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.

The Scholarship of Teaching and Learning (SoTL) aligns with many institutionally espoused values regarding the value of teaching and learning excellence. SoTL has increased in prestige and value in the past decade, but less information about the proliferation of SoTL within one institution is known. Through an examination of 10 years of curriculum vitae submitted for successful promotion and tenure, our study investigated faculty members' engagement in SoTL over time and differences in engagement by rank, race, gender, discipline, and type of SoTL artifact. Over the 10-year period, the percentage of faculty engaged in SoTL did not differ significantly. We did uncover differences by disciplinary type and race. Faculty were most likely to engage in grants and presentations related to SoTL and least likely to have a peer-reviewed journal artifact. Our findings suggest that although SoTL efforts continue to gain acceptance within the higher education milieu, institutional and disciplinary realities may be powerful determiners of SoTL participation.

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.

A survey of Iowa farmers was conducted to assess the farmers' perceptions and beliefs regarding auger safety and how these perceptions are put into practice. A questionnaire was designed to obtain information on the following auger-related topics: recognition of safe practices and appropriate shielding, personal practices and behaviors, risk perception, and personal beliefs. A random sample of 400 Iowa farmers (arrayed by county) was selected to be representative of Iowa farms. The response to the mailed survey was 23%. Some returned surveys were eliminated because the respondent was retired or did not own/operate augers, resulting in 57 usable questionnaires. Survey responses indicate that the majority of Iowa farmers who participated in this study can correctly identify appropriate shielding for auger intake that meet or exceed ASAE standards. Survey responses suggest that it is common practice in Iowa for youth (age 13 to 19) to operate augers. Iowa farmers in this study perceive the most important contributing factors to auger-related injuries to be operating an auger without shielding, failure to pay attention on the job, moving augers in a raised position, and using hand or feet to redirect the flow of grain. Survey results also indicate that carelessness is perceived to be the leading cause of auger-related injuries, and that auger related injuries can be avoided by having respect for the equipment, being careful, and using common sense. The results also show that the Iowa farmers who participated in this study commonly perform auger-related practices (e.g., moving an auger in a raised position) that they identify as being unsafe.

The PTO driveline is the most common means of transferring power from a tractor to towed machinery and stationary equipment. While equipment manufacturers install shielding to protect operators and bystanders from coming in contact with operating PTO components (particularly around the knuckle), entanglement is still a cause of some of the most catastrophic agricultural work-related injuries. This study investigated the influence of material type, material length, and angle of material introduction on entanglements with a spinning PTO shaft knuckle. These variables were tested using a laboratory PTO apparatus where 165 entanglements were recorded during the 720 trials conducted. The results indicate that lighter materials, such as cotton thread, have a significantly higher probability of becoming entangled than heavier materials, such as leather bootlaces. Materials that were longer (i.e., extend further below the midline of the PTO knuckle) have higher probabilities of becoming entangled than do shorter materials. The horizontal path that the material traveled across the centerline of the PTO shaft impacted the probability of entanglement. When the angle of intersection of the horizontal path of travel relative to the centerline of the PTO shaft is 90 degrees, or close to 90 degrees, a higher probability of entanglement occurs. All 165 entanglements occurred on the downward rotational side of the PTO knuckle regardless of which side the horizontal path of travel started from. The results of this study provide the first look at understanding the physical phenomena associated with the initial stages of PTO entanglements and set the stage for future research.

This article details a correction to: Ridpath, R. (2026). Best Practices in Conveyor Risk Assessment: A Qualitative Study of Certified Safety Professionals in Industrial Settings. Journal of Technology Studies, 51(1), 10–17. DOI: https://doi.org/10.21061/jts.448.