Assessment of agricultural land suitability using GIS and fuzzy analytical hierarchy process approach in Ranchi District, India. Issue 26 (13th December 2022)

• Record Type: Journal Article
• Authors: Sengupta, Soumita; Mohinuddin, Sk.; Arif, Mohammad; Sengupta, Bishwadip; Zhang, Wanchang
• Is Part Of: Geocarto international. Volume 37:Issue 26(2023)
• Page Start: 13337
• Page End: 13368
• Publication Date: 2022-12-13
• Subjects: Land suitability -- agriculture -- GIS -- fuzzy AHP; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2076925
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: The land is a vital source of agriculture. Agricultural land is utilized for the purpose of cropping. Delineation of potential agricultural land is pertinent when population pressure incrementally increases and a competitive trade-off between different land uses. The present study is conducted to evaluate the agrarian land suitability in the Ranchi district (Plateau region in India) using fuzzy analytical hierarchy process (FAHP). A land suitability evaluation technique is designed, combining nineteen thematic layers from five categories: climatic parameters, topographic features, soil characteristics, euclidean distance, and land elements. All thematic layers were combined into a single layer using the FAHP model to create agricultural land suitability map in a GIS environment. Our analysis demonstrates that 11.17% of the total area (509700 ha) is highly suitable, while about 20.73% and 13.81% of the study area is moderately suitable and marginally suitable for agricultural activities. The study also reveals that approximately 54.29% of the lands are not suitable for agriculture. Our findings provide additional information, and local governments can use the land suitability map to manage and develop agricultural resources. The study contributes to the studies on agricultural suitability by bringing in multiple variables centring on the FAHP method, which can be replicated in studies on plateau regions across the globe.