Location: Pest Management and Biocontrol Research
Project Number: 2020-42000-023-036-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Oct 1, 2025
End Date: Sep 14, 2027
Objective:
1. Develop state-specific machine learning models to predict mycotoxin contamination levels in corn.
2. Validate and refine fungal growth and mycotoxin contamination models with empirical field data.
Approach:
Predictive models for mycotoxin contamination risk have been developed utilizing data generated at the county-level, but collection of field-specific data prior to and throughout the growing season will provide information that can be used to further refine models. Multiple corn fields in different regions varying in environmental and agronomic factors in IL and TX will be selected for soil and crop sampling in 2024 and 2025. Soil samples and crop debris will be collected at different time points prior to and throughout the growing season, and mycotoxigenic fungal populations will be quantified and genetically characterized. Crop samples will be collected at harvest for analysis of fungal populations and mycotoxin concentrations. Data generated from field sampling will be used to support development and validation of models to predict mycotoxin outbreaks in the Midwest and Southern U.S. that will support greater implementation of integrated pest management (IPM)-based strategies for mycotoxin mitigation at region-specific scales.