Differentiation of nitrous oxide emission factors for agricultural soils.
ABSTRACT Nitrous oxide (N(2)O) direct soil emissions from agriculture are often estimated using the default IPCC emission factor (EF) of 1%. However, a large variation in EFs exists due to differences in environment, crops and management. We developed an approach to determine N(2)O EFs that depend on N-input sources and environmental factors. The starting point of the method was a monitoring study in which an EF of 1% was found. The conditions of this experiment were set as the reference from which the effects of 16 sources of N input, three soil types, two land-use types and annual precipitation on the N(2)O EF were estimated. The derived EF inference scheme performed on average better than the default IPCC EF. The use of differentiated EFs, including different regional conditions, allows accounting for the effects of more mitigation measures and offers European countries a possibility to use a Tier 2 approach.
- SourceAvailable from: Mahdi Al-Kaisi[Show abstract] [Hide abstract]
ABSTRACT: In-field management practices of corn cob and residue mix (CRM) as a feedstock source for ethanol production can have potential effects on soil greenhouse gas (GHG) emissions. The objective of this study was to investigate the effects of CRM piles, storage in-field, and subsequent removal on soil CO2 and N2O emissions. The study was conducted in 2010–2012 at the Iowa State University, Agronomy Research Farm located near Ames, Iowa (42.0°′N; 93.8°′W). The soil type at the site is Canisteo silty clay loam (fine-loamy, mixed, superactive, calcareous, mesic Typic Endoaquolls). The treatments for CRM consisted of control (no CRM applied and no residue removed after harvest), early spring complete removal (CR) of CRM after application of 7.5 cm depth of CRM in the fall, 2.5 cm, and 7.5 cm depth of CRM over two tillage systems of no-till (NT) and conventional tillage (CT) and three N rates (0, 180, and 270 kg N ha−1) of 32% liquid UAN (NH4NO3) in a randomized complete block design with split–split arrangements. The findings of the study suggest that soil CO2 and N2O emissions were affected by tillage, CRM treatments, and N rates. Most N2O and CO2 emissions peaks occurred as soil moisture or temperature increased with increase precipitation or air temperature. However, soil CO2 emissions were increased as the CRM amount increased. On the other hand, soil N2O emissions increased with high level of CRM as N rate increased. Also, it was observed that NT with 7.5 cm CRM produced higher CO2 emissions in drought condition as compared to CT. Additionally, no differences in N2O emissions were observed due to tillage system. In general, dry soil conditions caused a reduction in both CO2 and N2O emissions across all tillage, CRM treatments, and N rates.Applied Soil Ecology 05/2015; 89. · 2.21 Impact Factor
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ABSTRACT: Genome scale metabolic modelling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the ‘division-of-labour’ between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO3- or NH4+). The ‘division-of-labour’ between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step towards autonomous modelling of whole plant metabolism.Frontiers in Plant Science 01/2015; 6(4). · 3.64 Impact Factor
- Soil Research 01/2012; 50(3):188. · 1.24 Impact Factor
Differentiation of nitrous oxide emission factors for agricultural soils
Jan Peter Lesschen*, Gerard L. Velthof, Wim de Vries, Johannes Kros
Alterra, Wageningen UR, P.O. Box 47, 6700 AA Wageningen, The Netherlands
a r t i c l e i n f o
Received 25 March 2011
Accepted 1 April 2011
Tier 2 approach
a b s t r a c t
Nitrous oxide (N2O) direct soil emissions from agriculture are often estimated using the default IPCC
emission factor (EF) of 1%. However, a large variation in EFs exists due to differences in environment,
crops and management. We developed an approach to determine N2O EFs that depend on N-input
sources and environmental factors. The starting point of the method was a monitoring study in which an
EF of 1% was found. The conditions of this experiment were set as the reference from which the effects of
16 sources of N input, three soil types, two land-use types and annual precipitation on the N2O EF were
estimated. The derived EF inference scheme performed on average better than the default IPCC EF. The
use of differentiated EFs, including different regional conditions, allows accounting for the effects of more
mitigation measures and offers European countries a possibility to use a Tier 2 approach.
? 2011 Elsevier Ltd. All rights reserved.
Nitrous oxide (N2O) is one of the major greenhouse gasses with
a contribution of 8% to the anthropogenic global warming (IPCC,
2007). Fifty to sixty percent of the anthropogenic induced N2O
emissions comes from agriculture of which the major part is direct
emission from agricultural soils (Mosier et al., 1998). The N2O soil
emissions from applied fertilizer and manures are often estimated
using a default emission factor (EF). In the IPCC 2006 guidelines the
updated default EF for N inputs from mineral fertilizers, organic
amendments and crop residues (EF1) is 1% (IPCC, 2006; De Klein
et al., 2006), i.e. the direct fertilizer-derived N2O soil emission is
equalto 1%of theamountofNapplied.Thisfactorisbased ona large
number of measurements (Bouwman et al., 2002a,b; Stehfest and
Bouwman, 2006; Novoa and Tejeda, 2006), which lead to a mean
Nevertheless, a large variation in EFs exists (Stehfest and Bouwman,
2006; Flechard et al., 2007). Based on the Stehfest and Bouwman
(2006) data set the control emissions corrected EFs (n¼352) range
from 0.0% to 10.8% with an average of 1.1% and standard deviation of
1.7%. This large variation is due to differences in environmental
factors (e.g. climate and soil conditions), crop factors (e.g. crop type
fertilizer, application rate, time of application).
In this paper we elaborate a simple and transparent approach to
estimate direct N2O soil emission using EFs that depend on N-input
sources and environmental factors. The approach is mainly based
on literature data and expert knowledge and is intended for
implementation in large-scale models such as INTEGRATOR (De
Vries et al., this issue) and MITERRA-Europe (Velthof et al., 2009),
which contain spatially explicit information on fertilizer and
manure types and use, soil properties and climate, for use in
European wide applications. Although the focus is on Europe we
did not confine our literature survey to Europe, but used data from
experiments in agricultural systems of temperate regions. The EFs
used in our study all refer to values corrected for the control
emissions, thus representing the fertilizer induced emissions.
This paper starts with the conceptual framework and an over-
view of the different factors that control N2O emissions. We
reviewed literature data to quantify N2O EFs, using studies that
compared N2O emissions for different N inputs or for different
environmental factors. Based on these data and expert knowledge,
an inference scheme for N2O EFs for the different sources of N input
and different environmental conditions was established. Next, we
incorporated the developed inference scheme in the INTEGRATOR
model, applied this for the EU-27, and compared the calculated N2O
emissions from agricultural soils with the emissions using the
default IPCC EF. Finally, we evaluated our N2O EF inference scheme
based on the Stehfest and Bouwman (2006) data set.
2.1. Conceptual framework
The major factors that control N2O emission are N input and nitrate content,
oxygen content, available C content, temperature and pH (Firestone et al., 1980;
Granli and Bøckman, 1994; Sahrawat and Keeney, 1986; Tiedje, 1988). Table 1
gives an overview of the effects of these controlling factors on denitrification and
* Corresponding author.
E-mail address: firstname.lastname@example.org (J.P. Lesschen).
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/envpol
0269-7491/$ e see front matter ? 2011 Elsevier Ltd. All rights reserved.
Environmental Pollution 159 (2011) 3215e3222
the N2O/N2ratio of denitrification. N2O is also formed during nitrification. Nitrifi-
cation rates increase with increasing N content, oxygen content, temperature and
decreasing pH. However, highest N2O production is generally found during sub-
optimal conditions for nitrification, e.g. wet conditions.
For some of the controlling factors, i.e. oxygen content and available organic
carbon content, no direct data are available that can be used for a calculation of
large-scale N2O emissions. Therefore we used indirect parameters to account for the
effects of these two controlling factors. For oxygen content, the indirect controlling
factors that were included are soil type, precipitation, land use, manure application
technique and temperature. Soil type controls the oxygen content of the soil by soil
texture, biological oxygen consumption by degradation of organic matter and
groundwater level. Higher precipitation increases the risk of anaerobic conditions.
For land use a distinction is made between grassland and arable land, since grass-
land generally contains more soil organic carbon (Jenkinson, 1988) and has higher
oxygen consumption than arable land. The depth of manure application affects the
soil oxygen content and higher temperatures result in a higher biological activity
thus causing a higher oxygen demand.
For available organic carbon content, the indirect controlling factors that were
included are soil type, land use, manure type and crop residue type. With respect
to soil type a distinction is made between peat soils, which have much higher
organic carbon contents, clay soils and sandy soils. Regarding land use a distinc-
tion is made between grassland and arable land. We distinguished three manure
types and three crop residue types as such materials can have different available
organic carbon contents and CeN ratios (e.g. Velthof et al., 2002, 2003). Overall,
we thus included different N-input sources and the environmental factors land
use, soil type, precipitation, temperature and pH in our a priori N2O EF inference
2.2. Reference situation
Starting point of the methodology is the average EF for a reference situation
from which the effects of environmental factors on the EF are estimated. As starting
point the EF for fertilizer of 1% of the applied N is used, based on the IPCC 2006
guidelines (IPCC, 2006). In a two-year monitoring study of Velthof et al. (1996), the
EF of grassland on a sandy soil fertilized with nitrate based fertilizer was exactly 1%.
Therefore this experiment was used as the reference situation and the starting
point for the inference scheme. The conditions of this reference are: grassland
on a well drained sandy soil, neutral pH, annual precipitation of about 750 mm,
annual temperature of 10?C, andfertilized
(300e400 kgNha?1yr?1). Also in other studies with intensively managed grassland
in NW Europe an average EF of about 1% is often found (e.g. Clayton et al., 1997;
Flechard et al., 2007), although the annual value is affected by e.g. climatic factors
(Dobbie and Smith, 2003).
2.3. Description Stehfest and Bouwman (2006) data set
In this paper the N2O emission data set of Stehfest and Bouwman (2006) is used
in addition to other literature to establish the EF for some of the controlling factors.
This data set is an extension of the data set presented by Bouwman et al. (2002a),
and contains data for N2O emissions from soils under natural vegetation and agri-
cultural fields. The data set includes results from field studies that were published in
the peer-reviewed literature, including various parameters related to climate, soil,
management and measurement technique. The total data set contains 1372
measurements of N2O emissions. From this data set we selected the measurements
on agricultural sites and excluded the tropical sites, which resulted in a data set of
916 cases. From this selection, a corrected N2O EF was available for 352 measure-
ments, i.e. from experiments where the observed EF was corrected for the N2O
emission from the control plot without N input.
In the following sections a description and a comprehensive literature review of
the effects on the N2O emissions are given for each of the controlling factors. Based
on literature data, the Stehfest and Bouwman (2006) data set and expert knowledge
each of the controlling factors is parameterised relative to the reference situation.
2.4. Nitrogen input sources
The most important sources of nitrogen in soils are mineral fertilizers, animal
manure, N excreted during grazing, crop residues, atmospheric deposition, biolog-
ical N fixation, and mineralization of soil organic N. Many studies indicate that the
type of nitrogen affects N2O emission (e.g. Stehfest and Bouwman, 2006). Therefore,
we distinguished the following six nitrogen sources in the inference scheme:
eTwo types of mineral fertilizer: nitrate based fertilizer and ammonium based
fertilizer (not containing nitrate);
eThree types of manure: pig, poultry, and cattle. Moreover, the storage method
of manure(slurry or solid) and method of application (incorporation and surface
application) were distinguished;
eN excreted during grazing;
eThree types of crop residues: cereals, vegetables and arable crops;
eAtmospheric N deposition; and
eNet mineralization of soil organic N
We did not include biological N fixation, since Rochette and Janzen (2005)
indicated that it is not proven that biological N fixation itself is a source of N2O.
Based on their study, IPCC removed the process of biological N fixation as source of
N2O from their guidelines and only considered the N2O emissions from the crop
residues of legumes (IPCC, 2006).
2.4.1. Mineral fertilizer
Many types of fertilizers are used in agriculture, but the most common fertilizers
are ammonium nitrate based fertilizers, nitrate based fertilizers, ammonium based
fertilizers and urea and urea based fertilizers. Statistical analyses on the database
with measurements of N2O emissions (Bouwman, 1996; Stehfest and Bouwman,
2006) showed no significant effect of fertilizer type on N2O emission. However,
several studies in which different mineral fertilizers are compared in one experi-
ment often show large differences. In incubation studies, the N2O emission from
nitrate based fertilizer is much higher than from ammonium based fertilizer under
wet conditions (e.g. Pathak and Nedwell, 2001). The grassland studies of Clayton
et al. (1997), Dobbie and Smith (2003), Velthof et al. (1997) and Jones et al. (2005,
2007) also point at much higher N2O emissions from nitrate based fertilizer than
from fertilizer only containing ammonium, especially during wet conditions. The
average and median EF of these studies were respectively 2.5% and 1.1% for nitrate
based fertilizer (n¼28) and 0.65% and 0.42% for ammonium and urea based fertil-
izer (n¼26). Based on these data, the EF for ammonium based fertilizers on
grassland was set at 0.5 times the EFof nitrate based fertilizer. Ureawasnot included
separately, but considered as an ammonium based fertilizer, since hydrolysis of urea
leads to the formation of ammonium.
Only few studies were found that compared N2O emissions of different types of
N fertilizer on arable land. In a field experiment with oats by Leick and Engels (2001)
the N2O emission was slightly higher for the nitrate based fertilizer compared to
ammonium sulphate. This suggests that for arable soils, the difference in N2O
emission between nitrate and ammonium based fertilizer is smaller than for
grasslands. Therefore, the EF for ammonium fertilizer on arable land was set at 0.8
times that of nitrate fertilizer.
2.4.2. Animal manure
The N2O emissions from manure are affected by many factors, such as animal
type, storage type (slurry, solid or grazing), feeding, treatment and application
technique (Chadwick et al., 2000; Flessa and Beese, 2000; Velthof et al., 2003).
Relatively low N2O emissions (<1% of the N applied) have been found for animal
manures applied to grassland (Velthof et al., 1997; Chadwick et al., 2000). In these
soils, N2O emission from nitrate containing mineral fertilizers are often (much)
higher than from animal manures (Egginton and Smith, 1986). In a summary of all
available literatureon N2O emissions on grassland in the Netherlands (unpublished),
an average emission factor of 0.83% was found for nitrate based fertilizer (n¼26)
and 0.32% for injected cattle slurry (n¼7). Based on these results, we assumed that
cattle slurry injected to grassland has an EF of 0.5 times the EF of nitrate based
fertilizer (the reference situation).
In an incubation experiment of Velthof et al. (2003), the N2O EF was highest for
pig slurry (7.3e13.9%), lower for cattle slurry (1.8e3.0%) and lowest for poultry
manure (0.5e1.9%). Chadwick et al. (2000) also found lower N2O emissions from
cattle (dairy) slurry (EF 0.12-0.44%) than from pig slurry (0.12e0.97%). The higher
N2O emissions for pig slurry compared to cattle slurry and lowest N2O emissions for
solid manures can be explained by the composition of the manure, specifically the
fraction of ammonium in total N and the degradability of organic matter. Emission of
N2O from soil applied animal manures is controlled by the amount of applied N and
C. The higher the amount of applied mineral N and easily mineralizable N, the higher
the risk on N2O emission. The portion of NH4in total N is higher for slurries than for
solid manures and is higher for pig manure than for cattle and poultry manure.
Moreover, application of easily degradable C with manures increases potential
denitrification in the soil and thereby the risk on N2O emission. Paul and Beauchamp
(1989) showed that volatile fatty acids in animal manures are effective C sources for
denitrifying soil bacteria. Studies showthat amountof volatilefattyacids is higher in
Effect of changes in factors on denitrification and on the N2O/N2 ratio of
Increasing dissolved N content
Increasing oxygen content
Increasing available organic
J.P. Lesschen et al. / Environmental Pollution 159 (2011) 3215e3222
pig slurry than in cattle slurry (e.g. Guenzi and Beard, 1981; Kirchmann and
Lundvall, 1993; Sørensen, 1998). Based on the above mentioned data, the ratio in
EFs between the different manure types was set at 1:1:1:2:3 for poultry manur-
e:solid cattle manure:solid pig manure:cattle slurry:pig slurry. For other manure
types (e.g. from sheep and goats), the average of solid pig manure, solid cattle
manure and poultry manure was used.
Injection or incorporation of manure may increase N2O emission and denitrifi-
cation compared to surface-applied manure (e.g. Flessa and Beese, 2000; Velthof
et al., 2003). A two-year monitoring study by Velthof and Mosquera (2011)
showed that the N2O EF for shallow injection was a factor 2e4 higher compared
to broadcast application. However, a recent review of Webb et al. (2010) shows that
the effects of injection on N2O emission are variable and depend on the type of
injection technique and the circumstances during application. Therefore, the EF for
manure injected or incorporated in the soil was set at only 1.5 times that of surface-
A review of Oenema et al. (2008) of published data on N2O emissions from urine
and dung shows that N2O emissions from dung pats range from 0.1 to 0.7 percent
and emissions of N2O from urine patches range from 0.0 to 15.5%. This wide range
has been attributed to variations in urine composition, soil type and environmental
conditions. Smith et al. (1998) found higher N2O emissions for grazed land
compared to grassland that was cut. In a study of Velthof et al. (1996) on different
soil types, the EF for urine and dung was on average 2.2 times higher compared to
nitrate based fertilizer (Table 2). For the inference scheme we assumed that the EF
for grazing is two times the EF of nitrate based fertilizer. This also agrees with
Oenema et al. (1997) who found an overall mean EF of 2% for grazing.
2.4.4. Crop residues
Crop residues incorporated in the soil are a potentially important source of N2O.
Crop residues may affect the N2O emission from soils by: i) supply of easily
mineralizable N, which may be transformed into mineral N, ii) supply of easily
mineralizable C, which may enhance denitrifier activity and, thereby, N2O emission
from both soil mineral N and crop residue N, and iii) local increase of the oxygen
consumption in the soil. Limited, variable and often contradictory information
concerning N2O emissions from crop residues was found in a literature review by
Novoa and Tejeda (2006). This variability of N2O emissions can be partly explained
by differences in crop type, biochemical composition of the crop, management
practices, climate, and soil properties. The content of easily mineralizable organic
matter differs between crop residues, and is generally higher in fresh green mate-
rials than in straw (Henriksen and Breland, 1999). Baggs et al. (2000) found that
comparatively large emissions were measured after incorporation of material with
low C:N ratios. The highest flux was observed from N-rich lettuce residues. An
incubation study of Velthof et al. (2002) showed large differences in N2O emission
from crop residues, with very low emissions for cereals (0.0e0.75%), which have
a high C:N ratio, high emissions for vegetables (0.2e15%), which have a low C:N
ratio, and intermediate emissions for sugar beet (0.1e1.6%). Based on Velthof et al.
(2002), Harrison et al. (2002) and Novoa and Tejeda (2006), the following EFs are
assumed for the reference situation: 0.2% for crop residues of cereals, 2% for crop
residues of vegetables and 1% for crop residues of other arable crops.
2.4.5. Atmospheric deposition
The major form of atmospheric N on agricultural land in Europe is ammonium
(Galloway and Cowling, 2002). The amount of N deposited is small in comparison to
N applied via manures and fertilizers. In regions with high intensive livestock
systems, deposition may be higher than 30 kgNha?1yr?1, but in most countries
atmospheric deposition is less than 20 kgNha?1yr?1. Skiba et al. (2004) found no
evidence for different N2O emissions at different forms of N deposition (wet
deposition of NH4and NO3or dry deposition of NH3). Apart from forests (Denier van
der Gon and Bleeker, 2005), no quantitative data are available for the emission factor
of deposition on agricultural land. Since the deposited amount of NH4and NO3is
much moreevenlydistributed throughayear, it is likelythat the N2O emission factor
will be lower than for fertilizer. We therefore assumed that the EF for atmospheric N
deposition is 0.75 times the EF of ammonium based fertilizer.
2.4.6. Mineralization of soil organic matter
In mineral soils, the soil organic N contents are generally stable over a period of
several years, so that net mineralization is zero, and N2O emission will mostly be
small. Systems that have net mineralization are drained peat soils (Histosols) and
permanent grasslands that are converted into arable land (Velthof et al., 2010). In
both systems high N2O emission have been measured. Velthof et al. (1996) found
N2O emissions of 5.3 kgNha?1yr?1on unfertilized grassland on peat soils, whereas
on clay soils and sandy soils the average emissions were about 1.0 kg Nha?1yr?1.
The net N mineralization of these drained peat soils under grassland in the
Netherlands is estimated at 165 kgNha?1yr?1(Kuikman et al., 2005). Consequently,
the EF for mineralization is set at 2.6% for peat soils under the climatic conditions of
the reference situation. This value agrees with EFs found by Kasimir Klemedtsson
and Klemedtsson (2002) and Vinther et al. (2004), which ranged from 1.8 to 2.9%.
Sand and clay soils have low mineralization rates, therefore the same emissions
factors as for atmospheric deposition are used, since N is released slowly.
2.5. Land use
There are several mechanisms that make N2O emission from grasslands differ
from those of arable cropping systems. First of all, the organic matter content of
grasslands is higher than in arable cropping systems (e.g. Jenkinson, 1988). This
difference is mainly due to the absence of soil tillage in grasslands, in combination
with high C input by grass roots and residues and manure. In addition, the organic
matter of grasslands is much more available for bacteria because of the continuous
high input of fresh organic matter via root exudates and manure. The potential
denitrification rate is therefore higher for grasslands than for arable land (Bijay-
Singh et al., 1988). Because of the higher availability of C in grassland soils, the
application of C via manure has less effect on N2O emission from grasslands than
from arable land (e.g. Egginton and Smith, 1986; Velthof et al., 1997). In addition,
nitrification occurs less on grassland, since grass has a dense rooting systemwhich is
active during large part of the year. Therefore, applied N is rapidly (within a few
days e weeks) taken up by the grass or immobilized in the rooting system (Huntjes,
1971). By contrast, in arable cropping systems, mineral fertilizer and manure are
often applied before sowing or planting of the crop and it takes longer before the
crop can absorb the applied N. In this period, N content in the soil remains high for
a relatively long period and applied ammonium can be nitrified. The risk of N2O
emission differs between grassland and arable land, because of the mentioned
Several studies showed that N2O emission from manure on arable land is often
higher compared to mineral fertilizer (e.g. Petersen, 1999; van Groenigen et al.,
2004; Perälä et al., 2006). Based on these studies the EF for manure on arable
land was set at 1.5 times the EF of manure on grassland, whereas the EF for nitrate
and ammonium based fertilizer on arable land is set at respectively 0.5 and 0.8 times
the EF on grassland.
2.6. Soil type
In general, N2O emission increases with higher clay content of the soil, because
the chance on anaerobic conditions increases (Granli and Bøckman,1994; Sahrawat
and Keeney, 1986; Velthof and Oenema, 1995). van Groenigen et al. (2004) found
much higher N2O emissions on clay soil compared to sandy soil under maize land.
EFs for the sandy soil averaged 0.08%, 0.51% and 0.26% for calcium ammonium
nitrate fertilizer, cattle slurry, and combinations of both. For the clay soil, these
numbers were 1.18%, 1.21% and 1.69%, respectively. Peat soils have higher N2O
emissions than sand and clay soils because of i) the higher organic matter content
with related higher denitrification potential (Velthof et al., 1996) and ii) the higher
groundwater level (wet conditions). This was observed by several studies (e.g.
Duxbury et al., 1982; Velthof and Oenema, 1995).
For a more general comparison of the effect of soil type, we also calculated the
average N2O EF for sand, clay and peat soils, based on the Stehfest and Bouwman
(2006) data set. These EFs were respectively 0.86% (n¼161), 1.24% (n¼170) and
2.61% (n¼21) for sand, clay and peat soils. Based on these data and the before
mentioned literature, we set the following ratios for the relative EF of soil types:
sand:clay:peat¼1 (reference situation):1.5:2.
Precipitation is an important indicator for the risk of anaerobic conditions in
soils. Many studies find clear relations with rainfall and soil moisture (e.g. Smith
et al., 1998; Flynn et al., 2005; Zhang and Han, 2008; Rochette et al., 2008). Espe-
cially for water filled poor space, which is related to precipitation, significant posi-
tive correlations with N2O emission are found by many studies. Since there are no
studies available with field experiments that measured N2O emissions under similar
conditions but with different annual precipitation, we used as an alternative the
Stehfest and Bouwman (2006) data set to obtain a relation between average annual
precipitation and N2O EF. The measurements in the data set were aggregated by
location or study and for the European sites missing precipitation values were
added, based on an overlay with European precipitation data (Mitchell et al., 2004).
EF for applied mineral N fertilizer (calcium ammonium nitrate) and N excreted
during grazing, derived from 2-year monitoring studies at four grassland sites in the
Netherlands (Velthof et al., 1996).
EF grazing Ratio EF grazing:
J.P. Lesschen et al. / Environmental Pollution 159 (2011) 3215e3222
A linear regression analysis between the average N2O EF and the average annual
precipitation resulted in a significant relation (n¼45; P¼0.006) with a R2of 0.16
(Fig.1). The equation was rescaled to the reference situation with an average annual
precipitation of 750 mm, which lead to the following equation:
fp ¼ 0:00253 ? P ? 0:894
where fpis the precipitation adjustment factorand P the annual precipitation in mm.
To avoid extreme factors a minimum and maximum precipitation of respectively
400 mm and 1500 mm were used, which correspond with a factor of respectively
0.12 and 2.89. Most agricultural lands in Europe are located within these precipi-
Dobbie and Smith (2003) measured several grassland and arable land sites over
the UK for 2e3 years. Some of these sites were already monitored for several years
before (Clayton et al., 1997). On grassland, rainfall with its consequent effect on
WFPS was the main driving factor during the growing season. Annual EFs, uncor-
rected for background emissions, varied from 0.4 to 6.5% of the N applied.
Measurements by Meijide et al. (2009) in semi-arid Spain (430 mm) with applica-
tion of different manure types showed that all EFs were very low (<0.15%). These
studies indicate that the precipitation influence is rather strong and that we do not
overestimate the precipitation effect using the above mentioned regression.
Temperature affects directly the activity of the nitrifying and denitrifying
bacteria and the ratio N2O/N2, i.e. this ratio decreases when the temperature
decreases. Moreover, temperature controls biological oxygen consumption and this
may also affect the emission of N2O. Because of these opposite effects on N2O
emission, the net temperature effect on N2O emission is limited to a range of about
5e15?C, but may sharply increase at higher temperatures (e.g. Keeney et al., 1979).
At low temperatures, still N2O may be emitted, but at low rates.
Since there are no comparable field experiments that measured N2O emissions
under similar conditions but with different annual temperatures, we used as an
alternative the Stehfest and Bouwman (2006) data set to obtain a relation between
average annual temperature and N2O EF. However, based on the available data in the
data set no significant relation was found between annual temperature and N2O EF.
Although many studies do find significant effects of temperature on N2O emissions
(e.g. Goodroad and Keeney,1984; Smith et al.,1998; Dobbie et al.,1999), this effect is
mainly based on experiments on a daily or seasonal basis. However, on an annual
basis the temperature effect might be averaged out, since average biological activity
will be more or less the same at annual basis. In colder ecosystems soil fauna is
adapted to peak during the warmer periods, although even under freezing condi-
tions biological activityand N2O emission are observed (Röveret al.,1998). The same
holds for warmer climates, were biological activity peaks during the wetter periods.
Therefore we decided not to include a temperature effect in the final N2O EF
The pH directly affects the activity of nitrifying and denitrifying bacteria (e.g.
Granli and Bøckman,1994; Tiedje, 1988). Optimum activities are found in the range
of pH 7e8. However, the reduction of N2O toN2is moresensitive toacidic conditions
than the reduction of NO3to N2O, by which the ratio N2O/N2strongly increases at
decreasing pH (Firestone et al., 1980). However, the net effect on N2O emission is
uncertain, and inconsistent relationships are found between N2O emission and pH
(Goodroad and Keeney, 1984; Goodroad et al., 1984; Bandibas et al., 1994; Stevens
et al., 1998; Mørkved et al., 2007). Therefore we decided not to include the factor
pH in the EF inference scheme.
2.10. Evaluation of the N2O emission factor inference scheme
To evaluate our developed EF inference scheme we used the Stehfest and
Bouwman (2006) data set for comparison. First, we (re)classified or added the
information on land use, soil type and N-input classes for each of the 352
measurements. For most variables the information was already in the data set, but
precipitation data was missing in several cases, and we added values derived from
a European meteorological database (Mitchell et al., 2004) that contains monthly
values of temperature, precipitation and cloudiness for the years 1901e2000 for
land-based grid-cells of size 100?10’. Nevertheless, not for all measurements data
for each factor could be obtained, and only 225 cases were included in the
comparison. Next, wecalculated the N2O EFaccording tothe EF inferencescheme for
each of the measurements and compared them with the observed EF. Additionally,
we used the default IPCC EF and applied the empirical N2O emission model derived
by Stehfest and Bouwman (2006). This model was developed using the REML
directiveof Genstat, which was more appropriate for analyzing unbalanced data sets
with missing values than regression analysis. For N2O emission from agricultural
fields the following model was derived by Stehfest and Bouwman:
Eiþ N ? 0:0038 ? 1:5160 (2)
where Nemissionis the emission of N2O expressed in kg Nha?1yr?1, Eiis the effect
value for factor i and N is the N application rate (kgNha?1yr?1). The values used for
Ei are given in Stehfest and Bouwman (2006). The EF was derived for each
measurement by using Equation (2) for both zero N application and the amount of N
as used in the experiment. However, the empirical model of Stehfest and Bouwman
could only be used for 133 sites because of missing data for several factors. An
intercomparison of model results and measurements was thus made for both 225
sites (observations, method used in our study and IPCC tier 1 method) and for 133
sites (same methods plus the empirical model of Stehfest and Bouwman).
The performance of the three approaches (EF inference scheme, empirical N2O
emission model by Stehfest and Bouwman (2006) and the default IPCC EF) on the
data set was compared by four statistical measures, i.e. the Mean Error (ME),
Normalized Absolute Error (NAE), Normalized Root Mean Square Error (NRMSE) and
Pearson correlation coefficient. The ME is calculated as the average for the predicted
values minus the average for the observed values, whereas the NAE is calculated as
theaverage of the absolutedifferencebetweenpredicted values and observedvalues
divided by the number of observations. The ME expresses the bias in average values
of model predictions and observations, and gives a rough indication of over-
estimation (ME>0) or underestimation (ME<0) whereas the NAE gives an
impression of the absolute deviation between predictions and observations. The
NRMSE is another measure for these deviations, calculated as
where Piis predicted N2O EF value, Oiobserved N2O EF value, O is average of the
observed values and N is number of observations. The NRMSE is rather sensitive to
extreme values, because it is a quadratic measure. If models accurately describe data
that contains no errors, the NRMSE approximately represents the coefficient of
variation for calculated data.
3.1. The resulting inference scheme
The resulting inference scheme gives the N2O EF for 16 different
sources of N input, three soil types and two land-use types. Table 3
shows how the inference scheme is build up, starting at the EF of
1.0% for grassland on sandy soils, fertilized with nitrate fertilizer,
and an annual precipitation of 750 mm. For application in the field
situation, the EF values from Table 3 must be multiplied with the
precipitation adjustment factor based on the regression for the
average annual precipitation (Eq. (1)).
Fig. 2 shows the spatial patterns of N2O soil emissions, as
predicted by INTEGRATOR, for both the differentiated N2O EFs and
R2 = 0.16
Annual precipitation (mm)
N2O emission factor (%)
Fig. 1. Relation between observed N2O EFs and precipitation from the Stehfest and
Bouwman data set (observations are aggregated by location/study).
J.P. Lesschen et al. / Environmental Pollution 159 (2011) 3215e3222
the default IPCC EF of 1%. The overall picture is similar across
Europe, but regionally clear differences between the EF inference
scheme and the IPCC EF are observed. For example, for Poland and
eastern Germany the N2O emissions are lower with the EF infer-
ence scheme, whereas for the western part of the UK and Ireland
and the northern part of Spain the N2O soil emissions are higher
due to the precipitation effect. For some areas the effect of soil
type on the N2O emission is clearly visible, e.g. in the Netherlands.
According to the EF inference scheme the highest N2O emissions
occur in the western part with mainly clay and peat soils, while
the eastern and southern part with mainly sandy soils have lower
emissions. With the default IPCC EF, the southern regions in the
Netherlands with most manure have the highest emissions,
although these areas have sandy soils. The total N2O soil emission
as calculated with the N2O EF inference scheme is 292 kton
N2OeN for the EU-27, whereas the default IPCC EF results in a total
N2O emission of 315 kton N2OeN. In De Vries et al. (this issue), the
results of the INTEGRATOR model are compared to other European
wide modelling approaches. The estimated overall variation at EU-
27 is small for N2O emissions, but the variation in N2O fluxes
between models is large at the regional scale.
3.2. Evaluation of the N2O emission factor inference scheme
Table 4 presents a summary of the comparison with the
observed EFs from the Stehfest and Bouwman (2006) data set.
The EF inference scheme is performing better than the default IPCC
1% EF and the Stehfest and Bouwman model. Compared to the IPCC
1% EF, the EF inference scheme has a lower normalized absolute
error and normalized root mean square error, whereas the mean
error is smaller when comparing results for all 225 sites, but larger
when the comparison is limited to the 133 sites for which the
Stehfest and Bouwman model could be applied. Compared to the
latter model the EF inference scheme has a smaller mean error,
a lower normalized absolute errorand lower normalized root mean
square error and a significant and higher Pearson correlation
In Fig. 3, the frequency diagram of the resulting EFs is shown for
the different approaches. Neither, the empirical model of Stehfest
and Bouwman nor the EF inference scheme is able to simulate well
the lower end of the observed EFs. Both approaches lead to rather
normal distributions of the resulting EFs, whereas the observed EFs
have an asymptotic distribution. The empirical model of Stehfest
Fig. 2. Comparison of calculated N2O soil emissions based on the N2O EF inference scheme (left) and the default IPCC EF of 1% (right).
The developed N2O EF inference scheme for N-input sources (in %). The value 1.00 in bold is the starting point of the reference situation. The final EF is derived by multiplying
the value from the table with the precipitation adjustment factor, which is derived according to Equation (1).
Source N inputSandClay Peat
Grassland Arable landGrassland Arable landGrassland Arable land
Nitrate based fertilizer
Ammonium based fertilizer
Pig slurry low NH3application
Cattle slurry low NH3application
Pig slurry surface-applied
Cattle slurry surface-applied
Solid pig manure
Solid cattle manure
Crop residues cereals
Crop residues vegetables
Crop residues other arable crops
J.P. Lesschen et al. / Environmental Pollution 159 (2011) 3215e3222
and Bouwman gives no EFs below 0.25% and the inference scheme
only about a quarter of the observed number. Inversely, simulations
based on the inference scheme hardly result in values above 1.75%,
whereas the empirical model of Stehfest and Bouwman is per-
forming better here.
4. Discussion and conclusion
scheme that accounts for different N-input sources and environ-
mental conditions for agricultural land in temperate zones. The
comparison with observed EFs from the Stehfest and Bouwman
(2006) data set indicates that the EF inference scheme performs
on average better than the default IPCC EF and the empirical model
developed by Stehfest and Bouwman, as is shown by a higher score
on all four statistical measures (Table 4). This result demonstrates
that, despite high uncertainties in N2O emissions and poor quanti-
fication for some factors, differentiated N2O EFs can perform better
regional variation in soils, land use, crop management and climate
emissions. Although the total estimated direct N2O soil emission in
Europe is more or less the same for the IPCC 1% EF and our EF
inference scheme, the variation among countries is much larger, as
was also shown by Leip et al. (this issue).
Both the inference scheme and the empirical model of Stehfest
emissions and few extremely high N2O emissions (Fig. 3). It appears
to be difficult for empirical models to predict these low EFs, since
such models tend to focus on a correct estimate of the average
behaviour. A reason for the poorer performance of the empirical
model of Stehfest and Bouwman is probably related to its loga-
rithmic character, which can lead to very high N2O emissions for
high N application rates.
The developed inference scheme incorporates most driving
factors for N2O emission, including several sources of N input and
various environmental conditions, such as soil type, land use and
precipitation. An important driver that has not been accounted for
and that may largely affect the N2O emission for some countries in
a given year is the occurrence of freezing and thawing events
(Müller et al., 2002; Matzner and Borken, 2008). However, when
including this aspect, it requires a simple approximation, such as
the fraction of months in a given climate with a minimum
temperature below zero (Bloemerts and de Vries, 2009).
Uncertainty estimates are an essential element of a complete
GHG inventory of emissions, which help to prioritise national
efforts to reduce GHG emissions and the uncertainty of inventories
in the future. N2O emission from agricultural soils is one of the
sources with the largest uncertainty in GHG emissions. These
uncertainties are caused by uncertainties related to the emission
factors, natural variability, activity data, lack of coverage of
measurements, spatial aggregation, and lack of information on
specific on-farm practices. In the IPCC 2006 guidelines the uncer-
tainty range of the 1% EF is 0.3e3.0%. The presented EF inference
scheme may be used to obtain more realistic estimates of N2O
emissions from soils at regional or continental scale. However, an
uncertainty assessment for the differentiated EFs is difficult due to
the lack of quantitative information. A possibility to estimate the
uncertainty of EFs for a range of experimental conditions is to
perform a Bayesian calibration of the EFs using measured N2O
emissions from various sites under different conditions. Lehuger
et al. (2009) were able to find global estimates and their uncer-
tainty for emission parameters, despite the large variations in
parameter values across experimental sites. Del Grosso et al. (2010)
used a Monte Carlo analysis to estimate uncertainties for N2O soil
emissions as modelled with the DAYCENT model. The total N2O soil
emission of 201 Gg N for US cropland had an estimated 95%
confidence interval of about ?35% toþ50%, although it tendedtobe
larger at the regional level. The uncertainties of the simulated N2O
soil emissions by INTEGRATOR are currently being assessed using
a Monte Carlo analysis (Kros et al., 2011).
Despite the uncertainty given above, the range in EFs is still
lower than the possibility raised by Crutzen et al. (2008) that the
overall EF for agricultural N is 4?1% of the newly fixed N. Although
this estimate includes both direct and indirect emissions, it seems
to indicate a mismatch between emissions determined by bottom-
up studies, as applied in all the studies evaluated in this paper, and
a global top-down approach. However, one has to be aware that the
EFs derived in our study and the average 1% EF from the IPCC apply
for each of the different sources of N input, whereas the 4% of
Crutzen et al. (2008) equals the sum of the N cascade, i.e. N2O that is
emitted during application of fertilizer, subsequently followed by
N2O that is emitted from crop residues after it has been taken up by
crops or during the application of manure due to N in crops that are
fed to animals. Finally, there are indirect N2O emissions from
leaching and atmospheric deposition. Thus the sum of all these
emissions might come close to the 4% of newly fixed N as found by
Crutzen et al. (2008). Davidson (2009) found that 2.0% of manure N
and 2.5% of fertilizer N is finally converted to N2O.
The current IPCC Tier 1 approach for N2O from agricultural soils,
i.e. the default EF1of 1%, does not account for effects of either crop
< 0.25 0.25 -
N2O emission factor (%)
EF inference scheme
EF Stehfest and Bouwman (2006)
Fig. 3. Frequency distribution of N2O EFs that were observed and derived from the EF
inference scheme and the empirical model of Stehfest and Bouwman (the IPCC 1% is
not included to prevent distortion of the graph, since this would be only one bar in the
0.75e1.25 class with a frequency of 133).
Summary of comparison with the observed EFs from the Stehfest and Bouwman
(2006) data set.
225 EF inference scheme
133EF inference scheme
Stehfest and Bouwman
*Significant at P<0.01.
aME¼mean error, NAE¼normalized absolute error, and NRMSE¼normalized
root mean square error.
J.P. Lesschen et al. / Environmental Pollution 159 (2011) 3215e3222
type, climatic conditions and crop management. As a result, the
methodology omits factors that are crucial in determining current
emissions, and has no mechanism to assess the potential impact of
future climate and land-use change (Flynn et al., 2005). Addition-
ally, a Tier 1 approach does not provide many incentives to apply
mitigation measures, since the effect is in manycases notexpressed
in the national GHG emissions inventory. One of the advantages of
our presented approach with differentiated EFs is the possibility to
account for the effects of additional mitigation measures, such as
changes in fertilizer or manure type. The EF inference scheme
might thus form a basis for countries to use a Tier 2 approach for
N2O soil emissions. Few countries already developed Tier 2
approaches for N2O soil emissions for specific sources, e.g. Canada
(Rochette et al., 2008) and New Zealand (De Klein and Ledgard,
2005). IPCC also encourages countries to use a Tier 2 approach, in
which N2O EFs are disaggregated based on environmental and crop
management related factors. Our presented N2O EF inference
scheme offers such a basis for European countries to use a Tier 2
The authors gratefully acknowledge funding by the European
Commission DG Research, 6th Framework Programme under the
NitroEurope Integrated Project (017841).Theresearchis co-funded by
the Dutch Ministry of Agriculture, Nature and Food Quality as part of
the strategic research programmes “Sustainable spatial development
of ecosystems, landscapes, seas and regions” and “Climate change”,
carried out by Wageningen University and Research Centre. Gert Jan
Reinds is thanked for providing the INTEGRATOR results.
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