KIT and the Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA) published this joint publication in which farmers were put in the driver’s seat. Within the programme ‘farmer empowerment for innovation in smallholder agriculture’ (FEISA) farmers were provided tools and skills to enhance collaboration with private enterprises, as well as service providers, in multi stakeholder ‘innovation triangles’ within value chains for the benefit of smallholder farmers.
Innovation systems can be defined in a variety of ways: they can be national, regional, sectoral, or technological. They all involve the creation, diffusion, and use of knowledge. Systems consist of components, relationships among these, and their characteristics or attributes. The focus of this paper is on the analytical and methodological issues arising from various system concepts. There are three issues that stand out as problematic. First, what is the appropriate level of analysis for the purpose at hand?
This report is concerned with the ‘who?’ ‘what?’ and ‘how?’ of pro-poor extension. It builds on the analytical framework proposed in the Inception Report of the same study (Christoplos, Farrington and Kidd, 2001), taking it forward by fleshing out the analysis with empirical information gathered from several countries during the course of the study (from primary data in Bolivia, Colombia, Nicaragua, Uganda and Vietnam, and from secondary sources in a range of other countries, including India), and drawing conclusions on the scope for action by governments and donors in a range of contexts.
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.
This paper is an attempt to take stock of the authors' work. In Section 2, the authors reflect upon the emergence and fairly rapid diffusion of the concept ‘national system of innovation’ as well as related concepts. In Section 3, they describe how the Aalborg-version of the concept evolved by a combination of ideas that moved from production structure towards including all elements and relationships contributing to innovation and competence building.
In times of market liberalization and structural adjustment, the agricultural sectors of developing countries face profound changes. To seize new market opportunities, farmers need to innovate. In order to innovate, farmers need new technologies and information on how to access and manage them, as well as better support services for the delivery of inputs and knowledge, and better infrastructure for delivering produce to the market.
There is increasing evidence that public organizations dedicated exclusively to research and development (R&D) in agribusiness need systematic management tools to incorporate the uncertainties and complexities of technological and nontechnological factors of external environments in its long-term strategic plans. The major issues are: What will be the agribusiness science and technology (S&T) needs be in the future? How to prepare in order to meet these needs?
The private sector dominates biotechnology research in industrialized countries, but there are major market failures in developing countries in accessing the new tools and technologies. The public sector, national and international, will have to play a major role in filling this gap. This paper provides an overview of options that countries of different sizes and capacities can employ to gain access to the research tools and technologies that they need to address issues of relevance to poor producers and consumers.
The increasing complexity of technology development and adoption is rapidly changing the effectiveness of scientific and technological policies. Complex technologies are developed and disseminated by networks of agents. The impact of these networks depends on the assets they command, their learning routines, the socio-economic environment in which they operate and their history.
This paper argues that impact assessment research has not made more of a difference because the measurement of the economic impact has poor diagnostic power. In particular it fails to provide research managers with critical institutional lessons concerning ways of improving research and innovation as a process. Paper's contention is that the linear input-output assumptions of economic assessment need to be complemented by an analytical framework that recognizes systems of reflexive, learning interactions and their location in, and relationship with, their institutional context.