Introduction
Agricultural practices contribute 5.4 Gt CO2 eq. yr-1
(range 11 %–12 %) to global greenhouse gas (GHG) emissions (IPCC,
2014; Tubiello et al., 2015). The technical potential to mitigate GHG
emissions from agriculture ranges between 5.5 and
6.0 Gt CO2 eq. yr-1 by 2030 (Smith et al., 2008), exceeding
current agricultural GHG emissions. The three major anthropogenic GHGs
are carbon dioxide (CO2), methane (CH4), and nitrous
oxide (N2O). The agricultural sector is responsible for 84 % of
global anthropogenic N2O emissions (Smith et al., 2008). N2O
emissions are primarily attributed to mineral and organic fertiliser applied
to soils, manure left on pastures, biomass burning, crop residues, and
increased mineralisation of soil organic matter (SOM) caused by the
cultivation of soils (IPCC, 2014; Tubiello et al., 2015). Due to the high
global warming potentials of CH4 and N2O (GWP; factor 34 and
298, respectively, on a per mass basis compared to CO2 based on a
100-year time horizon) (IPCC, 2013b), these gases are more important than the
CO2 fluxes from the agricultural sector. However, they remain far
less understood than CO2 fluxes because of interactions between
multiple underlying processes that are largely unexplored. In particular,
data resolving the dynamics of N2O fluxes from soils are still
scarce, as advances in instruments capable of high-frequency continuous
N2O concentration measurements that are steadily deployable in the field
have only become available in recent years (Eugster and Merbold, 2015).
Here we test a potential mitigation strategy for nitrous oxide emissions,
namely the substitution of fertiliser with biologically fixed nitrogen (BFN)
via clover on intensively managed grassland. Processes producing and
consuming N2O are numerous and their complex interactions and
dependencies on biotic and abiotic factors are generally known but not yet
fully understood (Butterbach-Bahl et al., 2013). Nevertheless, it is known
that N2O emissions in grasslands strongly depend on management
practices (Hörtnagl et al., 2018; Li et al., 2013; Snyder et al., 2009),
and reducing N2O emissions while maintaining yields can thus
contribute to climate smart agriculture (CSA) (Lipper et al., 2014). For
mitigating N2O emissions from soils, a range of options (e.g.
nitrification inhibitors, liming of acid soils, precision fertiliser use,
legumes) are available (Bell et al., 2015; Flessa, 2012; de Klein and Eckard,
2008; Li et al., 2013; Luo et al., 2010; Paustian et al., 2016; Smith et al.,
2008). The most important strategies focus on increasing the nitrogen use
efficiency (NUE) of plants by adjusting the rate, type, timing, and placement
of organic and inorganic nitrogen fertilisers. With such approaches, the
surplus of nitrogen (N) as the substrate for microbial communities producing
N2O can be reduced or avoided (Flessa, 2012; Galloway et al., 2003;
Snyder et al., 2009). Reducing N surplus comes along with other environmental
benefits such as reduced ammonia emissions (NH3) and nitrate
(NO3-) leaching, both potential sources of indirect (off-site)
N2O emissions. Similar to these mitigation strategies, forage legume
species of the Fabaceae family (e.g. white clover, red clover, lucerne, also
called alfalfa) grown in grass–legume mixtures have the potential to reduce
N2O emissions as a cost-effective mitigation strategy (Jensen et al.,
2012). In legume-rich systems, large parts of the plants' nitrogen (N) demand
can be provided from the atmosphere via biological nitrogen fixation (BNF)
instead of using fertiliser amendments (Ledgard et al., 2001; Suter et al.,
2015). Hence, N input via BNF instead of fertilisers has the potential to
avoid large N surpluses by provisioning N in a manner synchronous to plant
needs following their growth pattern (Crews and Peoples, 2005). Furthermore,
BNF is down-regulated by the plant when demand is low; fixed N is located
in the nodules and thus not freely available to microbiota in the soil
(Lüscher et al., 2014; Nyfeler et al., 2011).
Our mitigation approach investigated the potential for reductions in slurry
application accompanied by increased clover proportion in the pasture to
reduce N2O emissions at the field scale. Farmers currently use a
combination of home-produced slurries and bought mineral fertiliser. Our
suggestion is to apply the slurry to the fields which are amended with
mineral fertiliser. This would have an additional benefit of reducing
indirect GHG emissions, i.e. those during the manufacture of mineral
fertilisers.
Besides the obvious advantage of lower fertiliser amendments, grass–legume
mixtures typically achieve higher yields than average grass and legume
monocultures (“overyielding effect”) and often also higher yields than the
best-performing monoculture (“transgressive overyielding”), with legume
proportions of 40 %–70 % resulting in the highest yields (Finn et al., 2013;
Lüscher et al., 2014; Nyfeler et al., 2009). In addition, growing
selected legumes in mixtures with non-legumes could improve the resistance and
resilience of forage swards against climatic extremes such as severe drought
events (Hofer et al., 2017). Moreover, grass–legume mixtures are beneficial
to fodder composition as they are characterised by higher protein contents
than grass swards, and show well-balanced feeding values (Phelan et al.,
2015). Legume-rich fodder has high crude protein (CP) contents and was shown
to increase voluntary intake by 10 %–20 % (Dewhurst et al., 2003)
and to increase milk production (Dewhurst et al., 2003; Huhtanen et al.,
2007).
Despite the known advantages, introducing legumes causes some challenges for
farmers. For instance, maintaining a persistent optimal legume proportion of
30 %–60 % (30 %–50 %, Lüscher et al., 2014;
40 %–60 %, Nyfeler et al., 2011) is not trivial (Guckert and Hay,
2001). Conservation of legumes such as hay or silage can be more difficult than
for grasses due to lower contents of water-soluble carbohydrates (WSC) and
higher pH buffering capacities (Phelan et al., 2015). When protein-rich
forage is fed without sufficient WSC, N cannot be used efficiently by
livestock and N excretion from the animals increases (Phelan et al., 2015).
However, the balance between CP and WSC can be provided by carbohydrates from
other plant species in mixtures (Lüscher et al., 2014). Furthermore,
exceptionally high legume proportions (>80 %) and legume monocultures
can lead to similar N surplus due to high levels of BFN as found in
fertilised fields and consequently to high soil nitrate concentrations
(Weisser et al., 2017), which can subsequently lead to enhanced N2O
emissions (Jensen et al., 2012). So far, relatively few in situ measurements
at plot scale have been carried out to investigate the effect of legumes and
grass–legume mixtures on N2O emissions (e.g. studies by Klumpp et
al., 2011; Virkajärvi et al., 2010; Schmeer et al., 2014; Niklaus et al.,
2016; Li et al., 2011). The contribution of legumes to total field-scale
N2O emissions was attributed to the decomposition of N-rich plant
residues and N from root exudates (Millar et al., 2004; Rochette and Janzen,
2005). Although it was shown that some Rhizobium species are able to produce
N2O via rhizobial denitrification (O'Hara and Daniel, 1985; Rosen and
Ljunggren, 1996), direct N2O emissions from BNF are negligible
compared to N2O from denitrification rates for most investigated
species and hence result in no significant effect on field-scale N2O
emissions (Garcia-Plazaola et al., 1993; Rochette and Janzen, 2005).
To date, experimental studies investigating year-round N2O exchange
in grassland systems are scarce (Skinner et al., 2014), and measurements of
high temporal resolution in grassland relying on fertiliser input versus
grassland based on BFN are missing. Thus, the aim of this study was to test
the N2O mitigation strategy of substituting N fertiliser with BFN by
increasing the clover proportion in grassland. Therefore, we measured
N2O exchange and productivity in two adjacent grassland parcels, one
with an intensive “business as usual” management compared to a parcel for
which
fertiliser amendments were substituted by over-sowing clover. Our specific
objectives were (1) to quantify N2O emissions from both parcels,
(2) to identify the meteorological and soil chemical drivers of N2O
emissions, and (3) to assess if substituting N fertiliser with BFN was an
effective N2O mitigation strategy. We hypothesised considerably lower
N2O emissions in the clover parcel, lower soil nutrient availability
in the clover parcel (and thus no effect of legume proportions on N2O
emissions), and hypothesised fertilisation to play the dominant role in
driving N2O emissions in the control parcel. We further expected
minor differences in grassland yield between the two parcels and, as a
consequence, reduced N2O emission intensities in the clover parcel.
(a) Experimental set-up and measured variables at the
experimental research site Chamau (CH-Cha). The clover parcel (north) is
managed to increase nitrogen inputs from the atmosphere via increased
biologically fixed nitrogen (BFN). This was achieved by over-sowing with
clover in March 2015 and April 2016. In contrast, the control parcel under
conventional management (south) obtains most N in the form of organic
fertiliser (i.e. slurry) and only small N inputs via BNF. Blue dots represent
soil sampling locations. (b) Footprint climatology of the years
2013–2016 with footprint contour lines of 10 % to 90 % in 10 %
steps using the Kljun et al. (2015) footprint model (background
picture used with kind permission from Swisstopo, https://map.geo.admin.ch/, last access:
5 June 2018). The prevailing wind direction was from the north.
Management activities carried out at the control and clover parcels
during the experimental years 2015 and 2016 according to the field book
entries of the farmer. For organic fertiliser amendments, the results of
laboratory analyses (slurry composition) are given.
Year
Parcel
Start
End
Management
Specification
Amount
Dry
Organic
Organic C
pH
total N
NH4-N
NO3-N
C / N
P
P2O5-P
K
K2O-K
Ca
Mg
S
Total
Total
Unit
Unit
matter
matter
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(g kg-1
(kg ha-1)
(kg ha-1)
DM
N
ha-1
(%)
(%)
DM)
DM)
DM)
DM)
DM)
DM)
DM)
DM)
DM)
DM)
DM)
2015
Clover
2015-03-13
2015-03-13
Over-sowing, rolling
Seed mixture OHHEBE, FIONA,TEDI*
20.0
kg
2015-04-21
2015-04-22
Mowing, swathing, bringing in silage
Grass silage
11.1
dt FS
2015-06-02
2015-06-03
Mowing, swathing, put hay on wagon
Hay
44.4
dt FS
2015-06-15
2015-06-19
Grazing
Sheep
28.1
2015-06-30
2015-06-30
Drainage grubber
2015-07-01
2015-07-06
Grazing
Sheep
31.1
2015-08-20
2015-08-21
Mowing, swathing, spinning, bringing in silage
Grass silage
25.0
dt FS
2015-09-28
2015-09-28
Mowing, swathing, put hay on wagon
Hay
22.2
dt FS
Control
2015-03-11
2015-03-11
Organic fertiliser, trail hose
Liquid slurry
21.5
m3
2.2
67.6
392.1
7.8
82.3
49.3
0.3
4.8
11.0
25.1
55.7
67.2
33.4
6.6
5.5
474.0
39.0
2015-04-21
2015-04-22
Mowing, swathing, bringing in silage
Grass silage
23.9
dt FS
2015-04-29
2015-04-29
Organic fertiliser
Liquid slurry
28.6
m3
2.6
71.5
414.5
7.7
61.2
37.1
<0.001
6.8
9.5
21.7
64.6
77.8
27.8
7.2
5.3
744.5
45.6
2015-06-02
2015-06-03
Mowing, swathing, put hay on wagon
Hay
50.0
dt FS
1.7
66.6
386.2
7.5
69.4
47.8
<0.001
5.6
11.2
25.7
85.1
102.5
29.3
9.0
5.4
517.7
35.9
2015-06-09
2015-06-09
Organic fertiliser, trail hose
Liquid slurry
30.5
m3
2015-06-30
2015-06-30
Drainage grubber
2015-07-06
2015-07-06
Mowing, swathing, spinning, bringing in silage
Grass silage
34.1
dt FS
2015-07-09
2015-07-09
Organic fertiliser, trail hose
Liquid slurry
35.5
m3
2.6
65.8
381.2
8.0
74.5
47.7
<0.001
5.1
15.1
34.6
72.8
87.7
41.8
9.1
6.6
921.8
68.7
2015-08-20
2015-08-21
Mowing, swathing, spinning, bringing in silage
Grass silage
37.5
dt FS
2015-08-25
2015-08-25
Organic fertiliser, trail hose
Liquid slurry
27.3
m3
2.6
65.8
381.5
8.0
87.9
55.8
5.7
4.3
15.6
35.8
67.2
81.0
45.8
8.1
6.3
709.1
62.3
2015-09-28
2015-09-28
Mowing, swathing, put hay on wagon
Hay
40.9
dt FS
2015-10-08
2015-10-08
Organic fertiliser, trail hose
Liquid slurry
28.2
m3
2.0
63.8
370.1
8.1
79.0
45.7
3.5
4.7
14.5
33.2
78.2
94.3
42.8
8.0
6.5
563.6
44.5
2016
Clover
2016-01-26
2016-02-10
Grazing
Sheep
5.5
2016-04-06
2016-04-06
Over-sowing, rolling
Seed mixture OHHEBE, FIONA,TEDI*
20.0
kg
2016-05-25
2016-05-27
Mowing, swathing, spinning, bringing in hay
Hay
66.7
dt FS
2016-07-04
2016-07-04
Mowing, swathing, spinning, bringing in silage
Grass silage
22.2
dt FS
2016-08-13
2016-08-14
Mowing, swathing, spinning, bringing in silage
Grass silage
15.3
dt FS
2016-09-22
2016-09-24
Mowing, swathing, spinning, silage bales
Grass silage
20.0
dt FS
2016-11-22
2016-11-30
Grazing
Sheep
5.5
Control
2016-01-26
2016-02-10
Grazing
Sheep
5.5
2016-03-23
2016-03-23
Organic fertiliser, trail hose
Liquid slurry
21.5
m3
1.6
66.9
387.6
8.0
72.8
43.4
1.0
5.3
10.7
24.5
84.5
101.8
29.5
7.1
5.7
343.7
25.0
2016-05-25
2016-05-27
Mowing, swathing, spinning, bringing in hay
Hay
81.8
dt FS
2016-06-01
2016-06-01
Organic fertiliser, trail hose
Liquid slurry
25.5
m3
1.6
65.3
378.8
8.0
80.6
45.9
1.5
4.7
12.4
28.4
79.9
96.3
36.0
8.1
6.9
407.3
32.8
2016-07-04
2016-07-04
Mowing, swathing, spinning, bringing in silage
Grass silage
23.9
dt FS
2016-07-16
2016-07-16
Organic fertiliser, trail hose
Liquid slurry
24.5
m3
2.8
68.8
398.9
8.2
71.2
49.8
<0.001
5.6
12.0
27.4
66.5
80.1
35.3
8.0
6.2
687.3
48.9
2016-08-13
2016-08-14
Mowing, swathing, spinning, bringing in silage
Grass silage
23.9
dt FS
2016-08-17
2016-08-17
Organic fertiliser, trail hose
Liquid slurry
23.2
m3
1.6
67.0
388.4
8.0
110.0
60.3
<0.001
3.5
13.7
31.4
72.8
87.7
42.2
9.2
6.7
370.9
40.8
2016-09-22
2016-09-24
Mowing, swathing, spinning, silage bales
Grass silage
24.5
dt FS
2016-09-30
2016-09-30
Organic fertiliser, trail hose
Liquid slurry
26.8
m3
1.2
66.5
385.3
8.0
103.0
55.0
<0.001
3.7
13.8
31.7
80.3
96.7
39.3
9.5
6.8
321.8
33.1
2016-11-22
2016-11-30
Grazing
Sheep
5.5
* Two varieties of Trifolium repens L., variety HEBE, FIONA,
and one variety of Trifolium pratense L. TEDI; 20 kg seeds ha-1;
1/3 of each sort, identical mixture and amounts in both years; acquired
from UFA Samen, fenaco Genossenschaft, Winterthur, Switzerland.
Material and methods
Site description
The experiment was set up at the Swiss FluxNet site Chamau (CH-Cha), located
in the valley of the Reuss River on the Swiss Plateau, approximately 30 km
southwest of Zurich (47∘12′36.8′′ N, 8∘24′37.6′′ E;
393 m a.s.l.). The site has been well investigated in terms of CO2
exchange (Burri et al., 2014, using static chambers (SC); Zeeman et al.,
2010, using EC), as well as for N2O and CH4 exchange under
management that is typical for Swiss grasslands located on the Swiss Plateau
(Imer et al., 2013, using SC for N2O and CH4 and EC for
CO2; Merbold et al., 2014, using EC for all three gases; Wolf et al.,
2015, using EC and SC for N2O). Two grassland parcels of 2.2 and
2.7 ha are located adjacent to each other and have a similar management
history, i.e. permanent grassland since at least 2002 with a restoration year
in 2012 (Merbold et al., 2014). The most abundant species are English
ryegrass (Lolium perenne) (a mixture of early and late varieties),
common meadow grass (Poa pratensis), red fescue (Festuca rubra), timothy (Phleum pratense), white clover (Trifolium repens; small leaf varieties PEPSI, HEBE and big leaf varieties FIONA,
BOMBUS), red clover (Trifolium pratense; variety BONUS) sown in
2012 and complemented by the volunteer species dandelion (Taraxacum officinale), and rough meadow grass (Poa trivialis). Each parcel is
usually mown four to six times per year for silage or hay production
(Table 1). Each harvest is commonly followed by a fertiliser amendment,
predominantly in the form of liquid slurry (average ± SD over 11 years
(2003–2014) 266±75 kg N ha-1 yr-1).
The meteorological conditions at the site are characterised by an average
annual temperature of 9.1 ∘C and an average annual precipitation sum
of 1151 mm (Sieber et al., 2011). The soil is a gleysol–cambisol, with bulk
densities in 0–0.2 m of depth ranging between 0.9 and 1.3 g cm-3 (Roth,
2006) and a soil pH of about 6.5 (Labor Ins AG, Kerzers, Switzerland, in
2014).
Experimental set-up and management activities
The field experiment comprised a control and a clover treatment parcel
(Fig. 1). The control parcel was managed similarly to previous years,
including the common management activities described above (harvest,
fertiliser application, and occasional grazing; Table 1). The eddy covariance
tower, including meteorological sensors, was located at the border between
the two parcels (Fig. 1). We used the two years 2013 and 2014 as reference
years (no treatment). In order to test the N2O mitigation option, the
treatment parcel was over-sown in March 2015 and April 2016 with clover
(Trifolium pratense L. and two varieties of Trifolium repens L.) to increase the clover proportion of the sward in the clover
parcel. In contrast to the control parcel on which 296 and
181 kg N ha-1 were added in 2015 and 2016, respectively (Table 1), no
fertiliser was applied on the clover parcel during the experiment. To assist
clover establishment and increase the clover proportion in the clover parcel,
the parcel was grazed with sheep after over-sowing in mid-June and the beginning
of July 2015 to keep the grass species short and thus reduce competition
during the clover establishment phase. The control parcel was mown once
instead of being grazed during this time (beginning of July). All other
harvests took place on the same day on both parcels (see Table 1 for specific
management data including dates, slurry composition, and sowing rate).
Management activities comprised the regular harvest activities (mowing,
swathering, and subsequent biomass removal) on both parcels, with subsequent
slurry applications in the control parcel, besides occasional grazing, plus
the over-sowing of the clover parcel. During our reference years 2013 and
2014, management was identical in both parcels in 2013, while in 2014 instead
of mowing, cattle were grazing in the control parcel, whereas the clover
parcel was mown, resulting in similar reference fluxes from both parcels.
Yields and exports of C and N were quantified by analysing biomass sampled
destructively during each harvest event (see Sect. 2.7 on vegetation
samples) for C and N contents in the years 2015–2016. The fraction of N
originating from BNF in the harvested biomass (2015–2016) was quantified via
the 15N natural abundance method (Unkovich, 2008). Combined with
the legume biomass obtained by destructive biomass sampling at all harvest
dates, we were able to calculate the total amounts of BFN in the harvested
biomass. Beyond our own observations, detailed management information for the
years 2001–2016 was recorded by the farm staff in a field book. The overall
amount of organic and mineral fertiliser applied to the field was documented,
and
subsamples of the applied slurry were taken on the day of application (since
2007) and analysed in an external laboratory (LBU, Eric Schweizer AG, Thun,
Switzerland). Slurry applied to the control parcel was digested cattle and
pig slurry obtained from a local biogas plant (for chemical composition, see
Table 1). Records in the field book also included information on herbicide
application, harrowing, rolling, and over-sowing (for details, see Table 1).
Greenhouse gas flux measurements
Greenhouse gas exchange (CO2, N2O, CH4,
H2O) was continuously measured at the site with the eddy
covariance (EC) technique using a mast located at the boundary between the
two parcels (Fig. 1). The choice of the EC tower location resulted in the
fetch being located most of the time either in one or the other parcel,
taking advantage of the two prevailing wind directions. The flux measurement
set-up consisted of a 3-D sonic anemometer (Solent R3, Gill Instruments,
Lymington, UK), an open-path infrared gas analyser for CO2 and
H2O concentrations (LI-7500, LI-COR Biosciences, Lincoln, NE, USA),
and a quantum cascade laser absorption spectrometer (QCLAS) capable of
measuring N2O, CH4, and H2O concentrations
(mini-QCLAS, Aerodyne Research Inc., Billerica, MA, USA) (Merbold et al.,
2014) at 10 Hz resolution. The air inlet for the laser absorption
spectrometer was located at a height of 2.1 m, just below the sonic
anemometer head. The air was pulled through a 6 m long tube to the QCLAS
located in a temperature-controlled weatherproof box. Data acquisition and
data storage were conducted according to the set-up described in Eugster and
Plüss (2010). From the high-frequency measurements of these sensors, 10
and 30 min flux averages of the respective trace gases were calculated. The
basic EC system, measuring CO2 and H2O exchange, has been
running since 2005 (Eugster and Zeeman, 2006; Zeeman et al., 2010) and was
complemented with the field-suitable QCLAS for high-frequency (10 Hz)
N2O concentration measurements in 2012 (Merbold et al., 2014). Thus,
more than 2 years of reference fluxes from both parcels under similar
management regimes were collected before the beginning of the study presented
here.
Meteorological and soil microclimate measurements
Meteorological variables measured at the Chamau site included air temperature
and relative humidity (2 m of height; Hydroclip S3 sensor, Rotronic AG,
Switzerland), all components of the radiation balance (2 m of height; CNR1,
Kipp & Zonen B.V., Delft, the Netherlands), incoming and reflected
photosynthetic active radiation (2 m of height; PARlite sensor, Kipp and Zonen,
Delft, the Netherlands), and precipitation (1 m of height; tipping bucket rain
gauge model 10116, Toss GmbH, Potsdam, Germany) (Table S1 in the Supplement,
Fig. 1). Less than 2 m from the tower, basic soil microclimate
measurements were carried out. These measurements included volumetric soil
water content (at 0.04 and 0.15 m of depth; ML2x sensors, Delta-T Devices Ltd.,
Cambridge, UK) and soil temperature (at 0.01, 0.02, 0.05, 0.10, and 0.15 m
of depth; TL107 sensors, Markasub AG, Olten, Switzerland). In addition to the
sensors close to the tower, each parcel was equipped with a similar set of
soil sensors in 2015 (see soil plots, Fig. 1) to compare potential
differences in soil microclimatic conditions and subsequent effects on GHG
fluxes. Soil pH (at 0.1 m of depth) and soil oxygen (O2)
concentration (at 0.1, 0.2 m of depth) were automatically measured using
in-house custom-made sensors (based on ISFET pH sensor kit, Sentron, Roden,
Netherlands and EC410 oxygen sensors, SGX Sensortech, Chelmsford, UK). In
addition, soil water content (at 0.05, 0.1, 0.2, 0.5, 0.8 m of soil depth;
EC-5, Decagon, Pullman, WA, USA), soil temperature (at 0.05, 0.1, 0.2, 0.5,
0.8 m of soil depth; T109, Campbell Scientific Inc., Logan, UT, USA), matrix
potential (at 0.1, 0.2 m of soil depth; Tensiometer T8, UMS GmbH, Munich,
Germany), and soil heat flux (at 0.02 m of soil depth; HFP01, Hukseflux B.V.,
Delft, Netherlands) were recorded. Some of the soil water content sensors
stopped functioning on 18 June 2015 (at 0.05, 0.1, 0.2 m) and were thus
replaced on 6 August 2015 (Decagon 5TM, Pullman, WA, USA). The signals of these
sensors were sampled at 10 s intervals and stored as 10 min averages on a
data logger (CR1000; Campbell Scientific Inc., Logan, USA). Sensors at the
tower and in its vicinity were previously connected to a CR10X model
(Campbell Scientific Inc., Logan, USA) and since March 2016 to a newer data
logger (CR1000; Campbell Scientific Inc., Logan, USA).
Soil nutrient availability
For determining ammonium (NH4+), nitrate (NO3-), and
dissolved organic carbon (DOC) concentrations in the soil, topsoil samples
were taken down to 0.2 m of depth. The nominally biweekly sampling was
intensified to daily intervals for seven consecutive days following slurry
application (see also Wolf et al., 2015). Five samples per parcel were taken
along a transect within the average footprint of the EC measurements.
Extraction of NH4+, NO3-, and DOC was achieved by
shaking 15 g of fresh soil with 50 mL 0.5 M K2SO4 for 1 h
and subsequent filtering (Whatman no. 42 ashless filter paper, 150 mm
diameter, GE Healthcare AG, Glattbrugg, Switzerland) into centrifuge tubes
(50 mL tubes, PP, Greiner Bio-One GmbH, St. Gallen, Switzerland). From the
extract, a subsample was acidified for the measurement of DOC by combustion
in a total organic C and N analyser (multi N/C TOC analyser 2100S, Analytik
Jena AG, Jena, Germany). NH4+ and NO3- were
analysed colourimetrically (Vis V-1200, VWR International, Radnor, PA, USA).
Thereafter, the remaining soil samples were dried for 1 week at
105 ∘C and weighed before and after drying in order to determine the
gravimetric soil water content.
Characteristics of the exported biomass from the control and clover
parcels in 2015 and 2016 for legumes, non-legumes, and total biomass (legumes
and non-legumes). Numbers in brackets give the respective standard errors.
The legume proportion is based on the annual biomass exported. C and N
content and δ15N values refer to mean values across all samples.
BFN refers to N derived from the atmosphere in harvested clover biomass.
Means sharing the same superscript (per row) are not significantly different
from each other (Tukey's HSD, p<0.05); no significance tests were
applied for percentages and ratios.
Variable (unit)
2015
2016
Control
Clover
Control
Clover
Biomass export (DM t ha-1)
Total
12.8 (±0.5)a
10.4 (±0.7)b
11.9 (±0.4)ab
11.0 (±0.5)ab
Biomass export (DM kg ha-1)
Legumes
1860 (±176)a
2240 (±141)b
503 (±80)ab
4840 (±355)ab
Non-legumes
11 000 (±541)a
8170 (±666)b
11 400 (±462)a
6150 (±493)b
Legume proportion (%)
Total
15 (±12)
21 (±8)
4 (±5)
44 (±20)
C content (%)
Legumes
45.3 (±1.1)
45.6 (±0.3)
42.9 (±0.9)
43.8 (±0.6)
Non-legumes
45.1 (±1.4)
45.2 (±0.4)
43.0 (±1.0)
43.0 (±1.0)
N content (%)
Legumes
3.36 (±0.24)
3.56 (±0.14)
3.30 (±0.14)
3.08 (±0.18)
Non-legumes
2.18 (±0.12)
2.25 (±0.16)
1.94 (±0.19)
1.85 (±0.17)
δ15N (‰)
Legumes
-0.47 (±0.54)
-0.72 (±0.21)
-0.37 (±0.55)
-0.76 (±0.24)
Non-legumes
4.77 (±0.83)
4.48 (±0.42)
5.10 (±0.94)
3.45 (±0.55)
C (kg ha-1)
Total
5780 (±222)a
4720 (±289)b
5120 (±221)ab
4760 (±228)b
Legumes
843 (±78)a
1020 (±70)a
216 (±24)b
2120 (±123)c
Non-legumes
4940 (±235)a
3700 (±295)b
4900 (±220)a
2640 (±275)c
N (kg ha-1)
Total
301 (±10)a
264 (±13)b
238 (±13)ab
262 (±8)b
Legumes
63 (±6)a
80 (±5)a
17 (±2)b
149 (±9)c
Non-legumes
238 (±9)a
184 (±13)a
221 (±11)a
113 (±9)a
BFN (kg ha-1)
Legumes
55 (±5)a
72 (±5)a
14 (±2)b
130 (±8)c
Vegetation sampling and determination of biological nitrogen
fixation
Vegetation samples were taken from each parcel at each harvest date by
destructive sampling using harvest frames (0.1 m2; n=10 for each
parcel per date randomly sampled within the EC footprint clipped at a mowing
height of 0.05 m; Table S1). Vegetation was separated into legumes and
non-legumes (grasses and forbs) to assess the legume proportion in the dry
biomass. The only legume species found on site were the sown clover species
Trifolium pratense L. and Trifolium repens L. Vegetation
samples were dried at 70 ∘C for 1 week and weighed before and
after drying to estimate the water content. Milling of dry biomass samples
was done separately for legumes and non-legumes, and a subsample of 5 mg was
weighed into tin capsules for further analyses of total C, N,
δ13C, and δ15N (n=5 for each parcel per
date). C and N concentrations, as well as δ13C and
δ15N values, were analysed with a Flash EA 1112 series
elemental analyser (Thermo Italy, former CE Instruments, Rhodano, Italy)
coupled to an isotope ratio mass spectrometer (DELTAplusXP, Finnigan MAT,
Bremen, Germany). Estimates of BFN were based on the δ15N
measurement. The percentage of shoot N derived from BNF
(%Ndfa; nitrogen derived from the atmosphere) in legume biomass
was calculated with the 15N natural abundance method (Boddey et
al., 2000; Unkovich, 2008), following Eq. (1):
%Ndfa=δ15Nref-δ15Nlegumeδ15Nref-B×100,
where %Ndfa is the percentage of legume shoot N derived from
the atmosphere, δ15Nref is the δ15N value of a
non-fixing reference plant (i.e. grass species) growing in the proximity of
the legume, and δ15Nlegume is the δ15N value
of the legume shoot. The B value is the δ15N signature of
the legume species growing without N available from soil. B was estimated as
the weighted mean of B values of Trifolium repens L. reported in
the literature (-1.48×2/3) and Trifolium pratense L.
(-0.94×1/3) (B values from Unkovich, 2008, Appendix 4). Weights
were chosen according to the sown legume species composition of 2/3 white
clover and 1/3 red clover. The %Ndfa in legume shoots was
calculated for each legume biomass sample taken. The non-legumes cut within
the same harvest frame as the legumes were used as a reference, delivering the
δ15Nref value (Carlsson and Huss-Danell, 2014). For
annual values, harvests, and their components, uncertainty estimates were
calculated with the Gauss uncertainty propagation (Table 2). Vegetation
development was tracked via leaf area index (LAI) measurements (LAI-2000,
LI-COR Biosciences, Lincoln, NE, USA) carried out on both parcels biweekly as
well as before and after mowing or grazing activities. Vegetation height and
plant development as well as grazing activities within the footprint were
further monitored via standard webcams (IN-5907HD, INSTAR Deutschland GmbH,
Huenstetten, Germany).
Eddy covariance flux post-processing
Net ecosystem fluxes of CO2, N2O, and CH4 were
quantified by the eddy covariance (EC) method as the covariance between
turbulent fluctuations calculated by Reynolds averaging 10 min blocks of
data of vertical wind speeds and trace gas molar densities (CO2) or
mixing ratios (N2O, CH4). Molar densities of CO2
were corrected for water vapour transfer effects (Webb et al., 1980).
Frequency response corrections applied to raw fluxes accounted for high-pass
(Moncrieff et al., 2004) and low-pass filtering (CO2: Horst (1997);
N2O and CH4: Fratini et al. (2012). N2O and
CH4 fluxes were additionally corrected for spectral losses due to
instrument separation (Horst and Lenschow, 2009). All fluxes were calculated
using the EddyPro software (v6.1.0, LI-COR Inc., Lincoln, NE, USA).
Before flux calculations, the statistical quality of the raw time series was
checked (Vickers and Mahrt, 1997). Raw high-frequency data used in flux
calculations were rejected (1) if raw measurements were outside a physically
plausible range (vertical wind speed: ±5 m s-1; CO2: 200
to 900 ppm, N2O: below 250 ppb, CH4: below 1700 ppb),
(2) if spikes, defined as data points outside predefined sigma (σ)
plausibility ranges (vertical wind speed: ±5σ, CO2: ±3.5σ, N2O and CH4: ±8σ), accounted for more
than 1 % of the respective raw time series, or (3) if more than 10 %
of available raw data were statistically different from the overall trend in
a specific 10 min period. Raw CO2 measurements were only used for
flux calculations if the window dirtiness signal from the open-path infrared
gas analyser did not exceed 80 % on average per 10 min data block.
Half-hourly fluxes were rejected (1) if fluxes were outside predefined
ranges (CO2: ±50 µmol m-2 s-1;
N2O: between -50 and 100 nmol m-2 s-1; CH4:
between -400 and 800 nmol m-2 s-1), (2) if the steady-state
test (Foken and Wichura, 1996) was outside ±30 %, or (3) if the test
on developed turbulent conditions was outside ±30 % (Foken et al.,
2004; Foken and Wichura, 1996). The analytical flux footprint model by Kljun
et al. (2015) was used for footprint calculations.
The boundary between the two parcels is oriented approximately in the east–west
direction (75∘ from north; Fig. 1). Each 10 min flux average
was attributed to a parcel only if a minimum of 80 % of the flux
footprint was in the direction of the respective parcel (i.e. footprint
weights from the direction of the respective parcel divided by the total of
all flux footprint weights >80 %). Similar methods with EC fluxes from
one set-up being attributed to certain land use categories according to the
respective footprint area were successfully used before (e.g. Biermann et
al., 2014; Gourlez de la Motte et al., 2018; Neftel et al., 2008; Rogiers et
al., 2005; Sintermann et al., 2011). After quality control, data coverage for
N2O exchange for both years was 62 % of the entire period
(details in Table 3). We observed moderate diurnal variations in flux origin
from the two parcels (Fig. S2 in the Supplement). Nevertheless, a similar share of
quality-controlled N2O fluxes was obtained from the control
(48 %) and the clover parcel (52 %) during the observation period.
The net effect in N2O emission differences represents a conservative
estimate, as N2O emissions from the clover parcel are more likely to
be overestimated and fluxes from the control parcel are more likely to be
slightly underestimated (Fig. S2). Our aim was to analyse flux data
originating from either one or the other parcel and avoid mixed GHG fluxes
due to wind direction changes during the flux-averaging interval. As the
standard 30 min averaging interval often resulted in mixed flux signals, we
reduced the averaging period to 10 min, which resulted in a clearer
representation of the temporal dynamics of GHG fluxes from each individual
parcel. On grassland systems in flat terrain (such as the Chamau site), eddies
with a timescale of 1–5 min are dominant, and thus fluxes based on a
10 min averaging interval adequately represent the atmospheric exchange of
GHGs (Lenschow et al., 1994). Our comparison of flux data (full time series)
based on 10 and 30 min averaging intervals showed that the average of
10 min N2O fluxes was only 2.3 % lower than the 30 min
N2O fluxes. Daily averages were calculated based on all data points
per parcel that fulfilled quality criteria 0 (best quality fluxes) or 1
(fluxes suitable for general analysis such as annual budgets) (Mauder and
Foken, 2004).
Comparison of N2O fluxes between parcels
We applied non-parametric bootstrapping in order to estimate the mean annual
N2O fluxes from both parcels and their respective confidence
intervals. From all available 10 min fluxes, we took 1000 bootstrapping
samples of each day per parcel. Averaging over time results in the
bootstrapping estimate of the average annual flux, while the 0.025 and 0.975
percentiles of the bootstrapping distribution reveal the 95 % confidence
intervals for the mean flux per parcel.
Relative flux differences between parcels were defined as the difference of
daily averages between clover and control parcels with respect to the average
flux from the control, calculated based on all days for which data from both
parcels were available. This was done to minimise potential biases associated
with periods of unequal coverage of both parcels. Calculations were done
following Eq. (2).
ΔF/F=FClover‾-FControl‾FControl‾
FClover and FControl are daily average fluxes from
the clover and the control parcels, respectively. Before being able to
identify differences in N2O exchange during the experimental periods,
2 years of flux data (2013 and 2014) were used to quantify how much the
fluxes and the productivity from the two parcels deviated under exactly the
same (2013) and similar (2014) management practice. For the calculation of
CO2 equivalents (CO2 eq.) we used factor 298, which is the
current IPCC global warming potential including climate–carbon feedbacks on a
100-year basis (IPCC, 2013a).
Data availability of the GHG flux measurements over the 2-year
experimental period (a) before quality assessment and quality control (QAQC)
(flagged 0, 1, and 2; after Foken et al., 2004) and (b) after QAQC (acceptable
quality flagged 0 and 1; after Foken et al., 2004). The reference for 100 %
is a year without data gaps.
(a)
Acquired measurement hours
Data coverage before QAQC
before QAQC (h)
(%)
CO2 flux
N2O flux
CH4 flux
CO2 flux
N2O flux
CH4 flux
2015
Both parcels
6958
7969
7964
79
91
91
Control parcel
4089
4826
4823
47
55
55
Clover parcel
2869
3143
3141
33
36
36
2016
Both parcels
7456
7734
7734
85
88
88
Control parcel
3911
4485
4485
45
51
51
Clover parcel
2302
2518
2518
26
29
29
(b)
Acquired measurement hours
Data coverage after QAQC
after QAQC (h)
(%)
CO2 flux
N2O flux
CH4 flux
CO2 flux
N2O flux
CH4 flux
2015
Both parcels
4930
5984
5223
56
68
60
Control parcel
1418
2120
1837
16
24
21
Clover parcel
2298
2395
2091
26
27
24
2016
Both parcels
3787
5040
4250
43
58
49
Control parcel
1081
1895
1581
12
22
18
Clover parcel
1548
1921
1615
18
22
18
Management and rain-event-specific N2O exchange
Three management event types and one natural event type were analysed in more
detail. These included organic fertiliser application, harvesting (mowing),
sheep grazing, and rain events following dry weeks. When fertilisation took
place less than 7 days after harvest, days after fertilisation were
classified as fertilisation and thus not associated with the harvest event.
If days after harvest overlapped days before fertilisation, these days were
excluded from the fertilisation class. In this case, the data displayed and
analysed only refer to days after harvest but not to days before
fertilisation in order to avoid misleading references. A rain event was
defined with >4 mm precipitation following a dry period with <1.5 mm
collected during the 7 days preceding the rain event. When a fertilisation
event took place at the same time as the rain event (9 August 2015 and
16 July 2016), the event was classified as a fertilisation event but not as a
rain event. Grazing overlapped a rain event on 15 June and 1 July 2015, and
thus these days were excluded from the rain event analysis. A pre-analysis
was conducted for all these events, comparing N2O emissions during
7 days before the event to 7 days after the start of the event (incl.
starting date). Grazing showed no significant differences between emissions
before and during grazing, nor did rain events. These categories were
therefore not considered in the generalised additive model (GAM; see
Sect. 2.11).
Statistical analysis
In order to assess the influence of management and environmental drivers of
N2O fluxes, we used semi-parametric generalised additive modelling
(Wood, 2006). We expected non-linear effects of some predictor variables on
N2O emissions, such as soil water content and oxygen concentration.
The GAM is adequate for including these non-linear effects because it
prescribes no parametric relationship between predictors and the response
variable. Instead, the model fits smoothing splines (piecewise defined
polynomials) to the relationship between each predictor and the response
variable, allowing for highly flexible curves if needed (i.e. if improving the
goodness of fit), but resulting in the smoothest possible relationship (i.e.
linear relationship) if suitable. The response variable was predicted by the
sum of all these smooth functions (“additive”). The degree of smoothing for
each additive function was determined using generalised cross-validation
(GCV).
The response variable was the log-transformed N2O flux in order to
better meet the assumptions of normally distributed residuals. The additive
model with a log-transformed response corresponds to a model
with multiplicative effects in the original scale. Thus, the predictors'
effects influence N2O fluxes multiplicatively. The influence of
management (i.e. fertilisation and harvest) and environmental driver
variables (e.g. soil meteorological variables, soil chemical variables) on
N2O emissions was investigated based on daily averages of measured
10 min flux data and the corresponding environmental variables. For introducing
management influence in the regression analysis, dates were labelled
according to three a priori selected management categories only:
post-fertilisation (F), post-harvest (H), and no management (here defined as
no management during the previous week) (0) in combination with the treatment
clover (Clo) or control (Ctr). Thus, five management categories existed
(Ctr-F, Ctr-H, Ctr-0, Clo-H, Clo-0). The control parcel without recent
management activity (Ctr-0) served as the reference level in comparison to
all other management categories. As grazing intensity is low at the site and
grazing did not show any influence on N2O exchange, we did not
include grazing in the GAM analysis. The full set of predictors included soil
temperature, soil water content, oxygen concentration, NH4-,
NO3+ and DOC concentration for substrate availability, net
ecosystem exchange (NEE) of CO2 as a proxy for plant activity, and
the categorical variable for management activity.
Meteorological conditions during 2015 and 2016. (a) Average
daily air temperature (2 m) and (b) average daily photosynthetically
active radiation (2 m). The grey bars indicate the sub-daily variability
(quartiles based on 10 min values). (c) Daily precipitation sums
during 2015 and 2016 (1 m).
All predictors were included as non-linear terms in the first step, and the
basic GAM was fitted using generalised cross-validation as the criterion for
the parameter choice resulting in the best fit. This method resulted in
several terms being included in the GAM as linear predictors (empirical
degrees of freedom, edf = 1). These were finally treated as linear terms
in order to obtain their effect sizes. For linear predictors such as soil
temperatures, effect sizes can be interpreted as in linear regression models.
Soil water content and oxygen concentration showed a non-linear influence on
log N2O emissions (reverse U shape), as estimated by the GAM to
require more degrees of freedom (edf > 1). These were kept as (non-linear)
smooth terms in the GAM. Stepwise backward elimination was applied for model
selection, whereby the number of predictors was reduced until the local
minimum value of the Akaike information criterion (AIC) was found. Residual
analysis showed that the final model residuals were in line with the
assumptions of a Gaussian distributed, homoscedastic error term with a mean
of zero.
Due to focusing the analysis on in situ measured data only, models that
included the soil sampling variables are limited to the observational days on
which manually sampled data were available (full model and optimised model).
To check the consistency of these results (i.e. effect sizes) with results from a
wider range of observations (year-round continuous measurements) we built a
model (“simple model”) based on only the major driver variables soil
temperatures, SWC, and management as predictors, with the advantage of
including more observations due to the wide coverage of these variables.
Negative N2O fluxes were analysed separately, but no significant
effects of the same set of predictors on N2O uptake were found. For
autocorrelated time series (i.e. soil microclimatic variables) the t test on
the differences was corrected for autocorrelation by calculating the
effective sample sizes according to Wilks (2011:147) and using the effective
sample sizes in the tests, resulting in adjusted standard errors and
p values (seadj; padj). All statistical analyses
were performed with the open source software R (R Core Team, 2016) using the
“mgcv” package (Wood, 2011) for generalised additive modelling.
Soil meteorological conditions during 2015 and 2016.
(a) Average daily soil temperature (0.1 m of depth),
(b) average daily soil water content (0.1 m of depth), and
(c) average daily soil oxygen concentration (0.1 m of depth) at the
control (left, red) and clover parcel (right, blue). The bars indicate the
sub-daily variability (ranges of 10 min values).
Results
General environmental conditions
Mean annual temperatures in 2015 and 2016 were 10.3 and 9.7 ∘C,
respectively (Fig. 2a). Thereby 2015 was 0.2 ∘C warmer and 2016 was
0.4 ∘C colder than the previous 5 years, which averaged
10.1 ∘C. Daily photosynthetically active radiation (PAR) followed
the typical seasonal pattern (Fig. 2b). Annual precipitation was 1029 mm in
2015 and 1202 mm in 2016, which is 7 % lower and 9 % higher,
respectively (Fig. 2c), than the 5-year mean annual precipitation (1101 mm).
While both years were characterised by a typical wet beginning of the growing
season (MAM with 376 mm in 2015 and 379 mm in 2016) similar to the 5
years prior to our period of analysis, the peak growing season (JJA) in 2015
was considerably drier (260 mm of precipitation) than in 2016 (396 mm;
Fig. 2c). The growing season, defined by Tair exceeding 5 ∘C
for at least five subsequent days, started on 17 March 2015 and
30 January 2016. The starting dates of net CO2 uptake for at least 10
subsequent days, an alternative indicator for the start of the growing season,
were 27 February 2015 and 8 March 2016, similar to previous years.
Soil microclimate
An important precondition for the N2O mitigation experiment is to
check for approximately equal soil microclimatic conditions in both parcels,
i.e. to exclude the possibility that soil microclimatic variables acted as
confounders in the experiment. Soil temperatures were similar in the control
(mean 14.5 ∘C) and the clover parcel (13.6 ∘C) with
measured differences being smaller than the sensor accuracy of ±1 ∘C. While air temperature fell below 0 ∘C, soil
temperature at 0.1 m of depth never fell below 0 ∘C during the course
of the experiment (Fig. 3a). This was also the case for the two reference
years 2013 and 2014. Volumetric soil water content (at 0.1 m of depth) were
similar in the control (33±4 %) and the clover parcel (31±5 %). The difference between treatments was within the sensor accuracy
of ±3 % (Fig. 3b). Oxygen concentration (at 0.1 m of depth) ranged
between 15 % and 21 % during three-quarters of the measurement period
and decreased consistently to 0 % during spring in both years (Fig. 3c).
Moreover, temporal patterns seen in O2 concentration were not
significantly different in both parcels (measured difference 0.3±0.2 % seadj; padj=0.075).
Oxygen concentration during summer (JJA) 2015 was higher compared to
2016 (t=2.64; padj=0.03) as a consequence of less rainfall
compared to summer 2016 (Fig. 2c). Soil oxygen concentration was inversely
related to soil water content.
(a) Ammonium-N concentration, (b) nitrate-N
concentration, and (c) dissolved organic carbon concentration per unit
of dry soil at the control (left, red) and clover parcel (right, blue) during
2015 and 2016. Black arrows indicate slurry applications, which only took
place in the control parcel. The numbers above the arrows indicate the amount of
N (kg ha-1) added to the parcel.
Soil mineral N and DOC concentration
The ammonium (NH4+) concentration in the soil peaked on each day
of slurry application in the control parcel and declined during the following
few days (Fig. 4a). The NH4+-N concentration measured in the
topsoil ranged between 0.4 and 19.2 mg NH4+-N kg-1 dry
soil in the control parcel during the 2 years of observations. A
significantly lower NH4+-N concentration was measured in the
clover parcel (0.6–11.1 mg NH4+-N kg-1 dry soil; paired
Wilcoxon test, p<0.01). While the NH4+-N concentration
peaked after fertilisation events in the control parcel, no consistent
patterns were observed in the clover parcel to which no fertiliser was
applied. The soil nitrate (NO3-) concentration ranged between
1.7 and 27.7 mg NO3--N kg-1 dry soil in the control
parcel (Fig. 4b). Similar to the observations found for NH4+-N,
significantly lower soil nitrate levels
(0.6–18.9 mg NO3--N kg-1 dry soil) were found in the
clover parcel (paired Wilcoxon test, p<0.01). The NO3--N
concentration significantly increased over the course of the season in the
control parcel (Mann–Kendall test, 2015: τ=0.50, p<0.001; 2016:
τ=0.40, p<0.001). Such a trend was not observed in the clover
parcel in 2015, while it was significant in 2016 (Mann–Kendall test, 2015:
τ=0.15, p>0.05; 2016: τ=0.35, p<0.01) (Fig. 4b).
Dissolved organic carbon (DOC) measured regularly from soil samples resulted
in a range of 42–234 mg C kg-1 dry soil in the control parcel
(Fig. 4c). Again, significantly lower values were measured for DOC in the
clover parcel (0.6–160 mg C kg-1 dry soil) (paired Wilcoxon test, p<0.01) compared to the control. As observed for NO3--N, the
DOC concentration significantly increased with the growing season in the
control parcel in both years and in the clover parcel in 2016 (Mann–Kendall
test, control parcel, 2015: τ=0.25, p<0.01; 2016: τ=0.23,
p<0.05; clover parcel, 2015: τ=0.14, p>0.5; 2016: τ=0.26, p<0.05) (Fig. 4b, c). Overall,
soil mineral N and DOC concentrations were lower in the clover parcel.
(a) Yields and intake by grazing at the control (left, red)
and clover parcel (right, blue); (b) total aboveground biomass.
Circles represent the total biomass (legumes and non-legumes), and filled
triangles display the remaining biomass after harvest (stubble), which
was measured once (sampling date 21 April 2015) and assumed to be
approximately similar during subsequent harvests. (c) Clover
proportion in dry biomass, (d) leaf area index (LAI), (e) C
content, and (f) N content in biomass. Diamonds represent the
legumes and triangles non-legumes. (g) Vegetation heights derived
from webcam images; (h) amounts of total N removal at harvest
(semi-transparent), including total amount of BFN in the removed biomass
(saturated).
Sward productivity and vegetation composition
Total annual yields (mean ± SE) in the control parcel were 12.8±0.5 t dry matter (DM) ha-1 in 2015 and 11.9±0.4 t DM ha-1 in 2016, while yields in the clover parcel were 10.4±0.7 and 11.0±0.5 t DM ha-1 in 2015 and 2016,
respectively (Table 2). Previous years' yields for both parcels were 9.3±3.2 t DM ha-1 yr-1 in the control and 6.6±2.3 t ha-1 yr-1 in the parcel, which was transformed into the
experimental parcel during the years 2015 and 2016 based on data of all
years with complete records between 2007 and 2013 (mean difference between
parcels 2007–2013 of -2.7 t ha-1 yr-1; experiment difference
2015–2016 -2.4 and -0.9 t ha-1; Table S2). Thus, yield differences
between the two parcels in 2015 and 2016 were in the range of yield
differences observed during previous years, with yields being 19 % (2015)
and 9 % (2016) lower at the clover parcel compared to the control parcel
(Fig. 5a). The living aboveground biomass remaining on the parcel after
mowing was 1.0±0.3 t DM ha-1 on the control parcel and 0.8±0.4 t DM ha-1 on the clover parcel (measured on 21 April 2015;
Fig. 5b).
(a) Annual N2O exchange at the control (red) and clover
parcels (blue) for the reference years 2013–2014 and the experimental years
2015–2016. (b) Relative differences between N2O exchange in
the control and clover parcels for the reference years (grey) and the
experimental years (white). Boxes indicate the interquartile range based on
non-parametric bootstrapping; bold black lines within boxes indicate the
medians.
The average clover proportion in harvested biomass in 2015 was 14.5 % in the
control parcel and 21.4 % in the clover parcel. The difference in clover
proportion between the two parcels was more visible in 2016, with 4.1 %
clover proportion in the control parcel and 44.2 % in the clover parcel.
When analysing individual sampling dates, differences in clover proportion
between the control and clover parcel were highly variable in 2015, with
substantially higher values for the clover parcel in the months April and
June and slightly lower clover proportion in August compared to the
control parcel. In 2016, clover proportions increased and stabilised in the
clover parcel, while they decreased in the control parcel with the progress of
the growing season (Fig. 5c). Leaf area index (LAI) ranged between 0.4 and
5.9, with a maximum at the first harvest each year (Fig. 5d). Average C
concentrations in the biomass of all harvests were similar across parcels and
plant functional types (legumes 42.9 %–45.6 %, non-legumes
43.0 %–45.2 % C in biomass across parcels and years; Table 2,
Fig. 5e). Average N concentrations in the biomass were always higher in
legumes (3.3±0.2 %) compared to non-legumes (2.1±0.2 %)
(Fig. 5f). The C / N ratios (data not shown) of total annual yields were
slightly higher in the control (19.2±1.7 and 19.8±2.8) than in
the clover parcel (17.1±1.0 and 16.7±2.1) for both years. Vegetation height reflected the vegetation dynamics and reached
similar maxima on the control parcel (41 and 59 cm) and the clover parcel
(44 and 60 cm) in 2015 and 2016 (Fig. 5g). C in annual yields
at the control parcel was higher (5.8±0.2 t ha-1) compared to
the clover parcel (4.7±0.3 t ha-1) in 2015, while C in biomass
was similar for the control parcel (5.1±0.3 t ha-1) and the
clover parcel (4.8±0.2 t ha-1 yr-1) in 2016 (Table 2). The N
exported was similar across parcels in the second year (control: 238±13 kg ha-1 yr-1; clover: 262±8 kg ha-1 yr-1;
Table 2). Biological nitrogen fixation via rhizobia associated with clover (N
derived from the atmosphere – Ndfa) resulted in BFN in harvested
biomass of 55.6±5.3 kg N ha-1 yr-1 and 14.2±1.7 kg N ha-1 yr-1 in the control parcel and 71.6±5.0 and 130±8.0 kg N ha-1 yr-1 in the clover parcel during the first and
the second year of the experiment, respectively (Table 2, Fig. 5h).
Differences in N2O exchange between control and clover
parcel
Average N2O fluxes (with 95 % confidence interval, CI, from the
bootstrapping given in parentheses) in the control parcel in 2015 were
4.1 kg N2O-N ha-1 yr-1
(CI 3.8–4.2 kg N2O-N ha-1 yr-1) and
1.9 kg N2O-N ha-1 yr-1
(CI 1.8–2.0 kg N2O-N ha-1 yr-1) in the clover parcel.
In 2016, average N2O fluxes were higher for both parcels
(6.3 kg N2O-N ha-1 yr-1, CI 6.0–6.5 kg
N2O-N ha-1 yr-1 in the control and
3.8 kg N2O-N ha-1 yr-1,
CI 3.7–3.9 kg N2O-N ha-1 yr-1 in the clover parcel)
(Fig. 6a). Annual N2O fluxes in the clover parcel were 54 %
(51 %–57 % as 95 % confidence intervals) and 39 %
(36 %–42 %) lower than at the control parcel in 2015 and 2016,
respectively (Fig. 6b). During the reference year 2013, average N2O
fluxes in the control parcel were
4.7 kg N2O-N ha-1 yr-1
(4.6–4.8 kg N2O-N ha-1 yr-1) and in the clover parcel
4.8 kg N2O-N ha-1 yr-1
(4.6–4.9 kg N2O-N ha-1 yr-1) and thus did not differ
significantly. N2O emission intensities (yield-scaled N2O
emissions) during the experiment were 0.31 g N2O-N kg-1 DM
in the control parcel and thus higher than the
0.18 g N2O-N kg-1 DM observed in the clover parcel in 2015.
A similar pattern was observed in 2016, with N2O emission intensities
of 0.53 g N2O-N kg-1 DM versus
0.37 g N2O-N kg-1 DM in 2016 for the control and clover parcel,
respectively.
N2O fluxes (bold lines: average; colour bands: interquartile
range of daily means across all events in 2015 and 2016) in the control and
the clover parcels from 1 week before to 2 weeks after management events:
after (a) organic fertiliser application, (b) harvests,
(c) grazing events, and (d) rain events. The black dashed
line indicates the start of an event.
Results of generalised additive models (GAMs) (a) including all
variables (full model), (b) reduced after stepwise backward elimination,
dismissing DOC and nitrate (optimised model), and (c) simplified including only
management, soil temperature (TS), and volumetric soil water content (SWC).
The control parcel without recent management (Ctr-0) was used as the
reference level for the categorical variable management, and thus the constant
represents predictions for Ctr-0 and the effect sizes of all other management
categories depict differences compared to Ctr-0. The effect sizes are
displayed with their standard errors and p values for all linear terms. For
the non-linear terms soil water content and oxygen concentration, the
respective empirical degrees of freedom (edf) and p values are shown. The
effect sizes are direct model outputs, while the values used in the text were
back-transformed to increase comprehensibility.
Dependent variable: log N2O flux
(a) Full model
(b) Optimised model
(c) Simple model
Covariates
Effect size (±SE)
p value
Effect size (±SE)
p value
Effect size (±SE)
p value
Parametric coefficients:
Control after harvest (Ctr-H)
0.30 (±0.24)
0.223
0.13 (±0.22)
0.567
0.17 (±0.07)
0.012*
Control after fertilisation (Ctr-F)
0.46 (±0.19)
0.016*
0.40 (±0.17)
0.025*
0.31 (±0.06)
<0.0001***
Clover no management (Clo-0)
0.14 (±0.18)
0.432
0.11 (±0.18)
0.529
-0.02 (±0.03)
0.567
Clover after harvest (Clo-H)
0.24 (±0.22)
0.269
0.20 (±0.22)
0.359
0.10 (±0.07)
0.129
TS (∘C)
0.03 (±0.01)
0.023*
0.03 (±0.01)
0.004**
0.03 (±0.002)
<0.0001***
CO2 Flux (µmol m-2 s-1)
0.02 (±0.01)
0.018*
0.02 (±0.01)
0.025*
NH4-N (µg g-1)
0.02 (±0.01)
0.167
0.02 (±0.01)
0.074
NO3-N (µg g-1)
-0.01 (±0.01)
0.231
DOC (µg g-1)
0.002 (±0.001)
0.303
Constant
-4.22 (±0.25)
<0.0001***
-4.17 (±0.23)
<0.0001***
-3.97 (±0.04)
<0.0001***
Approximate significance of smooth terms
edf
p value
edf
p value
edf
p value
SWC
2.33
0.119
1.87
0.048*
1.98
<0.0001***
O2 concentration
2.81
0.0001***
2.72
0.0003***
Observations
90
93
891
Adjusted r2
53.5 %
54.5 %
26.3 %
Explained deviance
60.9 %
60.2 %
26.9 %
GCV score
0.1183
0.1152
0.1761
* p<0.05, ** p<0.01, *** p<0.001.
Effects of management activities on N2O exchange
We observed increased N2O fluxes after fertilisation in the control
parcel, with maximum daily N2O fluxes reaching
17.4 mg N2O-N m-2 d-1 on 25 August 2015 (Fig. S1a),
a day of slurry amendment. The effect of fertiliser
amendment on N2O fluxes depended on the environmental conditions
during and after the fertilisation event. While several events (e.g.
10 June 2015, 25 August 2015, 16 July 2016, and 17 August 2016; Fig. S1a) were
followed by increased N2O emissions, other events (e.g. 1 June 2016)
did not show such an effect (Fig. S1a; interquartile range displayed in
Fig. 7a). N2O fluxes decreased to background levels within a few
(3–7) days after fertiliser application. Harvest had a moderate influence on
N2O emissions on both parcels (Fig. 7c). Maximum daily N2O
fluxes after harvest were 7.0 mg N2O-N m-2 d-1 on
5 July 2016 (Fig. S1a). Average N2O fluxes on both parcels were
significantly higher the weeks after harvest (average of both parcels:
2.0 mg N2O-N m-2 d-1) compared to average fluxes during
the preharvest weeks (1.4 mg N2O-N m-2 d-1) (Fig. 7b).
Neither grazing nor rain events significantly affected N2O exchange
(Fig. 7c, d).
Influence of management and environmental variables on N2O
emissions as predicted by the generalised additive model (GAM). Significant
effects were found for (a) management, (b) soil temperature
(TS, 0.1 m of depth), (c) soil water content (SWC, 0.1 m of depth),
(d) oxygen concentration (O2, 0.1 m of depth), and
(e) carbon dioxide (CO2) flux; while not significant,
(f) ammonium-N concentration (NH4-N, 0–0.2 m of depth)
still improved the model (lowered the AIC). No significant influence was
found for (g) nitrate-N concentration (NO3-N, 0–0.2 m
of depth) and (h) dissolved organic carbon concentration (DOC,
0–0.2 m of depth). Measurements are displayed as squares for “no
management”, with upward triangles for harvests at the control (red) and clover
(blue) parcels and downward triangles (red) for fertilisation (control).
Predictions are displayed if lowering AIC as solid lines for the category
“no management”, as dashed lines for harvests, and as dot-dashed lines for
fertilisation based on average values for all other drivers.
Influence of potential drivers on N2O exchange
Nitrous oxide emissions significantly increased after fertiliser application
(Ctr-F compared to Ctr-0, p<0.05) compared to N2O fluxes
during periods of no management on the same (control) parcel (Fig. 8a,
Table 4). The effect size showed 2.5-fold N2O emissions during the
7 days following slurry amendment compared to no management (resulting
from applying the back-transformation to the fertilisation effect: 100.4=2.5; Table 4). The effects of management influence N2O fluxes
jointly with other measured driver variables, such as soil moisture, soil
temperature, NH4+-N, NO3--N, and DOC concentration
in the soil. After mowing no significant increase in N2O emissions
was found for the optimised model in either of the parcels (Table 4b). In
contrast, a difference in N2O emissions after harvest was observed for
the simple model on the control parcel (Table 4c). If the difference in sward
composition itself affected N2O emissions (e.g. via plant residues or
rhizodeposition), we expected a significant effect of the clover treatment
compared to the control during times without management (Ctr-0, which was the
reference compared to Clo-0; Table 4). Due to the absence of such an effect,
we deduce that the increased clover proportions at the clover parcel did not
affect N2O emissions.
Soil microclimate affected N2O emissions in both parcels. Soil
temperature significantly influenced N2O emissions (p<0.05),
indicating a 7 % (±2 %) increase in N2O per ∘C
temperature increase (p<0.05; Table 4, Fig. 8b). Soil temperature had the
highest explanatory power (r2=0.17) for the prediction of
log-transformed N2O flux as a single explanatory variable (data not
shown). Besides soil temperature, volumetric soil water content showed a
significant non-linear effect on N2O emissions (p<0.05; Fig. 8c).
The humpback-shaped functional relationship between volumetric soil water
content and log-transformed N2O emissions (Fig. 8c) shows an increase
until 34 % and a decrease above 36 % volumetric soil water content.
Similarly, oxygen concentration significantly affected N2O emissions
(p<0.05; Fig. 8d). Oxygen concentration was non-linearly related to
N2O emissions, showing the lowest N2O emissions
(10-4 µmol m-2 s-1) at 0 % oxygen
concentration. N2O emissions increased until a maximum was reached at
17 %–19 % oxygen concentration and then decreased with further
increasing oxygen concentration to atmospheric concentrations of 20.9 %
(Fig. 8d). The net ecosystem exchange of CO2, which was used here as a
proxy for plant activity, affected N2O emissions (p<0.05;
Fig. 8e) with a 4 % (±2 %) decrease in N2O emissions per
µmol m-2 s-1 net carbon dioxide uptake. Inclusion of
NH4+-N concentration improved the prediction of N2O
emissions (Table 4, Fig. 8f), leading to an emission increase of 5 %
(±3 %) per µmol m-2 s-1. Note that large
NH4+-N concentrations only occurred after fertilisation, and thus
the NH4+-N effect was mainly influenced by these dates, while it
did not play a role for the other management categories. In contrast,
the NO3--N concentration did not improve the prediction of
N2O emissions (Table 4, Fig. 8g). Also, DOC concentrations showed no
effect on N2O emissions (Table 4, Fig. 8h). The slopes of the
relationship between drivers and predicted N2O emission are flatter
than expected from visual inspection of the observed values (Fig. 8), as the
predictions here depict the dependency of N2O emissions on the
respective driver alone (based on averages of all other drivers) in contrast
to observations, which depict combinations of the effects of several drivers. The
effects of soil temperature, soil water content, and management in the full
and the optimised model (Table 4a and b) were consistent with the simple
model (Table 4c) that included only these three variables and therefore more
observations (n=891 versus n=93). Including additional variables
(O2, NH4+-N, NEE of CO2) besides soil
temperature and soil water content increased the explained variance in
N2O emissions from 26.3 % in the simple model (Table 4c) to
54.5 % in the optimised model (Table 4b).
Discussion
We quantified ecosystem N2O exchange at a fertilised control parcel
(“business as usual”) and an unfertilised clover parcel for which we increased
the clover proportion (“mitigation management”). The mitigation management
was composed of two major changes compared to the “business as usual”
practice: (1) omitted fertilisation and (2) over-sowing clover, leading to an
increased clover proportion in the experimental sward (i.e. 21 % versus
15 % in 2015, 44 % versus 4 % in 2016). Our analysis showed that
the difference in N2O emissions between the two parcels can be
attributed to the absence of fertilisation on the clover parcel. An increased
clover proportion could still have increased N2O emissions in the
clover parcel due to N-rich clover residues and N from root exudates
(Rochette and Janzen, 2005), thereby offsetting the effect of reduced
fertilisation. However, we measured similar N2O fluxes originating
from the two parcels of different clover proportion during periods without
management, indicating that differences in clover proportion alone (i.e.
excluding recent management effects) resulted in unchanged N2O
emissions (i.e. plant residues and root exudates affected N2O
emissions similarly on the clover and the control parcel). We quantified the
effects of environmental drivers on N2O emissions and identified soil
temperature, soil oxygen concentration, soil water content, and NEE of
CO2 as the main environmental drivers of N2O emissions. The
assessment of the mitigation strategy revealed reductions in N2O
emissions, an increase in BFN, and stable yields under mitigation management.
This study covered 2 years and did not include potential effects of
the incorporation of clover into the soil during ploughing (which takes place
every 8–10 years). Long-term effects of the mitigation strategy on the N
budget of the site, as well as implications on the farm level (e.g. the
feasibility to use the slurry to replace mineral fertiliser elsewhere, fodder
composition), should be investigated in future studies. In summary, our
results indicate that N2O emissions can be effectively reduced at
ecosystem scale through enhancing the clover proportion (and BFN) in
permanent grassland while reducing organic fertiliser inputs and still
meeting the N requirements of plants.
Summary of studies investigating N2O emissions
simultaneously in permanent grasslands of at least two different clover
proportions. We included studies with >200 days of temporal coverage
and at least biweekly sampling of N2O emissions or, if
discontinuously sampled, included a sensible strategy used by the authors in
order to estimate annual fluxes.
Source
Treatment
Nfert
Clover
N2O
(kg N ha-1 yr-1)
%
(kg N2O-N ha-1 yr-1)
Ammann et al. (2009)
Low clover
230
21
1.60
Ammann et al. (2009)
High clover
0
32
-0.10
Jensen et al. (2012)
Fertilised pasture
n/a
0
4.49
Jensen et al. (2012)
Unfertilised grass
0
0
1.20
Jensen et al. (2012)
Grass–clover
0
n/a
0.54
Jensen et al. (2012)
Pure clover
0
100
0.79
Klumpp et al. (2011)
Low clover
157
19
1.72
Klumpp et al. (2011)
High clover
157
35
1.52
Li et al. (2011)
Ryegrass grazed
226
0
7.82
Li et al. (2011)
Fertilised ryegrass–white clover grazed
58
20–25
6.35
Li et al. (2011)
Unfertilised ryegrass–white clover grazed
0
20-25
6.54
Li et al. (2011)
Ryegrass background
0
0
2.38
Li et al. (2011)
Grass–clover background
0
20–25
2.45
Schmeer et al. (2014)
Uncompacted grass
360
15
8.74
Schmeer et al. (2014)
Compacted grass
360
15
13.31
Schmeer et al. (2014)
Uncompacted lucerne grass
0
70
2.46
Schmeer et al. (2014)
Compacted lucerne grass
0
70
2.22
Šimek et al. (2004)
No clover
210
0
2.28
Šimek et al. (2004)
High clover
20
60
1.50
Šimek et al. (2004)
Pure clover
20
100
1.50
This study 2015
Low clover
296
15
3.82
This study 2016
Low clover
181
4
6.27
This study 2015
High clover
0
21
1.89
This study 2016
High clover
0
44
4.07
Virkajärvi et al. (2010)
No clover
220
0
3.65
Virkajärvi et al. (2010)
High clover
0
75
7.00
N/a – not applicable.
N2O emissions in the fertilised grassland parcel
N2O emissions in the control parcel totaled 4.1 and
6.3 kg N2O-N ha-1 yr-1 for the 2 years, respectively,
corresponding to 1.4 % and 3.5 % of the applied fertiliser N. Annual
N2O emissions are of the same order of magnitude as the values
reported from the site in previous years (2010 and 2011) by Imer et
al. (2013), who estimated 2.2–7.4 kg N2O-N ha-1 yr-1
based on manual N2O measurements using static GHG chambers. Similar
N2O emissions of 4.5 kg N2O-N ha-1 yr-1
(0.3–18.2 kg N2O-N ha-1 yr-1) from other fertilised
grassland sites were reported by Jensen et al. (2012) in a synthesis paper
covering 19 site years. Fertilised grassland sites in central Europe,
particularly grasslands at higher altitudes, typically gave lower N2O
emissions (0.19–5.28 kg N2O-N ha-1 yr-1 across
site years, or 0.1 %–2.5 % of fertiliser input) compared to our
site, which showed the highest emissions with respect to both absolute
N2O emissions and emissions as a percentage of fertiliser N
input (2.55–7.89 kg N2O-N ha-1 yr-1 or
1.1 %–3.6 % of fertiliser N input across site years 2010–2013) as
reported by Hörtnagl et al. (2018) compared to 1.4 %–3.5 % of
fertiliser N in our study (2015 and 2016). For a more targeted comparison,
here we considered only the non-restoration site years and excluded 2012,
which showed high N2O emissions particularly related to grassland
restoration. The Hörtnagl et al. (2018) study covered the years 2010–2013
for
our site but used a different gap-filling method. The high emissions from our
site were explained by warm temperatures (∼20 ∘C), combined
with moist to wet soil moisture conditions after fertiliser events, and
therefore particularly favourable conditions for N2O production
compared to conditions at other sites. Hörtnagl et al. (2018) used a
conservative method to estimate fluxes during periods without measurement
(running median gap filling, resulting in low estimates when gaps are filled
during emission peaks). In this study, gaps for annual estimates were filled
with the arithmetic average because this method appropriately represents an
average of peak and background emissions, rather than predominantly
representing background emissions as with the running median method. In
summary, our year-round measurements of N2O emissions are higher than
the multi-site averages due to the fertiliser regime and site conditions, but
within plausible ranges compared to other sites.
N2O emissions in the unfertilised clover parcel
N2O emissions in the clover parcel during our 2-year observation
period totaled 1.9 and 3.8 kg N2O-N ha-1 yr-1 in
2015 and 2016, respectively. These N2O emissions were clearly lower
than the values observed in the control parcel during both years. In 2015,
the difference can be attributed to the difference in fertilisation between
parcels, as the clover proportion was still similar in both parcels (control
parcel: 15 %; clover parcel: 21 % clover). In 2016, large differences
in clover proportion (control parcel: 4 %; clover parcel: 44 %
clover) resulted in similarly lower N2O emissions on the clover
parcel as in 2015. However, N2O emissions in the clover parcel were
high compared to other unfertilised grass–clover mixtures with zero or low
fertiliser inputs (<50 kg N) for which average emissions of
0.54 kg N2O-N ha-1 yr-1
(0.10–1.30 kg N2O-N ha-1 yr-1) were reported by Jensen
et al. (2012) based on eight site years. Further non-fertilised grass–clover
mixtures showed annual N2O emissions of up to
2.5 kg N2O-N ha-1 yr-1 (Li et al., 2011; Table 5).
Thus, our measurements exceeded the typical range of values in the second
year by 50 %. Regular N amendments at the Chamau site in the past might
have led to immobilisation of N via microbes and subsequent enrichment of the
soil organic N (SON) pool (Conant et al., 2005; Ledgard et al., 1998). This
in turn is known to lead to higher background N2O emissions in
relation to N2O emissions observed from sites under long-term
extensive management. In addition, high total N deposition (NH3,
NO3, HNO3, NO2) in the study area (in total
33.8 kg N ha-1 yr-1 in 2015; Rihm and Achermann, 2016) might
foster background N2O emissions due to increased NH4-+
and NO3- availability (Butterbach-Bahl et al., 2013).
Additionally, NH3 deposition on the clover parcel originating from
NH3 emissions from the adjacent control parcel is likely to be the
cause of increased soil NH4-+ concentrations after the event on
17 August 2016. Furthermore, a possible explanation for the relatively high
N2O emissions from our clover parcel in 2016 related to the meteorological
conditions, which were wetter during summer and therefore more favourable for
N2O production than during 2015. High background N2O
emissions in the clover parcel in 2016 were reflected by similarly high
background N2O emissions in the control parcel, indicating that these
were mainly driven by other factors (favourable meteorological conditions,
sufficient N substrate availability) and not by the sward composition itself.
Effects of management and environmental drivers on N2O
emissions
Our aim was to identify the main drivers of N2O emissions and
therefore we investigated the effects of management (fertilisation, harvest,
grazing, and over-sowing leading to increased clover proportion) and
environmental variables on N2O emissions. Fertilisation of the
control parcel had the largest effect on N2O emissions. Increased N
availability due to fertilisation is widely known as a main driver of
N2O emissions, which makes it a key factor for mitigating N2O
emissions (Bouwman et al., 2002; Smith et al., 1997). Nevertheless, the effects
of fertilisation on N2O emissions vary widely across grassland sites
and years (0.01 %–3.56 % in Flechard et al., 2007;
0.1 %–8.6 % in Hörtnagl et al., 2018; 1.4 % and 3.5 % of
fertiliser N across years in this study), indicating that fertilisation alone
is insufficient to explain N2O emissions and highlighting the
need to take additional drivers into account. We further observed increased
N2O emissions following harvest events on the control parcel, which
may be explained as a consequence of increased rhizodeposition (Bolan et al.,
2004; Butenschoen et al., 2008). Subsequently, greater availability of labile
C compounds can lead to increased microbial activity, accompanied by
increased production of N2O (Rudaz et al., 1999). Higher N2O
fluxes following cutting were similarly observed on a pasture in central
France (up to 3.7 mg N2O-N m-2 d-1 in Klumpp et al.,
2011; up to 7.0 mg N2O-N m-2 d-1 in this study).
Grazing had only a minor influence on the overall N2O budget of the
Chamau site with 3.71 % of N2O-N emitted during grazing periods,
and data analysis showed that N2O fluxes did not significantly
respond to the presence of animals (Fig. 7c). We attribute this observation
to low stocking densities and short duration of grazing (Table 1). Other
studies with higher stocking densities have shown that more intensive grazing
led to increased N2O emissions (van Groenigen et al., 2005; Oenema et
al., 1997). These were attributed to C and N from animal excreta and to soil
compaction by treading and trampling animals, creating anaerobic soil
conditions (Flechard et al., 2007; Lampe et al., 2006; Oenema et al., 1997).
An important finding from this study is that increased clover proportion, and
subsequently increased BFN, did not increase N2O emissions, as shown
by comparing N2O emissions between the two parcels during periods
without management (Table 5c, Clo-0). In other words, substrate from
the decomposition of plant residues and from root exudates may affect N2O
emissions, but this effect was similar on both parcels, independent of the
higher clover proportion and BFN in the clover parcel. This is in contrast to
a study on a boreal grass–clover mixture in which significant N2O
emissions were observed in spring, largely exceeding the fertilised grassland
control (Virkajärvi et al., 2010). These higher emissions were explained
by increased substrate available to microbial communities producing
N2O in the surface layer after spring thaw (Wagner-Riddle et al.,
2008). Nitrous oxide emissions from BNF itself (rhizobial denitrification)
have been shown to be possible (O'Hara and Daniel, 1985). Nevertheless, due
to its small magnitude the contribution to field-scale N2O emissions
is negligible (Rochette and Janzen, 2005). Previous results from a laboratory
incubation by Carter and Ambus (2006), who investigated N2O emissions
from unfertilised soils for up to 36 weeks, showed that recently fixed
N2 in a white clover–ryegrass mixture contributed as little as 2.1±0.5 % to total N2O emissions. In agreement with our result,
measurements from permanent grasslands in Ireland, where winter freeze–thaw
cycles are very rare, showed that annual N2O emissions in
unfertilised ryegrass (2.38±0.12 kg N2O-N ha-1 yr-1) were not significantly
different from an unfertilised grass–clover sward (2.45±0.85 kg N2O-N ha-1 yr-1) with clover proportions of
20–25%, hence providing evidence that N2O emission due to BNF
itself and clover residual decomposition were negligible (Li et al., 2011).
Our findings are in line with these observations and add the insight that
clover proportions of up to 44 %, as found in our study, will not result
in increased N2O emissions.
The effects of temperature and soil water content on N2O emissions as
found in our study are in line with established knowledge (Butterbach-Bahl et
al., 2013; Flechard et al., 2007). Furthermore, directly measured soil oxygen
concentrations, which have hardly been used in field-scale studies before,
improved the prediction of N2O emissions (Table 4). Our data showed
that larger plant C uptake (negative NEE) of CO2 as a proxy for plant
activity was associated with reduced N2O emissions, which supports
the hypothesis that plant roots are in competition for available N with
microbes and often reduce the N availability to microbes (Merbold et al.,
2014). Thus, we observed lower N2O emissions at higher levels of
photosynthesis. Our analysis showed that inclusion of NH4+-N
concentration in the statistical analysis improved the prediction of
N2O emissions, while NO3--N and DOC were of less
importance for the prediction of N2O emissions. Comparable results
for the influence of NH4+ and NO3- were found at an
Irish grassland (Rafique et al., 2012). In summary, fertilisation was the
dominant predictor of N2O emissions, while soil temperature, soil
water content, soil oxygen concentration, and NEE of CO2 were
significant environmental drivers. Concluding from all management effects,
the decrease in annual N2O emissions under the mitigation strategy
was primarily caused by the absence of fertilisation, while a potential
effect of the increase in clover proportion and increased BFN offsetting
these emission reductions was absent.
Effect of the mitigation strategy on productivity and biological
nitrogen fixation
An important precondition for the acceptance of any climate change mitigation
strategy is that yields need to be maintained at similar levels as under
conventional management. Differences in biomass yields between the control
and clover parcels were only minor (19 % and 9 % lower in the clover
parcel in 2015 and 2016, respectively) and comparable to the observed
differences between the two parcels prior to the mitigation experiment (Table S2).
Maintaining high yields without fertilisation can be explained by the
increased BFN in the clover parcel and positive interactions between clover
and grass (“overyielding effect”) (Lüscher et al., 2014; Nyfeler et
al., 2009). Additionally, high SON content due to the previous year's fertiliser
amendments are expected to contribute to the persistently high production
levels (Table 2). Similar productivity levels of an unfertilised grass–clover
mixture (three cuts, 9 % less DM) compared to an adjacent intensive
grass–clover mixture (230 kg N fertiliser, 4–5 cuts) were also found at a
site 50 km from the Chamau field site in the past (Ammann et al., 2009).
Furthermore, our findings are consistent with findings from the more
comprehensive study by Nyfeler et al. (2009), who found large overyielding
effects in comparable Swiss grassland systems; i.e. grass–clover yields at
50 kg N ha-1 yr-1 and 50 % to 70 % clover were as productive
as grass monocultures fertilised with 450 kg N ha-1 yr-1. The
overyielding effect has been reported across a wide range of climates and
soil types (Finn et al., 2013; Kirwan et al., 2007), indicating that our
result of maintained productivity levels under the mitigation strategy is
likely to be reproducible across a wider range of site conditions.
Biologically fixed nitrogen found in shoot biomass was slightly higher in the
clover parcel (72 kg N ha-1 yr-1) compared to the control
parcel (55 kg N ha-1 yr-1) in 2015 due to only small
differences in clover proportion between the two parcels. During the second
year, over-sowing was more effective and BFN found in shoot biomass in
the clover parcel totaled 130 kg N ha-1 yr-1, while only
14 kg N ha-1 yr-1 was measured in the control parcel. Previous
studies reported similar amounts of BFN for mown and grazed pasture systems
(Ledgard and Steele, 1992; Nyfeler et al., 2011), with maxima being as high
as 323 kg N ha-1 yr-1 as observed in a comparable grass–clover
mixture (Nyfeler et al., 2011). This indicates that biologically fixed
nitrogen at Chamau could reach higher amounts than observed during our
experiment. Clover proportions at our site varied seasonally, with a minimum
in spring and maximum in summer in both parcels. Such seasonal cycles in
clover proportions occur due to species' developmental cycles, but also
the competitive advantages and disadvantages of the respective species. Drier
conditions, observed for instance in summer (JJA), result in competitive
advantages of the clover compared to grasses, as N2 fixation is
less sensitive to dry conditions than uptake of mineral N (Hofer et al.,
2017; Lüscher et al., 2005). Furthermore, inter-annual variability of
clover proportions can be an additional management challenge for farmers
whose aim is to keep a persistent sward composition (Lüscher et al.,
2014).
Lower SON content (3490 kg N ha-1) in a grass–clover mixture compared
to a 200 kg ha-1 yr-1 fertilised grassland
(4350 kg N ha-1) was observed after 13 years of management comparable
to our experiment (Ledgard et al., 1998). It is well known that N exports
exceeding inputs leads to a decreasing SON pool. Potential losses in SON were
shown to be closely linked to losses in soil organic C (SOC) (Ammann et al.,
2009; Conant et al., 2005) and can therefore compromise the soil's
CO2 sink strength. Thus, detailed investigations on the effect of the
clover treatment on SON, SOC content, and CO2 exchange are recommended
to comprehensively evaluate the mitigation strategy in the long term.
Effect of the mitigation strategy on N2O emissions and
emission intensities
We found that the mitigation strategy effectively reduced N2O
emissions by 54 % (51 %–57 %) and 39 % (36 %–42 %)
in 2015 and 2016 as well as N2O emission intensities by 41 % and
30 % in 2015 and 2016, respectively. Past studies carried out in
temperate grasslands consistently found reductions in N2O emissions
when reducing fertiliser and increasing BFN through legumes (Table 5). The
magnitude of relative N2O emission reductions ranged from 34 %
(Šimek et al., 2004) to 100 % (Ammann et al., 2009), with absolute
N2O emission reductions of 0.8 kg N ha-1 yr-1
(Šimek et al., 2004) to 11.1 kg N ha-1 yr-1 (Schmeer et
al., 2014). The variability across studies can be attributed to differences
in meteorological and soil conditions as well as variations in the
experimental set-up (i.e. fertiliser rates applied, realised legume
proportions, grass and legume species; Table 5). Much higher N2O
emissions from an unfertilised grass–clover mixture (92 % increase)
compared to N2O emissions from a grass sward fertilised with
220 kg N ha-1 yr-1 were observed under boreal climate
conditions in eastern Finland due to large springtime emissions associated
with freeze–thaw cycles (Virkajärvi et al., 2010). Such an effect could
not be found at our site, although soils also freeze occasionally during the
cold season, but at most in the top few centimetres. Although our tested
mitigation strategy seems to be beneficial for permanent grasslands, Basche
et al. (2014) and Lugato et al. (2018) have shown that incorporation of
clover into the soil may lead to increased N2O fluxes and thus may
not be the best mitigation strategy for croplands and temporary grasslands
for which ploughing is done much more frequently.
In summary, the implementation of the mitigation option tested here was found
to be effective at permanent grassland in the temperate zone. It is cheap
and simple as it requires few management activities, which would favour
farmer willingness for implementation (Vellinga et al., 2011).