Journal of Agricultural Safety and Health最新文献

Highlights: Risk assessment is required for new machine designs, including autonomous, robotic, and other highly automated agricultural equipment. Meaningful data is needed to inform many risk assessment processes, yet there is no historical incident data on novel machine forms that have never been widely deployed. The establishment of an exposure database could supply needed data for machine risk assessment with new machine forms. Engineering standards are a bedrock for safe design, including newly developed standards for highly automated and autonomous machines. Outreach and awareness of engineering design standards are needed for engineers, non-engineers, regulators, and students.

Abstract: The 2022 SAFER AG workshop convened stakeholder groups that addressed emerging challenges related to safety, research needs, workforce implications, community-level impacts, and other issues connected to the development and eventual widespread deployment of autonomous and highly automated agricultural machines. This article summarizes the key findings, discussions, and recommendations of one of the workshop's working groups that focused their efforts on risk assessment and the data needed to inform risk assessment processes during machine design, engineering design, safety standards, and regulatory efforts. Participants engaged in a consensus-building process to identify gaps in current knowledge and practice, particularly concerning new machine forms for which historical safety data is limited or nonexistent. The group recommended innovative solutions, including creating an exposure-based database and exposure definitions that could serve as a foundation for future risk assessment processes. Workshop participants emphasized the critical role of consensus engineering standards in ensuring the safe design of autonomous, robotic, and other highly automated equipment, with a clear sense that engineering standards must be considered early in the design phases. The group also recommended that any desired regulatory activity at various levels (local, state, national, and international) must consider the knowledge that is already embedded in pre-existing engineering standards with the hope that future regulation aligns with these standards and leverages the expertise and care that goes into the consensus standards development process. Recommendations for outreach and education efforts targeting small manufacturers, regulators, students in ABET-accredited engineering programs, and academia are also outlined.

Highlights: Over 50% of the young adult agricultural workers surveyed reported never wearing a helmet, seatbelt, or hearing protection when operating an ATV or UTV. Young adult agricultural workers' mean ATV and UTV safety scores ranged from 1 to 4.44 on a scale of 1 to 5. Gender, supervisor influence, and risk-taking behaviors significantly influenced young agricultural workers ATV and UTV safety scores.

Abstract: ATV and UTV crashes are a leading cause of agricultural-related injury and death in the United States. Young adult agricultural workers (under the age of 25) contribute substantially to the agricultural workforce and often utilize ATVs and UTVs in their work. However, it is unknown if they have adopted recommended safe operating behaviors that could reduce the potential for injury or death. As such, we evaluated young adult agricultural workers' reported ATV and UTV operating practices and safety influences. Agricultural students from four Midwestern colleges and universities responded to an online survey about ATV and UTV operating behaviors, social influences (supervisor, peer, and parental risk-taking behaviors), and individual risk-taking orientation and tendencies. An ATV/UTV safety score was calculated from participants' responses, and linear regression methods were used to examine associations between scores and other aspects of risk-taking. Out of 193 respondents, a large proportion of participants reported never wearing hearing protection (68.6%), a seatbelt (59.2%), or a helmet (58.1%) while operating an ATV or UTV. The analysis identified gender (p < 0.001), global risk-taking (p = 0.001), risk-taking orientation at work (p = 0.012), and supervisor influence (p = 0.015) as significant factors influencing young adult agricultural workers' safety scores. The findings suggest that gender, individual risk-taking tendencies, and supervisor influence play crucial roles in safety behavior, indicating that risk management initiatives for young adult agricultural workers should not only focus on individual behavior change but also consider the broader social and workplace influences on safety.

Highlights: Percent agreement score showed moderate acceptability in preparing participants to complete the SFS application. Using self-directed learning to guide the training provided evidence for successful SFS application completion. Researchers plan to modify the self-directed learning approach to increase future SFS training success rates.

Abstract: This study evaluates the use of Self-Directed Learning (SDL) and Performance-Based Assessment (PBA) to improve the accuracy of adult educators' completion of the Safe Farm Steward (SFS) application. PBA is a form of assessment commonly utilized in youth education, but there is limited evidence of its implementation among adults. Through implementing SDL, the researchers introduced a farm safety model to extension agents, agriculture teachers, and college professors to improve their performance assessment. The cohort attended a three-day professional development training followed by farm visitations; the training and visitations employed materials that incorporated SDL techniques. This study's findings indicate that the professional development training was moderately acceptable in preparing the cohort to accurately complete the SFS application. Though the cohort did not meet the expected score on each individual construct, the overall PBA demonstrated moderate acceptability compared to the expected score. The researchers highlight gaps in professional development where recommendations for improvement could be made.

Highlights: Farmworkers are at high risk of heat-related illness, especially those working in polytunnels. There is a gap in knowledge regarding working conditions inside polytunnels. Polytunnels are prevalent in California's Central Coast region. Recommendations to reduce the risk of heat-related illness among workers in polytunnels were formulated.

Abstract: Polytunnels-also known as hoop houses-are used worldwide to grow certain crops year-round, primarily to protect plants from precipitation and cool temperatures. Farmworkers may be at increased risk in polytunnels due to higher temperatures and relative humidity. In the Central Coast region of California, polytunnels are commonly used to grow berries and other crops, but information on measures used to reduce heat stress in farmworkers working in polytunnels or how many workers are potentially exposed to these conditions is scarce. The purpose of this study was to: (1) estimate the area under polytunnels and the number of workers in them in California's Central Coast region; (2) assess current practices to manage heat and protect workers in polytunnels; and (3) use this information to develop proposed best practices for protecting farmworkers in polytunnels. Using satellite imagery and crop production records, the area under polytunnels in the region was estimated at 5,162 ha with a conservatively estimated 46,000 farmworkers. Through key informant interviews, we found that farms are generally following OSHA worker safety regulations. However, additional measures may be needed to protect workers because environmental conditions inside polytunnels are variable and difficult to predict. For instance, wet bulb globe temperature would be a more accurate measure of heat stress risk than temperature alone. We propose recommendations that follow the hierarchy of controls to reduce the risk of heat-related illness among workers inside these structures.

Highlights: Over half of agricultural youth directors rarely discuss mental health issues such as depression and suicidal thoughts. There is a significant correlation between the frequency of mental health discussions and agricultural youth directors' self-reported knowledge and confidence levels. Integrating tailored mental health training into professional development programs could enhance the effectiveness of youth mentoring in agricultural settings.

Abstract: This study evaluates how frequently agricultural youth directors discuss mental health topics with youth and explores the influence of directors' knowledge and confidence on these discussions. We conducted a cross-sectional online survey assessing mental health knowledge and confidence among 242 directors from 4-H, Extension, and FFA in Illinois. The results indicate that discussions on sensitive mental health issues such as depression, suicide, traumatic experiences, isolation, eating disorders, self-harm, and substance use are infrequent and correlate with agricultural youth directors' limited knowledge and confidence in addressing these issues. The findings underscore the importance of mental health literacy as an essential part of professional development for agricultural youth directors and advocate for integrating mental health considerations into youth development models in agriculture. This research fills a gap in the existing literature on mental health in agricultural settings by pinpointing key areas for improving agricultural youth directors' ability to support youth mental health effectively.

Highlights: Outdated safety regulations pose challenges for autonomous agricultural tractors. Cal/OSHA denied the petition to update regulations for autonomous tractors. The industry's experimental variance shows potential but lacks sufficient data. Recommendations include third-party safety testing and creating an advisory group.

Abstract: As of August 2024, California's agricultural tractor safety regulations, developed over half a century ago, are still focused on classic tractors with human operators. These regulations are problematic when applied to autonomous equipment. Since agricultural equipment has advanced, producers have faced challenges in complying with existing regulations for autonomous machinery in California. A petition (No. 596) was submitted in December 2021 to the State of California Department of Industrial Relations and reviewed in March 2023. The petition requesting modification of the agricultural tractor's traditional regulation was recently submitted to the Occupational Safety and Health Standards Board (OSHSB). The OSHSB denied both petitions. This article discusses more details related to California agricultural tractor safety regulations, the petition to modify the traditional regulations, discussions on OSHSB meetings regarding the petition, and several suggestions to resolve the current issue.

Highlights: Findings confirmed that out-of-condition grain is a primary causal factor in grain entrapment and engulfment. The advisory panel confirmed that grain quality has implications for grain dust explosions. Findings highlighted a lack of in-depth knowledge expected from an expert panel, specifically on aspects of protective grain quality traits.

Abstract: Out-of-condition grain has been identified as a primary causal factor in grain entrapments and engulfments. The quality of grain also has implications for grain dust explosions. Limited research has examined exactly which elements of grain condition influence worker safety in grain handling. This research project aimed to establish an advisory panel to examine and provide input on how elements of grain condition relate to worker safety risks in grain handling. A purposeful sampling technique was used to obtain a sample of grain handling and storage experts to function in an advisory role for the project. A primary aim of this research was to understand the problem further, provide input on tested variables, and guide educational and dissemination efforts. As is true for qualitative methodologies, those selected as part of the targeted sample cannot be generalized to other experts in the field of grain handling. The final sample contained six industry representatives, five academic professionals, and two insurance/regulatory professionals. Participants interviewed had varied expertise with grain-based safety events. Of those interviewed, 23% of participants had personal experience, 54% had bystander or investigator experience, and 23% had training experience. Semi-structured interviews were conducted to further understand the problem, provide input on important elements in safe grain handling, and guide educational and dissemination efforts. Interviews were analyzed with a primary objective to identify elements of grain condition that play a role in the incidence of grain entrapment, grain engulfments, or grain dust explosions. NVivo 14 was used to conduct a thematic analysis, and four overall themes were identified, which included challenges to worker safety in the grain handling industry, areas where improved communication is needed, grain quality indicators that may play a role in safety incidents, and available mitigation strategies. The themes are the opinions of the advisory panel and may not reflect those of the entire grain handling industry.

Highlights: An AI-driven system for predicting and preventing ATV crashes was developed. Machine learning model achieved rollover prediction accuracy of over 99%. The system has the potential to significantly reduce ATV-related injuries and fatalities by enabling preemptive actions.

Abstract: All-Terrain Vehicle (ATV) crashes have become a public health concern in the U.S. over the past decades, resulting in numerous fatalities and hospitalizations. Most of those incidents could have been prevented if riders could better assess their ability to handle risks. Currently, risk factors associated with ATV incidents have already been studied. However, little effort has been made toward developing practical applications that assist the rider in preventing crashes. Commercial ATV safety systems, such as Farm Angel, focus on post-crash detection and emergency medical services (EMS) alerting rather than preventive measures. Machine learning prediction models can be used to assist riders in taking preventive measures to avoid an imminent crash. In this study, we developed a system that leverages the predictive power of machine learning algorithms to assess the likelihood of a crash in real-time and alert the riders, thus allowing them to prevent the crash. To the best of our knowledge, this is the only system ever developed for ATVs specifically that can predict rollover incidents. The crash likelihood is estimated by a deep neural network that considers the ride parameters (e.g., ATV speed, turning radius, and roll and pitch angles), ATV characteristics (e.g., width, length, wheelbase), and human factors (i.e., presence of a rider). The ATV characteristics and the presence of a rider are retrieved from the rider's input through a smartphone application developed specifically for this study. The ride parameters are retrieved from an embedded system (attached to the ATV). Validation and performance tests indicated that: (1) the proposed device has a rollover prediction system with an accuracy superior to 99%; (2) the system can detect roll and pitch angles with average errors of 0.26 and 0.54 degrees, respectively; and (3) the system can detect the ATV's speed with an average error of 0.75 m s-1.