In this new study, researchers developed a first-of-its-kind knowledge-guided machine learning model for agroecosystem
A team of researchers led by the University of Minnesota has significantly improved the performance of numerical predictions for agricultural nitrous oxide emissions. The first-of-its-kind knowledge-guided machine learning model is 1,000 times faster than current systems and could significantly reduce greenhouse gas emissions from agriculture.
The research was recently published in Geoscientific Model Development, a not-for-profit international scientific journal focused on numerical models of the Earth. Researchers involved were from the University of Minnesota, the University of Illinois at Urbana-Champaign, Lawrence Berkeley National Laboratory, and the University of Pittsburgh.
Compared to greenhouse gases such as carbon dioxide and methane, nitrous oxide is not as well-known. In reality, nitrous oxide is about 300 times more powerful than carbon dioxide in trapping heat in the atmosphere. Human-induced nitrous oxide emissions (mainly from agricultural synthetic fertilizer and cattle manure) have also grown by at least 30 percent over the past four decades.
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