Understanding the future and evolution of agri-food systems: A combination of qualitative scenarios with agent-based modelling

Abstract

The agri-food system is a vital and complex social-ecological system characterized by interactions between humans and the environment. For such systems, qualitative scenarios (QS) can generate pictures of possible futures, which combined with quantitative modelling allow identifying pathways towards improved resilience and exploring system uncertainties. However, the interpretation of qualitative narratives into quantitative simulation parameters remains challenging due to system complexity. In this study, we translate QS into quantitative agent-based model (ABM) parameters and estimate the likelihood of each scenario in the evolved agri-food system in response to individual actions. We implement a five-step approach consisting of: i) the generation of the QS, ii) the parameterization of the ABM iii) the translation of scenario assumptions into ABM parameters, iv) the validation of our results via an expert workshop, v) the assessment of the scenarios in the evolved system. The results of an illustrative example reveal that, with the implementation of cropping diversification, the system will evolve to a combination of scenario 1, 2, 3 and 4 at probabilities of 9 %, 35 %, 30 % and 26 %, respectively, which change under different management options. Overall, we conclude that QS-ABM combination is a promising approach to provide robust quantitative projections of the agri-food system future.