Despite efforts over recent years to improve the status of agriculture in sub-Saharan Africa, little change has been noted, due partially to the fact that efforts have come from individual entities, which had short-term funding or lacked the necessary expertise to scale up research outputs. Disconnect between researchers and end-users has further hindered the success of such efforts.
Malgré les efforts déployés ces dernières années pour améliorer la situation de l’agriculture en Afrique subsaharienne, peu de changements ont été observés. Cet insuccès est dû, en partie, au fait que ces efforts ont été consentis par diverses entités de petite taille, aux capacités de financement à court terme et sans l’expertise nécessaire pour diffuser les résultats de leurs travaux de recherche. De plus, ces initiatives ont aussi pâti de la déconnexion entre la recherche et les utilisateurs finaux.
The nature of the issues around which Agricultural Research for Development (ARD) partnerships are formed requires a different way of conceptualizing and thinking to that commonly found in many agricultural professionals. This brief clarifies the components of a system of interest to an ARD partnership.
Networks and organizations need to find ways to be more effective in pursuing their objectives and thus seek to “learn” to be able to respond, innovate and adapt to complex, changing social and environmental conditions, thus bringing about social change. An essential capacity for ARD (Agricultural Research for Development) partnerships is therefore the ability to reflect and learn. Learning is not simply about increasing knowledge and skills or changing attitudes; it is about making sense of complexity to act more effectively.
This brief illustrates the different forms of knowledge, and the ways to create and manage it.
This paper discusses a range of approaches and benchmarks that can guide future design of value chain impact evaluations. Twenty studies were reviewed to understand the status and direction of value chain impact evaluations. A majority of the studies focus on evaluating the impact of only a few interventions, at several levels within the value chains. Few impact evaluations are based on well-constructed, well-conceived comparison groups. Most of them rely on use of propensity score matching to construct counterfactual groups and estimate treatment effects.
Following the remarkable success of performance testing in the commercial sector, the Agricultural Research Council's Animal Improvement Institute (ARC–AII) initiated a beef cattle performance testing scheme for smallholder farmers in 1996. The scheme, which became known as Kaonafatsho ya Dikgomo (Sotho for animal improvement), has been running well in the Northern and North West Provinces and is set to spread gradually to the rest of the country.
This learning module on Applying innovation system concept in agricultural research for development has been prepared to serve as a tool in achieving the objective of strengthening the capacity of project staff and other researchers and actors who are believed to have a key role to play in ushering in market-led agricultural transformation. This includes national, regional, international and private sector agricultural researchers, university lecturers, and others engaged in biophysical as well as social science research.
This flyer is about the AgriFood chain toolkit, which has been launched in 2013 by the CGIAR programme on Policies, institutions and markets.The AgriFood chain toolkit acts as a clearing house and learning platform – using the power of information and communication technologies to bring together people and resources.
The aim of this paper is to show the importance of monitoring genetic improvement programmes using the examples of an improvement programme for the Sahiwal breed in Kenya and a progeny testing scheme for Friesian cattle in Kenya. The paper is based on reports by Rege et al. (1992) and Rege and Wakhungu (1992) for the Sahiwal project and Rege (1991a and 1991b) for the progeny testing scheme for Friesian cattle.