Javed Rizvi

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.

Mid-infrared (MIR) spectroscopy is emerging as one of the most promising technologies, as it is a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. This study was conducted to evaluate the potential of mid-infrared spectroscopy for the rapid and nondestructive measurement of some important soil properties of Alfisols. A total of 336 georeferenced soil samples fromthe 0–15 cm soil layer of Alfisols that were collected from the eastern Indian states of Odisha and Jharkhand were used. The partial least-squares regression (PLSR), random forest, and support vector machine regression techniques were compared for the calibration of the spectral data with the wet chemistry soil data. The PLSR-based predictive models performed better than the other two regression techniques for all the soil properties, except for the electrical conductivity (EC). Good predictions with independent validation datasets were obtained for the clay and sand percentages and for the soil organic carbon (SOC) content, while satisfactory predictions were achieved for the silt percentage and the pH value. However, the performance of the predictive models was poor in the case of the EC and the extractable nutrients, such as the available phosphorus and potassium contents of the soil. Specific regions of the MIR spectra that contributed to the prediction of the soil SOC, the pH, and the clay and sand percentages were identified. The study demonstrates the potential of the MIR spectroscopic technique in the simultaneous estimation of the SOC content, the sand, clay, and silt percentages, and the pH of Alfisols from eastern India.

Trees are an integral part of the sustainable farming practices that can withstand extreme weather events, pest risks, and optimize land and water productivity to achieve food, fuel, fodder and nutritional security while safeguarding the environmental flows. This study was undertaken to analyze the landscape potential for the South Asian region in the geospatial domain utilizing the FAO’s land suitability criteria. The key datasets were derived from satellite remote sensing at a global and regional scale for land, soil, climate, and topography and were used to model the agroforestry suitability across South Asia. Furthermore, the agroforestry suitability categories and tree cover dominance were investigated with respect to the total geographical area, agriculture land cover and with climate variables to understand the present and future trends. The comprehensive analysis revealed that 69% of the total geographical area retains 55% and greater suitability for agroforestry. The analysis revealed that nearly 73.4% of the landscape is absent (0%) of tree cover, 7.1%, shows 1–10% and 19.5% area having more than 10% tree cover. The tree dominance/hotspot analyses in the agriculture land were found notably high in the multiple farming components such as home gardens. The single crop of irrigated and rain-fed croplands showed high land suitability towards agroforestry. Such land can be utilized to enhance the tree cover that suits locally as per the farmer's need based on a community-driven participatory approach to bring the sustainability and resilience in degraded landscapes (FAO in Agroforestry for landscape restoration, 2017). The future climate data analysis showed a significant change in the distribution of temperature and precipitation that will influence future farming practices in South Asia. The agroforestry suitability and tree cover mapping results/analysis will assist crucially the agroforestry policymakers/planners in the various South Asian countries to implement and extend it to the new area. The analysis clearly shows that the advent of big data, remote sensing and GIS provide insights into the agroforestry interventions and scaling which further helps in building resilient landscapes for sustainable agri-food systems, livelihoods, safeguarding the environmental security and supporting some of the important sustainable development goals (SDGs).