Catena 101 (2013) 17–23
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Catena
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Fractal features of soil particle-size distribution and total soil nitrogen distribution in a
typical watershed in the source area of the middle Dan River, China
Guoce Xu a, b, Zhanbin Li a, b, c,⁎, Peng Li 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 and Ministry of Water Resources,
Yangling, Shaanxi 712100, PR China
Graduate School of Chinese Academy of Sciences, Beijing 100039, PR China
c
Key Laboratory of Northwest Water Resources and Environment Ecology of MOE, Xi'an University of Technology, Xi'an Shaanxi 710048, PR China
b
a r t i c l e
i n f o
Article history:
Received 4 December 2011
Received in revised form 21 September 2012
Accepted 21 September 2012
Keywords:
Fractal dimension
Land-use
Slope
Total soil nitrogen
Vertical distribution
a b s t r a c t
Fractal scaling theory was employed to analyze the fractal dimension of soil particle-size distribution (PSD)
for different plant communities with similar soil types in a watershed in the middle Dan River Valley,
China. A total of 296 soil samples were collected from 78 sites. PSD and total soil nitrogen (TSN) were determined in soil from depths of 0–60 cm in four soil horizons for different plant communities. Soils in this area
typically comprise silt and fine sand. The fractal dimensions of the six selected plant communities ranged
from 2.73–2.89, with fractal dimension (Dm) values of grassland and forestland being lower (2.73–2.78)
than those of cropland (2.81 and 2.89). There was an obvious decreasing trend in TSN content with increasing
depth under the various plant communities. Spatial patterns of TSN changed significantly with land-use
types. Organic nitrogen was the main component of soil nitrogen. There was a strong positive correlation between the fractal dimension and the silt and clay content (n = 78, R2 = 0.96, P b 0.01), with increasing Dm
values corresponding to higher silt and clay contents. The Dm value and TSN content both indicated positive
correlations with silt and clay content at a depth of 20–60 cm. These results demonstrate that fractal dimension analysis offers a useful approach to quantify and assess the degree of soil degradation among similar soil
types, but that anthropogenic disturbances can have a great impact on the fractal dimensions for different
land-use types. Cropland was prone to soil degradation, especially on steep slopes. Consequently, improved
conservation measures are needed to enhance and sustain soil and water quality, and to prevent further soil
degradation in the middle Dan River.
© 2012 Elsevier B.V. All rights reserved.
1. Introduction
Soil particle-size distribution (PSD) is one of the most important soil
physical attributes due to its great influence on water movement, productivity, and soil erosion. In areas with high soil erosion rates induced
by water such as rainfall and runoff, fine particle-size fractions (accompanied by nutrients) are selectively removed or deposited during soil
erosion processes (Wang et al., 2008). A soil aggregate is made of closely
packed sand, silt, clay, and organic particles. Applications of fractal geometry in soil science have shown that soil exhibits fractal characteristics, being a porous medium with different particle compositions that
have irregular shapes and self-similar structures (Liu et al., 2009; Rieu
and Sposito, 1991a,b; Tyler and Wheatcraft, 1992). Martinez-Mena et
al. (1999) used the fractal dimension of soil aggregates (Dn) as an indicator of soil erodibility. They estimated Dn from the fractal model proposed
by Rieu and Sposito (1991b) using dry-sieving of soil aggregates. The
⁎ Corresponding author at: State Key Laboratory of Soil Erosion and Dry-land Farming on
the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences,
Yangling, Shaanxi 712100, PR China. Tel./fax: +86 29 82312658.
E-mail addresses: [email protected] (G. Xu), [email protected] (Z. Li).
0341-8162/$ – see front matter © 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.catena.2012.09.013
results of their study confirmed that higher Dn values were associated
with lower aggregate stabilities and that the Dn can be a useful index
for characterizing and describing soil erodibility. Different types of
land-use and vegetation largely influence PSD by either enhancing or
inhibiting soil erosion (Basic et al., 2004; Erskine et al., 2002; Fullen et
al., 2006; Martínez-Casasnovas and Sánchez-Bosch, 2000; Wang et al.,
2008). Wang et al. (2006b) determined the fractal characteristics of
soils under different land-use patterns. The results of their study showed
that land-use had considerable effects on the fractal dimension of PSD
and other soil properties. Such studies support the use of fractal theory
for quantifying soil structure, soil erodibility, and soil permeability
(Huang and Zhan, 2002; Perfect and Kay, 1995; Rieu and Sposito,
1991a,b). In addition, fractal parameters have also become important
in understanding and quantifying soil degradation and land desertification (Su et al., 2004).
Soil erosion and water loss not only affect the fractal dimension of soil
particle-size distribution, but also leads to non-point source pollution
(Huang and Zhan, 2002; Munodawafa, 2007; Tyler and Wheatcraft,
1992). In agricultural ecosystems, total soil nitrogen (TSN) and total
soil phosphorus (TSP) are the major determinants and indicators of soil
fertility and quality, which are closely related to soil productivity
18
G. Xu et al. / Catena 101 (2013) 17–23
Fig. 1. Study area and soil sample sites.
(Al-Kaisi et al., 2005; Søvik and Aagaard, 2003; Wang et al., 2009). In an
agricultural context, nitrogen and phosphorus are the main non-point
source pollutants of surface water and groundwater (Søvik and
Aagaard, 2003; Wang et al., 2009). The reduction of TSN and TSP levels
can result in decreased soil nutrient supply, fertility, porosity, penetrability, and consequently, soil productivity (Huang et al., 2007). Furthermore, the removal of excess nitrogen and phosphorus from soil by
leaching or by rainfall scouring and soil erosion may lead to environmental problems, such as agricultural non-point source pollution and water
quality degradation in both freshwater and marine ecosystems. Understanding the impact of land-use changes on water resources is recognized as one of the most challenging issues in environmental hydrology
(e.g. Stonestrom et al., 2009).
The South to North Water Diversion Project is one of the largest projects conducted in centuries in China. The project, which is designed to
solve the water shortage problem in vast areas of northern China, will
divert up to 45 billion m3 of water per year – an amount roughly equivalent to the annual volume of the Yellow River in a normal year – from
the lower (eastern route), middle (middle route), and upper reaches
(western route) of the Yangtze River in southern China by 2050 (Jiang,
2009). The middle route has a capacity of transferring a total of 13 billion
m3 of water annually from Danjiangkou Reservoir on the Hanjiang
River, a tributary of the Yangtze River, to North China, including
Henan, Hebei, Tianjin and Beijing, for irrigation, industrial and domestic
usage (Dong et al., 2011). The present study focused on the Dan River,
which is the water source area of the middle route of the diversion project. The Dan River originates from Shangzhou in Shaanxi province, with
a drainage area of about 1.68× 10 4 km2 and a length of 443 km. The
project seeks to promote Northern China's economic growth by relaxing
water constraints in a region now facing severe water shortage (Feng et
al., 2009; Jiang, 2009; Wang et al., 2006a). However, water quality is severely degraded in the middle route. This is partly a result of serious
uncontrolled agricultural non-point source pollution. Increased fertilizer
application and livestock waste have contributed large amounts of nutrients to downstream water bodies (Liu and Qiu, 2007), resulting in
very poor water quality in the main rivers. Accordingly, water pollution
control is vital for maintaining good water quality in the middle route.
Soil erosion and nutrient loss are two important aspects influencing
water quality. The spatial variability of TSN levels, which may be greatly
affected by land-use, plays an important role in both agriculture and the
environment. This is especially so with regard to soil fertility, soil quality, and water-body eutrophication (Wang et al., 2009).
Few studies have applied fractal theory to determine the effects of
different plant communities or land management on PSDs for similar
soils (Liu et al., 2009; Wang et al., 2006b, 2008). Researchers have seldom studied the structure and spatial distribution of TSN or investigated
the effect of slope gradient on topsoil (Ribolzi et al., 2011). Furthermore,
there have been almost no studies investigating the soil fractal features
associated with different plant communities in the middle line area. In
this study, we employed the fractal scaling theory developed by Tyler
and Wheatcraft (1992) to analyze PSD and Dm for six different plant
communities with similar soil types. We then evaluated the relationships between selected soil properties and the fractal dimension of
PSD. The purpose of the study was to: 1) analyze the fractal dimension
Table 1
Percentages of different particle-size distributions for the 0–10 cm soil layer for each plant community.
Land-use type
Cropland
Grassland
Forestland
Plant community
Zea mays
Camelia sinensis
Herbose
Quercus acutissima
Pinus tabulaeformis
Platycladus orientalis
Code
ZM
CS
HB
QA
PT
PO
Slope (°)
17
20
20
20
18
21
Soil particle-size distribution (mm)
Gravel
Coarse sand
Fine sand
Silt
Clay
2.0–1.0
1.0–0.5
0.5–0.25
0.25–0.1
0.1–0.05
0.05–0.002
b0.002
0.4
0.8
0.5
0.1
0.2
0.8
4.4
9.6
8.2
7.6
4.9
8.4
5.8
10.1
9.9
14.8
9.9
13.2
5.8
9.0
11.8
12.2
15.3
15.7
7.1
10.2
12.4
11.6
15.7
12.2
66.2
54.3
51.1
47.2
50.1
45.4
10.2
6.0
6.1
6.5
3.9
4.2
Fractal dimension(Dm)
R2 (n = 6)
2.89
2.81
2.78
2.76
2.75
2.73
0.92
0.95
0.94
0.95
0.90
0.95
G. Xu et al. / Catena 101 (2013) 17–23
19
Fig. 2. A–D. Relationships between soil fractal dimension and; A) silt-clay, B) fine sand, C) coarse sand, and D) gravel content in the 0–10 cm soil layer (n = 78, P b 0.01).
of soils involved in different land-uses and determine if the Dm of the
soil particle-size distribution can be used as an integrating index for
quantification of soil degradation; and 2) gain more insight into the
TSN structure and the relationship between PSD and TSN.
Linn.). The land-use types investigated were mainly woodland, farmland
and grassland. The studied plots have been under their present land-use
for at least 10 years.
2.2. Soil sampling and analysis
2. Materials and methods
2.1. Description of study area
The study is located in the Yingwugou Watershed (110°53′38″–
110°55′18″E, 33°31′23″–33°30′35″N), 2 km southeast of Shangnan
County, Shaanxi Province, China (Fig. 1). The study area is situated in
the water source area of the Middle Route of the South-to-North Water
Diversion Project and belongs to the Dan River Basin. The watershed
has an altitude ranging between 464–600 m a.s.l., and covers an area
of 1.86 km2. The climate is warm and semi-humid, with an average annual temperature of 14 °C. The average annual amount of sunshine is
1974 h and the mean annual frost-free period is 216 d. The mean annual
precipitation is 814 mm, 50% of which occurs between July and September. The topography is characterized by low mountains and hills. The soil
types in this area are typically yellow-brown (Haplumbrepts) and sandy
(Arenosols) soils common to the mountain regions of Shaanxi. The parent rock material is granite, gneiss and limestone. The major arboreal
vegetation in the area consists of pine (Pinus tabuliformis Carr.) and
robur (Castanea mollissima Blume.). The dominant crops are peanut
(Arachis hypogaea L.), corn (Zea mays L.) and wheat (Triticum aestivum
Soil samples were collected from 0–60 cm in the Yingwugou Watershed during October 2010. The 0–60 cm layer was divided into four soil
layers, 0–10, 10–20, 20–40 and 40–60 cm. At each plot unit, samples of
each soil layer were collected from five points (equidistant length along
an S-shaped sampling line) using a soil sampling auger with a diameter
of 5.0 cm, after which the five replicate samples in each soil layer were
homogenized by hand mixing. A total of 296 soil samples from 78 sites
were collected. The numbers of soil samples (n) for the 0–10, 10–20,
20–40 and 40–60 cm soil layers were 78, 78, 74 and 66, respectively.
A global positioning system (GPS) was used to determine sampling locations. Site properties, including land-use type, slope, aspect, altitude,
soil type, and vegetation species and coverage were recorded for the
purpose of analyzing correlations between these factors and spatial variations in soil nutrients.
Six typical plant communities within the study area were selected
according to the three land-use types (Table 1). The plant communities were defined as follows: Camelia sinensis crop (CS); Zea mays crop
(ZM); Quercus acutissima forest (QA); Pinus tabulaeformis forest (PT);
Platycladus orientalis forest (PO); and Herbose (HB). According to
land-use type, the forestlands (QA, PT and PO) were within protected
Table 2
Relationships between soil fractal dimension and; A) silt-clay, B) fine sand, C) coarse sand, and D) gravel content in four soil layers.
Depth(cm) Gravel
0–10
10–20
20–40
40–60
Coarse sand
Fine sand
Silt-clay
linear fitting
(R2)
Pearson
correlation
linear fitting
(R2)
Pearson
correlation
linear fitting
(R2)
Pearson
correlation
linear fitting
(R2)
Pearson
correlation
0.30
0.14
0.20
0.16
−0.551⁎⁎
−0.379⁎⁎
−0.452⁎⁎
−0.401⁎⁎
0.49
0.30
0.43
0.51
−0.702⁎⁎
−0.544⁎⁎
−0.653⁎⁎
−0.711⁎⁎
0.48
0.54
0.58
0.43
−0.691⁎⁎
−0.733⁎⁎
−0.762⁎⁎
−0.658⁎⁎
0.96
0.95
0.96
0.94
0.981⁎⁎
0.973⁎⁎
0.980⁎⁎
0.972⁎⁎
⁎⁎ Correlation is significant at P b 0.01 (two-tailed).
Number of samples
(n)
78
78
74
66
20
G. Xu et al. / Catena 101 (2013) 17–23
The mean particle diameter is taken as the arithmetic mean of the upper
and lower sieve sizes (Liu et al., 2009). Taking the logarithm of both
sides in Eq. (2), we derived Eq. (3) to solve for Dm:
Table 3
Soil fractal features in different slopes for ZM.
Slope (°)
Fractal dimension (Dm)
10
21
25
28
0–10 cm
10–20 cm
20–40 cm
40–60 cm
2.84
2.88
2.89
2.59
2.81
2.92
2.94
2.87
2.86
2.96
2.96
2.94
2.83
2.93
2.95
2.92
Dm ¼ 3−
logMðr < Ri Þ=MT
log ðRi =R max Þ
ð3Þ
3. Results
forestland preserve area (Zhang and Cui, 2007). CS is used for Chinese
tea production, ZM is used for cropland production (primarily corn),
and HB represents ungrazed grassland. The six plant communities in
the study were located on south-facing, mid-slope areas.
Soil samples were air-dried, divided, and passed through either a
0.25-mm or 1.0-mm sieve. The samples were then analyzed for TSN
using the Kjeldahl digestion procedure (Bremner and Tabatabai, 1972).
For samples that were passed through the 1.0-mm sieve, soil NO3−\N
and NH4+\N were analyzed with an Auto Discrete Analyzer (ADA,
CleverChem200, Germany). Soil samples were air dried and machinesieved through a 2.0-mm screen to remove roots and other debris.
Soil particle-size distribution was described in terms of the percentages
of silt and clay (b0.05 mm), fine sand (0.05–0.25 mm), coarse sand
(0.25–1.0 mm) and gravel (1.0–2.0 mm). Soil particle composition
was measured by laser diffraction using a Mastersizer2000 particle size
analyzer (Malvern Instruments, Malvern, England).
2.3. Soil fractal model theory
The definition of a fractal can be given based on the relationship
between number and size in a statistically self-similar system and defined by the following equation (Mandelbort, 1982; Turcotte, 1986):
−D
NðX > xi Þ ¼ kxi
ð1Þ
where N (X ≥ xi) is the cumulative number of objects or fragments
greater than a characteristic size xi, k is the number of elements at a
unit length scale, and D is the fractal dimension. However, the applicability of Eq. (1) for PSD analysis is limited because complete and accurate calculations of N values are typically unavailable from
conventional PSD experimental data.
In an effort to compensate for the lack of N values, Tyler and
Wheatcraft (1992) estimated the fractal dimension of PSD, Dm, based
on the following expression:
3−D
MðrbRi Þ=M T ¼ ðRi =R max Þm
ð2Þ
where M is the cumulative mass of particles of the ith size, r less than Ri,
MT is the total mass, Ri is the mean particle diameter (mm) of the ith size
class, and Rmax is the mean diameter of the largest particle, respectively.
3.1. Soil particle-size distribution and fractal features
As shown in Table 1, there are considerable differences in PSD among
the six plant communities in this study. The predominant soil particle
size is silt. The percentages of silt range from 45.4–66.2% with a mean
value of 52.4%. Fine sand ranges between 12.9–31.0% with a mean
value of 23.2% and coarse sand ranges from 10.3–22.4% with a mean
value of 17.8%. The content of clay and gravel is relatively lower, with
the content of clay ranging from 3.9–10.2% (mean value 6.1%), and
that of gravel from 0.1–0.8% (mean value 0.5%). Soils of this nature are
classified as yellow-brown soils (Haplumbrepts) according to the Chinese soil texture classification system (Lin, 2002; Xiong and Li, 1990).
Table 1 shows Dm values for PSD calculated using Eq. (2) for each
of the six plant communities. Analysis of variance reveals significant
differences in Dm between crop and forest communities (P b 0.01).
The greatest differences in PSD among plant communities are in the
percentages of silt. Silt content of CS (54.3%) and ZM (66.2%) are considerably higher than the mean value of 47.6% for forest communities
(QA, PT and PO). Moreover, differences between crop communities
and forest communities are characterized by a decrease in the fractal
dimension, indicating that the soils in cropland (CS and ZM) are
better. This is because sites with deep soils and good conditions
were used to plant crops. Most forest communities grew in rocky and
poor conditions.
3.2. Relationship between fractal dimension and soil particle-size
distribution
Linear regression analyses were performed to determine the strength
of the relationships between Dm values and the contents of gravel, coarse
sand, fine sand, and silt-clay in the 0–10 cm soil layer (Fig. 2A–D) and
the other three soil layers. The statistical results obtained from the 78
sites show that the fractal dimension of PSD has a strong positive correlation with the silt-clay content (Fig. 2A, n=78, R2 =0.96, Pb 0.01), a
negative correlation with the fine sand content (Fig. 2B, n =78, R2 =
0.48, Pb 0.01) and coarse sand (Fig. 2C, n =78, R2 =0.49, Pb 0.01), and
a weak negative correlation with the gravel content (Fig. 2D, n =78,
R2 =0.30, Pb 0.01). Similar results were observed for the other three
soil layers (Table 2). Thus, regression analysis indicates that soils with
higher contents of silt and clay, and lower fractions of fine and coarse
sand have higher Dm values. Values of Dm do not appear to be related
to gravel content. Other studies have produced similar results in very different landscapes and under contrasting climate conditions (Liu et al.,
2009; Su et al., 2004; Wang et al., 2006b, 2008).
3.3. Slope and soil fractal features
Fig. 3. TSN content for each of the different plant communities in four soil layers.
The Dm selected from four ZM sites under different slopes in four soil
layers is shown in Table 3. The Dm value in the 0–10 cm layer is bigger
than those in the 10–20 cm and 40–60 cm soil layers at the slope angle
of 10°. As slope increases, the Dm values in the 0–10 cm layer become
smaller than those in the soil layers from 10–20 to 40–60 cm. This characteristic becomes statistically significant, especially at the slope angle
of 28°. The Dm value is 2.59, which is much smaller than those of the
soil layers from 10–20 to 40–60 cm.
G. Xu et al. / Catena 101 (2013) 17–23
21
Table 4
Mean percentages of NO3−\N, NH4+\N and other nitrogen forms in four soil layers.
Depth (cm)
0–10
10–20
20–40
40–60
NO3−-N(mg·kg−1)
NH4+-N(mg·kg−1)
Other nitrogen forms
Min.
Max.
Mean percentage (%)
Min.
Max.
Mean percentage (%)
Mean percentage (%)
1.73
2.07
1.38
1.56
12.42
30.17
11.78
14.65
0.99
1.68
2.82
3.94
1.98
2.06
2.24
1.84
77.06
32.85
23.81
20.48
2.32
2.38
3.02
4.55
96.69
95.94
94.16
91.51
3.4. Total soil nitrogen features of plant communities
The contents and vertical distribution of TSN under six different
plant communities are presented in Fig. 3. There is an obvious decreasing trend in total nitrogen content from 0–10 to 40–60 cm
among plant communities. The maximum TSN contents in the 0–10,
10–20, 20–40 and 40–60 cm soil layers are 1.53, 1.12, 0.674 and
0.412 g kg −1, respectively, while the minimum TSN contents are
0.50, 0.367, 0.256 and 0.317 g kg −1, respectively. Forestland and
grassland soils maintained high TSN contents in both the 0–10 and
10–20 cm layers. The difference in TSN contents among the six
plant communities decreases as soil depth increases.
Number of samples (n)
78
78
74
66
phenomenon might be related to leaching and migration of soil organic matter.
The relationship between Dm and TSN content was investigated by
linear regression analyses (Fig. 4A–D). The R 2 values for the 0–10 and
10–20 cm soil layers are 0.01 (n = 78, P > 0.05) and 0.02 (n = 78,
P > 0.05), respectively (Fig. 4A–B); therefore, no significant correlation is evident between the Dm values and TSN content in the
0–20 cm soil layer. The Dm values positively correlate with TSN content in the 20–40 (Fig. 4C, R 2 = 0.19, n = 74, P b 0.01) and 40–60 cm
soil layers (Fig. 4D, R 2 = 0.31, n = 66, P b 0.01), which correspond to
the correlation of PSD and TSN. The Dm value and TSN content both
positively correlate with silt-clay content in the 20–60 cm soil layer.
3.5. Total soil nitrogen structure analysis
4. Discussion
The mean percentages of NH4+\N, NO3−\N and other nitrogen
forms range from 0.99–3.94%, 2.32–4.55% and 91.51–96.69%, respectively (Table 4) based on analysis of 296 soil samples. The percentages of NH4+\N and NO3−\N are generally less than the deeper soil
layer and the NH4+-N percentage is less than that of NO3−\N in the
same soil layer. The minimum and maximum values of NH4+\N and
NO3−\N in each soil layer also indicate the same result. Lower percentages of NH4+\N and NO3−\N imply that nitrogen content is dominated by the other nitrogen forms. The percentages of the other
forms of nitrogen decrease as depth increases and range from 96.69
to 91.51%. These findings indicate that organic nitrogen is the main
type of soil nitrogen.
3.6. Relationships between soil particle-size distribution, fractal dimension
and total soil nitrogen
Table 5 compares the correlation coefficients between TSN and
PSD in the four soil layers based on the analysis of 78 sites. TSN has
a significant positive correlation with coarse sand in the 0–10 and
10–20 cm soil layers. However, as depth increases, TSN shows a negative correlation with coarse sand, although this relationship is not
statistically significant. In the 20–40 and 40–60 cm soil layers, TSN
shows a significant positive correlation with silt-clay. A significant
negative correlation exists between TSN and fine sand from the
10–20 to 40–60 cm soil layers. TSN is not strongly correlated with
gravel in each soil layer. The TSN content is linearly correlated with
SOC and the positive correlation coefficient is significant at P b 0.01
(Guo, 1992; Tong et al., 2009). These findings indicate that soil organic matter decomposes into small particles as soil depth increases. This
Table 5
Pearson correlation coefficients between PSD and TSN in four soil layers.
Size
Gravel
Coarse sand
Fine sand
Silt-clay
0.310⁎⁎
0.268⁎
−0.207
−0.303⁎
−0.177
−0.383⁎⁎
−0.382⁎⁎
−0.462⁎⁎
−0.121
0.098
0.406⁎⁎
0.538⁎⁎
Depth (cm)
0–10
10–20
20–40
40–60
0.077
0.063
−0.136
−0.178
⁎ Correlation is significant at P b 0.05 (two-tailed).
⁎⁎ Correlation is significant at P b 0.01 (two-tailed).
PSD is commonly used in soil classification, as well as for estimating
various related soil properties (Hillel, 1980). PSD affects the movement
and retention of water, solutes, heat and air in soil. In land degradation
processes, a decrease in water-holding capacity, losses in soil nutrients,
and the diminution of soil structure are indicative of the selective removal of fine particle-size fractions and can often be attributed to various human activities. Thus, identifying changes in PSD may provide
useful indications that soils have been subjected to degradation or nutrient changes caused by certain human activities (Liu et al., 2009; Wang et
al., 2008).
Fractal dimension analysis has been used to quantitatively describe
soil texture, soil aggregate fragmentation, and related soil properties
(Perfect, 1997). The current study demonstrates that the Dm value of
PSD increases as soil becomes finer in texture (Liu et al., 2009). The
Dm values of CS (2.81) and ZM (2.89) were higher than the values of forest areas (QA, PT and PO). There was also a significant difference
(Pb 0.01) in fractal dimensions between crop and grass communities.
These findings are contrary to those of Liu et al. (2009), who found
that the Dm values of forestland and grassland were larger than those
of cropland. This difference was primarily because the land-use types
in the present study were not protected forestland or commercial forestland. These findings suggest that anthropogenic disturbances, especially in the distribution of forestland and grassland, have resulted in
various levels of soil degradation in the study area. Overall, the Dm
values in the Yingwugou study area varied from 2.59 to 2.93 among
plant communities, indicating that the soils had high quality. The
range of Dm values present in our study was relatively high, and plant
communities with higher Dm values corresponded to better soil conditions. Therefore, the use of Dm values based on PSDs could provide
more information than PSD or soil texture alone.
We also assessed the linear fitting for the fractal model as it applies
to the different plant communities observed in this study. The results
for the six plant communities are shown in the scatter diagram in
Fig. 5, with linear regression analysis between the log of (Ri / Rmax) on
the x-axis and the log of M(r b Ri)/MT on the y-axis. There was a strong
linear trend in the data for each of the communities and strong correlations as indicated by the R2 values being>0.90 in all cases. These results
are similar to those of Grout et al. (1998) and Liu et al. (2009) and indicate that using the fractal dimension of PSD as a descriptor for soils is
effective.
22
G. Xu et al. / Catena 101 (2013) 17–23
Fig. 4. A–D. Relationships between soil fractal dimension and TSN content in four soil layers: A) n=78 and P>0.05, B) n=78 and P>0.05, C) n=74 and Pb 0.01, and D) n=66 and Pb 0.01.
The difference in TSN contents among the six plant communities
became smaller as soil depth increased. This could be explained by
the very low inorganic nitrogen percentage in TSN, which is often
b5% (Sainju et al., 2002). TSN levels in farmland benefit from the
use of NPK fertilizers (Halvorson et al., 1999), while forestland and
grassland restoration is encouraged by organic N, which primarily
originates from humus and forest litter. The organic N content in
humus and forest litter is much higher than the inorganic N from
NPK fertilizers. This also explains why the TSN contents did not differ
greatly among the four soil layers in farmland and were much higher
in the 0–10 cm soil layer in forestland and grassland. The largest TSN
content is present at the PO site in the 0–10 cm layer, whereas the
lowest TSN content is present at the CS site in the 20–40 cm layer.
This was likely due to the relatively higher soil organic matter for
the PO site, while the low TSN content at the CS site is a result of farming activities, soil structure and nitrogen transport. The Dm values of
forestland and grassland were smaller than those of cropland, while
the TSN contents of forestland and grassland are higher than those
of cropland. This could be explained by Tables 3 and 5. The fractal dimension in topsoil is related to soil erosion, which removes relatively
finer soil particles and causes the content of silt-clay to decrease.
However, the TSN contents are significantly positively correlated
with coarse sand in the 0–10 cm and 10–20 cm soil layers. This also
explains why the fractal dimension showed no significant correlation
with the TSN content in the 0–10 cm and 10–20 cm soil layers. The
high TSN contents and the unreasonable land-use types induced the
degraded water quality together in the water source area of the
South-to-North Water Diversion Project, which corresponded to the
high total nitrogen content observed during water monitoring.
The Dm values in topsoil at a slope angle of less than 25° showed
little difference, while the Dm value decreased greatly in topsoil at a
slope angle of 28°. These findings indicate that there is a critical
point between 25° and 28°. Once the angle of the cropland is greater
than 25°, the topsoil is subject to serious soil erosion. Therefore, Dm
values are relatively smaller on steep slopes that are prone to severe
soil erosion. Accordingly, the cropland on steep slopes should be
converted to woodland and grassland to strengthen soil and water
conservation.
5. Conclusions
The fractal dimension of PSD is sensitive to the soil coarsening process and fractal dimension analysis of soil particle-size distribution offers a useful approach to quantify and assess the degree of soil
degradation among similar soil types. There is an obvious decreasing
trend in TSN content with increasing depth under the various plant
communities. In addition, anthropogenic disturbances have had a great
impact on the fractal dimensions of different land-use types in the
study area. Most forest communities are forced to grow in rocky and
poor conditions, while crops are planted in deep soils under good conditions. This resulted in various levels of soil degradation and is likely to induce greater soil erosion. These differences also led to the Dm values of
forestland and grassland being smaller than those of cropland. Finally,
the unreasonable land-use type and high TSN contents likely resulted
in easy soil erosion and degraded water quality. Soil erosion is also
found to have a critical point between 25° and 28°. Consequently, the
forestland should be protected effectively and the cropland should be
returned to forestland and grassland, especially on steep slopes, to enhance and sustain soil quality and water quality in the area and to
avoid further soil degradation. Further research should be conducted to
determine if optimizing the landscape pattern can regulate water quality.
Acknowledgements
Fig. 5. Relationship between log (Ri / Rmax) and log M(r b Ri)/MT for different plant communities (n = 6).
This research was supported by the National Basic Research Program
of China (No. 2011CB411903 and No. 2011CB403302) and the Natural
Science Foundations of China (No. 41071182 and No. 40971161).
Additionally, we thank the reviewers for their useful comments and
suggestions.
G. Xu et al. / Catena 101 (2013) 17–23
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