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As warming approaches 2°C, India’s climate policy must build institutions to manage impacts that adaptation alone cannot prevent

Large-scale soil sampling efforts have been undertaken in India since 2015 under the Soil Health Card (SHC) scheme. This study integrates 39 million+ soil measurements from SHC into a geospatial framework to study two important soil properties— Soil Organic Carbon (SOC) content and pH. The study provides maps of mean and uncertainty at village level for SOC content and pH in surface (0–15cm) agriculture soils in India, and further analyzes the varying relationship between them across major Agro-Ecological Regions (AERs) in the country. The resultant spatial SOC layer also gave an opportunity to assess two global SOC maps — 1. SoilGrids (250m) and 2. Global Soil Data for Earth System Modelling (GSDE—30 arcsec). Mean SOC content in different AERs varied from 0.39% to 1.06% while mean pH varied from 5.4 to 8.0. An AER-wise analysis indicated a spatially varying relationship betweenSOC and pH with 11 AERs showing negative correlation and 4 showing positive and no correlation each. The mean SOC contents from GSDE were around half that of SHC for most AERs, while those estimated by SoilGrids were more than twice that of SHC in 16 of the 19 AERs. The implications of these results for Indian SOC stock estimates and climate change mitigation potential are discussed in this paper. Overall, SHC data can complement and augment large scale soil datasets. It can find applications in a diverse set of fields like soil monitoring, carbon budgeting, soil zonation studies, as well as in crop and carbon cycle modelling studies.

The integration of Artificial Intelligence (AI) is not just an incremental improvement; it has the potential to fundamentally alter the landscape of how we understand the risks emanating from a warming planet that outpace our resilience. 

Increasing Soil Organic Carbon (SOC), the largest terrestrial carbon pool, through proper land management has been suggested as a nature-based solution to mitigate climate change. In this context, it is important to understand the impacts of land transformations on regional SOC stocks. The study spatially analyzed the tree plantation expansion in Kerala, India, along with other land transformations in the last five decades and its effect on surface (0–30 cm) Soil Organic Carbon (SOC) density and stocks. This study adopted a machine learning-based predictive modelling approach by combining: (1) a detailed two-time period land use map separating major plantation types; (2) legacy soil data representing ground SOC measurements for each land use category; (3) other climatic, topographic and soil variables that affect the spatial variation of SOC, in order to spatially assess the changes in SOC stocks in Kerala due to land use changes over the last five decades (1972–2020). The study highlighted significant local hotspots of losses and gains that the traditional area-based methods do not fully capture. Interestingly, although there was a large increase in the area under tree cover in the last five decades, SOC gains in certain regions were compensated by losses in other regions leading to a very small change (~ 2%) in the overall SOC pool size. Land use and soil type were the most important predictors of SOC based on the developed Random Forest model. The findings highlighted that afforestation with tree plantations might not always lead to an increase in SOC stocks at regional scales. Its effect on SOC stocks varied by plantation type and previous land use. These implications must be considered while adopting climate mitigation strategies. Also, spatially explicit evaluation of various plantation types improves SOC source sink modelling and should be considered for preparing more accurate regional & national SOC inventories.