New model for capturing the variations of fertilizer‐induced emission factors of N₂O

Abstract

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%.

Quantifying nitrogen leaching response to fertilizer additions in China's cropland

“…The cost function in BRRT v2 that was minimized was the Bayesian Information Criterion (BIC). Detailed methodology of the BRRT v2 was described by Zhou et al (2015). Note that regularized linear regression (Rosset and Zhu, 2007) and condition index were applied for individual bottom nodes within the procedure of BRRT v2, avoiding over-fitting in model calibration and colinearity among different f k , respectively (Zhou et al, 2015).…”

“…Detailed methodology of the BRRT v2 was described by Zhou et al (2015). Note that regularized linear regression (Rosset and Zhu, 2007) and condition index were applied for individual bottom nodes within the procedure of BRRT v2, avoiding over-fitting in model calibration and colinearity among different f k , respectively (Zhou et al, 2015). PKU-NLEACH model was also used to simulate the spatial patterns of N leaching (under 1-m soil depth) over China's cropland in 2008 at a 1-km scale.…”

“…The R 2 , RMSE, and BIC were applied to assess their predictive accuracy performance. We finally applied a relative importance analysis (Zhou et al, 2015) to understand the extent to which factors control the difference in LR-N rate relationships and the spatial variation of LR. The relative importance was defined as the magnitude of the increase in the mean squared errors (MSEs) of PKU-NLEACH model when one of the selected f k is randomly permuted (Zhou et al, 2015).…”

An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty

“…The detailed methodology of the BRRT v2 and EILP models can be found in Zhou et al [19,26] or in supplementary information, Text S1 and S2.…”

“…Although the ANN approach is one powerful platform for developing an approximation function, it has not yet been shown to provide explicit expressions of nonlinear stressor-response relationships and effectively quantify uncertainties in dynamic water body systems. Recently, the Bayesian recursive regression tree Version 2 (BRRT v2) model was developed for rapidly capturing the nonlinear stressor-response relationship of general aquatic systems [26], generating a series of interval linear regression equations that covered the pertinent ranges of stressors and responses reflected in the original nonlinear simulation model. Given its computational efficiency, predictive accuracy and capability of explicitly quantifying uncertainty in previous works [26], the BRRT v2 may be employed as a surrogate for a simulation model in an indirect SOM framework by replacing the process-oriented simulation model with interval linear regression equations.…”