Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems

The potent greenhouse gas nitrous oxide (N2O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N2O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited geographic coverage; process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured soil parameters. We used sub-daily N2O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model...