Location: Food and Feed Safety ResearchTitle: Predictive models to manage mycotoxin outbreaks in the USA
|BARNETT, KRISTIN - Illinois Department Of Agriculture|
|Rajasekaran, Kanniah - Rajah|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/28/2023
Publication Date: N/A
Technical Abstract: Mycotoxin contamination of corn results in significant agroeconomic losses and poses serious health issues worldwide. We utilized machine learning and historical aflatoxin and fumonisin contamination levels in-order-to develop models that can confidently predict mycotoxin contamination of corn in Illinois, a major corn producing state in the USA. We used historical daily meteorological data from a 14-year period combined with corresponding aflatoxin and fumonisin contamination data from the State of Illinois to engineer input features that link weather, fungal growth, aflatoxin production. These features in combination with biogeochemical soil properties, and satellite acquired vegetation index were used for gradient boosting (GBM) and bayesian network (BN) modeling. The GBM and BN models developed can predict mycotoxin contamination with overall 93% accuracy. Analyses for aflatoxin and fumonisin with GBM showed that meteorological and satellite-acquired vegetative index data between corn growing seasons significantly influence mycotoxin contamination levels at harvest. Prediction of high aflatoxin contamination levels was linked to high aflatoxin risk index in weeks during the months of March/April/June/July. Correspondingly, high levels of fumonisin contamination were linked to high precipitation levels in week during February/March/September and high vegetative index in January. During corn flowering time weeks in June, higher temperatures range increased prediction of high levels of fumonisin contamination, while high aflatoxin contamination levels were linked to high aflatoxin risk index. Meteorological events prior to corn planting in the field have high influence on predicting aflatoxin and fumonisin contamination levels at the end of the year. These early-year events detected by the models can directly assist farmers and stakeholders to make informed decisions to prevent mycotoxin contamination of Illinois grown corn.