How to map soil organic carbon stocks at field scale in South Africa?

Reliable soil organic carbon stocks (SOCS) maps are important for accurately assessing carbon credits. To address this gap, the study aimed to determine whether conventional mapping using ordinary kriging (OK) or digital soil mapping (DSM) with machine learning (ML) could be used for mapping SOCS at a field scale in South Africa. At two fields, fifty samples from three depths, 0–5, 5–15 and 15–30 cm were collected and analysed for soil organic carbon (SOC) content using the total dry combustion method. For dry bulk density (ρb), a pedo-transfer function was created for predicting the ρb at unsampled locations. Maps for SOC content, ρb and SOCS were created using OK and DSM and assessed with evaluation statistics including root mean square error (RMSE) and Lin’s concordance correlation coefficient (ρ c ). A calculate-first approach (calculate SOCS and then map it) and map-first approach (map SOC, ρb and then calculate SOCS) were compared. Findings of this study demonstrated that DSM should be used rather than OK for mapping SOCS at the field scale in South Africa. Utilising DSM will lead to more accurate and reliable SOCS maps. However, the results also indicated that the SOC content, ρb and SOCS maps need to be improved. To increase the accuracy of maps generated using DSM, additional soil samples need to be collected, and field scale covariates should be incorporated. The results were inconclusive regarding whether the calculate-first approach or the map-first approach should be used when utilising OK or DSM. Nevertheless, the study concluded that the calculate-first approach should be considered, because this approach is simpler and might lead to more reliable and accurate SOCS estimations.