An agent-based model of insect resistance management and mitigation for Bt maize: A social science perspective

Abstract

2 24 25 Abstract 26 Managing and mitigating agricultural pest resistance to control technologies is a complex 27 system in which biological and social factors spatially and dynamically interact. We build a 28 spatially explicit population genetics model for the evolution of pest resistance to Bt toxins 29 by the insect Ostrinia nubilalis and an agent-based model of Bt maize adoption, emphasizing 30 the importance of social factors. The farmer adoption model for Bt maize weighed both 31 individual profitability and adoption decisions of neighboring farmers to mimic the effects 32 of economic incentives and social networks. The model was calibrated using aggregate 33 adoption data for Wisconsin. Simulation experiments with the model provide insights into 34 mitigation policies for a high-dose Bt maize technology once resistance emerges in a pest 35 population. Mitigation policies evaluated include increased refuge requirements for all farms, 36 localized bans on Bt maize where resistance develops, areawide applications of insecticidal 37 sprays on resistant populations, and taxes on Bt maize seed for all farms. Evaluation metrics 38 include resistance allele frequency, pest population density, farmer adoption of Bt maize and 39 economic surplus generated by Bt maize. 40 41 Based on economic surplus, the results suggest that refuge requirements should remain the 42 foundation of resistance management and mitigation for high-dose Bt maize technologies.43 For shorter planning horizons (< 16 years), resistance mitigation strategies did not improve 44 economic surplus from Bt maize. Social networks accelerated the emergence of resistance, 45 making the optimal policy intervention for longer planning horizons rely more on increased 3 46 refuge requirements and less on insecticidal sprays targeting resistant pest populations.47 Overall, the importance social factors play in these results implies more social science 48 research, including agent-based models, would contribute to developing better policies to 49 address the evolution of pest resistance. 50 51 Author Summary 52 Bt maize has been a valuable technology used by farmers for more than two decades to 53 control pest damage to crops. Using Bt maize, however, leads to pest populations evolving 54 resistance to Bt toxins so that benefits decrease. As a result, managing and mitigating 55 resistance has been a serious concern for policymakers balancing the current and future 56 benefits for many stakeholders. While the evolution of insect resistance is a biological 57 phenomenon, human activities also play key roles in agricultural landscapes with active pest 58 management, yet social science research on resistance management and mitigation policies 59 has generally lagged biological research. Hence, to evaluate policy options for resistance 60 mitigation for this complex biological and social system, we build an agent-based model that 61 integrates key social factors into insect ecology in a spatially and dynamically explicit way. 62 We demonstrate the significance of...

Tolerant crops increase growers’ yields but promote selfishness: how the epidemiology of disease resistant and tolerant varieties affect grower behaviour

“…Though tolerance and resistance to disease have been widely researched in plant biology, little thought has been given to the distinct epidemiological consequences of deploying tolerant or resistant varieties upon a community of growers, and none to how this affects the decisions of growers to partake in control. Growers’ behaviour more broadly, and its effects on epidemic outcomes remains largely overlooked in plant disease epidemiology (with some exceptions including Milne et al (2016), McQuaid et al (2017a), Szyniszewska et al (2019), Milne et al (2020), Bate et al (2021), Saikai et al (2021) and Murray-Watson et al (2022)). Here we investigated the effect of a fixed proportion of “improved” crop (with either tolerant or resistant characteristics) on growers’ profits and subsequently how this affected growers’ use of improved crop when given the choice.…”

“…Similarly, it suggests growers using tolerant crops incentivise others to do so too, as they will have higher yields when infected. To investigate these dynamics, we included growers’ behaviour in our disease spread model, with growers evaluating profitability based on the “grower vs. alternative strategy” method described in Murray-Watson et al (2022) (which takes the same form as decision models in Milne et al (2016), McQuaid et al (2017a) and Saikai et al (2021)). In this model, growers compared their own outcome from the previous season (i.e.…”

“…We use the “grower vs. alternative” mechanism for decision making, as set out in Murray-Watson et al (2022), McQuaid et al (2017a), Milne et al (2016), Saikai et al (2021). In this behavioural model, growers compare their outcome from the previous season with the expected profit of the alternative strategy (i.e.…”

“…Game theory has been increasingly used to better understand some of the drivers of human behaviour in response to an epidemic (Chang et al (2020)). Models derived from game-theoretic principals have also been used to examine the behaviour of growers who are making decisions about crop management (Milne et al (2016), McQuaid et al (2017a), Saikai et al (2021), Murray-Watson et al (2022)). Here, we use a gametheoretic model to examine the effects of the contrasting externalities of tolerant and resistant crop.…”

How growers make decisions impacts plant disease control

“…The “grower vs.” models are closer to previous usage in plant disease epidemiology (e.g. [13], [12] and [17]), with the outcome obtained by the grower for their last crop used as the point of comparison. Table 3 provides a summary of the symbols used in the behavioural model.…”