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 () and the fraction of nitrification as N₂O emissions (). Using our global database on and , we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating and N₂O emission from nitrification. We then applied the SGB technique for global prediction. The potential was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas by mean annual precipitation, soil clay content, soil pH, soil total N. The global 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.

硝化作用是需氧土壤中N ₂ O产生的主要途径。硝化作用和相关的N ₂ O排放的测量和模型模拟具有挑战性。在这里，我们创新地集成了数据挖掘和机器学习功能，以预测硝化率（）和硝化分数作为N ₂ O排放量（）。在和上使用我们的全局数据库，我们发现基于机器学习的随机梯度增强（SGB）模型在估算硝化作用和N ₂ O排放方面优于三个基于过程的模型。然后，我们将SGB技术应用于全局预测。潜力受长期平均年温度，土壤碳氮比和土壤pH值的驱动，而受年平均降水量，土壤黏土含量，土壤pH值，土壤总氮的影响。全球变化超过200倍（0.006％–1.2％） ，这对在基于过程的模型中使用恒定值的普遍做法提出了挑战。这项研究为使用机器学习方法预测N动态和温室气体排放的先进过程模型提供了见解。