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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Atmos. Chem. Phys., 26, 703–721, https://doi.org/10.5194/acp-26-703-2026, https://doi.org/10.5194/acp-26-703-2026, 2026
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We estimate ammonia fluxes over the contiguous U.S. from 2008 to 2022 using a directional derivative approach applied to satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS). The resulting flux estimates reveal pronounced seasonal and spatial patterns driven by agricultural activities and indicate substantial local deposition to surrounding vegetation, underscoring the need for improved monitoring and management strategies.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, John R. Evans, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Robert T. Furbank, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Viridiana Silva-Perez, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data, 18, 245–265, https://doi.org/10.5194/essd-18-245-2026, https://doi.org/10.5194/essd-18-245-2026, 2026
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We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5448, https://doi.org/10.5194/egusphere-2025-5448, 2025
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Fan Sun, Yu Cui, Jiayin Su, Mark W. Shephard, Shailesh K. Kharol, Yifan Zhang, Xuejing Shi, Junqing Zhang, Huili Liu, Qitao Xiao, Xiao Lu, Zhao-Cheng Zeng, Timothy J. Griffis, and Cheng Hu
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Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci., 29, 6393–6417, https://doi.org/10.5194/hess-29-6393-2025, https://doi.org/10.5194/hess-29-6393-2025, 2025
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Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He
Earth Syst. Sci. Data, 17, 2933–2952, https://doi.org/10.5194/essd-17-2933-2025, https://doi.org/10.5194/essd-17-2933-2025, 2025
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Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data, 17, 2713–2733, https://doi.org/10.5194/essd-17-2713-2025, https://doi.org/10.5194/essd-17-2713-2025, 2025
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We have developed new maps that reveal how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2500 gauges and a wealth of climate and environmental information. The maps are a critical step in understanding and predicting how carbon moves through our environment, hence making them a useful tool for tackling climate challenges.
Jinyun Tang and William J. Riley
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Jinyun Tang and William J. Riley
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Jinyun Tang, William J. Riley, and Qing Zhu
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Cheng Hu, Jiaping Xu, Cheng Liu, Yan Chen, Dong Yang, Wenjing Huang, Lichen Deng, Shoudong Liu, Timothy J. Griffis, and Xuhui Lee
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Chongya Jiang, Kaiyu Guan, Genghong Wu, Bin Peng, and Sheng Wang
Earth Syst. Sci. Data, 13, 281–298, https://doi.org/10.5194/essd-13-281-2021, https://doi.org/10.5194/essd-13-281-2021, 2021
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Photosynthesis, quantified by gross primary production (GPP), is a key Earth system process. To date, there is a lack of a high-spatiotemporal-resolution, real-time and observation-based GPP dataset. This work addresses this gap by developing a SatelLite Only Photosynthesis Estimation (SLOPE) model and generating a new GPP product, which is advanced in spatial and temporal resolutions, instantaneity, and quantitative uncertainty. The dataset will benefit a range of research and applications.
Cited articles
Barton, L., Wolf, B., Rowlings, D., Scheer, C., Kiese, R., Grace, P., Grace, P., Stefanova, K., and Butterbach-Bahl, K.: Sampling frequency affects estimates of annual nitrous oxide fluxes, Scientific Reports, 5, 15912, https://doi.org/10.1038/srep15912, 2015.
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nature Computational Science, 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021.
Beucler, T., Rasp, S., Pritchard, M., and Gentine, P.: Achieving conservation of energy in neural network emulators for climate modeling, arXiv [preprint], arXiv:1906.06622, 2019.
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing analytic constraints in neural networks emulating physical systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021.
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R., and Zechmeister-Boltenstern, S.: Nitrous oxide emissions from soils: how well do we understand the processes and their controls?, Philos. T. Roy. Soc. B, 368, 20130122, https://doi.org/10.1098/rstb.2013.0122, 2013.
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches, arXiv [preprint], arXiv:1409.1259, 2014.
Chung, J., Gulcehre, C., Cho, K. H., and Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv [preprint], arXiv:1412.3555, 2014.
Davidson, E. A., Keller, M., Erickson, H. E., Verchot, L. V., and Veldkamp, E.: Testing a conceptual model of soil emissions of nitrous and nitric oxides: using two functions based on soil nitrogen availability and soil water content, the hole-in-the-pipe model characterizes a large fraction of the observed variation of nitric oxide and nitrous oxide emissions from soils, Bioscience, 50, 667–680, https://doi.org/10.1641/0006-3568(2000)050[0667:TACMOS]2.0.CO;2, 2000.
Del Grosso, S. J., Parton, W. J., Mosier, A. R., Ojima, D. S., Kulmala, A. E., and Phongpan, S.: General model for N2O and N2 gas emissions from soils due to dentrification, Global Biogeochem. Cy., 14, 1045–1060, https://doi.org/10.1029/1999GB001225, 2020.
Fassbinder, J. J., Griffis, T. J., and Baker, J. M.: Evaluation of carbon isotope flux partitioning theory under simplified and controlled environmental conditions, Agr. Forest Meteorol., 153, 154–164, https://doi.org/10.1016/j.agrformet.2011.09.020, 2012.
Fassbinder, J. J., Schultz, N. M., Baker, J. M., and Griffis, T. J.: Automated, Low-Power Chamber System for Measuring Nitrous Oxide Emissions, J. Environ. Qual., 42, 606, https://doi.org/10.2134/jeq2012.0283, 2013.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Open Access Te Herenga Waka-Victoria University of Wellington, https://doi.org/10.25455/wgtn.16869671.v1 (last access: 26 October 2021), 2021.
Grant, R. F.: A review of the Canadian ecosystem model ecosys: Modeling Carbon and Nitrogen Dynamics for Soil Management, 1st edn., CRC Press, Boca Raton, FL, USA, 173–264, ISBN 1566705290, 2001.
Grant, R. F. and Pattey, E.: Mathematical modeling of nitrous oxide emissions from an agricultural field during spring thaw, Global Biogeochem. Cy., 13, 679–694, https://doi.org/10.1029/1998GB900018, 1999.
Grant, R. F. and Pattey, E.: Modelling variability in N2O emissions from fertilized agricultural fields, Soil Biol. Biochem., 35, 225–243, https://doi.org/10.1016/S0038-0717(02)00256-0, 2003.
Grant, R. F. and Pattey, E.: Temperature sensitivity of N2O emissions from fertilized agricultural soils: Mathematical modeling in ecosys, Global Biogeochem. Cy., 22, GB4019, https://doi.org/10.1029/2008GB003273, 2008.
Grant, R. F., Pattey, E., Goddard, T. W., Kryzanowski, L. M., and Puurveen, H.: Modeling the effects of fertilizer application rate on nitrous oxide emissions, Soil Sci. Soc. Am. J., 70, 235–248, https://doi.org/10.2136/sssaj2005.0104, 2006.
Grant, R. F., Black, T. A., Jassal, R. S., and Bruemmer, C.: Changes in net ecosystem productivity and greenhouse gas exchange with fertilization of Douglas fir: Mathematical modeling in ecosys, J. Geophys. Res.-Biogeo., 115, G04009, https://doi.org/10.1029/2009JG001094, 2010.
Grant, R. F., Neftel, A., and Calanca, P.: Ecological controls on N2O emission in surface litter and near-surface soil of a managed grassland: modelling and measurements, Biogeosciences, 13, 3549–3571, https://doi.org/10.5194/bg-13-3549-2016, 2016.
Hamrani, A., Akbarzadeh, A., and Madramootoo, C. A.: Machine learning for predicting greenhouse gas emissions from agricultural soils, Sci. Total Environ., 741, 140338, https://doi.org/10.1016/j.scitotenv.2020.140338, 2020.
Hanson, P. C., Stillman, A. B., Jia, X., Karpatne, A., Dugan, H. A., Carey, C. C., Stachelek, J., Ward, N. K., Zhang, Y., Read, J. S., and Kumar, V.: Predicting lake surface water phosphorus dynamics using process-guided machine learning, Ecol. Model., 430, 109136, https://doi.org/10.1016/j.ecolmodel.2020.109136, 2020.
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., Moore, A. D., Brown, H., Whish, J. P. M., Verrall, S., Fainges, J., Bell, L. W., Peake, A. S., Poulton, P. L., Hochman, Z., Thorburn, P. J., Gaydon, D. S., Dalgliesh, N. P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F. Y., Wang, E., Hammer, G. L., Robertson, M. J., Dimes, J. P., Whitbread, A. M., Hunt, J., van Rees, H., McClelland, T., Carberry, P. S., Hargreaves, J. N. G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., and Keating, B. A.: APSIM – evolution towards a new generation of agricultural systems simulation, Environ. Modell. Softw., 62, 327–350, https://doi.org/10.1016/j.envsoft.2014.07.009, 2014.
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., and Saynisch-Wagner, J.: Towards neural Earth system modelling by integrating artificial intelligence in Earth system science, Nature Machine Intelligence, 3, 667–674, https://doi.org/10.1038/s42256-021-00374-3, 2021.
Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J., Steinbach, M., and Kumar, V.: Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles, in: Proceedings of the 2019 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Calgary, Alberta, Canada, 2–4 May 2019, 558–566, https://doi.org/10.1137/1.9781611975673.58, 2019.
Jia, X., Willard, J., Karpatne, A., Read, J. S., Zwart, J. A., Steinbach, M., and Kumar, V.: Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles, ACM/IMS Transactions on Data Science, 2, 20, https://doi.org/10.1145/3447814, 2021.
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., and Kumar, V.: Theory-guided data science: A new paradigm for scientific discovery from data, IEEE T. Knowl. Data En., 29, 2318–2331, 2017.
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N., Meinke, H., Hochman, Z., and McLean, G.: An overview of APSIM, a model designed for farming systems simulation, Eur. J. Agron., 18, 267–288, https://doi.org/10.1016/s1161-0301(02)00108-9, 2003.
Khandelwal, A., Xu, S., Li, X., Jia, X., Stienbach, M., Duffy, C., Nieber, J. and Kumar, V.: Physics guided machine learning methods for hydrology, arXiv [preprint], arXiv:2012.02854, 2020.
Kim, T., Jin, Z., Smith, T. M., Liu, L., Yang, Y., Yang, Y., Peng, B., Phillips, K., Guan, K., Hunter, L. C., and Zhou, W.: Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach, Environ. Res. Lett., 16, 075008, https://doi.org/10.1088/1748-9326/ac0d21, 2021.
Kraft, B., Jung, M., Körner, M., Koirala, S., and Reichstein, M.: Towards hybrid modeling of the global hydrological cycle, Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, 2022.
Liu, L. and Jin, Z.: Code and data for “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” (v1.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.5504533, 2021.
Metivier, K. A., Pattey, E., and Grant, R. F.: Using the ecosys mathematical model to simulate temporal variability of nitrous oxide emissions from a fertilized agricultural soil, Soil Biol. Biochem., 41, 2370–2386, https://doi.org/10.1016/j.soilbio.2009.03.007, 2009.
Miller, L. T.: Assessing Agricultural Nitrous Oxide Emissions and Hot Moments Using Mesocosm Simulations, Master Thesis, University of Minnesota, University of Minnesota Digital Conservancy, https://hdl.handle.net/11299/219276, last access: 15 September 2021.
Miller, L. T., Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R. T., Baker, J. M., and Frie, A. L.: Response of nitrous oxide emissions to future changes in precipitation and individual rain events, J. Environ. Qual., https://doi.org/https://doi.org/10.1002/jeq2.20348, accepted, 2022.
Necpálová, M., Anex, R. P., Fienen, M. N., Del Grosso, S. J., Castellano, M. J., Sawyer, J. E., Iqbal, J., Pantoja, J. L., and Barker, D. W.: Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling, Environ. Modell. Softw., 66, 110–130, https://doi.org/10.1016/j.envsoft.2014.12.011, 2015.
Oord, A. van den, Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K.: Wavenet: A generative model for raw audio, arXiv [preprint], arXiv:1609.03499, 2016.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta, P., Dubash, N. K., Edenhofer, O., Elgizouli, I., Field, C. B., Forster, P., Friedlingstein, P., Fuglestvedt, J., Gomez-Echeverri, L., Hallegatte, S., Hegerl, G., Howden, M., Jiang, K., Jimenez Cisneroz, B., Kattsov, V., Lee, H., Mach, K. J., Marotzke, J., Mastrandrea, M. D., Meyer, L., Minx, J., Mulugetta, Y., O'Brien, K., Oppenheimer, M., Pereira, J. J., Pichs-Madruga, R., Plattner, G. K., Pörtner, H. O., Power, S. B., Preston, B., Ravindranath, N. H., Reisinger, A., Riahi, K., Rusticucci, M., Scholes, R., Seyboth, K., Sokona, Y., Stavins, R., Stocker, T. F., Tschakert, P., van Vuuren, D. and van Ypserle, J. P.: Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change, IPCC, 151, ISBN 978-92-9169-143-2, 2014.
Peng, B., Guan, K., Tang, J., Ainsworth, E. A., Asseng, S., Bernacchi, C. J., Cooper, M., Delucia, E. H., Elliott, J. W., Ewert, F., Grant, R. F., Gustafson, D. I., Hammer, G. L., Jin, Z., Jones, J. W., Kimm, H., Lawrence, D. M., Li, Y., Lombardozzi, D. L., Marshall-Colon, A., Messina, C. D., Ort, D. R., Schnable, J. C., Vallejos, C. E., Wu, A., Yin, X., and Zhou, W.: Towards a multiscale crop modelling framework for climate change adaptation assessment, Nat. Plants, 6, 338–348, https://doi.org/10.1038/s41477-020-0625-3, 2020.
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S. K., Karpatne, A., Hansen, G. J. A., Hanson, P. C., Watkins, W., Steinbach, M., and Kumar, V.: Process-guided deep learning predictions of lake water temperature, Water Resour. Res., 55, 9173–9190, https://doi.org/10.1029/2019WR024922, 2019.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and Carvalhais, N.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Robertson, M., BenDor, T. K., Lave, R., Riggsbee, A., Ruhl, J. B., and Doyle, M.: Stacking ecosystem services, Front. Ecol. Environ., 12, 186–193, https://doi.org/10.1890/110292, 2014.
Rohe, L., Apelt, B., Vogel, H.-J., Well, R., Wu, G.-M., and Schlüter, S.: Denitrification in soil as a function of oxygen availability at the microscale, Biogeosciences, 18, 1185–1201, https://doi.org/10.5194/bg-18-1185-2021, 2021.
Saha, D., Basso, B., and Robertson, G. P.: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems, Environ. Res. Lett., 16, 024004, https://doi.org/10.1088/1748-9326/abd2f3, 2021.
Soil Survey Staff: Gridded soil survey geographic (gSSURGO)
database for the United States of America and the
Territories, Commonwealths, and Island Nations served by
the USDA-NRCS United States Department of Agriculture,
Natural Resources Conservation Service, https://gdg.sc.egov.usda.gov/, last access: 15 September 2021.
Solazzo, E., Crippa, M., Guizzardi, D., Muntean, M.,
Choulga, M., and Janssens-Maenhout, G.: Uncertainties in the
Emissions Database for Global Atmospheric Research (EDGAR) emission
inventory of greenhouse gases, Atmos. Chem. Phys., 21, 5655–5683,
https://doi.org/10.5194/acp-21-5655-2021,
2021.
Syakila, A. and Kroeze, C.: The global nitrous oxide budget revisited, Greenhouse Gas Measurement and Management, 1, 17–26, https://doi.org/10.3763/ghgmm.2010.0007, 2011.
Thompson, R. L., Lassaletta, L., Patra, P. K., Wilson, C., Wells, K. C., Gressent, A., Koffi, E. N., Chipperfield, M. P., Winiwarter, W., Davidson, E. A., Tian, H., and Canadell, J. G.: Acceleration of global N2O emissions seen from two decades of atmospheric inversion, Nature Clim. Change, 9, 993–998, https://doi.org/10.1038/s41558-019-0613-7, 2019.
Thornley, J. H. and France, J.: Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences, Cabi, ISBN 9780851990101, 2007.
Tian, H., Xu, R., Canadell, J. G., Thompson, R. L., Winiwarter, W., Suntharalingam, P., Davidson, E. A., Ciais, P., Jackson, R. B., Janssens-Maenhout, G., Prather, M. J., Regnier, P., Pan, N., Pan, S., Peters, G. P., Shi, H., Tubiello, F. N., Zaehle, S., Zhou, F., Arneth, A., Battaglia, G., Berthet, S., Bopp, L., Bouwman, A. F., Buitenhuis, E. T., Chang, J., Chipperfield, M. P., Dangal, S. R. S., Dlugokencky, E., Elkins, J. W., Eyre, B. D., Fu, B., Hall, B., Ito, A., Joos, F., Krummel, P. B., Landolfi, A., Laruelle, G. G., Lauerwald, R., Li, W., Lienert, S., Maavara, T., MacLeod, M., Millet, D. B., Olin, S., Patra, P. K., Prinn, R. G., Raymond, P. A., Ruiz, D. J., van der Werf, G. R., Vuichard, N., Wang, J., Weiss, R. F., Wells, K. C., Wilson, C., Yang, J., and Yao, Y.: A comprehensive quantification of global nitrous oxide sources and sinks, Nature, 586, 248–256, https://doi.org/10.1038/s41586-020-2780-0, 2020.
Venterea, R. T., Maharjan, B., and Dolan, M. S.: Fertilizer source and tillage effects on yield-scaled nitrous oxide emissions in a corn cropping system, J. Environ. Qual., 40, 1521–1531, https://doi.org/10.2134/jeq2011.0039, 2011.
Wagner-Riddle, C., Congreves, K. A., Abalos, D., Berg, A. A., Brown, S. E., Ambadan, J. T., Gao, X., and Tenuta, M.: Globally important nitrous oxide emissions from croplands induced by freeze–thaw cycles, Nat. Geosci., 10, 279–283, https://doi.org/10.1038/ngeo2907, 2017.
Willard, J., Jia, X., Xu, S., Steinbach, M., and Kumar, V.: Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems, arXiv [preprint], arXiv:2003.04919, 2020.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental‐scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS‐2): 1. Intercomparison and application of model products, J. Geophys. Res.-Atmos., 117, D03109, https://doi.org/10.1029/2011jd016048, 2012.
Zhang, Y. and Niu, H.: The development of the DNDC plant growth sub-model and the application of DNDC in agriculture: a review, Agr. Ecosyst. Environ., 230, 271–282, https://doi.org/10.1016/j.agee.2016.06.017, 2016.
Zhang, Y., Li, C., Zhou, X., and Moore III, B.: A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture, Ecol. Model., 151, 75–108, https://doi.org/10.1016/S0304-3800(01)00527-0, 2002.
Zhou, W., Guan, K., Peng, B., Tang, J., Jin, Z., Jiang, C., Grant, R., and Mezbahuddin, S.: Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for US Midwestern agroecosystems, Agr. Forest Meteorol., 307, 108521, https://doi.org/10.1016/j.agrformet.2021.108521, 2021.
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...
