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Accumulating evidence indicates that N 2 O emission factors (EFs) vary with nitrogen additions and environmental variations. Yet the impact of the latter was often ignored by previous EF determinations. We developed piecewise statistical models (PMs) to explain how the N 2 O EFs in agricultural soils depend upon various predictors such as climate, soil attributes, and agricultural management. The PMs are derived from a new Bayesian Recursive Regression Tree algorithm. The PMs were applied to the case of EFs from agricultural soils in China, a country where large EF spatial gradients prevail. The results indicate substantial improvements of the PMs compared with other EF determinations. First, PMs are able to reproduce a larger fraction of the variability of observed EFs for upland grain crops (84%, n = 381) and paddy rice (91%, n = 161) as well as the ratio of EFs to nitrogen application rates (73%, n = 96). The superior predictive accuracy of PMs is further confirmed by evaluating their predictions against independent EF measurements (n = 285) from outside China. Results show that the PMs calibrated using Chinese data can explain 75% of the variance. Hence, the PMs could be reliable for upscaling of N 2 O EFs and fluxes for regions that have a phase space of predictors similar to China. Results from the validated models also suggest that climatic factors regulate the heterogeneity of EFs in China, explaining 69% and 85% of their variations for upland grain crops and paddy rice, respectively. The corresponding N 2 O EFs in 2008 are 0.84 ± 0.18% (as N 2 O-N emissions divided by the total N input) for upland grain crops and 0.65 ± 0.14% for paddy rice, the latter being twice as large as the Intergovernmental Panel on Climate Change Tier 1 defaults. Based upon these new estimates of EFs, we infer that only 22% of current arable land could achieve a potential reduction of N 2 O emission of 50%.

Ammonia (NH3) released to the atmosphere leads to a cascade of impacts on the environment, yet estimation of NH3 volatilization from cropland soils (VNH3) in a broad spatial scale is still quite uncertain in China. This mainly stems from nonlinear relationships between VNH3 and relevant factors. On the basis of 495 site-years of measurements at 78 sites across Chinese croplands, we developed a nonlinear Bayesian tree regression model to determine how environmental factors modulate the local derivative of VNH3 to nitrogen application rates (Nrate) (VR, %). The VNH3-Nrate relationship was nonlinear. The VR of upland soils and paddy soils depended primarily on local water input and Nrate, respectively. Our model demonstrated good reproductions of VNH3 compared to previous models, i.e., more than 91% of the observed VR variance at sites in China and 79% of those at validation sites outside China. The observed spatial pattern of VNH3 in China agreed well with satellite-based estimates of NH3 column concentrations. The average VRs in China derived from our model were 14.8 ± 2.9% and 11.8 ± 2.0% for upland soils and paddy soils, respectively. The estimated annual NH3 emission in China (3.96 ± 0.76 TgNH3·yr(-1)) was 40% greater than that based on the IPCC Tier 1 guideline.