Agriculture serves as the backbone of Bankura District, West Bengal, contributing substantially to its economic sustenance. This study endeavors to ascertain optimal land utilization for agriculture, crucial for sustainable development in the semi-arid region. Employing a comprehensive approach, it integrates data from diverse sources, encompassing parameters like slope, elevation, land use land cover (LULC), normalized difference water index (NDWI), soil type, proximity to river and road, geology, rainfall, and aspect. Through weighted overlay analysis, based on the Analytic Hierarchy Process (AHP), the study identifies and models potential agricultural zones. The resultant classification delineates five categories: very highly suitable, highly suitable, moderately suitable, marginally suitable, and currently not suitable. Cross-validation with district reports, Google Earth imagery, and field surveys bolster the credibility of findings, yielding an overall accuracy of 92.14%. Despite the substantial areas identified as agriculturally suitable, the study emphasizes the imperative of adopting sustainable management practices and resilient farming techniques to augment agricultural output. The insights garnered from this research hold significant implications for local farmers, regional planners, and governmental bodies. They can inform judicious decision-making processes, facilitate the identification of prime agricultural sites, foster agricultural expansion, and nurture self-sufficient local economies.

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Modeling Agricultural Land Suitability Using MCDM-AHP Techniques in Semi-arid Region of West Bengal, India

  • Prosenjit Kayal,
  • Susmita Das,
  • Indrajit Roy Chowdhury

摘要

Agriculture serves as the backbone of Bankura District, West Bengal, contributing substantially to its economic sustenance. This study endeavors to ascertain optimal land utilization for agriculture, crucial for sustainable development in the semi-arid region. Employing a comprehensive approach, it integrates data from diverse sources, encompassing parameters like slope, elevation, land use land cover (LULC), normalized difference water index (NDWI), soil type, proximity to river and road, geology, rainfall, and aspect. Through weighted overlay analysis, based on the Analytic Hierarchy Process (AHP), the study identifies and models potential agricultural zones. The resultant classification delineates five categories: very highly suitable, highly suitable, moderately suitable, marginally suitable, and currently not suitable. Cross-validation with district reports, Google Earth imagery, and field surveys bolster the credibility of findings, yielding an overall accuracy of 92.14%. Despite the substantial areas identified as agriculturally suitable, the study emphasizes the imperative of adopting sustainable management practices and resilient farming techniques to augment agricultural output. The insights garnered from this research hold significant implications for local farmers, regional planners, and governmental bodies. They can inform judicious decision-making processes, facilitate the identification of prime agricultural sites, foster agricultural expansion, and nurture self-sufficient local economies.