Crop suitability analysis for the coastal region of India through fusion of remote sensing, geospatial analysis and multi-criteria decision making.

Crop suitability analysis plays an important role in identifying and utilizing the areas suitable for better crop growth and higher yield without deteriorating the natural resources. The present study aimed to identify suitable areas for rice and coconut cultivation across the coastal region of India using the analytic hierarchy process (AHP) integrated with geographic information systems (GIS) and remote sensing. A total of nine parameters were selected for suitability analysis including elevation, slope, soil depth, drainage, texture, pH, soil organic carbon, rainfall, temperature and a land use land cover (LULC) constraint map.

Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models.

Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered.

Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India.

Coconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield.