ICARDA scientists along with CGIAR LIVESTOCK developed a cloud-based genetic database platform to boost breed improvement programs in community-based livestock breeding programs in Ethiopia. 

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.

Breeding programs for local breeds kept by small farmers in developing countries are a major challenge. Animal recording of pedigree and performance under conditions of subsistence livestock farming is remain difficult or next to impossible. 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.

- Lack of automated data capture systems affects timely feedback and accuracy of information for breeding decisions.
- CGIAR researchers and national research partners have adopted a digital genetic database, Dtreo, that is enhancing genetic improvement by providing timely and accurate animal ranking information to communities.
- Dtreo is a digital genetic database that is flexible and easy to use, that allows users to capture and save data offline. Data is uploaded to the database once an internet connection has been established.

Digital platform enhances genetic progress in community-based sheep and goat breeding programs in Ethiopia:

- Up-to-date information on estimated breeding values and animal rankings is directly channeled to breeder organizations and used for selection decisions.

- The digital platform motivated use of more complicated evaluation models which improve accuracy of breeding values considerably.

- When upscaled, this will help create a permanent multi-country source of information.

Ethiopia is a home for diverse livestock including small ruminants and has the largest population of livestock in Africa. Livestock is kept for export earnings, food security, economic growth, poverty reduction and employment opportunities. Small ruminants are an important resource for livelihood and food security improvement serving as sources of food, income, risk mitigation, property security, monetary saving, investment, and providing other social and cultural benefits.

IFAD’s technical assistance programme INSURED (Insurance for rural resilience and economic development) has been building knowledge about how to strengthen women producers’ access to climate risk insurance. Working with partners, INSURED supported research, and fieldwork in Ethiopia including group discussions with smallholders about insurance options. A checklist was drawn up for insurance designers and implementers to help them reach out to women every step of the way.

Geographic information system (GIS) data is often used to map socio-economic data with a spatial component. This data, which is obtained from multiple open-source databases, complements official statistics and generates additional spatial inputs to statistical and econometric analyses. IFAD uses impact assessments using data from face-to-face interviews in order to determine the impact of their projects on strategic goal and objectives. However, the COVID-19 pandemic meant these interviews could no longer take place.

Food insecurity remains a major challenge to rural households in Eastern Ethiopia. To improve food and nutrition security of vulnerable households in eastern Ethiopia, several agricultural technologies have been scaled-up by Haramaya University for more than six decades. However, the impact of these technologies on household nutritional outcomes was not systematically studied. This study examined the impact of selected agricultural technologies on household food and nutrition security. Cross-sectional data were generated from 248 randomly selected rural households.

The brochure summarizes main features and goals of the EU funded CDAIS project, jointly implemented by FAO and AGRINATURA to support the TAP Action Plan.