Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso)

Abstract

Although the evaluation of soil organic carbon (SOC) stocks across different types of land use and major reference soil groups is essential for mitigating climate change, there remains, to date, limited comprehensive understanding of whole tropical soil profiles. Therefore, this study aimed to explain the amount of SOC stocks in different land-use systems and across various soil groups, as well as its spatial pattern in the topsoil (0–30 cm) and subsoil (30–100 cm) within the savannah zone of Burkina Faso. Roughly 70 soil profiles were considered along with additional auger sampling to account for spatial variation in both cropland (CR) and savannah (SA). The machine learning technique random forest regression (RFR) and multiple linear regression (MLR) were used for modeling the surface and subsurface SOC stocks. For model calibration, covariates including land use, topographic, texture, and climatic data were considered as surrogate for soil forming factors. The prediction maps produced by the calibrated models were validated by an independent dataset. The results indicated that about 53% of the SOC stock over 1 m depth was held in the upper 30 cm. Only a marginal difference was recorded between the topsoil SOC stock in SA (41.4 t C ha−1) and CR (39.1 t C ha−1) soils. For the subsoil, a significant difference (p < 0.05) was observed between the SOC stock of CR soils recording about 40.2 t C ha−1 and SA soils with 26.3 t C ha−1. Among the reference soil groups, the Gleysols located at lower elevation positions revealed the highest SOC stocks over 0–30 cm (44 t C ha−1) and 100 cm depth (86.6 t C ha−1). The Stagnosols (45.2 t C ha−1) followed by the Gleysols (42.7 t C ha−1) recorded the highest SOC stocks over 30–100 cm. The variability of SOC stock in the topsoil was primarily related to site-specific elements, such as particle-size fraction and wetness index, while its distribution in the subsoil was mainly associated with the topographical orientation represented by the slope aspect. Compared to the MLR, RFR estimated mean top- and subsoil SOC stocks of the catchment fairly well, along with lower statistical error metrics, though extreme values were not covered. Nevertheless, the findings on SOC stocks reinforce the view that the semi-arid ecosystems of West Africa still offer a significant opportunity for carbon sequestration for both topsoil and subsoil, and these results represent a baseline for future modeling of SOC dynamics in the region.