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 satellite-acquired 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.

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

Background and Objectives: Fumonisin (FUM), a secondary metabolite from Fusarium spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois‐centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa‐centric model. Corn samples (n = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (n = 89).Findings: Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois‐ and Iowa‐centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois‐ and Iowa‐centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%.Conclusions: FUM‐GBM analyses determined the top influential predictor for the Illinois‐centric model was satellite‐acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa‐centric model was precipitation (PRCP) in October.Significance and Novelty: Results indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.

Highlights Meeting the 0.9% tolerance level was challenging under most conditions. Non-GM loads in the simulation were able to meet a 1.5% or 3% tolerance level under specific conditions. Field isolation distance plays a large role in a non-GM load meeting the posted tolerance level Abstract. The open-air growth environment used in maize production makes it nearly impossible to ensure 100% purity of specified genetic traits. One measure of successful coexistence is a low level of unintended material in seed, grain, and feed or food products, termed “adventitious presence” (AP). To allow the coexistence of genetically modified (GM) and non-genetically modified (non-GM) maize, tolerance levels regulate how much AP of genetically modified corn is allowed in each unit of maize. This research sought to model four factors contributing to levels of adventitious presence: seed purity, field isolation distance, combine cleanout, and grain elevator receipt and handling practices. Monte Carlo simulation was used to test nine scenarios to determine the feasibility of successfully meeting three tolerance levels for adventitious presence (0.9%, 1.5%, and 3.0%). After 50,000 iterations for each model, sensitivity analysis was performed to identify factors that play an important role in whether the load meets the posted tolerance level or not. Results suggest that non-GM maize loads would not meet a tolerance level of 0.9% in most cases. Non-GM maize loads were found to meet tolerance levels of 1.5% and 3.0% in certain cases. The most significant factors affecting the probability of the unit of maize meeting the posted tolerance level were field isolation distance, elevator handling practices, and seed purity. Keywords: Adventitious presence, Coexistence, Identity preservation, Monte Carlo simulation, Transgenic grain.

The safety climate of an agricultural workplace may be affected by several things, including the level of trust that workers have in their work group supervisor and organizational management. Safety climate has been used by previous safety researchers as a measure of worker perceptions of the relative importance of safety as compared with other operational goals. Trust has been linked to several positive safety outcomes, particularly in hazardous work environments, but has not been examined relative to safety climate in the perennially hazardous work environment of a commercial grain elevator. In this study, 177 workers at three Midwest grain elevator companies completed online surveys measuring their perceptions of trust and safety at two administrative levels: organizational management and work group supervisors. Positive and significant relationships were noted between trust and safety climate perceptions for organizational managers and for work group supervisors. Results from this research suggest that worker trust in organizational management and work group supervisors has a positive influence on the employees' perceptions of safety climate at the organizational and work group levels in an agricultural workplace.

Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers’ compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive models in cost estimation. The results show that the GLM with gamma distribution has the highest predictivity power (R2 = 0.79). Injury characteristics and worker’s occupation were predictive of large claims’ occurrence and costs. The conclusions of this study are useful in modifying and estimating insurance pricing within high-risk agribusiness industries. The approach of this study can be used as a framework to forecast workers’ compensation claims amounts with rare, high-cost events in other industries. This work is useful for insurance practitioners concerned with statistical and predictive modeling in financial risk analysis.