The efficacy of spatio-temporal predictors in forecasting the risk of Cydia nigricana infestation

Abstract

Abstract The ability to estimate the risk of pest infestation can help cultivators to reduce pesticide application and provide guidance that would result in better management decisions. This study tested whether different combinations of spatial and temporal risk factors can be used to predict the damage potential of Cydia nigricana, a major pest in field pea (Pisum sativum). Over four consecutive years, the abundance of pea moth was monitored by placing pheromone traps at different field pea cultivation sites. We also assessed the phenological development stages and the percentage of damaged seeds per 100 pods collected from each growing pea field in a region of approximately 30 km in diameter. The study found the significant infestation risk indicators to be the time of flowering, the date on which male pea moths are first detected in the monitoring traps, and the minimum distance (MD) to pea fields that were planted and harvested in the previous growing season. The combination of all three factors using a general additive model (GAM) approach yielded the best results. The model proposed by this study accurately discriminated between low-infestation and high-infestation fields in 97% of cases.