Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning.

Autor:	Castano-Duque L; Food and Feed Safety Research Unit, Southern Regional Research Center, Agriculture Research Service, United States Department of Agriculture, New Orleans, LA, United States., Winzeler E; Dale Bumpers Small Farms Research Center, Agriculture Research Service, United States Department of Agriculture, Booneville, AR, United States., Blackstock JM; Dale Bumpers Small Farms Research Center, Agriculture Research Service, United States Department of Agriculture, Booneville, AR, United States., Liu C; Microbiology and Agrochains Wageningen Food Safety Research, Wageningen, Netherlands., Vergopolan N; Atmospheric and Ocean Science Program, Princeton University, Princeton, NJ, United States., Focker M; Microbiology and Agrochains Wageningen Food Safety Research, Wageningen, Netherlands., Barnett K; Agricultural Products Inspection, Illinois Department of Agriculture, Springfield, IL, United States., Owens PR; Dale Bumpers Small Farms Research Center, Agriculture Research Service, United States Department of Agriculture, Booneville, AR, United States., van der Fels-Klerx HJ; Microbiology and Agrochains Wageningen Food Safety Research, Wageningen, Netherlands., Vaughan MM; Mycotoxin Prevention and Applied Microbiology Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, Peoria, IL, United States., Rajasekaran K; Food and Feed Safety Research Unit, Southern Regional Research Center, Agriculture Research Service, United States Department of Agriculture, New Orleans, LA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in microbiology [Front Microbiol] 2023 Nov 01; Vol. 14, pp. 1283127. Date of Electronic Publication: 2023 Nov 01 (Print Publication: 2023).
DOI:	10.3389/fmicb.2023.1283127
Abstrakt:	Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2023 Castano-Duque, Winzeler, Blackstock, Liu, Vergopolan, Focker, Barnett, Owens, van der Fels-Klerx, Vaughan and Rajasekaran.)
Databáze:	MEDLINE
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