Development of an Agent-Based Model to Evaluate Rural Public Policies in Medellín, Colombia

Abstract

Rural areas near large cities do not satisfy the food needs of the city’s population. In Medellín, Colombia, these areas satisfy only 2% of the city’s food needs, highlighting an urgent need to review and improve policies supporting agriculture. This study was conducted over a ten-year period since the release of the Medellín policy related to land use. The model uses agent-based modelling, geographic analysis and dichotomous variables, combining these structures to create a decision-making element and thus identify changes to examine in relation to current land use and detect properties with a potential for conversion to agricultural use. By evaluating post-processed geographic layers, land use in agricultural rural environments is prioritized, setting up clusters of homogeneous zones and finding new areas of rural influence. The implications of this study extend beyond Medellín, offering a model that can be applied to other regions facing similar challenges in agricultural productivity and land use. This research supports informed and effective decision-making in agricultural policy, contributing to improved food security and sustainable development. The results show that some properties are susceptible to policy changes and provide a framework for the revision of local regulations, serving as a support tool for decision-making in rural public policies by giving the local administration key factors to update in the current policies. The findings are relevant to local stakeholders, including policymakers and rural landowners, suggesting that several properties are susceptible to policy changes promoting agriculture and supporting informed decision-making in agricultural policy, contributing to food security and sustainable development. Also, this approach promotes efficient and sustainable agriculture, highlighting the importance of geographic analysis and agent-based modelling in policy planning and evaluation.