Journal of Agricultural Safety and Health最新文献

Highlights: There is a need to close the gap in farmers' knowledge about natural pesticides and safer alternatives to agrochemicals. A system for routine, affordable medical care would encourage clinical presentation to capture occupational health data. Appropriate content and medium are critical for impact among the high number of farmers completing only primary school.

Abstract: Globally, low- and middle-income countries bear the highest disease burden from the indiscriminate use of agrochemicals. Few recent studies directly assess agrihealth and food safety hazards from pesticide use in the smaller Caribbean islands. This study aims to close this gap and support the development of evidence-based policy and interventions for agrihealth. Secondary data was analyzed from a retrospective cross-sectional survey to examine the use of hazardous pesticides in crop production, handling and application practices, and farmers' health in a Caribbean island, St. Vincent and the Grenadines. The implications for health and food safety are discussed with recommendations to improve agrihealth in the country. Over 90% of farmers reported use of commercial pesticides in crop production. The active ingredients of commonly used products were classified as moderately, highly, and extremely hazardous by the World Health Organization, which is significant for health and food safety. Statistically significant associations were not found between the use of pesticides and farmers' demographics, indicating these factors did not affect the pattern of usage. Over 40% of the farmers reported experiencing symptoms of arthritis and wheezing, respectively, and 15% reported respiratory problems. Mixing 2 or more pesticides was common (41.5%), of which 56.8% reported no knowledge of natural pesticides available in the country. Knowledge about natural pesticides was significantly associated with region (p < 0.05). A range of personal protective equipment was used but appeared not to be in full complement. Training in pesticide handling, storage, and application was limited. There is a need to strengthen systems to monitor stewardship in agrochemical use, farmers' health surveillance, and agricultural food safety practices. Studies are needed to further examine the public health and food safety effects in the context of the wider community. This study, however, provides a basis for immediate interventions to improve health and food safety in the country.

Highlights: Compared visual-auditory and visual-tactile modalities for AAM supervision. Both modalities are highly usable, with preferences shaped by farming experience. Non-farming participants responded faster to visual-auditory cues. User background influenced warning design effectiveness and interface needs.

Abstract: As autonomous agricultural machines (AAMs) become more prevalent, effective communication through warning systems is essential for safe and efficient remote supervision. This study evaluated the usability and cue effectiveness of two bimodal warning modalities (visual-auditory (VA) and visual-tactile (VT)) during a simulated remote supervision task. Usability was assessed from the supervisor's perspective using the Computer System Usability Questionnaire (CSUQ). Cue effectiveness was examined through objective performance-based measures aligned with three situation awareness levels: (1) noticeability and response time as measures of perception, (2) accuracy and comprehension time as measures of comprehension, and (3) accuracy and projection time as measures of projection. Thirty participants (5 with farming experience and 25 without farming experience) interacted with a simulated human-machine interface (HMI). Both modalities received high usability ratings, although the visual-tactile was rated more favorably by farming participants. Projection accuracy was significantly lower for farming participants using visual-auditory cues (86.7%), χ²(3, N = 540) = 5.37, p = .147. Although comprehension accuracy differed slightly across modalities-particularly among farming participants-these differences were minimal and not statistically significant. In contrast, non-farming participants exhibited significantly faster saccadic and manual response times to visual-auditory warnings, t(24) = 3.16, p = .004 and t(24) = 3.56, p = .002, respectively. Comprehension and projection times did not differ significantly by modality. Subjective preferences aligned with performance trends; non-farming participants favored visual-auditory cues, while farming participants preferred visual-tactile cues. Correlation analyses indicated that faster comprehension strongly predicted higher usability, and task accuracy was perfectly correlated with usability ratings. These findings underscore the influence of user experience on multimodal warning efficacy and support the need for adaptive, user-informed interface designs in AAM supervision.

Highlights: Swine farmworkers face high risk of flu exposure, which can extend to their household members. Access to the flu vaccine for swine farmworkers is essential to protect both human and animal health. Having health insurance is crucial for increasing flu vaccination rates among Latino swine farmworkers.

Abstract: Swine production in the United States is vital to food security. Although there has been a decline in the number of hog farms across the country, there has been an expansion of large-scale operations. These larger facilities can create environments that facilitate the zoonotic transmission of viruses such as influenza, posing risks not only to workers and their families but also to public health. The seasonal influenza vaccine can reduce the risk of infection and subsequent transmission. The purpose of this study was to explore predictors of seasonal influenzavaccine uptake among Latino immigrant swine farmworkers and their household members. Descriptive statistics, correlations, and logistic regression were used to assess factors associated with seasonal influenza vaccine uptake among participants (n=62). About 60% of the participants reported that they had received the seasonal influenza vaccine within the previous year. Self-reported seasonal influenza vaccine uptake was positively associated with reporting a regular healthcare provider and having health insurance coverage. The odds of a Latino immigrant swine farmworker or a household member receiving the seasonal influenza vaccine were seven times higher for those with health insurance. Ensuring that agricultural workers and members of their household have access to the seasonal influenza vaccine and encouraging vaccine uptake is a critical strategy for preventing the spread of influenza and ensuring both human and animal health.

Highlights: Three AI systems were used and analyzed on their ability to create farm emergency plans. AI were presented with three farm emergency scenarios to access their completeness and accuracy of response. AI was not able to present complete farm emergency plans, as human intervention was needed for a complete FEP. AI responded well for individual emergency scenarios presented, containing key safety points.

Abstract: The ability of three artificial intelligence systems (ChatGPT, Microsoft Copilot, and Google Gemini) to generate functional Farm Emergency Plans (FEP) for a typical Midwestern row crop grain farm was evaluated. Four prompts, each of increasing levels of specificity, were used with the three AI systems, yielding twelve distinct FEPs. A rubric was developed to evaluate each of the twelve AI products against the findings of a review of relevant current literature including academic, government, not-for-profit, and insurance sources to identify essential and consistent components of a FEP. Both ChatGPT and Microsoft Copilot were found to provide valuable starting points for developing FEPs when detailed prompts were provided, while Google Gemini results were less useful. However, none of the systems were capable of independently generating FEPs at the time of this study. Plans that were deemed as unreliable or incomplete enough for application were primarily due to the diverse nature of agricultural operations, limited resources on agricultural emergency preparedness, and the lack of maturity of current AI systems. Findings showed the essential need of using AI systems in collaboration with human guidance and input from other evidence-based sources to create effective FEPs. Similar results were confirmed in which the AI systems were prompted for emergency responses to three specific farm-related emergencies as part of the FEP: (1) flowing grain entrapment, (2) hazardous agricultural chemical spills, and (3) anhydrous ammonia exposure. The need for additional input was found to be essential. Outcomes were limited in scope to the particular type of farm selected for testing and the ability of the AI systems when they were queried on 30 September 2024; 12 February 2025; and 7 March 2025. Since AI systems rapidly continue to mature as they are "exercised," further inquiries will, therefore, yield different outcomes, because AI has become more sophisticated and developed every day. It should also be noted that for "best practices," the inquirer should provide AI with any resources that they have found and provide multiple inquiries to gain the best and most accurate results. This study demonstrated the potential that AI offers to agricultural producers, specifically in emergency preparedness and response, while emphasizing prompt development and user competency to verify AI outputs.

Highlights: Autonomous and highly automated agricultural machines require data-informed risk assessment integrated into design. Long-term incident data gaps for risk assessment necessitate alternative, creative solutions and data sharing. Consensus, voluntary engineering standards should guide safe design for all types of machines, but often lag behind rapid technological change. Broader awareness, access, and alignment of engineering design standards with emerging risks are essential for safe adoption.

Abstract: The introduction of highly automated and autonomous agricultural machinery has led to concerns about the availability of appropriate historical data for engineering design risk assessment to ensure protection from injury and other unexpected events. During the transition period from traditional farm tractors and machines to those more highly automated, industry-based, consensus standards remain a vital tool in the design process. This paper examines the role of risk assessment methods and consensus-based standards in improving safety for these technologies. It includes cited literature presented in the form of a primer or overview at the request of participants at the 2022 SaferAg workshop. It reviews key risk assessment concepts, common methodologies, and the specific data needs and limitations that arise when historical incident records are unavailable. The paper also outlines how standards are developed, their connection to regulation, and recent updates to ISO 18497 relevant to autonomous agricultural equipment. Key challenges include lag times in standard development, uneven access to standards, and gaps in awareness among both designers and policymakers engaged in new regulatory efforts. Addressing these challenges will require coordinated efforts in data collection, standard refinement, and stakeholder education to ensure safe and effective deployment of emerging agricultural machine forms.

Highlights: Maximum high temperature and maximum relative humidity were found to be important in predicting corn moisture. Year, state, and maximum relative humidity were found to be important in predicting engulfment and entrapment. Findings warrant further investigation to examine exactly what is changing and impacting corn moisture and engulfment and entrapment incidents.

Abstract: Despite the clear hazard of out-of-condition grain, limited research has explored the role of specific indicators of grain condition and their impact on the rate of grain entrapments and engulfments. This project focuses on corn because it is the primary crop involved with documented grain entrapments and engulfments. The primary aim of the project was to identify significant factors associated with the moisture of harvested corn and to analyze the relationship between these factors and the occurrence of engulfment and entrapments. First, it was found that both year and state were associated with corn moisture. Further, the relationship between corn moisture and weather variables in the U.S. Corn Belt was investigated. It was found that both maximum high temperature and relative humidity predict corn moisture. A secondary goal was to measure the relationship between selected weather factors, the moisture of commodity corn, and on-farm and commercial entrapment and engulfment incidents in the U.S. Corn Belt. Year, state, and maximum relative humidity were found to be important in predicting the occurrence of engulfments and entrapments. Future work should examine other quality factors using modeling, including test weight, total damage, foreign material, and more detailed weather data to consider several prediction alternatives.

Highlights: Compressed air strategy was evaluated as a grain entrapment prevention method. Nozzle types affected compressed air efficiency. Open ½ inch nozzles performed best. Mid-scale experiment confirmed compressed air utility in breaking grain clumps. Dust, fungal, and noise levels exceeded maximum limits during operations, and PPE must be worn properly before using compressed air to break grain clumps.

Abstract: Grain entrapment, a severe and often fatal agricultural hazard, continues to pose a significant challenge in grain storage and handling. These incidents are often due to out-of-condition grain blocking outlets, leading to workers frequently entering the grain bin to dislodge grain. This study evaluates the utility of compressed air as a preventive measure to break up grain clumps located at bin outlets by conducting pilot and full-scale experiments using an air compressor. This study also evaluated potential hazards due to the use of air compressors. Three nozzle types were tested: open ½ inch, Crimped ½ inch, and the AirSpade. The findings indicated that the open ½ inch nozzle was the most efficient, with an average clearing time of 15 minutes per run, outperforming the crimped and AirSpade nozzles. Noise levels during operation ranged up to 105 dBA, with dust and fungal spore concentrations exceeding safety limits inside the grain bins and returning to acceptable levels shortly after operation. Full-scale testing indicates that compressed air can be useful in unclogging bins. The study underscores the potential of compressed air to enhance grain handling safety, offering practical safety recommendations and suggesting the need for further research to optimize and standardize its use in preventing grain entrapment.

Highlights: ½ open nozzle and ½ crimped nozzle were the most effective nozzles in removing out-of-condition grain clumps. ½ open nozzle and ½ crimped nozzle effectiveness were significantly correlated with the hardness of the grain clump. High pressure compressed air could be an effective method for breaking grain clumps stuck in the center sump. A shop air compressor is not effective in breaking up out-of-condition grain clumps.

Abstract: Grain entrapment is an agricultural injury in which a person enters a grain bin or silo to dislodge a blockage caused by out-of-condition grain and becomes partially or fully entrapped or engulfed in grain. Each year in the US, approximately 33 grain entrapments occur, where roughly 50% of the entrapped people die. In 2022, at least 42 individuals were entrapped with grain, the largest number of incidents since 2010, when 60 incidents were reported. The persistence of this issue despite substantial investment in training programs highlights the urgent unmet need for testing alternative prevention solutions to reduce grain entrapments. This study aims to test and validate the effectiveness and safety of using high-powered air compressors as tools to break apart out-of-condition grain blockages. A small-scale experiment was conducted with an air compressor and five different nozzles. The effectiveness of each nozzle was measured against hardness, moisture content, and percent grains in each clump. The ½ inch open nozzle and ½ inch crimped nozzle were found the most effective nozzles in reducing the size of grain clumps. Both nozzle results were highly correlated with the hardness of the clump. Results indicate that compressed air could be an effective solution to address grain clumping and blocking auger sumps.

Highlights: Thermal infrared array sensors can detect simulated human presence at distances up to 3m. Simulated human models for testing thermal infrared arrays need to be heated. Testing models should be designed to incorporate clothing, PPE, and features that change heat distribution. It is critical to match the pixel observable area and the expected size of heated regions at critical distances.

Abstract: This project consists of two sets of experiments using low-cost thermal infrared arrays-the TPAM 166 L3.9 array and the AMG8833, which were operated as 16-pixel arrays. These sensors were tested to determine how well they detected a simulated human model. The TPAM 166 L3.9 was tested with a heated, water-filled, clothed model representing a standing adult human. The AMG8833 was tested with a heated and non-heated version of the ISO 18497 simulated human model, which represented a seated human. These sensors were able to respond to heated models at distances up to 3 m from the sensor. The unheated human model did not produce a response. For heated models, the strength of the detection increases at shorter distances if the warmest parts of the model were visible to the sensor. As the warm parts of the model approached the sensors, the number of pixels impacted and the temperatures that they detected increased. However, detection decreased as the simulated human model approached at distances less than 1.5 m if the sensor was focused on a clothed section of the model. For deployment, the pixel observable area should match the expected size of heated regions considering PPE, clothing, and operational considerations. It is critical to consider pixel observable area and pixel response levels rather than the sensor level characteristics, as detection will occur at the pixel level.