Precision Technologies for Small/Medium Sized Businesses Throughout the Beef Value Chain

Abstract

Precision livestock farming (PLF) promises enhanced animal welfare, production, ease of management, environmental impacts, and profitability. However, existing PLF technologies are often focused on large farms and on intensive production systems. The development of PLF technologies to support the sustainability of small- to medium-sized businesses across the beef value chain must be responsive to the unique needs and characteristics of these businesses and their operators. The overarching goal of the work presented in this dissertation was to explore the development of PLF technologies for extensive production systems and small/medium-sized businesses throughout the beef value chain. Manure is a high-value fertilizer produced by the beef value chain; however, it is also a source of environmental concern due to contributions of leached nutrients to non-point source pollution. Precision technologies may help manage non-point source pollution. We explored the viability of two sensing systems in identifying defecation and urination events of livestock in pasture systems to aid precision management. Both carbon dioxide and motion sensing on the tail of cattle showed limited capacity to predict these events despite extensive data analysis. The poor performance appears to be driven largely by the highly imbalanced data, suggesting that these data imbalances should be addressed prior to further testing. In addition to understanding the nutritional value of forages, improved monitoring of rumen conditions may eventually contribute to supporting ration formulation choices across the beef value chain. We designed an open-source system for monitoring ruminal pH, temperature, dissolved oxygen, and oxidation reduction potential for prediction of volatile fatty acid (VFA) concentrations. All VFA concentrations were predicted with high accuracy (concordance correlation coefficient > 0.993), though the study was limited by testing a small number of animals. Pending further investigation with a greater number of animals and diets, there is potential for this system to aid in monitoring VFA supply and supporting ration formulation decisions. Cow-calf production in Virginia is pasture-based, and understanding the nutritional value of forage throughout the year is critical to support feeding choices and optimize financial viability. We designed a low-cost, open-source spectral sensor to predict the quality of cool season, grass pastures across the grazing season. This spectral sensor costs roughly $80 and was able to predict dry matter, acid detergent fiber, neutral detergent fiber, and crude protein with root mean squared prediction errors of 10-20%, showing promise for farm-level forage quality monitoring, though additional verification work is needed. Meat is the primary end product of the beef value chain. A number of precision meat processing technologies exist; however, these technologies are primarily designed for large operations. To address the needs of smaller processing facilities, we developed a survey to gather producer perceptions of automation and identify next steps for technology development. The survey identified areas of opportunity for automation and specifics on how collaborative automation might occur, including safety and quality considerations. These results can guide development of collaborative PLF technologies to support small/medium sized meat processors. Overall, each technology studied focuses on solutions that do not require a high level of technical expertise to build, operate, and maintain; that are affordable; and that address management needs of key stakeholders throughout the beef value chain. Although these technologies represent a diversity of adoption readiness levels, each may eventually support enhanced management of beef production systems.