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Mycotoxins such as deoxynivalenol (DON) in wheat grain pose a threat to food and feed safety. Models predicting DON levels mostly require field specific input data that in turn allow predictions for individual fields. To obtain predictions for entire regions, model results from fields commonly have to be aggregated, requiring many model runs and the integration of field specific information. Here, we present a novel approach for predicting the percentage of winter wheat samples with DON levels above the EU maximum legal limit (ML) based on freely available agricultural summary statistics and meteorological data for an entire region using case study data from Luxembourg and Switzerland. The coefficient of variation of the rainfall data recorded ±7 days around wheat anthesis and the percentage of fields with a previous crop of maize were used to predict the countrywide percentage of winter wheat grain samples with DON levels &gt; ML. The relationships found in the present study allow for a better assessment of the risk of obtaining winter wheat samples with DON contaminations &gt; ML for an entire region based on predictors that are freely available in agricultural summary statistics and meteorological data.

Frequency of Deoxynivalenol Concentrations above the Maximum Limit in Raw Winter Wheat Grain during a 12-Year Multi-Site Survey