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.

中文翻译：

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减少估算土壤微生物碳储量的不确定性


土壤有机碳（SOC）是陆地生态系统中最大的碳库，在减缓气候变化和提高土壤生产力方面发挥着至关重要的作用。微生物源碳（MDC）是持久性SOC库的主要组成部分。然而，由于样本量有限和忽视细菌群组成的影响，目前用于估计 MDC 比例贡献的公式受到不确定性的困扰。在这里，我们编制了全面的全球数据集，并采用机器学习方法来完善我们对 MDC 对总碳储存贡献的定量理解。我们的努力使现行估计的相对标准误差平均减少了 71%，并最大限度地减少了细菌群组成的全球变化对 MDC 估计的影响。我们的估计表明，MDC 贡献了大约 758 Pg，约占全球土壤碳储量的 40%。我们的研究更新了MDC估计的公式，提高了准确性并保持了简单性和实用性。鉴于 MDC 池独特的生物化学和功能，我们的研究对建模工作和预测当前和未来气候情景下的陆地-大气碳平衡具有直接影响。