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

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. Results of random forest model provided the most useful information about H. zea population dynamics. We found that the location and year of trap location was the most meaningful predictor of H. zea abundance. The importance of local host crop abundance was less meaningful than the prior predictors. We also tested the importance of trap density to recover forecasting signals in North Carolina. Results showed that the trap density could be reduced by approximately 25% while still recovering reasonable predictions of H. zea density. However, it is clear from prior work that some trap locations are more important for prediction, so further assessment of the specific roles individual trap nodes present would be useful. Together this study highlights opportunities to improve annual H. zea forecasting across the US Cotton Belt.