| Autor: |
Xiang, Daifeng, Wang, Gangsheng, Tian, Jing, Li, Wanyu |
| Předmět: |
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| Zdroj: |
Nature Communications; 4/15/2023, Vol. 14 Issue 1, p1-14, 14p |
| Abstrakt: |
Knowledge about global patterns of the decomposition kinetics of distinct soil organic matter (SOM) pools is crucial to robust estimates of land-atmosphere carbon fluxes under climate change. However, the current Earth system models often adopt globally-consistent reference SOM decomposition rates (kref), ignoring effects from edaphic-climate heterogeneity. Here, we compile a comprehensive set of edaphic-climatic and SOM decomposition data from published incubation experiments and employ machine-learning techniques to develop models capable of predicting the expected sizes and kref of multiple SOM pools (fast, slow, and passive). We show that soil texture dominates the turnover of the fast pools, whereas pH predominantly regulates passive SOM decomposition. This suggests that pH-sensitive bacterial decomposers might have larger effects on stable SOM decomposition than previously believed. Using these predictive models, we provide a 1-km resolution global-scale dataset of the sizes and kref of these SOM pools, which may improve global biogeochemical model parameterization and predictions. The predictive power of earth system models may be improved by better representation of decomposition processes. Here, the authors use incubation data and machine learning to estimate soil organic matter decomposition kinetic parameters as a reference for global modelling. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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