Odebiri Omosalewa, Mutanga Onisimo, Odindi John, Slotow Rob, Mafongoya Paramu, Lottering Romano, Naicker Rowan, Matongera Trylee Nyasha, Mngadi Mthembeni
School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, VIC 3125, Australia.
Geoderma Reg. 2024 Jun;37:e00817. doi: 10.1016/j.geodrs.2024.e00817.
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 (R ≥ 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.
土壤有机碳(SOC)储量对于土地管理策略和缓解气候变化至关重要。然而,由于数据限制,以及荒漠化和土地退化导致的SOC储量复杂的空间和地下变异性,了解南非干旱和半干旱地区的SOC分布仍然是一项挑战。因此,为了支持土壤和土地利用管理实践,并推进缓解气候变化的努力,迫切需要在南非干旱和半干旱地区提供更精确的SOC储量估计。因此,本研究采用遥感方法来确定SOC储量的空间地下分布以及四个土壤深度(即0 - 30厘米、30 - 60厘米、60 - 100厘米和100 - 200厘米)处环境协变量的影响。使用两种基于回归的算法,即极端梯度提升(XGBoost)和随机森林(RF),研究发现前者(均方根误差值范围为7.12吨/公顷至29.55吨/公顷)与后者(均方根误差值范围为7.36吨/公顷至31.10吨/公顷)相比,是SOC的更优预测器。尽管如此,两个模型在研究的土壤深度上进行区域尺度SOC预测时都达到了令人满意的精度(R≥0.52)。此后,通过变量重要性分析,该研究证明了降雨和温度等气候变量对不同深度SOC储量的影响。此外,研究揭示了SOC储量存在显著的空间变异性,并且SOC储量随土壤深度增加。总体而言,这些发现增进了对南非干旱和半干旱地区SOC动态的理解,并强调了考虑特定地点的地形气候特征对于可持续土地管理和缓解气候变化的重要性。此外,该研究为地下SOC分布提供了有价值的见解,这对于制定碳固存策略、指导土地管理实践以及为干旱和半干旱环境中的环境政策提供信息至关重要。