Multi-criteria Decision-Making Approaches to Agricultural Land Suitability Classification of Malda District, Eastern India

Abstract

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.