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

Abstract

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.

How growers make decisions impacts plant disease control

“…The “grower vs.” models are closer to previous usage in plant disease epidemiology (e.g. [ 12 , 13 ] 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.…”

“…An alternative means of assessing profitability, is used in [ 12 , 13 ] and [ 17 ] (though the inclusion of grower characteristics in [ 13 ] means it deviates from pure game-theoretic assumptions of rationality as growers may adopt a strategy that will earn them lower profits). In these two-strategy models, growers compare their own profit with the average profit of those playing the alternative strategy (the strategy that the grower does not currently use; for a controller, this would be non-control and vice versa).…”

How growers make decisions impacts plant disease control

“…When conducting our further analysis into the effects of parameters and misperception, we focused on the “grower vs. alternative” evaluation, where individual growers compare their own profits with the expected profit of the alternative strategy. The reasoning behind this was twofold: first, this has been used previously in plant epidemiological literature ([12], [13] and [17]). Secondly, and most importantly, we believe this comparison to be most similar to what real growers might be expected to do ([18], [53], [20]).…”

“…first, this has been used previously in plant epidemiological literature ( [12], [13] and [17]). Secondly, and most importantly, we believe this comparison to be most similar to what real growers might be expected to do ( [18], [53], [20]).…”

“…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.…”