CSIRO Agriculture & Food, Canberra, ACT, Australia.
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Glob Chang Biol. 2017 Oct;23(10):4430-4439. doi: 10.1111/gcb.13767. Epub 2017 Jun 26.
Soil organic carbon (SOC) dynamics are regulated by the complex interplay of climatic, edaphic and biotic conditions. However, the interrelation of SOC and these drivers and their potential connection networks are rarely assessed quantitatively. Using observations of SOC dynamics with detailed soil properties from 90 field trials at 28 sites under different agroecosystems across the Australian cropping regions, we investigated the direct and indirect effects of climate, soil properties, carbon (C) inputs and soil C pools (a total of 17 variables) on SOC change rate (r , Mg C ha yr ). Among these variables, we found that the most influential variables on r were the average C input amount and annual precipitation, and the total SOC stock at the beginning of the trials. Overall, C inputs (including C input amount and pasture frequency in the crop rotation system) accounted for 27% of the relative influence on r , followed by climate 25% (including precipitation and temperature), soil C pools 24% (including pool size and composition) and soil properties (such as cation exchange capacity, clay content, bulk density) 24%. Path analysis identified a network of intercorrelations of climate, soil properties, C inputs and soil C pools in determining r . The direct correlation of r with climate was significantly weakened if removing the effects of soil properties and C pools, and vice versa. These results reveal the relative importance of climate, soil properties, C inputs and C pools and their complex interconnections in regulating SOC dynamics. Ignorance of the impact of changes in soil properties, C pool composition and C input (quantity and quality) on SOC dynamics is likely one of the main sources of uncertainty in SOC predictions from the process-based SOC models.
土壤有机碳(SOC)动态受气候、土壤和生物条件的复杂相互作用调节。然而,SOC 与这些驱动因素及其潜在的连接网络之间的关系很少被定量评估。利用澳大利亚种植区不同农业生态系统 28 个地点的 90 个田间试验的 SOC 动态观测数据和详细的土壤特性,我们研究了气候、土壤特性、碳(C)输入和土壤 C 库(共 17 个变量)对 SOC 变化率(r,Mg C ha -1 yr -1 )的直接和间接影响。在这些变量中,我们发现对 r 影响最大的变量是平均 C 输入量和年降水量以及试验开始时的总 SOC 储量。总体而言,C 输入(包括作物轮作系统中的 C 输入量和牧场频率)占 r 相对影响的 27%,其次是气候(包括降水和温度)占 25%,土壤 C 库(包括库大小和组成)占 24%,土壤特性(如阳离子交换量、粘粒含量、容重)占 24%。路径分析确定了气候、土壤特性、C 输入和土壤 C 库在确定 r 方面的相互关联网络。如果去除土壤特性和 C 库的影响,r 与气候的直接相关性会显著减弱,反之亦然。这些结果揭示了气候、土壤特性、C 输入和 C 库的相对重要性及其在调节 SOC 动态方面的复杂相互关系。忽略土壤特性、C 库组成和 C 输入(数量和质量)变化对 SOC 动态的影响可能是基于过程的 SOC 模型中 SOC 预测不确定性的主要来源之一。