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Human factors play an important role in the management of safety and quality in an agricultural work environment. Although employee actions and decisions have been identified as a key component of successful occupational safety programs and quality management programs, little attention has been given to the employees' role in these types of programs. This research explored two safety relationships that have theoretical connections but little previous research: the relationship between safety climate and quality climate, and the relationship of the safety and quality climates between the organizational level and the group level within a workplace. Survey data were collected at three commercial grain handling facilities from 177 employees. Employees also participated in safety and quality decision-making simulations. Significant positive predictions were noted for safety and quality climate. Decision-making predictions are also discussed. This research suggests that organizational safety is an important predictor of group safety. In addition, recognizing the larger role that supervisors play in group workplace behavior, more should be done to increase employee perceptions of grouplevel involvement in quality climate to promote more quality-oriented decision-making by employees.

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Research studies have shown that tractor rollovers are the leading cause of work-related death in U.S. production agriculture. Previous studies have also shown that while rollover protective structures (ROPS) are the most effective means of preventing these deaths, it is estimated that over half of the tractors in use on U.S. farms are not equipped with ROPS. To gauge the impact of a ROPS retrofit policy, tractor sales in Central Iowa were monitored for a three-month period in early 1998 to determine the proportion of tractors without ROPS being sold by equipment dealers versus those being sold through other channels such as auctions, farm sales, and private transfer. During the study period, 549 tractors sales were documented in Central Iowa. Of these tractors, 72% were equipped with ROPS. Of the 152 that were sold without ROPS, 43% were sold by equipment dealers. ROPS retrofits were readily available for 92% of the tractors that were not equipped with them at the time of the sale. A fully implemented ROPS retrofit program for equipment dealers would have reduced the number of tractors sold without ROPS in Central Iowa by over 40%. The results suggest that such a policy could have a significant impact in reducing the number of farm fatalities and thus should be investigated further.

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.