Assessment of Some Soil Properties by Spatial Variability in Saline and Sodic Soils in Arsanjan Plain, Southern Iran
Spatial patterns for several soil parameters such soil texture, Exchangeable Sodium Percentage (ESP), Electrical Conductivity (ECe), soil pH, Cation Exchange Capacity (CEC) were examined in saline and sodic soils in Arsanjan plain, Southern Iran, in order to identify their spatial distribution for implementation of a site-specific management. Soil samples were collected from 0-30, 30-60 and 60-90 cm soil depths at 85 sampling sites. Data were analyzed both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied between soil parameters. Soil pH and ESP had the minimum and maximum variability at all depths, respectively. Soil properties indicated moderate to strong spatial dependence. ECe exhibited moderate spatial dependence at three depths; pH and ESP had a moderate spatial dependence at 0-30 cm and strong spatial dependence at 30-60 and 60-90 cm depths. Clay and CEC exhibited strong spatial dependence for the 0-30 cm and weak spatial dependence at 30-60 and 60-90 cm depths. Sand and silt had a non-spatial dependence at 0-30 cm and weak spatial dependency at 30-60 and 60-90 cm depths. The spatial variability in small distances of ECe, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1168 m. The results reported herein indicated that the strong spatial dependency of soil properties would lead to the extrinsic factors such as ground water level and drainage. It is important to know the spatial dependence of soil parameters, as management parameters with strong spatial dependence will be more readily managed and an accurate site-specific scheme for precision farming more easily developed.
Precision agriculture uses principles of farming according
to the field variability, which requires new requirements for estimating
and mapping spatial variability of soil properties. In the last decades,
important contributions has been made by geostatistics to understand soil
distribution patterns within the landscape, which are required for effective
land management. Samples close to each other have more similar properties
than those located far from each other. However, the classical statistic,
assuming the measured data independent, is not capable to analyze the
spatial dependency of the variable (Cemek et al., 2007). The interpolation
techniques commonly used in agriculture include Inverse Distance Weighting
(IDW) and kriging (Kravchenko and Bullock, 1999; Ardahanlioglu et al.,
2003). Both methods estimate values at unsampled location based on the
measurements from the surrounding locations with certain weights assigned
to each measurement. IDW is easier to implement, while kriging is more
time-consuming and cumbersome. However, kriging provides a more accurate
description of the data spatial structure and produces valuable information
about estimation error distribution (Kravchenko and Bullock, 1999). Surface
soil properties (Brejda et al., 2000), soil nutrient content (Newman
et al., 1997), soil chemical properties (Lee et al., 2001; Ardahanlioglu
et al., 2003), nitrate leaching (Ersahin, 2001) and distribution
of soil physical characteristics in soils (Sepaskhah et al., 2005)
could be analyzed with geostatistical methods to predict spatial variation
in soil properties.
Although these studies provided very precise information
for site-specific recommendations, such information for soils of Arsanjan
plain with semiarid condition is lacking and thus, need to be assessed.
Moreover, it is important to consider the fact that spatial variability
of soils depends on the specific soil studied. The Arsanjan plain is one
of the most important agricultural production areas of Fars province (Southern
Iran). Crop productivity is threatened in this region due to the lack
of an outlet for drainage water, high groundwater level and low quality
of water using as irrigation water. Therefore, the assessment of salinity,
sodicity and other important soil characteristics in this plain is needed
to establish data of salt and sodic affected soils and to evaluate their
spatial variability for site-specific management. The aims of this study
were to (1) examine spatial variability in Exchangeable Sodium Percentage
(ESP), Electrical Conductivity (ECe), soil pH, particle size distribution,
Cation Exchange Capacity (CEC) and (2) assess spatial distribution patterns
of saline and sodic soils in Arsanjan plain.
MATERIALS AND METHODS
Description of the study area: This study was conducted within Arsanjan plain that was
located in Fras province, southern Iran (29° 43` to 29° 47` N
latitude and 53° 09` to 53° 16` E longitude). The mean annual
precipitation, evaporation and temperature are 323.8 mm, 989.1 mm and
18.2°C, respectively. Soil moisture and temperature regime are xeric
and thermic, respectively. The prominent soils of Arsanjan plain are somewhat
affected with salinity and/or sodicity because of high evaporation. Extensive
areas of the Arsanjan plain have become and continue to be degraded by
salinization due to the use of low-quality irrigation water with inappropriate
irrigation methods. As a result, agricultural production of the Arsanjan
plain has declined significantly in the last two decades.
Soil sampling and laboratory analysis: Soil samples in September 2006, in the 85 sampling site
(10187 ha) were collected from 0-30, 30-60 and 60-90 cm depths, georefrenced
using GPS receiver (accuracy of ±5 m), analyzed for ESP, ECe, pH,
CEC and particle size distribution. ESP was determined using the ammonium
acetate (NH4OAc) method (Thomas, 1982); soil pH was measured
with a glass electrode pH meter (McLean, 1982). Soluble salts were calculated
by measurement of ECe in the soil extraction by the use of a conductivity
meter (Rhoades, 1982). CEC was determined using the sodium saturation
method (Rhoades, 1986). Particle size distribution was determined by hydrometer
method (Gee and Bauder, 1986).
Statistical analysis and interpolation: The data analyses were conducted in three stages: (a)
normality tests were applied (Kolmogrov-Smirnov); (b) distribution was
analyzed by classical statistics (mean, maximum, minimum, standard deviation,
skewness and coefficient of variations); (c) geostatistical parameters
were calculated for each variable as a result of corresponding semivariogram
analysis. Skewness is the most common form of departure from normality.
If a variable has positive skewness, the confidence limits on the variogram
are wider than they would otherwise be and consequently, the variances
are less reliable. A logarithmic transformation is considered where the
coefficient of skewness is greater than 1 and a square-root transformation
applied if it is between 0.5 and 1 (Webster and Oliver, 2001). Exploratory
statistical analyses were performed by SPSS (1997) software. A semivariogram
was calculated for each soil property as follows (Isaaks and Srivastava,
1989; Journel and Huijbregts, 1978):
||Experimental semivariogram value at distance interval
||Number of sample value pairs within the distance interval h
||Sample values at two points separated by the distance interval h
All pairs of points separated by distance h (lag h) were
used to calculate the experimental semivariogram. Semivariograms were
calculated both isotropically and anisotropically. Spherical, exponential
or pure nugget models were fitted to the empirical semivariograms. Model
selection for semivariograms was done on the basis of regression (r2),
visual fitting and Residual Sum of Squares (RSS). To define different
classes of spatial dependence for the soil variables, the ratio between
the nugget semivariance and the total semivariance or sill was used (Cambardella
et al., 1994). Geostatistical software (GS+5.1, 2001; Gamma Design
Software) was used to conduct semivariogram and special structure analysis
for soil variables.
RESULTS AND DISCUSSION
A histogram, box plot and normal plot were constructed for all soil properties,
revealing three and two outliers for pH at 0-30 and 60-90 cm depths, respectively.
Their removal significantly reduced the coefficient of skewness (lower
than 0.4) avoiding the need for data transformation. Two (21.2, 19.2)
and one (0.2) potential outliers with ECe at depth of 0-30 and 30-60 cm,
respectively, found from exploratory analysis for electrical conductivity.
The bulk of the data has an ECe of approximately 5 dS m-1,
which dramatically affects the normality of the distribution. However,
these outliers data
||(A, B) maps of estimated silt (%) and ECe (dS m-1)
in 0-30 cm depth, respectively and (C, D) experimental and modeled
semivariograms for silt (%) and ECe (dS m-1) in 0-30 cm
|a: Standard deviation; b:
Coefficient of variation
are of most interest to the analysis of salinity and hence, they are
kept in the dataset. Furthermore, since the coefficients of skewness in
some soil properties (ECe and ESP at three depths and sand at 0-30 cm)
are greater than 1 (Table 1), the natural logarithm is
applied for a kriging analysis to stabilize the variance (Goovaerts, 1997).
Applying ordinary kriging to logarithmic transformed data is the essence
of lognormal kriging. Explanatory analysis for CEC revealed two potential
outliers (34.6 and 31.2), however, visualization showed that this value
is located on the periphery of the study area and therefore it will not
be included in many lags. It also has relatively large values contiguous
to it. Consequently, the decision was to include the data in the analysis.
Although the coefficient of skewness for CEC at 0-30 cm is located in
the range where a square-root transformation is appropriate, it is that
outlying value on the periphery that is skewing the data, so the data
were left in its original
(+): Spatial distribution
(S-strong spatial dependence (<25%); M-Moderate spatial dependence
(26-75%); W-weak spatial dependence (>75%); Pure Nugget- no spatial
correlation (100%) and their spatial distribution
model *: Residual
sum of squares (often the model with the lowest RSS chooses as optimal)
form. The exploratory analysis and descriptive statistics
of the other soil parameters at each depth suggested that they were all
normally distributed and therefore no transformation was needed for geostatistical
The coefficient of variations (CV) of soil properties except pH was fairly
high, indicating that soil properties were generally heterogeneous (Table
1). The highest CV value was for ESP, while the CV values for pH was
the lowest in all the three depths. In general, the CV for other soil
characteristics, except ECe, decreased with soil depth. However, the mean
values of pH, ECe and ESP increased, whereas the mean values of CEC decreased
with soil depth due to the fact that, clay content decreased with soil
depth. Application of poor quality water would result in increase in pH,
ECe and ESP. A highly significant positive correlation was observed between
soil salinity and water content in Entisols, with high clay content and
low infiltration capacity (Miyamoto and Chacon, 2005). Another reason
for higher values of these soil properties in the lower layers was due
to the decrease in clay content with depth (Table 1).
Kachanoski et al. (1988) found that ECe was affected by volumetric
water content and increased with increasing water content when clay content
Anisotropic semivariograms did not show any differences in spatial dependence
based on direction and therefore isotropic semivariograms were chosen.
The geostatistical analysis indicated different spatial distribution models
and spatial dependence levels for the soil parameters. Exponential, spherical
and pure nugget models were used to define soil properties (Table
2). Nugget effect was higher for CEC, clay, silt, sand and ESP compared
to pH and ECe. This indicated that these soil properties had spatial variability
in small distances. The nugget effect of ECe, pH, clay and ESP were generally
increased with depth. The large nugget semivariance and the non-spatial
dependence for silt and sand (Fig. 1A, B
for silt at 0-30 cm, slope of semivariogram was close to zero, 100%, nugget
semivariance/sill ratio) suggest that an additional sampling of these
variables at smaller lag distances and in larger numbers is needed to
detect spatial dependence. However, under no research circumstances (which
means in a commercial context) a larger sampling density is usually not
feasible. The recent research showed that using geostatistical and remote
sensing approaches for mapping soil surface characteristics could improve
the prediction quality (Lopez-Granados et al., 2005).
When the distribution of soil properties is strongly or moderately spatially
correlated (for example for EC at 0-30 cm depth, indicated in Fig.
2C, D), the mean extent of these patches is given
by the range of the semivariogram. A larger range indicates that observed
values of the soil variable are influenced by other values of this variable
over greater distances than soil variables which have smaller ranges (Samper-Calvete
and Carrera-Ramirez, 1996). Range value varied from 1168 m (Ece in the
30-60 cm depth) to 17191 m (clay at 60-90 cm depth). Thus, clay had a
range of more than 17000 m at 30-60 cm depth. This indicates that clay
contents influenced the neighboring values of clay over greater distances
than other soil variable e.g., ECe, which had a range of less than 1200
m at 0-30 cm depth.
Generally, range values of ECe and pH were smaller than that of the other
soil properties. Soil properties exhibited both a consistent and non-consistent
spatial pattern regarding the sampling depth at three locations. Some
soil properties such as ESP, clay and CEC following a different spatial
distribution at each depth, showed a moderate spatial dependence in 0-30
cm depth and a strong spatial dependence in other tow depths (Table
2). Similarity, ECe and pH showed a similar trend at three sampling
depths and followed the same spatial pattern. Cambardella and Kallen (1999)
reported a similar consistent and non-consistent spatial distribution
according to the sampling depths e.g., NH4-N showed three spatial
patterns: Moderate spatial dependence at 0-10 cm depth, no spatial dependence
at 10-20 cm depth and strong spatial dependence 20-30 cm depth, while
pH exhibited a strong spatial dependence at all depths.
The low nugget variance/total variance ratio and small
range values for some soil properties exhibited patchy distribution pattern.
The patchy distribution can be related to the groundwater level and topography.
This study emphasizes that even though the previous agricultural management
was similar, the spatial distribution and spatial dependence level of
soil properties can be different. These results confirm the importance
of collecting information in every agricultural region to select the proper
a site specific system. Long-term field management histories should be
known, since even the same farming practices, clearly effectively affects
both spatial distribution and the level of spatial dependence. Strong
spatial dependency of soil variables may be controlled by intrinsic variations
in soil characteristics (Cambardella et al., 1994). The results
presented here suggested that extrinsic factors such as ground water level,
drainage and irrigation systems would be important factors affecting in
strong spatial dependency of soil properties. Soil salinity (ECe) and
sodicity (ESP) had generally high values in the northeast side of the
study area. Values for ESP and ECe ranged between high and very high in
the Northeast side, suggesting that proper soil management and drainage
techniques are needed to decrease soil salinity and sodicity in these
regions. Jackknifing analysis was used to test if the chosen semivariogram
models accurately predicted soil properties at unsampled locations. The
results indicated that the mean reduced error was near zero and the squared
differences between the jackknifed and the original values were lowest
for the fitted models. This means that the kriging estimates are accurate
and the spatial relationships derived from the studied part of the research
site may be applicable to other areas with similar characteristics in
the Arsanjan plain.
In general, most of the studied soil properties indicated
strong spatial dependency in 0-30 cm depth, while they exhibited moderate
spatial dependency in the 30-60 and 60-90 cm depths. Geostatistical range
values for most soil properties, were greater than 1200 m, indicating
that soil-sampling distance for further sampling designs should be taken
as 1200 m. The nugget effect of ECe, CEC, pH, clay and ESP were generally
higher in 0-30 cm than in 30-60 and 60-90 cm depths. The majority of soil
properties showed a strong spatial dependency at small distances in the
topsoils. This could be attributed to different in the fluctuation and
drainage of the groundwater in the Arsanjan plain and other places in
arid and semiarid areas with similar conditions. The results emphasized
that irrigation was created salinity and sodicity problem in the study
area and probably low quality of irrigation water; extreme water use and
insufficient drainage are mainly responsible for such condition. Besides,
this study suggested that distribution maps of these soil properties may
be used to develop indicator maps, which can separate areas within the
Arsanjan plain, according to their management and reclamation requirements.
Recently, the amount of irrigation water was decreased in order to lower
the adverse effects of irrigation water on soil properties. In the study
area, furrow irrigation is in progress. Sprinkle or subsurface irrigation
methods is recommended instead of furrow irrigation to decrease the amount
of irrigation water used. Also, local areas with high salinity and sodicity
or having salinity and sodicity risk should be continuously monitored
for depth of groundwater table and groundwater salinity to avoid upward
transport of soluble salts with evaporation during irrigation season.
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