ARTICLE IN PRESS
Journal of Environmental Management 85 (2007) 690–695
www.elsevier.com/locate/jenvman
Soil organic carbon decomposition and carbon pools in
temperate and sub-tropical forests in China
L. Yanga,b,c, J. Pana,, Y. Shaoa, J.M. Chend, W.M. Jud, X. Shie, S. Yuanf
a
College of Resources and Environment of Nanjing Agriculture University, Nanjing 210095, PR China
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, PR China
c
Graduate School of Chinese Academy of Sciences, Beijing 100039, PR China
d
Department of Geography, University of Toronto, 100 St. George St., Room 5047, Toronto, Ont., Canada M5S 3G3
e
Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, PR China
f
Land Institute of College of Public Management, Zhejiang Gongshang University, Hangzhou 310035, PR China
b
Received 4 December 2005; received in revised form 31 August 2006; accepted 19 September 2006
Available online 14 November 2006
Abstract
Decomposition of soil organic carbon (SOC) is a critical component of the global carbon cycle, and accurate estimates of
SOC decomposition are important for forest carbon modeling and ultimately for decision making relative to carbon sequestration and
mitigation of global climate change. We determined the major pools of SOC in four sites representing major forest types in
China: temperate forests at Changbai Mountain (CBM) and Qilian Mountain (QLM), and sub-tropical forests at Yujiang (YJ) and
Liping (LP) counties. A 90-day laboratory incubation was conducted to measure CO2 evolution from forest soils from each site, and
data from the incubation study were fitted to a three-pool first-order model that separated mineralizable soil organic carbon into active
(Ca), slow (Cs) and resistant (Cr) carbon pools. Results indicate that: (1) the rate of SOC decomposition in the sub-tropical zone was
faster than that in the temperature zone, (2) The Ca pool comprised 1–3% of SOC with an average mean residence time (MRT) of 219
days. The Cs pool comprised 25–65% with an average MRT of 78 yr. The Cr pool accounted for 35–80% of SOC, (3) The YJ site in
the sub-tropical zone had the greatest Ca pool and the lowest MRT, while the QLM in the temperature zone had the greatest MRT for
both the Ca and Cs pools. The results suggest a higher capacity for long-term C sequestration as SOC in temperature forests than in subtropical forests.
r 2006 Elsevier Ltd. All rights reserved.
Keywords: Soil organic carbon decomposition; Carbon pool; Active carbon pool; Slow carbon pool; Resistant carbon pool
1. Introduction
Soil organic carbon (SOC) represents the largest carbon
reservoir in terrestrial ecosystems, and is estimated
at about 1500 Pg C globally, or 2 times that of the
atmosphere and 2.3 times that of the total terrestrial
vegetation (Schimel, 1995). Approximately 70% of the
global soil C inventory resides in forest ecosystems
(Hudson et al., 1994). A small change in forest soil C
inventories can thus result in a large change in atmospheric
CO2 concentration (Raich and Schlesinger, 1992). The
study of dynamic changes and mechanisms of forest SOC is
Corresponding author. Tel.: +86 25 84395329; fax: +86 25 57714759.
E-mail address: [email protected] (J. Pan).
0301-4797/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jenvman.2006.09.011
thus essential in understanding and mitigating global
climate change (Fang et al., 1996). The chemical
components of SOC are complex, involving a wide array
of organic constituents (Sollins et al., 1999) with mean
resistant times (MRT) that range over three orders of
magnitude (Goh et al., 1989; Paul et al., 2001a). In general,
SOC can be divided into an active pool (turnover time
0.1–4.5 a), a slow pool (turnover time 5–50 a) and a passive
pool (50–3000 a) (Parton et al., 1987). Prior research
suggests that the three-pool first-order model can
accurately predict dynamic changes in forest SOC (Deans
et al., 1986; Gregorich et al., 1989; Cabrera, 1993).
Accurate assessment of the different carbon pools of forest
SOC is an important step in understanding mechanisms of
soil C cycling and dynamic change of carbon pools.
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L. Yang et al. / Journal of Environmental Management 85 (2007) 690–695
691
Table 1
Properties of soil samples in LP, CBM, YJ and QLM sites
Sample
Depth (cm)
Organic carbon (g kg1)
pH (%)
Clay (%)
Soil type (CST)
Vegetation
LP3
LP7
YJ1
YJ2
CBM0
CBM14
CBM22
QLM2
QLM7
QLM8
0–20
0–15
0–15
0–18
0–11
0–11
0–7
0–30
0–30
0–30
21.17
24.73
30.76
26.59
57.72
74.41
120.34
89.03
71.93
70.18
4.84
4.42
4.35
4.33
5.04
5.07
5.24
8.11
8.31
8.24
17.9
22.9
28.40
35.6
9.39
8.69
7.18
13.70
12.50
13.30
Perudic Ferrosols
Perudic Ferrosols
Udic Ferralsols
Udic Ferralsols
Udic Isohumosols
Boric Argosols
Udic Isohumosols
Ustic Argosols
Ustic Isohumosols
Ustic Isohumosols
Cunninghamia lanceolata
Evergreen broadleaf
Evergreen broadleaf
Cunninghamia lanceolata
Pteridophyta
Spruce-fir
Poplar and Birch
Picea crassifolia
Sabinaprezew alskii
Shrub
While there are recent studies on total SOC stocks of
forests in China, estimates of sizes and turnover rates of the
three SOC pools have not been reported in forest soils of
China. The objectives of this work were: (a) to describe
SOC decomposition by incubation analysis of different
soils under constant temperature (25 1C) and water content
(60% WHC) for major forest types in China, and (b) to
determine SOC pool sizes and turnover rates for these
forest types according to the three-pool first-order model.
2. Materials and methods
2.1. Study sites
The Liping (LP) site is located in the Guizhou Province,
China (1091100 E, 261200 N) with a mean annual temperature (MAT) of 15 1C and rainfall of 1321 mm. The site is in
the sub-tropical zone of China, with plantation stands
of Chinese fir (Cunninghamia lanceolata). Soil type
corresponds to Perudic Ferrosols in the CST classification
(Chinese Soil Taxonomy, 2001). The Yujiang (YJ) site, also
in the sub-tropical zone, is located in the Jiangxi Province,
China (1161410 E, 281040 N) with a MAT of 18.1 1C and
1741 mm of rainfall annually. Ferralsols were formed
under Chinese fir (Cunninghamia lanceolata) and evergreen
broadleaf forests (Wu et al., 1997). The Changbai
Mountain (CBM) site is located in the southeast of Jilin
province, northeast China (1271380 E, 411420 N), and the
elevation varies in the range from 720 to 2691 m above the
sea level. The climate belongs to the temperate continental
mountainous climate with a MAT of 5 1C and 1050 mm of
precipitation annually. The typical soil types in the area are
Boric Argosols and Udic Isohumosols. The site has
obvious vertical vegetation zones, including broad-leaved
Korean pine forest with an elevation from 500 to 1000 m,
dark coniferous forest with an elevation from 1100 to
1700 m, and Erman’s birch forest with an elevation from
1700 to 2000 m. The broad-leaved Korean pine forest is the
dominating vegetation type (Wang et al., 2003a, b). The
Qilian Mountain (QLM) site is located in the Gansu
province, northwest China (991500 E, 381300 N) and has a
semiarid climate with a MAT of 0.3 1C and 440 mm of
precipitation annually. The typical soil types are Ustic
Isohumosols and Argosols in the CST classification with
main parent rock of calcareous rock. Influenced by the
topography and climate, vegetation type in the site is
mountainous pasture and forest, which includes Picea
crassisolia, Sabina przewalski and shrub forests (Chang
et al., 2005).
2.2. Samples and analysis methods
Soil samples from the four experimental sites were
collected using truck-mounted hydraulic soil probes in
2002 and 2003. Ten samples were collected according to
climate, soil and vegetation types, each of which included
three replicates (Table 1). Geographic coordinates and the
elevations of the four sites were obtained using a satellite
differential global positioning system (GPS).
Moist soil samples were air-dried and sieved to pass a
2 mm screen. Recognizable plant fragments were removed
by hand picking. Soil carbonates were removed by adding
100 ml of 250 mM HCL to 20 g soil and shaking for 1 h.
Soils were washed with deionized water to remove excess
Cl (Collins et al., 2000). Total C was measured by wet
oxidation using dichromate in acid medium followed by
the FeSO4 Titration method (Nelson and Sommers, 1975),
and pH was measured in 0.01 M CaCl2 (1:5 Soil: Solution
by volume) using a glass electrode (Sparks, 1996).
One-hundred g of each sample were incubated in 250 ml
glass jars in the dark at 25 1C and 60% water holding
capacity for 90 days. Water holding capacity was estimated
by a volumetric soil water method (Elliott et al., 1994). The
jars were normally closed but opened periodically to
maintain aerobic conditions. Water loss in the jars was
monitored by weight and replenished after opening. No
leaching occurred during the course of incubation. The
evolved CO2 was trapped in 25 ml, 0.4 N NaOH. Control
jars contained no soil. Evolved CO2 was precipitated by the
addition of BaCl2 and measured by titration of residual
NaOH to pH 7.0 with 0.4 N HCL. The evolved CO2 was
measured daily during the first week and every 3–4 days in
the following 2 weeks till the end of the incubation period.
The size of the resistant C pool (Cr) (Leavitt et al., 1997)
was determined by the residue of acid hydrolysis. Acid
hydrolysis consisted of refluxing 1 g soil in hot, 6 M HCL
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L. Yang et al. / Journal of Environmental Management 85 (2007) 690–695
for 18 h. The soluble materials were separated by filtration
followed by repeated evaporation to remove the HCL. The
residue of hydrolysis was rinsed with deionized water and
dried at 55 1C and ground to pass a 180 mm screen. The
total soil and the residue of hydrolysis were combusted to
CO2 (Paul et al., 2001a, b).
2.3. Model description
The three-pool first-order model (Paul et al., 2001a, b)
separates the mineralizable organic carbon into active, slow
and resistant C pools and can be presented as:
Ct ¼ Ca eK a t þ Cs eK s t þ Cr eK r t ,
(1)
where Ct is total organic carbon at time t, Ca and Cs are the
sizes of the active and slow pools; and Ka and Ks are
decomposition rate constants for the active and slow pools.
Cr is the size of the resistant carbon pool as estimated by
acid hydrolysis. Mean residence time (MRT) for each
component was calculated as the reciprocal of the
decomposition rate constant in the three-pool first-order
model. Radiocarbon dating of the residue of the acid
hydrolysis may be used to determine MRT for Cr. Carbon
dating is relatively expensive and therefore C dates are
often unavailable, and the MRT of Cr is commonly
assumed to be 1000 yr (Paul et al., 2001a, b). The
laboratory-derived values were scaled to the field according
to mean-annual temperature (MAT) by assuming a Q10 of
2ð25MATÞ=2 (Collins et al., 2000). The size of the slow pool
is defined as Cs ¼ CSOC Ca Cr with CSOC representing
the total SOC at time of sampling.
2.4. Model fitting and statistical analysis
Eq. (1) was fitted with a non-linear regression (SPSS
10.5) that uses the Marquardt algorithm and an iterative
process to find the parameter values that minimize the
residual sum of squares. The resultant pool sizes and their
mineralization rate constants could be sensitive to the
initially assigned parameter values and the iterative step
size. Generally, the automatically estimated initial parameters resulted in acceptable parameter values. In some
cases, initial parameter values and the iterative steps size
were adjusted by hand to obtain results in reasonable
ranges; for example, rate constants could not be negative,
and the sum of active, slow and resistant carbon pools
should not exceed total SOC.
The standard deviations of the parameters, the residual
mean square (RMS) and the F-values of the curve fits were
calculated (Little and Hills, 1975).
3. Results
rapid decomposition in the initial incubation stages and
gradually which gradually reached a steady state. During
the SOC decomposition process, the amount of decomposition in the first week accounted for 14–41% of total
decomposition.
In the LP and YJ sites, which belong to the sub-tropical
zone, rates of SOC decomposition differed substantially.
The decomposition rates in the LP site from Cunninghamia
lanceolata forest were faster than those in the YJ site. There
was the same trend under evergreen broadleaf forest.
Comparing the decomposition rates between the Cunninghamia lanceolata and evergreen broadleaf forests, the
maximum decomposition rate of the former was higher
than that of the latter (Fig. 1).
In the CBM and QLM sites, which belong to the
temperate zone, the rates of SOC decomposition varied
greatly (Fig. 2). During the incubation period, the
maximum SOC decomposition rate occurred during the
second day of incubation. After the initially high decomposition rate, there was a gradual decrease in all samples.
There were only small differences in the rates of SOC
decomposition. In the CBM site, SOC decomposition rates
from different types of vegetations varied and followed the
order spruce-fir4poplar and birch4Pteridophyta. In the
QLM sites, SOC decomposition rates from different types
of vegetation followed the order Picea crassifolia4
Sabinaprezew alskii4shrub forests.
3.2. SOC pools and dynamics
The distributions of the three pools of SOC have distinct
differences in different forests mainly because chemical
components of litterfall vary greatly which could cause
different effects on SOC decomposition rate. Although the
sizes of the SOC pools in the study areas were different,
20
18
CO2-C evolved (mg C kg-1day-1)
692
16
Cunninghamia lanceolata(LP)
14
Cunninghamia lanceolata(YJ)
Evergreen broadleaf(LP)
12
Evergreen broadleaf(YJ)
10
8
6
4
2
0
3.1. Characteristic of SOC decomposition
Although the rates of SOC decomposition were different
in different forests, qualitative trends were similar with
0
10
20
30
40
50
60
Incubation time (days)
70
80
90
Fig. 1. Decomposition rates of SOC from two vegetations under subtropical zone.
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there was a commonality in that the sizes of Ca were
smaller (Table 2). The Ca pool comprised 1–3% of SOC
with an average MRT of 219 days. Because Ca is active, a
small change in pool size can cause a marked change in
atmospheric CO2 concentration and cause a sharp effect on
the global climate. Therefore, it is vital to take measures to
decrease the loss of Ca and increase the stock of the stabile
C pool. The Cs pool comprised 25–65% with an average
MRT of 78 yr. The Cr pool accounted for 35–80%. There
was a greater retention of C in the forests. These results are
consistent with the existence of at least a small, labile
carbon pool and a much larger, more recalcitrant pool
(Collins et al., 2000).
The proportion of Ca in the YJ site was greater than in
the other study sites and this site had the lowest MRT for
both the Ca and Cs pools. This indicated that the soil in the
YJ site was active. The QLM site had the most stable pool,
and the CBM site had the second most stable pool (Table
2). So the stable C pools followed the order
QLM4CBM4LP4YJ which was consistent with the
temperature change in the study sites.
4. Discussion
Our data indicated that SOC decomposed rapidly during
the early incubation stages and gradually slowed down to a
CO2-C evolved (mg C kg-1 day-1)
25
Spruce-fir(CBS)
Poplar and birch(CBS)
20
Pteridophyta(CBS)
Picea crassifolia(QLS)
15
Sabinaprezew alskii(QLS)
shrub(QLS)
10
5
0
0
10
20
30
40
50
60
Incubation time (days)
70
80
90
Fig. 2. Decomposition rates of SOC from different vegetations under
temperate zone.
693
comparative steady state. During the early stages of
incubation, the decomposed SOC consisted mostly of
accumulations of Ca, presumably derived from vegetation.
The Ca pool comprised 1–3% of the SOC with an average
MRT of 219 days. The Cs pool comprised about 25–65%
with an average field MRT of 78 yr. The Cr pool accounted
for 35–80%. The sizes of the Cs and Cr pools indicated
that the SOC in the cold arid QLM site was the most
stable. Among the different forests sampled, the SOC
contents followed the order CBM4QLM4YJ4LP. The
ratio of the Ca pool to the CSOC contents followed the
order YJ4LP4CBM4QLM, with MRT showing the
inverse order YJ (MRT ¼ 8 days)4LP (MRT ¼ 47
days)4CBM (MRT ¼ 56 days)4QLM (MRT ¼ 80 days).
These orders correspond closely to the rank order of mean
annual temperature (MAT) across the study areas: YJ
(18.1 1C)4LP (15 1C)4CBM (5 1C)4QLM (3 1C). Our
results are thus consistent with the conclusion that
temperature is the most important environmental factor
that affects microbial processes in soils and consequently
SOC decomposition dynamics. Although a significant
correlation between soil temperature and SOC decomposition is well established (Singh and Gupta, 1977; Raich and
Schlesinger, 1992; Lloyd and Taylor, 1994; Kirschbaum,
1995; Katterer et al., 1998), there is no agreement about
which function to use to describe this relationship.
Litterfall properties could also affect SOC decomposition under the same environment. In particular, the lignin
concentration and lignin/N ratios of litterfall, both
generally higher in conifers than in broadleaved trees
(Perry et al., 1987; Petersen et al., 1997), are expected to be
negatively related to decomposition rates. Our results
indicate that SOC decomposition rate in Cunninghamia
lanceolata plantations was actually faster than that in
evergreen broadleaf forests in the sub-tropical zone,
contradicting some other researchers’ conclusions (Wu
et al., 1996). The relationships between the decomposition
rate and litterfall properties need to be studied further.
Soil texture, especially soil clay content, is also an
important factor influencing SOC dynamics under the
same climatic conditions. It was apparent that the fine
texture of clay soil reduced the amount of SOC mineralization which contributed to accumulation of SOC. Clay
Table 2
Pool sizes and laboratory mean residence times (MRT) of soil for the active, slow and resistant carbon pools from LP, YJ, CBM and QLM sites
Sample
Depth (cm) Ca (g kg1) MRTLab (days) Ca/SOC (%) Cs (g kg1) MRTLab (yr) Cs/SOC (%) Cr (g kg1) MRTLab (yr) Cr/SOC (%)
LP3
LP7
YJ1
YJ2
CBM0
CBM14
CBM22
QLM2
QLM7
QLM8
0–20
0–15
0–15
0–18
0–11
0–12
0–7
0–30
0–30
0–30
0.21
0.31
0.47
0.48
0.65
0.69
1.38
1.33
1.73
0.65
47
50
8
7
56
11
35
45
161
47
0.99
1.25
1.53
1.81
1.13
0.93
1.15
1.49
2.41
0.92
11.6
12.55
6.65
5.24
36.54
18.75
64.08
36.5
27.56
28.81
3
27
2
2
12
4
25
16
33
3
54.79
50.75
21.61
19.71
63.32
25.20
53.25
41.00
38.31
41.05
9.36
11.87
23.64
20.87
20.52
54.97
54.88
52.53
44.38
41.37
500
500
620
620
250
250
250
173
173
500
44.22
50.00
76.86
78.48
35.55
73.87
45.61
59.00
61.70
58.95
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Cr (g kg-1)
694
decomposition, it was found that temperature (MAT) was
a good predictor of SOC values and the active pool size,
but that other factors, such as vegetation type, may modify
SOC and pool composition. Further evaluation of the
relationships between SOC decomposition and environmental factors is required.
60
50
40
30
20
10
0
0
10
20
Clay content (%)
30
40
Fig. 3. The relationships between clay content and resistant carbon pool
from different study sites.
content affected the turnover of active carbon pools and
the stabilization efficiency of slow carbon pools (Sorenhen,
1981; Hassink, 1994). In our observations, there was no
significant correlation between the rate of SOC decomposition and soil clay content (Fig. 3). Similarly, Gregorich
et al. (1991) found that soil texture had no significant effect
on the decomposition rate of SOC. However, Wang et al.
(2003a, b) observed that there was a negative correlation
between clay content and the rate of SOC decomposition,
consistent with the results of Franzluebbers et al.
(1999a, b). A common practice in modeling is to assume
that the rate of SOC decomposition decreases with
increasing clay content (Jensen et al., 1994; Coleman and
Jenkinson, 1996). Soil textural effects on SOC decomposition could be confounded by clay mineralogy, chemistry of
SOM, microbial composition, inhibiting or toxic factors
such as extreme pH or heavy metals, and other soil
properties that are related to the clay content of the soils
tested. Such confounding effects are more difficult to
discern when only a small number of soils are used (Wang
et al., 2003a, b).
5. Conclusions
The dynamics of SOC decomposition followed a twophase pattern in which SOC was rapidly decomposed in the
initial incubation stages and its decomposition gradually
slowed down in a comparative steady stage. The reason
was that SOC was composed of two parts: active (easily
mineralizable) and slow and resistant carbon pools
(anti-mineralizable components). This could be described
by a three-pool first-order model. Many researchers have
shown that the three-pool first-order model could be used
to interpret the dynamics of forest SOC. Pool sizes and
MRT of the three pools were determined by the model. The
Ca pool comprised 1–3% of SOC with an average MRT
of 219 days. The C pool comprised 25–65% with an
average MRT of 78 yr. The Cr pool accounted for
35–80%. The analyses of pool sizes and MRT give
accurate estimates of SOC dynamics that may be used in
decision making related to global climate change. However
the pool sizes and MRT of the three pools from different
forest soils had obvious differences which showed that
SOC decomposition was affected by the environment and
other factors. By analyzing the factors that control SOC
Acknowledgements
This research was sponsored by the Canadian International Development Agency (CPR/00/G33/A/1G/99) and
the National Natural Science Foundation of China
(Project 40231016). Part of this work was completed while
the author was a visiting scholar at University of Toronto,
Canada. The authors thank Dr. Chen Minzhen, Prof. Tian
Qingjiu, Prof. Pan Genxing, Prof. Li Lianqing, Ms. Zhang
Yongqin, Dr. Li Zhiwei, Ms. Lu Xiongjie, Dr. Hui
Fengming, Dr. Jin Zhenyu, and Dr. Xia Xueqi for various
assistance.
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