Adaptive farming strategies for dynamic economic environment

This paper aims to forecast the economic impacts of changing land-use in UK uplands. We assume that farmers adaptively learn and respond to a dynamic economic environment. The main research approach is the use of evolutionary algorithms for dynamic optimization. We use this approach to study how the changes of agricultural subsidy policy (CAP reform) affect farmers' land-use decisions. We compare the experimental results from our simulated evolution versus the predictions made by agricultural experts. We have found that evolutionary algorithms for dynamic optimization forecast farmers' land-use decision in line with experts' predictions. This study also shows that maintenance of the diversity of the solution set is important for evolutionary algorithms to continuously track dynamic optimums. This work provides a framework to integrate other natural, social and economic factors in future.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Nanlin Jin, Mette Termansen, Klaus Hubacek, Joseph Holden, Mike Kirkby
• Keywords: Artificial Intelligence, CEC 2007, Dynamic Optimization, Evolutionary Algorithms, Land-use Decision