BACKGROUND: Farmers around the world have used Bt maize for more than two decades, delaying resistance using a highdose/refuge strategy. Nevertheless, field-evolved resistance to Bacillus thuringiensis (Bt) toxins has been documented. This paper describes a spatially explicit population genetics model of resistance to Bt toxins by the insect Ostrinia nubilalis and an agent-based model of farmer adoption of Bt maize incorporating social networks. The model was used to evaluate multiple resistance mitigation policies, including combinations of increased refuges for all farms, localized bans on Bt maize where resistance develops, area-wide sprays of insecticides on fields with resistance and taxes on Bt maize seed for all farms. Evaluation metrics included resistance allele frequency, pest population density, farmer adoption of Bt maize and economic surplus. RESULTS: The most effective mitigation policies for maintaining a low resistance allele frequency were 50% refuge and localized bans. Area-wide sprays were the most effective for maintaining low pest populations. Based on economic surplus, refuge requirements were the recommended policy for mitigating resistance to high-dose Bt maize. Social networks further enhanced the benefits of refuges relative to other mitigation policies but accelerated the emergence of resistance. CONCLUSION: These results support using refuges as the foundation of resistance mitigation for high-dose Bt maize, just as for resistance management. Other mitigation policies examined were more effective but more costly. Social factors had substantial effects on the recommended management and mitigation of insect resistance, suggesting that agent-based models can make useful contributions for policy analysis.

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...

Despite the promise of precision agriculture for increasing the productivity by implementing site-specific management, farmers remain skeptical and its utilization rate is lower than expected. A major cause is a lack of concrete approaches to higher profitability. When involving many variables in both controlled management and monitored environment, optimal site-specific management for such high-dimensional cropping systems is considerably more complex than the traditional low-dimensional cases widely studied in the existing literature, calling for a paradigm shift in optimization of site-specific management. We propose an algorithmic approach that enables farmers to efficiently learn their own site-specific management through on-farm experiments. We test its performance in two simulated scenarios-one of medium complexity with 150 management variables and one of high complexity with 864 management variables. Results show that, relative to uniform management, site-specific management learned from 5-year experiments generates $43/ha higher profits with 25 kg/ha less nitrogen fertilizer in the first scenario and $40/ha higher profits with 55 kg/ha less nitrogen fertilizer in the second scenario. Thus, complex site-specific management can be learned efficiently and be more profitable and environmentally sustainable than uniform management.