Michigan State University scientists have created an innovative, scalable system to make carbon markets more reliable and effective, helping farmers adopt regenerative practices for greater climate impact.
In a significant step towards improving agricultural carbon markets, researchers at Michigan State University have developed a more accurate and scalable system for measuring climate benefits from regenerative agriculture practices.
The research, led by agricultural systems scientist Bruno Basso, aims to solve the problem of setting accurate baselines for carbon credit calculations.
Current systems often use fixed baselines that fail to consider soil carbon changes and emissions from standard agricultural practices, which can distort carbon credit calculations. This undermines market trust.
“The choice of baseline can dramatically influence carbon credit generation; if the model is inaccurate, too many or too few credits may be issued, calling market legitimacy into question,” Basso, a John A. Hannah Distinguished Professor at MSU, said in a news release.
Published in the journal Scientific Reports, the study encompasses 46 million hectares of cropland across 12 Midwestern states. The research provides stakeholders with a scalable, scientifically robust framework that offers both credibility and simplicity, enabling broader adoption of regenerative agriculture.
Addressing Carbon Markets and Regenerative Agriculture
Regenerative agriculture practices, such as cover cropping, reduced tillage and diversified crop rotations, help restore soil health, enhance biodiversity and reduce greenhouse gas emissions. Carbon markets offer a financial mechanism to accelerate the transition to these sustainable practices by compensating farmers for verified climate benefits.
However, the effectiveness of these markets depends on reliable, science-based measurement systems. The MSU team’s innovative approach integrates multiple models, field data and remote sensing to provide a comprehensive and accurate assessment of these benefits.
A Breakthrough Multi-Model Ensemble Approach
To improve the accuracy of soil carbon predictions, the MSU scientists used a multi-model ensemble (MME) framework involving eight validated crop and biogeochemical models.
This approach covers 40,000 locations in 934 counties across the Midwest. The MME reduces uncertainty in soil carbon predictions from 99% (with single models) to 36%.
“This is a game changer for carbon markets,” added Basso. “It delivers a level of accuracy and scalability — from individual fields to entire regions — that current systems lack.”
The MME system allows for the creation of practice-based dynamic baselines, reducing data collection burdens and making it easier for farmers to participate.
Comprehensive Climate Impact Assessment
Unlike many other methods, the MSU-led study evaluates both soil organic carbon (SOC) sequestration and nitrous oxide emissions to determine the net climate impact. This comprehensive approach ensures that carbon credits reflect true climate mitigation.
“This comprehensive assessment ensures that carbon credits represent true climate mitigation,” added co-author Tommaso Tadiello, a postdoctoral fellow in MSU’s Department of Earth and Environmental Sciences.
“A practice that increases soil carbon may improve soil health, but it may not deliver actual climate benefits if it simultaneously increases nitrous oxide emissions,” Basso added. “Our method provides a full accounting of the net climate effect.”
The study found that a combination of no-till and cover cropping could deliver an average net mitigation of 1.2 metric tons of carbon dioxide-equivalent per hectare annually, potentially reducing 16.4 teragrams of carbon dioxide-equivalent in the study area.
Source: Michigan State University