4.7 Article

Factor contribution to soil organic and inorganic carbon accumulation in the Loess Plateau: Structural equation modeling

期刊

GEODERMA
卷 352, 期 -, 页码 116-125

出版社

ELSEVIER
DOI: 10.1016/j.geoderma.2019.06.005

关键词

Soil organic carbon; Soil inorganic carbon; Carbon sequestration; Controlling factor; Relative contribution; Path analysis

资金

  1. National Natural Science Foundation of China [41877037, 41425006, 41101218]
  2. Western Light Project of Chinese Academy of Sciences [K318001103]

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The effects of climate, soil characteristics and management on soil carbon accumulation have been extensively investigated. However, the relative importance of these factors remains unclear, especially in arid and semiarid regions. Here we evaluated the contribution of the environmental variables (geographical location, climate, soil type, and land use type) to soil organic and inorganic carbon accumulation in the 0-100 cm soil layers across the Loess Plateau in China. A structural equation modeling (SEM) was used to distinguish direct from indirect effects of factors on soil carbon accumulation based on covariance structures. The results showed that environmental temperature and moisture were the primary controls of soil organic carbon density (SOCD) variation. The total effects (the sum of direct and indirect effect) of soil type and land use on SOCD were less than half of those of environmental temperature and moisture. In addition, the direct and negative effect of environmental temperature on SOCD increased, and the direct and positive effect of environmental moisture on SOCD decreased with soil depth. For the soil inorganic carbon densities in the 0-100 cm soil layers, soil organic carbon (SOC) content acted as the most important factor controlling the variations in soil inorganic carbon density (SICD). Environmental temperature and moisture mainly affected indirectly SICD by mediating through its impacts on soil type, SOC content, or soil pH. Less than 40% of variation in soil carbon accumulation for 0-100 cm soil depth is explained in the model. The unexplained variance highlights the need for the data on soil physicochemical properties, quality of organic carbon inputs, and soil microorganisms.

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