Enhancing predictions of nitrous oxide emissions from agricultural soils using a classification‐swap machine learning approach

Accurate prediction of N₂O emissions in agricultural systems is essential for developing effective climate-smart practices. This study introduces a novel ensemble approach, termed the "Class-Swap" machine learning model, which employs two independent Random Forest (RF) models trained separately on background and hot-moment emissions. A statistical anomaly detection algorithm first classifies each flux observation, and the model then swaps between the two RF models accordingly, enabling emission-specific predictions based on distinct biogeochemical drivers. The objective was to evaluate the performance of this approach against traditional RF modeling in predicting N₂O fluxes from a long-term continuous cotton crop rotation in west Tennessee, which includes different tillage, N fertilization, and cover cropping treatments. The Class-Swap approach consistently outperformed traditional RF models on an independent unseen holdout dataset, achieving higher R² values (0.33-0.34 vs. 0.08-0.25) and lower root mean square error (9.8-9.9 vs. 10.5-11.6 g N₂O-N ha^-1 day^-1), while accurately capturing the magnitude and temporal dynamics of emissions-something traditional RF models failed to replicate. Key predictors varied by emission type: in the background emission model, moderate to high soil moisture (0.45-0.70 WFPS), soil <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msubsup><mml:mi>NH</mml:mi> <mml:mn>4</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> <mml:annotation>${\mathrm{NH}}_4^ + $</mml:annotation></mml:semantics> </mml:math> , and increased soil CO₂ fluxes positively contributed to N₂O fluxes; in the hot-moment model, fluxes were primarily driven by large precipitation events and high soil moisture (>0.65 WFPS) conditions. This study underscores the effectiveness of differentiating between the distinct biogeochemical dynamics underlying background emissions and hot moments in predictive modeling. Further assessment across diverse sites, years, and agroecosystems remains necessary to validate the potential of the Class-Swap approach as a robust alternative for predicting dynamic soil N₂O fluxes.