Researchers propose a unified, scalable framework to measure agricultural greenhouse gas emissions
Researchers propose a unified, scalable framework to measure ... University of Illinois Urbana-Champaign
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Increased Government Investment in Climate Change Mitigation Spurs Development of Supercomputing Solution for Measuring Farm Field-Level Greenhouse Gas Emissions
CHAMPAIGN, Ill. — Increased government investment in climate change mitigation is prompting agricultural sectors to find reliable methods for measuring their contribution to climate change. With that in mind, a team led by scientists at the University of Illinois Urbana-Champaign proposed a supercomputing solution to help measure individual farm field-level greenhouse gas emissions.
Introduction
Although locally tested in the Midwest, the new approach can be scaled up to national and global levels and help the industry grasp the best practices for reducing emissions.
Methodology
The new study, directed by natural resources and environmental sciences professor Kaiyu Guan, synthesized more than 25 of the group’s previous studies to quantify greenhouse gas emissions produced by U.S. farmland. The findings – completed in collaboration with partners from the University of Minnesota, Lawrence Berkeley National Laboratory and Project Drawdown, a climate solutions nonprofit organization – are published in the journal Earth Science Reviews.
Supercomputing Solution
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The team built a solution based on “agricultural carbon outcomes,” which it defines as the related changes in greenhouse gas emissions from farmers adopting climate mitigation practices like cover cropping, precision nitrogen fertilizer management, and use of controlled drainage techniques.
“We developed what we call a ‘system of systems’ solution, which means we integrated a variety of sensing techniques and combined them with advanced ecosystem models,” said Bin Peng, co-author of the study and a senior research scientist at the U. of I. Institute for Sustainability, Energy and Environment. “For example, we fuse ground-based imaging with satellite imagery and process that data with algorithms to generate information about crop emissions before and after farmers adopt various mitigation practices.”
“Artificial intelligence also plays a critical role in realizing our ambitious goals to quantify every field’s carbon emission,” said Zhenong Jin, a professor at the University of Minnesota who co-led the study. “Unlike traditional model-data fusion approaches, we used knowledge-guided machine learning, which is a new way to bring together the power of sensing data, domain knowledge, and artificial intelligence techniques.”
Implications and Applications
The study also details how emissions and agricultural practices data can be cross-checked against economic, policy, and carbon market data to find best-practice and realistic greenhouse gas mitigation solutions locally to globally – especially in economies struggling to farm in an environmentally conscious manner.
To compute the vast amount of information from millions of individual farms, the team is using supercomputing platforms available at the National Center for Supercomputing Applications. “Access to the resources at NCSA allows for this monumental task,” Guan said.
“The real beauty of our tool is that it is both very generic and scalable, meaning it can be applied to virtually any agricultural system in any country to obtain reliable emissions data using our targeted procedure and techniques,” Peng said.
Challenges and Future Adoption
The challenge of this work will be encouraging widespread adoption of the system, the researchers said.
“Given the U.S. government’s $19 billion investment in the Inflation Reduction Act and the upcoming Farm Bill, farmers will be able to adopt more conservation practices,” Guan said. “This work will help researchers and policymakers to ‘speak the same language’ by using this tool that we believe is very valuable in this time of increasing government investment in climate mitigation.”
“Bringing more scientific rigor to estimating emissions on farmlands is a huge task. We need credible tools that are simple and practical,” said Paul West, a senior scientist at Project Drawdown and a collaborator on this research. “Our research brings a big step closer to meeting the challenge.”
Funding
The Department of Energy, the National Science Foundation, the Foundation for Food and Agriculture Research, and FoodShot Globe funded this study.
About the Researchers
Guan is the founding director of the Agroecosystems Sustainability Center and is also affiliated with the College of Agricultural, Consumer and Environmental Sciences, the Grainger College of Engineering, the College of Liberal Arts and Sciences, NCSA, and iSEE at Illinois.
SDGs, Targets, and Indicators
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 13: Climate Action | Target 13.2: Integrate climate change measures into national policies, strategies, and planning | Indicator not mentioned in the article |
| SDG 15: Life on Land | Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought, and floods, and strive to achieve a land degradation-neutral world | Indicator not mentioned in the article |
| SDG 2: Zero Hunger | Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters, and that progressively improve land and soil quality | Indicator not mentioned in the article |
1. Which SDGs are addressed or connected to the issues highlighted in the article?
The SDGs that are connected to the issues highlighted in the article are SDG 13: Climate Action, SDG 15: Life on Land, and SDG 2: Zero Hunger.
2. What specific targets under those SDGs can be identified based on the article’s content?
Based on the article’s content, the specific targets that can be identified are:
– Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
– Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought, and floods, and strive to achieve a land degradation-neutral world.
– Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters, and that progressively improve land and soil quality.
3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?
No specific indicators are mentioned or implied in the article that can be used to measure progress towards the identified targets.
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Source: news.illinois.edu
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