Artificial intelligence and supercomputers now make it possible to measure the amount of greenhouse gas emissions from every individual farm at a national scale, a new research study shows.
The breakthrough is a critical first step in developing a credible measurement, monitoring, reporting and verification of agricultural emissions, according to a news release. Research findings were recently published in Nature Communications, based on a framework the team published in Earth-Science Reviews.
Funded by the Foundation for Food & Agriculture Research, FoodShot Global, the Department of Energy and the U.S. National Science Foundation, the study was co-led by University of Illinois Urbana-Champaign’s Agroecosystem Sustainability Center Founding Director and Blue Waters professor Kaiyu Guan and University of Minnesota professor Zhenong Jin. The team and collaborators developed the AI and supercomputer solution to quantify the related changes in greenhouse gas emissions from adopting climate mitigation practices like cover cropping and precision nitrogen fertilizer management, according to the release.
“This solution is a scalable, reliable way to measure and predict on individual farm fields the agricultural carbon fluxes, crop yields and changes in soil carbon stocks and can help the industry speak uniformly about best practices for reducing farmland emissions,” Guan said. “This breakthrough could allow the agricultural and food sectors to quantify their carbon footprints in producing or sourcing raw agricultural products so that they can design strategies to reduce [greenhouse gas] in their supply chain and objectively assess different [greenhouse gas]-reducing strategies.”
The research team built their predictive modeling tool using Knowledge-Guided Machine Learning, an emerging machine learning research framework proposed by a group of computer scientists, according to the release. Pioneered by this team, the KGML model for agriculture, or KGML-Ag, uses the power of satellite remote sensing, computational models and AI techniques to cost-effectively produce accurate results more than 10,000 times faster than traditional process-based models, even with limited data, the release said.
“Building the KGML is very challenging due to the need of data and knowledge from various domain.” said Licheng Liu, lead author of the KGML-Ag work and a research scientist at University of Minnesota. “Fortunately, our team brings together the experts in field measurements, domain sciences and AI techniques, allowing us to achieve this significant breakthrough.”
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, the release said.
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, the release said.
“The strength of our tool is that it is both generic and scalable, and it can be potentially applied to different agricultural systems in any country,” said Bin Peng, co-author of the study and an assistant professor at the University of Illinois Crop Sciences Department. “There are many effective farming practices that reduce [greenhouse gas] emissions, but if everyone measures them differently, we’ll never be able to objectively understand how well these practices work. This research helps agriculture stakeholders ‘speak the same language’ about farmland greenhouse gas emissions and will foster more scientific rigor in estimating those emissions.”
