The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.

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.