For the first time, researchers at the University of Minnesota Twin Cities (UMN) and the University of Illinois Urbana-Champaign (UIUC) have demonstrated that it is possible to provide accurate, high-resolution predictions of carbon cycles in agroecosystems, which could help mitigate the impacts of climate change.
The study by scholars from the UMN-led National Artificial Intelligence Institute for Climate-Land Interactions, Mitigation, Adaptation, Tradeoffs and Economy (AI-CLIMATE) and UIUC-led Agroecosystem Sustainability Center was recently published in Nature Communications, a peer-reviewed, open access, scientific journal.
The study’s findings are a critical first step in developing a credible Measurement, Monitoring, Reporting, and Verification (MMRV) of agricultural emissions that can be used to incentivize the implementation of climate smart practices while boosting rural economies. This follows the national strategy, set by the White House, highlighting the need to quantify greenhouse gas emission across sectors with a goal of net-zero emissions by no later than 2050.
Accurate, scalable, and cost-effective monitoring and reporting of greenhouse gas emissions are needed to verify what are called “carbon credits” or permits that offset greenhouse gas emissions. Farmers can be reimbursed for practices that reduce greenhouse gas emissions. Agriculture accounts for about 25 percent of greenhouse gas emissions, but large corporations can be hesitant about purchasing these credits without knowing how much carbon is being stored.
Right now, to accurately gather carbon data, a farmer would need to hire someone to come to their farm, take what is called a soil core (vertical profile of the soil), and send that back to the lab for analysis.
“To gather the amount of data needed at each individual farm, it could cost the farmers time and money that they may not be willing to give,” said Licheng Liu, the lead author and a research scientist in the University of Minnesota Department of Bioproducts and Biosystems Engineering.
The emerging field of Knowledge-Guided Machine Learning (KGML), pioneered by researchers at the University of Minnesota, combines the strength of artificial intelligence (AI) and process-based models from physical sciences. With observations in the United States Corn Belt, the KGML-ag framework significantly surpasses both process-based and pure machine learning models in accuracy, especially with limited data.
Remarkably, KGML-ag operates over 10,000 times faster than traditional process-based models, delivering high-resolution and high-frequency predictions cost-effectively.
"These knowledge-guided machine learning (KGML) techniques are fundamentally more powerful than standard machine learning approaches and traditional models used by the scientific community to address environmental problems,” said Vipin Kumar, a University of Minnesota Regents Professor and William Norris Endowed Chair in the Department of Computer Science and Engineering and a researcher in the AI-CLIMATE Institute, whose group has pioneered the development of the KGML framework.
Instead of taking soil cores at every farm, with KGML-ag, researchers can use the power of satellite remote sensing, computational models, and AI to provide an estimate of carbon in each individual field. This allows for compensation to individual farmers that are fair and accurate. The researchers say this is key to fostering trust in carbon markets and supporting the adoption of sustainable practices.
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Categories: Illinois, Energy