[en] Nitrous oxide (N2O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N2O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on ~12,000 N2O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R2 = 0.84, RMSE = 16.4 g N ha-1 d-1) and held-out testing sites (R2 = 0.84, RMSE = 6.2 g N ha-1 d-1). Analyses identified six dominant N2O drivers: soil organic carbon (SOC), NH4+, NO3-, water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N2O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N2O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.
Disciplines :
Agriculture & agronomy
Author, co-author :
Sharma, Prateek ; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824
Basso, Bruno ; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824 ; W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060 ; U.S. Department of Energy, Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824
Manuraj, Aditya; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824
Murillo, Michael S; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824
Millar, Neville ; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824
Tadiello, Tommaso ; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824
Sharma, Mukta; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824
Delandmeter, Mathieu ; Université de Liège - ULiège > Département GxABT > Plant Sciences ; Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824
Robertson, G Philip ; W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060 ; U.S. Department of Energy, Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824 ; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824
Language :
English
Title :
Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N2O) fluxes from US croplands.
Publication date :
10 March 2026
Journal title :
Proceedings of the National Academy of Sciences of the United States of America
ISSN :
0027-8424
eISSN :
1091-6490
Publisher :
Proceedings of the National Academy of Sciences, United States
DOE - United States. Department of Energy NSF - National Science Foundation USDA - United States Department of Agriculture USB - United Soybean Board MSU - Michigan State University F.R.S.-FNRS - Fonds de la Recherche Scientifique
Funding text :
FNRS funds number : 44221 (Research Fellow grant awarded to M. Delandmeter) Support for this research was provided by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under Award Number DE-SC0018409; the NSF Long-term Ecological Research Program (DEB 2224712) at the Kellogg Biological Station; USDA NIFA (Award no. 2020-67021-32799); Michigan State University AgBioResearch; the CERCA-FFAR project; Climate Trace; and the Soil Inventory Project. M.D. was funded by the F.R.S.-FNRS, Belgium. We gratefully acknowledge the US Department of Agriculture, Natural Resources Conservation Service (USDA-NRCS) for the gSSURGO soils data and the USDA Agricultural Research Service GRACEnet/REAP program for providing N2O flux data via the USDA Ag Data Commons. Several sites are also part of the USDA ARS Long- Term Agroecosystem Research Network, for which we also acknowledge support.
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