This project report from Wageningen UR (as a contribution to the CGIAR Humid Tropics research program) examines critical issues for reflection when designing and implementing research for development in innovation platforms’. The current document therefore aims to increase awareness about the complexity of research in innovation. The underlying idea is that innovation platforms can facilitate institutional changes and support system innovations through increased interaction, negotiation and learning between stakeholders, including (new) roles of research(ers).  

Innovation Platforms (IPs) are seen as a promising vehicle to foster a paradigm shift in agricultural research for development (AR4D). By facilitating interaction, negotiation and collective action between farmers, researchers and other stakeholders, IPs can contribute to more integrated, systemic innovation that is essential for achieving agricultural development impacts. However, successful implementation of IPs requires institutional change within AR4D establishments.

Multi-stakeholder (MS) platforms, such as innovation platforms (IP), public-private partnerships (PPP) are becoming more common but what they can achieve in innovation and scaling is limited and depends on different factors. This poster and the broader research paper provide evidence what MS platforms can and cannot achieve in their early phases and give insights about effectiveness and efficiency of Agricultural Research for Development (AR4D) interventions such as CGIAR research programs (CRPs) in low and middle income countries.

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.

Rapid appraisal of agricultural innovation systems (RAAIS) is a participatory, diagnostic tool for integrated analysis of complex agricultural problems. RAAIS facilitates interaction between different groups of stakeholders in collecting and analysing data. The poster briefs what RAAIS is, when to use it, what is available and where it has been used.

Consumer concerns are leading to changes in China’s food markets and demands for higher quality food. In this article, we explore the role of farmer cooperatives in China in linking farmers with high-quality food markets. We consider food quality a social construct and farmer cooperatives key players in the ‘quality battlefield’. Using a case study approach, we investigate the everyday practices of three farmer cooperatives.

Innovation platforms are fast becoming part of the mantra of agricultural research and development projects and programs with an innovation objective.

In this paper, is first described the design and development process of a modular ICT application system called GeoFarmer. Geofarmer was designed to provide a means by which farmers can communicate their experiences, both positive and negative, with each other and with experts and consequently better manage their crops and farms. We designed GeoFarmer in a collaborative, incremental and iterative process in which user needs and preferences were paramount.

Agricultural extension in sub-Saharan Africa has often been criticised for its focus on linear knowledge transfer, and limited attention to systemic approaches to service delivery. Currently, the region is experiencing a new-ICT revolution and there are high expectations of new-ICTs to enhance interaction and information exchange in extension service delivery. Using an innovation systems perspective, we distinguish the roles demand-articulation, matching demand and supply, and innovation process management for innovation-intermediaries.

The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers.