An integrated and dynamic approach to agricultural land‐use change modeling at country‐level to regional scale: Application to Iran

Abstract

AbstractAgricultural land use change is the result of interactions between different driving factors and processes at different scales. Most of models have been proposed for the land use change simulations only consider the suitability of lands and spatial competition between different land uses at microscales. But agricultural land use projection involves assessment of macro‐level socioeconomic variables and driving forces. This paper suggests a dynamic modeling approach that integrates demand‐driven changes in agricultural land area at macro‐level with spatially explicit distribution processes at regional‐scale. This approach is based on combination of two core models with dynamic top‐down and bottom‐up feedback loops between them, dynamic simulation model, and land use change (LUC) model. Without the spatial considerations, the dynamic model is used to project the agricultural land demands influenced by economic, demographic, technologic, and regulatory variables and their interactions at country‐level. In addition, LUC model is used to simulate the downscaling of these demands between country regions based on spatial consideration of land suitability, change elasticity, spatial policies and restrictions, and competitive advantage of agriculture. Sensitivity analysis and empirical validation indicated the reliability and capability of the model for addressing the complexity of current agricultural land use changes and for investigating long‐term scenarios in the future. Finally, the model is used to explore the future dynamics of Iran agricultural land use during 2015–2040 with eight‐year pace. The simulation results for Iran show that the water availability is the most determining factor in the distribution of agricultural lands in a way that a continuing downward trend in agriculture land areas will occur in east and northeast, as well as an upward trend in north and southwest regions of the country. The outcome of this study enhances our capacity to consider approaches from different disciplines in an integrated framework for LUC modeling and provide a decision support tool for land use planning, policy making, and managements of agricultural sector.