New approach for predicting nitrification and its fraction of N2O emissions in global terrestrial ecosystems

Nitrification is a major pathway of N 2 O production in aerobic soils. Measurements and model simulations of nitrification and associated N 2 O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate ( R nit ) and the fraction of nitrification as N 2 O emissions ( f N 2 O Nit ). Using our global database on R nit and f N 2 O Nit , we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating R nit and N 2 O emission from nitrification. We then applied the SGB technique for global prediction. The potential R nit was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas f N 2 O Nit by mean annual precipitation, soil clay content, soil pH, soil total N. The global f N 2 O Nit varied by over 200 times (0.006%–1.2%), which challenges the common practice of using a constant value in process-based models. This study provides insights into advancing process-based models for projecting N dynamics and greenhouse gas emissions using a machine learning approach.