Academic Journal

Identification of Potential Agricultural Land Pooling Sites in Kaski District using AHP and GIS

Bibliographic Details
Title: Identification of Potential Agricultural Land Pooling Sites in Kaski District using AHP and GIS
Authors: Dhaka Ram Gaire, Shailendra Shahi, Oshi Humagain, Bir Bahadur Khatri, Umesh Bhurtyal, Milan KC
Source: Journal of Land Management and Geomatics Education. 7:1-7
Publisher Information: Nepal Journals Online (JOL), 2025.
Publication Year: 2025
Description: This study identifies potential agricultural land pooling sites in Kaski District using an integrated Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approach. Agricultural land pooling seeks to address land fragmentation and promote efficient collective farming. The study considered four major criteria – slope, land parcel area, proximity to roads, and irrigation access following the Agricultural Land Pooling Programme Implementation Guidelines 2077 from the Ministry of Agriculture and Land Management, Gandaki Province. Criteria weights were calculated using AHP as follows: Slope (60.57%), Area (23.18%), Irrigation Access (11.04%), and Road Proximity (5.21%). A GIS-based weighted overlay method was applied to create a suitable map, classifying land into four categories: Most Suitable, Suitable, Moderately Suitable, and Low Suitable. The results revealed that about 48% of the land parcels fall under the “Most Suitable” and “Suitable” categories, mostly located in the district’s central and southern regions. Field validation was carried out using Kobo Collect, resulting in an overall accuracy of 82.35% and a Kappa coefficient of 0.76, indicating a strong agreement between the model and ground data. This research demonstrates that combining AHP with GIS is an effective method for land pooling site selection and offers valuable insights for expanding future land consolidation initiatives.
Document Type: Article
ISSN: 2676-1408
DOI: 10.3126/jlmge.v7i1.83175
Accession Number: edsair.doi...........bc77e2ce3f8285382e85527ea83f3c91
Database: OpenAIRE
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