Reducing the uncertainty in estimating soil microbial-derived carbon storage

Area of Science:

• Environmental Science
• Soil Science
• Biogeochemistry

Background:

• Soil organic carbon (SOC) is a critical terrestrial carbon pool vital for climate change mitigation and soil health.
• Microbial-derived carbon (MDC) forms the persistent SOC pool, but its contribution is uncertain.
• Existing MDC estimation formulas lack accuracy due to small sample sizes and unaddressed bacterial group effects.

Purpose of the Study:

• To refine quantitative understanding of microbial-derived carbon (MDC) contributions to soil organic carbon (SOC) storage.
• To develop improved, accurate, and practical formulas for estimating MDC.
• To minimize uncertainties in MDC estimations caused by global variations in bacterial communities.

Main Methods:

• Compilation of a comprehensive global dataset on soil organic carbon and microbial communities.
• Application of machine learning approaches to analyze the dataset and refine estimation models.
• Statistical analysis to quantify MDC contributions and reduce estimation errors.

Main Results:

• MDC contributes approximately 40% (758 Pg) to the global soil carbon stock.
• Relative standard errors in MDC estimations were reduced by an average of 71%.
• The influence of global bacterial group composition variations on MDC estimation was minimized.

Conclusions:

• Updated formulas enhance the accuracy and practicality of estimating microbial-derived carbon.
• Accurate MDC quantification is crucial for improving climate models and predicting land-atmosphere carbon balance.
• This research provides a more robust understanding of carbon cycling in terrestrial ecosystems.