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Vertical Distribution of Soil Carbon, Nitrogen, and Phosphorus in Typical Chinese Terrestrial Ecosystems
Characterization of the vertical distribution of soil organic carbon(C), nitrogen(N), and phosphorus(P) may improve our ability to accurately estimate soil C, N, and P storage. Based on a database of 21 354 records in 74 long-term monitoring plots from 2004 to 2013 in the Chinese Ecosystem Research...
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Published in: | Chinese geographical science 2015-10, Vol.25 (5), p.549-560 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Characterization of the vertical distribution of soil organic carbon(C), nitrogen(N), and phosphorus(P) may improve our ability to accurately estimate soil C, N, and P storage. Based on a database of 21 354 records in 74 long-term monitoring plots from 2004 to 2013 in the Chinese Ecosystem Research Network(CERN), we built fitting functions to quantify the vertical distribution of soil C, N, and P(up to 100 cm depth) in the typical Chinese terrestrial ecosystems. The decrease of soil C, N, and P content with depth can be well fitted with various mathematical functions. The fitting functions differed greatly between artificial(agriculture) and natural(desert, forest, and grassland) ecosystems, and also differed with respect to soil C, N, and P content. In both the artificial and natural ecosystems, the best fitting functions were exponential functions for C, quadratic functions for N, and quadratic functions for P. Furthermore, the stoichiometric ratios of soil C, N, and P were ranked in descending order: grassland 〉 forest 〉 agriculture 〉 desert, and were also associated with climate. This study is the first to build the fitting functions for the profile distribution of soil C, N, and P in China at a national scale. Our findings provide a scientific basis to accurately assess the storage of C, N, and P in soils at a large scale, especially for the integrative analysis of historical data. |
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ISSN: | 1002-0063 1993-064X |
DOI: | 10.1007/s11769-015-0756-z |