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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #361325

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock

Author
item KOLTES, JAMES - Iowa State University
item Cole, John
item CLEMMENS, ROXANNE - Iowa State University
item DILGER, RYAN - University Of Illinois
item KRAMER, LUKE - Iowa State University
item Lunney, Joan
item MCCUE, MOLLY - University Of Minnesota
item MCKAY, STEPHANIE - University Of Vermont
item MATEESCU, RALUCA - University Of Florida
item MURDOCH, BRENDA - University Of Idaho
item REUTER, RYAN - Oklahoma State University
item Rexroad, Caird
item ROSA, GUILHERME - University Of Wisconsin
item SERAO, NICK - Iowa State University
item White, Stephen
item Woodward-Greene, Jennifer
item WORKU, MILLIE - North Carolina A&t State University
item ZHANG, HONGWEI - Iowa State University
item REECY, JAMES - Iowa State University

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/29/2019
Publication Date: 12/17/2019
Citation: Koltes, J.E., Cole, J.B., Clemmens, R., Dilger, R.N., Kramer, L.M., Lunney, J.K., Mccue, M.E., Mckay, S., Mateescu, R., Murdoch, B.M., Reuter, R., Rexroad Iii, C.E., Rosa, G.J.M., Serao, N.V.L., White, S.N., Woodward Greene, M.J., Worku, M., Zhang, H., Reecy, J.M., editors. 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Frontiers in Genetics. 10:1197. https://doi.org/10.3389/fgene.2019.01197.
DOI: https://doi.org/10.3389/fgene.2019.01197

Interpretive Summary: New technologies such as low-cost sensors, digital cameras, and other on-farm technologies have resulted in a flood of on-farm data that are largely under-used. Dramatic decreases in the cost of whole-genome DNA sequencing and related technology allow us to characterize livestock at the molecular level, too. Increased training is needed so that animal scientists have the skills needed to manage and analyze these large collections of data so that they can be used to solve problems for farmers and consumers. This paper describes recommendations developed by the attendees of the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017. Critical needs for investments in infrastructure for people, data, and technology were identified by this group. Many opportunities exist for public and private entities to use big data to answer important research questions to the benefit of society as the need to feed a rapidly growing population increases.

Technical Abstract: 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 manuscript 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. 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 of a rapidly growing population.