A genetic database tool for data capture in small ruminant community-based breeding programs

Genetic improvement on local breeds kept by small farmers in developing countries is challenging. Even though good pedigree and performance recording is crucial and an important component of breeding programs, it remain difficult or next to impossible under conditions of subsistence livestock farming. This means that standard genetic evaluations, as well as selection and planning of mating based on estimates of the animals' genotypes, cannot be done at any level in the population of the target breed or genetic group. However, the International Center for Agricultural Research in the Dry Areas (ICARDA) partnering with the National Agricultural Research Systems (NARS) has been implemented sheep and goat community-based breeding programs (CBBPs) in Ethiopia since 2010. Currently, a total of about 40 CBBPs each with average of 80 household and 1000 flock size in four sheep (Menz, Bonga, Doyogena and Horro) and three goat (Abergelle, konso and Borena) breeds are available. Many other CBBPs has also been established throughout the countries by different institutions (Research Centers, Universities, and Biodiversity Institute); and African (e.g. Tanzania, Sudan, Uganda, Malawi) and Asian (e.g Brazil,Iran, Mongolia) countries are implementing sheep and goat CBBPs. It means pedigree and performance data recording is being accumulated and getting larger and larger. Though selection of best sires has been a routine practice in the CBBPs, retaining best animal for breeding challenged by many factors. Sale of animals by owners before selection event for pressing cash need tied with delay in data capture, analysis and giving on time feedback to the community is appeared as main challenge. With the recent advances in computer science, ICT and mobile technology, ICARDA is therefore developed a digital database system called AniCloud which can accelerate the data capture, analysis and feedback system which is crucial to assist the small ruminant breeding program at lower cost, high storage capacity, high fidelity and fast computing speed. Thus, the aim of this paper is to explain the current