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Modelling agricultural land suitability for vegetable crops farming using RS and GIS in conjunction with bivariate techniques in the Uttar Dinajpur district of Eastern India
This study aims to evaluate agricultural land suitability for vegetable crop farming in the Uttar Dinajpur district. For performing the land suitability, the bivariate statistical techniques like frequency ratio, entropy index, and weights of evidence models are taken into consideration. Influencing parameters such as elevation, slope, geology, geomorphology, stream density, rainfall, aquifer type, geohazard, Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), groundwater level, soil type, topsoil depth, bulk density, Cation Exchange Capacity (CEC), nitrogen, soil pH, Organic Carbon Content (OCC), distance from Krishi Bazar, distance from the road, distance from the river were used. The estimated results show that 16.93% (531.949 sq. km) of the study district is highly suitable for vegetable cultivation, while 26.643% (873.116 sq. km) is moderately suitable, 20.380% (640.337 sq. km) of the geographical land is marginally suitable and 9.341% (293.488 sq. km) is very less suitable for vegetable farming. Models’ validation using the ROC curve resulted that the bivariate techniques like frequency ratio (0.927), entropy index (0.888), and weights of evidence (0.907) are highly effective methods for site selection for vegetable farming in the study region. The factors such as soil, rainfall, aquifer type, groundwater level, nitrogen, and distance from Krishi Bazaar are the most influencing parameters to perform the outcome.
Modelling agricultural land suitability for vegetable crops farming using RS and GIS in conjunction with bivariate techniques in the Uttar Dinajpur district of Eastern India
This study aims to evaluate agricultural land suitability for vegetable crop farming in the Uttar Dinajpur district. For performing the land suitability, the bivariate statistical techniques like frequency ratio, entropy index, and weights of evidence models are taken into consideration. Influencing parameters such as elevation, slope, geology, geomorphology, stream density, rainfall, aquifer type, geohazard, Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), groundwater level, soil type, topsoil depth, bulk density, Cation Exchange Capacity (CEC), nitrogen, soil pH, Organic Carbon Content (OCC), distance from Krishi Bazar, distance from the road, distance from the river were used. The estimated results show that 16.93% (531.949 sq. km) of the study district is highly suitable for vegetable cultivation, while 26.643% (873.116 sq. km) is moderately suitable, 20.380% (640.337 sq. km) of the geographical land is marginally suitable and 9.341% (293.488 sq. km) is very less suitable for vegetable farming. Models’ validation using the ROC curve resulted that the bivariate techniques like frequency ratio (0.927), entropy index (0.888), and weights of evidence (0.907) are highly effective methods for site selection for vegetable farming in the study region. The factors such as soil, rainfall, aquifer type, groundwater level, nitrogen, and distance from Krishi Bazaar are the most influencing parameters to perform the outcome.
Modelling agricultural land suitability for vegetable crops farming using RS and GIS in conjunction with bivariate techniques in the Uttar Dinajpur district of Eastern India
Debabrata Sarkar (author) / Sunil Saha (author) / Prolay Mondal (author)
2023
Article (Journal)
Electronic Resource
Unknown
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