Environmental Research Letters ( IF 5.8 ) Pub Date : 2021-03-02 , DOI: 10.1088/1748-9326/abe4f5 Baobao Pan 1 , Shu Kee Lam 1 , Enli Wang 2 , Arvin Mosier 1 , Deli Chen 1
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.
中文翻译:
预测全球陆地生态系统中硝化作用及其N 2 O排放量比例的新方法
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。