DOI: 10.5061/dryad.pvmcvdnzx
title: Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N<sub>2</sub>O) fluxes from US croplands

description: 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 approximately 12,000 N2O chamber measurements at 17 U.S.
 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 (WFPS), soil temperature, and 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.
description: # Data from: Coupled machine learning-ecosystem ensemble models
 substantially improve predictions of nitrous oxide (N~2~O) fluxes from US
 croplands Dataset DOI: [10.5061/dryad.pvmcvdnzx](10.5061/dryad.pvmcvdnzx)
 ## Description of the data and file structure We present here the data
 that were used for the analysis presented in: Coupled machine
 learning-ecosystem ensemble models substantially improve predictions of
 nitrous oxide (N~2~O) fluxes from US croplands. ### Files and variables
 Files: Dataset_S1.xlsx, Dataset_S2.csv, Dataset_S3.csv, ####
 **Description:**  **Description of data sheets** **Dataset S1A columns** *
 **Site_ID:** Numeric identifier for the experimental site. *
 **Treatment_ID:** Numeric code for the management treatment applied at
 that site * **DataUse:** To assign data to be used for model training
 (development) and testing (held-out evaluation) * **State/Province:**
 State acronym * **Latitude decimal deg:** Site location latitude *
 **Longitude decimal deg:** Site location longitude * **Start Data Year:**
 Starting year of data used * **End Data Year:** Ending year of data used *
 **Cover crop:** Type of cover crop used within the treatment * **Rotation
 Descriptor:** Describe the rotation of crops within the treatment *
 **Tillage Descriptor:** Describe tillage type within the treatment *
 **Residual Removal:** Describe residual management within the treatment *
 **Irrigation:** Describe if irrigation was applied or not within the
 treatment * **N Treatment Descriptor:** Describe nitrogen amendments
 within the treatment * **Reference:** Reference for the data **Dataset
 S1B**: This sheet contains the reference list for the data used **Dataset
 S2 columns** * **Date:** Gas sampling days * **Site_ID:** Numeric
 identifier for the experimental site. * **Treatment_ID:** Numeric code for
 the management treatment applied at that site * **DataUse:** To assign
 data to be used for model training (development) and testing (held-out
 evaluation) * **Observed N2O:** Daily average N2O flux measured (g N2O-N
 ha-1d-1) * **Predicted N2O:** Daily average N2O flux predicted by
 multimodel hybrid framework (g N2O-N ha-1d-1) * **NH4:** Process-based
 models simulated daily NH4-N content in the top 30-cm soil layer (kg ha-1)
 * **SOC:** Process-based models simulated daily soil organic carbon in the
 top 30-cm soil layer (kg ha-1) * **NO3:** Process-based models simulated
 daily NO3-N content in the top 30-cm soil layer (kg ha-1) * **ST:**
 Process-based models simulated daily average soil temperature in the top
 30-cm soil layer (°C) * **WFPS:** Process-based models simulated daily
 water-filled pore space in the top 30-cm soil layer (fraction) * **ABG:**
 Process-based models simulated daily above-ground biomass (kg ha-1) *
 **BG:** Process-based models simulated daily below-ground biomass (kg
 ha-1) * **SRAD:** Average solar radiation for the last five days before
 gas sampling (Watt m-2) * **Tmax:** Average maximum air temperature for
 the last three days before gas sampling  (°C) * **APrecip:** Average
 precipitation in the last fifteen days before gas sampling (mm) *
 **Wspd:** Average wind in the last fifteen days before gas sampling (m
 s-1) * **LAI:** Process-based models simulated daily leaf area index (m2
 m2) * **Nstress:** Process-based models simulated the daily nitrogen
 stress factor (fraction) * **Wstress:** Process-based models simulated the
 daily water stress factor (fraction) * **PET:** Process-based models
 simulated daily potential evapotranspiration (mm) * **SE:** Process-based
 models simulated daily soil evaporation (mm) * **SPrecip:** Cumulative
 precipitation in the last two days before gas sampling (mm) * **SH:**
 Average specific humidity in the last three days before gas sampling (g
 kg-1) * **RH:** Average relative humidity in the last fifteen days before
 gas sampling (%) **Dataset S3 columns** * **Date:** Gas sampling days *
 **Site_ID:** Numeric identifier for the experimental site. *
 **Treatment_ID:** Numeric code for the management treatment applied at
 that site * **DataUse:** To assign data to be used for model training
 (development) and testing (held-out evaluation) * **SD:** Monte Carlo
 standard deviation of the simulated daily N₂O flux distribution (g N2O-N
 ha-1d-1) * **CV**: Monte Carlo coefficient of variation of the simulated
 daily N₂O flux distribution (%) * **CI05:** 5th percentile (lower 90 %
 confidence bound) of the Monte Carlo flux distribution(g N2O-N ha-1d-1) *
 **CI95:** 95th percentile (upper 90 % confidence bound) of the Monte Carlo
 flux distribution(g N2O-N ha-1d-1)