PMID- 36493831
OWN - NLM
STAT- MEDLINE
DCOM- 20230119
LR  - 20230119
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 862
DP  - 2023 Mar 1
TI  - Cropland carbon stocks driven by soil characteristics, rainfall and elevation.
PG  - 160602
LID - S0048-9697(22)07705-1 [pii]
LID - 10.1016/j.scitotenv.2022.160602 [doi]
AB  - Soil organic carbon (SOC) can influence atmospheric CO(2) concentration and then 
      the extent to which the climate emergency is mitigated globally. It follows the 
      elucidation of the driving factors of cropland SOC stocks, which is fundamental 
      to reducing soil carbon loss and promoting soil carbon sequestration. Here, we 
      examined the influence of 16 environmental variables on SOC stocks and 
      sequestration based on three machine learning soil mapping methods, i.e. multiple 
      linear regression (MLR), random forest (RF) and extreme gradient boosting 
      (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan 
      Province, China in 2010. We employed a structural equation model (SEM) to 
      extricate the driving mechanisms of environmental variables on SOC stocks at the 
      regional scale. Our results show that XGBOOST had the most reliable performance 
      in predicting SOC stocks, explaining 66 % of the total SOC stock variation. 
      Croplands with high SOC stocks were distributed in low-altitude and 
      water-sufficient areas. The partial dependence of SOC on precipitation showed a 
      trend of increasing and then slowly decreasing. In addition, the grid-based SEM 
      results clearly presented the direct and indirect routes of environmental 
      variables' impacts on cropland SOC stocks. Soil properties regulated by 
      elevation, were the most influential natural factor on SOC stocks. Precipitation 
      and elevation drove SOC stocks through direct and indirect effects respectively. 
      Our SEM combined with machine learning approach can provide an effective 
      explanation of the driving mechanism for SOC accumulation. We expect our proposed 
      modelling approach can be applied to other regions and offer new insights, as a 
      reference for mitigating cropland soil carbon loss under climate emergency 
      conditions.
CI  - Copyright (c) 2022 Elsevier B.V. All rights reserved.
FAU - Chen, Fangzheng
AU  - Chen F
AD  - College of Land Science and Technology, China Agricultural University, Key 
      Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, 
      Beijing, PR China.
FAU - Feng, Puyu
AU  - Feng P
AD  - College of Land Science and Technology, China Agricultural University, Key 
      Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, 
      Beijing, PR China. Electronic address: fengpuyu@cau.edu.cn.
FAU - Harrison, Matthew Tom
AU  - Harrison MT
AD  - Tasmanian Institute of Agriculture, University of Tasmania, Newnham, Launceston, 
      Tasmania 7248, Australia.
FAU - Wang, Bin
AU  - Wang B
AD  - New South Wales Department of Primary Industries, Wagga Wagga Agriculture 
      Institute, Wagga Wagga, New South Wales 2650, Australia.
FAU - Liu, Ke
AU  - Liu K
AD  - Tasmanian Institute of Agriculture, University of Tasmania, Newnham, Launceston, 
      Tasmania 7248, Australia; Engineering Research Center of Ecology and Agricultural 
      Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, 
      Hubei, China.
FAU - Zhang, Chenxia
AU  - Zhang C
AD  - College of Land Science and Technology, China Agricultural University, Key 
      Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, 
      Beijing, PR China.
FAU - Hu, Kelin
AU  - Hu K
AD  - College of Land Science and Technology, China Agricultural University, Key 
      Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, 
      Beijing, PR China.
LA  - eng
PT  - Journal Article
DEP - 20221207
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
RN  - 0 (Soil)
RN  - 7440-44-0 (Carbon)
SB  - IM
MH  - *Soil/chemistry
MH  - *Carbon/chemistry
MH  - Carbon Sequestration
MH  - Altitude
MH  - Crops, Agricultural
OTO - NOTNLM
OT  - Cropland
OT  - Driving mechanisms
OT  - Machine learning
OT  - Soil organic carbon
OT  - Structural equation modelling
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/12/10 06:00
MHDA- 2023/01/20 06:00
CRDT- 2022/12/09 19:32
PHST- 2022/09/24 00:00 [received]
PHST- 2022/11/21 00:00 [revised]
PHST- 2022/11/26 00:00 [accepted]
PHST- 2022/12/10 06:00 [pubmed]
PHST- 2023/01/20 06:00 [medline]
PHST- 2022/12/09 19:32 [entrez]
AID - S0048-9697(22)07705-1 [pii]
AID - 10.1016/j.scitotenv.2022.160602 [doi]
PST - ppublish
SO  - Sci Total Environ. 2023 Mar 1;862:160602. doi: 10.1016/j.scitotenv.2022.160602. 
      Epub 2022 Dec 7.