Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches

Agroforestry in the form of intercropping, boundary plantation, and home garden are parts traditional land management systems India. Systematic implementation agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity microclimates, mitigating climate change, providing food fodder livelihood. The current study collected ground data for patches Belpada block, Bolangir district, Odisha state, site-suitability analysis employed 15 variables on climate, soil, topography, proximity, wherein use cover (LULC) map was referred to prescribe appropriate interventions. random forest (RF) machine learning model applied estimate relative weight determinant variables. results indicated high accuracy (average suitability >0.87 by validation data) highlighted dominant influence socioeconomic compared show that >90% agricultural area is suitable interventions, bund plantation based cropping intensity. settlement wastelands were found be ideal gardens bamboo block plantations, respectively. spatially explicit provide a baseline managers planners. Moreover, adopted approach can hosted cloud-based platforms different agro-ecological zones India, employing local regional national scale interventions would agriculture implement develop policies.