Reevaluating the Drivers of Fertilizer-Induced N2O Emission: Insights from Interpretable Machine Learning

Direct nitrous oxide (N2O) emissions from fertilizer application are the largest anthropogenic source of global N2O, but the factors influencing these emissions remain debated. Here, we compile 1134 observations of fertilizer-induced N2O emission factor (EF) from 229 publications, covering various regions and crops globally. We then employ an interpretable machine learning model to investigate the driving factors of fertilizer-induced N2O emissions. Our results reveal that pH, soil organic carbon, precipitation, and temperature are the most influential factors, overweighing the impacts of management practices. Nitrogen application rate has a positive impact on the EF, but the effect diminishes as nitrogen application rate increases, which has been overestimated in previous studies. Soil pH has three-stage influence on EF: positive when 7.3 ≤ pH ≤ 8.7, significantly negative between 6.8 and 7.3, and insignificant at lower pH levels (4.7 ≤ pH ≤ 6.8). Moreover, we confirm the nonlinear contributions of temperature and precipitation to EF, which may cause an unexpected increase in N2O emission under climate change. Our research provides crucial insights for global N2O modeling and mitigation strategies.