We developed an agent-based model to simulate farmers’ decision-making in a study area named Isfahan-borkhar aquifer. This model proposes a socio-hydrological model to simulate farmers’ crop selection process. In this software, farmers’ behaviors are influenced by aquifer conditions varying across space and over time (e.g., water table fluctuations) and psychological parameters. In addition to farmers’ understanding of economic constraints, their heterogeneous perceptions of aquifer conditions are simulated using the theory of planned behavior within an agent-based model. This framework facilitated high spatiotemporal resolution in socio-hydrological models by incorporating heterogeneity of farmers’ features and simulating a distributed environment.

The agent-based model WEEM (Woodlot Establishment and Expansion Model) as described in the journal article, has been designed to make use of household socio-demographics (household status, birth, and death events of households), to better understand the temporal dynamics of woodlot in the buffer zones of Budongo protected forest reserve, Masindi district, Uganda. The results contribute to a mechanistic understanding of what determines the current gap between intention and actual behavior in forest land restoration at farm level.

This is a coupled conceptual model of agricultural land decision-making and incentivisation and species metacommunities.

The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.

To investigate the potential of using Social Psychology Theory in ABMs of natural resource use and show proof of concept, we present an exemplary agent-based modelling framework that explicitly represents multiple and hierarchical agent self-concepts