Agriculture, Ecosystems and Environment 142 (2011) 184–194
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Agriculture, Ecosystems and Environment
journal homepage: www.elsevier.com/locate/agee
Effect of environmental factors on regional soil organic carbon stocks across the
Loess Plateau region, China
Zhipeng Liu a,c , Ming’an Shao b,∗ , Yunqiang Wang a,c
a
State Key Laboratory of Soil Erosion and Dry-land Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences,
Ministry of Water Resources, Yangling, Shaanxi 712100, PR China
b
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, PR China
c
Graduate School of Chinese Academy of Sciences, Beijing 100049, PR China
a r t i c l e
i n f o
Article history:
Received 13 August 2010
Received in revised form 1 May 2011
Accepted 2 May 2011
Available online 25 May 2011
Keywords:
Soil organic carbon density
Environmental factors
Geostatistics
Spatial pattern
Land-use
a b s t r a c t
Accurate knowledge of regional soil organic carbon (SOC) stocks and the effects of environmental factors
on SOC is crucial, both from the perspective of regional carbon budgets and appropriate landscape management of SOC. However, little information is available regarding the regional SOC stocks in the Loess
Plateau region in China. Thus, the objectives of this study were to estimate the current regional SOC
stocks and to analyze the relationship between SOC and pertinent environmental factors, i.e. precipitation, temperature, elevation, slope gradient, clay plus silt content (<20 ␮m) and land use. We investigated
upper (0–20 and 20–40 cm) and deeper (0–100 and 100–200 cm) soil layers at 382 sampling sites across
the entire Loess Plateau region (620,000 km2 ). Regional spatial distribution of soil organic carbon density
(SOCD) was depicted in a map and SOC stocks were calculated for different soil depths using a geostatistical method. Analysis of variance (ANOVA) was used to analyze the effects of environmental factors on
SOCD. Results showed that the mean SOCD was 2.64 kg C m−2 in the 0–20 cm soil layer and 4.57 kg C m−2
in the 0–40 cm soil layer, and it was estimated that 1.64 and 2.86 Pg (1 Pg = 1015 g) of organic carbon
were stored in these soil layers, respectively. Estimates for deeper soil layers indicate that mean SOCD in
the 0–100 and 0–200 cm layers was 7.70 and 12.45 kg C m−2 , respectively, while the total organic carbon
stocks amount to 4.78 Pg C (0–100 cm) and 5.85 Pg C (0–200 cm), respectively. Precipitation, temperature,
elevation, clay plus silt contents and land use showed significant regional impacts on SOCD. Generally,
SOC contents are higher in soils on mountains (with relatively high elevations and low temperatures) and
valleys (with low elevations and high precipitation). The results also show that human activities have
heavily affected SOC accumulation. Measured SOCD under cropland was relatively higher than under
grassland and forestland. The study provides an overview of the current spatial pattern and stocks of
SOC, as well as the effects of environmental factors on SOCD, across the entire Loess Plateau region
and may be of further use in optimizing strategies for ecological restoration and regional SOC dynamic
modeling as an important initial input.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Soil organic carbon (SOC) is an important soil component that
plays key roles in the functions of both natural ecosystems (greatly
influencing soil structure, fertility, and water-holding capacity) and
agricultural systems, in which it also affects food production and
quality (Lal, 2004a). Recently, attention has focused on organic carbon pools in soils because of its critical role in the global carbon
cycle and its potential for mitigating or exacerbating atmospheric
levels of greenhouse gases. (Raich and Potter, 1995; Davidson and
∗ Corresponding author. Tel.: +86 29 87012405; fax: +86 29 87012334.
E-mail address: [email protected] (M. Shao).
0167-8809/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.agee.2011.05.002
Janssens, 2006). The global SOC stock has been estimated to be
about 1400–1500 Pg C in the upper 100 cm soil layer (Post et al.,
1982; Eswaren et al., 1993; Batjes, 1996), which is approximately
twice the amount of C in the atmosphere and three times the
amount stored in terrestrial vegetation (Smith, 2004). Thus, slight
reductions in SOC contents due to changes in land-use, soil management, or rates of soil erosion, could significantly raise the CO2
concentration in the atmosphere. However, soils have the potential
to mitigate increasing atmospheric CO2 concentrations through C
sequestration with maximum potential global sequestration estimates ranging from 0.45 to 0.9 Pg C year−1 (Batjes, 1999; Bruce
et al., 1999; Lal, 2004b).
Soil carbon sequestration may be capable of playing a major
role in attempts to meet the aims of the United Nations Frame-
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
work Convention on Climate Change to limit rises in atmospheric
CO2 thereby mitigating their potential effects (Schlesinger, 2000;
Ingram and Fernandes, 2001). Accurate assessment of the spatial
pattern and stocks of SOC, especially at national and sub-national
scales, is an indispensable step when evaluating sequestration
potentials. Therefore, SOC stock inventories have been widely
established: at a global level (Eswaren et al., 1993; Sombroek
et al., 1993; Batjes, 1996), and in North America (Lacelle et al.,
1997), South America (Batjes, 2000), various European countries
(Arrouays et al., 2001; Batjes, 2002; Krogh et al., 2003), India
(Bhattacharyya et al., 2000), Brazil (Bernoux et al., 2002) and China
(Fang et al., 1996; Pan, 1999; Wang et al., 2000; Jin et al., 2001; Li
et al., 2003; Xie et al., 2004; Yu et al., 2007a,b).
Stocks of SOC have also been investigated in various subnational regions, including northern (Zhao et al., 2006b), eastern (Li
et al., 2001b, 2002), southwestern (Zhang et al., 2008) and southeastern (Zhao et al., 1997) regions of China. However, most of these
studies focused on humid or semi-humid areas of China, and there
is little information on either regional SOC stocks or their relationships with pertinent environmental factors in the large-scale arid
and semiarid inland regions, which have been regarded as major
potential carbon sinks (Squires, 1998; Lal, 2002; Ardö and Olsson,
2003). The Loess Plateau region (620,000 km2 ) in China, which is
typically arid or semiarid, has deep loess soils and is subject to
severe soil erosion (Shi and Shao, 2000). Due to natural drought
conditions, intensive human disturbance and severe soil erosion,
the region has the lowest soil organic carbon density (SOCD) in
China (Yu et al., 2007a). However, it is possible to increase organic
carbon content, as well as to sequestrate more carbon, in this
region’s soils through appropriate reforestation of degraded soils
and ecosystems whose resilience capacity is intact (Lal, 2004a; Pan,
2008).
To acquire accurate estimates of regional SOC stocks, reliable
datasets providing information on pertinent soil variables in all of
the areas, or types of sites, within the entire region are required.
Several areal distributions of SOC in China, Chinese regions and
other countries have been estimated by scaling up estimates
obtained from soil sampling points and maps showing the distributions of related variables (soil type, land-use type, soil polygons
or vegetation type) (Krogh et al., 2003; Yu et al., 2007b). A serious
concern with this approach is that the division of areas according to
classified types is based on Boolean logic, ignoring the spatial continuity between the plots and spatial variability within the plots
(Zhu, 1997). Thus, both the accuracy and the reliability of the area
datasets are questionable (Pan, 1999). Furthermore, previous largescale evaluations of SOC stocks in China have generally been based
on soil data (including soil profile records, soil bulk density, SOC
concentration and proportions of rock fragments) gathered by the
Second National Soil Survey of China (1979–1983) (Wang et al.,
2000; Li et al., 2001a, 2002; Zhao et al., 2006b; Zhang et al., 2008).
These estimates were based on decades old information, which may
have subsequently changed significantly, and it was not possible to
establish a baseline year. To better understand the SOC reservoir, it
is necessary to update regional SOC information with intensive and
contemporary measurements and to use a geostatistical method to
avoid dependence on various maps having artificial boundaries.
The SOC content varies from place to place, controlled by a
series of environmental factors at different spatial scales (Powers
and Schlesinger, 2002), such as climate variables (Ganuza and
Almendros, 2003; Dai and Huang, 2006; Davidson and Janssens,
2006), topography (Rezaei and Gilkes, 2005; Liu et al., 2006), soil
texture (Hassink, 1996; Percival et al., 2000; Heviaa et al., 2003),
vegetation and land-use types (Su et al., 2006; Wang et al., 2009),
and human management (Sainju et al., 2008). However, large-scale
studies on the relationship between SOC and affecting factors are
not sufficient (Lal, 2004b; Chaplot et al., 2010) and a good under-
185
standing on this issue is essential to develop suitable management
strategies to enhance carbon sequestration.
Therefore, the two objectives of the study presented here were:
(1) to acquire contemporary measurements of SOCD and a database
of pertinent environmental factors, and then to use a geostatistical method to evaluate the SOC stocks in various layers of soils in
China’s Loess Plateau region; and (2) from a large-scale perspective,
to analyze the effects of those environmental factors on SOC stocks.
2. Materials and methods
2.1. Study area
The Loess Plateau region, as defined here and illustrated
in Fig. 1a, is located in northwestern China within latitudes
33◦ 43 –41◦ 16 N and longitudes 100◦ 54 –114◦ 33 E, between the
upper and middle reaches of the Yellow River, covering a total
area of 620,000 km2 , about 6.5% of the area of China. The
region is surrounded by mountains (the Yinshan, Qinling, Taihang and Riyue-Helan Mountains to the north, south, east and
west, respectively), and it has highly complex topography, including sub-plateaus, basins, hills and gullies, with altitudes ranging
from 200 to 3000 m. The region is dominated by a temperate,
arid and semi-arid continental monsoon climate. The mean annual
temperature ranges from 3.6 to 14.3 ◦ C, and the mean annual precipitation ranges from 150 to 800 mm, most (55–78%) of which
falls between June and September, and decreases along a southeast to northwest transect (Yang and Shao, 2000). The soils are
mainly derived from loess, and are of diverse types. The most
widespread soil is clay–loam in texture, with sandier soils in the
northwest and clayier soils in the southeast (Wang et al., 2010b).
The natural vegetation has been largely destroyed by deforestation and cultivation. Much of the region (280,000 km2 ) is subject
to severe soil erosion (Shi and Shao, 2000). A large quantity of
the eroded mass is transported into the Yellow River, which carries a sediment load of about 16.4 × 109 t year−1 (SSCCWRA, 1989).
Land degradation, desertification, soil erosion and declining soil
fertility threaten the environment. However, since the 1950s the
Chinese Government has made efforts to control soil erosion and
restore the ecosystem and, in 1999, an extensive ecological rehabilitation program, designated “Grain-for-Green”, was initiated in
the Loess Plateau region. The program has now been operating
for about 10 years and the natural environment in parts of the
Plateau is improving as annual crops are replaced by perennial
plants.
2.2. Estimation of soil organic carbon density (SOCD)
2.2.1. Field sampling and laboratory analysis
In order to obtain updated SOCD data, 382 sampling sites were
investigated throughout the Loess Plateau region, from April to
November, 2008 (Fig. 1b). In the field, the approximate location of each sampling site was controlled by the spacing distance
(30–50 km) between sites, which was measured by a vehicle’s
milometer during travelling. Furthermore, the actual sampling sites
were randomly selected to represent the main topography, landuses and vegetation types within the range of vision; a global
positioning system (GPS) (5-m precision) was then used to identify the site’s longitude, latitude and elevation. Notes were made
of land-use type, vegetation species and coverage by observation;
aspect and slope measured with a geological compass; and information on human activities (irrigation, fertilizer use and crop yield)
collected from surveys of the local inhabitants.
Disturbed soil samples were collected with a 5-cm diameter
soil auger, extracted in 10-cm incremental subsamples that were
186
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
Fig. 1. Locations of (a) the Loess Plateau region in China and (b) the sampling sites.
then mixed together by hand. Soil samples representing the upper
soil layers were collected from the 0–20 cm and 20–40 cm depths
at five different places randomly selected within a 10-m radius
at each site; for each layer, the five samples were then mixed by
hand to form one representative sample for that layer at the site.
Soil samples representing deeper soil layers were collected from
0–100 cm and 100–200 cm depths from one place at each site. In
total, 1528 soil samples were collected to determine SOC concentrations, i.e., one sample from four soil layers (0–20, 20–40, 0–100
and 100–200 cm) at each of the 382 sampling sites. In addition,
undisturbed soil cores from the 0–20 cm and 20–40 cm soil layers
were collected and sealed in air-tight containers for measurements
of bulk density.
All disturbed soil samples were air-dried, sealed in air-tight bags
and taken to the laboratory. Samples passed through a 0.25-mm
mesh were used to determine SOC concentration (g kg−1 ), using the
Walkley-Black method (Nelson and Sommers, 1982). The extraction efficiency of SOC by the Walkley-Black method varies between
60 and 86%, with a mean recovery of 76% (Walkley and Black, 1934).
In this study, the SOC data were adjusted with a correction factor of
1.32 to estimate 100% organic C recovery (Meersmans et al., 2009;
Wei et al., 2010). Samples passed through a 2-mm mesh were used
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
187
to determine soil mechanical composition (%) by laser diffraction
using a Mastersizer 2000 (Malvern Instruments, Malvern, England)
(Liu et al., 2005). Soil bulk density (g cm−3 ) was determined by measuring the original volume of each soil core and the dry mass of it
after oven-drying at 105 ◦ C.
were log-transformed and an ordinary point kriging method was
used for interpolation, without trend removal. The predicted map
quality of the SOCD was tested by cross validation with replacement (Isaaks and Srivastava, 1989). The geostatistical analysis was
conducted using GS + software (version 7.0).
2.2.2. Calculation of SOCD
SOCD (kg C m−2 ) was calculated using the following equation
(Zhang et al., 2008):
2.3.2. Derivation of an accumulation process using GIS
After spatial interpolation, a SOCD surface was created to
cover the entire area of the Loess Plateau region. This surface was exported as a raster layer with a defined resolution
(3000 m × 3000 m) in which every grid square was assigned both a
SOCD value and an area value. The next step was to use an accumulation process as follows:
SOCDh =
n
Li × SOCi × bi × (1 − Fi /100)
100
i=1
where SOCDh is the total amount of soil organic carbon between
the soil surface and depth h per unit area (kg C m−2 ); n is the number of layers considered; i is the ith layer and Li , SOCi , bi , and Fi
are the thickness (cm), SOC concentration (g kg−1 ), bulk density
(g cm−3 ), and the proportion (%) of coarse (>2 mm) fragments in
the ith layer, respectively. The occurrence of coarse particles in the
loess soils of the study region was rare, so Fi was considered to
be negligible. We could determine SOCD to a depth of 40 cm more
reliably by using the data obtained for the two layers, 0–20 cm and
20–40 cm, which included the measured soil bulk densities. To estimate SOCD in the 0–100 cm and 100–200 cm soil layers, a mean
soil bulk density value for loess soils (1.25 g cm−3 ) was used (Wang
et al., 2010a).
2.3. Estimation of SOC stocks
2.3.1. Geostatistics
To estimate SOC stocks, data on both the SOCD (kg C m−2 )
and the area (m2 ) of the study region are required. Geostatistics
(Webster, 1985) can be used to convert an SOCD dataset from
discrete points to a spatially continuous surface with optimal interpolation. Based on regionalized variable theory (Matheron, 1963),
geostatistics uses semivariograms (Yost et al., 1982) to quantify
spatial autocorrelations and provide input parameters for spatial
interpolation, i.e., kriging (Krige, 1951). An empirically derived
semivariogram can be expressed as:
1 2
[Z(xi + h) − Z(xi )]
2N(h)
N(h)
(h) =
i=1
where ␥(h), the semivariogram estimator, is half the expected
squared difference between paired data separated by a distance
h (Webster and Oliver, 2000), Z(xi + h) and Z(xi ) are observations at
positions xi + h and xi , respectively, and N(h) denotes the number of
data pairs.
Standard theoretical models (spherical, Gaussian, exponential and linear), were then fitted to the empirical semivariogram
derived from the measured data. The best fitted model, with the
smallest residual sum of squares and the largest coefficient of
determination, was used to provide input parameters for kriging
interpolation. Essentially, the predictions obtained in this manner
are like weighted moving means:
Z ∗ (x0 ) =
n
i Z(xi )
i=1
where Z* (x0 ) is the predicted value at position x0 , Z(xi ) is the known
value at sampling site xi , and n is the number of sites within the
neighborhood searched for the interpolation. The weight, i , is
decided by spatial autocorrelation according to the semivariogram
(Burgess and Webster, 1980), under the conditions of unbiased
error and minimized estimation variance. Here, original datasets
SOCSh =
n
SOCDi × Areagrid × 10−12
i=1
where SOCSh is the total amount of soil organic carbon stock (Pg C;
1015 g C) at depth h in the study region, n is the total grid number
of the raster, i is the ith grid square, SOCDih is soil organic carbon
density (kg C m−2 ) for the ith grid square calculated to depth h, and
Areagrid is the area (m2 ) of each grid square, set by the defined resolution. These calculations were performed using the GIS software
package Arcmap Desktop (version 9.1) with the Spatial Analyst
module.
2.4. Pertinent environmental factors and classification
To assess the relationships between SOCD and pertinent environmental factors at the regional scale, the following variables were
examined: precipitation, temperature, elevation, land use, slope
gradient and clay plus silt content (<20 ␮m) (Hassink, 1996).
Precipitation and temperature datasets were derived as annual
means of meteorological records (1951–2001) from 68 weather stations distributed throughout the region, and kriging interpolation
was employed to create a continuous data surface of mean annual
precipitation and mean annual temperature. Then the spatial coordinates of the sampling points were used to extract meteorological
parameters for each sampling point from that data surface.
According to standard classification criteria for arid and semiarid areas, the sampling sites (and associated datasets) were
divided into three groups with annual precipitation of: <250,
500–250 and >500 mm (Table 1). The annual temperature and elevation datasets were also divided into three groups: <5, 10–5, and
>10 ◦ C and <1000, 1000–1500 and >1500 m (ISTCASLP, 1990, 1991).
Land use was classified into three major groups: cropland, forestland and grassland. According to their distribution histograms,
slope gradients and clay plus silt contents were divided into five (0◦ ,
<10◦ , 10◦ –20◦ , 20◦ –30◦ , and >30◦ ) and three groups (<70%, 70–85%,
and >85%), respectively (Table 1).
2.5. Statistical analysis
The mean and standard deviation values were used to represent
SOCD in different soil layers. The Kolmogorov–Smirnov (K–S) test
was used to determine whether the data were normally distributed.
In our study, precipitation, temperature and elevation values were
normally distributed; SOCD values were log-normally distributed;
and land use, slope gradient and clay plus silt content were neither normally nor log-normally distributed. The Runs and Levene
tests were used to confirm sample independence and homogeneity of variance. One-way analysis of variance (ANOVA) was used
to analyze the effect of each environmental factor on SOCD within
the groups defined in Section 2.4. Subsequently, Duncan’s Multiple
Range test was used to identify statistically significant differences
(p < 0.05) among the mean values of SOCD within each group of each
188
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
Table 1
Description of the class divisions of environmental factors considered to affect soil organic carbon contents.
Class
Precipitation (mm)
Temperature (◦ C)
Land use
Elevation (m)
Slope (◦ )
CSC (%)
1
2
3
4
5
<250 (n = 34)
250–500 (n = 210)
>500 (n = 138)
<5 (n = 37)
5–10 (n = 235)
>10 (n = 110)
Cropland (n = 153)
Forestland (n = 128)
Grassland (n = 101)
<1000 (n = 120)
1000–1500 (n = 192)
>1500 (n = 70)
0 (n = 297)
0–10 (n = 22)
10–20 (n = 27)
20–30 (n = 20)
>30 (n = 16)
<70 (n = 107)
70–85 (n = 135)
>85 (n = 140)
CSC: clay plus silt content (<20 ␮m); n = number of samples.
environmental factor. By using this method, effects of precipitation, temperature, elevation, slope, and clay plus silt content were
detected within each land use type; and effects of land use type
were detected within each of the groups defined by precipitation,
temperature, elevation, slope and clay plus silt content. Statistical
analysis was carried out using SPSS 13.0.
3. Results and discussion
3.1. The SOC pool
3.1.1. SOCD Statistics
From the data for the 1528 soil samples, the mean SOCD
(kg C m−2 ) across the region was calculated to be 2.64 and
4.57 kg C m−2 in the 0–20 cm and 0–40 cm soil layers, respectively, and estimated to be 7.70 and 12.45 kg C m−2 in the 0–100
and 0–200 cm layers, respectively (Table 2). These values were
lower than reported values for previously investigated regions in
China. For example, it was reported that the SOCD was 6.8 and
21.4 kg C m−2 for 0–20 cm and 0–100 cm soil layers, respectively,
in eastern tropical and subtropical regions (Li et al., 2001a); and
4.27 kg C m−2 for the 0–20 cm soil layer of paddy soils in northeast China (Wang et al., 2007), which was twice as high as the
corresponding value that we found (2.64 kg m−2 ). In the southwest
mountain regions (Zhang et al., 2008), the SOCD was 14.43 kg C m−2
for the 0–100 cm layer, about two times more than for the entire
Loess Plateau region (7.70 kg C m−2 ). Across the total area of China
(9,280,000 km2 ; Yu et al., 2007a), the SOCD was estimated to be
9.60 kg C m−2 for the 0–100 cm soil layer, about 25% more than the
value for our study region.
Table 3 showed geostatistical parameters derived from logtransformed SOCD values for the 0–40 cm and 0–100 cm soil layers.
No anisotropy was detected (Wang et al., 2009), and isotropic
semivariograms were best fitted by a spherical model to obtain
continuous curves. The ranges (maximum correlated distances) of
log-transformed SOCD values for the 0–40 cm and 0–100 cm layers
were 339 and 363 km, respectively. The ranges were much larger
than the sampling interval (30–50 km), indicating that our sampling design was sufficient to accurately assess the regional SOC
stocks. The nugget-to-sill ratios were 0.451 and 0.507, indicating
moderate spatial dependence (Cambardella et al., 1994) for the
region.
Kriging interpolation gave a continuous surface of SOCD covering the entire Loess Plateau region. Fig. 2 shows the SOCD
distribution map for the 0–40 cm soil layers. Cross validation
showed that the regression coefficient, when measured and estimated values were compared, was 0.977, close to 1; the mean
prediction error (ME) was 0.0087, close to 0; the root mean square
prediction error (RMSE) was 0.75, which was small. All these indices
indicated that the interpolated SOCD map was accurate and credible.
3.1.2. Estimation of current SOC stocks
Current SOC stocks were calculated for the four soil layers. These
updated values were 1.64, 2.86, 4.79 and 5.85 Pg organic carbon
for the 0–20, 0–40, 0–100 and 0–200 cm soil layers, respectively.
The SOC stock in the 0–40 cm soil layers accounted for about 60%
of the estimated total SOC mass in the 0–100 cm soil layers. The
stock in the 100–200 cm soil layers was relatively low, accounting for about 18% of the total SOC mass in the 0–200 cm soil
layers.
Globally, SOC stocks in 0–20 and 0–100 cm soil layers have been
estimated to be 455 Pg C (Potter et al., 1993) and 1550 Pg C (Lal,
2004a), respectively. If the values obtained in the cited studies and
those which we obtained are accurate, SOC stocks in the 0–20 cm
and 0–100 cm of soils in China’s Loess Plateau region contribute
0.36% and 0.31% to the global SOC stored in these respective layers.
In addition, our results indicate that the 0–20 and 0–100 cm soil
layers of the Loess Plateau, which covers nearly 6.5% of the area
of China, currently holds about 8.21% and 5.32% of the total SOC
stocks in these layers in China, respectively (Pan, 2008). The Loess
Plateau region accounts for 21.0% of the land area in northwest
China, but appears to hold 87.1% and 33.3% of the total SOC stock
in the 0–20 cm and 0–100 cm soil layers, respectively, of this part
of China (Pan, 1999).
In addition to the studies mentioned above, several earlier studies assessed SOC stocks in the Loess Plateau region, using traditional
methods and datasets collected several decades ago. Using data
gathered by the Second National Soil Survey of China (1979–1983),
Xu et al. (2003) estimated that 1.07 Pg SOC was stored in the upper
20 cm soils in the Loess Plateau region (within an area covering
429,800 km2 ). During 1985–1988, a soil survey was conducted
throughout the Loess Plateau region by members of the Institute
of Soil and Water Conservation, Chinese Academy of Sciences. The
results indicated that the SOC stock amounted to 1.23 Pg C in the
0–20 cm soil layers within an area of 592,900 km2 (unpublished
data). Our estimate for the organic carbon stock in the 0–20 cm
soil layers within the area we considered (covering 620,000 km2 )
was 1.64 Pg C, at a similar level to the finding of the earlier study. It
indicated that the SOC stocks of the large regions can indeed be calculated with less extensive and long time soil surveys. Moreover,
the estimation can be free from the crisp boundaries and uncertainties in various classification maps. We updated the regional SOC
stock information with our currently measured data and extended
the maximum estimated depth to 200 cm. Since the methodology
used to estimate SOC stocks in the present and previous investigations differed, it is difficult to determine temporal trends in SOC
stocks from the available results.
3.2. Effects of environmental factors on SOCD
Soil organic carbon is influenced by a large number of related
factors and the regulating processes are highly complex and vary
spatially in both vertical and horizontal directions. Compared to
the deeper soil layers, upper soil layers interact more directly with,
and are more sensitive to, climate, vegetation and human activities.
In our study, we focused on the relationships between SOCD in
the 0–40 cm soil layers and six pertinent environmental factors, i.e.
precipitation, temperature, elevation, slope, clay plus silt contents
(<20 ␮m) and land use.
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
189
Table 2
Soil organic carbon density (SOCD) (kg C m−2 ) in different soil layers under different land uses.
SOC pool
SOCD20 c
SOCD40
SOCD100
SOCD200
All samples
Cropland
na
Meanb
382
382
382
382
2.64
4.57
7.70
12.45
±
±
±
±
1.77
2.88
4.36
6.57
Grassland
n
Mean
153
153
153
153
3.03
5.36
8.82
14.54
±
±
±
±
1.87a
3.20a
4.44a
5.18a
Forestland
n
Mean
101
101
101
101
2.01
3.52
6.48
10.30
±
±
±
±
1.17b
2.07c
3.78b
5.27c
n
Mean
128
128
128
128
2.65
4.42
7.27
12.04
±
±
±
±
1.87b
2.72b
4.17b
6.28b
Different lowercase letters within rows indicate significant differences among land use types according to Duncan’s Multiple Range Test (P < 0.05).
a
n is the number of samples.
b
Mean ± standard deviation.
c
SOCD20 , SOCD40 , SOCD100 , SOCD200 refers to SOCD calculated for 0–20 cm, 0–40 cm, 0–100 cm, 0–200 cm soil layers, respectively.
Table 3
Geostatistical parameters for soil organic carbon density (SOCD) (kg C m−2 ) in the 0–40 cm and 0–100 cm soil layers.
SOC pool
Model
r2
Nugget (C0 )
Sill (C + C0 )
Proportion (C0 /C + C0 )
Range (km)
Anisotropy ratio
SOCD40 a
SOCD100
Spherical
Spherical
0.947
0.925
0.032
0.034
0.071
0.067
0.451
0.507
339
363
2.22
1.92
a
SOCD40 , SOCD100 refers to SOCD in the 0–40 cm and 0–100 cm soil layers, respectively; r2 is the coefficient of determination.
3.2.1. Effects of precipitation and temperature
It is generally accepted that climatic factors, especially precipitation and temperature, are the most important determinants of
SOC contents (Homann et al., 1995; Alvarez and Lavado, 1998),
due to their effects on the quantity and quality of organic residue
soil inputs and on the rates of soil organic matter mineralization and litter decomposition (Quideau et al., 2001; Heviaa et al.,
2003).
Fig. 3a shows the effects of precipitation on SOCD. Generally,
SOCD was significantly higher in areas where the precipitation was
greater than 500 mm than where it was less than 500 mm (p < 0.05).
Precipitation would be expected to influence SOCD since higher
precipitation is generally associated with higher rates of vegetation growth, and thus, with higher rates of organic carbon input
and SOC accumulation. However, there was no effect of increased
precipitation on SOCD when amounts were less than 500 mm, since
no significant differences in SOCD were detected between arid
(<250 mm) and semiarid (250–500 mm) areas. Increasing susceptibility to soil erosion could reduce the positive effect of increased
precipitation on SOCD in these areas, by decreasing SOC accumulation through removal of surface litter and particles attached to
organic carbon (Lal, 2004b). The semiarid areas are more susceptible to soil erosion by water than the arid areas due to the greater
amount of rainfall; storms are also more intense and of longer duration. The semiarid areas are more likely to be cultivated than the
arid areas, so that recently tilled bare soils are exposed to the erosive power of rainfall. In contrast, the surfaces of uncultivated arid
soils are often protected to a degree by biological surface crusts
Fig. 2. Spatial distribution of soil organic carbon density (kg C m−2 ) in 0–40 cm layers of soils in the Loess Plateau region.
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
Soil Organic Carbon Density
0-40 cm (kg C.m-2)
8
8
Cropland + Forestland + Grassland
Cropland
Forestland
Grassland
b
6
(a)
b b
b
b
a
a a
4
a
a
aC
2
a
b
aC
bF
aC
aG
0
bF
aF
< 250
cG
bG
250-500
Soil Organic Carbon Density
0-40 cm (kg C.m-2)
190
b
6
b
b
a
F
b
6
a
b
b
ba
aC
aC
b
0
a
a a
4
2
(b)
F
aC
aG
<5
F
aF
b
aG
5-10
Mean Annual Temperature (ºC)
F
aG
b
aG
< 1000
bG
1000-1500
> 1500
Elevation (m)
Cropland + Forestland + Grassland
Cropland
Forestland
Grassland
b
(d)
a
b
6
b
a
a
a
4
a a
2
a
0
b
b
aC
bC
bC
bF
aF
aG
> 10
bC
aF
b
8
Soil Organic Carbon Density
0-40 cm (kg C.m-2)
Soil Organic Carbon Density
0-40 cm (kg C.m-2)
Cropland + Forestland + Grassland
Cropland
Forestland
b b
Grassland
c
b
aC
bC
Mean Annual Precipitation (mm)
8
a
a a
a
2
(c)
a
b
4
0
> 500
Cropland + Forestland + Grassland
Cropland
b
Forestland
Grassland
aG
< 70
bF
b
70-85
G
c
G
> 85
Clay plus Silt (<20 µm) Content (%)
Fig. 3. (a–d) Mean soil organic carbon density (kg C m−2 ) in 0–40 cm soil layers for different precipitation, temperature, elevation, clay plus silt content and different land uses.
Different lowercase letters denote significant differences among precipitation, temperature, elevation, clay plus silt content classes for a given land use class (superscripts C,
F, G denote cropland, forestland, and grassland; no superscript denotes all land uses combined); different lowercase italic letters denote significant differences among land
uses within an individual precipitation, temperature, elevation, clay plus silt content (p < 0.05) (Duncan’s Multiple Range Test). The error bars represent ± one standard error.
(Kenapen et al., 2007). Finally, as indicated by the sampling site
data, the semiarid areas have steeper slopes than the arid area.
Other studies have shown that the most severe soil erosion in
the Loess Plateau region occurs in the semiarid area, in the middle reaches of the Yellow River (Tang, 1991; Xin et al., 2009).
Another factor affecting SOCD was temperature, which decreased
with decreasing precipitation from the southeast to the northwest
in our study region (Yang and Shao, 2000). Lower temperatures
could result in reduced SOC breakdown, thereby increasing SOC
accumulation and weakening the positive effect of precipitation
(Townsend et al., 1995; Trumbore et al., 1996).
Fig. 3a also shows different effects of precipitation on SOCD
under different land use types. Under cropland, precipitation
did not significantly affect SOCD (p < 0.05). However, cultivation
processes on the Loess Plateau, such as land leveling and terracing, fertilization, tillage and crop residue management, tended to
increase SOC accumulation in all areas while irrigation mitigated
shortages in precipitation. Under forestland, SOCD was significantly higher in areas with precipitation either less <250 mm or
>500 mm, compared to that between 250–500 mm (p < 0.05). The
high SOCD under relatively low and high precipitations could be
attributed to low decomposition of litter under those precipitation
regimes (Seneviratne et al., 1998). For grassland, SOCD significantly
increased with increases in precipitation. Different responses of
land use to precipitation could be attributed to the nature of the
different plant species and diverse complex interactions between
them and climatic conditions, soil water conditions, microorganisms and human activities.
Similarly, Fig. 3b shows both an overall effect of temperature on
SOCD and the various effects of temperature on SOCD under different land use types. When land use type is not considered, SOCD
was significantly lower in areas where temperature was intermediate (5–10 ◦ C) than in both relatively cooler (<5 ◦ C) and warmer
(>10 ◦ C) areas, between which there was no significant difference in
mean SOCD values (p < 0.05). Mean annual precipitation was higher
(547 mm) in the areas with temperatures less than 10 ◦ C compared
with 396 mm in areas with temperatures between 5 and 10 ◦ C. The
combination of warmer temperatures and wetter conditions could
lead to better biomass productivity and greater SOC accumulation.
Relatively higher SOCD in areas with temperatures greater than 5 ◦ C
could be attributed to the slower microbial and chemical breakdown of SOC due to the lower temperatures, as well as the drier
conditions. The absence of significant differences between areas
with temperatures less than 5 ◦ C or greater than 10 ◦ C may coincidentally result from differing interaction effects of precipitation
and temperature on SOCD. An additional factor to consider is that
areas with intermediate temperatures were mainly located in the
central Loess Plateau, which is subject to severe soil erosion (Tang,
1991).
Effects of temperature on SOCD also differed among the various
land use types. Under cropland and grassland, temperature had no
significant effect on SOCD (p < 0.05). In these cases, it suggests other
factors have greater effects on SOCD than temperature alone, or that
the integrated effects of climate, topography and human activities
dampened the independent effect of temperature. Under forestland, SOCD was significantly higher in areas with lower (<5 ◦ C) and
higher (>10 ◦ C) temperatures than in those areas with intermediate temperatures (5–10 ◦ C) (p < 0.05). This pattern was consistent
with the overall effect of temperature on SOCD as mentioned above,
which suggests that the presence or absence of forests in these
regions defined by temperature is a dominant factor in determining
SOCD.
Fig. 4. Mean annual precipitation and temperature as a function of elevation in the
Loess Plateau region, based on datasets for 382 sampling sites.
3.2.2. Effects of elevation
As shown in Fig. 3c, when land use type is not considered,
mean SOCD values were significantly lower in areas with intermediate elevations (1000–1500 m) than in areas with relatively
higher (>1500 m) or lower (<1000 m) elevations, and there was no
significant difference in the mean SOCD values for these two latter elevation classes (p < 0.05). Under cropland and forestland in
the same elevation class, the mean SOCD was statistically the same
(p < 0.05). This was also true for grassland except at the lower elevations (<1000 m) where mean SOCD values were significantly lower
than under the other two land types. Furthermore, under grassland
there were no significant differences between the mean SOCD values in areas at elevations below 1500 m but SOCD was significantly
higher at elevations above 1500 mm (p < 0.05).
The effect of elevation was complex and was probably indirect.
For example, there is an association between elevation and climatic
parameters such as precipitation and temperature (Trumbore et al.,
1996; Garten et al., 1999). This is demonstrated in Fig. 4, which
shows that both precipitation and temperature tend to decline
with increases in elevation in the Loess Plateau region according
to our data. Lower temperatures could reduce SOC turnover rates,
leading to increases in SOC levels; Leifeld et al. (2005) suggested
that such increases may be in the order of 0.75–2.1 mg g−1 per
100 m increase in elevation in Swiss agricultural soils. However, at
lower elevations the wetter and warmer conditions could increase
biomass productivity, leading to increased organic carbon inputs.
Elevation is also associated with geomorphology that could influence soil erosion and geological deposition processes (Tan et al.,
2004; Dai and Huang, 2006), consequently affecting topsoil SOC
values. In the Loess Plateau region the most severe erosion occurs
from the slopes in an area where the elevations are between 1000
and 1500 m (Tang, 1991; Xin et al., 2009). The same areas have
relatively moderate precipitation that fall in intense storms. Soil
erosion leads to the removal of SOC and other nutrients, together
with soil particles, in runoff waters that enter the river system and
are taken out of the region by the Yellow River (Shi and Shao, 2000)
in amounts estimated to be 16.4 × 109 t year−1 (SSCCWRA, 1989).
Such soil losses also reduce the region’s SOC stocks. Alternatively,
valleys and basins with elevations less than 1000 m are the main
deposition areas for eroded material. These erosion and deposition
processes likely account, at least in part, for the observed differences in SOCD values at the lower (<1000 m) and intermediate
(1000–1500 m) elevations.
3.2.3. Effects of slope gradient
Fig. 5 shows the mean SOCD values for the five classes of slope
gradients (Table 1). There was no significant difference in SOCD values for slope equals 0◦ and for slopes greater than 0◦ (p < 0.05). An
ANOVA of the effects of slope on SOCD showed no significant differences among the four classes with slope greater than 0◦ (p < 0.05).
Soil Organic Carbon Density 0-40 cm (kg C.m-2)
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
191
8
6
4
2
0
0
0-10
10-20
20-30
> 30
Slope Gradient (º)
Fig. 5. Mean soil organic carbon density (kg C m−2 ) in 0–40 cm soil layers on different slope gradients. The error bars represent ± one standard error.
Considering the close relationship between slope and precipitation
in soil erosion processes on the Loess Plateau (Tang, 1991), we also
analyzed the effects of slope on SOCD in the areas with precipitation between 250–500 mm. For this analysis, SOCD was divided
into three slope based groups, i.e. 0◦ , 0–15◦ , and >15◦ . However, the
effects of slope on SOCD were still not significant (p < 0.05).
Our results were in contrast to many studies, which have indicated a strong relationship between slope and SOC accumulation
(Wang et al., 2001; Tan et al., 2004; Liu et al., 2006; Wang et al.,
2009). This may be attributable to the relative scales of the studies.
Our study had a large-scale perspective within which slope would
be considered to be a small-scale factor of the overall topography.
The effects of slope on SOCD could thus be masked by other largescale factors affecting it. In addition, a large number of dry-land
terraces constructed in the hilly regions probably weakened the
effects of slope.
3.2.4. Effects of clay plus silt content (<20 m)
Fig. 3d shows the mean SOCD values in areas with different clay
plus silt content (CSC), which ANOVA indicated had a significant
impact on SOCD (p < 0.05). When land use was not considered, the
mean SOCD value was significantly higher in areas with CSC > 70%
than in those with CSC < 70% (p < 0.05); there were no significant differences between the SOCD values of the two areas with
CSC > 85% and 70–85% (p < 0.05). The same patterns were found
under cropland and forestland. However, under grassland mean
SOCD values consistently and significantly increased with increasing CSC (p < 0.05).
Other studies have reported that SOC was significantly related
to the CSC of soils (e.g., Hassink, 1996). In our study, fine particles (<20 ␮m) dominated the sampled soils, which had a mean CSC
of 73.6%. This was attributed to the fine parent material of loess
soils in the Loess Plateau region (Zhao et al., 1999). The positive
effects of higher CSC on SOCD are likely due to the ability of clay and
silt particles to adsorb organic matter and to protect it, to varying
degrees, from microbial decomposition (Zhao et al., 2006a). Moreover, soil mechanical composition could also indirectly affect SOCD
by impacting soil aggregation, hydrology, aeration and temperature
(Saxon and Rawls, 2006; Shao et al., 2006).
3.2.5. Effects of land use
Natural factors and human activities have strong interactive
effects. Climate, topography and soil characteristics influence
human decisions regarding land use while, in turn, human activ-
Soil Organic Carbon Density 0-40 cm (kg C.m-2)
192
Z. Liu et al. / Agriculture, Ecosystems and Environment 142 (2011) 184–194
10
Mean
Medium
5th/95th percentile
8
a
6
b
c
4
2
0
the Chinese government’s “Grain-for-Green” program. It is possible
that SOC levels in the soils of the less mature forests and grasslands
are in the process of accumulating to higher levels than would
occur in cropland in the same locations. Our results only present
a large-scale overview of the current SOCD in different land use
types across the Loess Plateau region; they do not indicate that
cropland could sequestrate more organic carbon than forestland
and grassland. It is probable that the potential for further carbon sequestration is high under all land use types. It is possible
that additional appropriate management practices, such as fertilization, soil erosion control and artificial fencing for forestland and
grassland, and the use of mulches, conservation tillage, and applications of organic and green manure for cropland would enhance
SOC sequestration in the Loess Plateau region.
4. Conclusion
Cropland
Forestland
Grassland
Land use
Fig. 6. Mean soil organic carbon density (kg C m−2 ) in 0–40 cm soil layers under
three land uses. Difference lowercase letters denote significant differences determined by Duncan’s Multiple Range Test (p < 0.05).
ities and the choice of land use can change the natural factors, such
as soil physical, chemical and biological properties.
Our results indicated that land use had significantly impacted
SOCD (p < 0.05). As shown in Fig. 6, the mean SOCD values for the
three land-use-based groups were significantly different (p < 0.05),
being highest for cropland, intermediate for forestland and lowest
for grassland. When the effect of these three land use types were
compared within the groups of precipitation, temperature, elevation and CSC (Fig. 3), cropland had consistently and significantly
higher SOCD values than forestland and grassland (p < 0.05).
This result was not expected, since it was opposite to the
findings of other studies on the Loess Plateau, which found that
cropland soils had lower SOC contents compared to those under
forestland and grassland (Wang et al., 2001; Li et al., 2005; Chen
et al., 2007). It has also been reported that SOC could be depleted by
conversion of natural vegetation to cropland due to reduced organic
matter inputs and tillage effects that increased decomposition rates
(Post and Kwon, 2000). However, most of these studies were conducted on a small scale, investigating individual plots, slopes or
catchments that had relatively homogeneous environments. In our
large-scale study, the entire Loess Plateau region was considered
and climate, topography and soil conditions varied greatly.
Flat areas with favorable soil and water conditions, such as valleys and basins, are favored for use as croplands. Due to the natural
conditions of such places, SOC content would be relatively higher in
these areas even without human influence. Management practices
often involve addition of organic and inorganic fertilizers, which
further help to maintain and increase SOCD (Glendining et al., 1996;
Liu et al., 2006). It has been reported that, in the Loess Plateau
region, the inputs of organic and inorganic fertilizers amounted
to about 86.7 × 107 kg/year and 111.3 × 107 kg/year, respectively,
during the 1980s (ISTCASLP, 1990), with further increases in more
recent years. Moreover, irrigation and soil erosion control measures
in the cropland areas also had positive effects on SOC accumulation
(Lal, 2004a,b). In contrast, forestland and grassland were mainly
located on the slopes of hilly areas, where the soils were sandier in
the northern part of the study region. Vegetation growth and SOC
accumulation would be limited by inferior soil and water conditions, severe soil erosion and lack of appropriate management in
these areas.
Another factor is that many of the forestland and grassland areas
have been established relatively recently (within the last 10 to
60 years) in order to combat soil erosion and degradation under
In this study, we examined SOC stocks and the effects of environmental factors on SOCD across the entire Loess Plateau region
of China. The study and results can be summarized as follows:
• The methodology was based on intensive field sampling and
geostatistical analysis, accounting for spatial autocorrelation and
variability. This allowed large-scale, updated, SOC stocks to be
estimated, without relying on various limited and outdated soil
survey datasets and maps of classes. A spatial distribution map
of SOCD across the entire Loess Plateau region was produced.
• The SOCD was estimated not only for the upper soil layers but
also for deeper layers, to a maximum depth of 200 cm. Compared
to other investigated regions in China, the Loess Plateau region
was found to have relatively low levels of SOC.
• The 0–20 and 0–100 cm layers of China’s Loess Plateau region
currently store about 0.36% and 0.31%, respectively, of the global
SOC stocks in these layers, and 8.21% and 5.32% of the total SOC
stocks in these layers in China.
• From a regional perspective, precipitation, temperature, elevation, land use, and the clay plus silt content (<20 ␮m) of the soil
all had significant effects on SOCD (p < 0.05). Generally, SOCD was
highest in soils on mountains (with relatively high elevations and
low temperatures) and valleys (with low elevations and high precipitation). Higher fine soil particle contents were associated with
higher SOCD values. In addition, SOCD was higher under cropland than under forestland or grassland at the regional scale of
the entire Loess Plateau.
• Adding data from more sites and/or small-scale studies to this
framework could increase the accuracy of the SOC estimations.
Acknowledgements
This research was supported by the Innovation Team Program
of the Chinese Academy of Sciences, the Program for Innovative
Research Team of Ministry of Education, China (No. IRT0749), and
the National Natural Science Foundation of China (41071156).
Authors wish to thank the editor and reviewers for their valuable
comments and suggestions. Special thanks also go to Dr. Robert
Horton and Mr. David Warrington for their zealous help in improving the manuscript.
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