239 Grazing Distribution of Beef Cattle: Acronyms of Technology and Spatial Measures. (30th November 2020)

• Record Type: Journal Article
• Authors: Thomas, Milton G; Dillon, Jasmine; Bailey, Derek; Pierce, Courtney; Speidel, Scott; Enns, Mark
• Is Part Of: Journal of animal science. Volume 98(2020)Supplement 3
• Page Start: 125
• Page End: 125
• Publication Date: 2020-11-30
• Subjects: cattle -- grazing -- precision agriculture; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skaa054.214
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Grazing distribution is an important aspect of pasture management, yet measuring traits for sustainable forage consumption is challenging. Uplands are often un-grazed by beef cattle due to rugged terrain while riparian zones are often heavily grazed; thus, sustainable consumption may be achieved if improved landscape utilization by cattle is encouraged. Modifying grazing behaviour with fencing and (or) water-source and feeding location(s) is effective in improving grazing patterns; however, these infrastructure improvements are typically expensive, supporting the concept of genetic selection for improved grazing distribution. Efforts funded by the Western Sustainable Research and Education Program (WSARE; SW15-015) using global positioning systems (GPS) indicated a genetic influence on quantitative traits describing grazing distribution of 330 Angus-influenced cows (i.e. distance from water, slope, elevation, vertical climb, etc.). Collars fitted with GPS devices for data collection accrued measures at intervals of 5 to 15 min and 10 m resolution for 3-19 weeks in 16 pastures on 14 ranches and experiment stations. Genome-wide association studies involving trait-measures and high-density genotypes (n = 777, 962 single nucleotide polymorphisms; SNP) indicated these traits were polygenic. Combining SNP genotypes with trait measures and pedigree has become the norm in genetic evaluation and improvement processes (i.e. genome-enhanced expected progeny difference (GE-EPD). These processes require data from large numbers of animals (n > 10, 000). Collecting grazing distribution phenotypes with GPS collars is accurate, but time-consuming; therefore, collaborative research is being conducted in the 2019-2020 academic year exploring the use of unmanned aerial vehicles (UAV) and cameras to ascertain spatial measures of beef cow grazing distribution. This collaboration involves scientists in the Colorado State University Drone Center, Department of Mechanical Engineering, and Department of Animal Sciences. The project objective is to determine if UAV can expedite data collection to support development of genetic evaluation and improvement programs for grazing distribution.