Forecasting models for mycotoxins in cereal grains during cultivation are useful for pre-harvest and post-harvest mycotoxin management. Some of such models for deoxynivalenol (DON) in wheat, using two different modelling techniques, have been published. This study aimed to compare and cross-validate three different modelling approaches for predicting DON in winter wheat using data from the Netherlands as a case study. To this end, a published empirical model was updated with a new mixed effect logistic regression method. A mechanistic model for wheat in Italy was adapted to the Dutch situation. A new Bayesian network model was developed to predict DON in wheat. In developing the three models, the same dataset was used, including agronomic and weather data, as well as DON concentrations of individual samples in the Netherlands over the years 2001–2013 (625 records). Similar data from 2015 and 2016 (86 records) were used for external independent validation. The results showed that all three modelling approaches provided good accuracy in predicting DON in wheat in the Netherlands. The empirical model showed the highest accuracy (88%). However, this model is highly location and data-dependent, and can only be run if all of the input data are available. The mechanistic model provided 80% accuracy. This model is easier to implement in new areas given similar mycotoxin-producing fungal populations. The Bayesian network model provided 86% accuracy. Compared with the other two models, this model is easier to implement when input data are incomplete. In future research, the three modelling approaches could be integrated to even better support decision-making in mycotoxin management.

Our climate is projected to change gradually over time. Mycotoxin occurrence in cereal grains is both directly and indirectly related to local weather and to climate changes. Direct routes are via the effects of precipitation, relative humidity, and temperatures on both fungal infection of the grain and mycotoxin formation. Indirect routes are via the effects of the wind dispersal of spores, insect attacks, and shifts in cereal grain phenology. This review aimed to investigate available modeling studies for climate change impacts on mycotoxins in cereal grains, and to identify how they can be used to safeguard food safety with future climate change. Using a systematic review approach, in total, 53 relevant papers from the period of 2005–2020 were retrieved. Only six of them focused on quantitative modeling of climate change impacts on mycotoxins, all in pre-harvest cereal grains. Although regional differences exist, the model results generally show an increase in mycotoxins in a changing climate. The models do not give an indication on how to adapt to climate change impacts. If available models were linked with land use and crop models, scenario analyses could be used for analyzing adaptation strategies to avoid high mycotoxin presence in cereal grains and to safeguard the safety of our feed and food.