This Economic and Sector Work paper, “Enhancing Agricultural Innovation: How to Go Beyond the Strengthening of Research Systems,” was initiated as a result of the international workshop, “Development of Research Systems to Support the Changing Agricultural Sector,” organized by the Agriculture and Rural Development Department of the World Bank in June 2004 in Washington, DC.
We are facing complex societal problems such as climate change, human conflict, poverty and inequality, and need innovative solutions. Multi-stakeholder processes (MSPs) are more and more seen as a critical way of coming to such innovative solutions. It is thought that when multiple stakeholders are able to meet, share experiences, learn together and contribute to decisions, new and innovative ways of dealing with problems are found and turned into action. Still, much remains to be understood about the role and effectiveness of social learning in multi-stakeholder settings.
The present manual provides a reference framework for the strategic and operational work in the field of capacity development. It is addressed to all staff of ADC in Austria as well as in the coordination offices, to non-governmental and implementing organisations, to stakeholders in partner countries, other donors and members of the public interested in development policy.
This report refers to the workshop which was held on October 21-25, 2013 at ILRI Campus in Nairobi, Kenya. The workshop involved a variety of sessions which made use of presentations, card exercises, group work and discussions to facilitate the engagement of the participants in sharing, learning, discussing and planning around CapDev in CGIAR. This report provides an overview of the workshop sessions, focusing mainly on the key discussion topics, results and next steps.
The first phase in the development of the Common Framework on Capacity Development for Agricultural Innovation systems (CD for AIS) consisted of the review of the existing literature, building up a repository of relevant documentation on agricultural innovation in general and AIS and CD for AIS. This report summarizes this first phase. In particular, Section 1 covers this brief introduction. Sections two and three focus on the review of relevant literature, presenting the methodology used and the structure of the repository itself.
The present case study investigated a policy-induced agricultural innovation network in Brandenburg.
This report has the aim of contributing to the PRO AKIS overall goal of exploring and identifying the possibilities, conditions and requirements of rural networks to enhance the farmers’ ability to create, test, implement and evaluate innovation in cooperation with other actors.In particular, the report presents two cases: the Small Fruit Cluster (SFC) and the Drosophila Suzukii Monitoring (DSM) network. The SFC is a nationwide, multi-actor network composed of several actors, interacting in the small fruit sector in Portugal.
As the name suggests, the original aim of the Rural Knowledge Network (RKN) was to make more information available specifically about markets, to smallholder farmers. The core idea was to provide information to farmers and traders about current market prices in different markets around the country. This was done by building a network of entrepreneurs who regularly collected the price information and sent it to a central collecting Internet platform facility.
This report provides a synthesis of all findings and information generated through a “stocktaking” process that involved a desk study of Prolinnova documents and evaluation reports, a questionnaire to 40 staff members of international organizations in agricultural research and development (ARD), self-assessment by the Country Platforms (CPs) and backstopping visits to five CPs. In 2014, the Prolinnova network saw a need to re-strategise in a changing context, and started this process by reviewing the activities it had undertaken and assessing its own functioning.
This report describes the 2012 NAIS Assessment was piloted in 4 countries: Botswana, Ghana, Kenya and Zambia. Data were collected through a survey questionnaire, open-ended interview questions, and data mining of secondary sources. A team led by a national coordinator took charge of data collection from various partner organizations in each country.