Introduction
Most natural peatlands act as a sink for atmospheric carbon dioxide
(CO2) and as a source for methane (CH4) (Blodau, 2002; Whalen,
2005; Drösler et al., 2008). The net climate effect of natural peatlands
regarding the greenhouse gas (GHG) fluxes, however, is close to zero
(Drösler et al., 2008). In the last century, drainage and the
intensification of agriculture turned European peatlands into hot spots for
GHG emissions (Drösler et al., 2008). Increased CO2 and nitrous
oxide (N2O) emissions have been observed from drained peatlands as a
result of enhanced decomposition of organic matter (Martikainen et al., 1993;
Silvola et al., 1996). The gases mentioned (CO2, CH4 and N2O)
act as climatically relevant greenhouse gases (IPCC, 2007). Additionally,
N2O and CH4 contributes to the chemical destruction of
stratospheric ozone (Crutzen, 1979; Solomon, 1999).
Through the ratification of several international agreements on climate
protection (e.g., UNFCCC 1992; Kyoto protocol 1997 – specified by the Bonn
Agreements and Marrakesh Accords; several EU decisions) Germany is obliged to
publish annual national greenhouse gas emissions inventories according to the
Intergovernmental Panel on Climate Change (IPCC) guidelines. However, the
national climate reporting in the Land-use, Land-Use Change and Forestry
(LULUCF) sector as well in the Agriculture, Forestry and Other Land-uses
(AFOLU) sector is challenging with regard to organic soils. This is mainly
because reliable measurements of GHGs from temperate drained peatlands are
rare and observed GHG fluxes show a large temporal and spatial variability
ranging from -2 to 31 t CO2-C ha-1 yr-1 and 2 to 38 kg
N2O-N ha-1 yr-1 (IPCC, 2014). Furthermore, the definition of
Histosols is complex (Couwenberg, 2011), and several national and
international classification systems exist for organic soils. For the climate
reporting under LULUCF/AFOLU, the IPCC guidelines require at least ≥ 10 cm thickness of the soil or peat layer and an organic carbon (Corg) content of ≥ 12 % in the case of a soil thickness ≤20 cm for peat soils. Thus,
the IPCC definition of peat soils is broader than the definition of Histosols
in the world reference base for soil resources (WRB, 2008). In the German
classification system (KA5) (Ad-hoc-AG Boden, 2005), a
distinction is made between soil horizons with ≥ 30 % soil organic
matter (SOM) content (called organic horizon) and those, containing
15–30 % SOM (called anmoor horizon). Particularly at the boundary
between mineral and organic soils, the conversion from Corg to SOM
leads to uncertainties due to different conversion factors which are commonly
used for mineral soils and peat soils according to the KA5 (Tiemeyer et al.,
2013). Depending on the conversion factor (1.72 for mineral soils or 2 for
peat soils), the maximum limit of the IPCC requirement is between 21 and
24 % SOM (Tiemeyer et al., 2013). To date, soils which are, by definition
in the transition between mineral and organic soils were mostly neglected in
the national GHG inventory of most countries (Leiber-Sauheitl et al., 2014).
In the Danish greenhouse gas inventory, for example, GHG emissions from very
thin and shallow organic soils, which do not meet the definition of organic
soils according to the IPCC, were also considered. Due to a lack of
information about the release of the GHG emissions of those soils, a fixed
emission factor, half as much as for typical organic soils (> 12 %
Corg), has been introduced in Denmark for soils containing
6–12 % organic carbon (Nielsen et al., 2012).
According to estimates, peatlands in Germany account for approximately
4.9 % of the national GHG emissions although they only account for
5.1 % of the total area (NIR, 2010; Drösler et al., 2011). Drained peatlands
even represent the largest single source for GHG emissions outside the energy
sector in Germany (Drösler et al., 2011; NIR, 2010). Hence, according to
the IPCC guidelines, drained peatlands are identified as a key category which
results in Germany being obliged to calculate the annual GHG emission
inventory on the basis of nationally specific emission factors (EF; Tier 2 or
Tier 3 methods). The main reason for the critical climate balance is caused
by the fact that more than two-thirds of the German peatlands are intensively
used as grassland or arable land (Drösler et al., 2008). Both land-use
types have been regarded as the main producers of CO2 and N2O from
farmed organic soils (Kasimir-Klemedtsson et al., 1997; Kroeze et al., 1999;
Drösler et al., 2008; International Peat Society, 2008). Highest GHG
emissions from drained organic soils were related to management activities
such as tillage and fertilization, which enhance microbial SOM decomposition
and nitrogen turnover (Kandel et al., 2013). Beside management practices,
several other physical and chemical factors control the intensity of
mineralization processes (Heller and Zeitz, 2012) in which soil temperature
and soil moisture are considered to be the primary regulators for CO2
emissions from soils (Silvola et al., 1996; Maljanen et al., 2001; Hardie et
al., 2011). However, recent studies have shown that in particular the SOM
quality and its labile and more recalcitrant fractions act as key variables
affecting the decomposability of SOM, and thus they control CO2 fluxes
from peatlands (Byrne and Farrell, 2005; Heller and Zeitz, 2012; Leifeld et
al., 2012). Beside the macromolecular organic composition (e.g.,
polysaccharides, lignin, and aliphatic biopolymers) of the peat-forming
vegetation, the SOM quality of peat strongly depends on hydrological and
geomorphological building conditions during peat formation (Heller and Zeitz,
2012). Additionally, peat and SOM quality is strongly affected by human
impact, which leads to peat shrinking, secondary decomposition and
mineralization (Heller and Zeitz, 2012). It can be assumed that with
increasing peat humification, aggregation and organomineral association gain
in importance in the SOM stabilization. Thus, a decrease in CO2
emissions from soils which are by definition at the transition between
mineral soils and peat, can be expected compared to peat soils with higher
SOM contents. The objective of this study was to quantify GHG emissions from
arable lands and grasslands on two types of drained organic soils with
different Corg contents in southern Germany. We hypothesize (i)
that GHG emissions significantly increase with increasing soil organic
carbon (SOC) content in the soil and (ii) that GHG
emissions from arable soils exceed GHG emissions from intensively managed
grassland soils.
Material and methods
Study area and experimental design
The study was conducted at a drained fen peatland 30 km northeast of Munich
(Freisinger Moos; 48∘21′ N, 11∘41′ E; 450 m a.s.l.).
From 1914 onwards, the Freisinger Moos (FSM) was systematically drained for
intensive cultivation (Zehlius-Eckert et al., 2003). Today about 40 % of
the whole area (1570 ha) is used as grassland and 20 % as arable land
(Schober et al., 2008).
According to the climate station at Munich airport, located 7 km east of the
study sites, the 30-year mean annual temperature was 8.7 ∘C and the
mean annual precipitation was 834 mm (1981–2010). Annual atmospheric N
deposition amounted to 6.22 and 7.20 kg N ha-1 yr-1 in 2010 and
2011. Data of N deposition were collected by the Bavarian State Institute of
Forestry at a German Level II monitoring plot (Forest Intensive Monitoring
Programme of the UNECE), located at a distance of 7 km to the sites
investigated.
Schema of the experimental design.
In October 2009, we selected two adjacent areas, one used as intensive
grassland and the other as arable land. Both areas are characterized by a
distinct gradient in their soil organic carbon (SOC) content in the topsoil
(Table 1), which increases from the southeast to the northwest. In March 2010
the arable land was split into two halves to simulate two different crop
rotations (maize (Zea mays) and oat (Avena sativa); see
Table 3) along the SOC gradient (labeled A1 and A2). At the grassland area a
similar design was implemented to investigate the effect of two different
organic fertilizers (labeled G1, fertilized with cattle slurry, and G2,
fertilized with biogas digestate). Within these areas we selected two sites
with maximally different SOC contents per land use type (Fig. 1). According
to the WRB (2006), soil types at the sites were classified as Mollic Gleysol
(labeled Cmedium) and as Sapric Histosol (labeled Chigh)
(N. Roßkopf, personal communication, 2010). At each site two plots were
selected according to the management type (Fig. 1). A detailed description of
the experimental design of the grassland sites and the chemical and physical
composition of the applied fertilizers is given in Eickenscheidt et
al. (2014b) and Table 2. The arable land was managed according to
organic-farming criteria but without any fertilization during the
investigated period.
Physical and chemical properties of the investigated plots.
Site
Soil type
Organic carbon (%)
C/N ratio
pH (CaCl2)
Bulk density (gcm-3)
Mean GW level above surface (cm)
0–10 cm
10–20 cm
0–20 cm
0–20 cm
0–10 cm
10–20 cm
2010
2011
A1Cmedium
Mollic Gleysol
9.6±0.1
9.3±0.2
10
5.24
0.72±0.03
0.85±0.08
-56 (-86/0)
-67 (-86/-4)
A1Chigh
Sapric Histosol
16.9±0.2
17.2±0.2
12
5.61
0.63±0.05
0.67±0.04
-45 (-90/7)
-49 (-76/5)
A2Cmedium
Mollic Gleysol
9.4±0.
9.2±0.1
10
5.24
0.83±0.06
0.90±0.06
-56 (-86/0)
-67 (-86/-4)
A2Chigh
Sapric Histosol
16.1±0.9
16.8±0.2
12
5.61
0.67±0.11
0.77±0.08
-45 (-90/7)
-49 (-76/5)
G1Cmedium
Mollic Gleysol
10.5±0.2
9.4±0.1
10
4.10
0.71±0.09
0.90±0.06
-65 (-91/-2)
-72 (-92/0)
G1Chigh
Sapric Histosol
17.2±0.
16.7±0.1
11
4.24
0.53±0.09
0.64±0.05
-45 (-64/-1)
-52 (-66/-3)
G2Cmedium
Mollic Gleysol
10.9±0.2
10.1±0.1
10
4.10
0.81±0.09
0.88±0.03
-63 (-92/0)
-72 (-97/0)
G2Chigh
Sapric Histosol
16.4±0.1
15.6±0.1
11
4.24
0.57±0.08
0.67±0.03
-45 (-67/-1)
-50 (-65/-3)
Values presented are means ± SD.Values in brackets are minimum and maximum values.A: arable land.G: grassland.
At each plot, three PVC collars for GHG measurements (inside dimension
75 × 75 cm) were permanently inserted 10 cm into the soil at a
distance of 1.5–2 m to each other. In the case of management activities,
collars were removed for a short period on the arable land. To prevent
oscillations of the peat through movements during the measurements,
boardwalks were installed. In March 2010, climate stations were set up at
each site, midway between the two plots (see Fig. 1; for the arable land,
climate stations represent temperatures from the management of the A1 plots),
for the continuous recording (every 0.5 h) of air temperature
(Tair) and humidity at 20 cm above soil surface; in addition, soil
temperature was measured at a depth of -2, -5 and -10 cm (ST2, 5, 10) and soil moisture content was measured at a depth of -5 cm. In
addition, two further climate stations, additionally equipped with sensors to
measure air temperature at 200 cm above the soil surface and photon flux
density of the photosynthetically active radiation (PAR), were operated in
close proximity (1.5 km) to the areas investigated. For measuring the
groundwater table, plastic perforated tubes (JK casings DN 50, 60 mm
diameter, 1 m length) were inserted close to each collar for the
plot-specific measurements of groundwater (GW) tables during gas flux
measurements at the grassland plots. On the arable land only, three tubes
were inserted between the two plots of the same soil type. In April 2010, we
equipped one tube per plot or, in the case of the arable land one tube per
soil type, with a water level logger (Type MiniDiver, Schlumberger water
services), which recorded the water tables every 15 min. Additionally, to
the recorded data, plot-specific soil temperatures at three soil depths
(-2, -5 and -10 cm) were determined with penetration thermometers at
the beginning and end of each gas flux measurement.
Physical and chemical properties of the applied slurries and
digestates (data derived from Eickenscheidt et al.,
2014b).
Cattle slurry
Biogas digestate
1.
2.
3.
4.
1.
2.
3.
4.
Application
Application
Application
Application
Application
Application
Application
Application
(14 June 2010)
(25 August 2010)
(27 May 2011)
(22 September 2011)
(14 June 2010)
(25 August 2010)
(27 May 2011)
(22 September 2011)
Fertilizer
20
20
25
20
20
20
25
20
quantity
(m3 ha-1)
Total
47
64
70
85
49
52
78
35
nitrogen
(kg ha-1)
NO3-
0
0
0
0
0
0
0
0
(kg N ha-1)
NH4+
20
28
23
33
22
28
51
17
(kg N ha-1)
C / N ratio
12
11
11
9
8
7
2
5
pH (CaCl2)
–
–
6.8
7.0
–
–
7.7
7.4
Date and type of conducted management events.
Date
Julian day
Management events
A1
A2
G
24 September 2009
–
seed sowing (Secale cereale)
seed sowing (Secale cereale)
–
26 March 2010
85
–
–
leveling
30 March 2010
89
–
plowing and seed sowing(Avena sativa and 15% Vicia faba minor)
–
7 April 2010
97
–
–
rolling
13 April 2010
103
–
harrowing
–
28 April 2010
118
plowing
–
–
30 April 2010
120
seed sowing (Zea mays)
–
–
24 May 2010
144
grubbing
–
harvesting
11 June 2010
162
grubbing
–
–
14 June 2010
165
–
–
manuring
6 July 2010
187
grubbing and hilling
–
–
20 August 2010
232
–
–
harvesting
22 August 2010
234
–
harvesting
–
25 August 2010
237
–
–
manuring
28 August 2010
240
–
milling
–
4 September 2010
247
–
–
–
23 September 2010
266
–
–
herbicide for commonsorrel (Rumex acetosa)
15 October 2010
288
harvesting
–
–
30 October 2010
303
mulching
–
–
16 March 2011
440
–
–
leveling
1 April 2011
456
plowing and seed sowing(Avena sativa and 20 % Vicia sativa)
–
–
18 April 2011
473
–
plowing
–
26 April 2011
481
–
grubbing and seed sowing(Zea mays)
–
30 April 2011
485
harrowing
harrowing
–
8 May 2011
493
–
harrowing
–
19 May 2011
504
–
mattocking
–
23 May 2011
508
–
–
harvesting
27 May 2011
512
–
–
manuring
14 June 2011
530
–
hilling
–
1 August 2011
578
–
–
harvesting
16 August 2011
593
harvesting
–
–
18 August 2011
595
milling
–
–
27 August 2011
604
plowing and seed sowing(Secale cereale)
–
–
13 September 2011
621
–
–
harvesting
22 September 2011
630
–
–
manuring
28 September 2011
636
–
harvesting
–
Biomass yield, soil sampling and laboratory analyses
Crop and grass yield was determined by harvesting the biomass inside the PVC
collars with scissors at each harvesting event (same cutting height as that
used by the farmers) (Table 3). To determine the annual crop or grass yield,
samples were oven-dried at 60 ∘C for 48 h and the phytomass of each
harvesting event per year was summed. To determine the total carbon
(Ctot) and total nitrogen (Ntot) content, the total
phytomass was milled (0.5 mm) and a pooled and homogenized sample from each
PVC collar and harvesting event was analyzed by the AGROLAB Labor GmbH
(Bruckberg, Germany).
Mineral N (Nmin= NH4+–N + NO3-–N) contents
of each plot were determined according to VDLUFA (1997). Samples were taken
during every CH4 / N2O gas flux measurement. For the
determination of Ctot and organic carbon (Corg), a mixed
soil sample of nine individual samples was collected close to each collar at
two soil depths (0–10, 10–20 cm) using an auger with a diameter of 3 cm.
After having been dried for 72 h at 40 ∘C, soil samples were sieved
to 2 mm to remove stones and living roots. Analyses were conducted at the
Division of Soil Science and Site Science (Humbold Universität zu Berlin,
Germany). For the determination of bulk density and porosity, three
undisturbed core cutter samples (100 cm3) were randomly taken at four
depths (0–5, 5–10, 10–15, 15–20 cm) for each plot.
GHG measurements
We measured fluxes of N2O and CH4 every second week from
December 2009 to January 2012 using the static manual chamber method
(Livingston and Hutchinson; 1995). We used opaque chambers
(0.78 m × 0.78 m × 0.5 m; Ps-plastic, Eching, Germany),
which were configured according to Drösler (2005), having two handles at
the top, a permanent thermometer for chamber inside temperature
(Mini-Thermometer, TFA), and a closed-cell rubber tube at the bottom to
ensure airtightness when the chamber was positioned on the collars.
Furthermore, a vent close to the chamber bottom was connected to a 100 cm
PVC tube (4 mm wide) to avoid pressure differences during chamber closure
and a rubber valve (M20 cable gland, Kleinhuis) for the extraction of gas
samples was installed at the top of the chamber additionally ensuring
pressure release during chamber placement (Elsgaard et al., 2012). At periods
when the vegetation grew higher than the chamber height (0.5 m), extensions
were used between the collar and chamber (white, opaque; volume varied
between 309 and 1236 L). N2O and CH4 gas flux rates were
calculated from the linear change in gas concentration over time (four gas
samples; sampling time was 0, 20, 40 and 60 minutes or 0, 40, 80, 120 min in
the case of two or more extensions), considering chamber air temperature and
atmospheric pressure. Gas fluxes were accepted when the linear regression was
significant (P≤0.05). In the case of small N2O or CH4 fluxes,
fluxes were also accepted if the coefficient of determination was ≥ 0.90 and the regression slope was between -1 and 1 ppb min-1. The
cumulative annual mean exchange rate was calculated by linear interpolation
between the measurement dates. To minimize diurnal variation in the flux
pattern, N2O and CH4 sampling was always carried out between 09.00
and 11.30. We removed the gas fluxes measured in 2010 from the data set due
to errors in the gas chromatography (GC) analysis and due to long vial
storage. To improve GC accuracy a methanizer was installed in late 2010.
Further, it was ensured that vial storage time did not exceed 2 weeks in
2011. A detailed description of gas sampling and gas chromatograph settings
is given in Eickenscheidt et al. (2014a, b). The N2O and CH4 fluxes
mentioned as well as soil properties, Nmin values and biomass yield
data from the grassland sites are derived from Eickenscheidt et al. (2014b).
For CO2 flux measurements we used the closed dynamic manual chamber
system which was described in detail by Drösler (2005) and Elsgaard et
al. (2012). The chamber configuration was identical with
N2O / CH4 chambers as mentioned above. CO2 measurement
campaigns took place at irregular time intervals (8–60 days), depending on
weather conditions, management activities and the phenological stage of
plants (Tables S1–S8 in the Supplement). Measurement campaigns always
started 1 h before sunrise and lasted till late afternoon to cover the full
range of the PAR and air and soil temperatures. Opaque and transparent
chambers (same dimension as for N2O and CH4 measurements) were
alternately used at each of the three collars per plot during the time course
of a measurement campaign to obtain the ecosystem respiration
(RECO) and the net ecosystem exchange (NEE). In total up to 55 NEE
measurements and 33 RECO measurements were conducted per
measurement day and plot (Tables S1–S8). As for N2O and CH4
measurements, extensions were installed between the collar and chamber in
case of vegetation growing higher than the chamber height was (transparent or
opaque; volume varied between 309 and 1236 L). Chambers were connected to an
infrared gas analyzer (IRGA, LI-820, LI-COR, USA), which continuously
determined the CO2 headspace concentration. In the case of extensions
being used, chamber air from each level of an extension (every 0.5 m) was
drawn and merged to guarantee a reliable mixture signal from inside the
chamber. Additionally, contrary to chambers used for N2O / CH4
measurements, three fans (SUNON® Super
Silence MAGLev®-Lüfter) continuously
operated during the CO2 measurement to ensure a constant mixing of the
chamber air (wind speed in chamber headspace ∼ 1.5–2 m s-1).
Chamber enclosure time was 120 s for transparent chambers and 240 s for
opaque chambers. The CO2 concentration, air temperature from inside the
chamber and site-specific PAR was recorded every 5 s with a data logger (GP1
Data logger, Delta-T Devices, UK). To prevent heating of the air in the
transparent chambers, freezer packs (1–10 pieces) were positioned in the
airstream of the fens at the inner surface of the PVC collar (Drösler,
2005; Beetz et al., 2013). Single measurements where the PAR changed by more
than 15 % of the starting value or the temperature inside the chamber
increased by more than 1.5 ∘C compared to the outside air
temperature were discarded and the measurement was repeated (Leiber-Sauheitl
et al., 2014). CO2 gas fluxes were calculated by linear regression.
Nonsignificant gas fluxes (P≥ 0.05) with slopes close to zero or zero
(equilibrium between gross primary production – GPP – and RECO)
were not discarded (Alm et al., 2007; Leiber-Sauheitl et al., 2014). For NEE
flux calculation, a minimum time interval of 25 s was used, whereas for
RECO fluxes a minimum interval of 60 s was applied.
Modeling of CO2 net ecosystem exchange
The NEE of CO2 is defined as the product of the
GPP and the ecosystem respiration (RECO)
(Chapin et al., 2006).
NEE=GPP+RECO
In the present study we followed the atmospheric sign convention in which a
positive NEE is defined as a net flux of CO2 to the atmosphere
(Elsgaard et al., 2012).
Modeling of ecosystem respiration
The measured RECO fluxes are the sum of autotrophic
(Ra) and heterotrophic (Rh) respiration. Both
Ra and Rh are mainly controlled by temperature (Lloyd
and Taylor, 1994; Tjoelker et al., 2001). For each measurement campaign and
plot, the dependency between RECO and temperature was modeled
according to Lloyd and Taylor (1994), who developed an Arrhenius-type
relationship to predict soil respiration rates (Eq. 2).
RECO=Rref⋅eE0⋅1Tref-T0-1T-T0,
where RECO is given in milligrams of
CO2-C m-2 h-1, Rref is the respiration at the
reference temperature (mg CO2-C m-2 h-1), E0 is
activation energy (K), Tref is the reference temperature
(283.15 K), T0 is the temperature constant for the start of biological
processes (227.13 K), and T is air or soil temperature (K).
In response to the phenological stage of the plants, management activities or
changing soil moisture conditions, the applied temperature as an explanatory
variable could change during the year. Therefore, the RECO model
was fitted to the appropriate temperature type (air temperature at 20 cm or
soil temperature at -2, -5 or -10 cm) which showed the best
explanatory power for RECO. At the grassland site, we used
site-specific climate station temperatures since we assume that they were
comparable to plot-specific temperatures due to the comparable management and
close proximity. At the A1 plots, RECO modeling was based on
plot-specific climate station temperature files, whereas at the A2 plots,
RECO modeling was based on the continuous climate data set of the
A1 plots. This procedure probably produced some uncertainty for
RECO modeling in the A2 plots, but due to the inaccuracy in
manually observed temperatures, plot-specific temperature model building
would have resulted in a higher uncertainty in these two plots. In the case
of the temperature span being too small for model building (e.g., in winter
or due to snow cover) or in the case of it not being possible to observe a
significant relationship between RECO and temperature (e.g., after
ploughing), an average CO2 flux was calculated for the measurement
campaign. Annual sums of RECO were calculated by summing 0.5 hourly
RECO fluxes recalculated from Eq. (2), based on the linear
interpolated parameters Rref and E0 of two consecutive
measurement campaigns and the continuous site or plot-specific time series of
air and soil temperatures (Elsgaard et al., 2012). In the case of management
events (e.g., harvesting and ploughing) or snow cover, Rref and
E0 were kept constant from the previous measurement campaign until the
management date. After the management, parameters were taken from the
subsequent measurement campaign (Leiber-Sauheitl et al., 2013). However, in
the case of harvesting at the grassland plots, estimated parameters were
linearly interpolated over this period. Estimated parameters and the
temperatures used for RECO are shown in Tables S1–S8.
Modeling of gross primary production
We estimated GPP as the product of measured NEE minus modeled RECO
at the same time step, since it is not possible to determine GPP through
measurements. The relationship between GPP and PAR was modeled by a
Michaelis–Menten-type rectangular hyperbolic function proposed by Falge et
al (2001) (Eq. 3).
GPP=α⋅PAR1-PAR2000+α⋅PARGPP2000
GPP is given in milligrams of CO2-C m-2 h-1, α is
the initial slope of the curve (light use efficiency; mg
CO2-C m-2 h-1 µmol-1 m-2 s-1),
the photon flux density of PAR is given in µmol m-2 s-1,
and GPP 2000 is gross primary production at PAR 2000 (mg
CO2-C m-2 h-1).
Prior to modeling GPP, we corrected the plot-specific PAR values since the
acrylic glass of the transparent chambers reflected or absorbed at least
5 % of the incoming radiation (Ps-plastic, Eching, Germany)
(Leiber-Sauheitl et al., 2014). Annual sums of GPP were calculated based on
the linear interpolation of α and GPP2000 between two
consecutive measurement campaigns and the continuous time series of the PAR
(Drösler, 2005; Elsgaard et al., 2012). In the case of management events
(e.g., harvesting and ploughing), α and GPP2000 were kept
constant from the preceding measurement until the management time and were
set to zero at the 0.5 hour time step during the working process. Thereafter,
parameters were immediately linearly interpolated from the subsequent
measurement campaign for the grassland plots. For the arable land plots,
parameter interpolation started after the establishment of the seed.
Estimated parameters are shown in Tables S1–S8.
Model evaluation and uncertainties analysis
For RECO and NEE model evaluation, we used Pearson's correlation
coefficient (r), Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe,
1970), percent bias (PBIAS) and the ratio of the root mean square error to
the standard deviation of measured data (RSR) (Moriasi et al., 2007).
According to Moriasi et al. (2007), model simulation can be judged
satisfactory if NSE > 0.50 and RSR ≤ 0.70. For PBIAS, the optimal
value is 0.0, with low-magnitude values indicating accurate model simulation.
Additionally, positive PBIAS values indicate model underestimation bias, and
negative values indicate model overestimation bias (Gupta et al., 1999;
Moriasi et al., 2007). To account for the uncertainties in annual
RECO and annual GPP modeling, annual sums from the upper and lower
limits of the determined parameters (Rref, E0, α,
GPP2000), based on their standard errors (SE) were estimated
(Drösler, 2005; Elsgaard et al., 2012). However, quantifying total model
uncertainties is challenging because of the multiple sources of errors (Beetz
et al., 2013) and due to a lack of independent data for gap-filling
verification. The main uncertainty in the present study may derive from
management activities where no additional measurements were conducted and
parameters were kept constant (e.g., Rref and E0 at the
grassland) or set to zero (e.g., α and GPP2000 at the
grassland).
Estimation of NECB and GWP
A simple net ecosystem carbon balance (NECB) was calculated for each plot
based on the NEE, the carbon export of harvested phytomass, the carbon input
through organic fertilizer application and the cumulative annual CH4
exchange (Elsgaard et al., 2012; Beetz et al., 2013).
To assess the global warming potential (GWP) from the different plots, the
net emissions of carbon equivalents of NECB and N2O were summed
according to Beetz et al. (2013). For the conversion of CH4 and N2O
to CO2 equivalents, radiative forcing factors of 25 and 298 were used
(Forster et al., 2007).
Statistical analyses
Statistical analyses were conducted using R 3.0.1 (R Development Core Team,
2013). The assumption of the normality of residuals was tested using the
Lilliefors or Shapiro–Wilk test and by plotting the quantile–quantile
plots. The homogeneity of variances in residuals was checked using the Levene
or Breusch–Pagan test and by plotting the residuals against the fitted
values. Where necessary, data were Box–Cox transformed prior to analyses.
For the comparison of cumulative modeled GPP, RECO and NEE as well
as for annual yields and Nmin values, we used a two-factorial ANOVA
with land use and soil type as fixed effects (including an interaction term
in the model), neglecting the individual plot-specific standard error for
modeled CO2 values. Nonsignificant terms were removed from the model
structure. In the case of significant differences among the means, we used
Tukey's honest significant differences test (TukeyHSD). For GW level we used
the nonparametric Kruskal–Wallis rank sum test and the nonparametric
pairwise Wilcoxon rank sum test with Bonferroni correction for multiple
comparisons. In order to test two independent sample means regarding the 2
investigated years 2010 and 2011, we used the Welch two-sample t test
(Corg contents, bulk density, yields) or the nonparametric
Mann–Whitney U test (for Nmin). Due to temporal
pseudoreplication of time series data (N2O, CH4 field
measurements), we applied linear mixed-effects models (Crawley, 2007;
Hahn-Schöfl et al., 2011; Eickenscheidt et al., 2014a, b). For N2O
fluxes we set up a basic model with land-use type and soil type as fixed
effects and the spatial replication (individual plot) nested in time as
random effect. We extended the basic model by a variance function due to
observed heteroscedasticity. Furthermore, N2O fluxes showed significant
serial correlation. To take this into account, a first-order temporal
autoregressive function was included in the model. Autocorrelation was tested
using the Durbin–Watson test and by plotting the empirical autocorrelation
structure. The model extension was proved by the Akaike information criterion
(AIC). For multiple comparisons we conducted Tukey contrasts using the
general linear hypotheses function from the “multcomp” package (Hothorn et
al., 2013). CH4 fluxes did not satisfy the necessary requirements for
the linear mixed-effects model; therefore, CH4 analysis were restricted
to the nonparametric Mann–Whitney U test. We accepted significant
differences if P≤0.05. Results in the text are given as means ±1
standard error.
Results
Environmental variables
Temperatures between the two investigated land-use types and soil types did
not differ considerably. In 2010 and 2011, air temperature at a height of
20 cm ranged from -17.5 to 39.5 ∘C. Annual mean air temperature
at a height of 20 cm was 7.7 and 8.1 ∘C at the GCmedium
and GChigh sites in 2010 and 8.6 ∘C at both grassland
sites in 2011. Soil temperature at a soil depth of -2 cm averaged
10.3 ∘C at the GCmedium site and 10.5 ∘C at the
GChigh site in 2011. On the arable land, air temperature at a
height of 20 cm ranged from -15.0 to 39.5 ∘C in 2010 and 2011. In
2010 annual mean air temperature at a height of 20 cm was 8.2 and
8.1 ∘C at the ACmedium and AChigh sites and 8.8
and 8.7 ∘C at the ACmedium and AChigh in 2011.
Soil temperature at a soil depth of -2 cm averaged 10.1∘C at both
arable land sites in 2011. Longer periods of snow cover occurred from
1 January to 12 March 2010, 28 November 2010 to 10 January 2011 and from
24 January to 5 February 2011 (see also Figs. 4 and 5). In 2011, the annual
sum of PAR was 17 % higher than in the year 2010. Annual precipitation
amounted to 850 mm (2010) and 841 mm (2011) in the period investigated,
which was slightly above the 30-year mean of the period 1981–2010. Mean
annual groundwater levels of the Chigh sites were significantly
higher (all P<0.001) than those at the Cmedium sites in 2010 and
2011 (Table 1). Furthermore, the GW level at the arable sites was
significantly higher (all P<0.001) than at the grassland sites in both
years investigated. Longer periods of flooding and water saturation were only
observed at the AChigh sites for the period from 1 to 17 June 2010.
Soil properties and mineral nitrogen contents
Total organic carbon contents and bulk density in the 0–10 cm and
10–20 cm soil layers significantly (all P < 0.01) differed between
the two soil types investigated (Table 1). At the grassland sites pH values
in the 0–20 cm soil layer were approximately one unit lower than for the
arable land (Table 1). Observed C / N ratios at a soil depth of 0–20 cm
were between 10 and 12 (Table 1), indicating nitrogen-rich conditions at all
plots. Extractable Nmin contents of the soils ranged from 1 to
178 mg N kg-1 at the arable sites and from 2 to 115 mg N kg-1
at the grassland sites (Figs. 2, 3). In both years, the Nmin
contents at the grassland sites significantly (P < 0.001) exceeded
those from the arable site (Fig. 3). Furthermore, the Nmin contents
of the Chigh sites were significantly (P < 0.01) higher than
those of the Cmedium sites (Fig. 3), but this did not apply when
the arable land was considered separately. Slightly higher Nmin
contents were found at a soil depth of 10–20 cm than at a soil depth of
0–10 cm, but differences were only significant for the grassland sites
(P < 0.05). In both years, Nmin was mainly dominated by
NO3-, , whereas NH4+ was only of minor importance.
However, at the AChigh sites the proportion of NO3- at a
soil depth of 0–10 cm was lower (approximately 80 %) than at the
ACmedium sites (approximately 97 %), whereas at the grassland
sites no differences were found between the two soil types investigated
(91–95 %).
Cumulative RECO, GPP, NEE, CH4 and N2O
exchange rates as well as C import through fertilizer and C export due to
crop or grass yield.
Plot/year
cultivated crop
RECO(gCm-2yr-1)
GPP(gCm-2yr-1)
NEE(gCm-2yr-1)
Fertilizer input∗(gCm-2yr-1)
Yield∗(gCm-2yr-1)
CH4∗(gCm-2yr-1)
N2O∗(gNm-2yr-1)
A1Cmedium/10
silage maize
2473±272
-1454±114
1019±386
–
193±53
–
–
A1Cmedium/11
oat grains
2992±230
-1862±126
1130±356
–
74±8
0.51±0.17
0.27±0.01
A1Chigh/10
silagemaize
2012±284
-873±110
1139±394
–
58±23
–
–
A1Chigh/11
oat grains
2117±123
-1302±77
815±200
–
135±7
0.22±0.04
0.23±0.05
A2Cmedium/10
oat grains + straw
2704±544
-1449±103
1255±647
–
227±27
–
–
A2Cmedium/11
maizegrains
2354±309
-2360±237
-6±546
–
457±71
-0.03±0.05
0.39±0.06
A2Chigh/10
oat grains + straw
2907±482
-1200±137
1707±619
–
145±19
–
–
A2Chigh/11
maizegrains
2538±329
-2188±253
350±582
–
330±79
-0.10±0.07
0.86±0.21
G1Cmedium/10
grass 2cuts
3954±671
-2131±180
1823±851
126
297±32
–
–
G1Cmedium/11
grass 3cuts
4099±300
-2414±195
1685±495
267
344±63
-0.06±0.09
0.12±0.01
G1Chigh/10
grass 2cuts
3736±491
-2152±140
1584±631
126
325±41
–
–
G1Chigh/11
grass 3cuts
4026±707
-2633±138
1393±845
267
455±41
-0.07±0.02
0.18±0.02
G2Cmedium/10
grass 2cuts
3683±453
-2131±213
1552±666
76
342±39
–
–
G2Cmedium/11
grass 3cuts
4265±379
-2880±177
1385±556
53
543±58
-0.11±0.05
0.19±0.02
G2Chigh/10
grass 2cuts
3521±1041
-2093±152
1428±1193
76
380±43
–
–
G2Chigh/11
grass 3cuts
4316±562
-2962±178
1354±740
53
593±132
-0.02±0.02
0.31±0.09
∗ Data from grassland plots derived from Eickenscheidt et al. (2014).A: arable land.G: grassland.10: year 2010.11: year 2011.
Mineral nitrogen contents (mg N kg-1) for the arable land
(a) and the grassland (b) at a soil depth of 0–10 cm for
the years 2010 and 2011. Data from grassland plots (b) derived from
Eickenscheidt et al. (2014b).
Box plots of mineral nitrogen contents (mg N kg-1) at a soil
depth of 0–10 cm (A: arable land; G: grassland). Box plot showing median
(central thick lines) and 25 and 75 % quartile ranges around the median
(box width). Circles present extreme values (≤ 1.5 times the
interquartile range).
Time series of modeled CO2 fluxes (g
CO2-C m-2 d-1) and cumulative NEE (g
CO2-C m-2 yr-1) for each site in 2010 and 2011: (a)
grassland, cattle slurry, Cmedium; (b) grassland cattle
slurry, Chigh; (c) grassland biogas digestate
Cmedium; (d) grassland, biogas digestate, Chigh.
Grey bars mark the period with snow cover. Dashed lines indicate management
activities (see Table 3).
Model evaluation statistics from observed RECO vs.
modeled RECO.
2010
2011
Site
r
NSE
PBIAS
RSR
r
NSE
PBIAS
RSR
A1Cmedium
0.90
0.70
-7.93
0.55
0.98
0.95
-0.17
0.22
A1Chigh
0.98
0.96
0.44
0.19
0.98
0.97
1.79
0.18
A2Cmedium
0.93
0.81
-5.68
0.44
0.94
0.89
-0.23
0.33
A2Chigh
0.96
0.92
2.60
0.29
0.98
0.96
0.00
0.20
G1Cmedium
0.96
0.93
1.54
0.27
0.95
0.91
-2.40
0.31
G1Chigh
0.89
0.75
-6.27
0.50
0.97
0.95
0.03
0.23
G2Cmedium
0.93
0.86
0.80
0.37
0.98
0.96
0.06
0.19
G2Chigh
0.93
0.82
-4.65
0.42
0.97
0.94
0.92
0.25
r: Pearson's correlation coefficient.NSE: Nash–Sutcliffe efficiency.PBIAS: percent bias.RSR: ratio of the root mean square error to the SD of measured data.
Time series of modeled CO2 fluxes (g
CO2-C m-2 d-1) and cumulative NEE (g
CO2-C m-2 yr-1) for each site in 2010 and 2011: (a)
arable land, 2010 maize, 2011 oat, Cmedium; (b) arable
land, 2010 maize, 2011 oat, Chigh; (c) arable land, 2010
oat, 2011 maize, Cmedium; (d) arable land, 2010 oat, 2011
maize, Chigh. Grey bars mark the period with snow cover. Dashed
lines indicate management activities (see Table 3).
Biomass yield
The mean annual crop or grass yield ranged from 58 ± 23 to
457 ± 71 g C m-2 yr-1 for the arable land and from
297 ± 32 to 593 ± 132 g C m-2 yr-1 for the grassland
in 2010 and 2011 (see also Eickenscheidt et al., 2014b) (Table 4). For both
land-use types, the crop or grass yield was significantly (P < 0.01)
lower in the year 2010 than in the year 2011 (38 % lower at the A sites
and 31 % lower at the G sites). However, it has to be taken into
consideration that, at the grassland sites, three instead of two cuts were
carried out in 2011. On the arable land a longer period with partial flooding
and high water saturation damaged or killed some of the maize seedlings as
well as the oat plants in June 2010, especially at the Chigh sites.
Furthermore, in 2010 the entire plants were harvested at both arable land
sites and used as silo maize or oat corn plus straw, whereas in 2011 only the
grains that were grown under both management practices were harvested and the
remaining plants were left on the field (Table 3). In both years
investigated, the yield from the grassland sites significantly exceeded those
from the arable land (all P < 0.001), whereas no significant
differences were found between the two soil types observed.
Box plots of cumulative RECO (a), GPP
(b) and NEE (c) for the two soil types and land-use types.
Box plot showing median (central thick lines) and 25 and 75 % quartile
ranges around the median (box width).
CO2 fluxes
The modeling showed that the air temperature in 20 cm above soil surface and
soil temperature at a soil depth of -2 cm are the main drivers of
RECO in the present study, while soil temperatures at soil depths
of -5 and -10 cm mostly showed distinctly weaker correlations
(Tables S1–S8). For the arable land, 88 % of the calculated models based
on Tair and only 12 % on ST2, whereas at the grassland
sites 54 % of the models were based on Tair and 39 % on
ST2. Model evaluation statistics from observed RECO versus
modeled RECO generally revealed a good model performance, with a
slight tendency towards model overestimation bias for the year 2010 (mean
PBIAS -2.39). Pearson's correlation coefficients for observed
RECO versus modeled RECO ranged between 0.89 and 0.98,
NSE values ranged from 0.70 to 0.97 and RSR values were ≤ 0.55
(Table 5). According to the annual temperature trend, RECO showed a
clear seasonality with maximum flux rates during the summer time. In 2010,
highest daily RECO fluxes of up to 41 g
CO2-C m-2 d-1 were modeled at the A2Cmedium (oat)
and G1Cmedium plot, whereas in 2011, distinctly lower maximum daily
RECO fluxes of up to 28 and 32 g CO2-C m-2 d-1
were modeled for the A2Chigh (maize) plot and the G2Chigh
plot, respectively (Figs. 4 and 5). At the grassland sites, annual sums of
modeled RECO ranged from 3521 ± 1041 (G2Chigh/10)
to 4316 ± 562 g CO2-C m-2 yr-1
(G2Chigh/11), which was significantly (P < 0.001) higher than
at the arable sites where RECO ranged from 2012 ± 284
(A1Chigh/10, maize) to 2992 ± 230 g
CO2-C m-2 yr-1 (A1Cmedium/11, oat; Table 4,
Fig. 6a). Differences in RECO between the two soil types
investigated were only small and not significantly different (Fig. 6a).
Like RECO, GPP showed a clear seasonal trend with increasing
CO2 uptake capacity, with an increasing PAR intensity in summer time. In
2010, the highest maximum daily GPP of up to -25 g
CO2-C m-2 d-1 was modeled for the arable land (maize,
Cmedium) and up to -20 g CO2-C m-2 d-1 for the
grassland (G2Chigh), whereas in 2011, distinctly higher GPP values
of up to -35 g CO2-C m-2 d-1 were modeled for both maize
plots and up to -28 g CO2-C m-2 d-1 for the
G2Chigh plot (Figs. 4 and 5). At the grassland sites annual sums of
GPP ranged between -2093 ±152 (G2Chigh/10) and
-2962 ± 178 g CO2-C m-2 yr-1
(G2Chigh/11), which was significantly (P < 0.01) higher than
at the arable sites, where GPP ranged between -873 ± 110
(A1Chigh/10, maize) and -2360 ± 237 g
CO2-C m-2 yr-1 (A2Cmedium/11, maize; Table 4,
Fig. 6b). Differences in GPP between the two soil types were not significant.
Calculated NEEs were in good agreement with observed NEE. Nevertheless, the
calculated percent bias revealed a tendency of model overestimation for both
years (mean PBIAS -7.5 in 2010 and -6.1 in 2011). Pearson's correlation
coefficients for observed NEE versus calculated NEE ranged from 0.79 to 0.98,
NSE values ranged from 0.61 to 0.96 (Table 6). The mean RSR value was 0.36.
Annual NEE differed significantly (P < 0.01) between the two land-use
types with lower NEE values at the arable sites, ranging from
-6 ± 546 (A2Cmedium/11, maize) to 1707 ± 619 g
CO2-C m-2 yr-1 (A2Chigh/10, oat), than at the
grassland sites, where NEE ranged from 1354 ± 740
(G2Chigh/11) to 1823 ± 851 g CO2-C m-2 yr-1
(G1Cmedium/10; Table 4, Fig. 6c). Differences between the two soil
types were not significant for NEE.
Model evaluation statistics from observed NEE vs. modeled NEE.
2010
2011
Site
r
NSE
PBIAS
RSR
r
NSE
PBIAS
RSR
A1Cmedium
0.94
0.87
-11.84
0.36
0.97
0.93
1.41
0.26
A1Chigh
0.94
0.88
-7.94
0.35
0.98
0.96
-4.94
0.21
A2Cmedium
0.85
0.72
3.03
0.53
0.96
0.92
-3.64
0.28
A2Chigh
0.79
0.61
3.63
0.63
0.96
0.91
-9.56
0.29
G1Cmedium
0.90
0.80
-10.98
0.45
0.92
0.84
-10.47
0.40
G1Chigh
0.91
0.82
-12.07
0.43
0.94
0.88
-10.04
0.35
G2Cmedium
0.95
0.89
-13.23
0.33
0.96
0.92
-5.43
0.28
G2Chigh
0.94
0.87
-10.71
0.36
0.94
0.89
-6.22
0.34
r: Pearson's correlation coefficient.NSE: Nash–Sutcliffe efficiency.PBIAS: percent bias.RSR: ratio of the root mean square error to the SD of measured data.
N2O and CH4 fluxes
Nitrous oxide emissions were generally low at all plots (Fig. 7). N2O
fluxes rarely exceeded 50 µg N m-2 h-1. However, single
N2O peaks with maximum flux rates of up to 2832 µg
N m-2 h-1 were detected on 3 June at both maize plots as well as
on 6 September at both oat plots with maximum flux rates of up to
289 µg N m-2 h-1. At the grassland sites, highest
N2O fluxes of up to 992 µg N m-2 h-1 were found
immediately after fertilizer application (see Eickenscheidt et al., 2014b).
In general, N2O fluxes from the arable sites were significantly
(P < 0.01) higher than at the grassland sites (Fig. 8a). Furthermore,
N2O fluxes from the Chigh sites significantly (P < 0.05)
exceeded N2O fluxes from the Cmedium sites, but this did not
apply when the arable land was considered separately (Table 4). Significant
differences within the land-use types, regarding N2O flux rates, were
only found between the grassland plots, where the application of biogas
digestate significantly (P < 0.01) enhanced the N2O fluxes
compared to the application of cattle slurry (see Eickenscheidt et al.,
2014b). For the arable land distinctly different N2O flux rates between
maize and oat were not found, but the single peak emissions observed led to
significantly (P < 0.01) higher annual cumulative N2O emissions at
the maize plots (Table 4, Fig. 8a). N2O peaks accounted for 75 and
87 % of the annual N2O balances at the maize plots, whereas at the
oat plots peaks account for 63 and 54 % of the annual N2O sums (at
Cmedium and Chigh, respectively). Annual cumulative
N2O emissions ranged from 0.12 ± 0.01 g N m-2 yr-1
(G1Cmedium) to 0.86 ± 0.21 g N m-2 yr-1
(A2Chigh, maize; Table 4).
Time series of measured N2O fluxes (a: arable land;
b: grassland) and CH4 fluxes (c: arable land;
d: grassland) for the year 2011. Data from grassland plots
(b, c) derived from Eickenscheidt et al. (2014b).
Box plots of cumulative annual N2O emissions (a) and
cumulative annual CH4 emissions (b) for the two soil types and
land-use types. Box plot showing median (central thick lines) and 25 and
75 % quartile ranges around the median (box width).
Estimated global warming potential for a time horizon of
100 years.
Site/period
GWP100
NEEcorrected∗(gCO2eq.m-2yr-1)
GWP100 CH4(gCO2eq.m-2yr-1)
GWP100 N2O(gCO2eq.m-2yr-1)
GWP100 balance[gCO2eq.m-2yr-1]
A1Cmedium/11
4419±1336
16.96±5.65
126.32±4.68
4562±1346
A1Chigh/11
3487±760
7.32±1.33
107.61±23.39
3601±785
A2Cmedium/11
1655±2264
-1.00±1.33
182.47±28.07
1837±2293
A2Chigh/11
2496±2426
-3.33±1.66
402.36±98.25
2895±2526
G1Cmedium/11
6467±2048
-2.00±2.99
56.14±4.68
6521±2056
G1Chigh/11
5802±3252
-2.33±0.67
84.21±9.36
5884±3262
G2Cmedium/11
6881±2253
-3.66±1.66
88.89±9.36
6967±2264
G2Chigh/11
6951±3200
-0.67±0.67
145.04±42.11
7095±3243
∗ Corrected for C export and C import.
Most of the time, all sites showed a weak uptake of CH4 or zero fluxes.
CH4 peaks up to 173 µg C m-2 h-1 were occasionally
found immediately after fertilization at the G1 sites (see Eickenscheidt et
al., 2014b). Moreover, a high CH4 peak event of up to 2177 µg
C m-2 h-1 occurred on 14 July 2011 at the oat plots. Generally,
CH4 fluxes of the arable sites significantly (P < 0.01) exceeded
CH4 fluxes of the grassland sites, whereas no differences were found
between the two soil types investigated (Figs. 7 and 8b). Significantly
different CH4 fluxes within the land-use types could not be observed
regarding the annual fluxes in 2011. However, considering the annual
cumulative exchange rates, CH4 emissions of the oat plots significantly
(P < 0.05) exceeded those of the maize plots. The observed weak
CH4 emissions or uptakes amounted to cumulative annual CH4 exchange
rates ranging between -0.11 ± 0.05 g C m-2 yr-1
(G2Cmedium) and 0.51 ± 0.17 g C m-2 yr-1
(A1Cmedium, oat; Table 4). However, as previously mentioned for
N2O, the single CH4 peak event observed at the arable sites
determines the cumulative sum of CH4 and turns the plots from a sink
into a source of CH4.
NECB and GWP
Taking into consideration the C export from harvested phytomass, C import
from fertilization, and CH4–C and CO2–C exchange (NEE),
calculated NECB ranged from 451 ± 617 (A2Cmedium, maize) to
1894 ± 872 g C m-2 yr-1 (G2Chigh). Estimated
GWPs ranged from 1837 ± 2293 (A2Cmedium, maize) to 7095
±3243 g CO2eq. m-2 yr-1 (G2Chigh),
revealing a very high release of greenhouse gases from all plots (Table 7).
However, CO2 dominated the GWP of all plot by nearly 100 % (ranging
between 97 and 99 % and, for maize, between 86 and 90 %), whereas the
contributions of N2O and CH4 were almost negligible, with the
exception of the maize plots.
Discussion
Magnitude of GHG fluxes
The observed annual CO2 emissions were in the upper range or sometimes
higher than CO2 exchange rates reported in the literature from temperate
or boreal drained arable lands (e.g., Maljanen et al., 2001, 2007, 2010;
Grønlund et al., 2008; Höper et al., 2008; Leifeld et al., 2011;
Elsgaard et al., 2012; Drösler et al., 2013) and grasslands (e.g.,
Maljanen et al., 2001; Grønlund et al., 2006, 2008; Maljanen et al., 2010;
Elsgaard et al., 2012; Beetz et al., 2013; Drösler et al., 2013; Leifeld
et al., 2014; Renou-Wilson et al., 2014). No differences in the CO2
release of the Cmedium and Chigh sites were found in the
current study, and no information about CO2 fluxes of soils comparable
to those of the Cmedium sites were available in the literature.
Observed CO2 emissions from the arable land were in the range of or in
some cases double (4.51–12.04 t CO2-C ha yr-1) the IPCC default
emission factor from the Tier 1 approach for drained boreal and temperate
arable lands (7.9 t CO2-C ha yr-1; IPCC, 2014), whereas CO2
emissions observed at the grassland sites were more than 3 times higher
(15.81–18.94 t CO2-C ha yr-1) than the IPCC default emission
factor for deeply drained temperate grasslands (6.1 t
CO2-C ha yr-1; IPCC, 2014). However, comparison of CO2
exchange rates is difficult since annual variability is very high. For
example, Leifeld et al. (2014) reported that the NECB of a temperate
grassland in Germany ranged from 0.98 to 19.46 t C ha-1 yr-1,
with a 5-year mean of 9.06 ± 6.64 t C ha-1 yr-1. In this
study the highest value was observed for the period 2010 to 2011, which was
in good agreement with the values estimated by us during this period. The
finding is also in line with Kasimir-Klemendtsson et al. (1997), who reported
net CO2 exchange rates ranging from 8 to 115 t
CO2 ha-1 yr-1 for farmed organic soils, demonstrating the
high bandwidth of measured CO2 balances.
Observed cumulative annual N2O emissions were distinctly lower than the
default emission factor from the Tier 1 approach for boreal and temperate,
drained arable land (13 kg N2O-N ha-1 yr-1; IPCC. 2014) and
for temperate deeply drained, nutrient-rich grassland (8.2 kg
N2O-N ha-1 yr-1; IPCC, 2014). In line with this, several
other authors reported much higher N2O emissions from organic soils
ranging from 0 to 61 kg N2O-N ha-1 yr-1 for arable lands
(Kasimir-Klemendtsson et al., 1997; Augustin et al., 1998; Flessa et al.,
1998; Petersen et al., 2012; Drösler et al., 2013) and ranging from 1.15
to 41 kg N2O-N ha-1 yr-1 for grasslands (Velthof et al.,
1996; Augustin et al., 1998; Flessa et al., 1997 and 1998; van Beek et al.,
2010, 2011; Kroon et al., 2010;
Petersen et al., 2012; Beetz et al., 2013; Drösler et al., 2013).
As expected, observed CH4 fluxes from all plots were low, which is in
line with generally low groundwater levels and the absence of aerenchymous
plant species which can transport CH4 from an anaerobic layer to the
atmosphere, bypassing the oxic zone at the soil surface (Grosse et al., 1992;
Svensson and Sundh, 1992; Whalen, 2005). Cumulative annual CH4 emissions
or uptakes were in the range reported for other deeply drained arable lands
and grasslands (Maljanen et al., 2010; Petersen et al., 2012; Beetz et al.,
2013; Drösler et al., 2013; Renou-Wilson et al., 2014) and also fit in
well with the IPCC default emission factor for boreal and temperate drained
arable land (0 kg CH4 ha-1 yr-1; IPCC, 2014). However, the
IPCC gives an emission factor for a temperate deeply drained, nutrient-rich
grassland (16 kg CH4 ha-1 yr-1; IPCC, 2014) that is
distinctly higher than our estimations.
Uncertainties in GHG fluxes and modeling
Several factors probably influenced the accuracy of estimated CO2
exchange rates. Firstly, the infrared gas analyzer used, LI-820, is just able
to measure CO2 concentrations, without considering spectral
cross-sensitivity due to absorption band broadening and inherent instrument
cross-sensitivity. Both cause an overestimation of the CO2 mole fraction
in samples containing water vapor. Furthermore, the dilution effect of
CO2 in H2O can cause a proportionate decrease in the sample
CO2 concentration. In particular, the increase in water vapor due to
evaporation and/or transpiration leads to the fact that carbon uptake will be
overestimated, whereas the carbon release will respond vice versa (see
application note no. 129 from LI-COR). This is in line with Pérez-Priego
et al. (2015), who found that the increase in water vapor concentration in
the headspace leads to one of the most important systematic errors affecting
CO2 flux estimations when using closed chambers provided that no
corresponding correction is performed. According to Welles et al. (2001), the
largest error due to increasing water vapor and the dilution effect will
occur on wet soils with low CO2 fluxes
(dc / dt < 1 ppm s-1) and dry, sunny, conditions, when
chamber air temperature and water vapor can rise rapidly. Only in advective
high-flux situations when the rate of increasing water vapor is less than
1 % of the rate of increasing chamber CO2 may dilution effects be
ignored. This finding was also confirmed by Matsuura et al. (2011). However,
neither corrections for cross-sensitivity and band broadening nor a dilution
correction were applied in the present study. Nevertheless, the cooling
system used partially reduced the dilution effect by ensuring a more or less
constant air temperature and additionally by affecting air moisture and
H2O condensation, albeit to an unknown extent. However, it must be
pointed out that modeled GPP will possibly be overestimated, whereas modeled
RECO will possibly be underestimated, resulting in significantly
higher calculated NEE values. For future ecosystem CO2-exchange studies
we strongly recommend the use of a different infrared gas analyzer or the
concurrent measurement of the relative humidity and temperature to perform a
dilution correction to reduce significant errors in CO2 flux
measurements as proposed by Welles et al. (2001) and Pérez-Priego et
al. (2015).
Secondly the RECO models based only on temperature changes
disregarding the effect of soil moisture or GW level. Thus, changing soil
moisture contents or GW levels between two consecutive measurements campaigns
were neglected since we assume a linear change in derived model parameters
(see also Beetz et al., 2013; Leiber-Sauheitl et al., 2014). Thirdly, some
uncertainty in RECO models occurred at both A2 plots since no
plot-specific temperature models were used. Due to the inaccuracy of the
manually determined temperatures, we decided not to model plot-specific
temperatures for both A2 plots. However, we assume that the use of air
temperatures from climate stations of the adjacent arable plots is less
problematic for RECO modeling since 88 % of RECO
models were fitted to the air temperature, which is considered to be
comparable between the two different plots. Fourthly, management activities
such as ploughing at the arable sites probably produced peak CO2
emissions, which we may have missed. Additionally, it can be assumed that
after harvesting at the grassland sites, RECO decreased due to the
reduced phytomass. However, additional measurement campaigns to capture this
effect did not take place in the current study and no corresponding data were
found in the literature. Furthermore, it is well known that the application
of organic fertilizers produced short-term CO2 emission peaks, which
were also not sufficiently detected. However, both sources of errors may even
have an opposite effect. Fifthly, for GPP, the linear interpolation of
parameters produced some uncertainties since it can be assumed that plant
growth after cutting did not increase linearly (Horrocks and Valentine, 1999;
Beetz et al., 2013). However, with the available data set, it was not
possible to quantify the error by the used interpolation approach of
parameters since the data set was too small for cross validation and no
additional measurements for an independent model validation were conducted.
In addition, despite high model accuracy, the calculated PBIAS revealed a
slight model overestimation bias for RECO and NEE for both years
(RECO only in 2010). Thus, modeled RECO and calculated
NEE rates should be considered to be a conservative estimation. However,
modeled values fit well with values reported in the literature (see Fig. 9).
Several studies have indicated that dissolved organic C can significantly
contribute to terrestrial C balances (e.g., Worrall et al., 2009; Dinsmore et
al., 2010; Renou-Wilson et al., 2014). Thus, for the calculation of NECB from
drained organic soils, fluvial C losses should additionally be considered in
future investigations.
Relationship of GPP to biomass export from temperate peatlands.
Solid symbols represents grassland sites (intensive and extensive); hollow
symbols represents arable lands.
Observed N2O fluxes showed a high temporal variability with long periods
of low background emissions and a few high peaks, mainly after management
activities. Measurement frequency was increased after fertilization at the
grassland plots for at least 2 weeks (see Eickenscheidt et al., 2014b), but
due to our regular measurement intervals in the remaining year we cannot rule
out that we may have missed high-N2O events driven by changing climate
conditions (e.g., drying–rain or freeze–thaw events) and/or management
activities, particularly at the arable sites. N2O peaks are known to
last from a couple of days up to several weeks (Stolk et al., 2011). Due to
our measurement intervals and interpolation approach, observed N2O and
CH4 peaks distinctly altered the cumulative annual budgets, increasing
the overall uncertainties in estimated GHG emissions. Furthermore,
Christiansen et al. (2011) and Juszczak (2013) found that fluxes estimated in
non-mixed chambers (without fans) were significantly underestimated (by up to
58 %) compared to the measured reference fluxes. Moreover, all gas fluxes
were calculated solely by ordinary linear regression models, which
potentially carries the risk of underestimating gas fluxes when compared to
calculations using nonlinear functions (see, e.g., Pihlatie et al., 2013).
Thus, it is possible that we systematically underestimated N2O and
CH4 fluxes. However, for future investigations into GHG emissions we
strongly advocate, firstly, the combined use of automatic and manual chamber
systems and, secondly, the testing of linear versus nonlinear models for gas
flux calculation to obtain a higher accuracy of data.
Soil organic carbon effects
With the exception of N2O, significantly different GHG emissions between
the two soil types investigated were not found in the present study, although
significantly different SOC contents in the upper soil horizon were detected.
The observation contrasts strongly with our hypothesis that GHG emissions
significantly increase with increasing SOM content (hypothesis i).
NECB plotted against the effective C stock, which is defined as the fraction
of aerated carbon in the soil profile (according to Leiber-Sauheitl et al.,
2014) (calculated NECB did not include CH4 losses).
Regarding CO2 fluxes, the current findings are, however, in line with
investigations from Leiber-Sauheitl et al. (2014), who reported that CO2
emissions were not related to different SOM contents in the upper horizon of
an extensive grassland in northern Germany. By contrast, Veenendaal et
al. (2007) and Renou-Wilson et al. (2014) assumed that their different
estimated respiration rates for grassland sites were driven by different SOC
or SOM contents. However, it can be assumed that not only the SOM content
itself acts as a key factor controlling the CO2 release but that the
proportion of SOM which is exposed to mineralization, which in turn is driven
by drainage depth, also does so. Therefore, we calculated the effective C
stock as the fraction of aerated carbon in the soil profile according to
Leiber-Sauheitl et al. (2014) (Fig. 10). No relationship was found between
the effective C stock and the C flux components (expressed as NECB), which
were also reported by Leiber-Sauheitl et al. (2014) and Tiemeyer et
al. (2014). Moreover, Pohl et al. (2015) found that the static SOC stocks
showed no significant effects on C fluxes of maize in a heterogenous
peatland, whereas the dynamic C (SOCdyn) and N (Ndyn)
stocks and their interaction with GW level strongly influenced the C gas
exchange. We also tried to apply the concept of SOCdyn and
Ndyn stocks as described in Pohl et al. (2015); however, neither
contrasting them with the GW level nor contrasting them with the
SOCdyn or with the Ndyn had any explanatory power in our
study. However, Fig. 10 shows that at the grassland sites, C stocks available
for mineralization processes are comparable (40–45 kg C m-2),
probably explaining the equal CO2 loss rates from this land-use type.
Temperature and soil moisture are considered to be the primary regulators for
CO2 emissions from soils (Silvola et al. 1996; Maljanen et al., 2001;
Hardie et al., 2011), since they directly affect microbial activity and the
rate of enzymatic processes (Michaelis and Menten, 1913; Tietema et al.,
1992). In the present study, temperatures are found to be equal at all sites
due to their close proximity, whereas the soil moisture contents differed
significantly between the Chigh and Cmedium sites mainly
due to the GW oscillation. It is well known that the water level height has a
strong influence on CO2 emissions from peatlands as it directly affects
the oxygen availability for microbial activity as was reported in several
studies (e.g., Silvola et al., 1996; Berglund and Berglund, 2011;
Renou-Wilson et al., 2014; Leiber-Sauheitl et al., 2014). Beside abiotic
factors, substrate chemistry, in particular the SOM quality and its labile
and more recalcitrant fractions, are considered to act as key variables
affecting the decomposability of SOM and thus controlling CO2 fluxes
from peatlands (Byrne and Farrell, 2005; Heller and Zeitz, 2012; Leifeld et
al., 2012). For example, Leifeld et al. (2012) showed that the soil
respiration rate of a disturbed temperate peatland was strongly controlled by
its polysaccharide content; the O-alkyl-C content, in particular, was found
to be a useful proxy for respiration rates. SOM quality was not examined in
our study, but both soil types at all plots investigated exhibited highly
decomposed organic material (H10, according to Von Post's humification scale;
N. Roßkopf, personal communication, 2013). This is typical for organic
soils which have been drained and intensively managed for a long time and is
in line with Leifeld et al. (2012), who found that organic matter quality
declines with ongoing decomposition, resulting in low polysaccharide contents
and a lower availability for heterotrophic metabolism. Nevertheless, observed
NECB revealed very high C loss rates from the SOC pool. Leifeld et al. (2014)
suggested that intensive management, drainage and changed climate drivers
accelerate peat decomposition today and therefore outweighed declining peat
quality. Additionally, Reiche et al. (2010) reported that the degree of
humification is not suitable for the prediction of CO2 and CH4
fluxes from anaerobic decomposition, which stands in contrast to assumptions
made by Glatzel et al. (2004). However, observed equally narrow C / N
ratios (10–12) in the upper soil reveal firstly a high organic-matter
quality, easy to mineralize, and secondly comparable SOM qualities at all
plots, probably explaining why no significantly different C loss rates
between the two different soil types were found in the present study.
In line with CO2, CH4 fluxes were also not different between the
two soil types investigated, but this can mainly be attributed to the
intensive drainage and thus soil aeration, which effectively inhibited
microbial methanogenesis at the Cmedium and Chigh sites.
It is known that the availability and quality of organic substrates
influences the amount of CH4 produced. Nevertheless, several studies
indicate that high CH4 fluxes in bogs are mainly controlled by labile
organic substrates such as root exudates or plant litter and not by bulk peat
(Minchin and McNaughton, 1984; Chanton et al., 1995; Bridgham et al., 1998;
Whalen, 2005; Hahn-Schöfl et al., 2011).
In contrast to CO2 and CH4 fluxes, N2O fluxes from the
Chigh sites significantly exceeded N2O fluxes from the
Cmedium sites. This can probably be attributed to the more
favorable soil conditions for denitrification, supported by higher
Nmin contents and higher groundwater levels at these sites
(Eickenscheidt et al., 2014b). In both years Nmin was mainly
dominated by NO3-, demonstrating that net nitrification entirely
controls net nitrogen mineralization at all plots. Thus, nitrification
provided the substrate for denitrification and, additionally, may itself have
contributed to N2O production. In general, N2O production processes
are various and can occur simultaneously within close proximity (Davidson et
al., 1986; Butterbach-Bahl et al., 2013). Both nitrification as well as
denitrification depend on the availability of labile organic compounds as C
and/or energy source (Butterbach-Bahl et al., 2013), in which autotrophic
nitrification depends particularly on the availability of CO2 for cell
growth (Delwiche and Finstein, 1965). However, for denitrification the actual
regulation by C is currently not yet understood (Baggs and Philippot, 2011),
but it can be assumed that sufficient metabolizable C was widely available at
all plots investigated.
Land-use and management effects
On peatlands, GW level and land-use type are closely linked. From a
meta-analysis of 53 German peatlands, Tiemeyer et al. (2013) found that the
mean annual GW level was lower for arable land than for intensive grassland,
with median GW levels of approximately -70 and -37 cm below the soil
surface. The GW levels observed in our study were on average lower for the
arable land and higher for the grassland compared with the average of the
meta-analysis. In general, intensive farming of peatlands presupposes low GW
levels, since most of the arable crops are not adapted to low oxygen contents
in the rhizosphere, as could be seen in the present study, where the
temporarily high GW level or flooding caused plant damage and yield losses at
the arable sites in 2010. The effect of reduced biomass productivity due to
high GW levels which inhibited photosynthesis by slowing the rate of gas
diffusion through the vegetation (Lohila, 2008) was also reported by
Renou-Wilson et al. (2014). Both annual sums of GPP as well as yields were in
good agreement with those reported from other peatlands as can be seen in
Fig. 9. Statistical analysis revealed significantly higher yields at the
grassland sites than at the arable sites, but it has to be taken into account
that at the arable sites only the grains were harvested in 2011 and up to
3.84 and 9.05 t DM ha-1 remained on the field in the oat and maize
plots, respectively. Due to the continuous plant cover over the whole year at
the grassland plots, annual sums of GPP were significantly higher at these
plots than at the arable plots in 2010 as well as in 2011.
As with GPP, modeled annual sums of RECO differed significantly
between the two land-use types with distinctly higher RECO values
at the grassland sites. As mentioned above, RECO is strongly
controlled by temperature since it stimulates both Ra and
Rh, as can be seen in the pronounced seasonality of
RECO. From the model fits it can be suggested that the more
frequent model adaptation with Tair (88 %) reveals a higher
share of Ra at the arable site than at the grassland sites. At
the latter, approximately 40 % of the RECO models were based on
ST2, perhaps demonstrating a more balanced ratio of Ra to
Rh. Nevertheless, the proportion of the different respiration
compartments of RECO is unknown, but Silvola et al. (1996) reported
that root-derived respiration from grasslands established on peatland
accounted for 35–45 % of total soil respiration. Furthermore, Maljanen
et al. (2001) found that root-associated respiration was distinctly higher on
grasslands than on arable lands. However, the significantly higher
RECO at the grassland sites may, firstly, be related to the higher
biomass production at these sites, because a higher GPP also results in
higher above- and belowground autotrophic respiration (Leiber-Sauheitl et
al., 2014; Renou-Wilson et al., 2014). Moreover, the increased transport of
photosynthates to the plant rhizosphere due to the higher GPP may favor
bacterial metabolism through increased root exudates (Mounier et al., 2004;
Henry et al., 2008; Sey et al., 2010), additionally enhancing Rh.
Secondly, the organic fertilizer application at the grassland plots
stimulates microbial growth and thus SOM mineralization (Gutser et al., 2005;
Jones et al., 2007). Additionally, a large part of the C from the organic
fertilizer will quickly be metabolized to CO2 (Vuichard et al., 2007).
Several authors (see, e.g., Dao, 1998; Maljanen et al., 2010) reported that
regularly ploughed and fertilized arable lands are larger sources of CO2
than non-tilled arable land soils or grasslands, due to aerating and the
mixing of crop residues into the soil. However, in the current study the
effect of management is difficult to capture.
Despite higher modeled GPP values, the distinctly higher modeled
RECO values led to significantly higher calculated NEE values at
the grassland sites than at the arable sites. With the exception of the maize
plot at the Cmedium site in the year 2011, all plots show positive
NEE balances in both years investigated, as expected for drained organic
soils and as commonly reported in the literature (e.g., Maljanen et al.,
2001; Grønlund et al., 2006, 2008; Maljanen et al., 2010; Elsgaard et al.,
2012; Beetz et al., 2013; Drösler et al., 2013). However, the huge
CO2 uptake capacity during the short growth period of the maize plants
compensates for the soil CO2 release due to the microbial decomposition
of organic matter at least in the year 2011. Nevertheless, as seen in the
NECB, the C export also reversed the maize cultivation on the
Cmedium site to a C source. Previous studies of annual NEE from
maize on organic soils are rare in the literature, but our results are in
line with Drösler et al. (2013), who reported NEE values ranging from
-216.2 to 443.8 g C m-2 yr-1. As mentioned above, it has to be
taken into account that in the year 2011 only the grains were harvested at
all arable plots. Assuming that silage maize would have been produced instead
of maize grains or the straw was additionally harvested at the oat plots,
NECB would in part be doubled and more comparable to calculated grassland
values.
According to Maljanen et al. (2010) the better aeration of regularly ploughed
arable land leads to a larger sink of atmospheric CH4 than that at
permanent grasslands. This contrasted with our results, where the CH4
fluxes from the arable plots significantly exceeded CH4 fluxes from the
grassland plots. However, all measured CH4 fluxes were very low and
CH4 emissions and uptakes were almost negligible in the NECB of the
plots, as was also reported by several other authors for drained organic
soils (e.g., Maljanen et al., 2010; Petersen et al., 2012; Schäfer et
al., 2012; Drösler et al., 2013; Renou-Wilson et al., 2014). Moreover,
the C import through fertilization contributed only marginally (3–14 %)
to the NECB of the grassland plots.
In the course of the present study, fertilization was found to enhance
N2O fluxes at the grassland sites, where the application of biogas
digestate led to significantly higher N2O emissions than cattle slurry
application did (for further discussion see Eickenscheidt et al., 2014b).
From a meta-study of European organic soils, Leppelt et al. (2014) found that
the amount of N fertilizer was directly linked to N2O fluxes from
grasslands, whereas no significant relationship between N fertilization and
N2O fluxes from arable lands were found. Nevertheless, N2O fluxes
from the arable plots significantly exceeded those of the grassland sites, as
was also reported by Maljanen et al. (2007, 2010) and Petersen et al. (2012)
and additionally confirmed by Leppelt et al. (2014) for European organic
soils. Observed N2O peaks at the arable sites can be related to
harvesting and/or several consecutive tillage steps (e.g., ploughing,
milling, and mattocking) in the previous weeks. This is in line with Silvan
et al. (2005), who supposed that higher N2O fluxes from arable lands are
related to the higher N availability for microbial denitrification in the
absence of plants. No fertilizer was applied to the arable plots, which is
also reflected in the significantly lower Nmin contents and perhaps
in pH values that are higher in arable plots than in the grassland plots.
However, it is well known that drainage and intensive management enhanced the
degradation of SOM and thus stimulates net nitrogen mineralization and
nitrogen transformation processes (Kasimir-Klemedtsson et al., 1997;
Freibauer et al., 2004; Goldberg et al., 2010). Several authors reported an
annual N supply through peat mineralization of approximately 70–425 kg
N ha-1 yr-1 (Schothorst, 1977; Flessa et al., 1998; Sonneveld and
Lantinga, 2011; Leppelt et al., 2014). Taking into account the calculated
soil carbon losses and plot-specific C / N ratios of the upper soil or
peat layer, estimated SOM mineralization leads to an annual N supply of
approximately 451–1720 kg N ha-1 yr-1. This estimation seems
very high but regardless of the high uncertainties it clearly indicates that
sufficient N must be available for nitrification and denitrification,
independently of fertilizer application as previously assumed by Leppelt et
al. (2014). Furthermore, the admixture of Vicia sativa or
Vicia faba minor, both N2 fixing leguminoses further increase
the soil Nmin pool of the arable sites through the release of
N-rich root exudates (Rochette et al. 2004; Sey et al., 2010) as well as
their incorporation into the soil, albeit to an unknown extent.
In conclusion, taking together estimated GHG emissions, calculated GWPs
clearly differ between the two land-use types investigated, with distinctly
higher GWPs observed at the grassland plots than on the arable land. However,
all plots show a very high release of GHGs, demonstrating the unsustainable
agricultural use of drained organic soils and the current need for the
implementation of mitigation strategies and restoration measures. We
hypothesized that GHG emissions from arable soils exceed GHG emissions from
intensively managed grassland soils. The contrary was found in the present
study; therefore, we have to reject hypothesis ii. However, from the present
results it can be concluded that mainly the management, and not the land-use
type itself or the SOC content, is responsible for the amount of GHGs
released from the intensive farming of drained organic soils.
Implications for the climate reporting under LULUCF/AFOLU
For the climate reporting under LULUCF/AFOLU, the IPCC guidelines consider
GHG emissions from peat soils that have a soil or peat layer that is at least
≥ 10 cm thick and a Corg content of ≥ 12 % in the
case of a soil thickness of ≤ 20 cm. However, the intensive
cultivation of organic soils leads to a continuous decrease in the amount of
SOM, and thus the area of soils which fulfil the requirements of the IPCC
guidelines for organic soils have rapidly declined in the last decades. For
example, Nielsen et al. (2012) reported an average annual decrease in organic
soils of approximately 1400 ha in Denmark since 1975. The remaining soils
often contain > 6 % Corg and not the required > 12 %
(Nielsen et al., 2012). Contrary to mineral soils or natural peatlands in
equilibrium, Nielsen et al. (2012) assume that drained and managed soils
having > 6 % Corg will evidently lose carbon until a new
equilibrium is reached. Since no data were available in the literature for
these soils, Nielsen et al. (2012) decided to allocate a fixed emission
factor that is half of what was measured for soils having > 12 %
Corg to account for these losses in the Danish greenhouse gas
inventory. However, despite being subject to high uncertainties, our results
reveal that the GHG emission potential of soils intermediate between mineral
and organic soils can be as high as or sometimes higher than for typical
drained organic soils under intensive agricultural use. This is in line with
observations from Leiber-Sauheitl et al. (2013) for extensive grasslands. To
avoid a significant underestimation of GHG emissions in the LULUCF/AFOLU
sector, there is a corresponding need to adjust the IPCC guidelines for
drained inland organic soils accordingly. The new 2013 Supplement to the IPCC
guidelines for national GHG inventories on wetlands distinguishes several
emission factors for different land-use types, climate regions, nutrient
statuses and drainage intensities (IPCC, 2014). We suggest establishing a
further category which provides emission factors for different land-use types
at former drained peatlands or associated organic soils, which do not fulfil
the necessary requirements of typical organic soils but also contain high
amounts of Corg. To define reliable emissions factors for these
soils, further investigations regarding their potential to release GHGs are
needed. Furthermore, it has to be clarified to what extent the composition of
the SOM is responsible for the magnitude of GHG release from drained organic
soils.