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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]
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Database: Engineering Source
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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]
ISSN:00278424
DOI:10.1073/pnas.2401916121