Spatial distribution of soil carbon stocks in a semi-arid region of India

Abstract

We predicted total carbon (TC) stocks in the 0–30 cm soil depth of two southern states in India using field observations, environmental covariates and geospatial approaches. We compared the Geographically Weighted Regression Kriging (GWRK) with Linear Regression Kriging (LRK) approach to predict the TC stocks. Greater spatial heterogeneity in TC stocks (2–15 kg m−2) were predicted with lower estimation errors (RMSE = 2 kg m−2 R2 = 0.63) in GWRK in comparison to the LRK approach (RMSE = 3 kg m−2, R2 = 0.55). The average decrease in the prediction error was 39% in GWRK in comparison to the LRK approach. The total TC stock in the 0–30 cm depth of the study area was estimated at 1.5 Pg C with upper and lower prediction intervals of 1 and 2 Pg C, respectively. The cropland stored largest (65%, 1 Pg C) amount of TC stocks followed by forest (21%, 0.31 Pg C) and plantation (8%, 0.12 Pg C) land cover types. Among soil types the Alfisols stored largest (33%, 0.49 Pg C) amount of TC stocks followed by Inceptisols (23%; 0.35 Pg C) and Entisols (18%, 0.27 Pg C). The uncertainty in TC stock predictions ranged from 41 to 75% and 69 to 91% under various land covers and soil types, respectively. Highest uncertainties in predicted TC stocks were associated with the forest land cover type and Mollisols soil order. Similarly, lowest uncertainties were found in the built up areas and Aridisols soil order. Our results suggest that GWRK is a useful approach to spatially predict the TC stocks at regional scales.