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To date, most quad bike educational programs have featured an operatorfocused approach, focusing on adherence to administrative controls, personal responsibility, and personal protective equipment. Though these programs lead to shifts in 'safety knowledge', they result in very little change in actual rider behavior. In this perspectives article, we highlight discussions from a recent ATV Safety Symposium and USDA-NIFA review of agricultural ATV safety in the U.S. that highlight the dire need for building agricultural community awareness of quad bike engineering controls such as CPDs, wider and more stable frame designs, and others. Although CPDs were introduced 15 years ago, we continue to observe low awareness of this and other important quad bike safety features among young adults in agriculture. We believe that it will be critical to apply some of the recommendations outlined in this article to improve future outreach programs focused on quad bike safety for agricultural occupational use. If rural, agricultural communities learn to accept and respect these life-saving technologies, future standards, policies, and legislative actions are more likely to be well-received. [ABSTRACT FROM AUTHOR]

The 2015 CS-CASH (Central States Center for Agricultural Safety and Health, 2015) Injury Surveillance Surveys showed that around 19% of injuries to agricultural producers are related to tractors or large agricultural machinery, yet only a limited number of studies are found that address tools and methods for monitoring safety behaviors of agricultural machinery operators in real-time. The current safety behavior monitoring approaches require an in-person presence, which can be both time- and cost-inefficient, and the other available methods lack a feedback element to alert operators in realtime. As a result, the research presented in this study aimed to develop an automated approach to monitoring tractor operators' safety behaviors through the use of a trained machine learning (ML) model and a feedback system to alert operators when they engage in unsafe practices. For the ML model development, a skeleton-detecting algorithm called OpenPose was used to detect real-time human postures in a livestreaming video feed from a camera installed in the tractor cab. The model was then trained on three separate categories of tractor operators' safety operating behaviors, and this trained classifier was used to label operators' safety behaviors in real time based on the three safety classes. A feedback mechanism controlled by an onboard microcontroller was then used to alert the operators when unsafe operating behavior was detected to facilitate safe practices. This monitoring system, named Ag-OMS (Agricultural Machinery Operators Monitoring System), monitored the ingress/egress operators' behaviors in real-time entering and exiting the tractor cab. The Ag-OMS successfully identified the ingress/egress operators' behaviors with an accuracy of 97% on the testing datasets for all safety risk categories. [ABSTRACT FROM AUTHOR]

There is significant interest in biomass production ranging from government agencies to the private sector, both inside and outside of the traditional production agricultural setting. This interest has led to an increase in the development and production of biomass crops. Much of this effort has focused on specific segments of the process, and more specifically on the mechanics of these individual segments. From a review of scientific literature, it is seen that little effort has been put into identifying, classifying and preventing safety hazards in on-farm biomass production systems. This commentary describes the current status of the knowledge pertaining to health and safety factors of biomass production and storage in the US and identifies areas of standards development that the biomass industry needs from the agricultural safety and health community. [ABSTRACT FROM AUTHOR]