Research Brief: New study could help reduce agricultural greenhouse gas emissions

In this new study, researchers developed a first-of-its-kind knowledge-guided machine learning model for agroecosystem, called KGML-ag that includes less obvious variables such as soil water content, oxygen level, and soil nitrate content related to nitrous oxide production and emission.

A team of researchers led by Licheng Liu from the Department of Bioproducts and Bioengineering (BBE) 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. BBE Assistant Professor Zhenong Jin also contributed significantly to this work.