Weather-based models for forecasting Fusarium head blight risks in wheat and barley: A review

Fusarium head blight (FHB) is one of the most devastating crop diseases worldwide, significantly reducing the yield and quality of small-cereal crops such as wheat and barley when favourable weather conditions exist during anthesis. Additionally, FHB-associated mycotoxins significantly impact global food and feed safety. Controlling FHB with fungicides applied near anthesis reduces visual FHB symptoms and associated mycotoxin production, thereby lowering disease-related costs. However, when weather conditions are unfavourable for FHB occurrence, fungicide application can be costly and environmentally undesirable. Thus, fungicides should be used sparingly only when the pathogen is present and weather conditions are favourable. Modelling of FHB risk using weather data has grown rapidly in recent decades and plays an essential role in integrated crop disease management. In this review, several weather-based FHB models are selected and described in detail. The models were developed globally for assessing the real-time risk of FHB epidemics in various regions/countries. Most of these models are site-specific and predict FHB visual observations such as the incidence and severity of FHB, Fusarium-damaged kernels (FDK), and also deoxynivalenol (DON) levels. The review also highlights the limitations of these existing models, including their narrow applicability, low accuracy for high-risk contamination situations, and omissions of certain factors. Also discussed are potential avenues for improvement and enhanced predictive capabilities including consideration of additional disease risk factors as well as a broader range of varieties. These predictive models can assist producers, regulatory agencies, and industry to mitigate potential food and feed security and safety concerns.

Automated summary

This review discusses the importance and development of FHB risk modeling using weather data. By selecting and describing several models, the review highlights the strengths and limitations of these models and explores potential avenues for improvement and enhanced predictive capabilities. These predictive models are of significant importance in mitigating potential food and feed security concerns.