Using environmental covariates to predict soil organic carbon stocks in Vertisols of Sudan

Soil organic carbon (SOC) stocks account for nearly 75% of the active carbon pool in terrestrial ecosystems. SOC is also one of the most important soil quality components and has a vital role in agricultural productivity. The main objective of this study was to test the ability of regression kriging (RK) to predict the spatial distribution of SOC stocks in the Vertisols of Gezira State, Sudan using environmental covariates (ECOVs) and legacy soil data. The variables involved 2098 legacy soil datasets (LSDs). Seven ECOVs were used: precipitation (PR), temperature (T), normalized difference vegetation index (NDVI), land use/cover (LULC), digital elevation model (DEM), terrain slope (TS), and valley depth (VD), all downloaded from the repository of the United States Geology Survey (USGS) except PR and T. The RK model was validated using root mean squared error (RMSE), normalized root mean squared error (nRMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). The NDVI and topography were the major ECOVs affecting spatial SOC distribution and its stocks in the area. The results showed that low elevation areas had higher SOC content compared to higher regions and the SOC stocks were 4.14 Kg C ha 1, 0.86 Kg C ha 1, 0.11 Kg C ha 1, 0.02 Kg C ha 1, and 0.004 Kg C ha 1 at gradient levels of <5., 5-10., 10-15., 15-20., and < 20., respectively. Additionally, the spatial prediction revealed that agricultural land use (cropland) had the greatest SOC stocks (4.49 Kg C ha 1), whereas grasslands and forest lands were the lowest (0.01 Kg C ha 1). The RK model performed fairly well (RMSE = 1.83 g kg 1; nRMSE = 36%; MAE = 1.7%; MSE = 3.63%; R2 = 0.98) and gave satisfactory results to predict SOC stocks in the top 30 cm of the Vertisols. The findings of this study highlight that Vertisols could play a vital role in climate change mitigation via high SOC stocks and ability for C sequestration. It was recommended that the RK model should be extended to include much denser LSDs and test it with additional ECOVs combined with most specific soil properties that would have significant influence on SOC stocks in Vertisols, particularly soil pH, clay content, and clay mineralogy to achieve higher accuracy.

Automated summary

This study tested the ability of regression kriging to predict soil organic carbon stocks in Sudan and found that the normalized difference vegetation index and topography were the major factors affecting the distribution of soil organic carbon.