Introduction
Nitrous oxide (N2O) is the single largest contributor to global
stratospheric ozone depletion (Ravishankara et al., 2009) and a potent
greenhouse gas (GHG). N2O is formed naturally in soils and aquatic
environments, primarily as a by-product of the microbial processes of
nitrification and denitrification (e.g. Davidson et al., 2000; Wrage et al.,
2001). Agricultural activities such as the use of nitrogen fertilisers and
livestock farming have dramatically altered the natural nitrogen cycle in
agricultural environments resulting in significantly increased global
emissions of N2O since pre-industrial times (IPCC, 2007). Agriculture
is believed to be the largest source of global anthropogenic N2O
emissions with estimates as high as 80 % of all anthropogenic emissions
due directly or indirectly to agricultural activities (Isermann, 1994; IPCC,
2007).
Large-scale N2O flux estimates for terrestrial sources are often
subject to large and poorly defined uncertainties which can limit the
effectiveness of mitigation efforts in the agricultural sector (e.g. Bouwman
et al., 1995; Oenema et al., 2005). Even estimates of N2O fluxes from
agricultural sources at much finer scales (i.e. the plot and farm scale) can
be highly uncertain. This is predominately caused by the large temporal and
spatial variability of N2O fluxes due to the high heterogeneity of soil
properties and microbiological processes (Parkin, 1987; Zhu, J. et al., 2013;
Chadwick et al., 2014). Soil properties which are believed to increase
N2O emissions by influencing the nitrification and denitrification
processes include available nitrogen (in the form of ammonium
(NH4+) and nitrate (NO3-)), available organic carbon,
oxygen supply and pH (Bateman and Baggs, 2005; Davidson et al.,
2000;
Although it is known that these properties can alter N2O production in
soils, it is still difficult to accurately simulate the net effect on
N2O fluxes from areas (that are often considered to be homogeneous land
cover) such as agricultural fields used for arable crops and grazing of
livestock due to the heterogeneous nature of microbial populations and
nitrogen availability in soils (Conen et al., 2000; Jarecki et al., 2008;
Oenema et al., 1997).
The two main flux measurement methods applied on the field scale for
N2O in agricultural areas are the flux chamber method and the eddy
covariance method (e.g. Jones et al., 2011; Skiba et al., 2013). Chamber
fluxes are measured over a number of enclosed areas (typically < 1 m2) on a field, and a mean or median flux estimate is extrapolated to
the farm, field or regional scale: the combination of upscaling with the
large spatial variability of N2O sources often results in very
significant uncertainty when estimating N2O fluxes (Velthof et al.,
1996). The advantage of using the eddy covariance method is that it can
measure and integrate flux data directly over areas greater than 100 m2
continuously without disturbing the soil or air environment. For large
homogeneous areas, which are well represented by an integrated value of
flux, the eddy covariance approach is ideal, but it does not address the
issue of spatial variability on reported fluxes within the measurement area.
Eddy covariance also requires fast, sensitive equipment that often demands
high power supply, and so it can be an expensive option (Hensen et al.,
2013).
In this experiment, a high precision dynamic chamber method (Cowan et al.,
2014) was used to make 100 flux measurements of N2O from an
intensively managed grassland field which contained several features
associated with elevated N2O fluxes. Soil NH4+,
NO3-, total carbon, total nitrogen, water filled pore space (WFPS%), bulk density and pH were recorded from 55 out of
100
flux measurement locations. The aims of the experiment were (i) to measure
the spatial variability of N2O fluxes at a field scale, (ii) to try to
identify the main drivers of this variability and (iii) to provide better
understanding of how N2O flux estimates from agricultural soils can be
improved.
Materials and methods
Field site
The locations of 100 flux measurements (markers) made over a
6.78 ha grazed grassland field using the closed loop dynamic chamber method
(bottom). Details of the high density measurement areas in the north of the
field are expanded (top). Features present in the field are outlined,
including the tree shaded area (Sh), the two small patches of silage remains
(S1 & S2) and the manure heap (M). The stream runs across the north of
the field through the shaded area.
Flux measurements were carried out at an intensively managed grassland field
owned by the University of Edinburgh (55∘52′1.2144′′ N,
3∘12′39.564′′ W) (Fig. 1). This 6.78 ha field contained
approximately 140 sheep (a mixture of ewes and lambs)
during the 3-day measurement period between 8 and
10 July 2013. Measurements were made continuously between 10:00 and
16:00 GMT on these days. This field had been used to graze predominately sheep
for at least the last decade with regular nitrogen fertiliser application.
The field contained several interesting features that provided the
opportunity to measure N2O fluxes from soils with a wide range of
properties. The vast majority of the field (98.62 % of the study area)
could be classed as typical grazed grassland in which sheep were free to
roam during the measurement period. The sheep had been present on the field
for several months, giving us the opportunity to measure from suspected
hotspots of N2O flux where sheep droppings had collected on the grass.
A drinking trough was situated in a shaded area under several large mature
trees with wide leaf coverage at the north end of the field. The sheep had
spent a lot of time in this shaded area due to the warm weather during the
past 2–3 weeks before measurements were made. This behaviour was
observed during recent measurements carried out in adjacent fields unrelated
to this study. Several flux measurements were made in the shaded area to
investigate the effect that the recent increase in sheep density in this
area had on N2O flux.
Patches of decayed grass silage were visible in two small areas of the
field. These patches remained after silage bales had been placed in the
fields to feed the sheep over the winter months. The patches had scarred the
grassland leaving small areas of bare soil, with decayed grass matter still
present. Fluxes from both of these patches were measured during the
experiment. A small running stream crosses the north side of the field which
helped with drainage. Several flux measurements were made from the stream
using the dynamic chamber to investigate if it was a significant source of
N2O.
One particular area of interest was a large manure heap which was situated
in the north-east corner of the field. This heap was a semi-permanent feature
which had been used to fertilise a nearby barley field on several occasions.
The heap reached a height of up to 3 m and covered approximately
100 m2 of the field, with a wider perimeter of contaminated
soil. The area of influence of the manure heap contamination was uncertain
due to consistent build up and removal of the heap over several years. A
scarred area around the heap was visible with no grass present for several
metres. The scarred grassland was used as an indicator of the area of
contamination of the manure heap. Measurements were made on the heap, from
soils near the base of the heap and on the contaminated soils surrounding
the heap at varying distances to investigate the spatial variability of this
particular feature of the field.
Dynamic chamber method
N2O flux measurements were made using a non-steady-state flow-through
(or closed dynamic) chamber system which circulated air between a flux
chamber and a quantum cascade laser (QCL) gas analyser via an air pump
(SH-110, Varian Inc, CA, USA) (for a full description of the system see
Cowan et al., 2014). A compact continuous wave QCL (CW-QC-TILDAS-76-CS,
Aerodyne Research Inc., Billerica, MA, USA) was used to measure gas mixing
ratios within the dynamic chamber system (with a detection limit of
approximately 30 nmol mol-1 s-1 for N2O). The instrument was
secured inside a four wheel drive vehicle to allow mobile measurements. A
diesel generator was kept on a tow trailer which provided electricity to the
system. The chamber was placed onto circular aluminium collars which were
inserted several centimetres into the soil (on average 5 cm) and almost flush to the
soil, prior to each measurement. Neoprene sponge formed an airtight seal
between the chamber and the collar. When used to measure from the stream in
the field, the chamber was held steady in place by hand with the bottom
slightly under the surface of the water. Two 30 m lengths of 3/8 in. ID
Tygon® tubing were attached to both the inlet of
the analyser and the outlet of the pump. This provided a 30 m radius from
the vehicle in which the chamber could be placed. A flow rate of
approximately 6 to 7 L min-1 was used between the analyser and the
chamber.
Fluxes of N2O were calculated using linear and non-linear asymptotic
regression methods using the HMR package for the statistical software R
(Levy et al., 2011; Pedersen et al., 2010). Using a mixture of
goodness-of-fit statistics and visual inspection, the regression method that
provided the best fit for the time series of concentration was chosen for
each individual measurement. The rate of change in the concentration of a
particular gas was then used to calculate the soil flux for each
measurement according to Eq. (1).
F=dCdt0⋅ρVA,
where F is gas flux from the soil (µmol m-2 s-1),
dC/dt0 is the initial rate of change in concentration with time in
µmol mol-1 s-1, ρ is the density of air in mol m-3, V is the volume of the chamber in m3 and A is the ground area
enclosed by the chamber in m2.
Soil sampling and analysis
Fifty-five of the 100 locations from which dynamic chamber
measurements were made were selected for soil analysis. From these locations,
5 cm deep soil samples were taken from inside the chamber collar using a 2 cm wide corer immediately after the flux measurement was completed. These
soils were used to calculate soil pH and available nitrogen in the form of
ammonium (NH4+) and nitrate (NO3-) via KCl extraction
(see below). Soil cores were taken immediately after the flux measurement
using a sharp metal cutting cylinder (7.4 cm diameter, 5 cm deep) which was
carefully hammered into undisturbed soil. Samples were used to calculate
total carbon and nitrogen content of the soil, soil moisture content (via
oven drying at 100 ∘C) and WFPS% as well as bulk density.
WFPS% was calculated from the bulk density soil samples using Eq. (2)
(Rowell, 1994).
WFPS%=Vcont×1001-rbrd,
where WFPS% is the percentage of porous volume in the soil filled by
water, Vcont is the volumetric water content of the soil, rb is the
bulk density of the soil in g cm-3 and rd is the particle density
of the soil (assumed as 2.65 g cm-3) (Rowell, 1994).
KCl extractions were carried out on 15 g un-dried soil samples (kept frozen
until extraction) using 1 mol L-1 KCl solution. Concentrations of
NH4+ and NO3- were measured using a Bran+Luebbe
AutoAnalyzer (SPX Flow Technology, Norderstedt, Germany). The mass of
available nitrogen in the soil was calculated using Eq. (3).
N=C×Vm,
where N is the mass of nitrogen in the form of NH4+ or
NO3- in grams (per kilogram of soil), C is the concentration of
NH4+ or NO3- measured in the analysis of KCl extract in
mg L-1, V is the volume of solution in which the soil sample was mixed
with KCl in L and m is the mass of dry soil mixed with the KCl solution in
grams.
Results
Variation in N2O fluxes at the field scale
Fifty flux measurements of N2O were made on grazed
grassland: the sampled locations which contained visible sheep droppings are
represented by the darker bars. Error bars represent the uncertainty in each
flux measurement which was calculated using a propagation of regression,
volume, temperature and pressure uncertainties (See Cowan et al., 2014).
The 3-day measurement period (8 to 10 July 2013) was very
dry with no rainfall and relatively low soil moisture contents (ranging from
9 to 50 % WFPS). Daily temperatures were similar, with mean daytime soils
temperatures recorded as 15.7, 16.6 and 15.9 ∘C on the 8,
9 and 10 of July, respectively. Flux measurement locations
were chosen using a mixture of a grid approximately 30×30 m
across the field and a selection of feature areas in which multiple
measurements were made in close proximity (See Fig. 1). Fifty measurements
were made on what was considered “normal” grassland across the field. This
provided an estimate of the spatial variability of N2O flux across the
field without interference from the hotspot features. Chamber placement on
the grassland area included some locations where sheep droppings were
present. These locations were noted during measurements when visible. Fluxes
from the grassland followed a geometric (log-normal) distribution ranging
between 2 and 227 µg N2O-N m-2 h-1, with an arithmetic
and geometric mean value of 25 and 13 µg N2O-N m-2 h-1, respectively (Fig. 2). No negative fluxes of N2O were
measured during this experiment at any of the locations. Droppings were
present at locations where the two largest fluxes were measured from the
grassland (227 and 132 µg N2O-N m-2 h-1), although
fluxes measured at other locations which contained droppings were not always
larger than those observed on clear (dropping-free) grassland (Fig. 2).
Silage and shaded patch fluxes
Two features which were measured in more detail were patches of the field
which contained the remains of decayed grass silage and a large area shaded
by trees in which the sheep had spent much of their time due to the warm
weather. A total of seven flux measurements were made over two patches of
decayed grass silage (Fig. 3a). Only small residues of the grass silage
were visible, mixed in with the soil in these areas as the sheep had
consumed the majority of it months before the measurement period. The
patches were easily visible due to the lack of grass on the bare soil where
the silage bales had been left. N2O fluxes measured from these plots
were higher than those measured from the grassland area. Fluxes varied from
1160 to 13 393 µg N2O-N m-2 h-1 (Fig. 3a). The
arithmetic and geometric mean values of these fluxes were 3745 and 2664 µg N2O-N m-2 h-1, respectively.
(a) Flux measurements made on patches of decayed grass
silage. Measurements 1–3 were taken from the first patch (referred as S1
in Fig. 1) and the remaining four were measured from the second (referred as
S2 in Fig. 1). (b) Flux measurements made from a shaded area with
increased sheep density. The first two of these measurements were made near
the centre of the shaded area. Fluxes from both features were made during
the same 3-day measurement period between 8 and 10 July 2013. Error bars represent the uncertainty in flux measurement
calculated using a propagation of errors from regression, volume,
temperature and pressure.
Five flux measurements were made in the shaded area in which the sheep had
access to a water trough. These fluxes varied between 200 and 9600 µg N2O-N m-2 h-1 (Fig. b). The arithmetic and geometric
mean values of these fluxes were 2983 and 1217 µg N2O-N m-2 h-1, respectively. The precise area which had been influenced
by increased sheep activity was difficult to measure with certainty, although
an increased number of animal droppings, clumps of wool and damp urine
patches were visible in this area of the field. The two measurements made in
the centre of the shaded area appeared to contain more animal droppings and
emit higher fluxes, whereas the outer perimeter appeared more similar to the
surrounding grassland area and fluxes were lower. It was likely that the
additional presence of sheep had influenced N2O production in this
area, although the effect of the shade (on soil moisture content) and a
difference in organic material composition (due to leaf litter) provided by
the tree may have also contributed.
Drainage stream fluxes
Flux measurements were made using the chamber from a stream: nine sampling
points were chosen where the stream was wide enough to fit the chamber onto
the surface of the water with flux values shown in Fig. 4. The stream was
approximately 5 m away from the north edge of the study area. These
measurements of flux were not as reliable as the measurements made on the
soil, due to the unavoidable disturbance on water pressure and flow caused
by the chamber. These flux estimates can still be used as a rough
approximation of the N2O which is emitted from the stream as it passes
through this field. Fluxes from the stream varied from 1 to 22 µg N2O-N m-2 h-1 with arithmetic and geometric mean values of
9.5 and 7.1 µg N2O-N m-2 h-1, respectively. These
fluxes were similar in magnitude to some of those measured from the
grassland area, although hotspots were not observed in the stream, even in
areas with higher turbulence in which de-gassing of N2O would be
expected to increase (Reay et al., 2003). It is not possible to determine
the magnitude of N2O fluxes which may have occurred further downstream
as a result of inputs from the field. The measurements were made only as an
indicator of the fluxes from the stream within the field area.
N2O fluxes measured from different locations in a
drainage stream in the grazed grassland field. Hotspots of N2O flux
were not observed in the stream measurements. Uncertainty was calculated for
each measurement, as was done for the fluxes measured from soils in the
field.
Manure heap fluxes
Ten N2O flux measurements were made directly on top of the manure heap
located on the field at differing heights (0.5 to 3 m). Care was made not to
physically disturb the chamber during measurements to prevent additional
gases escaping from the porous manure surface. Fluxes varied in magnitude
significantly across the heap with measured values ranging between
approximately 660 and 79 000 µg N2O-N m-2 h-1
(Fig. 5). Two of the measurements recorded very high N2O fluxes exceeding
35 000 µg N2O-N m-2 h-1. No relationship between the
height of the heap and N2O flux was observed from these measurements.
Seven sampling points were taken near the foot of the heap: fluxes recorded
from these locations showed a similar mixture of very large and
comparatively small fluxes of N2O, varying by up to 3 orders of
magnitude, between 85 and 31 250 µg N2O-N m-2 h-1.
Again, no clear spatial pattern was observed in the fluxes around the heap.
A further six flux measurements were made at distances of 5 to 10 m and
five more were made at 10 to 20 m from the heap. The arithmetic and
geometric mean fluxes recorded from the 5 to 10 m range were 6759 and
1986 µg N2O-N m-2 h-1, respectively. The arithmetic
and geometric mean fluxes recorded from the 10 to 20 m were 466 and 91 µg N2O-N m-2 h-1, respectively. These results suggest
that the influence of the manure heap on N2O fluxes decreases
dramatically after a distance of approximately 10 m (See Fig. 5).
N2O flux measurements from a semi-permanent manure heap
located on the grassland field. Vertical dashed lines split the measurements
into groups separated by distance from the heap with the left side of the
figure being the nearest and right side the furthest from heap. The darkest
bars in the figure represent measurements made on top of the actual manure
heap. Next are the measurements made from the base of the heap, then those
made 5 to 10 m and 10 to 15 m from the heap.
Summary of relevant soil properties of all 55 soil
measurements made during flux measurements. Soil samples were taken from
inside the chamber area immediately after flux measurements were completed.
The mean values and range (in brackets) of measurements from each variable
within the field are included in the table.
Feature
Soil samples
Area (m2)
NH4+ (g N kg-1)
NO3- (g N kg-1)
Total carbon (g C kg-1)
Total nitrogen (g N kg-1)
pH
WFPS (%)
Bulk density (g cm-3)
Grass
38
66861
0.060 (0.008–0.745)
0.017 (0.001–0.198)
60.269 (43.458–103.707)
4.708 (3.368 –9.494)
5.63 (4.74–6.62)
24.7 (8.9 –36.7)
0.754 (0.566–0.968)
Silage remains
5
36
0.247 (0.037–0.934)
0.161 (0.046–0.243)
77.010 (44.252–118.652)
5.872 (3.779–8.501)
6.42 (5.21–8.28)
42.8 (38.0–50.0)
0.848 (0.667–1.061)
Shaded area
3
210
0.287 (0.037–0.489)
0.087 (0.009 –0.239)
51.841 (9.678–105.96)
4.277 (0.835–9.178)
7.38 (6.1–3.18)
33.6 (24.2–44.3)
0.953 (0.833–1.079)
Stream
0
183
NA
NA
NA
NA
NA
NA
NA
Manure heap
0
102
NA
NA
NA
NA
NA
NA
NA
Manure heap perimeter
7
a
0.987 (0.089–2.175)
0.103 (0.002–0.587)
216.996 (107.652–354.828)
18.750 (8.045–34.099)
8.33 (6.97–9.41)
22.8 (14.1–31.9)
0.423 (0.172–0.846)
Manure perimeter (5–10 m)
1
b
0.036
0.398
52.346
5.440
6.00
33.6
0.955
Manure perimeter (10–15 m)
1
406
0.008
0.002
111.563
9.641
7.21
10.7
0.792
NA: No samples recorded. a As Manure heap. b Total manure perimeter
area of influence estimated as 406 m.
Variation in soil properties at the field scale
Soil measurements were made from 55 of the 100 flux
measurement locations (Table 1). The majority of these samples (n= 38)
were taken from the grassland area to assess the natural heterogeneity of
the soil throughout the field. The remaining soil samples were taken from
the visible hotspot features of the field to investigate the causes of
elevated N2O emissions (n= 17).
The most variable of the soil properties across the grassland area were the
concentrations of the available reactive nitrogen in the form of
NH4+ and NO3- (see Table 1). Locations with elevated
NH4+ also generally recorded higher NO3- concentrations,
although this relationship was not consistent at all locations (R2= 0.56). Soil samples taken from patches of decayed grass silage and the
shaded area indicated that these small areas had significantly greater
concentrations of NH4+ and NO3- (p < 0.001)
compared to the grassland area. Reactive nitrogen concentrations in soils
from the perimeter of the manure heap also showed wide variations, with some
extremely large (2.2 g N kg-1) and small (0.1 g N kg-1) values
being measured (Table 1).
Total carbon and nitrogen content of the soil from the grassland area showed
less variation than the reactive nitrogen content, with a small number of
elevated outlier values. The ratio of carbon to nitrogen content of the
soils (12:1) was consistent across the measurement locations (R2= 0.94). Total soil carbon and nitrogen concentrations from the shaded area
and silage remains were similar in magnitude to the grassland area
measurements. The manure heap perimeter was the exception to this,
presenting some very high concentrations of carbon and nitrogen. Total
carbon and nitrogen content of the soils around the manure heap varied from
small concentrations similar to the grassland soil (8 and
107 g C kg-1) to concentrations as large as 34 and 355 g C kg-1 (Table 1).
Soil pH varied little between most of the measurement locations in the
grassland area with the majority of the grazed field confidently estimated
at pH levels of 5.6 ± 0.34 (n= 38), in agreement with measurements
made in similar managed grazed fields in this area. Soil pH from the silage
remains and tree shaded area was generally more alkaline (pH 6.9 ± 1.5)
than from the grassland area. The soils from the manure heap perimeter were
highly alkaline (pH 8.3 ± 0.85) (Table 1).
WFPS% values across all measurement locations in the field ranged
between 9 and 50 % with a mean value of 26.5 %. The bulk density of the
soil in the field with the exception of the manure heap perimeter ranged
between 0.6 and 1.1 g cm-3 with a mean value of 0.8 g cm-3. Due to
the heterogeneous nature of soils there were several outliers for each of
the soil properties measured across the field (Table 1).
Correlation between soil properties and N2O flux
Multiple linear regression was used to investigate the relationships between
the soil properties presented in Table 1 (also soil porosity) and N2O
flux. Due to the wide ranging and uneven distribution of values measured for
both N2O flux and soil properties, the common logarithm (hereafter
referred to as log10) of several of these measurements (N2O flux,
NH4+, NO3-, total carbon and total nitrogen content)
was used for the multiple linear regression. Correlations of soil properties
were carried out with multiple linear regression analysis using the statistical
software R. The soil properties from all of the features in the field were
processed together as one group (n= 55).
Linear regression was carried out firstly using all of the measured
soil properties for each of the fits. After the initial fit, the properties
which were not statistically significant (p > 0.1) were removed
and the fit was run again using only the significant values (See Table 2).
Concentrations of NH4+ in soils were found to correlate well with
pH and total carbon and nitrogen (R2= 0.64; Fig. 6a). High
total carbon and nitrogen contents were indicative of an increased presence
of total organic carbon (TOC) in the soils.
Multiple linear regression correlation of soil properties and
N2O flux as plotted in Fig. 6.
Estimate
SD
p value
(a) Y= log10(NH4+)
Intercept
-2.56
0.76
< 0.01
pH
0.37
0.05
< 0.001
log10(Carbon g Kg-1)
-1.14
0.62
< 0.1
log10(Nitrogen g Kg-1)
1.53
0.79
< 0.1
(b) Y= log10(NO3-)
Intercept
-402.47
205.04
< 0.1
log10(NH4-N g Kg-1)
0.48
0.130
< 0.001
log10(Carbon g Kg-1)
-6.7
0.87
< 0.001
log10(Nitrogen g Kg-1)
8.58
1.13
< 0.001
WFPS%
0.04
0.01
< 0.001
Soil porosity
403.81
205.12
< 0.1
Bulk density g cm-1
154.86
77.39
< 0.1
(c) Y= log10(N2O Flux)
Intercept
-4.33
1.29
< 0.01
log10(NH4-N g Kg-1)
-0.25
0.20
< 0.1
log10(NO3-N g Kg-1)
0.76
0.10
< 0.001
pH
0.60
0.10
< 0.001
WFPS%
0.04
0.01
< 0.001
Soil porosity
3.85
1.34
< 0.01
Concentrations of NO3- correlated strongest with TOC,
NH4+ total nitrogen and WFPS% present in the soil
(R2= 0.77; Fig. 6b). NO3- concentrations were presumed
to be indicative of microbial nitrification activity in the soil as it is
the primary product of this process. Fluxes of N2O
(log10(N2O)) correlated strongly with NO3-, pH and WFPS% (R2= 0.86; Fig. 6c). The soil property with the most
significant correlation with N2O flux was NO3- (See Table 2).
Multiple linear regression used to identify relationships
between NH4+ (a), NO3- (b) and N2O flux
(c) with soil properties measured during flux measurements from grazed
grassland (See Table 2 for fitting parameters). All 55 soil samples
collected from multiple features present in the field were included in the
regression analysis.
Interpolation of N2O fluxes at a field scale
The simplest way to estimate the total daily N2O flux from the field
during the measurement period is to combine the relevant area and mean flux
recorded for each of the features of the field. Due to the uneven
distribution of flux magnitude and the many large hotspots of flux measured
using the chamber method in this experiment, geometric mean values are most
suitable to determine fluxes across the field scale (Table 3). Using the
geometric mean values, an estimate of 47.7 g N2O-N d-1 was emitted
from the field site during the measurement period (see Table 3; 122.5 g N2O-N d-1 estimated using the arithmetic mean). The grassland area
of the field which accounts for 98.62 % of the study area contributed 45 % (21.3 g N2O-N) of the estimated daily N2O flux from the
field. The silage remains and shaded area contributed 5 and 13 % to the
total emissions, respectively. The manure heap and soils contaminated by the
heap contributed a very large 38 % (18 g N2O-N) of the total flux
estimate which comes from a relatively small area of the field (0.8 %; Table 3).
Discussion
Variation in N2O fluxes at the field scale
N2O fluxes measured from the grazed grassland area of the field
(excluding the hotspot areas) were highly variable (between 2 and 227 µg N2O-N m-2 h-1). This is a common phenomenon which is
verified in many N2O flux measurement experiments (e.g. Oenema et al.,
1997; Skiba et al., 2013). Flux magnitude was unpredictable across the
grassland and in some cases varied by 2 orders of magnitude across
relatively short distances (< 10 m). Eighty percent of the fluxes
measured from the grassland area were below
30 µg N2O-N m-2 h-1. Fluxes of N2O comparable to this magnitude are often measured
from grazed fields in different climates in between fertilisation events
(Clayton et al., 1997; Luo et al., 2013; Oenema et al., 1997). The advantage
of using the closed loop dynamic chamber (Cowan et al., 2014) in this experiment
was that the extremely high precision (1 µg N2O-N m-2 h-1) allowed us to confidently report very low individual N2O
fluxes across the field and compare these measurements with the relevant
soil properties collected from within the measurement plot at each
individual location.
Geometric mean flux values and estimated cumulative flux from
each of the measured features across the field scale. 95 % confidence
intervals (CIs) are included.
Field feature
Area
Geometric mean flux
95 % CI
Cumulative flux
95 % CI
(m2)
(µg N2O-N m-2 h-1)
(g N2O-N d-1)
Grazed grassland
66861
13.3
(4.7–37.2)
21.3
(7.6–59.8)
Silage remains
36
2663.6
(1220–5815)
2.3
(1.1–5.0)
Shaded area
210
1217.1
(252–5881)
6.1
(1.3–29.6)
Stream
183
7.1
(2.9–17.5)
0
(0.0–0.1)
Manure heap
102
3195.2
(656–15562)
7.8
(1.6–38.1)
Manure perimeter
50
4469.7
(573–34875)
5.4
(0.7–41.9)
Manure outer perimeter
366
550.9
(66–4628)
4.8
0.6
Total
67808
47.7
(12.8–215.1)
The largest fluxes in the field were measured from the hotspot features
present (up to 79 000 µg N2O-N m-2 h-1). Fluxes from
the shaded area and the silage heap remains were consistently higher than
those measured on the grassland area. The shaded area presented an increased
number of sheep, with the resultant increase in animal waste freshly
deposited there (NH4+). Fluxes measured from the silage heap
remains were surprisingly high. Decaying plant matter is known to emit
N2O (Hellebrand, 1998), but it is unclear whether the emissions from
these patches are due to the additional organic materials present in the
soil or to the increased sheep activity and resultant urine and faeces
deposits. The larger pH values from the shaded areas, as well as the manure
heap and perimeter suggest that animal waste was the most likely source of
N2O. The combination of large concentrations of mineral N and organic C
in a high pH environment are ideal conditions for denitrification (Hofstra
and Bouwman, 2005; Saggar et al., 2013), which is most probably the main
source of the N2O here.
Fluxes of N2O from the stream were relatively small (1 to 22 µg N2O-N m-2 h-1) compared with those measured from the rest of
the field. Significantly higher fluxes have been measured from drainage
streams at the Bush Estate in previous experiments (100 to 1000 µg N2O-N m-2 h-1) using a different methodology (Reay et al.,
2003). Dry conditions in the run up to the measurement period had decreased
any leachate from the soils entering the stream. Past experiments have
reported N2O flux measurements from agricultural streams similar in
magnitude to those made in the surrounding soils (Baulch et al., 2011);
however, it is likely that the N2O fluxes measured in this experiment
are lower than they would have been had the measurements taken place on a
wetter date when drainage waters containing N2O and other nitrogen
compounds from surrounding fields would also have been entering the stream.
Flux measurements made on and around the manure heap were on average 420 times higher than the fluxes measured for the grassland
area of the field. The large spatial variability of N2O flux observed
from the heap was similar to that of a previous experiment carried out on
the farm estate using static chamber measurements, although reported fluxes
are an order of magnitude smaller in this study (Skiba et al., 2006). Solid
manure heaps are a known large source of N2O emissions and several
studies have estimated emission factors for such heaps (Amon et al., 2001;
Chadwick et al., 1999; Skiba et al., 2006). Emission factors for manure
heaps are often calculated by volume of stored manure. This implies a large
degree of variability, following from the different components of animal waste
as well as the age of the waste and how it is stored (Amon et al., 2001).
Application of the manure as fertiliser is often considered in the emission
factor of animal waste as well as storage (Chadwick et al., 1999, 2011; Velthof et al., 2003). Measurements made in this experiment
did not account for manure volume or calculate an emission factor for the
heap; however, this study highlights that an additional factor may also need
to be taken into account for a more accurate estimate of the emission factor
of solid manure storage (i.e. the legacy emissions of a manure heap). Very
high N2O fluxes (up to 10 825 µg N2O-N m-2 h-1)
were measured from the area around the manure heap which had become
contaminated with the animal waste. Our data have shown that these areas
that are highly enriched with available nitrogen compounds, and organic
matter remain after the manure heap has been removed and can continue to
emit N2O for months, as was observed for the patches of silage heap
remains (manure was spread in autumn, 9 months prior to measurements).
The high emissions and lasting effect of these areas may contribute
significantly to the overall emission factor of solid manure heaps and
agriculture as a whole when the large volumes of animal waste and storage
from livestock farms are considered.
Correlation between soil properties and N2O flux
High concentrations of NH4+ and NO3- are known to
increase N2O fluxes from soils as they are the primary nutrients
required for the microbial processes of nitrification and denitrification in
which N2O is produced and then released into the atmosphere (Davidson et
al., 2000). Animal urine and droppings are a known source of urea
CO(NH2)2 and ammonia (NH3), which are both alkaline and
convert to NH4+ in the presence of water (Freney et al., 1983). A
strong positive correlation between NH4+ concentrations and soil pH was observed across the field (See Table 2). As ruminant
(sheep and cattle) urine is normally slightly alkaline, the increased pH in
the small hotspot areas suggested that increased alkaline animal waste
deposition was the reason for the increase in pH and resultant available
NH4+ in the soil. This relationship has also been observed in
other studies (e.g. Haynes and Williams, 1992). Organic matter in the soils
(total C and N) also correlated with NH4+ concentrations in the
soils (See Table 2). Mineralisation of animal waste, and plant materials
such as silage, continues to provide NH4+ to soils over extended
periods (Martins and Dewes, 1992; Van Kessel and Reeves, 2002). All of the
N2O flux hotspot features of the field contained elevated
concentrations of NH4+ in the soil (See Table 1); however, the
concentration of NH4+ was not found to correlate significantly
with N2O fluxes (See Table 2).
NO3- concentrations in the soil correlated well with available
NH4+ and organic matter (See Fig. 6b). The physical properties
of the soil were also influential as NO3-correlated strongly with
WFPS%, and weakly with bulk density and soil porosity. Elevated
NO3- concentrations in the soil can be associated with high
rates of nitrification as NO3- is the primary product of the
nitrification process. The strong correlation between NO3- with
the available NH4+ and organic material present in the hotspot
features of the field provides strong evidence that elevated concentrations
of NO3- in these areas are due to nitrification occurring at an
increased rate. The soils measured in this study were relatively dry (9–50 % WFPS) and therefore more conducive for nitrification than
denitrification (Bateman and Baggs, 2005; Davidson et al., 2000). However,
the presence of organic matter would have created the necessary anaerobic
conditions required for denitrification in localised microsites through
increased O2 consumption required for organic matter decomposition
(Sexstone et al., 1985). No significant correlation between organic carbon
and N2O flux was observed in this data set. Organic carbon is known to
be a limiting factor of denitrification rates in some soils (McCarty and
Bremner, 1992); however, it is possible that the lack of correlation between
carbon and N2O flux measured in this experiment is due to the abundance
of carbon available in the soils.
Correlation between N2O flux and the measured soil properties showed
that NO3- concentrations were the most significant factor (Table 2). The strength of the correlation with NO3- and lack of
correlation with NH4+ does not explain if fluxes are
predominantly caused by either microbial nitrification or denitrification.
The presence of NO3- indicates that nitrification is definitely
happening at these sites; however, the lack of correlation between
NH4+ and N2O flux suggests that denitrification may be the
primary source of emissions. Another possibility is that conditions are
favourable for the conversion of NH4+ to N2O via microbial
nitrifier denitrification. In certain conditions, the nitrifier
denitrification process can be responsible for the majority of N2O
released from soils (Kool et al., 2010; Zhu, X. et al., 2013).
The correlations observed between N2O flux and the measured soil
properties in this study indicate that areas in which the concentrations of
available nitrogen compounds are higher emit more N2O; therefore,
available nitrogen input is likely the primary driver of the spatial
variability observed in N2O flux measurements. This relationship
between soil NO3- and NH4+ concentrations and N2O
flux is also observed in similar studies (e.g. Turner et al., 2008). Our
conclusion from the correlation analysis is that the high spatial
variability of N2O flux across the grazed field is primarily due to the
uneven distribution of nitrogen deposition in the form of animal waste.
There remains a high degree of uncertainty in the relationship between the
soil properties and N2O flux. This study suggests NH4+,
NO3- and organic matter can be used as proxies to predict where
fluxes will be higher in the field; however, exact fluxes are more difficult
to estimate due to the large number of variables which affect the rates of
microbial processes. Many studies have identified similar soil properties
which affect the rate of N2O emissions from agricultural soils
(Butterbach-Bahl et al., 2013; Dobbie and Smith, 2003); however, due to the
multiple simultaneous microbial processes which produce N2O it is
difficult to identify a clear relationship between soil properties and flux.
Relationships between N2O flux with temperature, WFPS% and nitrogen
content in soils are often observed, yet a consistent method for predicting
N2O from agricultural soils based on soil measurements still eludes
researchers (Flechard et al., 2007; Smith et al., 2003).
Multiple linear regression correlation between flux and soil properties
reported in studies similar to our own predicted very different significance
values for each of the measured soil properties depending on environmental
factors (Šimek et al., 2006; Turner et al., 2008). In order to advance
our understanding of these processes, more detailed experiments are
required in a variety of geographical and environmental conditions to better
predict the behaviour of microbial processes in soils with high available
nitrogen concentrations. Alternatively, a more controlled analysis of
individual soil properties and microbial processes can be examined under
laboratory conditions using similar high precision chamber methodology.
Ideally the use of this equipment could be paired with 15N labelled nitrogen
compounds (such as urea) and denitrification inhibitors to investigate the
biological mechanisms in N2O production and determine relationships
between these processes and soil properties.
Interpolation of N2O fluxes at a field scale
Using mean values to interpolate N2O flux at the field scale results in
very high uncertainty values due to the high spatial variability of the
N2O fluxes (Table 3). From this experiment, the total daily flux is
estimated to be between 12.8 and 215.1 g N2O-N d-1. These high
uncertainties highlight the weakness of the chamber methodologies inability
to account for spatial variability of N2O flux over large areas and the
importance of spatial variability when N2O flux estimates are made
using simple interpolation methods on a large scale. These results also
highlight the need for a better understanding of how agricultural flux
measurements are made using current methodology. Flux chamber placement is
vital in understanding the variability of N2O flux across a field.
Without a good understanding of N2O hotspots and the appropriate
positioning of chambers to include (or exclude) these areas, chamber methods
will not be able to provide effective comparable results between
experiments.
Other methods of interpolation exist when using chamber measurements;
however, these also struggle to account for the spatial variability of
N2O at larger scales. Fluxes measured from the field in this experiment
showed some predictability in spatial patterns as fluxes were higher in
certain hotspot locations, although knowledge of these locations is required
to observe this predictability as there was little relationship observed
between N2O flux and distance between measurements. Hotspot locations
which are not visible by eye are much more difficult to investigate.
Variance diagrams highlight this lack of predictability across the field,
showing a random distribution with no clear spatial pattern visible in the
flux or the corresponding soil properties across the field (Fig. 7).
The nature of the unpredictable spatial variability of N2O fluxes is a
huge barrier which limits the use of many methods of spatial interpolation
of the flux across a large scale such as a field. Taking many chamber
measurements across a small area is one way to improve this method (Turner
et al., 2008); however, this becomes impractical at larger scales and a
compromise needs to be made between field coverage and the number of chamber
measurements taken.
Variograms for N2O flux, NO3-, NH4+
and total carbon measured across the field scale. Log-normal distributions
were used as in Fig. 6 and Table 2. The x axis is the distance between
measurement locations in metres and the y axis is the semivariance in all of the
respective measurements made for the entire field.
Another method of measuring N2O fluxes at a field scale which has
advanced in recent years due to the increasing precision of rapid gas
analysers would be eddy covariance (Eugster et al., 2007; Kort et al.,
2011). Eddy covariance does not suffer from the same interpolation issues as
the chamber method and can provide a relatively confident estimate of mean
N2O flux across a large area (> 100 m2). The weakness
of the eddy covariance method is that it would not be able to distinguish
between sources and provide information on hotspot fluxes. Areas in which
animals spend a lot of time to shelter from the elements such as the shaded
area in this field-scale study present problems for eddy covariance
measurements as any physical objects which alter turbulence in the air (such
as trees or foliage in our case) can prevent measurements from taking place.
From the results in this experiment we would suggest that both methods
should be deployed in tandem to investigate N2O flux at the field scale
as both methods have significant weaknesses that the other can compliment.
Conclusions
Spatial variability remains one of the largest sources of uncertainty when
measuring N2O flux from agricultural soils. Results from this study
suggest that additional nitrogen applied to fields in the form of animal
waste is the primary source of anthropogenic N2O emissions from grazed
agricultural soils (with the exception of fertiliser events). The wide and
often random distribution of this nitrogen in the soils is one of the major
causes of the spatial variability observed in N2O emissions. This
inherent variability of soil properties limits the ability to reduce
uncertainty in N2O emission estimates that can be achieved by taking a
practical number of flux measurements using a chamber method. In order to
reduce uncertainties in large-scale emission budgets, it is effective to
identify hotspots of N2O fluxes and determine the causes of these
increased emissions. Identifying areas in which N2O fluxes are
significantly higher than the majority of the experimental area can reduce
overall uncertainty in results by defining different emission factors.
This study highlights the requirement of a better understanding of spatial
variability of N2O fluxes from intensively grazed grasslands. Without a
basic understanding of how hotspots of N2O are formed and the lifetime
of these hotspots, it is difficult to determine the true effect of these
areas, which may be significant over wider areas such as on the farm scale.
Field-, farm-, national- and global-scale emission budgets of agricultural
contributions to N2O emissions are often dominated by emission factors
which account for the soil conditions of the majority of the area of a
field. These budgets may be significantly underestimating N2O fluxes in
some cases, especially for livestock farms with high stocking densities.