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

Abstract

Nitrification is a major pathway of N2O production in aerobic soils. Measurements and model simulations of nitrification and associated N2O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate (Rnit) and the fraction of nitrification as N2O emissions (fN2ONit). Using our global database on Rnit and fN2ONit, we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating Rnit and N2O emission from nitrification. We then applied the SGB technique for global prediction. The potential Rnit was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas fN2ONit by mean annual precipitation, soil clay content, soil pH, soil total N. The global fN2ONit 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.