A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock.

Autor:	Koltes JE; Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States., Cole JB; Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, United States., Clemmens R; College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States., Dilger RN; Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States., Kramer LM; Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States., Lunney JK; Animal Parasitic Diseases Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States., McCue ME; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, United States., McKay SD; Department of Animal and Veterinary Sciences, College of Agriculture and Life Sciences, University of Vermont, Burlington, VT, United States., Mateescu RG; Department of Animal Sciences, University of Florida, Gainesville, FL, United States., Murdoch BM; Department of Animal and Veterinary Science, University of Idaho, Moscow, ID, United States., Reuter R; Department of Animal and Food Sciences, College of Agricultural Sciences and Natural Resources, Oklahoma State University, Stillwater, OK, United States., Rexroad CE; Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States., Rosa GJM; Department of Dairy Science, University of Wisconsin-Madison, Madison, WI, United States., Serão NVL; Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States., White SN; Animal Disease Research Unit, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States.; Department of Veterinary Microbiology and Pathology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States.; Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States., Woodward-Greene MJ; Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States., Worku M; Department of Animal Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC, United States., Zhang H; Department of Electrical and Computer Engineering, College of Engineering, Iowa State University, Ames, IA, United States., Reecy JM; Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2019 Dec 17; Vol. 10, pp. 1197. Date of Electronic Publication: 2019 Dec 17 (Print Publication: 2019).
DOI:	10.3389/fgene.2019.01197
Abstrakt:	Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in Agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, "big data," analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g., "big data" training), data (e.g., data transfer, management, and analytics), and technology (e.g., development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics, and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food in a rapidly growing population. (Copyright © 2019 Koltes, Cole, Clemmens, Dilger, Kramer, Lunney, McCue, McKay, Mateescu, Murdoch, Reuter, Rexroad, Rosa, Serão, White, Woodward-Greene, Worku, Zhang and Reecy.)
Databáze:	MEDLINE
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