Abstract
Crop-specific land suitability analysis can better support a region's agricultural base and enhance its resource utilization efficiency. In this study, we employ the Analytical Hierarchic Process-based Multi-Criteria Decision-Making approach together with geospatial inputs/methods to assess land suitability for betel leaf cultivation in Tamluk Subdivision of Purba Medinipur district in West Bengal, India. Seventeen individual parameters grouped into five categories – physiographic, land use and land cover, climatic, pedologic and infrastructural – were used. The respective ranks of the influencing factors were assigned based on collated farmer responses and expert opinions obtained during an extensive field survey, while a pair-wise comparison matrix was used to calculate their respective weights. Internal variations within individual factors were categorized by assigning scores based on their importance to betel leaf production, as ascertained from published literature and fieldwork. Results show that only 6.86% of the region is highly suitable and available for betel leaf cultivation, 30.15% area is moderately suitable, while 11.26% is marginally suitable but can be enhanced through apt land/water management practices. A 500 × 500 m grid overlay further elicited location specific combinations of the different land suitability classes and the presence/absence of waterbodies required for irrigation. It also ascertained the locational aptness of current betel leaf plots while identifying new expansion sites for this crop on the basis of grids having more than 40% of suitable lands with available water resources. The study presents possibly the first documentation of land suitability specifically for betel vine plots in Eastern India and thus thus provides a valuable insight into this high-value cash crop agribusiness, which can be useful for framing crop-specific policies. The methods used herein can be readily employed for similar agricultural assessments (in respect of betel vine or other crops) in other regions.
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