Predicting when and why N₂O hot moments occur across global agroecosystems with a generalizable, low-input machine learning model

Soil nitrous oxide (N₂O) emissions in agroecosystems are temporally variable. Brief ‘hot moments’ driven by abrupt changes in local climatic conditions and substrate availability contribute disproportionately to cumulative annual emissions. The ability to accurately predict such temporal dynamics and high-impact events, particularly across heterogeneous and novel environments, can help to concentrate mitigation efforts and improve hot moment representation in global models. We used a novel application of classification machine learning (ML) modeling to simplify the prediction of hot moments at high temporal resolution. Leveraging this simplicity, our models used limited management, environmental, and climate data requirements designed to promote accessibility. Models were rigorously evaluated for generalizable performance on temporally and spatially stratified datasets, predicting on unseen years and a set of novel holdout sites across 29 diverse agricultural experiments worldwide. Of five ML models evaluated, XGBoost demonstrated best predictive power and generalizability (Matthews correlation coefficient: ∼0.45), identifying key emissions events from both unseen years and unseen holdout sites which contributed around half of cumulative emissions at on average ∼2-fold the overall flux rate. Statistical anomaly detection of hot moments rapidly produced high quality and repeatable training data, offering comparable performance and practical advantages over hand-labeled training data toward developing even larger models in the future. Model behavior aligned with known drivers of N₂O emissions, with predictions of hot moments particularly influenced by wetting events, the type and rate of fertilizer, and temperature. These results demonstrate a practical and broadly accessible pathway toward applying machine learning to improve soil N₂O flux predictions, and to empower global land managers to maximize the efficacy of strategies to mitigate agricultural GHG emissions.