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

Abstract

The ability to estimate the risk of pest infestation can help farmers 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 may be used to predict the damage potential of pea moth, Cydia nigricana Fabricius (Lepidoptera: Tortricidae), a major pest in field pea (Pisum sativum L., Fabaceae). Over four consecutive years, the abundance of pea moth was monitored by placing pheromone traps at various 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 to pea fields that were planted and harvested in the previous growing season. The combination of all three factors using a general additive model approach yielded the best results. The model proposed by this study accurately discriminated between low‐infestation and high‐infestation fields in 95% of cases.

GIS-Based Tool for Pest Specific Area-Wide Planning of Crop Rotation Distance with Land Use Data

“…It cannot be recommended to use a smaller buffer, since with the use of satellite data, we do not know if a previous year's pea site was treated with insecticides or not. Even though the function almost seems to be linear within the examined range, the underlying function has been proven to be an exponential decay function by Huusela-Veistola and Jauhiainen [24], Thöming et al [23], Weisz et al [12], and Riemer et al [25] in their studies. The MD for grain peas of 1-3 km was effective in reducing the risk of pea moth infestation, depending on the year and cropping system [24].…”