An integrated socio-economic agent-based modeling framework towards assessing farmers’ decision making under water scarcity and varying utility functions

Abstract

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers’ behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.