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Optimal trap cropping investments to maximize agricultural yield Matthew H Holden School of Mathematics and Physics, The University of Queensland, St Lucia, 4072, Australia Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, 4072, Australia Abstract Trap cropping is a pest management strategy where a grower plants an attractive “trap crop” alongside the primary crop to divert pests away from it. We propose a simple framework for optimizing the proportion of a grower’s field or greenhouse allocated to a main crop and a trap crop to maximize agricultural yield. We implement this framework using a model of pest movement governed by trap crop attractiveness, the potential yield threatened by pests, and functional relationships between yield loss and pest density drawn from the literature. Focusing on a simple case in which pests move freely across the field and are attracted to traps solely by their relative attractiveness, we find that allocating 5–20 percent of the landscape to trap plants is typically required to maximize yield and achieve effective pest control in the absence of pesticides. For highly attractive trap plants, growers can devote less space because they are more effective; less attractive plants are ineffective even in large numbers. Intermediate attractiveness warrants the greatest investment in trap cropping. Our framework offers a transparent and tractable approach for exploring trade-offs in pest management and can be
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Intermediate attractiveness warrants the greatest investment in trap cropping. Our framework offers a transparent and tractable approach for exploring trade-offs in pest management and can be extended to incorporate more complex pest behaviors, crop spatial configurations, and economic considerations. Keywords: ecological pest management, integrated pest management, inter-cropping, companion planting, organic agriculture, trap crop Recommendations for Resource Managers • Planting a highly attractive “trap crop” that lures pests away from the main crop in an agricultural field can control pests, improve yield, and lower reliance on chemical pesticides. • Because trap plants occupy precious space that could otherwise be used to grow harvestable crops, it is important to optimize their share of the field. • Our results suggest that allocating 5–20% of the field to trap crops may be required to maximize yield, depending on pest pressure and trap plant attractiveness. 1 Introduction Pests remain one of the most significant threats to agricultural productivity worldwide, reducing yields and increasing the need for chemical interventions . However, widespread pesticide use has led to well-documented issues, including the evolution of pest resistance , harm to non-target species and pollinators , and environmental contamination . These concerns have motivated a growing interest in ecologically based pest management approaches that reduce reliance on chemical controls while
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
and pollinators , and environmental contamination . These concerns have motivated a growing interest in ecologically based pest management approaches that reduce reliance on chemical controls while maintaining productivity . Among these, trap cropping has emerged as a promising, environmentally friendly tactic for manipulating pest behavior to protect the main crop from pests . 1 arXiv:2508.05896v1 [q-bio.PE] 7 Aug 2025 Trap cropping is a strategy where a more attractive plant species or variety is deliberately planted to draw pests away from the main crop, thereby reducing damage and improving overall yield . The effectiveness of trap cropping has been demonstrated in a variety of agricultural systems, including cotton, brassicas, cucurbits, and legumes . However, despite its widespread use, practical guidance on how much land to allocate to the trap crop is lacking. Many studies remain empirical, focusing on evaluating the attractiveness of candidate trap plants for specific crop–pest pairs or small-scale field trials, with limited generalization to broader agroecological conditions . Moreover, quantitative theory linking system parameters to optimal spatial allocation remains underdeveloped. Mathematical and simulation models have played a critical role in advancing our understanding of trap cropping and related ecological interventions such as intercropping and companion planting. Early models focused on insect pest dispersal and behavioral responses to attractants or
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
of trap cropping and related ecological interventions such as intercropping and companion planting. Early models focused on insect pest dispersal and behavioral responses to attractants or traps embedded within cropping systems . Since then, a range of modeling approaches have been used to explore how pest movement, crop attractiveness, and spatial layout interact to influence trap crop performance . Simulation-based studies have shown that trap effectiveness depends not only on the strength of attraction but also on spatial configuration and pest mobility. For example, models incorporating biological control agents alongside trap crops reveal strong context dependence, with outcomes shaped by both natural enemy dynamics and landscape structure . Individual-based models (IBMs) have been used to account for fine-scale behavioral heterogeneity and movement biases in response to trap placement , while recent work has examined how plant dispersion and patch shape influence trapping efficiency . A broader synthesis of movement modeling in cropping systems underscores the importance of aligning spatial designs with pest dispersal mechanisms and highlights trade-offs between model tractability and ecological realism . While these studies provide rich insight into pest–crop dynamics and demonstrate the potential of spatially targeted interventions, they do not offer general analytic solutions for the optimal proportion of land to devote to trap cropping. Our work aims to fill this
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
demonstrate the potential of spatially targeted interventions, they do not offer general analytic solutions for the optimal proportion of land to devote to trap cropping. Our work aims to fill this gap by proposing a stylized but tractable framework that captures key trade-offs and yields explicit recommendations for growers. We start by developing a simple mathematical framework to determine the optimal proportion of a grower’s field that should be allocated to a trap crop to maximize total yield. Our framework is intentionally stylized and analytically tractable. Analytic solutions are especially valuable because they (i) improve transparency by making a direct link between assumptions and outcomes easy to interpret and interrogate, (ii) their solutions serve as baselines for validating more complex future simulation models, and (iii) they allow for general insights to be computed and communicated efficiently. The model is built around two central parameters: the proportion of yield that would be lost to pests in the absence of trap cropping and the relative attractiveness of the trap crop, which determines the likelihood that pests are diverted away from the main crop. Both parameters can be estimated from experimental data, expert elicitation, or synthesis studies on pest behavior and damage functions . To illustrate the utility of the framework, we analyze a simplified scenario in which pests move freely across the field and select plants purely based on relative
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
behavior and damage functions . To illustrate the utility of the framework, we analyze a simplified scenario in which pests move freely across the field and select plants purely based on relative attractiveness. This assumption corresponds to highly mobile pests, such as adult moths that lay their eggs in batches over several days or weeks across multiple plants . The assumption of high mobility relative to crop area is especially plausible in smaller greenhouse environments, since pests can rapidly traverse the entire planting area . Under these conditions, we derive explicit expressions for expected yield and characterize the optimal fraction of land to devote to trap cropping. Using plausible parameter values from the literature, we find that between 5–20% of the field must typically be allocated to trap crops for effective pest control in the absence of pesticides. Interestingly, we show that the relationship between trap crop attractiveness and optimal area is non-monotonic: highly attractive traps require relatively little area; traps with low attractiveness are ineffective even in large numbers; and intermediate levels of attractiveness require the largest investment. Our framework contributes to the growing body of theory guiding sustainable pest management and can be extended to incorporate more complex dynamics such as pest reproduction, spatial heterogeneity, and economic costs. 2 2 Framework In this work, we consider a scenario with N pests in a field damaging
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
extended to incorporate more complex dynamics such as pest reproduction, spatial heterogeneity, and economic costs. 2 2 Framework In this work, we consider a scenario with N pests in a field damaging the main crop harvested by the grower. To reduce this damage, the grower may plant a trap crop to divert pests, potentially increasing main crop yield. A fundamental question in implementing trap cropping is how much land to allocate to trap plants and, consequently, how much to assign to the main crop. Trap plants are typically not harvested and therefore do not directly contribute to yield. In fact, they take up space that would otherwise be used for planting productive main crop plants. However, trap crops can indirectly increase the grower’s yield by reducing pest density on the main crop. This creates a tradeoff: growers are reluctant to allocate space to non-producing plants, but desire lower pest densities. Assume that the yield of a single main crop plant depends on the pest density on that plant. In other words, a plant’s yield is y(ρ), where ρ is the number of pests on that plant. Let n be the total number of plants that can fit in the field. Let nc be the number of cash (main crop) plants; then n −nc is the number of trap plants. Since trap plants are not harvested, and assuming all cash and trap plants are otherwise equal, the total yield across the whole field is given by: Y (nc) = y (ρ(nc)) · nc (1) This equation says that total yield is just the average yield of a
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
all cash and trap plants are otherwise equal, the total yield across the whole field is given by: Y (nc) = y (ρ(nc)) · nc (1) This equation says that total yield is just the average yield of a cash plant, given the average pest density on cash plants, multiplied by the number of cash plants. As the grower increases the number of cash plants, the multiplication by nc on the right has an increasing effect, but y(ρ(nc)) is more complicated. If trap plants are effective at reducing pest densities on cash plants, then this term, yield per plant, can decrease when the grower adds cash plants through increases in pest density, ρ. However, note that y is not guaranteed to decrease with nc (even though it decreases in ρ) because if trap plants are not effective at trapping or retaining pests, then pests are diluted across an increased number of cash plants. With N pests in the field, the average pest density is ρ(nc) = x(nc)N nc , (2) where x(nc) is the proportion of insects on the cash crop as a function of the number of cash crops. To maximize total yield, one can differentiate equation (1), which results in general insight into the optimal solution. It says the optimal number of cash plants should satisfy, y(ρ(nc)) = −dy dnc · nc, (3) provided some conditions on the functions in equations (1) and (2) hold (see section 7). Equation (3) implies that at the optimal number of cash plants, the yield per plant equals the marginal loss in yield caused by adding another cash plant, summed
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
(1) and (2) hold (see section 7). Equation (3) implies that at the optimal number of cash plants, the yield per plant equals the marginal loss in yield caused by adding another cash plant, summed over all plants. Roughly, if you added a cash plant, the yield of that cash plant should equal the reduced yield summed over all plants due to the increased pest density. If the yield of adding another cash plant is greater than the yield loss it would cause due to pests, it would be optimal to increase the number of cash plants in the landscape. Conversely, if the yield from that plant is not greater than the loss it causes, it is optimal to add more trap plants (i.e., decrease the number of cash plants in the landscape). While flexible, the framework requires specifying two key relationships: y(ρ), and x(nc). In the following sections, we demonstrate how empirically supported functions from the literature can be used in this framework to achieve practical recommendations for growers deciding how many trap plants to deploy in their agricultural field or greenhouse. 3 Yield–Pest relationships The relationship between yield and pest density has been studied both theoretically and empirically in the literature . Many candidate relationships have been proposed. However, they all share some common properties. First, there is some assumed maximum yield per plant in the absence 3 0 2 4 6 8 10 0 20 40 60 80 100 Pest density per plant Yield per plant Linear Reciprocal Exponential Figure 1:
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
common properties. First, there is some assumed maximum yield per plant in the absence 3 0 2 4 6 8 10 0 20 40 60 80 100 Pest density per plant Yield per plant Linear Reciprocal Exponential Figure 1: Yield curves, y(ρ), as a function of pest density per plant ρ, with N = 1,000, β = 0.8, k = 100, and n = 10. of pests, call this maximum plant yield k, in other words, y(0) = k. Yield is also assumed to decrease monotonically with pest density, dy/dρ < 0 for all ρ > 0. Common functional forms for y(ρ) include linear declines, exponential and reciprocal curves (which drop sharply at low densities and saturate at high densities), and S-shaped curves, where yield remains high at low pest densities, declines rapidly around a threshold, and then saturates again . For our application, we also consider the maximum proportion of yield lost in the absence of trap plants. That is, we parameterize the yield functions such that y(N/n) = (1−β)k, where β is the proportion of yield lost per plant due to N total pests in a field of n plants in the absence of pest management. For example, if β = 1, N pests will wipe out the entire grower’s yield in the absence of trap crops, whereas if β = 0.5, half of the grower’s yield is lost. Linear yield loss If we assume the yield of a single plant decreases linearly with pest density from a maximum yield of k to (1 −β)k when pest density is N/n, we obtain the yield function, y(ρ) = k 1 −βn N ρ . (4) Reciprocal yield Assuming yield declines reciprocally
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
with pest density from a maximum yield of k to (1 −β)k when pest density is N/n, we obtain the yield function, y(ρ) = k 1 −βn N ρ . (4) Reciprocal yield Assuming yield declines reciprocally with pest density, i.e., y(ρ) = α/(1 + γρ), and parameterizing such that y(0) = k and y(N/n) = (1 −β)k, we obtain: y(ρ) = k(1 −β)N (1 −β)N + βnρ. (5) Exponential yield Similarly, exponential yield, parameterized in the same way, can be expressed as y(ρ) = k(1 −β)nρ/N. (6) 4 4 Pest distribution and movement For simplicity, we assume the pest population is held constant. However, we still require a method to allocate pests between trap and cash plants, since yield depends on both the number of cash plants and the pest load they experience. We describe the pest distribution using a single variable xt, the proportion of the pest population on the main crop (cash plants) at time t. Therefore, 1−xt is the proportion of the pest population on the trap crop (trap plants) at time t. Assume the movement dynamics of the pests are described by a simple difference equation. xt+1 = f(xt), (7) If f has a stable equilibrium that is reached quickly, this equilibrium can be used in equation (2). Below, we describe a simple example model first proposed by Holden et al. and then use it to compute the optimal allocation of land to cash and trap plants. Consider the case where pests leave plants at each time step with probability l. Pests that leave a cash plant settle on another cash plant with
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
optimal allocation of land to cash and trap plants. Consider the case where pests leave plants at each time step with probability l. Pests that leave a cash plant settle on another cash plant with probability s, while those leaving a trap plant settle on a cash plant with probability σ. Under these assumptions, the proportion of the pest population on the main crop changes through time iteratively according to the difference equation (7) with, f(x) = (1 −l)x + lsx + lσ(1 −x). (8) The first term accounts for pests that remain on the main crop, the second for those that left and returned, and the third for pests that moved from the trap crop to the main crop. The equilibrium of this model is x∗= σ 1 + σ −s, (9) which is approximately the proportion of the pest population on the main crop in the long run. Because equations (7) and (8) define a linear difference equation, the equilibrium is globally asymptotically stable. Therefore, the equilibrium is globally asymptotically stable. There is even an analytic expression for xt, and it can be used to show that solutions, no matter the initial conditions, approach the equilibrium exponentially quickly. Therefore, it suffices to look at equilibrium pest density as a proxy for cumulative pest load as long as crop damage is consistently related to pest density. So far, our model is simple. We have assumed very little about the spatial structure of the field. The only critical assumption is that all trap plants are treated as
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
related to pest density. So far, our model is simple. We have assumed very little about the spatial structure of the field. The only critical assumption is that all trap plants are treated as identical, and all cash plants are treated identically as well. This assumption implies that pests leaving any trap plant have the same probability of settling on a cash plant, regardless of their starting location. The same applies to pests leaving cash plants. However, this assumption is likely to hold approximately in many systems. If trap plants are planted in rows, for example, from the eyes of a pest on a trap plant, locally, the world looks the same. Note we have yet to introduce any specific model for movement. A specific movement model is needed to determine the settlement probabilities s and σ. To specify s and σ, we assume a common pool dispersal model, in which pests can access all plants each time step. Let nT be the number of trap plants in the field, and nC be the number of cash plants. Also, assume a trap plant is a times more attractive than a single cash plant. Since pests view all plants at each time step, their prior location does not affect their settlement decision, and therefore, s = nC anT + nC = σ. (10) In this movement model, the equilibrium in equation (9) simplifies to x∗= s, providing a simple formula relating the proportion of pests on the cash crop directly to the number of cash crops, nC. Substituting our expression for x∗for x(nC) then allows us to
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
to x∗= s, providing a simple formula relating the proportion of pests on the cash crop directly to the number of cash crops, nC. Substituting our expression for x∗for x(nC) then allows us to proceed using the framework to determine the optimal number of cash plants (and hence trap plants) to allocate to the landscape. It is important to note that in such a simplified, stylized model, we are assuming that pests leave trap plants and cash plants with the same probability, l, and trap plants only accumulate more pests than cash plants via their superior attraction once an insect has initiated movement. There is some support for this in the literature for whiteflies as pests of crops in greenhouses . 5 5 Optimal number of cash/trap plants to maximize grower yield Given the movement model described in equations (7) – (10) with the simplest yield–pest density relationship, linear yield loss as pest density increases, the optimal number of cash plants in the field to maximize total yield can be derived analytically as n∗ C = a −√βa a −1 n. (11) To understand this expression, let us first consider the case where β = 1. Recall that β = 1 means the total number of pests threatening the farm is so great that yield would be zero in the absence of trap crops. In such a case, the optimal number of trap plants, n −n∗ C, makes up a high proportion of the landscape. For example, if a trap plant were four times as attractive as a cash plant, the grower would have to devote a third of the
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
number of trap plants, n −n∗ C, makes up a high proportion of the landscape. For example, if a trap plant were four times as attractive as a cash plant, the grower would have to devote a third of the landscape to trap plants to maximize yield. Even if the trap crop is 100 times more attractive than the cash crop, the grower would still need to dedicate 9/99 proportion of the landscape to trap plants (nearly 10 percent). These numbers are quite large as they mean the grower is giving up 10% of their field to plants that aren’t producing anything they can harvest. As we decrease β, this conclusion becomes less severe, but still a substantial proportion of the landscape needs to be devoted to trap plants. For example, even if 40 percent of the grower’s yield is threatened by pests (a typical yield loss due to higher pest densities in organic agriculture), and the trap plant is four times as attractive as the cash plant, then nine percent of the landscape should be devoted to trap plants. If the trap plant is 100 times more attractive, then the optimal allocation of the landscape to trap plants is five percent. Under nonlinear yield loss functions, such as exponential and reciprocal yield loss, the effects are similar. For reciprocal yield, the optimal number of cash plants is n∗ C = " a(1 −β) + β − p β2 + βa(1 −β) (1 −β)(a −1) # n, (12) if the quantity is in [0, n], or n otherwise. For exponential yield, the optimal number of cash plants is n∗ C = " 2a −ln(1 −β) − p ln(1
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
n∗ C = " a(1 −β) + β − p β2 + βa(1 −β) (1 −β)(a −1) # n, (12) if the quantity is in [0, n], or n otherwise. For exponential yield, the optimal number of cash plants is n∗ C = " 2a −ln(1 −β) − p ln(1 −β)(ln(1 −β) −4a) 2(a −1) # n. (13) These expressions are more complex than for the linear yield relationship. However, note a key commonality across all three expressions: the optimal number of cash plants in equations (11) – (13) is directly proportional to n. This means the bracketed term in each equation can be interpreted as the optimal proportion of the landscape allocated to the cash plant. To visualize how these expressions differ, in the next section, we compare them for all possible parameter combinations. 6 Illustrative results We start by parameterizing the model using reported quantities available in the literature to form a plausible baseline. For example, it has been shown in choice experiments that 98% of greenhouse whiteflies, Trialeurodes vaporariorum, a common agricultural pest, choose to settle on an eggplant trap plant versus a poinsettia cash plant . This implies that eggplant is 49 times more attractive than poinsettia when the pest has decided to move. It is commonly suggested that without the use of pesticides, a roughly 40 percent reduction in yield is typical in agricultural landscapes . We will use these two parameter values as an illustrative baseline, but will also explore the whole parameter space. If a = 49 and β = 0.4, we find that roughly 91.5 –
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
in agricultural landscapes . We will use these two parameter values as an illustrative baseline, but will also explore the whole parameter space. If a = 49 and β = 0.4, we find that roughly 91.5 – 92.9 percent of a grower’s greenhouse or field should be allocated to cash plants to maximise yield (Figure 2). The nonlinear yield–pest relationships lead to more space being required for trap cropping: 8.5% for reciprocal yield, equation (12), and 7.8% for exponential yield, equation (13), compared to the case when yield declines linearly with pest density (7.1% trap plants). 6 This occurs because the yield loss per pest is steeper at low pest densities under these relationships, so even small increases in pest density across many cash plants have a large negative impact on total yield. 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 Cash plant proportion Proportion of a pest−free landscape's yield Linear Reciprocal Exponential Figure 2: Proportion of the total pest-free field’s yield (i.e., a field planted only with cash plants) achieved in a system with pests threatening up to 40% of the yield (β = 0.4), shown as a function of the proportion of the field devoted to trap plants. Curves correspond to a linear (blue solid), reciprocal (purple dotted), and exponential (red dashed) yield–pest relationship, assuming the trap plant is 49 times more attractive than the cash plant (a = 49). Note that when there are no trap plants, the grower loses 40% of their yield (right endpoint), with
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
relationship, assuming the trap plant is 49 times more attractive than the cash plant (a = 49). Note that when there are no trap plants, the grower loses 40% of their yield (right endpoint), with steep gains from adding only a few trap plants (moving left). The optimal proportion of the field to devote to trap plants is 7-8.5%, depending on the yield-pest relationships. Specifically, the optimal cash plant (trap plant) proportion is 92.9% (7.1%), 92.2% (7.8%), and 91.5% (8.5%) for the linear, exponential, and reciprocal yield–pest density relationships, respectively. In the case where there are all trap plants and no cash plants, every additional cash plant adds approximately an additional yield of y(ρ). In other words, in a landscape of no cash plants, adding a cash plant generates a pest-free plant’s worth of yield, because all the pests are on the many trap plants. This explains the linear increasing relationship between total yield and the proportion of the landscape with cash plants 7 for low cash plant proportions (see bottom left of Figure 2). Once the proportion of cash plants begins to exceed 80 percent, then the incremental yield of an additional cash plant starts to be reduced by the associated increased pest density across all cash plants in the system, with an optimal cash plant proportion of 91.5–92.9 percent. At approximately 95 percent of the landscape devoted to cash plants, each additional cash plant severely reduces yield compared to an equivalent
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
an optimal cash plant proportion of 91.5–92.9 percent. At approximately 95 percent of the landscape devoted to cash plants, each additional cash plant severely reduces yield compared to an equivalent investment in trap plants due to pest damage (see the rapid drop off in the right of Figure 2). However, a = 49 represents an extremely attractive plant. The question remains: What happens when the trap plant is less attractive? In Figure 3a, we see that for a = 2, and β = 0.4, trap cropping is entirely ineffective at increasing yield. This is because in such cases, the pest is only weakly drawn towards trap plants, and therefore, the reduction in pest pressure on cash plants is minimal; the lost yield from sacrificing land to unharvestable trap crops outweighs the small gains from pest control. 8 0.4 0.5 0.6 0.7 0.8 0.9 Linear Reciprocal Exponential a) a=2 0.4 0.5 0.6 0.7 0.8 0.9 b) a=5 0.5 0.6 0.7 0.8 0.9 1.0 0.4 0.5 0.6 0.7 0.8 0.9 c) a=25 Cash plant proportion Proportion of a pest−free, 100% cash plant, landscape's yield Figure 3: Grower yield as a function of the proportion of cash plants for trap cropping systems with less attractive trap plants than the baseline, where trap plants are (a) twice, (b) five times, and (c) 25 times more attractive than the cash plant. For a trap plant that is only twice as attractive as a cash plant, trap cropping is ineffective, and the grower should plant only cash plants, accepting the yield loss caused by pests. For more attractive trap
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
plant that is only twice as attractive as a cash plant, trap cropping is ineffective, and the grower should plant only cash plants, accepting the yield loss caused by pests. For more attractive trap plants, 5 and 25 times more attractive, approximately 7–10% of the field should be allocated to trap plants. As attractiveness is increased further to five, meaning a pest is five times more likely to settle on a trap plant than a cash plant, it is optimal to devote roughly ten percent of the land to the trap crop (Figure 3b). However, differences in prevented yield loss are less sensitive to the number of trap plants than for more attractive trap plants (compare Figure 3b to Figure 2 and Figure 3c). Interestingly, from figures 2 and 3, the optimal number of trap plants is bigger for intermediate attractiveness values of 5 and 25 than for 49. This is because intermediate attractiveness is sufficient to divert pests but not highly efficient, so more area is needed to sufficiently reduce pest pressure and maximize yield. This suggests, counterintuitively, that growers using intermediately attractive trap plants may need to invest 9 more land in trap cropping than those using highly attractive varieties. The figures also show that the nonlinear yield-pest relationships (red and purple) can increase the optimal number of trap crops or decrease it compared to the linear pest-density model, depending on the attractiveness parameter (the order of the colored circles, representing
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
can increase the optimal number of trap crops or decrease it compared to the linear pest-density model, depending on the attractiveness parameter (the order of the colored circles, representing optimal yield, changes when comparing Figure 3b and to Figure 3c). These reversals reflect that, at intermediate attractiveness levels, nonlinear damage responses can either amplify or buffer the effects of pest distribution. This depends on which part of the damage curve is most influential. To investigate this further, we plotted the optimal proportion of cash plants in equations (11) – (13) versus attractiveness, for the baseline yield at risk of 40 percent (Figure 4a). When trap plants are between one and 2.6 times more attractive than the main crop, the whole field should be cash plants, as trap cropping is not effective enough to make up for lost yield. From an attractiveness of 3.6 to eight, the optimal proportion of cash plants declines rapidly to a minimum of 88 percent of the landscape under all yield-pest density relationships. However, the decline is slightly more rapid for the linear case, where the 88 percent minimum occurs at an attractiveness of eight, compared to the exponential and reciprocal cases, where the minimum is achieved at higher attractiveness values of 10 and 12.5, respectively. After achieving the minimum, for larger attractiveness values, the optimal proportion of the landscape devoted to cash plants gradually rises again. This is because, as trap crops
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
12.5, respectively. After achieving the minimum, for larger attractiveness values, the optimal proportion of the landscape devoted to cash plants gradually rises again. This is because, as trap crops become highly effective, less land allocation is required to achieve adequate pest control. For even nearly perfect attractiveness, e.g., 100 times more attractive than the cash crop, the grower still needs to devote five, six, and seven percent of the landscape to the trap crop, for the linear, exponential, and reciprocal relationships, respectively. 0 20 40 60 80 100 0.6 0.7 0.8 0.9 1.0 a) Trap plant attractiveness Linear Reciprocal Exponential 0.0 0.2 0.4 0.6 0.8 1.0 b) Proportion of yield at risk Optimal cash plant proportion Figure 4: The optimal proportion of the landscape to allocate to the main crop (cash plants) as a function of (a) trap plant attractiveness and (b) the proportion of yield at risk from pests. For intermediately attractive trap plants (5–20 times as attractive as the cash plant), more than 10% of the landscape should be sacrificed to trap plants, as they provide sufficient efficacy to achieve pest reduction, but are not effective enough to work at low densities. For low attraction (a < 4), trap cropping is ineffective. For highly attractive trap plants, their efficacy is high enough that a smaller proportion of the landscape can achieve the necessary reduction in pest densities to maximize yield. Optimal trap cropping is even more sensitive to the
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
their efficacy is high enough that a smaller proportion of the landscape can achieve the necessary reduction in pest densities to maximize yield. Optimal trap cropping is even more sensitive to the maximum yield at risk due to pests, β, than the attraction parameter. We plotted the optimal proportion of cash plants in equations (11) equation (13) versus yield at risk, β, for the baseline trap plant attractiveness of 49 percent (Figure 4b). In general, the optimal proportion of the landscape dedicated to the cash crop declines monotonically as there is more yield 10 at risk due to high pest densities (see decreasing trend in Figure 4b). This is particularly more severe for the more nonlinear pest yield relationships. For the reciprocal yield relationship, this is particularly severe. If 100% of the yield is at risk, the cash crop should make up roughly 60% of the landscape to maximize yield. For high, but more typical, yield at risk, such as 60 and 80 percent, optimal cash crop allocations correspond to 87 and 80 percent, respectively. For linear and exponential relationships, the trend is similar but less severe. The relationship is strongest for the reciprocal yield curve because even low pest density causes a steep drop-off in yield. Therefore, more trap plants are required to achieve optimal yield. The exponential is an intermediate case between this severely nonlinear yield–pest relationship and the more gradual, linear, pest–yield relationship. However, even in the
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
to achieve optimal yield. The exponential is an intermediate case between this severely nonlinear yield–pest relationship and the more gradual, linear, pest–yield relationship. However, even in the linear case, if 100 percent of the yield is at risk, still 88 percent of the field needs to be devoted to cash plants to maximize yield. To see the differences between the yield achieved at all cash crop proportions for different yields at risk, see Figure 5. 0.2 0.4 0.6 0.8 1.0 Linear Reciprocal Exponential a) β=0.2 0.2 0.4 0.6 0.8 1.0 b) β=0.6 0.5 0.6 0.7 0.8 0.9 1.0 0.2 0.4 0.6 0.8 1.0 c) β=0.8 Cash plant proportion Proportion of a pest−free landscape's yield Figure 5: Grower yield as a function of the proportion of cash plants for trap cropping systems with different levels of yield at risk compared to the baseline in Figure 2: when (a) 10%, (b) 30%, and (c) 60% of maximum yield is at risk due to damage from pests. 11 In the previous plots, we have fixed one parameter to be at the baseline value while varying the other. A complete sensitivity analysis of the optimal cash crop allocation across all possible combinations of attractiveness and yields at risk is displayed as a heatmap in Figure 6. Across all yield-pest density relationships, yields at risk, and attractiveness, the vast majority of the parameter space corresponds to the case when 8095 percent of the landscape should be dedicated to the cash crop to maximize agricultural yield. Low optimal cash plant proportions of
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
majority of the parameter space corresponds to the case when 8095 percent of the landscape should be dedicated to the cash crop to maximize agricultural yield. Low optimal cash plant proportions of less than five percent only occur when less than 30 percent of the grower’s yield is at risk (left second darkest region). Trap cropping should be avoided only when trap plants are both unattractive and low yields are at risk (bottom-left dark regions in all panels) 0.0 0.2 0.4 0.6 0.8 20 40 60 80 100 Linear 0.0 0.2 0.4 0.6 0.8 20 40 60 80 100 Exponential 0.0 0.2 0.4 0.6 0.8 20 40 60 80 100 Reciprocal 0.5 0.75 0.8 0.85 0.95 0.95 1 Optimal proportion (nc * n) Attractiveness Proportion of yield at risk, β Figure 6: Heatmap of the optimal proportion of the landscape to allocate to a cash crop across a grid of yields-at-risk, β, and trap attractiveness, a. Darker regions represent greater investment in cash crops (low trap crop area). The white and lightest regions indicate 25–50% of the landscape is allocated to cash crops, implying substantial trap cropping. In most of the parameter space, optimal trap cropping investments require 5–20% of land area, as the white and next lightest regions, along with the darkest region, are small. The figure also demonstrates the result that intermediate attraction warrants the greatest trap cropping investment, compared to highly attractive and unattractive trap crops, is general across parameterizations. That is for all yield-pest dentistry
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
attraction warrants the greatest trap cropping investment, compared to highly attractive and unattractive trap crops, is general across parameterizations. That is for all yield-pest dentistry relationships (all panels) and parameter values for yields at risk (horizontal axis), as long as the trap crop is attractive enough to warrant some investment, the cash plant allocation that maximizes yield is highest for trap plants that are only a few times more attractive than the cash plant. To see this, examine vertical transects through each figure. Note there is a dark region on each side of the transect and a lighter region for intermediate attraction values for a wide range of yields at risk. 7 Generalities: existence of a single optimal cash plant proportion In sections 3 – 6, we focused on a specific model of pest movement and crop yield to demonstrate the framework and improve intuition. This required specifying several functional relationships. The question remains whether such results are possible across a wide range of functional forms and models. Below, we present general conditions on these functions, such that they guarantee a unique optimal number of cash plants (a sufficient condition). Theorem 1. There is a unique optimal number of cash plants, n∗ C ∈(0, n], satisfying equation (3), that maximizes total yield, Y (nC), if the following three properties hold: 1. The function x is positive, continuously differentiable, and strictly increasing on (0, n). 2. The function
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
(3), that maximizes total yield, Y (nC), if the following three properties hold: 1. The function x is positive, continuously differentiable, and strictly increasing on (0, n). 2. The function E, defined by E(nC) = nC x(nC) dx dnC −1, (14) is positive and strictly increasing with respect to nC on nC ∈(0, n). 12 3. The function y is continuously differentiable, and y′/y is monotonic in ρ. Below, we provide a biological explanation of the theorem. However, see the appendix for a rigorous proof. The intuition behind condition 1 is simple: As you increase the number of cash plants (and therefore decrease the number of trap plants), it requires that the density of pests per plant increases. In other words, trap plants have to reduce pest density on the cash plants as you add more trap plants to the landscape. This is almost surely satisfied for any trap cropping system; otherwise, the trap plant is ineffective and would not be considered by the grower. Note that equation (14) in condition 2 is just the elasticity of pest density on a single cash plant with respect to the number of cash plants. This is because nC ρ dρ dnC = nC x(nC) dx dnC −1 := E(nC). (15) Elasticity, in economics, is the proportional rate of change of one quantity with respect to a proportional change in the other quantity. For example, if the elasticity in equation (15) is two, then, approximately, a one percent increase in cash plants will cause a two percent increase in the proportion of pests on a cash plant.
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
For example, if the elasticity in equation (15) is two, then, approximately, a one percent increase in cash plants will cause a two percent increase in the proportion of pests on a cash plant. Therefore, condition 2 says that the elasticity of pest density on a single plant with respect to the number of cash plants present should be an increasing function. To provide intuition for condition 2, remember that total yield across the field is the product of yield per cash plant times the number of cash plants, as seen in equation (1). Consider the case where there are only 100 cash plants. In this case, a one percent boost in cash plants only adds a single plant, increasing yield by the yield from only that one plant, roughly y(ρ). Now, in the case where there are 1,000 cash plants, increasing the number of plants by one percent increases yield by roughly ten times y(ρ). Because yield scales with the number of cash plants, any yield loss per plant resulting from additional cash plants must be steep enough to offset the benefits of increasing plant numbers Since we have already assumed that the yield of a plant decreases as you increase the density of the pests (see section 3), condition 3 of the theorem just restricts the behavior of this decrease. It requires that either every additional individual pest added to a plant decreases yield more than the one before, or each pest decreases yield less than the one before, but it never switches back and forth depending on how many
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
additional individual pest added to a plant decreases yield more than the one before, or each pest decreases yield less than the one before, but it never switches back and forth depending on how many pests are on the plant. Theorem 1 means that for many mathematical models of pest movement and yield-pest relationships, one can set the derivative of Y to zero and solve for the unique optimal number of cash and trap plants, either analytically or numerically, without needing any global optimization algorithms. It is easy to show that the model of pest movement in equations (7) – (9) and all proposed yield-pest density relationships in equations (4) – (6) satisfy the conditions in Theorem 1. However, a yield-pest density curve with an inflection point will fail to satisfy condition 3, and therefore, multiple local optima will need to be checked in such a model. 8 Discussion Our results show that trap cropping can substantially improve agricultural yield, but only under specific conditions. Using a simple yet flexible framework, we derived analytic expressions for the optimal number of cash and trap plants, demonstrating how optimal strategies depend critically on two parameters: the proportion of yield at risk due to pests (β), and the relative attractiveness of the trap crop compared to the cash crop (a). When trap crops are only marginally more attractive than the cash crop (e.g., a = 2), trap cropping fails to improve yield, even under extreme pest pressure. In contrast,
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
to the cash crop (a). When trap crops are only marginally more attractive than the cash crop (e.g., a = 2), trap cropping fails to improve yield, even under extreme pest pressure. In contrast, when trap crops are highly attractive (e.g., a = 49), yield gains can be substantial even with relatively small land allocations to trap plants. Our theoretical results provide general conditions under which a unique optimal solution for the number of cash plants exists. In particular, the condition that the elasticity of pest density on a cash plant with respect to the number of cash plants is increasing (Condition 2 in Theorem 1) plays a central role. This elasticity captures how pest distribution responds to changes in landscape composition and connects directly to the efficiency of trap cropping. These results complement other theoretical work on pest suppression 13 strategies that use elasticity-based reasoning, in diverse fields from agricultural economics to invasive species management . Our findings have clear practical relevance. First, they demonstrate that highly attractive trap crops can offer strong control with minimal land sacrifice. However, even the most attractive trap crops require 5–10% of the landscape to be devoted to non-harvestable plants when pest pressure is severe. This finding is particularly important for organic and low-input systems, where pesticides are avoided and trap cropping is one of few viable pest management options . Second, the analysis
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
is severe. This finding is particularly important for organic and low-input systems, where pesticides are avoided and trap cropping is one of few viable pest management options . Second, the analysis emphasizes that blanket recommendations about trap crop allocation are unlikely to be effective. Instead, effective implementation requires system-specific knowledge: the value of β and a must be estimated for each crop–pest combination. Our formulas provide a valuable tool for translating this information into actionable trap cropping recommendations. As with any modeling approach, our framework makes simplifying assumptions. We assume a fixed pest population (N constant), homogeneous mixing of pests across the landscape, and identical cash and trap plants aside from attractiveness. These assumptions ignore important ecological complexities, such as spatial heterogeneity in plant placement within a field, pest dispersal behaviors, and pest reproduction . Furthermore, we consider an agricultural field or greenhouse in isolation, ignoring its place within a landscape of natural areas, roads, and other agricultural fields, where optimal strategies may depend on neighboring pest management strategies . Additionally, our model also treats trap plant effectiveness solely in terms of attractiveness, without considering pest arrestment, retention, or mortality on trap plants. In systems where trap plants kill or immobilize pests (e.g., through glandular trichomes or nectar-feeding
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
attractiveness, without considering pest arrestment, retention, or mortality on trap plants. In systems where trap plants kill or immobilize pests (e.g., through glandular trichomes or nectar-feeding traps), trap cropping will likely be more effective, and our model’s recommendations may not hold. Nonetheless, our framework is both general and flexible. We chose to demonstrate it with a deliberately simple and analytically tractable model, designed to clarify the fundamental trade-offs in trap cropping and to serve as a theoretical foundation for future extensions. By distilling the problem to its essential elements, the model provides general insights that remain easily interpretable and transferable. Specifically, the fact that analytic solutions are achieved and only depend on two parameters allowed us to explore the whole parameter space, presenting a complete analysis. This means future researchers adapting the model to incorporate more complex mechanisms have a concrete baseline that they can robustly compare the results to. Several avenues merit further exploration. Spatially explicit models could help examine how the arrangement of trap and cash plants influences pest dynamics, building on work in habitat fragmentation and landscape ecology . Stochastic models could incorporate variable pest dispersal patterns or plant preferences. Further, while our framework was designed for trap cropping, other systems where the grower sacrifices space via companion planting to
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
variable pest dispersal patterns or plant preferences. Further, while our framework was designed for trap cropping, other systems where the grower sacrifices space via companion planting to increase yield in the rest of the field may also work for our framework. For example, a modified version of the model could be used to optimize the amount of area devoted to companion plants that attract beneficial organisms such as pollinators, which, unlike pests, enhance rather than reduce crop yield . In this context, the spatial trade-off between yield-producing and service-providing plants remains, but the ecological mechanism is reversed. Such applications may help inform broader ecological management strategies in agriculture. Trap cropping remains a promising pest management strategy, especially for growers seeking to minimize chemical inputs. However, its success depends critically on the amount of space the grower allocates to trap plants. Our framework offers a tractable approach for identifying optimal land allocations. As such, it provides a foundation for both future theoretical exploration and practical decision-making in sustainable pest management.
|
{
"Authors": "Matthew H Holden",
"Published": "2025-08-07",
"Summary": "Trap cropping is a pest management strategy where a grower plants an\nattractive \"trap crop\" alongside the primary crop to divert pests away from it.\nWe propose a simple framework for optimizing the proportion of a grower's field\nor greenhouse allocated to a main crop and a trap crop to maximize agricultural\nyield. We implement this framework using a model of pest movement governed by\ntrap crop attractiveness, the potential yield threatened by pests, and\nfunctional relationships between yield loss and pest density drawn from the\nliterature. Focusing on a simple case in which pests move freely across the\nfield and are attracted to traps solely by their relative attractiveness, we\nfind that allocating 5-20 percent of the landscape to trap plants is typically\nrequired to maximize yield and achieve effective pest control in the absence of\npesticides. For highly attractive trap plants, growers can devote less space\nbecause they are more effective; less attractive plants are ineffective even in\nlarge numbers. Intermediate attractiveness warrants the greatest investment in\ntrap cropping. Our framework offers a transparent and tractable approach for\nexploring trade-offs in pest management and can be extended to incorporate more\ncomplex pest behaviors, crop spatial configurations, and economic\nconsiderations.",
"Title": "Optimal trap cropping investments to maximize agricultural yield",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
GPT-4 AS EVALUATOR: EVALUATING LARGE LANGUAGE MODELS ON PEST MANAGEMENT IN AGRICULTURE Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang* ABSTRACT In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent. We aimed to prove the feasibility by evaluating the content of the pest management advice generated by LLMs, including the Generative Pre-trained Transformer (GPT) series from OpenAI and the FLAN series from Google. Considering the context-specific properties of agricultural advice, automatically measuring or quantifying the quality of text generated by LLMs becomes a significant challenge. We proposed an innovative approach, using GPT-4 as an evaluator, to score the generated content on Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness. Additionally, we integrated an expert system based on crop threshold data as a baseline to obtain scores for Factual Accuracy on whether pests found in crop fields should take management action. Each model’s score was weighted by percentage to obtain a final score. The results showed that GPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories. Furthermore, the use of instruction-based prompting containing domain-specific knowledge proved the feasibility of LLMs as an effective tool in agriculture, with an accuracy rate of 72%, demonstrating
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
the use of instruction-based prompting containing domain-specific knowledge proved the feasibility of LLMs as an effective tool in agriculture, with an accuracy rate of 72%, demonstrating LLMs’ effectiveness in providing pest management suggestions. Index Terms— Large Language Model, Prompt Engineering, Large Language Model Evaluation, Agriculture, Pest Management 1. INTRODUCTION Language models (LMs), as computer algorithms or systems, are capable of understanding and generating human language, contributing a core component of the field of natural language processing (NLP) [1]. Language models are trained on a vast corpus of text data [2], enabling the model to capture word order or contextual associations, which allows the model to predict the next word or a sequence of words based on a particular probability distribution given an input [2, 3, 4]. LLMs are sophisticated LMs with a considerably larger scale, encompassing billions or hundreds of billions of parameters, and are typically founded upon deep learning methodologies [1]. In contrast to standard LMs, LLMs necessitate massive data for training, thereby enabling LLMs with a broad expanse of knowledge and generalization capabilities. LLMs exhibit enhanced adaptability to a diverse range of tasks and domains [5, 6]. Large, pre-trained language models (PLMs) like BERT (Bidirectional Encoder Representations from Transformers) and GPT have significantly altered the NLP landscape, delivering state-of-the-art results across
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
pre-trained language models (PLMs) like BERT (Bidirectional Encoder Representations from Transformers) and GPT have significantly altered the NLP landscape, delivering state-of-the-art results across various tasks [7]. Traditional NLP methods require handcrafted features and task-specific training, whereas PLMs use a generic latent feature representation learned from extensive training on a wide range of texts adapted for specific NLP tasks [7]. LLMs such as GPT-3.5 and GPT-4 have demonstrated remarkable capabilities as general-purpose computational tools, conditioned by natural language instructions. The efficacy of these models in task performance is substantially contingent upon the quality of prompts used to guide them. Notably, most effective prompts are crafted manually by humans [8]. Prompt engineering emerges as a pivotal area within AI, dedicated to optimising prompts to proficiently direct AI models, particularly those grounded in machine learning and NLP. The emerging research domain includes the design, refinement, and implementation of prompts or instructions that steer the output of LLMs, facilitating the completion of diverse tasks. Generally, LLMs are pre-trained on a massive corpus of unlabeled data to capture a broad understanding of language and knowledge. Followed by small fine-tuning, LLMs are adapted to task-specific datasets to particular applications of interest [9]. Consequently, identifying appropriate evaluation metrics for LLMs across diverse
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Followed by small fine-tuning, LLMs are adapted to task-specific datasets to particular applications of interest [9]. Consequently, identifying appropriate evaluation metrics for LLMs across diverse domains has emerged as a novel and significant research theme. Due to the efficiency in understanding and generating human language, LLMs have been applied across various domains, including finance, medicine and education. However, their adoption in agriculture has been limited, constrained by the field’s specialized nature and the paucity of research exploring their potential in this area. The main contributions of our paper can be summarized as follows: 1 arXiv:2403.11858v1 [cs.CL] 18 Mar 2024 1. Feasibility Study of LLMs for Pest Management Advice Generation in Agriculture: We demonstrate the viability of LLMs in the agricultural pest management domain. 2. Innovative Evaluation Methodology: We introduce a novel approach using GPT-4 for multi-dimensional assessment of generated pest management suggestions. 3. Effective Application of Instruction-Based Prompting Techniques: Our findings highlight a 72% accuracy in LLMdriven pest management decisions through instruction-based prompting that incorporates domain-specific knowledge. 4. Nuanced Differences Between GPT-3.5 and GPT-4: Our research uncovers subtle differences between GPT-3.5 and GPT-4 in decision-making on pest management, emphasizing the importance of model selection in agricultural contexts. 2. RELATED WORK 2.1.
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Our research uncovers subtle differences between GPT-3.5 and GPT-4 in decision-making on pest management, emphasizing the importance of model selection in agricultural contexts. 2. RELATED WORK 2.1. Application of LLMs “FinBERT” is a LM tailored in financial domain, a variant of the BERT model where lies in the specialized pre-training on financial texts, enabling the adaptability to handle the distinctive language and expressions prevalent in the financial sector. “FinBERT” has been applied for financial text mining [10], financial sentiment analysis [11], and financial communications [12]. However, the specialization of “FinBERT” is limit on effectiveness in domains outside of finance as the model’s performance is highly dependent on the quality and representativeness of the financial corpus used for training [10, 11, 12]. Beyond “FinBERT”, Xiao-Yang et al. [13] have introduced “FinGPT”, a novel model based on the transformer architecture, aimed at enhancing the applicability of LLMs in the financial domain. “FinGPT” addresses the limitations in data acquisition and processing faced by traditional financial LLMs by automating the collection of real-time financial data from the Internet. In evaluating LLMs in educational domains, Kung et al. [14] demonstrated that ChatGPT could achieve scores at or near the passing threshold for all three components of the United States Medical Licensing Exam without specific training or reinforcement, underscoring the potential of LLMs to
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
achieve scores at or near the passing threshold for all three components of the United States Medical Licensing Exam without specific training or reinforcement, underscoring the potential of LLMs to support medical education and possibly influence clinical decision-making processes. Similarly, Thirunavukarasu et al. [15] discussed the use of LLMs in healthcare, which covered development and applications in clinics. The review guides clinicians on using LLM technology for patient and practitioner benefits. In agriculture, Dr Som [16] explored the potential applications of OpenAI’s LLM, ChatGPT. Specifically, the paper discusses using ChatGPT across various agricultural tasks, including crop forecasting, soil analysis, crop disease and pest identification. Dr Som highlights that ChatGPT exhibits professional competence in analyzing agricultural data to generate accurate and timely reports, alerts, and insights, facilitate informed decision-making, and enhance customer service. However, it is noted that ChatGPT’s predictions’ accuracy relies heavily on input data quality. Inaccurate, biased, or incomplete data can significantly impact the model’s outputs. Moreover, AI systems like ChatGPT can assist decision-making but are not a substitute for human intuition and experience in complex agricultural environments [16]. Besides, Silva et al. [17] evaluate the capability of LLMs, including GPT-4, GPT-3.5, and Llama2, in responding to agriculturally-related queries. The queries were
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
agricultural environments [16]. Besides, Silva et al. [17] evaluate the capability of LLMs, including GPT-4, GPT-3.5, and Llama2, in responding to agriculturally-related queries. The queries were sourced from agricultural examinations and datasets from the United States, Brazil, and India. The study assessed the accuracy of answers produced by LLMs, the effectiveness of retrieval-augmented generation (RAG) and ensemble refinement (ER) techniques, and the comparative performance against human respondents. Silva et al. [17] discovered that in various tasks, GPT-4 performed better than GPT-3.5 and Llama2, achieving an impressive 93% accuracy rate in the certified crop adviser (CCA) exam. Additionally, in the study by Jiajun et al. [18], the application of LLMs, particularly GPT-4, in agriculture for pest and disease diagnosis is explored. Jiajun [18] introduces a novel approach that combines the deep logical reasoning capabilities of GPT-4 with the visual comprehension abilities of the You Only Look Once (YOLO) network. The paper evaluates the YOLO-PC, a new lightweight variant of YOLO, using metrics such as accuracy rate (94.5%) and reasoning accuracy (90% for agricultural diagnostic reports), assessing the quality of model-generated text in correlation with the recognized information [18]. 2.2. Prompt & Prompt Engineering Prompts are a mechanism for interaction with large language models (LLMs) to accomplish specific tasks [19]. Prompts act as essentially instructions
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
information [18]. 2.2. Prompt & Prompt Engineering Prompts are a mechanism for interaction with large language models (LLMs) to accomplish specific tasks [19]. Prompts act as essentially instructions directed towards LLMs, comprise the input provided by users and guide the model to generate answers for the response [20]. The nature of the inputs are vary, encompassing explanations, queries, or any other form of input, contingent upon the intended application of the model [19]. In contrast to traditional supervised learning, where models are trained to predict output from input using a probability distribution, prompt-based learning operates on LLMs that directly model textual probabilities. Prompt-based learning involves modifying the original input into a text string prompt with unfilled slots using templates. Subsequently, prompts are populated using the probabilistic capabilities of the LLM to generate the final 2 string [21]. Essentially, prompt engineering represents a practice of engaging effectively with AI systems to optimise the utility [22]. In addition, prompt engineering has been applied in various domains such as medical [22, 23, 24], generative art [25], multilingual legal judgment prediction [26], and the extraction of accurate materials data [27]. As delineated in the “Prompt Engineering Guide [28]”, constructing an effective prompt can involve integrating four elements or a combination: Instruction, Context, Input Data and Output Indicator. Instruction
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
in the “Prompt Engineering Guide [28]”, constructing an effective prompt can involve integrating four elements or a combination: Instruction, Context, Input Data and Output Indicator. Instruction refers to a specific task or directive to guide the model to perform a designated operation. Context encompasses providing supplementary information or background, instrumental in steering the model towards more accurate responses. Input Data pertains to the specific question or input content the model solicits to respond to. Lastly, the Output Indicator concerns the desired type or format of the model’s output. The iterative development process also outlined four prompt guidelines [29]: • Be clear and specific: The prompts should be unambiguous and detailed enough to guide the model precisely towards the intended task or output. • Analyze why the result does not give the desired output: If the output from the prompt does not meet expectations, it is crucial to analyze the reasons behind the discrepancy. • Refine the idea and the prompt: Based on the analysis, adjustments should be made to both the underlying idea and the wording or structure of the prompt to improve results. • Repeat: The process is not linear but cyclical, after refining, the new prompt is tested, and the cycle of analysis and refinement continues until the desired outcome is achieved. 3. EXPERIMENT DESIGN 3.1. Experiment Models This section provides an overview of the two LLMs evaluated in the experiment: Section
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
and refinement continues until the desired outcome is achieved. 3. EXPERIMENT DESIGN 3.1. Experiment Models This section provides an overview of the two LLMs evaluated in the experiment: Section 3.1.1 covers the GPT series from OpenAI, specifically GPT-3.5 and GPT-4, while Section 3.1.2 describes the FLAN-T5 model developed by Google. 3.1.1. GPT The transformer architecture, proposed by Vaswani et al. in the paper “Attention is All You Need” [30], became the cornerstone for the GPT [31, 32]. The OpenAI GPT model, introduced in the paper ”Improving Language Understanding by Generative PreTraining” [33], undergoes pre-training through language modelling on a substantial dataset to capture long-range dependencies within the text. Due to the GPT model’s advanced capability to understand and generate human-like text [34], it becomes an ideal choice for exploring complex agriculture tasks and serves as the experimental model. Specifically, the GPT-3.5 and GPT-4 models were used in the experiments. GPT-3.5 and GPT-4 are successive generations of artificial intelligence language models developed by OpenAI. The GPT-3.5 model is proficient in understanding and generating natural language or code and has been optimized specifically for chat-based interactions through the Chat Completion API. However, it remains applicable to non-chat tasks. The GPT-4 model, as a large multi-modal model, exhibits a broader comprehension of general knowledge and reasoning capabilities, enabling it to
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
API. However, it remains applicable to non-chat tasks. The GPT-4 model, as a large multi-modal model, exhibits a broader comprehension of general knowledge and reasoning capabilities, enabling it to solve complex problems with greater accuracy compared to GPT-3.5 and its predecessors [35]. 3.1.2. FLAN-T5 The T5 model significantly advances natural language processing through its novel unified framework. T5 converts all language problems into a text-to-text format, facilitating extensive exploration of transfer learning techniques. Employing a combination of supervised and self-supervised training methods, including a novel use of corrupted tokens for pre-training, T5 sets new benchmarks across a range of NLP tasks by leveraging its encoder-decoder architecture and the extensive “Colossal Clean Crawled Corpus” [36]. FLAN-T5 is an evolution of the original T5 model, which was fine-tuned on over a thousand additional tasks and expanded language coverage. FLAN-T5 significantly enhances performance and versatility, even in few-shot scenarios, achieving state-of-the-art results on various benchmarks [37]. The FLAN-T5 model is available in various sizes, including Small, Base, Large, XL, and XXL, with the XXL version being the largest, encompassing 11 billion parameters. Unlike the GPT model, FLAN models require downloading the checkpoints locally to generate the response. Given the considerations for computational speed and memory constraints, the FLAN-T5XL variant is selected as
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
model, FLAN models require downloading the checkpoints locally to generate the response. Given the considerations for computational speed and memory constraints, the FLAN-T5XL variant is selected as a more practical option for experimental use containing 3 billion parameters. The pre-trained model 3 “google/flan-t5-xl” weights and configuration are loaded using the transformers library. The weights and configurations are based on the previously saved checkpoint containing all the model parameters. 3.2. Baselines This section elucidates the methodology for generating labelled samples used to construct pest scenarios based on the expert system. To assess the ability of LLMs to determine whether specific pest scenarios necessitate action, a baseline of labelled samples is essential. Section 3.2.1 delineates the composition of the expert system, including four data files, while Section 3.2.2 elaborates on the process of generating labelled samples from the expert system’s data for the construction of pest scenarios. 3.2.1. Expert System As the baseline for this experiment, an Expert System is used to evaluate the Factual Accuracy of three different Large Language Models (LLMs) below on whether pests found in the crop fields necessitate management actions. The Expert System comprises four datasets extracted from the AHDB’s Encyclopaedia of Pests and Natural Enemies in Field Crops [38]. These datasets include two in structured data ‘JSON’ format: ‘pest to affected crop.json’ and
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
four datasets extracted from the AHDB’s Encyclopaedia of Pests and Natural Enemies in Field Crops [38]. These datasets include two in structured data ‘JSON’ format: ‘pest to affected crop.json’ and ‘pest to threshold.json’ and two in ‘XLSX’ format ‘thresholds database.xlsx’ and ‘pest to management.xlsx’. • File ‘pest to affected crop.json’ summarises various pests and the crops. It lists different types of pests where each pest is associated with one or more crops. • File ‘pest to threshold.json’ provides information on the thresholds for pests, specifying when action should be taken to manage them. Each entry includes the pest name and a threshold description, which details the criteria for deciding when to take action, such as the temperature, location, plant stages, pest density levels and the extent of crop damage. • File ‘thresholds database.xlsx’ features a first column listing the names of pests, with other columns containing threshold information extracted from the file ‘pest to threshold.json’. The threshold includes pest density metrics such as ‘per square meter’, ‘per plant’, ‘leaf area eaten’, ‘per trap’, ‘of petioles damaged’, ‘of plants are infested’, ‘of tillers infested’, ‘per pheromone trap’, ‘per ear’, ‘per trap on two consecutive occasions’, ‘per yellow sticky trap’, and ‘per gram of soil’. • File ‘pest to management.xlsx’ has two columns, the first listing the names of pests and the second detailing management suggestions for Non-chemical control
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
sticky trap’, and ‘per gram of soil’. • File ‘pest to management.xlsx’ has two columns, the first listing the names of pests and the second detailing management suggestions for Non-chemical control solutions that meet the criteria for affected plants and thresholds achieved. Notably, the Expert System is designed to output Non-chemical control solutions only when all specified conditions are met, defined as action is necessitated. As the benchmark for Factual Accuracy, the Expert System is not engaged in evaluating the accuracy, being designated as unequivocally 100% accurate. Only the outputs, Non-chemical control solutions, from expert systems are subject to evaluation by GPT-4, focusing on Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness. 3.2.2. Generation of Input Samples from Expert System Figure 1 shows the process for generating labelled pest samples. Files ‘pest to affected crop.json’, ‘thresholds database.xlsx’, and ‘pest to management.xlsx’ are used for the generation of labelled pest samples, serving as inputs for constructing prompts for LLMs. Although the data across these files are indexed by pest name, variations exist in the pests included due to the differing extraction methods employed from the AHDB database [38]. By querying pests of the same species, 25 types of pests, along with their affected crops, thresholds and Non-chemical control solutions, have been extracted. The ‘generate densities’ function provides a
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
pests of the same species, 25 types of pests, along with their affected crops, thresholds and Non-chemical control solutions, have been extracted. The ‘generate densities’ function provides a mechanism for determining ‘true’ and ‘false’ density values. By iterating through a list of density-related columns in file ‘thresholds database.xlsx’, the function searches for non-null entries that signify recorded density thresholds. When encountering a valid density value, the function performs a series of operations to cleanse and standardize the data, including removing percentage symbols or relational operators. Subsequently, the numerical density value is manipulated to generate a series of ‘true’ densities, inflating the original value by adding a random integer ranging from 1 to 10 to simulate density conditions exceeding the threshold for pest management action. Conversely, ‘false’ densities are generated by subtracting a random integer from the original value, ensuring the resultant value does not fall below zero. These reduced values represent conditions below the pest management threshold, indicating no action is needed. The generated ‘true’ and ‘false’ density values are then appended with the original measurement metric (e.g., ‘per plant’, ‘per square meter’) and a percentage symbol if the original value was expressed as a percentage. 4 Data Extraction and Indexing thresholds_database.xlsx Generate True (exceeding threshold) and False (below threshold) density values by
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
symbol if the original value was expressed as a percentage. 4 Data Extraction and Indexing thresholds_database.xlsx Generate True (exceeding threshold) and False (below threshold) density values by manipulating the original density values with random integers. Density Values Generation thresholds_database.xlsx pest_to_management.xlsx pest_to_affected_crop.json Query and extract pests by indexing pest names, obtaining 25 pests with their affected crops, thresholds, and non-chemical control solutions. 25 Pests Affected Crops Generation pest_to_affected_crop.json True Densities False Densities True Crops False Crops Samples Generation Pest Name: … True Crops: … False Crops: … True Densities: … False Densities: … Control Solutions: … Random Temperature Random Location 1. Generate Positive Samples (1) by pairing True crops with True density values. 2. Generate Negative Samples (0) by either pairing True crops with False densities or False crops with any density values. 3. Augment samples with random temperature and latitude parameters. 4. Randomly select one positive (labelled 1) and one negative (labelled 0) sample for each pest, resulting to a total of 50 samples for experiment. Positive Samples (1): True Crops + True Densities Negative Samples (0): True Crops + False Densities False Crops + True Densities False Crops + False Densities Positive Sample Pest Name: … Crop: … Density: … Temperature: … Location: … Label: 1 Negative Sample Pest: … Crop: … Density: … Temperature: …
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Crops + True Densities False Crops + False Densities Positive Sample Pest Name: … Crop: … Density: … Temperature: … Location: … Label: 1 Negative Sample Pest: … Crop: … Density: … Temperature: … Location: … Label: 0 Generate True (crops affected by the pest) and False (crops unaffected by the pest) crops by classifying the data file pest_to_affected_crop.json. Fig. 1: Generation of Input Samples from Expert System. This image outlines a process for generating labelled pest samples, detailing steps from data extraction and indexing of pests, through generating true and false density values and crops, to the creation of positive and negative samples for experiment. The core of the sample generation process is to create the combinations that represent an action that is needed (labelled as ‘1’) and not needed (labelled as ‘0’) for various pest conditions. This bifurcation is achieved by deliberately pairing crops and pest density values under varying conditions. Positive samples are formulated by coupling ‘true’ crops (crops affected by the pest) with ‘true’ density values. Conversely, negative samples emerge from the strategies: pairing ‘true’ crops with ‘false’ densities and ‘false’ crops (crops unaffected by the pest) with either ‘true’ or ‘false’ densities. These combinations are augmented with randomly generated temperature and latitude location parameters to diversify the dataset further. Considering computational resources and experimental costs, also ensuring an even
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
are augmented with randomly generated temperature and latitude location parameters to diversify the dataset further. Considering computational resources and experimental costs, also ensuring an even distribution of positive and negative samples, one positive sample (labelled as ‘1’) and one negative sample (labelled as ‘0’) are randomly selected for each of the 25 pest types. Eventually, a total of 50 samples are generated for experimentation. The samples are indexed in pest names, with other columns containing crops, pest density, temperature and location. Among these, crops and pest density determine the label as 1 or 0, whereas temperature and location only enrich the scene and do not affect the label. 3.3. Experiment Prompting This section lists the prompts constructed using different techniques in the experiment. Four prompt techniques: zero-shot prompting described in Section 3.3.1, few-shot prompting in Section 3.3.2, instruction-based prompting in Section 3.3.3, and self-consistency prompting in Section 3.3.4, incorporate samples of pest scenarios generated in Section 3.2.2 into prompts. These prompts serve as inputs for LLMs to generate responses, which are then evaluated. 3.3.1. Zero-shot Prompting Zero-shot prompting refers to providing instructions or requests to LLMs without needing prior examples or contextual information. Zero-shot prompting necessitates the ability of the model to comprehend and respond to tasks or queries not directly encountered before
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
LLMs without needing prior examples or contextual information. Zero-shot prompting necessitates the ability of the model to comprehend and respond to tasks or queries not directly encountered before [39]. The model relies on the extensive knowledge and understanding acquired during the training phase for zero-shot prompting. For instance, when posed with a question that has not previously been addressed, the model can still understand and attempt to provide an answer [40]. 5 For zero-shot prompting, 50 input samples are iteratively filled into the following prompt template via a loop: I discovered {Pest} in my {Crop}, with a density of {Density}. The temperature was {Temperature}, and the location was at {Location}. Could you please provide some control and management suggestions? This prompt is then input into a GPT or FLAN model. 3.3.2. Few-shot Prompting In contrast to zero-shot prompting, few-shot prompting provides relevant examples to guide the model to understand and execute a task. Few-shot prompting can be employed to facilitate in-context learning, where demonstrations in the prompt guide the model towards enhanced performance [41]. According to Min et al. [42], in the context of few-shot learning, both the label space and the distribution of the input text defined by the demonstrations are crucial for performance, irrespective of the accuracy of individual labels. Additionally, the format of the demonstrations, including random labels, significantly influences
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
by the demonstrations are crucial for performance, irrespective of the accuracy of individual labels. Additionally, the format of the demonstrations, including random labels, significantly influences effectiveness, which is better than not using any labels. The core of the few-shot learning approach is encapsulated within a create_prompt function. The function filters 50 input samples to select only samples with a label of ‘1’ and a pest different from the current input pest. It randomly selects three samples and constructs a few-shot prompt containing questions and answers. Each question is formulated from the pest, crop, density, temperature, and location of the selected input samples, same as the zero-shot prompting template, followed by the respective Non-chemical control solutions from file ‘pest to management.xlsx’. Finally, the prompt adds a new question using the current input sample without providing an answer. The template of the few-shot prompt is shown below: Question: I discovered {Pest 1} in my {Crop 1}, with a density of {Density 1}. The temperature was {Temperature 1}, and the location was at {Location 1}. Could you please provide some control and management suggestions? Answer: {Non-chemical control solutions for Pest 1} Question: I discovered {Pest 2} in my {Crop 2}, with a density of {Density 2}. The temperature was {Temperature 2}, and the location was at {Location 2}. Could you please provide some control and management suggestions? Answer: {Non-chemical
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
2}, with a density of {Density 2}. The temperature was {Temperature 2}, and the location was at {Location 2}. Could you please provide some control and management suggestions? Answer: {Non-chemical control solutions for Pest 2} Question: I discovered {Pest 3} in my {Crop 3}, with a density of {Density 3}. The temperature was {Temperature 3}, and the location was at {Location 3}. Could you please provide some control and management suggestions? Answer: {Non-chemical control solutions for Pest 3} Question: I discovered {Pest} in my {Crop}, with a density of {Density}. The temperature was {Temperature}, and the location was at {Location}. Could you please provide some control and management suggestions? Answer: 3.3.3. Instruction-based Prompting As mentioned in Section 2.2, constructing an effective prompt can involve any of the four elements: Instruction, Context, Input Data and Output Indicator [28]. Giray [43] discussed the importance of understanding the prompt component and its role in facilitating effective communication with the model. Through prompt design with these four elements, Giray [43] found one can guide model behaviour and improve response quality, ensuring output is precise and meaningful. The template of the instruction-based prompt is: Instruction: Generate comprehensive and sustainable pest management suggestions based on the given crop, pest type and density, and environmental conditions, including temperature and location. Context: Pest management in
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
comprehensive and sustainable pest management suggestions based on the given crop, pest type and density, and environmental conditions, including temperature and location. Context: Pest management in agriculture requires balancing control measures with environmental sustainability. Different crops and pests respond to varied strategies, and local environmental conditions significantly influence the effectiveness of these strategies. Input Data: For example: For pest: {Pest} The affected crops are: {Affected Crops} The threshold is: {Threshold} The non-chemical control solution could be: {Non-chemical control solutions} Output Indicator: Question: I discovered {Pest} in my {Crop}, with a density of {Density}. The temperature was {Temperature}, and the location was at {Location}. Could you please provide some control and management suggestions? Please first determine whether management measures are needed, then output your own control solution in about 200 words. The Instruction defines the pest management task for the model and guides the model to focus on the data in the input question. The context explains why pest management in agriculture is essential, helping the model better understand the broader implications of pest management and the necessity of tailoring suggestions to specific scenarios. The structured example 6 systematically introduces the input data, incorporating placeholders for designated variables. Precisely, the {Pest} variable corresponds to the pest
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
to specific scenarios. The structured example 6 systematically introduces the input data, incorporating placeholders for designated variables. Precisely, the {Pest} variable corresponds to the pest identified in the inquiry, while the {Affected Crops} are derived from the ‘pest to affected crop.json’ file. Similarly, the {Threshold} values are extracted from the ‘thresholds database.xlsx’ file, and the {Non-chemical control solutions} are obtained from the ‘pest to management.xlsx’ file. All input data variables are dynamically populated based on the specific pest mentioned in the question. 3.3.4. Self-consistency Prompting Self-consistency is an advanced prompting technique introduced by Wang et al. [44] building upon chain-of-thoughts (CoT). This innovative approach involves generating diverse reasoning paths rather than relying on the most immediately probable path. Self-consistency then deduces the most consistent answer by aggregating across these varied reasoning paths. Self-consistency prompting summarised the responses from the zero-shot, few-shot, and instruction-based prompting and gave a final response. The template of self-consistency prompting is: Given these three responses: Response 1: {Response 1 from zero-shot prompting} Response 2: {Response 2 from few-shot prompting} Response 3: {Response 3 from instruction-based prompting} Create a summary response that combines the best elements of question: I discovered {Pest} in my {Crop}, with a density of {Density}.
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Response 3: {Response 3 from instruction-based prompting} Create a summary response that combines the best elements of question: I discovered {Pest} in my {Crop}, with a density of {Density}. The temperature was {Temperature}, and the location was at {Location}. Could you please provide some control and management suggestions? 3.4. GPT-4 as Evaluator Twelve combinations emerge when integrating FLAN, GPT-3.5, and GPT-4 models with four prompting methodologies. Each combination is subjected to fifty input pest samples characterized by varying density and environmental conditions, generating respective responses. These responses are then evaluated by GPT-4 regarding the accuracy and the linguistic quality of the generated pest management suggestions. The prompt guiding the GPT to serve as an evaluator for determining the necessity of action in responses and assessing the linguistic quality of these responses draws inspiration from the article “G-EVAL: NLG Evaluation using GPT-4 with Better Human Alignment”, where the article introduces the G-EVAL framework, designed for evaluating the quality of text generated by Natural Language Generation (NLG) systems [45]. For accuracy evaluation, the prompt begins with the ‘Evaluation Guide’, which instructs the GPT to assess and decide whether action is required. The prompt followed with the ‘Evaluation Criteria’ to inform the GPT that this is a binary evaluation to assign ‘1’ or ‘0’. Through the Evaluation Steps, the GPT is guided with
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
action is required. The prompt followed with the ‘Evaluation Criteria’ to inform the GPT that this is a binary evaluation to assign ‘1’ or ‘0’. Through the Evaluation Steps, the GPT is guided with the CoT sequence, asked to decide whether the information presented within a response indicates that an action is required (‘1’) or not required (‘0’) in the following prompt and the corresponding response. The template for accuracy evaluation is shown below: Evaluation Guide: You will be provided with a prompt and the corresponding response for pest management. Your task is to evaluate the response based on the criteria below and decide whether action is required based on the response. Please read and understand these instructions carefully. Refer back to this document as needed during your evaluation. Evaluation Criteria: Action Required (1 or 0) This is a binary evaluation to determine if action is needed based on the response provided. Evaluation Steps: 1. Carefully read the pest management suggestion in the response, identifying the main content, pay special attention to the first sentence in the response, as it generally contains the decision of whether to take actions. 2. Analyze the response to see if it states whether action is required or not required to manage the pest. 3. Assign a score based on the evaluation criteria: 0 means no action is needed, 1 means the suggestion requires action. 4. If the response suggests the action is optional, needs further observation or
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Assign a score based on the evaluation criteria: 0 means no action is needed, 1 means the suggestion requires action. 4. If the response suggests the action is optional, needs further observation or continuous monitoring, leaves room for doubt, lacks clearly direction, contains not be necessary or not immediate control, or if you cannot determine with complete certainty that it indicates for management action, please mark it as 0. Here are the prompt and response you need to evaluate: Prompt: {Prompt} Response: {Response} Please state whether action is required (Answer 0 or 1 ONLY): The linguistic quality evaluation contains six dimensions: Coherence, Logical Consistency, Fluency, Relevance, Comprehensiveness, and Exhaustiveness. The structure of the prompt for linguistic quality evaluation is similar to accuracy evaluation, 7 comprising an Evaluation Guide with instructions, Evaluation Criteria that include scoring standards, and Evaluation Steps based on a CoT approach. Except for some differences in details and descriptors, the principal distinction lies in the judgment required from the GPT. For accuracy evaluation, the GPT is tasked with making a binary decision regarding the necessity of action. In contrast, evaluating linguistic quality required the GPT to assign scores ranging from 1 to 10 for each of the six dimensions. 4. RESULTS Model & Prompting Coherence Consistency Fluency Relevance Comprehensibility Exhaustiveness FLAN zero-shot 2.52 2.52 3.30 2.36 2.76 2.96
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
ranging from 1 to 10 for each of the six dimensions. 4. RESULTS Model & Prompting Coherence Consistency Fluency Relevance Comprehensibility Exhaustiveness FLAN zero-shot 2.52 2.52 3.30 2.36 2.76 2.96 FLAN few-shot 2.68 3.00 3.42 2.44 3.32 3.46 FLAN instruction-based 3.70 3.92 4.84 5.06 5.04 4.36 FLAN self-consistency 2.64 3.22 4.04 1.94 3.92 3.18 GPT-3.5 zero-shot 8.82 8.24 9.90 8.74 9.54 7.54 GPT-3.5 few-shot 8.14 8.24 9.86 9.26 8.36 6.28 GPT-3.5 instruction-based 8.28 8.20 9.60 8.92 9.14 6.92 GPT-3.5 self-consistency 7.98 8.00 9.80 7.70 9.44 7.16 GPT-4 zero-shot 9.14 8.88 10.00 9.86 9.38 8.74 GPT-4 few-shot 8.32 8.46 9.98 9.46 8.92 7.14 GPT-4 instruction-based 8.62 8.76 9.64 9.46 9.32 7.68 GPT-4 self-consistency 8.72 8.90 10.00 9.30 9.88 8.14 Table 1: Linguistic quality of different models and prompting methods evaluated by GPT-4 Model & Prompting TP TN FP FN Accuracy Precision Recall F1 Score Final Score FLAN zero-shot 20 6 19 5 0.52 0.51 0.80 0.62 37.22 FLAN few-shot 10 10 15 15 0.40 0.40 0.40 0.40 34.32 FLAN instruction-based 14 14 11 11 0.56 0.56 0.56 0.56 49.32 FLAN self-consistency 24 1 24 1 0.50 0.50 0.96 0.66 38.94 GPT-3.5 zero-shot 25 4 21 0 0.58 0.54 1.00 0.70 75.98 GPT-3.5 few-shot 17 8 17 8 0.50 0.50 0.68 0.58 70.14 GPT-3.5 instruction-based 24 12 13 1 0.72 0.65 0.96 0.77 79.86 GPT-3.5 self-consistency 25 0 25 0 0.50 0.50 1.00 0.67 70.08 GPT-4 zero-shot 24 4 21 1 0.56 0.53 0.96 0.69 78.40 GPT-4 few-shot 21 7 18 4 0.56 0.54 0.84 0.66 74.68 GPT-4
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
1 0.72 0.65 0.96 0.77 79.86 GPT-3.5 self-consistency 25 0 25 0 0.50 0.50 1.00 0.67 70.08 GPT-4 zero-shot 24 4 21 1 0.56 0.53 0.96 0.69 78.40 GPT-4 few-shot 21 7 18 4 0.56 0.54 0.84 0.66 74.68 GPT-4 instruction-based 24 9 16 1 0.66 0.60 0.96 0.74 79.88 GPT-4 self-consistency 25 0 25 0 0.50 0.50 1.00 0.67 74.94 Table 2: Performance metrics of different models and prompting methods with final scores Tables 1 and 2 respectively present the linguistic quality of different models and prompting methods evaluated by GPT-4, and the performance metrics of different models and prompting methods with the final scores for each model. The linguistic quality evaluation involves scoring the responses based on the generated pest management suggestions across 50 samples, each representing an average derived from these responses. In performance metrics, the TP (True Positives), TN (True Negatives), FP (False Positives), and FN (False Negatives) are used as foundational elements for calculating Accuracy, Precision, and Recall. To calculate the final scores for each “Model & Prompting” combination, we use a weighted average approach based on pre-determined weights for various evaluation metrics. Specifically, the weights for Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness are each allocated 10%, while Accuracy is assigned a higher weight of 40%. In the computation of the Final Score, the metrics of Coherence, Consistency, Fluency, Relevance,
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
and Exhaustiveness are each allocated 10%, while Accuracy is assigned a higher weight of 40%. In the computation of the Final Score, the metrics of Coherence, Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness are evaluated on a scale from 1 to 10. In contrast, Accuracy is averaged, falling in a range from 0 to 1. To harmonize these scores for a unified presentation in a percentage format, the scores for the linguistic quality are multiplied by 10, and the Accuracy score is multiplied by 100, facilitating a standardized evaluation outcome expressed on a 100-point scale. 8 The mathematical formulation for the final score for each model can be expressed as follows: Final Score = 0.1 × (V alCoherence + V alConsistency + V alFluency + V alRelevance + V alComprehensibility + V alExhaustiveness) × 10 + 0.4 × V alAccuracy × 100 (1) Where V alCoherence, V alConsistency, V alFluency, V alRelevance, V alComprehensibility and V alExhaustiveness respectively represent the numerical values scored by GPT-4 for the dimensions of Coherence, Consistency, Fluency, Relevance, Comprehensibility and Exhaustiveness as listed in Table 1, and V alAccuracy denotes the average accuracy value in Table 2. From Table 1, it can be observed that the performance of the different models and their application of different prompting methods on the various dimensions of language quality. Specifically, the FLAN model scores low on each assessed dimension, showing its understanding and
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
and their application of different prompting methods on the various dimensions of language quality. Specifically, the FLAN model scores low on each assessed dimension, showing its understanding and generating language limitations. For example, FLAN zero-shot scored no more than 3.3 on cohesion, logical consistency, fluency, relevance, comprehensibility, and exhaustiveness, indicating that the FLAN model struggles to handle complex language tasks effectively without specific training or guidance in generating pest management suggestions. In contrast, the GPT-3.5 and GPT-4 models scored significantly higher than the FLAN model on all dimensions, especially GPT-4, which achieved a perfect score of 10 on fluency and scored higher than 8 on the remaining dimensions. This result also demonstrates the excellent ability of GPT-3.5 and GPT-4 to generate high-quality, logically consistent, and relevant suggestions in pest management. It is worth noting that the same model scores roughly the same on all dimensions of linguistic quality using different prompting methods, suggesting that variations in prompting method have a limited impact on the language quality of the model output. For example, the scores of the FLAN model under different prompting methods are different, but the overall performance is still poor. In contrast, the GPT-3.5 and GPT-4 models maintain a high level of performance on all dimensions regardless of the prompting method used. Table 2 shows differences in
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
performance is still poor. In contrast, the GPT-3.5 and GPT-4 models maintain a high level of performance on all dimensions regardless of the prompting method used. Table 2 shows differences in performance metrics across models and prompting methods, focusing on accuracy, precision, recall, and F1 scores. Evidently, the GPT-3.5 and GPT-4 models outperform the FLAN model across nearly all metrics, indicating their superior ability to generate pest management advice. Interestingly, while most models exhibit high recall rates, their accuracy and precision remain low. This suggests that although the models can identify positive samples which require action in pest scenarios, they did the wrong classification in scenarios scenarios that not require an action, leading to a high rate of FP. Moreover, the performance impact of different prompting methods on the same model varies. For example, the accuracy of the instruction-based method outperforms other prompting methods for the same model. This is attributed to including pest threshold levels and affected crops in the instruction-based prompts, enabling LLMs to make better-informed judgments in pest management based on the information provided in the prompts. The instruction-based method with GPT-3.5 demonstrates the best performance in accuracy, precision, recall, and F1 scores. Unexpectedly, it even surpasses GPT-4. Examination of model responses reveals that although GPT-4 may better understand the content of prompts or appear
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
in accuracy, precision, recall, and F1 scores. Unexpectedly, it even surpasses GPT-4. Examination of model responses reveals that although GPT-4 may better understand the content of prompts or appear “smarter”, it occasionally makes judgments such as “Although your current density is not at the advised threshold level, preventive measures should be taken before populations reach damaging thresholds” (indicating action despite not reaching the threshold) or “Although ... are not typical pests of ..., they may occasionally be found on various crops” (classifying a non-affected pest as potentially affecting other crops, thereby suggesting that action is needed). This leads to GPT-4 inaccurately classifies negative samples. Meanwhile, GPT-3.5 adheres strictly to the thresholds specified in the prompts, more inclined to conclude that “... does not currently reach the treatment threshold, management measures may not be immediately necessary” so that making more accurate judgments on negative samples. The self-consistency prompting exhibits the poorest performance among all prompting methods. Despite its ability to correctly identify almost all positive samples, it incorrectly classifies nearly all negative samples as positive, suggesting that self-consistency prompts the model to judge nearly every scenario as requiring action. This outcome is due to the prompt containing a directive to “Create a summary response that combines the best elements”, asking the model to summarize
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
judge nearly every scenario as requiring action. This outcome is due to the prompt containing a directive to “Create a summary response that combines the best elements”, asking the model to summarize based on responses from zero-shot, few-shot, and instruction-based prompting. Given the model’s inherent insensitivity to negative samples, such summarization further deteriorates precision. The instruction-based scores of GPT-4 are comparable to those of GPT-3.5, both around 79, indicating a clear advantage in pest management scenarios when the model is provided with affected crops and threshold information in the prompt. In contrast, FLAN model scores are generally lower, with its instruction-based score reaching 49.32 but still below those of GPT series models, reflecting FLAN’s limitations in agricultural domain knowledge. The self-consistency prompting method performs relatively better in GPT-3.5 and GPT-4 models but still scores below instruction-based prompting due to its tendency to classify nearly all negative scenarios as positive. Zero-shot and few-shot methods score lower across all models, likely due to their lack of sufficient contextual information to guide the model in generating the most relevant and accurate advice. 9 5. CONCLUSION In conclusion, this study evaluated the ability of different LLMs to generate suggestions for pest management in agriculture using different prompting methods. By simulating various pest scenarios, we understood the strengths and
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
the ability of different LLMs to generate suggestions for pest management in agriculture using different prompting methods. By simulating various pest scenarios, we understood the strengths and limitations of using different LLMs and prompting methods, bridging the gap between the lack of research on LLMs in agriculture. GPT-3.5 and GPT-4 showed accuracy and relevance in delivering pest management solutions, demonstrating the potential of GPT-4 as an agricultural support tool. However, LLMs tended to generate generic suggestions and showed less sensitivity in facing negative samples. This highlights the need for continuous model updating and domain-specific fine-tuning. Instruction-based prompting led to a significant increase in the accuracy of LLMs, confirming that the addition of relevant knowledge domains has an indispensable role in generating responses to LLMs. In the future, we aim to enhance prompting methodologies to enable LLMs to generate more precise evaluations by integrating domain-specific knowledge. Simultaneously, by refining the prompts, we aspire for LLMs to deliver more detailed and user-friendly responses. Given that this technology primarily targets farmers, it is advantageous if responses can provide differentiated pest control methods tailored to various pest stages, including recommendations on varying dosages for management, intervals for prevention and control or subsequent monitoring. 6.
|
{
"Authors": "Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng, Kang Liu, Po Yang",
"Published": "2024-03-18",
"Summary": "In the rapidly evolving field of artificial intelligence (AI), the\napplication of large language models (LLMs) in agriculture, particularly in\npest management, remains nascent. We aimed to prove the feasibility by\nevaluating the content of the pest management advice generated by LLMs,\nincluding the Generative Pre-trained Transformer (GPT) series from OpenAI and\nthe FLAN series from Google. Considering the context-specific properties of\nagricultural advice, automatically measuring or quantifying the quality of text\ngenerated by LLMs becomes a significant challenge. We proposed an innovative\napproach, using GPT-4 as an evaluator, to score the generated content on\nCoherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and\nExhaustiveness. Additionally, we integrated an expert system based on crop\nthreshold data as a baseline to obtain scores for Factual Accuracy on whether\npests found in crop fields should take management action. Each model's score\nwas weighted by percentage to obtain a final score. The results showed that\nGPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.\nFurthermore, the use of instruction-based prompting containing domain-specific\nknowledge proved the feasibility of LLMs as an effective tool in agriculture,\nwith an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing\npest management suggestions.",
"Title": "GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Mathematical modeling for sustainable aphid control in agriculture via intercropping Alfonso Allen-Perkins1,2, Ernesto Estrada3,4,5* November 16, 2021 1Instituto de Física, Universidade Federal da Bahia, 40210-210 Salvador, Brazil; 2Complex System Group, Universidad Politécnica de Madrid, 28040-Madrid, Spain; 3Institute of Applied Mathematics (IUMA), Universidad de Zaragoza, Pedro Cerbuna 12, E-50009 Zaragoza, Spain; 4ARAID Foundation, Government of Aragón, 50018 Zaragoza, Spain; 5Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, 13560-970 São Carlos, São Paulo, Brazil. *Corresponding author: Ernesto Estrada, email: estrada66@unizar.es Abstract Agricultural losses to pest represent an important challenge in a global warming scenario. Intercropping is an alternative farming practice that promotes pest control without the use of chemical pesticides. Here we develop a mathematical model to study epidemic spreading and control in intercropped agricultural fields as a sustainable pest management tool for agriculture. The model combines the movement of aphids transmitting a virus in an agricultural field, the spatial distribution of plants in the intercropped field, and the presence of “trap crops” in an epidemiological Susceptible-Infected-Removed (SIR) model. Using this model we study several intercropping arrangements without and with trap crops and find a new intercropping arrangement that may improve significantly pest management in
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
(SIR) model. Using this model we study several intercropping arrangements without and with trap crops and find a new intercropping arrangement that may improve significantly pest management in agricultural fields respect to the commonly used intercrop systems. 1 Introduction The sustainable intensification of agriculture is imperative for feeding a growing world population while minimizing its negative environmental impact. The world population will increase to between 9.6 and 12.3 billion in 2100 [1], and for feeding these additional 2-4 billion people, a duplication (100-110%) of crop production relative to its 2005 level is needed [2]. Today, 10% of ice-free land on Earth is used for crop cultivation [3], and returning half of Earth’s terrestrial ecoregions to nature will mean global losses of 15–31% of cropland and of 3–29% of food calories [4]. Thus, increasing crop yield without extending the size of cultivation areas nor by intensifying the use of current technologies is a vital complex problem to be solved in the coming years. Agricultural yield is substantially reduced by pests [5, 6, 7, 8, 9], which cause losses of 10-16% to crop production [5, 6, 7, 8, 9], which may represent real threats for entire world regions [10]. In addition to these scenarios, there is increasing concern that climate change can increase plant damage from pests in future decades [11, 12, 13, 14, 15, 16]. Bebber et al. [17] have demonstrated that pests and pathogens have shifted poleward by 2.7 ±
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
that climate change can increase plant damage from pests in future decades [11, 12, 13, 14, 15, 16]. Bebber et al. [17] have demonstrated that pests and pathogens have shifted poleward by 2.7 ± 0.8 km/yr since 1960. This will produce lower numerical response of biological control agents, which can be translated into higher probabilities of insect pest outbreaks. Deutsch et al. [18] estimated that global yield losses of rice, maize and wheat grains are projected to increase in the range of 10 to 25% per degree of global mean surface warming. Thus, in a projected scenario of 2◦C-warmer climate the mean increase in yield losses owing only to pest pressure extend to 59, 92, and 62 metric megatons per year for wheat, rice and maize, respectively [18]. These losses cover most of the globe as can be seen in the Fig. 2 in ref. [18], but they are primarily centered in temperate regions. From the agricultural point of view, a particularly important class of insect pests are the aphids (aphididae) [19]. Aphids are by far the most important transmissors of plant viruses, being reported to transmit about 50% of insect-borne plant viruses (approximately 275 virus species). There are about 4,700 aphids described from which about 190 transmit plant viruses (see Chapter 15 of [19]). From the economic point of view this virus transmission by aphid represents global losses estimated on tens of millions to billions US$ of yield loss per annum [20, 21, 22]. In the UK alone the damage on cereals
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
point of view this virus transmission by aphid represents global losses estimated on tens of millions to billions US$ of yield loss per annum [20, 21, 22]. In the UK alone the damage on cereals made by aphids has been estimated to be around 60-120 million pounds annually [23]. Thus, mathematical modeling is seen as an important tool to predict and mitigate the effects of viruses on agriculture [24, 25]. Today, there are several alternative approaches for the sustainable intensification of agriculture based on agroecological and adaptive management techniques [26]. A recent work reports evidences that organic farming, for instance, promotes 1 arXiv:1903.05043v2 [q-bio.PE] 12 May 2019 pest control [27]. An example is intercropping, consisting in growing two or more crops in the same field, which has proved to be important for pest control in several crops [28, 29, 30] (see Supplementary Table 1). Intercropping is known since the 16th-18th centuries when Iroquoian farmers inter-planted the Three Sisters: corn, bean, and squash [31]. Intercropping is known to reduce the levels of infestation by stemborers and increases insect pest parasitism [32]. These practices have been extended across the globe as can be seen in Fig. 1 of ref. [30]. Meta-analysis of 552 experiments in 45 papers published between 1998 and 2008 showed that intercropping produces significant improvement for herbivore suppression, enemy enhancement, and crop damage suppression [33] respect to monocrop. Brooker et
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
between 1998 and 2008 showed that intercropping produces significant improvement for herbivore suppression, enemy enhancement, and crop damage suppression [33] respect to monocrop. Brooker et al. [34] have concluded that intercropping “could be one route to delivering ’sustainable intensification’” of agriculture. In the particular case of aphids, there are many reports on the successful use of intercropping strategies for controlling aphid-transmitted viral diseases [35, 36, 37]. In a recent review, a series of companion plants that can be potentially used in intercropping strategies for controlling aphids have been reported, together with several strategies for controlling aphid-produced diseases [38]. Here we develop and implement a mathematical model that allow us to study intercropping as a sustainable pest management tool for agriculture. Our main goal is to investigate which are the best spatial arrangements for controlling aphid-transmitted viruses in agricultural scenarios by avoiding the propagation of aphids through the crop field. For this purpose we combine the movement of aphids in the agricultural landscape [39, 40, 41, 42] with the spatial distribution of plants in the intercropped field, in an epidemiological Susceptible-Infected-Removed (SIR) [43] model. The model allows us to implement “trap crops”–plants which attract or detract insects to protect target crops [44, 45, 46, 47, 48]. Using this approach we find that a new intercropping arrangement proposed
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
allows us to implement “trap crops”–plants which attract or detract insects to protect target crops [44, 45, 46, 47, 48]. Using this approach we find that a new intercropping arrangement proposed here–particularly when combined with trap crops–can improve significantly pest management in agricultural fields respect to the commonly used intercrop systems. 2 Theoretical Methods For the development of the theoretical model to be used in this work we make the following assumptions: 1. The infection is transmitted to plants by an aphid–a vector. That is, a susceptible plant receives the infection, e.g., a virus, from an infectious plant through a vector. 2. Recovered (removed) plants represent those not only dead but also those which are useless for commercial purposes, i.e., those substantially damaged as to be used for consumption. 3. The number of plants in the field is fixed. 4. When a susceptible vector is infected by a plant, there is a fixed time τ during which the infectious agent develops in the vector. At the end of this time, the vector can transmit the virus to a susceptible plant. 5. The number of infectious vectors is very large and at a given time t its amount is proportional to I (t −τ). These assumptions are an adaptation of the ones made by Cooke [49] for implementing a time-delay Susceptible-InfectedRecovered (SIR) model to study a vector-borne infection transmission to a given population. The corresponding equations read as follows: ˙Si (t) = −βSi (t) X j Ij (t −τ)
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
Susceptible-InfectedRecovered (SIR) model to study a vector-borne infection transmission to a given population. The corresponding equations read as follows: ˙Si (t) = −βSi (t) X j Ij (t −τ) , ˙Ii (t) = βSi (t) X j Ij (t −τ) −µIi (t) , ˙Ri (t) = µIi (t) , (2.1) where Si is the probability of plant i of being susceptible to the infection, Ii is the probability of plant i of being infective after having been infected by the disease, and Ri is the probability of plant i of being removed, β and µ, are the birth and death rates of the disease, respectively, and j spans only to the plants that are able to spread the disease by contact to plant i. Note that Si (t) + Ii (t) + Ri (t) = 1, and, consequently, ˙Si (t) + ˙Ii (t) + ˙Ri (t) = 0. This model has been subsequently studied in the literature by several authors as a vector-borne disease transmission model (see for instance [50, 51, 52, 53]). For other approaches to modeling vector-borne virus transmission on plants see for instance [54]. Here we generalize Cooke’s model [49] in order to account for the probability that a vector hops not only to a neighboring plant but also to a more distant one in the field: ˙Si (t) = −βSi (t) X j fijIj (t −τ) , (2.2) 2 ˙Ii (t) = βSi (t) X j fijIj (t −τ) −µIi (t) , (2.3) ˙Ri (t) = µIi (t) , (2.4) where fij is a function of the “separation” between the plants i and j, and j spans to all the plants in the field. There are two possibilities of accounting for this separation between plants. The first is
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
fij is a function of the “separation” between the plants i and j, and j spans to all the plants in the field. There are two possibilities of accounting for this separation between plants. The first is to consider the Euclidean distance between the corresponding two plants, i.e., ρij = q (xi −xj)2 + (yi −yj)2, where xi and yi are the Cartesian coordinates of the plant i in the plane. Notice that this distance is not capturing all the subtleties of the real separation between the plants as two plants can be of different height, and a third coordinate should be introduced. In this case we can consider that the probability fij of moving from plant i to plant j is proportional to certain function of this distance, e.g., decaying as a power-law fij ∝ρ−s ij or decaying exponentially fij ∝exp (−λρij), where s, λ ∈R+. The second approach is to consider the plant-to-plant separation in terms of the number of hops that an aphid needs to take to go from plant i to plant j using other intermediate plants. That is, let us consider that the aphid in question has an exploration radius equal to r. This means that if the aphid is on plant i it can hop directly to a plant which is at a distance smaller than r from i. In order to hop to a plant k separated by two radii from i it has to use two steps. That is, if we connect two plants by an edge if their geographic separation is ρij ≤r, then the plant-to-plant (topological) separation dij is given by the number of edges in the shortest path
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
steps. That is, if we connect two plants by an edge if their geographic separation is ρij ≤r, then the plant-to-plant (topological) separation dij is given by the number of edges in the shortest path connecting the two nodes in the resulting graph G = (V, E) with vertices V and edges E. In this case we again can consider that the probability fij of moving from plant i to plant j is proportional to certain function of this distance, e.g., decaying as a power-law fij ∝d−s ij or decaying exponentially fij ∝exp (−λdij), where s, λ ∈R+. Let us consider some of the potential differences between these two ways of accounting for the interplant separation. 2.1 The rationale of the model: Through-space vs. plant-to-plant aphid mobility From the complex movements that an aphid can display in a crop field (see Chapter 10 in [19] and [55, 56]), here we focus only on their exploratory movement inside a crop field. This includes mainly displacements to neighboring plant (primary movement) or a distant plant inside the same field. We exclude from here those unintentional movements of aphids such as the displacement by air currents that can transport them at very long geographic distances. Thus, with this restriction in mind we analyze the main differences in considering a model that includes geographic or topological distance for epidemic transmission. In doing so, we have identified three main factors in favor of the use of the topological interplant separation which are based on the main
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
or topological distance for epidemic transmission. In doing so, we have identified three main factors in favor of the use of the topological interplant separation which are based on the main behavioral characteristics of aphids exploratory movement inside crop fields [58, 59, 60, 61]. These three principles are the following: (i) first come first served, which essentially tells that an aphid flying in one direction will land in the first available plant independently of the distance at which it is from its starting position; (ii) a bird in the hand is worth more than two in the bush, indicating that the probability that an aphid moves from a plant i to another j decays with the number of other plants in the path between i and j; (iii) go back before it is too late, which indicates that an aphid flying in a direction without plants would prefer to return to its starting point. These principles are detailed in the Supplementary Note 1. 2.2 SIR model with topological distances As a consequence of the previous hypothesis we conclude that the use of the topological interplant separation is appropriate for our modeling purposes. Therefore, the SIR model on the field is expressed as [66]: ˙Si (t) = −βSi X j ˜AijIj (t −τ) , (2.5) ˙Ii (t) = βSi (t) X j ˜AijIj (t −τ) −µIi (t) , (2.6) ˙Ri (t) = µIi (t) , (2.7) where ˜A = Pdmax d=1 d−sAd , d ≤dmax, dmax is diameter of the graph, i.e., the largest separation between two plants (in terms of steps), and the matrix Ad captures the (long-range)
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
= µIi (t) , (2.7) where ˜A = Pdmax d=1 d−sAd , d ≤dmax, dmax is diameter of the graph, i.e., the largest separation between two plants (in terms of steps), and the matrix Ad captures the (long-range) mobility of the pest between plants (see Fig. 2.1). 3 Figure 2.1: Inter-plants movements of an aphid in an agricultural plot with intercropping (see Supplementary Note 1). The hop of an aphid from an infected plant to a susceptible one separated by d steps is given by d−s (see Supplementary Note 1). The d-path adjacency matrices Ad used in the current formulation are generalizations of the concept of adjacency matrix. In the Supplementary Note 2 we give a formal definition of them and an example (see also [62, 63, 64]). We will always consider a connected network here. Let dij = d be the shortest-path distance between the nodes i and j in a network G. Then, the d-path adjacency matrix is defined by Ad (i, j) = 1 if dij = d 0 otherwise. (2.8) We consider any 1 ≤d ≤dmax, where d = 1 provides the “classical” adjacency matrix and where dmax is the diameter of the network. Then, we combine all the d-path adjacency matrices by using a transformation, such that ˜A = A1 + 2−sA2 + · · · + d−s maxAdmax, (2.9) where s is an empirical parameter controlling the insect mobility. The transformation in Eq. (2.9) is denoted as Mellin d-path transformation. In this case, the entries of ˜A are defined as follow: eA (i, j) = d−s ij if i ̸= j 0 if i = j. (2.10) Notice that the transformed adjacency
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
in Eq. (2.9) is denoted as Mellin d-path transformation. In this case, the entries of ˜A are defined as follow: eA (i, j) = d−s ij if i ̸= j 0 if i = j. (2.10) Notice that the transformed adjacency matrix ˜A is symmetric in the case of undirected networks. Then, when the aphid has very poor mobility s →∞, all the entries of ˜A, except those equal to one, become zeroes, which indicates that the aphid can only perform hops to nearest neighbors. On the other hand, when the aphid has a very large mobility s →0, every entry of ˜A becomes one, which means that the aphid can hop from one plant to another with equal probability independently of their separation in the field. 2.3 Markovian formulation of the epidemiological model. Following the framework introduced in [69], we formulate a Markovian dynamics that, in principle, is valid for any epidemic prevalence. For that reason, hereafter, we restrict ourselves to this Markovian approach. Let pi(t) be the probability that a node i is infected at time t. Then, in the SIR model, the Markovian equations reads as follows: pi(t + 1) = pi(t)(1 −µ) + (1 −pi(t) −ϱi(t))qi(t −τ) , (2.11) ϱi(t + 1) = ϱi(t) + µpi(t) , (2.12) where ϱi(t) is the probability that node i is removed at time t. Note that the term 1 −pi(t) −ϱi(t) is just si(t), the probability that a node i is susceptible at time t. The expression for the infection probability qi(t −τ) is [66] qi(t −τ) = 1 − N Y j=1 h 1 −β ˜Aijpj(t −τ) i , (2.13) which represents the probability
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
that a node i is susceptible at time t. The expression for the infection probability qi(t −τ) is [66] qi(t −τ) = 1 − N Y j=1 h 1 −β ˜Aijpj(t −τ) i , (2.13) which represents the probability that, when node i is healthy at time t, it becomes infected at time t + 1. The expression qi is calculated as 1 minus the probability that the node i is not infected by any infectious contact. This last probability is 4 the product over all the possible contacts of node i, considering that a node j transmits the disease to i with probability β ˜Aijpj, after the delay time τ. Note that if node j is not connected to i (i.e., if dij > 1 and s →∞), ˜Aij = 0, then the corresponding term in the product is equal to 1, since j cannot infect i regardless of its state, pj(t −τ). We should notice that this Markovian formulation holds for any disease incidence, while Eqs. (2.5) and (2.6) are only valid when the disease prevalence is small. To explain this, take Eq. (2.13) for qi(t−τ) and consider that the prevalence is small, pi ≪1 ∀i, and for this reason let us denote pi = xi. Then, the product in (2.13) transforms into: 1 −PN j=1 β ˜Aijxj. the new expression for qi(t) in Eq. (2.11), and passing from discrete to continuous time, we recover a similar expression to that in Eq. (2.6) for the evolution of the infected state of node i. For more details the reader is referred to [70]. The rate of propagation of the aphid-borne viral infection across an agricultural field is defined here as v = Number of
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
the infected state of node i. For more details the reader is referred to [70]. The rate of propagation of the aphid-borne viral infection across an agricultural field is defined here as v = Number of susceptible plants that become removed at equilibrium time to reach equilibrium . (2.14) Finally, for the sake of simplicity, in this work we suppose that the secondary crop of the intercropped systems is not susceptible to the disease and, consequently, its plants can not become infected (i.e. pi = 0 for every plant i that belongs to the secondary crop). However, note that the presence of a secondary crop may modify the interactions between the plants of the main cultivar (i.e. dij and ˜Aij) and, consequently, their respective probabilities qi(t −τ). In the next section we define the intercropping arrangements used in this work. Besides, the secondary crop can be used to implement “trap crops”, which may alter mobility of an aphid, i.e. ˜Aij (see subsection 2.4.3). 2.4 Computational arrangements 2.4.1 Intercropping arrangements. The intercropped systems considered here and shown in Fig. 2.2 are: the strip intercropping in which strips of the main cultivar are inserted between strips of the secondary crop; row intercropping in which rows of the main and secondary crops are alternated one-by-one; column intercropping, the same as before but by columns instead of by rows; chessboard intercropping in which a plant of the main crop is inserted in the rows and columns between every two
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
column intercropping, the same as before but by columns instead of by rows; chessboard intercropping in which a plant of the main crop is inserted in the rows and columns between every two susceptible plants; patches intercropping in which squared patches of the main crop are alternated with squared patches (of the same size) of the secondary crop; random intercropping in which plants of the secondary crop are randomly inserted among those of the main crop. The first two intercropping arrangements—strips [48, 71] and rows [72, 73, 74]—are frequently used in experimental designs and field applications. It is important to remark that in all cases we have considered exactly the same amount of plants of the main crop such that the results obtained here are not due to size effects. 5 Figure 2.2: Intercrop arrangements. Different organizations of intercrops between two species studied in this work with r = ∆(see Networks construction). Light green nodes represent the main crop and the dark green nodes represent the secondary crop, which is considered to be not susceptible to the disease spreading on the field. In the case of trap crop strategies the dark green nodes represent the plants with semiochemical activity to trap the pest to be controlled. The square lattices connecting the nodes correspond to the interconnection networks considered here. 2.4.2 Networks construction. Our arrangements consist of rectangular plots of lengths x = a and y = a−1. These plots guarantee that all
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
correspond to the interconnection networks considered here. 2.4.2 Networks construction. Our arrangements consist of rectangular plots of lengths x = a and y = a−1. These plots guarantee that all simulations are carried out on fields of equal area. The rectangular plots have been shown–both theoretically and experimentally–to delay more the propagation of epidemics than square plots with the same area and density of plants [75]. We consider the distribution of a major crop intercropped with a secondary crop, which may or may not be a trap crop. In the intercropped field we maintain a separation between plants equal to ∆(see Fig. 2.3). In this case the plant-to-plant connectivity, based on their separation, is represented by a squared partition of the plot. We simply normalize all the distances by dividing them by ∆. Then, two plants which are nearest neighbors are one step apart, a second nearest neighbor is two steps apart and so forth. In general, every plot consists of 20 rows and 50 columns. There is a plant at every intersection for a total of 1000 plants. As we have a unit rectangle with a = 1.6059, the value of ∆is 0.033, and we use a connection radius r = ∆, such that the plants are adjacent (connected in the network) only to those immediately to the left, right, up and down. In the case of the intercropped systems we always replaced 500 plants of the main crop by the same quantity of plants of the secondary crop. In the Supplementary Note 3 we analyze the case in
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
down. In the case of the intercropped systems we always replaced 500 plants of the main crop by the same quantity of plants of the secondary crop. In the Supplementary Note 3 we analyze the case in which the separation between rows and columns in the plot are smaller than ∆, which is equivalent to consider the radius of primary movement of the aphid equal to r = √ 2∆. 6 Figure 2.3: Schematic representation of the intercropping of two species in a rectangular plot of unit area and largest edge length a. The separation between plants is given by ∆. 2.4.3 Implementation of the “trap crops”. Although trap crops can be formed either by “push” crops or by the combination of “push-pull” crops [44, 45, 46, 47], here for the modeling purpose we combine all the trap crop effects into a single one. Basically we consider that trap crop diminishes or completely avoids the propagation of a pest in a path beyond the place in which the trap is located. Consequently, if there are more than one trap in the path between two susceptible plants we only consider the effect of one of them. An additive or multiplicative effect of the traps can be easily implemented using the current mathematical framework (see further), but it is not done here for the sake of simplicity. In this case the secondary crop is located between the paths connecting the infected and the susceptible plants. Mathematically, let us consider two plants i1 and id+1, and the shortest-path i1, i2, · · · , id, id+1 of length d
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
is located between the paths connecting the infected and the susceptible plants. Mathematically, let us consider two plants i1 and id+1, and the shortest-path i1, i2, · · · , id, id+1 of length d between them. To model trap crops, we modify the strength of the long-range mobility of the aphid between i1 and id+1 as follows: ˜A i1,id+1 = d−γs d−s if there is at least one trap crop between ii and id+1 otherwise , (2.15) where γ ≥1 is the trap strength. When γ = 1, there is no trap crop as we recover the original equation for the epidemic dynamics with long-range movements. On the other hand, when 1 < γ < ∞, movement of the aphid is reduced beyond the point in which the trap is located. For instance, when the trap crop is very effective, i.e., γ →∞, the movement of the aphid from i1 to id+1 is completely interrupted, which means that the trap is perfect. In the Fig. 2.4(b) we illustrate the effects of a secondary crop in which we obtained the probability qi that the plants in the right part are infected once the three plants on the left side are infected by the pest. To do so, we suppose that pi = 0 for i spanning over the secondary crop and the plants of the main cultivar that are in the right side of each arrangement, and pi = 1 otherwise. According to Fig. 2.4, the shortest path distance between the infected and the susceptible plants is always d = 2, and there is a secondary crop plant between them. Under those conditions, Eq. (2.13) reduces to qi = 1 −(1 −β2−sγ)3 for
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
path distance between the infected and the susceptible plants is always d = 2, and there is a secondary crop plant between them. Under those conditions, Eq. (2.13) reduces to qi = 1 −(1 −β2−sγ)3 for each plant in the right side. Supposing τ = 0 (no delay), s = 2.5 (aphid with large mobility) and β = 0.5, when γ = 1 (no trap), the infectability of the susceptible plants is 24.2%, which represents the effects of an intercropped secondary species. However, when the strength of the trap is γ = 2, the probability that the susceptible plants are infected drops to less than 5%. This probability is reduced to zero as γ is subsequently increased. 7 Figure 2.4: (a) Intercropping with ’push’ (or ’push-pull’) strategies where semiochemicals [57, 65] are released from trap crops (the photograph is courtesy of Rachel Monger (Immanuel International)). (b) Effects of the strength of the trap crop γ (dark plants) on the probability of plants i to get infected qi (see text for explanations) once the plants on the left of the Fig. are infected. 2.4.4 Simulations. Using the Markovian formalism, i.e. Eqs. (2.11)-(2.13), we perform 100 random realizations for each field arrangement, secondary crop (with or without trap) and aphid mobility (fast and slow). In each independent realization, the propagation is initialized by infecting randomly a single susceptible plant on the border of the field. Following [66], we set here µ = 0.5, since we are not trying to characterize any particular disease. For µ =
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
is initialized by infecting randomly a single susceptible plant on the border of the field. Following [66], we set here µ = 0.5, since we are not trying to characterize any particular disease. For µ = 1, for instance, the recovery is too fast to see the spatial propagation and, conversely, in the case µ = 0 the dynamics would be an SI dynamics. We decided to lie between these two limiting cases. For the dynamics of the disease we calculate the total amount of Markovian time t∗in which the probability of being susceptible is larger than the probability of being removed (i.e, 1 −ϱi −pi > ϱi), for each plant of the main cultivar. To estimate the epidemic thresholds for a given value of γ, we calculate the average stationary fraction of removed plants (over 100 independent realizations), R(β, µ), for 50 logarithmically spaced values of β, between 0.02 and 1.0, when µ = 0.5, where R(β, µ) = (1/Ni) lim t→∞ P i ϱi(t), i spans over the plants of the main cultivar and Ni is the total amount of plants of that crop. Then, using a linear interpolation, we find the epidemic threshold τ E in each field. We recall that the epidemic threshold is the smallest value of β/µ for each arrangement that satisfies the condition that R(β, µ) > 0. Visualization of results in the form of rain clouds were performed using Matlab® codes available from Allen et al. [76] 3 Results and discussion 3.1 Influence of time-delay According to the results previously reported by Tchuenche and Nwagwo [52], the effects of
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
using Matlab® codes available from Allen et al. [76] 3 Results and discussion 3.1 Influence of time-delay According to the results previously reported by Tchuenche and Nwagwo [52], the effects of the time delay τ are mainly observed at the initial times of the propagation dynamics and are focused on the population of susceptible plants. For relatively large time the evolution of the SIR dynamics with and without time delay are almost indistinguishable (see Fig. 2 in [52]). For instance, in Fig. 3.1 we can observe that the results without time delay, i.e., τ = 0 are qualitatively similar to those for τ = 1, with almost the same epidemic threshold and very similar shape of the propagation curves. We explore here the effects of τ on the epidemic dynamics when the vector mobility is incorporated into the model. Using the Markovian formulation described previously in Eqs. (2.11)-(2.13), we obtained the evolution of the infected population 8 of plants in crop field consisting of a square lattice as described before for two different values of the aphid mobility s in the Mellin transformed Markovian SIR equations. The results are illustrated in Fig. 3.1 were we have used β = 0.5, µ = 0.5, r = ∆and s = 2.5 (a) and s = 1.0 (b). It can be seen that the inclusion of a time delay in the model makes that the peak in the number of infected plants is displaced to longer times. For large aphid mobility (s = 1.0) it is observed that the shapes of the peaks of infection are very similar to each
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
makes that the peak in the number of infected plants is displaced to longer times. For large aphid mobility (s = 1.0) it is observed that the shapes of the peaks of infection are very similar to each other for different values of the time delay 0 ≤τ ≤10. When the mobility of the aphids is relatively low (s = 2.5) the rate of propagation of the infection changes significantly for different values of τ, particularly for very large time delays. For instance, the values of the rate of propagation for a given time-delay, v (τ), obtained from Eq. (2.14), are as follow: v (0) = 32.25, v (1) = 26.32, v (2) = 22.22, v (3) = 18.87, v (4) = 16.67, v (5) = 14.70, v (10) = 9.17. However, for the case of large aphid mobility these rates of propagation are not changed significantly with the time delay: v (0) = 43.48, v (1) = 40.00, v (2) = 38.46, v (3) = 35.71, v (4) = 34.48, v (5) = 33.33, v (10) = 27.78. That is, for relatively low time delays the results in the disease propagation on plants are very similar to those without time-delays. Also, when the the aphid mobility is relatively large, the time delay does not affect significantly the propagation rate of the disease. 0 20 40 60 80 100 120 t 0.0 0.1 0.2 0.3 0.4 0.5 pi(t) (a) 0 5 10 15 20 25 30 35 40 t 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 pi(t) τ = 0 τ = 1 τ = 2 τ = 3 τ = 4 τ = 5 τ = 10 (b) Figure 3.1: Evolution of the number of infected plants in a square plot with the variation of the time delays τ. The modeling is performed with β =
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
τ = 0 τ = 1 τ = 2 τ = 3 τ = 4 τ = 5 τ = 10 (b) Figure 3.1: Evolution of the number of infected plants in a square plot with the variation of the time delays τ. The modeling is performed with β = 0.5, µ = 0.5, r = ∆and s = 2.5 (a) and s = 1.0 (b). As a consequence of the previous analysis and for the sake of keeping our model as simple as possible we are not considering explicitly the time delay in the further calculations in this work. The biological justification for this simplification is as follows. The interaction of the virus and aphid is controlled by the following phases (see Chapter 15 of [19]): (i) acquisition, where the aphid takes up virions from an infected plant, (ii) retention, where the aphid carries the virions at specific sites, (iii) latency, which refers to the inability of an aphid to inoculate immediately a virus following acquisition, and (iv) inoculation, which is the release of retained virions into the tissues of a susceptible plant. There are three types of transmission of a virus to a plant (see Chapter 15 of [19]). In the non-persistent (NP) transmission, the acquisition and inoculation are very fast and requires only a very brief stylet penetration, which delays less than one minute. In this case there is no latency period and the whole cycle of transmission can be completed within a few minutes. In the semi-persistent (SP) transmission, the acquisition and inoculation requires periods of about 15 minutes. In this case there is no latency periods
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
can be completed within a few minutes. In the semi-persistent (SP) transmission, the acquisition and inoculation requires periods of about 15 minutes. In this case there is no latency periods either and the aphids retain the ability to inoculate for periods of up to 2 days following acquisition. Finally, in the persistent (P) transmission the virus acquisition requires period between hours to days, there is a latency period and the retention is for days to weeks. From the about 270 viruses transmitted by aphids more than 200 are transmitted by NP transmission (see Chapter 15 of [19]). The results to be considered here using a SIR model without time delays is then equivalent to model the aphid-borne transmission of viruses to plants using either NP or SP transmission. 3.2 Impact of intercrop arrangements on virus propagation. In Fig. 3.2 we illustrate the results of the simulations of the propagation of an aphid-borne virus in the 6 intercropped fields without traps (γ = 1.0) studied here as well as in the monocrop. In Fig. 3.2 (a) we show the results for an aphid with relatively low mobility (s = 4.0) and in Fig. 3.2 (b) we give the same for a relatively high mobility aphid 9 (s = 2.5). To compare the dynamics of the different arrangements, firstly, we analyze their respective results before they reach equilibrium (t = 10). In the case in which s = 4.0 it is clear that the disease is propagated in a relatively slow fashion and for t = 10 only 18.3% of plants are removed in the
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
before they reach equilibrium (t = 10). In the case in which s = 4.0 it is clear that the disease is propagated in a relatively slow fashion and for t = 10 only 18.3% of plants are removed in the monocrop. As can be seen in this figure all intercrop arrangements produce significant decrease in the number of removed plants. The smallest decay in the number of removed plants is observed for the patches configuration in which the percentage of removed plants is 10.1%, followed by the strips configuration with 6.6%. On the other hand, the most efficient arrangement is the chessboard one, which reduces the number of removed plants practically to zero (only 0.3% of removed plants). In Fig. 3.2 (b) we illustrate the results for the case in which the pest has a relatively large mobility. Here the picture observed is significantly different from the one in the previous case. First, the level of plants removed in the monocrop is 95.1%, indicating an almost complete destruction of the crop in a relatively short time (t = 10) when the pest is highly mobile. The range of amelioration of the infection across the fields is here very wide, ranging from the 10% of decrease in removed plants observed for the patches arrangement (85.5% of removed plants) up to about 80% of decrease obtained with the chessboard arrangement (16.3% of removed plants). Notice that the frequently used intercrop arrangement of strips produces, together with that of patches, the smallest improvement in the number of removed
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
arrangement (16.3% of removed plants). Notice that the frequently used intercrop arrangement of strips produces, together with that of patches, the smallest improvement in the number of removed plants. Thus, although the results are quantitatively very different for the cases of low and high mobility of the aphid, they are qualitatively similar in identifying the worse arrangements (patches and strips) as well as the best one (chessboard). In both cases the order of effectivity in reducing the impact of an aphid-borne virus propagation is: chessboard > columns > random > rows > strips > patches. In Fig. 3.2 (c) and (d) we illustrate a snapshot of the aphid-borne propagation of a virus across the different intercropping systems with s = 4.0 and s = 2.5, respectively. In order to compare all the different arrangements we always start the epidemic by infecting the same node, i.e., the one at the bottom-left corner of the field. The colors in the plots represent the time t∗in which the plant remains susceptible without becoming removed by the vector-borne virus disease. That is, a low value of this time indicates that the plant is removed relatively soon by the virus disease. In order to interpret quantitatively the results in these plots we use the rate v of propagation of the aphid-borne virus previously defined in Eq. (2.14). It can be seen that in the monocrop the epidemic is propagated in a wave-like way, typical of diffusion processes. The values of v in the monocrop are 23.26 (s
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
virus previously defined in Eq. (2.14). It can be seen that in the monocrop the epidemic is propagated in a wave-like way, typical of diffusion processes. The values of v in the monocrop are 23.26 (s = 4.0) and 32.26 (s = 2.5). That is, when the aphid has relatively low mobility there is an average infection of 23.26 plants per unit time. This rate is increased to 32.26 plants when the pest mobility is increased, due to the fact that the aphids can now hop to wider regions of the plots. Reminiscences of the wave-like kind of propagation of the vector-borne virus are observed in all the intercrop arrangements studied. In the intercropped systems (without trap crops, γ = 1.0) the propagation rates of the virus are: for s = 4.0, chessboard (0.03) < random (5.46) < columns (7.35) = rows (7.35) < strips (10.0) < patches (11.36); for s = 2.5, chessboard (9.62) < random (12.19) < columns (13.16) = rows (13.16) < strips (14.70) < patches (15.62). In closing, the chessboard arrangement is significantly better in reducing the propagation of aphid-borne viruses in agricultural fields than the rest of the arrangements when there are no trap crops in the intercrop. The random arrangement also performs very well in terms of both the number of plants removed by the infection and the rate of propagation of the epidemic. Finally, it is worth recalling that the prior results depend on the radius for primary dispersal of the aphid. See Supplementary Note 3 for the case when the separation between
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
of the epidemic. Finally, it is worth recalling that the prior results depend on the radius for primary dispersal of the aphid. See Supplementary Note 3 for the case when the separation between rows and columns is smaller than here and the pest can hop not only across the rows and columns, but also diagonally between rows, i.e., when the radius for primary dispersal of the aphid is r = √ 2∆instead of r = ∆. When the pest mobility is relatively low (s = 4.0), the best arrangements are the rows and columns intercrops with about 10% of affected plants vs. 78% affected in the monocrop for t = 10. However, when the pest has high mobility (s = 2.5), none of the intercropping systems is able to stop the propagation of the pest across the field, with percentages of affected plants similar to that in the monocrop (98.6%). An obvious measure to mitigate this problem is to increase the separation of the rows and columns in the crop field, or even–as shown in the experiments by Khan et al. [28]–to increase the separation between rows keeping a smaller separation between columns. 10 0 0.05 0.1 0.15 0.2 0.25 0.3 -20 0 20 Strips 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Row 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Column 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Chess 0 0.05 0.1 0.15 0.2 0.25 0.3 -20 0 20 Patches 0 0.05 0.1 0.15 0.2 0.25 0.3 -20 0 20 Random 0 0.05 0.1 0.15 0.2 0.25 0.3 -10 0 10 Monocrop (a) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Strips 0 0.1 0.2 0.3 0.4 0.5 0.6
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
0.3 -20 0 20 Patches 0 0.05 0.1 0.15 0.2 0.25 0.3 -20 0 20 Random 0 0.05 0.1 0.15 0.2 0.25 0.3 -10 0 10 Monocrop (a) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Strips 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -10 0 10 Row 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -10 0 10 Column 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -10 0 10 Chess 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -10 0 10 Patches 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -5 0 5 Random 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Monocrop (b) Strips Row Column Chess Patches Random Monocrop 𝑡∗ (c) Strips Row Column Chess Patches Random Monocrop 𝑡∗ (d) Figure 3.2: Aphid-borne virus propagation on intercropped fields without traps. Results of the simulations for a SIR epidemics at t = 10 with r = ∆, β = 0.5, µ = 0.5 for different intercropping strategies without trap crops, i.e., the strength of the trap crop is γ = 1.0. Raincloud plots of the proportion of dead plants for a viral infection propagated by aphids: (a) Aphid with reduced mobility (s = 4.0) and (b) with larger mobility (s = 2.5). The clouds show the kernel distribution of the proportion of dead plants for different realizations of the epidemics. Below, the raw data is plotted (the rain) together with their corresponding box and whisker plots. Illustration of the evolution of infection across fields with different intercropping systems: (c) Aphid with reduced mobility (s = 4.0) and (d) with larger mobility (s = 2.5). In both panels the time t∗is given in a
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
the evolution of infection across fields with different intercropping systems: (c) Aphid with reduced mobility (s = 4.0) and (d) with larger mobility (s = 2.5). In both panels the time t∗is given in a color scale (see text), and the propagation is initialized by infecting the plant on the bottom-left corner of the plot. 3.3 Impact of intercrops with trap crop on aphid-borne virus propagation. We now move to the analysis of the intercrop systems with trap crops. To have an idea of the many systems in which the current results can be applied the reader is referred to the Tables 1 and 2 in Hokkanen’s paper [44], where many examples of one main crop intercropped with a trap crop are given. We consider here the existence of trap crops which are not perfect, i.e., they allow certain propagation of the aphid-borne viral infection (see Supplementary Note 4 for results with a perfect trap). Thus, we use γ = 2.0 and analyze the cases of relatively low (s = 4.0) and relatively large (s = 2.5) aphid 11 mobility. In Fig. 3.3 we illustrate the results of our simulations for these systems using the different arrangements studied here. As can be seen for the case of relatively low mobility (s = 4.0) there are significant reduction in the percentages of removed plants for all intercrop systems. The percentages of removed plants for each intercrop are: chessboard (0.2%), columns (1.4%), random (1.5%), rows (1.8%), strips (3.4%) and patches (4.7%). We remind the reader that the percentage of
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
The percentages of removed plants for each intercrop are: chessboard (0.2%), columns (1.4%), random (1.5%), rows (1.8%), strips (3.4%) and patches (4.7%). We remind the reader that the percentage of removed plants in the monocrop is 18.1%. When the pest has a relatively large mobility (s = 2.5), 95.1% of plants are removed in the monocrop, while in each of the intercrops they are: chessboard (0.2%), random (2.8%), columns (4.4%), rows (6.3%), patches (9.1%), and strips (17.4%). Notice that here there are some important changes in the order of the arrangements in terms of their effectivity in reducing the propagation of the infection. When the aphid is of high mobility the best arrangements are the chessboard and the random one. The worse arrangement, and the only one having more than 10% of removed plants, is the strip one. Also notice that the percentage of removed plants in the chessboard arrangement is exactly the same for s = 2.5 and s = 4.0, indicating a high stability in the efficiency of this arrangement. It is important to remark one more time that these reductions in the number of removed plants are the consequence of the different topological patterns emerging from the intercrop arrangements. That is, these differences are not a dilution effect due to the fact that the number of susceptible and immune plants are kept the same in every arrangement. We now analyze the rate of propagation of the aphid-borne virus across the agricultural fields intercropped with a trap crop
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
of susceptible and immune plants are kept the same in every arrangement. We now analyze the rate of propagation of the aphid-borne virus across the agricultural fields intercropped with a trap crop (see Fig. 3.3 (c) and (d)). The rate of propagation of the virus follow a different order as for the case of intercrops without traps (γ = 1.0). That is, for s = 4.0, we find: chessboard (0.05) < random (1.67) < columns (4.59) < rows (4.67) < strips (7.04) < patches (7.94). For s = 2.5, chessboard (0.04) < random (4.18) < columns (7.58) < rows (8.06) < strips (10.87) < patches (11.63). Here again there is a significantly high improvement, in terms of diminishing the impact and the rate of propagation of a virus across an agricultural field, when the chessboard arrangement is used. See Supplementary Note 3 for the case when the separation between rows and columns is smaller than here, i.e., when the radius for primary dispersal of the aphid is r = √ 2∆instead of r = ∆. These results agree with those previously reported using a different stochastic simulation model [67]. 12 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Strips 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Row 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Column 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Chess 0 0.05 0.1 0.15 0.2 0.25 0.3 -40 -200 20 40 Patches 0 0.05 0.1 0.15 0.2 0.25 0.3 -20 0 20 Random 0 0.05 0.1 0.15 0.2 0.25 0.3 -10 0 10 Monocrop (a) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Strips 0 0.1 0.2 0.3 0.4
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
0.3 -40 -200 20 40 Patches 0 0.05 0.1 0.15 0.2 0.25 0.3 -20 0 20 Random 0 0.05 0.1 0.15 0.2 0.25 0.3 -10 0 10 Monocrop (a) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Strips 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Row 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -40 -200 20 40 Column 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -40 -200 20 40 Chess 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Patches 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Random 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -20 0 20 Monocrop (b) Strips Row Column Chess Patches Random Monocrop 𝑡∗ (c) Strips Row Column Chess Patches Random Monocrop 𝑡∗ (d) Figure 3.3: Aphid-borne virus propagation on intercropped fields with trap crops. Results of the simulations for a SIR epidemics at t = 10 with r = ∆, β = 0.5, µ = 0.5 for different intercropping strategies with trap crops of strength γ = 2.0. Raincloud plots of the proportion of dead plants for a viral infection propagated by aphids: (a) Aphid with reduced mobility (s = 4.0) and (b) with larger mobility (s = 2.5). The clouds show the kernel distribution of the proportion of dead plants for different realizations of the epidemics. Below, the raw data is plotted (the rain) together with their corresponding box and whisker plots. Illustration of the evolution of infection across fields with different intercropping systems: (c) Aphid with reduced mobility (s = 4.0) and (d) with larger mobility (s = 2.5). In both panels the time t∗is given in a color scale
|
{
"Authors": "Alfonso Allen-Perkins, Ernesto Estrada",
"Published": "2019-05-12",
"Summary": "Agricultural losses to pest represent an important challenge in a global\nwarming scenario. Intercropping is an alternative farming practice that\npromotes pest control without the use of chemical pesticides. Here we develop a\nmathematical model to study epidemic spreading and control in intercropped\nagricultural fields as a sustainable pest management tool for agriculture. The\nmodel combines the movement of aphids transmitting a virus in an agricultural\nfield, the spatial distribution of plants in the intercropped field, and the\npresence of `trap crops' in an epidemiological Susceptible-Infected-Removed\n(SIR) model. Using this model we study several intercropping arrangements\nwithout and with trap crops and find a new intercropping arrangement that\nimproves significantly pest management in agricultural fields respect to the\ncommonly used intercrop systems.",
"Title": "Mathematical modeling for sustainable aphid control in agriculture via intercropping",
"Topic": "Pests & Diseases",
"source": null,
"summary": null,
"title": null
}
|
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