An impact assessment of IITAs benchmark area approach - Douthwaite, Boru; Baker, D.; Weise, Stephan F.; Gockowski, J.; Manyong, Victor M.; Keatinge, J.D.H. (2003)
Here we evaluate the IITA Benchmark Area Approach (BAA), which is being used to deliver general improvements to rural livelihoods in sub-Saharan Africa. The approach began evolving just nine years ago so a formal ex-post impact assessment is not yet appropriate. Hence, we evaluated the approach by comparing it against existing “best practice.” We first established that existing “best practice” in developing sustainable improvements to complex agricultural systems is represented by integrated natural resource management (INRM) and current thinking in farming systems research (FSR). We then derived nine “best practice” criteria and evaluated the BAA against them, finding that the approach is delivering, or has the potential to deliver, on all nine. Hence the BAA is an important process innovation. The IITA BAA is a way of operationalizing INRM and ecoregional research by (1) conducting research in a characterized benchmark area that contains within it farming system dynamics and diversity that are representative of a portion of a wider agroecological zone; (2) developing “best-bet” innovations and processes; and (3) developing the knowledge networks amongst key stakeholders that are necessary for scaling-out and scaling-up. IITA’s experience in developing and implementing the BAA can provide useful Lesson to other international agricultural research centers attempting to put INRM into practice. These include the need to start small and simple and move quickly from characterization to building knowledge networks that will lead to scaling-up. It is these “social” scaling-up processes, in addition to the “technical” characterization processes, that are the international public goods INRM needs to show it can produce to be truly successful. An intellectual challenge facing the BAA is to develop characterization approaches that take into account the social and cultural factors known to influence the likelihood of adoption. If this is successful, then it should be possible to use geographic information systems (GISs) to match not just a technology that is likely to work in a new area, but the extension approach required to socially construct it. A second challenge is demonstrating that scaling-up occurs after the “best-bet” innovations and processes have been developed and knowledge networks have been built.