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.
The IAR4D concept has generated a large volume of success stories on many Innovation Platforms where it was implemented for the proof of concept and on the platforms of Complementary projects. It is noteworthy that in course of developing the IAR4D concept FARA engaged is series of trial efforts to arrive at a valid framework for the implementation of the IAR4D concept, the Innovation platform was developed from these thoughts and harmonization of knowledge and experience.
This background note for the development of an AIS Investment Sourcebook provides a menu of tools and guidance to invest in agricultural innovation in different contexts. The content is drawn on tested good practice examples and innovative approaches with emphasis on lessons learned, benefits and impacts, implementation issues, and replicability
This sourcebook contributes to identifying, designing, and implementing the investments, approaches, and complementary interventions that appear most likely to strengthen Agricultural innovation systems (AIS) and to promote agricultural innovation and equitable growth. It emphasizes the lessons learned, benefits and impacts, implementation issues, and prospects for replicating or expanding successful practices. The information in this sourcebook derives from approaches that have been tested at different scales in different contexts.
Given the diversity and context-specificity of innovation systems approaches, in March 2007 the World Bank organized a workshop in which about 80 experts (representing donor agencies, development and related agencies, academia, and the World Bank) took stock of recent experiences with innovation systems in agriculture and reconsidered strategies for their future development. This paper summarizes the workshop findings and uses them to develop and discuss key issues in applying the innovation systems concept. The workshop’s recommendations, including next steps for the wider
The central question in increasing productivity and generating incomes in African agriculture is how to move from technology generation to innovations that respond to constraints of agricultural production along the value chains. This question was considered in the context of subsistence agriculture, smallholder production systems, inefficient marketing and investments by the private sector, a preponderance of public interventions, and inadequate policies.
This paper relates the European Innovation Partnerships (EIP) to be implemented by Operational Groups (OGs) in Basilicata. New relationships and regeneration produced a “bio-economic Cluster”, creating “smart” specialization and a system linking research, innovation and the enterprise world. The Cluster consolidated competence and knowledge in small and medium enterprises, including agriculture and forest farms and encouraged the dissemination and implementation of innovative products and processes.
This paper applies the framework for pro-poor analysis to welfare changes from a CGE-microsimulation model to analyze what are the better or worse models for agriculture modernization, and to estimate the contribution of growth and redistribution to changes in poverty in DRC. The findings indicate that labor-using technological change generates absolute and relative pro-poor effects whereas capital-using technological change leads to immiserizing growth.
Multi-actors networks are increasingly used by farmers to link between them and to be interactively connected with other partners, such as advisory organizations, local governments, universities, and non-farm organizations. Given the importance assigned to the agricultural innovation by EU resorting to the networking between the research chain actors and the farmers, a strong focus on enhancing the creation of learning and innovation networks is expected.
Science and technology (S&T) are major contributors to food security, poverty reduction, and economic growth, as has been proven in Asia since the early-1970s through the Green Revolution in agriculture. Continuing to secure such gains, however, is becoming an increasingly complex undertaking. More than ever, quantitative data are vital for measuring, monitoring, and benchmarking the performance of agricultural S&T systems, including their inputs and outcomes.