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Annual net CO2 fluxes from drained organic soils used for agriculture
in the hemiboreal region of Europe
Arta Bārdule1, Raija Laiho2, Jyrki Jauhiainen2, Kaido Soosaar3, Andis Lazdiņš1, Kęstutis Armolaitis4,
Aldis Butlers1, Dovilė Čiuldienė4, Andreas Haberl5, Ain Kull3, Milda Muraškienė4, Ivika Ostonen3,
Gristin Rohula-Okunev3, Muhammad Kamil-Sardar3, Thomas Schindler3, Hanna Vahter3, Egidijus
5
Vigricas4, Ieva Līcīte1
1Latvian State Forest Research Institute "Silava", Salaspils, LV-2169, Latvia
2Natural Resources Institute Finland (Luke), Helsinki, P.O. Box 2, 00791, Finland
3Department of Geography, University of Tartu, Tartu, 51014, Estonia
4Department of Silviculture and Ecology, Lithuanian Research Centre for Agriculture and Forestry, Kėdainiai distr., 58344,
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Lithuania
5Michael Succow Foundation, partner in the Greifswald Mire Centre, Greifswald, 17489, Germany
Correspondence to: Arta Bārdule (arta.bardule@silava.lv)
Abstract. Carbon dioxide (CO2) emissions from drained organic soils used for agriculture contribute significantly to the overall
anthropogenic greenhouse gas budget in land use, land-use change and forestry (LULUCF) sector. To justify the
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implementation of climate change mitigation measures on these lands, it is important to estimate at least the regional variation
in annual net CO2 fluxes. This study presents the first estimates of annual net CO2 fluxes from drained nutrient-rich organic
soils in cropland (8 sites) and grassland (12 sites) in the hemiboreal region of Europe, represented by Estonia, Latvia and
Lithuania. The study sites represented both deep, and shallow highly decomposed, organic soils, categorized based on the
concentration of organic carbon in the top 20-cm soil layer. In each site, CO2 flux measurements were conducted at least over
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two years. To estimate annual net CO2 fluxes, ecosystem respiration (Reco) and soil heterotrophic respiration (Rhet) were
measured using a manual chamber technique, and carbon (C) input to soil through plant residues was estimated. Reco was
strongly dependent on temperature, particularly soil temperature at 10 cm depth, but rather independent of soil water-table
level and soil moisture. The overall mean annual net CO2 fluxes, calculated as the difference between annual CO2 output (Rhet)
and annual C input (plant residues), was 4.8 ± 0.8 t CO2-C ha–1 yr–1 in cropland and 3.8 ± 0.7 t CO2-C ha–1 yr–1 in grassland,
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while the means for “true” or deep organic soil were 4.1 ± 0.7 t CO2-C ha–1 yr–1 in cropland and 3.2 ± 0.6 t CO2-C ha–1 yr–1 in
grassland. Both the annual Reco and net CO2 fluxes for shallow highly decomposed organic soils, currently not recognized as
organic soil by the Intergovernmental Panel on Climate Change (IPCC), were of similar magnitude or even higher than those
from deep organic soil, suggesting a need to separate them from mineral soils in emission estimation.
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1 Introduction
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Organic soils drained for agriculture contribute significantly to anthropogenic greenhouse gas (GHG) emissions and are carbon
dioxide (CO2) emission hotspots in the agricultural and land use, land-use change and forestry (LULUCF) sectors in many
countries (Tubiello et al., 2015; Tiemeyer et al., 2016; Tubiello et al., 2016; Säurich et al., 2019a; European Environment
Agency, 2023a). When evaluating the overall impact of drained organic soils used for agricultural production on the
greenhouse effect, CO2 is considered the most important GHG (Houghton et al., 2001; Maljanen et al., 2007). Maljanen et al.
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(2007) reported that CO2 emissions accounted for around 80 % of the total emissions of CO2, methane (CH4), and nitrous oxide
(N2O) in drained organic croplands soils in the boreal region. The soil CO2 emissions result from two main processes:
autotrophic respiration, which is the respiration of living plant roots, and heterotrophic respiration (Rhet), which involves soil
biota such as microorganisms responsible for decomposing litter and soil organic matter (SOM) (Kuzyakov, 2006; Berglund
et al., 2011; Bader et al., 2017; Tang et al., 2020a; Tang et al., 2020b). SOM-derived CO2 emissions, along with estimates of
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C input to soil by vegetation, are key components in the assessment of soil as a source or sink of atmospheric CO2 (Kuzyakov,
2006; Tiemeyer et al., 2016).
According to the European Union (EU) GHG inventory for the year 2021, 4.1 Mha or 1 % of the total land area in the EU
comprised managed organic soils under cropland and grassland, corresponding to emissions of 76 Mt of CO2 (European
Environment Agency, 2023a). Thus, these soils are responsible for the largest share (~70 %) of GHG emissions from managed
45
organic soils in the EU (European Environment Agency, 2024). The largest area of drained organic soils used for agriculture
is in Eastern and Northern Europe. As of 2019, this region comprised 45 % of the worldwide agricultural land (FAO, 2020).
In order to achieve the international climate change mitigation goals, like the Paris Agreement (UNFCCC, 2015) and the
European Green Deal (Fetting, 2020), an increase in the sequestration of atmospheric CO2 and a reduction in GHG emissions
from organic soils, especially from soils drained for agricultural use, is urgently required. For effective mitigation actions, it
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needs to be known where and why the emissions are highest, and how they respond to changes in factors regulating them.
It is well documented that improved soil aeration caused by lowering the soil water-table level (WTL) through ditch drainage,
and mechanical disturbance (e.g., repeated ploughing) as well as liming and fertilization, improve conditions for SOM
mineralization and the associated CO2 production (Nykänen et al., 1995; Lohila et al., 2004; Maljanen et al., 2007). However,
CO2 emissions from drained organic soils vary considerably. They depend on complex interactions of many physical and
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chemical factors, including local climate and physical soil conditions (mainly soil temperature, moisture, and WTL), soil
properties (e.g., peat type, composition, degree of decomposition), as well as the type and intensity of management, including
the type of vegetation (Oleszczuk et al., 2008; Norberg et al., 2016; Tiemeyer et al., 2016; Minasny et al., 2017, Bader et al.,
2018; Fairbairn et al., 2023).
Relative to the number of the factors in effect and their potential interactions, as well as variation in management practices and
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intensity, there is still comprehensive information on the annual net CO2 fluxes from drained organic soils used for agriculture
from a rather limited number of sites. For instance, the IPCC (Hiraishi et al., 2014) default CO2 emission factors for drained
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nutrient-rich organic soils in the temperate and boreal regions are based on data from 39 sites for croplands and 60 sites for
grasslands. The categories temperate and boreal are broad and comprise a lot of variation in climatic, hydrological and
geomorphological conditions that are likely to shape the emissions, but currently there is too little data to adjust the emission
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factors correspondingly. Further, many of the GHG emission studies have focused on deep peat soils known as Histosols,
which have high soil organic carbon (SOC) content. Only few studies have highlighted the important contribution of organic
soils with comparatively low SOC concentration (<15.0 %, Tiemeyer et al., 2016), and even soils not falling under the
definition of organic soils provided by the IPCC (Eggleston et al., 2006), to total GHG emissions (Leiber-Sauheitl et al., 2014;
Eickenscheidt et al., 2015; Liang et al., 2024). These soils include formerly drained peatlands undergoing transformation into
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organo-mineral soils due to prolonged agricultural activities. Thus, the total GHG emissions from soils used in agriculture may
be underestimated if such soils are treated as mineral soils in the estimation, but their emissions are actually higher.
In the Baltic states, which, according to the vegetation classification (Ahti et al., 1968), fall within the hemiboreal region of
Europe, the share of croplands and grasslands with organic soils comprises 3–6 % and 5–19 % of the total land area and
correspond to emissions of up to 156 % and 75 % of total net GHG emissions in cropland and grassland, respectively (Estonia’s
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National GHG inventory, 2023; Latvia’s National GHG inventory, 2023; Lithuania’s National GHG inventory, 2023). To
provide knowledge-based recommendations for land-use and climate policymakers regarding the management of organic soils,
the magnitude of ecosystem CO2 fluxes and the factors affecting them need to be quantified under climatic and management
conditions that are relevant nationally or at least regionally (Wüst-Galley et al., 2020). In the hemiboreal region of Europe that
falls between the boreal and temperate regions, region-specific CO2 emission factors for cropland and grassland with drained
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organic soils have not been elaborated so far, due to insufficient data availability.
The primary aims of this study were to produce the first estimates on annual net CO2 fluxes from drained organic soils in
cropland and grassland in the Baltic states, and to elaborate corresponding CO2 emission factors for this hemiboreal region of
Europe. In addition, we evaluated the impacts of organic carbon (OC) concentration in topsoil and other potentially controlling
environmental variables on the magnitude of the CO2 fluxes. The study was conducted at 20 study sites covering managed
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grasslands and croplands with both deep, and shallow highly decomposed, organic soils, grouped depending on the OC
concentration in the topsoil layer.
2 Material and methods
2.1 Study sites
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The study was conducted in Estonia, Latvia and Lithuania, which are part of the hemiboreal vegetation region of Europe. In
total, 20 study sites were included in the study (Fig. 1, Table 1, Table S1): 8 sites in cropland (arable land) and 12 sites in
grassland with low management intensity (grazing or fodder production that involves up to two grass cuttings per year). The
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sites, established on formerly drained peatlands, included both i) deep organic soils with an OC concentration above 12 % in
the 0–20 cm soil layer, and ii) shallow highly decomposed organic soils with an OC concentration below 12 % in the 0–20 cm
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soil layer. The latter type of soil, in the current classification, does not meet the IPCC criterion for organic soils (Eggleston et
al., 2006). The thickness of the soil organic layer ranged from 16 to 72 cm (mean 43 ± S.E. 7 cm) in cropland and from 17 to
95 cm (46 ± 7 cm) in grassland (Table 1). All cropland sites were deep drained (mean WTL > 30 cm) according to the IPCC
(Hiraishi et al., 2014), while the grassland sites included both deep drained (n = 10) and shallow drained (mean WTL < 30 cm,
n = 2) sites (Table 1, Fig. S1). Description of the vegetation species composition in the grassland sites is summarized in Table
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S2. All study sites represented a steady-state level of land use, i.e., the land had been used for agricultural production for at
least the past 20 years. The long-term average (1991–2020) annual air temperature was 6.3 °C in Estonia, 6.9 °C in Latvia, 7.4
°C in Lithuania, while the average annual precipitation was 665 mm in Estonia, 681 mm in Latvia and 679 mm in Lithuania
(Climate Change Knowledge Portal, 2023).
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Figure 1. Location of the study sites in the Baltic States (Estonia, Latvia and Lithuania) belonging to the hemiboreal vegetation
region of Europe (maps prepared using QGIS 3.34.4).
Table 1. Description of the study sites with drained nutrient-rich organic soil in agricultural land in the Baltic States.
Land use
type
Country
Study site
(name,
identification
code)*
Soil group
(WRB,
2014)
Management during the
study period (type of
cultivated arable crop/
perennial grass, tillage, N
input with fertilization)
Mean thickness
of organic soil
layer (range),
cm
Mean soil water-
table level ± S.E.
(range), cm below
the surface
Cropland
Latvia
Diervanīne I,
CL_LV_1D
Histosols
Winter wheat; annual tillage;
N input 120 kg N ha–1 yr–1
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87.3 ± 3.9
(12–155)
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Land use
type
Country
Study site
(name,
identification
code)*
Soil group
(WRB,
2014)
Management during the
study period (type of
cultivated arable crop/
perennial grass, tillage, N
input with fertilization)
Mean thickness
of organic soil
layer (range),
cm
Mean soil water-
table level ± S.E.
(range), cm below
the surface
Diervanīne II,
CL_LV_2D
Histosols
Maize; annual tillage; N input
120 kg N ha–1 yr–1
57
96.2 ± 2.8
(53–160)
Gaveņpurvs,
CL_LV_3D
Histosols
Winter wheat; annual tillage;
N input 120 kg N ha–1 yr–1
45
41.7 ± 3.3
(-3–93)
Mārupe,
CL_LV_4D
Histosols
Maize; annual tillage; N input
120 kg N ha-1 yr-1
72
86.3 ± 2.3
(33–140)
Lazdiņi I,
CL_LV_5S
Gleysols
Winter wheat, winter rape;
annual tillage; N input 189 kg
N ha–1 yr–1
18
(15–20)
59.1 ± 1.3
(30–100)
Lazdiņi II,
CL_LV_6S
Gleysols
Beans; annual tillage; no
information on N input
16
(10–21)
54.7 ± 3.4
(1–91)
Estonia
Saverna I,
CL_EE_1D
Histosols
Maize; annual tillage; no
information on N input
33
(30–40)
46.7 ± 0.9
(29–78)
Lithuania
Dobilija,
CL_LT_1D
Histosols
Winter wheat, spring wheat,
winter rape; no-tillage > 5
years; N input 188 kg N ha–1
yr–1
45
(45–45)
> 150
(110–>150)
Grassland
Latvia
Kašķu,
GL_LV_1D
Histosols
Perennial grass (managed);
no-tillage; no N input
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91.1 ± 3.3
(1–150)
Krista,
GL_LV_2D
Histosols
Perennial grass (managed);
no-tillage; no N input
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25.5 ± 2.9
(-2–98)
Stabulnieku,
GL_LV_3D
Histosols
Perennial grass (managed);
no-tillage; no N input
50
42.2 ± 3.1
(-4–110)
Rucava,
GL_LV_4D
Gleysols
Perennial grass (managed);
no-tillage; no N input
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(30–32)
30.3 ± 2.7
(-3–91)
Lazdiņi III,
GL_LV_5S
Gleysols
Perennial grass (managed);
no-tillage; no N input
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(20–35)
47.7 ± 2.1
(1–85)
Andrupēni,
GL_LV_6S
Phaeozems
Perennial grass (managed);
no-tillage; no N input
22
(15–30)
94.2 ± 1.5
(47–127)
Lazdiņi IV,
GL_LV_7D
Phaeozems
Perennial grass (managed);
no-tillage; no N input
43
(20–70)
46.3 ± 2.1
(0–125)
Ķegums,
GL_LV_8S
Umbrisols
Perennial grass (managed);
no-tillage; no N input
17
(10–25)
83.0 ± 2.3
(0–146)
Estonia
Maramaa I,
GL_EE_1D
Histosols
Perennial grass (managed);
no-tillage; no N input
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(30–40)
22.6 ± 0.9
(-3–51)
Saverna II,
GL_EE_2D
Histosols
Perennial grass (managed);
no-tillage; no N input
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(40–50)
58.4 ± 1.0
(32–84)
Maramaa II,
GL_EE_3D
Histosols
Perennial grass (managed);
no-tillage; no N input
92
(75–100)
30.6 ± 1.4
(-1–96)
Lithuania
Dubrava,
GL_LT_1D
Histosols
Perennial grass (managed);
no-tillage; no N input
95
(78–120)
43.3 ± 3.7
(-3–150)
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* Sites characterized as ‘deep organic soils' are marked with upper index D, while sites characterized as ‘shallow highly
decomposed organic soils ' are marked with upper index S.
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2.2 Measurements of ecosystem respiration
To estimate ecosystem respiration (Reco), which includes both soil heterotrophic (Rhet) respiration from organic matter
decomposition and autotrophic respiration of above- and belowground plant biomass, gas sampling was conducted once or
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twice a month (Table S1). The measurement periods varied between sites as shown in Table S1, falling between December
2016 and June 2023. One to five plots per site (Table S1) were prepared for gas sampling by installing permanent circular
collars (area 0.1995 m2) in the soil, extending down to five-cm depth, at least one month before the first gas sampling to avoid
the disturbance to the vegetation affecting the results. Gas sampling was conducted using manually-operated closed static
opaque chambers (volume 0.0655 m3). The chambers were positioned air-tightly on the collars and during the next 30- (Latvia,
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Lithuania) or 60-minute (Estonia) period, four consecutive gas samples (100 cm3) were taken in 10- (Latvia, Lithuania) or 20-
minute (Estonia) intervals, respectively, using underpressurized (0.3 mbar) glass vials. All measurements were made during
daytime, randomizing the time of measurement events among sites and plots.
The CO2 concentration in the Reco gas samples was determined using a gas chromatograph (GC) method. The gas samples
were analyzed using Shimadzu GC-2014 equipped with ECD detector (Shimadzu Corporation, Kyoto, Japan) at the Laboratory
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of the Geography Department, University of Tartu (Estonia) and Shimadzu Nexis GC-230 equipped with ECD detectors
(Shimadzu USA manufacturing, Inc., Canby, OR, USA) and LabSolutions software 5.93 at the Latvian State Forest Research
Institute Silava (LVS EN ISO 17025:2018-accredited laboratory, Latvia). The uncertainty of the method was estimated to be
20 ppm of CO2.
2.3 Flux calculations and data quality check
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Quality control of the data (GC results) involved assessment of the fit of the CO2 concentrations in the gas samples to a linear
regression representing the gas concentration change in time in the closed chamber. Data were excluded from further data
processing if the coefficient of determination (R2) of the regression was lower than 0.9 except when the difference between
the maximum and minimum CO2 concentration in the four consecutive gas samples of a measurement event was lower than
the uncertainty of the CG method.
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Instantaneous Reco was calculated based on the equation of Ideal gas law using the slope of the linear regression describing the
change in the CO2 concentration over time following Eq. (1):
, (1)
where Reco is instantaneous ecosystem respiration (mg CO2-C m–2 h–1), M is the molar mass of CO2-C (12.01 g mol–1), P is air
pressure in the chamber during sampling (assumption) (101 300 Pa), V is chamber volume (0.0655 m3); Slope is the slope of
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the constructed linear regression describing the change in CO2 concentration over time (ppm h–1), R is the universal gas
constant (8.314 m3 Pa K–1 mol–1), T is air temperature (K), and A is collar area (0.1995 m2).
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2.4 Measurements of soil heterotrophic respiration
On 13 study sites (four croplands and nine grasslands), soil heterotrophic respiration (Rhet) was measured to allow comparison
between the use of direct Rhet measurements versus Rhet estimates derived from Reco measurements in estimation of annual net
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CO2 fluxes. Three measurement points per plot (i.e., nine measurement points per study site, Table S1) with an area of 0.36
m2 were established in the previous growing season prior the measurements of soil heterotrophic respiration (Rhet) were started.
Vegetation was removed, soil trenching to a depth of at least 40 cm was done to exclude existing roots, and a geotextile was
installed to prevent new root ingrowth into the measurement points. Soil Rhet was measured once or twice a month during the
vegetation periods (April–November) (Table S1) using a CO2 gas analyzer (EGM-5, P.P. Systems, Amesbury, MA, USA) and
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opaque fan-equipped chambers with a volume of 0.017 or 0.023 m3. Rhet measurements were conducted by positioning a
chamber (area 0.07 m2) open lower edge air-tightly on bare soil without a collar.
The duration of each Rhet measurement was 180 seconds, during which the CO2 concentration in the closed chamber was
recorded every second. Measurement results (CO2 concentration, ppm) were used to construct linear regressions reflecting
changes in CO2 concentration over time. To avoid possible disturbance from chamber positioning, concentration values
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obtained during the first 15 and the last 30 seconds of the measurement period (180 seconds in total) were excluded from the
regression. Similarly to Reco, instantaneous Rhet was calculated based on the equation of Ideal gas law using the slope of the
constructed linear regression (Eq. (1)).
2.5 Estimation of C stock in above- and belowground parts of vegetation
To estimate the vegetation C stocks, above- and belowground plant biomass was sampled in each plot with at least three
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replicates, once to thrice per study period. The biomass sampling dates for each study site are summarized in Table S1. The
sampling areas (1 m distance between replicates) were representative for each the Reco measurement plot avoiding atypical
microrelief and disturbance of vegetation in CO2 flux measurement points (permanent circular collars). The sampling area of
aboveground biomass was 625 cm2 in Latvia, 1600 cm2 in Lithuania, and 10000 cm2 in Estonia, and the sampling area of
belowground biomass was 625 cm2 in Latvia, 1600 cm2 in Lithuania, and 15 soil cores (diameter 48 mm) were randomly
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sampled per each site in Estonia. The belowground part was sampled by excavating roots down to 20–30 cm depth. Vegetation
samples were transported to the laboratory, and their dry mass was determined after drying at 65–70°C temperature for 48 h
or till a constant mass was reached. Before drying, the samples of belowground biomass were cleaned of soil particles by
washing with cold tap water and using wet sieving. Total C and nitrogen (N) concentrations in all biomass samples were
determined with the elementary analysis method (elemental analyzer Elementar El Cube) according to the LVS ISO
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10694:2006 and LVS ISO 13878:1998, respectively.
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2.6 Soil sampling and analyses
At each plot, soil was sampled in one to three replicates using a soil sample probe from the following depths: 0–10 cm, 10–20
cm, 20–30 cm, 30–40 cm, 40–50 cm, 50–75 cm, and 75–100 cm. The soil samples were first pretreated for physico-chemical
analyses including drying at temperature not exceeding 40 °C and sieving (aperture size of 2 mm) according to the LVS ISO
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11464:2005. The following soil variables were then determined: soil pH according to LVS EN ISO 10390:2021 (suspension
of soil in 1 mol L–1 potassium chloride (KCl) solution, pH KCl; pH-meter Adrona AM 1605); total C (TC) and total N (TN)
concentrations by dry combustion according to the LVS ISO 10694:2006 and LVS ISO 13878:1998 (elemental analyzer
Elementar El Cube); carbonate concentration using a digital soil calcimeter UGT/BD Inventions FOG II Calcimeter Field Kit;
ash content according to the LVS EN ISO 18122:2022; and concentrations of HNO3-extractable potassium (K), calcium (Ca),
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magnesium (Mg) and phosphorus (P) according to the LVS EN ISO 11885:2009 with the inductively coupled plasma-optical
emission spectrometry (ICP-OES) method (Thermo Fisher Scientific iCAP 7200 Duo). Organic C (OC) concentration was
calculated as the difference between TC and inorganic C (carbonate) concentration or by multiplying the SOM content derived
using results of ash content by a factor of 0.5, thus assuming that SOM is 50 % carbon (Pribyl, 2010). In addition, the soil
OC/TN ratio (C/N ratio) was calculated.
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2.7 Other environmental variables
Concurrently with the soil respiration measurements, the following environmental variables were measured in each plot: air
temperature; soil temperature at depths of 5, 10, 20, 30 and 40 cm; soil moisture (volumetric water content) at 5 cm depth; soil
water-table level (WTL) using groundwater wells installed vertically down to a depth of 1.5–1.6 m.
2.8 Estimation of annual soil net CO2 fluxes and CO2 emission factors
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Annual net CO2 fluxes from soil were calculated as the difference between annual CO2 output (annual soil Rhet) and annual C
input into the soil with above- and belowground parts of vegetation (plant residues). We initially intended to utilize the directly
measured Rhet values for these calculations; however, preliminary analyses showed that the directly measured Rhet values,
which unlike Reco do not include autotrophic respiration, were higher than Reco in several study sites (Fig. S2, Fig. S3). Under
similar conditions, Rhet should not be higher than Reco. Use of the directly measured Rhet values would thus have resulted in
195
overestimation of the CO2 output. Consequently, mean annual soil Rhet was calculated assuming that i) our Reco is equal to soil
surface respiration (Rs), and that ii) the proportion of annual soil Rhet from Rs is 64 %, based on results of previous studies
(n=61, Fig. S4) conducted in temperate and boreal regions (Jian et al., 2021). These assumptions were consistent with the most
conservative approach and should clearly avoid underestimation of Rhet since our Reco values additionally included the dark
respiration of the aboveground plant biomass, not included in the Rs. Annual Reco was calculated for each study site separately
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as a cumulative value consisting of mean hourly values of Reco multiplied by the number of hours in a day and days in the
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respective month covering all months in calendar year and expressed as t CO2-C ha–1 yr–1. The annual CO2 output from soil
(annual soil Rhet) was then estimated as the 64 % value of the annual Reco.
For cropland, the annual C input into the soil by the vegetation was divided into three components: aboveground harvest
residues, belowground harvest residues, and belowground biomass litter. C input from aboveground harvest residues was
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calculated as the difference between C stock in the measured total aboveground biomass and C stock in harvested products
(Eq. 2). C stock in harvested products was calculated using a harvest index (HI, Table 2), which is the ratio of harvested
product to total aboveground biomass (Palosuo et al., 2015), resulting in Eq. (2):
, (2)
where Annual C inputAGBHR is annual C input from aboveground harvest residue (t C ha–1 yr–1), C stockAGB is C stock in total
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aboveground biomass (t C ha–1), HI is harvest index (Table 2).
For cropland, C input from belowground harvest residues was assumed to be equal to the C stock in the measured belowground
biomass. The assumptions of annual C input into soil with belowground biomass litter are summarized in Table 2. For study
sites where data on above- and/or below-ground biomass was not available, theoretical values summarized in Table 2 were
used.
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Table 2. The estimated annual C inputs into soil with above- and belowground parts of vegetation (arable crops, perennial grass) in
cases for which no data was collected in this study.
Arable crop,
perennial grass
Harvest index (HI)
Annual C inputs into soil, t C ha–1 yr–1
Aboveground harvest
residues
Belowground biomass
Belowground biomass
litter
Total
Winter wheat
0.39 a
3.00 a
0.50 a
0.21 a
3.71 a
Spring wheat
0.44 a
2.21 a
0.43 a
0.18 a
2.81 a
Maize
0.84 b
0.95 a,b
0.72 a
0.30 a
1.97 a,b
Beans
0.28 a
3.11 a
0.23 a
0.09 a
3.43 a
Rape
0.35 b
1.95 b
0.58 b
0.40 b
2.92 b
Fallow
0.00 a
1.50 a
0.25 a
0.10 a
1.86 a
Perennial grass
0.84 b
0.81 a,b
1.14 a
0.77 a
2.71 a,b
a Source: Latvian State Forest Research Institute “Silava”, 2024
b Source: Palosuo et al., 2015
For grassland, it was either assumed that the C input into soil with aboveground parts of vegetation equalled the C stock in
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aboveground biomass in the end of vegetation season, or the C input was calculated using harvest index (Table 2), depending
on study site and management practices. The C input into soil with belowground parts of vegetation was calculated assuming
that the root turnover rate is 0.41 according to Palosuo et al. (2015). For study sites where data on above- and/or belowground
biomass was not available, values summarized in Table 2 were used.
Mean annual net CO2 fluxes from soil, corresponding to emission factors as outlined by IPCC, were calculated from the site-
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level annual net fluxes.
https://doi.org/10.5194/egusphere-2024-2523
Preprint. Discussion started: 5 September 2024
c
Author(s) 2024. CC BY 4.0 License.
10
2.9 Statistical analysis
Statistical analyses and visualization were conducted using the software environment R (version 4.3.3) and RStudio 2023.12.1
(R Core Team, 2024). The datasets of CO2 fluxes (both Reco and Rhet) were not normally distributed according to the results of
the Shapiro-Wilk normality test, both when all study sites were pooled and when each study site was tested separately (p <
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0.001). To evaluate the differences between independent variables, for instance, differences in soil physico-chemical variables,
Reco and Rhet between different types of land use (cropland, grassland), soil types (deep organic soil, shallow highly
decomposed organic soil) or drainage (deep drained, shallow drained), Wilcoxon rank sum exact test and pairwise comparisons
using Wilcoxon rank sum test with continuity correction was used; plot mean values were used for analysis.
Spearman’s correlation coefficient (ρ) was used to assess the degree of dependence between pairs of variables. To explain the
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variation in mean instantaneous Reco calculated as average of monthly means (Y) among study sites (plot-level mean values
were used), partial least squares (PLS) regression (multivariate method suitable for dealing with variables that are linearly
correlated to each other, such as soil physico-chemical variables) was used. PLS regression analysis includes evaluation of X
variables depending on their importance in explaining Y expressed as variables important for the projection (VIP values). X
variables with VIP values below threshold of 0.5 were considered as insignificant and were not used in the PLS regression,
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while X variables with VIP values exceeding 1.0 were considered as important. All statistical analyses were carried out with
a significance level of 95 % (α = 0.05). Results are expressed as mean values ± standard error (S.E.) unless stated otherwise.
3 Results
3.1 Soil physico-chemical variables
The soils of the study sites were characterized by high variation in both thickness of the soil organic layer (Table 1) and soil
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OC concentration, as well as other physico-chemical variables (Fig. 2, Fig. S5, Fig. S6). In the topsoil (0–20 cm layer), OC
concentration ranged from <120 g kg–1 in sites where the mean thickness of the soil organic layer was <30 cm and soil organic
matter was highly mineralized and mixed with the underlying mineral soil as a result of soil ploughing, i.e., the shallow highly
decomposed organic soils, to 526.8 g kg–1 in sites with deep organic soil. In the topsoil of deep organic soils, the mean OC
concentration in cropland was 365.0 ± 59.2 g kg–1, and in grassland 276.0 ± 36.8 g kg–1. In the topsoil of shallow highly
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decomposed organic soils, the mean OC concentration was significantly lower in both cropland (up to 73.7 ± 11.8 g kg–1) and
grassland (up to 81.9 ± 22.7 g kg–1). Similarly, significantly higher TN concentrations were found in the topsoil of deep organic
soils compared to shallow highly decomposed organic soils (25.7 ± 3.9 vs. 3.6 ± 2.3 g kg–1 in cropland and 20.0 ± 2.4 vs. 5.7
± 0.8 g kg–1 in grassland, respectively). The mean P con