
Research Article


Effect of Different Land Use Treatments on Soil Structural Quality and Relations with Fractal Dimensions 

A.A. Zolfaghari
and
M.A. Hajabbasi



ABSTRACT

In this study, the feasibility of making the use of
fractal dimension to quantify soil aggregate stability in different land
use systems was investigated. For this purpose the nonlinear fractal
dimension (D_{nl}) and the Mean Weight Diameter (MWD) of aggregates
were compared. In October 2005, soil samples from three sites with four
adjacent landuse types namely: forest area (F), cultivated lands adjacent
to forest (CAF), pasture (P) and cultivated lands adjacent to pasture
(CAP) were collected. Cultivated pasture (CAP) had the largest value of
D_{nl}, while pasture (P) had the smallest value of D_{nl}.
Difference of D_{nl} between forest and pasture was not significant,
while both of them significantly differed from that of cultivated forest
(CAF) and cultivated pasture (CAP) in this parameter. There were significant
differences between forest and pasture for the measured MWD. Coefficient
of variations (CVs) between MWD_{ }and D_{nl} were also
contrasted and the low value of CV indicated the higher precision of the
method used. The lowest CVs belonged to D_{nl}, demonstrating
that D_{nl} was more accurate than MWD methods. Fractal dimension
had negative correlation with MWD, SOM, Hydraulic Conductivity (HC) and
macroaggregates (>0.25 mm) and positive correlation with Bulk Density
(BD) and Total Porosity (TP).







INTRODUCTION
Rapid population growth in the residential areas of central Zagrous in
Iran, has increased the demand for farmland and food production. One way
to expand the cropland is clear cutting the forests and converting pastures
to agricultural fields. This would result in lowering soil structure,
loss of soil quality and consequently destruction of natural ecosystem
(Hajabbasi et al., 1997).
Soil structure is critical for germination and growth of plants and also
transport of water and contaminants through the unsaturated zone underlying
agricultural fields (Pirmoradian et al., 2005).
A scale is needed to quantify soil structure variation. Aggregatesize
distribution (ASD), is a representative of soil structure and is potentially
a useful way, even if not exhaustive, of expressing soil structural quantity.
The need to characterize soil ASD with a single parameter has long been
recognized. Early workers simply used the percentage by weight of aggregates
greater than some specified, but arbitrary, sieve size (Pirmoradian et
al., 2005). However, much information is lost by this approach. As
a result several empirical indices have been proposed for describing the
entire distribution with a single value. Van Bavel (1949) used meanweight
diameter (MWD) to integrate aggregatesize distribution obtained by mechanical
sieving. Mazurak (1950) suggested that the geometric mean diameter may
be more appropriate.
Recent findings in fractal theory introduced scaling parameters, as fractal
dimension that may be suitable for characterizing aggregatesize distribution
in soil (Pirmoradian et al., 2005; Gulser, 2006). According to
Mandelbrot (1982), fractals are characterized by a powerlaw relation
between the number and size of objects. The value of fractal dimension
D is equal to the absolute value of the exponent in the relation:
where, N>x is the cumulative number of objects greater than x and
k is a constant corresponding to the number of fragments of unit length.
The value of D depends on the shape of individual objects within the distribution
and the overall extent of aggregate fragmentation. The higher value of
D is associated with the greater aggregate fragmentation. This means that
the shape of aggregate may be similar in various ranges of aggregate size.
However, it may be assumed that the value of D is scale invariant in shape.
Rasiah et al. (1992) used the fractal dimension to evaluate the
influence of cropping and wetting treatments and aggregate size on the
fragmentation of soil aggregates. Significant relation between aggregate
number and fragmentation fractal dimension was observed. The estimated
D value varied with cropping and wetting treatments. Perfect and Kay (1991),
Rasiah et al. (1992), Pirmoradian et al. (2005) and Gulser
(2006) reported that fractal theory may be used to characterize soilaggregate
size distribution from different cropping treatments. Higher D values
will indicate greater soil fragmentation. Gulser (2006) measured the relation
between fractal dimension and Organic Carbon (OC) content and bulk density.
He proposed that fractal dimensions decreased with increasing SOC content,
MWD and with decreasing Bulk Density (BD).
The objectives of this study were to determine the effect of different
land use treatment on soil structural quality, investigate the ability
of fractal dimension to quantify soil aggregate stability in different
land use and find relationships between fractal dimension and some soil
parameters such as meanweight diameter, bulk density and soil organic
matter.
MATERIALS AND METHODS
Study Area
The study area is located within the northern parts of Karoon watershed
in the central Zagros, Iran (31°11` N and 51°14` E) with an altitude
of about 2050 m above the sea level. The soil was classified as coarsesilty,
carbonatic, calcixerollic and xerocherpts. The landused pattern included:
forest, unaltered pastures and cultivated lands both adjacent to the forests
and pastures. The prevailing climate is Mediterranean with a long term
mean annual precipitation of 502 mm. The forest area is approximately
340,000 hectare with a history of at least 20 years of anthropogenic activities
such as cropping and pasture grazing. Dominant tree species of these sparse
forests is oak (Quercus brontii). This area has been subject to
deforestation and tillage disturbance where barley and wheat have been
cultivated continuously since 1987. Plant cover of long term pastures
ranges from 70 to 90% depending on the severity of grazing. Dominant grass
species in pasture include Astragalus spp.
Soil Analyses
A twofactor analysis of variance under randomized complete block design
consisting of four replications was used. Soil samples were collected
from three sites with four adjacent landuse types: forest area (F), cultivated
lands adjacent to forest (CAF), pasture (P) and cultivated lands adjacent
to pasture (CAP) in October 2005. Some characteristic of the soil used
in this study were evaluated in soil samples taken from the 07 and 715
cm soil depth of the field (Table 1). After soil samples
were air dried and passed through a sieve with 2 mm size opening, some
soil properties determined were as follows:
Table 1: 
Some soil physical and chemical properties of the experiment
site 

*F: Forest area, CAF: Cultivated lands adjacent to forest,
P: Pasture and CAP: Cultivated lands adjacent to pasture 
Particle size distribution was determined by hydrometer method (Gee and
Bauder, 1986). Soil Organic Mater (SOM) content was determined by modified
WallkleyBlack method (Nelson and Sommers, 1982). Saturated Hydraulic
Conductivity (HC) in each site was measured as three replicates by the
constant head method according to (Klute and Dirksen, 1986). The samples
were used for saturation and consecutively ovendried at 105°C to
determine the Total Porosity (TP) and Bulk Density (BD). Total porosity
was calculated in undisturbed watersaturated samples of 100 cm^{3}
assuming no air trapped in the pores and validated using dry bulk density
and a particle density of 2.65 g cm^{3} (Danielson and Sutherland,
1986).
Aggregate Size Distribution
The wet sieving method of Kemper and Rosenau (1986) with a set of 2,
1, 0.5, 0.25 and 0.1 mm diameter sieves was used to determine aggregate
size distribution. After passing soil sample through an 8 mm sieve, approximately
50 g of the soil was put on the first sieve of the set and was gently
moistened to avoid sudden rupture of the aggregate. After moistening,
the set was sieved in water at 50 oscillations per minute. After 10 min
of oscillation, soil remaining on each sieve was dried and then sand and
aggregate were separated (Kemper and Rosenau, 1986). For determination
of aggregate size distribution, the weight ratio of aggregates of each
sieve (>2, 21, 10.5, 0.50.25 and < 0.1 mm) to the total weight
of aggregates was calculated.
Mean weight diameter was calculated as follows:
where, X_{i} is the mean diameter in mm of the openings of two
consecutive sieves and W_{i} the weight ratio of aggregates remaining
on the ith sieve.
Estimation of Fractal Dimension
The number of aggregates left on ith sieve of a nest of sieves can be
computed from aggregate mass data as follows (Tyler and Wheatcraft, 1992):
where, N_{i} is the number of aggregates left on ith sieve of
a nest of sieve, M(x_{i}) is the aggregate massing on the ith
sieve of a nest of sieves and X_{i} is the size of aggregate
in mm. Inserting Eq. 3 into Eq. 1 and
assuming scaleinvariant density and shape of aggregates, the following
equation is derived for the estimation of D from masssize distribution:
where, M(x) is the total mass of aggregate with sizes less than x. Soil
samples were used in a dry sieve and aggregate bulk density of different
size classes was measured by Rasiah and Biederbeck (1995).
Fractal dimension in Eq. 4 was determined by nonlinear
D_{nl} method. Marquradt`s (1963) optimization technique was used
for nonlinear fitting. Statistical analysis of data was conducted using
the SAS computer software package.
RESULTS AND DISCUSSION
Aggregate Bulk Density
Since the bulk density was measured on pooled samples, statistical analysis
was not possible. However, the variation in the data was small (12901410
kg m^{3}). Therefore, the aggregate bulk density was scaleinvariant
and the average of these values (1332 kg m^{3}) was used as the
bulk density of soil aggregate for determination of D in Eq.
4.
Soil Properties
Effects of the change in land use on the mean values of Soil Organic Matter
(SOM), Bulk Density (BD), saturated Hydraulic Conductivity (HC) and Total
Porosity (TP) are given in Table 2. Soils under cultivation
had higher bulk density than the adjacent soils under forest and pasture.
The forest soil had the lowest and the cultivated pastures had the highest
bulk density values. Soil bulk density was not different between the pasture
and forest sites. The loss of SOM by the conversion of the pasture and
forest into cultivated fields probably caused a higher bulk density in
the cultivated soils. Relative to SOM of the forest and pasture soils,
SOM of the cultivated soils decrease by 30 and 31% for 015 cm layer,
respectively (Table 2). Similar finding were reported
by Hajabbasi et al. (1997) that deforestation and subsequent tillage
practices resulted in a 50% decrease in SOM for a soil depth of 030 cm
in central Zagrous Mountain regions. There was a significant difference
in total porosity between cultivated soils and forests and pastures soils.
Relative reduction in the total porosity was about 8 and 13% for forest
and pasture soil, respectively (Table 2). The forest
and pasture soils did not differ in total porosity for the layer of 015
cm. Similar finding were reported by Celik (2005) that cultivation of
forest and pasture resulted a 5% decrease in total porosity for soil in
depth of 010 cm. In this study, there was a significant difference in
saturated Hydraulic Conductivity (HC) between forest and the rest of treatments.
No significant difference between pasture and cultivated forest and pasture
was observed. While the forest had highest hydraulic conductivity, the
cultivated forest had the lowest value at the depth of 015 cm.
Fractal Dimension Variation for Different Land Use Treatments
Statistical analysis of data indicated that there was no significant difference
between values of D_{nl} in different depths (probability p =
0.05). Therefore, the mean values of D_{nl} were used in further
statistical analysis. The values of D_{nl} for different land
use treatments ranged from 2.8533.024 (Table 3). Cultivated
pasture (CAP) had the largest value of D_{nl} while pasture (P)
had the smallest value of D_{nl}. Difference of D_{nl}
between forest and pasture was not significant, while both of them were
significantly different when compared to cultivated forest (CAF) and cultivated
pasture (CAP) in this parameter.
Table 2: 
Effect of changing land use on some parameters 

*F: Forest area, CAF: Cultivated lands adjacent to forest,
P: Pasture and CAP: Cultivated lands adjacent to pasture. Means followed
by same letter(s) in each column are not significantly different at
1% level of probability 
Changes in soil structure are often accompanied by changes in management
practices and may affect the effectiveness of these changes. Quantification
of these changes requires the adoption of certain parameters. Soil aggregate
composition has been found to be a good indicator of the changes in soil
structure. The fragmentation fractal dimension can be inferred from the
role of biological processes in soil structure formation. Inorganic and
relatively persistent organic binding agents are important in the stabilization
of microaggregates (<0.25 mm in diameter) by implementation of different
kinds of mechanisms (Zhao et al., 2006). In forest and pasture
sites, soil environment is more favorable for microbial activity, so there
were more water stable aggregates in forest soil and pasture compared
to cultivation lands. Study results showed that changing forest and pasture
to cultivated land increased the values of nonlinear fractal dimension.
In most studies, it has been found that the value of fractal dimensions
increases with increasing fragmentation and higher fractal dimension values
indicate a distribution dominated by smaller fragment (Perfect and Kay,
1991; Millan et al., 2002). In this study, the higher fractal dimensions
were observed in soils dominated by smaller aggregates in cultivated forest
and cultivated pasture. The lower fractal dimensions have always been
observed in soils with larger aggregates present in forest and pasture.
Mean Weight Diameters
Statistical analysis of data showed no significant difference between
values of MWD at different depths thus, the mean values of MWD were used
in further statistical analysis. Meanweight diameters (MWD in mm) for
different land use treatments are shown in Table 3. The
observed range of MWD was between 0.260.58 mm. The meanweight diameter
of soil aggregates was significantly greater in the forest and pasture
soils than in the cultivated forest and cultivated pasture. Cultivation
caused an approximate decrease of 48 and 32% MWD in the forest and pasture,
respectively compared to that of the undisturbed sites. The presence of
the macroaggregates is positively associated with organic matter concentration
(Duiker et al., 2003). Cultivation broke up soil aggregates and
exposed previously inaccessible organic matter to microbial attack and
accelerated decomposition and mineralization of SOM (Shepherd et al.,
2001). In this study, a significant difference in MWD between forest and
pasture was observed, while Celik (2005) was not able to detect a significant
difference between forest and pasture in Mediterranean highland which
is practically more acceptable.
Relationship between the values of D_{nl} and MWD is shown in
Fig. 1. The bestfit equation for this relationship is
as follow:
D_{nl}
= 0.22 ln(MWD) +2.7 
(5) 
with a value for R^{2} of 0.47. Nonlinear relationship between
D_{nl} and MWD indicated that MWD was unable to quantify soil
aggregate stability in the similar manner as that of D_{nl}.
Table 3: 
Nonlinear D_{nl} and meanwight diameter MWD
for different land use 

F: Forest area, CAF: Cultivated lands adjacent to forest,
P: Pasture and CAP, Cultivated lands adjacent to pasture. Means followed
by same letter(s) in each column are not significantly different at
1% level of probability 

Fig. 1: 
Relationship between the nonlinear fractal dimension
D_{nl} and the meanweight diameter MWD, R^{2}, coefficient
of determination 
Table 4: 
Relationships among the fractal dimensions and MWD and
some soil parameters 

*Correlation is significant at 5% level. **Correlation
is significant at 1% level 
There was no systematic variation for MWD to describe the aggregate stability
(Table 4). For example, the value of MWD of pasture
(P) was lower than that of forest (F). This trend of variation for MWD
cannot be explained theoretically, because soil aggregate fragmentation
should be practically similar in forest and pasture. D_{nl} in
this study was not significantly different between forest and pasture.
These results implicated that practically D_{nl} may be more appropriate.
Comparison of CVs of MWD with fractal dimension showed larger CVs of MWD.
These findings indicated that fractal dimension was more accurate and
more precise than MWD.
Relationship Between Fractal Dimensions and Other Parameters
Nonlinear fractal dimension was found by applying Equation 5 into each
wet sieving soil data set. The relationships among fractal dimension D_{nl},
MWD, SOM and other parameters are given in Table 4.
Fractal dimension had negative correlation with MWD, SOM, HC and macroaggregates
(>0.25 mm) and a positive correlation with BD and TP.
The significant negative correlations between fractal dimensions and
MWD indicated that D_{nl }values increased with decreasing aggregate
size due to changes in land use. Perfect and Kay (1991), Pirmoradian et
al. (2005) and Gulser (2006) reported that MWD was negatively correlated
with fractal dimension. Fractal dimensions had a significant negative
correlation with SOM content. Forest provided the highest SOM content
and the lowest fractal dimension. Increments in SOM content in forest
and pasture treatments increased the proportion of larger aggregates in
the distribution and caused a decrease in fractal dimensions. In numerous
studies, it was found that fractal dimensions were negatively correlated
with SOM content and values of aggregate stability (Rasiah et al.,
1993; Gulser, 2006). Fractal dimensions had a significant linear correlation
with bulk density and total porosity. The highest values of fractal dimensions
and BD were obtained in the cultivated pasture. Decreasing macroaggregation
content in soil due to changes in land use caused an increase in fractal
dimension, bulk density and an increase in total porosity.
CONCLUSION
Cultivated forest and pasture significantly decreased SOM, MWD and TP
and increased BD when compared to forest and pasture. Changing forest
to cultivated forest had higher negative effects on SOM and MWD than changing
pasture to cultivated pasture.
The proportion of macroaggregates in the fractions (>0.25 mm) was
decreased due to change in land use. Increases in the number of stable
macroaggregates are associated with good sustainable soil structure. The
values of D_{nl} decreased as the number of stable macroaggregates
in the soil increased. Lower fractal dimension values indicated a distribution
dominated by larger fragments rendering aggregates more resistant to fragmentation.
The relationships between the fractal dimensions and the other parameters
showed that fractal dimensions decreased with increasing SOM, MWD and
macroaggregates (>0.25 mm) and decreased with BD and TP. When the values
of coefficient of variations (CVs) between MWD and D_{nl} were
compared, lower value of CV indicated the higher precision of the method.
In this study the lowest CVs were detected in D_{nl}, resembling
D_{nl} as more accurate and precise than MWD. Due to strong theoretical
base of the fractal dimension, results of this analysis can be used to
evaluate the soil aggregate stability. As a result, change in land use
decreases soil structure through decreasing macroaggregates.

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