Abstract

High-quality agricultural carbon credits that incentivize regenerative practices can help address climate change through greenhouse gas (GHG) abatement and CO2 sequestration. Generating large volumes of such credits requires rigorous crediting methodologies. The Soil Enrichment Protocol (SEP) by the Climate Action Reserve (CAR) aims to unlock this type of crediting potential. The SEP includes new expert-driven standards for validating the use of soil biogeochemical modeling to generate credits. Technical experts at Indigo Ag participated in the SEP working group and are supporting implementation of the first project, CAR 1459_RP1, on hundreds of thousands of acres in the US. The authors share their thoughts on new approaches enabled by the SEP as both contributors to the theory behind and practitioners of these approaches. The SEP enables scalable, high-quality credits through four main advances: (1) allowing flexibility in the use of biogeochemical models that meet explicit performance requirements, (2) enabling a new approach to field-level, modeled baselines, (3) supporting a hybrid approach of credit generation using both soil measurement and modeling, and (4) requiring a new type of credit uncertainty quantification that accounts for multiple sources of uncertainty. Together these advances support agricultural credit quantification that enables payments to offset transitional costs for growers, at large enough scales to create a robust market, with a level of rigor that ensures any credited emission reductions have real climate impact. Innovations in soil analyses, advances in research, and improvements in data collection could further improve the potential for agricultural carbon credits to scale.

Highlights

  • To ensure long-term success at a global scale, carbon markets must be based on confidence in driving emissions reductions

  • To support large-scale practice change, growers need to be directly compensated for the carbon credits they generate on their operations

  • The registry engaged with expert working groups that represented multiple perspectives, including growers, scientists, environmental NGOs, and industry representatives, and included public comment periods. This process resulted in an independently validated, publicly available, and scalable methodology with four critical advances from previous protocols: (1) a flexible approach in soil biogeochemical model use within a single project, (2) a new approach to generate field-level, modeled baselines, (3) a combined measurement and modeling approach to credit generation, and (4) a novel uncertainty quantification approach. These advances enable the Soil Enrichment Protocol (SEP) to yield high-quality carbon credits from the quantification and verification of greenhouse gas (GHG) emission reductions associated with soil enrichment projects on agricultural lands

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Summary

INTRODUCTION

To ensure long-term success at a global scale, carbon markets must be based on confidence in driving emissions reductions. The registry engaged with expert working groups that represented multiple perspectives, including growers, scientists, environmental NGOs, and industry representatives, and included public comment periods This process resulted in an independently validated, publicly available, and scalable methodology with four critical advances from previous protocols: (1) a flexible approach in soil biogeochemical model use within a single project, (2) a new approach to generate field-level, modeled baselines, (3) a combined measurement and modeling approach to credit generation, and (4) a novel uncertainty quantification approach. These advances enable the SEP to yield high-quality carbon credits from the quantification and verification of GHG emission reductions associated with soil enrichment projects on agricultural lands. This approach creates a baseline using static historic data and incorporates dynamic modeling to re-calculate the baseline every year in response to ongoing real-world changes in project crop rotation and weather (see Supplementary Materials for further discussion)

A Hybrid Sampling and Modeling Approach
CONCLUSION
DATA AVAILABILITY STATEMENT
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