The iswc has made progress in the prediction method of regional deep soil organic carbon

Update time:2023-04-27
Recently, researcher Wei Xiaorong from the team of Shao Mingan academician of the Institute of Soil and Water Conservation, published a paper entitled “An empirical approach to predict regional organic carbon in deep soils” in Science China Earth Sciences. And, the research result was granted software copyright.

Accurate prediction of deep soil organic carbon at regional scales is the scientific basis for terrestrial ecosystem carbon cycle model simulations and carbon pool assessments. Field sampling combined with laboratory analysis is the most commonly used approach to obtain deep soil organic carbon (SOC) data and has been widely applied for more than a century. This approach provides the most accurate measurement of deep SOC concentration but is highly time-consuming and labor-intensive and is not practical at large spatial scales. Alternatively, developing mathematical functions to predict SOC in deep soils offers a quick technique for regional assessment. The depth distribution function describing the vertical distribution of SOC with soil depth has been used to estimate the deep SOC concentration in various regions and ecosystems. This method requires SOC data collected from multiple layers with a depth of at least 100 cm to obtain the parameters of the function. Additionally, the fittings among various functions have been rarely compared, leading to large arbitrariness in selecting the depth distribution function and lower fit goodness of selected function for the measured data. Moreover, application of such method is mainly focused at the site scale. These drawbacks of the currently used approaches restrict the accurate estimation of deep soil SOC at regional or larger spatial scales.

Wei Xiaorong et al. composed regional SOC datasets from the measured and International Soil Reference and Information Centre (ISRIC) Soil Information System database. The datasets were used to compare the results of the currently used 7 depth distribution functions in fitting the vertical distribution patterns of SOC to select the optimal depth distribution function. Then, the team developed a prediction approach of deep SOC at the regional scale, through analyzed the relationships of the optimal depth distribution function parameters and soil properties from 0-40 cm topsoil layers. The approach was demonstrated to accurately and rapidly predict deep SOC at sample sites and regional scales, providing technical support for accurate assessment of ecosystem carbon pools.

The work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant Nos. XDA23070202 and XDB40020000), the National Key Research and Development Program (Grant No. 2022YFF1302804), the National Natural Science Foundation of China (Grant Nos. 41977068 and 41622105), and the Program from Chinese Academy of Sciences (Grant No. QYZDB-SSW-DQC039). 

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