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Tytuł pozycji:

Decipher soil organic carbon dynamics and driving forces across China using machine learning.

Tytuł:
Decipher soil organic carbon dynamics and driving forces across China using machine learning.
Autorzy:
Li H; Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, China.; Key Laboratory of Degraded and Unused Land Consolidation Engineering, The Ministry of Natural Resources of China, Xi'an, China.
Wu Y; Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, China.; Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co. Ltd, Xi'an Jiaotong University, Xi'an, China.
Liu S; National Engineering Laboratory for Applied Technology of Forestry and Ecology in South China, Central South University of Forestry and Technology, Changsha, China.
Xiao J; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, USA.
Zhao W; Key Laboratory of Ecohydrology and River Basin Science, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou, China.
Chen J; Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.
Alexandrov G; A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russian Federation.
Cao Y; Xi'an Institute for Innovative Earth Environment Research, Xi'an, China.
Źródło:
Global change biology [Glob Chang Biol] 2022 May; Vol. 28 (10), pp. 3394-3410. Date of Electronic Publication: 2022 Mar 16.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Publication: : Oxford : Blackwell Pub.
Original Publication: Oxford, UK : Blackwell Science, 1995-
MeSH Terms:
Carbon*/analysis
Soil*
Carbon Sequestration ; China ; Machine Learning
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Contributed Indexing:
Keywords: SOC; climate change; factorial simulation experiments; fertilization; random forest; vegetation growth
Substance Nomenclature:
0 (Soil)
7440-44-0 (Carbon)
Entry Date(s):
Date Created: 20220307 Date Completed: 20220415 Latest Revision: 20220514
Update Code:
20240104
DOI:
10.1111/gcb.16154
PMID:
35253325
Czasopismo naukowe
The dynamics of soil organic carbon (SOC) play a critical role in modulating global warming. However, the long-term spatiotemporal changes of SOC at large scale, and the impacts of driving forces remain unclear. In this study, we investigated the dynamics of SOC in different soil layers across China through the1980s to 2010s using a machine learning approach and quantified the impacts of the key factors based on factorial simulation experiments.Our results showed that the latest (2000-2014) SOC stock in the first meter soil (SOC 100 ) was 80.68 ± 3.49 Pg C, of which 42.6% was stored in the top 20 cm, sequestrating carbon with a rate of 30.80 ± 12.37 g C m -2  yr -1 since the 1980s. Our experiments focusing on the recent two periods (2000s and 2010s) revealed that climate change exerted the largest relative contributions to SOC dynamics in both layers and warming or drying can result in SOC loss. However, the influence of climate change weakened with soil depth, while the opposite for vegetation growth. Relationships between SOC and forest canopy height further confirmed this strengthened impact of vegetation with soil depth and highlighted the carbon sink function of deep soil in mature forest. Moreover, our estimates suggested that SOC dynamics in 71% of topsoil were controlled by climate change and its coupled influence with environmental variation (CE). Meanwhile, CE and the combined influence of climate change and vegetation growth dominated the SOC dynamics in 82.05% of the first meter soil. Additionally, the national cropland topsoil organic carbon increased with a rate of 23.6 ± 7.6 g C m -2  yr -1 since the 1980s, and the widely applied nitrogenous fertilizer was a key stimulus. Overall, our study extended the knowledge about the dynamics of SOC and deepened our understanding about the impacts of the primary factors.
(© 2022 John Wiley & Sons Ltd.)

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