土壤学
孵化
土壤碳
环境科学
分解
动力学
碳纤维
碳循环
化学
环境化学
土壤科学
生态学
生态系统
土壤水分
生物
数学
生物化学
物理
算法
量子力学
复合数
作者
Daifeng Xiang,Gangsheng Wang,Jing Tian,Wanyu Li
标识
DOI:10.1038/s41467-023-37900-3
摘要
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.
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