KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

<p>Agricultural nitrous oxide (<span class="inline-formula">N<sub>2</sub>O</span>) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating <span class="inline-formula">N<sub>2</sub>O&l...

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Bibliographic Details
Main Authors: L. Liu, S. Xu, J. Tang, K. Guan, T. J. Griffis, M. D. Erickson, A. L. Frie, X. Jia, T. Kim, L. T. Miller, B. Peng, S. Wu, Y. Yang, W. Zhou, V. Kumar, Z. Jin
Format: Article
Language:English
Published: Copernicus Publications 2022-04-01
Series:Geoscientific Model Development
Online Access:https://gmd.copernicus.org/articles/15/2839/2022/gmd-15-2839-2022.pdf

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https://gmd.copernicus.org/articles/15/2839/2022/gmd-15-2839-2022.pdf

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