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Reducing the uncertainty in estimating soil microbial-derived carbon storage.

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Title: Reducing the uncertainty in estimating soil microbial-derived carbon storage.
Authors: Han Hu1,2, Chao Qian3,4 cliang823@gmail.com, Ke Xue3,4, Georg Jörgensen, Rainer5, Keiluweit, Marco6, Chao Liang7,8, Xuefeng Zhu7,8, Ji Chen9,10,11, Yishen Sun1,2, Haowei Ni1,2, Jixian Ding1, Weigen Huang1,2, Jingdong Mao12, Rong-Xi Tan3,4, Jizhong Zhou13, Crowther, Thomas W.14, Zhi-Hua Zhou3,4, Jiabao Zhang1, Yuting Liang1,2 ytliang@issas.ac.cn
Source: Proceedings of the National Academy of Sciences of the United States of America. 8/27/2024, Vol. 121 Issue 35, p1-24. 32p.
Subjects: Carbon in soils, Soil productivity, Carbon cycle, Machine learning, Climate change
Abstract: Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors in prevailing estimations by an average of 71% and minimized the effect of global variations in bacterial group compositions on estimating MDC. Our estimation indicates that MDC contributes approximately 758 Pg, representing approximately 40% of the global soil carbon stock. Our study updated the formulas of MDC estimation with improving the accuracy and preserving simplicity and practicality. Given the unique biochemistry and functioning of the MDC pool, our study has direct implications for modeling efforts and predicting the land-atmosphere carbon balance under current and future climate scenarios. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 179594857
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Reducing the uncertainty in estimating soil microbial-derived carbon storage.
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  Data: <searchLink fieldCode="AR" term="%22Han+Hu%22">Han Hu</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Chao+Qian%22">Chao Qian</searchLink><relatesTo>3,4</relatesTo><i> cliang823@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ke+Xue%22">Ke Xue</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Georg+Jörgensen%2C+Rainer%22">Georg Jörgensen, Rainer</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Keiluweit%2C+Marco%22">Keiluweit, Marco</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Chao+Liang%22">Chao Liang</searchLink><relatesTo>7,8</relatesTo><br /><searchLink fieldCode="AR" term="%22Xuefeng+Zhu%22">Xuefeng Zhu</searchLink><relatesTo>7,8</relatesTo><br /><searchLink fieldCode="AR" term="%22Ji+Chen%22">Ji Chen</searchLink><relatesTo>9,10,11</relatesTo><br /><searchLink fieldCode="AR" term="%22Yishen+Sun%22">Yishen Sun</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Haowei+Ni%22">Haowei Ni</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jixian+Ding%22">Jixian Ding</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Weigen+Huang%22">Weigen Huang</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jingdong+Mao%22">Jingdong Mao</searchLink><relatesTo>12</relatesTo><br /><searchLink fieldCode="AR" term="%22Rong-Xi+Tan%22">Rong-Xi Tan</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Jizhong+Zhou%22">Jizhong Zhou</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Crowther%2C+Thomas+W%2E%22">Crowther, Thomas W.</searchLink><relatesTo>14</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhi-Hua+Zhou%22">Zhi-Hua Zhou</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Jiabao+Zhang%22">Jiabao Zhang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yuting+Liang%22">Yuting Liang</searchLink><relatesTo>1,2</relatesTo><i> ytliang@issas.ac.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink>. 8/27/2024, Vol. 121 Issue 35, p1-24. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Carbon+in+soils%22">Carbon in soils</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+productivity%22">Soil productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+cycle%22">Carbon cycle</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors in prevailing estimations by an average of 71% and minimized the effect of global variations in bacterial group compositions on estimating MDC. Our estimation indicates that MDC contributes approximately 758 Pg, representing approximately 40% of the global soil carbon stock. Our study updated the formulas of MDC estimation with improving the accuracy and preserving simplicity and practicality. Given the unique biochemistry and functioning of the MDC pool, our study has direct implications for modeling efforts and predicting the land-atmosphere carbon balance under current and future climate scenarios. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=179594857
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        Value: 10.1073/pnas.2401916121
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        Text: English
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        PageCount: 32
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    Subjects:
      – SubjectFull: Carbon in soils
        Type: general
      – SubjectFull: Soil productivity
        Type: general
      – SubjectFull: Carbon cycle
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Climate change
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