The amount and geographic distribution of N2O emissions over China remain largely uncertain. In this study, county-level and 0.1° × 0.1° gridded anthropogenic N2O emission inventories for China (PKU-N2O) in 2008 are developed based on high-resolution activity data and regional emission factors (EFs) and parameters. These new estimates are compared with previous inventories, and with two sensitivity tests: one that uses high-resolution activity data but the default IPCC methodology (S1) and the other that uses regional EFs and parameters but starts from coarser-resolution activity data. The total N2O emissions are 2150 GgN2O/yr (interquartile range from 1174 to 2787 GgN2O/yr). Agriculture contributes 64% of the total, followed by energy (17%), indirect emissions (12%), wastes (5%), industry (2.8%), and wildfires (0.2%). Our national emission total is 17% greater than that of the EDGAR v4.2 global product sampled over China and is also greater than the GAINS-China, NDRC, and S1 estimates by 10%, 50%, and 17%, respectively. We also found that using uniform EFs and parameters or starting from national/provincial data causes systematic spatial biases compared to PKU-N2O. Spatial analysis shows nonlinear relationships between N2O emission intensities and urbanization. Per-capita and per-GDP N2O emissions increase gradually with an increase in the urban population fraction from 0.3 to 0.9 among 2884 counties, and N2O emission density increases with urban expansion.

Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr–1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways.

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.

China has experienced rapid agricultural development over recent decades, accompanied by increased fertilizer consumption in croplands; yet, the trend and drivers of the associated nitrous oxide (N2O) emissions remain uncertain. The primary sources of this uncertainty are the coarse spatial variation of activity data and the incomplete model representation of N2O emissions in response to agricultural management. Here, we provide new data‐driven estimates of cropland‐N2O emissions across China in 1990–2014, compiled using a global cropland‐N2O flux observation dataset, nationwide survey‐based reconstruction of N‐fertilization and irrigation, and an updated nonlinear model. In addition, we have evaluated the drivers behind changing cropland‐N2O patterns using an index decomposition analysis approach. We find that China's annual cropland‐N2O emissions increased on average by 11.2 Gg N/year2 (p < .001) from 1990 to 2003, after which emissions plateaued until 2014 (2.8 Gg N/year2, p = .02), consistent with the output from an ensemble of process‐based terrestrial biosphere models. The slowdown of the increase in cropland‐N2O emissions after 2003 was pervasive across two thirds of China's sowing areas. This change was mainly driven by the nationwide reduction in N‐fertilizer applied per area, partially due to the prevalence of nationwide technological adoptions. This reduction has almost offset the N2O emissions induced by policy‐driven expansion of sowing areas, particularly in the Northeast Plain and the lower Yangtze River Basin. Our results underline the importance of high‐resolution activity data and adoption of nonlinear model of N2O emission for capturing cropland‐N2O emission changes. Improving the representation of policy interventions is also recommended for future projections.

Nitrous oxide emission factors (N2O-EF, percentage of N2O-N emissions arising from applied fertilizer N) for cropland emission inventories can vary with agricultural management, soil properties and climate conditions. Establishing a regionally-specific EF usually requires the measurement of a whole year of N2O emissions, whereas most studies measure N2O emissions only during the crop growing season, neglecting emissions during non-growing periods. However, the difference in N2O-EF (EF) estimated using measurements over a whole year (EFwy) and those based on measurement only during the crop-growing season (EFgs) has received little attention. Here, we selected 21 studies including both the whole-year and growing-season N2O emissions under control and fertilizer treatments, to obtain 123 EFs from various agroecosystems globally. Using these data, we conducted a meta-analysis of the EFs by bootstrapping resampling to assess the magnitude of differences in response to management-related and environmental factors. The results revealed that, as expected, the EFwy was significantly greater than the EFgs for most crop types. Vegetables showed the largest ΔEF (0.19%) among all crops (0.07%), followed by paddy rice (0.11%). A higher ΔEF was also identified in areas with rainfall ≥600 mm yr −1 , soil with organic carbon ≥1.3% and acidic soils. Moreover, fertilizer type, residue management, irrigation regime and duration of the nongrowing season were other crucial factors controlling the magnitude of the ΔEFs. We also found that neglecting emissions from the non-growing season may underestimate the N2O-EF by 30% for paddy fields, almost three times that for non-vegetable upland crops. This study highlights the importance of the inclusion of the non-growing season in the measurements of N2O fluxes, the compilation of national inventories and the design of mitigation strategies. Capsule Fallow-season N2O emissions must be included when calculating emission factors (EFs); neglecting them lowers the EFs by 30% for paddy rice and 10% for non-vegetable crops.

Smart cropland management practices can mitigate greenhouse gas (GHG) emissions while safeguarding food security. However, the integrated effects on net greenhouse gas budget (NGHGB) and grain yield from different management practices remain poorly defined and vary with environmental and application conditions. Here, we conducted a global meta‐analysis on 347 observation sets of non‐CO2 GHG (CH4 and N2O) emissions and grain yield, and 412 observations of soil organic carbon sequestration rate (SOCSR). Our results show that for paddy rice, replacing synthetic nitrogen at the rate of 30%–59% with organic fertilizer significantly decreased net GHG emissions (NGHGB: −15.3 ± 3.4 [standard error], SOCSR: −15.8 ± 3.8, non‐CO2 GHGs: 0.6 ± 0.1 in Mg CO2 eq ha−1 year−1) and improved rice yield (0.4 ± 0.1 in Mg ha−1 year−1). In contrast, intermittent irrigation significantly increased net GHG emissions by 11.2 ± 3.1 and decreased rice yield by 0.4 ± 0.1. The reduction in SOC sequestration by intermittent irrigation (15.5 ± 3.3), which was most severe (>20) in alkaline soils (pH > 7.5), completely offset the mitigation in CH4 emissions. Straw return for paddy rice also led to a net increase in GHG emissions (NGHGB: 4.8 ± 1.4) in silt‐loam soils, where CH4 emissions (6.3 ± 1.3) were greatly stimulated. For upland cropping systems, mostly by enhancing SOC sequestration, straw return (NGHGB: −3.4 ± 0.8, yield: −0.5 ± 0.6) and no‐tillage (NGHGB: −2.9 ± 0.7, yield: −0.1 ± 0.3) were more effective in warm climates. This study highlights the importance of carefully managing croplands to sequester SOC without sacrifice in yield while limiting CH4 emissions from rice paddies.

a b s t r a c tAgricultural soils account for more than 50% of nitrogen leaching (L N ) to groundwater in China. When excess levels of nitrogen accumulate in groundwater, it poses a risk of adverse health effects. Despite this recognition, estimation of L N from cropland soils in a broad spatial scale is still quite uncertain in China. The uncertainty of L N primarily stems from the shape of nitrogen leaching response to fertilizer additions (N rate ) and the role of environmental conditions. On the basis of 453 site-years at 51 sites across China, we explored the nonlinearity and variability of the response of L N to N rate and developed an empirical statistical model to determine how environmental factors regulate the rate of N leaching (LR). The result shows that L N -N rate relationship is convex for most crop types, and varies by local hydro-climates and soil organic carbon. Variability of air temperature explains a half (~52%) of the spatial variation of LR. The results of model calibration and validation indicate that incorporating this empirical knowledge into a predictive model could accurately capture the variation in leaching and produce a reasonable upscaling from site to country. The fertilizer-induced L N in 2008 for China's cropland were 0.88 ± 0.23 TgN (1s), significantly lower than the linear or uniform model, as assumed by Food and Agriculture Organization and MITERRA-EUROPE models. These results also imply that future policy to reduce N leaching from cropland needs to consider environmental variability rather than solely attempt to reduce N rate .