Agricultural innovation systems require strong linkage between research and extension organizations in particular, and among the various actors engaged in the agricultural sector in general. In the context of Ethiopia and the Amhara regional state, the agricultural research and extension system is characterized by a large number of actors in a fragmented and underdeveloped innovation system, resulting in very low national and regional innovation capacities. Farmers are generally viewed as passive recipients of technology.
This report assesses trends in investments and human resource capacity in agricultural R&D in countries in West Asia and North Africa (WANA), focusing on developments during 2009–2012. The analysis is based on information from a set of country factsheets prepared by the Agricultural Science and Technology Indicators (ASTI) program of the International Food Policy Research Institute (IFPRI), using comprehensive datasets derived from primary surveys targeting over 300 agencies in 11 countries during 2013–2014.
National governments, especially in sub-Saharan Africa, have limited budgets and are forced to make difficult funding decisions regarding the provision of social services and the support of agricultural programs. These provisions can play a critical role in rural incomes and agricultural production but due to data constraints, the effects of different types of social services on agricultural productivity in this region have not been analyzed in detail.
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.
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.
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 offers a perspective on the Agricultural Knowledge and Innovation System. The first chapter gives an introduction to the subject and explains the role of SCAR and of the Strategic Working Group AKIS. The second chapter investigates the AKIS and their role in innovation, including the policy context of the European Innovation Partnership “Agricultural productivity and sustainability”. Chapter 3 discusses the relation in a globalised world between Agricultural Research (AR) and Agricultural Research for Development (ARD).
This report elaborates on how to use the agricultural knowledge and innovation systems framework to promote innovation at different levels with special focus on European issues related to the implementation of Horizon 2020. It is of value as a conceptual and methodological reference regarding the Agricultural Knowledge and Innovation Systems (AKIS).
This brief explores the definition of Agricultural Knowledge and Information System (AKIS) and the inventory of AKIS in Europe.
The paper explores the strength of social networks in the agricultural innovation systems (AISs) in Ghana and the effect of AISs on adoption of improved farm technology. The paper uses social network analysis (SNA) tools to identify, map and analyze the AISs and the two-stage Heckman selection model. Combining qualitative and quantitative methods allows testing the differential effects of social networks on technology adoption in the Ghananian Plantain Sector