This paper presents the processes, general guidelines lessons and experiences pertaining to “good practices” for organizing and forming Agricultural Innovation Platforms in the Lake Kivu Pilot Learning Site, covering three countries (Uganda, Rwanda and Democratic Republic of Congo) with widely differing social political environments to address agricultural development challenges.
This review seeks to assess the usefulness of innovation systems approaches in the context of the Integrated Agricultural Research for Development (IAR4D) in guiding research agendas, generating knowledge and use in improving food security and nutrition, reducing poverty and generating cash incomes for resource-poor farmers. The report draws on a range of case studies across sub-Saharan Africa to compare and contrast the reasons for success from which lessons can be learned.
This paper examines how the different institutional innovations arising from various permutations of linkages and interactions of ARD organizations (national, international advanced agricultural research centres and universities) influenced the different outcomes in addressing identified ARD problems.
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.
The chapter is a part of the book Integrated Agricultural Research for Development: from Concept to Practice.