Predicting the Unpredictable: A Systematic Review of FHB Risk Models in Wheat
Research Poster Health & Life Sciences 2025 Graduate ExhibitionPresentation by Olanrewaju Shittu
Exhibition Number 214
Abstract
Fusarium head blight (FHB) poses a significant threat to global wheat production, leading to yield losses and the contamination of grain with deoxynivalenol (DON), raising concerns about food safety. Accurate forecasts are essential for effective management, resulting in the development of various forecasting models. This systematic review assesses prediction models for FHB and DON, highlighting methodologies, performance metrics, and existing research gaps. We searched Citation Index, Emerging Sources Citation Index, Scopus, CAB Abstracts, and PubMed for the keywords ‘wheat’ and ‘FHB’ using various terms without applying limits or filters. We screened the titles and abstracts of 14,258 papers and conducted full-text reviews on 406 articles specifically focused on FHB or DON forecasting. Preliminary findings indicate that weather variables are the most frequently used predictors, with temperature and humidity being the key factors. Some models also incorporate rainfall and the crop growth stage to enhance accuracy. The models can be classified into three categories: empirical (e.g., logistic regression), machine learning (e.g., neural networks, random forests), and mechanistic (e.g., weather-based risk models). Many studies rely on internal validation, limiting their applicability across different agroecosystems. This review emphasizes the need for external validation, real-time sensor data use, and genetic factors integration to improve forecasting. Future research should focus on ensemble modeling and decision support systems to strengthen FHB risk prediction and disease management for wheat growers.
Importance
Fusarium Head Blight (FHB) is a major wheat disease that threatens production by reducing yields and contaminating grain with toxins like deoxynivalenol, which poses risks to food safety. Farmers rely on forecasting models to predict when FHB will occur and take timely action, but existing models vary in accuracy and utility across different environments. This study used a systematic review to assess the strengths, limitations, and potential improvements of different models. This research supports the development of better forecasting systems by identifying the most effective approaches and highlighting gaps in current prediction tools. Improving FHB prediction will help farmers make informed management decisions, reduce economic losses, and enhance food security by ensuring a safer and more stable wheat supply.