Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation - Odebiri, Omosalewa; Mutanga, Onisimo; Odindi, John; Slotow, Rob; Mafongoya, Paramu; Lottering, Romano; Naicker, Rowan; Matongera, Trylee Nyasha; Mngadi, Mthembeni (Geoderma Regional, 2024)
Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R2 ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments. •SOC stocks increase with depth, peaking at 100–200 cm in arid and semi-arid regions.•Climatic variables, especially rainfall and temperature, consistently influenced SOC stocks.•Each depth had varying influential variables; hence the need for depth-specific modeling.•XGBoost outperformed the RF model in estimating SOC stocks across all depths.