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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
Published on: October 16, 2018
Large-scale spatial assessment of soil organic carbon, pH and their interrelation in Indian agricultural soils using
Saketh Kandadai1, Vinay Kumar Dadhwal2
1School of Natural Sciences and Engineering, National Institute of Advanced Studies, Bengaluru, India.
Abstract:
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 (0-15 cm) 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 (250 m) 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 between SOC 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.
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