Knowledge-guided Machine Learning Enables Sustainable Fertilizing Management Practices to Reduce N2O Emission across Multiple Spatial and Temporal Scales
Lexuan Ye1, Licheng Liu2, Yufeng Yang3, Ziyi Li4, Wang Zhou5, Bin Peng5, Zhenong Jin6 and Kaiyu Guan7, (1)University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, Urbana, United States, (2)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, St. Paul, United States, (3)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Minneapolis, MN, United States, (4)University of Illinois at Urbana-Champaign, Urbana, IL, United States, (5)University of Illinois at Urbana Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (6)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, (7)University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States
