Linking Corn Earworm Populations and Management to Landscapes Across North America

Le résumé fourni par la source

Accurately modeling highly mobile pests in agricultural landscapes is a considerable challenge that limits population prediction in support of management. For some specialist insect herbivores, a close association to a limited host range enables reasonably accurate prediction of infestation risk. Moreover, simplified agricultural systems have generated successful tools that improve integrated pest management (IPM) practices due to a very predictable sequence of host utilization. In contrast, accurately modeling generalist pests with broad host ranges in dynamic cropping systems requires a deep understanding of ecological interactions and dispersal patterns among host plant patches in space and time. Corn earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae) is an example of a challenging polyphagous pest that cycles through multiple crops in agricultural systems, but also has different movement patterns from local dispersal to long-distance migration. Helicoverpa zea has been one of the most well studied agricultural pests in North America and, in turn, has generated many approaches to monitoring (i.e. pheromone and black light networks) and modeling. Unfortunately, these data-driven tools are vulnerable to major changes in production practices that include, but are not limited to, the adoption of genetically engineered crops expressing Bacillus thuringiensis toxins and an ever-shifting set of production practices. The focus of this chapter is a targeted review of fundamental H. zea literature that has spurred mechanistic and statistical modeling approaches to improve understanding of this pest beyond the field edge. Recent advancements in geospatial data analytics have enabled a rapid radiation in H. zea analytic approaches and improved risk prediction capabilities. The motivation for this chapter is to generate new interest and approaches to study this pest’s ecology and distribution using contemporary methods. We also highlight key knowledge gaps that, provided with innovative spatial data, could improve inference to motivate H. zea management improvements in agroecosystems.

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