There have been repeated calls for a ‘new professionalism’ for carrying out agricultural research for development since the 1990s. At the centre of these calls is a recognition that for agricultural research to support the capacities required to face global patterns of change and their implications on rural livelihoods, requires a more systemic, learning focused and reflexive practice that bridges epistemologies and methodologies.
This is a chapter of the book Innovation platforms for agricultural development edited by Iddo Dror, Jean-Joseph Cadilhon, Marc Schut, Michael Misiko and Shreya Maheshwari.
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.
Capacity development (CapDev) is increasingly acknowledged as a crucial part of agricultural development. In the CGIAR Strategic Results Framework (SRF), CapDev is included as a ‘cross-cutting issue’ and as a strategic enabler of Research for Development (R4D) impact for CGIAR and its partners. It goes far beyond the transfer of knowledge and skills through training, and cuts across multiple levels.
This document is intended to serve as a resource for assessing capacity needs in a project or programme. A capacity needs assessment (CNA) is a process for identifying a project’s perceptions (through staff, partners and stakeholders) on various capacity areas that impact the work they do. The process helps identify challenges and opportunities for enhancing key skills thereby enhancing the project’s ability to achieve its objectives. The overall goal of a CNA is to determine the gap between required and existing capacities.
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.
A paradigm shift is needed to reposition the world’s AFS from being an important driver of environmental degradation to being a key contributor for the global transition to sustainability. Such a transformation can only happen through both generation of new knowledge and enhanced translation of knowledge into use. This achievement requires the generation of new knowledge and enhanced translation of knowledge into use, entailing considerable efforts in terms of research and innovation.
The paper, prepared for the "High Level Policy Dialogue on Investment in Agricultural Research for Sustainable Development in Asia and the Pacific" (Bangkok Thailand; 8-9 December 2015), presents the Common Framework on Capacity Development for Agricultural Innovation Systems (CDAIS).The framework is a core component of the Action Plan of the TAP, a G20 Initiative, aiming to increase coherence and effectiveness of capacity development for agricultural innovation that lead to sustainable change and impact at scale.
Innovation Platforms (IPs) have become a popular vehicle in agricultural research for development (AR4D). The IP promise is that integrating scientific and local knowledge results in innovations that can have impact at scale. Many studies have uncovered how IPs work in various countries, value chains and themes. The conclusion is clear: IPs generate enthusiasm and can bring together stakeholders to effectively address specific problems and achieve ‘local’ impact.
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.