Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Yea

Warm temperatures and drought conditions in the United States (US) Corn Belt in 2012 raised concern for widespread aflatoxin (AFL) contamination in Iowa corn. To identify the prevalence of AFL in the 2012 corn crop, the Iowa Department of Agriculture and Land Stewardship (IDALS) conducted a sample of Iowa corn to assess the incidence and severity of AFL contamination. Samples were obtained from grain elevators in all of Iowa’s 99 counties, representing nine crop reporting districts (CRD), and 396 samples were analyzed by IDALS using rapid test methods. The statewide mean for AFL in parts per billion (ppb) was 5.57 ppb. Regions of Iowa differed in their incidence levels, with AFL levels significantly higher in the Southwest (SW; mean 15.13 ppb) and South Central (SC; mean 10.86 ppb) CRD () regions of Iowa. This sampling demonstrated high variability among samples collected within CRD and across the entire state of Iowa in an extreme weather event year. In years when Iowa has AFL contamination in corn, there is a need for a proactive and preventive strategy to minimize hazards in domestic and export markets.This article is published as Branstad-Spates, Emily H., Erin L. Bowers, Charles R. Hurburgh, Philip M. Dixon, and Gretchen A. Mosher. "Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year." International Journal of Food Science 2023 (2023). doi: https://doi.org/10.1155/2023/9959998. Copyright © 2023 Emily H. Branstad-Spates et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year

Warm temperatures and drought conditions in the United States (US) Corn Belt in 2012 raised concern for widespread aflatoxin (AFL) contamination in Iowa corn. To identify the prevalence of AFL in the 2012 corn crop, the Iowa Department of Agriculture and Land Stewardship (IDALS) conducted a sample of Iowa corn to assess the incidence and severity of AFL contamination. Samples were obtained from grain elevators in all of Iowa’s 99 counties, representing nine crop reporting districts (CRD), and 396 samples were analyzed by IDALS using rapid test methods. The statewide mean for AFL in parts per billion (ppb) was 5.57 ppb. Regions of Iowa differed in their incidence levels, with AFL levels significantly higher in the Southwest (SW; mean 15.13 ppb) and South Central (SC; mean 10.86 ppb) CRD (p<0.05) regions of Iowa. This sampling demonstrated high variability among samples collected within CRD and across the entire state of Iowa in an extreme weather event year. In years when Iowa has AFL contamination in corn, there is a need for a proactive and preventive strategy to minimize hazards in domestic and export markets

Gradient boosting machine learning model to predict aflatoxins in Iowa corn

Introduction: Aflatoxin (AFL), a secondary metabolite produced from filamentous fungi, contaminates corn, posing significant health and safety hazards for humans and livestock through toxigenic and carcinogenic effects. Corn is widely used as an essential commodity for food, feed, fuel, and export markets; therefore, AFL mitigation is necessary to ensure food and feed safety within the United States (US) and elsewhere in the world. In this case study, an Iowa-centric model was developed to predict AFL contamination using historical corn contamination, meteorological, satellite, and soil property data in the largest corn-producing state in the US. Methods: We evaluated the performance of AFL prediction with gradient boosting machine (GBM) learning and feature engineering in Iowa corn for two AFL risk thresholds for high contamination events: 20-ppb and 5-ppb. A 90%–10% training-to-testing ratio was utilized in 2010, 2011, 2012, and 2021 (n  =  630), with independent validation using the year 2020 (n  =  376). Results: The GBM model had an overall accuracy of 96.77% for AFL with a balanced accuracy of 50.00% for a 20-ppb risk threshold, whereas GBM had an overall accuracy of 90.32% with a balanced accuracy of 64.88% for a 5-ppb threshold. The GBM model had a low power to detect high AFL contamination events, resulting in a low sensitivity rate. Analyses for AFL showed satelliteacquired vegetative index during August significantly improved the prediction of corn contamination at the end of the growing season for both risk thresholds. Prediction of high AFL contamination levels was linked to aflatoxin risk indices (ARI) in May. However, ARI in July was an influential factor for the 5-ppb threshold but not for the 20-ppb threshold. Similarly, latitude was an influential factor for the 20-ppb threshold but not the 5-ppb threshold. Furthermore, soil-saturated hydraulic conductivity (Ksat) influenced both risk thresholds. Discussion: Developing these AFL prediction models is practical and implementable in commodity grain handling environments to achieve the goal of preventative rather than reactive mitigations. Finding predictors that influence AFL risk annually is an important cost-effective risk tool and, therefore, is a high priority to ensure hazard management and optimal grain utilization to maximize the utility of the nation’s corn crop.This article is published as Branstad-Spates EH, Castano-Duque L, Mosher GA, Hurburgh CR Jr, Owens P, Winzeler E, Rajasekaran K and Bowers EL (2023) Gradient boosting machine learning model to predict aflatoxins in Iowa corn. Front. Microbiol. 14:1248772. doi: 10.3389/fmicb.2023.1248772. Posted with permission. © 2023 Branstad-Spates, Castano-Duque, Mosher, Hurburgh, Owens, Winzeler, Rajasekaran and Bowers. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.<br