Introduction
Peatlands are one of the most important ecosystems for the terrestrial carbon
(C) and nitrogen (N) cycle, storing up to 500 Mg C ha-1 and –
particularly in nutrient-rich fens – 120 Mg N ha-1 (Yu et al., 2011;
McDonald et al., 2006; Kunze, 1993). Throughout the world, the drainage and
subsequent agricultural cultivation of peatlands has increased soil organic
carbon (SOC) mineralisation rates and the associated CO2 emissions
(Couwenberg et al., 2010; Kasimir-Klemedtsson et al., 1997; Nykänen et
al., 1995), resulting in the creation of small-scale mosaics of soil types
with extremely variable SOC stocks, especially in the case of fens. The
respective soil types range from deep peat soils to humus-rich sandy soils,
which are not classified as peat soils due to an SOC content of < 12 %
(IUSS Working Group WRB, 2007). These individual soil types are typically
found at similar relative elevations within an increasingly undulating
landscape, and the groundwater level (GWL) is often subject to considerable
short-term fluctuations. As a result of the tight coupling between soil types
and elevation, mean GWL may differ considerably between individual soil types
(Aich et al., 2013; Heller and Zeitz, 2012; Dawson et al., 2010; Teh et al.,
2011; Dexler et al., 2009; Müller et al., 2007; Schindler et al., 2003).
These sites are typically used as grassland or cropland (Joosten and Clark,
2002; Byrne et al., 2004).
The relevance of these soil type mosaics originating from drained fen
peatlands as a source or sink for greenhouse gases such as CO2 and
CH4, especially if the land is used for cropland, still cannot be exactly
determined. In particular, knowledge about the influence of variable soil C
stocks on the C gas exchange is still limited. In light of the extreme
complexity of site conditions, it seems unlikely that the common focus on
interactions between C stocks and particularly relevant control parameters
such as groundwater and temperature (Adkinson et al., 2011; Berglund et al.,
2010; Kluge at al., 2008; Jungkunst and Fiedler, 2007; Daulat et al., 1998)
will result in reliable and generalisable conclusions about the C gas fluxes
in degraded fens, mainly because this approach fails to account for the
plant-induced C gas input counteracting the C gas emissions determined by
soil characteristics and microorganisms.
Therefore, new insights are much more likely to be derived from
system-oriented studies analysing all interrelated C gas fluxes, e.g.
CH4 exchange, CO2 uptake during photosynthesis and CO2
emission via respiration, together with the underlying processes and control
mechanisms (Chapin III et al., 2009; Schmidt et al., 2011). Indeed, there are
numerous indications suggesting that this approach may also be promising for
the C gas exchange of drained fen sites.
Short- and long-term fluctuations in GWL and its interactions with soil
and plants very likely also play a key role in the C cycle of other
groundwater-influenced soil types similar to true peat soils (Couwenberg et
al., 2011; Berglund and Berglund, 2011; Flanagan et al., 2002; Augustin et
al., 1998; Martikainen et al., 1995; Nykänen et al., 1995). For peat
soils, many studies have documented the impact of GWL on the interactions between
soil C dynamics and gaseous C emissions in the form of CH4 and CO2,
the latter originating from autotrophic root respiration and heterotrophic
microbial respiration. Ultimately, these GWL effects are a result of the
ratio between SOC stocks located in the aerobic, i.e. above-GWL, and the
anaerobic, i.e. below-GWL, zone (Laine et al., 1996). However, very few
(Leiber-Sauheitl et al., 2014; Jans et al., 2010; Jungkunst et al., 2008;
Jungkunst and Fiedler, 2007) studies have investigated Gleysols and
groundwater-influenced sandy soils, which make up a significant portion of
fen landscapes. It also remains unclear whether the impact of GWL on the gas
exchange is modified by the highly variable density typical of the SOC-rich soil
horizons of drained peatlands.
Knowledge gaps also limit the quantification of direct GWL effects on
plant-mediated CO2 uptake via photosynthesis. Site-adapted plants
growing on undisturbed peat soils and perennial grasses cultivated on
groundwater-influenced soils can tolerate changing GWL without considerable
deterioration of photosynthetic performance (Farnsworth and Meyerson, 2003;
Crawford and Braendle, 1996). In contrast, GWL fluctuations likely have a
particularly strong impact on annual crops cultivated on drained peatlands,
as most crops typically react to waterlogging, i.e. anoxic soil conditions as
a result of high GWL, with reduced photosynthesis, plant respiration and
growth (Zaidi et al., 2003; Asharf, 1999; Singh, 1984; Wenkert et al., 1981).
Other studies indicate that crops cultivated on groundwater-influenced soils
feature better growth when GWLs are low (Glaz et al., 2008), but it is unclear
whether this is a direct result of improved aeration or an indirect effect of
increased soil volume, allowing for better root development and thus
increased nutrient uptake (Glaz et al., 2008; Livesley et al., 1999).
Characteristics of study sites: soil type, elevation and
0–1 m stocks of soil organic C and total N.
Site
Soil typea
Elevation
SOCstocks
Total Nstocks
(m a.s.l.)
(kg SOC m-2)b
(kgNtm-2)b
AR
Haplic Arenosol
29.6
8.0
0.7
GL
Mollic Gleysol
29.0
37.8
3.1
HS
Hemic Histosol
28.8
86.9
5.4
a WRB (2006); b 0–1 m soil
depth.
Despite the system-orientated approach mentioned above, it can therefore be
assumed that the amounts of soil C and N located above the temporally
variable GWL – hereafter referred to as dynamic C and N stocks – are of
essential relevance to plant- and microbially mediated C gas fluxes on
drained peatland soils. Moreover, investigations into the effects of dynamic
C and N stocks may yield new insights into the mechanisms controlling the C
dynamics at these sites. This would be a significant advancement with
respect to a comprehensive and generalisable understanding of the CO2
and CH4 source and sink capacity of drained arable fen peatlands.
The present study tests the above-mentioned assumption by means of
multiyear manual-chamber measurements, subsequent modelling and complex
statistical analysis of all relevant C gas fluxes in maize cultivated on different groundwater-dependent soil types
representing a steep SOC gradient; the gas fluxes are the net CO2
exchange resulting from gross primary production (plant photosynthesis) and
ecosystem respiration (sum of plant and soil respiration) and the CH4
exchange. In particular, the study focuses on
answering the following research questions:
Are there differences among soil types regarding the dynamics and the
intensity of the C (CO2 and CH4) gas exchange of drained arable
peatland soils?
(a) Which factors and factor interactions influence the C gas exchange of
drained arable peatland soils? (b) In particular, what is the influence of
the amount and the dynamics of soil C and N stocks located in the aerobic
zone above GWL on the C gas exchange of drained arable peatland soils?
Materials and methods
Site description and land use history
The study sites are located near the village of Paulinenaue, in the shallow
and drained peatland complex “Havelländisches Luch” of NE Germany
(51 km west of Berlin; 52∘41′ N, 12∘43′ E). This
peatland was first drained at the beginning of the 14th century (Behrendt,
1988). A systematic amelioration for the entire Luch took place from 1718
until 1724 and included the construction of ditches and dams to drain the
formerly swampy terrain and to provide access to the land. Grasslands with
hay production dominated the Luch at that time. In order to prevent
repeated flooding and to increase grassland productivity, a second
amelioration with deeper drainage ditches was implemented between 1907 and
1925. A substantial increase in total ditch length occurred between 1958 and
1961, when approx. 1000 km of new ditches were established in the area
(Behrendt, 1988). The next huge effort to increase productivity was started in
the early 1970s by the so-called “Komplexmelioration”, which lasted until
the late 1980s. The basic idea was to establish a system of pumping stations
and related ditches in order to increase and lower the groundwater table
dynamically throughout the vegetation period depending on the actual plant
water demand. In addition, fertiliser application rates, including organic
manure, increased and the acreage of arable land doubled at the expense of
grassland. After the reunification of Germany in 1989, a substantial
de-intensification took place, resulting in the reconversion of arable land
to grassland, the reduction of fertiliser input and the abandonment of hydraulic
technical devices for economic reasons.
The region is characterised by a continental climate with a mean annual air
temperature of 9.2 ∘C and a mean annual precipitation of 530 mm
(1982–2012).
The study sites are located along a representative and steep landscape
gradient in terms of soil organic carbon stocks (SOCstocks;
0–1 m), which is related to topographic position (Table 1): AR, a Haplic
Arenosol developed from aeolian sands with low SOCstocks
(8 kg C m-2 m-1) in an elevated topographic position (29.6 m a.s.l.); GL, a
Mollic Gleysol developed from peat overlying fluvial sands with medium
SOCstocks (38 kg C m-2 m-1) at 29.0 m a.s.l.; and
HS, a Hemic Histosol developed from peat featuring high SOCstocks
(87 kg C m-2 m-1) at the edge of a local depression
(28.8 m a.s.l.). Moreover, the vertical distribution of C and N differs
between sites: at AR almost all SOC and N is concentrated in the plough layer
(Ap horizon), whereas GL and HS show larger portions of SOC and N in subsoil
horizons (Fig. S1 in Supplement).
All sites were identically managed during the study period (Table S1 in
Supplement), i.e. cultivated with a monoculture of grain maize with annually
changing varieties. The AR and HS sites are located 150 m apart within the
same managed field, while GL is located 1.5 km from AR and HS. However, field
operations such as tillage, sowing, fertilisation and harvest were conducted
almost concurrently at all sites. Maize was fertilised with diammonium
phosphate (DAP) containing 22 kg N ha-1 and 24 kg P ha-1 in
the course of sowing, followed approx. 2 weeks later by fertilisation with
calcium ammonium nitrate (CAN) containing 100 kg N ha-1. During
harvest, total plant biomass within the measurement plots was collected,
chipped, dried at 60 ∘C to constant weight and weighed. Grain yield
was not recorded due to technical complications. Total plant biomass
subsamples were analysed for C content at the ZALF Central Laboratory. After
harvesting, all sites were mulched and ploughed.
Environmental controls
Half-hourly values of air temperature (20 cm height), soil temperatures
(2, 5 and 10 cm depth), PAR (photosynthetically active radiation) and precipitation were continuously recorded by
a climate station installed within 1 km of the sites. Site-specific air and
soil temperatures were manually measured simultaneously with CO2 and
CH4 flux measurements. Site-specific half-hourly air and soil
temperature models were derived from correlations between the respective
climate station temperature records and site-specific manual temperature
data. Sunshine hours and long-term climate data originate from the Potsdam
station of the German Weather Service (DWD).
GWL at GL and HS was measured manually every 2 weeks using short 1.5 m dip
wells. The measured piezometric heads are considered representative of the
phreatic water levels in the peat layer because the organic soil layer
directly overlies a sand aquifer without any major low-conductance soil
horizons in between. At HS, GWL was additionally recorded every 15 min by a
data logger (Mini-diver, Schlumberger). Time series modelling was used to fill
several small data gaps and to obtain continuous daily GWL data for the
entire study period. The applied PIRFICT (predefined impulse response function in continuous time) approach (von Asmuth et al., 2008)
implemented in the Menyanthes software (von Asmuth et al., 2012a) is
a physically based statistical time series model specifically developed to
model hydrologic time series, including shallow GWL fluctuations. As input,
the model requires continuous precipitation (DWD station Kleßen) and
evapotranspiration data (FAO56 Penman-Monteith; DWD station Kyritz) and
optional control parameters, e.g., in our case, deep GWL data recorded from a
local dip well (Ministry of Rural Development, Environment and Agriculture – LUGV, Brandenburg). The calibrated model explained
80–87 % of the data variance, a good result for this data and model type
(von Asmuth et al., 2012b). Confidence intervals of GWL time series
predictions were obtained by means of stochastic simulation (see von Asmuth
et al., 2012a). Due to the short distance between AR and HS and the highly
significant correlation of GWL at these sites (R2 = 0.836), daily
GWL values for AR were calculated by shifting the modelled time series of HS
with a constant offset of 0.9 m.
Concept and calculation of dynamic C and N stocks
The concept of “dynamic” groundwater-dependent C and N stocks was developed
to account for the interaction of the most important drivers of the C gas
fluxes in peatlands, namely GWL and soil C and N stocks. The underlying idea
is to derive a quantitative, dynamic proxy for the aerated, unsaturated zone
which determines the actual nutrient and O2 availability and is
therefore highly relevant for root and shoot growth, microbial activity and,
consequently, all C gas fluxes. Using daily GWL data, it was determined for
each 1 cm soil layer up to a depth of 1 m whether the respective layer was
saturated with groundwater or not. In daily time steps, SOC and N stocks were
then calculated for all non-saturated 1 cm layers and cumulated over the
entire non-saturated soil profile, i.e. above GWL, to generate daily
dynamic SOC (SOCdyn) and N (Ndyn) stocks. For further
analysis, daily SOCdyn and Ndyn values were averaged
monthly and annually.
Gas flux measurements
Periodic trace gas measurements were carried out at three permanently
installed soil collars (0.75 × 0.75 m) at each site. In the summer
of 2007, due to flooding, soil collars at the HS site had to be relocated within
a radius of 10 m to (i) technically allow for gas flux measurements and
(ii) ensure that all soil collars contained flood-affected but viable plants
in order to maintain comparability with the GL and AR sites, where maize
mortality was not increased by flooding.
Throughout the entire study period, CH4 measurements were conducted one
to two times per month using static non-flow-through non-steady-state
opaque chambers (vol. 0.296 m3; Livingston and Hutchinson, 1995;
Drösler, 2005) for a total of 51–60 campaigns per site. At HS, CH4
measurements were terminated already in October 2010 due to management
constraints. The exchange of CH4 was measured by taking four consecutive
100 mL gas samples from the chamber headspace at 20 min intervals (closure
time 60 min), subsequently analysed using a gas chromatograph (Shimadzu GC
14B, Loftfield, Göttingen, Germany) equipped with a flame ionisation
detector.
CO2 exchange was measured using dynamic flow-through non-steady-state
transparent (net ecosystem exchange – NEE; light transmission of 86 %)
and opaque (ecosystem respiration – Reco) chambers (Livingston and
Hutchinson 1995, Drösler 2005) attached to an infrared gas analyzer
(Li-820, Lincoln, NE, USA). Full-day CO2 measurement campaigns with
repeated (30–50) individual chamber measurements (closure time 3–5 min)
were conducted regularly every 4–6 weeks from May 2007 to April 2011, for a total
of 29–37 full campaigns per site. Further details on the CO2 measurement
methodology are given in Hoffmann et al. (2015).
Flux calculation and gap filling
Flux calculation for CO2 and CH4 was based on the ideal gas
equation accounting for chamber volume and area, air pressure, and average
air temperature during the measurement. CH4 fluxes were calculated with
the R package “flux 0.2-2” (Jurasinski et al., 2012), using linear
regression analysis with stepwise backward elimination of outliers based on
the normalised root mean square error (NRMSE ≥ 0.2) up to a minimum of
three data points. Fluxes with NRMSE > 0.4 were rejected. The calculated
flux rates were then averaged for the respective measurement day and linearly
interpolated to determine annual CH4 exchange.
For CO2, the R script of Hoffmann et al. (2015) was used for flux
calculation as well as the subsequent separation into and modelling of
Reco, gross primary production (GPP) and NEE. Measurements
<30 s were rejected and measurements > 1 min were shortened by a
death band of 10 % at the beginning and end, respectively (Kutzbach et
al., 2007). For each measurement, the final flux rate was selected from all
potential flux rates generated by a moving window approach using a stepwise
algorithm, numerous quality criteria and the Akaike information criterion
(AIC; for details see Hoffmann et al., 2015). For Reco, gap filling
between measurement campaigns was performed using campaign-specific
temperature-dependent Arrhenius-type models by Lloyd and Taylor (1994). GPP
fluxes were calculated by subtracting modelled Reco fluxes from
measured NEE fluxes, and then modelled using campaign-specific hyperbolic
PAR-dependent models (Wang et al., 2013; Elsgaard et al., 2012;
Michaelis–Menten, 1913). Average measured flux rates were used if no
significant fit was achieved for campaign-specific Reco or GPP
models (Hoffmann et al., 2015). Half-hourly NEE values were calculated from
modelled Reco and GPP fluxes (Hoffmann et al., 2015; Drösler,
2005) and cumulated from 1 May to 30 April of the following year (Table S1),
resulting in four consecutive annual CO2 balances. Negative values
represent a C gas flux from the atmosphere to the ecosystem; positive values
a flux from the ecosystem to the atmosphere. The uncertainty of the annual
CH4 and CO2 exchange was quantified using a comprehensive error
prediction algorithm described in detail by Hoffmann et al. (2015).
Data analysis
Daily values for CH4 efflux, GPP, Reco and NEE were cumulated
monthly for a total of 48 monthly data sets per site to reduce the effects of
temporal autocorrelation. The respective environmental controls were
cumulated (sunshine hours, precipitation and linear modelled biomass) or
averaged (for GWL, SOCdyn, Ndyn, and air and soil
temperature) for each month. Gas flux balances for longer time periods may
vary considerably depending on the duration of the respective cumulation
period. As the wavelet analysis of daily NEE data for inherent signals
revealed strong annual dynamics (Stoy et al., 2013; Fig. S2), a 365-day
cumulation period was used to calculate gas flux balances. Additional
variability in annual balances can result from arbitrarily chosen starting
dates of the cumulation period. To account for this uncertainty in the
calculation of annual balances, a 365-day moving window was shifted in
monthly time steps through the entire study period, resulting in a total of
111 data sets (37 per site) for annual NEE, GPP, Reco and CH4
efflux and the respective environmental control parameters.
Seasonal dynamics of (from top to bottom) daily precipitation,
average daily GWL including 95 % confidence intervals (dotted lines), and
daily dynamic SOCdyn and Ndyn stocks by site (for 0–1 m
depth).
Subsequently, generalised linear model (GLM) analyses (SPSS generalised linear – GENLIN – procedure)
were performed to determine the influence of environmental controls and their
interactions on the cumulated annual CH4, Reco, GPP and NEE
balances as well as the GPP : Reco ratio. Models were defined
using a gamma probability distribution and a log link function and calculated
in a stepwise backward elimination procedure, dropping non-significant
variables until no further improvement of the AIC was achieved (correction
for finite sample sizes: AICc). Parameter and interaction effects
were evaluated based on the Wald χ2 statistic, appropriate for
non-normally distributed continuous variables. Prior to analysis, CH4
data were log-transformed after adding the minimum CH4 value to each
data value in order to allow for the application of the GLM log link function.
Analogously, absolute values of GPP were used for the analysis and NEE data
were transformed to positive values by adding the minimum NEE value to each
data value.
Multiple nonlinear regression analyses were performed to derive a model for
NEE based on GWL and SOCdyn, Ndyn,
SOCdyn : Ndyn ratio and biomass, representing the main
GLM parameter groups. For model calculation, data were averaged for 12
site-specific GWL classes to account for uncertainty from GWL model data.
The class number was determined using Sturges' rule, appropriate for n<200
(Scott, 2009). All data analyses were performed using the R (R 3.0.3) and
SPSS (SPSS 19.0.1, SPSS Inc.) software.
Dynamics of daily (a) cumulated PAR (grey vertical bars)
and average air temperature at 20 cm height (black line) and (b)
modelled CO2-C fluxes (grey line: Reco; black line: GPP),
including 95 % confidence intervals (dotted lines) by site. Shaded areas
indicate the period between maize sowing and harvest (dashed vertical line);
tp indicates ploughing, and tc indicates cultivation (sowing, fertilisation).
Results
Environmental controls
During the study period (May 2007–April 2011), weather conditions were
somewhat cooler (8.7 ∘C) and wetter (634 mm) compared to the
long-term average (1982–2012; 9.2 ∘C; 530 mm). Particularly the
2010/11 measurement year considerably deviated from the long-term temperature
average, with an annual air temperature that was 1.5 ∘C below the
long-term average – 1 SD (data not shown). While PAR and air temperature
showed high daily and seasonal dynamics (Fig. 2a), no pronounced seasonal
patterns were observed for precipitation (Fig. 1). Instead, precipitation
featured an extremely high interannual variability with particularly heavy
rainfalls during the summer months of 2007 (May–July; Fig. 1). The
precipitation sum during this period (507 mm) exceeded the long-term average
(179 mm) by > 180 % (data not shown). Reflecting the precipitation
dynamics, GWL showed similar temporal dynamics at the three sites, but at
different levels. In summer, GWL remained generally low, with the exception
of July–August 2007. The HS site, which consistently featured the highest
average GWL (-0.5 m; Fig. 1, Table S2), was flooded during this period
(GWL + 0.2 m; data not shown).
The SOCdyn and Ndyn stocks calculated based on the
modelled GWL showed the highest fluctuations at the HS site (Fig. 1). During
times of high GWL, such as in summer 2007, the HS and GL site featured
drastically lowered SOCdyn and Ndyn values, amounting to
only 6.2 kg C m-2 and 0.5 kg N m-2, respectively, with
SOCdyn and Ndyn reduced to 0 during flooded periods.
In contrast, pronounced peak values at HS were calculated for the low-GWL
summer months during the rest of the study period, with monthly averages of
21–86 kg C m-2 and of 2–5 kg N m-2. The HS site always
featured the highest annual SOCdyn (52 kg C m-2) and
Ndyn (4 kg N m-2) stocks, except in 2007/08 (Fig. 1;
Table S2).
Daily and annual carbon gas exchange
All sites generally featured very low daily CH4 fluxes (-0.01 to
0.01 g CH4-C m-2 d-1) throughout the study period
(Fig. S3). However, considerable CH4 emission peaks were observed at the
HS and GL sites during times of flooding or high GWL, e.g. during summer 2007
and spring 2008. At HS, this resulted in a maximum CH4 flux of
1.2 g CH4-C m-2 d-1 on 1 August 2007, which is approx. 60
times higher than the median flux (0.02 g CH4-C m-2 d-1) at
this site. As a result of the flooding, annual CH4 emissions in 2007/08
at HS amounted to 28 ± 4 g CH4-C m-2 yr-1 and were
thus nearly 100 times higher than observed for HS in the following years
(0.3 ± 0.5 and ± 0.2 g CH4-C m-2 yr-1) and at
least 25 times higher than observed for AR and GL
(< 1.2 ± 0.6 g CH4-C m-2 yr-1; Table 2). However,
as the high annual CH4 emissions at HS in 2007/08 result from a peak
described by three measurement campaigns during the flooded period (Fig. S3),
they are also associated with a higher uncertainty
(± 3.7 in 2007/08 vs. ±0.5 and
±0.2 g CH4-C m-2 yr-1 in 2008/09 and 2009/10; Table 2).
The modelled CO2 exchange rates (for model evaluation statistics, see
Table S3) reflected the daily and seasonal dynamics of air temperature and
PAR, with generally higher fluxes in the growing season compared to fall and
winter (Fig. 2a, b). In summer, peak GPP fluxes considerably exceeded the
amplitude of Reco fluxes. At all sites, the CO2 exchange was
also influenced by management events, with particularly pronounced peaks of
Reco following tillage. In addition, GPP was immediately reduced to
0 after maize harvest due to the removal of the photosynthetically active
aboveground plant biomass. In general, the organic GL and HS sites showed the
highest CO2 exchange intensity, with maximum Reco and GPP
fluxes of 23 and -46 g CO2-C m-2 d-1, respectively,
observed at the HS site (Fig. 2a, b). However, during the wet summer of 2007,
the mineral AR site featured the highest intensity of CO2 exchange,
resulting in cumulated annual Reco and GPP fluxes that were 25–44
and 52–61 % higher, respectively, than in the following years
(2008–2011, Table 2). In contrast, at HS, the 2007 flooding resulted in
strongly reduced CO2 flux intensities and large net annual CO2-C
losses (NEE of 493 ± 83 g CO2-C m-2) compared to the
following years. Although the CO2 fluxes measured during the flooded
period are associated with higher error values compared to periods without
flooding (Table 2), the modelled results are plausible, clearly reflecting
the negative effects of flooding on plant growth and thus plant C exchange.
Hence, in 2007/08, cumulated annual Reco and GPP fluxes at AR were
76 and 49 % higher than at the HS site (Table 2).
Annual fluxes of CO2 (Reco, GPP and NEE) and
CH4 by site and year (± model error; 95 % confidence
interval) and average fluxes (±1 SD) for the entire study period
(2007/08–2010/11) excluding the flooded year 2007/08.
Site
C flux
Year
Periodic average
(gCm-2yr-1)
2007/08
2008/09
2009/10
2010/11
2007/08–2010/11
2008/09–2010/11
AR
CH4
0.17 (0.07)
0.15 (0.32)
-0.10 (0.06)
0.00 (0.04)
0.06 (0.06)
0.01 (0.07)
Reco
2880 (183)
1729 (32)
1267 (21)
1547 (40)
1856 (354)
1514 (134)
GPP
-2889 (52)
-1670 (34)
-1143 (34)
-1534 (58)
-1810 (377)
-1449 (158)
NEE
-9 (190)
59 (47)
125 (40)
13 (70)
47 (30)
66 (32)
GL
CH4
1.19 (0.61)
-0.10 (0.03)
-0.04 (0.10)
-0.17 (0.08)
0.22 (0.32)
-0.10 (0.04)
Reco
1733 (191)
2131 (30)
1288 (51)
1409 (36)
1640 (189)
1609 (263)
GPP
-1799 (43)
-2279 (43)
-1895 (97)
-1809 (40)
-1946 (113)
-1994 (144)
NEE
-65 (196)
-148 (52)
-607 (110)
-400 (54)
-305 (123)
-385 (133)
HS
CH4
27.57 (3.70)
0.26 (0.51)
0.30 (0.20)
n.a.a
n.a.a
n.a.a
Reco
1479 (55)
1853 (33)
2131 (68)
1995 (52)
1864 (141)
1993 (80)
GPP
-985 (62)
-2065 (61)
-2535 (53)
-2382 (122)
-1992 (350)
-2327 (139)
NEE
493 (83)
-212 (70)
-404 (86)
-387 (133)
-127 (212)
-334 (62)
a Data not available.
Excluding 2007/08, the average NEE during the study period at the mineral AR
site was close to 0, with 50 ± 32 g CO2-C m-2 yr-1
(Table 2), whereas the organic sites were net CO2-C sinks, with
-385 ± 133 (GL) and
-334 ± 61 g CO2-C m-2 yr-1 (HS). Including the
flood-dominated year of 2007/08 resulted in a 62 and 21 % reduction in
the overall NEE at the HS and GL sites, respectively. In contrast, when
2007/08 is included in the overall 2007–2011 average for the AR site,
cumulated Reco and GPP increase by 63 and 67 %, respectively,
while NEE remains unaffected.
Summary statistics of generalised linear model (GLM) analysis
describing the influence of site and environmental controls (GWL, climate,
soil, plants) on cumulative annual CH4 efflux, Reco, GPP,
NEE and the ratio of GPP:Reco.
CH4 [g CH4-C m-2a-1]
Reco [g CO2-C m-2a-1]
GPP [g CO2-C m-2a-1]
NEE [g CO2-C m-2a-1]
GPP:Reco
Wald χ2
p
Wald χ2
p
Wald χ2
p
Wald χ2
p
Wald χ2
p
Intercept
1.312
0.252
7.626
0.006a
14.311
≤0.001a
96.005
≤0.001a
29.743
≤0.001a
Site
72.812
≤0.001a
25.571
≤0.001a
26.040
≤0.001a
90.685
≤0.001a
65.869
≤0.001a
Climate
Air temperature
11.218
0.001a
33.135
≤0.001a
18.706
≤0.001a
30.960
≤0.001a
17.566
≤0.001a
Soil temperature
1.666
0.197
14.456
≤0.001a
5.927
0.015a
36.618
≤0.001a
18.096
≤0.001a
Precipitation
19.008
≤0.001a
9.093
0.003a
4.827
0.028a
11.562
0.001a
17.588
≤0.001a
Sunshine hours
10.201
0.001a
21.158
≤0.001a
9.646
0.002a
b
b
Year
b
6.004
≤0.001a
8.210
0.004a
7.629
0.006a
4.650
0.031a
Year × Air temp.
b
50.403
≤0.001a
37.758
≤0.001a
b
9.919
0.002a
Year × Sunshine hours
b
37.816
≤0.001a
24.348
≤0.001a
b
b
Soil temp. × Air temp.
12.791
≤0.001a
b
b
29.049
≤0.001a
12.913
≤0.001a
Soil temp. × Sunshine h.
11.667
0.001a
20.182
≤0.001a
11.059
0.001a
29.049
≤0.001a
b
Plants
Biomass
b
17.810
≤0.001a
23.071
≤0.001a
49.537
≤0.001a
7.361
0.007a
Biomass × Site
b
72.633
≤0.001a
70.273
≤0.001a
80.039
≤0.001a
33.074
≤0.001a
Biomass × Sunshine h.
b
16.733
≤0.001a
23.268
≤0.001a
b
b
GWL
GWL
3.173
0.075
273.627
≤0.001a
13.516
≤0.001a
2.667
0.102
38.940
≤0.001a
GWL × Site
27.256
≤0.001a
b
17.779
≤0.001a
b
61.005
≤0.001a
GWL × Precipitation
b
b
b
6.653
0.010a
23.737
≤0.001a
Soil
SOCdyn
5.843
0.016a
15.668
≤0.001a
8.330
0.004a
32.101
≤0.001a
18.340
≤0.001a
Ndyn
8.683
0.003a
26.541
≤0.001a
8.479
0.004a
23.224
≤0.001a
b
SOCdyn:Ndyn
0.869
0.351
b
13.120
≤0.001a
106.424
≤0.001a
4.146
0.042a
SOCdyn × Site
24.005
≤0.001a
93.546
≤0.001a
25.348
≤0.001a
b
13.538
0.001a
Ndyn × Site
b
93.868
≤0.001a
25.267
≤0.001a
8.349
0.004a
b
SOCdyn:Ndyn × Site
73.365
≤0.001a
b
26.078
≤0.001a
92.340
≤0.001a
66.370
≤0.001a
SOCdyn × GWL
17.551
≤0.001a
b
b
b
b
Ndyn × GWL
22.532
≤0.001a
b
9.169
0.002a
64.724
≤0.001a
b
a Significant factors (α=0.05). b Redundant
parameter–parameter interaction.
Impact of environmental controls on carbon gas exchange
Despite the wide range of control parameters included in the complex
analysis, site (i.e. soil) had a significant (p value ≤ 0.05) effect
on all gas fluxes (Table 3). The generally highly significant
(p value ≤ 0.001) interactions between site and controls such as
biomass, GWL and soil parameters show that the selected study sites
represented a wide range of the respective control parameters. Especially
annual CH4-C emissions were dominated by site, suggesting the presence
of additional important control factors not considered in this analysis.
However, little residual variability indicates that most of the variability
in annual Reco and GPP was explained by the factors included in the
GLM analyses, with more residual variability remaining for NEE and the
GPP : Reco ratio.
While climate played a minor role in determining annual CH4-C emissions
via the effect of precipitation on GWL, climate controls were more relevant
for CO2 exchange (Table 3). There, the importance of climate was higher
for cumulated GPP and Reco than for NEE and the
GPP : Reco ratio. The impact of climate variability on CO2
exchange was even more pronounced on the monthly scale, as indicated by
highly significant interactions between climate controls and month of year
(data not shown). Biomass was as important as climate in determining
annual GPP, whereas for Reco, biomass and its interactions were less
relevant than climate (Table 3). In contrast, the derived variables NEE and
GPP : Reco were less influenced by biomass than the individual
fluxes Reco and GPP.
Summary statistics of multiple nonlinear regression
analysis of the form NEE=poly(GWL)+liny(1;2;3 or 4) describing the
influence of GWL and one environmental parameter, either (1) SOCdyn, (2) Ndyn, (3) SOCdyn:Ndyn or (4)
biomass, on cumulative annual NEE: mean absolute error (MAE), RMSE observations standard deviation ratio (RSR), adjusted coefficient of
determination (Adj. R2), modified index of agreement (md), percent bias (PBIAS), Nash–Sutcliffs model efficiency (NSE), Akaike Information Criterion (AIC) and Bayesian information criterion (BIC).
Summary statistic
Environmental parameter
1 SOCdyn
2 Ndyn
3 SOCdyn:Ndyn
4 Biomass
MAE [gm-2y-1]
80.99
83.86
78.99
84.78
RSR
0.353
0.362
0.355
0.354
Adj. R2
0.869
0.862
0.867
0.868
md
0.847
0.842
0.850
0.840
PBIAS [%]
0.000
0.000
0.000
0.000
NSE
0.872
0.866
0.871
0.871
AIC
503.38
505.37
503.88
503.67
BIC
515.20
517.20
515.70
515.49
Note: bold values highlight the best value for each summary
statistic across the four models.
Direct groundwater influence was particularly pronounced for Reco,
GWL being by far the most important GLM parameter (Table 3). Groundwater
influence on CH4-C emissions and the GPP: Reco ratio was
expressed mainly through the interaction between GWL and site.
Groundwater-dependent soil parameters and their interactions with site and
GWL dominated annual CH4-C emissions (Table 3). Soil parameters were
also the main controls on NEE, particularly the
SOCdyn : Ndyn ratio and its interactions with site.
Dynamic soil parameters and their associated interactions thus were of higher
relevance for the derived variables NEE and GPP : Reco than for
the NEE flux components Reco and GPP. This indicates differences
between Reco and GPP with respect to their reaction to changing
GWL and soil parameters, i.e. a shift in the ratio between Reco,
and GPP throughout the range of GWL, SOCdyn and Ndyn
stocks. In contrast, static SOCstocks and Nstocks showed
no significant (p value ≥ 0.05) effect on cumulated annual or
monthly fluxes of either Reco, GPP or NEE (data not shown).
Nonlinear regression analysis of annual NEE versus GWL and either
SOCdyn, Ndyn, SOCdyn : Ndyn or
biomass across all sites resulted in highly significant two-parameter models
(Table 4; Fig. 3). While all models explained > 86 % of the overall
variability of annual NEE, model fit was best for GWL and SOCdyn,
likely because the study sites represent a wide range of SOCdyn.
For all sites, the model shows a negative NEE optimum for a GWL of 0.8–1.0 m
below the soil surface, with NEE increasing at higher or lower GWL (Fig. 3).
In contrast, the model reflects a linear effect of SOCdyn on NEE
with more negative NEE for higher SOCdyn. Depending on
SOCdyn, NEE changes to positive values at a GWL above -0.43 m (for
SOCdyn=60 kg C m-2) or -0.61 m
(SOCdyn = 30 kg C m-2). However, the relations shown cannot be assumed as valid outside the measured ranges of SOCdyn
and GWL.
Result of nonlinear regression analysis between NEE, GWL and
SOCdyn originating from 365-day moving-window analysis averaged
over 12 GWL classes per site (for model statistics, see Table 4).
Displayed grid represents the derived model surface with (i) estimated model
area covered by direct measurements (solid black) and (ii) non-empirically
approved model area computed by extrapolation (grey). Modelled NEE is
separated according to positive (solid lines) and negative (dashed lines)
values.
Discussion
Soil influence on C gas exchange
As indicated in the introduction, data about the CO2 exchange of
groundwater-influenced arable soils are generally scarce, particularly for
maize, although some data are available for organic soils. Although the
maximum CO2 fluxes observed during a 1-year study of maize cultivated on
a Haplic Gleysol in the Netherlands (Jans et al., 2010) are ∼ 25 %
lower compared to the Gleysol studied (Fig. 2), the flux dynamics and the
cumulative net CO2 exchange of the organic soil types are relatively
similar in both studies, with a mean annual NEE of -385 (Gleysol) and -334 g
CO2-C m-2 yr-1 (Histosol) in this study (Table 2) vs.
-332 g CO2-C m-2 yr-1 (Jans et al., 2010). Moreover, the
dynamics and the intensity of the CO2 exchange observed for the
groundwater-influenced soils in this study are of the same order of magnitude
as reported for maize cultivated on soils without groundwater influence
(Gilmanov et al., 2013; Kalfas et al., 2011; Zeri et al., 2011; Ceschia et
al., 2010). The observed biomass yield of maize
(257–3117 g DM m-2 yr-1: DM – dry matter) is also in line with previous studies
(500–2800 g DM m-2 yr-1; Zeri et al., 2011; Verma et al.,
2005). According to Gilmanov et al. (2013) and Ceschia et al. (2010), maize
cultivation generally resulted in a net annual CO2 sink across a wide
range of sites in America and Europe but – like in this study – with
considerable variability between sites and years (+89 to
-573 g CO2-C m-2 yr-1).
The results of this study demonstrate for the first time a considerable
influence of groundwater-influenced soils on crop CO2 exchange,
particularly on cumulative NEE (Tables 3, 4, Fig. 3), thus clearly providing a positive answer to research question (1) regarding the soil effect. Surprisingly, the C-rich
drained organic soils showed a strong net CO2 uptake (Table 2), while
the C-poor Arenosol was a small net CO2 source. This observation cannot
be entirely explained by the interaction between GWL and the potentially
mineralisable soil C stocks. Hence, an integrated consideration of all
relevant C gas fluxes and their regulation within the plant–soil system is
required, which is discussed in detail below. We are unaware of any previous
study ever reporting such an effect, likely because any systematic effects
may only be observed in longer-term studies due to the high interannual
variability of C gas fluxes. This strongly supports the high relevance of
such investigations for the accurate evaluation of the C dynamics of
groundwater-influenced arable soils.
Relevance of interactions between GWL and maize ecophysiology
Apart from soil type and SOC content, the study sites are mainly
differentiated by different average GWL, which our study results show to be a
crucial factor determining the high short- and long-term variability of maize
C gas exchange across the entire range of groundwater-influenced soils.
Previous studies have mainly shown an influence of GWL on CH4 fluxes
from peat soils, mainly reporting an exponential increase in CH4 fluxes
for rising GWL with particularly high CH4 losses for GWL ≥ –0.2 m
(Couwenberg et al., 2011; Jungkunst and Fiedler, 2007; Drösler, 2005;
Fiedler and Sommer, 2000). Annual CH4 emissions (-0.2 to
1.2 g CH4-C m-2 yr-1) for GWL between -1.6 and -0.6 m
and peak fluxes during flooding (≤ 28 g CH4-C m-2 yr-1; GWL of -0.3 m) observed at the
HS site are similar to values of Couwenberg et al. (2011) and
Drösler (2005). However, for crops cultivated on groundwater-influenced
mineral soils, little data are available on the impact of GWL on CH4
fluxes (e.g. Pennock et al., 2010).
CO2 exchange has also been intensively studied for organic soils, but
mostly for pristine peatlands and grasslands on peat soils (e.g. Leiber-Sauheitl et al., 2014; Berglund and Berglund, 2011; Couwenberg et al.,
2011), while data on maize are lacking. For peatland NEE, one study reports a
linear decrease with rising GWL over a range of -0.4 to -0.1 m, with
maximum NEE observed at -0.4 m (Leiber-Sauheitl et al., 2014). Couwenberg
et al. (2011) also observed decreasing NEE when GWL rose above -0.5 m, but
net CO2-C uptake was only reported for very high GWL above -0.1 m. In
contrast, in this study, maize NEE was largely negative across the entire
range of GWL recorded in the groundwater-influenced soils studied (-2.1 m
to +0.2 m), changing to positive values when GWL rose above -0.4 m to
-0.6 m. Moreover, the GWL–NEE relationship for maize shows a clearly
nonlinear relationship to GWL, with a distinct optimum at considerably lower
GWL (between -0.8 and -1.0 m; Fig. 3) than observed for grasslands.
Further studies are required to determine whether this is a general pattern
applicable to other groundwater-influenced soil types and crops.
Our study results further indicate that Reco and GPP also feature
specific GWL optima (data not shown). For example, maximum Reco
fluxes were observed for GWL of -0.8 to -1.0 m, similar to data from
grassland on four GWL-influenced soil types (Fiedler et al., 1998). Similar
to the Reco of maize at the organic HS and GL sites, Reco
fluxes in grasslands on organic soils typically decrease with rising GWL
(Leiber-Sauheitl et al., 2014; Berglund and Berglund, 2011; Laine et al.,
1996; Silvola et al., 1996), particularly if GWL rises above the soil surface
(Koebsch et al., 2013). The impact of GWL on GPP was relatively small in this
study (Table 3), except for the effect of the 2007 flooding, which resulted
in a drastic reduction in GPP (Table 2) as also observed by Koebsch et
al. (2013) after rewetting.
Most of the study results concerning the individual CO2 fluxes can be
explained by the interactions between GWL and maize plant activity because
the magnitude and the variability of GPP and Reco are most
pronounced during the short period from May to September, which corresponds
to the growing period of maize (Fig. 2). For example, the drastic reduction
in the CO2 fluxes during the flooding in 2007 at HS and GL (Figs. 1, 2)
was very likely caused by the previously mentioned negative effect of anoxic
soil conditions on maize metabolism. On the other hand, the lower CO2
fluxes during the summer of 2009, especially at the AR site, probably result
from an inhibition of maize gas exchange due to drought stress (Vitale et
al., 2008; Jones et al., 1986), i.e. long periods of very low GWL
(Figs. 1, 2). Apart from these extreme situations, GWLs were mostly at soil
depths which were favourable to the metabolism and the productivity of a
C4 plant such as maize (Tollenaar and Dwyer, 1999).
For example, maize features considerably higher gas exchange activity under
maximum PAR and temperature conditions than all C3 grasses and crops (Zeri et
al., 2011; Kutsch et al., 2010). As a consequence, although the main growing
period of maize (∼ 2 months) is much shorter than that of most
C3
plants (3–4 months), the CO2 flux intensity of maize throughout this
short active period is large enough to result in higher annual cumulative
Reco and GPP values compared to C3 crops (Beetz et al., 2013;
Klumpp et al., 2011; Zeri et al., 2011; Flanagan et al., 2002). It is very
likely that the GWL optima of GPP and Reco can be traced back to
this fact, e.g. as indicated by the enhanced amplitudes of the GPP as well
as the Reco fluxes at the AR site during the wet summer 2007
compared to years with lower GWL (Fig. 2). However, the interactions between
GWL and maize growth do not offer explanations for the observed differences
in cumulative NEE among sites and the functional relationship between NEE and
GWL.
Relevance of interactions between GWL and dynamic soil C and N stocks
The strong effect of GWL on the C gas exchange is likely also the reason for
the lack of any effect of total, i.e. static, soil C and N stocks on daily,
monthly or annual C gas exchange. In the few existing studies on this
subject, an impact of soil C and N stocks on C gas fluxes was only found if GWL was either constant (Mundel, 1976) or irrelevant to the soil water
regime (Lohila et al., 2003). Moreover, in agreement with the results of this
study, Leiber-Sauheitl et al. (2014) found no relationships between static
soil C and N stocks and the C gas exchange of Gleysols with highly variable
GWL during a 1-year study. In contrast, our study revealed a very strong
effect of mainly GWL-determined dynamic soil C and N stocks on C gas dynamics
(Table 3), thus indicating a higher relevance of SOC and N stocks located in
the aerobic zone above the GWL for plant and soil gas exchange than of total
soil SOCstocks and Nstocks in the soil profile.
However, the functional GWL-related mechanisms mentioned in the introduction
cannot fully explain the results of this study. Several observations
indicate that the influence of the dynamic soil C and N stocks on the C gas
exchange extends beyond the mere GWL effect:
All C gas fluxes are differently and specifically influenced by the dynamic
soil C and N stocks (Tables 3, 4).
Compared to the GWL, the effects of dynamic soil C and N stocks on NEE are
considerably stronger than on the individual Reco and GPP fluxes,
also reflected by the associated shift in the GPP : Reco ratio
(Table 3). It must be pointed out that these two parameters differ in their
informational value: while NEE is the absolute difference between the
opposing CO2 fluxes Reco and GPP, the GPP : Reco
ratio reflects the relative proportion of these fluxes, thus giving
indications for the reasons for changing NEE values. Interestingly, the
dynamic C : N ratio shows a similarly strong effect on these two
parameters. The potential relevance of these observations for explaining the
study results is also discussed in Sect. 4.2.
The effects of the GWL and the dynamic soil C or N stocks on the cumulative CO2
fluxes clearly differ with respect to their type and direction (Fig. 3, Table 4).
Despite a limited number of sites, clustering of sites with respect to GWL
range and a single crop, the results of this study are considered consistent
and plausible for the range of measured GWL and soil C stocks, as the results
from several very different statistical methods point to the same
conclusions. Still, subsequent studies which consider other sites and plants
are required to determine whether the discussed conclusions regarding the type and
intensity of the effect of dynamic soil C and N stocks on cumulative NEE,
their differentiated effects on GPP and Reco as well as their
interactions with GWL are generally valid. A reassessment of data from
previous studies using continuous GWL data (if available) for the calculation
of dynamic soil C and N stocks could be helpful to determine whether similarly
strong effects of dynamic soil C and N stocks on C gas dynamics exist for
other sites and plants. System-oriented investigations, which aim to
understand the underlying processes and mechanisms, might reveal whether and how
the observed phenomena are related and from which underlying processes they
originate.
The nature and relevance of mechanisms causing the effect of the dynamic
soil C and N stocks
Potential mechanisms
A common observation may be used as a starting point for a comprehensive
explanation: crop growth on groundwater-influenced soils is mainly influenced
by rooting depth, which in turn is mostly influenced by GWL (e.g. for maize,
see Kondo et al., 2000). In this context, stress due to O2 deprivation only
plays a minor role, i.e. via the GWL-defined lower limit of the rootable
soil volume (Glaz et al., 2008; Livesley et al., 1999). More importantly,
larger root systems enable the improved supply of plants with nutrients and water
(especially at the AR site), likely resulting in increased photosynthetic
capacity and thus higher primary productivity. The link between increasing N
content and increased GPP was previously documented in studies by Flanagan et
al. (2002) and Ashraf et al. (1999). Interestingly, several long-term field
trials with crops grown on mineral soils also show that changing SOC stocks
not only depend on crop rotation and organic fertiliser amount but also on
the nutrient supply to the crops per se. In these trials, the mere
application of mineral fertiliser results in a significant increase in soil
organic matter compared to non-fertilised treatments (Jung and Lal, 2011;
Banger et al., 2010; Thomas et al., 2010; Christopher and Lal, 2007; Sainju
et al., 2006). In addition to other crops, this also applies to maize (Kaur et al.,
2007).
In particular, the N supply plays a key role: up to a threshold, the gradual
increase in mineral N fertiliser amount generally results in higher SOC and
SON stocks (e.g. for maize, see Kaur et al., 2007; Blair et al., 2006a, b). Pot
experiments with maize indicate that N fertilisation increases the input of
newly assimilated C more than CO2 emissions from root respiration and
the mineralisation of soil organic matter (Gong et al., 2012; Conde et al.,
2005), thus resulting in the accumulation of SOC. Moreover, in field trials,
mineral N fertilisation reduced the decomposition rate of maize residues in
the soil (Grandy et al., 2013). Therefore – apart from the impact of C
export (removal during harvesting) and import (input through organic
fertilisation) on the soil C budget – it seems highly likely that the N
fertilisation of arable crops contributes to an increase in SOC stocks by
promoting C input through gross and net primary productivity more than C loss
via ecosystem respiration. Although this has not yet been experimentally
confirmed in its entirety, scientific evidence on the individual effects of N
fertilisation on the SOC stocks of arable soils without groundwater influence
makes this hypothesis plausible.
Indications of similar mechanisms on groundwater-influenced soils
Several results of this study suggest a strong N influence on C gas fluxes. All
sites received a total of 122 kg N ha-1 yr-1 throughout the
entire study period, providing sufficient N for plant growth. The dynamic
soil N stocks and the SOCdyn : Ndyn ratio had strong
effects on cumulative NEE and the GPP : Reco ratio (Table 3).
Formally, this also holds true for the dynamic SOC stocks, but – unlike for
N – this effect results from the tight correlation of soil C and N contents
rather than from direct effects of organic-matter production or
decomposition. The large influence of GWL on dynamic soil N stocks, reflected
by strong interaction, indicates that both parameters control N
mineralisation. It has been repeatedly observed both for organic and mineral
soils that the lowering of GWL, i.e. an increase in the dynamic N stocks
due to improved soil aeration, increases N mineralisation, while a rising
GWL, i.e. decreasing dynamic N stocks, results in the opposite (Eickenscheid
et al., 2014; McIntyre et al., 2009; Venterink et al., 2002; Hacin et al.,
2001; Goettlich, 1990; Reddy and Patrick, 1975).
Increased dynamic soil N stocks are equivalent to an improved N supply to
plants and microorganisms, which should be similar in effect to the N
fertilisation in the above-mentioned long-term field trials. In this study,
the tight correlation between the dynamic soil N stocks and the maize biomass
development during the vegetation period (r2 = 0.817; data not
shown) indicates that most of the N that was mineralised when GWLs were low and root
systems deep likely played a significant role in plant N supply and thus
plant development – regardless of the fertilisation-induced N impulse and
the fact that the monthly biomass values were not measured but calculated
using a simple linear approach. Similarly strong biomass and dynamic C and N
stock effects on cumulative NEE (Table 3) further support this line of
thought, as an increased biomass production stimulated by higher N
availability is always associated with increased CO2 input into the
plant–soil system via gross primary production.
In other words, the N supply in the plant–soil system and its effects on C
formation and transformation processes likely also play a key role in the C
gas exchange of groundwater-influenced soils by promoting CO2 input via
gross primary production more than CO2 emission via ecosystem
respiration. The observed effects of the dynamic soil C and N stocks on
cumulative NEE can thus be plausibly explained. However, the relatively low
optimum GWL for minimising NEE (Fig. 3) likely requires additional
explanatory mechanisms. For example, an improved plant water and nutrient
supply, e.g. with macronutrients such as P and K, could increase root and shoot
growth and thus CO2 input, as observed for soils without groundwater
influence (Ladha et al., 2011; Poirier et al., 2009; Al-Kaisi et al., 2008;
Reay et al., 2008; Kaur et al., 2007).
Future improvements of the dynamic stocks concept
Most of the functional mechanisms discussed above are somewhat speculative
and require subsequent validation by means of experiments which consider all
mentioned processes of the plant–soil system and their respective regulating
factors. Special attention should be paid to the determination of the scope
of all relevant processes, as several studies state that the input of N and
other nutrients does not always have only positive effects on net CO2
exchange and the C sink function of arable soils (Thangarajan et al., 2013;
Hofmann et al., 2009; Mulvaney et al., 2009; Al-Kaisi et al., 2008; Khan et
al., 2007).
Moreover, the concept of dynamic soil C and N stocks is only an indicator of
real dynamic stocks because in this study dynamic stocks were modelled
exclusively based on GWL dynamics. Further developments might include
precipitation-related topsoil water dynamics or soil hydraulic properties
(e.g. capillary fringes), which might considerably reduce dynamic soil C
and N stocks. The concept of dynamic stocks could also be expanded to other
plant nutrients such as plant-available P or K. However, these suggested
refinements require very detailed high-resolution data on soil and plant
properties and processes, including their vertical variability in the soil
profile, and were thus beyond the scope of this study.
Conclusions
Results clearly showed that the soils studied differ considerably with
respect to the intensity and dynamics of C gas exchange. In order to
accurately assess the climate impact of arable sites on drained peatlands,
it is therefore necessary to consider the entire range of
groundwater-influenced mineral and organic soil types and their respective
areal extent within a heterogenous soil landscape.
While climatic controls such as PAR, temperature and precipitation mainly have
short-term effects on C gas fluxes, the effects of dynamic soil C and N
stocks are clearly observable on all temporal scales. It is to be determined
by future studies in how far this also applies to (i) crops other than maize,
(ii) other land use forms such as grasslands and iii) other
groundwater-influenced sites. Dynamic soil C and N stocks may be major
controlling factors of C gas fluxes and of the CO2 source or sink function
of the entire range of wetlands, and they may potentially be of higher and more global
relevance than GWL and vegetation, which are the main factors favoured to
date (Couwenberg et al., 2011; Byrne et al., 2004). The insight that the
effect of the dynamic soil C and N stocks very likely results from the
regulation of C formation and transformation processes by N and –
potentially – the nutrient and water supply as such may be of particular
importance. This mechanism would be a favourable prerequisite for the
development of generalisable process-based models, which would be very useful
in providing more precise estimates of the impact of important factors such
as climate, site conditions and land use on the C gas fluxes in wetlands.
Overall, the results presented and subsequent analyses show the enormous
potential of combining long-term measurements of C gas fluxes with
process-oriented analyses of the functional mechanisms and their regulation
within the soil–plant system when aiming for an improved understanding of
the biogeochemistry of wetlands.