license: cc-by-4.0
language:
- en
task_categories:
- text-generation
- question-answering
tags:
- agriculture
- korean-natural-farming
- knf
- regenerative-agriculture
- tropical-farming
- smallholder
- philippines
- instruction-tuning
pretty_name: ROOTMODEL KNF Philippines v1
size_categories:
- n<1K
rootmodel-knf-philippines-v1
Adaptive agricultural instruction dataset for regenerative tropical farming informed by Korean Natural Farming (KNF), built from a working farm in Nabua, Camarines Sur, Bicol, Philippines.
Released for the AutoScientist Challenge — Agriculture (Part 2, 2026). To the maintainer's knowledge, no equivalent KNF-specific instruction dataset currently exists in the public domain.
"Modern AI was trained on the internet. ROOTMODEL is trained on living soil."
What this dataset is
59 instruction → response records teaching the decision logic of a KNF-informed biological farming system: read the signal (crop stage, symptom, soil and weather condition), correct soil biology first, then apply the stage-appropriate biological input at the right dilution, method and timing — and record the outcome.
JSONL, with fields:
| field | meaning |
|---|---|
task |
reasoning task (stress_classification, input_selection, fermentation_readiness, outcome_comparison, recovery_prediction, field_observation, microbial_imbalance, land_history, pest_cycle, knf_input_function, knf_knowledge) |
source |
provenance — see below |
instruction |
task prompt |
input |
field or lab context |
output |
target response |
Data provenance
Records are split by provenance and never blended:
nabua_field_log(37 records) — real observations from the Nabua farm: dated interventions and outcomes, substrate readings, fermentation batches, and documented management transitions (2016 chemical → organic → KNF; 2019 pig-litter integration; ASF culling; Typhoon Kristine 2024; Typhoon Inday July 2026; 2026 reboot).knf_canonical(22 records) — established KNF domain knowledge: input functions (IMO, LAB, FPJ, FFJ, OHN), standard dilution ranges, fermentation-readiness signals, stage-to-input mapping, biological-first decision logic.
This separation lets a reviewer see exactly which records are general knowledge and which are ground-truth field data.
Field observation schema
Date · Plot ID · Crop · Growth Stage · Weather · Symptom ·
Soil Condition · KNF Input Applied · Dilution Ratio ·
Application Method · Time Applied · Result After 72h · Notes
KNF inputs covered
| Input | Function | Stage |
|---|---|---|
| IMO | Soil microbial inoculation | Land prep, transplanting |
| LAB | Fermentation activator, anaerobe suppression | Foliar spray, soil drench |
| FPJ | Vegetative growth stimulation | Vegetative |
| FFJ | Flowering and fruiting support | Reproductive |
| OHN | Pest resistance, stress support | Stress response |
Recording discipline
- No date is estimated. Precision is recorded as exact, approximate or month-only.
- No outcome is written before its 72-hour window has elapsed.
- "No visible change" and "identification unconfirmed" are valid records.
- Failures, confounds and interrupted observation days are logged rather than omitted.
- Identifications are revised in place when better evidence arrives, with the revision noted.
Limitations
- Small corpus. Field observations centre on IMO application and fermentation readiness; other KNF inputs are represented canonically.
- Dilution ranges are standard practice and should be validated against local results.
- Specific to humid, typhoon-affected lowland tropical Philippines. Transfer to temperate or arid systems is not claimed.
- Several observations carry stated confounds (rainfall, co-introduced organisms, weed-seed load) recorded alongside the result.
Citation
@dataset{rootmodel_knf_philippines_v1_2026, title = {ROOTMODEL KNF Philippines v1}, author = {Sales, Ralph Anthony}, year = {2026}, note = {Nabua, Camarines Sur, Bicol, Philippines.}, url = {https://huggingface.co/datasets/GreenRalph/rootmodel-knf-philippines-v1} }