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Journal Articles Journal of Pest Science Year : 2023

Modeling fall armyworm resistance in Bt-maize areas during crop and off-seasons

Maysa Tomé
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Igor Weber
Adriano Garcia
Josemeri. Jamielniak
Mirian Hay-Roe
Wesley Godoy

Abstract

Entomologists have often used computational modeling to study the dynamics of insects in agricultural landscapes. Recently, important issues such as the movement of adults and immatures associated with insect resistance to GMO (genetically modied organism) crops, have been addressed using computational models. Further studies are needed, especially of structured landscapes composed of GMO plants and alternative hosts that insect pests can exploit. We developed an individual-based model using the cellular automata approach (CA) to investigate how an intercropping system composed of transgenic maize ( Bacillus thuringiensis), refuge areas (non-Bt maize), and grasses combined with off-season periods might inuence the evolution of resistance in Spodoptera frugiperda (Lepidoptera : Noctuidae), one of the leading agricultural pests targeted by GMOs. We designed the Bt and non-Bt plants in two different arrangements: a) a seed mixture and b) alternate rows, adding grasses in areas adjacent to the eld. We added the seasonal planting dynamics (crop season and off-season), to evaluate a total of six agricultural scenarios. We followed a crop calendar from the United States to create simulations close to agricultural practice. The results showed that the frequency of the resistance allele was strongly related to the landscape arrangements and their dynamics. Since the adult insects are mobile, the seed- mixture scenario increased the frequency of the resistance allele the most, followed by alternate rows. Finally, grass elds can help to manage S. frugiperda Bt resistance in the agricultural scenarios.

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hal-04193185 , version 1 (01-09-2023)

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Maysa Tomé, Igor Weber, Adriano Garcia, Josemeri. Jamielniak, Eric Wajnberg, et al.. Modeling fall armyworm resistance in Bt-maize areas during crop and off-seasons. Journal of Pest Science, 2023, 96 (4), pp.1539-1550. ⟨10.1007/s10340-022-01531-2⟩. ⟨hal-04193185⟩
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