk\nMLN013\n321 ab\n77.33 klm\n80.67 klmn\nMLN006\n535.5 abcd\nMLN016\n287 a\n81 abcdefgh\n83 abcdefghijk\nMLN019\n378 abc\n71.33 cdefghijklm\n70.33 bcdefghijkl\nMLN019\n551.2 bcde\nMLN008\n292 a\n77.33 bcdefgh\n78.67 bcdefghijk\nPA405\n385 abc\n61.67 abcd\n62.67 abcd\nMLN052\n555.9 bcdef\nMLN007\n298 a\n76 bcdefgh\n82.67 cdefghijk\nMLN028\n453 abcd\n70.67 cdefghijklm\n73 defghijklm\nMLN016\n565.8 bcdef\nN211\n454 ab\n64.67 abcdefg\n66.33 abcdef\nMLN020\n474 abcde\n63 abcdefg\n60 ab\nMLN018 573.4 bcdefg\nMLN009\n459 ab\n72.33 abcdefgh\n75 abcdefghijk\nMLN056\n566 abcdef\n66 abcdefghij\n70.33 bcdefghijkl\nMLN002 582.8 bcdefgh\nMLN050\n636 bc\n58.33 ab\n59.33 ab\nCML444\n1311 hi\n73.67 ghijklm\n74.33 efghijklmn\nMLN056 601.1 bcdefgh\nKS23-6\n844 cd\n77.33 bcdefgh\n77.67 abcdefghijk CML312\n1323 i\n70 cdefghijklm\n70.33 bcdefghijkl\nKS23-6 606.7 bcdefgh\nCML312 1132 fgh 70.67 abcdefgh 73.33 abcdefghijk CML442\n1326 i\n70.67 cdefghijklm\n73.33 defghijklm\nN211\n663.8 bcdefgh\nCML444 1166 fgh\n84.33 fgh\n86 efghijk\nCML 539\n1363 i\n66 abcdefghij\n68.67 abcdefghij\nCML444 666.8 bcdefgh\nCML 539 1165 fgh 69.33 abcdefgh\n71.33 abcdefghij\nOH28\n1369 i\n69.67 cdefghijklm\n72.33 defghijklm\nCML312 722.2 bcdefgh\nCML442 1178 fgh 76.33 bcdefgh\n75.67 abcdefghijk MLN006 960bcdefghijk\n58.67ab\n63.76abcde\nCML 539\n890.2 efgh\nPA405\n1182 fgh 67.33 abcdefgh\n70.67 abcdefghij\nOH28\n913.5 gh\nOH28\n1259 h\n73.33 abcdefgh\n75 abcdefghijk\nCML442\n922.8 h\nMean\n1019.94\n73.099\n76.12\nMean\n1116.7\n69.932\n72.609\nMean\n740.38\ns.e.\n69.15\n5.993\n6.159\ns.e.\n196.7\n3.142\n3.352\ns.e.\n79.94\ncv%\n6.8\n8.2\n8.1\ncv%\n17.6\n4.5\n4.6\ncv%\n10.8\n*Mean followed by the same letters in the same column are nor significantly different from each other at P< 0.001\nThe symptoms were first observed at 14 dpi from the lower leaves and spread slowly to the newly emerging leaves\n \n0\n1\n2\n3\n4\n5\n6\n14dpi\n35dpi\n56dpi\nMean\nMLN disease severity (scale 1-5) \nDays post inoculation \nAtlas\nMLN001\nMLN013\nMLN006\nMLN019\nMLN052\nMLN016\nMLN018\nMLN002\nMLN056\nKS23-6\nN211\nCML444\nCML312\nPA405\nCML 539\nOH28\nCML442\n"
+    },
+    {
+      "page": 8,
+      "text": "222   The Open Agriculture Journal, 2018, Volume 12\nKaranja et al.\nin the MCMV experiment. At 20dpi, 45% of the germplasm screened had a disease severity score < 3, while at 56dpi,\nonly 15% of the germplasm were <3. Only 9% of the total germplasm in the 2 years period of screening showed\nacceptable levels of tolerance to MCMV. Among the top-performing were; MLN001, MLN006 MLN012, MLN016,\nMLN008, MLN007 and MLN009 which had a disease severity score <2.0 at 56dpi. N211, unlike the others, developed\nsymptoms slowly with the first symptoms been observed at 20 dpi and with moderate symptoms at 56dpi (Fig. 5).\nNearly all plants inoculated with MLN (SCMV and MCMV combined) developed symptoms within 10 dpi. At\n35dpi, 95% germplasm screed had a mean disease severity score of < 2.5. However, symptoms for genotypes MLN001\nand MLN006 developed moderately with a final score of < 3.0 at 56 dpi (Fig. 6). The disease severity on susceptible\ngermplasm was high and reached a maximum score of 5 and highest AUDPC value of >650 Table 1. More than 60% of\ngermplasm under MLN showed stunted growth and dead heart (Fig. 2b) or died before reaching knee height. Since the\nplants were allowed to grow to maturity, >50% of the germplasm did not tussle. For those that managed to produce\ntassel,  they  did  not  shed  pollen  (sterile)  (Fig.  2e).  Majority  of  the  germplasm  did  not  produce  silk.  The  tolerant\ngermplasm N211, KS23-6, MLN001, MLN006, MLN013 and MLN019 managed to tassel and produced silk. However,\nthe ears were either deformed, partially filled or had husk cover senesced prematurely. If a maize field is infected with\nMLN late in the growing cycle, an increased dry husk (Fig. 2f) and a high number of rotten ears are observed (Karanja\net al unpublished).\nOther than Sart and Atlas sorghums, no other germplasm was found to be completely immune to MCMV based on\nELISA tests Table 2. However, N211 and MLN016 can be considered to be moderately tolerant to MCVM given the\nlow ELISA readings Table 2. Moreover, inbred MLN013, MLN041 and MLN019 were found to be resistant to SCMV\nas no virus was detected with ELISA analysis. In germplasm MLN042, MLN020, PA405 and CML 444, SCMV virus\nwere detected when analysed with ELISA suggesting low virus titter hence tolerance to SCMV.\nTable 2. Confirmation of enzymes-linked immunosorbent assay (ELISA) results for maize germplasm with the lowest and\nhigh disease severity under MCMV and SCMV screening.\nMCMV\nSCMV\nGenotype\nElisa Reading\nReaction Type \na\nGenotype\nElisa Reading\nReaction Type \na\nBuffer\n0.18\nMLN013\n0.09\nR\nAtlas\n0.25\nR\nMLN041\n0.09\nR\nHealth control (*3)\n0.41\nMLN019\n0.09\nR\nMLN006\n0.42\nT\nBuffer\n0.09\nN211\n0.47\nT\nMLN042\n0.1\nR\nMLN016\n0.66\nS\nAtlas\n0.1\nR\nCML442\n0.74\nS\nMLN020\n0.11\nR\nMLN009\n0.74\nS\nPA405\n0.13\nR\nOH28\n0.87\nS\nCML444\n0.15\nR\nMLN007\n0.94\nS\nOH28\n0.2\nT\nMLN001\n0.95\nS\nCML442\n0.23\nT\nCML 539\n1.03\nS\nMLN028\n0.24\nT\nMLN012\n1.03\nS\nCML 539\n0.25\nT\nCML312\n1.09\nS\nCML312\n0.25\nT\nCML444\n1.11\nS\nHealth Control (*3)\n0.26\nPositive control\n1.15\nSart\n0.28\nHighly Susceptible\nCML395\n1.2\nHighly Susceptible\nMLN006\n0.29\nHighly susceptible\nMLN050\n1.25\nHighly Susceptible\nPositive control\n1.07\nHeritability\n0.85\n0.96\nMean\n0.86\n0.2\nLSD0.05\n0.38\n0.08\nCV (%)\n25.17\n22.66\n*Three times the reading for health control;\na R=Resistant; T=Tolerant; S=Susceptible\nHowever, there is a need to use real-time quantitative PCR (RTqPCR) to confirm these given the low sensitivity of\nELISA.\nA negative genetic and phenotypic correlation was observed under MLN between 56 days post inoculation (dpi),\ndisease severity score pooled mean (dspm), Area Under Disease Progress Curve (AUDPC) and 50% days to anthesis\n"
+    },
+    {
+      "page": 9,
+      "text": "Response of Selected Maize Inbred Germplasm to Maize\nThe Open Agriculture Journal, 2018, Volume 12   223\n(Anth) and silking (SLK) Table 3. Most of the plants in MLN experiment were stunted or died resulting in missing data\non anthesis and silking, thus high cv(%) of 51.2 Table 1. However, the majority of plants under SCMV and MCMV\nmanaged to flower despite the severe symptoms even for the checks Table 1.\nTable 3. Genotypic (upper triangle) and phenotypic (lower triangle) correlation estimates for 56 days post inoculation (dpi),\ndisease severity score pooled mean (dspm), Area Under Disease Progress Curve (AUDPC) and 50% days to anthesis (Anth)\nand silking (SLK) measured in the screen house under; a) MCMV, b) MLN and c) SCMV\na) MCMMV\nTraits\n52dpi\ndspm\nAUDPC\nAnth.\nSlk\n52dpi\n1\n0.92***\n0.99***\n0.11*\n0.12*\ndspm\n0.95***\n1\n0.96***\n0.16*\n0.16*\nAUDPC\n0.99***\n0.99***\n1\n0.13*\n0.14*\nAnth.\n0.14*\n0.20*\n0.17*\n1\n0.93**\nSLk\ne\n0.15*\n0.20*\n0.17*\n1.00\n1\nb) MLN\n52dpi\n1\n0.89***\n0.93***\n-0.51**\n-0.60**\ndspm\n0.92***\n1\n0.99***\n-0.48*\n-0.52**\nAUDPC\n0.96***\n0.99***\n1\n-0.51**\n-0.56**\nAnth\n-0.57**\n-0.52**\n-0.56**\n1\n0.80***\nSlk\n-0.73**\n-0.62**\n-0.67**\n0.93***\n1\nc) SCMV\n52dpi\n1\n0.97***\n0.99***\n0.04\nns\n0.08\nns\nMean\n0.97***\n1\n0.99***\n0.03\nns\n0.06\nns\nAUDPC\n0.99***\n0.99***\n1\n0.04\nns\n0.07\nns\nAnth\n0.04\nns\n0.03\nns\n0.04\nns\n1\n0.96**\nSlk\n0.09\nns\n0.07\nns\n0.08\nns\n0.98***\n1\n*significant at P<0.05; **significant at P<0.01; ***significant at P<0.001; ns, not significant\n5. DISCUSSION\nThe  analysis  of  disease  severity  revealed  that  the  genotypic  effect  of  SCMV,  MCMV  and  MLN  was  highly\nsignificant  on  all  assessment/scoring  dates  as  well  as  on  the  pooled  mean  and  AUDPC  values.  The  susceptible\ngenotypes; CML444, CML 312, CML442, CML 539 and OH 28, showed the highest disease scores at all assessment\ndates, pooled mean and AUDPC. Other than Atlas sorghum, no maize genotype was observed to be immune to MLN.\nEvaluation of the germplasm suggests that MLN041 and MLN042 may be carrying the desirable genes for SCMV\nresistance and MLN001, MLN006, MLN016 and N211 for MCMV tolerance. This study has also confirmed that\nPA405 developed in Pennsylvania is tolerant to SCMV strain used in this study. Inbred PA405 was reported to show\ncomplete resistance to MDMV and SCMV inoculation under both field and greenhouse conditions [29] with six major\ngenes conferring resistance to SCMV and MDMV being identified [30 - 34]. Further studies need to be conducted in\nMLN042 and MLN041 to confirm and identify the number of genes conforming to resistance Inbred N211 which\nshowed delayed symptoms expression with the first symptoms appearing at 20 dpi could be considered have partial\ntolerant to MCMV since a low viral load was detected at 25 dpi using ELISA. The delayed symptoms development in\nN211 was also observed by [12]. Similar observations were also made by Kovacs [35] after screening different maize\ninbred lines and hybrids with MCMV. Nelson [36] also observed that the level of resistance to MCMV varied widely\namong the maize lines screened suggesting that MCMV resistance is controlled by many minor genes. The extended\nincubation period observed in N211 is an indication of the presence of resistance genes which are providing a certain\ndegree of MCMV resistance. Similar results of delayed symptoms were observed under MLN for the inbred N211,\nKS23-6, MLN016 and MLN009. This could be associated to reduce synergistic interaction between SCMV and MCMV\ngave their high level of tolerance to MCMV [37]. Zihao [38] reported that the co-infection of MCMV and SCMV\nincreases the accumulation of MCMV [38]. The accumulation of MCMV genomic RNAs and the expression of MCMV\nCP have been observed to be higher in MLN-infected leaves than single-infected leaves, with no obvious difference\nobserved  for  SCMV  RNA  and  SCMV  CP  expression  levels  between  MLN  and  single-infected  leaves  [38].  This\nsuggests that the symptoms expression in MLN for the inbred MLN042 and MLN041 were for MCMV given their high\nlevel of resistance to SCMV. This study also validated the presence of MLN tolerance to MLN013 (CKDHL120312)\nand MLN019 (CKDHL140918) as earlier reported by CIMMYT [39]. However, more study needs to be conducted on\n"
+    },
+    {
+      "page": 10,
+      "text": "224   The Open Agriculture Journal, 2018, Volume 12\nKaranja et al.\nthese  germplasm  together  with  N211,  KS23-6,  MLN001,  MLN006,  MLN009  and  MLN016  to  understand  the\nmechanism  underlying  the  synergistic  interaction  between  MCMV  and  SCMV.  The  failure  of  >50%  of  screened\ngermplasm to reach flowering and/or delayed flowering, male sterility, poorly filled cobs and a high number of rotten\nears under MLN trials is an indication of the high impact of the disease on yield.\nCONCLUSION\nThe results presented here reveal that the studied maize germplasm has sufficient variability for response to SCMV,\nMCMV and MLN. SCMV seems to play an important role in increasing MLN symptoms in MCMV susceptible maize\ngenotypes  and  can  be  considered  as  a  catalyst  to  its  multiplication.  This  indicates  that  in  developing  MLN\ntolerant/resistant maize varieties, more focus should be on identifying and using SCMV tolerant maize germplasm.\nThese tolerant genotypes are now serving as donors in the introgression of the tolerance into the Kenyan adapted maize\nbackgrounds and development of improved MLN tolerant varieties combined with multiple abiotic and biotic stress\ntolerance traits. This will go a long way in restoring and ensuring sustainable maize productivity and improving the\nlivelihoods of the smallholder farmers who form 75% of the major maize producers in Kenya.\nIn addition, the inbred lines should be used to conduct further research on the mechanism responsible for the healthy\npollen-diseased silk relationship, SCMV and MCMV synergism as earlier suggested by Mikel [40].\nETHICS APPROVAL AND CONSENT TO PARTICIPATE\nNot applicable.\nHUMAN AND ANIMAL RIGHTS\nNo Animals/Humans were used for studies that are base of this research.\nCONSENT FOR PUBLICATION\nNot applicable\nCONFLICT OF INTEREST\nThe authors declare no conflict of interest, financial or otherwise.\nACKNOWLEDGEMENTS\nWe thank KALRO, CIMMYT and USDA-ARS for providing seeds of inbred lines used in this study. We are\ngrateful to KAPAP and CIMMYT for proving the funds to support this study.\nREFERENCES\n[1]\nMuasya WNP, Diallo AO. Development of early and extra early drought and low nitrogen-tolerant varieties using exotic and local germplasm\nfor the dry mid-altitude ecology. In: Friesen DK, Palmer AFE, Eds. Proceedings of the seventh eastern and southern africa regional maize\nconference. February 5-11, 2001; Nairobi, Kenya. 2001; pp. 253-9.\n[2]\nGroote Hugo De , Odongo Odendo M. Assessment of Farmers’ Preferences and Constraints to Maize Production in Moist Midaltitude Zone of\nWestern Kenya. October 21-26, 2001; In: 5\nth International Conference of the African Crop Science Society; Lagos, Nigeria.\n[3]\nFood Security Statistics Http://www.fao.org/economic/ess/food-security-statistics/en/\n[4]\nAdams IP, Harju VA, Hodges T, et al. First report of maize lethal necrosis disease in Rwanda. New Dis Rep 2014; 29: 22-4.\n[http://dx.doi.org/10.5197/j.2044-0588.2014.029.022]\n[5]\nWangai AW, Redinbaugh MG, Kinyua ZM, et al. First report of Maize chlorotic mottle virus and maize lethal necrosis in Kenya. Plant Dis\n2012; 96(10): 1582-3.http://apsjournals.apsnet.org/loi/pdis\n[http://dx.doi.org/10.1094/PDIS-06-12-0576-PDN]\n[6]\nWangai A, Kinyua ZM, Otipa MJ, et al. Maize (Corn) Lethal Necrosis Disease . 2012. KARI Information Brochure [ed. by Ministry Of\nAgriculture]\n[7]\nDoupnik B Jr. Status of corn lethal necrosis Proceedings of the 34\nth Annual Corn and Sorghum Research Conference. Chicago, USA. 1979;\npp. 16-34.\n[8]\nCabanas  D,  Watanabe  S,  Higashi  CHV,  Bressan  A.  Dissecting  the  mode  of  Maize  chlorotic  virus  (Tombusviridae:  Machlomovirus)\ntransmission by franliniella williamsi (Thysanoptera: Thripidae). J Econ Entomol 2013; 106: 16-24.\n[http://dx.doi.org/10.1603/EC12056] [PMID: 23448010]\n"
+    },
+    {
+      "page": 11,
+      "text": "Response of Selected Maize Inbred Germplasm to Maize\nThe Open Agriculture Journal, 2018, Volume 12   225\n[9]\nBrandes EW. Artificial and insect transmission of sugarcane mosaic. J Agric Res 1920; 9: 131-8.\n[10]\nJensen SG, Wysong DS, Higley P M. Seed transmission of maize chlorotic mottle virus. Plant Dis 1991; 75(5): 497-8.\n[http://dx.doi.org/10.1094/PD-75-0497]\n[11]\nCastillo-Loayza J. Maize virus and virus-like diseases in Peru. In: Williams LE, Gordon DT, Nault LR, Eds. In: International Maize Virus\nDisease Colloquium andWorkshop.; OARDC, Wooster, OH. 1976; pp. 40-4.\n[12]\nMahuku G, Lockhart BE, Wanjala B, et al. Maize lethal necrosis (MLN) an emerging threat to maize-based food security in sub-Saharan\nAfrica. Phytopathology 2015; 105(7): 956-65.\n[http://dx.doi.org/10.1094/PHYTO-12-14-0367-FI] [PMID: 25822185]\n[13]\nLukanda M, Owati A, Ogunsanya P, et al. First report of Maize chlorotic mottle virus infecting maize in the Democratic Republic of the\nCongo. Plant Dis 2014; 98: 1448-9.\n[http://dx.doi.org/10.1094/PDIS-05-14-0484-PDN]\n[14]\nRedinbaugh MG, Pratt PC. Virus resistance. In: Bennetzen JL, Hake SC, Eds. Handbook of maize: its biology. New York: Springer 2009; pp.\n251-68.\n[http://dx.doi.org/10.1007/978-0-387-79418-1_13]\n[15]\nNiblett CL, Claflin LE. Corn lethal necrosis - a new virus disease of corn in Kansas. Plant Dis Rep 1978; 62(1): 15-9.\n[16]\nUyemoto JK. Biology and control of maize chlorotic mottle virus. Plant Dis 1983; 67(1): 7-10.\n[http://dx.doi.org/10.1094/PD-67-7]\n[17]\nUyemoto JK, Bockelman DL, Claflin LE. Severe outbreak of corn lethal necrosis disease in Kansas. Plant Dis 1980; 64(1): 99-100. formerly\nPlant Disease Reporter.\n[http://dx.doi.org/10.1094/PD-64-99]\n[18]\nPhillips NJ, Uyemoto JK, Wilson DL. Maize chlorotic mottle virus and crop rotation: effect of sorghum on virus incidence. Plant Dis 1982;\n66: 376-9.\n[http://dx.doi.org/10.1094/PD-66-376]\n[19]\nGururani MA, Venkatesh J, Upadhyaya CP, Nookaraju A, Pandey SK, Park SW. Plant disease resistance genes: current status and future\ndirections. Physiol Mol Plant Pathol 2012; 78: 51-65.\n[http://dx.doi.org/10.1016/j.pmpp.2012.01.002]\n[20]\nAli F, Yan J. Disease resistance in maize and the role of molecular breeding in defending against global threat. J Integr Plant Biol 2012; 54(3):\n134-51.\n[http://dx.doi.org/10.1111/j.1744-7909.2012.01105.x] [PMID: 22333113]\n[21]\nGowda M, Das B, Makumbi D, et al. Genome-wide association and genomic prediction of resistance to maize lethal necrosis disease in\ntropical maize germplasm. Theor Appl Genet 2015; 128(10): 1957-68.\n[http://dx.doi.org/10.1007/s00122-015-2559-0] [PMID: 26152570]\n[22]\nZambrano JL, Francis DM, Redinbaugh MG. Identification of resistance to maize rayado fino virus in maize inbred lines. Plant Dis 2013; 97:\n1418-23.\n[http://dx.doi.org/10.1094/PDIS-01-13-0037-RE]\n[23]\nJones MW, Redinbaugh MG, Louie R. The Mdm1 locus and maize resistance to Maize dwarf mosaic virus. Plant Dis 2007; 91: 185-90.\n[http://dx.doi.org/10.1094/PDIS-91-2-0185]\n[24]\nLouie R. Vascular pucture of maize kernel for the mechanical transmission of maize white line mosaic-virus and other viruses of maize.\nPhytopathology 1995; 85: 139-683.\n[http://dx.doi.org/10.1094/Phyto-85-139]\n[25]\nMcMullen MD, Louie R. The linkage of molecular markers to a gene controlling the symptom response in maize to maize dwarf mosaic virus.\nMol Plant Microbe Interact 1989; 2: 309-14.\n[http://dx.doi.org/10.1094/MPMI-2-309]\n[26]\nLouie R. Effects of genotype and inoculum protocols on resistance evaluation of maize to maize dwarf mosaic virus strains. Phytopathology\n1986; 76: 769-73.\n[http://dx.doi.org/10.1094/Phyto-76-769]\n[27]\nCampbell CL, Madden LV. Introduction to Plant Disease Epidemiology. New York: John Wiley & Sons 1990.\n[28]\nJegger MJ, Viljanen-Rollinson SLH. The use of the area under the disease-progress curve to assess quantitative disease resistance in crop\ncultivars. Theor Appl Genet 2001; 102: 32-40.\n[http://dx.doi.org/10.1007/s001220051615]\n[29]\nLouie R, Knoke JK, Findley WR. Elite maize germplasm: Reactions to maize dwarf mosaic and maize chlorotic dwarf viruses. Crop Sci 1990;\n30: 1210-5.\n[http://dx.doi.org/10.2135/cropsci1990.0011183X003000060010x]\n[30]\nDussle CM, Melchinger AE, Kuntze L, Stork A, Lubberstedt T. Molecular mapping and gene action of Scm1 and Scm2, two major QTLs\ncontributing to SCMV resistance in Maize. Plant Breed 2000; 119: 299-303.\n[http://dx.doi.org/10.1046/j.1439-0523.2000.00509.x]\n"
+    },
+    {
+      "page": 12,
+      "text": "226   The Open Agriculture Journal, 2018, Volume 12\nKaranja et al.\n[31]\nDussle M, Quint M, Xu L, Melchinger E, Lübberstedt T. Conversion of AFLP fragments tightly linked to SCMV resistance genes Scmv1 and\nScmv2 into simple PCR-based markers. Theor Appl Genet 2002; 105(8): 1190-5.\n[http://dx.doi.org/10.1007/s00122-002-0964-7] [PMID: 12582898]\n[32]\nGemechu AL, Chiemsombat P, Attathom S, Reanwarakorn K, Lersrutaiyotin R. Cloning and sequence analysis of coat protein gene for\ncharacterization of sugarcane mosaic virus isolated from sugarcane and maize in Thailand. Arch Virol 2006; 151(1): 167-72.\n[http://dx.doi.org/10.1007/s00705-005-0643-5] [PMID: 16195785]\n[33]\nRosenkranz E, Scott GE. Determination of the number of genes for resistance to maize dwarf mosaic virus strain A in five corn inbred lines.\nPhytopathology 1986; 74: 71-6.\n[http://dx.doi.org/10.1094/Phyto-74-71]\n[34]\nUyemoto JK, Claflin LE, Wilson DL, Raney RJ. Maize chlorotic mottle and maize dwarf mosaic viruses; effect of single and double\ninoculations on symptomatology and yield. Plant Dis 1981; 65(1): 39-41.\n[http://dx.doi.org/10.1094/PD-65-39]\n[35]\nKovacs G, Gaborjanyi R, Duong HN, Vasdinyei R. Susceptibility of maize inbred lines and hybrids to potyviruses under greenhouse and field\nconditions. Cereal Res Commun 1994; 4: 347-51.\n[36]\nNelson S, Brewbaker J, Hu J. Maize Chlorotic Mottle Virus. Plant Dis 2011; 79: 1-6.\n[37]\nRetnosari A, Suvia W, Weny N, et al. Full sequence of the coat protein gene is required for the induction of pathogen-derived resistance\nagainst sugarcane mosaic virus in transgenic sugarcane. Mol Biol Rep 2018; 16.\n[38]\nXia Z, Zhao Z, Chen L, et al. Synergistic infection of two viruses MCMV and SCMV increases the accumulations of both MCMV and\nMCMV-derived siRNAs in maize. Sci Rep 2016; 6: 20520.\n[http://dx.doi.org/10.1038/srep20520] [PMID: 26864602]\n[39]\nYoseph B, Manje G, Suresh LM, et al. Genetic analysis of tropical maize inbred lines for resistance to maize lethal necrosis disease.\nEuphytica 2017; 213: 224.\n[http://dx.doi.org/10.1007/s10681-017-2012-3]\n[40]\nMikel MA, D’Arcy CJ, Rhodes AM, Ford RE. Effect of maize dwarf mosaic virus Infection on sweet corn pollen and silk. Phytopathology\n1982; 72: 428-31.\n[http://dx.doi.org/10.1094/Phyto-72-428]\n© 2018 Karanja et al.\nThis is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a\ncopy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and\nreproduction in any medium, provided the original author and source are credited.\n"
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+      "text": "Genetic trends in the\nKenya Highland Maize\nBreeding Program\nbetween 1999 and 2020\nDickson O. Ligeyo 1, Edward Saina 1, Bornface J. Awalla 1*,\nClay Sneller\n2, Walter Chivasa 3, Lennin Musundire 3*,\nDan Makumbi 3, Mable Mulanya 4, Dragan Milic 3,\nSamuel Mutiga 3, Abraham Lagat 3, Biswanath Das 3\nand Boddupali M. Prasanna 3\n1Department of Food Crops and Research Institute, Kenya Agricultural and Livestock Research\nOrganization, Kitale, Kenya, 2Department of Horticulture and Crop Science, The Ohio State University,\nWooster, OH, United States, 3Global Maize Program, International Maize and Wheat Improvement\nCenter, (CIMMYT), Nairobi, Kenya, 4Integrated Breeding Platform (IBP), Nairobi, Kenya\nOptimization of a breeding program requires assessing and quantifying empirical\ngenetic trends made through past efforts relative to the current breeding\nstrategies, germplasm, technologies, and policy. To establish the genetic\ntrends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade\n(1999–2020) historical dataset from the Preliminary Variety Trials (PVT) and\nAdvanced Variety Trials (AVT) was analyzed. A mixed model analysis was used\nto compute the genetic gains for traits based on the best linear unbiased\nestimates in the PVT and AVT evaluation stages. A positive significant genetic\ngain estimate for grain yield of 88 kg ha−1 year−1 (1.94% year−1) and 26 kg ha−1\nyear−1 (0.42% year−1) was recorded for PVT and AVT, respectively. Root lodging,\nan important agronomic trait in the Kenya highlands, had a desired genetic gain\nof −2.65% year−1 for AVT. Results showed improvement in resistance to Turcicum\nLeaf Blight (TLB) with −1.19% and −0.27% year−1 for the PVT and AVT, respectively.\nSimilarly, a significant genetic trend of −0.81% was noted for resistance to Gray\nLeaf Spot (GLS) in AVT. These findings highlight the good progress made by\nKHMP in developing adapted maize hybrids for Kenya’s highland agroecology.\nNevertheless, the study identified significant opportunities for the KHMP to make\neven greater genetic gains for key traits with introgression of favorable alleles for\nvarious traits, implementing a continuous improvement plan including marker-\nassisted forward breeding, sparse testing, and genomic selection, and doubled\nhaploid technology for line development.\nKEYWORDS\nmaize, genetic gain, Kenya national breeding program, highland ecology, breeding\nFrontiers in Plant Science\nfrontiersin.org\n01\nOPEN ACCESS\nEDITED BY\nMulatu Geleta,\nSwedish University of Agricultural Sciences,\nSweden\nREVIEWED BY\nGurjeet Singh,\nTexas A and M University, United States\nGanapati Mukri,\nIndian Agricultural Research Institute (ICAR),\nIndia\n*CORRESPONDENCE\nBornface J. Awalla\njumawalla@yahoo.com\nLennin Musundire\nL.Musundire@cgiar.org\nRECEIVED 12 April 2024\nACCEPTED 10 June 2024\nPUBLISHED 01 July 2024\nCITATION\nLigeyo DO, Saina E, Awalla BJ, Sneller C,\nChivasa W, Musundire L, Makumbi D,\nMulanya M, Milic D, Mutiga S, Lagat A,\nDas B and Prasanna BM (2024) Genetic\ntrends in the Kenya Highland Maize\nBreeding Program between 1999 and 2020.\nFront. Plant Sci. 15:1416538.\ndoi: 10.3389/fpls.2024.1416538\nCOPYRIGHT\n© 2024 Ligeyo, Saina, Awalla, Sneller, Chivasa,\nMusundire, Makumbi, Mulanya, Milic, Mutiga,\nLagat, Das and Prasanna. This is an open-\naccess article distributed under the terms of\nthe Creative Commons Attribution License\n(CC BY). The use, distribution or reproduction\nin other forums is permitted, provided the\noriginal author(s) and the copyright owner(s)\nare credited and that the original publication\nin this journal is cited, in accordance with\naccepted academic practice. No use,\ndistribution or reproduction is permitted\nwhich does not comply with these terms.\nTYPE Original Research\nPUBLISHED 01 July 2024\nDOI 10.3389/fpls.2024.1416538\n"
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+      "text": "1 Introduction\nMaize (Zea mays L.) is the primary cereal crop in Kenya, covering\napproximately 2.1 million ha of land, whereas over 75% of the area is\ndevoted to cereal production (FAOSTAT, 2013). The majority (~81%)\nof the maize farmers in Kenya are small-scale growers who cultivate\nthe crop on less than 2 ha of land. With a per capita consumption\nrange of 98 kg–103 kg, maize is a major source of staple food calories,\naccounting for 65% and 36% of the total caloric intake by the Kenyan\npopulation (De Groote et al., 2005; Kirimi et al., 2011; Marenya et al.,\n2021). Maize productivity increased from 1.3 t ha−1 in 1979 to 1.5 t\nha−1 in 2021, but the current yield is still far below the global average\nof 5.8 t ha−1 (FAOSTAT, 2013). Furthermore, the current annual grain\nproduction of 3.3 million tons only caters for approximately 75%–80%\nof the country’s demand (Erenstein et al., 2022). The low maize\nproduction in sub-Saharan Africa (and Kenya in particular) stems\nfrom the inability of the small-scale farmers to adopt good agronomic\npractices that would optimize the performance of the improved\nvarieties across agroecologies and overcome the Genotype ×\nEnvironment × Management (G × E × M) interactions (Dao et al.,\n2015; Cairns et al., 2021). Genetic innovations for enhanced crop\nresilience could reduce the G × E × M effects, increasing maize\nproduction by up to 25% (Cairns and Prasanna, 2018).\nDeploying climate-resilient crops and climate-smart\nagricultural practices is critical for closing yield gaps and reducing\nthe high risk and vulnerability, particularly for smallholder farmers\n(Cairns et al., 2021). To minimize G × E × M effects, the Kenyan\nbreeding programs and their partners have focused on development\nof adapted and resilient maize varieties for cultivation across\ndifferent agroecological zones, including the moist transitional\nhighlands, highland tropics, mid-altitudes, moist transitional, dry-\ntransitional, dry mid-altitudes, and tropical lowlands agroecologies\n(Jatzold and Kutsch, 1982; Miruka et al., 2012; De Groote et al.,\n2023). The highland agroecological zone (1,700 m to 2,300 m above\nsea level, masl; mean annual rainfall of 1,000 mm to 1,800 mm) is\nimportant to Kenyan food security because it is less prone to\ndrought, heat, and flooding than other zones (Kostandini et al.,\n2013). The highlands agroecological zone covers 12% of the maize\narea and produces approximately one-third of the maize grain in\nKenya (De Groote and Omondi, 2023). A recent analysis of the\npotential effects of climate change shows that maize yields will\nreduce by 10%–20% in Sub-Saharan Africa (SSA) by 2050 but the\nyield potential in the highlands of Kenya and Ethiopia will increase\ndue to an increase in temperature (Philip et al., 2010; Cairns et al.,\n2013; Tesfaye et al., 2015). Unfortunately, the area classified as\nhighlands in Kenya will decrease by 35% due to climate change\n(Tesfaye et al., 2015). Increasing maize yields in farmers’ fields\nrequires an integrated approach of various interventions, including\nincreased adoption of new climate-resilient varieties, access by\nsmallholders to the seed of improved varieties, increased fertilizer\nuse, adoption of new agronomic management practices, and\nsupportive policies (Cairns et al., 2021).\nThe Kenyan Highland Maize Program (KHMP) focuses on\ndeveloping improved varieties for the highlands and is one of the six\nmaize improvement initiatives of the Kenya Agricultural and Livestock\nResearch Organization (KALRO) (Miruka et al., 2012; Schroeder et al.,\n2013). The program began with informal maize breeding operations in\nthe 1930s at Njoro in Nakuru and was streamlined when the operations\nwere relocated to Kitale in 1955 (Gerhart 1975). The initial core\nactivities involved collections of suitable local white kernel parental\ngermplasm, which was used to develop the first two synthetic varieties,\nKitale Synthetic I (KSI) and Kitale Synthetic II (KSII), at Kitale in 1955\n(Harrison, 1970). To strengthen the breeding pool, additional new\ngenetic stocks were also introduced from similar agroecologies of\nCentral and Southern America (e.g., the Ecuadorian landrace,\nEcuador 573 and Costa Rica 296) through the International Maize\nand Improvement Center (CIMMYT) (Harrison, 1970). The imported\ngermplasm was used to develop the first maize hybrids (e.g., H611\ndeveloped from Ecuador 573 × KSII and released in 1964), which had\nhigher heterosis for grain yield (140%) than the earlier synthetic\nvarieties (Darrah et al., 1972, 1978). KHMP uses Ecuador 573 and\nKSII as the heterotic groups in well-defined hybrid development\npipelines (Wende et al., 2018).\nIn the last three decades, 394 improved maize varieties have been\nreleased in Kenya, with 87 (22%) coming from the KALRO maize\nbreeding programs; 17 of the 394 varieties (4%) originate from the\nKHMP (KEPHIS, 2023). A high (80%–95%) rate of adoption of\ncertified seed in the Kenyan highlands has been achieved through\nvarietal development, advocacy, and marketing programs, which are\nimplemented by KALRO, the Kenya Seed Company Limited (a\nparastatal with a mandate for certified seed production and\nmarketing), international partners, and other private seed\ncompanies (Smale and Olwande, 2011; Wang et al., 2017).\nAssessing genetic trends for the major traits can provide\ninsights into optimizing the pipelines to enhance genetic gains\nfor traits of interest to a breeding program (Khanna et al., 2022).\nBreeders can assess the response or gain in a breeding program by\nusing “the breeder’s equation” (Eberhart, 1970; Xu et al., 2017).\nThe breeder’s equation estimates potential genetic gains in a\npopulation undergoing cycles of selection and recombination\nand is based on estimates of variances (Falconer and Mackay,\n1996; Rutkoski et al., 2022). Recent interest has been in assessing\nthe genetic gains in breeding programs that are not entirely\nfocused on population improvement through selection cycles\n(Badu-Apraku et al., 2015). There are limited studies on genetic\ngains in the highlands breeding programs in Africa. A previous\ngenetic gain report for the KHMP was published in the 1970s\nbased on older highland maize genotypes, some of which were\nprogenitor populations of the germplasm used in this study\n(Eberhart and Harrison, 1973). Kebede et al. (2020) reported\ngenetic gains of 16.26 kg ha−1 year−1 for GY in Ethiopia’s\nhighland maize breeding program. However, no recent studies\nhave been done on KHMP. Therefore, this study aims to assess the\nhistorical genetic gains based on the trends of the changes in the\ngenetic values for GY, yield-related traits, and major diseases over\na 22-year breeding period at the KHMP and to utilize the results to\nidentify areas of improvement, including potential optimization of\nthe breeding pipeline.\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n02\n"
+    },
+    {
+      "page": 3,
+      "text": "2 Materials and methods\n2.1 Description of the germplasm and trials\nData on germplasm developed by the KHMP, which included\nthree-way hybrids (70%), top cross hybrids (15%), single-cross\nhybrids (15%), and commercial check hybrids, evaluated in trials\nbetween 1999 and 2020, was used in the study. Approximately 10%\nof the evaluated genotypes had parental components derived from\nthe doubled haploid (DH) technology. In contrast, the rest of the\ngenotypes were developed using the pedigree breeding method in\nwhich selected parental inbred lines were hybridized to generate F1\nhybrid seed and individual plants were selected from F2 and\nadvanced up to F6 prior to crossing with two single cross testers\nrepresenting the Kitale synthetic and Ecuador composite heterotic\ngroups, respectively, to generate three-way hybrids. Single-cross\nhybrids were generated from hybrid combinations between inbred\nlines from the pedigree breeding method and DH technology. These\nsingle-cross hybrids were also used as parental combinations to\ngenerate double-cross hybrids. Top crosses were generated from\ncross combinations between open-pollinated varieties, inbred lines\ndeveloped within the KHMP, and introductions from partners,\nnamely, Ethiopia’s Highland maize breeding program and Uganda’s\nNational Agricultural Research Organization (NARO)’s Highland\nmaize breeding program and CIMMYT’s Global Maize Program.\nVarious local and commercial check varieties were used in the trials\nfor years, but common checks were maintained to provide\nconnectivity and allow genetic gain estimates. The number of\nentries evaluated across the years varied, with most of the\ngenotypes in the Preliminary Variety Trials (PVT, 86%) and the\nAdvanced Variety Trials (AVT, 80%) being tested in just 1 year,\n12% and 15% in the second year for the PVT and AVT, respectively;\n2% and 5% evaluated in the third year for the PVT and AVT,\nrespectively; only 1% of the entries tested in the fourth year for the\nAVT (Table 1).\n2.2 Experiment design and\ntrial management\nThe KHMP uses the product development pipeline, which\ninvolves the stage gate advancement protocol across the different\nstages (from PVT to AVT). Each advancement stage is defined\nrelative to activities and key decisions based on defined threshold\nvalues for key traits (Supplementary Table 1) taken between the\nstages. An alpha lattice design was used in the PVT and AVT\nexperiments. In the PVT, 36 entries year−1 and 394 entries with\nthree replications over 13 years were evaluated in 55 experiments.\nSimilarly, in AVT, 25 entries year−1 and 399 entries, with four\nreplications over 22 years, were assessed in 146 experiments\nbetween 1999 and 2020 across 27 locations in the Highlands of\nKenya (Table 2; Figure 1). The experiments were planted in three-row\nplots, 3-m-long rows with 0.75-m row spacing and 0.30-m in-row\nspacing, with a final plant density of approximately 44,444 plants\nha−1. AVT were planted in two-row plots, 5-m-long rows with row\nspacing of 0.75-m and 0.25-m in-row spacing, with a final plant\ndensity of approximately 53,333 plants ha−1. The trials were fertilized\nusing basal fertilizer (18:46:0–N:P: K) at an average rate of 190 kg\nha−1 at planting and top dressing using Top CAN (26% N) at 190 kg\nha−1 split into two equal applications of 95 kg ha−1, with the first half\napplied at the early vegetative stage and the second at the booting\n(pre-flowering) stage as recommended in the PVT and AVT. The\nmajor traits offocus were GY (kg ha−1), plant and ear height (PH and\nEH) (cm), root and stalk lodging (RL and SL) (%), ear rot (ER) (%),\nhusk cover (HC) (%), and disease severity scores (1–5) for Grey leaf\nspot (GLS) and Turcicum leaf blight (TLB). Hand weeding was used\nto keep the trial area weed-free during the crop-growing period.\n2.2.1 Trait measurements\nData were collected at all the sites applying standard procedures\nused at the International Maize and Wheat Improvement Center\n(CIMMYT 1985) for the following traits: (a) plant and ear height\nwas measured 2–3 weeks after flowering on 5–10 selected plants,\nmeasuring the distance from the plant base to the point where the\ntassel starts to branch and the distance in centimeters from the plant\nbase to the node bearing the uppermost ear, respectively;\n(b) percentage stalk and root lodging was recorded as a\npercentage of plants per plot that had their stems broken below\nthe ears but not above the ears and percentage of plants per plot\nwhich had their stems inclined at least 30° or more from the\nperpendicular at the base of the plant where the root zone stars,\nrespectively; (c) ear rot (ER): incidence of ear and kernel rots caused\nby Diplodia spp., Fusarium spp., or Gibberella spp., counted and\nexpressed as a percentage of the number of harvested ears; (d) husk\ncover (HC): percentage number of ears in each plot that had any\nportion of the ear exposed relative to the total number of ears per\nplot; (e) severity of GLS and TLB diseases were scored 3 weeks after\nflowering on a score scale of 1–5 (where 1 = no visible leaf damage;\n5 = severe damage of >50% of the leaf area) (Ngwira and Pixley,\n2000; Bankole et al., 2022). All plants were hand-harvested\nTABLE 1\nSummary of the composition of the Kenya highland maize\nPreliminary Varietal Trials (PVT) and Advanced Varietal Trials (AVT).\nFactors\nPVT\nAVT\nNumber of years\n13\n20\nNumber of experiments\n55\n146\nNumber of replications\n3\n4\nNumber of entries per year\n36\n25\nTotal number of entries\n394\n399\nTotal number of breeding lines\n392\n396\nNumber of breeding lines tested for 1 year\n339\n319\nTrial design layout\nAlpha\nlattice\nAlpha\nlattice\nNumber of breeding lines tested for 2 years\n47\n58\nNumber of breeding lines tested for 3 years\n6\n14\nNumber of breeding lines tested for 4 years\n0\n5\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n03\n"
+    },
+    {
+      "page": 4,
+      "text": "and shelled, and grain weight was measured in grams (g). Shelled\ngrain weight (g) was used to estimate GY adjusted to 12.5%\nmoisture content at harvest and expressed to kg ha−1.\n2.3 Data analysis\nThe analysis of variance (ANOVA) for individual environments\nwas carried out for GY, agronomic traits, and disease severity using\na model in which genotypes and replications were fitted as random\neffects. An analysis of variance was performed within each\nexperiment using the model (Equation 1):\nyij = m + gi + rj + eij\n(1)\nwhere yij is the mean phenotypic value of the trait within an\nexperiment, gi is the random effect of the ith genotype with gi ∼\nN(0,   s 2\ng ), rj is the random effect of the jth replication with rj ∼\nN(0,   s 2\nr ), and eij is the residual error with eij ∼N(0,   s 2\ne ).\nAnalysis of variance was conducted over all experiments within\neach year using the model (Equation 2):\nTABLE 2 Agro-ecology details of Kenya highland multi-location trial sites (1999–2020).\nLocation\nLatitude\nLongitude\nAltitude\n(masl)\nAnnual average\ntemperature (°C)\nMean rainfall\n(mm)\nAverage\nannual\nRH\nMin\nMax\nBaraton\n0.236\n35.185\n1,975\n12\n23\n1,648\n62\nBrigadier\n0.787\n35.038\n1,830\n10\n30\n1,100\n78\nChepareria\n1.381\n35.256\n1,723\n14\n30\n1,134\n71\nChepkoilel\n0.643\n35.343\n2,205\n10\n26\n1,103\n75\nCherangani\n1.123\n35.273\n1,870\n11\n29\n1,300\n71\nChorlim\n1.137\n34.773\n2,400\n10\n29\n1,450\n71\nEndebess\n1.139\n34.910\n1,900\n11\n29\n1,280\n69\nKabianga\n−0.486\n35.188\n1,780\n10\n30\n1,300\n81\nKakamega\n0.430\n34.765\n1,540\n17\n35\n1,395\n69\nKapsabet\n0.229\n35.230\n1,950\n9\n29\n1,600\n78\nKimilili\n0.929\n34.746\n1,723\n11\n29\n1,500\n75\nKisii\n−0.773\n34.894\n1,879\n15\n27\n1,500\n62\nKitale\n1.043\n35.017\n1,900\n11\n29\n1,450\n71\nLurende\n0.806\n34.663\n1,700\n11\n29\n1,500\n77\nMakutano-Pokot\n1.250\n35.833\n2,025\n14\n31\n1,429\n75\nMolo\n−0.228\n35.705\n2,482\n12\n26\n1,430\n70\nMuguga\n−1.298\n36.721\n2,050\n11\n30\n1,200\n74\nNaitiri\n0.787\n34.817\n1,740\n11\n29\n1,100\n78\nNalondo\n0.707\n34.704\n1,370\n11\n29\n1,300\n73\nNangeni\n0.585\n34.468\n1,340\n16\n33\n1,300\n73\nNasokol/\nMakutano\n1.271\n35.082\n2,050\n11\n29\n1,429\n75\nNdalu\n0.859\n35.001\n1,820\n11\n29\n1,450\n71\nNjoro\n−0.374\n36.060\n1,800\n7\n31\n1,000\n61\nOljororok\n−0.039\n36.351\n2,400\n7\n31\n1,500\n73\nSabwani\n1.214\n34.889\n1,880\n10\n30\n1,034\n65\nSang’alo\n0.591\n34.701\n1,420\n16\n33\n1,500\n73\nTongaren\n0.798\n34.954\n1,713\n11\n29\n1,100\n73\nMasl, meters above sea level; °C, degrees Celsius; mm, millimeters; RH, relative humidity (%).\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n04\n"
+    },
+    {
+      "page": 5,
+      "text": "yijk = m + gi + lk + r(l)jk + glik +   eijk\n(2)\nwhere yijk is the mean phenotypic value of the trait within an\nexperiment within a year, lk is the random effect of the kth location\nwith lk ∼N(0,   s 2\nl ), glik is the interaction of the ith genotype with\nthe kth location with glik ∼N(0,   s 2\ngl), r(l)jk. is the random effect of\nthe jth replication nested in the kth location with r(l)jk ∼N(0,   s 2\nr(l)),\nand eijk is the residual error with eijk ∼N(0,   s 2\ne ).\nGY (kg ha−1) and agronomic performance of genotypes evaluated\nin the current study highlighted variation among the genotypes\n(Table 1) in the PVT and AVT experiments. Broad sense heritability\nwas determined for all traits within each experiment. The variance\ncomponents from the above models were used to calculate entry mean\nheritability within each experiment as (Equation 3):\nH =\ns 2\ng\ns 2g + 〈s2e\nr 〉\n(3)\nwhere s 2\ne is the genetic variance, s 2\ne is the error variance, and r is the\nnumber of replications.\nSimilarly, the mean broad-sense heritability within a year was\ncalculated as (Equation 4):\nH =\ns 2\ng\ns 2g + 〈\ns 2\ngl\nl 〉+ 〈s 2e\nlr 〉\n(4)\nwhere s2\ngl   is the genotype × location variance, l is the number of\nlocations for the trial, and r is the number of replications.\n2.4 Genetic gain estimates\nThe rate of genetic gain using data from the KHMP PVT (2003–\n2020) and AVT (1997–2020) trials that were conducted across\nKenya highland environments (Figure 1) were estimated. To\ncompute genotypic Best Linear Unbiased Estimates (BLUEs) for\nuse in subsequent regression models, a mixed model, which\nincluded entries (genotypes) as fixed effects was fitted using the\nmodel (Equation 5):\nyijklm = u + gi + yj + lk + r(l)lk + gyij + glik + glyijk + cm +   eijkl\n(5)\nwhere yijklm is the phenotype of the ith entry in the jth year in the kth\nlocation and the lth replication, gi is the fixed effect of the ith genotype,\nyj is the effect of the jth year with yj ∼N(0,   s 2\ny ), lk is the effect of kth\nlocation with lk ∼N(0,   s 2\nl ), r(l)lk is the effect of the lth rep nested in\nthe kth location with r(l)lk ∼N(0,   s 2\nr(l)), gyij is the interaction of the\nith genotype with the jth year with gyij ∼N(0,   s 2\ngy), glik is the\ninteraction of the ith genotype with the kth location with ), glyijk is\nthe interaction of the ith genotype with the jth year and kth location\nwith glyijk ∼N(0,   s 2\ngly), cm is the fixed effect of control (1 for a check,\n0 for a breeding line), and eijklm is the error with eijklm ∼N(0,   s2\ne ).\nGenotype and check effects were considered fixed, and all other effects\nwere considered random.\nA concept referred to as retrogressive analysis (Ci et al., 2011;\nBadu-Apraku et al., 2015) was used to estimate the genetic gain trends\n(hereinafter referred to as genetic gains) for the key traits in trials\nevaluated between 1999 and 2020 within the KHMP. In the model,\nFIGURE 1\nKenya highland multi-location trial sites for preliminary and advanced maize varietal trials.\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n05\n"
+    },
+    {
+      "page": 6,
+      "text": "the best linear unbiased estimates (BLUEs), which were considered to\nbe the genetic gain values of individual maize traits, were generated in\na mixed statistical analysis model (Henderson, 1975). The genetic\ngain was estimated by regressing each genotype’s estimated value in\nthe first year the genotype was tested (FYT) in the 2003 to 2020 data\nset for PVT and in the 1997 to 2020 data set for AVT. The FYT was\ndefined as the base year the genotype entered the trial (PVT or AVT).\nThe genetic gain was reported based on the direction of the trend\n(slope) and was declared to be significant if the probability of the\nslope was less than 0.05 and had an R2 greater than 0.05.\n3 Results\n3.1 Broad-sense heritability and quality of\ndata in the experiments\nThe broad-sense heritability (H) estimates expressed as a\npercentage were classified according to Robinson et al. (1949)\ninto three classes: low 0%–30%, medium 31%–60%, and >60% as\nhigh. The results revealed that in the PVT, GY and HC had medium\nH (Table 3). In this trial series, 25% and 11% of the experiments had\nH <0.2 for GY and HC, respectively. A higher proportion of\nexperiments with H >0.2 was observed for the other agronomic\ntraits and foliar diseases. In the AVT GY, EH, PH, SL, and HC had\nmedium H (Table 4). Low H2 was recorded for RL, foliar disease\nseverity scores for GLS TLB and ER in both AVT and PVT\n(Tables 3, 4). Experiments with H > 0.2 in the PVT (75%) and\nAVT (84%) for GY were used to compute the best linear unbiased\nestimates (BLUEs) for each genotype referred to as genetic values in\nthis study. Similarly, for agronomic traits, data sets with H > 0.2\nranged from 34% to 89% in the PVT and 35%–90% for agronomic\ntraits and disease severity were used to compute BLUEs.\n3.2 Genetic trends for grain yield,\nagronomic traits, and diseases\nThe BLUEs were used in linear regression analysis to assess the\ngenetic gain values for GY and other agronomic traits in the PVT\nand AVT. The regression model accounted for 16.5% and 2.4% of\nthe variation for GY for genotypes in the PVT and AVT,\nrespectively (Table 5; Figure 2). There was significant (p < 0.05)\npositive annual genetic gain in GY for the genotypes in the PVT and\nAVT. The PVT genotypes had a mean GY of 4,530 kg ha−1 and an\nannual genetic gain estimate of 88 kg ha−1 or 1.94% year−1 (Table 5;\nFigure 2). In the AVT, genotypes had a higher mean GY than the\nPVT genotypes (6,170 kg ha−1) but a smaller annual genetic gain\nestimate of 26 kg ha−1 or 0.42% year−1.\nThe regression model accounted for variation ranging from\n2.5% to 7.2% for agronomic traits and 3.6% to 8.7% for diseases in\nthe PVT (Table 5). Similarly, the regression model accounted for\n18.6%, 4.4%, 2.8%, and 3.7% for EH, PH, RL, and GLS, respectively,\nin the AVT. Results of genetic trends revealed an increase in PH of\n0.92 cm year−1 at a rate of 0.32% year−1 in the PVT and AVT. On\nthe other hand, an increase in EH of 0.51 cm year−1 (0.27% year−1)\nwas observed for PVT genotypes, whereas AVT genotypes had an\nincrease of 0.40 cm year−1 (0.23% year−1). Root lodging and SL\nincreased at a rate of 13.8% year−1 and 2.2% year−1, respectively, in\nthe PYT. In contrast, a rate of −2.65% year−1 was observed for RL in\nthe AVT. There was a significant (p < 0.05) increase in HC at a rate\nof 0.064% year−1 and 0.92% year−1 in the PVT and AYT,\nrespectively. Ear rot had a significant (p < 0.05) genetic gain of\n0.14% year−1 with a mean of 7.8% and 0.011% year−1 with a mean of\n3.3% for the PVT and AVT, respectively. Disease severity scores for\nTLB and GLS showed a significant (p < 0.05) decrease of −0.03\nyear−1 (−1.19% year−1) with a mean score of 2.3 and −0.02 year−1\n(−0.81% year−1), and a mean score of 1.7, for PVT genotypes (TLB)\nand AVT genotypes (GLS), respectively. A distinction was recorded\nfor GLS with PVT genotypes that had a significant (p < 0.05 increase\nin disease severity scores of 0.02 year−1 (1.01% year−1) and a mean\nscore of 2.2.\n3.3 Consistency in the performance of\nputatively superior maize genotypes\nTo assess the efficiency of the advancement process, the\nperformance of some superior genotypes for GY that were\nTABLE 3 Summary of maize trait values and heritability within the experiment and years for the PVT conducted between 1999–2020.\nTrait\nYears (n)\nExperiments (n)\nTrait values\nHeritability (H)\nMean\nRange\nMean\n% <0.2\nGrain yield (kg ha−1)\n12\n52\n4,530\n1,700–10,100\n0.40\n25.0\nEar height (cm)\n13\n50\n191\n127–249\n0.23\n53.5\nRoot lodging (%)\n12\n47\n0.5\n0–11\n0.13\n66.0\nStalk lodging (%)\n12\n47\n10.2\n1–35\n0.26\n41.3\nEar rot (%)\n13\n53\n7.8\n0–15\n0.29\n41.5\nPoor husk cover (%)\n13\n53\n4.9\n1–14\n0.45\n11.3\nGrey leaf spot (1–5)\n11\n24\n2.3\n1–3\n0.16\n54.2\nTurcicum leaf blight (1–5)\n13\n28\n2.2\n1–4\n0.20\n60.7\nAn experiment was defined as a year/location combination.\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n06\n"
+    },
+    {
+      "page": 7,
+      "text": "advanced from the PVT to the AVT was tracked. The advancement\nof these entries from the PVT stage to the AVT stage was\nintroduced in 2006 in the KHMP. These were the putatively\nsuperior genotypes (PSG), and their number ranged from one in\n2019 to 20 in 2009 (Table 6). The average number of PSG advanced\nwas 8.4 per year between 2006 and 2020. We compared the mean\nGY of the PSG in the PVT to the mean GY of all other PVT\ngenotypes and to the commercial check hybrid H6213. Within the\nPVT, the PSG average GY is 114.7% of all other entries, ranging\nfrom 98% to 139.7%. The GY of the PSG in the PVT was, on\naverage, 102% of the check hybrid H6213, with a range of 96.1 to\n119%. A comparison of the GY of the PSG in the subsequent AVT\nto the mean GY of all other AVT entries and to the check H6213\nwas carried out. The PSG average GY in the AVT was 105.4% of all\nother genotypes, with a range of 97.7% to 118.8% (Table 6). The GY\nof the PSG in the AVT was, on average, 94.7% of the check H6213,\nwith a range of 76.2% to 120.2%.\nFirstly, the consistency of performance of the three PSG\nidentified in the PYT of 2006 that were evaluated in the AYT of\n2007 was assessed. These three genotypes had an average GY of\n5,290 kg ha−1 in the PYT in 2006, whereas the mean of other entries\nin this stage was 4,870 kg ha−1. In the AYT of 2007, the three PSG\nhad a consistently higher GY (5,490 kg ha−1) compared with the\nAYT mean of 5,190 kg ha−1 for the rest of the entries. Overall, the\nTABLE 5 Genetic trends in maize trait values for grain yield, agronomic traits, and disease resistance in PVT and AVT between 1999 and 2020.\nTrait\nPVT\nAVT\nMagnitude* of\ngain in trait value\nAnnual gain in\ntrait value (%)a\nP-value\nR2\nMagnitude of\ngain in trait value\nAnnual gain in\ntrait value (%)\nP-value\nR2\nGrain yield\n(kg ha−1)\n88\n1.94\n<0.0001\n0.165\n26\n0.42\n0.0290\n0.024\nPlant\nheight (cm)\n–\n–\n–\n–\n0.923\n0.32\n<0.0001\n0.186\nEar\nheight (cm)\n0.513\n0.27\n<0.0001\n0.044\n0.403\n0.23\n<0.0001\n0.044\nRoot\nlodging (%)\n0.065\n13.08\n0.0089\n0.025\n−0.047\n−2.65\n0.0020\n0.028\nStalk\nlodging (%)\n0.225\n2.20\n<0.0001\n0.072\n0.032\n0.42\n0.2590\n0.004\nEar rot (%)\n0.011\n0.14\n0.5590\n0.001\n0.006\n0.17\n0.6390\n0.001\nPoor husk\ncover (%)\n0.064\n1.31\n0.0002\n0.036\n0.039\n0.92\n0.0086\n0.019\nTurcicum leaf\nblight (1–5)\n−0.026\n−1.19\n<0.0001\n0.071\n−0.004\n−0.27\n0.1280\n0.008\nGrey leaf spot\n(1–5)\n0.024\n1.01\n<0.0001\n0.087\n−0.016\n−0.81\n0.0005\n0.037\n*Magnitude is the slope of the regression equation; apercentage increases or decreases relative to the mean of the trait value.\nTABLE 4 Summary of maize trait values and heritability within the experiments and years for the AVT conducted between 1999 and 2020.\nTrait\nYears (n)\nExperiments (n)\nTrait values\nHeritability (H)\nMean\nRange\nMean\n% <0.2\nGrain yield (kg ha−1)\n18\n127\n6,170\n900–14,100\n0.51\n15.7\nPlant height (cm)\n20\n137\n290\n156–401\n0.45\n17.5\nEar height (cm)\n20\n138\n175\n26–421\n0.52\n10.1\nRoot lodging (%)\n20\n139\n1.8\n0–21\n0.16\n64.7\nStalk lodging (%)\n19\n131\n7.6\n0–86\n0.37\n26.0\nEar rot (%)\n20\n137\n3.3\n0–39\n0.26\n39.4\nPoor husk cover (%)\n19\n139\n4.3\n0–23\n0.49\n15.1\nGrey leaf spot (1–5)\n19\n75\n1.9\n1–3\n0.27\n56.0\nTurcicum leaf blight (1–5)\n20\n79\n1.7\n1–3\n0.28\n45.6\nAn experiment was defined as a year/location combination.\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n07\n"
+    },
+    {
+      "page": 8,
+      "text": "PSG yielded 1.15-fold more than other genotypes in the PYT, but\nthe magnitude of difference (1.05-fold) relative to the other entries\nwas marginally lower in the following year in the AYT.\nSecondly, a comparison of the means of some specific genotypes\nmeant for advancement in the PYT to the means of all other entries in\nthe PYT trial was carried out. The advancement with the largest\nnumber of PSG (n = 20; 2009) in the PYT had a mean GY of 7,400 kg\nha−1. These PSG were evaluated in the 2010 AYT and gave a mean\nGY of 6,990 kg ha−1, approximately 1.18-fold the yield of other entries\nin that trial but 12% less than the check (H6213). In 2012, the PSG\nhad a mean GY of 7,810 kg ha−1 in PYT, and when these entries were\nevaluated in the 2013 AYT, they gave a mean GY of 5,900 kg ha−1,\nwhich was 2% less than the GY of other entries and 1.2-fold more\nthan the commercial check hybrid. Based on the occurrence of the\nmeans of GY, the probabilities for having higher GY for PSG than the\nother entries each year were (GYPSG>GYOE) = 0.86 for PYT and P\n(GYPSG>GYOE) = 0.71 for AYT. However, the mean GY in the PYT\nPSG was not likely to be different from the commercial check hybrid,\nas P(GYPSG>GYH6213) = 0.5. Overall, the PSG were more likely to\nhave a lower mean GY than the commercial check, as P\n(GYPSG>GYH6213) = 0.14) in the AYT (Table 6).\n4 Discussion\nIt is a common practice in both the public and private sectors to\nuse key performance indicators (KPIs), defined as quantifiable\nparameters, to ensure that activities are aligned toward achieving\norganizational goals through increasing transparency and\naccountability for overall long-term performance (Zakaria et al.,\n2011; Covarrubias-Pazaran, 2020). Thus, the number of varieties\nreleased by the public sector crop breeding programs has been\nfrequently used as a KPI. However, varietal releases alone do not\nreflect the efficiency of a breeding program nor the impact of a\nbreeding pipeline (Prasanna et al., 2022a). Estimates of genetic gains\nare an important KPI to measure the genetic progress, assess\nB\nA\nFIGURE 2\nRegression of grain yield BLUEs of maize inbred lines onto the first year of testing for all genotypes in the KMHP preliminary varietal trial (A, PVT) and\nadvanced varietal trial (B, AVT).\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n08\n"
+    },
+    {
+      "page": 9,
+      "text": "breeding efficiency, identify areas of improvement, and investment\nfor accelerated genetic gains in delivering improved varieties to the\nfarmers (Asea et al., 2023; Tarekegne et al., 2024).\nThe objective of this study was to estimate genetic gain for GY\n(kg ha−1), agronomic traits, and disease severity using historical\ndata in the PVT (2003–2020) and AVT (1999–2020) variety trials\nconducted by the Kenya highland maize breeding program\n(KHMP), a program under Kenya Agricultural and Livestock\nResearch Organization (KALRO). Previous genetic gain report for\nKHMP was published in the 1970s (Eberhart and Harrison, 1973).\nThe success of a crop breeding program mainly depends on the\npresence of genetic variation and the heritability of the traits under\nconsideration. Broad-sense heritability was medium (>31%‒60%)\nfor GY and HC in PVT and AVT. A similar heritability range was\nobtained for PH, EH, and SL in AVT. These results indicate that\ngenetic progress for these traits can be achieved through careful\nselection and the use of simple selection methods like pedigree\nselection. In contrast, low heritability was recorded for RL, GLS,\nTLB, and ER in both AVT and PVT and PH and EH in the PVT,\nwhich suggests that improvement of these traits may be\nconsiderably difficult due to the masking effect of the\nenvironment on genotypic expression. This also shows the\ncomplex nature of inheritance of certain traits like resistance to\nsome foliar diseases. Some of the traits with low heritability require\nselection methods that will progressively build desirable/favorable\nalleles to improve the population. The KHMP can improve the\nefficiency of selection for resistance to major diseases, especially ER,\nGLS, and TLB, by using artificial inoculation in trials at appropriate\nsites during hybrid evaluation.\nIn the current study, the general genetic gain estimates were\nhigher in PVT relative to AVT across traits. The high genetic gain\ntrend in PVT relative to AVT is mainly accounted for by the inbred\nlines in the PVT that are in early-stage generations (F3–F5), whereas\nthe inbred lines in AVT are advanced (>F6). Genetic variance is\nusually higher in the early generations but reduces in the advanced\ngenerations due to selection. The GY genetic gain estimate for PVT\nwas 88 kg ha−1 year−1, whereas that for AVT was 26 kg ha−1 year−1.\nWhile the germplasm is not similar, the genetic gain estimate in\nPVT in this study was comparable with the 81 kg ha−1 year−1\nreported for Uganda (Asea et al., 2023), 62 kg ha−1 year−1 for\nEthiopia Highland maize (Kebede et al., 2020), 61 kg ha−1 year−1\nreported for the Zimbabwe National Breeding Program (Mazibuko\net al., 2024), and 109.4 kg ha−1 year−1 CIMMYT hybrid breeding\npipeline in Eastern and Southern Africa (Masuka et al., 2017). The\nGY genetic gain estimates of 26 kg ha−1 year−1 in the AVT was\nhigher than that reported by the CIMMYT East Africa Product\nProfile-Highland breeding pipeline −70 kg ha−1 year−1 (Prasanna\net al., 2022b). The lower genetic gain estimates in AVT relative to\nPVT may be due to the difference in the germplasm sample from\nthe given testing stage. According to Covarrubia-Pazaran (2020),\nthe use of early testing trials (within program management), i.e.,\nPVT, and late testing trials (within program management), i.e.,\nAVT to calculate genetic gain estimate uses different samples, but\neach will have different properties that affect the accuracy of the\ngenetic gain estimate and Target Population Environment (TPE)\ncoverage. The use of early-generation trials (PVT) provides a better\nestimate of the evolution of genetic variance, whereas advanced-\nstage genotypes provide a better estimate of the rate of genetic gain.\nIn addition, the low annual genetic gain estimate in AVT genotypes\nmay be attributed to the long crop growth cycle in the highland\nbreeding program (Eberhart and Harrison, 1973). This calls for the\nadoption of DH technology to accelerate inbred line development.\nKenya Highland maize market segment has a defined Target\nProduct Profile (TPP)1 (Supplementary Table 1), a blueprint for the\ndesign of new varieties that indicates the traits2 required in a new\n1\nhttps://glomip.cgiar.org/target-product-profiles\n2\nhttps://www.cimmyt.org/blogs/market-segmentation-and-target-\nproduct-profiles-tpps-developing-and-delivering-impactful-products-for-\nfarming-communities/\nTABLE 6\nA comparison of grain yield (kg ha−1) for putatively superior entries (PSG), other entries and the check variety (H6213) based on evaluations\nin the PVT and AVT between 2006 and 2019.\nPVT\nyear\n(PSG) for\nadvancement\nto AVT (n)\nGrain yield (t/ha) in the PVT\nAVT\nyear\nGrain yield (t/ha) in the AVT\nCheck-\nH6213\nPSG\nOther\nentries\n(OE)1\nPSG\nvs.\nOE\n(%)\nPSG vs.\nH6213\n(%)\nCheck-\nH6213\nPSG\nOE2\nPSG\nvs.\nOE\n(%)\nPSG vs.\nH6213\n(%)\n2006\n9\n5,290\n4,870\n108.7\n2007\n5,770\n5,490\n5,190\n105.7\n95.1\n2007\n12\n5,180\n5,020\n4,600\n109.2\n96.9\n2008\n7,120\n5,420\n5,500\n98.6\n76.2\n2008\n7\n4,610\n4,620\n3,740\n123.5\n100.1\n2009\n5,980\n5,870\n5,540\n105.9\n98.2\n2009\n20\n6,220\n7,400\n6,580\n112.5\n119\n2010\n8,240\n6,990\n6,560\n106.7\n84.9\n2010\n3\n6,150\n5,910\n4,230\n139.7\n96.1\n2011\n7,570\n6,690\n5,630\n118.8\n88.4\n2012\n6\n7,810\n7,820\n7,010\n111.6\n100.1\n2013\n4,910\n5,900\n6,040\n97.7\n120.1\n2019\n1\n6,030\n6,030\n6,150\n98\n100\n2020\n7,010\n7,010\n6,690\n104.7\n100\nMean\n8.29\n6,000\n6,010\n5,310\n114.7\n102\n6,660\n6,200\n5,880\n105.4\n94.7\nPVT, Preliminary Variety Trials; AVT, Advanced Variety Trial. Other entries tested in the PVT and AVT are putatively superior genotypes (PSG), OE1, and OE2, respectively.\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n09\n"
+    },
+    {
+      "page": 10,
+      "text": "variety to meet or exceed the requirements of farmers, processors,\nand consumers (Donovan et al., 2022). As a result, agronomic traits\nin KHMP have been deliberately selected based on thresholds set for\nessential or nice-to-have traits for this TPP. The genotypes\nadvanced from the PVT into AVT had a general trend of reduced\near height, thus indicating a deliberate selection strategy for the\nbreeding program to advance genotypes with lower ear placement,\nan essential trait for Kenya’s highland maize market segment. A\nsimilar trend was also noted for RL and SL in PVT, which had\nincreased genetic gain estimates relative to the decrease noted in\nAVT genotypes. This highlights that KHMP has made a concerted\neffort to select and advance genotypes from PVT to AVT with RL\nand SL threshold values lower than commercial check varieties, thus\nincreasing the chances of releasing varieties that have good\nstandability (root and stalk lodging). Furthermore, in\ncollaboration with CIMMYT, KHMP has recently acquired elite\ninbred lines with short stature from the CIMMYT-Ethiopian\nInstitute of Agricultural Research (EIAR) highland maize\nbreeding program and has begun to introgress the trait into its\nbreeding populations. This will eventually lead to greater use of\nparental inbred lines for a new generation of short-statured hybrids\nadapted to the Kenya highland agroecology.\nThe Kenya highland TPE (Table 2; Figure 1) is prone to ear and\nfoliar diseases due to the conducive climatic conditions (high\nrainfall, high humidity, and temperature) for disease\ndevelopment. Consequently, KHMP has defined resistance to\nTLB, GLS, and ER as an essential trait, and this still needs\ncontinuous improvement. There was a trend toward increased\nresistance to TLB (−1.19% year−1) and GLS (−0.81% year−1) in\nthe AVT, indicating that the defined selection strategy for these\ndiseases and advancement decision from PVT to AVT was optimal.\nIn the PVT, genotypes had reduced GLS resistance, as evidenced by\nincreased severity scores of 0.02 year−1 (1.01% year−1). The\ninconsistency may be due to the use of natural disease sites as\nopposed to artificial inoculation. Lack of adequate pathogen\npressure may lead to reduced disease incidence in some locations\nand, hence, low disease severity scores (Kenworthy, 1966; Ward\net al., 1999; Jakhar et al., 2017; Njeru et al., 2023). Without artificial\ninoculation, screening maize at disease hotspots would be the best\nalternative to attain high disease pressure, thus enhancing genetic\ngain during selection (Lopez-Zuniga et al., 2018).\nGood husk cover (tip of the ear fully covered to ensure restricted\nentry and damage by water, opportunistic insects, birds, and\npathogens) is an essential trait in the KHMP breeding pipeline\n(Supplementary Table 1). The results indicated little progress in the\nreduction of the incidence of poor husk cover in both PVT 1.31%\nyear−1 and AVT (0.92% year−1). KHMP should consider infusing\ngermplasm with good husk coverage from other programs,\nespecially CIMMYT, to enhance gains for husk cover in the\ndesired direction. Development of varieties with a good husk\ncover would be valuable because the ongoing climate change will\nlead to an increase in temperature, and hence, maize will be\ncultivated in warmer conditions, which favors maize ear rot\ninfections (Warfield and Davis, 1996; Miedaner and Juroszek,\n2021; Nnadi and Carter, 2021). Poor husk cover is related to the\nhigh incidence of ear/cob rots and insect pests like Fall Armyworms\n(Prasanna et al., 2022a). Enhancing resistance to ER would improve\ngrain quality and prevent contamination by mycotoxins, which are\nharmful to livestock and humans and are widespread in Kenya\n(Ajanga and Hillocks, 2000; Mutiga et al., 2017).\nThe analysis of the GY of PSG which were advanced to the AVT\nshowed that while they had a GY of 114.7% of all other PVT\ngenotypes, they had a GY of just 105.4% of other AVT, and they\nwere just 94.7% of the GY of the commercial check hybrid H6213 in\nthe AVT. There is a significant genotype × year interaction affecting\nadvancements to the AVT, yet this seems to have had less effect on\nadvancements to the PVT. The average GY of the PVT experiments\nwas 26% lower than the average GY of the AVT. It is possible that\nperformance in low-GY sites may not translate to GY in high-GY\nsites. Adopting accurate and efficient phenotyping strategies, with\noptimal replication over years in environments with known levels of\nreproducibility for target agroecological zones, could enhance the\nefficiency in selection for advancement (Duvick, 2005; Gunundu\net al., 2023). The focus of the KHMP breeding pipeline has been to\nimprove GY, which averaged 4,530 kg ha−1 in the PVT and 6,100 kg\nha−1 in the AVT. The mean differences in GY between the two\nevaluation stages suggest the superiority of the fraction of genotypes\nselected for advancement in terms of GY. Although there were\nsignificant, positive genetic gain estimates for GY in the PVT and\nAVT, most of the putatively superior genotypes (or advanced\nhybrids) yielded less than the commercial check hybrid (H6213)\nreleased by the Kenya Seed Company in 2002.\nIn this study, most of the genotypes selected in the PVT and\nadvanced in the breeding pipeline evaluation stages had lower GY\nthan the commercial check hybrid. While the primary aim of this\nstudy was to document current genetic trends within the KALRO\nhighland maize breeding pipeline, the compilation of historical\ndatasets provides the opportunity to identify key areas to improve\nbreeding efficiency. The introduction of germplasm from similar\nagroecological zones in Central and South America played a major\nrole in the establishment of the KHMP breeding pipeline for the\nKenyan highlands agroecology (Harrison, 1970). Similar to the\nCIMMYT-Ethiopia highland maize breeding program, the genetic\npool in the KALRO highland maize program still relies on parental\ninbred lines developed from Ecuador-573, Kitale-SYN, and Pool-9A\n(Prasanna et al., 2022a). Genetic trends in hybrid maize breeding\npipelines are a function of the gains in female and male parental\ninbred lines. The pedigree data for the current study showed that\nmost of the male inbred lines in the single-cross female parents of\nthe three-way hybrids remained the same throughout the study,\nwith only six male parents of the single-cross hybrid female parents\naccounting for 75% of all candidate hybrids tested in the AVT. Five\nof these males of single-crosses were present in hybrids in the first\nyear of testing and were still key male parental inbred lines in\nhybrids in 2020, and only one male of the single-crosses first entered\nthe AVT in 2006. The observed low performance of the PSG\nsuggests that for the program to move beyond the current GY\nperformance ceiling, it must utilize international networks to\nrevamp the breeding pipeline with superior exotic elite inbred\nlines, which are adapted to similar agroecologies (Harrison, 1970;\nAtlin et al., 2017), beyond the original introductions from Central\nand South America that were used to initiate hybrid maize\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n10\n"
+    },
+    {
+      "page": 11,
+      "text": "development in the Kenyan highlands (Harrison, 1970). The\nfindings of this two-decade study will enable the program to\nreorient its breeding strategy strategically by adopting more\nefficient modern breeding operations for better resource\nutilization and enhanced genetic gains.\nTo further enhance the genetic trends across all important traits\nin the TPP, several practical breeding modernization approaches\ncould be adopted in the KHMP, as outlined below:\ni. Adopt a product profile-based breeding approach, where\nthe efforts are aimed at developing products for the current\nmarket demands. Market-led breeding operations could\ncompel the program to adopt cost-effective methods that\nwould fast-track the release of improved varieties. To\nidentify the cost drivers for the breeding operations and\nthe potential ways of optimizing breeding metrics, the\nprogram has participated in the costing of its pipeline\nusing the University of Queensland Breeding Costing Tool\n(UQBCT) through the CGIAR Accelerated Breeding\nInitiative (ABI).\nii. Implementation of doubled haploid (DH) technology for\nfaster development of genetically homozygous inbred lines\nand accelerated hybrid development (Chaikam et al.,\n2019). CIMMYT uses DH technology for line\ndevelopment, and KHMP can use the service, with the\ncost covered through special projects.\niii. Using an artificial disease screening facility for TLB, GLS,\nand MLN will ensure accurate selection and advancement\nof genotypes with these essential traits for KHMP. KALRO\nutilizes the CIMMYT-operated MLN Screening Facility at\nNaivasha and an artificial foliar disease screening facility at\nKakamega in Kenya. These facilities could potentially be\nused for KHMP.\niv. Adoption of sparse testing for breeding trials: Field\nphenotyping is one of the major cost drivers in most of\nthe breeding programs (Lane et al., 2020). Sparse testing\ncan enhance the evaluation of many entries across multiple\nenvironments, improving breeding efficiency, and\noptimizing use of resources. In sparse testing for\nmultienvironment breeding trials, not all genotypes of\ninterest are grown in each environment but the alleles of\ninterest are tested across specific environments based on\nprior genotyping data (Atanda et al., 2022). Sparse testing\ncould be coupled with an expansion in the testing network\nwithin East Africa while leveraging the leadership of\ninternational collaborative partners like CIMMYT in\nAfrica. Increasing the size of the testing network is a key\ndriver of enhancing genetic gains in maize yields in the US\n(Cooper et al., 2014) and Eastern and Southern Africa\n(Masuka et al., 2017; Prasanna et al., 2022b).\nv. Digitization of breeding program operations, i.e.,\nelectronic data capturing and use of data management\nsystems like Breeding Management System (BMS) or the\nEnterprise Breeding System (EBS), will enhance data\nturnaround time and data quality for data-driven\nselection decisions and advancement of genotypes within\nthe breeding pipeline.\nvi. Adoption of molecular markers for forward breeding to\nintegrate qualitative traits and genomic selection for\nsimultaneous improvement of multiple traits (Moose and\nMumm, 2008). Forward breeding can be used for selection\nto enrich populations for favorable alleles before field\nphenotyping. Coupling forward breeding with genomic\nselection to simultaneously improve the germplasm for\nmultiple traits, including those that are inherited\nquantitatively, such as GY, root, and stalk lodging, ear\nrot, GLS, TLB, and MLN would be an excellent strategy to\nincrease genetic gains (Poland et al., 2009; Larkin et al.,\n2019). KHMP can leverage current efforts by CIMMYT’s\nGlobal Maize Program to implement molecular breeding\nin conjunction (Crossa et al., 2014; Prasanna et al., 2022b;\nOmondi et al., 2023).\n5 Conclusions\nThe Kenya Highland Maize Program (KHMP) focuses on\nbreeding and deploying elite, climate-resilient improved maize\nhybrid varieties. This study estimated genetic gains for grain yield\n(GY), agronomic traits, and disease resistance traits in the PVT and\nAVT conducted between 1999 and 2020. The study revealed\nsignificant genetic gains for GY, EH, PH, HC, and disease\nresistance (GLS and TLB). Thus, the KHMP has made desired\nstrides in hybrid maize development over the years, resulting in\ndeveloping and releasing highland maize varieties that are\nproductive, short stature, and disease-resistant. The genetic gain\ntrends for some traits were not consistently in the desired direction.\nTo further improve the genetic gains, the KHMP plans to adopt a\nproduct-profile-based breeding approach, breeding methods, and\ntechnologies that can increase breeding efficiency and genetic gains.\nData availability statement\nThe datasets presented in this study can be found in online\nrepositories. The names of the repository/repositories and accession\nnumber(s) can be found in the article/Supplementary Material.\nAuthor contributions\nDL: Data curation, Investigation, Methodology, Writing –\noriginal draft, Writing – review & editing. ES: Conceptualization,\nFormal analysis, Methodology, Software, Writing – original draft,\nWriting – review & editing. BA: Conceptualization, Data curation,\nVisualization, Writing – original draft. CS: Methodology, Project\nadministration, Resources, Visualization, Writing – original draft,\nWriting – review & editing. WC: Formal analysis, Investigation,\nResources, Validation, Writing – review & editing. LM:\nConceptualization, Data curation, Resources, Supervision,\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n11\n"
+    },
+    {
+      "page": 12,
+      "text": "Visualization, Writing – original draft, Writing – review & editing.\nDMa: Conceptualization, Formal analysis, Funding acquisition,\nMethodology, Validation, Writing – review & editing. MM: Data\ncuration, Formal analysis, Validation, Writing – review & editing.\nDMi: Formal analysis, Methodology, Resources, Writing – review &\nediting. SM: Formal analysis, Investigation, Visualization, Writing –\nreview & editing. AL: Data curation, Formal analysis, Visualization,\nWriting – review & editing. BD: Funding acquisition, Investigation,\nSupervision, Validation, Writing – review & editing. BP: Funding\nacquisition, Investigation, Project administration, Supervision,\nWriting – review & editing.\nFunding\nThe author(s) declare financial support was received for the\nresearch, authorship, and/or publication of this article. All the field\ntrials reported in this study were primarily supported by funding\nfrom the Kenyan Government and the Bill & Melinda Gates\nFoundation and USAID-funded projects titled Improved Maize\nfor African Soils (IMAS, Grant No. OPPGD1017), and Stress\nTolerant Maize for Africa (STMA, Grant No. OPP1134248)\nprojects; the Excellence in Breeding Platform (EiB) and the Bill &\nMelinda Gates Foundation, USAID, and FFAR provided financial\nsupport to conduct this analysis through the project Accelerating\nGenetic Gains for Maize and Wheat Improvement (BMGF Grant\nnumber INV-003439).\nAcknowledgments\nThe Government of Kenya financed the development of the\nhighland maize germplasm used in the study through KALRO. The\ndata curation, management, and analysis for the KALRO highland\nmaize program was supported by the Excellence in Breeding\nprogram (EiB, INV-008091) funded by the Bill and Melinda\nGates Foundation. We acknowledge the role of the Integrated\nBreeding Platform (https://www.integratedbreeding.net) team for\ncuration of historical data into the Breeding Management System\n(BMS). We wish to acknowledge the technical and logistical support\nof Joyce Malinga from KALRO Headquarters, Nairobi; the\nCIMMYT maize breeding team, including Jill Cairns; Gustavo\nTeixeira, Liz Jones, Young Wha Lee, Ana Oliveira, and Manilal\nWilliam from the Excellence in Breeding program of CGIAR.\nConflict of interest\nThe authors declare that the research was conducted in the\nabsence of any commercial or financial relationships that could be\nconstrued as a potential conflict of interest.\nPublisher’s note\nAll claims expressed in this article are solely those of the authors\nand do not necessarily represent those of their affiliated\norganizations, or those of the publisher, the editors and the\nreviewers. Any product that may be evaluated in this article, or\nclaim that may be made by its manufacturer, is not guaranteed or\nendorsed by the publisher.\nSupplementary material\nThe Supplementary Material for this article can be found online\nat: https://www.frontiersin.org/articles/10.3389/fpls.2024.1416538/\nfull#supplementary-material\nReferences\nAjanga, S., and Hillocks, R. J. (2000). Maize cob rot in Kenya and its association\nwith stalk borer damage. J. Crop Prot. 19, 297–300. doi: 10.1016/s0261–2194(00)\n00020-x\nAsea, G., Kwemoi, D. B., Sneller, C., Kasozi, C. L., Das, B., Musundire, L., et al.\n(2023). Genetic trends for yield and key agronomic traits in pre-commercial and\ncommercial maize varieties between 2008 and 2020 in Uganda. Front. Plant Sci. 14.\ndoi: 10.3389/fpls.2023.1020667\nAtanda, S. A., Govindan, V., Singh, R., Robbins, K. R., Crossa, J., and Bentley, A. R.\n(2022). Sparse testing using genomic prediction improves selection for breeding targets in\nelite spring wheat. Theor. Appl. Genet. 135, 1939–1950. doi: 10.1007/s00122–022-04085–0\nAtlin, G. N., Cairns, J. E., and Das, B. (2017). Rapid breeding and varietal\nreplacement are critical to adaptation of cropping systems in the developing world\nto climate change. Glob. Food Sec. 12, 31–37. doi: 10.1016/j.gfs.2017.01.008\nBadu-Apraku, B., Fakorede, M. A. B., Oyekunle, M., Yallou, G. C., Obeng-Antwi, K.,\nHaruna, A., et al. (2015). Gains in grain yield of early maize cultivars developed during\nthree breeding eras under multiple environments. Crop Sci. 55, 527–539. doi: 10.2135/\ncropsci2013.11.0783\nBankole, F. A., Badu-Apraku, B., Salami, A. O., Falade, T. D. O., Bandyopadhyay, R.,\nand Ortega-Beltran, A. (2022). Identification of early and extra-early maturing tropical\nmaize inbred lines with multiple disease resistance for enhanced maize production and\nproductivity in Sub-Saharan Africa. Plant Dis. 106, 2638–2647. doi: 10.1094/pdis-12–\n21-2788-re\nCairns, J. E., Chamberlin, J., Rutsaert, P., Voss, R. C., Ndhlela, T., and Magorokosho,\nC. (2021). Challenges for sustainable maize production of smallholder farmers in sub-\nSaharan Africa. J. Cereal Sci. 101, 103274. doi: 10.1016/j.jcs.2021.103274\nCairns, J. E., Hellin, J., Sonder, K., Araus, J. L., Macrobert, J. F., Thierfelder, C., et al.\n(2013). Adapting maize production to climate change in sub-Saharan Africa. Food\nSecur. 5 (3), 345–360. doi: 10.1007/s12571–013-0256-x\nCairns, J. E., and Prasanna, B. M. (2018). Developing and deploying climate-resilient\nmaize varieties in the developing world. Curr. Opin. Plant Biol. 45, 226–230.\ndoi: 10.1016/j.pbi.2018.05.004\nChaikam, V., Molenaar, W., Melchinger, A. E., and Boddupalli, P. M. (2019).\nDoubled haploid technology for line development in maize: technical advances and\nprospects. Theor. Appl. Genet. 132, 3227–3243. doi: 10.1007/s00122–019-03433-x\nCi, X., Li, M., Liang, X., Xie, Z., Zhang, D., Li, X., et al. (2011). Genetic Contribution\nto Advanced Yield for Maize Hybrids Released from 1970 to 2000 in China. Crop Sci. 51\n(1), 13–20. doi: 10.2135/cropsci2010.04.0207\nCooper, M., Messina, C. D., Podlich, D., Totir, L. R., Baumgarten, A., Hausmann, N. J.,\net al. (2014). Predicting the future of plant breeding: complementing empirical evaluation\nwith genetic prediction. Crop Pasture Sci. 65 (4), 311–336, 326. doi: 10.1071/CP14007\nCovarrubias-Pazaran, G. E. (2020)Genetic gain as a high-level key performance\nindicator. In: CGIAR Excellence in Breeding Platform, Breeding Scheme Optimization\nManuals. Available online at: http://Excellenceinbreeding.org/toolbox/tools/eib-\nbreeding-schemeoptimization-manuals (Accessed 8th April 2024).\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n12\n"
+    },
+    {
+      "page": 13,
+      "text": "Crossa, J., Pérez, P., Hickey, J., Burgueño, J., Ornella, L., Cerón-Rojas, J., et al. (2014).\nGenomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112\n(1), 48–60. doi: 10.1038/hdy.2013.16\nDao, A., Sanou, J., Gracen, V., and Danquah, E. Y. (2015). Indentifying farmers’\npreferences and constraints to maize production in two agro-ecological zones in\nBurkina Faso. Agric. Food Secur. 4. doi: 10.1186/s40066–015-0035–3\nDarrah, L. L., Eberhart, S. A., and Penny, L. H. (1972). A maize breeding methods\nstudy in Kenya. Crop Sci. 12, cropsci1972.0011183X001200050016x. doi: 10.2135/\ncropsci1972.0011183X001200050016x\nDarrah, L. L., Eberhart, S. A., and Penny, L. H. (1978). Six years of maize selection in\n‘Kitale Synthetic II’, ‘Ecuador 573’, and ‘Kitale composite a’ using methods of the\ncomprehensive breeding system. Euphytica 27, 191–204. doi: 10.1007/BF00039135\nDe Groote, H., Muteti, F. N., and Bruce, A. Y. (2023). On-farm storage loss estimates\nof maize in Kenya using community survey methods. J. Stored Prod. Res. 102, 102107.\ndoi: 10.1016/j.jspr.2023.102107\nDe Groote, H., and Omondi, L. B. (2023). Varietal turn-over and their effect on yield\nand food security – Evidence from 20 years of household surveys in Kenya. Glob. Food\nSecur. 36, 100676. doi: 10.1016/j.gfs.2023.100676\nDe Groote, H., Owuor, G., Doss, C., Ouma, J., Muhammad, L., and Danda, K. (2005).\nThe Maize Green Revolution in Kenya Revisited. J. Dev. Agric. Econ. 3 (1), 32–49.\nDonovan, J., Coaldrake, P., Rutsaert, P., Bänzinger, M., Gitonga, A., Naziri, D., et al.\n(2022)Market intelligence for informing crop-breeding decisions by CGIAR and\nNARES. In: Market Intelligence Brief Series 1. Available online at: https://hdl.handle.\nnet/10883/22248 (Accessed 8th April 2024).\nDuvick, D. N. (2005). Genetic progress in yield of United States maize (Zea mays L.).\nMaydica 50, 193–202.\nEberhart, S. A. (1970). Factors effecting efficiencies of breeding methods. Afr. Soils 15,\n655–680.\nEberhart, S. A., and Harrison, M. N. (1973). Progress from half-sib selection in Kitale\nstation maize. East Afr. J. Agric. Forest. 39, 12–16. doi: 10.1080/00128325.1973.11662611\nErenstein, O., Jaleta, M., Sonder, K., Mottaleb, K., and Prasanna, B. M. (2022). Global\nmaize production, consumption and trade: trends and R&D implications. Food Secur.\n14, 1295–1319. doi: 10.1007/s12571–022-01288–7\nFalconer, D. S., and Mackay, T. F. C. (1996). Introduction to Quantitative Genetics.\n(San Francisco, California: Benjamin-Cummings Pub. Co).\nFAOSTAT. (2013). Statistical databases and data sets of the Food and Agriculture\nOrganization of the United Nations. FAO. Available at: http://faostat.fao.org/default.\naspx. (accessed June 2024).\nGerhart, D. Z., and Likens, G. E. (1975). Enrichment Experiments for Determining\nLimitation: Four Methods Compared. Limnol. Oceanogr. 20:649–53.\nGunundu, R., Shimelis, H., and Mashilo, J. (2023). Genomic selection and enablers\nfor agronomic traits in maize (Zea mays): A review. Plant Breed 142, 573–593.\ndoi: 10.1111/pbr.13127\nHarrison, M. N. (1970). “\"Maize improvement in East Africa,\",” in Crop improvement in\nEast Africa. Ed. C. L. A. Leakey (Commonwealth Agricultural Bureau, England), 27–36.\nHenderson, C. R. (1975). Best linear unbiased estimation and prediction under a\nselection model. Biometrics 31 2, 423–447. doi: 10.2307/2529430\nJakhar, D. S., Singh, R., Pargat Singh, S. K., and Ojha, V. (2017). Turcicum leaf blight:\nA ubiquitous foliar disease of maize (Zea mays L.). Int. J. Curr. Microbiol. Appl. Sci. 6,\n825–831. doi: 10.20546/ijcmas.2017.603.097\nJatzold, R., and Kutsch, H. (1982). Agro-ecological zones of the tropics, with a sample\nfrom Kenya. Tropenlandwirt 83, 15–34.\nKebede, M., Mekbib, F., Abakemal, D., and Bogale, G. (2020). Genetic gain of maize\n(Zea mays L.) varieties in Ethiopia over 42 year (1997-2015). Afr. J. Agric. Res. 15 (3),\n419–430. doi: 10.5897/ajar2019.14564\nKenworthy, J. M. (1966). Temperature conditions in the tropical highland climates of\nEast Africa. Afr. Geogr. Rev. 4), 1–11.\nKEPHIS (2023). “Kenya National Crop Variety List”, in: pdf. (ed.). A report.\nAvailable online at: https://kilimo.go.ke/wp-content/uploads/2024/02/NATIONAL-\nVARIETY-LIST-APRIL-2023.pdf (Accessed on 8th November 2023).\nKhanna, A., Anumalla, M., Catolos, M., Bartholomé, J., Fritsche-Neto, R., Platten, J.\nD., et al. (2022). Genetic trends estimation in IRRI's rice drought breeding program and\nidentification of high-yielding drought-tolerant lines. Rice 15 (1), 14. doi: 10.1186/\ns12284–022-00559–3\nKirimi, L., Sitko, N., Jayne, T. S., Karin, F., Muyanga, M., Sheahan, M., et al. (2011) A\nfarm gate-to-consumer value chain analysis of Kenya’s maize marketing system\n(Accessed 1st August 2023).\nKostandini, G., La Rovere, R., and Abdoulaye, T.. (2013). Potential impacts of\nincreasing average yields and reducing maize yield variability in Africa. Food Policy\n43:213–26. doi: 10.1016/j.foodpol.2013.09.007\nLane, H. M., Murray, S. C., Montesinos−López, O. A., Montesinos−López, A., Crossa,\nJ., Rooney, D. K., et al. (2020). Phenomic selection and prediction of maize grain yield\nfrom near-infrared reflectance spectroscopy of kernels. Plant Phenome J. 3 (1).\ndoi: 10.1002/ppj2.20002\nLarkin, D. L., Lozada, D. N., and Esten Mason, R. (2019). Genomic selection—\nconsiderations for successful implementation in wheat breeding programs. Agronomy 9\n(9), 479. doi: 10.3390/agronomy9090479\nLopez-Zuniga, L. O., Wolters, P., Davis, S., Weldekidan, T., Kolkman, J. M., Nelson,\nR. J., et al. (2018). Using maize chromosome segment substitution line populations for\nthe identification of loci associated with multiple disease resistance. G3-Genes Genom.\nGenet. 9, 189–201. doi: 10.1534/g3.118.200866\nMarenya, P. P., Wanyama, R., Alemu, S., and Woyengo, V. (2021). Trait preference\ntrade-offs among maize farmers in western Kenya. Heliyon 7, e06389. doi: 10.1016/\nj.heliyon.2021.e06389\nMasuka, B., Atlin, G. N., Olsen, M., Magorokosho, C., Labuschagne, M., Crossa, J.,\net al. (2017). Gains in maize genetic improvement in Eastern and Southern Africa: I.\nCIMMYT hybrid breeding pipeline. Crop Sci. 57 (1), 168–179. doi: 10.2135/\ncropsci2016.05.0343\nMazibuko, P., Mutengwa, C., Magorokosho, C., Kutywayo, D., and Kamutando, C.\nN. (2024). Genetic gains of grain yield among the maize cultivars released over a\ncentury from the national breeding program of Zimbabwe. Agronomy 14, 246.\ndoi: 10.3390/agronomy14020246\nMiedaner, T., and Juroszek, P. (2021). Global warming and increasing maize\ncultivation demand comprehensive efforts in disease and insect resistance breeding\nin north-western Europe. Plant Pathol. 70, 1032–1046. doi: 10.1111/ppa.13365\nMiruka, M. K., Okello, J. J., Kirigua, V. O., and Murithi, F. M. (2012). The role of the\nKenya Agricultural Research Institute (KARI) in the attainment of household food\nsecurity in Kenya: A policy and organizational review. Food Secur. 4, 341–354.\ndoi: 10.1007/s12571–012-0197–9\nMoose, S. P., and Mumm, R. H. (2008). Molecular plant breeding as the foundation\nfor 21st-century crop improvement. Plant Physiol. 147, 969–977. doi: 10.1104/\npp.108.118232\nMutiga, S. K., Morales, L., Angwenyi, S., Wanaina, J. M., Harvey, J. W., Das, B., et al\n(2017). Association between agronomic traits and aflatoxin accumulation in diverse\nmaize lines grown under two soil nitrogen leaves in Eastern Kenya. Field Crops Res. 205,\n124–134. doi: 10.1016/j.fcr.2017.02.007\nNgwira, P., and Pixley, K. V. (2000). \"Eastern Africa Regional Maize Nursery: Project\nReport for 1997 and 1998.\" (Harare, Zimbabwe: CIMMYT).\nNjeru, F., Wambua, A., Muge, E., Haesaert, G., Gettemans, J., and Misinzo, G. (2023).\nMajor biotic stresses affecting maize production in Kenya and their implications for\nfood security. PeerJ 11, 11:e15685. doi: 10.7717/peerj.15685\nNnadi, N. E., and Carter, D. A. (2021). Climate change and the emergence of fungal\npathogens. PloS Pathog. 17, e1009503. doi: 10.1371/journal.ppat.1009503\nOmondi, D. O., Dida, M. M., Berger, D. K., Beyene, Y., Nsibo, D. L., Juma, C., et al.\n(2023). Combination of linkage and association mapping with genomic prediction to\ninfer QTL regions associated with gray leaf spot and northern corn leaf blight resistance\nin tropical maize. Front. Genet. 14. doi: 10.3389/fgene.2023.1282673\nPhilip, K. T., Jones, P. G., Alagarswamy, G., Andresen, J., and Herrero, M. (2010).\nAdapting to climate change: Agricultural system and household impacts in East Africa.\nAgric. Syst. 103, 73–82. doi: 10.1016/j.agsy.2009.09.003\nPoland, J. A., Balint-Kurti, P. J., Wisser, R. J., Pratt, R. C., and Nelson, R. J. (2009).\nShades of gray: The world of quantitative disease resistance. Trends Plant Sci. 14, 21–29.\ndoi: 10.1016/j.tplants.2008.10.006\nPrasanna, B. M., Bruce, A., Beyene, Y., Makumbi, D., Gowda, M., Asim, M., et al.\n(2022a). Host plant resistance for fall armyworm management in maize: relevance,\nstatus and prospects in Africa and Asia. Theor. Appl. Genet. 135 (11), 3897–3916.\ndoi: 10.1007/s00122–022-04073–4\nPrasanna, B. M., Burgueño, J., Beyene, Y., Makumbi, D., Asea, G., Woyengo, V., et al.\n(2022b). Genetic trends in CIMMYT’s tropical maize breeding pipelines. Sci. Rep. 12,\n20110. doi: 10.1038/s41598–022-24536–4\nRutkoski, J. E., Krause, M. R., and Sorrells, M. E. (2022). “\"Breeding methods:\nPopulation improvement and selection methods,\",” in Wheat Improvement (Springer\nInternational Publishing, Cham, Switzerland), 83–96.\nSchroeder, C., Onyango K’Oloo, T., Ranabhat, N., Jick, N., Parzies, H., and Gemenet,\nD. (2013). Potentials of hybrid maize varieties for small-holder farmers in Kenya: A\nreview based on swot analysis. Afr. J. Food Agric. Nutr. Dev. 13, 7562–7586.\ndoi: 10.18697/ajfand.57.11360\nSmale, M., and Olwande, J. (2011) Is older better? Maize hybrid change on household\nfarms in Kenya. Available online at: https://www.tegemeo.org/ (Accessed 12July2023).\nTarekegne, A., Wegary, D., Cairns, J. E., Zaman-Allah, M., Beyene, Y., Negera, D.,\net al. (2024). Genetic gains in early maturing maize hybrids developed by the\nInternational Maize and Wheat Improvement Center in Southern Africa during\n2000–2018. Front. Plant Sci. 14. doi: 10.3389/fpls.2023.1321308\nTesfaye, K., Gbegbelegbe, S., Cairns, J. E., Shiferaw, B., Prasanna, B. M., Sonder, K.,\net al. (2015). Maize systems under climate change in sub-Saharan Africa: Potential\nimpacts on production and food security. Int. J. Clim. Change Strateg. Manage. 7, 247–\n271. doi: 10.1108/IJCCSM-01–2014-0005\nWang, Y., Vitale, J., Park, P., Adams, B., Agesa, B., and Korir, M. (2017).\nSocioeconomic determinants of hybrid maize adoption in Kenya. Afr. J. Agric. Res.\n12, 617–631. doi: 10.5897/ajar2016.11958\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n13\n"
+    },
+    {
+      "page": 14,
+      "text": "Ward, J., Stromberg, E., Nowell, D., and Nutter, J. F. (1999). Gray leaf spot: A disease\nof global importance in maize production. Plant Dis. 83, 884–895. doi: 10.1094/\nPDIS.1999.83.10.884\nWarfield, C. Y., and Davis, R. M. (1996). Importance of the husk covering on the\nsusceptibility of corn hybrids to fusarium ear rot. Plant Dis. 80, 208–210. doi: 10.1094/\nPD-80-0208\nWende, A., Shimelis, H., and Gwata, E. T. (2018). “Genetic variability for resistance\nto leaf blight and diversity among selected maize inbred lines,” in M. A. El-Esawi ed.\nMaize Germplasm - Characterization and Genetic Approaches for Crop Improvement\n(London, UK: IntechOpen Limited). Available at: https://www.intechopen.com/\nchapters/56913.\nXu, Y., Li, P., Zou, C., Lu, Y., Xie, C., Zhang, X., et al. (2017). Enhancing genetic gain\nin the era of molecular breeding. J. Exp. Bot. 68, 2641–2666. doi: 10.1093/jxb/erx135\nZakaria, Z., Yaacob, M. A., Yaacob, Z., Noordin, N., Sawal, M. Z. H. M., and Zakaria,\nZ. (2011). Key performance indicators (KPIs) in the public sector: A study in Malaysia.\nAsian Soc Sci. 7, 102–107. doi: 10.5539/ass.v7n7p102\nLigeyo et al.\n10.3389/fpls.2024.1416538\nFrontiers in Plant Science\nfrontiersin.org\n14\n"
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+      "text": "ORIGINAL RESEARCH\npublished: 15 June 2022\ndoi: 10.3389/fsufs.2022.702405\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n1\nJune 2022 | Volume 6 | Article 702405\nEdited by:\nTodd Andrew Crane,\nInternational Livestock Research\nInstitute (ILRI), Kenya\nReviewed by:\nNathaniel Jensen,\nIndependent Researcher, Morris, NY,\nUnited States\nGirma Hailu,\nInternational Centre of Insect\nPhysiology and Ecology (ICIPE), Kenya\n*Correspondence:\nPaswel Phiri Marenya\np.marenya@cgiar.org\nSpecialty section:\nThis article was submitted to\nClimate-Smart Food Systems,\na section of the journal\nFrontiers in Sustainable Food Systems\nReceived: 29 April 2021\nAccepted: 05 May 2022\nPublished: 15 June 2022\nCitation:\nMarenya PP, Wanyama R, Alemu S\nand Woyengo V (2022) Building\nResilient Maize Production Systems\nWith Stress-Adapted Varieties:\nFarmers’ Priorities in Western Kenya.\nFront. Sustain. Food Syst. 6:702405.\ndoi: 10.3389/fsufs.2022.702405\nBuilding Resilient Maize Production\nSystems With Stress-Adapted\nVarieties: Farmers’ Priorities in\nWestern Kenya\nPaswel Phiri Marenya 1*, Rosina Wanyama 1, Solomon Alemu 2 and Vincent Woyengo 3\n1 The International Maize and Wheat Improvement Center (CIMMYT), Sustainable Agrifood Systems Program, Nairobi, Kenya,\n2 Consultative Group on International Agricultural Research (CGIAR), Standing Panel on Impact Assessment (SPIA), Addis\nAbaba, Ethiopia, 3 Kenya Agricultural and Livestock Research Organization, Small Ruminant Research Center, Kakamega,\nKenya\nMaize cropping systems in Kenya, as is true in many other places in Africa, face multiple\nbiotic and abiotic stressors not least climatic ones. Guided by farmers’ priorities, maize\nbreeding programs can contribute to the needed resilience against these changes by\ndeveloping and mainstreaming new generations of maize varieties adapted to these\nchallenges. Using data from 1,400 farmers and applying a multi-criteria choice analysis,\nthis study reports on smallholder farmers’ relative valuation of stress tolerance traits. The\nresults showed that farmers were willing to pay significant premiums for tolerance to\ndrought, striga, low nitrogen (nitrogen use efficiency) and fall army worm infestation, in\nthat order. Large scale incorporation of these traits in legacy varieties as well as new ones,\ncan contribute to enhancing maize system resilience and adaptation to changing growing\nconditions. For seed systems development, these traits can provide the basis for making\nstrong business cases for the replacement of old varieties with new, stress-adapted ones.\nKeywords: Africa, climate change adaptation, climate-ready maize varieties, maize-genetic improvement,\nresilience, smallholder farming systems\nINTRODUCTION\nAs smallholder farming systems form the majority of maize production in Kenya, it is imperative\nto enhance their productivity, resilience, and adaptability to climate changes. Part of the toolkit\nto achieve these aims involves mainstreaming stress- and climate-adapted maize varieties. Maize\nproduction in most sub-Saharan African countries has yet to reach its potential due to a\ncombination of factors, including drought, degraded and infertile soils, diseases, pests, weeds,\nand the limited use of improved seed or chemical fertilizer (International Maize and Wheat\nImprovement Center; CIMMYT, 2013). An estimated 40% of Africa’s maize-growing area faces\noccasional drought stress, resulting in yield losses of 10–25% (CIMMYT, 2013). A combination of\nsupply and demand factors can also reduce access to modern seeds. For example, the adoption of\nstress-tolerant maize varieties in eastern and southern Africa was conditioned by their availability,\ninadequate information, a lack of resources, high seed prices, and their perceived attributes (Fisher\net al., 2015).\nIn recent years, national and international breeding systems have implemented programs to\nproduce stress-tolerant varieties focusing on tolerance to drought, weeds, low soil nitrogen, and\ninsects. For example, Kenya Agricultural and Livestock Research Organization (KALRO), the\n"
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+      "page": 2,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nnational research body in Kenya, has implemented a number of\nsuch breeding programs. The multi-year and multi-million dollar\nDrought Tolerant Maize for Africa project (2006–2015), which\nwas implemented in 12 other African countries, is an example.\nFurther, the Improved Maize for African Soils project (2010–15)\nfocused on breeding for efficient nitrogen use.\nThe Water-Efficient Maize for Africa project (2008–2018)\nwas meant to produce varieties that were suited for moisture-\nstressed environments, while the Insect-Resistant Maize for\nAfrica project (1999–2004) aimed to promote insect resistance.\nRecently, the Stress-Tolerant Maize for Africa project (2016–\n2020) was implemented to consolidate some of the previously\nmentioned legacy breeding programs to focus on multiple stress-\ntolerant breeding goals, such as combating drought, invasive,\ncompetitive plant species, and low nitrogen, among others. The\nfocus on these stress elements is understandable, as these affect\nKenya and other countries in the region in various ways.\nDrought\nDrought is estimated to cause up to a 39% seasonal reduction\nin yield in much of sub-Saharan Africa (Daryanto et al., 2016).\nAccording to CIMMYT (2013), frequent droughts—or those\naffecting 25% of the maize area—often lead to 50% of losses\nduring the production season. The same study (CIMMYT, 2013)\nindicated that 40% of the maize area suffers from occasional\ndroughts, with yield losses of 10–25%. An ex-ante assessment of\nthe benefits of diffusion of drought-tolerant maize varieties in\n13 African countries, including Kenya, would range from 9 to\n$1.5 billion (Rovere et al., 2014), revealing the potential economic\nfallout from unmitigated effects of droughts.\nStriga\nThe striga weed (Striga hermonthica), also known by its common\nname “witch weed,” is a parasitic plant of cereals, including maize.\nIt depletes the host’s nutrients by inserting its roots into those\nof the host’s root tissues. The resulting nutrient malnutrition can\ncreate extremely low or absent yields depending on the severity of\ninfestation or the host plant’s tolerance (Ejeta, 2007). In the 1990s,\nstriga was estimated to impact as much as 50–100 million ha in\nsub-Saharan Africa (Lagoke et al., 1991; Kanampiu et al., 2018),\nwith consequent yield losses ranging from 20 to 80% (Khan et al.,\n2006; Ejeta, 2007). Striga’s negative effects can be observed in\nexpansive fields over a short period of time due to its diverse\nseed-dispersal mechanisms, including contaminated crop seeds,\nfarm implements, wind, and animals, among others (Berner et al.,\n1996; Ejeta, 2007).\nClimate change has expanded striga’s range of habitats\nacross maize-growing areas. Mechanical, chemical, and biological\ncontrol methods are all used to some degree, such as physical\nweeding, crop rotation, both trap and catch crops, and the use\nof herbicides and fertilizers (Gressel et al., 2004). However, none\nhave successfully eliminated the striga menace, and especially\nin Africa. The weed’s physiology and adaptability has made it\ndifficult to control, as it has a unique life cycle with broad genetic\nvariation in its reproductive process and it infects a wide range\nof crops (Ejeta, 2007; Hearne, 2009). Host plant resistance is\na promising strategy in controlling striga, and especially when\ncombined with beneficial agronomic practices, including soil\nnutrient management.\nLow-Nitrogen Soils\nMaize is a nutrient-demanding crop sensitive to nutrient\ndeficiencies, especially nitrogen (Emede and Alika, 2012).\nFurther, large amounts of nutrients are often required to achieve\nthe genetic promise of high yields (Arisede et al., 2020), while\nmany African soils are depleted of macro-nutrients (Drechsel\net al., 2001; Pasley et al., 2020). While Smaling et al. (1993)\nestimated nitrogen (N) depletion due to maize production at\n42 kg/N per ha annually, many farmers apply limited nutrients\ndue to a lack of funds to purchase fertilizers, a long-standing\nissue (Kamara et al., 2006). The fertilizers that are distributed are\napplied in small quantities. Sheahan and Barrett (2017) estimated\nthat most of Africa only uses ∼5 to 12 kg/N fertilizer applications\nper hectare.\nThe development of N-efficient maize that can withstand\nlow levels of nitrogen in many African soils is an important\ncontribution to enabling low-input producers to best use the\navailable soil N, in addition to the limited amounts they can\nafford (Arisede et al., 2020). Maize varieties with high nitrogen\nuse efficiency (NUE) do so through a higher recovery efficiency\nof N (Arisede et al., 2020), which reduces N losses and provides a\ncost advantage for farmers who can only afford limited amounts\nof N, such as the majority of African smallholders (Sheahan and\nBarrett, 2017). Broadly and in the long run, farmers must be\ncareful, as excessive N applications can increase greenhouse gas\nemissions. Some estimates suggest that over half of applied N\nis lost through leaching, and therefore, is not utilized by plants\n(Arisede et al., 2020).\nGlobally, nitrogen from fertilizers contribute to 30% of\nthe total nitrous oxide, which contributes to air pollution\nand the accumulation of greenhouse gases in the atmosphere\n(Dobermann and Cassman, 2005; Skiba and Rees, 2014). This\nsuggests a strong case for N use efficiency through agronomic\nand genetic approaches. Therefore, the pathway toward increased\ninput use and productivity, even in low-input-use environments,\nshould involve identifying methods to increase the efficiency\nof nitrogen use. Subsequently, small-scale farmers with low\nagricultural inputs can use crop varieties selected and bred for\nlow-N tolerance as a reliable economic method to increase maize\nyields. This also provides a sustainable approach to mitigate\nclimate change (Badu-Apraku et al., 2018).\nFall Armyworm\nThe fall armyworm (Spodoptera frugiperda JE Smith)—hereafter\n“FAW”—is an insect that originated in the Americas, and was\nfirst reported in Africa in early 2016 (Goergen et al., 2016). The\nFAW has the remarkable ability to migrate over long distances\non prevailing winds, produce many eggs, and thrive in a variety\nof hosts and environments (Day et al., 2017; Prasanna et al.,\n2018). By 2018, it had been identified in 30 countries across the\ncontinent, including Kenya, with losses of up to 40% estimated\nin Ghana and Zambia (Day et al., 2017), 47% in Kenya, and\n32% in Ethiopia (Kumela et al., 2019). Further, Prasanna et al.\n(2018) illustrated the devastating effects of FAW by reporting that\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n2\nJune 2022 | Volume 6 | Article 702405\n"
+    },
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+      "page": 3,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nthe insect can feed on more than 80 different crop species, such\nas maize, sorghum, rice, sugarcane, pasture grasses, and millet.\nThe Center for Agriculture and Biosciences International, CABI\n(2017) estimated the FAW’s potential economic devastation as\n8.3–20.6 million metric tons of crops per year, with impacts in\na dozen maize-producing countries in Africa accounting for 21–\n53% of yield losses in a 3-year period. In monetary terms, these\nlosses amount to up to $6.2 billion.\nDe Groote et al. (2020) found that over 80% of farmers in\nKenya have already been affected by FAW and reported over\n30% in yield losses, with variations observed across ecological\nzones. A range of integrated pest-management practices (IPMs)\ncan be deployed to manage the FAW (Prasanna et al., 2018).\nWhile pesticides can be effective, the environmental and\nhealth risks demand that appropriate IPMs be promoted, and\nespecially in resource-constrained environments in Africa with\nlittle awareness of their safe use and abundant regulatory\ngaps. Therefore, developing host plant resistance is a crucial\nelement in effective IPMs to manage the FAW. In all cases,\nIPM strategies that combine good agronomic practices and\nmeasures of FAW resistance in maize can be deemed a higher\npriority than chemical-dependent interventions from a human\nor environmental health perspective and regardless of farmers’\neconomic or educational circumstances.\nGiven these challenges, maize breeding programs can provide\nthe necessary resilience by developing and promoting a new\ngeneration of maize varieties that are bred to withstand or\ntolerate these stresses. These efforts should be guided by farmers’\npriorities for the new varieties to quickly gain broad acceptance.\nThis study was designed to understand the willingness to sacrifice\nyield for stress-tolerant varieties. We consider the tolerance to\nfour stresses: drought, the striga weed, fall armyworm, and low\nnitrogen (measured by a lower top-dressing requirement).\nTHE CONTEXT: SEED MARKETS IN KENYA\nAND FARMER SEED CHOICES\nThe seed markets in Kenya and most of the maize markets in\nAfrica, are characterized by what is typically called low variety\nturnover (newer varieties are slow to gain market share to replace\nold ones). The number of varieties that are drought tolerant\nhave increased in recent years. Varieties tolerant to FAW are just\nbeginning to come onto commercial markets. Varieties tolerant\nto striga and low N have been around for some time. The\nchallenge is that due to a variety of reasons around farmers’\ndecision inertia, weak marketing strategies and low profits for\nseed companies in investing in new varieties (because it takes a\nlong time to gain market share), new superior varieties remain\npoorly commercialized.\nRegarding prices, seed prices usually do not vary based on\ntraits (Rutsaert and Donovan, 2020). This is a major weakness\nthat is frequently cited as an impediment for introducing new\nvarieties. The experiential nature of new seed varieties often\nmakes many farmers hesitant to buy these new varieties without\nfirst observing them in a low-cost way and for a period of\ntime (leading to the slow variety turnover cited above). This\nalso discourages seed companies from investing in the upfront\npromotional costs, thereby creating a viscous cycle of slow\nmarket insertion of new varieties. In focus group discussions,\nseed price was not mentioned as an attribute that farmers\nconsidered when deciding on their seed purchases, reflecting the\nundifferentiated nature of the market. This is an area that requires\ncareful attention in research and development.\nIn summary, seed prices do not vary by traits, this is\na weakness of the seed sector in Kenya (and the region).\nThis is the main reason we use yield to assess farmers’\nvaluation of these traits (this information is itself valuable)\nto inform breeding investments or innovative trait-based\ncommercialization efforts. This paper provides much-needed\ninsights into farmers prioritization of the relevant traits to\nprovide the basis for feedback to seed companies, breeders, policy\nmakers and others to inform maize improvement priorities.\nMATERIALS AND METHODS\nSampling and Data\nThe study was conducted in the mid-altitude, moist, transitional\nmaize-growing areas of western Kenya. The sample was limited\nto the administrative and agro-ecological characteristics of the\nregion’s Busia and Siaya counties. These counties were some\nof the implementation sites for a stress-tolerant maize breeding\nand seed-development project conducted by the KALRO and\nCIMMYT. The dominance of smallholder maize production\nsystems and maize’s role as a prominent staple in farmers’\ncrop portfolios were factors also considered in developing the\nsampling for this study. Finally, we considered the relatively\nlow penetration of stress-tolerant hybrids in these counties’ and\njurisdictions’ seed markets when selecting them for inclusion in\nthis study. Among the four sub-counties selected—Central Alego\nand Alego-Usonga in Siaya County; and Butula and Samia in\nBusia County—eight wards were selected: two in Siaya and six\nin Busia.\nThe final stage of sampling occurred by randomly selecting\nvillages from each ward based on the sampling principle of\nprobability proportional to size. As more villages were sampled\nfrom the larger wards, these wards contributed more to the\nsample. In each village, 40 households were randomly selected to\ngenerate a final random sample of 701 households. Two adults\nfrom each household separately participated in the interview,\nwith questions relating to household data answered by the head of\nthe household as the household representative. These questions\nwere not re-administered to the second respondent (typically\nthe spouse or another adult member of the family). After\nthe head of household finished answering their questions and\nparticipating in the choice experiment (to be described below),\nthe spouse or other adult in the household with significant\nmaize-production responsibilities participated in the full choice\nexperiment. This second respondent also answered a subset\nof the other interview questions related to the management\nof the plots they were responsible for. This process generated\na total of 1,400 respondents: 701 were household heads, 419\nwere spouses, and 280 were other adult household members.\nOf the 1,400 respondents, 732 (52.3%) were female. The map\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n3\nJune 2022 | Volume 6 | Article 702405\n"
+    },
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+      "page": 4,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nFIGURE 1 | Map of study areas in western Kenya and sample sizes per ward.\ndisplayed in Figure 1 summarizes the sampled districts, villages,\nand households.\nConceptual Framework for Choice\nExperiment\nThis study implemented a choice experiment to elicit farmers’\nrelative preferences for selected yield traits. Diverse literature has\ndiscussed choice experiment methods in the transport, health,\nmarketing, and environmental economics fields (Hensher et al.,\n2005; Louviere et al., 2010; Schipmann and Qaim, 2011; Veettil\net al., 2011; Meemken et al., 2017; Ochieng et al., 2017; Wanyama\net al., 2019). The farmers in this study were presented with two\nhypothetical maize varieties with varying levels of the identified\ntraits: yield and the tolerance to drought, striga, low nitrogen, and\nthe FAW. In using a choice experiment approach, we construe\nfarmers’ choices of their preferred variety as following a random\nutility framework, calculated as\nUij = Vij + εij,\n(1)\nwhere U is the utility of farmer i associated with choice j, V\nis the deterministic component, and ε is the random element.\nThe choice of j among several alternatives means that the utility\nderived from j is greater that the utility derived from all other\nattributes. Assuming Yi is a random variable that indicates the\nchoice made, and given the choice between alternatives j and k,\nthe probability that farmer i chooses alternative j is\nProb\n\u0000Yi = j\n\f\f j, k = Prob\n\u0002\u0000Vij + εij\n\u0001\n> (Vik + εik)\n\u0003\n,\nfor all j ̸= k; k = 1, 2 . . . J.\nTherefore, the utility Uij depends on attributes of (choice) variety\nj, and farmer i’s individual characteristics. Assuming m is a vector\nof farmer characteristics and A is a vector of the alternative’s\nattributes, it follows that\nVij = βmij + α′Ai,\n(2)\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n4\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 5,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nwhere β and α are the corresponding vectors of the parameters\nto be estimated.\nWe use a mixed logit (MIXL) model as an improvement over\nthe multinomial logit (MNL) and conditional logit (CL) models\n(McFadden, 1973; Hoffman and Duncan, 1988; McFadden and\nTrain, 2000; Hensher and Greene, 2003). The MNL is appropriate\nwhen the focus is on the individual as a unit of analysis, and\nuses the individual’s characteristics as explanatory variables;\nin contrast, the CL focuses on a set of alternatives for each\nindividual, and the characteristics of these alternatives are used\nas explanatory variables (Hoffman and Duncan, 1988). The MNL\nand CL can be calculated as\nPij = exp\n\u0000Miβj\n\u0001\n/\nJ\nX\nk=1\nexp(Miβk) for multinomial logit; and (3)\nPij = exp\n\u0000Aijα\n\u0001\n/\nJ\nX\nk=1\nexp(Aikα) for conditional logit, (4)\nwhere M represents the characteristics of individual i, Aij denotes\nthe characteristics of alternative j for individual i, and β and α\nare the corresponding vectors of parameters that represent the\ninfluence of individual characteristics and choice attributes.\nA strong assumption for the MNL and CL models is the\nrequirement that the unobserved effects be independently and\nidentically distributed (IID) across the alternatives in the choice\nset, according to the extreme Type 1 distribution (Dahlberg and\nEklöf, 2003). The IID assumption results in a rigid property\nof “independence from irrelevant alternatives” (IIA; Hoffman\nand Duncan, 1988; Ben-Akiva and Bierlaire, 1999). The IIA\nproperty assumes that the entire population has a homogeneous\npreference structure that restricts the β to be the same for all\nmembers of the population (Holmes and Adamowicz, 2003).\nSpecifically, if εij for all i, then j is the probability that a given\nindividual i chooses alternative j within the choice set Zn, which\nis given by\np\n\u0000j\n\f\fzi\n\u0001\n=\nexp(µvi)\nP\nkεzi exp(µvk)\n(5)\nThe IIA property is difficult to satisfy when only two alternatives\nare presented (Benson et al., 2016). The IIA requirement can be\nsatisfied by allowing the unobserved part of the utility function\nto follow a multivariate normal distribution, letting the residuals\ncorrelate with each other, and estimating the model with a\nmultinomial probit model (Dahlberg and Eklöf, 2003).\nAs another approach suggested in literature, the MIXL\nmodel (Hall et al., 2004; Hensher et al., 2005; Hole, 2007) can\naccommodate random taste variations as well as correlations\nin unobserved factors, which relaxes the IIA assumption\n(McFadden and Train, 2000; Campbell et al., 2006; Train, 2009).\nWe use the MIXL model to calculate the utility derived by farmer\ni from choosing maize variety j on choice occasion t, as\nUijt = (β + ri) xijt + εij r = 1, . . . .R; j = 1, . . . J; t = 1, . . . T,(6)\nwhere β is the vector of the mean attribute utility weights in the\npopulation, ri is the vector of person i’s specific deviation from\nthe mean. The random error term εijt is still assumed to be an\nindependently and identically distributed extreme value. The ri\ncan take several distributional forms, such as the multivariate\nnormal of (0, P). As one of the variables used in the choice\nset is the seed price (cost) variable, we assume a log-normal\ndistribution because it allows the price variable to take on a\nnegative sign (Train, 2009; Fiebig et al., 2010). Assuming non-\ncorrelation in the attributes, the estimation model takes the\nform of\nYijt = γ Pijt + δAijt + εnjt,\n(7)\nwhere Y is a binary decision variable that assumes a value of one if\nfarmer i chooses variety j in choice scenario t, and zero otherwise;\nP denotes the price attribute; and A is a vector of other attributes,\nincluding yield and the tolerance to drought, the fall armyworm,\nlow nitrogen, and striga. It is possible that the responses in\nEquation (that is Y) are correlated within households. In our case\nthe MIXL estimation was clustered at household level, and the\nstandard errors are specified as robust.1 For robustness checks,\nthe standard errors are also clustered at village level. While the\nnumber of villages in our sample is not enough (16 villages),\nthe results would still provide a good ground for comparison\nto identify any significant differences. A negative coefficient for\nγ implies that farmers generally prefer lower seed prices. A\npositive coefficient for δ implies that another variety of attributes\npositively influences the selection of a particular variety.\nHeterogeneity and Estimating Willingness\nto Pay for Traits\nThe core of the MIXL model is preference heterogeneity, which\ncan be detected when a statistically significant standard deviation\nis estimated. Therefore, several interaction terms are included to\ncapture these heterogeneities, as\nYijt = γ Pijt + δAijt + λ(Aijt∗Mi) + εijt,\n(8)\nwhere M represents a vector of socioeconomic characteristics.\nThe estimates in Equations (7) and (8) can be used to calculate\nthe willingness to pay (WTP) for the selected attributes. This is\ncomputed by obtaining the partial derivatives of price (P) relative\nto other attributes (A) and multiplying this value by −1 (Hole\nand Kolstad, 2012), as\nWTP = ∂P\n∂Aj\n= −δj\nγ\nWe also calculate an alternative WTP measure with the yield\ncoefficient instead of the coefficient for the price variable. To\n1The mixed logit (MIXL) which is meant to take care of heterogeneity uses a\ngroup variable that allows the model to be estimated by clustering at the household\nrespondent and household level. We used the variable “group_id” which was\ngenerated using three variables household ID (household cluster), respondent type\n(respondent heterogeneity) and experiment session id (to capture any correlations\nbetween experimental sessions).\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n5\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 6,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nTABLE 1 | Summary of maize traits identified and used in the experiments.\nTraits\nDescription\nLevelsa\nReference\nlevel\nPrice (Kshs/2 kg\npacket)\nThe buying price for\na maize seed\n300, 400, 500, 600, 700,\n800\nYield (90 Kg\nbags/acre)\nAmount of dry grain\nthat farmers can\nobtain per unit of\nland usually in bags\nof 90-kg per acre.\n6, 9, 12, 15, 18, 21, 24\nDrought tolerant\n(DT)\nWhen there is a dry\nspell the tolerant\nmaize stays green.\nThe variety which\nmoderately or\ndrought tolerant has\nthe quality of yielding\nat least half of the\nnormal yields when\nthere is mid-season\nmoderate\n(no-catastrophic)\ndrought.\nNot tolerant, moderately\ntolerant, completely\ndrought tolerant\nNot drought\ntolerant\nFall army worm\ntolerant (FAT)\nNot resistant, moderately\nresistant, completely\nresistant\nNot tolerant\nto fall army\nworm\nNitrogen use\nefficient (tolerant\nto low N) – NUE\nNot resistant, moderately\nresistant, Completely\nresistant\nNot tolerant\nto low\nnitrogen\nTolerant to striga\n(ST)\nNot tolerant, moderately\ntolerant, Completely\ndrought tolerant\nNot tolerant\nto striga\naIn all these cases, not tolerant (resistant) meant total yield failure (a loss of more than\n14 90–kg bags/acre or more than 90% yield failure, given a typical good yield of 15\n90-kg bags/acre). Moderately tolerant meant at 25-50% (4–8 90-kg bags/acre) yield\nfailure and tolerant meant no more than 10% (2 90-kg bags/ha) yield failure based on\n15 bags per acre reference point in most of the communities and based on the focus\ngroup discussions.\ndistinguish the two, the latter is considered the “willingness to\nsacrifice yield” (WTSY), and is calculated as\nWTSY = ∂Y\n∂Aj\n= δj\nγ\nNote that we do not multiply δj\nγ by−1 because the yield coefficient\nis (intuitively) positive.\nChoice Experiment Design\nData was collected using a structured choice experiment\naccompanied by a demographic and farm survey questionnaire.\nAs previously stated, the experiments were conducted separately\nin each household, with both the household head and the spouse,\nor another adult household member who independently operated\na maize plot. If the household had only one single adult head, with\nonly minor children as the other members, or where only one\nadult operated a maize plot, then only one member—typically the\nhousehold head—was interviewed.\nA series of 18 focus group discussions (FGDs) were used\nto identify the key traits to use in the CE. These FGDs were\nmeant to narrow down the key stress tolerance traits of concern,\nor those most likely to influence farmers’ choices of maize\nvarieties. Three FGDs occurred in each host village,2 and two\nof these meetings were exclusively men or women, with a third\nmixed; each FGD had 6–12 participants. The aim was to gather\ninformation for designing a parsimonious experiment that would\nnot impose an undue cognitive load on the participants. We\nachieved this following the 18 FGDs by restricting the choices to\nthe following six attributes, as listed in Table 1: seed prices; yield;\nand tolerance to the four stresses of drought, low nitrogen, striga,\nand fall armyworm.\nSecond, the FGDs were also meant to help us determine the\nreasonable ranges to use without creating choice options that\nwere too complex. Therefore, we aimed to develop a choice\nexperiment that was familiar in the local contexts, but that also\nused the most important choice criteria. The actual experimental\nsessions were conducted using the levels estimated from the\nFGDs; these were designed using the NGENE software suite\n(ChoiceMetrics, 2012) using a D-efficient design (Hall et al.,\n2002; ChoiceMetrics, 2012). To facilitate a recognition of the\nchoices, 12 combinations (sessions) generated from NGENE\nwere illustrated in color (Figure 2). The choice experiment was\nexplained to respondents before each session, and the respondent\nwas allowed to ask any clarifying questions. Twelve pages similar\nto Figure 2 were produced, with each trait combination derived\nfrom corresponding NGENE output. The respondent was shown\none page at a time; the interviewer held the page with the picture\nfacing the respondent and the interviewer read out the trait\ncombinations. Each respondent was allowed ∼30 to 60 s to review\nthe pictures and make a choice. The choice experiment was\nimplemented in the local Swahili language spoken in the research\ncommunities and by all the interviewers.\nKEY RESULTS FROM THE CHOICE\nEXPERIMENT AND MIXED LOGIT MODEL\nESTIMATIONS\nTable 2\nsummarizes\nthe\ndemographic\ndata\nused\nin\nthe\neconometric\nmodels.\nAs\ntwo\nhousehold\nmembers\nwere\ninterviewed, 50% of respondents were heads of household,\n30% were spouses, and ∼20% were other adult household\nmembers. Of the respondents, 48% were male, 42% were younger\nthan 35, and 66% relied on primary agricultural production as\nthe main source of income, or representing 50% or more of their\nannual income. The respondents’ average age (42 years) was\nslightly less than that often observed among farmers (∼50 years).\nThe respondents had an average of 7.5 years of education, or a\nprimary education, with primary school in Kenya involving eight\nyears of education. Overall, youths (those under 35 years) were\nstrongly represented in the maize production sector (42%), but\nthese respondents heavily rely on agriculture. Their low levels\nof education—or no more than a primary school education on\naverage—would suggest the low accumulation of skills, as one\n2These were not the same villages in the final sample, but were located within the\nsame wards.\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n6\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 7,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nFIGURE 2 | An example of choice experiment session (session 1) comparing traits for two maize varieties.\noften requires a post-primary education to begin acquiring trade\nskills. Consequently, the respondents heavily rely on primary\nagriculture and other forms of low-skilled economic activities.\nChoice Experiment Results\nWillingness to Pay for Maize Traits With Price as Cost\nVariable\nThe upper panel in Table 3 displays the WTP for maize traits.3 In\nthe pooled samples with or without interaction terms,4 the WTPs\nfor the various traits were the same after including interaction\neffects. The WTP for FAW tolerance (FAT) was slightly higher\n3The detailed MIXL results are presented in Tables S1–S3 in the Supplementary\nonline Materials to reduce the amount of data presented in the body of the\nmanuscript.\n4For each subsample, we ran two models, one with and the other without\ninteraction (heterogeneity) terms.\nin Siaya, which is possible if FAWs or similar insect infestations\nwere more prevalent in Siaya. Generally, the WTP for FAT was\n79% greater among respondents older than 35 compared to\nthe under-35 cohort. The WTP for yield was the lowest of all\ntraits. A noteworthy observation based on the data displayed\nin Table 3 is that the differences in the various sub-samples’\nWTP were not large. Table S1 in the Supplementary Materials\nnotes 13 interaction terms, but only four are significant; this is\nconsistent with weak heterogeneity in preferences and with the\nlargely similar WTPs reported in Table 3.\nWillingness to Sacrifice Yield (WTSY) for Maize Traits,\nWith Yield as the Cost Variable\nThe lower panel in Table 3 notes the WTSY estimates; the WTSY\nfor lower seed prices is comparable to that for low N tolerance.\nIn measuring WTSY, we observe that drought tolerance (DT) has\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n7\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 8,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nthe highest WTSY of all traits across locations and demographic\ncategories. The second-highest WTSY was for striga tolerance\n(ST). Regarding DT, the WTSY increases from that for moderate\nto full DT. The same occurs for ST, where the WTSY for full\nST is twice that of moderate ST. Regarding low N tolerance, we\nnote that the WTSY for moderately low N tolerant is higher\nthan the WTSY for fully low N tolerant. Across all the sub-\nsamples in Table 3, the WTSY for full low N tolerance was\nan average of 26% smaller than the WTSY for Moderate N\ntolerance, with a difference of nearly 40% in the pooled sample\nwith interactions.\nThe younger (under 35) demographic appears to have the\nhighest WTSY for lower seed prices among all categories,\nmirroring the pooled sample result. Younger farmers may be\njust starting out and have fewer resources, and their yield\nconsiderations are not prioritized, as they lack the complimentary\ninputs to achieve the varieties’ yield potential. As with WTP\nestimates, the WTSY estimates were fairly similar overall (by\nvisual inspection of Table 3); most differences occurred between\nmen and women, but appear to be generally small. The\ndifferences between WTP and WTSY estimates were most\ndiscernible when compared based on the geographic variation\nbetween Busia and Siaya. Specifically, the sub-sample from Siaya\nexhibited a higher WTP and WTSY than Busia for DT, FAT, and\nNUE. This suggests that geographic differences may be at play.\nDISCUSSIONS AND IMPLICATIONS FOR\nMAIZE SYSTEMS’ RESILIENCE\nFarmers highly value stress-tolerant traits. Overall, the model\nestimates indicate that farmers prioritize all the stress-tolerant\nattributes, and may be willing to pay a premium in terms of\nhigher seed price or sacrifice yield to gain crops with these\nattributes. The small coefficient estimates for yield compared to\nthe other traits reflect not that yield is a trivial attribute, but that\nthe high yield potential per se has reduced economic value in\nthe presence of drought, striga and fall armyworm infestations,\nand low N (see Figure 3). It is conceivable that within this\nlimit, lower-yielding varieties with these kinds of attributes may\nTABLE 2 | Sample characteristics.\nVariable\nDescription\nMean (n = 1,400)\nRespondent type\nHousehold head (%)\n50.00\nSpouse (%)\n30.07\nAnother adult member (%)\n19.93\nSex\n=1 if respondent is male (%)\n47.71\nAge\n=1 if respondent is below 35\nyears\n41.57\nEducation\nYears of schooling\n7.53\nOccupation\n=1 if main occupation of\nrespondent is agriculture (%)\n66.21\nCounty\nSiaya (%)\n50.14\nBusia (%)\n49.86\nNumber of households\n701\nmaintain market share as farmers may be willing to accept\nsome yield penalty in favor of stress tolerance. Fortunately,\nmodern breeding techniques have led to maize hybrids with\nthese traits, but that also exhibit comparable or higher yields as\nlegacy hybrids and having stress tolerance traits that the latter\nmay lack.\nOur results are broadly consistent with recently published\nliterature. Kassie et al. (2017) found that farmers in Zimbabwe\nmore highly value stress-tolerance traits—or drought tolerance,\nin their case—even more than such grain characteristics as\nclosed tips, cob size, and flint texture. These authors also\nconclude that designing and implementing innovative ways of\npromoting drought-tolerant maize while raising awareness of\nthese issues would enhance their adoption in risk-prone farming\ncommunities. Although Sibiya et al. (2013) examined rice crop\nin South Africa, they discovered that local rice landraces were\npreferred for their tolerance to abiotic stresses and stable yields.\nRegarding impact of seed price on choice of variety, Sibiya\net al. (2013) observed that the scarce use of hybrids was attributed\nto the high seed costs, and that these varieties are not tolerant\nto acidic, low-nitrogen soils (common issues in their study area\nin KwaZulu Natal). In our case, the WTSY for seed price was\nhigh and ranked after DT and ST (Figure 4). It is often argued\nthat seed price may not be a major determinant of seed demand.\nHowever, our study and results by Sibiya et al. (2013) suggest\nthat farmers may be critically sensitive to seed price. If a new\nvariety lacks new traits and its main selling point is not in stress\ntolerance, farmers may choose more familiar yet lower-yielding\nvarieties due to their less expensive retail price. However, in the\nlocal seed markets, there is little trait-based price differentiation.\nOur conclusion therefore would apply when more sophisticated\nseed markets emerge, as mimicked in our experiment.\nWhile the local seed markets are particularly not driven by\nprice competition (Rutsaert and Donovan, 2020), it is conceivable\nthat a more nuanced approach to seed markets may be warranted,\nas seed prices could reflect unique varieties’ combinations of\nattributes. In other words, seed markets should move toward\nattribute-based pricing. This will provide the chance for farmers\nto identify the seed product with the desired groups of attributes,\nwhich could act as a cornerstone of product differentiation and\nmarket segmentation.\nMoreover,\nKamara\net al. (2006) examined\ngeographic\nvariations in preferences in Nigeria. Specifically, these authors\nobserved that farmers in relatively market-driven production\nsystems preferred high-yielding, drought-tolerant varieties, while\nthose in resource-poor areas with low potential preferred\nearlier-maturing varieties to provide food security during\nperiods of scarcity. Efisue et al. (2008) reported on rice;\nwhich although a different crop than maize, is still relevant\nwhen discussing farmers’ trait preferences. These authors noted\nthat while farmers in irrigated ecologies in Mali preferred\nhigh-yielding, long-duration rice varieties, those in the less\nfavorable ecologies preferred tall plants with a short duration.\nThis suggests that market segmentation could be based on\ngeographic segmentation. One exception involves gender, as\nsubtle differences have been observed that could influence\nmarketing within a single geographic area.\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n8\nJune 2022 | Volume 6 | Article 702405\n"
+    },
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+      "page": 9,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nTABLE 3 | Mean willingness to pay (WTP) and willingness to sacrifice (WTSY) for maize variety attributes, by location and demographics.\nPooled base Pooled with\ninteraction\nSiaya\nBusia\nMale\nrespondent\nFemale\nrespondent\nBelow 35\nyears\nAbove 35\nyears\nNon-Agric\noccupation\nAgric\nOccupation\nWTP (Cost variable = seed price)\nYield\n0.0340A\n0.0229***\n0.0359***\n0.0303***\n0.0351***\n0.0306***\n0.0238***\n0.0425***\n0.0298***\n0.0348***\nModerate\ndrought\ntolerant\n2.369***\n2.374***\n2.772***\n2.252***\n2.807***\n2.164***\n2.529***\n2.362***\n2.451***\n2.472***\nDrought\ntolerant\n2.550***\n2.485***\n2.828***\n2.481***\n2.933***\n2.365***\n2.680***\n2.535***\n2.519***\n2.648***\nSlight\nresistant to\nFAW\n0.563***\n0.560***\n0.576***\n0.452***\n0.466***\n0.621***\n0.465***\n0.617***\n0.691***\n0.445***\nResistant to\nFAW\n0.516***\n0.111***\n0.655***\n0.363***\n0.484***\n0.527***\n0.518***\n0.566***\n0.682***\n0.445***\nModerately\nNUE\n1.117***\n1.121***\n1.259***\n1.071***\n1.343***\n0.990***\n1.200***\n1.091***\n0.940***\n1.227***\nNUE\n0.844***\n0.697***\n1.033***\n0.754***\n1.019***\n0.752***\n0.900***\n0.844***\n0.784***\n0.907***\nModerately\nST\n0.531***\n0.533***\n0.516***\n0.513***\n0.419***\n0.599***\n0.430***\n0.611***\n0.581***\n0.483***\nST\n1.596***\n1.631***\n1.493***\n1.584***\n1.475***\n1.685***\n1.517***\n1.639***\n1.708***\n1.452***\nWTSY (Cost variable = yield)\nSeed price\n25.57***\n39.83***\n22.80***\n23.66***\n21.21***\n27.86***\n31.38***\n21.52***\n26.57***\n23.37***\nModerately\nDT\n55.07***\n85.90***\n54.39***\n46.73***\n50.84***\n54.78***\n69.01***\n46.36***\n53.86***\n52.18***\nDT\n60.37***\n93.06***\n57.64***\n53.71***\n54.94***\n61.27***\n75.23***\n50.65***\n57.79***\n57.86***\nModerately\nFAT\n15.26***\n23.69***\n15.68***\n13.36***\n12.09***\n17.68***\n16.28***\n14.18***\n21.30***\n12.32***\nFAT\n13.50***\n5.099***\n16.03***\n11.94***\n12.37***\n15.22***\n17.31***\n12.54***\n18.48***\n12.06***\nModerately\nNUE\n26.28***\n41.17***\n25.46***\n22.84***\n24.90***\n25.44***\n33.06***\n21.54***\n20.88***\n26.87***\nNUE\n19.58***\n24.74***\n20.64***\n16.23***\n18.91***\n19.04***\n24.53***\n16.45***\n17.06***\n19.84***\nModerately\nST\n15.74***\n24.62***\n14.20***\n15.86***\n12.64***\n18.52***\n16.96***\n14.80***\n19.23***\n13.50***\nST\n40.40***\n63.38***\n36.12***\n38.69***\n32.03***\n46.04***\n46.60***\n35.08***\n46.72***\n35.20***\nN\n1400\n1400\n702\n698\n668\n732\n582\n818\n473\n927\nAp < 0.05, **p < 0.01, ***p < 0.001; FAW, fall army worm.\nRegarding gender preference heterogeneity, Anja et al. (2017)\nfound gender-related differences in variety preferences. In\nmost of the studies reviewed, men exhibited a preference for\nproduction- and marketing-related traits, while women focused\non production- and use-related traits. However, when men\nand women faced similar constraints, they tended to mention\nsimilar trait preferences. As a general observation, women\nfocused more on traits related to post-harvest processing and\nfood preparation, such as storability, grain characteristics, losses\nduring the decortication process, and the resulting flour’s swelling\ncapacity. Women also mentioned various food security-related\ntraits, such as early maturity, the potential for multiple harvests,\nand production stability.\nAn apparent duality exists in women’s roles in choosing maize\nvarieties. The predominant cultural norms reveal that women\nare responsible for food processing and preparation, and this is\noften reflected in how they weigh grain qualities for example.\nHowever, women as farmers prefer the same attributes as male\nfarmers. Finally, it is important to recognize that when women\nexpress strong preferences for specific consumption traits, those\npreferences are most likely those expressed by their households,\nin that this captures the preferences of the women, men and\nother members in the household. Will only consumption traits\nmatter when targeting women farmers with information on\nnew varieties? It is more likely that this will require a better\nunderstanding of who in the household chooses its varieties.\nThe second consideration will be whether the varieties\nconcerned are primarily for subsistence, in that they are\nconsumed at home, or whether they are intended for the market.\nRegarding the former, both consumption and stress-tolerance\ntraits will have to be collectively considered in the household.\nRegarding the latter, the main issue may be stress tolerance\nand storability, and particularly if farmers store dry grain for\nsome time before sale. As maize grain is often not graded on\nconsumption traits, farmers may pay less attention to these issues\nif the varieties are primarily meant for market sale.\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n9\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 10,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nFIGURE 3 | Pairwise comparisons of WTP and WTSY between traits.\nCONCLUSIONS\nMaize cropping systems must respond accordingly to biotic\nand abiotic stressors exacerbated by climatic changes. One way\nto accomplish this is for maize improvement programs to\ndevelop and promote varieties that are simultaneously tolerant\nto multiple stressors. Consequently, this study examined farmers’\nvaluation of maize traits and how they may prioritize them.\nWe used a choice experiment approach similar to those more\nbroadly used in product development research and applied a\nmixed-logit model to examine data from 1,400 smallholder\nfarmers responding from two counties in the mid-altitude\ntransitional and mid- to low-potential maize-growing regions\nin western Kenya. These areas are characterized by smallholder\nfarmers operating low-capital farming enterprises dominated\nby subsistence concerns and limited commercialization. The\nresults revealed that farmers were willing to pay significant\npremiums for seed tolerant to drought, striga, low nitrogen\n(nitrogen use efficiency), and fall armyworm infestation, in that\norder. The large-scale incorporation of these traits in both legacy\nand new varieties could enhance the maize system’s resilience\nand adaptation.\nIn\nconclusion,\nthe\nmulti-criteria\nprioritization\ndata\npresented\nhere\nsuggests\nthat\nto\nmainstream\nclimate-\nresilient,\nstress-adapted\nmaize\nvarieties\nin\nthe\ncountry’s\nmaize production systems, drought- and striga-tolerant and\nnitrogen-efficient characteristics must be prioritized in the\ncountry’s\nmaize-breeding\nprograms.\nSeed\ncompanies\nwill\nmore\nlikely\ncommercialize\nthese\nstress-adapted\nvarieties\nif\nin\naddition\nto\nstress\ntolerance\ntraits,\nthe\npreferred\nconsumption traits are integrated into these new varieties,\nor vice versa.\nThese trait combinations can provide a basis for seed\ncompanies\ndevelop\ntheir\nseed\nbusinesses\nby\nreplacing\nolder varieties with those that are new and stress-adapted.\nIn\nterms\nof\nsegmentation,\nan\napproach\nthat\nprioritizes\ngeographic\nmarket\npartitioning\nmay\nbe\nrequired\nbased\non\npredominant\nstresses\nin\nspecific\nareas.\nWithout\nbalancing\nthese\nmultiple\ndimensions,\nit\nis\npossible\nthat\nnew\nstress-adapted\nvarieties\nmay\nstruggle\nto\ngain\na\nsignificant\nmarket\nshare.\nHowever,\ncreating\nresilient,\nwell-adapted\nmaize\nsystems\nwill\nrequire\nthe\nwidespread\ncommercialization of new generations of maize consistent with\nfarmers’ priorities.\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n10\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 11,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nFIGURE 4 | Differences between men and women in WTP and WTSY for maize traits.\nDATA AVAILABILITY STATEMENT\nThe raw data supporting the conclusions of this article will be\nmade available by the authors, without undue reservation.\nETHICS STATEMENT\nThe studies involving human participants were reviewed\nand approved by Institutional Research Ethics Committee,\nInternational Maize and Wheat Improvement Center. Written\ninformed consent for participation was not required for this\nstudy in accordance with the national legislation and the\ninstitutional requirements.\nAUTHOR CONTRIBUTIONS\nPM conceived and designed the study and the experiments,\nrun the surveys and experiments, analyzed and interpreted\nthe data, and wrote the paper. RW analyzed data and wrote\nMIXL sections of the paper. SA implemented the surveys,\nreviewed survey instruments, and participated in data analysis\nand interpretation. VW participated in study design, supervised\nfield implementation, interpreted the data, and wrote discussion\nsections of the paper. All authors contributed to the article and\napproved the submitted version.\nFUNDING\nWe gratefully acknowledge funding from the Bill & Melinda\nGates Foundation (Grants INV-003439 and OPP1134248) and\nthe United States Agency for International Development (Grant\nMTO 069033) through the Accelerating Genetic Gains for Maize\nand Wheat and Stress Tolerant Maize for Africa Projects from\nunder which the research activities were done. We gratefully\nacknowledge additional support from the CGIAR Research\nPrograms Maize Agri-Food Systems (CRP MAIZE) and Policies,\nInstitutions, and Markets (CRP PIM) for supporting the lead\nauthor PM.\nSUPPLEMENTARY MATERIAL\nThe Supplementary Material for this article can be found\nonline\nat:\nhttps://www.frontiersin.org/articles/10.3389/fsufs.\n2022.702405/full#supplementary-material\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n11\nJune 2022 | Volume 6 | Article 702405\n"
+    },
+    {
+      "page": 12,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nREFERENCES\nAnja, C., Weltzien, E., Rattunde, F., and Ashby, J. A. (2017). “Gender\ndifferentiation of farmer preferences for varietal traits in crop improvement:\nevidence and issues,” in Working Paper No. 2. CGIAR Gender and Agriculture\nResearch Network [Cali: CGIAR System Management Office and International\nCenter for Tropical Agriculture (CIAT)].\nArisede, C., Mainassara, Z. A., Jill, C., Amsal, T., Cosmos, M., Bish. (2020). Low-\nN stress tolerant maize hybrids have higher fertilizer N recovery efficiency and\nreduced N-dilution in the grain compared to susceptible hybrids under low N\nconditions. Plant Prod. Sci. 23, 417–426. doi: 10.1080/1343943X.2020.1746188\nBadu-Apraku, B., Fakorede, M. A. B., Annor, B., and Talabi, A. O. (2018).\nImprovement in grain yield and low-nitrogen tolerance in maize cultivars of\nthree eras. Exp. Agric. 54, 805–823.\nBen-Akiva, M., and Bierlaire, M. (1999). “Discrete choice methods and their\napplications to short term travel decisions,” in Handbook of Transportation\nScience (Boston, MA: Springer), 5–33.\nBenson, A. R., Kumar, R., and Tomkins, A. (2016). “On the relevance of irrelevant\nalternatives,” in Proceedings of the 25th International Conference on World Wide\nWeb (Montreal, QC: International World Wide Web Conferences Steering\nCommittee), 963–973.\nBerner, D., Carsky, R., Dashiell, K., Kling, J., and Manyong, V. (1996). A land\nmanagement based approach to integrated Striga hermonthica control in\nsub-Saharan Africa. Outlook Agric. 25, 157–164.\nCABI (2017). How to Identify Fall Armyworm. Plantwise. CABI. pp 4, Available\nonline at: www.plantwise.org/fallarmyworm (accessed July 2nd, 2020).\nCampbell, D., Hutchinson, W. G., and Scarpa, R. (2006). Using Discrete\nChoice Experiments to Derive Individual-Specific WTP Estimates for Landscape\nImprovements Under Agri-Environmental Schemes: Evidence From the Rural\nEnvironment Protection Scheme in Ireland. Working Paper No. 26.2006. FEEM\n(Fondazione Eni Enrico Mattei). doi: 10.2139/ssrn.884443\nChoiceMetrics (2012). Ngene 1.1.1 User Manual & Reference Guide. Sydney\nChoiceMetrics.\nCIMMYT (2013). The Drought Tolerant Maize for Africa project. DTMA\nBrief. Available online at: https://www.cimmyt.org/news/a-decade-of-world-\nleading-maize-and-wheat-research/ (accessed July 02, 2020).\nDahlberg, M., and Eklöf, M. (2003). Relaxing the IIA Assumption in Locational\nChoice Models: A Comparison Between Conditional Logit, Mixed Logit, and\nMultinomial Probit Models. Uppsala: Nationalekonomiska institutionen.\nDaryanto, S., Wang, L., and Jacinthe, P.-A. (2016). Global synthesis of\ndrought effects on maize and wheat production. PLoS ONE 11, e0156362.\ndoi: 10.1371/journal.pone.0156362\nDay, R., Abrahams, P., Bateman, M., Beale, T., Clottey, V., Cock, M., et al. (2017).\nFall armyworm: impacts and implications for Africa. Outlooks Pest Manag. 28,\n196–201.\nDe Groote, H., Kimenju, S. C., Munyua, B., Palmas, S., Kassie, M., and Bruce, A.\n(2020). Spread and impact of fall armyworm (Spodoptera frugiperda JE Smith)\nin maize production areas of Kenya. Agriculture, Ecosystems and Environment.\n292, 106804.\nDobermann, A., and Cassman, K. G. (2005). Cereal area and nitrogen use efficiency\nare drivers of future nitrogen fertilizer consumption. Sci. China Life Sci. 48,\n745–758.\nDrechsel, P., Kunze, D., and de Vries, F. P. (2001). Soil nutrient depletion and\npopulation growth in sub-Saharan Africa: a Malthusian nexus? Popul Environ.\n22, 411–423. doi: 10.1023/A:1006701806772\nEfisue, A., Tongoona, P., Derera, J., Langyintuo, A., Laing, M., and Ubi, B.\n(2008). Farmers’ Perceptions on rice varieties in Sikasso region of Mali\nand their implications for rice breeding. J. Agron. Crop Sci. 194, 393–400.\ndoi: 10.1111/j.1439-037X.2008.00324.x\nEjeta, G (2007). “The Striga scourge in Africa: a growing pandemic,” in Integrating\nNew Technologies for Striga Control: Towards Ending the Witch-Hunt, eds G.\nEjeta, and J. Gressel (Singapore: World Scientific), 3–16.\nEmede, T. O., and Alika, J. E. (2012). Variation in agronomic characters among\nhigh and low nitrogen S2 maize (Zea mays L) lines grown in high and low\nnitrogen environments. Maydica. 57, 139–146.\nFiebig, D. G., Keane, M. P., Louviere, J., and Wasi, N. (2010). The generalized\nmultinomial logit model: Accounting for scale and coefficient heterogeneity.\nMark. Sci. 29, 393–421.\nFisher, M., Abate, T., Lunduka, R. W., Asnake, W., Alemayehu, Y., and\nMadulu, R. B. (2015). Drought tolerant maize for farmer adaptation to\ndrought in sub-Saharan Africa: determinants of adoption in eastern and\nsouthern Africa. Clim. Change 133, 283–299. doi: 10.1007/s10584-015-\n1459-2\nGoergen, G., Kumar, P. L., Sankung, S. B., Togola, A., and Tamò, M. (2016).\nFirst report of outbreaks of the fall armyworm Spodoptera frugiperda (JE\nSmith)(Lepidoptera, Noctuidae), a new alien invasive pest in West and Central\nAfrica. PloS one. 11, e0165632.\nGressel, J., Hanafi, A., Head, G., Marasas, W., Obilana, A. B., Ochanda, J., et al.\n(2004). Major heretofore intractable biotic constraints to African food security\nthat may be amenable to novel biotechnological solutions. Crop Prot. 23,\n661–689.\nHall, J., Kenny, P., King, M., Louviere, J., Viney, R., and Yeoh, A. (2002). Using\nstated preference discrete choice modelling to evaluate the introduction of\nvaricella vaccination. Health Econ. 11, 457–465. doi: 10.1002/hec.694\nHall,\nJ.,\nViney,\nR.,\nHaas,\nM.,\nand\nLouviere,\nJ.\n(2004).\nUsing\nstated\npreference\ndiscrete\nchoice\nmodeling\nto\nevaluate\nhealth\ncare\nprograms.\nJ.\nBus.\nRes.\n57,\n1026–1032.\ndoi:\n10.1016/S0148-2963(02)0\n0352-1\nHearne, S. J. (2009). Control—the Striga conundrum. Pest Manag., 65, 603–614.\nHensher, D. A., and Greene, W. H. (2003). The mixed logit model: the state of\npractice. Transportation 30, 133–176. doi: 10.1023/A:1022558715350\nHensher, D. A., Rose, J. M., and Greene, W. H. (2005). Applied Choice Analysis: A\nPrimer. New York, NY: Cambridge University Press.\nHoffman, S. D., and Duncan, G. J. (1988). Multinomial and conditional\nlogit discrete-choice models in demography. Demography 25, 415–427.\ndoi: 10.2307/2061541\nHole, A. R. (2007). Fitting mixed logit models by using maximum simulated\nlikelihood. Stata J. 7, 388–401. doi: 10.1177/1536867X0700700306\nHole, A. R., and Kolstad, J. R. (2012). Mixed logit estimation of willingness\nto pay distributions: a comparison of models in preference and WTP space\nusing data from a health-related choice experiment. Empir. Econ. 42, 445–469.\ndoi: 10.1007/s00181-011-0500-1\nHolmes, T. P., and Adamowicz, W. L. (2003). “Attribute-based methods,” in A\nPrimer on Nonmarket Valuation (Dordrecht : Springer), 171–219.\nKamara, A., Kureh, I., Menkir, A., Kartung, P., Tarfa, B., and Amaza, P. (2006).\nParticipatory onfarm evaluation of the performance of drought tolerant maize\nvarieties in the Guinea Savannas of Nigeria. J. Food Agric. Environ. 4, 192–196.\nKanampiu, F., Makumbi, D., Mageto, E., Omanya, G., Waruingi, S., Musyoka,\nP., et al. (2018). Assessment of management options on striga infestation\nand maize grain yield in Kenya. Weed Sci. 66, 516–524. doi: 10.1017/wsc.\n2018.4\nKassie, G. T., Abdulai, A., Greene, W. H., Shiferaw, B., Abate, T., Tarekegne,\nA., et al. (2017). Modeling preference and willingness to pay for drought\ntolerance (DT) in maize in rural Zimbabwe. World Dev. 94, 465–477.\ndoi: 10.1016/j.worlddev.2017.02.008\nKhan, Z. R., Pickett, J. A., Wadhams, L. J., Hassanali, A., and Midega, C. A.\n(2006). Combined control of Striga hermonthica and stemborers by maize–\nDesmodium spp. intercrops. Crop Prot. 25, 989–995.\nKumela, T., Simiyu, J., Sisay, B., Likhayo, P., Mendesil, E., Gohole, L., et al.\n(2019). Farmers’ knowledge, perceptions, and management practices of the new\ninvasive pest, fall armyworm (Spodoptera frugiperda) in Ethiopia and Kenya.\nInt. J. Pest Manag. 65, 1–9.\nLagoke, S. T. O., Parkinson, V., and Agunbiade, R. M. (1991). “Parasitic weeds and\ncontrol methods in Africa,” in Combating Striga in Africa: Proceedings of an\nInternational Workshop on Striga, ed S. K. Kim (Ibadan: IITA), 3–14.\nLouviere, J. J., Flynn, T. N., and Carson, R. T. (2010). Discrete choice\nexperiments\nare\nnot\nconjoint\nanalysis.\nJ.\nChoice\nModell.\n3,\n57–72.\ndoi: 10.1016/S1755-5345(13)70014-9\nMcFadden, D. (1973). Conditional Logit Analysis of Qualitative Choice Behavior.\nBerkeley, CA: Institute of Urban and Regional Development.\nMcFadden,\nD.,\nand\nTrain,\nK.\n(2000).\nMixed\nMNL\nmodels\nfor\ndiscrete\nresponse.\nJ.\nAppl.\nEconometr.\n15,\n447–470.\ndoi: 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1\nMeemken, E. M., Veettil, P. C., and Qaim, M. (2017). Toward improving the design\nof sustainability standards—a gendered analysis of farmers’ preferences. World\nDev. 99, 285–298. doi: 10.1016/j.worlddev.2017.05.021\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n12\nJune 2022 | Volume 6 | Article 702405\n"
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+    {
+      "page": 13,
+      "text": "Marenya et al.\nStress-Adapted Maize Varieties for Kenya\nOchieng, D. O., Veettil, P. C., and Qaim, M. (2017). Farmers’ preferences\nfor\nsupermarket\ncontracts\nin\nKenya.\nFood\nPolicy\n68,\n100–111.\ndoi: 10.1016/j.foodpol.2017.01.008\nPasley, H. R., Camberato, J. J., Cairns, J. E., Zaman-Allah, M., Das,\nB., and Vyn, T. J. (2020). Nitrogen rate impacts on tropical maize\nnitrogen use efficiency and soil nitrogen depletion in eastern and southern\nAfrica. Nutrient Cycl. Agroecosyst. 116, 1–12. doi: 10.1007/s10705-020-\n10049-x\nPrasanna, B. M., Huesing, J. E., Eddy, R., and Peschke, V. M. (2018). Fall\nArmyworm in Africa: A Guide for Integrated Pest Management, 1st Edn. Mexico,\nCDMX: CIMMYT.\nRovere, R. L., Abdoulaye, T., Kostandini, G., Guo, Z., Mwangi, W., MacRobert, J.,\net al. (2014). Economic, production, and poverty impacts of investing in maize\ntolerant to drought in Africa: an ex-Ante assessment. J. Dev. Areas 48, 199–225.\ndoi: 10.1353/jda.2014.0016\nRutsaert, P., and Donovan, J. (2020). Sticking with the old seed: input value\nchains and the challenges to deliver genetic gains to smallholder maize farmers.\nOutlook Agric. 49, 39–49.\nSchipmann, C., and Qaim, M. (2011). Supply chain differentiation, contract\nagriculture,\nand\nfarmers’\nmarketing\npreferences:\nthe\ncase\nof\nsweet\npepper in Thailand. Food Policy 36, 667–677. doi: 10.1016/j.foodpol.2011.\n07.004\nSheahan,\nM.,\nand\nBarrett,\nC.\nB.\n(2017).\nTen\nstriking\nfacts\nabout\nagriculture input use in Sub-Saharan Africa. Food Policy 67, 12–25.\ndoi: 10.1016/j.foodpol.2016.09.010\nSibiya, J., Tongoona, P., Derera, J., and Makanda, I. (2013). Farmers’ desired\ntraits and selection criteria for maize varieties and their implications for maize\nbreeding: a case study from KwaZulu-Natal Province, South Africa. J. Agric.\nRural Dev. Tropics Subtropics 114, 39–49. Available online at: http://nbn-\nresolving.de/urn:nbn:de:hebis:34-2013030542599\nSkiba, U., and Rees, B. (2014). Nitrous oxide, climate change and agriculture. CAB\nRev. 9, 1–7.\nSmaling, E. M. A., Stoorvogel, J. J., and Windmeijer, P. N. (1993). Calculating\nsoil nutrient balances in Africa at different scales. Fertil Res. 35, 237–250.\ndoi: 10.1007/BF00750642\nTrain, K. E. (2009). Discrete Choice Methods With Simulation. Cambridge\nUniversity Press.\nVeettil, P. C., Speelman, S., Frija, A., Buysse, J., and Van Huylenbroeck,\nG. (2011). Complementarity between water pricing, water rights and\nlocal\nwater\ngovernance:\na\nBayesian\nanalysis\nof\nchoice\nbehaviour\nof\nfarmers in the Krishna river basin, India. Ecol. Econ. 70, 1756–1766.\ndoi: 10.1016/j.ecolecon.2011.04.018\nWanyama, R., Gödecke, T., Jager, M., and Qaim, M. (2019). Poor consumers’\npreferences for nutritionally enhanced foods. Br. Food J. 121, 755–770.\ndoi: 10.1108/BFJ-09-2018-0622\nConflict of Interest: The authors declare that the research was conducted in the\nabsence of any commercial or financial relationships that could be construed as a\npotential conflict of interest.\nPublisher’s Note: All claims expressed in this article are solely those of the authors\nand do not necessarily represent those of their affiliated organizations, or those of\nthe publisher, the editors and the reviewers. Any product that may be evaluated in\nthis article, or claim that may be made by its manufacturer, is not guaranteed or\nendorsed by the publisher.\nCopyright © 2022 Marenya, Wanyama, Alemu and Woyengo. This is an open-access\narticle distributed under the terms of the Creative Commons Attribution License (CC\nBY). The use, distribution or reproduction in other forums is permitted, provided\nthe original author(s) and the copyright owner(s) are credited and that the original\npublication in this journal is cited, in accordance with accepted academic practice.\nNo use, distribution or reproduction is permitted which does not comply with these\nterms.\nFrontiers in Sustainable Food Systems | www.frontiersin.org\n13\nJune 2022 | Volume 6 | Article 702405\n"
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+      "text": "Maize Seed Contamination and Seed Transmission of Maize Chlorotic\nMottle Virus in Kenya\nEsther N. Kimani,1,2,† Samuel M. Kiarie,3 Cyrus Micheni,3 Laureen G. Muriki,3 Douglas W. Miano,2 Isaac Macharia,4\nGary P. Munkvold,5 William M. Muiru,2 B. M. Prasanna,6,† and Anne Wangai3,6\n1 Crop Biotechnology, Kabete Center, Biotechnology Research Institute, Kenya Agricultural and Livestock Research Organization (KALRO),\nNairobi, Kenya\n2 Department of Plant Science and Crop Protection, University of Nairobi, Kangemi, Nairobi, Kenya\n3 Plant Pathology Department, Kabete Center, Food Crops Research Institute, Kenya Agricultural and Livestock Research Organization (KALRO),\nNairobi, Kenya\n4 Kenya Plant Health Inspectorate Services (KEPHIS), Nairobi, Kenya\n5 Department of Plant Pathology and Microbiology, Seed Science Center, Iowa State University, Ames, IA, 50011-1050, U.S.A.\n6 Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, United Nations Avenue – Gigiri,\nNairobi, Kenya\nAccepted for publication 23 June 2021.\nAbstract\nMaize chlorotic mottle virus (MCMV) causes maize lethal necrosis\ndisease in combination with a cereal-infecting potyvirus, leading\nto high yield losses. There is limited information on seed infec-\ntion or contamination rate by MCMV and its comparison with\ntransmission rate to maize seedlings. This study was conducted\nto determine the extent of seed contamination in seed lots from\nMCMV-infected maize fields in Kenya and the transmission of\nMCMV from seeds to seedlings. To determine the contamination\nlevels, whole seeds were ground and the extract tested for the\npresence of MCMV using double antibody sandwich enzyme-\nlinked immunosorbent assay (DAS-ELISA). Seedling grow-outs\nwere tested for seed transmission of MCMV using DAS-ELISA and\nreal-time reverse transcription polymerase chain reaction (real-\ntime RT-PCR) methods. The seed contamination rates of the four\nseed lots tested ranged from 4.9 to 15.9%. MCMV transmission\nfrequency for 37,617 seedlings, tested in 820 pools of varying\nseed amounts by DAS-ELISA, was 0.17%, whereas a transmission\nfrequency of 0.025% was obtained from 8,322 seedlings tested\nin 242 pools by real-time RT-PCR. Seeds from plants mechani-\ncally inoculated with MCMV had an overall seed transmission\nrate of 0.04% in 7,846 seedlings tested in 197 pools. The study\nshowed that even with substantial contamination of maize seed\nwith MCMV, the transmission of the virus from the seed to seed-\nlings was low. Nevertheless, even low rates of transmission can\nbe significant under field conditions where insect vectors can\nfurther spread the disease from infected seedlings, unless dis-\neased plants are detected in time and properly managed.\nKeywords: maize lethal necrosis, maize chlorotic mottle virus,\nseed contamination, seed transmission\nMaize has the highest production among the cereal crops glob-\nally and is the important cereal in Latin America and sub-Saharan\nAfrica (Ranum et al. 2014). In Kenya, maize is the main staple\nfood; the crop had an average annual production of 3.57 million\nmetric tons in the years 2014 to 2018 (FAOSTAT 2021), with an\naverage consumption of 67.3 kg/person in 2019 (KNBS 2020).\nSince 2011, when the maize lethal necrosis (MLN) disease was\nfirst reported in Kenya (Wangai et al. 2012), maize production in\nthe country has been threatened by MLN, which was estimated to\nhave caused crop losses of 0.5 million tons in 2013 (De Groote\net al. 2016). An increase in maize chlorotic mottle virus (MCMV)\nincidence has been reported in Kenya, Uganda, and Ethiopia in\n2018–2019 (Prasanna et al. 2020). MLN is a result of infection of\nmaize plants by MCMV in combination with maize-infecting\npotyvirids, such as sugarcane mosaic virus (SCMV), maize dwarf\nmosaic virus (MDMV), Johnson grass mosaic virus in the genus\nPotyvirus, or wheat streak mosaic virus in the genus Tritimovirus\n(Goldberg and Brakke 1987; Stewart et al. 2017; Uyemoto et al.\n1980; Wangai et al. 2012).\nMaize chlorotic mottle virus is the sole member of the genus\nMachlomovirus, in the family Tombusviridae. Globally, MCMV\nwas first reported in the 1970s in Peru and subsequently in dif-\nferent parts of the world, including eastern Africa (Prasanna et al.\n2020; Redinbaugh and Stewart 2018). The genome sequence of\nthe MCMV-Kenya isolate is 99% identical to that in Rwanda\n†Corresponding authors: E. N. Kimani; esther.kimani@gmail.com and\nB. M. Prasanna; b.m.prasanna@cgiar.org\nCurrent address for Anne Wangai: P.O. Box 9369-00100, Nairobi, Kenya.\nFunding: This work was supported by a subgrant under a project led by the\nInternational Maize and Wheat Improvement Center (CIMMYT) and funded by Bill\nand Melinda Gates Foundation (INV-006697/OPP1138693), and the CGIAR\nResearch Program on Maize (MAIZE). MAIZE receives Windows 1 and 2 support\nfrom the Governments of Australia, Belgium, Canada, China, France, India, Japan,\nKorea, Mexico, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the\nUnited Kingdom, the United States, and the World Bank. USDA 2017 Norman E.\nBorlaug International Agricultural Science and Technology Fellowship funded the\ntravel and work of the first author at the Iowa State University, U.S.A.\nThe author(s) declare no conflict of interest.\nCopyright © 2021 The Author(s). This is an open access article\ndistributed under the CC BY 4.0 International license.\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 496\nPlant Health Progress \u0001 2021 \u0001 22:496–502\nhttps://doi.org/10.1094/PHP-02-21-0018-RS\nResearch\n"
+    },
+    {
+      "page": 2,
+      "text": "(Adams et al. 2014), has 98 to 99% similarity to MCMV isolates\nfrom China (Adams et al. 2013), and is 96 to 97% identical to\nthe MCMV isolates from Kansas and Nebraska in the United\nStates (Adams et al. 2013; Wamaitha et al. 2018). The virus is\ntransmitted through vectors, including corn thrips (Frankliniella\nwilliamsi), maize rootworms (Diabrotica undecimpunctata, Dia-\nbrotica longicornis, and Diabrotica virgifera), cereal leaf beetles\n(Oulema melanopus), and flea beetles (Systena frontalis and\nChaetocnema pulicaria) (Cabanas et al. 2013; Jensen 1985;\nJiang et al. 1992). Transmission is also reported to be through\nseed and soil, as well as mechanically by contaminated farm\nimplements (Mahuku et al. 2015; Nelson et al. 2011).\nSeed transmission of MCMV was reported from a study con-\nducted on 25 seed lots in Hawaii with a transmission rate of 0 to\n0.33% (Jensen et al. 1991), whereas another study reported no\nseed transmission (Bockelman et al. 1982). Testing of 25 indi-\nvidual seeds obtained from MCMV-infected maize plants in\nKenya resulted in 72% of the seeds being positive for MCMV\n(Mahuku et al. 2015). None of the earlier studies attempted to\ncomprehensively determine the possibility of seed contamination\nleading to transmission of MCMV to next-generation seedlings\nusing a large sample size. The present study was therefore under-\ntaken to evaluate the extent of contamination of seed lots by\nMCMV and further transmission of the East African isolate of\nMCMV to the grown-out seedlings.\nExperimental Material\nCommercial maize seed lots harvested in 2015 and 2017 from\nthe Western region of Kenya, designated as seed lots K27, K4,\nA, and B (Table 1), were contributed by a seed company. These\nseed lots from commercial hybrid seed production fields were\nrejected by the Kenya Plant Health Inspectorate Services\n(KEPHIS) after laboratory testing, due to detection of MCMV.\nIn Kenya, seed production fields are regularly inspected by\nKEPHIS for possible presence of MLN three times prior to\nmaturity, and laboratory testing is carried out on pooled samples\nof 7-day-old seedlings (two-leaf stage), using real-time reverse\ntranscription polymerase chain reaction (real-time RT-PCR)\n(Shango et al. 2019). The four seed lots were sampled according\nto International Seed Testing Association guidelines (ISTA\n2016) for studying the levels of contamination and seed trans-\nmission of MCMV. The cultivars of the seed lots were different\nexcept for lots K4 and B. Group testing of the samples in pools\nwas adopted (Albrechtsen 2006a) to reduce the cost of laboratory\nanalysis per sample. Further studies on seed transmission were\ncarried out using seeds from maize plants (hybrid maize cultivars\nH614, DK777, and DUMA 43) mechanically inoculated with\nMCMV at Kenya Agricultural and Livestock Research Organiza-\ntion (KALRO)–Njoro quarantine field. H614 and DUMA 43 are\nhighly susceptible to MCMV, and DK777 is partially resistant to\nMLN (having a score of 5.0 on a scale of 1 to 9).\nThe work was done at the Molecular Pathology Laboratory at\nKALRO, Food Crops Research Institute–Kabete Center in Kenya\n(KALRO-Kabete). Seed contamination of seed lots K4 and K27\nwas tested at the Seed Science Center, Iowa State University,\nAmes, IA, U.S.A.\nTesting of Maize Seed for Contamination with MCMV\nWhole maize seeds were sampled from the four seed lots. The\nsamples obtained were then grouped into pools of 20 seeds each\nfor lots A and B, and tests were carried out at KALRO-Kabete,\nKenya; the pools were 50 seeds each for lots K27 and K4, and\nthe tests were carried out at Iowa State University, Ames, IA,\nU.S.A. Whole seed pools were then ground to a coarse texture\nfor MCMV testing. The total number of seeds tested for the four\nseed lots was 4,560, 3,860, 1,850, and 3,100 for lots A, B, K27,\nand K4, respectively. An IKA Tube Mill control grinder (Tube\nMill CS000, IKA, Staufen, Germany) was used for grinding the\npools of 20 dry seeds, whereas a Waring 7011HS blender (War-\ning Commercial, Stamford, CT) was used to grind the pools of\n50 seeds in enzyme-linked immunosorbent assay (ELISA) gen-\neral extraction buffer.\nDouble antibody sandwich ELISA (DAS-ELISA) was used to\ndetect MCMV. Antibodies developed against MCMV were coated\non a high protein-binding capacity polystyrene 96-well flat-\nbottomed microplate (F96 Maxisorp, Nunc, Thermo Fisher\nScientific, Waltham, MA). The samples from seed lots K27 (37\nsubsamples) and K4 (62 subsamples) were tested using a commer-\ncial ELISA reagent set for MCMV (Agdia, Elkhart, IN), following\nthe manufacturer’s instructions. The general extraction buffer was\nprepared in the laboratory using 0.2% chicken egg albumin,\n0.13% sodium sulfite, and 2% polyvinylpyrrolidone (Sigma-\nAldrich, St. Louis, MO) in 1× phosphate-buffered saline with\nTween (PBST). The buffer was added to the seed (ratio of 1:1)\nand the mixture shaken by hand vigorously prior to soaking over-\nnight in a conical flask and grinding the next day. Each sample\nwas replicated twice on the plate. The optical density absorbance\nvalues were measured using a microplate spectrophotometer\n(PowerWave HT, BioTek Instruments, Winooski, VT) at wave-\nlength of 405 nm 1 h after incubation at room temperature. The\ncontrols included in all the microplates were two negative controls\n(general extraction buffer and MCMV-free maize seed [20 seeds\nground together] obtained from the Seed Science Center, Iowa\nState University, U.S.A.) and a positive control (maize seed from\nMCMV-infected plants obtained from the MLN screenhouse at\nKALRO-Kabete, Kenya).\nDAS-ELISA was carried out on samples from seed lots A and\nB at KALRO-Kabete, using MCMV antibodies obtained from\nPlant Pathology Department, University of Minnesota, U.S.A.\nThe DAS-ELISA procedure was similar to that of the Agdia\nELISA reagent set for MCMV with slight modifications, and the\nbuffers were prepared in the laboratory with similar chemical\ncomposition. The plates were incubated at 37\u0001C for 1 h, except\nTABLE 1\nDetails of the seed lots used in the study\nParameter\nLot A\nLot B\nLot K27\nLot K4\nHarvesting date\n2017\n2017\n2015\n2015\nSampling date\n2018\n2018\n2017\n2017\nSource of seed\nBlended, rainfed fields\nBlended, irrigated fields\nSingle, rainfed field\nBlended, rainfed fields\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 497\n"
+    },
+    {
+      "page": 3,
+      "text": "the substrate step, which was incubated at room temperature for\n1 h. The primary antibodies were diluted at a ratio of 1:1,000 in\ncarbonate buffer, whereas the alkaline phosphatase conjugated\nantibody was diluted at a ratio of 1:4,000 in enzyme conjugate\nbuffer (buffer composition similar as those of Agdia). The opti-\ncal density absorbance values of the reaction were measured\nusing a microplate reader (Elx808 BioTek Instruments) at a\nwavelength of 405 nm, 1 h after incubation at ambient tempera-\nture. Similar controls as those described above were used in all\nthe microplates. The MCMV-free maize seed was obtained from\na seed company and tested for confirmation.\nSamples from both experiments with an average optical absor-\nbance value from the two replicates that was higher than twice\nthat of the negative maize seed control and extraction buffer\nwere considered positive for MCMV. Data were collected on the\nnumber of samples that were positive and negative for MCMV.\nThe most probable percentage of infected seeds was calculated\nusing a binomial statistical method formula at 95% confidence\nlevel (Chen and Swallow 1990):\nP = 1– Y=N\nð\nÞ1=n\nh\ni\n´ 100\nwhere n is the number of seeds per group sample, N is the num-\nber of groups, and Y is the assay-negative groups.\nDetection of MCMV in Seedling Grow-Outs from Commer-\ncial Seed Contaminated with MCMV\nSeed to seedling transmission of MCMV was tested by germi-\nnating seeds from the four seed lots (Table 1) by planting the sam-\npled seeds and from the healthy certified seed (DK777) in trays\nfilled with vermiculite mixed with sterile soil at a ratio of 1:2, at\nKALRO-Kabete, in a seed laboratory, at average day room tem-\nperature of 21\u0001C, with an average of 12 h light and 12 h night.\nFifty seeds were sown in each tray, and leaves from germinated\nseedlings from each tray were sampled 10 days after planting (at\ntwo-leaf stage) (Shango et al. 2019). Insecticides were sprayed\nweekly to control possible insect vectors transmitting MCMV.\nSeedlings at the two-leaf stage were counted, recorded, and sam-\npled into one sample bag as one pooled sample. Contamination of\nsamples was controlled by handling the seedlings on each tray\nwith a clean set of gloves. The samples were transported to the\nlaboratory in a cool box and stored at –40\u0001C. Testing was carried\nout using DAS-ELISA with MCMV antibodies (from the Univer-\nsity of Minnesota) as described above.\nA total of 37,617 seedlings from the four seed lots (Table 2)\nwere tested using DAS-ELISA in 820 pools with a varying\nnumber of seedlings from each tray, ranging from seven to 50\ndepending on germination count during sampling. The samples\nwere crushed using a tissue homogenizer and sample mesh bag\n(Agdia) in general extraction buffer as described above, at a ratio\nof 1:10 (w/v). Three controls were included in each plate: two\nnegative controls (general extraction buffer and leaf sample from\nhealthy certified seed) and one MCMV-positive leaf from maize\nplants infected with MCMV (maintained at Biosafety Green-\nhouse, KALRO-Kabete, Kenya).\nMCMV Detection in Seedling Grow-Outs by Real-Time\nRT-PCR\nSeedlings from seed lots A and B were also tested using real-\ntime RT-PCR. RNA was extracted from leaves of 8,322 seed-\nlings, which were in 242 pools, using a CTAB modified protocol\n(Monger et al. 2001). The pool sizes had samples from a range\nof 14 to 50 seedlings. Real-time RT-PCR was carried out using\nMCMV primers designed from the coat protein of MCMV (Liu\net al. 2016): forward primer 59-GAGTCCTGCCAATCCAAAG\nTG-39, reverse primer 59-TGGGTGGGTCAAGGCTTACTA-39,\nand\nprobe\n59-FAM-AGCCGCCGCCCACTCTCCAG-BHQ-39.\nThe PCR mix consisted of 0.5 µl of forward and reverse primers,\n0.3 µl of fluorescence-labeled probe, 2 µl of RNA template,\n1.4 µl of diethyl pyrocarbonate-treated water, and SensiFAST\nProbe Hi-Rox one-step kit (BIO 77001, Bioline USA, Taunton,\nMA) components comprising 0.2 µl of RNase inhibitor, 0.1 µl of\nreverse transcription, and 5 µl of SensiFAST probe Hi-Rox\none-step buffer (with Hot Start DNA polymerase). The reaction\nwas done in a real-time PCR equipment (StepOnePlus, Applied\nBiosystems,\nThermo\nFisher\nScientific)\nwith\nthe\nfollowing\ncycling conditions: complementary strand synthesis step at 50\u0001C\nfor 10 min, 95\u0001C for 1 min, and then 40 cycles of 94\u0001C for 10 s\nand 60\u0001C for 1 min. Data collected was the threshold cycle (Ct)\nvalue. Samples that had Ct values lower than the Ct values of\nthe negative controls (healthy maize leaf sample and nontem-\nplate reaction) were considered positive for MCMV.\nSeed transmission for each of the pooled samples from the\nfour seed lots, based on DAS-ELISA and real-time RT-PCR\nmethods, was calculated using the pooled prevalence tests using\nEpitools epidemiological calculators (Sergeant 2018) applying a\nmaximum-likelihood estimate of prevalence of the virus for the\npooled samples. In this case, we applied the variable pool sizes\nand perfect test (Sergeant 2018), using the data for each pool\nsize, number of pools in the pool size, and number of positive\npools per size for each of the seed lots. The fixed pool size and\nperfect test method were applied where the pool sizes were the\nsame in the seed lot. The prevalence of the virus was obtained at\na 95% confidence interval and the seed transmission reported as\na percentage.\nDetection of MCMV from Seedling Grow-Outs\nGerminated from Seeds from Mechanically\nInoculated Plants\nOne hundred sixty-five plants from three hybrid cultivars each\n(H614, DK777, and DUMA 43) were mechanically inoculated\nwith MCMV at the four-leaf stage at KALRO-Njoro quarantine\nfield in 2017 and 2018. MCMV inoculum was obtained at the\nKALRO biosafety level II greenhouse, where the MCMV-\ninfected plants were grown under controlled conditions for\nresearch use. Leaves from the MCMV-infected plants were\nblended with 0.1 M phosphate buffer at a ratio of 1:10 (weight/\nvolume). The mixture was sieved using a muslin cloth, and carbo-\nrundum was added just before inoculation. A motorized sprayer\nTABLE 2\nDAS-ELISA-based detection of maize chlorotic mottle virus\n(MCMV)-contaminated maize in seedlings grown from\nMCMV seed lots\nSeed\nlot\nNo. of\nseedlings\ntested\nNo. of positive\npoolsa/total\nno. of pools\ntested\nEstimated seed\ntransmission\nrate\nA\n17,098\n15/360\n0.09%\nB\n15,319\n4/356\n0.03%\nK27\n4,800\n0/96\n0\nK4\n400\n2/8\n0.57%\na Samples were tested in pools with varying pool sizes.\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 498\n"
+    },
+    {
+      "page": 4,
+      "text": "was used to apply the inoculum on the seedlings. MCMV insect\nvectors and fall armyworm (Spodoptera frugiperda) were con-\ntrolled by spraying insecticides weekly by alternating alphacyper-\nmethrin (Alphaguard 10EC) and chlorpyrifos (Pyrinex 48EC)\ncombined with either chlorantraniliprole and abamectin (Voliam\nTargo) or emamectin benzoate (Escort 19EC).\nThe seeds from the infected maize plants were harvested at\nmaturity and dried to a moisture content of 13% before storage.\nThree hundred seeds for each cultivar were directly tested by\nDAS-ELISA in group sizes of 20 seeds each, and MCMV was\ndetected from 98% of the replicated samples. The harvested\nseeds were planted and treated as in the previously described\nseed transmission experiments for commercial seed lots; 7,846\ngrow-out seedlings were tested for presence of MCMV using the\nDAS-ELISA method, in pool sizes ranging from 20 to 50 seed-\nlings. The data on the number of positive pools and the number\nof pools tested per pool size were analyzed as described above\nusing the Epitools epidemiological calculators variable pool sizes\nand perfect test (Sergeant 2018).\nExtent of Contamination of the Commercial Seed\nLots by MCMV\nThe four naturally contaminated seed lots (A, B, K4, and K27)\nwere found to be contaminated by MCMV at varying levels. The\naverage contamination rate for the four seed lots was 8.75%,\nwith a range of 4.90% (K27) to 15.93% (B) (Table 3) at a 95%\nconfidence level.\nSeed Transmission of MCMV from Commercial\nSeed Contaminated with MCMV to Seedlings\nThe results from the total number of pooled samples tested\nusing DAS-ELISA indicated that 21 pooled samples tested posi-\ntive for MCMV out of a total of 820 pooled samples analyzed in\nthe study (Table 2). The numbers of pools that were found posi-\ntive for MCMV were 15 from lot A, four from lot B, and two\nfrom lot K4. A total of 37,617 seedlings were tested from the\nfour seed lots. The average seed transmission rate for the four\nseed lots was 0.17% (Table 2) when tested using DAS-ELISA.\nThe four seed lots had varying seed transmission rates, with lot\nK4 having the lowest number of samples tested (400) but record-\ning the highest seed transmission rate (0.57%).\nTransmission of MCMV from the contaminated seed of seed\nlots A and B to the next-generation seedlings was analyzed using\nthe real-time RT-PCR method. Only two composite samples\nwere found to be positive for MCMV (Table 4), with Ct values\nlower than those of the negative healthy control, which ranged\nfrom 29.94 to 34.04 for different plates. The average seed trans-\nmission of MCMV for the seed lots tested by real-time RT-PCR\nwas 0.025%. These two samples that tested positive for MCMV\nin real-time RT-PCR were both negative when tested using\nDAS-ELISA, reflecting the sensitivity of real-time RT-PCR in\ndetection of a small amount of viruses in samples (Liu et al.\n2016). However, in this study, the high cost of detection by real-\ntime RT-PCR limited its use on all the samples that were tested\nusing the DAS-ELISA method.\nTransmission of MCMV from Seeds Obtained from\nPlants Mechanically Inoculated with MCMV\nThree seedling pools obtained from seeds that were mechani-\ncally inoculated with MCMV tested positive. These were from\nthe seeds of DK777 (one pool) and H614 (two pools) harvested\nin 2017 (Table 5). There was no detection of MCMV in the sam-\nples from 2018. Seed transmission rates of 0.1 and 0% were cal-\nculated from the infected plants harvested in 2017 (2,944\nseedlings) and 2018 (4,902 seedlings), respectively. Based on the\ndata from this study, an average seed transmission rate of 0.04%\nwas calculated.\nSeed transmission of viruses occurs when the virus is passed\nfrom the contaminated seed to the seedling grown out during the\nprocess of germination and growth. In most such cases, seed\ntransmission occurs when there is invasion of the embryo\nTABLE 3\nDAS-ELISA-based estimation of percentage of maize chlorotic mottle virus (MCMV) contamination in the four maize seed lots\nSeed lot\nTotal number of\nseeds tested\nNumber of MCMV+ seed\npoolsa/total number of\nseed pools tested\nProportion of\npositive samples\nCalculated % MCMV\ncontamination rateb\nA\n4,560\n165/228\n0.72\n6.23%\nB\n3,860\n187/193\n0.96\n15.93%\nK27\n1,850\n34/37\n0.92\n4.90%\nK4\n3,100\n61/62\n0.98\n7.92%\na Samples were tested in pools, with a pool size of 20 seeds for lots A and B and 50 seeds for lots K27 and K4.\nb Contamination rate was calculated using a binomial statistical method, 95% confidence level.\nTABLE 4\nSeed transmission of maize chlorotic mottle virus in\nseedlings germinated from contaminated seeds, as tested\nusing real-time RT-PCR\nSeed\nlot\nNo. of\nseedlings\ntested\nNo. of positive\nsample pools/no.\nof pools tested\nMinimum seed\ntransmission rate\nA\n4,837\n1/143\n0.02%\nB\n3,485\n1/99\n0.03%\nTABLE 5\nData on the number of seedlings tested for each cultivar\nand the number of pools that tested positive for maize\nchlorotic mottle virus by DAS-ELISA\nCultivar\n2017\n2018\nSeedlings\ntested\nPositive\npoolsa\nSeedlings\ntested\nPositive\npoolsa\nDK777\n1,031\n1b\n1,640\n0\nDUMA 43\n796\n0\n1,630\n0\nH614\n1,117\n2b\n1,632\n0\na Pool sizes varied from 20 to 50 seedlings.\nb The positive pools had 50 seedlings each.\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 499\n"
+    },
+    {
+      "page": 5,
+      "text": "(Johansen et al. 1994; Sastry 2013a). In other cases, although\nthe virus may not be present in the embryo, contamination of\nthe seed coat or presence of virus in the outer layers of the endo-\nsperm can lead to external seed transmission (Bernardo et al.\n2018; Sastry 2013a), such as for cucumber green mottle mosaic\nvirus in watermelon (Sui et al. 2019). The virus, whether inacti-\nvated or viable, can be detected by microscope, serological, and\nnucleic-acid based methods. Results from the present study\nshowed that seeds sampled from commercial hybrid maize seed\nlots in Kenya were contaminated with MCMV at varying levels\nranging from 4.9 to 15.93%. The previously reported contamina-\ntion rate from 260 maize seeds obtained from 26 seed lots in\nKenyan markets was 6%; MCMV was detected in 12 of the 26\nseed lots (Mahuku et al. 2015). This was within the same range\nof the findings in this study with an average contamination rate\nof 8.75%. These levels of seed contamination by a virus are rela-\ntively low, compared with virus contamination in vegetables,\nfruits, and legumes; for example, contamination could be as high\nas 100 and 93.85% by cucumber green mottle mosaic virus in\nwatermelon and melon seeds, respectively (Wu et al. 2010).\nVirus contamination detected in whole seeds by serological and\nnucleic acid-based methods may include viable, transmissible\nvirus in the embryo as well as inactivated virus found externally\nor in various seed tissues (Albrechtsen 2006b; Crowley 1957).\nVirus inactivation can occur during seed maturation and also\nduring storage of the seeds (Dombrovsky and Smith 2017). The\ntesting of whole seeds for viruses in most cases does not give a\nclear indication of the risk of seed transmission into seedlings\n(Johansen et al. 1994).\nIn the present study, seed transmission of MCMV when tested\nusing DAS-ELISA ranged from 0 to 0.57% for samples collected\nfrom commercial seed lots and from 0 to 0.01% for samples\nfrom maize plants of three cultivars (DK777, H614, and DUMA\n43) that were mechanically inoculated with MCMV. The loca-\ntion of the virus in seed tissues was not determined; therefore, it\nis uncertain whether seed transmission resulted from external\ncontamination or internal infection. Jensen et al. (1991) reported\nMCMV transmission rates from maize seeds obtained from four\nseed lots in the United States as 0.03, 0.067, 0.2, and 0.33%,\nrespectively, from about 42,000 seedlings, whereas no seed\ntransmission of MCMV was revealed from 2,153 maize inbred\nseedlings and 1,898 hybrid seedlings (Bockelman et al. 1982). In\nanother study, analysis of seeds from MCMV-infected maize\ninbred lines (24) in Kenya revealed seed transmission in one\npooled sample from a total of 480 seedlings (Kitira 2018). From\nthese studies reported, it is evident that the transmission of\nMCMV through seed is consistently lower than 1%. Low seed\ntransmission rates have also been documented for other maize-\ninfecting viruses, with the tombusvirids having very few seed-\ntransmitted viruses (Sastry 2013b). Transmission of other viruses\nin maize via seed at low levels has been reported for MDMV,\nwith one seed transmission in 22,189 seedlings (Mikel et al.\n1984), and no transmission of SCMV in 480 seedlings of seeds\nfrom inbred lines infected with SCMV in Kenya (Kitira 2018);\ntransmission of high plains virus in sweet corn was three out of\n38,473 seedlings tested (Forster et al. 2001). In contrast, exam-\nples of high seed-transmission rates for viruses include 30% by\nsoybean mosaic virus (Domier et al. 2007), 25.3% for bean com-\nmon mosaic virus in bean cultivars (Mandour et al. 2013), and\n46.6% transmission of dolichos yellow mosaic virus in lablab\nbean (Suruthi et al. 2018). Seed transmission of viruses is influ-\nenced by many factors, including the host and cultivar, the\nisolate, environmental conditions, and timing of infection (Sastry\n2013a; Simmons and Munkvold 2014). Despite no transmission\ndetected for SCMV in Kenya (Kitira 2018), Li et al. (2007)\nreported 91 positive seed-transmitted cases out of 1,193 seed-\nlings for SCMV.\nMost seed transmission of viruses involves the presence of\nvirus in the embryo, leading to transmission of viruses to the\nnext progeny (Sastry 2013a). Cytoplasmic connections lead to\nmore embryo infections and to higher transmission of viruses\nfrom the seed; legumes have more cytoplasmic connections com-\npared with maize and, thus, have more seed-transmitted viruses\n(Sastry 2013a). The level of seed contamination may not be cor-\nrelated to the rate of seed transmission (Dombrovsky and Smith\n2017), as in the present study, in which we found lower seed\ntransmission in seed lots with higher seed contamination of\nMCMV and vice versa. The results are similar to findings with\nvarying levels of contamination by cowpea aphid borne mosaic\nvirus (CABMV). Increasing seed contamination levels at 0, 0.25,\nand 1% did not lead to an increase in the incidence of virus in\n31-day-old cowpea seedlings of cultivars with low ability to\ntransmit virus via seed (N\u0001eya et al. 2007). However, there was\nan increase in incidence when the contamination rate was 5%,\nand there was no significant difference in the incidence of the\nvirus when cultivars with high ability to transmit CABMV via\nseed were used. The presence of viruses on the seed coat or in\nthe endosperm is common for most viruses, but if the virus is\nabsent from the embryo, infection in seedlings was found to be\nrare (Johansen et al. 1994). The cultivars used in the present\nstudy are all highly susceptible to MCMV/MLN infection,\nexcept DK777, which is tolerant to MLN. The exact factors con-\ntributing to transmission of MCMV, even at extremely low rates,\nfrom contaminated seed to grow-out seedlings are still unknown.\nHowever, for some viruses it is possible for virus found on seed\nextract to infect seedlings, such as pepino mosaic virus infection\nof tomato and Nicotiana benthamiana seedlings (Ling 2008).\nConclusions\nMaize is grown extensively in Kenya. Even less than 0.1%\nseed transmission rate of MCMV, if not properly detected and\ncontrolled, could have significant implications. The seeding rate\nfor maize in Kenya is about 55,000 seeds per hectare; thus, a\nseed transmission rate of 0.1% would mean about 55 MCMV-\ninfected plants per hectare. These infected plants, if not detected\nin time and discarded, could serve as a source of MCMV inocu-\nlum. Further, favorable weather conditions and the presence of\nMCMV vectors, especially the corn thrips that are widely preva-\nlent in Kenya (Mahuku et al. 2015), could translate into signifi-\ncant infection across the field and ultimately decreased crop\nyield.\nUnderstanding the probable reasons and the effects of different\nlevels of contamination of maize seed by MCMV, and the impli-\ncations of such contamination to the next-generation seedlings\ngrown especially by the farmers, is important for effective man-\nagement of MLN. Despite the very low seed transmission rate\nobserved for MCMV recorded in the present study, the implica-\ntions of maize seed contamination by MCMV are still signifi-\ncant. Therefore, effective and continued implementation of\nvarious management options and continued implementation of\nphytosanitary measures in production of seeds that are free from\nviruses causing MLN are critical to reduce the spread and impact\nof MLN and overall to improve the production of maize.\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 500\n"
+    },
+    {
+      "page": 6,
+      "text": "Acknowledgments\nThis work was supported, in part, by the Bill & Melinda Gates Foun-\ndation\n(grant\nnumber:\nINV-006697/OPP1138693),\nproject\nname\n“Understanding and Preventing Seed Transmission of Maize Lethal\nNecrosis (MLN) in Africa”. Under the grant conditions of the Founda-\ntion, a Creative Commons Attribution 4.0 Generic License has already\nbeen assigned to the Author Accepted Manuscript version that might\narise from this submission. The work is also financially supported by\nthe CGIAR Research Program on Maize (MAIZE). MAIZE receives\nW1&W2 support from the Governments of Australia, Belgium, Canada,\nChina, France, India, Japan, Korea, Mexico, Netherlands, New Zealand,\nNorway, Sweden, Switzerland, the United Kingdom, the United States,\nand the World Bank. We also acknowledge Derrick A. Mayfield, who\nassisted with the laboratory activities at the Seed Science Center, Iowa\nState University. Henry Onzere and George Mosota provided immense\nsupport during planting of the seeds, harvesting, and grinding of leaf\nand seed samples at KALRO-Kabete.\nLiterature Cited\nAdams, I. P., Harju, V. A., Hodges, T., Hany, U., Skelton, A., Rai, S., Deka,\nM. K., Smith, J., Fox, A., Uzayisenga, B., Ngaboyisonga, C., Uwumukiza,\nB., Rutikanga, A., Rutherford, M., Ricthis, B., Phiri, N., and Boonham, N.\n2014. First report of maize lethal necrosis disease in Rwanda. New Dis.\nRep. 29:22.\nAdams, I. P., Miano, D. W., Kinyua, Z. M., Wangai, A., Kimani, E., Phiri,\nN., Reeder, R., Harju, V., Glover, R., Hany, U., Souza-Richards, R., Deb\nNath, P., Nixon, T., Fox, A., Barnes, A., Smith, J., Skelton, A., Thwaites,\nR., Mumford, R., and Boonham, N. 2013. Use of next-generation sequenc-\ning for the identification and characterization of Maize chlorotic mottle\nvirus and Sugarcane mosaic virus causing maize lethal necrosis in Kenya.\nPlant Pathol. 62:741-749.\nAlbrechtsen, S. E. 2006a. Group testing of seeds. Pages 231-234 in: Testing\nMethods for Seed-Transmitted Viruses: Principles and Protocols. CABI\nPublishing, Wallingford, U.K.\nAlbrechtsen, S. E. 2006b. Seed transmission of viruses. Page 13 in: Testing\nMethods for Seed-Transmitted Viruses: Principles and Protocols. CABI\nPublishing, Wallingford, U.K.\nBernardo, P., Redinbaugh, M. G., and Barriball, K. 2018. Understanding\nMaize chlorotic mottle virus transmission through seed: Localization and\ninfectivity. (Abstr.) Phytopathology 108:S1.245.\nBockelman, D. L., Claflin, L. E., and Uyemoto, J. K. 1982. Host range and\nseed transmission studies of maize chlorotic mottle virus in grasses and\ncorn. Plant Dis. 66:216-218.\nCabanas, D., Watanabe, S., Higashi, C. H. V., and Bressan, A. 2013. Dissecting\nthe mode of maize chlorotic mottle virus transmission (Tombusviridae:\nMachlomovirus) by Frankliniella williamsi (Thysanoptera: Thripidae).\nJ. Econ. Entomol. 106:16-24.\nChen, C. L., and Swallow, W. H. 1990. Using group testing to estimate a\nproportion, and to test the binomial model. Biometrics 46:1035-1046.\nCrowley, N. C. 1957. Studies on seed transmission of plant virus diseases.\nAust. J. Biol. Sci. 10:449-464.\nDe Groote, H., Oloo, F., Tongruksawattana, S., and Das, B. 2016.\nCommunity-survey based assessment of the geographic distribution and\nimpact of maize lethal necrosis (MLN) disease in Kenya. Crop Prot. 82:\n30-35.\nDombrovsky, A., and Smith, E. 2017. Seed transmission of Tobamoviruses:\nAspects of global disease distribution. In: Advances in Seed Biology. J. C.\nJimenez-Lopez, ed. IntechOpen, London, U.K.\nDomier, L. L., Steinlage, T. A., Hobbs, H. A., Wang, Y., Herrera-Rodriguez,\nG., Haudenshield, J. S., McCoppin, N. K., and Hartman, G. L. 2007.\nSimilarities in seed and aphid transmission among Soybean mosaic virus\nisolates. Plant Dis. 91:546-550.\nFAOSTAT. 2021. Maize production data for Kenya. Accessed 12 August\n2021. http://www.fao.org/faostat/en/#data/QCL.\nForster, R. L., Seifers, D. L., Strausbaugh, C. A., Jensen, S. G., Ball, E. M.,\nand Harvey, T. L. 2001. Seed transmission of the High plains virus in\nsweet corn. Plant Dis. 85:696-699.\nGoldberg, K.-B., and Brakke, M. K. 1987. Concentration of maize chlorotic\nmottle virus increased in mixed infection with maize dwarf mosaic virus,\nstrain B. Phytopathology 77:162-167.\nISTA. 2016. Sampling. Pages 2i–2-40 in: International Rules for Seed Test-\ning. International Seed Testing Association, Bassersdorf, Switzerland.\nJensen, S. G. 1985. Laboratory transmission of maize chlorotic mottle virus\nby three corn rootworms. Plant Dis. 69:864-868.\nJensen, S. G., Wysong, D. S., Ball, E. M., and Higley, P. M. 1991. Seed\ntransmission of maize chlorotic mottle virus. Plant Dis. 75:497-498.\nJiang, X. Q., Meinke, L. J., Wright, R. J., Wilkinson, D. R., and\nCampbell, J. E. 1992. Maize chlorotic mottle virus in Hawaiian-grown\nmaize: Vector relations, host range and associated viruses. Crop Prot.\n11:248-254.\nJohansen, E., Edwards, M. C., and Hampton, R. O. 1994. Seed transmission\nof viruses: Current perspectives. Annu. Rev. Phytopathol. 32:363-386.\nKitira, F. G. 2018. Determining the role of seed and soil in the transmission\nof viruses causing maize lethal necrosis disease. M.Sc. thesis. University\nof Nairobi, Nairobi, Kenya.\nKNBS. 2020. Economic Survey 2020. Kenya National Bureau of Statistics,\nNairobi, Kenya.\nLi, L., Wang, X., and Zhou, G. 2007. Analyses of maize embryo invasion by\nSugarcane mosaic virus. Plant Sci. 172:131-138.\nLing, K.-S. 2008. Pepino mosaic virus on tomato seed: Virus location and\nmechanical transmission. Plant Dis. 92:1701-1705.\nLiu, Z., Zhang, L., Yang, C., and Xia, X. 2016. Development of real-time\nreverse transcription PCR for detection of Maize chlorotic mottle virus\nbased on a novel molecular marker. Cogent Food Agric. 2:1224047.\nMahuku, G., Lockhart, B. E., Wanjala, B., Jones, M. W., Kimunye, J. N.,\nStewart, L. R., Cassone, B. J., Sevgan, S., Nyasani, J. O., Kusia, E.,\nKumar, P. L., Niblett, C. L., Kiggundu, A., Asea, G., Pappu, H. R.,\nWangai, A., Prasanna, B. M., and Redinbaugh, M. G. 2015. Maize lethal\nnecrosis (MLN), an emerging threat to maize-based food security in sub-\nSaharan Africa. Phytopathology 105:956-965.\nMandour, A. M., Abdelmaksoud, H. M. M., and El-Tahlawey, A. M. 2013.\nSeed transmission of bean common mosaic virus. Egypt. J. Agric. Res.\n91:403-409.\nMikel, M. A., D’Arcy, C. J., and Ford, R. E. 1984. Seed transmission of\nmaize dwarf mosaic virus in sweet corn. Phytopathol. Z. 110:185-191.\nMonger, W. A., Seal, S., Cotton, S., and Foster, G. D. 2001. Identification of\ndifferent isolates of Cassava brown streak virus and development of a\ndiagnostic test. Plant Pathol. 50:768-775.\nNelson, S., Brewbaker, J., and Hu, J. 2011. Maize chlorotic mottle. Plant\nDis. 79:1-6.\nN\u0001eya, B. J., Zabr\u0001e, J., Millogo, R. J., Ginko, S., and Konat\u0001e, G. 2007. Effect\nof cowpea seeds contamination rate by the Cowpea aphid borne mosaic\nvirus. Pak. J. Biol. Sci. 10:4365-4374.\nPrasanna, B. M., Suresh, L. M., Mwatuni, F., Beyene, Y., Makumbi, D.,\nGowda, M., Olsen, M., Hodson, D., Worku, M., Mezzalama, M., Molnar,\nT., Dhugga, K. S., Wangai, A., Gichuru, L., Angwenyi, S., Alemayehu,\nY., Grønbech Hansen, J., and Lassen, P. 2020. Maize lethal necrosis\n(MLN): Efforts toward containing the spread and impact of a devastating\ntransboundary disease in sub-Saharan Africa. Virus Res. 282:197943.\nRanum, P., Pe~na-Rosas, J. P., and Garcia-Casal, M. N. 2014. Global maize\nproduction, utilization, and consumption. Ann. N. Y. Acad. Sci. 1312:105-\n112.\nRedinbaugh, M. G., and Stewart, L. R. 2018. Maize lethal necrosis: An\nemerging, synergistic viral disease. Annu. Rev. Virol. 5:301-322.\nSastry, K. S. 2013a. Mechanism of seed transmission. Pages 85-95 in: Plant\nVirus and Viroid Diseases in the Tropics. Springer, Dordrecht, The\nNetherlands.\nSastry, K. S. 2013b. Identification and taxonomic groups, Pages 55-58 in:\nSeed-Borne Plant Virus Diseases. Springer, Dordrecht, The Netherlands.\nSergeant, E. S. G. 2018. Epitools Epidemiological calculators. https://\nepitools.ausvet.com.au/.\nShango, P., Onyango, C., Oganda, J. K., Macharia, I., and Kariuki, G.\n2019. Seed certification as a means of curbing emerging diseases: A case\nstudy of maize lethal necrosis disease in Kenya. J. Agric. Res. Adv. 1:\n39-43.\nSimmons, H., and Munkvold, G. 2014. Seed transmission in the Potyviridae,\nPages 3-15 in: Global Perspectives on the Health of Seeds and Plant\nPropagation Material.\nStewart, L. R., Willie, K., Wijeratne, S., Redinbaugh, M. G., Massawe, D.,\nNiblett, C. L., Kiggundu, A., and Asiimwe, T. 2017. Johnsongrass mosaic\nvirus contributes to maize lethal necrosis in East Africa. Plant Dis. 101:\n1455-1462.\nSui, X., Li, R., Shamimuzzaman, M., Wu, Z., and Ling, K.-S. 2019.\nUnderstanding the transmissibility of cucumber green mottle mosaic virus\nin watermelon seeds and seed health assays. Plant Dis. 103:1126-1131.\nSuruthi, V., Nakkeeran, S., Renukadevi, P., Malathi, V. G., and Rajasree, V.\n2018. Evidence of seed transmission of dolichos yellow mosaic virus, a\nbegomovirus infecting lablab-bean in India. VirusDis. 29:506-512.\nUyemoto, J. K., Bockelman, D. L., and Claflin, L. E. 1980. Severe outbreak\nof corn lethal necrosis disease in Kansas. Plant Dis. 64:99-100.\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 501\n"
+    },
+    {
+      "page": 7,
+      "text": "Wamaitha, M. J., Nigam, D., Maina, S., Stomeo, F., Wangai, A., Njuguna,\nJ. N., Holton, T. A., Wanjala, B. W., Wamalwa, M., Lucas, T., Djikeng,\nA., and Garcia-Ruiz, H. 2018. Metagenomic analysis of viruses associated\nwith maize lethal necrosis in Kenya. Virol. J. 15:90.\nWangai, A. W., Redinbaugh, M. G., Kinyua, Z. M., Miano, D. W., Leley,\nP. K., Kasina, M., Mahuku, G., Scheets, K., and Jeffers, D. 2012. First\nreport of Maize chlorotic mottle virus and maize lethal necrosis in Kenya.\nPlant Dis. 96:1582.\nWu, H. J., Qin, B. X., Chen, H. Y., Peng, B., Cai, J. H., and Gu, Q. S. 2010.\nThe rate of seed contamination and transmission of cucumber green\nmottle mosaic virus in watermelon and melon. Sci. Agric. Sin. 44:1527-\n1532.\nPLANT HEALTH PROGRESS\n\u0001 2021, Vol. 22, No. 4\n\u0001 Page 502\n"
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+      "text": "1\nVol.:(0123456789)\nScientific Reports |        (2022) 12:16043  \n| https://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports\nA suite of agronomic factors \ncan offset the effects of climate \nvariability on rainfed maize \nproduction in Kenya\nKevin Ong’are Oluoch1*, Hugo De Groote2, Zachary M. Gitonga2, Zhenong Jin3,4 & \nKyle Frankel Davis1,5\nAchieving food security in sub-Saharan Africa (SSA) is a multidimensional challenge. SSA reliance on \nfood imports is expected to grow in the coming decades to meet the population’s demand, projected \nto double to over 2 billion people by 2050. In addition, climate change is already affecting food \nproduction and supply chains across the region. Addressing these multiple food security challenges \nwill necessitate rapid enhancements in agricultural productivity, which is influenced by a host of \ndemographic, agronomic, and climatic factors. We use statistical approaches to examine rainfed \nmaize in Kenya, where maize cultivation and consumption are widespread and central to livelihoods \nand national food security. We find that improving a suite of agronomic factors, such as applying \nfertilizer, planting certified seeds, and extension services, will have a greater effect on rainfed maize \nproductivity than demographics and can offset the effects of climate change. These findings could also \noffer insights into similar challenges for other crops in Kenya and other SSA countries.\nSub-Saharan Africa (SSA) faces multiple food security challenges. Currently, 22% of the people living in SSA are \n­undernourished1, and the region relies on large amounts of food imports to meet local ­demand2. In addition, cli-\nmate change is already affecting food production and supply chains across ­SSA3,4. Compounding these issues, the \npopulation of SSA is projected to double from about 1 billion people currently to over 2 billion by ­20505, which \nwill make it home to one in four people ­globally6. Addressing current food security challenges while reducing \nimport reliance, meeting rising food demand, and coping with the effects of climate change will necessitate rapid \nenhancements in agricultural productivity across the SSA region, particularly for staple cereals, which constitute \n50% of the current average calorie intake in developing ­countries2,7,8. While past studies have demonstrated that \nmany parts of SSA do indeed possess an immense potential to increase cereal yields through improved access \nto irrigation, fertilizers, and other ­inputs9,10, a host of demographic, agronomic, and climatic factors converge \nto exercise influence on yield outcomes. Yet, these factors are rarely considered together in studies evaluating \nthe extent to which SSA farmers can feasibly enhance cereal yields. Such integrated considerations are essential \nin SSA where most farmers practice rainfed agriculture on relatively small family-owned plots—80% of farms \nare less than 2 ­hectares11—and grow a diversity of crops that serve multiple purposes, including supporting \nhousehold nutrition, diversifying their marketable goods, and mitigating drought ­risk12.\nThe demographics of SSA farmers differ from those of developed countries, where large-scale agriculture is \nmore widely practiced. Previous work has examined how specific demographic characteristics can influence yield \noutcomes for SSA ­farmers13,14. For instance, one recent study showed that labor supply(in hours per day) per \nworker—who are household members—is low in SSA because farmworkers engage in other economic activities \nor primarily work in another sector of the ­economy14. Consequently, smallholder farms often have lower per \ncapita income as compared to larger ­farms15, smallholder yield has been found to inversely correlated with farm \nsize in ­Kenya12,16. Other studies examining the relationship between yield and gender of smallholder farmers \nhave described how male farmers may achieve better yields outcomes than female farmers because cultural \nnorms give them better land ownership and farming management rights (to acquire inputs, labor, and extension \nOPEN\n1Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA. 2International Maize and \nWheat Improvement Center (CIMMYT), Nairobi, Kenya. 3Department of Bioproducts and Biosystems Engineering, \nUniversity of Minnesota - Twin Cities, Saint Paul, MN, USA. 4Institute on the Environment, University of Minnesota \n- Twin Cities, Saint Paul, MN, USA. 5Department of Geography and Spatial Sciences, University of Delaware, \nNewark, DE, USA. *email: oluoch@udel.edu\n"
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+    {
+      "page": 2,
+      "text": "2\nVol:.(1234567890)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\nservices)13,15,17. Other work on smallholder demographics found that educated farmers have better uptake of \nimproved ­technologies13,15. All this previous work indicates that the demographics of smallholder farmers are \nessential to consider in developing effective strategies to improve food production in SSA.\nClimate variability and change continue to disrupt rainfed agriculture in SSA primarily through changing \nrainfall patterns, rising annual temperatures, and increasing extreme ­events18. Extensive research has examined \nthe relationship between climatic factors and rainfed yield outcomes in SSA. While climate change-induced \nchanges in SSA’s suitability for maize (Zea mays) production will vary by agro-ecological zone, the overarching \ntrend across agro-ecological zones indicates declining ­yields19. Rainfed production of cereals in SSA is projected \nto increase due to the use of improved technologies, but potentially attainable yields are likely to be reduced \ndue to changing climatic ­conditions20. Heavier rain will also increase nitrogen leaching, leading to reduced \nplant uptake and lower yields in nitrogen-deficient -soils21. Other studies focused on the effects of temperature \nhave generally found a negative relationship between temperatures and crop ­productivity3,22,23. However, rising \ntemperatures may increase yields in certain regions (i.e., the Ethiopian highlands and the continent’s southern \nregion)22. Smallholder farmers in SSA are some of the most vulnerable to the impacts of climate variability and \nchange and identifying opportunities to increase their yields can improve their adaptive ­capacity24,25.\nSub-optimal agronomic factors have led to lower yields compared to the attainable rain-limited crop \n­yield10,13,26. This yield gap between actual and attainable crop yields is estimated to be up to 80% for certain \ncrops in SSA ­countries27. This under-productivity is primarily the result of low uptake of improved inputs such \nas certified seeds and ­fertilizer12,15,28,29, a lack of economic incentives, or services, and insufficient capital for \nsmallholder ­farmers25. Further, improved farm equipment are often designed with large farms in mind, unsuitable \nfor hilly and stony smallholder farms, and uneconomical except through rental schemes or farmers’ associations; \nas a result, their per capita ownership is also low in ­SSA30,42. Improving physical and economic access to these \ninputs and services is another vital component for enhancing smallholder productivity.\nAs evidenced above, a large body of work has sought to understand the relationship between smallholder \nyields and demographic, agronomic, or climatic factors in isolation. However, little work has evaluated the rela-\ntive importance of these three sets of factors together in ultimately determining rainfed yield outcomes. Here \nwe explore this knowledge gap by examining the case of rainfed maize in Kenya, where maize cultivation and \nconsumption is widespread, central to farmer livelihoods, and essential for national food security. We leverage \ndetailed, nationally representative farmer survey data for 2010 and 2013 to evaluate the relative importance \nof demographic, agronomic, and climatic factors in influencing maize yields. By examining the relative effect \nof these factors, this study aims to identify the factors that offer the greatest opportunity for improvements of \nrainfed maize yields in Kenya, to understand whether factors under farmers’ control can overcome the effects \nof climate variability on yields, and to draw broader inferences that are generalizable to similar challenges in \nrainfed production of other crops in Kenya and in other SSA countries.\nResults\nWe examined the significance and effect-size of each predictor variable within our three groupings: farmer’s \ndemographic information, agronomic factors, and climatic conditions (Fig. 1 and supplementary Table 3).\nModel coefficients for the farmer demographic variables had relatively small magnitudes, with farmer educa-\ntion having a significant (p = 0.0293) and modest relationship to maize yield. We find that one standard deviation \nFigure 1.   Effect sizes of variables with a significant relationship with normalized maize yield. The bars of \nsimilar color across each point (A) show the standard error. The effect sizes of climatic variables (B) with a \nsignificant relationship with normalized maize yield is non-linear because of the squared terms.\n"
+    },
+    {
+      "page": 3,
+      "text": "3\nVol.:(0123456789)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\nincrease in farmer’s education corresponded to a 0.049 unit increase in normalized maize yield. Gender, house-\nhold size, age, and farming experience showed no statistically significant association with maize yield.\nFarmer agronomic factors had the highest number of variables with a significant relationship with normal-\nized maize yield. Farmers who used fertilizer had a 0.362 unit increase in normalized maize yield, while those \nwho planted certified maize seeds were associated with 0.197 units higher productivity than non-certified maize \nseeds users. For every standard deviation increase in the maize plot size, normalized maize yield was reduced \nby 0.046—a confirmation of the inverse field size-yield ­relationship12,16. As the normalized time to the nearest \nmarket increased by a single unit, the normalized maize yield decreased slightly (− 0.048). At the same time, \nfarmers who had accessed extension services in the current or a previous season had increases of 0.116 and 0.195, \nrespectively, in normalized maize yield units. Access to credit, and distance to (government) extension services \ndid not significantly correlate with normalized maize yield (other factors, such as the use of certified seeds and \nthe application of fertilizer, through which access to credit and distance to extension services may impact maize \nyields may be influencing their effect-size and statistical significance).\nClimatic conditions showed significance in the associations of maximum temperature and normalized maize \nyield. The linear term of maximum temperature had the largest effect-size of 1.727. However, the squared maxi-\nmum temperature term had a negative effect of − 1.712, suggesting that the change we observed in normalized \nmaize yield due to maximum temperature has an optimal value beyond which temperature begins to adversely \nimpact yields. One standard deviation increase in annual precipitation linear term had a positive effect of 0.161, \nand the squared annual precipitation term had a marginally significant coefficient of 0.135.\nTo verify our results, we created models for each agroecological zone. Overall, the estimated coefficients \nwere consistent in size across all agroecological zones except the climatic variables, and the combined effect of \nagronomic factors had the most significant impact on normalized maize yield, followed by climatic conditions \nand, lastly, farmer demographics.\nDiscussion\nOur analysis provides new understanding of the relative importance of demographic, agronomic, and climatic \nfactors in influencing maize yields in Kenya and provides valuable insights into the ways in which these factors \nmay combine to determine yield outcomes for other crops and other countries. We find that agronomic factors \nhave a relatively high influence on yield compared to farmer demographics and offer the greatest opportunity \nfor improvements of rainfed maize yields and counteracting the effects of climatic factors. No single agronomic \nfactor has an effect-size large enough to offset the effects of climatic conditions, and adoption of agronomic \nfactors can be correlated—use of fertilizer and certified seed is an example (Table 1). Thus, a suite of agronomic \nfactors is necessary to improve smallholder yields while adapting to climate change (Fig. 2).\nWe show that farmers who plant certified seeds, apply fertilizer, or access extension services register higher \nmaize yields; however, due to lack of data, we did not study how the maize yield would vary depending on the \ntype of certified seed planted, how the farmer applied fertilizer, or what extension services they accessed. Efforts \nTable 1.   Correlation matrix of all variables.\nFarmer’s age\nFarmer’s \neducation-\nyears\nFarming \nexperience in \nyears\nHousehold size\nFarmer’s \nmarital status\nFarmer’s \ngender\nCredit \nservices\nPlanted \ncertified \nseeds\nAgricultural \nextension \n(Current)\nAgricultural \nextension \n(Previous)\nUsed fertilizer\nMaize plot \nsize\nDistance to \nextension \nservices\nTime to \nmarket\nMaximum \ntemperature\nPrecipitation\nFarmer’s Age\n1\nFarmer’s \nEducation-\nYears\n− 0.361\n1\nFarming \nExperience in \nYears\n0.7173\n − 0.3217\n1\nHousehold \nSize\n− 0.0306\n0.0352\n− 0.0277\n1\nFarmer’s Mari-\ntal Status\n0.1644\n− 0.316\n0.1924\n− 0.152\n1\nFarmer’s \nGender\n− 0.1229\n0.3271\n− 0.1795\n0.1141\n− 0.8303\n1\nCredit \nServices\n− 0.0214\n0.1285\n− 0.0294\n0.043\n− 0.0478\n0.0162\n1\nPlanted Certi-\nfied Seeds\n− 0.0473\n0.1811\n− 0.0352\n0.0085\n− 0.1044\n0.1021\n0.0398\n1\nAgricultural \nExtension \n(Current)\n0.0432\n0.0935\n0.0122\n0.0247\n− 0.0486\n0.0278\n0.1138\n0.139\n1\nAgricultural \nExtension \n(Previous)\n0.0414\n− 0.0177\n0.0662\n− 0.0217\n− 0.0121\n0.0198\n− 0.0361\n0.0457\n0.0859\n1\nUsed Fertilizer\n− 0.0174\n0.1842\n− 0.0263\n− 0.0739\n− 0.044\n0.0614\n0.0436\n0.3948\n0.0428\n− 0.0061\n1\nMaize Plot \nSize\n0.0743\n0.0686\n0.0475\n0.1494\n− 0.0167\n0.0098\n0.035\n0.0212\n0.0737\n0.0414\n− 0.0823\n1\nDistance to \nExtension \nservices\n− 0.0257\n− 0.0364\n− 0.0452\n0.0479\n0.0061\n0.0029\n− 0.0274\n− 0.0551\n− 0.0035\n− 0.0743\n− 0.1128\n0.059\n1\nTime to \nMarket\n0.0081\n− 0.0402\n0.019\n0.0054\n0.0321\n− 0.0334\n− 0.0556\n− 0.0548\n0.0097\n0.025\n��� 0.0855\n0.0326\n0.0761\n1\nMaximum \nTemperature\n− 0.015\n− 0.1055\n− 0.0549\n0.1853\n− 0.006\n3.00E− 04\n− 0.0154\n− 0.175\n0.0539\n− 0.0152\n− 0.2809\n0.1187\n0.0255\n0.0546\n1\nPrecipitation\n− 0.0769\n0.0465\n− 0.0816\n0.0245\n0.003\n− 0.003\n− 0.0211\n0.1579\n− 0.0092\n− 0.0424\n0.2949\n− 0.123\n0.0142\n− 0.0635\n− 0.4148\n1\n"
+    },
+    {
+      "page": 4,
+      "text": "4\nVol:.(1234567890)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\naiming to increase yields to feed the growing SSA population and mitigate the effects of climate variability and \nchange should focus on the factors that can most enhance productivity. By comparing the relative effect sizes of \neach demographic, agronomic, and climatic factor, we show that the mean relative effect-size of agronomic fac-\ntors is less than that of climatic factors, providing evidence that a suite of agronomic factors is needed to offset \nthe effects of climate variability, consistent with other ­studies10,31–35.\nUnlike climatic factors over which farmers have no control, agronomic factors are the set of factors most \nat agency of SSA farmers. Consequently, efforts to close maize yield gaps—and by extension, the yield gaps for \nother staple crops—should prioritize targeted improvements in agronomic factors. In particular, our findings \nprovide evidence that faster access to markets, providing extension services, planting certified seeds and applying \nfertilizer can have an immediate and positive effect on yield outcomes. We also confirm that smallholder farmers \nwith smaller fields tend to be more productive than those with larger fields, as smallholder farmer’s precision \nin implementing agronomic factors relates inversely to farm ­size16,30. We note though that our findings do not \ndownplay the effect of demographics in potentially improving yields. We find that farmers with higher levels of \neducation tend to have better yield outcomes, which can potentially be attributed to their increased awareness of \nfarming factors and market ­dynamics15. Other farmer demographics, such as gender, have been shown elsewhere \nto affect ­yield17 despite not having a significant relationship with yield in this study.\nWhile further research is needed to understand interactions of demographic, agronomic, and climatic factors \nin determining smallholder yields, our study points to multiple opportunities for holistic approaches to improve \nfarmer productivity and to offset adverse impacts of climate change and variability. While our findings are based \non rainfed maize cultivation in Kenya, we provide a generalizable methodology and set of results that can be \napplied to other crops and countries in SSA. Though smallholder options for improved productivity differ based \non the crop of interest, government policies, agroecological environments, and climatic conditions, our findings \nindicate that improved agronomic factors can play an important role in addressing the overarching challenges \nof population growth, yield gaps, and changing climate.\nMethods\nWe evaluated the sensitivity of rainfed maize yields in Kenya to demographic, agronomic, and climatic factors \nusing a linear model. We developed the linear model with farmer survey data collected in two calendar years \n(2010, 2013) covering six agro-ecological zones and thirty-two counties.\nStudy area. \nThe study focused on the main maize growing areas in Kenya. Kenya is in East Africa and has \nforty-seven counties (i.e., level-one administrative units). As of 2010, The forty-seven counties were districts \nunder the Kenyan national government. After the March 2013 general elections, the districts’ restructuring into \ncounties was completed in line with a devolved system of government outlined in a new Kenya constitution \npassed in 2010 (for consistency, the word counties is used even for 2010). The study covered thirty-two counties \nacross the southern part of Kenya. In 2010, these thirty-two counties accounted for 91% of total maize produc-\ntion (Fig. 3) and covered 92% of the total area under rainfed maize ­production36.\nSurvey: data collection, processing, and cleaning. \nThe International Maize and Wheat Improve-\nment Centre (CIMMYT), in collaboration with the Kenya Agricultural and Livestock Research Organization \n(KALRO), conducted nationally representative household surveys in the major maize growing areas. The data \nand the surveys have been previously used to analyze trends in ­mechanization37 and fertilizer ­use33.\nThe 2010 and 2013 surveys used a two-stage stratified design with six maize agro-ecological zones (AEZs) as \n­strata31, census clusters or sublocations as primary sampling units, and maize growing households as secondary \nsampling units. The first survey, done in 2010, covered 120 sublocations with 1344 households. The second sur-\nvey, done in 2013, interviewed the same farmers with a 20% replacement with randomly sampled ­households34.\nCIMMYT- Nairobi and KALRO surveyed the farmers per the guidelines of the Declaration of Helsinki, and \nthe data was provided for this study by CIMMYT-Nairobi, to the authors, after signing a confidentiality agree-\nment. The farmers provided all information in the survey after being taken through and signing a consent form. \nFigure 2.   Change in maize yield under selected agronomic factors. Lines show the changes in normalized \nmaize yield associated with a standard deviation increase in maximum temperature in the presence of different \ncombinations of agronomic factors.\n"
+    },
+    {
+      "page": 5,
+      "text": "5\nVol.:(0123456789)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\nWe combined data sets from the two surveys and removed personal identifying information and household \nobservations with missing entries. The combined cleaned data for the two years had 17 variables covering 2197 \nhouseholds -1099 in 2010 and 1098 in 2013- in 32 counties (Supplementary Tables 1 and 2).\nThe 17 variables include one target variable (maize yield in kg ­ha−1) and sixteen independent variables. We \norganized the independent variables into three broad groups: farmer’s demographic information, agronomic fac-\ntors, and climatic conditions. There were six variables on farmer demographics: gender, marital status, age, size of \nhousehold, years in farming, and years of education; eight variables on agronomic factors: area under maize, use \nof certified maize seeds, use of fertilizer, access to extension services (in current and previous seasons), distance \nto the nearest extension service center, access to credit services, and, time to the nearest market; and two on \nclimatic conditions (i.e., variables largely out of a farmer’s control): maximum temperature (at 2 m height), and \ntotal precipitation in the growing season. The original dataset also included a minimum temperature variable, \nFigure 3.   Maize cultivation and survey counties. Gridded maize yield data (the year 2010) came from IFPRI 36. \nThe 32 counties covered by the survey are highlighted in light grey with dark brown boundaries, while the other \ncounties have dashed boundaries. Generated using ggplot2 package (version 3.3.5) R version 4.1.2 (Rstudio \nversion 2022.02.0 + 443 in windows 10).\n"
+    },
+    {
+      "page": 6,
+      "text": "6\nVol:.(1234567890)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\nwhich we excluded from the analysis, as it provided near-identical information to maximum temperature in \nexplaining yield variability. We mapped the spatial distributions of the independent variables to identify spatial \ntrends in the data (Supplementary Figs. 1–30).\nWe transformed each independent variable to the appropriate data structure before using them in the linear \nmodel. First, we added the squared terms of the climate variables (maximum temperature and precipitation) \nas new variables because of the non-linear relationship between climate and yield. Then, we set all binary vari-\nables (Table 2) as dummy variables with their absence as the reference value, set marital status as a categorical \nvariable of three levels, and normalized the numeric variables. We normalized numeric variables to remove the \neffects of measurement units in two steps: subtracting the mean from each observation and dividing this differ-\nence by the standard deviation. Before normalizing, the climate variables were divided into groups based on the \nagroecological zones, and each group normalized separately. Lastly, we calculated the variance inflation factor \n(VIF) to eliminate colinear independent variables based on a standard VIF threshold of five. The VIF value of \nall variables was less than the threshold, so we did not remove any.\nLinear model. \nUsing the normalized data, we developed a linear model to examine how sensitive maize \nyield (kg ­ha−1) was to farmers’ demographic information, agronomic factors, and climatic conditions ­following38 \n­and39. We computed the linear model and performed the data normalization and VIF calculation steps described \nin  \"Survey: data collection, processing, and cleaning\" section in R using functions from stats, fmsb, and R base \npackages. We organized the developed functions as a new R package and anonymized the data used in this study \nbefore including it in the R package, which we called “yieldest.”The R package is publicly available on ­GitHub40 \nand Harvard ­Dataverse41. Using normalized maize yield as the target variable and the normalized independent \nvariables, we could compare the magnitude of coefficients (i.e., effect size) in the linear model to evaluate the \nrelative influence of corresponding predictor variables in determining rainfed maize yields.\nEquipment and settings. \nWe generated all figures in this manuscript using ggplot2 package R version \n4.1.2 (Rstudio version 2022.02.0 + 443 in windows 10) with data from our results and gridded maize yield data \n(the year 2010) ­from35.\nData availability\nThe datasets generated and/or analyzed during the current study are available in the Harvard Dataverse reposi-\ntory, https://​doi.​org/​10.​7910/​DVN/​UIWQQH. Contact corresponding author for data requests.\nReceived: 15 February 2022; Accepted: 26 August 2022\nReferences\n\t 1.\t FAO, IFAD, UNICEF, WFP & WHO. The state of food security and nutrition in the world 2020. FAO, IFAD, UNICEF, WFP and \nWHO. https://​doi.​org/​10.​4060/​ca969​2en (2020).\n\t 2.\t FAOSTAT, F. FAOSTAT Statistical Database. FAO (2022).\n\t 3.\t Chapman, S. et al. Impact of climate change on crop suitability in Sub-Saharan Africa in parameterized and convection-permitting \nregional climate models. Environ. Res. Lett. 15, 094086. https://​doi.​org/​10.​1088/​1748-​9326/​ab9daf (2020).\nTable 2.   Summary of study data characteristics after standardization.\nVariable\nCategory\nUnit\nMinimum\nMaximum\nMedian\nSD (Standard Deviation)\nFarmer’s age\nFarmers’ demographics\nYears\n− 2.31\n3.17\n− 0.02\n1\nFarmer’s education\nFarmers’ demographics\nYears of schooling\n− 1.68\n4.07\n0\n1\nFarmer’s experience\nFarmers’ demographics\nYears\n− 1.73\n3.52\n− 0.12\n1\nFarmer’s household size\nFarmers’ demographics\nPersons\n− 2.01\n5.51\n− 0.13\n1\nFarmer’s Relationship status\nFarmers’ demographics\n(Nominal scale: 0 to 3)\n0\n3\n0\n0.81\nFarmer’s gender\nFarmers’ demographics\n(Binary: 0 for female and 1 for male)\n0\n1\n1\n0.4\nThe farmer has access to credit\nFarmers’ agronomic factors\n(Binary: 0 for false and 1 for true)\n0\n1\n1\n0.5\nThe farmer planted certified seeds\nFarmers’ agronomic factors\n(Binary: 0 for false and 1 for true)\n0\n1\n1\n0.44\nAgricultural extension-current season\nFarmers’ agronomic factors\n(Binary: 0 for false and 1 for true)\n0\n1\n0\n0.5\nAgricultural extension-previous season\nFarmers’ agronomic factors\n(Binary: 0 for false and 1 for true)\n0\n1\n1\n0.24\nThe farmer used fertilizer\nFarmers’ agronomic factors\n(Binary: 0 for False and 1 for True)\n0\n1\n1\n0.5\nSize of plot under maize\nFarmers’ agronomic factors\nHectares\n− 0.68\n20.68\n− 0.29\n1\nDistance from the farm to the extension \nservices\nFarmers’ agronomic factors\nKilometers\n− 0.83\n12.34\n− 0.29\n1\nTime of travel from the farm to the \nmarket\nFarmers’ agronomic factors\nMinutes\n− 0.8\n17.59\n− 0.19\n1\nMaximum temperature in the maize \nfarm’s location (growing season)\nFarmers’ climatic conditions\nDegree Celsius\n− 3.1\n2.04\n0.24\n1\nTotal precipitation (growing season)\nFarmers’ climatic conditions\nMillimeters\n− 1.23\n5.96\n− 0.05\n1\n"
+    },
+    {
+      "page": 7,
+      "text": "7\nVol.:(0123456789)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\n\t 4.\t Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2, 54–65. https://​\ndoi.​org/​10.​1038/​s43016-​020-​00196-3 (2021).\n\t 5.\t UN-DESA. World population prospects 2019: Data booklet. UN. https://​doi.​org/​10.​18356/​3e9d8​69f-​en (2019).\n\t 6.\t Suzuki, E. World’s population will continue to grow and will reach nearly 10 billion by 2050. https://​blogs.​world​bank.​org/​opend​\nata/​worlds-​popul​ation-​will-​conti​nue-​grow-​and-​will-​reach-​nearly-​10-​billi​on-​2050 (2019).\n\t 7.\t Harrison, P. (ed.) World Agriculture: Towards 2015/2030: Summary Report (Food and Agriculture Organization of the United \nNations, 2002).\n\t 8.\t Wei, D. & Davis, K. F. Culturally appropriate shifts in staple grain consumption can improve multiple sustainability outcomes. \nEnviron. Res. Lett. https://​doi.​org/​10.​1088/​1748-​9326/​ac32fc (2021).\n\t 9.\t Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257. https://​doi.​org/​10.​1038/​\nnatur​e11420 (2012).\n\t10.\t van Ittersum, M. K. et al. Can Sub-Saharan Africa feed itself?. Proc. Natl. Acad. Sci. 113, 14964. https://​doi.​org/​10.​1073/​pnas.​16103​\n59113 (2016).\n\t11.\t Lowder, S. K., Skoet, J. & Raney, T. The number, size, and distribution of farms, smallholder farms, and family farms worldwide. \nWorld Dev. 87, 16–29. https://​doi.​org/​10.​1016/j.​world​dev.​2015.​10.​041 (2016).\n\t12.\t Ricciardi, V., Mehrabi, Z., Wittman, H., James, D. & Ramankutty, N. Higher yields and more biodiversity on smaller farms. Nat. \nSustain. 4, 651–657. https://​doi.​org/​10.​1038/​s41893-​021-​00699-2 (2021).\n\t13.\t Assefa, B. T., Chamberlin, J., Reidsma, P., Silva, J. V. & van Ittersum, M. K. Unravelling the variability and causes of smallholder \nmaize yield gaps in Ethiopia. Food Secur. 12, 83–103. https://​doi.​org/​10.​1007/​s12571-​019-​00981-4 (2020).\n\t14.\t McCullough, E. B. Labor productivity and employment gaps in Sub-Saharan Africa. Food Policy 67, 133–152. https://​doi.​org/​10.​\n1016/j.​foodp​ol.​2016.​09.​013 (2017).\n\t15.\t Jayne, T. S. et al. Are medium-scale farms driving agricultural transformation in Sub-Saharan Africa?. Agric. Econ. 50, 75–95. \nhttps://​doi.​org/​10.​1111/​agec.​12535 (2019).\n\t16.\t Muyanga, M. & Jayne, T. S. Revisiting the farm size-productivity relationship based on a relatively wide range of farm sizes: Evidence \nfrom Kenya. Am. J. Agr. Econ. 101, 1140–1163. https://​doi.​org/​10.​1093/​ajae/​aaz003 (2019).\n\t17.\t Slavchevska, V., Doss, C. R., de la O Campos, A. P. & Brunelli, C. Beyond ownership: Women’s and men’s land rights in Sub-Saharan \nAfrica Oxford Dev. Stud. 49, 2–22. https://​doi.​org/​10.​1080/​13600​818.​2020.​18187​14 (2021).\n\t18.\t Derrick Ngoran, S., Dogah, K. & XiongZhi, X. Assessing the impacts of climate change on water resources: The Sub-Saharan Africa \nperspective. J. Econ. Sustain. Dev. 6(1), 185–193 (2015).\n\t19.\t Ramirez-Cabral, N. Y. Z., Kumar, L. & Shabani, F. Global alterations in areas of suitability for maize production from climate change \nand using a mechanistic species distribution model (CLIMEX). Sci. Rep. 7, 5910. https://​doi.​org/​10.​1038/​s41598-​017-​05804-0 \n(2017).\n\t20.\t Blanc, E. The Impact of Climate Change on Crop Yields in Sub-Saharan Africa (Scientific Research Publishing, 2012).\n\t21.\t Falconnier, G. N. et al. Modelling climate change impacts on maize yields under low nitrogen input conditions in Sub-Saharan \nAfrica. Glob. Change Biol. 26, 5942–5964. https://​doi.​org/​10.​1111/​gcb.​15261 (2020).\n\t22.\t Dale, A., Fant, C., Strzepek, K., Lickley, M. & Solomon, S. Climate model uncertainty in impact assessments for agriculture: A \nmulti-ensemble case study on maize in Sub-Saharan Africa. Earth’s Future 5, 337–353. https://​doi.​org/​10.​1002/​2017E​F0005​39 \n(2017).\n\t23.\t Talib, M. N. A., Ahmed, M., Naseer, M. M., Slusarczyk, B. & Popp, J. The long-run impacts of temperature and rainfall on agricul-\ntural growth in Sub-Saharan Africa. Sustainability 13, 595. https://​doi.​org/​10.​3390/​su130​20595 (2021).\n\t24.\t Campbell, B. M., Thornton, P., Zougmoré, R., van Asten, P. & Lipper, L. Sustainable intensification: What is its role in climate smart \nagriculture?. Curr. Opin. Environ. Sustain. 8, 39–43. https://​doi.​org/​10.​1016/j.​cosust.​2014.​07.​002 (2014).\n\t25.\t Stuch, B., Alcamo, J. & Schaldach, R. Projected climate change impacts on mean and year-to-year variability of yield of key small-\nholder crops in Sub-Saharan Africa. Clim. Dev. 13, 268–282. https://​doi.​org/​10.​1080/​17565​529.​2020.​17607​71 (2021).\n\t26.\t Djurfeldt, G. et al. Using panel survey and remote sensing data to explain yield gaps for maize in Sub-Saharan Africa. J. Land Use \nSci. 13, 344–357. https://​doi.​org/​10.​1080/​17474​23X.​2018.​15117​63 (2018).\n\t27.\t GYGA. Global yield gap atlas. www.​yield​gap.​org/​home (2021).\n\t28.\t Kihara, J., Tamene, L. D., Massawe, P. & Bekunda, M. Agronomic survey to assess crop yield, controlling factors and management \nimplications: A case-study of Babati in Northern Tanzania. Nutr. Cycl. Agroecosyst. 102, 5–16. https://​doi.​org/​10.​1007/​s10705-​\n014-​9648-3 (2015).\n\t29.\t Senthilkumar, K. et al. Quantifying rice yield gaps and their causes in Eastern and Southern Africa. J. Agron. Crop Sci. 206, 478–490. \nhttps://​doi.​org/​10.​1111/​jac.​12417 (2020).\n\t30.\t Sheahan, M., Barrett, C. B. & Sheahan, M. B. Understanding the agricultural input landscape in Sub-Saharan Africa: Recent plot, \nhousehold, and community-level evidence. SSRN Scholarly Paper, Social Science Research Network, Rochester, NY. https://​papers.​\nssrn.​com/​abstr​act=​24876​12 (2014).\n\t31.\t Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people. Science 327, 812. https://​doi.​org/​10.​1126/​scien​ce.​\n11853​83 (2010).\n\t32.\t Hassan, R.M., Njoroge, K., Njore, M., Otsyula, R. & Laboso, A. Adoption patterns and performance of improved maize in Kenya. \nMaize technology development and transfer: A GIS application for research planning in Kenya. Cab International 107–136 (1998).\n\t33.\t Jena, P. R., De Groote, H., Nayak, B. P. & Hittmeyer, A. Evolution of fertiliser use and its impact on maize productivity in Kenya: \nEvidence from multiple surveys. Food Secur. 13, 95–111 (2021).\n\t34.\t Ogada, M. J. & Nyangena, W. Complementarity of inorganic fertilizers and improved maize varieties and farmer efficiency in maize \nproduction in Kenya. Afr. Rev. Econ. Finance 11, 76–100 (2019).\n\t35.\t Wainaina, P., Tongruksawattana, S. & Qaim, M. Tradeoffs and complementarities in the adoption of improved seeds, fertilizer, and \nnatural resource management technologies in Kenya. Agric. Econ. 47, 351–362 (2016).\n\t36.\t IFPRI. Global spatially-disaggregated crop production statistics data for 2010 version 2.0. International Food Policy Research \nInstitute, Ed., Harvard Dataverse. https://​doi.​org/​10.​7910/​DVN/​PRFF8V (2019).\n\t37.\t De Groote, H., Marangu, C. & Gitonga, Z. Evolution of agricultural mechanization in Kenya 401–422 (2020).\n\t38.\t DeFries, R., Mondal, P., Singh, D., Agrawal, I., Fanzo, J., Remans, R. & Wood, S. Synergies and trade-offs for sustainable agriculture: \nNutritional yields and climate-resilience for cereal crops in central India. In 2nd International Global Food Security Conference \n44–53. https://​doi.​org/​10.​1016/j.​gfs.​2016.​07.​001 (2016).\n\t39.\t Davis, K. F., Chhatre, A., Rao, N. D., Singh, D. & DeFries, R. Sensitivity of grain yields to historical climate variability in India. \nEnviron. Res. Lett. 14, 064013. https://​doi.​org/​10.​1088/​1748-​9326/​ab22db (2019).\n\t40.\t Ong’are Oluoch, K. R Package used in the analysis: Can a suite of agronomic factors offset the effects of climate change and vari-\nability on rainfed maize production? New evidence from Kenya. https://​github.​com/​Kevin​Oluoch/​yield​est (2022).\n\t41.\t Oluoch, K. O. Replication data for: Can a suite of agronomic factors offset the effects of climate change and variability on rainfed \nmaize production? New evidence from Kenya. Harvard Dataverse. https://​doi.​org/​10.​7910/​DVN/​UIWQQH (2022)\n\t42.\t Makini, F., Mose, L., Kamau, G., Wawire, N., Salasya, B., Mulinge, W., Makelo, M. & Thuranira, E. Mechanization and skills devel-\nopment for productivity growth, employment and value addition: Insights from KENYA.\n"
+    },
+    {
+      "page": 8,
+      "text": "8\nVol:.(1234567890)\nScientific Reports |        (2022) 12:16043  | \nhttps://doi.org/10.1038/s41598-022-19286-2\nwww.nature.com/scientificreports/\nAcknowledgements\nThis work was supported in part by the University of Delaware General University Research fund; and Borel \nGlobal Fellowship.\nAuthor contributions\nK.O.O., Z.J., and K.F.D. conceived the study, while H.G. and Z.M.G. contributed the data used in the study. K.O.O. \ndid the analysis, prepared the manuscript template, and prepared the tables and figures. H.G., Z.M.G., Z.J., and \nK.F.D. provided text for different sections. All authors edited and reviewed the manuscript.\nCompeting interests \nThe authors declare no competing interests.\nAdditional information\nSupplementary Information The online version contains supplementary material available at https://​doi.​org/​\n10.​1038/​s41598-​022-​19286-2.\nCorrespondence and requests for materials should be addressed to K.O.O.\nReprints and permissions information is available at www.nature.com/reprints.\nPublisher’s note  Springer Nature remains neutral with regard to jurisdictional claims in published maps and \ninstitutional affiliations.\nOpen Access   This article is licensed under a Creative Commons Attribution 4.0 International \nLicense, which permits use, sharing, adaptation, distribution and reproduction in any medium or \nformat, as long as you give appropriate credit to the original author(s) and the source, provide a link to the \nCreative Commons licence, and indicate if changes were made. The images or other third party material in this \narticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the \nmaterial. If material is not included in the article’s Creative Commons licence and your intended use is not \npermitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from \nthe copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.\n© The Author(s) 2022\n"
+    }
+  ]
+}

chatbot/pipeline/embed.py

@@ -0,0 +1,86 @@
+"""
+Embed wiki and PDF chunks and store them in two separate Chroma collections.
+
+Collections:
+  - "wiki"  : primary retrieval layer (wiki pages)
+  - "pdfs"  : secondary/fallback retrieval layer (raw PDF text)
+
+Embedding model: all-MiniLM-L6-v2 (local, no API key needed, fast on CPU)
+"""
+
+import json
+from pathlib import Path
+
+import chromadb
+from sentence_transformers import SentenceTransformer
+
+CHUNKS_DIR = Path(__file__).parent.parent / "chunks"
+DB_DIR = Path(__file__).parent.parent / "db"
+DB_DIR.mkdir(exist_ok=True)
+
+MODEL_NAME = "all-MiniLM-L6-v2"
+BATCH_SIZE = 64
+
+
+def load_chunks(path: Path) -> list[dict]:
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def build_collection(client: chromadb.Client, name: str, chunks: list[dict], model: SentenceTransformer):
+    # Delete and recreate for a clean build
+    try:
+        client.delete_collection(name)
+    except Exception:
+        pass
+    collection = client.create_collection(name, metadata={"hnsw:space": "cosine"})
+
+    texts = [c["text"] for c in chunks]
+    ids = [f"{name}_{i}" for i in range(len(chunks))]
+
+    metadatas = []
+    for c in chunks:
+        meta = {
+            "source": c.get("source", ""),
+            "layer": c.get("layer", name),
+            "page": str(c.get("page", 0)),
+        }
+        if "title" in c:
+            meta["title"] = c["title"]
+        if "section" in c:
+            meta["section"] = c["section"]
+        if "page_type" in c:
+            meta["page_type"] = c["page_type"]
+        metadatas.append(meta)
+
+    print(f"  Embedding {len(texts)} chunks in batches of {BATCH_SIZE}...")
+    for start in range(0, len(texts), BATCH_SIZE):
+        batch_texts = texts[start:start + BATCH_SIZE]
+        batch_ids = ids[start:start + BATCH_SIZE]
+        batch_meta = metadatas[start:start + BATCH_SIZE]
+        embeddings = model.encode(batch_texts, show_progress_bar=False).tolist()
+        collection.add(documents=batch_texts, embeddings=embeddings, ids=batch_ids, metadatas=batch_meta)
+        print(f"    {min(start + BATCH_SIZE, len(texts))}/{len(texts)}")
+
+    print(f"  Collection '{name}': {collection.count()} documents stored")
+    return collection
+
+
+def main():
+    print(f"Loading embedding model: {MODEL_NAME}")
+    model = SentenceTransformer(MODEL_NAME)
+
+    client = chromadb.PersistentClient(path=str(DB_DIR))
+
+    print("\n[1/2] Building wiki collection...")
+    wiki_chunks = load_chunks(CHUNKS_DIR / "wiki_chunks.json")
+    build_collection(client, "wiki", wiki_chunks, model)
+
+    print("\n[2/2] Building PDF collection...")
+    pdf_chunks = load_chunks(CHUNKS_DIR / "pdf_chunks.json")
+    build_collection(client, "pdfs", pdf_chunks, model)
+
+    print("\nDone. Vector store saved to:", DB_DIR)
+
+
+if __name__ == "__main__":
+    main()

chatbot/retriever.py

@@ -0,0 +1,92 @@
+"""
+Tiered retrieval: query wiki first; fall back to PDF chunks if wiki confidence is low.
+
+Chroma uses cosine distance (lower = more similar, range 0–1).
+WIKI_FALLBACK_THRESHOLD: if the best wiki result has distance > this value,
+also pull results from the PDF collection.
+"""
+
+from dataclasses import dataclass
+from pathlib import Path
+
+import chromadb
+from sentence_transformers import SentenceTransformer
+
+DB_DIR = Path(__file__).parent / "db"
+MODEL_NAME = "all-MiniLM-L6-v2"
+
+WIKI_FALLBACK_THRESHOLD = 0.45   # cosine distance; above this = low wiki confidence
+N_WIKI = 5                        # top-k from wiki
+N_PDF = 4                         # top-k from PDFs when fallback triggers
+
+
+@dataclass
+class Chunk:
+    text: str
+    source: str
+    layer: str          # "wiki" or "pdf"
+    distance: float
+    title: str = ""
+    section: str = ""
+    page: int = 0
+    page_type: str = ""
+
+    def citation(self) -> str:
+        if self.layer == "wiki":
+            label = self.title or self.source
+            if self.section:
+                return f"{label} — {self.section}"
+            return label
+        else:
+            page_str = f" (p.{self.page})" if self.page else ""
+            return f"{self.source}{page_str}"
+
+
+class Retriever:
+    def __init__(self):
+        self._model = SentenceTransformer(MODEL_NAME)
+        self._client = chromadb.PersistentClient(path=str(DB_DIR))
+        self._wiki = self._client.get_collection("wiki")
+        self._pdfs = self._client.get_collection("pdfs")
+
+    def _query(self, collection, query: str, n: int) -> list[Chunk]:
+        embedding = self._model.encode([query]).tolist()
+        results = collection.query(
+            query_embeddings=embedding,
+            n_results=n,
+            include=["documents", "metadatas", "distances"],
+        )
+        chunks = []
+        for doc, meta, dist in zip(
+            results["documents"][0],
+            results["metadatas"][0],
+            results["distances"][0],
+        ):
+            chunks.append(Chunk(
+                text=doc,
+                source=meta.get("source", ""),
+                layer=meta.get("layer", "unknown"),
+                distance=dist,
+                title=meta.get("title", ""),
+                section=meta.get("section", ""),
+                page=int(meta.get("page", 0)),
+                page_type=meta.get("page_type", ""),
+            ))
+        return chunks
+
+    def retrieve(self, query: str) -> tuple[list[Chunk], bool]:
+        """
+        Returns (chunks, pdf_fallback_triggered).
+        Always includes wiki results. Appends PDF results when best wiki
+        distance exceeds WIKI_FALLBACK_THRESHOLD.
+        """
+        wiki_chunks = self._query(self._wiki, query, N_WIKI)
+
+        best_wiki_distance = wiki_chunks[0].distance if wiki_chunks else 1.0
+        use_pdf_fallback = best_wiki_distance > WIKI_FALLBACK_THRESHOLD
+
+        pdf_chunks = []
+        if use_pdf_fallback:
+            pdf_chunks = self._query(self._pdfs, query, N_PDF)
+
+        return wiki_chunks + pdf_chunks, use_pdf_fallback

requirements.txt

@@ -0,0 +1,7 @@
+streamlit==1.57.0
+anthropic>=0.99.0
+python-dotenv>=1.0.0
+sentence-transformers>=3.0.0
+chromadb>=1.5.0
+tiktoken>=0.13.0
+torch>=2.0.0