A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock
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 mol...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-12-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.01197/full |
| _version_ | 1819265572244815872 |
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| author | James E. Koltes John B. Cole Roxanne Clemmens Ryan N. Dilger Luke M. Kramer Joan K. Lunney Molly E. McCue Stephanie D. McKay Raluca G. Mateescu Brenda M. Murdoch Ryan Reuter Caird E. Rexroad Guilherme J. M. Rosa Nick V. L. Serão Stephen N. White Stephen N. White Stephen N. White M. Jennifer Woodward-Greene Millie Worku Hongwei Zhang James M. Reecy |
| author_facet | James E. Koltes John B. Cole Roxanne Clemmens Ryan N. Dilger Luke M. Kramer Joan K. Lunney Molly E. McCue Stephanie D. McKay Raluca G. Mateescu Brenda M. Murdoch Ryan Reuter Caird E. Rexroad Guilherme J. M. Rosa Nick V. L. Serão Stephen N. White Stephen N. White Stephen N. White M. Jennifer Woodward-Greene Millie Worku Hongwei Zhang James M. Reecy |
| author_sort | James E. Koltes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T20:47:30Z |
| format | Article |
| id | doaj.art-5b602cccbd9e41d89c47cbd3cb9d29d1 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-23T20:47:30Z |
| publishDate | 2019-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-5b602cccbd9e41d89c47cbd3cb9d29d12022-12-21T17:31:45ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-12-011010.3389/fgene.2019.01197480865A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in LivestockJames E. Koltes0John B. Cole1Roxanne Clemmens2Ryan N. Dilger3Luke M. Kramer4Joan K. Lunney5Molly E. McCue6Stephanie D. McKay7Raluca G. Mateescu8Brenda M. Murdoch9Ryan Reuter10Caird E. Rexroad11Guilherme J. M. Rosa12Nick V. L. Serão13Stephen N. White14Stephen N. White15Stephen N. White16M. Jennifer Woodward-Greene17Millie Worku18Hongwei Zhang19James M. Reecy20Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United StatesAnimal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, United StatesCollege of Agriculture and Life Sciences, Iowa State University, Ames, IA, United StatesDepartment of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United StatesDepartment of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United StatesAnimal Parasitic Diseases Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD, United StatesDepartment of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, United StatesDepartment of Animal and Veterinary Sciences, College of Agriculture and Life Sciences, University of Vermont, Burlington, VT, United StatesDepartment of Animal Sciences, University of Florida, Gainesville, FL, United StatesDepartment of Animal and Veterinary Science, University of Idaho, Moscow, ID, United States0Department of Animal and Food Sciences, College of Agricultural Sciences and Natural Resources, Oklahoma State University, Stillwater, OK, United States1Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States2Department of Dairy Science, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States3Animal Disease Research Unit, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States4Department of Veterinary Microbiology and Pathology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States5Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States1Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States6Department of Animal Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC, United States7Department of Electrical and Computer Engineering, College of Engineering, Iowa State University, Ames, IA, United StatesDepartment of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United StatesAutomated 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.https://www.frontiersin.org/article/10.3389/fgene.2019.01197/fullautomated phenotypingprecision agricultureprecision livestock farmingphenomicssensors |
| spellingShingle | James E. Koltes John B. Cole Roxanne Clemmens Ryan N. Dilger Luke M. Kramer Joan K. Lunney Molly E. McCue Stephanie D. McKay Raluca G. Mateescu Brenda M. Murdoch Ryan Reuter Caird E. Rexroad Guilherme J. M. Rosa Nick V. L. Serão Stephen N. White Stephen N. White Stephen N. White M. Jennifer Woodward-Greene Millie Worku Hongwei Zhang James M. Reecy A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock Frontiers in Genetics automated phenotyping precision agriculture precision livestock farming phenomics sensors |
| title | A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock |
| title_full | A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock |
| title_fullStr | A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock |
| title_full_unstemmed | A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock |
| title_short | A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock |
| title_sort | vision for development and utilization of high throughput phenotyping and big data analytics in livestock |
| topic | automated phenotyping precision agriculture precision livestock farming phenomics sensors |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.01197/full |
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