Gretchen A. Mosher

Agricultural Worker Injury Comparative Risk Assessment Methodology: Assessing Corn and Biofuel Switchgrass Production Systems

Keeping workers safe is a continuing challenge in agricultural production. Risk assessment methodologies have been used widely in other industries to better understand systems and enhance decision making, yet their use in production agriculture has been limited. This article describes the considerations and the approach taken to measure the difference in worker injury risks between two agricultural production systems. A model was developed specifically for the comparison of worker injury risk between corn and biofuel switchgrass production systems. The model is composed of injury and exposure values that were used in a Monte Carlo simulation. The output of this risk assessment shows that approximately 99% of the values from the Monte Carlo simulation rank corn production as a greater worker injury risk than biofuel switchgrass production. Furthermore, the greatest contributing factors for each production system were identified as harvest, and that finding aligns with current literature.

Does a NIR system provide low-cost alternative to on-farm feed and forage testing? A Techno-economic analysis

Improperly balanced diet not only impacts the quality of animal products but also the quantity of profits of a livestock operation. Typically, the nutrient and chemical content of feed ingredients and forages are determined using well-established wet chemistry tests. However, these tests can be expensive and timeconsuming. Moreover, the increasing use of distiller’s by-products which are known to have large variations in chemical and nutrient content warrants a real-time on-farm feed and forage testing system. Near Infra-Red (NIR) spectroscopy systems have been documented as a quick and effective on-site testing tool across several industries. While NIR systems are being adopted for on-farm feed and forage testing, very little is known about the economic impacts of such an investment for a livestock operator. This study developed a baseline model and an excel based spreadsheet application for performing Return On Investment (ROI) analysis to determine the feasibility of using an on-farm NIR testing system. ROI was calculated based on the nutrient cost saved or spent determined from the difference of estimated nutrient content and actual calculated value. Assumptions were made for the cost of NIR system, calibration and labor expenses. What-if analyses were also performed to determine the impact of variation in nutrient content on the ROI. The finding of this study will help promote low-cost alternatives for on-farm feed and forage testing, thus positively impacting the quality of animal product and minimizing costs.