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.

土地适宜性分析对于明智的农业决策至关重要，可以揭示一个地区的自然潜力和局限性。本研究的主要目的是利用地统计学、模糊层次分析法 (FAHP) 算法和 GIS 工具，确定印度孙德尔本地区土地是否适合种植主要粮食作物，如 Kharif 稻、Rabi 稻和绿豆。利用当地专家的见解来确定 19 个主题层的相对重要性，包括气候、土壤、环境和社会经济因素。使用 GIS 中的 FAHP 模型将这些组合起来，生成农田适宜性地图。使用球形和高斯半变异函数模型对土壤参数进行了最佳拟合，显示出最佳性能。土地适宜性分析显示，高度适宜（S1）区域以拉比稻（21.65%）最为广泛，其次是卡里夫稻（16%）和绿豆（11.8%）。中等适宜（S2）区域占主导地位，其中卡里夫稻（68.70％）和拉比稻（65.32％）的面积明显大于绿豆（44.28％）的面积。由于有机物含量低、盐胁迫、pH值酸性、沙壤土质地、土层深度浅以及灌溉水质量差，一些小的限制限制了这些地区的发展。哈里夫稻 (14.97%)、拉比稻 (12.62%) 和绿豆 (37.88%) 的边缘适宜 (S3) 地区范围较小，而不适宜 (N) 地区则很少 (0.33–6.04%)。使用曲线下面积 (AUC) 验证了 FAHP 程序在适宜性评估中的可靠性，发现该曲线对于 Kharif 米 (81.20%)、Rabi 米 (83.30%) 和 Green gram (79.41%) 非常重要。 研究结论是，GIS 中的组合 FAHP 算法是准确评估生产特定作物的土地适宜性的实用方法。