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Woodward-Greene MJ, Kinser JM, Huson HJ, Sonstegard TS, Soelkner J, Vaisman II, Boettcher P, Masiga CW, Mukasa C, Abegaz S, Agaba M, Ahmed SS, Maminiaina OF, Getachew T, Gondwe TN, Haile A, Hassan Y, Kihara A, Kouriba A, Mruttu HA, Mujibi D, Nandolo W, Rischkowsky BA, Rosen BD, Sayre B, Taela M, Van Tassell CP. 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. Front Genet 2023; 14:1200770. [PMID: 37745840 PMCID: PMC10512022 DOI: 10.3389/fgene.2023.1200770] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/25/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day's images, or even an entire sampling trip's images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection.
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Affiliation(s)
- M. Jennifer Woodward-Greene
- 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
| | - Jason M. Kinser
- School of Physics, Astronomy, and Computational Sciences, College of Science, George Mason University, Fairfax, VA, United States
| | - Heather J. Huson
- Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
| | | | - Johann Soelkner
- Department of Sustainable Agricultural Systems, Division of Livestock Sciences, BOKU—University of Natural Resources and Life Sciences, Vienna, Austria
| | - Iosif I. Vaisman
- Bioinformatics and Computational Biology Program, School of Systems Biology, College of Science, George Mason University, Manassas, VA, United States
| | - Paul Boettcher
- Food and Agriculture Organization of the United Nations, Animal Production and Health Division, Rome, Italy
| | - Clet W. Masiga
- Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), Entebbe, Uganda
| | | | - Solomon Abegaz
- Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia
| | - Morris Agaba
- Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | - Sahar S. Ahmed
- Cell Biology Department, Biotechnology Research Institute, National Research Centre, Giza, Egypt
| | - Oliver F. Maminiaina
- Department of Zootechnical, Veterinary and Piscicultural Research (DRZVP), National Center for Applied Research in Rural Development (CENRADERU), Antananarivo, Madagascar
| | - Tesfaye Getachew
- International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia
| | - Timothy N. Gondwe
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Aynalem Haile
- International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia
| | - Yassir Hassan
- Department of Animal Genetic Resources Development, Animal Production Research Center, Ministry of Animal Resources, Khartoum North, Sudan
| | | | | | | | - Denis Mujibi
- International Livestock Research Institute, Nairobi, Kenya
| | - Wilson Nandolo
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Barbara A. Rischkowsky
- International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia
| | - Benjamin D. Rosen
- Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
| | - Brian Sayre
- Department of Biology, Virginia State University, Petersburg, VA, United States
| | - Maria Taela
- Agrarian Research Institute of Mozambique, Directorate of Animal Science, Maputo, Mozambique
| | - Curtis P. Van Tassell
- Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
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