Abstract
Mycotoxin contamination in wheat is strongly influenced by weather conditions, yet how contamination may evolve under future climate and socioeconomic change remains poorly understood at the European scale. Here, we develop a hybrid modelling framework combining machine learning and crop phenology to assess scenario-dependent changes in mycotoxin contamination. The framework integrates historical monitoring data with climate projections under multiple Shared Socioeconomic Pathways. Model evaluation shows strong performance for low-contamination conditions, while the ability to distinguish higher contamination levels remains limited due to class imbalance. Results indicate an overall increase in contamination risk under climate change, particularly for deoxynivalenol (DON), with relatively higher risk in coastal and northwestern Europe. These findings highlight the role of climate-driven shifts in crop phenology and weather conditions, and provide a scenario-based framework for exploring future mycotoxin risk patterns rather than precise quantitative predictions.
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Acknowledgements
This study was financed by the project KB-37-002-038. The Authors kindly thank Dr. Allard de Wit from Wageningen Environmental Research, and Maurits van den Berg from the European Commission Joint Research Centre (JRC) for pointing to the WOFOST model and the JRC data. The Authors also kindly extend thanks to Wouter Hoenderdaal, Rosan Hobe and Cheng Liu from Wageningen Food Safety Research for their assistance in processing data. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies that support CMIP6 and ESGF.
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Wang, X., Rodés-Bachs, C. & Van der Fels-Klerx, H.J. Projected mycotoxin contamination in European wheat under future climate and socioeconomic scenarios.
npj Sci Food (2026). https://doi.org/10.1038/s41538-026-00858-9
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DOI: https://doi.org/10.1038/s41538-026-00858-9
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