Modeling deoxynivalenol contamination of wheat in northwestern Europe for climate change assessments.

Abstract

Climate change will affect mycotoxin contamination of feed and food. Mathematical models for predicting mycotoxin concentrations in cereal grains are useful for estimating the impact of climate change on these toxins. The objective of the current study was to construct a descriptive model to estimate climate change impacts on deoxynivalenol (DON) contamination of mature wheat grown in northwestern Europe. Observational data from 717 wheat fields in Norway, Sweden, Finland, and The Netherlands were analyzed, including the DON concentrations in mature wheat, agronomical practices, and local weather. Multiple regression analyses were conducted, and the best set of explanatory variables, mainly including weather factors, was selected. The final model included the following variables: flowering date, length of time between flowering and harvest, wheat resistance to Fusarium infection, and several climatic variables related to relative humidity, temperature, and rainfall during critical stages of wheat cultivation. The model accounted for 50 % of the variance, which was sufficient to make this model useful for estimating the trends of climate change on DON contamination of wheat in northwestern Europe. Application of the model in possible climate change scenarios is illustrated.