Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N2O) fluxes from US croplands.

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.