Agriculture, Ecosystems and Environment 254 (2018) 202–212

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Agriculture, Ecosystems and Environment

journal homepage: www.elsevier.com/locate/agee

Soil carbon dioxide emissions due to oxidative peat decomposition in an oil T

palm plantation on tropical peat
⁎
Kiwamu Ishikuraa, , Takashi Hiranoa, Yosuke Okimotoa, Ryuichi Hiratab, Frankie Kiewa,c,
Lulie Mellingc, Edward Baran Aeriesc, Kim San Loc, Kevin Kemudang Musinc,
Joseph Wenceslaus Wailic, Guan Xhuan Wonga,c, Yoshiyuki Ishiid
a
Research Faculty of Agriculture, Hokkaido University, Kita-ku, Kita 9, Nishi 9, Sapporo, Hokkaido, 060-8589, Japan
b
Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, 305-8506, Japan
c
Sarawak Tropical Peat Research Institute, Lot 6035, Kuching, Kota Samarahan Expressway, 94300, Kota Samarahan, Sarawak, Malaysia
d
Institute of Low Temperature Science, Hokkaido University, Kita-ku, Kita 19, Nishi 8, Sapporo, Hokkaido, 060-0819, Japan

A R T I C L E I N F O A B S T R A C T

Keywords: Soil carbon dioxide (CO2) efflux was measured continuously for two years using an automated chamber system
Automated chamber system in an oil palm plantation on tropical peat. This study investigated the factors controlling the CO2 efflux and
Carbon dioxide efflux quantified the annual cumulative CO2 emissions through soil respiration and heterotrophic respiration, which is
Groundwater level equivalent to oxidative peat decomposition. Soil respiration was measured in close-to-tree (< 2.5 m, CT) and far-
Heterotrophic respiration
from-tree (> 3 m, FT) plots, and heterotrophic respiration was measured in root-cut (RC) plots by a trenching
Soil respiration
method. The daily mean CO2 efflux values (mean ± 1 standard deviation) were 2.80 ± 2.18, 1.59 ± 1.18,
Subsidence
Trenching and 1.94 ± 1.58 μmol m−2 s−1 in the CT, FT, and RC plots, respectively. Daily mean CO2 efflux increased
exponentially as the groundwater level or water-filled pore space decreased, indicating that oxidative peat
decomposition and gas diffusion in the soil increased due to enhanced aeration resulting from lower ground-
water levels. Mean annual gap-filled CO2 emissions were 1.03 ± 0.53, 0.59 ± 0.26, and
0.69 ± 0.21 kg C m−2 yr−1 in the CT, FT, and RC plots, respectively. Soil CO2 emissions were significantly
higher in the CT plots (P < 0.05), but did not differ significantly between the FT and RC plots. This implies that
root respiration was negligible in the FT plots. Heterotrophic respiration accounted for 66% of soil respiration.
Annual CO2 emissions through both soil and heterotrophic respiration were smaller than those of other oil palm
plantations on tropical peat, possibly due to the higher groundwater levels, land compaction, and continuous
measurement of soil CO2 efflux in this study. Mean annual total subsidence was 1.55 to 1.62 cm yr−1, of which
oxidative peat decomposition accounted for 72 to 74%. In conclusion, water management to raise groundwater
levels would mitigate soil CO2 emissions from oil palm plantations on tropical peatland.

1. Introduction Furukawa et al., 2005; Couwenberg et al., 2010; Hooijer et al., 2012).
Peat is usually compacted using heavy machinery before planting in
Peatland stocks approximately one-third of the global terrestrial Malaysia to enhance its bearing capacity for trees and to increase soil
carbon (C) in 3% of the global terrestrial area (Maltby and Immirzi, moisture via capillary water rise (Dislich et al., 2016). This compaction
1993), and approximately 25 Mha are in Southeast Asia, especially in practice is expected to depress peat oxidative decomposition due to the
Indonesia and Malaysia (Page et al., 2011). However, tropical peatland increase in soil water content and decrease in soil gas diffusivity
has been rapidly reclaimed since the 1990s, mainly for oil palm and (Melling et al., 2005, 2013a).
Acacia plantations. By 2015, oil palm plantations had expanded to It has been reported that CO2 emissions from tropical drained
cover an area of 4.3 Mha on peat in Indonesia and Malaysia (Miettinen peatland are an important part of the global C cycle (Sjögersten et al.,
et al., 2016). Because the agricultural use of tropical peatland is com- 2014; Miettinen et al., 2017), and therefore it is important to quantify
monly accompanied by drainage, the aerobic mineralization of peat soil oxidative peat decomposition or heterotrophic respiration (RH) from
is promoted, resulting in large carbon dioxide (CO2) emissions (e.g., total soil respiration (RS) separately. However, there have been few

⁎
Corresponding author at: Laboratory of Ecological and Environmental Physics, Research Faculty of Agriculture, Hokkaido University, Kita-ku, Kita 9, Nishi 9, Sapporo, Hokkaido 060-
8589, Japan.
E-mail addresses: ishikura@chem.agr.hokudai.ac.jp, ishikura.kiwamu@gmail.com (K. Ishikura).

https://doi.org/10.1016/j.agee.2017.11.025
Received 8 September 2017; Received in revised form 24 November 2017; Accepted 27 November 2017
0167-8809/ © 2017 Elsevier B.V. All rights reserved.
K. Ishikura et al. Agriculture, Ecosystems and Environment 254 (2018) 202–212

studies of RH in tropical peatland, despite RH being an important ground, except for some areas with fern plants. In 2014, the palm trees
component of RS that corresponds to oxidative peat decomposition. For were 9 years old, and the canopy height was about 8 m. Oil palm
oil palm plantations on peat, some studies have measured RH periodi- plantations are commonly replanted every 25–30 years (Basiron, 2007),
cally at intervals of one or more months for periods equal to or less than so the study site was in the first cycle of cultivation. The following
1 year (Melling et al., 2005, 2013a, 2013b; Dariah et al., 2014; Husnain fertilizers were applied together four times a year (January, March,
et al., 2014; Marwanto and Agus, 2014; Sakata et al., 2015; Comeau July–August, and September–October) within 1 m of each stem:
et al., 2016). Due to the limitations of field studies, the controlling 74–147 kg N ha−1 yr−1 of urea, 7–9 kg P ha−1 yr−1 of rock phosphate,
factors of soil CO2 efflux are not well understood at the process level. and 239–311 kg K ha−1 yr−1 of muriate of potash (KCl). Copper, zinc,
For example, it was reported that no significant relationship exists be- and boron were applied as micronutrients at 8–16 kg ha−1 yr−1 in
tween RS and groundwater level (GWL) (Jauhiainen et al., 2008), May–June every year, and kieserite (MgSO4·H2O) was also applied at
probably due to the disconnection of capillary force under dry condi- rates of 80 kg ha−1 in October 2014, 119 kg ha−1 in May 2015, and
tions, resulting in soil moisture in the topsoil becoming decoupled from 80 kg ha−1 in January 2016, respectively.
the GWL (Ishikura et al., 2017). Soil moisture in the topsoil can be a
better predictor than GWL for soil CO2 efflux (Melling et al., 2005, 2.2. Experimental design and chamber measurement
2013a), because soil moisture is affected more by capillary rise than
GWL when a peat soil is compacted (Price, 1997; Michel et al., 2001). In April 2014, an experimental area without fern plants was es-
However, the relationship between soil CO2 efflux and soil moisture in tablished, and the following treatments were applied (Fig. 2):
tropical peat ecosystems is still not well understood. For a better un- Close-to-tree (CT, four plots): distance from the nearest tree < 2.5
derstanding, long-term continuous measurement of both RS and RH is m, corresponding to RS.
necessary to capture diurnal variation, detect the response of soil CO2 Far-from-tree (FT, four plots): distance from the nearest tree > 3 m,
efflux to dynamic environmental variations, and reduce the un- corresponding to RS.
certainties in assessment of annual CO2 emissions. To our knowledge, Root-cut (RC, four plots): distance from the nearest tree > 3 m with
no studies have measured RS and RH continuously in an oil palm trenching, corresponding to RH.
plantation on peat. In each RC plot, four stainless steel plates were inserted surrounding
Oxidative peat decomposition induces subsidence together with an area of 40 × 80 cm2. The depth of insertion was 80 cm, which was
physical consolidation and shrinkage (Stephens and Stewart, 1976; almost equivalent to the lowest GWL. In May 2014, 1 month later, an
Wösten et al., 1997; Hooijer et al., 2012). If the contribution of peat automated chamber system was installed in the experimental area. The
oxidation to total subsidence is determined, CO2 emissions through peat system consisted of 16 chambers, an infrared CO2 analyzer (LI-820, LI-
decomposition can be estimated from subsidence monitoring COR, Inc., Lincoln, Nebraska, USA), a programmable data logger
(Couwenberg and Hooijer, 2013). However, the extent of this con- (CR1000, Campbell Scientific Inc., Logan, Utah, USA), and an air pump
tribution has not yet been determined, because it depends on peat and solenoid valves (Hirano et al., 2009). The chamber consisted of an
conditions, such as GWL and the time since drainage. Field studies in- opaque polyvinyl chloride (PVC) cylinder (height: 40 cm; inner dia-
volving simultaneous measurement of peat subsidence and oxidative meter: 25 cm). Chambers were inserted 2–3 cm deep into the soil. One
peat decomposition could enable this to be determined, but only a few chamber was installed in each CT and FT plot, and two chambers were
studies have been reported (Wakhid et al., 2017). installed in each RC plot (Fig. 2).
Therefore, RS and RH due to oxidative peat decomposition were An opaque PVC lid was attached to the chamber top that opened
measured continuously for two years using an automated chamber vertically and closed under the control of the data logger. Each chamber
system, together with GWL, soil moisture, and peat subsidence in an oil closed for 225 s in sequence, one after the other, and it took 1 h for all
palm plantation established on tropical peat. The objectives of this chambers to close/open in rotation. The air in the headspace of each
study were to investigate seasonal changes in RS and RH in relation to chamber was circulated through the CO2 analyzer when the chambers
soil water conditions, quantify annual cumulative RS and RH values, and were closed. The CO2 concentration was measured at 10-s intervals and
evaluate the contribution of oxidative peat decomposition to sub- recorded in the data logger. In August 2015, a greenhouse gas analyzer
sidence. (Ultraportable Greenhouse Gas Analyzer 915-0011, Los Gatos Research,
Inc., San Jose, California, USA) was placed in the air circulation line to
2. Material and methods measure CO2, methane, and water vapor concentrations. Although
measurements began in May 2014, data for a two-year period from
2.1. Site description January 2015 were used, because additional CO2 emissions resulting
from the decomposition of dead roots left in the trenched plots were
This study was conducted in an oil palm (Elaeis guineensis Jacqu.) expected to occur for several months after trenching (Hanson et al.,
plantation (2°11′N, 111°50′E) in a watershed of the Rajang River in 2000; Comeau et al., 2016). One palm tree fell on a chamber in a CT
Sibu, Sarawak, Malaysia (Fig. 1) at an elevation of approximately 25 m plot in August 2015, and the chamber was then moved to an FT posi-
above sea level. The mean annual air temperature and precipitation tion. Thus, the number of CT plots decreased to three, while the number
between 2004 and 2016 were 26.5 ± 0.2 °C and of FT plots increased to five in 2016. CO2 data from the CO2 analyzer
2915 ± 213 mm yr−1 (mean ± 1 standard deviation (SD)), respec- were primarily used; data from the greenhouse gas analyzer were used
tively, at the Sungai Salim B meteorological station (Department of as an alternative when the LI-820 malfunctioned. During the two years
Irrigation and Drainage Malaysia), which is 7.4 km from the study site. of 2015 and 2016, 23% of the data was lost, mainly due to power
In September 2004, a mixed peat swamp forest on an ombrotrophic problems.
peat dome was converted to an oil palm plantation, with the installation Soil CO2 efflux was calculated from the increase in CO2 concentra-
of ditches and water gates; artificial compaction to prevent palms from tion in the chamber headspace during the 90–220 s after the chamber
leaning and toppling was performed during land preparation. The soil closing:
type was a Sapric Histosol (IUSS Working Group WRB, 2015), with a
PH dC
peat depth of 12.7 m. Palm seedlings were planted on a triangular grid F=
RTair dt (1)
spacing of 8.5 m between trees (153 trees ha−1; Fig. 2), and the ground
−2 −1
was sparsely covered by fern plants (Stenochlaena palustris (Burm. f.) where F is soil CO2 efflux (μmol m s ), P is air pressure
Bedd.). The lower fronds of oil palm trees were periodically lopped and (101.325 kPa), H is the aboveground height of a chamber, R is the gas
piled in inter-row spaces. Thus, little leaf litter accumulated on the constant (8.314 Pa m3 K−1 mol−1), Tair is air temperature (K), and dC/

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Fig. 1. Map of the study site.

emissions resulting from leaf litter decomposition were not included in

either RS or RH in this study. The CO2 fluxes measured by the two CO2
analyzers did not differ significantly from each other (Fig. S1).

2.3. Environmental properties

Precipitation was observed at a height of 1 m in an open space about

5 m away from the experimental area. However, some data were lost
due to power problems, and precipitation data from the Sungai Salim B
meteorological station were therefore used to record annual precipita-
tion. The friction velocity (u*, m s−1) was measured at a height of 21 m
above the vegetation canopy using a sonic anemometer (CSAT3,
Campbell Scientific Inc.) and was used as an index of atmospheric
turbulence.
Fig. 2. Allocation of chambers, subsidence pole, and groundwater level (GWL) pipe. CT, Air and soil temperatures (°C) at a depth of 5 cm were measured
FT, and RC represent chamber treatments for close-to-tree (< 2.5 m), far-from-tree using thermocouple thermometers in the same two chambers in FT
(> 3 m), and root-cut plots, respectively. plots. The GWL (m, negative values represent belowground) was mea-
sured using a piezometer (HTV-050KP, Sensez, Tokyo, Japan) at one
dt is the rate of increase of the CO2 concentration (μmol mol−1 s−1). point (Fig. 2), and volumetric soil water content (m3 m−3) at 0–30-cm
The quality of soil CO2 efflux data was controlled as follows: depth was measured using a time-domain reflectometry (TDR) sensor
(CS616, Campbell Scientific Inc.) in FT and RC plots, respectively
1 Significant slope: the Pearson’s correlation coefficient for the rate of (Fig. 2). Half-hourly means of these belowground variables were also
increase in the CO2 concentration should be higher than 0.661376 recorded to the data logger used for the chamber system. Missing daily
(P < 0.01, n = 14); mean GWLs were gap-filled by a tank model (He and Inoue, 2015). The
2 Stationary slope: the rates of increase in CO2 concentration from 90 water retention curve was fitted to the relationship between daily mean
to 150 s and from 160 to 220 s after closing were calculated sepa- GWL and soil water content using van Genuchten’s model (van
rately. The difference between the means of the two rates and the Genuchten, 1980):
rate during the whole period (90–220 s) should be less than 30%
θsat − θres
(Aguilos et al., 2013); θ = θres +
(1 + (αh)n)1 − 1/ n (2)
3 Outliers: the CO2 flux should be within 0–40 μmol m−2 s−1.
where θ is the soil water content, θsat is the saturated soil water content
After these quality control criteria were applied, 43% of the data (equivalent to the porosity explained below), h is the pressure head
remained available. Because no litter accumulated in any chamber, CO2 (=−100 × GWL, cm), and α, n, and θres are fitting parameters,

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respectively. Missing daily mean soil water contents were gap-filled GWL or WFPS using a regression equation (Eqs. (4) or (5)), and annual
from the GWL using the water retention curve (Fig. S3). cumulative CO2 emissions were summed for each chamber. The dif-
In June 2014, six undisturbed soil cores of 100 cm3 were taken to a ferences in the means of the annual cumulative CO2 emissions among
depth of 60 cm at intervals of 10 cm using a stainless soil core cylinder. years (2015 and 2016) and treatments (CT, FT, and RC plots) were
Bulk density (Mg m−3) and porosity (m3 m−3) were determined using a tested by a two-way analysis of variance (ANOVA) and a multiple
digital soil volume analyzer (DIK-1110, Daiki Rika Company, Saitama, comparison using the Tukey–Kramer method. The ratios of RH to RS
Japan). A further three disturbed soil samples were taken to a depth of were calculated by dividing the annual soil CO2 emissions of RC plot by
60 cm at intervals of 10 cm, and the total C and nitrogen (N) contents those of CT and FT plots.
(%) were analyzed by the dry combustion method (TruMac CN, LECO Subsidence through the physical processes of shrinkage/swelling
Corporation, St. Joseph, Michigan, USA). Other undisturbed soil cores and consolidation (cm period−1) was determined monthly as the dif-
of 100 cm3 were taken from depths of 0–5, 5–10, 10–20, and 20–30 cm ference between total subsidence and SRH. It was assumed that physical
every month during the study period, and the volumetric soil water subsidence (Sphys) progressed logarithmically over time due to sec-
content was determined using the digital soil volume analyzer. ondary consolidation and fluctuated with GWL by shrinkage/swelling
Volumetric soil water content measured by the TDR sensor was cali- (Eq. (6)). In this simple model, C leaching, especially dissolved organic
brated using the soil water content measured by the soil core method. carbon (DOC) efflux through groundwater discharge (Moore et al.,
Water-filled pore space (WFPS, m3 m−3) was calculated from the pro- 2011), was incorporated into the first term on the right side. However,
portion of soil water content to soil porosity. the regression of cumulative subsidence over time can be spurious,
In February 2017, disturbed topsoil at a depth of 0–30 cm was because cumulative subsidence is probably a unit root process. There-
sampled in four replicate locations around the experimental area. Soil fore, a unit root test (augmented Dickey–Fuller test) was performed for
pH (1:2.5H2O) was measured using a digital pH meter (827 pH Lab, Sphys. As a result, Sphys was significantly stationary (P < 0.05) so a
Metrohm AG, Herisau, Switzerland). Ash content (%) was analyzed by linear regression was performed using the following equation:
loss-on-ignition (TGA701, LECO Corporation) at 800 °C for more than
Sphys = a · log10(Days) + b · (GWLi − GWL0) (6)
1 h. To measure fine root biomass (diameter: < 2 mm), 100 cm3 soil
cores were taken at a depth of 0–10 cm at 1, 2, and 3 m from the four where the term Days is the number of days from the beginning of
nearest palm trees, respectively. The soil samples were washed and subsidence measurement (June 23, 2014), GWL0 is the daily mean GWL
sieved through a 2-mm mesh. Living fine roots were picked out by vi- on the initial date (=−0.73 m), GWLi is the daily mean GWL when
sual assessment and elasticity, and dried at 75 °C for more than 48 h to subsidence was measured, and a and b are regression coefficients.
measure biomass. Because soil CO2 efflux was available only from January 2015, SRH in
2014 was estimated from the GWL using Eq. (4). Finally, the smoothed
2.4. Subsidence and the contribution of peat decomposition annual total subsidence (Ssm, cm yr−1) was calculated using the fol-
lowing equation:
In May 2014, a subsidence pole with a marking disk was installed
vertically into the soil until it reached the mineral soil beneath the peat. ⎛ Days y + 1 ⎞
Ssm = a⋅log10 ⎜ + b⋅(GWL y − GWL 0) + SRHy
Because the measurement height of the pole from the ground surface Days y ⎟ (7)
⎝ ⎠
was affected by the roughness of the ground, the marking disk was used
to take an average. The disk could move freely along the anchored pole, where y is the target year (2015 or 2016), Daysy is the number of days
and thus was always resting on the ground surface. The heights from after January 1 in y since the beginning of subsidence measurement
the disk on the ground to the top of the pole were measured manually (June 23, 2014), GWL y is the annual mean GWL in y, and annual SRH y
on four sides and averaged every month from June 2014. Annual sub- is subsidence through RH in y.
sidence was calculated as the cumulative subsidence from January in All data analyses were conducted using R software (R Core Team,
one year to January in the following year. 2017).
Subsidence through oxidative peat decomposition (SRH,
cm period−1) was calculated using the following equation (Wakhid 3. Results
et al., 2017):
3.1. Environmental properties
CumulativeRH
SRH =
10·BD·TC (3) The soil pH was 3.9 ± 1.0 (mean ± 1 SD) and the soil C content
−2 −1 was 52.8 ± 0.8%; such a low pH and high C content are typical
where cumulative RH (kg C m period ) is the cumulative CO2
emission in RC plots, BD (Mg m−3) is bulk density, and TC (g C g−1) is properties of ombrotrophic peat. Bulk density was 0.16 ± 0.04 Mg
the total C content of the soil. m−3 at a depth of 0–10 cm and 0.12 ± 0.02 Mg m−3 at a depth of
0–60 cm (Table S1). The ash content was relatively high at 7.1 ± 7.9%
2.5. Data analysis because of fertilizer applications. Fine root biomass values were
165 ± 91, 86 ± 63, and 93 ± 25 g m−2 at a depth of 0–10 cm at 1,
Nonlinear mixed-effects modeling was applied to analyze the de- 2, and 3 m from the nearest tree, respectively.
pendencies of the daily mean soil CO2 efflux (μmol m−2 s−1) on GWL Annual precipitation amounts in 2015 and 2016 were similar and
(m) and WFPS (m3 m−3) for each of the CT, FT, and RC plots by fitting close to the mean annual precipitation recorded over a 13-year period
the following equations: (2915 mm yr−1) (Fig. 3a, Table 1). Daily mean GWL varied from −0.89
to −0.23 m (Fig. S2), with similar annual means in 2015 and 2016
CO2 efflux = R 0 ·exp(b·GWL) (4) (Fig. 3b, Table 1). The WFPS did not change between 2015 and 2016
(Table 1) and values were similar in the RC and FT plots. No significant
CO2 efflux = R 0 ·exp(b·(1−WFPS)) (5)
difference was found in daily mean soil temperatures between 2015 and
where R0 and b are regression coefficients, and chambers are treated as 2016 (Table 1).
the random coefficients of R0 and b. The residual maximum likelihood
(REML) estimation method was used for regression analysis, and the 3.2. Diurnal changes in soil carbon dioxide efflux
goodness-of-fit was evaluated by the coefficient of determination (R2).
The daily mean soil CO2 efflux was gap-filled from the daily mean Soil temperature at a depth of 5 cm displayed a typical diurnal

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Fig. 3. Variations in (a) monthly precipitation and (b) monthly mean GWL. Error bars
denote 95% confidence intervals.

Table 1 Fig. 4. Diurnal changes in (a) air and soil temperatures and friction velocity (u*) and (b)
Annual sum of precipitation, and annual mean groundwater level (GWL), water-filled soil CO2 flux in the CT, FT, and RC plots. Error bars denote 95% confidence intervals.
pore space (WFPS, 0–30 cm depth) and soil temperature (5 cm depth).

Variables Treatment Mean ± 1SD Table 2

Results of a multiple regression analysis for hourly soil CO2 efflux (μmol m−2 s−1) with
2015 2016 soil temperature (°C), groundwater level (GWL, m), difference between air and soil
temperatures (ΔTair-soil), and friction velocity (u*, m s−1). Std. coeff. represents the
Precipitation (mm yr−1) 3000 2910 standardized regression coefficient.
GWL (m) FT −0.62 ± 0.09 −0.57 ± 0.09
WFPS (m3 m−3) FT 0.70 ± 0.03 0.69 ± 0.03 Treatment Predictor Coefficient Std. coeff. P-value R2
RC 0.68 ± 0.03 0.68 ± 0.03
Soil temperature (°C) FT 26.8 ± 1.5 27.8 ± 2.2 CT Intercept 2.89 < 0.001 0.128
(n = 8618) Soil temperature −0.03 −0.014 0.18
FT: far-from-tree (> 3 m); RC: root-cut. GWL −10.2 −0.296 < 0.001
ΔTair-soil −0.20 −0.197 < 0.001
u* −0.57 −0.032 < 0.05
pattern with a minimum at 7 h and a maximum at 14 h. The diurnal FT Intercept −2.91 < 0.001 0.107
range was about 10 °C (Fig. 4a). Soil temperature was higher than air (n = 9506) Soil temperature 0.07 0.006 0.09
temperature in the nighttime by about 2 °C on average (Fig. 4a). The u* GWL −7.37 −0.303 < 0.001
ΔTair-soil −0.08 −0.109 < 0.001
was higher in the daytime than in the nighttime (Fig. 4a). In contrast,
u* −0.65 −0.049 < 0.001
soil CO2 efflux was higher in the nighttime than in the daytime in all RC Intercept −1.80 < 0.001 0.064
treatments (Fig. 4b). Its diurnal pattern was a mirror image of those of (n = 17617) Soil temperature 0.002 0.001 < 0.001
temperature and u*. GWL −6.37 −0.205 < 0.001
To examine the effects of temperature, soil water condition, and u* ΔTair-soil −0.11 −0.125 < 0.001
u* −0.92 −0.057 < 0.001
on diurnal changes in soil CO2 efflux, a multiple regression was per-
formed for hourly soil CO2 efflux with soil temperature, GWL, the dif- CT: close-to-tree (< 2.5 m); FT: far-from-tree (> 3 m); RC: root-cut.
ference between air and soil temperature (ΔTair-soil, defined as air
temperature minus soil temperature), and u* as predictors (Table 2). (Fig. 5a). A significant negative relationship was found between soil
Soil temperature was a significant predictor only in RC plots, while CO2 efflux and u* in each treatment when u* was lower than around
GWL, ΔTair-soil, and u* were significant predictors in all treatments. 0.4 m s−1 (Fig. 5b).
From their standardized regression coefficients, ΔTair-soil was the
strongest predictor followed by u*, with no general diurnal change
expected in GWL. Hourly soil CO2 efflux was plotted against ΔTair-soil 3.3. Seasonal changes in soil carbon dioxide efflux
and u*, respectively (Fig. 5). Soil CO2 efflux had a significant negative
relationship with ΔTair-soil in each treatment when ΔTair-soil was negative To remove bias due to diurnal changes, the daily mean soil CO2

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Fig. 5. Relationship of soil CO2 efflux with the dif-

ference between air and soil temperatures (ΔTair-soil,
air minus soil) or friction velocity (u*) in the CT, FT,
and RC plots. Efflux data were binned into deciles by
ΔTair-soil or u*. A linear regression was applied to
data at ΔTair-soil < 0 °C and u* < 0.4 m s−1. Error
bars denote 95% confidence intervals.

efflux was calculated only when the number of available data points 3.4. Annual cumulative soil carbon dioxide emission
was larger than six in both the daytime (7–18 h) and nighttime
(19–6 h), respectively. The daily mean soil CO2 efflux values during the The daily mean soil CO2 flux was gap-filled from the GWL using the
two years were 2.80 ± 2.18, 1.59 ± 1.18, and model (Eq. (4), Fig. 7), and annual sums were calculated (Table 3).
1.94 ± 1.58 μmol m−2 s−1 (mean ± 1 SD) in the CT, FT, and RC Annual cumulative soil CO2 emissions did not differ between 2015 and
plots, respectively (Fig. 6). Nonlinear mixed-effects models using GWL 2016 (F1,27 = 1.59, P = 0.22), but differed significantly among the CT,
and WFPS [Eqs. (4) and (5), respectively] were significantly fitted FT, and RC plots (F2,27 = 4.05, P < 0.05). The highest soil CO2
(P– < 0.001) to daily mean soil CO2 efflux (Fig. 7). It was found that emission was measured in the CT plots (1.03 ± 0.53 kg C m−2 yr−1),
the daily mean soil CO2 efflux increased significantly as the GWL or and the lowest was measured in the FT plots (0.59 ± 0.26 kg C m−2
WFPS decreased. The regression with GWL produced higher R2 values yr−1). The RH/RS ratios were 0.66 and 1.16 for RC/CT and RC/FT,
than that with WFPS in the CT and FT plots, while the regression with respectively (Table 3).
WFPS produced slightly higher R2 values than the regression with GWL
in RC plots (Fig. 7).
3.5. Subsidence

The ground surface subsided, but oscillated in correspondence with

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4. Discussion

4.1. Factors controlling soil carbon dioxide efflux

Soil CO2 efflux displayed a clear diurnal pattern that was almost in
reverse parallel with soil temperature (Fig. 4). The hourly soil CO2 ef-
flux had significant negative relationships with GWL, ΔTair-soil, and u*
(Table 1). First, the effects of ΔTair-soil and u* on the diurnal change in
soil CO2 efflux are considered.
Soil temperature was higher than air temperature at night (Fig. 4a),
and soil CO2 efflux increased significantly as ΔTair-soil decreased when
ΔTair-soil was negative (Table 2, Fig. 5a). Ganot et al. (2014) found that
soil CO2 efflux was promoted by the upward mass flow due to thermal
convection in porous mineral soils when the soil temperature was
higher than the air temperature. In this study, unsaturated peat soil was
more porous than mineral soil. Therefore, our results imply that soil
CO2 efflux was promoted by thermal convection in the unsaturated peat
profile. Furthermore, the nighttime thermal convection probably de-
creased soil CO2 concentrations more than diffusion, which potentially
suppressed soil CO2 efflux during the following daytime period under
stable thermal conditions. On the other hand, Lai et al. (2012) reported
that soil CO2 flux was underestimated by the closed chamber method
during periods with a high u* in boreal peatland because wind pumps
out the soil air just below the ground surface, which decreases the soil
CO2 concentration. The chamber method can measure only the diffu-
sive CO2 efflux in the closed space, but cannot measure the CO2 mass
flow due to atmospheric turbulence. In this study, the hourly soil CO2
efflux decreased significantly as u* increased (Fig. 5b), which may have
led to an underestimation of soil CO2 efflux in the daytime when u* was
high on average (Fig. 4a). Therefore, the diurnal change probably re-
sulted from the combination of an increased flux in the nighttime due to
thermal convection, a decreased flux in the daytime due to the after-
effect of nighttime thermal convection, and an underestimated flux in
the daytime due to pumping by atmospheric turbulence. In the calcu-
lation of the daily soil CO2 efflux, the positive and negative effects of
thermal convection can be compensated for by making continuous
measurements, although some underestimation due to mass flow
through pumping is inevitable in a porous soil when the chamber
method is applied, especially in unsaturated peat soils.
Soil CO2 flux increases with soil temperature. The diurnal changes
in soil temperature at a depth of 5 cm, with a diurnal range of about
5 °C (Fig. 4a), could have had a positive effect on soil CO2 efflux.
Fig. 6. Seasonal changes in daily mean soil CO2 efflux in the CT, FT, and RC plots. Circles However, the effect of soil temperature was not significant in the CT
and lines represent measured and gap-filled values, respectively. Error bars and the gray and FT plots (Table 2). The effect of soil temperature was weaker than
area denote 95% confidence intervals of measured and estimated soil CO2 efflux among that of the other environmental properties in the RC plots, although it
chambers, respectively.
was significant due to the large sample size (Table 2). Oxidative peat
decomposition would have occurred within the 60-cm deep unsaturated
the GWL (Fig. 8). Total annual subsidence values were determined in- peat horizon because the annual mean GWL was about −0.6 m
stantaneously from two measurements in January to be 1.23 and (Table 1). Thus, soil CO2 efflux resulting from the total peat decom-
2.02 cm yr−1 in 2015 and 2016, respectively (Table 4). The annual SRH position was not directly related to soil temperature, which was similar
was calculated from the annual RH, the bulk density, and C content of to the oxidative peat decomposition observed in a burnt ex-peat swamp
60-cm-thick surface peat using Eq. (3). The result shows that the oxi- forest in Central Kalimantan, Indonesia (Hirano et al., 2014).
dative subsidence values were 1.22 and 0.99 cm yr−1 in 2015 and The effects of GWL and WFPS on the daily mean soil CO2 efflux were
2016, respectively (Table 4). As a result, the contributions of oxidative then considered. The soil CO2 flux had a negative exponential re-
peat decomposition to total subsidence were 100 and 49% in 2015 and lationship with the GWL in all treatments (Fig. 7), which indicates that
2016, respectively, with a mean of 74%. soil CO2 efflux was promoted by lowering of the GWL. A negative re-
Physical subsidence (Sphys) was significantly fitted with Eq. (6) as lationship between soil CO2 efflux and GWL has been reported in var-
Sphys = 1.42·log10(Days) − 6.46·(GWLi + 0.73) (P < 0.01). The result ious tropical peatlands (Furukawa et al., 2005; Hirano et al., 2009,
indicated that the ground subsided physically by 0.65 cm for every 2014; Couwenberg et al., 2010; Sundari et al., 2012; Ishikura et al.,
GWL lowering of 10 cm. Annual smoothed subsidence (Ssm) values were 2017), indicating that peat decomposition is promoted by lowering of
1.83 and 1.27 cm yr−1, respectively, in 2015 and 2016. The SRH ac- the GWL. Lowering of the GWL decreases WFPS and enhances aeration
counted for 67 and 78% of the smoothed subsidence in 2015 and 2016, of the soil. As a result, oxidative mineralization of organic matter and
respectively, with a mean of 72% (Table 4). gas diffusivity in the soil is accelerated, resulting in increased soil CO2
efflux (Linn and Doran, 1984). The relationship between soil CO2 efflux
and WFPS in the 30-cm-thick surface peat was also significant, but was
weaker than the relationship with GWL in the CT and FT plots, while

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K. Ishikura et al. Agriculture, Ecosystems and Environment 254 (2018) 202–212

Fig. 7. Relationships of daily mean soil CO2 efflux

with (a) GWL and (b) water-filled pore space (WFPS)
in the CT, FT, and RC plots.

Table 3
Annual cumulative soil CO2 emissions (mean ± 1 standard deviation (n)) and the ratio of heterotrophic to total soil respiration (RH/RS). Mean values with the same letter are not
significantly different (P > 0.05).

Year Annual cumulative soil CO2 emission (kg C m−2 yr−1) RH/RS ratio

CT (RS) FT (RS) RC (RH) RC/CT RC/FT

2015 1.13 ± 0.63 (3) 0.63 ± 0.30 (4) 0.76 ± 0.24 (8) 0.68 1.20
2016 0.94 ± 0.53 (3) 0.55 ± 0.25 (5) 0.61 ± 0.16 (8) 0.65 1.12
Mean 1.03 ± 0.53 b 0.59 ± 0.26 a 0.69 ± 0.21 ab 0.66 1.16

CT: close-to-tree (< 2.5 m); FT: far-from-tree (> 3 m); RC: root-cut.

the relationships were similar in the RC plots (Fig. 7). In contrast, between surface soil water and groundwater occurred under dry con-
Ishikura et al. (2017) suggested that WFPS might be better able to ex- ditions. However, at the site used in the current study, such a dis-
plain the variation in peat soil CO2 efflux than GWL in Central Kali- connection probably did not occur (Fig. S3) because GWL was con-
mantan, Indonesia, because disconnection of the capillary force trolled by water gates and remained relatively high. Melling et al.

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K. Ishikura et al. Agriculture, Ecosystems and Environment 254 (2018) 202–212

provides a default CO2 emission factor of 1.1 kg C m−2 yr−1, with 95%
confidence intervals of 0.56–1.7 kg C m−2 yr−1, from tropical peat in
oil palm plantations for their Tier 1 methodology, although this value
was derived from results obtained by the closed chamber and sub-
sidence methods. The annual RH measured in the RC plots was almost
equivalent to the bottom 95% confidence interval of the Tier 1 method.
The annual RS in the CT plots was lower than the range of
1.22–1.81 kg C m−2 yr−1 reported in previous studies conducted in oil
palm plantations on peat (Melling et al., 2005, 2013b; Dariah et al.,
2014; Sakata et al., 2015). The annual RH was lower than the previously
reported range of 0.69–1.80 kg C m−2 yr−1 (Melling et al., 2013b;
Dariah et al., 2014; Husnain et al., 2014; Marwanto and Agus, 2014).
The low RS and RH in this study may have been caused by the higher
annual mean GWL (−1.24 to −0.58 m) than reported in previous
studies (Table 1) and by the exclusion of leaf litter decomposition. In
Fig. 8. Cumulative instantaneous total subsidence (Sinstan, closed circles), cumulative
addition, previous studies calculated annual soil CO2 emissions either
oxidative subsidence (SRH, open circles) and daily mean GWL (gray line). Error bars
denote 95% confidence intervals. by linear interpolation of the monthly CO2 flux (Melling et al., 2005,
2013b; Sakata et al., 2015) or by simply averaging periodic CO2 flux
measurements for less than 1 year (Dariah et al., 2014; Husnain et al.,
(2013a) found that WFPS was a better predictor of soil CO2 efflux than
2014; Marwanto and Agus, 2014), whereas in this study annual CO2
was GWL over a WFPS range of 0.6–0.9 m3 m−3. However, in the
emissions were calculated from the quality controlled continuous flux,
current study the range was narrower (0.60–0.75 m3 m−3), although
and data gaps were filled using continuous GWL data. The difference in
the GWLs were similar between this study and that of Melling et al.
the calculation methods used would also affect the reported annual soil
(2013a). The bulk density in Melling et al. (2013a) was higher
CO2 emissions. The contribution of oxidative peat decomposition to
(0.21–0.23 Mg m−3) than that recorded in this study (Table 4, S1).
total soil respiration calculated as RC/CT (Table 3) was comparable
Therefore, the effect of a capillary rise on WFPS might have been higher
with the range of 60–86% reported in other oil palm plantations on
in Melling et al. (2013a) than in this study due to the higher bulk
tropical peat (Dariah et al., 2014; Comeau et al., 2016), except for 38%
density and lower porosity. These are reasons why WFPS was not a
in Melling et al. (2013a, 2013b).
better predictor than GWL in this study.
When considering other land uses on tropical peat, the RS in this
study was lower than the values of 1.23 and 1.35 kg C m−2 yr−1 re-
ported in swamp forests (Sundari et al., 2012), 1.68–4.20 kg C m−2
4.2. Annual cumulative carbon dioxide emission
yr−1 reported in an Acacia plantation (Jauhiainen et al., 2012),
3.29 kg C m−2 yr−1 reported in a rubber plantation (Wakhid et al.,
In this study, 57% of the data from continuous CO2 flux measure-
2017), and 1.11–1.60 kg C m−2 yr−1 reported in a sago palm planta-
ments during the two years was lost due to power problems and quality
tion (Melling et al., 2005, 2013b). The RH in this study was also lower
controls. To calculate annual cumulative CO2 emissions, the data gaps
than the ranges of 0.70–0.83 kg C m−2 yr−1 reported in swamp forests
were filled on a daily basis from GWL using negative exponential
(Itoh et al., 2017), 1.91–3.78 kg C m−2 yr−1 reported in an Acacia
equations (Fig. 7). Although gap filling causes uncertainties in the as-
plantation (Jauhiainen et al., 2012), and the value of 1.41 kg C m−2
sessment of annual emissions, these uncertainties are limited because
yr−1 reported in a rubber plantation (Wakhid et al., 2017), while it was
the seasonal variation in GWL was not large at this study site (Fig. 3b,
similar to the range of 0.60–0.76 kg C m−2 yr−1 reported in sago palm
S2). The annual values are expected to be more reliable than those
plantations (Melling et al., 2005, 2013b; Watanabe et al., 2009). In
reported in previous studies, because previous studies estimated annual
Acacia plantations, GWL tends to be lower than in oil palm plantations
values from data collected at intervals of one month or longer.
(Hergoualc’h and Verchot, 2011), which would enhance oxidative peat
Annual soil CO2 emission was significantly larger from the CT plots
decomposition.
than from the RC plots, and RH accounted for 66% of RS (Table 3). In
contrast, the annual soil CO2 emission from the FT plots did not differ
significantly from that of the RC plots (Table 3). Dariah et al. (2014) 4.3. Subsidence
measured soil CO2 efflux at different distances from tree stems in an oil
palm plantation on tropical peat and reported that root respiration was The annual total subsidence that was determined instantaneously
negligible at distances of more than 3 m. The distance of each FT plot was higher in 2016 than in 2015, whereas the annual smoothed sub-
from the nearest palm tree was greater than 3 m. Thus, the soil CO2 sidence was higher in 2015 (Table 4); the interannual difference was
efflux in the FT plots was mostly derived from RH, and root respiration larger for instantaneous subsidence. Instantaneous subsidence was
was probably negligible. calculated simply from two measurements at annual intervals, and
The Intergovernmental Panel on Climate Change (IPCC, 2014) therefore was dependent on peat surface oscillation due to short-term

Table 4
Annual subsidence as a result of oxidative peat decomposition (RH) and its contribution to total subsidence. Bulk density and total carbon (C) content of surface peat with 60-cm thickness
are shown. The total annual subsidence rate was determined instantaneously from January’s measurements and smoothed using Eq. (7). Values are shown as means ± 1 standard
deviation.

Year Annual RH Bulk density Total C content Subsidence through RH Total subsidence rate Contribution of RH to subsidence

(kg C m−2 yr−1) (Mg m−3) (%) (cm yr−1) (cm yr−1) (%)

instantaneous smoothed instantaneous smoothed

2015 0.76 ± 0.24 0.12 ± 0.02 52.8 ± 0.8 1.22 ± 0.45 1.23 ± 0.49 1.83 100 67
2016 0.61 ± 0.16 0.99 ± 0.32 2.02 ± 0.44 1.27 49 78
Mean 0.69 ± 0.21 1.11 ± 0.39 1.62 ± 0.46 1.55 74 72

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K. Ishikura et al. Agriculture, Ecosystems and Environment 254 (2018) 202–212

variations in GWL. Therefore, the smaller instantaneous subsidence in University for chamber preparation. Malaysian Meteorological
2015 was caused by the higher GWL in January 2016 (Fig. 8). In ad- Department (Sarawak branch) and Department of Irrigation and
dition, the interannual order of instantaneous subsidence Drainage, Sarawak supported us to provide the meteorological data for
(2015 < 2016) was inconsistent with that of annual RH this study. This study was supported by the Sarawak State Government
(2015 > 2016) (Table 4). Because such surface oscillation due to and Federal Government of Malaysia. This study was conducted under
short-term GWL variation was excluded in the smoothed subsidence, the Joint Research Program of the Institute of Low Temperature
annual values reflected the interannual variation in GWL (Table 1) and Science, Hokkaido University, and was also supported by the Japan
their order of magnitude was consistent with that of the annual RH Society for the Promotion of Science (JSPS) KAKENHI (no. 25257401),
(2015 > 2016). However, the means of annual subsidence for the two the Environment Research and Technology Development Fund (no. 2-
years were similar in the two approaches. 1504) of the < GS6 > Environmental Restoration and Conservation
The annual total subsidence in this study (Table 4) was lower than Agency and the Ministry of the Environment, Japan < /GS67 > , the
the range of 2.0–5.4 cm yr−1 reported in previous studies of oil palm Asahi Glass Foundation, and a Grant for Environmental Research
plantations on peat (Wösten et al., 1997; Hooijer et al., 2012; Projects from The Sumitomo Foundation.
Couwenberg and Hooijer, 2013), and was also lower than the 5 cm yr−1
in an Acacia plantation (Hooijer et al., 2012) and 5.96 cm yr−1 in a Appendix A. Supplementary data
rubber plantation (Wakhid et al., 2017) on tropical peat. These studies
reported a higher oxidative peat decomposition of 0.69–2.13 kg C m−2 Supplementary data associated with this article can be found, in the
yr−1 than was found in this study, which probably resulted in higher online version, at https://doi.org/10.1016/j.agee.2017.11.025.
subsidence. Previous studies in tropical peatland also reported a lower
bulk density of 0.12–0.14 Mg m−3 than was found in this study, except References
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