A team led by researchers from the University of Minnesota's Digital
Agriculture Group has significantly improved the performance of
numerical predictions for agricultural nitrous oxide emissions. The
researchers developed the first-of-its-kind knowledge-guided machine
learning model for agroecosystem called KGML-ag.

KGML-ag was constructed using a special procedure that incorporates
knowledge from an advanced agroecosystem computational model, called
ecosys. It includes less obvious variables such as soil water content,
oxygen level, and soil nitrate content related to nitrous oxide
production and emission. In small, real-world observations, the KGML-ag
turns out to be much more accurate than either ecosys or pure machine
learning models and is 1,000 times faster than previously used
computational models.

Compared to GHGs such as carbon dioxide and methane, nitrous oxide is
not as well-known, but it is about 300 times more powerful than carbon
dioxide in trapping heat in the atmosphere. Human-induced nitrous oxide
emissions from agricultural synthetic fertilizer and cattle manure have
also grown by at least 30 percent over the past four decades.

Researchers involved in the study were from the University of Minnesota,
the University of Illinois at Urbana-Champaign, Lawrence Berkeley
National Laboratory, and the University of Pittsburgh.

New study could help reduce agricultural greenhouse gas emissions |
University of Minnesota (umn.edu)
[twin-cities.umn.edu]