Introduction
Organic soils represent a major global sink for atmospheric carbon (C).
Although they cover only 3 % of the earth's terrestrial surface (Tubiello
et al., 2016), they store up to 30 % of the global soil organic carbon
(SOC) pool (Parish et al., 2008). In Europe, more than 50 % of the former
peatland area has been degraded by peat mining and conversion of land use,
including drainage, to improve their suitability for agriculture or forestry
(Joosten, 2010). Drainage aerates the soil so that plants of interest for
agriculture and forestry can grow and make these soils manageable. The change
from anaerobic to aerobic conditions, however, triggers rapid decomposition
of peat that had accumulated under the conditions of waterlogging. This
transforms the former C-sink into a major source of atmospheric carbon
dioxide (CO2) and makes peatlands an important contributor to global
climate change (Freeman et al., 2004). Around 85 % of the global annual
CO2 emission of 915 Mt CO2-C from drained peatlands are estimated
to originate from organic soils now used as croplands (Tubiello et al.,
2016). With rates of 6.5–9.4 t C ha-1 a-1 net CO2 fluxes
from organic soils now used as croplands were on average found to be higher
than from organic soils under grassland, which were estimated to vary between
1.8 and 7.3 t C ha-1 a-1 (IPCC, 2014). However, recent studies
reported emission rates of 7.6 ± 2.0 t C ha-1 a-1 on
organic soils managed as grassland in Germany and thus much higher rates than
previously found for this type of land use (Tiemeyer et al., 2016). Drained
organic soils under forest can act as both net sinks or sources of
atmospheric CO2 (Cannell et al., 1993; Minkkinen and Laine, 1998;
Minkkinen et al., 1999; Wüst-Galley et al., 2016), although they are in
general considered to represent a source with average net CO2 emissions
of 2.0–3.3 t C ha-1 a-1 in the temperate zone (IPCC, 2014).
Temperature and soil moisture regime, which depends on drainage
depth, among other factors, have the greatest influence on peat decay in drained organic soils (Hogg
et al., 1992; Berglund, 1995; Scanlon and Moore, 2000; Chimner and Cooper,
2003; Couwenberg et al., 2010; Leifeld et al., 2012). However, there are
substantial differences in CO2 emissions from organic soils with similar
drainage and cultivation properties. The protection of organic matter (OM)
against decomposition by mechanisms such as occlusion in aggregates and
binding to mineral surfaces, which are important for the stabilization of OM
in mineral soils (Six et al., 2002), are of minor importance in organic soils
due to the lack or low abundance of minerals (Han et al., 2016). Therefore,
the intrinsic decomposability of organic matter is considered another major
factor influencing the rate of peat decomposition and a major cause of
substantial variation in CO2 emissions at different sites (Chimner
and Cooper, 2003; Byrne and Farrell, 2005; Höper, 2007; Wickland and
Neff, 2008; Reiche et al., 2010).
Although intrinsic decomposability of SOM cannot be addressed directly,
useful indicators of the latter are the relative abundances of labile and
recalcitrant C moieties, which shift towards progressively higher proportions
of the recalcitrant C with decomposition (Beer et al., 2008; Tfaily et al.,
2014) and result in selective enrichment and depletion of specific
functionalities (Leifeld et al., 2017; McAnallen et al., 2017). It is
important to recognize that during peat formation, most of the net primary
production contained in the initial mass of plant residues are lost due to
mineralization, and only 10–20 % is transformed and accumulated as peat
in the water-saturated zone of a peat bog or fen (Clymo, 1984). Although
decomposition acts slowly on accumulating peat of undisturbed (i.e. water-saturated organic) soils, it is believed that primarily the most labile OM
moieties are lost. Due to fresh peat layers accumulating on top of older
ones, age and depletion in labile compounds increase with soil depth.
Incubation studies of peat samples and carbon loss studies with undisturbed
organic soils found smaller CO2 emission rates from deeper peat layers,
which was related to the absence of labile compounds i.e. a lower intrinsic
decomposability of soil organic matter (SOM) (Hogg et al., 1992; Scanlon and
Moore, 2000; Wang et al., 2010; Hardie et al., 2011; Leifeld et al., 2012).
Using solid-state 13C-NMR, DRIFT/FTIR spectroscopy and pyrolysis-GC/MS,
various studies of OM composition of undisturbed peat profiles have shown a
gradual change with increasing depth towards a relative enrichment of
compounds that are recalcitrant against decomposition under anoxic
conditions, such as lignins and polyphenols (Freeman et al., 2004), while the
contents of labile oxygen-rich compounds, such as polysaccharides, were found
to decrease (Leifeld et al., 2012; Biester et al., 2014; Sjögersten et
al., 2016).
Elemental ratios of oxygen (O),
hydrogen (H), and nitrogen (N) to carbon
are widely used as indicators of the relative abundance of different groups
of compounds such as phenols, lipids and polysaccharides, and proteins.
Lignins and polyphenols have molar O / C ratios in the range of 0.2–0.6
and H / C ratios between 0.9 and 1.5, while the respective ratios of
carbohydrates range from 0.8 to 0.9 for O / C and from 1.4 to 1.8 for
H / C (Kim et al., 2003). In line with the molecular and spectroscopic
analyses mentioned before, both ratios were found to decrease with increasing
depth in peat (Klavins et al., 2008; Biester et al., 2014; Wüst-Galley et
al., 2016). On the other hand, both fresh plant residues and undisturbed
peat usually have high C / N ratios (Loisel et al., 2012). When peat
becomes exposed to oxic conditions, mineralization seems to lead to relative
enrichment of N, explaining why decreased C / N ratios are found in
organic topsoils compared to undrained peat layers or bottom layers of
drained organic soils (Malmer and Holm, 1984; Kuhry and Vitt, 1996; Krueger
et al., 2015). While undisturbed organic soils have a low bulk density,
drainage leads to subsidence processes and increasing bulk densities in the
topsoils (Rogiers et al., 2008; Leifeld et al., 2011a, b).
The temperature sensitivity of peat mineralization, as expressed by its Q10
value, is a useful parameter for characterizing the intrinsic decomposability of
SOM (Hogg et al., 1992; Biasi et al., 2005; Davidson and Janssens, 2006;
Conant et al., 2008; Boddy et al., 2008; Karhu et al., 2010; Hilasvuori et
al., 2013). In line with the biochemical and elemental evidence reviewed
above, it was reported to increase with increasing resistance of peat soils
against OM decomposition (Scanlon and Moore, 2000), soil depth and peat age
(Hardie et al., 2011; Hilasvuori et al., 2013).
Despite its likely important role in determining future C losses from drained
peatland, the influence of SOM composition on peat decomposition in managed
organic soils is not well studied. While decomposition rates seem to decline
with increasing peat age, i.e. profile depth, the oxic conditions, occurring
after drainage onset, lead to fast SOM decomposition. As for undisturbed
organic soils, we expect that post drainage decomposition primarily acts on
the most labile OM moieties. However, the much faster decomposition of labile
SOM might alter the depth interaction found in undisturbed peat soils.
Further, recent inputs from plant residues may supply the topsoils with
labile OM. Around 20 % of carbon in organic soils under agriculture is
derived from crop residues and thus decomposes more rapidly (Bader et al.,
2017). The fractions of OM derived from peat and recent inputs and their
decomposability in drained organic soils may, however, substantially vary
with land use, site conditions and time since land use conversion. Schulze
et al. (2009) reported that inputs of fresh organic matter residues were
smaller in croplands than in grasslands or forests, suggesting that SOM might
be on average more aged and thus less decomposable. In situ measurements of
CO2 fluxes from managed organic soils reveal slower decomposition of
peat under forest (IPCC, 2014). Together, smaller peat loss rates and higher
residue input make us expect that SOM decomposition rates under controlled
conditions are fastest in forest topsoils.
In this study, we analysed the relationship between SOM properties, specific
decomposition rates (CO2-C mg-1 SOC) and their temperature
sensitivities to peat samples taken from depths between 0 and 200 cm of 21
drained organic soils in Switzerland managed as cropland, perennial grassland
or forest. These sites embody three major uses for drained peatlands as
they occur in Europe (Joosten, 2010) and are also representative of the
situation in Switzerland where most former peatlands are drained and managed
(Wüst-Galley et al., 2015). We measured decomposition rates in incubation
experiments under standardized lab conditions and interpreted the current
decomposition status of peat using SOM properties such as (i) carbon stocks,
bulk densities and the elemental ratios O / C, H / C and C / N as
well as (ii) the temperature sensitivity towards decomposition, expecting that
specific decomposition rates of SOM decline with depth,
specific decomposition rates of SOM in managed organic soils
correlate with its composition and are inversely related to the temperature
sensitivity of decomposition,
specific decomposition rates of topsoil SOM are largest in
the forest and smallest in the croplands.
Methods
Sampling sites
The soil samples used for this study were taken from organic soils
distributed across Switzerland that were identified using the map of Wüst
et al. (2015). Apart from current land use (grassland, cropland, forest),
they differed in the type of drainage system (ditches in forest, pipes in
crop- and grassland), time since drainage onset and drainage intensity,
altitude (MASL) and climate (Table 1). All sites were classified as fens,
although we found bog-derived peat layers within the top 30 and 40 cm of the
soil profiles at two sites (SK_FL, K_FL). Cropland management comprised
rotations typical for Switzerland with maize, winter wheat, ley and rapeseed as major crops. Sites were conventionally tilled. Grasslands were used
for cutting and haymaking, not grazing, and are fertilized according to the Swiss
Fertilization Recommendations (Flisch et al., 2009). Forest sites were
managed and their vegetation was not peat-forming.
Soil sampling
Between October 2013 and June 2015, we sampled a total of 84 peat cores from
all 21 sites (4 cores per site). All cores were taken to a maximum depth of
1 m. If the underlying mineral layer was reached before 1 m depth, coring was
discontinued. We used a Belarusian peat corer (cuts a half-cylindrical
undisturbed core of diameter 4 cm) for soils with low bulk densities and a
motorized Humax corer (cuts a cylindrical core of diameter 5 cm) for denser
soils. The samples were stored at 4 ∘C for up to 2 months until
analysis. We applied the method of Rogiers et al. (2008) to account for soil
compaction during sampling for any sample and divided the cores into
segments corresponding to 5–10 cm depth increments. This corresponded,
depending on the type of soil corer used and length of the increment, to
sample volumes of between 31 and 196 cm3 per segment. In total this
resulted in 1605 soil samples. Some cores had interlayers of mineral
sediment identified by a different colour (grey), a high bulk density and their SOC content was lower than 150 g kg-1. These interlayers
were excluded from the analysis. The soil of one site (BI_FL) had no limnic
layer and therefore was classified as a murshic Histosol; all others were
classified as murshic limnic Histosols (WRB, 2014).
Soil analysis
Soil pH was measured for two to three samples of each core (307 samples in
total) using a flat surface electrode (pH 100, Extech Instruments, USA)
calibrated at pH 7.00 and pH 4.01. Aliquots of fresh soil (10 g dry matter)
were diluted in distilled water (2.5 parts water to 1 part material by mass),
shaken, left for 20 h and shaken again, before the pH measurements were
carried out.
List of sampling locations, including information on the land-use
type, peat thickness, approximate time since drainage onset, elevation
(MASL), mean annual temperature (MAT) and mean annual precipitation (MAP) of
each site.
Location name
Abbreviation of
Co-ordinates;
MASL (m)
Area size
Peat thickness1
Drainage
Drainage
MAT4
MAP5
location with
WGS (1984)
(ha)
(cm)
history2
class3
(∘C)
(mm)
land use
(CL: cropland,
GL: grassland,
FL: forest)
Birmensdorf
BI_FL
8.454, 47.357
560
2.6
95
Unclear; peat excavation nearby
s
9.2
1122
Brüttelen
B_CL
7.175, 47.033
438
3.1
290
Drained by 1864
d
9.9
1003
Chreienriet
F_FL
8.486, 47.434
440
6.9
330
Unclear; peat excavation site
d
9.4
1040
nearby until 1940
Cressier
C_CL, C_GL
7.047, 47.041
430
1.6, 1.6
120
Drained by 1864
d
10.0
1145
Gals
G_CL, G_GL, G_FL
7.065, 47.040
430
1.2, 0.8, 1.0
< 100
Drained by 1864
d
10.0
1145
Im Moos
IM_CL
9.573, 47.379
414
5.6
400
Drained by 1860; intensive
d
10.1
1297
drainage between 1942 and 1962
Katzensee
K_FL
8.495, 47.433
440
1.9
230
Unclear; peat excavation
s
9.4
1040
site nearby until 1940
Kirchenthurnen
K_GL
7.523, 46.821
540
9.9
302
Drained after 1860
d
8.9
1136
Lüchingen
L_CL
9.574, 47.378
414
4.4
400
Drained by 1860; intensive
d
10.1
1297
drainage between 1942 and 1962
Mühlethurnen
M_CL, M_GL
7.523, 46.821
540
8.2, 7.6
400
Drained after 1860; intensive
d
8.9
1136
7.523, 46.817
drainage in 1942
Rüthi
R_GL
9.536, 47.283
435
13.3
> 700
Drained by 1970
d
10.1
1533
Staatswald 1 + 2
SW1_FL, SW2_FL
7.092, 46.984
431
30.0, 48.4
142
drained by 1864; intensive
d
10.1
990
drainage in 1942
Summerigchopf
SK_GL, SK_FL
9.399, 47.212
1300
11.5, 2.2
147–202
Drain established between
s
6.0
1731
1935 and 1960
Treiten
T_CL
7.145, 47.010
439
29.3
238
Drained by 1864
d
9.9
1033
Vorderwengi
VW_GL, VW_FL
9.098, 47.196
1070
1.1, 0.9
100–146
grassland drained by 1935
s
6.2
2240
1 Peat thickness was determined by excavation of an additional
peat core down to the underlying sediment layer. 2 Information on
drainage was gained by viewing Siegfried topographical maps (1870–1949),
considering information on Swiss organic soils by Lüdi (1935) as well as aerial photographs. 3 Shallow drainage < 0.5 m
s, deep drainage > 0.5 m d. 4 MAT is the average for the years
1981–2010. 5 MAP is the average of the years 1971–1991 derived from
original data of MeteoSchweiz.
Prior to further chemical analysis, the samples were oven-dried at
105 ∘C and weighed to determine bulk density (g cm-3). The
dried samples were ground for 2 min at 25 rotations s-1 in a ball
mill (Retsch MM400) and subsampled to determine total carbon (Ctot), SOC,
hydrogen (H), nitrogen (N) and oxygen (O) contents. Ctot, H and N were
analysed after dry combustion of ground subsamples in an elemental analyser
(Hekatech, Germany). To determine SOC, we hydrolysed ground aliquots with
36 % HCl (acid fumigation) in a desiccator to remove any carbonates
before the samples were analysed in the elemental analyser. A third set of
ground subsamples were used to determine the O contents by means of the same
analyser after pyrolysis at 1000 ∘C. We corrected O contents for
inorganic O, assuming that all inorganic O was present in form of CaCO3.
The O / C and H / C ratios given in this paper represent mole ratios,
whereas the C / N ratios represent mass ratios. For analysis O / C
ratios and H / C ratios of samples having a SOC content lower than
150 g kg-1 were excluded from analysis. Soil carbon stocks
(t C ha-1) refer to the organic horizons summed over each profile and
thus do not include sediment layers that interspersed the profiles.
Incubation experiment
We selected at least two soil segments of each soil core from depths between
0–30, 30–60 and 60–100 cm for incubation to determine SOM
decomposability. Each segment was divided in two subsegments, whereas one
subsegment was incubated at 10 ∘C and the other subsegment at
20 ∘C for between 6 and 13 months. From the one location (M_CL)
where we had taken cores of > 100 cm length, we selected six
additional samples from the depth below 100 cm for incubation, resulting in
a total of 560 incubated samples. Prior to incubation, we thoroughly mixed
every segment, removed visible roots and adjusted the water potential to
-10 kPa using a hanging water column. The sample weight was
53.9 ± 0.7 g (mean ± standard error) at -10 kPa. Following
the method of Chapman (1971), we measured CO2 emission rates by means
of a Respicond VII analyser (Nordgren Innovation, Sweden) over three to four
measurement cycles of several weeks between November 2013 and March 2016. The
measurement principle is based on the change in electrical conductivity of
the NaOH solution with increasing uptake of CO2. In each cycle, we
vented the alkali CO2 traps (NaOH 0.6 M) of the analyser regularly after
50–60 mg of CO2 had been emitted to prevent O2 deficiency. In
addition, we exchanged the NaOH solution while the traps were vented. Between
measurement cycles, we kept the soil samples at the same temperature and
moisture level as during the cycles.
Data analysis
We only used CO2 data taken after the first 3 days of each measurement
cycle for data analysis to avoid artefacts that might have resulted from
moving the samples and adjusting their water content. Furthermore, we
excluded all negative emission rate values (0.45 % out of 1700 CO2
measurements taken on average per sample). Data gaps (83 % of the
timeline) between measurement cycles were filled by means of interpolation
using a robust linear regression on the log-transformed data. The specific
amount of SOC which was emitted from a sample as CO2 during 10 000 h
of incubation at 10 or 20 ∘C [mg CO2-C g-1 SOC], L,
was calculated as
L=(CO2sample-CO2blank)×12.0144.01SOCsample×msample,
where CO2sample is the amount of CO2 emitted from the sample over
10 000 h of incubation [mg CO2-C g-1 SOC], CO2blank
is the median of ambient CO2 accumulation collected in six blank vessels
over more than 6 months and extrapolated to 10 000 h (on average 27 mg),
SOCsample is the SOC content of the sample [g kg-1], and
msample is the mass of the soil sample [kg].
To determine Q10 values we applied the method used by others (e.g. Hogg et
al., 1992; Scanlon and Moore, 2000; Wang et al., 2010; Wetterstedt et al.,
2010; Hardie et al., 2011), dividing the 10 000 h length of the incubation
period at 10 ∘C by the time span over which samples incubated at
20 ∘C emitted the same amount of CO2-C per mg SOC as those
incubated at 10 ∘C emitted during 10 000 h. Given that the same
amount of SOC is lost at both temperatures, changes in OM composition during
incubation are also assumed to be the same and thus differences in the rates
are assumed to reflect only the influence of temperature and not that of
differences in composition. Q10 values are known to depend on incubation
temperatures. In order to compare our results with those of other studies we
calculated the activation energy (Ea in kJ mol-1) required for
decomposition of SOC using Q10 values.
Results of land-use effect analysis for the whole soil profile as
well as specifically in the topsoil (0–30 cm) and bottom layers
(> 30 cm), displayed for SOC concentration, C stocks, bulk density C / N, H / C and
O / C ratios, CO2 emissions at 10 and 20 ∘C and the resulting
Q10 values.
Land-use interaction1
P values between specific land-uses2
Attribute
χ2 value
P value
CL vs. FL
CL vs. GL
FL vs. GL
Soil pH
χ2(2) = 3.7
0.16
Soil pH (0–30 cm)
χ2(2) = 14.9
0.0006
0.0003
0.9
0.0021
Soil pH (> 30 cm)
χ2(2) = 0.7
0.7
SOC
χ2(2) = 10.7
0.005
0.14
0.28
0.002
SOC (0–30 cm)
χ2(2) = 14.5
0.0001
0.0001
0.5
0.009
SOC (> 30 cm)
χ2(2) = 3.0
0.2
Cumulative C stock
C stock (0–30 cm)
χ2(2) = 5.4
0.07
0.06
0.4
0.6
C stock (0–100 cm)
χ2(2) = 5.4
0.06
0.2
0.06
0.8
Bulk density
χ2(2) = 3.4
0.2
Bulk density (0–30 cm)
χ2(2) = 10.3
0.06
0.02
0.09
0.4
Bulk density (> 30 cm)
χ2(2) = 2.0
0.4
C / N ratio
χ2(2) = 5.9
0.05
0.06
0.9
0.1
C / N ratio (0–30 cm)
χ2(2) = 15.0
0.0005
0.0002
0.8
0.003
C / N ratio (> 30 cm)
χ2(2) = 2.2
0.3
H / C ratio
χ2(2) = 6.7
0.04
0.5
0.4
0.02
H / C ratio (0–30 cm)
χ2(2) = 6.3
0.04
0.6
0.4
0.03
H / C ratio (> 30 cm)
χ2(2) = 3.5
0.2
O / C ratio
χ2(2) = 11.5
0.003
0.4
0.7
0.001
O / C ratio (0–30 cm)
χ2(2) = 10.5
0.005
0.06
0.7
0.003
O / C ratio (> 30 cm)
χ2(2) = 8.5
0.014
0.008
1.0
0.003
CO2 10∘C
χ2(2) = 2.4
0.3
CO2 10 ∘C (0–30 cm)
χ2(2) = 2.9
0.2
CO2 10 ∘C (30–60 cm)
χ2(2) = 7.17
0.03
0.023
0.38
0.34
CO2 10 ∘C (> 60 cm)
χ2(2) = 1.6
0.4
CO2 20∘C
χ2(2) = 1.4
0.5
CO2 20 ∘C (0–30 cm)
χ2(2) = 6.5
0.04
0.03
0.2
0.7
CO2 20 ∘C (30–60 cm)
χ2(2) = 1.7
0.4
CO2 20 ∘C (> 60 cm)
χ2(2) = 1.2
0.5
Q10
χ2(2) = 3.5
0.2
Q10 (0–30 cm)
χ2(2) = 0.4
0.8
Q10 (30–60 cm)
χ2(2) = 1.1
0.6
Q10 (> 60 cm)
χ2(2) = 1.0
0.6
1 P value of ANOVA comparing linear mixed models with and
without the factor “land-use type”. 2 P value emitted using
least square means between land-use types.
Results of the depth influence analysis displayed for Q10 values,
CO2 emissions at 10 and 20 ∘C, SOC contents, bulk densities,
C / N ratios, H / C ratios and O / C ratios. Ea values (not shown) had similar
significance to Q10 values.
Depth interaction
P values between specific depth classes
Attributes
χ2 values
P value
0–30 vs. 30–60
0–30 vs. > 60
30–60 vs. > 60
Q10 values
χ2(2) = 46.2
9.56 × 10-11
0.05
< 0.0001
< 0.0001
Q10 cropland
χ2(2) = 16.1
0.0003
0.8
0.0002
0.002
Q10 forest
χ2(2) = 5.2
0.08
Q10 grassland
χ2(2) = 29.5
3.9 × 10-7
0.06
< 0.0001
0.009
CO2 emission (10 ∘C)
χ2(2) = 6.1
< 0.05
0.03
0.7
0.2
Cropland (10 ∘C)
χ2(2) = 1.5
0.5
Forest (10 ∘C)
χ2(2) = 17.3
0.0001
0.0001
0.01
0.5
Grassland (10 ∘C)
χ2(2) = 7.9
0.02
0.01
0.3
0.5
CO2 emission (20 ∘C)
χ2(2) = 0.9
0.6
Cropland (20 ∘C)
χ2(2) = 8.4
0.015
0.02
1.0
0.09
Forest (20 ∘C)
χ2(2) = 13.2
0.0001
0.0007
< 0.05
0.6
Grassland (20 ∘C)
χ2(2) = 3.5
0.17
pH
χ2(2) = 6.0
Cropland
χ2(2) = 19.4
6.2 × 10-5
< 0.02
< 0.0001
0.09
Forest
χ2(2) = 36.8
1 × 10-8
0.004
< 0.0001
0.001
Grassland
χ2(2) = 27.4
1.1 × 10-6
0.0001
< 0.0001
0.9
SOC
χ2(2) = 157.7
< 2.2 × 10-16
< 0.0001
< 0.0001
0.0001
Cropland
χ2(2) = 158.2
< 2.2 × 10-16
< 0.0001
< 0.0001
< 0.0001
Forest
χ2(2) = 3.8
0.15
0.2
1.0
0.3
Grassland
χ2(2) = 143.2
< 2.2 × 10-16
< 0.0001
< 0.0001
< 0.0001
Bulk density
χ2(2) = 57.6
< 3.1 × 10-13
< 0.0001
< 0.0001
0.9
Cropland
χ2(2) = 312.6
< 2.2 × 10-16
< 0.0001
< 0.0001
< 0.0001
Forest
χ2(2) = 31.6
1.4 × 10-7
0.7
< 0.0001
< 0.0001
Grassland
χ2(2) = 49.9
1.4 × 10-11
< 0.0001
< 0.0001
0.08
C / N ratio
χ2(2) = 325.5
< 2.2 × 10-16
< 0.0001
< 0.001
< 0.0001
Cropland
χ2(2) = 199
< 2.2 × 10-16
< 0.0001
< 0.001
< 0.0001
Forest
χ2(2) = 41.2
1.5 × 10-9
0.4
< 0.001
< 0.0001
Grassland
χ2(2) = 152.8
< 2.2 × 10-16
< 0.0001
< 0.001
< 0.0001
H / C ratio
χ2(2) = 19.9
< 4.7 × 10-5
< 0.0001
< 0.0002
0.7
Cropland
χ2(2) = 46.7
7.3 × 10-11
< 0.0001
< 0.0001
0.3
Forest
χ2(2) = 1.9
0.38
Grassland
χ2(2) = 52.6
3.7 × 10-12
< 0.0001
< 0.0001
0.07
O / C ratio
χ2(2) = 22.0
0.0005
< 0.06
0.03
0.9
Cropland
χ2(2) = 0.01
1.0
Forest
χ2(2) = 2.6
0.3
Grassland
χ2(2) = 6.3
0.04
0.03
0.5
0.5
Soil pH, bulk density, SOC content, cumulated C stocks, C / N ratios,
CO2 emissions and temperature sensitivity (Q10) displayed for the three
land-use types (cropland, grassland and forest) in relation to the profile
depth (cm, y axis). CO2 emissions are displayed at 10 (open symbols) and
20 ∘C (black symbols), while the area between dashed lines and
error bars represents the standard errors of the mean.
While R is the gas constant (8.314 J K-1 mol-1) and T is the
temperature used for incubation (K).
Ea=R×ln(Q10)(1T1-1T2)1000
Mixed linear models were used to analyse the effects of the various soil
parameters on SOM mineralization and their interactions with land use. The
function lmer from the package lme4 (Bates et al., 2015) was implemented
using the software R (R core Team, 2015) to run mixed linear models.
Heteroscedasticity or departure from normality was assessed graphically. In
order to avoid heteroscedasticity, we log-transformed topsoil C stocks and
bulk density data. We tested whether the factor “land use” had a
significant influence on the variation of each of the analysed variables
(α=0.05). To do this, the following two mixed models, 2 and 3,
were run for each dependent variable and compared using an ANOVA.
variable∼land.use+randomeffectsvariable∼randomeffects
Sampling depth, sampling location and site repetition were included as random
effects to account for the dependence among segments of the same core and
among cores from the same sampling location, respectively. In addition, we
included bulk density, SOC, nitrogen, hydrogen and oxygen contents as well
as the emitted CO2 as further random effects, given that there was no
collinearity with the tested variable and that the Akaikes criterion (AIC)
of the models revealed smaller scores with additional random effects. The
additional random effects used for each model are given in Tables 2 and 3.
Further, we determined the significance of land-use-specific differences (CL
vs. FL, CL vs. GL, FL vs. GL) using a least square means test for linear
models (lsmeans package).
We used the same approach to test the influence of the factor “soil depth” on
the target variables with interactions between the three sampling depths
(0–30, 30–60, > 60 cm) using the model
variable∼depth.interval+randomeffects
in addition to Eq. (3). To determine the significance of depth-specific
differences, we used a least square means test as mentioned before.
Coefficients of determination and correlation for CO2 emissions
measured at 20 ∘C and different soil attributes as explanatory
variables (profile depth, SOC content, bulk density, C / N, O / C and H / C ratio).
Ea values (not shown) behaved similarly to Q10 values.
Attribute
0–30 cm
30–100 cm
CO2 at 20 ∘C
Intercept
cor
P value
R2
Intercept
cor
P value
R2
Depth (cm)
< 2.0 × 10-16
-0.23
0.01
0.05
0.0001
0.11
0.2
0.01
SOC (g kg-1)
8.06 × 10-6
0.31
0.001
0.09
7.6 × 10-6
-0.01
0.9
4.8 × 10-5
Bulk density (g kg-1)
< 2.0 × 10-16
-0.27
0.003
0.07
< 2 × 10-16
-0.19
0.02
0.04
C / N ratio
0.002
0.28
0.002
0.08
3.5 × 10-6
-0.1
0.5
0.004
O / C ratio
1.33 × 10-6
-0.02
0.9
0.0002
2.1 × 10-5
0.06
0.4
0.01
H / C ratio
0.046
0.03
0.8
0.0005
0.292
0.07
0.4
0.01
pH
2.3 × 10-8
-0.29
0.001
0.09
2.3 × 10-8
-0.25
0.003
0.06
CO2 at 10 ∘C
Depth (cm)
1.7 × 10-13
0.19
0.2
0.02
0.0002
0.10
0.2
0.02
SOC (g kg-1)
1.7 × 10-5
-0.13
0.04
0.03
4.8 × 10-16
-0.24
0.002
0.06
Bulk density (g kg-1)
< 2.0 × 10-16
-0.22
0.02
0.04
< 2.0 × 10-16
-0.01
0.9
9.1 × 10-5
C / N ratio
0.0007
0.31
0.0007
0.09
1.05 × 10-5
-0.03
0.7
0.0008
O / C ratio
3.8 × 10-15
0.01
1.0
3.4 × 10-5
3.4 × 10-5
0.10
0.2
0.01
H / C ratio
0.5
0.14
0.1
0.2
0.3
0.24
0.002
0.06
pH
6.6 × 10-5
-0.14
0.1
0.02
0.0001
-0.09
0.3
0.007
Q10 values
Depth (cm)
< 2.0 × 10-16
0.18
0.12
0.01
0.0001
-0.30
0.0001
0.08
SOC (g kg-1)
< 2.0 × 10-16
-0.16
0.03
0.03
< 2.0 × 10-16
0.12
0.06
0.02
Bulk density (g kg-1)
< 2.0 × 10-16
-0.02
0.6
-0.006
< 2.0 × 10-16
-0.02
0.4
-0.002
C / N ratio
8.1 × 10-16
-0.10
0.15
0.01
1.2 × 10-15
-0.08
0.4
-0.002
O / C ratio
5.3 × 10-15
0.20
0.07
0.02
< 2.0 × 10-16
-0.13
0.3
0.0002
H / C ratio
0.0001
0.06
0.2
0.004
2.9 × 10-8
-0.13
0.06
0.02
pH
1.02 × 10-9
-0.03
0.8
0.0006
2.7 × 10-8
-0.02
0.8
0.0003
Van Krevelen plots of samples from (a) the upper 30 cm and
(b) depths below 30 cm. Symbols represent averages for relevant
core segments from each site, black bars represent the standard error of the
mean, grey surfaces represent the range of O / C and H / C for
lignin, carbohydrates and lipids, adapted from Preston and Schmidt (2006).
CO2 emissions at 10 (open symbols) and 20 ∘C (grey
symbols) displayed for upper soil layers (0–30 cm) and bottom layers
(30–100 cm) of all sampling locations. Sampling locations are sorted from
the lowest to highest CO2 emissions in (a). Same order of sites
was taken for (b). Error bars represent the standard error of the
mean. If a symbol lacks error bars, the standard error was smaller than the
symbol size or, as in the cases of upper soil layers SW1 and SW2, n=2.
Results
SOM characteristics
Soil pH, SOC content, C / N ratio and bulk density showed significant
land-use effects (Fig. 1, Table 2, Table S1). The lowest soil pH values were
found in the forest topsoil samples, whereas SOC content and C / N ratio
were the highest in these samples. Bulk density was highest in the cropland
topsoils. Below 30 cm depth, soil pH, SOC content, C / N ratio and bulk
density showed no land-use effect.
In the forest soil profiles, soil pH overall increased with depth, whereas it
decreased in the grassland and cropland soils (Table 3). Also bulk density
decreased with depth in the grassland and cropland soils, while SOC content
and C / N ratio increased. In the forest soils, SOC content, bulk density
and C / N ratio did not differ between topsoil (0–30 cm depth) and
subsoil samples (30–60 cm depth); however, below 60 cm depth SOC was
slightly lower than above, while bulk density and C / N ratio were higher
than above 60 cm depth (Fig. 1, Table 3). The cumulated topsoil C stocks
showed no land-use effects but tended to be larger in cropland and forest
than in grassland soils over the entire profile (Fig. 1, Table 2).
The molar H / C and O / C ratios of the organic matter fell between
the typical values of the ratios for carbohydrates and lignin, which is
displayed in a Van Krevelen plot (Fig. 2). The lowest values of both ratios
were found in the forest soils, the highest in the grassland and cropland
topsoils. Both ratios were lower in the topsoils than in the subsoils of the
cropland and grassland sites, while there was no difference between the two
depths in the forest soils (Table 3). At depths below 30 cm, the O / C
ratio was lower in the forest soils than in the other soils but without
a land-use effect in the H / C ratio.
CO2 emissions and Q10
The samples incubated at 10 ∘C emitted
32.56 ± 1.39 mg CO2-C g-1 SOC, while samples incubated at
20 ∘C emitted 74.06 ± 2.98 mg CO2-C g-1 SOC
(Fig. 1). At 10 ∘C we did not observe a land-use effect on CO2
emission (Fig. 1, Table 2), but at 20 ∘C the topsoil samples from
croplands emitted less CO2 than those from forests. This effect occurred
due to extraordinarily high emissions of the samples from two grassland and
two forest sites (VW_GL, VW_F, SK_GL, SK_F) (Fig. 3). Those four sites
experienced the least intensive drainage. Furthermore, these sampling sites
were situated at high altitude in a pre-alpine environment with lower mean
annual temperatures and higher precipitation than at the other sites
(Table 1). In pairwise comparisons between adjacent sites of different
land use (i.e. VW_GL vs. VW_FL, SK_GL vs. SK_FL, C_CL vs. C_GL and
G_CL vs. G_GL and G_FL; Table 1), land-use effects were only found for
the last site (Fig. 3).
Incubation studies with organic and mineral soils at different
moisture levels, soil depths and temperatures. If moisture level stays moist,
samples were incubated directly after being retrieved from the field, while
saturated samples were incubated under wetter, i.e. anaerobic conditions.
Similar stands for samples whose water content was similar to our samples.
Soil
Region
Moisture
∘C
Days
C / N
Depths
CO2 emissions
Q10
Ea
level
(cm)
(mg CO2-C g-1 SOC d-1)
(kJ mol-1)
This study
Drained Fens
Switzerland
-10 kPa
10
416
17.7
5–150
0.078
2.57
69.5
20
0.18
Chapman and Thurlow (1998)
Drained/undrained
UK (Scotland)
Moist
10
unclear
0–20
0.051*
3.2
80.0
Bogs
20
0.030*
Grover and Baldock (2012)
Bog
Australia
Moist
20
38
15–25
5–110
0.13–0.78
Hahn-Schöfl et al. (2011)
Fen
Germany
Saturated
20
346
15.3
0.013
Hardie et al. (2011)
Bog
UK
Drier
5
6
30
0-30
0.027
3.66
86.4
10
0.049
15
0.093
Hartley and Ineson (2008)
Mineral soil
UK
Drier
10
124
unclear
0.046
3.25
81.3
20
0.074
Hilasvuori et al. (2013)
Bog
Finland
Moist
10
short
83
0–44
0.016*
2
22.7
20
0.061*
Hogg et al. (1992)
Fen
Canada
Similar
8
120
40.6
5–40
0.083
1.9–2.2
62.0
16
0.282
24
0.381
Karhu et al. (2014)
Organic soil
UK (Scotland)
Similar
11.4
174
28.6
0–10
0.065
Organic soil
UK (Scotland)
7.6
174
36.5
0-10
0.105
Organic soil
UK
13.3
174
18.7
0–10
0.201
Mineral soil
UK
11.4
174
13.3
0–10
0.101
Mineral soil
Spain
21.5
174
14.3
0–10
0.293
Mineral soil
Spain
19
174
13.0
0–10
0.345
Mineral soil
Spain
19
174
18.6
0–10
0.448
Mineral soil
Italy
18.4
174
13.2
0–10
0.129
Karhu et al. (2010)
Mineral soil
Finland
8–25
540
–
0–30
–
3.0
45.0
Koch et al. (2007)
Organic soil
Austria
Moist
0–30
25
21.6
0–5
–
2.0
31.9
Leifeld and Fuhrer (2005)
Mineral soil
Switzerland
Similar
25
707
7.85
5–35
0.12
4.6
110.8
Neff and Hooper (2002)
Organic soil
USA (Alaska)
Unclear
10
352
34.6
0–10
0.32
1.9
22.9
30
0.75
Plante et al. (2010)
Mineral soil
USA
Similar
15
56
0–20
0.28
1.36–1.79
31.7
Reiche et al. (2010)
Fen
Germany
Saturated
15
31
30.1
0–40
0.0022
Reichstein et al. (2000)
Mineral soil
Switzerland
Similar
5
104
30.3
0.05
2.5–2.7
65.9
15
0.14
25
0.22
Scanlon and Moore (2000)
Fen
Canada
Moist
4
12
43
5–45
0.227
2.0
45.8
14
0.109
Wang et al. (2010)
Organic soil
China
Similar
5–20
40
28.5
10–30
0.31
2.2
53.3
Wickland and Neff (2008)
Organic soil
Canada
Similar
10
57
24.7
2–30
0.35
1.7
36.6
20
0.79
Yavitt et al. (2000)
Bog
Canada
Moist
12–22
2
0–54
–
1.4
32.5
* Study authors are not specific about the SOC content of
peat; therefore we assumed it to be 400 g kg-1, according to the findings
of undisturbed bog peat (Loisel et al., 2014).
At 10 ∘C, CO2 emissions of the topsoil samples from all sites
together were higher than from samples taken at 30 to 60 cm depth,
independent of land use (Table 3). When analysing the influence of depth
separately by land-use type, this effect was found to only manifest in grassland and
forest but not in cropland soils. We found no overall depth effect at
20 ∘C, but CO2 emissions of topsoil samples from forests were
higher than those of samples taken at lower depths, whereas we found the
opposite case for the cropland soils. Despite these depth effects,
the general relationship between emissions and soil depth was weak and not
consistent in its sign (Table 4).
Over the course of the incubation, CO2 emissions increased for 40 %
of the samples, as revealed in Table S1 by positive slopes of the regression
lines. These increases were independent of land use. In total, the CO2
emissions from these samples were almost 50 % higher than those from the
other samples that instead showed a trend of decreasing emissions.
Mean Q10 values were 2.57 ± 0.05. The Q10 did not differ between the
three land use types. It was lower below 60 cm depth in the cropland and
grassland but not in the forest soils (Fig. 1, Table 3). Activation
energies (Ea) calculated from Q10 values ranged around 48.1 and
123.5 kJ mol-1 and like Q10 values decreased with depth. There were
significant relationships between CO2 emission and SOC content, bulk
density and C / N ratio in general but they were weak (Table 4). The Q10
values showed similar relationships to these soil variables as CO2
emission.
Discussion
SOM characteristics
The SOC contents, bulk densities and C / N ratios found in the deeper
parts of soil profiles presented here were close to values that are typical
for undisturbed peat (Grover and Baldock, 2012; Loisel et al., 2014). They
also indicate that soils of our study sites were characteristic of European
fens and resembled typical properties of managed organic soils (Berglund,
1995; Kechavarzi et al., 2010; Eickenscheidt et al., 2015; Krueger et al.,
2015; Wüst-Galley et al., 2016; Brouns et al., 2016). Several studies
assume that deeper layer peat of managed organic soils is less decomposed
(Ewing and Vepraskas, 2006; Rogiers et al., 2008; Leifeld et al., 2011a,
b; Krueger et al.,
2015; Wüst-Galley et al., 2016). We therefore interpret the different SOM
characteristics found in the topsoils of our samples as indicators of
advanced decomposition triggered by drainage.
The land-use-specific differences manifested in different topsoil SOC
contents and C / N ratios (highest under forest) and topsoil bulk
densities (lowest under forest). The higher forest C / N ratios might be
explained by absence of the use of N fertilizers and lower bulk densities by
lower traffic with field machinery. In addition, differences in C / N
between land-use types may also suggest that peat decomposition was less
advanced in forests compared to croplands and grasslands. Further, depth
effects are lowest in forest soils, indicating a lower impact of soil
management that could also result in a lower decomposition of forest
topsoils. The relatively high carbon stocks found in cropland top soils are
most likely the result of subsidence after drainage and compaction from field
traffic, leading to increased soil bulk density in the uppermost layers. This
effect, with respect to C stocks, overrides the overall much smaller C
concentration in agriculturally managed organic soils.
The H / C and O / C ratios in the deeper layers of the studied soils
were similar to those found in undisturbed bogs and drained bogs used for
forestry in Switzerland (Zaccone et al., 2007; Wüst-Galley et al., 2016).
They indicate an enrichment of polyphenols and aromatic carbon with depth,
which is in line with the current understanding of peat development in
peatlands (Cocozza et al., 2003; Zaccone et al., 2007; Klavins et al., 2008;
Delarue et al., 2011; Leifeld et al., 2012, 2017). The increased H / C
and O / C ratios in the grass- and cropland topsoils can be attributed to
inputs of fresh plant litter to the topsoil via above- and belowground
residues, as such residues are rich in carbohydrates (Koegel-Knabner, 2002).
In a previous study, in which we used stable and radiocarbon isotopes to
label the SOC of two of the studied soils (C_CL and C_GL in Table 1), at
least 20 % of topsoil organic matter was not peat but derived from recent
plant litter (Bader et al., 2017). The results further indicated that the OM
derived from these fresh plant residues was a source of labile C that
contributed more to decomposition than the old, peat-derived SOM.
Comparison of daily CO2 emission rates from this study
(box plots) with rates found during other incubation studies (organic soils
and mineral soils). The curves represent the modelled CO2 emission rates
for organic soils from other studies (solid thick line)
rate = 0.06 × 100.08t and mineral soils (dashed thin line)
rate = 0.09 × 100.05t for temperatures between
0 and 30 ∘C.
The H / C and O / C ratios reflect the mixing ratio of these two SOM
sources. The H / C and O / C ratios in forest topsoils were lower
than of those under cropland and grassland and did not change with depth.
Interpreting these lower H / C and O / C ratios in the forest
topsoils as indicators of more advanced peat decomposition (Klavins et al.,
2008; Leifeld et al., 2012; Biester et al., 2014; Wüst-Galley et al.,
2016) would be in contradiction to our conjecture that land management
effects on peat decomposition, revealed by SOC, bulk density and C / N
ratio, are less pronounced for forests. We rather argue that the reason for the
low H / C and O / C ratio in the forest soils is a higher abundance
of lignin rich (wood derived) plant residues. A second mechanism for
comparably higher O / C and H / C ratios in cropland and grassland
soils could be that peat loss in the uppermost layers was higher under
agriculture than under forest, resulting in a relatively higher share of SOM
from recent inputs. Considering all the available evidence of SOM
characteristics, we conclude that peat decomposition is less advanced in
forest soils than in agricultural soils and also in line with field flux
measurements on managed organic soils that typically show faster
decomposition in croplands and grasslands than in forests (IPCC, 2014).
CO2 emissions and temperature sensitivity of decomposition
The studied soils emitted, on average, ca. 5–10 % of their SOC
(20 ∘C) as CO2, calculated for an incubation duration of
1 year. The advanced decomposition state of many of the samples might give
reason to expect that CO2 rates are below that of more intact peat or
mineral topsoils that contain a larger fraction of recent plant residues. To
understand whether SOM in the studied organic soils is particularly stable,
we compared its average daily carbon loss with data from studies that used
undisturbed to extensively managed organic soils or mineral soils (Table 5,
Fig. 4). Indeed, our values are on average below those from other organic
soil studies. However, their range overlaps with the uncertainty of the
regression line that is plotted through results from studies from other,
mostly intact or little degraded organic soils. Hence, the pronounced
oxidative decomposition after long periods of drainage might result in a
relatively smaller labile SOC pool, but the large variability between
experimental set-ups, incubation lengths and water contents among incubation
studies prevents a stronger line of interpretation. Interestingly, the
regression lines modelled for organic and mineral soils did not deviate
significantly from each other. Therefore, the pools size of labile carbon,
indicated by the decomposition rates, seem not to differ between these soil
classes. This comparison suggests that accumulation of recent, labile plant
materials that presumably account for most of the evolved CO2 is not
systematically different between mineral and organic soils.
Samples showing an increase in CO2 emission rate over time were
predominantly of subsoil origin, where SOC contents and C / N ratios
indicate a lower decomposition than in the topsoil. Furthermore, based on the
information we have on land use and drainage depths, it appears that most of
these samples were taken from soil layers that were protected from intensive
decomposition by water saturation. The long incubation period in our study
might have given aerobic decomposer communities time to develop and grow,
whereas time might not have been sufficient in shorter studies.
Like other studies on extensively managed or undisturbed organic soils that
investigated depth interaction of decomposition rates in the top 30 to 50 cm
(Hogg et al., 1992; Scanlon and Moore, 2000; Wang et al., 2010; Hardie et
al., 2011), we found a decrease of specific CO2 release with depth.
However, the relationship between CO2 emissions and depth was rather
weak in our case and not consistent for both incubation temperatures and the
different land uses. Compared to the studies on unmanaged organic soils,
reporting declines of a factor 2 to 30 (Hogg et al., 1992; Scanlon and Moore,
2000; Wang et al., 2010; Hardie et al., 2011), our differences were
substantially smaller. Drainage and decadal agricultural use of the studied
soils led to more intense decomposition processes in the topsoil, resulting
in little depth interaction or, for croplands, sometimes maybe even a
reversal of decomposability. Further, the abundance and decomposability of
crop residues have to be considered as a substantial CO2 source. For two
topsoils (C_GL and C_CL), Bader et al. (2017) showed that at least 20 %
of the SOM is crop residue derived and responsible for 40 % of the
emitted CO2. Assuming that the abundance of crop and plant residues is
highest in topsoils, it might be possible that decomposability of peat-derived SOM either does not depend on depth or topsoil peat decomposes at
smaller rates. Therefore, we cannot confirm our first hypothesis of
decreasing decomposition rates with depth.
As Table 5 shows, the Q10 values found in our study (2.74 ± 0.06) were
higher than Q10 values found elsewhere for similar sampling depths but in
unmanaged organic soils (Chapman and Thurlow, 1998; Hamdi et al., 2013;
Hardie et al., 2011; Hilasvuori et al., 2013; Hogg et al., 1992; Scanlon and
Moore, 2000; Yavitt et al., 2000). Also the temperature-independent Ea was
higher in the studied samples (69.4 ± 3 kJ mol-1) than in most
other studies on undisturbed organic soils (47.4 ± 7.2 kJ mol-1)
(Table 5). However, three studies (Chapman, 1971; Hardie et al., 2011; Hogg
et al., 1992) found similar or higher Ea values in northern organic soils. In
the case of Chapman and Thurlow (1998) they were also managed as grassland or
forest, whereas the other studies used peat from undisturbed organic soils.
Nevertheless, the high Ea of the studied samples might reflect the change in
chemical peat composition with decomposition after drainage towards higher
recalcitrance. In contrast to other studies on unmanaged organic soils
reporting no trend or increasing Q10 values with depth (Scanlon and Moore,
2000; Wang et al., 2010; Hardie et al., 2011; Hilasvuori et al., 2013), the
cropland and grassland profiles in our study had a lower Q10 below the 60 cm
depth. Various studies on SOM decomposition used Q10 values as an indicator
of SOM recalcitrance (Hogg et al., 1992; Biasi et al., 2005; Davidson and
Janssens, 2006; Conant et al., 2008, 2011; Hartley and Ineson, 2008;
Hilasvuori et al., 2013). Considering that the presence of labile crop
residues would decrease Q10 in the topsoil rather than in the subsoil, the
higher topsoil Q10 may be explained by an extended accumulation of
recalcitrant moieties. This proposed a high abundance of recalcitrant moieties
in topsoils of degrading organic soils is in line with the reported
differences in SOM composition in different layers as well with the pattern
of CO2 emissions. The latter show no substantial difference with depth
and indicate that a higher fraction of recent and labile plant residues in
topsoils is counterbalanced by a high recalcitrance of the highly degraded
peat. Comparing radiocarbon concentrations in SOC and emitted CO2 of two
sites also used for this study (C_CL, C_GL), Bader et al. (2017) estimated
that SOC from plant residue inputs is more labile than peat. The measured
radiocarbon contents for SOC were 75 to 80 pMC and therefore indicated that
peat of the topsoil must have experienced a substantial decomposition.
It is remarkable that despite the controlled conditions in our incubation
experiment the variation in cumulative loss of initial SOC of between 0.6 and
42.3 % (Fig. 4) was similar to or even larger than that observed in
field flux measurements (IPCC, 2014). This large variability suggests that
the composition of SOM is of similar importance to drainage, climate and
other site factors in controlling CO2 emissions from drained organic
soils. Nevertheless, the relationships between the measured SOM parameters used to
assess the biochemical decomposability, CO2 emissions and Q10 values
were rather weak and thus do not support our second hypothesis. This stands
in contrast to other studies which concluded that chemical composition is a
major factor of SOM decomposability in organic soils (Scanlon and Moore,
2000; Koch et al., 2007; Reiche et al., 2010; Hardie et al., 2011; Leifeld et
al., 2012). However, these studies focused mainly on single profiles of
undisturbed or extensively used organic soils. A recent study investigated
relationships between SOM parameters and decomposition rates of German
organic soils under controlled conditions (Säurich et al., 2017). These
authors mostly studied strongly disturbed fens with similar properties to the
soils in our study. Besides SOC contents, soil pH and C / N ratios,
Säurich et al. (2017) focused on other soil nutrients, stable isotopes
and microbial biomass. In line with our results, they could not identify
strong proxies for SOC decomposition by means of simple chemical attributes.
In order to explain the weak relationships between SOM composition and
CO2 release it should be considered that, in our case, the emitted
CO2 comprised, on average, only 3.2–7.4 % of the total SOC, while
the analysed SOM parameters in this and other studies represent bulk SOM. Our
methods allowed us to gain a broad overview of the chemical composition of
SOM, while decomposition might more tightly be bound to the abundance of
specific OM moieties.
Although land-use-affected SOM characteristics, such as elemental contents
and their ratios, the amount of CO2 emitted from the soils did not
differ among the three types of land use. We therefore have to also reject
our third hypothesis of a higher SOM decomposition rate in forest topsoils.
We assume that long-lasting drainage and management might have resulted in an
equivalent decomposition of most of the labile OM, along with its intrinsic
decomposability.