Journal of Agricultural Safety and Health最新文献

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.

Highlights: 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.

Abstract: 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.

Highlights: 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.

Abstract: 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.

Highlights: 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.

Abstract: 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.

Highlights: 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.

Abstract: 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.

Highlights: The safety guidelines outlined in ISO 18497 are not sufficient to guarantee the safe operation of autonomous agricultural machines. Since the risk assessment techniques specified in ISO 12100:2012 require historical failure data of the machine at hand, they cannot be used to effectively mitigate the risk associated with autonomous agricultural machines where such data are not readily available. Analysis from the perspective of ergonomics can potentially increase the safety of autonomous agricultural machines.

Abstract: The goal of this study was to analyze the safety implications of an autonomous agricultural machine (TerraPreta) using the standards ISO 18497 (ISO, 2018) and ISO 12100:2012 (ISO, 2012), as well as to investigate the ergonomics associated with the use of the autonomous agricultural machine. First, three engineers involved in the robot's manufacturing process were asked to evaluate the robot's functionalities compliance with the applicable safety standards and protective measures outlined in standard ISO 18497 (ISO, 2018). Second, while the robot was planting cover crop seeds, an attempt was made to identify and evaluate every risk connected to the robot using the risk assessment techniques outlined in ISO 12100:2012 (ISO, 2012). (1) Half (50%) of the functionalities of the autonomous agricultural machine complied with the safety requirements and protective measures described within the standard ISO 18497 (ISO, 2018). (2) The heavy reliance on past incident data of the risk assessment procedure described within the standard ISO 12100:2012 (ISO, 2012) makes it ineffective for new and revolutionary technologies such as autonomous agricultural machines where such data are not available. (3) Lifting a bag to fill the robot hopper with seeds was found to be a moderately hazardous activity associated with human-robot interaction. Multiple tentative solutions were provided to avoid this moderately hazardous activity.