Forecasting mycotoxins Forecasting – during the cultivation season – of mycotoxin presence at harvest can assist grain supply chain providers in taking timely actions to manage mycotoxin contamination in the downstream stages of the chain. Such forecasting models use weather data, sometimes in combination with agronomical information, to predict – beforehand – the toxins presence in the grain at harvest. Various types of models have been devel- oped, being mechanistic models and statistical models. Mecha- nistic models are based on the biology of the fungus, whereas statistical models are based on relationships between the input variables (weather and agronomics) and mycotoxin contami- nation. A recent approach is the combination of a mechanistic model with machine learning, which was demonstrated for aflatoxins in maize. In this approach, first fungal presence is es- timated using the mechanistic module for Aspergillus flavus. Next, the amount of aflatoxins produced by this fungus is esti- mated with the Bayesian network module, using weather data during different periods of maize cultivation as inputs.
Use of model outcomes Many models developed so far focus on DON in wheat and on aflatoxins in maize. Contamination of wheat with ZEA is highly (about 90%) related to contamination with DON, so predictions for DON are also highly relevant for ZEA. These two mycotoxins are the most relevant mycotoxins to the feed industry, given current regulation and impacts on animal and human health. The forecasting models focus not only on different grain- mycotoxin combinations but also on different regions and end- users. Regarding end-users, farmers and other providers of the grain chain should be distinguished. Farmers would need mycotoxin predictions around grain flowering so a last fungicide application can still be applied, depending on the model outcomes (high or low predicted mycotoxins in the grain at harvest. When the prediction is only available close to/at harvest, the farmer can use the predictions to decide upon separate storage and/or testing of the batch, but not for fungicide applications. Buyers/traders and feed industry can use model predictions to decide upon routing/processing within the chain, and/or risk-based testing. For instance, feed producers can use model predictions for DON in wheat for their compound feed production for pigs, given that pigs are a very sensitive animal species to DON; it is affecting their growth and fertility. In case of high predicted DON values in wheat, testing of the wheat batch would be advised before using it in feed production. In this way, the forecasting model can guide risk-based testing and analyses for mycotoxins, and thus save costs; only those batches with high predicted mycotoxin level will need to be sampled. Wageningen Food Safety Research (WFSR) develops forecasting models for different types of end- users, different grain-toxin combinations and various regions, including models for feed industry.
50 ▶ MYCOTOXINS | NOVEMBER 2021
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