Microbial-based measurement and modeling of agricultural carbon sequestration

Overview

This research addresses the challenge of accurately measuring and modeling agricultural carbon sequestration by focusing on the microbial mechanisms that drive soil organic carbon (SOC) persistence. The approach couples microbial activity metrics with soil organic matter (SOM) fractionation and advanced modeling to quantify how agricultural practices contribute to long-term soil carbon storage. By evaluating practices through microbial and biogeochemical lenses, the work enables more reliable assessment of carbon sequestration efficacy in agricultural systems.

Technical specifications

Core measurement framework:

• Microbial carbon use efficiency (CUE) measured by quantifying the incorporation of 13C-labeled substrate into microbial biomass versus loss as CO2
• Microbial growth and turnover assessed through microbial uptake of heavy water (H218O) into microbial DNA
• Microbial necromass quantified using amino sugar biomarkers, capturing the persistent carbon left behind after microbial death
• SOM fractionation combining particle size-based separation with density-based techniques (sonication, wet-sieving, and centrifugation) to isolate particulate and mineral-associated organic matter fractions
• Soil carbon modeling using a next-generation SOM model that integrates microbial contributions to SOM stabilization, including the Millennial model framework

Key insight: Mineral-associated organic matter, derived primarily from microbial necromass, correlates more strongly with persistent SOC than particulate organic matter, making microbial proxies effective indicators of long-term carbon storage potential.

Technology readiness level

This research is at an early-to-mid stage of development. Future validation will be conducted using soil samples from the Oxford College Organic Farm at Emory University, where organic farming practices have been maintained since 2014, providing a well-characterized long-term experimental site. The approach integrates established microbial tracing methods (13C labeling, H218O uptake) with proven SOM fractionation techniques and computational modeling. Further validation through field-scale application and cross-site comparison would advance the framework toward broader deployment.