Sharma Rajneesh, Levi Matthew R, Ricker Matthew C, Thompson Aaron, King Elizabeth G, Robertson Kevin
Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA; Now at the Department of Geography, University of Georgia, Athens, GA 30602, USA.
Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA.
Sci Total Environ. 2024 Jul 10;933:173060. doi: 10.1016/j.scitotenv.2024.173060. Epub 2024 May 7.
Soil organic carbon (SOC) is a dynamic soil property (DSP) that represents the largest portion of terrestrial carbon. Its relevance to carbon sequestration and the potential effects of land use on SOC storage, make it imperative to map across both space and time. Most regional-scale studies mapping SOC give static estimates and train different models for different periods with varying accuracies. We developed a flexible modeling approach called DSP-Scale to map SOC in both space and time. DSP-Scale uses ecological concepts and empirical data to predict DSP dynamics using inherent soil properties (static factors) and land cover changes (dynamic factors). We compiled SOC data for the 0-20 cm depth (SOC20) from 1441 points spanning a 25 million ha study area in the southeastern U.S. Coastal Plain, incorporating data from the Rapid Carbon Assessment, National Cooperative Soil Survey Soil Characterization database, and other regional studies. We developed a random forest model using climate, topography, soil survey, and land cover changes to predict SOC20 dynamics for five-year periods between 2001 and 2019. Our model explained 66 % and 59 % of the variation for the training and test data, respectively. Top predictors included mean annual precipitation, slope, and soil erosion class. Land cover 10 years before measurements of SOC20 was more important than current land cover for estimating SOC20. We estimated total SOC stocks of 207.1 and 208.3 Tg for 2001 and 2019, respectively. Highest gains of total SOC stock (0.9 Tg from 2001 to 2019) were associated with land cover change from mixed to evergreen forest. The greatest loss of total SOC stock (0.2 Tg) in the same period was associated with land cover change from pasture/hay to evergreen forest. We concluded that the DSP-Scale approach provides a flexible way to use dynamic and static factors affecting SOC stocks to predict changes in space and time at regional scales.
土壤有机碳（SOC）是一种动态土壤属性（DSP），它代表了陆地碳的最大部分。其与碳固存的相关性以及土地利用对SOC储存的潜在影响，使得有必要在空间和时间上进行制图。大多数绘制SOC的区域尺度研究给出的是静态估计值，并针对不同时期训练具有不同精度的不同模型。我们开发了一种名为DSP-Scale的灵活建模方法，用于在空间和时间上绘制SOC。DSP-Scale利用生态概念和经验数据，通过土壤固有属性（静态因素）和土地覆盖变化（动态因素）来预测DSP动态。我们汇编了美国东南部沿海平原一个2500万公顷研究区域内1441个点的0-20厘米深度的SOC数据（SOC20），纳入了快速碳评估、国家合作土壤调查土壤特征数据库以及其他区域研究的数据。我们使用气候、地形、土壤调查和土地覆盖变化开发了一个随机森林模型，以预测2001年至2019年期间每五年的SOC20动态。我们的模型分别解释了训练数据和测试数据中66%和59%的变异。最重要的预测因子包括年平均降水量、坡度和土壤侵蚀类别。对于估计SOC20，在测量SOC20前10年的土地覆盖比当前土地覆盖更重要。我们分别估计了2001年和2019年的总SOC储量为207.1和208.3太克。总SOC储量的最高增加量（2001年至2019年增加0.9太克）与土地覆盖从混交林变为常绿林有关。同期总SOC储量的最大损失量（0.2太克）与土地覆盖从牧场/干草变为常绿林有关。我们得出结论，DSP-Scale方法提供了一种灵活的方式，利用影响SOC储量的动态和静态因素来预测区域尺度上的时空变化。
