Digital mapping of soil carbon in a viticultural region of Southern Brazil

- We have predicted SOC for 5 different depths.
- Multiple Linear Regression showed to be more robust than Stepwise Multiple Linear Regression, Cubist or Random Forest.
- Soils under forest accumulated more SOC in the top and pasture in lower layers.
- PNVSC analysis could help to estimate the loss of carbon due changes in land use.

There is a need for soil C assessment in the soils of tropical and subtropical areas. We have aimed to quantify the spatial extent of SOC concentration and stocks under different land use and soil types in an 8118 ha area in southern Brazil. Common soils are Inceptisols, Ultisols and Mollisols, and the dominant land use is forest and vineyard. SOC data were modeled by 5 depths deriving values from spline functions. Regression kriging was used to model SOC concentration for each depth to 100 cm, and for producing a soil depth map. Uncertainty was estimated by empirical approach, using sequential Gaussian geostatistical simulation of the residuals. The Projected Natural Vegetation Soil Carbon (PNVSC) approach was used to evaluate changes in soil carbon due to land use change. Bulk density was estimated by pedotransfer functions. SOC stocks were calculated using the SOC prediction, bulk density and the soil depth map, and the stocks were corrected by cumulative mass coordinates. The models for SOC concentration prediction explained about 44% of the variance at 30-60 cm depth and with slightly lower values for other depths. Important covariates for prediction were Soil Order (Entisols), coordinate X, Aspect and the DEM. The model for the prediction of soil depth explained 43% of variance and important covariates were Soil Order (Entisol, Mollisol, Ultisol), Valley Depth and TWI. Soils under forest accumulated more carbon in the top 30 cm whereas soils under pasture had higher SOC levels with depth. Soils under arable crops and vineyard had the lowest SOC concentration. SOC concentration decreases by depth, as well as prediction intervals of uncertainty, until 60 cm depth. The SOC stocks (0-100 cm) varied between 104 t C/ha in vineyards on Alfisols, and 280 t C/ha in pasture areas on Oxisols. The PNVSC analysis showed that most soils had lost SOC compared to when they were projected to be under forest.