Fuzzy-AHP and GIS-Based Modeling for Food Grain Cropping Suitability in Sundarban, India

Abstract

Land suitability analysis is essential for informed farming decisions, revealing an area’s natural potential and limitations. The primary objective of this research is to determine the suitability of land for cultivating major food grain crops like Kharif rice, Rabi rice, and Green gram in the Sundarban region of India using geostatistics, the fuzzy-AHP (FAHP) algorithm, and GIS tools. Local experts’ insights were harnessed to ascertain the relative importance of 19 thematic layers encompassing climatic, soil, environmental, and socioeconomic factors. These were combined using the FAHP model in a GIS to produce a cropland suitability map. The soil parameters were best fitted using spherical and Gaussian semi-variogram models, which showed the best performance. Land suitability analysis revealed that highly suitable (S1) areas were most extensive for Rabi rice (21.65%), followed by those for Kharif rice (16%) and Green gram (11.8%). Moderately suitable (S2) areas dominated the landscape, with those for Kharif rice (68.70%) and Rabi rice (65.32%) exhibiting significantly larger extents than those for Green gram (44.28%). Minor limitations restricted these areas due to low organic content, salt stress, acidic pH, sandy-loamy soil texture, shallow soil depth, and poor-quality irrigation water. Marginally suitable (S3) areas for Kharif rice (14.97%), Rabi rice (12.62%), and Green gram (37.88%) were less extensive, while not suitable (N) areas were minimal (0.33–6.04%). The dependability of the FAHP procedure in suitability assessment was validated using the area under curve (AUC), which was found to be substantial for Kharif rice (81.20%), Rabi rice (83.30%), and Green gram (79.41%). The study concluded that the combined FAHP algorithm in GIS is a practical approach for assessing accurately land suitability for producing specific crops.