Role of an explorative model for learning about sustainable agricultural development in Burkina Faso

Agricultural development is complex, highly dynamic and differs among varying contexts. Decision-making for sustainable agricultural development cannot be based on generalized science-based knowledge, but should include context-specific knowledge and values of local stakeholders. Computer models seem a useful tool to integrate scientific knowledge include local-specific data, and explore local-specific solutions. In this paper we study whether and how a multiple goal linear program (MGLP) model could enhance learning for sustainable development. According to the learning theory, multi-actor learning is only productive when it consists of first-order (experiential) learning and second-order (social) learning. We applied an action-research approach and explored the value of an MGLP model SHARES (SHAred RESources) for learning by agricultural extension staff and farmers in an integrated rural development project in Burkina Faso. Fieldwork showed the main value of SHARES in the capacity to generate farm scenarios and trigger second-order learning about tacit frames-of-reference. People rarely engage in secondorder learning, but pursue different objectives and often remain trapped in confusing discussions and action. SHARES was a critical boundary-spanning object that facilitated communication between farmers and agricultural staff, enhanced mutual understanding, and the determination of area- and category-specific farm development goals.