Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network-Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes.

Autor: Woodward-Greene MJ; National Agricultural Library, USDA Agricultural Research Service, Beltsville, MD, United States.; Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States.; Bioinformatics and Computational Biology Program, School of Systems Biology, College of Science, George Mason University, Manassas, VA, United States., Kinser JM; School of Physics, Astronomy, and Computational Sciences, College of Science, George Mason University, Fairfax, VA, United States., Huson HJ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States., Sonstegard TS; Acceligen Inc., Eagan, MN, United States., Soelkner J; Department of Sustainable Agricultural Systems, Division of Livestock Sciences, BOKU-University of Natural Resources and Life Sciences, Vienna, Austria., Vaisman II; Bioinformatics and Computational Biology Program, School of Systems Biology, College of Science, George Mason University, Manassas, VA, United States., Boettcher P; Food and Agriculture Organization of the United Nations, Animal Production and Health Division, Rome, Italy., Masiga CW; Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), Entebbe, Uganda., Mukasa C; National Animal Genetic Resource Centre and Data Bank, Entebbe, Uganda., Abegaz S; Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia., Agaba M; Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania., Ahmed SS; Cell Biology Department, Biotechnology Research Institute, National Research Centre, Giza, Egypt., Maminiaina OF; Department of Zootechnical, Veterinary and Piscicultural Research (DRZVP), National Center for Applied Research in Rural Development (CENRADERU), Antananarivo, Madagascar., Getachew T; International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia., Gondwe TN; Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi., Haile A; International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia., Hassan Y; Department of Animal Genetic Resources Development, Animal Production Research Center, Ministry of Animal Resources, Khartoum North, Sudan., Kihara A; International Livestock Research Institute, Nairobi, Kenya., Kouriba A; Institut d'Économie Rurale, Bamako, Mali., Mruttu HA; Ministry of Livestock and Fisheries, Dodoma, Tanzania., Mujibi D; International Livestock Research Institute, Nairobi, Kenya., Nandolo W; Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi., Rischkowsky BA; International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia., Rosen BD; Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States., Sayre B; Department of Biology, Virginia State University, Petersburg, VA, United States., Taela M; Agrarian Research Institute of Mozambique, Directorate of Animal Science, Maputo, Mozambique., Van Tassell CP; Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States.

Publikováno v: Frontiers in genetics [Front Genet] 2023 Sep 06; Vol. 14, pp. 1200770. Date of Electronic Publication: 2023 Sep 06 (Print Publication: 2023).

PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements.

Autor: Woodward-Greene MJ; USDA-ARS-NEA Animal Genomics and Improvement Laboratory, Beltsville, MD, United States of America.; Bioinformatics and Computational Biology Department, George Mason University, Manassas, VA, United States of America., Kinser JM; Department of Computational and Data Sciences, George Mason University, Fairfax, VA, United States of America., Sonstegard TS; Recombinetics at Acceligen, St. Paul, Minnesota, United States of America., Sölkner J; BOKU University of Natural Resources and Life Sciences, Vienna, Austria., Vaisman II; Bioinformatics and Computational Biology Department, George Mason University, Manassas, VA, United States of America., Van Tassell CP; USDA-ARS-NEA Animal Genomics and Improvement Laboratory, Beltsville, MD, United States of America.

Publikováno v: PloS one [PLoS One] 2022 Oct 13; Vol. 17 (10), pp. e0275821. Date of Electronic Publication: 2022 Oct 13 (Print Publication: 2022).

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.

Publikováno v: Frontiers in genetics [Front Genet] 2019 Dec 17; Vol. 10, pp. 1197. Date of Electronic Publication: 2019 Dec 17 (Print Publication: 2019).