Land Suitability Analysis for Agricultural Landuse in the Pakyong District of Sikkim Himalayas with the help of Geospatial Tools and Techniques

This study presents an inclusive analysis of agricultural land suitability in the Pakyong district of Sikkim Himalaya using GIS and AHP techniques. Four levels of suitability classes identified by FAO were used in this study. To analyze and prepare the land suitability classes, eight significant parameters were taken based on their influencing factors, namely, slope, elevation, rainfall, temperature, soil texture, NDVI, distance from drainage, and distance from road. All eight parameters were given a certain weightage according to their influencing factors with the help of AHP calculation. FAO four-level land suitability classes were also used to assign the score or rank for individual parameters. All these criteria were taken and analyzed with the help of the Weighted Overlay Method in ArcGIS 10.8 to prepare a final land suitability map. Four suitability classes were identified. Highly suitable (S1) covered a total area of 47.371 sq km (10.40%) and was largely found along the western and south-western parts of the study area. Moderately suitable (S2) covered a total area of 266.727 sq km (58.56%) and was mostly found along the central, western, and southern parts of the study area. Whereas marginally suitable (S2) and not suitable (NS) covered a total area of 140.492 sq km (30.85%) and 0.811 sq km (0.178%), respectively, and were found along the northern, northeastern, and easternmost parts of the study area. Assessment of land suitability plays a vital role in delivering the required information or data for future sustainable agricultural planning and development.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-23
DOI
https://doi.org/10.5281/zenodo.17219464
Primary Topic
Food Security and Health in Diverse Populations
Type
article
Field-Weighted Citation Impact
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article

Land Suitability Analysis for Agricultural Landuse in the Pakyong District of Sikkim Himalayas with the help of Geospatial Tools and Techniques

Zenodo (CERN European Organization for Nuclear Research)
Food Security and Health in Diverse Populations
article

Land Suitability Analysis for Agricultural Landuse in the Pakyong District of Sikkim Himalayas with the help of Geospatial Tools and Techniques

article en

Abstract

This study presents an inclusive analysis of agricultural land suitability in the Pakyong district of Sikkim Himalaya using GIS and AHP techniques. Four levels of suitability classes identified by FAO were used in this study. To analyze and prepare the land suitability classes, eight significant parameters were taken based on their influencing factors, namely, slope, elevation, rainfall, temperature, soil texture, NDVI, distance from drainage, and distance from road. All eight parameters were given a certain weightage according to their influencing factors with the help of AHP calculation. FAO four-level land suitability classes were also used to assign the score or rank for individual parameters. All these criteria were taken and analyzed with the help of the Weighted Overlay Method in ArcGIS 10.8 to prepare a final land suitability map. Four suitability classes were identified. Highly suitable (S1) covered a total area of 47.371 sq km (10.40%) and was largely found along the western and south-western parts of the study area. Moderately suitable (S2) covered a total area of 266.727 sq km (58.56%) and was mostly found along the central, western, and southern parts of the study area. Whereas marginally suitable (S2) and not suitable (NS) covered a total area of 140.492 sq km (30.85%) and 0.811 sq km (0.178%), respectively, and were found along the northern, northeastern, and easternmost parts of the study area. Assessment of land suitability plays a vital role in delivering the required information or data for future sustainable agricultural planning and development.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 33%
Food Security and Health in Diverse Populations
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