Advancing Sclerotinia risk forecasting for winter rapeseed in Germany: integrating crop phenology and disease development into a decision support system

Abstract BACKGROUND Sclerotinia stem rot, caused by Sclerotinia sclerotiorum , threatens winter rapeseed ( Brassica napus ) production in Germany, with potential yield losses of up to 30%. The current SkleroPro model provides regional Sclerotinia risk assessments but has shown declining predictive accuracy. This study aims to enhance SkleroPro by integrating a newly developed phenological model to predict flowering stages and a sclerotia germination module to improve disease risk forecasting. RESULTS A phenological model was developed using temperature and photoperiod as key predictors. The model achieved a root mean square error (RMSE) of 3.83 days for predicting flowering stages (BBCH 58–70). A sclerotia germination model was created, with 79% accuracy, incorporating mean maximum temperature and relative humidity as predictors. Integration of these models into SkleroPro improved disease risk prediction, increasing accuracy from 39% to 66%. Sensitivity rose to 90%, ensuring a low risk of underestimating disease outbreaks. CONCLUSION The enhanced SkleroPro model improves disease risk forecasting by identifying high‐ and low‐risk windows for fungicide application, reducing unnecessary treatments while maintaining effective disease control. This decision support tool promotes sustainable winter rapeseed production. The model is currently undergoing further validation with the German Plant Protection Services before being made freely available to farmers. © 2025 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.