Journal of Agricultural Safety and Health最新文献

{"title":"Perspective: Closing the Regulatory Gap: Addressing Challenges for Autonomous Agricultural Equipment in California.","authors":"Farzaneh Khorsandi, Kent E Pinkerton, Minyoung Hong","doi":"10.13031/jash.16112","DOIUrl":"10.13031/jash.16112","url":null,"abstract":"<p><strong>Highlights: </strong>Outdated safety regulations pose challenges for autonomous agricultural tractors.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.</p><p><strong>Abstract: </strong>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.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 4","pages":"155-161"},"PeriodicalIF":0.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors Influencing Worker Safety in Grain Handling: An Advisory Panel Perspective.","authors":"Elzerie Derry, Gretchen A Mosher, Kingsly Ambrose","doi":"10.13031/jash.15915","DOIUrl":"10.13031/jash.15915","url":null,"abstract":"<p><strong>Highlights: </strong>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.</p><p><strong>Abstract: </strong>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.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 4","pages":"163-180"},"PeriodicalIF":0.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-Driven All-Terrain Vehicle Crash Prediction and Prevention System.","authors":"Farzaneh Khorsandi, Guilherme De Moura Araujo, Fernando Ferrei","doi":"10.13031/jash.16079","DOIUrl":"10.13031/jash.16079","url":null,"abstract":"<p><strong>Highlights: </strong>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.</p><p><strong>Abstract: </strong>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.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 4","pages":"139-154"},"PeriodicalIF":0.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the Feasibility of Selected, Commercially Available Wearable Devices in Detecting Agricultural-Related Incidents.","authors":"Aaron James Etienne, William E Field, Shawn G Ehlers, Roger Tormoehlen, Noah Joel Haslett","doi":"10.13031/jash.15985","DOIUrl":"10.13031/jash.15985","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;The purpose of this research was to validate a test procedure for using commercially available smart technologies in detecting an agricultural-related incident. A convenient selection of commercially available wearable devices was used to measure the inertial qualities of simulated incidents. Simulated ejections, falls, and upsets were performed to recreate leading causes of agricultural injuries and fatalities using an anthropomorphic test device. Only 2 of 27 simulated incidents triggered detection on the selected wearable devices tested. The results of this study were inconclusive in determining the feasibility of commercially available wearable devices in detecting agricultural-related incidents. More research is needed to develop an improved testing procedure. Additional collaboration is needed with manufacturers of wearable incident detection devices to clearly identify potential applications and limitations of their devices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstract: &lt;/strong&gt;A study was conducted to test a selection of commercially available wearable devices to determine their feasibility for triggering incident detection during a variety of simulated agricultural incidents with high risk of causing injury. The goal was to ultimately increase survivability outcomes for victims by enhancing notification and reducing response time from emergency services. A 50th percentile adult male anthropomorphic test device (ATD). was fitted with a convenient selection of commercially available wearable smart technologies to measure the responsiveness of the technology's incident detection software. Devices used for this testing were: (1) Garmin Vivoactive 4 smartwatch; (2) Apple Watch Series 7 (Bluetooth only and cellular models); and (3) Movesense Active tracking device. A Samsung Galaxy S22 smartphone and an Apple iPhone 12 smartphone were used to connect the wearable devices and measured impact through their internal inertial measurement unit (IMU) sensors. Simulated ejections from equipment, vertical falls, and vehicle overturns were performed with the ATD. Side upsets were simulated with the ATD positioned in the operator station of a 52-drawbar horsepower (dbp), two-wheel drive, standard front axle, diesel tractor, weighing 6500 pounds. The tractor was equipped with an approved ROPS. Side upsets were also simulated using a 22-horsepower zero-turn mower, with the ATD positioned in the operator seat. Falls were simulated from heights of up to 4.57 meters. After each simulated incident, devices were examined to determine whether or not incident detection was successfully triggered. Data was then collected from an internal sensor logging application installed on the selected devices. It was found that the incident detection feature on the identified wearable devices only triggered in specific scenarios. Only 2 of the 27 simulated incidents successfully triggered incident detection on one device. Only the Garmin Vivoactive 4 smartwatch tr","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 4","pages":"181-204"},"PeriodicalIF":0.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":">Documenting Baseline Efficacy of Grain Rescue Training for Emergency First Responders Through Pre- and Post-Testing, and Follow-Up Survey.","authors":"Yuan-Hsin Cheng, William E Field, Salah F Issa, Brian F French, Shawn G Ehlers, Edward J Sheldon","doi":"10.13031/jash.16012","DOIUrl":"10.13031/jash.16012","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;Pre- and post-tests, administered to 2,141 emergency first responder participants, showed an average improvement in test scores from 67% to 75%, highlighting the efficacy of the training. Interviews conducted within 3 years post-training revealed high participant satisfaction, with over 25% reporting adoption of key strategies discussed in the training by their fire/rescue service. Areas of concern were identified, including the lack of understanding related to certain hazards, such as free-flowing grain, which may put first responders at risk of secondary victimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstract: &lt;/strong&gt;Purdue University's Agricultural Safety and Health Program has provided leadership for nearly 40 years in the documentation of fatalities and injuries associated with agricultural confined spaces, especially those relating to grain storage, handling, and transport. Findings have been used to develop evidence-based resources to assist in the prevention and mitigation of these incidents, including the design of in-service training resources for emergency rescue and medical personnel responding to entrapments or engulfment in agricultural confined spaces. To enhance the efficacy and consistency of these training resources, a list of core competencies was developed with companion test questions by a panel of experts to validate the baseline understanding and knowledge gain of training participants. The test questions were pilot tested as pre- and post-tests and incorporated into a curriculum developed under a U.S. Department of Labor Susan Harwood Training Grant. The twenty-question pre- and post-tests were administered to 2,141 registered emergency first responder participants in training conducted primarily in Indiana. Participation was voluntary, providing 671 usable matched pre- and post-tests. On average, test scores improved from 67% to 75%. A question-by-question review highlighted areas of common knowledge as well as at least one topic in which the potential for confusion was increased by the instructional content. In addition, participants were interviewed within 3 years to assess the impact of the training received. Interviewees indicated a high level of satisfaction with the training, and over 25% indicated that their fire/rescue service adopted at least one of the seven key strategies discussed in the training. One key concern observed in training was the lack of understanding related to certain hazards, such as the nature of free-flowing grain, that may put first responders at risk of becoming secondary victims during rescue and extrication efforts. A need was identified for continued improvement of emergency first responder training through the incorporation of recent research findings on confined space rescue, greater attention to the prevention of secondary injuries, and more consistent instructor preparation in order to increase the probability of successful outcomes from incidents involving grain stora","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 3","pages":"123-138"},"PeriodicalIF":0.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Relationship Between Goat and Sheep Farmers' Stress and Their Demographics: A Pilot Study.","authors":"Suzanna R Windon, Carolyn Henzi","doi":"10.13031/jash.15820","DOIUrl":"10.13031/jash.15820","url":null,"abstract":"<p><strong>Highlights: </strong>Limited leisure time, insufficient sleep, and family members' health conditions were the top personal stressors. Occupational stressors were too much to do in so little time, worrying about the farm's future and financial issues. Governmental regulation, market prices, and unpredictable weather conditions were off-farm occupational stressors. The work hours during the busy season and farm size were significant predictors of farmers' stress. The farmer's age and years in the farm business were not significant predictors of the farmer's stress.</p><p><strong>Abstract: </strong>This pilot study aims to investigate goat and sheep farmers' stress amidst the COVID-19 pandemic. The authors developed a questionnaire based on existing literature to measure farmers' stress. The online questionnaire was sent to the 3000 goat and sheep farmers registered in the Penn State Extension Listserv. We used the technique described by Dillman et al. (2014) to collect online data. After cleaning the data, the response rate was 6.8% (n = 204). The mean and SD for farmer's stress were 3.0±.63 out of 5, occupational stress 3.11±.65, and personal stress 2.80 ± .82, respectively. During the COVID-19 pandemic, work hours during the busy season and farm size exhibited a positive low association with farmers' stress (r_s = .245 and r_s = .238, respectively). They predicted 10% of the total variation in farmers' stress. We propose that extension professionals and public health practitioners learn lessons from the COVID-19 pandemic in case other public health concerns arise. We suggest that future educational programs addressing stress among farmers prioritize specific strategies to reduce occupational stress and cope with uncertainty during health-related outbreaks or other crises. An interesting avenue for further investigation can involve examining other issues related to farmers' financial planning, time management (especially during the busy season), and their relationships with family members.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 3","pages":"107-122"},"PeriodicalIF":0.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automated On-The-Go Unloading System Reduces Harvest Operator Stress Relative to Manual Operation.","authors":"Travis A Burgers, Kusha Kamarei, Mukund Vora, Matthew Horne","doi":"10.13031/jash.15992","DOIUrl":"10.13031/jash.15992","url":null,"abstract":"<p><strong>Highlights: </strong>Stress was measured in harvest operators who performed on-the-go unloading manually and with an automated system. Automated unloading reduced the average grain cart and combine operator stress rate by 18% and 12%, respectively, compared to manual operation. Harvest operators usually worked more than 9 hours and often worked more than 12hours per workday during harvest. The use of automated unloading systems could positively affect the health of harvest operators.</p><p><strong>Abstract: </strong>On-the-go unloading improves harvest operational efficiency, but it requires skilled labor because it is challenging and stressful to balance numerous concurrent tasks. Harvest automation reduces workload, stress, and fatigue. The objective of this study was to determine if using a commercially available, automated on-the-go unloading system (Raven Cart Automation^TM, RCA, Raven Industries) would reduce operator stress compared to manual operation. Nine grain cart tractor operators and six combine operators participated in this study. Operators performed their typical harvest operation, except to alternate on-the-go unloading using RCA or operating manually. Skin conductance (electrodermal activity) was measured with an Empatica E4 wristband, and stressful events were quantified. Machine data was collected from the tractor and combine via CAN logs. Over 200 total unload events were analyzed. Grain cart and combine operators using RCA had an 18% (p = 0.022) and 12% (p = 0.18) reduction in stress rate, respectively, compared to operating the grain cart tractor manually. RCA reduced the tractor cross-track error standard deviation by 2.5 cm on straight passes (p < 0.0001). The use of an automated on-the-go unloading system reduces operator stress during harvest and could positively affect the health of operators, especially during the long harvest workdays.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 3","pages":"89-106"},"PeriodicalIF":0.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AgroGuardian: An All-Terrain Vehicle Crash Detection and Notification System.","authors":"Farzaneh Khorsandi, Guilherme De Moura Araujo, Fernando Ferreira Lima Dos Santos","doi":"10.13031/jash.15801","DOIUrl":"10.13031/jash.15801","url":null,"abstract":"<p><strong>Highlights: </strong>Off-road ATV incidents can be problematic due to long EMS alert times. An ATV crash-detection-and-report system is expected to reduce EMS response time. The developed system can accurately detect ATV rollovers. The alert time of our system is 10 times faster than the national U.S. average. Any rider using our system is 3 times more likely to survive an off-road crash.</p><p><strong>Abstract: </strong>All-Terrain Vehicle (ATV) incidents are a common cause of injury and death in the agricultural industry in the United States. Many ATV off-road crashes on farms and ranches may result in trauma requiring immediate care, but the injured rider is unable to seek help due to their injuries. Moreover, many of these crashes occur in isolated areas that may be difficult to access and have unreliable cellular phone service, making contact with emergency medical services (EMS) challenging. This study aimed at developing and testing a low-cost ATV crash detection device (AgroGuardian) that immediately alerts EMS and emergency contacts, even when the rider is unable to take action and/or there is no cellular phone service available. AgroGuardian includes an embedded data logging system, a smartphone application, and a remote database. The embedded system includes an Inertial Measurement Unit (IMU) for attitude estimation, a Global Positioning System (GPS) for location estimation, and a Rock7 modem for off-board communication. A smartphone application was developed for the users to input information about their vehicle (e.g., make and model) and emergency contacts. Also, it allows them to interact with their ATV data. An emergency signal along with the ATV's coordinates is transmitted through the Rock7 modem and received in the remote database when a rollover is detected by the system. This emergency signal is then processed and sent to EMS and emergency contacts. Our results indicated that the device: (1) is unlikely to miss an ATV rollover; (2) has a fast EMS notification time (40.7 s); and (3) the ATV localization system presented an average error of 2.34 m.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 2","pages":"53-74"},"PeriodicalIF":0.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial Agricultural Incident Analysis for the State of Indiana.","authors":"Aaron James Etienne, Noah Joel Haslett, William E Field","doi":"10.13031/jash.15919","DOIUrl":"10.13031/jash.15919","url":null,"abstract":"<p><strong>Highlights: </strong>29 recent agricultural-related fatalities or injuries occurring throughout the state of Indiana were analyzed using geospatial incident analysis. Proximity of each incident to nearby cellular towers was found through 5 and 10-mile spatial joins by their relationship with cellular towers, with no towers most likely to be found within 5 miles of a given incident and only one tower to be found within 10 miles of a given incident. Proximity of each incident to emergency services and the nearest hospital was performed through 5 and 10-mile spatial joins, with only one service provider most likely to be within the five-mile range of a given incident.</p><p><strong>Abstract: </strong>A total of 29 recent agricultural-related injuries and fatalities throughout the state of Indiana were identified and analyzed for their proximity to cellular towers and emergency medical services (EMS). The objective of this research was to identify relationships between selected agricultural incidents and the ability of the victim to successfully contact emergency services. The geographic information system (GIS) software ArcGIS Pro and ArcGIS Online were utilized for trend identification and analysis. Findings from this analysis showed that only one EMS provider was most likely to be found within five miles of a given incident location. This frequency increased to seven EMS providers when the proximity range was increased to ten miles of a given incident location. The analysis also showed that only one cellular tower was most likely to be within a 10-mile radius of a given incident. There were frequently no accessible towers within five miles of a given incident. In addition, identified incidents were overlaid on a digital elevation map (DEM) of Indiana for analysis on the relationship between elevation and the number of accessible cell towers in the area. Studies have confirmed that victims of serious agricultural-related injuries, especially while working alone, face significant barriers in alerting EMS of their need for assistance. Geospatial analysis techniques performed in this study can be utilized by other states to assess access to EMS and for larger-scale, agricultural incident analysis. These tools have the potential to improve detail in agricultural incident reporting.</p>","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 2","pages":"75-88"},"PeriodicalIF":0.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Advantages and Limitations of Current Risk Assessment and Hazard Analysis Methods when Applied on Autonomous Agricultural Machineries.","authors":"Guy R Aby, Salah F Issa, John F Reid, Cheryl Beseler, John M Shutske","doi":"10.13031/jash.15873","DOIUrl":"10.13031/jash.15873","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;The three main types of risk assessment and hazard analysis techniques applied on autonomous agricultural machines are: (1) Informal Group Analysis; (2) Hazard Analysis and Risk Assessment (HARA); and (3) Failure Mode and Effects Analysis (FMEA). Replicability is the main advantage of FMEA and HARA, while cost effectiveness is the main advantage of Informal Group Analysis. Subjectivity and the requirement for prior knowledge (data) are the main weaknesses of FMEA, HARA, and Informal Group Analysis when applied to novel and revolutionary autonomous agricultural machines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstract: &lt;/strong&gt;In the last ten years, the development of automated agricultural machinery has seen noteworthy advancements. Nevertheless, the successful commercialization of these technologies depends critically on their ability to operate safely. This study evaluated the advantages and limitations of current risk assessment and hazard analysis methods currently used to ensure the safety of autonomous agricultural machines. An online survey containing 18 questions was distributed to 711 participants identified as potential individuals who are currently working or have worked on autonomous agricultural machines to determine the type and frequency of risk assessment and hazard analysis methods applied on autonomous agricultural machines, examine the advantages and limitations of each method, and investigate the perceived effectiveness of each method. Frequency analysis was used to determine the most and least utilized risk assessment and hazard analysis methods. The advantages and limitations of each risk assessment and hazard analysis approach were compared. Descriptive statistics (counts, means, medians, percent) and frequency analysis of the variables were used. The three main types of risk assessment and hazard analysis techniques applied to autonomous agricultural machines. The methods are (a) Informal Group Analysis (e.g., Brainstorming), (b) Hazard Analysis and Risk Assessment (HARA), and (c) Failure Mode and Effects Analysis (FMEA). Replicability is perceived as the main advantage of FMEA and HARA, while cost-effectiveness is the main advantage of Informal Group Analysis. The need to have pre-existing data of the autonomous agricultural machine at hand to be able to perform risk assessment and subjectivity are the main limitations of FMEA, HARA, and Informal Group Analysis dealing with novel and revolutionary autonomous agricultural machines. Industry experts do not believe that the risk assessment and hazard analysis procedures now used are reliable and efficient enough to guarantee the safety of autonomous agricultural tractors. This study reveals important information about the current state of risk assessment and hazard analysis methods in the context of autonomous agricultural machinery. This knowledge can inform future research, policy development, and industry practices to ensure the safety of autonomous agricultural m","PeriodicalId":45344,"journal":{"name":"Journal of Agricultural Safety and Health","volume":"30 2","pages":"35-52"},"PeriodicalIF":0.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}