Ito et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-018-0215-4(2018) 5:55

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Emissions of nitrous oxide (N2O) from soil
surfaces and their historical changes in East
Asia: a model-based assessment
Akihiko Ito1,2* , Kazuya Nishina1, Kentaro Ishijima3, Shoji Hashimoto4,5 and Motoko Inatomi4

Abstract
This study assessed historical changes in emissions of nitrous oxide (N2O), a potent greenhouse gas and stratosphericozone-depleting substance, from the soils of East Asia to the atmosphere. A process-based terrestrial ecosystem model(VISIT) was used to simulate the nitrogen cycle and associated N2O emissions as a function of climate, land use,
atmospheric deposition, and agricultural inputs from 1901 to 2016. The mean regional N2O emission rate in the 2000s
was estimated to be 2.03 Tg N2O year−1 (1.29 Tg N year−1; approximately one-third from natural ecosystems and twothirds from croplands), more than triple the rate in 1901. A sensitivity analysis suggested that the increase of N2Oemissions was primarily attributable to the increase of agricultural inputs from fertilizer and manure. The simulated N2Oemissions showed a clear seasonal cycle and interannual variability, primarily in response to meteorological conditions
and nitrogen inputs. The spatial pattern of the simulated N2O emissions revealed hot spots in agricultural areas ofChina, South Korea, and Japan. The average N2O emission factor (emission per unit nitrogen input) was estimated tobe 1.38%, a value comparable to previous estimates. These biogeochemical modeling results will facilitate identifying
ways to mitigate global warming and manage agricultural practices in this region.
Keywords: Global warming, Land use change, Nitrogen cycle, Regional budget, Terrestrial ecosystem

Introduction
Nitrous oxide (N2O) is one of the long-lived anthropogenic greenhouse gases, which are attracting particularattention in the context of mitigating global warming. In
addition, N2O is recognized as the most importantozone-depleting substance in the stratosphere (Ravishankara et al. 2009). Despite the relatively small contribution
N2O has made to historical climatic warming (about 10%of the effect of carbon dioxide [CO2] on historical radiative forcing; Intergovernmental Panel on Climate Change[IPCC] 2013), N2O is among the most remarkable of thegreenhouse gases in several respects. First, N2O emissions
are intimately linked with the biogeochemical cycle of nitrogen (N), which has been greatly impacted by anthropogenic effects that include fertilizer use and industrial
activities that lead to atmospheric deposition of reactive* Correspondence: itoh@nies.go.jp
1
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki
305-8506, Japan
2
Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Full list of author information is available at the end of the article

nitrogen (e.g., Davidson 2009; Zaehle et al. 2011). Evaluation of the magnitude of N2O emissions requires a modelof the N cycle, and the data needed for calibration and validation of such a model differ from those used to estimate
CO2 and methane (CH4) emissions. Second, N2O emissions from soil, one of the major sources, are quite heterogeneous spatially and vary greatly temporally (e.g., Nishina
et al. 2012; Bellingrath-Kimura et al. 2015). In soils, instantaneous N2O production occurs within microzones(i.e., hot spots) of high-microbial activity and bursts ofN2O emissions often occur after rainfall and thawing offrozen soil (e.g., Kim et al. 2012; Muhr et al. 2008). Suchhigh variability is intriguing from the standpoint of biogeochemical mechanisms but confounds quantification ofemission rates with a small number of instruments.
Several studies have attempted to evaluate broad-scale(e.g., national, regional, and global) N2O budgets. Galloway
et al. (2004) have synthesized an overview of the global N
cycle from available data and have shown that global continental N2O emissions in 1860 and in the early 1990s wereabout 8 and 11 Tg N year−1, respectively. Del Grosso et al.

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Ito et al. Progress in Earth and Planetary Science(2018) 5:55

(2009) used the DAYCENT model to estimate that global
annual N2O emissions from corn, wheat, and soybean croplands in the 1990s totaled 1.15 Tg N year−1. Syakila andKroeze (2010) analyzed the global N2O budget from 1500
to 2006 and estimated the global agricultural and naturalsoil emissions in 2000 to be 4.9 and 6–7 Tg N year−1,
respectively. Xu-Ri et al. (2012) used the DyN-LPJ model tosimulate the global terrestrial N cycle and estimated total
N2O emissions in the twentieth century of 8.2–9.5 Tg
N year−1. Hashimoto (2012) has used an empirical model
to estimate mean global soil N2O emissions of 4.4 Tg
N year−1. Recently, more mechanistic models have beendeveloped and used to evaluate global N2O emissions fromsoils, the largest source to the atmosphere (Tian et al.
2018). However, large uncertainties remain in the present
estimates of the broad-scale greenhouse gas budget because
relevant data and understanding of biogeochemical
processes are incomplete.
The global N2O budget is very sensitive to human activities in Asia, especially East Asia. Rapid growth of thehuman population in Asia and associated economic expansion have led to a considerable increase of fertilizer use andindustrial emissions that have caused serious environmental
problems such as air pollution and water body eutrophication. This region is under the influence of the Asian monsoon climate system, and there is a clear gradient inprecipitation from wet coastal areas to dry inland areas. A
credible evaluation of the regional N2O budget of East Asia
requires taking account of such region-specific conditions.
Shindo et al. (2003), for example, used a model developed
for this region to conduct a study focused on N fluxes in
the food production–supply system and related environmental impacts. Several country-specific studies have also
been conducted. Yan et al. (2003) have used IPCC guidelines to estimate nitrogenous gas emissions of Asian countries. For China, Tian et al. (2011) used the Dynamic Land
Ecosystem Model and estimated N2O emissions from terrestrial ecosystems of 0.6 Tg N year−1. For Japanese forests,
Hashimoto et al. (2011) used a statistical model and estimated N2O emissions of 0.00194 Tg N year−1. Because of
the paucity of regional studies that used up-to-date forcing
data, it is still a challenging task to estimate N2O emissions
from East Asia.
In this study, we used a process-based terrestrial
biogeochemical model of the East Asia region and thehistorical record of various driving factors (climate, landuse, N inputs, and atmospheric CO2 concentrations) toestimate the history of N2O emissions from natural andagricultural soils. Riverine export of N and emissions
from inland waters and coastal areas are interesting aspects of the N cycle, but they were excluded from thescope of this study. We analyzed the spatial patterns andtemporal variability of the simulated N2O emissions.
Sensitivity simulations were conducted to relate thePage 2 of 13

contributions of the driving factors to changes of historical emissions. We then examined the contributions ofN2O emissions from East Asia to the regional and global
budgets. Finally, we assess the possibility of mitigating
the impact of those emissions for climate managementin the future, and we delineate future research needs
and opportunities for improving model reliability.

Methods/Experimental
Description of biogeochemical model

We used a process-based terrestrial ecosystem model,
Vegetation Integrative SImulator for Trace gases (VISIT;
Inatomi et al. 2010; Ito and Inatomi 2012a). The model
consists of component schemes that simulate the energy
budget, hydrology, carbon (C) cycle, and N cycle in various types of terrestrial ecosystems, and it can be applied
at global scales by selecting appropriate spatial grid systems. The model was developed for terrestrial biogeochemical and ecophysiological studies; the associatedwater and C cycle schemes have been described and examined in previous papers (Ito and Oikawa 2002; Itoand Inatomi 2012b). To consider the effects of land use,
the model simulates natural vegetation and croplands
separately at each grid. The hydrological scheme is represented by two (near-surface and below) soil water andsnow pools, and it simulates evapotranspiration fromthe soil surface and vegetation canopy surface, infiltration, and runoff discharge. The carbon cycle scheme
consists of three (leaf, stem, and root) plant and two (litter and humus) soil carbon pools and simulates carbon
flows, including photosynthetic assimilation, respiratory
emission, allocation to C pools, litterfall, and microbial
soil decomposition. Each material flow or process is simulated in a mechanistic manner, i.e., on the basis of environmental regulation and biological responsiveness.
The model has been validated by comparisons with measurements of atmosphere–ecosystem CO2 exchange (Ito2008; Inatomi et al. 2010) and satellite-based estimatesof photosynthetic parameters (Ito et al. 2017).
The N cycle scheme consists of two (photosynthetic
and non-photosynthetic) plant, two (litter and humus)
soil-organic and two (ammonium [NH4+] and nitrate[NO3−]) soil-inorganic pools, as well as a microbial pool(see Inatomi et al. 2010 for the schematic diagram). The
N cycle scheme simulates major N flows such as
mineralization,
nitrification,
denitrification,
NH3
volatilization, nitrate leaching, microbial immobilization,and plant assimilation. To facilitate broad-scale application, the model takes account of these processes with anintermediate complexity; i.e., the simulations use forcing
and characteristic data available at regional scales to account for specific processes. The principal processes that
emit N2O, nitrification and denitrification, are simulated
separately by using the generalized parameterization ofIto et al. Progress in Earth and Planetary Science(2018) 5:55

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Parton et al. (1996) developed on the basis of laboratory
and field data. The nitrification-associated N2O emission
rate (FN2Onit) is calculated as follows:

FN2 Onit ¼ fWnit  fTnit  fpHnit ðKnit þ MXnit  fNH4 Þ

ð1Þ
where fWnit, fTnit, fpHnit, and fNH4 are the coefficientsthat quantify the influence of soil water, temperature,
pH, and NH4+ availability on the nitrification rate; Knit is
the N turnover coefficient; and MXnit is the maximum
rate of N2O emission associated with nitrification when
there is excess soil NH4+. The denitrification-associated
N2O emission rate (FN2Oden) is calculated as follows:

FN2 Oden ¼ minðfNO3 ; fCO2 Þ  fWden = 1 þ RN2=N2Oð2Þwhere fNO3 and fCO2 represent the effects of soil NO3−and soil respiration (a proxy of C availability), fWden is ametric of the effect of soil water on denitrification; min() denotes the smallest value of the term in the parenthesis; and RN2/N2O is the N2 to N2O emission ratio fromdenitrification-associated emissions and is formulated asanother function of soil NO3−, soil respiration, and soil
moisture content. The effect terms, fNH4 and fNO3, andthe coefficients, fWnit, fTnit, fCO2, fWden, and RN2/N2O,
are provided as empirical functions (Parton et al. 1996)
and are calculated at each grid for every time step. The
fpHnit coefficient is calculated at each grid using soil
data and is assumed to be constant through time. Knitand MXnit are constant parameters. Soil moisture content simulated by the hydrology scheme and soil respiration by the carbon cycle scheme is used to estimate
N2O emissions.
The N cycle in the model thus responds directly to climate conditions such as temperature and precipitation.
Atmospheric CO2, which affects the ecosystem C cycle
by its fertilization effect (e.g., Norby et al. 2010), can influence the N cycle by altering plant-root N uptake andsoil respiration. The driving forces of the terrestrial N
cycle include biological N fixation (estimated using theparameterization of Cleveland et al. 1999), atmosphericdeposition of NH4+ and NO3−, and fertilizer inputs tocroplands. In this study, the external drivers for the ecosystem (i.e., atmospheric deposition and fertilizer input)
were obtained from appropriate datasets (explainedlater). Land use changes between natural (unmanaged)
and agricultural (managed) ecosystems also affectregional N budgets by altering cropland area and areconsidered by the model (Kato et al. 2013).

Simulations and analyses

The continental-scale simulations were conducted at a
spatial resolution of 0.5° × 0.5° in latitude and longitude,
and then results for East Asian countries (alphabetically,
China, Chinese Taipei, Japan, Mongolia, and North andSouth Korea) were extracted by using common-use national boundary data. The total land area was about
11.6 × 106 km2, and the total population in 2010 was about
1.58 billion. After a spin-up period of at least 300 years to
obtain stable carbon and N budgets under a stationary climate condition, the simulation was started in January
1901 and ended in December 2015. Atmospheric CO2
concentrations for the historical period were derived fromatmospheric observations (e.g., Keeling and Whorf 2009).
Monthly temperature, precipitation, humidity, and cloud
cover during the simulation period were obtained fromthe CRU TS3.25 dataset (Harris et al. 2014) for each grid.
Soil properties such as bulk density, clay/sand composition, and field capacity were derived from the International Satellite Land Surface Climatology Project
dataset (Hall et al. 2006). A map of soil pH was derived
from IGBP-DIS (2000). Transitional land use change wasderived from the global land use dataset compiled by
Hurtt et al. (2011) for climate studies. Croplands wereclassified on the basis of global crop distribution maps
(Monfreda et al. 2008) into the following three categories:
rice, generic C3 crops (except rice, e.g., wheat), and genericC4 crops (e.g., maize). To facilitate regional implementation of the model, agricultural management practices wereclassified in a generic way. For example, cultivation of legume species with symbiotic N fixers was not explicitly included. Historical chemical fertilizer and manure inputs
into croplands were derived from datasets produced by
Nishina et al. (2017) and Potter et al. (2010), respectively.
In the fertilizer dataset, country statistics from the Foodand Agriculture Organization were down-scaled to 0.5°
meshes after filling data gaps. Notably, the dataset differentiated fertilizer N into NH4+ and NO3− and consideredseasonal crop calendars of dominant crops. Historical
trends of atmospheric N deposition were derived from
Galloway et al. (2004); seasonal variability, based on the
results of atmospheric chemistry model simulation studies(Sudo et al. 2002; Dentener et al. 2006), was included. Figure 1 shows the spatial distribution of N inputs to East
Asian land areas in 2000 that was assumed in this study.
The baseline simulation experiment (called EX0) considered all driving factors, and the results were analyzed
to assess the characteristics of the N budget in thisregion. In addition, to separate the contribution of individual factors, we conducted the following series of sensitivity simulations:– EX1: fertilizer input was always fixed at the level in1901

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

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Fig. 1 Spatial distribution of nitrogen inputs in East Asia in 2000–2009. a Fertilizer, including both ammonium and nitrate, b organic nitrogen inmanure, c atmospheric deposition, including both ammonium and nitrate and both dry and wet depositions, and d biological nitrogen fixation– EX2: manure input was always fixed at the level in1901– EX3: atmospheric N deposition was always fixed at
the level in 1901
– EX4: land use was always fixed at the level in 1901
– EX5: fertilizer and manure inputs were always fixedat the levels in 1901
– EX6: land use, fertilizer, and manure inputs werealways fixed at the values in 1901
– EX7: manure was ignored (i.e., always zero)throughout the simulation
The EX1–4 simulations separated the effects of individual N inputs; EX5 separated the effect of direct N input to croplands; and EX6 separated the human impacts
on land systems. EX3 separated the effect of N inputs
from atmospheric deposition and indirectly included anthropogenic inputs. EX6 removed direct impacts of local
human manipulations and showed the effects caused byelevated CO2 concentration and climate change. EX7
was conducted to quantify the impact of ignoring manure inputs, which have been inconsistently considered
in terrestrial N model studies (discussed later).

The emission factor, which is defined as the mean atmospheric emission per unit mass of substrate or input
to a system, is a useful metric for greenhouse gas studies
because it facilitates estimates of emissions and clarification of regional characteristics. Here, the emission factor
for N2O was defined as the N2O emission per unit N input as chemical fertilizer, manure, and atmospheric deposition (Stehfest and Bouwman 2006) on a yearly basis.
The model simulation enabled us to estimate a representative emission factor for East Asia during a time when
N inputs were changing. Most results are shown on an
N2O weight basis (e.g., Tg N2O year–1) to facilitate conversion to a CO2-equivalent weight basis with a global
warming potential of 298 (IPCC 2013; 100-year horizon);the values can be converted to an N weight basis (e.g.,
Tg N year−1) by multiplying by 28/44.

Results
Regional N2O emissions

In the EX0 simulation (i.e., all inclusive and so presumably closest to reality), total N2O emissions from the soil
surface of East Asia in the 2000s (2000–2009) were estimated to be 2.03 Tg N2O year –1 (i.e., 1.29 Tg N year−1).

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

Natural ecosystems and croplands contributed 32.3%
and 67.7%, respectively, to the total emissions.
The spatial patterns of simulated N2O emissions fromnatural and cropland soils differed markedly (Fig. 2). In
most natural ecosystems (Fig. 2a, not weighted byland-use fractions), N2O emissions occurred at low rates
and land use patterns created a heterogeneous distributionof emissions. The relatively high rates in the middle of
China were consistent with the high rates of N depositionin that area (Fig. 1c). The N2O emissions from croplands
were relatively high (Fig. 2b, not weighted by land-use
fractions), especially in central China, western South
Korea, and parts of Japan. The total emission map (Fig. 2c,weighted by land-use fractions) showed that high-N2O
emissions occurred in central to southern parts of China
and parts of South Korea and Japan. In contrast, N2O
emissions were quite low in the interior regions covered
mainly by grasslands and alpine meadows.

Fig. 2 Emissions of N2O in East Asia in 2000–2009 in the EX0
simulation with VISIT. a Natural ecosystems per unit area, b
croplands per unit area, and c grid-based emissions obtained asa cropland-fraction weighted mean

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The simulated N2O emissions showed clear seasonalpatterns (Fig. 3). Regional N2O emissions were high from
June to September, the warm and rainy period of the monsoon climate cycle. Average regional N2O emission rates
varied from 0.03 Tg N2O month−1 in January to 0.38 Tg
N2O month−1 in July during the 2000s. The period of highemissions clearly corresponded to the season of agricultural cultivation and fertilizer application.

Historical changes in N2O emissions

In the EX0 experiment, regional soil N2O emissions
increased from 0.60 Tg N2O year–1 (0.38 Tg N year−1) in
1901 to 2.35 Tg N2O year –1 (1.50 Tg N year−1) in 2015
(Fig. 4a). The increase was especially evident after 1960,
i.e., the era of increased agricultural production stimulated by the Green Revolution. Between the 1960s andthe 2000s of EX0, N2O emissions from natural
ecosystems increased by 0.01 Tg N2O year−1 (+ 1.8%),
while emissions from croplands increased by 1.05 Tg
N2O year –1 (+ 331.2%). The dominant source therefore
shifted from natural ecosystems (66.9% of the total inthe1960s) to croplands (67.7% of the total in the 2000s).
Spatially, an increase of N2O emission was evident incropland areas (Fig. 5 and Additional file 1: Figure S1).
From 1901 to 2016, fertilizer and manure inputs to croplands in this region intensified from 5.8 Tg N year−1 to55.5 Tg N year−1, and atmospheric N deposition also
increased from 2.49 Tg N year−1 to 4.4 Tg N year−1(especially NH4+ deposition). Biological N fixation
showed a small increase of 0.2 Tg N year−1.
From the 1960s to the 2000s, temperatures over East
Asia increased by 0.88 °C on average, but annual precipitation did not change significantly (from 561 mm year−1
to 564 mm year−1). The very small increase of N2Oemissions driven by climate in the sensitivity simulation(EX6; Fig. 4), from 0.90 Tg N2O year−1 in the 1960s to
0.97 Tg N2O year−1 in the 2000s, indicated a weak influence of climate. When the effect of land use change wasincluded (EX5), the result did not vary much from thatof EX6 (i.e., from 0.91 Tg N2O year−1 to 0.99 Tg
N2O year−1), because N inputs from fertilizer and manure into croplands were kept at low levels.
In contrast, the sensitivity simulations for fertilizer,
manure, and N deposition inputs clearly suggested
strong influences from these driving factors. When N
deposition was fixed (EX3), regional N2O emissions in
the 2000s were estimated to be 1.87 Tg N2O year−1,
92.2% of the emission in EX0. When manure inputs
were fixed (EX2), regional N2O emissions were estimated to be 1.69 Tg N2O year−1, 83.5% of the emissions
in EX0. When chemical fertilizer input was fixed (EX1),
regional N2O emissions were estimated to be 1.42 Tg
N2O year−1, 70.3% of the emissions in EX0.

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

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Fig. 3 Monthly maps of soil N2O emissions in East Asia during 2000–2009 in the EX0 simulation with VISIT: from the top to bottom, northern
winter, spring, summer, and autumn

Cumulatively, the increases of the N inputs since 1950
led to about 70 Tg of additional N2O emissions to theatmosphere (Fig. 4b). Thus, the historical increase of soil
N2O emissions from this region was primarily (morethan half ) attributable to the increase of fertilizer input
to croplands and secondarily to increments of manure
use and atmospheric deposition.
Relationships with driving factors

The simulated annual soil N2O emissions from East Asia
were very weakly correlated with annual temperature
anomalies (Fig. 6a) but more closely correlated with annual precipitation anomalies (Fig. 6b). Note that linear

trends were removed from both the N2O emissions andclimate factors to focus on interannual variability (i.e.,
anomaly), defined as a year-to-year deviations from thelong-term linear trend. This result therefore implies thatinterannual variability of soil N2O emissions in this region
is limited mainly by soil wetness, which is largely determined by the precipitation anomaly (and partly by evapotranspiration variability). Among the N2O production
processes, denitrification, which occurs under anaerobic
conditions, was more closely related to precipitation
anomalies (coefficient of determination [R2] = 0.63) than
was nitrification (R2 = 0.08). This result is consistent withthe positive response of total N2O emissions toIto et al. Progress in Earth and Planetary Science(2018) 5:55

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outflows by NO3− leaching and NH3 volatilization withincreasing N inputs (data not shown). The simulated
N2O emission factor was not uniform throughout the
study region (Fig. 8b). The emission factor ranged fromlow values in interior dry regions and northern parts ofChina to high values in humid regions in southern partsof China, the Korean Peninsula, and Japan. The weakrelationship between the distribution of emission factors
and land use for agriculture (Fig. 8a, b) implied thatemission factors were primarily affected by climatic andintrinsic soil conditions.

Fig. 4 Interannual variability of total N2O emissions in East Asia
simulated by the VISIT model. a Annual emissions in the EX0 to EX7
simulations: EX0, control; EX1, fixed fertilizer; EX2, fixed manure input;
EX3, fixed atmospheric deposition; EX4, fixed land-use; EX5, fixed
fertilizer and manure; EX6, fixed fertilizer, manure, and land-use; andEX7, no manure. b Cumulative differences from the EX6 result

precipitation anomalies. During the twentieth century, thehighest annual N2O emission rate (2.28 Tg N2O year−1 inEX0) occurred in 1998, a very strong El Niño year withthe warmest temperature and highest precipitation of the
century.
The simulated regional N2O emissions were strongly
correlated with N inputs (Fig. 7 for EX0). The slope of alinear regression of N2O emission versus N inputs is ametric of N2O emission factor: in this study, 0.0216 Tg
N2O Tg− 1 N or 0.0138 Tg N-N2O Tg− 1 N (i.e., 1.38%).
The estimated emission factor for cropland, calculatedon the basis of direct N inputs to cropland, was almost
the same (1.38%) as the regional mean, which includes
both natural and agricultural ecosystems. The relationship between N inputs and N2O emissions during theperiod 1960–2015 was almost linear (R2 = 0.88), andthere were corresponding increases of residual N

Discussion
The model simulation allowed us to evaluate soil N2O
emissions and their spatial and temporal variability inEast Asia; the methodology we used may be applicableto other regions. Compared with global N2O emissions(18.3 Tg N2O year−1 in the 2000s; unpublished result ofa global simulation), the East Asia region accounts forabout 11% of the global total (5.9% of natural ecosystems
and 18.9% of croplands). Note that the global result iscomparable with previous studies (Syakila and Kroeze
2010; Wells et al. 2018): in 2002, 6–7 Tg N year−1 (9.4–
11 Tg N2O year−1) from soils and 4.9 Tg N year−1(7.7 Tg N2O year−1) from agriculture. Using the global
warming potential value of 298, we estimated the N2O
emission of 2.03 Tg N2O year− 1 to be equivalent to
605 Tg CO2-eq. year− 1 (on a carbon basis, 165 Tg
C year− 1). Soil N2O emissions can be a substantial fraction of the total greenhouse-gas budget for this region.
For example, an integrated synthesis of regional C budgets
by Piao et al. (2012) conducted an integrated synthesis ofregional C budgets and concluded that land ecosystems in
East Asia are a net CO2 sink of 224–413 Tg C year−1 atthe present time. The range of numbers reflects differences in estimation methods such as bottom-up summation and top-down inversion. The estimated soil N2O
emission is therefore likely to offset 40–74% of the mitigation effect of CO2 sequestration by terrestrial ecosystems
in this region. Reducing soil N2O emissions from the region should therefore contribute greatly not only to mitigation of global warming but also to amelioration ofenvironmental pollution (e.g., eutrophication and acidification) by reducing agricultural N use. For example, thedistribution of emission factors estimated in this study(Fig. 8b) may enable optimization of N fertilizer and manure use, which are related to both food production andenvironmental loads. Although not addressed in thisstudy, the simulation of N leaching and of NH3 and NO
emissions by the model leads to reductions of water use
and air pollution. In addition, changes of ecosystem N
stocks are linked with the degree of N limitation, which
can alter ecosystem structure (including biodiversity),
functions, and services (Aber et al. 1995). Such co-benefits

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Fig. 5 Historical changes of N2O emissions in the EX0 simulation with VISIT: a 1910, b 1940, c 1960, d 1970, e 1980, f 1990, g 2000, and h 2010

and trade-offs of N management are among the focal
themes of the International Nitrogen Management System(INMS, http://www.inms.international/). The INMS aims
at synthesizing the achievements and challenges on N research, and our research may therefore make some contribute to activities such as regional demonstration.

The large soil N2O emissions from this region are attributable to extensive use of land for agriculture and high N
inputs from chemical fertilizer, organic manure, and atmospheric deposition. Note that other anthropogenic emissionsof N2O from industrial and urban sectors (not included inthis study) are likely substantial in this region (e.g., Ohara

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

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Fig. 7 Relationship between nitrogen inputs and N2O emissions inEast Asia in the EX0-simulation results of the VISIT model. Each circle
represents an annual value. Nitrogen inputs include fertilizer,
manure, atmospheric deposition, and biological fixation

Fig. 6 Relationships between regional N2O emissions in East Asia andanomalies of meteorological factors: a temperature and b precipitation.
Annual values of the EX0 simulation results (i.e., standard run) by the
VISIT model were used after removal of long-term linear trends

et al. 2007). Emissions of N2O from the soil surface haveapparently increased during the last few decades in East
Asia, mainly because of human interventions in the N
cycle, as demonstrated by the sensitivity simulations. Thesesimulation results, in conjunction with data for other factors such as food production and environmental pollution,
should inform identification of strategies for mitigation of
global warming and N management in this region (Shibata
et al. 2015). The mean emission factor of 1.38% for N2O in
East Asia estimated in this study is slightly higher than thedefault value of the IPCC guideline, i.e., 1% for mineral
fertilizer and organic amendments to cropland, but within
the range of uncertainty (0.3–3.0%) (IPCC 2006). Several
meta-analysis studies have indicated that emission factors

derived from observations of soil N2O emissions vary
widely, from 0.03 to 10% (e.g., Kim et al. 2013; Charles etal. 2017). The mean value estimated in this study is within
this range, but there was much variability of both observations and model estimates (e.g., inter-grid standard deviation, 2.09%). The simulated N2O emission factor (Fig. 8b)varied between low values (~ 1%) in northern area and highvalues (> 1%) in the southern area, irrespective of agricultural contributions (Fig. 8a). Remarkably, higher emission
factors were found in the Tibetan Plateau, where N2O
emissions were low because of low N inputs (Fig. 1) andcool temperatures. In contrast, emission factors were low innorth-central China, which has been intensively cultivated(Additional file 1: Figure S1) and is receiving high N inputs(Fig. 1). Such regional heterogeneity in the emission factor
implies a need for diverse regulatory mechanisms. In themodel used in this study, N2O emission rates from both nitrification and denitrification were sensitive to soil moisture
conditions, which are affected by precipitation and soil
water-holding capacity. In this regard, the model estimationof N2O emissions from paddy fields could be improved byconsidering water management and associated characteristics of the N cycle specific to the land cover type. For example, an empirical study by Yan et al. (2003) obtained an
N2O emission factor of 0.93% (versus 1.38% in this study).
As noted by Lesschen et al. (2011), variability of N2O emission factors influences regional greenhouse gas accounting.
The model used in this study is one of the models usedin the terrestrial N model intercomparison and global
N2O synthesis project (Tian et al. 2018). Comparison ofsoil N2O emissions simulated by different biogeochemical

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

Fig. 8 Spatial characteristics of simulated soil N2O emissions in the
EX0-simulation results for 2000–2009 by VISIT. a Percentage
contributions of agricultural emissions, and b emission factors(percentage of N2O emissions per unit of nitrogen input)

models has shown that the VISIT model provides
broad-scale estimates comparable to those of other contemporary models. However, it is difficult to relate the regional N2O emissions simulated here with atmosphericmeasurements for validation because of the long atmospheric residence time of N2O, urban emissions, and exchanges of N2O with the stratosphere (Ishijima et al.
2010). The trends of the observed atmospheric N2O concentrations in East Asia increased linearly with time, butseasonal patterns were unclear (Additional file 1: FigureS2). Comparison with the results of atmospheric inversion
studies would provide further insights. For example,
Thompson et al. (2014) have reported surface N2O fluxes
inferred using atmospheric inversion models based on
chemistry transport models. Their estimates of N2O emissions in East Asia have indicated a spatial distribution
comparable to that simulated in the present study, e.g.,high emissions in parts of southern China and low emissions in interior drylands and northern areas. The peakemission intensity in parts of southern China, around
8 kg N ha−1 yr−1, is also comparable between the two studies. Recently, Wells et al. (2018) made a similar analysis
using a different atmospheric model and data-assimilation
techniques and obtained comparable results for total (i.e.,
including soil and other sources) N2O emissions. Saikawa
et al. (2014) have presented another model-based estimateof global and regional N2O emissions derived from atmospheric observational data. In their results, Southern Asia

Page 10 of 13(i.e., East, South, and Southeast Asia) was estimated to release N2O at a rate of 3–5 Tg N year−1 in 1995–2008; thisestimate is consistent with our model estimate of 3.3 Tg
N year−1 for the corresponding area.
It is difficult, however, to compare simulated regional
N2O fluxes directly with field observations based onchamber methods, because of the large difference inspatial scales: i.e., square meter for chamber measurements vs 104–106 km2 of model simulations. Recently,
several numerical techniques have been developed to extrapolate (i.e., scale up) field measurements to the global
scale (e.g., Jung et al. 2011; Tramontana et al. 2016). A
more realistic strategy for validating model estimateswould be a comparison with these up-scaled flux estimates. Hashimoto (2012) has provided a global datasetof soil greenhouse gas emissions extrapolated from fielddata using a statistical model based on extensive fielddata (e.g., Morishita et al. 2007). Spatial patterns of theestimated N2O emissions (Additional file 1: Figure S3a, arevised estimate of Hashimoto 2012) is roughly comparable with those of the present study. Both studies capture higher emissions from parts of southern China andfrom hot spots in central China. In general, however, theintensities of the emissions estimated by Hashimoto(2012) in the study region are lower (Additional file 1:
Figure S3b: − 25% on average), probably because his
study used data obtained mainly from temperate forests(Morishita et al. 2007). As noted by Hashimoto (2012),
statistical methods might capture background emissions
from croplands. The VISIT-estimated emissions innatural ecosystems are closer to the regional estimatesof Hashimoto (2012). Further validations using multiple
observational data, similar to the validation conductedin this study, will clearly be needed to evaluate andreduce uncertainties in model-based assessments.
Several limitations of this study remain. First, thisstudy used a single model (i.e., VISIT) and is therefore
susceptible to model-specific bias. Such a study basedon a small number of models is effective for conducting
many simulations to separate the influence of driving
factors, but a composite of multiple model results islikely to provide more robust outcomes. A recent modelintercomparison project (Tian et al. 2018) has provided
us with an opportunity to assess inter-model variability
and to obtain less biased estimates. Moreover, systematic
analyses of the formulations and parameters related topivotal processes are required to scrutinize inter-model
differences. Second, our incomplete understanding of N
biogeochemical processes leads to insufficient formulations in N models. The complexity of biochemical processes mediated by microbes such as mineralization,
nitrification, and denitrification (Butterbach-Bahl et al.
2013; Isobe and Ohte 2014) makes it difficult to formulate mechanisms accurately. Physical processes such as

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

freezing and thawing and their impacts on the N cycle(Yanai et al. 2011; Hosokawa et al. 2017) are sufficiently
complicated that most models do not adequately
characterize them. Also, human manipulations by agricultural practices such as reduced tillage (Koga et al. 2004),
enhanced-efficiency fertilizer (Akiyama et al. 2010), andinoculation of soils with microbes that process N more efficiently (Itakura et al. 2012) can affect N2O emissions but
have not been adequately incorporated into biogeochemical models. Third, there are still uncertainties in the forcing input data. In this study, we used popular datasets todrive the model. Recent studies have been providing
updated datasets for fertilizer (e.g., Lu and Tian 2017),
manure (e.g., Wolf et al. 2017; Zhang et al. 2017), and landuse (Lawrence et al. 2016). Use of these recent and forthcoming datasets in future studies is expected to considerably improve the accuracy of the simulated results.

Conclusions
Our estimated N2O emissions from the soil surface ofEast Asia of 2.03 Tg N2O year−1 in the 2000s based on aprocess-based model showed that the region was a significant source of N2O emissions to the atmosphere.
The estimated mean emission factor, 1.38%, was robust
through time but showed high-spatial heterogeneity.
Compared to previous studies, this study used
up-to-date forcing data, simulated longer periods, andprovided detailed emission maps. Despite the uncertainty of the model estimates, this study demonstrated
the effectiveness of model-based evaluations of regional
N2O budgets, which are difficult to obtain from direct
methods by tower and satellite measurements. The capability of male long-term analyses is another advantageof the model-based approach. Our historical simulation
results imply that there has been a significant increase ofregional soil N2O emissions, by a factor of three or
more, mainly because of human activities. Our results
also suggest opportunities for mitigation of climatic impacts through application of appropriate emission reduction measures such as optimization of fertilizer use andagricultural practices (e.g., tillage and water management). Because of the importance of N2O as a greenhouse gas, contemporary climate policies such as theParis Accord of the United Nations Framework Convention of Climate Change should pay more attention toN2O emissions. Also, management of N2O emissions
from soil should provide other benefits in terms of environmental quality through reduction of excessive N inputs to water and air and losses from soil. Further
studies are nevertheless required to elucidate the biogeochemical processes involved in N dynamics, the interactions between the N cycle and the water and C cycles,
and the best way to enlarge the spatial scale of models.

Page 11 of 13

Additional file
Additional file 1: Figure S1. Cropland fraction in East Asia derived fromHurtt et al. (2011). Figure S2. Comparison between the simulatedmonthly N2O emissions and observed atmospheric N2O concentrations inEast Asia. The observational data were obtained from the World Data
Center of Greenhouse Gases (https://gaw.kishou.go.jp/). Figure S3.
Comparison of the VISIT estimations (EX0) with the estimates ofHashimoto (2012, updated). (a) Annual N2O emissions and (b) grid-based
scatter diagram. Orange line shows linear regression (y [N2O emissions by
VISIT] = 1.25 x [N2O emissions by Hashimoto (2012)] + 0.0596, in kg N2O
ha− 1 yr.− 1; R2 = 0.44). (DOCX 1792 kb)
Abbreviations
CRU: Climate Research Unit; IGBP-DIS: International Geosphere-Biosphere
Program Data and Information System; VISIT: Vegetation Integrative
SImulator for Trace gases
Acknowledgements
This work is a contribution to the N2O-integrated synthesis of the Global
Carbon Project and the regional demonstration of the Integrated Nitrogen
Management System.
Funding
This study was supported by the Japan Society for the Promotion of
Sciences KAKENHI grant (nos. 26281014 and 17H01867) and by theEnvironmental Research Fund (2-1710) of the Ministry of the Environment,
Japan, and Environmental Restoration and Conservation Agency.
Availability of data and materials
Please contact author for data requests.
Authors’ contributions
AI designed the research, conducted simulations and analyses, and drafted
the manuscript. MI contributed to model development. KN, KI, and SH
examined results and contributed to the manuscript. All authors read andapproved the final manuscript.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki
305-8506, Japan. 2Japan Agency for Marine-Earth Science and Technology,
3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
3
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan. 4Forestry and Forest Product Research Institute, 1 Matsunosato,
Tsukuba, Ibaraki 305-8687, Japan. 5The University of Tokyo, 1-1-1 Yayoi,
Bunkyo-ku, Tokyo 113-8657, Japan.
Received: 30 May 2018 Accepted: 6 September 2018

References
Aber JD, Magill A, McNulty SG, Boone RD, Nadelhoffer KJ, Downs M, Hallett R(1995) Forest biogeochemistry and primary production altered by nitrogensaturation. Water Air Soil Poll 85:1665–1670
Akiyama H, Yan X, Yagi K (2010) Evaluation of effectiveness of enhancedefficiency fertilizers as mitigation options for N2O and NO emissions fromagricultural soils: meta-analysis. Glob Chang Biol 16:1837–1846. https://doi.
org/10.1111/j.1365-2486.2009.02031.x
Bellingrath-Kimura SD, Kishimoto-Mo AW, Oura N, Sekikawa S, Yonemura S, Sudo
S, Hayakawa A, MInamikawa K, Takata Y, Hara H (2015) Differences in thespatial variability among CO2, CH4, and N2O gas fluxes from an urban forest
soil in Japan. Ambio 44:55–66. https://doi.org/10.1007/s13280-014-0521-z

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

Butterbach-Bahl K, Baggs EM, Dannenmann M, Kiese R, Zechmeister-Boltenstern S(2013) Nitrous oxide emissions from soils: how well do we understand theprocesses and their controls. Phil Trans R Soc A:20130122. https://doi.org/10.
1098/rstb.2013.0122
Charles A, Rochette P, Whalen JK, Angers DA, Chantigny MH, Bertrand N (2017)
Global nitrous oxide emission factors from agricultural soils after addition oforganic amendments: a meta-analysis. Agr Ecosys Env 236:88–98. https://doi.
org/10.1016/j.agee.2016.11.021
Cleveland CC, Townsend AR, Schimel DS, Fisher H, Howarth RW, Hedin LO,
Perakis SS, Latty EF, Von Fischer JC, Elseroad A, Wasson MF (1999) Global
patterns of terrestrial biological nitrogen (N2) fixation in natural ecosystems.
Glob Biogeochem Cycles 13:623–645
Davidson EA (2009) The contribution of manure and fertilizer nitrogen toatmospheric nitrous oxide since 1860. Nat Geosci 2:659–662. https://doi.org/
10.1038/NGEO608
Del Grosso SJ, Ojima DS, Parton WJ, Stehfest E, Heistemann M, DeAngelo B, Rose
S (2009) Global scale DAYCENT model analysis of greenhouse gas emissions
and mitigation strategies for cropped soils. Glob Planet Chang 67:44–50.
https://doi.org/10.1016/j.gloplacha.2008.12.006
Dentener F, Drevel J, Lamarque JF, Bey I, Eickhout B, Fiore AM, Hauglusteine D,
Horowitz LW, Krol M, Kulshreshtha UC, Lawrence M, Galy-Lacaux C, Rast S,
Shindell D, Stevenson D, Van Noije T, Atherton C, Bell N, Bergman D, Butler T,
Cofala J, Collins B, Doherty R, Ellingsen K, Galloway J, Gauss M, Montanaro V,
Müller JF, Pitari G, Rodriguez J et al (2006) Nitrogen and sulfur deposition on
regional and global scales: a multimodel evaluation. Glob Biogeochem
Cycles 20:GB4003. https://doi.org/10.1029/2005GB002672
Galloway JN, Dentener FJ, Capone DG, Boyer EW, Howarth RW, Seitzinger SP,
Asner GP, Cleveland CC, Green PA, Holland EA, Karl DM, Michaels AF, Porter
JH, Townsend AR, Vörösmarty CJ (2004) Nitrogen cycles: past, present, andfuture. Biogeochem 70:153–226
Hall FG, de Colstoun EB, Collatz GJ, Landis D, Dirmeyer P, Betts A, Huffman GJ,
Bounoua L, Meeson B (2006) ISLSCP Initiative II global data sets: surface
boundary conditions and atmospheric forcings for land-atmosphere studies.
J Geophys Res 111:D22S01. https://doi.org/10.1029/2006JD007366
Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids ofmonthly climatic observations – the CRU TS3.10 dataset. Int J Climatol 34:
623–642. https://doi.org/10.1002/joc.3711
Hashimoto S (2012) A new estimate of global soil greenhouse gas fluxes using asimple data-oriented model. PLoS One 7:e41962. https://doi.org/10.1371/
journal.pone.0041962
Hashimoto S, Morishita T, Sakata T, Ishizuka S (2011) Increasing trends of soil
greenhouse gas fluxes in Japanese forests from 1980 to 2009. Sci Rep 1:116.
https://doi.org/10.1038/srep00116
Hosokawa N, Isobe K, Urakawa R, Tateno R, Fukuzawa K, Watanabe T, Shibata H(2017) Soil freeze–thaw with root litter alters N transformations during thedormant season in soils under two temperate forests in northern Japan. Soil
Biol Biochem 114:270–278. https://doi.org/10.1016/j.soilbio.2017.07.025
Hurtt GC, Chini LP, Frolking S, Betts RA, Feddema J, Fischer G, Fisk JP, Hibbard K,
Houghton RA, Janetos A, Jones CD, Kindermann G, Kinoshita T, Klein
Goldewijk K, Riahi K, Shevliakova E, Smith S, Stehfest E, Thomson A, Thornton
P, van Vuuren DP, Wang YP (2011) Harmonization of land-use scenarios forthe period 1500–2100: 600 years of global gridded annual land-use
transitions, wood harvest, and resulting secondary lands. Clim Chang 109:
117–161. https://doi.org/10.1007/s10584-011-0153-2
IGBP-DIS (2000) Global Soil Data Products CD-ROM. Oak Ridge National
Laboratory, Oak Ridge
Inatomi M, Ito A, Ishijima K, Murayama S (2010) Greenhouse gas budget of a cooltemperate deciduous broadleaved forest in Japan estimated using a processbased model. Ecosystems 13:472–483. https://doi.org/10.1007/s10021-010-9332-7
Intergovernmental Panel on Climate Change (IPCC) (2006) 2006 IPCC Guidancefor National Greenhouse Gas Inventories. Institute for Global Environmental
Strategies, Hayama
Intergovernmental Panel on Climate Change (IPCC) (2013) Climate Change 2013: The
Physical Science Basis. Cambridge University Press, Cambridge and New York
Ishijima K, Patra PK, Takigawa M, Machida T, Matsueda H, Sawa Y, Steele LP,
Krummel P, Langenfelds RL, Aoki S, Nakazawa T (2010) Stratosphericinfluence on the seasonal cycle of nitrous oxide in the troposphere as
deduced from aircraft observations and model simulations. J Geophys Res
115:S20308. https://doi.org/10.1029/2009JD013322
Isobe K, Ohte N (2014) Ecological perspectives on microbes involved in N-cycling.
Microb Env 29:4–16. https://doi.org/10.1264/jsme2.ME13159

Page 12 of 13

Itakura M, Uchida Y, Akiyama H, Hoshino YT, Shimomura Y, Morimoto S, Tago K,
Wang Y, Hayakawa C, Uetake Y, Sánchez C, Eda S, Hayatsu M, Minamisawa K(2012) Mitigation of nitrous oxide emissions from soils by Bradyrhizobium
japonicum inoculation. Nat Clim Chang 3:208–212. https://doi.org/10.1038/
NCLIMATE1734
Ito A (2008) The regional carbon budget of East Asia simulated with a terrestrial
ecosystem model and validated using AsiaFlux data. Agr For Meteorol 148:
738–747. https://doi.org/10.1016/j.agrformet.2007.12.007
Ito A, Inatomi M (2012a) Use and uncertainty evaluation of a process-basedmodel for assessing the methane budget of global terrestrial ecosystems.
Biogeosci 9:759–773. https://doi.org/10.5194/bg-9-759-2012
Ito A, Inatomi M (2012b) Water-use efficiency of the terrestrial biosphere: amodel analysis on interactions between the global carbon and water cycles.
J Hydrometeorol 13:681–694. https://doi.org/10.1175/JHM-D-10-05034.1
Ito A, Nishina K, Reyer CPO, François L, Henrot A-J, Munhoven G, Jacquemin I, Tian
H, Yang J, Pan S, Morfopoulos C, Betts R, Hickler T, Steinkamp J, Ostberg S,
Schaphoff S, Ciais P, Chang J, Rafique R, Zeng F, Zhao F (2017) Photosynthetic
productivity and its efficiencies in ISIMIP2a biome models: benchmarking for
impact assessment studies. Env Res Lett 12:085001. https://doi.org/10.1088/
1748-9326/aa7a19
Ito A, Oikawa T (2002) A simulation model of the carbon cycle in land
ecosystems (Sim-CYCLE): a description based on dry-matter production
theory and plot-scale validation. Ecol Model 151:147–179
Jung M, Reichstein M, Margolis HA, Cescatti A, Richardson AD, Arain MA, Arneth
A, Bernhofer C, Bonal D, Chen J, Gianelle D, Gobron N, Kiely G, Kutsch W,
Lasslop G, Law BE, Lindroth A, Merbold L, Montagnani L, Moors EJ, Papale D,
Sottocornola M, Vaccari F, Williams C (2011) Global patterns of landatmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived
from eddy covariance, satellite, and meteorological observations. J Geophys
Res 116:G00J07. https://doi.org/10.1029/2010JG001566
Kato E, Kinoshita T, Ito A, Kawamiya M, Yamagata T (2013) Evaluation of spatially
explicit emission scenario of land-use change and biomass burning using aprocess based biogeochemical model. J Land Use Sci 8:104–122. https://doi.
org/10.1080/1747423X.2011.628705
Keeling CD, Whorf TP (2009) Atmospheric CO2 concentrations - Mauna Loa
observatory, Hawaii, 1958–2008. Numerical data package NDP001. Oak Ridge
National Laboratory, Oak Ridge. https://doi.org/10.3334/CDIAC/atg.ndp001
Kim D-G, Hernandez-Ramirez G, Giltrap D (2013) Linear and nonlinear dependency
of direct nitrous oxide emissions on fertilizer nitrogen input: a meta-analysis.
Agr Ecosys Env 168:53–65. https://doi.org/10.1016/j.agee.2012.02.021
Kim D-G, Vargas R, Bond-Lamberty B, Turetsky MR (2012) Effects of soil rewetting
and thawing on soil gas fluxes: a review of current literature and suggestions for
future research. Biogeoscie 9:2459–2483. https://doi.org/10.5194/bg-9-2459-2012
Koga N, Tsuruta H, Sawamoto T, Nishimura S, Yagi K (2004) N2O emission andCH4 uptake in arable fields managed under conventional and reduced tillage
cropping systems in northern Japan. Glob Biogeochem Cycles 18:GB4025.
https://doi.org/10.1029/2004GB002260
Lawrence DM, Hurtt GC, Arneth A, Brovkin V, Calvin KV, Jones AD, Jones CD,
Lawrence PJ, de Noblet-Ducoudré N, Pongratz J, Seneviratne SI, Shevliakova
E (2016) The Land Use Model Intercomparison Project (LUMIP) contribution
to CMIP6: rationale and experimental design. Geosci Model Dev 9:2973–2998.
https://doi.org/10.5194/gmd-9-2973-2016
Lesschen JP, Velthof GL, de Vries W, Kros J (2011) Differentiation of nitrous oxide
emission factors for agricultural soils. Env Pollut 159:3215–3222. https://doi.
org/10.1016/j.envpol.2011.04.001
Lu C, Tian H (2017) Global nitrogen and phosphorus fertilizer use for
agriculture production in the past half century: shifted hot spots andnutrient imbalance. Earth Sys Sci Data 9:181–192. https://doi.org/10.5194/
essd-9-181-2017
Monfreda C, Ramankutty N, Foley JA (2008) Farming the planet: 2. Geographic
distribution of crop areas, yields, physiological types, and net primary
production in the year 2000. Glob Biogeochem Cycles 22:GB1022. https://doi.
org/10.1029/2007GB002947
Morishita T, Sakata T, Takahashi M, Ishizuka S, Mizoguchi T, Inagaki Y, Terazawa K,
Sawata S, Igarashi M, Yasuda H, Koyama Y, Suzuki Y, Toyota N, Muro M, Kinjo
M, Yamamoto H, Ashiya D, Kanazawa Y, Hashimoto T, Umata H (2007)
Methane uptake and nitrous oxide emission in Japanese forest soils and theirrelationship to soil and vegetation types. Soil Sci Plant Nutr 53:678–691
Muhr J, Goldberg SD, Borken W, Gebauer G (2008) Repeated drying-rewetting
cycles and their effects on the emission of CO2, N2O, NO, and CH4 in a forest
soil. J Plant Nutr Soil Sci 171:719–728. https://doi.org/10.1002/jpln.200700302

Ito et al. Progress in Earth and Planetary Science(2018) 5:55

Nishina K, Akiyama H, Nishimura S, Sudo S, Yagi K (2012) Evaluation of
uncertainties in N2O and NO fluxes from agricultural soil using a hierarchical
Bayesian model. J Geophys Res 117. https://doi.org/10.1029/2012JG002157
Nishina K, Ito A, Hanasaki N, Hayashi S (2017) Reconstruction of spatially detailed
global map of NH4+ and NO3− application in synthetic nitrogen fertilizer.
Earth Sys Sci Data 9:149–162. https://doi.org/10.5194/essd-9-149-2017
Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE (2010) CO2
enhancement of forest productivity constrained by limited nitrogen
availability. Proc Natl Acad Sci U S A 107:19368–19373. https://doi.org/10.
1073/pnas.1006463107
Ohara T, Akimoto H, Kurokawa J, Horii N, Yamaji K, Yan X, Hayasaka T (2007) An
Asian emission inventory of anthropogenic emission sources for the period
1980-2020. Atm Chem Phys 7:4419–4444
Parton WJ, Mosier AR, Ojima DS, Valentine DW, Schimel DS, Weier K, Kulmala AE(1996) Generalized model for N2 and N2O production from nitrification anddenitrification. Glob Biogeochem Cycles 10:401–412
Piao S, Ito A, Li SG, Huang Y, Ciais P, Wang XH, Peng SS, Nan HJ, Zhao C,
Ahlström A, Andres RJ, Chevallier F, Fang JY, Hartmann J, Huntingford C,
Jeong S, Levis S, Levy PE, Li JS, Lomas MR, Mao JF, Mohammat A, Muraoka H,
Peng CH, Peylin P, Poulter B, Shen ZH, Shi X, Sitch S, Tao S et al. (2012) The
carbon budget of terrestrial ecosystems in East Asia over the last two
decades. Biogeosci 9:3571–3586. https://doi.org/10.5194/bg-9-3571-2012
Potter P, Ramankutty N, Bennett EM, Donner SD (2010) Characterizing the spatial
patterns of global fertilizer application and manure production. Earth Interact
14:1–23. https://doi.org/10.1175/2009EI288.1
Ravishankara AR, Daniel JS, Portmann RW (2009) Nitrous oxide (N2O): thedominant ozone-depleting substance emitted in the 21st century. Science
326:123–125. https://doi.org/10.1126/science.1176985
Saikawa E, Prinn RG, Dlugokencky E, Ishijima K, Dutton GS, Hall BD, Langenfelds R,
Tohjima Y, Machida T, Manizza M, Rigby M, O'Doherty S, Patra PK, Harth CM,
Weiss RF, Krummel PB, van der Schoot M, Fraser PJ, Steele LP, Aoki S, Nakazawa
T, Elkins JW (2014) Global and regional emissions estimates for N2O. Atm Chem
Phys 14:4617–4641. https://doi.org/10.5194/acp-14-4617-2014
Shibata H, Branquinho C, McDowell WH, Mitchell MJ, Monteith DT, Tang J, Arvola
L, Cruz C, Cusack DF, Halada L, Kopáček J, Máguas C, Sajidu S, Schubert H,
Tokuchi N, Záhora J (2015) Consequence of altered nitrogen cycles in the
coupled human and ecological system under changing climate: the need forlong-term and site-based research. Ambio 44:178–193. https://doi.org/10.
1007/s13280-014-0545-4
Shindo J, Okamoto K, Kawashima H (2003) A model-based estimation of nitrogenflow in the food production-supply system and its environmental effects inEast Asia. Ecol Model 169:197–212
Stehfest E, Bouwman L (2006) N2O and NO emission from agricultural fields andsoils under natural vegetation: summarizing available measurement data andmodeling of global annual emissions. Nutrient Cycl Agroecosys 74:207–228.
https://doi.org/10.1007/s10705-006-9000-7
Sudo K, Takahashi M, Kurokawa J-I, Akimoto H (2002) CHASER: a global chemical
model of the troposphere. 1. Model description. J Geophys Res 107:4339.
https://doi.org/10.1029/2001JD001113
Syakila A, Kroeze C (2010) The global nitrous oxide budget revisited. GHG
Measure Manage 1:17–26. https://doi.org/10.3763/ghgmm.2010.0007
Thompson RL, Chevallier F, Crotwell AM, Dutton G, Langenfelds R, Prinn RG,
Weiss RF, Tohjima Y, Nakazawa T, Krummel PB, Steele LP, Fraser P, O'Doherty
S, Ishijima K, Aoki S (2014) Nitrous oxide emissions 1999 to 2009 from a
global atmospheric inversion. Atm Chem Phys 14:1801–1817. https://doi.org/
10.5194/acp-14-1801-2014
Tian H, Xu X, Lu C, Liu M, Ren W, Chen G, Melillo J, Liu J (2011) Net exchanges ofCO2, CH4, and N2O between China’s terrestrial ecosystems and theatmosphere and their contributions to global climate warming. J Geophys
Res 116:G02011. https://doi.org/10.1029/2010JG001393
Tian H, Yang J, Lu C, Xu R, Canadell JG, Jackson R, Arneth A, Chang J, Chen G,
Ciais P, Gerber S, Ito A, Huang Y, Joos F, Lienert S, Messina P, Olin S, Pan S,
Peng C, Saikawa E, Thompson RL, Vuichard N, Winiwarter W, Zaehle S, Zhang
B, Zhang K, Zhu Q (2018) The global N2O Model Intercomparison Project(NMIP): objectives, simulation protocol and expected products. Bull Am
Meteorol Soc 99:1231–1251. https://doi.org/10.1175/BAMS-D-17-0212.1
Tramontana G, Jung M, Schwalm CR, Ichii K, Camps-Valls G, Ráduly B, Reichstein
M, Arain MA, Cescatti A, Kiely G, Merbold L, Serrano-Ortiz P, Sickert S, Wolf S,
Papale D (2016) Predicting carbon dioxide and energy fluxes across global
FLUXNET sites with regression algorithms. Biogeosci 13:4291–4313. https://doi.org/10.5194/bg-13-4291-2016

Page 13 of 13

Wells KC, Millet DB, Bousserez N, Henze DK, Griffis TJ, Chaliyakunnel S, Dlugokencky
EJ, Saikawa E, Xiang G, Prinn RG, O’Doherty S, Young D, Weiss RF, Dutton GS,
Elkins JW, Krummel PB, Langenfelds R, Steele LP (2018) Top-down constraints
on global N2O emissions at optimal resolution: application of a new dimension
reduction technique. Atm Chem Phys 18:735–756. https://doi.org/10.5194/acp18-735-2018
Wolf J, Asrar GR, West TO (2017) Revised methane emissions factors and spatiallydistributed annual carbon fluxes for global livestock. Carbon Balance Manag
12:16. https://doi.org/10.1186/s13021-017-0084-y
Xu-Ri, Prentice IC, Spahni R, Niu HS (2012) Modelling terrestrial nitrous oxide
emissions and implications for climate feedback. New Phytol 196:472–488.
https://doi.org/10.1111/j.1469-8137.2012.04269.x
Yan X, Akimoto H, Ohara T (2003) Estimation of nitrous oxide, nitric oxide andammonia emissions from croplands in East, Southeast and South Asia. Glob
Chang Biol 9:1080–1096
Yanai Y, Hirota T, Iwata Y, Nemoto M, Nagata O, Koga N (2011) Accumulation of
nitrous oxide and depletion of oxygen in seasonally frozen soils in northern
Japan – snow cover manipulation experiments. Soil Biol Biochem 43:1779–1786.
https://doi.org/10.1016/j.soilbio.2010.06.009
Zaehle S, Ciais P, Friend AD, Prieur V (2011) Carbon benefits of anthropogenic
reactive nitrogen offset by nitrous oxide emissions. Nat Geosci 4:601–605.
https://doi.org/10.1038/NGEO1207
Zhang B, Tian H, Lu C, Dangal SRS, Yang J, Pan S (2017) Global manure nitrogenproduction and application in cropland during 1860–2014: a 5 arcmin gridded
global dataset for earth system modeling. Earth Sys Sci Data 9:667–678. https://doi.org/10.5194/essd-9-667-2017