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

Yuji Saikai, Terrance M. Hurley, Paul D. Mitchell

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


BACKGROUND: Farmers around the world have used Bt maize for more than two decades, delaying resistance using a high-dose/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.

Original languageEnglish (US)
Pages (from-to)273-284
Number of pages12
JournalPest management science
Issue number1
StatePublished - Aug 16 2020

Bibliographical note

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© 2020 Society of Chemical Industry


  • European corn borer
  • population genetics model
  • resistance mitigation policies
  • social networks
  • technology adoption


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