Developing a Carbon Credit Simulator
In recent years, there has been a growing interest in developing agricultural carbon markets and incentivizing farmers to adopt conservation practices (e.g. adopting cover crops, reduced tillage, and variable N rate) to increase carbon storage in soils as well as reduce greenhouse gases (GHGs) emissions. The GHGs removed or avoided, if quantifiable, can be certified as carbon credits to sell on the market, most often to corporations looking for carbon emission offsets to fulfill their sustainability commitments. In Minnesota, both public and private initiatives have been established to seek opportunities in the emerging carbon market. However, being able to make the right choice of practices that would generate the expected carbon credit while maintaining yield has become a major hurdle for farmers in practice. This is challenging because the potential to sequester carbon through those conservation practices can vary substantially across different fields depending on soil, weather, and management history. Minnesota farmers lack a science-based tool to navigate them through the emerging carbon market.
PhD student Yufeng Yang (Bioproducts and Biosystems Science, Engineering, and Management), in a project called “Agricultural Carbon Credit: An Enough Stimulus for Farmers to Go Green or Not,” is aiming to develop a carbon credit simulator (CCS) that integrates a process-based crop model and machine learning to estimate potential carbon credits through conservation practices at every corn and soybean field across Minnesota. The goal of the project is to provide an unbiased, transparent tool for Minnesota farmers and local conservation professionals to estimate potential benefits for individual farming operations.
Some funding for this project was provided by a 2022 University of Minnesota Informatics Institute MnDRIVE PhD Graduate Assistantship. The UMII MnDRIVE Graduate Assistantship program supports U of M PhD candidates pursuing research at the intersection of informatics and any of the five MnDRIVE areas:
- Robotics, Sensors and Advanced Manufacturing
- Global Food Ventures
- Advancing Industry, Conserving Our Environment
- Discoveries and Treatments for Brain Conditions
- Cancer Clinical Trials
This project is part of the Advancing Industry, Conserving Our Environment and Global Food Ventures MnDRIVE areas.
Research Computing partners:
- University of Minnesota Informatics Institute
Complete list of 2022 UMII MnDRIVE PhD Graduate Assistantships.
Image description: The study framework: assessment of carbon credits under different field conservation practices. Data sources include satellite fusion, soil C and N measurements and soil health dataset in Minnesota, and flux tower observations at Rosemount, Minnesota.
