Agricultural research impact assessment: issues, methods and challenges

Résumé En

The Research Impact Assessment (RIA) is expected to increase the efficiency with which public funds are used, and to improve more broadly the functioning of the research and innovation system and its contribution to address a wide range of socio-economic and environmental issues. Both standard economic approaches, which aim to estimate the economic benefits of research investments, and case-study approaches, which aim to analyse the processes of impact generation, have been applied to agricultural research in practice. Standard economic approaches generally focus on public research as information on private efforts in agricultural research is limited, and on economic impacts such as productivity growth. Case studies provide richer information, through a narrative, and highlight the complex relationships among the various variables, events and actors, but it is difficult to standardise results and scale them up. The challenge for RIA is to take into account broader impacts that go beyond science and economic impacts, and to improve knowledge on impact-generating mechanisms. This has become more difficult as agricultural research and innovation systems are increasingly open and complex, and changing quickly. Observation of practices applied to agricultural research in five selected organisations confirms the difference found in RIA between academic research and in practice. In both, the assessment systems pursue the same objectives: 1) Learning: enhance the know-how to produce an environment conducive to socio-economic impact; 2) Capacity building: spread the culture of socio-economic impact to its researchers; and 3) Reporting to stakeholders: from accountability purposes to advocacy targeted to various audiences. The accountability objective, including estimating returns on the financial investment, poses complex challenges and is in tension with the learning and capacity building objectives. The future of RIA will depend on the capacity to improve estimation methods and gather quality information (which also takes into account non-economic impacts) and the sharing of good practices.