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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. [ABSTRACT FROM AUTHOR]

Many pests damage pea crops, which potentially leads to reduced quality and yield losses. Since pests occur at different phenological growth stages of pea crops, the prediction of growth stages, for example as BBCH stages, is beneficial. In this study, three models have been developed to simulate growth stages of grain and green pea crops, for the latter with early and late sowing dates. All data, such as BBCH stages and air temperature, were collected in Germany in a three-year study under practical farming conditions at 415 sample sites. For the development of each model, a Gompertz regression model based on the observed data was performed. The model validation suggests that each model precisely and reliably predicts pea crop growth stages for spring-sown peas. Amongst others, the RMSEIndex for grain peas was 3.4; for green peas, early and late sowing dates, respectively, they were 3.4 and 4.5. SIMONTO-Pea (SIMulation of ONTOgenesis) is the first model that predicts detailed pea crop growth stages based on the BBCH scale. This innovation is especially beneficial for users such as advisors and farmers dealing with spring-sown pea crops as a decision support system in monitoring and pest management according to pea crop growth stages. [ABSTRACT FROM AUTHOR]