Agricultural Research for Development: implications for policy, practice and investmentwca2014-2301 Philip Dobie 1 2,*Sinead M. Mowlds 2 1World Agroforestry Centre, Nairobi, Kenya, 2University College Cork, Cork, Ireland
There has been a semantic shift from agricultural research “and development” to agricultural research “for development” (AR4D) or “in development”. This change reflects the need for agricultural research to become more focused on and accountable for its impacts on people’s livelihoods and health, and on the environment. In practice, this new paradigm recognizes that research takes place and is scaled up within complex adaptive systems. Approaches to agricultural research have been dominated by conventional linear research and development models, where scientific advances are assumed to drive the developments that result in changes that impact on people’s lives. Complex systems-based research, of the sort that dominates agroforestry, does not fit this simple model. Scholars have shown that most economic change in society does not often derive from single scientific breakthroughs, but frequently from the re-working of existing knowledge. Scientific-led change takes place in social contexts that are typified by complex interactions among a range of actors, and agroforestry presents a clear example of these complex interactions. Change in these systems is not linear, but consists of many feedback loops whereby knowledge and information flow through the system in different directions.
Many of the implications of understanding how to operate in complex adaptive systems are behavioural, and will require scientists to work with broader ranges of partners and through different working relationships than previously. Other necessary changes are institutional, and include how scientific institutions partner with others, what incentives are provided to scientists to work within complex adaptive systems and how the funders of science allocate funds to allow the merging of science and development. Major changes are needed in monitoring methodologies to allow more rapid learning from experience and the shortening of feedback loops. Better systems for tracking investment in Ar4D are needed.