Predicting Helicoverpa zea (Lepidoptera: Noctuidae) populations in the southern United States.

ABSTRACT: Forecasting insect pest populations before crops are planted can help improve management and reduce pesticide use. Pests with long dispersal potentials and wide host ranges are difficult to predict but often cause losses in crops across broad spatial scales. Here, we use corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), as a case study of a regional crop pest to develop a forecasting framework. Models used historical H. zea trap data to predict populations across 12 states in the southern United States. Three regression models and one machine learning model were used to evaluate predictive performance in either space or time (ie year over year): linear regression model with fixed-effects only, Bayesian spatial-intercept model with and without fixed effects, and random forest.

SUBMITTER: Yanchenko E 

PROVIDER: S-EPMC12817313 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature