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

Abstract

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

Cost–Benefit Evaluation of Walnut and Jujube Orchards under Fruit Tree–Crop Intercropping Conditions in Southern Xinjiang

“…Fruit planting greatly depends on climate, soil, topography, water resources, and other natural conditions. There are many studies on the suitability analysis of fruit planting based on environmental and socioeconomic conditions [19,20]. Planting fruit trees in suitable areas is more likely to produce higher yields and economic benefits.…”

Harnessing the Potentiality of Farm Landscape for Trees Based on Satellite Evaluation: A GIS Modeling Perspective

“…The ICRAF has the leading role in agroforestry research with the basic objective to utilize the potential of trees to make farm landscapes sustainable at various levels (ICRAF 2019b). Several projects are running in different parts of India including in the Odisha state to increase agroforestry extent at the local level with appropriate land and water management intervention (Singh et al 2021(Singh et al , 2022. The project entitled "Enabling smallholders in Odisha to produce and consume more nutritious food through agroforestry systems" was funded by the government of Odisha under the supervision of world agroforestry (ICRAF) to familiarize the agroforestry practice and its successful implementation of the project(ICRAF 2019a).…”

GIS-Based Cropland Suitability Prediction Using Machine Learning: A Novel Approach to Sustainable Agricultural Production

“…By creating and studying a technique for assessing the suitability accuracy utilizing the NDVI vegetation index from the multispectral Sentinel-2 images, the traditional GIS-based multicriteria analysis was primarily improved. To improve the subjective weight determination of AHP, Singh et al [73] applied a Random Forest machine learning algorithm to derive criteria weights based on the relative variable importance. Radočaj et al [17] proposed the novel peak NDVI method to identify the vegetation potential of soybean during the full maturity (R6) development stage, representing a measurable and exact approach for high repeatability in future seasons and other locations in the world.…”