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

Authors: Woodward-Greene, Jennifer; KINSER, JASON - George Mason University; HUSON, HEATHER - Cornell University; SONSTEGARD, TAD - Acceligen Inc; SOLKNER, JOHANN - University Of Natural Resources And Life Sciences, Vienna; VAISMAN, IOSIF - George Mason University; BOETTCHER, P - Food And Agriculture Organization Of The United Nations (FAO); MASIGA, C - Tropical Institute Of Development Innovation (TRIDI); MUKASA, C - Collaborator; ABEGAZ, S - Ethiopian Institute Of Agricultural Research; AGABA, M - Nelson Mandela African Institute Of Science And Technology; AHMED, S - National Research Centre; FRIDOLIN, M - Collaborator; GETACHEW, T - Lilongwe University Of Agriculture And Natural Resources; GONDOWE, T - Collaborator; HAILE, A - Collaborator; HASSAN, Y - Collaborator; KIHARA, A - International Livestock Research Institute (ILRI) - Kenya; KOURIBA, A - Collaborator; MRUTTU, H - Collaborator; MUJIBI, D - International Livestock Research Institute (ILRI) - Kenya; NANDOLO, W - Lilongwe University Of Agriculture And Natural Resources; RISCHKOWSKY, B - Collaborator; Rosen, Benjamin - Ben; SAYRE, B - Virginia State University; Van Tassell, Curtis - Curt

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/25/2023
Publication Date: 9/6/2023
Citation: Woodward Greene, M.J., Kinser, J.M., Huson, H.J., Sonstegard, T.S., Solkner, J., Vaisman, I.I., Boettcher, P., Masiga, C., Mukasa, C., Abegaz, S., Agaba, M., Ahmed, S.S., Fridolin, M., Getachew, T., Gondowe, T., Haile, A., Hassan, Y., Kihara, A., Kouriba, A., Mruttu, H.A., Mujibi, D., Nandolo, W., Rischkowsky, B., Rosen, B.D., Sayre, B., Van Tassell, C.P. 2023. 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. Frontiers in Genetics. 14:1200770. https://doi.org/10.3389/fgene.2023.1200770.
DOI: https://doi.org/10.3389/fgene.2023.1200770

Interpretive Summary: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) was developed under the auspices of the African Goat Improvement Network and the community base breeding program (CBBP) approach to directly engage small-holder farmers and their communities to enhance local goat herds. The development of the AGIN-ICP is described here as a case study to demonstrate the synergy and singularity of purpose embodied as AGIN participants from farmers to students to junior and seasoned researchers engaged in advancing science, and their individual careers. The initial draft of the protocol was called the AdaptMap Digital Phenotype Collection Method and was iteratively improved over several years in 12 countries by local farmers and research sampling teams. Using the AGIN platform, the objective was to develop an easy to use, low-cost procedure to collect digital phenotypic data including health status indicators (anemia status, age, weight), and body measurements, shapes, coat color and pattern using digital images and mobile technology. Working with many sampling teams in many situations showed that despite the simplicity of the protocol, if the steps were not executed with precision, the images collected may be difficult, or impossible to extract digital phenotypes as intended. Thus, the paper provides the detailed experience and rationale for the development of each step in the final AGIN-ICP. The images collected needed to be of sufficient quality to develop image processing software to extract and provide consistent digital physical characteristic measures, or phenotypes, over varied sampling teams and conditions. Data collected under this strategy could be used in genomic tools research and development, One Health detection and surveillance efforts, and animal health and management. Potential users include researchers, veterinarians, regional government or public health officials, farmers, and others.

Technical Abstract: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) was iteratively developed and tested over three years, in 12 countries with over 12,000 images taken for development of an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The work was accomplished as part of the multi-national 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). The AGIN-ICP collects images to extract several phenotype measures, including health status indicators (anemia status, age, and weight), and body measurements, shapes, 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. 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, veterinarians, regional government or other public health officials, researchers, and others. 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. It is critical to note, that while nothing of the very detailed tasks described here are difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample, a sampling day, or even an entire sampling trip’s images difficult, or unusable for extracting digital phenotypes. 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.