Data from: 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 that explained 65-89% of daily flux variance with very
few input variables –soil moisture, days after fertilization, soil
texture, air temperature, soil carbon, precipitation, and N fertilizer
rate. When applied to a long-term test site not used to train the model,
the model explained 38% of the variation observed in weekly to biweekly
manual chamber measurements from corn, and 51% upon coupling the ML model
with a cropping systems model that predicted daily soil N availability.
This represents a 2-3 times improvement over conventional process-based
models and with substantially fewer input requirements. This coupled
approach offers promise for better predictions of agricultural N2O
emissions and thus more precise global models and more effective
agricultural mitigation interventions.