Study Sites and Implementation of the Sphagnum Cultivation Areas
We measured GHGs at two Sphagnum cultivation areas (‘Provinzialmoor’, 52°40′ N, 07°06′ E and ‘Drenth’, 52°41′ N, 07°05′ E) and a near-natural peat bog (‘Meerkolk’, 52°38′ N, 07°08′ E) from March 2017 to March 2019 (Fig. 1). All sites are situated approximately 20 km northwest of Lingen, Lower Saxony, Germany. The climate is oceanic with an average annual precipitation of 791 mm and an average annual temperature of 9.8 °C (1971–2000, Lingen, German Weather Service).
Meerkolk, a last remainder of a once vast peatland complex, is a former bog pool characterized by partially floating peat moss and cotton grass mats with a peat thickness of about 350 cm. The upper 53 cm are weakly decomposed, and the lower part is highly decomposed with a high water content (Table 1). The site can be classified as an Ombric Fibric Histosol (IUSS Working Group WRB 2015). The dominating plant species are Sphagnum papillosum, Sphagnum pulchrum, Sphagnum cuspidatum, Rhynchospora alba, Molinia caerulea, Vaccinium oxycoccos, Erica tetralix, Drosera rotundifolia and Eriophorum angustifolium. Close to the measurement plots, the upper 5 cm of Sphagnum vegetation was harvested and used for the inoculation of parts of Provinzialmoor (P-MIX). As the area is—as nearly all potential donor sites in Germany—strictly protected, mosses were selectively manually harvested. Meerkolk is hereafter referred to as near-natural reference site. GHGs were measured at a control site (M-NAT) and at a harvest site where the upper 5 cm of vegetation were removed in April 2017 (M-HAR).
Table 1 Soil Properties of Sites Both Provinzialmoor and Drenth are former sites of industrial peat extraction with remaining peat thicknesses of about 90 cm and 45 cm, respectively. Both sites are Ombric Hemic Histosols. The lower part of the profile is moderately decomposed fen peat overlying a relictic gley and the upper part moderately to highly decomposed bog peat. The major difference between the two areas is that Provinzialmoor has been re-wetted in 2008 after the termination of peat extraction as a system of large (~ 1.6 to 4.2 ha) shallow polders, while Drenth is a narrow strip of seven polders installed in 2015 directly after terminating peat extraction without any previous re-wetting. Thus, Drenth is not surrounded by water bodies and re-wetted peatlands but by ongoing peat extraction. The inoculation of Sphagnum mosses was performed following the moss layer transfer technique (Quinty and Rochefort 2003). In brief, small fragments of peat mosses were spread evenly and covered with straw mulch (details in Graf and others 2017) and developed into new moss plants. Along with the Sphagnum mosses, vascular plants were also transferred. In order to prevent negative feedback of dominant vascular plants on peat moss development and substrate quality, the cultivation sites were mowed 1–2 times a year.
In Drenth, water is pumped from two ponds, which were additionally replenished with deeper ground water in dry periods. Excess irrigation water is channelled back to the ponds. The sites were inoculated with Sphagnum papillosum in October 2015. Due to the poor growth of mosses, parts were re-inoculated with Sphagnum palustre in April 2017. GHGs were measured at two of these polders (0.4 ha each): one irrigated by ditches (D-DITCH) and the other one by drip irrigation (D-DRIP). Drip irrigation was installed in April 2017 and the site was irrigated via subsurface drain pipes until then.
In Provinzialmoor, one of the polders (2.3 ha) was prepared for Sphagnum cultivation in 2015 by lowering the water table to the peat surface and profiling the ground. Different parts were inoculated with different peat moss species, that is, with Sphagnum papillosum in October 2015 (P-PAP), with Sphagnum palustre in March 2016 (P-PAL) and with a mix of Sphagnum hummock species from Meerkolk in October 2016 (P-MIX), although Sphagnum papillosum was the dominant species (Fig. 1). Water is supplied by the surrounding polders and is distributed via shallow ditches. To avoid inundation, surplus water is discharged to a drainage ditch. Unfortunately, drainage pipes (30 m drain spacing) have been discovered and destroyed only after termination of measurements. GHGs were also measured at the southern irrigation polder (P-POLDER) which was subjected to seasonal fluctuations of the water table (− 0.21 to 0.56 m). The measurement plots are located approximately 6 m away from the shore, with a sparse cover of submerged peat moss (Sphagnum cuspidatum).
Environmental Parameters
Hydrological and Meteorological Characteristics
A meteorological station in Provinzialmoor (Fig. 1) measured soil temperature (2 cm), air temperature and relative humidity (2 m), wind speed, photosynthetic active radiation and global radiation. All hydro-meteorological data were recorded in 30-min intervals. Each GHG measurements site consisted of three replicate plots, and at all plots, soil temperatures (2 cm) were measured from June 2017 onwards. At the meteorological station, 15% of the temperature and 2% of radiation data were missing and filled with data of the German Weather Service (station Lingen, 20 km away). In Meerkolk (M-NAT), 14% of soil temperature values were missing and replaced by meteorological station data. In Drenth (D-DRIP) and Provinzialmoor (P-MIX), it was 29% and 23%, respectively. At sites M-NAT, D-DRIP and P-MIX, near-surface soil moistures were recorded using GS3 capacitance sensors (Decagon Devices Inc., Pullman, WA, USA). Volumetric water contents θ (cm3 cm−3) were calculated from dielectric permittivities using the standard calibration of the device for potting and peat soils. To exclude the impact of freezing on the dielectric permittivity, values at soil temperature below 2 °C were discarded. Measurements of θ were transformed to water-filled pore space (WFPS) by dividing θ with the maximum θ of the time series. Additionally, relative humidity was measured near the soil surface (IST AG, Ebnat-Kappel, Switzerland).
Monitoring wells (slotted PVC tubes) were installed in the peat at all plots. To avoid measurements of deeper groundwater, they were installed in the peat layer only and thus fell periodically dry. Water table depth (WTD) was measured using Mini-Divers in combination with Baro-Divers for atmospheric pressure correction (Eijkelkamp, Giesbeek, the Netherlands). In the following, WTD below ground is noted with a negative sign and vice versa.
Soil Properties
At all study sites, soil profiles were dug. If sites were very close to each other, a profile was shared to minimize disturbance of the area (Table 1). From each horizon, samples for the determination of physical (six steel rings with a volume of 244.29 cm3 each) and chemical properties (grab samples) were taken. The degree of humification was determined according to von Post, and carbon (C) and nitrogen (N) contents were measured using an elemental analyser (LECO Corporation, St. Joseph, Michigan, USA).
Bulk density (BD) and porosity (ε) were determined as part of evaporation experiments with standard mass calculations based on the sample weight at the beginning and end of the experiment. Field capacity (θ at pF 1.8−θ at pF 4.2; pressure heads h (cm) are expressed as pF = log10(h)) was determined with soil hydraulic properties using the bimodal hydraulic function of van Genuchten (1980) (Durner 1994) derived by evaporation experiments using the ‘inverse method’ described in Dettmann and others (2019).
Water Quality
Biweekly, soil water samples were taken at each site and from the irrigation pond in Drenth and polders in Provinzialmoor. EC and pH values were measured in situ (WTW, Weilheim, Germany). Samples were filtered to 0.45 µm (PES, Merck Millipore, Tullagreen, Ireland), and concentrations of nitrate (NO3−), ammonium (NH4+), phosphate (PO43−), sulphate (SO42−) and calcium (Ca2+) were determined by ion chromatography (850 Professional Ion Chromatograph, Metrohm, Filderstadt, Germany). Concentrations of dissolved organic carbon (DOC) were calculated as the difference between total carbon and total inorganic carbon (DimaTOC 2000, Dimatec, Essen, Germany).
Vegetation Characteristics
Every spring and autumn, vegetation cover (mosses and vascular plants) and species composition were classified according to the Londo scale (Londo 1976) at each measurement plot. In addition, heights of Sphagnum lawns were recorded. The harvested biomass at the donor site (M-HAR) and the mowed vascular plants at the cultivation sites were dried to determine biomass. Subsequently, C and N contents were determined by elemental analysis (LECO Corporation, St. Joseph, Michigan, USA).
Determination of Greenhouse Gas Balances
GHGs were measured using manual static chambers (Livingston and Hutchinson 1995) at 8 sites (Fig. 1, Table 1). A ‘site’ represents a management/vegetation type in one of the three study areas ‘Meerkolk’, ‘Provinzialmoor’ and ‘Drenth’ and consists of three ‘plots’ as measurement replicates. GHG measurements were taken for two years. The period from 16 March 2017 to 15 March 2018 will hereafter be referred to as 2017 and the period from 16 March 2018 to 15 March 2019 as 2018. We follow the atmospheric sign convention and emissions of GHG are specified as positive values.
All plots were accessible by boardwalks in order to minimize soil disturbances. During measurements, chambers were placed on permanently installed PVC frames and were fixed gas-tightly via a rubber seal and clamps. Fans mixed the air inside the chambers in order to avoid concentration differences. An opening in the chamber wall, which was closed with a rubber plug after placement, prohibited possible pressure differences during the placement of the chambers. Additionally, a vent tube prevented differences in pressure between inside and surrounding air. When necessary, transparent chambers were cooled with icepacks. At the irrigation polder (P-POLDER), buoyant frames with a water-filled u-shape were deployed. The frames were able to follow the WTD of the polder and held in position by thin steel poles. Before measurements, they were fixed to the poles.
Carbon Dioxide
Measurement and Flux Calculation
Fluxes of CO2 were measured monthly in diurnal campaigns using transparent and opaque chambers (78 × 78 × 50 cm, transparent polycarbonate and PVC) to determine the net ecosystem exchange (NEE) and ecosystem respiration (Reco), respectively.
Campaigns started with one set of Reco measurements before sunrise, followed by one set of NEE measurements at sunrise and then continued in alternation until the maximum light intensity was reached at noon and the maximum soil temperature was reached in the afternoon. If possible, measurements were taken on sunny days to cover the whole range of environmental parameters. A minimum of four transparent and four opaque fluxes was measured per plot and campaign day. The CO2 concentration of the chamber air was measured with an infrared gas analyser (LI-820, LI-COR, Lincoln, Nebraska, USA) during chamber closure times of 120 (NEE) and 180 (Reco) seconds. Additionally, air temperature and humidity (Rotronic GmbH, Ettlingen, Germany) were recorded and the CO2 concentrations were corrected for water vapour concentrations (Webb and others 1980).
Fluxes were calculated using the linear regression of a moving window with the highest coefficient of determination (R2). The length of the moving window was adjusted according to the solar declination between 40 s at the longest day and 50 s at the shortest day. This was necessary as chamber air temperatures rapidly increased during summer and low fluxes required longer moving windows during winter. Fluxes with R2 < 0.75 were excluded from further calculations unless the increase or decrease of the CO2 concentration was smaller than 3% of the mean concentration. If the change of photosynthetic active radiation (PAR) exceeded a threshold of 10% of the initial value and/or the change of air temperature inside the chamber was higher than 1.5 °C, no flux was calculated.
Calculation of Annual Balances
To account for the seasonal development in vegetation response to environmental conditions, we used a campaign-based approach for annual balances (for example, Leiber-Sauheitl and others 2014).
First, response functions relating Reco to soil temperature were parameterized for each campaign day using the temperature dependent Arrhenius-type model of Lloyd and Taylor (1994):
$$ R_{{{\text{eco}}}} \left( T \right) = R_{{{\text{ref}}}} \times {\text{exp}}\left[ {E_{0} \times \left( {\frac{1}{{T_{{\text{ref }}} - T_{0} }} - \frac{1}{{T - T_{0} }}} \right)} \right] $$
(1)
where T is the measured soil temperature, Tref the reference temperature of 283.15 K and T0 the temperature constant for the start of biological processes (227.13 K) and Rref (respiration at the reference temperature (mg CO2–C m−2 h−1)) and E0 (an activation-like parameter (K)) are the estimated parameters. If the difference between minimum and maximum temperatures was smaller than 1.5 °C during the campaign day, the median of all Reco fluxes was used as Rref in Eq. 1.
Secondly, using these parameters and half-hourly data of soil temperature, annual time series of Reco were interpolated for each site (Leiber-Sauheitl and others 2014). For every time point, Reco was calculated as the distance weighted mean of the two values derived by using the parameters of the previous and subsequent campaign.
Thirdly, gross primary production (GPP) was calculated as the difference between measured NEE and the nearest modelled Reco flux. For each campaign, the rectangular hyperbolic light response equation based on the Michaelis–Menten (Johnson and Goody 2011) kinetics was parameterized (Falge and others 2001):
$$ {\text{GPP}}~\left( {{\text{PAR}}} \right) = ~\frac{{{\text{GPP}}2000~ \times ~\alpha ~ \times {\text{PAR}}}}{{{\text{GPP}}2000~ + ~\alpha ~ \times {\text{PAR}} - ~\frac{{{\text{GPP}}2000}}{{2000{\rm{\mu}} {\rm{mol}}\,{\rm{m}}^{{ - 2}}\, {\rm{s}}^{{ - 1}} ~}}~ \times {\text{PAR}}}} $$
(2)
where PAR is the photon flux density of the photosynthetic active radiation (µmol m−2 s−1) and GPP2000 (the rate of carbon fixation at a PAR value of 2000 (mg CO2-C m−2 h−1)) and α (the light use efficiency (mg CO2-C m−2 h−1/µmol m−2 s−1), that is, the initial slope of the fitted curve) are the estimated parameters. The PAR data of the measurement campaigns were corrected by a factor of 0.95 as the transparent chambers slightly reduce light transmission (PS-Plastic, Eching, Germany). If GPP parameters could not be fitted, the respective campaign was combined with the nearest campaign which best resembled the campaign’s environmental conditions, that is, ranges of PAR and GPP. This was especially the case at the polder site (P-POLDER), where fluxes were low and variable and all campaigns were pooled. Annual time series of GPP were interpolated in analogy to Reco using the campaign parameters and half-hourly PAR data. The effect of cutting vegetation on the GPP interpolation of the near-natural donor site (M-HAR) was accounted for by setting the GPP parameters to zero at this day. NEE values were obtained by summing up GPP and Reco.
Finally, annual balances and uncertainties of NEE were estimated by bootstrapping. The response functions for Reco and GPP were fitted again using random resamples of the campaign fluxes with replacement (number of bootstraps = 1000). From the bootstrapped fits, standard errors were calculated.
Methane and Nitrous Oxide
Measurement and Flux Calculation
Fluxes of CH4 and N2O were determined fortnightly using opaque chambers. Over a total closure time of 80 min, five consecutive chamber air gas samples were collected using semi-automatic sampling devices directly after placing the chamber and every 20 min from then on. Concentrations of CO2, CH4 and N2O were measured in the laboratory using a gas chromatograph (Shimadzu, Kyoto, Japan) equipped with an electron capture detector (ECD) for analysing CO2 and N2O and a flame ionization detector (FID) for analysing CH4.
Fluxes were determined using robust linear or nonlinear Hutchinson–Mosier (HMR, Pedersen and others 2010) regressions (R Core Team 2019; Fuß and others 2020). Linear or nonlinear fits were selected according to the kappa.max criterion introduced by Hüppi and others (2018). In brief, the robust linear regression was set as a default. HMR was selected, if the kappa value, that is, the nonlinear shape parameter, did not exceed kappa.max (h−1), that is, the quotient of the linear flux estimate and the minimal detectable flux multiplied by the closure time. This was the case for 28% of CH4 fluxes and 2% of N2O fluxes.
Decreases in CO2 concentration of more than 10 ppm compared to the previous measurement were interpreted as a hint towards a leak of the system or other shortcomings, and the respective data points were discarded. If there were less than four data points per measurement, no flux was calculated. Fluxes indicating an uptake of CH4 higher than 0.5 mg m−2 h−1 (n = 6) were regarded as implausible and discarded (Günther and others 2015; Hütsch 2001). Finally, fluxes of both CH4 and N2O were excluded (n = 54), if the respective CO2 flux was smaller than 30% of the maximum CO2 flux of the other two replicates.
Calculation of Annual Balances
Annual balances of CH4 and N2O and uncertainties were estimated using a combination of bootstrap and jackknife procedures (Günther and others 2015). In brief, one of the three replicate flux estimates was randomly selected for each campaign day. This way, 2000 random time series were generated. Out of these data, balances were calculated via linear interpolation, each time omitting one campaign day. The reported annual estimates and uncertainties represent the means of all jackknife balances and standard errors.
Site-specific and Areal Greenhouse Gas Balances
Methane and N2O entered the greenhouse gas balance of sites given their global warming potentials of 28 and 265 t CO2-eq. ha−1 y−1 over a timeframe of 100 years (Myhre and others 2013).
$$ {GHG{\text{-}}balance} \, \left( {t \, CO_{2}{\text{-} eq}. \, ha^{ - 1} y^{ - 1} } \right) \, = \, NEE \, + \, C_{Export} + \, 28 \, CH_{4} + \, 265 \, N_{2} O $$
(3)
Sphagnum donor material was harvested at M-HAR, and the cultivation sites were mowed. The respective C exports (t CO2 ha−1 y−1) are part of the GHG balance. The C import by Sphagnum fragments and straw was not accounted for as the inoculation took place before starting the measurements, and no straw was present during the measurement period anymore.
In order to derive total areal balances of the three Sphagnum farming sites differing in their irrigation system, emissions of the irrigation system, of dams and of projected biomass harvest have to be included. To do so, we used the following assumptions:
Size of the irrigation polders: The contribution of GHG emitted from the irrigation systems (polders and ditches) was determined by sizing the respective areas using scans of an aerial drone and assuming that the emission of irrigation ditches equals the measured emission of the P-POLDER site. The exact amount of irrigation water could not be determined in Provinzialmoor. Therefore, a theoretical size of 3.8 ha of an irrigation polder needed to balance water deficits of the cultivation area was estimated based on the maximum irrigation amount determined in Drenth (500 mm in 2018, Köbbing, personal communication) and a theoretical extractable water column of 0.30 m.
Peat dams: Peat dams surrounding the cultivation sites were constructed out of the upper layer of onsite peat and will largely decompose to CO2. For D-DITCH, D-DRIP and Provinzialmoor, areas of the surrounding peat dams of 0.17 ha, 0.17 ha and 0.31 ha were determined using scans of an aerial drone. We assumed that emissions from peat dams correspond to peat extraction sites in North-Western Germany (5.2 t CO2 ha−1 y−1, Tiemeyer and others 2020).
Re-distribution of Sphagnum fragments: We assumed that all materials harvested at the donor site were spread on the cultivation sites. Therefore, the harvested biomass enters the site-specific GHG balance of M-HAR, but not the areal GHG balances.
Biomass harvest: Over the course of this study, no harvest of Sphagnum biomass was conducted. However, we determined biomass and height of mosses at the cultivation sites (Grobe and others 2021). The linear regression (R2 = 0.43) between biomass and height of these data was used to derive biomasses for each measurement site from the height of mosses in our plots. It was further assumed that 70% of this biomass could be harvested and this estimated extractable biomass was divided by the number of years since the establishment of respective sites and included in the areal GHG balances (t CO2-eq. ha−1 y−1, Table 4). Areal GHG balances of the different irrigation systems were finally standardized per unit of estimated extractable Sphagnum biomass (t DM ha−1 y−1, Table 4).