Land suitability classification (LSC) is an approach of land evaluation, which measures the degree of appropriateness of land for a specific land use. LSC is governed by a myriad of factors at the local and regional level including physiographic, pedologic and a host of socioeconomic and infrastructural determinants. This has called for the application of different multi-criteria decision-making (MCDM) techniques in agricultural LSC. The present study has attempted and compared various MCDM-based agricultural LSCs for Malda District in Eastern India. The study is based on multiple parameters governing agriculture, considering not only the physiographic and pedological attributes (e.g., relief, slope, soil fertility, soil organic carbon, etc.) but also the socioeconomic ones (e.g., the percentage of people engaged in agriculture, cultivator–labor ratio, degree of electrification, etc.). Four major MCDM algorithms have been applied, i.e., composite ranks, composite Z-scores, analytical hierarchy process (AHP) and weighted principal component analysis (WPCA). The results were also compared with the crop productivity-based agricultural efficiency. It was observed that about 15.44% of the area of Malda District is highly suitable for agriculture, whereas limited suitability is displayed by about 12.68% of area. The remaining part falls under moderate and marginal suitability classes. Furthermore, WPCA and AHP are superior to the nonparametric techniques of MCDM, namely composite ranks and composite Z-score. Moreover, the results of WPCA are superior to those of AHP. Due to the inherent limitations of the AHP approach, this study proposes the use of WPCA in the domain of agricultural LSC.

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印度东部马尔达地区农用土地适宜性分类的多准则决策方法

土地适宜性分类（LSC）是一种土地评估方法，用于评估特定土地用途的土地适宜程度。LSC在地方和地区层面受众多因素的支配，包括地理，儿科以及许多社会经济和基础设施的决定因素。这就要求在农业LSC中应用不同的多标准决策（MCDM）技术。本研究尝试并比较了印度东部马尔达地区各种基于MCDM的农业LSC。该研究基于支配农业的多个参数，不仅考虑了地貌和土壤学属性（例如地形，坡度，土壤肥力，土壤有机碳等），还考虑了社会经济属性（例如从事农业的人口的百分比） ，中耕工人的劳动比例，电气化程度等）。已经应用了四种主要的MCDM算法，即复合等级，复合Z评分，层次分析法（AHP）和加权主成分分析（WPCA）。还将结果与基于作物生产力的农业效率进行了比较。据观察，马尔达区约有15.44％的面积非常适合农业生产，而适度性有限的地区约占12.68％。其余部分属于中等和边缘适用性等级。此外，WPCA和AHP优于MCDM的非参数技术，即复合秩和复合Z分数。而且，WPCA的结果优于AHP。由于AHP方法的固有局限性，本研究建议在农业LSC领域使用WPCA。