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Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020
It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for a...
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Published in: | Land (Basel) 2024-11, Vol.13 (11), p.1764 |
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description | It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources. |
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Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.</description><identifier>ISSN: 2073-445X</identifier><identifier>EISSN: 2073-445X</identifier><identifier>DOI: 10.3390/land13111764</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agricultural land ; Carbon content ; Carbon cycle ; Carbon cycle (Biogeochemistry) ; Carbon sequestration ; China ; Climate action ; Climate change ; Climatic changes ; Computer centers ; Correlation coefficient ; Correlation coefficients ; Datasets ; Ecological effects ; Forest management ; Geospatial data ; Global warming ; Grasslands ; Land resources ; Land use ; Organic carbon ; Organic soils ; Precipitation ; random forest ; Regions ; SOC ; Soil structure ; Soils ; Temperature effects ; Terrestrial ecosystems ; Vegetation</subject><ispartof>Land (Basel), 2024-11, Vol.13 (11), p.1764</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c293t-3d9bf9059e9223fce1d163435fb0a00ef5a7a39f9a165640f41b85b2bc0e733e3</cites><orcidid>0000-0002-2605-4774 ; 0000-0002-4168-6462 ; 0000-0003-4976-8881</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3133074960/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3133074960?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Zou, Fuyan</creatorcontrib><creatorcontrib>Yan, Min</creatorcontrib><creatorcontrib>Zhang, Liankai</creatorcontrib><creatorcontrib>Yang, Jinjiang</creatorcontrib><creatorcontrib>Chen, Guiren</creatorcontrib><creatorcontrib>Shan, Keqiang</creatorcontrib><creatorcontrib>Zhang, Chen</creatorcontrib><creatorcontrib>Xu, Xiongwei</creatorcontrib><creatorcontrib>Wang, Zhenhui</creatorcontrib><creatorcontrib>Xu, Can</creatorcontrib><title>Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020</title><title>Land (Basel)</title><description>It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.</description><subject>Agricultural land</subject><subject>Carbon content</subject><subject>Carbon cycle</subject><subject>Carbon cycle (Biogeochemistry)</subject><subject>Carbon sequestration</subject><subject>China</subject><subject>Climate action</subject><subject>Climate change</subject><subject>Climatic changes</subject><subject>Computer centers</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Datasets</subject><subject>Ecological effects</subject><subject>Forest management</subject><subject>Geospatial data</subject><subject>Global warming</subject><subject>Grasslands</subject><subject>Land resources</subject><subject>Land use</subject><subject>Organic carbon</subject><subject>Organic soils</subject><subject>Precipitation</subject><subject>random forest</subject><subject>Regions</subject><subject>SOC</subject><subject>Soil structure</subject><subject>Soils</subject><subject>Temperature effects</subject><subject>Terrestrial ecosystems</subject><subject>Vegetation</subject><issn>2073-445X</issn><issn>2073-445X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU1LBDEMhgdRUNSbP6Dg1dVk0s5Mj7J-guBBBW8lM9OOXXZb7YyC_96uK7LJISG8eRKSojhBOCfScLHk0CMhYl3JneKghJpmUqrX3a18vzgexwVk00iNVAfF69V34JXvxPyNw2BH4YPIIHGV_JcPg7jhboppFNGJp-iX4jENHNZyTm0Ma_X8zQcWLsWVKAFQTDHHEo6KPcfL0R7_xcPi5eb6eX43e3i8vZ9fPsy6UtM0o163ToPSVpcluc5ijxVJUq4FBrBOcc2knWasVCXBSWwb1ZZtB7YmsnRY3G-4feSFeU9-xenbRPbmtxDTYDhNvltaUzbSkVQoXZ5QI2rgWiuGpoWee9lk1umG9Z7ix6cdJ7OInynk9Q0hEdRSV5BV5xvVwBnqg4tT4i57b_MhY7DO5_plgw2RlJpyw9mmoUtxHJN1_2simPXvzPbv6AeRyodu</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Zou, Fuyan</creator><creator>Yan, Min</creator><creator>Zhang, Liankai</creator><creator>Yang, Jinjiang</creator><creator>Chen, Guiren</creator><creator>Shan, Keqiang</creator><creator>Zhang, Chen</creator><creator>Xu, Xiongwei</creator><creator>Wang, Zhenhui</creator><creator>Xu, Can</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2605-4774</orcidid><orcidid>https://orcid.org/0000-0002-4168-6462</orcidid><orcidid>https://orcid.org/0000-0003-4976-8881</orcidid></search><sort><creationdate>20241101</creationdate><title>Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020</title><author>Zou, Fuyan ; 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Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/land13111764</doi><orcidid>https://orcid.org/0000-0002-2605-4774</orcidid><orcidid>https://orcid.org/0000-0002-4168-6462</orcidid><orcidid>https://orcid.org/0000-0003-4976-8881</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural land Carbon content Carbon cycle Carbon cycle (Biogeochemistry) Carbon sequestration China Climate action Climate change Climatic changes Computer centers Correlation coefficient Correlation coefficients Datasets Ecological effects Forest management Geospatial data Global warming Grasslands Land resources Land use Organic carbon Organic soils Precipitation random forest Regions SOC Soil structure Soils Temperature effects Terrestrial ecosystems Vegetation |
title | Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020 |
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