New approach for predicting nitrification and its fraction of N<sub>2</sub>O emissions in global terrestrial ecosystems

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Environmental Research Letters

سال: 2021

ISSN: ['1748-9326']

DOI: https://doi.org/10.1088/1748-9326/abe4f5