Results from the gender capacity assessment shows, in general, that development and research organizations lack the knowledge and skills to integrate gender into their agricultural programs. Addressing gender-inequity in agriculture will require increased investment in skills and knowledge for value chain actors and enablers.
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.
Multi-stakeholder or innovation platforms are increasingly seen as a promising vehicle for agricultural innovation and development. In the field of agricultural research for development (AR4D), such platforms are an important element of a commitment to more intentional, structured and long-term engagement among sector stakeholders.
The CGIAR research program on livestock and fish aims to sustainably increase the productivity of small-scale livestock and fish systems so as to increase the availability and affordability of meat, milk and fish for poor consumers across the developing world. The purpose of this document is to lay out a Monitoring, Evaluation and Learning (MEL) Framework for the program. The Framework provides a concise narrative of why the M&E system is important, how it operates, what kinds of data it will collect and who is responsible for data collection and analysis.
The flyer points up an overview of: (i) new approaches to capacity building and institutional change; (ii) ICRA’s role in the South; (iii) ICRA’s role in the North and in linking South and North.
In the 90’s first steps were taken in Cuba to strengthen family farming. A participatory seeds breeding, multiplication and diffusion project started, a challenge to Cuban scientists, not used to involve farmers in the decision making process and recognizing them as equal partners. This project further evolved to become the Local Agricultural Innovation Programme, Spanish acronym PIAL (Programa de Innovación Agropecuaria Local).
Capacity development is regarded by CGIAR as an effective vehicle for sustainable development, when embedded within broader CGIAR Research Programs (CRP). This document offers guidelines on how CGIAR and boundary partners (or those partners who take up and adapt research results for the next level of users) can successfully develop and implement strategies which support this process of integration.
Farmers and businesses need to adapt constantly if they are to survive and compete in the rapidly evolving environment associated with the contemporary agricultural sector. Rethinking agricultural research as part of a dynamic system of innovation could help to design ways of creating and sustaining conditions that will support the process of adaptation and innovation. This approach involves developing the working styles and practices of individuals and organizations and the incentives, support structures and policy environments that encourage innovation.
This policy brief consolidates lessons learned from an in-depth literature review on small-scale farmer (SSF) innovation systems and a two-day expert consultation on the same topic, hosted in Geneva by Quaker United Nations Office (QUNO) in May 2015. This review draws together published literature on the evolution of the concept, how on-farm innovation systems function in practice, and the roles of outside actors in supporting them.
The capacity of existing monitoring and decision making tools in generating evidence about the performance of R4D with multi-stakeholder processes, such as innovation platforms (IPs), public private partnerships (PPP), participatory value chain management (PVCM) is very limited. Results of these tools are either contextual and qualitative such as case studies that can not be used by other R4D interventions or quantitative i.e. impact assessments that do not inform what works in R4D.