Machine learning reveals dynamic controls of soil nitrous oxide (N2O) emissions from diverse long-term cropping systems

<p>Soil nitrous oxide (N₂O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17-years (2003-2019) of measurements at the Kellogg Biological Station LTER/LTAR site to better understand controls of N₂O emissions in four corn–soybean–winter wheat rotations employing Conventional, No-till, Reduced input, and Biologically-based/organic inputs. We used a Random Forest machine learning model to predict daily N₂O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29 to 42% of daily N₂O flux variability in test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing N₂O emissions. In the Conventional system, the model identified ammonium (>15 kg N ha^-1) and daily temperature (>23 °C) as the most influential variables; in the No-till system, climate variables, precipitation, and temperature were important variables. In low input and organic systems, where red clover (<em>Trifolium repens</em> L.; before corn) and cereal rye (<em>Secale cereale</em> L.; before soybean) cover crops were integrated, nitrate was the predominant variable, followed by precipitation and temperature. In low input and biologically-based systems, red clover residues increased soil nitrogen availability to influence N₂O emissions. Long-term data facilitated machine learning for predicting N₂O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.</p>