This paper (Part I) present a case study of work conducted by the International Centre for Tropical Agriculture (CIAT) to adapt network mapping techniques to a rural and developing country context. It reports on work in Colombia to develop a prototype network diagnosis tool for use by service providers who work to strengthen small rural groups. It is complemented by a further paper in this issue by Louise Clark (Part II) which presents work to develop a network diagnosis tool for stakeholders involved in agricultural supply chains in Bolivia.
This concept note has been developed within the context of the EU-funded CDAIS project, which is jointly implemented by AGRINATURA-EEIG and the Food and Agriculture Organization of the United Nations (FAO) to support the TAP Action Plan in eight pilot countries in Africa (Angola, Burkina Faso, Ethiopia, Rwanda), Asia (Bangladesh, Laos) and Central America (Guatemala, Honduras) .
CDAIS is a global partnership that aims to strengthen the capacity of countries and key stakeholders to innovate in the context of complex agricultural systems, to improve rural livelihoods. The goal of the Capacity Development for Agricultural Innovation Systems (CDAIS) project is to promote innovation that meets the needs of small farmers, small and medium-sized agribusiness, and consumers.
CDAIS is a global partnership that aims to strengthen the capacity of countries and key stakeholders to innovate in the context of complex agricultural systems, to improve rural livelihoods. The goal of the Capacity Development for Agricultural Innovation Systems (CDAIS) project is to promote innovation that meets the needs of small farmers, small and medium-sized agribusiness, and consumers.
La FAO a adopté une approche multidimensionnelle pour aider les exploitants familiaux pauvres à faire face aux difficultés qu’ils rencontrent au quotidien et renforcer leur capacité de création de revenus, afin de réduire la pauvreté rurale.
This paper presents a case study of the work carried out by CIAT to facilitate the creation of a community of practice, using Dgroups and taking advantage of this virtual space to apply a qualitative monitoring technique called Most Significant Change. The experience reported here mixed key ingredients to create and facilitate a community of practice to facilitate knowledge sharing and communication flow among 14 learning and knowledge sharing centres in Latin America and the Caribbean.
This paper outlines key areas of intervention that are identified as the core of FAO's strategy on strengthening Agricultural Innovation Systems (AIS) across multiple areas of work (e.g. research and extension, agroecology, biotechnology, green jobs, resourcing etc.) for achieving sustainable rural development.
The three system CGIAR research programs on Integrated Systems for the Humid Tropics, Dryland Systems and Aquatic Agricultural Systems have included “capacity to innovate” as an intermediate development outcome in their respective theories of change. The wording of the intermediate development outcome is “increased systems capacity to innovate and contribute to improved livelihoods of low-income agricultural communities.” This note captures the CGIAR's collective thinking about this intermediate development outcome from a systems perspective to clarify it and inspire other programs.
Global agriculture will face multiple challenges over the coming decades. It must produce more food to feed an increasingly affluent and growing world population that will demand a more diverse diet, contribute to overall development and poverty alleviation in many developing countries, confront increased competition for alternative uses of finite land and water resources, adapt to climate change, and contribute to preserving biodiversity and restoring fragile ecosystems.
This paper describes the learning selection approach to enabling innovation that capitalizes on the complexity of social systems at different scales of analysis. The first part of the paper describes the approach and how it can be used to guide the early stages of setting up a “grassroots” innovation process. The second part of the paper looks at how the learn selection model can be used “top-down” to guide research investments to trigger large-scale systemic change.