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
This paper provides a review of agronomic management practices supporting sustainable crop production systems and intensification, and testifying to developments in the selection of crops and cultivars. The paper also describes crop farming systems taking a predominantly ecosystem approach and it discusses the scientific application of this approach for the management of pest and weed populations.
This brief explores the definition of Agricultural Knowledge and Information System (AKIS) and the inventory of AKIS in Europe.
Innovation is an important challenge for European agriculture, but little is known about the performance of the Agricultural Knowledge and Innovation Systems (AKIS). This report contributes towards this knowledge, as it reports on experiences from different countries and regions. The systems are very different between countries, regions and sectors.
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 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).
The government of Ethiopia gives great attention to agriculture and rural development for the country’s economy development. Dairy development is one of the components of agricultural development. To improve dairy production in certain locality, dairy producers should able to access and use appropriate knowledge for the particular problem at the right time. This research was conducted to assess agricultural knowledge management system and its challenges and opportunities of knowledge management processes in Bure district.
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.