The aim of the study was to strengthen the capacities of the farmers in a participatory process to adapt to climate change. It was assumed that an innovation platform could support generation and exchange of knowledge on climate change, exchange and identification and implementation of options for adaptation tailored to local needs by the participating farmers

This methodological framework is based on Life Cycle Assessment (LCA) and multi-criteria assessment methods. It integrates CSA-related issues through the definition of Principles, Criteria and Indicators, and involves farmers in the assessment of the effects of CSA practices. To reflect the complexity of farming systems, the method proposes a dual level of analysis: the farm and the main cash crop/livestock production system. After creating a typology of the farming systems, the initial situation is compared to the situation after the introduction of a CSA practice.

This chapter reports on the different functions fulfilled by existing mechanisms for supporting collective innovation in the agricultural and agrifood sectors in the countries of the Global South in order to identify the potential contributions the research community can make to strengthen them. The authors show that a variety of mechanisms are needed to create enabling conditions for innovation and to provide a step-by-step support to innovation communities, according to their capacities and learning needs.

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.