Re-quantifying the emission factors based on field measurements and estimating the direct N 2 O emission from Chinese croplands

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1 GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 18,, doi: /2003gb002167, 2004 Re-quantifying the emission factors based on field measurements and estimating the direct N 2 O emission from Chinese croplands Xunhua Zheng, Shenghui Han, Yao Huang, Yuesi Wang, and Mingxing Wang Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China Received 2 October 2003; revised 17 February 2004; accepted 3 March 2004; published 12 June [1] The authors collect 54 direct N 2 O emission factors (EFds) obtained from 12 sites of Chinese croplands, of which 60% are underestimated by 29% and 30% are overestimated by 50% due to observation shortages. The biases of EFds are corrected and their uncertainties are re-estimated. Of the 31 site-scale EFds, 42% are lower by 58% and 26% are higher by 143% than the Intergovernmental Panel on Climate Change default. Periodically wetting/drying the fields or doubling nitrogen fertilizers may double or even triple an EFd. The direct N 2 O emission from Chinese croplands is estimated at gn 2 O-N yr 1 in the 1990s, of which 20% is due to vegetable cultivation. The great uncertainty of this estimate, 79% to 135%, is overwhelmingly due to the huge uncertainty in estimating EFds ( 78 ± 15% to 129 ± 62%). Direct N 2 O emission intensities significantly depend upon the economic situation of the region, implicating a larger potential emission in the future. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 0322 Atmospheric Composition and Structure: Constituent sources and sinks; 0330 Atmospheric Composition and Structure: Geochemical cycles; 1615 Global Change: Biogeochemical processes (4805); KEYWORDS: nitrous oxide, emission factor, uncertainty, direct emission, inventory, croplands Citation: Zheng, X., S. Han, Y. Huang, Y. Wang, and M. Wang (2004), Re-quantifying the emission factors based on field measurements and estimating the direct N 2 O emission from Chinese croplands, Global Biogeochem. Cycles, 18,, doi: /2003gb Introduction [2] Nitrous oxide (N 2 O) is one of the major greenhouse gases closely associated with anthropogenic activities. The current budget of atmospheric N 2 O is imbalanced, indicating an increase in atmospheric N 2 O burden by 3.8 Tg N 2 O-N yr 1 (1 Tg = g) [Intergovernmental Panel on Climate Change (IPCC), 2001]. This rate is almost equivalent to the global anthropogenic N 2 O emission rate (4.4 Tg N 2 O-N yr 1 ) from agricultural activities, of which ca. 80% is due to the emission from agricultural soils [IPCC, 2001]. Parties to the United Nations Frame Convention on Climate Change (UNFCCC) (Article 12 of the UNFCCC) are obligated to periodically provide national inventories on emissions/removals of greenhouse gases (GHGs). Nitrous oxide (as one of the major GHGs not controlled by the Montreal Protocol) released from crop production is required to be included in the inventories (Decision 10 of the Conference of Parties to the UNFCCC). Crop production, wherein anthropogenic reactive nitrogen (Nr) is created and consumed, inevitably causes N 2 O emissions directly and indirectly [IPCC, 1997, 2000]. Of the direct and indirect emissions, the former is usually the majority. For instance, up to 75% of the annual total N 2 O Copyright 2004 by the American Geophysical Union /04/2003GB002167$12.00 released from anthropogenic reactive nitrogen input into croplands of China is presently due to direct emission (estimated by the authors using the IPCC [1997, 2000] default methodology). Accordingly, the estimate uncertainty for the N 2 O emission inventory of crop production is reasonably due to that of the direct emission. We therefore mainly focus on the direct emission issues in this study. [3] Regarding the direct N 2 O emission from agricultural soils induced by fertilizer N application, as one of the sources for anthropogenic N 2 O emission, the IPCC [1997, 2000] concept is directly followed. The direct N 2 O emission from the croplands of a given region is defined as the product of anthropogenic Nr input (from synthetic nitrogen fertilizers, animal manure, biologically fixed nitrogen, returned crop residues, and deposited atmospheric nitrogen derived from anthropogenic activities) and the direct N 2 O emission factor (hereinafter referred to as EFd). An EFd is defined as the fraction of the Nr input released in the form of N 2 O within the current seasonal or annual period [IPCC, 1997, 2000]. In this definition, the N 2 O emission due to N remnants from fertilizers applied in previous years, which should also be regarded as a direct anthropogenic emission, is ignored, as so far no data have been available to quantify it. The site-scale EFd of a given cropland (E) is estimated with equation (1) below [e.g., Xing, 1998; Yan et al., 2003; Veldkamp and Keller, 1997], where E F and E n are the seasonally or annually released N 2 O-N from a unit area of nitrogen-fertilized and no-nitrogen control fields, 1of19

2 respectively, which are quantified by observing the net N 2 O emission fluxes, and N is the amount of fertilizer nitrogen seasonally or annually applied in the unit area of the fertilized field. For observational cases without a no-nitrogen control, the EFd is also roughly estimated with equation (1) by assuming E n = 0, i.e., ignoring the background emission [e.g., Huang et al., 1995]. This method to quantify EFds by either considering or ignoring the background emission has been widely applied [e.g., Yan et al., 2003; Xing, 1998; Zheng et al., 2000; Dobbie and Smith, 2003; Brown et al., 2001]. In practice, whether the EFd value quantified with this method can well represent the real emission factor and/or how large its estimate uncertainty is may depend on several influencing factors. To date, however, this issue has been seldom addressed. E ¼ ðe F E n Þ=N: ð1þ [4] Direct N 2 O emission from croplands at site and/or regional scales occurs essentially with great spatial and temporal variability [e.g., Dobbie and Smith, 2003; Veldkamp and Keller, 1997]. At the site scale, temporal variability diurnally, seasonally, and interannually exists, while spatial variability is mainly caused by heterogeneity in soil properties and agricultural management (e.g., water, nutrient, crop, tillage, and residue management) [e.g., Dobbie and Smith, 2003; Brown et al., 2001; Veldkamp and Keller, 1997; Williams et al., 1992]. Knowledge of EFds of various sites and their uncertainties is substantially essential for determining regional EFds with bottom-up methods by considering regional homogeneity in climate, soils, and agricultural management. Any field measurements to obtain an accurate site-scale EFd should well cover the high temporal and spatial variability. Ideally, such measurements at a site should involve the field treatments of all local dominant management practices in terms of crop type, rotation regime, irrigation/drainage, tillage, and fertilization, and N 2 O emission fluxes of all field treatments should be diurnally, seasonally, annually, and interannually measured. So far, however, few data based on such ideal measurements have been available. In fact, most of the available measurements [e.g., Dong et al., 2001; Xing and Zhu, 1997] were not initially designed for accurately quantifying EFds. Because too few accurately quantified EFds are available for the complex and diverse croplands, and since the IPCC [1997, 2000] default EFd or those from other countries/ regions [e.g., Brown et al., 2001; Veldkamp and Keller, 1997] may not be suitable for the specific croplands of China, we should make use of, as much as possible, the available measurements in compiling the national inventory of direct N 2 O emission from Chinese croplands. In terms of accurately estimating EFds, the available measurements may inevitably have all or some of the following five shortages: (1) intermittent measurements at an interval of at least 1 day (i.e., low measurement frequency, hereinafter referred to as LMF), (2) lack of measurements during nongrowing period of crops (i.e., less-representative observation periods, LRP), (3) lack of non-fertilized controls (LNF), (4) dominant management practices not well represented by field treatments (MNR), and (5) too few interannual replicates (FAR). These shortages usually lead to enormous biases and/or large uncertainties for the site-scale EFds estimated with equation (1). [5] The biases due to LMF for the estimated EFds may be occasionally positive [e.g., Brumme and Beese, 1992] but mostly negative [e.g., Veldkamp and Keller, 1997; Smith and Dobbie, 2001]. LRP usually produces negative biases for the estimated EFds since the seasonal or annual N 2 O emissions for the fertilized and the unfertilized field treatments are underestimated, while LNF inevitably leads to overestimated EFds because the background N 2 O emission is ignored. [6] Nitrous oxide emission from croplands mainly occurs in pulses, and each pulse emission event usually lasts for only 1 3 days [e.g., Zheng et al., 2000]. Many available observations, however, were intermittently performed at an interval of 3 10 days [e.g., Dong et al., 2001] or even longer [e.g., Veldkamp and Keller, 1997] and some explosive or pulse emission peaks were therefore inevitably missed. For example, a 4-day explosive emission peak occurred on June 1997, which was caused by wetting the dry fallow fields through irrigation, and accounted for 46% (ranging from 35% to 62%) of the annually accumulative N 2 O released from the rice-wheat rotation cropland of southeastern China (X. Zheng, unpublished data, 2003). Another 4-day explosive emission caused by a heavy rainfall event during the late wheatgrowing season of accounted for 20% (ranging from 13% to 26%) of the annually accumulative N 2 O released from the rice-wheat rotation cycle [Zheng et al., 2000]. On the one hand, negative biases may be produced for EFd estimates if pulse emissions are not captured by low-frequency intermittent measurements [e.g., Smith and Dobbie, 2001; Veldkamp and Keller, 1997]. On the other hand, if peak emissions are occasionally captured while valley emissions are occasionally missed, positive biases may be inevitable [e.g., Brumme and Beese, 1992]. [7] Nitrous oxide emission from croplands receiving anthropogenic Nr happens not only during the crop-growing seasons but also during the non-growing periods. Accordingly, negative bias may be produced for the estimated EFds due to lack of measurements during the non-growing periods. [8] On the basis of 75 available observations taken in China, the background N 2 O emissions, which were observed with no-nitrogen controls, account for 20 53% of the seasonally or annually accumulative N 2 O released from fertilized fields (this study, data not shown). This indicates a great positive bias for an EFd estimated with equation (1) by ignoring the background emissions. Of the 54 available EFds of Chinese croplands (Table 1), however, 43% were initially estimated on the basis of the field measurements without no-nitrogen controls. The negative or positive biases need to be corrected and the uncertainties assessed before the available EFds are applied in compiling an inventory for direct N 2 O emission from Chinese croplands. [9] A multiyear observation over fixed field treatments in a rice-wheat rotation agro-ecosystem of southeastern China (Table 1) shows a high interannual variability in annual 2of19

3 Table 1. Available Initial Site-Scale Direct N 2 O Emission Factors (EFds) From Chinese Croplands and Related Information Initial site-scale EFds e (kgn 2 O-N kg 1 N) Site a Field Type ID b Year c Interval d Mean (n f ) Range g Ref. h LN-SY maize/fallow rotation d i (1) NA j d i (4) rice/drying rotation d k (1) NA 3 HB-SJZ maize/wheat rotation d k (2) , 5, 6 wheat season d k (3) , 8 SD-YC maize/wheat rotation d k (2) HN-FQ wheat/cotton rotation d k (2) , d k (3) summer crop season d k (1) NA d k (4) winter wheat season d k (2) , 10 rice/drying rotation d k (1) NA 6, 13 JS-NJ: Field 1 rice/wheat rotation d (1) NA d (1) NA d (1) NA rice season d i (1) NA d k (1) NA d k (1) NA 15 non-rice season d k (1) NA d k (1) NA d i (1) NA 14 Field 2 rice/wheat rotation d (3) rice season d (3) non-rice season d (3) Field 3 rice season d i (4) , 13, 19 JS-JR wheat season d i (2) JS-WX rice/wheat rotation d (3) d (4) rice season d (3) d (4) non-rice season d (3) d (4) JS-SZ rice/wheat rotation d k (4) , h k (3) , h (3) h (3) rice/drying rotation h k (2) , 20 non-rice seasons d k (4) , h i (3) , h i (3) h (3) rice season d i (2) , d i (3) h i (3) , h i (3) h i (3) SC-YT non-rice season d (1) NA 14 winter wheat season d (1) NA 14 JX-YT double rice/dry rotation d k (2) double rice seasons d k (1) NA d k (2) rape seed season d k (1) NA 6 GZ-GY upland crop rotation d k (2) GD-GZ rice season d k (2) , 23 a Refer to Figure 1 and its caption for the full site names and locations indicated with longitude and latitude. b Series number. c Series number for each year of the observation period. d Observation frequency, for example, 7 d means to measure N 2 O emission fluxes once every 7 days; h is hours. e Uncorrected emission factors. f Number of the field treatments with fertilizer-n amended. g Given by the minimum and maximum values of the field treatments. h References: 1, Huang et al. [1995]; 2, Huang et al. [1998]; 3, Chen et al. [1995]; 4, Zeng et al. [1995]; 5, Song et al. [1997]; 6, Xing [1998]; 7, Wang et al. [1994]; 8, Su et al. [1992]; 9, Dong et al. [2001]; 10, Xing and Zhu [1997]; 11, H. Xu et al. [2000]; 12, Ding et al. [2001]; 13, Xu et al. [1997]; 14, unpublished data from the authors; 15, Zou et al. [2003b]; 16, Jiang et al. [2003]; 17, Huang et al. [2001]; 18, Zou et al. [2003a]; 19, Cai et al. [1997]; 20, Zheng et al. [2000]; 21, Xiong et al. [2002]; 22, W. Xu et al. [2000]; 23, Xu et al. [1995]. i Values directly cited from the listed references. j Not available. k Values calculated using the available data from the listed references. 3of19

4 EFds, with a variation coefficient (CV) of 75% [Zheng et al., 2000; X. Zheng, unpublished data, 2003]. The available multitreatment observations at six sites of Chinese croplands (Table 1) indicate a high site-scale spatial variability in seasonal or annual EFds of an individual site, with a CV of 57% (this study). These suggest that MNR and FAR may make the initially estimated EFds poorly represent the real site-scale EFds because the observations poorly cover the high spatial and/or interannual variability. [10] In this study, we collect all the available data from field measurements on N 2 O emissions from different Chinese croplands, re-quantify the site-scale EFds and reestimate their uncertainties by assessing the biases or uncertainties due to observational shortages, apply the requantified EFds and re-estimated uncertainties to compile an inventory of direct N 2 O emission from croplands of Mainland China in the 1990s, and discuss the direct N 2 O emission inventories, uncertainties, and further research needs. 2. Data Sources and Calculation Methods 2.1. Data Sources [11] There are two major sources of data used in this study. One is the available data from field measurements of N 2 O emissions from Chinese croplands and related information, which are collected from the published literature and unpublished data measured by the authors in the past decade. The observed data of N 2 O emissions from croplands of 12 sites located in nine provinces of China (Figure 1) are collected and organized. Table 1 lists all the initially estimated site-scale EFds, which contain biases or unsuitable uncertainties, and some related information (more detailed information is not shown). These data and information are used for re-quantifying site-scale EFds and reestimating their uncertainties. Another source is statistical data. Data at the provincial level on arable lands, yields and harvest areas of crops, livestock population, and commercial nitrogen fertilizer consumption in the 1990s are collected from the Rural Statistical Yearbook of China (annually published by China Statistics Press, Beijing) and used for estimating the anthropogenic Nr input of each cropland category with the IAP-N model [Zheng et al., 2002]. Meanwhile, some statistical data on gross domestic products (GDP) of China at the provincial and national levels ( GDP of the United States and the globe ( and arable land area and synthetic fertilizer nitrogen consumption of the United States and the globe ( are also used. Figure 1. Location of the sites for observation of N 2 O direct emission from croplands of China. 1, Shenyang, Liaoning (abbreviated as LN-SY) ( N, E); 2, Shijiazhuang, Hebei (HB-SJZ) ( N, E); 3, Yucheng, Shandong (SD-YC) ( N, E); 4, Fengqiu, Henan (HN-FQ) ( N, E); 5, Nanjing, Jiangsu (JS-NJ) (Field 1: N, E. Field 2: N, E. Field 3: N, E); 6, Jurong, Jiangsu (JS-JR) ( N, E); 7, Wuxi, Jiangsu (JS-WX) ( N, E); 8, Suzhou, Jiangsu (JS-SZ) ( N, E); 9, Yanting, Sichuan (SC-YT) ( N, E); 10, Yingtan, Jiangxi (JX-YT) ( N, E); 11, Guiyang, Guizhou (GZ-GY) ( N, E); 12, Guangzhou, Guangdong (GD- GZ) ( N, E) Calculation Methods Correction of Site-Scale Mean EFd and Uncertainty Re-Estimation [12] Owing to ignoring the observational shortages, the initially estimated EFds listed in Table 1 are likely to greatly overestimate or underestimate the real site-scale EFds and their uncertainties Correction of Site-Scale Mean EFd [13] Applying Monte Carlo stochastic mathematics [e.g., Sun et al., 2000], the mean EFd for an individual site (Table 1) is corrected with the method described in Appendix A, assuming that the biases or errors induced by any observational shortage of LMF, LRP, and LNF statistically have a normal distribution. The bias extent, by which the real site-scale EFd for a given year is underestimated or overestimated due to any of these observational shortages, is randomly selected within a given range. The most essential step to correct the mean EFd for an individual site, therefore, is on the basis of case studies to statistically estimate the range of underestimation or overestimation due to LMF, LRP, and LNF, respectively. The methods for estimating the ranges of underestimation or overestimation due to LMF, LRP, and LNF are described in the following paragraphs. [14] With the measurements continuously taken at an interval of 4 hours in a rice-wheat rotation agro-ecosystem of southeastern China [Zheng et al., 2000], we investigate the biases for estimating seasonally or annually accumulative N 2 O emissions due to low measurement frequency using the methods described by Brumme and Beese [1992] and Smith and Dobbie [2001]. Table 2 lists the results based on the continuously measured data of three automated chambers (data not shown). The sign of the biases shown in Table 2 is consistent with those of Smith and Dobbie [2001] for a N-fertilized grassland under a humid temperate climate and Veldkamp and Keller [1997] for a N-fertilized banana plantation under a humid tropical climate but reverse to that of Brumme and Beese [1992] for a limed and N-fertilized forest under a humid temperate 4of19

5 Table 2. Biases to Underestimate Seasonal or Annual N 2 O Emissions due to Unsuitable Observation Intervals 1-Day Interval 2-Day Interval 3-Day Interval 4-Day Interval 5-Day Interval 7-Day Interval 10-Day Interval Number of observations Bias, a % 19 ± 2 18 ± 4 19 ± ± ± ± ± 13 a Mean ±1 standard deviation. climate. The negative biases indicate that intermittent measurements at a frequency of even as high as once every day are likely to lead to an underestimate for the seasonally or annually accumulative N 2 O emission from the rice-wheat cropping ecosystem. For occasions with multimeasurements diurnally taken, underestimation or overestimation due to LMF is not regarded to occur. Otherwise, the biases for estimating the seasonally or annually accumulative N 2 O emissions from N-fertilized fields and no-nitrogen controls of a rice-wheat rotation cropland (Table 2) are directly applied to quantify the underestimation or overestimation range (expressed as a percentage) due to LMF, using the statistical method described in Appendix B. [15] The multiyear data continuously measured in a ricewheat rotation cropland [Zheng et al., 2000] indicate that lack of measurement during the non-growing periods, which usually last for only 15 to 25 days, may lead to an underestimation for the seasonally or annually accumulative N 2 O emission from nitrogen-fertilized fields by 6% to 52% (with a mean of 32%) and for the seasonally or annually accumulative background emission by 10% to 66% (with a mean of 47%). These may accordingly lead to an underestimation of the site-scale EFd by 3% to 34% (with a mean of 19%). The great variation in the negative biases may be due to changes during the non-growing periods in precipitation, irrigation, tillage, and so on. For occasions with observation well covering the whole investigation period or the entire rotation cycle, underestimation or overestimation due to LRP is not regarded to occur. Otherwise, a default range of 34% to 3% may be directly applied for the underestimation of site-scale EFds due to LRP. Instead of directly applying this default range, the underestimation or overestimation range due to RNP may also be estimated at a 95% confidence using the statistical methods described in Appendix C. For a cropping system with a dry fallow period following a single paddy rice season, the EFds are likely to be underestimated due to LRP. For instance, the observation carried out in by Zheng et al. [2000] shows an underestimation of 45%, which is applied in this study as a default for paddy rice-fallow (dry) fields. [16] About 40 field observations conducted in Chinese croplands (Table 1) indicate that lack of no-nitrogen controls or ignorance of the background N 2 O emissions probably leads to an overestimation of site-scale EFds by 15% to 111% (this study), with the biases greatly varying among different cropping systems. For instance, the range of the positive biases is between 13% to 74% (n = 9) for the ricegrowing season, 29% to 69% (n = 9) for the non-rice period, and 24% to 68% (n = 9) for the whole rice-wheat rotation cycle (this study). These values are directly applied as the default ranges of overestimation due to LNF for the corresponding periods of rice-wheat ecosystems. For other cropping ecosystems that lack no-nitrogen controls, we directly apply 15% to 111% as the default range of overestimation. For occasions with available no-nitrogen controls, underestimation or overestimation due to LNF is not regarded to occur Uncertainties of the Re-Estimated Site-Scale EFds [17] The uncertainty of an individual site-scale EFd, which usually depends upon the site-scale spatial variability and interannual temporal variability, is re-estimated on the basis of available multitreatment and multiyear measurements. Multiple-field treatments and multiyear replicates are used to estimate the site-scale spatial CV and the interannual CV, respectively. On the basis of observations of multiple field treatments at each of the six sites, i.e., the Shenyang site in Liaoning [Huang et al., 1995, 1998], the Shijiazhuang site in Hebei [Zeng et al., 1995; Su et al., 1992; Wang, 1994; Wang et al., 1994; Song et al., 1997], the Fengqiu site in Henan [Xing and Zhu, 1997; Xing, 1998; Xu et al., 2000a; Ding et al., 2001], and the Nanjing [Huang et al., 2001; Jiang et al., 2003; Zou et al., 2003a, 2003b], Suzhou [Zheng et al., 2000], and Wuxi (X. Zheng, unpublished data, 2003) sites in Jiangsu province, a site-scale spatial CV is estimated to be 57% (this study), which is applied as a default for estimating the uncertainty of a sitescale EFd quantified with a single-treatment observation. For estimating the uncertainty of a site-scale EFd quantified with multitreatment observations, the CV among the EFds of individual treatments is directly applied. Ideally, the interannual CV should be determined with multiyear observations carried out on a fixed field treatment. To date, however, very few such observations have been available in China or even globally. Fortunately, one such multiyear observation was carried out during at a suburb of Suzhou, China [Zheng et al., 2000; Xing and Zhu, 1997; Xing, 1998], resulting in an interannual CV of 75% (this study). This value is applied as the default for the EFds based on single-year observations. Integrating the spatial and interannual CVs, the uncertainty for estimating the EFd of an individual site is statistically quantified at a 95% confidence using the methods described in Appendix D Up-Scaling Site-Scale EFds and Their Uncertainties [18] For the final purpose of compiling an inventory for direct N 2 O emission from croplands, site-scale EFds and their uncertainties have to be upscaled to appropriate regions. Considering the complexity of China-specific croplands and the availability and accessibility of various statistical data for quantifying the annual anthropogenic Nr input by cropland types, we define a three-level hierarchical classification for the Chinese croplands, and then we upscale the corrected site-scale EFds and their re-estimated uncertainties to corresponding third-level categories. 5of19

6 Table 3. Classification of Croplands and Methods in the IAP-N Model [Zheng et al., 2002] for Calculating Anthropogenic Reactive Nitrogen (Nr) Input Into Each Third-Level Cropland Category a Code Description of Each Category Calculation Methods for Quantifying Nr Input I 1-1 upland vegetables A v R v I 1-2 year-round upland crops excluding vegetables (N a +N d ) (A u A v )/(A u +A r A v )+N bnf_u I 2-1 single paddy rice plus fallow (dry) (N a +N d ) A r /(A u +A r A v )+N bnf_r II 1-1 upland vegetables Same as I 1-1 II 1-2 year-round upland crops excluding vegetables Same as I 1-2 II 2-1 single paddy rice plus fallow (dry) Same as I 2-1 III 1-1 upland vegetables Same as I 1-1 III 1-2 year-round upland crops excluding vegetables Same as I 1-2 III 2-1 single paddy rice plus fallow (dry) Same as I 2-1 IV 1-1 upland vegetables Same as I 1-1 IV 1-2 year-round upland crops excluding vegetables (N a +N d ) [(A u A v ) (A r 2 A re W) 0.7]/(A u -A v +A r )+N bnf_u IV 2-1 single paddy rice plus fallow (dry) (N a +N d ) (A r 2 A re W) 0.3/(A u A v +A r ) +N bnf_r (A r 2 A re W) 0.3/A r IV 2-2 single paddy rice plus fallow (year-round flooding) (N a +N d ) W/(A u A v +A r )+N bnf_r W/A r IV 3-1 non-rice period of upland crop plus single paddy rice (N a +N d ) (A r 2 A re W) 0.7/(A u A v +A r ) IV 3-2 rice season of upland crop plus single paddy rice (N a +N d ) (A r 2 A re W) 0.7/(A u A v +A r ) +N bnf_r (A r 2 A re W) 0.7/A r IV 4-1 double paddy rice plus fallow (dry) or green manure (N a +N d ) 2 A re /(A u A v +A r )+N bnf_r 2 A re /A r IV 4-2 double paddy rice plus fallow (year-round flooding) (N a +N d ) W/(A u A v +A r )+N bnf_r W/A r V 1-1 upland vegetables Same as I 1-1 V 1-2 year-round upland crops excluding vegetables (N a +N d ) (A u A v A re 0.3)/(A u A v +A r )+N bnf_u V 2-1 non-rice period of upland crop + single paddy rice (N a +N d ) (A r 2 A re )/(A u A v +A r ) V 2-2 rice season of upland crop + single paddy rice (N a +N d ) (A r 2 A re )/(A u A v +A r )+N bnf_r (A r 2 A re )/A r V 3-1 double paddy rice plus upland crops (N a +N d ) (3 A re 0.3)/(A u A v +A r )+N bnf_r 2 A re 0.3/A r V 3-2 double paddy rice plus fallow (dry) (N a +N d ) (2 A re 0.7)/(A u A v +A r )+N bnf_r 2 A re 0.7/A r VI 1-1 upland vegetables Same as I 1-1 VI 1-2 year-round upland crops excluding vegetables (N a +N d ) [A u A v (A r W) 0.7]/(A u A v +A r )+N bnf_u VI 2-1 non-rice period of upland crop plus single paddy rice (N a +N d ) (A r W) 0.7/(A u +A r ) VI 2-2 rice season of upland crop plus single paddy rice (N a +N d ) (A r W) 0.7/(A u A v +A r )+N bnf_r (A r W) 0.7/A r VI 3-1 single rice plus fallow (dry) or green manure (N a +N d ) (A r W) 0.3/(A u A v +A r )+N bnf_r (A r W) 0.3/A r VI 3-2 single rice plus fallow (year-round flooding) (N a +N d ) W/(A u -A v +A r )+N bnf_r W/A r a Abbreviations: I, including the eight northwestern provinces (Xinjiang, Qinghai, Xizang, Shanxi, Gansu, Shaanxi, Inner Mongolia, and Ningxia); II, including the three northeastern provinces (Heilongjiang, Jilin, and Niaoning); III, including the northern five provinces (Beijing, Tianjing, Hebei, Henan, and Shangdong); IV, including the nine provinces along the Yangtze River (Zhejiang, Shanghai, Jiangsu, Anhui, Jiangxi, Hunan, Hubei, Sichuan, and Chongqing); V, including the four southeastern coastal provinces (Guangdong, Guangxi, Hainan, and Fujian); VI, including the two southwestern plateau provinces (Yunnan and Guizhou); A r, total paddy rice harvest area of a province; A re, early rice harvest area of the double rice cropping system of a province; A w, area of year-round flooded croplands [Chinese Academy of Agricultural Sciences, 1986; National Office for Soil Survey, 1996] of a province. In Yunnan, Guizhou, Sichuan, and Chongqing provinces, the year-round flooded croplands are assumed to be cultivated with single paddy rice, i.e., W = A w. In Hunan and Hubei provinces, one half of the year-round flooded croplands are assumed to be cultivated with double paddy rice and the other half with single paddy rice, i.e., W = A w /2. In other provinces, the area of year-round flooded croplands are ignorable, i.e., W = 0. A v, harvest area of upland vegetables of a province; R v, fertilizer-n application rate for individual vegetable crop growing season, which was reported to be 694 kgn ha 1 in Gansu province [Liu et al., 1998], 266 kgn ha 1 in Qinghai province [Hao et al., 2001], 303 kgn ha 1 in Shanxi province [Cheng et al., 2003], 782 kgn ha 1 in Beijing [Chen and Zhang, 1996], 665 kgn ha 1 in Shandong province [Li et al., 2002], 649 kgn ha 1 in Shanghai [Yu et al., 2001], 442 kgn ha 1 in Jiangsu province [Guo et al., 2001], 314 kgn ha 1 in Anhui [Wang et al., 2001], 645 kgn ha 1 in Sichuan [Li et al., 2000], 586 kgn ha 1 in Fujian [Wu, 2002], 396 kgn ha 1 in Hainan [Hu et al., 2001] and 340 kgn ha 1 in Yunnan [Lu et al., 2003]. The reported R v for a province is applied for the adjacent provinces missing available R v.n a, amount of Nr applied to a province for crops excluding upland vegetables, which consists of nitrogen from synthetic fertilizers (TN fer ), animal manure (F AM ) and returned crop residues (including straw, roots, and stubble) (F CR ), i.e., N a =TN fer +F AM A v R v +F CR. N d, amount of anthropogenic Nr deposition from the atmosphere into the croplands of a province. N bnf_r, amount of BNF (biological nitrogen fixation) in paddy rice production in a province. N bnf_u, amount of BNF in production of upland legume crops in a province. Data for A r, A u,a re,a v, and TN fer are available at the provincial level in statistical yearbooks on agriculture. Data for some variables are obtained from the literature, for example, A w and R v (see above parts of this caption). Those variables for the formulas listed in Table 3 without data directly available from statistics and the literature are indirectly calculated with the IAP-N model [Zheng et al., 2002]. [19] By referring to climatic heterogeneity, the Chinese croplands are classified into six regions [Shandong Agricultural College, 1984], which are encoded as I, II,..., and VI. By referring to rotation types and number of crops annually harvested, each first-level region is further divided into second-level ones, which are encoded by adding a subscript number to the first-level category code, such as I 1, III 2, and so on. By referring to rotation regimes or crop types, each second-level region is further classified into third-level categories, which are differentiated with another subscript number following a dash, such as I 1-1, III 2-1, and so on. Table 3 lists each third-level cropland category and its definition. [20] Using the information related to each site-scale EFd on number of crops annually harvested, crop rotation regimes, or crop types, and considering ecological and climatic similarities, the re-quantified site-scale EFds and their re-estimated uncertainties are upscaled to regions of third-level cropland categories under three principles. For a third-level category with at least two established site-scale EFds, the average value of all EFds is simply regarded as the regional mean, while the average of the low values for the uncertain ranges of individual site-scale EFds and that of the high values are directly applied as the regional uncertain range (Principle-I). Variability in management 6of19

7 and soils may be the cause of variability that is not captured when using established EFds from too few sites. When there is more than one site, the EFds are simply averaged, which may be too simplistic in view of the actual variability. However, this problem may have been partially overcome by using a classification referring to management which is much more important than soil types in causing spatial variability [Veldkamp and Keller, 1997; Matson et al., 1996]. For a third-level cropland category with single site-scale EFd, the mean estimate of this EFd is directly applied as the regional mean, and the regional uncertainty range is estimated with a default regional CV (Principle-II). The default regional CV for permanent upland crop fields, the rice-growing season of paddy rice-upland crop rotation fields, the non-rice period of paddy rice-upland crop rotation fields, and a rotation cycle of paddy rice and upland crops is estimated to be 48%, 62%, 72%, and 71%, respectively (see section 3.1). For a third-level cropland category without an available sitescale EFd, the EFd estimate for a neighboring region with similar attributes is directly applied (Principle-III). Following these principles, the regional EFds of individual thirdlevel categories are estimated at the provincial level Quantification of Regional Anthropogenic Nr Inputs [21] The IAP-N model [Zheng et al., 2002], which is driven by provincial (or national, or regional, or global) statistical data on annual commercial fertilizer-n consumption, production, and harvest area of various crops, livestock population, and rural population, is applied for calculating the anthropogenic Nr input into each third-level category of croplands. Table 3 lists the detailed formulas in the model to calculate the regional Nr input. There are four assumptions for establishing these calculation formulas, which are (1) the application rate of fertilizer-nitrogen for a unit area of an individual paddy rice growing season is equal to that of upland crops excluding vegetables and legumes [e.g., Chen et al., 1995; Dong et al., 2001; Huang et al., 1995; Xing and Zhu, 1997; Xing, 1998]; (2) permanently flooded paddies are only distributed in six provinces, i.e., Sichuan, Chongqing, Yunnan, Guizhou, Hunan, and Hubei [Chinese Academy of Agricultural Sciences, 1986], with all the permanently flooded paddies in the former four provinces cultivated with single rice, and half of those in the later two provinces cultivated with double rice while another half are cultivated with single rice; (3) 70% of the single paddy rice-upland crop/(dry) fallow rotation fields in IV and VI are cultivated with upland crops during the non-rice period and 30% are (dry) fallow, while 30% of the paddy rice-upland crop/(dry) fallow rotation fields in V are cultivated with upland crops during the non-rice period and 70% are (dry) fallow; and (4) the biological nitrogen fixation (BNF) of legume green manure and legume cover crops are ignored due to lack of available data. Details for defining the variables of the formulas and their data sources are given in the caption for Table 3. It should be noticed that the IAP-N model used in this study directly applies the parameters recommended by the IPCC [1997, 2000] methods for quantifying the Nr input from livestock manure, since so far too few China-specific parameters Table 4. Corrected Site-Scale Direct Emission Factors (EFds) and Their Re-Estimated Uncertainties Corrected Site-Scale EFds, kgn 2 O-N kg 1 N ID a Mean Uncertainty Range Uncertainty, b % a Corresponding to the ID of Table 1. b Extent of uncertainties relative to the corrected mean EFds. have been available. The anthropogenic Nr inputs of individual third-level cropland categories are calculated at the provincial level Calculation of the N 2 O Emissions in the 1990s [22] For each third-level cropland category, the mean, low, and high estimates of direct N 2 O emission in each year of the 1990s are calculated at the provincial scale by simply multiplying the regional EFds with the regional anthropogenic Nr input. Then the 10-year mean estimates by provinces and by cropland categories and regional averages on an arable land area basis are calculated. The regional average of direct N 2 O emission on an arable land area basis is hereinafter referred to as regional direct N 2 O emission intensity. 3. Results 3.1. Re-Quantified Site-Scale EFds and Their Re-Estimated Uncertainties [23] Table 4 lists the re-quantified site-scale EFds and their re-estimated uncertainty range, while Figure 2 illustrates the initially estimated and re-quantified site-scale EFds by four cropland groups. These results indicate that 60% of the initially estimated EFds listed in Table 1 are underestimated by 29%, and 30% are overestimated by 50%, while most of the initially estimated uncertainties 7of19

8 Figure 2. Re-quantified and initially estimated direct N 2 O emission factors (EFds) for various sites. The abbreviations of site names are found in the caption of Figure 1. The uncertainties are indicated with error bars. In Figure 2a, five pairs are annual EFds and the remainder are seasonal EFds. An initially estimated EFd is a datum with the uncorrected biases or errors due to observational shortages. are not representative, due to observational shortages. This suggests that using the initially estimated EFds to estimate direct N 2 O emissions from croplands, as Yan et al. [2003] and Xing [1998] have done, would inevitably produce great biases or errors. [24] As Table 4 shows, the uncertainties of individual requantified site-scale EFds are obviously smaller than the IPCC [1997, 2000] default (62% ± 18% versus 80%). It is logical that individual site-scale EFds are more accurate than the IPCC default which is established based on multisite observations [Bouwman, 1996]. [25] The mean, low, and high estimates of all the requantified site-scale EFds (on average being , , and kgn 2 O-N kg 1 N, respectively) are on average very close to the IPCC [1997, 2000] defaults (0.0125, , and kgn 2 O-N kg 1 N, respectively), indicating that the IPCC defaults may be directly applicable for China as a whole but are likely to be not applicable for individual parts of it. Of the 31 site-scale EFds in Table 4, about 42%, with an average of , , and kgn 2 O-N kg 1 N for the mean, low, and high estimates, respectively, are significantly lower than the IPCC default by 58%, and 26%, with an average of , , and kgn 2 O-N kg 1 N for the mean, low, and high estimates, respectively, are obviously higher than the IPCC default by 143%. Meanwhile, up to two thirds of the locally quantified EFds are far beyond the IPCC default uncertainty range. These facts suggest that directly using the IPCC default may greatly underestimate or overestimate the direct N 2 O emissions from particular croplands. [26] The re-quantified site-scale EFds from various sites are grouped by four cropland field types, which are (1) permanent upland crop fields (hereinafter referred to as G-A), (2) paddy rice fields during the rice growing season (G-B), (3) the non-rice season of paddy rice-upland crop rotation fields (G-C), and (4) the paddy rice-upland crop/dry fallow rotation fields (G-D) (Figure 2). Within each group, the site-scale EFds vary greatly across various regions, with a CV of 48% (n = 5) for G-A, 62% (n = 7) for G-B, 72% (n = 7) for G-C, and 71% (n = 8) for G-D, indicating a relatively smaller spatial variation in permanent upland fields. On average, the site-scale EFds in G-A ( ± kgn 2 O-N kg 1 N, n = 10) are comparable with those in G-B ( ± kgn 2 O-N kg 1 N, n = 7). However, those in G-C ( ± kgn 2 O-N kg 1 N, n = 7) are significantly higher than in G-A or G-B by 228% (P < 0.05) while those in G-D ( ± kgn 2 O-N kg 1 N, n = 8) tend to be higher, but not significantly, than in G-A by 90% (P < 0.20). These results imply that periodic alternation of wetting and drying the soils due to water management, for example, paddy riceupland rotation regimes, may greatly enlarge EFds. This further provides a strong reason to use separate EFds for various cropland categories in terms of management. [27] Figure 3 plots the re-quantified site-scale EFds for G-B, G-C, G-D, and rotation cycles of G-A against fertilizer-n application rates. As Figure 3 shows, no overall correlation obviously appears between site-scale EFds and fertilizer-n application rates. If only the observational sites located at the same cropland type within the same region or under the similar climate and soil conditions, for example, 8of19

9 Figure 3. Site-scale direct N 2 O emission factors (EFds) under various fertilizer-n application rates. The error bars indicate the uncertainty ranges. The numbers beside the data points are site codes illustrated in Figure 1. The dashed elliptic cycles indicate the sites for obtaining the regression curves shown in Figures 3b, 3c, and 3d. those encoded with 5, 7, and 8, are taken into account, however, a positive correlation is significantly revealed (P < 0.1) (Figures 3b, 3c, and 3d). The regression fitting curves illustrated in Figure 3 indicate that to double the fertilizer-n application rate within the linear range may at least triple the EFds, likely due to more excess fertilizer nitrogen available for N 2 O formation. This implies that overapplication of fertilizer nitrogen, which has been commonly happening in some developed countries and a few developed regions in China [Zheng et al., 2002], may significantly enlarge EFds Regional EFds and Their Uncertainties [28] By up-scaling the re-quantified site-scale EFds and their re-estimated uncertainties, the regional EFds for individual third-level cropland categories are estimated (Table 5). Of the 29 categories of croplands in mainland China, the regional EFds of about three fourths, corresponding to 81% of the national crop harvest area and 80% of the national Nr input, are estimated to be ± kg N 2 O-N kg 1 N (ranging from to kg N 2 O-N kg 1 N), which are obviously lower than the IPCC default by 48%. Meanwhile the regional EFds of the remaining one fourth, corresponding to only 19% of the national crop harvest area and 20% of the national Nr input, are estimated to be ± kg N 2 O-N kg 1 N (ranging from to kg N 2 O-N kg 1 N), which are significantly higher than the IPCC default by 78%. As Table 5 indicates, the uncertainties for the regional EFds on average range from 93% (mean minus 1SD for the deviations of the low estimate from the mean estimate of EFds) to 191% (mean plus 1SD for the deviations of the high estimate from the mean estimate of EFds). They are obviously much larger than either those of the site-scale EFds or that of the IPCC default, due to too sparse site-scale EFds poorly covering the essentially high spatial variability. This suggests that adding more new observational sites while developing suitable methods (e.g., promising model tools) to provide more EFds well covering the high spatial variability are absolutely necessary Estimates of Regional Anthropogenic Nr Input in the 1990s [29] By feeding the IAP-N model [Zheng et al., 2002] with provincial statistics data, anthropogenic Nr incorporated into individual cropland categories in the 1990s is calculated (Table 5). The results show that Chinese croplands annually received 28.9 ± 3.1 Tg N (ranging from 24 to 33 Tg N) in the 1990s, of which 68% was from application of synthetic fertilizers, 12% from application of animal manure, 6% from returning of crop residues, 8% from biologic nitrogen fixation, and 6% from atmospheric deposition of anthropogenic Nr derived from NO x produced in combustion processes as well as NO x and NH 3 volatilized from fertilizers and manure. Of the annual total Nr, about 55% was incorporated into the permanent uplands cultivated with non-vegetable crops. Owing to much higher nitrogen application rates in individual growing seasons of vegetables, kg N ha 1 [e.g., Li et al., 2002; Wu, 2002; Lu et al., 2003], than that of non-vegetable crops, kg N ha 1 [e.g., Xing, 1998; Xing and Zhu, 1997; Dong et al., 2001], vegetable fields accounting for only 7% of the national crop harvest area received 17% of the annual Nr input. 9of19

10 Table 5. Regional Direct Emission Factor, Anthropogenic Nr Input, and Direct N 2 O Emission From Various Types of Chinese Croplands in the 1990s Cropland Category Direct Emission Factor, kg N 2 O-N kg 1 N Annual Nr input in the 1990s, 10 6 kg N yr 1 Emission in the 1990s, 10 9 gn 2 O-N yr 1 Mean Range Mean Range Mean Range I I I II II II III III III IV IV IV IV IV IV IV IV V V V V V V VI VI VI VI VI VI Total of Chinese croplands Meanwhile, 28% of the annual Nr input was incorporated into the rice-based croplands Estimates of Direct N 2 O Emissions From Mainland China in the 1990s [30] Table 5 lists the annual direct N 2 O emissions from individual third-level cropland categories in the 1990s. Totally, about gn 2 O-N yr 1 (ranging from 59 to gn 2 O-N yr 1 ) was annually released from the croplands of mainland China in the 1990s, with 80% released in the humid regions receiving only 65% of the total Nr input. The humid regions are referred to as II, IV, V, and VI, where 13%, 43%, 18%, and 5% of the national direct N 2 O emission occurred, respectively. The permanent uplands cultivated with non-vegetable crops received 55% of the national Nr input but only contributed 47% of the national direct N 2 O emission, while those cultivated with vegetables received 17% of the total Nr but contributed 20% of the national direct N 2 O emission. The rice-based cropping systems, which received 28% of the national Nr input, accounted for 33% of the national direct N 2 O emission, of which 18% was due to the emission in the rice-growing seasons during which 23% of the national Nr input was consumed while 15% of the national direct N 2 O emission occurred in the non-rice seasons during which only 5% of the national Nr was consumed. The relatively low percentage for N 2 O emission in the rice-growing seasons is due to the lowest EFds ( ± kgn 2 O-N kg 1 N) in comparison with other occasions, whereas the relatively high percentage for N 2 O emission in the non-rice seasons of the rice-based cropping systems is attributed to the highest EFds ( ± kgn 2 O-N kg 1 N) in comparison with other occasions (Table 5). These results imply that direct N 2 O emission from croplands is regulated by climate at a large regional scale in terms of humidity status and by agricultural practices at regional or site scales in terms of water and fertilizer management. As Table 5 indicates, about 59% of the national direct N 2 O emission in the 1990s resulted from croplands with regional EFds lower than the IPCC default and the remaining 41% resulted from those with EFds higher than the IPCC default. This suggests that the croplands with high EFds are substantially essential for the overall national direct N 2 O emission even though their area and Nr consumption amount is relatively small. [31] The regional totals or averages on an arable land area basis of direct N 2 O emission in the 1990s were quite different among the first-level regions (Figure 4). The maximum of regional totals appeared in IV and the minimum in VI, while the maximum of regional direct N 2 O emission intensities (defined as the annual direct N 2 O emission on an arable land area basis) appeared in V and the minimum in I. There was a great spatial variability in regional direct N 2 O emission intensities, with a CV of 74% 10 of 19