A two-stage GIS-based EWM–AHP and Monte Carlo simulation framework for land suitability modelling in estate crop planning in tropical regions

This paper presents a two-stage modelling framework for land suitability evaluation that integrates geographic information system (GIS)-enabled multi-criteria decision analysis (MCDA) with economic simulation under uncertainty. The first stage applies a hybrid MCDA method combining entropy weighting and the analytical hierarchy process to generate spatially explicit suitability maps incorporating biophysical, social, and sustainability criteria. In the second stage, Monte Carlo simulation is used to evaluate the economic performance of alternative land use scenarios, addressing variability in key input parameters such as yield, cost, and price. Applied in a tropical case study context, the framework enables probabilistic assessment of land allocation strategies and supports more robust decision-making in estate crop planning. By decoupling suitability modelling from deterministic economic assumptions, this approach enhances the transparency, flexibility, and realism of land use evaluation. The integration of spatial MCDA and stochastic simulation demonstrates a transferable method for supporting land use decisions in data-limited but uncertainty-prone environments.