Carbon credit accuracy for agricultural markets improved by new baseline system testing

New research from Michigan State University, led by agricultural systems scientist Bruno Basso, addresses a major problem in agricultural carbon markets: how to set an accurate starting point, or “baseline,” for measuring climate benefits. Most current systems use fixed baselines that don’t account for the soil carbon changes and emissions that would occur if business-as-usual practices were maintained on fields. Such inaccuracies can distort carbon credit calculations and undermine 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,” said Basso, a John A. Hannah Distinguished Professor in the Department of Earth and Environmental Sciences, the Department of Plant, Soil and Microbial Sciences and the W.K. Kellogg Biological Station at MSU.

“Our dynamic baseline approach provides flexible scenarios that capture the comparative climate impacts of soil organic carbon, or SOC, sequestration and nitrous oxide emissions from business-as-usual practices and the new regenerative system.”

The research, published in the journal Scientific Reports, covers 46 million hectares of cropland across the U.S. Midwest, provides carbon market stakeholders with a scalable, scientifically robust crediting framework. It offers both the investment-grade credibility and operational simplicity needed to expand regenerative agriculture.

Regenerative agriculture and carbon markets

Regenerative agriculture includes practices like cover cropping, reduced or no tillage, diversified rotations, adaptive grazing and agroforestry. These methods restore soil health, enhance biodiversity, increase system resilience and help mitigate climate change by building SOC and reducing greenhouse gas emissions.

Carbon markets offer a promising financial mechanism to accelerate regenerative transitions. By compensating farmers for verified climate benefits, they can act as either offset markets (for external buyers) or inset markets (within agricultural supply chains). However, the integrity of these markets hinges on reliable, science-based measurement, reporting and verification systems that integrate modeling, field data and remote sensing.

A breakthrough multi-model ensemble approach

To overcome limitations in traditional modeling, the MSU scientists and colleagues from different institutions in the U.S. and Europe deployed a multi-model ensemble, or MME, framework, using eight validated crop and biogeochemical models across 40,000 locations in 934 counties spanning 12 Midwestern states. The MME avoids model selection bias, lowering uncertainty in soil carbon predictions from 99% (with single models) to just 36% (with the MME).

“This is a game changer for carbon markets,” said Basso. “It delivers a level of accuracy and scalability—from individual fields to entire regions—that current systems lack.”

The MME platform also enables the creation of precalculated, practice-based dynamic baselines, reducing the burden of data collection and easing participation for producers.

Improved mitigation assessments

Unlike many approaches that consider only SOC, the MSU lead team’s study evaluates both SOC sequestration and nitrous oxide emissions to determine net climate impact.

“This comprehensive assessment ensures that carbon credits represent true climate mitigation,” said Tommaso Tadiello, postdoctoral fellow in MSU’s Department of Earth and Environmental Sciences and co-author of the study.

“A practice that increases soil carbon may improve soil health,” added Basso, “but it may not deliver actual climate benefits if it simultaneously increases nitrous oxide emissions. Our method provides a full accounting of the net climate effect.”

The research team found that the combination of no-till and cover cropping delivered an average net mitigation of 1.2 metric tons of carbon dioxide-equivalent per hectare annually, potentially abating 16.4 teragrams of carbon dioxide-equivalent across the study area.