We have won a bid to undertake a Defra-funded research project investigating the development of models for farmer and land manager behaviours and decision-making.

The UK’s exit from the European Union, and subsequent withdrawal from the Common Agricultural Policy (CAP), will give the UK the ability to design and implement new agricultural and environmental policies that work for our land and our farmers.

Traditional models of farmer decisions are largely based on extrapolations of previous economic conditions and an assumption that individuals will behave in accordance with strict rationality. Given the variability in economic conditions, and recognising that behavioural factors influenced by social relationship play a large part in the impact of policy, having models available which incorporate this complexity will likely be extremely important to policy makers.

A range of approaches will be evaluated, with special focus placed on the value of, and limitations of, agent-based models. These types of model can outline how the behaviours of decision-makers at a farm-level could change in response to economic, geographic and policy-related factors, and be influenced by their own underlying motivations, as well as the choices of other land managers in their social network.

David Baxter, our Head of Natural Economy, who is leading the project, said:

“We are delighted to be continuing to build our experience in agriculture policy, and to be able to explore the limitations and opportunities of a range of radically different models that can support policy makers. The opportunity to work with researchers at Edinburgh University, who have applied some of these approaches in a range of UK and international policy settings, will help ensure our recommendations strike a balance between radicalism in approach, and being grounded in the real world.”

The project is due to complete in late December. We will be working in collaboration with a team from the University of Edinburgh.

Photo courtesy of Jim Roberts, Flickr, CC BY-ND 2.0.