Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Apr 2022
Development and technical paper |
| 07 Apr 2022
| 07 Apr 2022
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Licheng Liu
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Shaoming Xu
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Jinyun Tang
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Kaiyu Guan
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Timothy J. Griffis
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Matthew D. Erickson
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Alexander L. Frie
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Xiaowei Jia
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA
Taegon Kim
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Department of Smart Farm, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
Lee T. Miller
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Bin Peng
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Shaowei Wu
School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA
Yufeng Yang
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Wang Zhou
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Vipin Kumar
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Zhenong Jin
CORRESPONDING AUTHOR
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
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43 citations as recorded by crossref.
- Advancing agroecosystem modelling of nitrogen losses with machine learning S. Lam et al. https://doi.org/10.1016/j.ecz.2024.100006
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- Process‐Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond M. Wesselkamp et al. https://doi.org/10.1111/ele.70012
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- Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands P. Sharma et al. https://doi.org/10.1073/pnas.2524808123
- Nitrous Oxide Emission Prediction Using IoT Soil and Weather Sensor Data P. Killeen et al. https://doi.org/10.1145/3776747
- Effects of agricultural management and of climate change on N2O emissions in an area of the Brazilian Cerrado: Measurements and simulations using the STICS soil-crop model F. da Silva et al. https://doi.org/10.1016/j.agee.2023.108842
- Nitrogen fertilizers and the future of sustainable agriculture: a deep dive into production, pollution, and mitigation measures M. Tufail et al. https://doi.org/10.1080/00380768.2024.2361068
- A deep transfer learning framework for mapping high spatiotemporal resolution LAI J. Zhou et al. https://doi.org/10.1016/j.isprsjprs.2023.10.017
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- Interoperable agricultural digital twins with reinforcement learning intelligence M. Kallenberg et al. https://doi.org/10.1016/j.atech.2025.101412
- N2Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions W. Yang et al. https://doi.org/10.1088/1748-9326/ae440d
- The Cycles agroecosystem model: Fundamentals, testing, and applications A. Kemanian et al. https://doi.org/10.1016/j.compag.2024.109510
- Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics S. Chen et al. https://doi.org/10.1145/3637491
- Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States H. Wander et al. https://doi.org/10.1021/acs.est.3c05784
- Assessment of greenhouse gas (GHG) emissions in Indonesia using the first order decay (FOD) model: implications of waste bioavailability, biodegradability, and bioactivity R. Permatasari et al. https://doi.org/10.1080/26395940.2025.2539875
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- Hysteretic temperature sensitivity in wetland CH4 emission modeling S. Chen et al. https://doi.org/10.1016/j.agrformet.2025.110704
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- RETRACTED: Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest L. Ye et al. https://doi.org/10.1016/j.agrformet.2025.110750
- Assessing reclamation potential of abandoned drylands using knowledge-guided machine learning (KGML) and remote sensing K. Liu et al. https://doi.org/10.1016/j.watres.2025.124623
- Tracking the future of global N2O gas emissions with data-driven forecasts G. Önder https://doi.org/10.1016/j.jastp.2025.106577
- Agroecosystem modeling and precision agriculture for sustainable nitrogen management J. Yin et al. https://doi.org/10.1016/j.ijagro.2025.100053
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
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- Knowledge‐Guided Machine Learning for Global Change Ecology Research Z. Jin et al. https://doi.org/10.1111/gcb.70742
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- Achieving system-level decoupling in intensive agriculture via zoning-based cleaner production strategies: Insights from the Huang-Huai-Hai plain N. Chang et al. https://doi.org/10.1016/j.jclepro.2026.148517
- Reactive Nitrogen from Agriculture: A Review of Emissions, Air Quality, and Climate Impacts L. Luo et al. https://doi.org/10.1007/s40726-025-00360-y
- Physiological process-based feature engineering enables robust estimation of crop gross primary production under data-limited conditions M. Horikoshi et al. https://doi.org/10.1016/j.agrformet.2026.111191
- Developing a hybrid data-driven and informed model for prediction and mitigation of agricultural nitrous oxide flux hotspots N. Vemuri https://doi.org/10.3389/fenvs.2024.1353049
- Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast P. Xue et al. https://doi.org/10.3390/rs14112640
- Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. https://doi.org/10.1038/s41467-023-43860-5
- Knowledge-guided machine learning with multivariate sparse data for crop growth modelling J. Han et al. https://doi.org/10.1016/j.fcr.2025.109912
- Progress and Perspectives of Crop Yield Forecasting With Remote Sensing: A review G. Xiao et al. https://doi.org/10.1109/MGRS.2025.3571906
43 citations as recorded by crossref.
- Advancing agroecosystem modelling of nitrogen losses with machine learning S. Lam et al. https://doi.org/10.1016/j.ecz.2024.100006
- Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty J. Yang et al. https://doi.org/10.1016/j.postharvbio.2024.113009
- Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al. https://doi.org/10.1016/j.eja.2025.127769
- Process‐Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond M. Wesselkamp et al. https://doi.org/10.1111/ele.70012
- A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q. Yang et al. https://doi.org/10.1016/j.rse.2023.113880
- Causal machine learning methods for understanding land use and land cover change F. Eigenbrod et al. https://doi.org/10.1007/s10980-025-02279-7
- Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Q. Yang et al. https://doi.org/10.1016/j.compag.2024.108911
- Knowledge-guided graph machine learning improves corn yield mapping in the U.S. Midwest J. Yang et al. https://doi.org/10.1016/j.rse.2026.115287
- Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models L. Li et al. https://doi.org/10.1007/s00376-023-3181-8
- Artificial intelligence in environmental remote sensing: Progress, way forward and key considerations C. Maniyar et al. https://doi.org/10.1177/27539687251357020
- Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands P. Sharma et al. https://doi.org/10.1073/pnas.2524808123
- Nitrous Oxide Emission Prediction Using IoT Soil and Weather Sensor Data P. Killeen et al. https://doi.org/10.1145/3776747
- Effects of agricultural management and of climate change on N2O emissions in an area of the Brazilian Cerrado: Measurements and simulations using the STICS soil-crop model F. da Silva et al. https://doi.org/10.1016/j.agee.2023.108842
- Nitrogen fertilizers and the future of sustainable agriculture: a deep dive into production, pollution, and mitigation measures M. Tufail et al. https://doi.org/10.1080/00380768.2024.2361068
- A deep transfer learning framework for mapping high spatiotemporal resolution LAI J. Zhou et al. https://doi.org/10.1016/j.isprsjprs.2023.10.017
- Toward impact-based monitoring of drought and its cascading hazards A. AghaKouchak et al. https://doi.org/10.1038/s43017-023-00457-2
- Transfer learning in environmental remote sensing Y. Ma et al. https://doi.org/10.1016/j.rse.2023.113924
- Interoperable agricultural digital twins with reinforcement learning intelligence M. Kallenberg et al. https://doi.org/10.1016/j.atech.2025.101412
- N2Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions W. Yang et al. https://doi.org/10.1088/1748-9326/ae440d
- The Cycles agroecosystem model: Fundamentals, testing, and applications A. Kemanian et al. https://doi.org/10.1016/j.compag.2024.109510
- Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics S. Chen et al. https://doi.org/10.1145/3637491
- Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States H. Wander et al. https://doi.org/10.1021/acs.est.3c05784
- Assessment of greenhouse gas (GHG) emissions in Indonesia using the first order decay (FOD) model: implications of waste bioavailability, biodegradability, and bioactivity R. Permatasari et al. https://doi.org/10.1080/26395940.2025.2539875
- Applying Knowledge-Guided Machine Learning to Slope Stability Prediction T. Pei et al. https://doi.org/10.1061/JGGEFK.GTENG-11053
- Hysteretic temperature sensitivity in wetland CH4 emission modeling S. Chen et al. https://doi.org/10.1016/j.agrformet.2025.110704
- A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. https://doi.org/10.1016/j.earscirev.2023.104462
- RETRACTED: Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest L. Ye et al. https://doi.org/10.1016/j.agrformet.2025.110750
- Assessing reclamation potential of abandoned drylands using knowledge-guided machine learning (KGML) and remote sensing K. Liu et al. https://doi.org/10.1016/j.watres.2025.124623
- Tracking the future of global N2O gas emissions with data-driven forecasts G. Önder https://doi.org/10.1016/j.jastp.2025.106577
- Agroecosystem modeling and precision agriculture for sustainable nitrogen management J. Yin et al. https://doi.org/10.1016/j.ijagro.2025.100053
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
- Cyberinfrastructure for sustainability sciences C. Song et al. https://doi.org/10.1088/1748-9326/acd9dd
- Integrating machine learning with agroecosystem modelling: Current state and future challenges M. Aderele et al. https://doi.org/10.1016/j.eja.2025.127610
- Knowledge‐Guided Machine Learning for Global Change Ecology Research Z. Jin et al. https://doi.org/10.1111/gcb.70742
- Earth-Economy Modeling: Advances in Linking Economic and Ecosystem Models J. Johnson et al. https://doi.org/10.1146/annurev-resource-013024-033043
- Achieving system-level decoupling in intensive agriculture via zoning-based cleaner production strategies: Insights from the Huang-Huai-Hai plain N. Chang et al. https://doi.org/10.1016/j.jclepro.2026.148517
- Reactive Nitrogen from Agriculture: A Review of Emissions, Air Quality, and Climate Impacts L. Luo et al. https://doi.org/10.1007/s40726-025-00360-y
- Physiological process-based feature engineering enables robust estimation of crop gross primary production under data-limited conditions M. Horikoshi et al. https://doi.org/10.1016/j.agrformet.2026.111191
- Developing a hybrid data-driven and informed model for prediction and mitigation of agricultural nitrous oxide flux hotspots N. Vemuri https://doi.org/10.3389/fenvs.2024.1353049
- Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast P. Xue et al. https://doi.org/10.3390/rs14112640
- Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. https://doi.org/10.1038/s41467-023-43860-5
- Knowledge-guided machine learning with multivariate sparse data for crop growth modelling J. Han et al. https://doi.org/10.1016/j.fcr.2025.109912
- Progress and Perspectives of Crop Yield Forecasting With Remote Sensing: A review G. Xiao et al. https://doi.org/10.1109/MGRS.2025.3571906
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the...
