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Transcript of Spatial variability of salinity and alkalinity of a field having salination risk in semi-arid...
Environ Monit Assess (2007) 127:55–65
DOI 10.1007/s10661-006-9258-x
O R I G I N A L A R T I C L E
Spatial variability of salinity and alkalinity of a field havingsalination risk in semi-arid climate in northern TurkeyKenan Kılıc · Sinan Kılıc
Received: 26 September 2005 / Accepted: 13 April 2006 / Published online: 23 August 2006C© Springer Science + Business Media B.V. 2006
Abstract Spatial variability of salinity and alkalinity
is important for site-specific management since they
are the most important factors influencing soil qual-
ity and agricultural production. The objectives of this
study were to analyze spatial variability in salinity and
alkalinity and some soil properties affecting salinity
and alkalinity, using classical statistics and geostatis-
tical methods, in an irrigated field with low-quality ir-
rigation water diverted from drainage canals. A field
of 5 da was divided into 10 m × 10 m grids (5 lines
in the east-west direction and 10 lines in the north-
south direction). The soil samples were collected from
three depths (0–30, 30–60 and 60–90 cm) at each grid
corner. The variation coefficients of OM and sand con-
tents were higher than other soil properties. OM had
the maximum variability, with a mean of 1.63% at 0–
30 cm depth and 0.71% at 30–60 cm depth. Significant
correlations occurred between ESP, EC and each of Ca,
Mg, K and CaCO3 contents of the soils (p < 0.01). Ex-
perimental semivariograms were fitted to spherical and
gaussian models. All geostatistical range values were
greater than 36 m. The soil properties had spatial vari-
ability at small distances at 60–90 cm depth. EC was
variable within short distances at 30–60 cm depth. The
nugget effect of ESP increased with soil depth. Kriged
Kenan Kılıc (�) · Sinan KılıcDepartment of Soil Science, Faculty of Agriculture,University of Gaziosmanpasa, Taslıciftlik, 60250, Tokat,Turkeye-mail: [email protected]
contour maps revealed that soils had a salinisation and
alkalisation tendency at 60–90 cm depth based on spa-
tial variance structure of the EC and ESP values. Spatial
variability in EC and ESP can depend on ground water
level, quality of irrigation water, and textural differ-
ences.
Keywords Salinity . Alkalinity . Geostatistics .
Spatial variability . Turkey
1 Introduction
Salinity (EC) and alkalinity (ESP) are the two of the
most important problems on agricultural production
in arid and semi-arid regions and are threatening for
sustainable agricultural management (Ceuppens et al.,1997; Boivin et al., 2002). The amount of agricultural
lands having salinity and alkalinity problems increase
continuously as related to climate, topography, ground-
water level and quality of irrigation water (Postel, 1989;
Ayers and Westcot, 1989). About 400 million hectares
of agricultural lands are under salinity in the world are.
The most common reasons of salinity and alkalinity
are low precipitation, high evapotranspiration, and low
quality of irrigation water. Saline soils contain soluble
salts in sufficient quantities to interfere the growth of
most crop plants but they do not contain enough ex-
changeable sodium to alter soil characteristics (Seatz
and Peterson, 1965). However, alkali soils include
Springer
56 Environ Monit Assess (2007) 127:55–65
exchangeable sodium in a sufficient quantity to inter-
fere the growth of most crops (Bohn et al., 1985).
There is a close relationship between soil properties
and salinity and alkalinity (Kachanoski et al., 1988).
When clay content is low, EC is affected by volumet-
ric water content, and EC increased with increasing
water content (Kachanoski et al., 1988). There was a
significant relationship between EC and the depth of
claypan in the soil profile, and EC was determined to
increase with increasing the depth of claypan (Doolittle
et al., 1994; Sudduth et al., 1999). The claypan formed
in the agricultural lands complicates water movement
through soil profile due to low pore size (Rhoades
et al., 1992).
Soil properties may show both vertical and horizon-
tal variability along a field. The structure of variability
in soil properties showed differences according to sam-
pling spacing, soil properties, and method used in the
study (Trangmar et al., 1985). Miyamoto et al. (2005)
reported that soil sampling for salinity appraisal was
the most problematic in Entisols, but could be made
simply if a detailed soil map was available. Soil sam-
pling of Entisols can be made based on the soil type
distributions (Miyamoto et al., 2005). Several methods
are available to evaluate soil salinity and determine the
effects of soil management systems on salinity and al-
kalinity. Some researchers (Kelleners and Chaudhry,
1998; Sharma and Rao, 1998; Miyamoto and Chacon,
2005), used conventional statistics to evaluate extent of
soil salinity and many others used geostatistical meth-
ods (Nielsen et al., 1983; Miyamoto and Cruz, 1987;
Utset et al., 1998; Miyamoto et al., 2005).
Soil properties are continuous variables and values
at any location can vary according to direction and dis-
tance of separation from neighboring samples (Burgess
and Webster, 1980). Salinity and alkalinity exhibit spa-
tial dependence within some localized region. The clas-
sical statistic is insufficient for interpolation of spatial
dependent variables, because it supposes random vari-
ation and does not takes into consideration of spatial
correlation, distance of sampling and location of sam-
ples (Trangmar et al., 1985).
Management practices in the study area have caused
to salinity and alkalinity in the subsurface layers, espe-
cially in 60–90 cm depth. Salinity and alkalinity have
been increased dramatically with soil management sys-
tems such as irrigated cultivation of the soils, existing
farm irrigation management, high groundwater level,
and applying poor quality water. The soils in Kazova
of Tokat Province in the middle Black sea region of
Turkey are commonly surface irrigated, with low qual-
ity of irrigation water having 980 μS cm−1 EC and
SAR value of 1.28 (Saltalı et al., 1999).
The soils of the study area (Aquic Ustifluvent) have
high variability in short distances as they formed on
alluvial materials. This property of the soils can com-
plicate to analysis of spatial variability in soil proper-
ties. However, assessment of spatial variability in salin-
ity and alkalinity will play an important role for site-
specific management. The objectives of this study were
to (i) assess relationships between some soil proper-
ties and EC and ESP values; and (ii) evaluate spatial
variability of EC and ESP for the purpose of identi-
fying the possible trends in soil salinity and alkalin-
ity to avoid further deterioration in soil quality in the
region.
2 Material and Methods
2.1 Study area
The study area is located in Kazova of Tokat Province
in the middle Black sea region of Turkey at an alti-
tude of 620 m above sea level, covering approximately
40.000 ha suitable for irrigation. Soils have a slope
ranging from 0 to 2%, and poorly drained with the
textures of clay and clayey loam. The soils of the study
area formed on river terraces of different materials de-
posited during Holocene, and classified as Aquic Us-
tifluvent according to Soil Taxonomy (Tasova, 1997).
Water and temperature regimes of study area are us-
tic and mesic, respectively. Average annual rainfall is
436 mm, 52% of which falls in the period from Decem-
ber to May, air temperature is 12◦C, and soil tempera-
ture at 50 cm soil depth is 6.2◦C (State Water Works,
1999). Common red mottles located about 90 cm soil
depth indicated a high groundwater level.
The soils have been under conventional tillage sys-
tem, with moldboard plough (at 20 cm soil depth), cul-
tivator (about 15 cm soil depth) and disc harrow (about
10 cm soil depth) for a long period of time. The soils
in the study area have been under production of cereals
and maize with sprinkler and furrow irrigation systems.
The quality of irrigation water in the study area
was low, with 980 μS cm−1 EC and 1.28 SAR values
(Saltalıet al., 1999). This irrigation water can be used
suitable drainage conditions. Saltalı and Derici (1999)
Springer
Environ Monit Assess (2007) 127:55–65 57
Fig. 1 Location of the study area in Turkey, topography map of the experimental field and the sampling sites (dots). The mean sea levelaltitudes of the contours are in meters. Each contour is 0.1 cm.
pointed out that salt content of soils in the study area
changed temporally depending on growing plant vari-
ety. Upward movement of salts was the highest when
cereals are produced as compared to the other produc-
ing plants such as sunflower, watermelon and sugar
beets. Percent salt content in the topsoil was higher in
October than June (Saltalı and Derici, 1999). The soils
are regularly flooded even after the installation of the
irrigation scheme. These floods caused leaching of eas-
ily soluble salts in spite of the poor quality of irrigation
water. The grayish-blue reduction matrix colors and the
presence of abundant iron mottling below 60 cm depth
Springer
58 Environ Monit Assess (2007) 127:55–65
Fig. 2 Spatial pattern of soil EC for each soil depth
Fig. 3 Spatial pattern of soil ESP for each soil depth
suggested that the groundwater level showed large an-
nual fluctuations.
2.2 Soil sampling and analysis
The study area (100 m × 50 m) was divided into10 m
× 10 m grids (10 lines in the north-south direction and
5 lines in the east-west direction). Total 50 grid points
were obtained and soil samples were collected at these
points from three depths (0–30, 30–60 and 60–90 cm)
in August 2003. At the sampling time, maize was sown
in the field.
The soil samples were air-dried and passed through
a 2-mm sieve. The particle-size distribution was de-
termined by Bouyoucos hydrometer method (Gee
and Bouder, 1986). Soil organic matter content was
Springer
Environ Monit Assess (2007) 127:55–65 59
measured using the modified Walkley-Black wet oxi-
dation procedure (Nelson and Sommers, 1982). Soil pH
and electrical conductivity (ECe) were measured with
glass electrode in a saturation extract. Cation exchange
capacity (CEC), exchangeable sodium, potassium, cal-
cium and magnesium were measured after extraction
with ammonium acetate (Rhoades, 1986).
2.3 Statistical and geostatistical methods
Statistical analysis of data includes examination of the
mean, maximum and minimum values, standard de-
viation (SD), variance, skewness, kurtosis and coeffi-
cient of variation (CV). The Shapiro-Wilks normality
test was conducted to test the hypothesis that assum-
ing each soil properties has a normal distribution, and
these variables without normal distribution subjected to
log-transformation. Variance and sample means of soil
properties were compared using LSD test. The linear
correlation between pairs of variables was also exam-
ined, as it often reveals relationships, and helps in the
choice of variables for the kriging estimation (Webster
and Oliver, 1990). Statistical analyses of variables were
performed with SPSS 10 (2000).
Spatial variation of salinity and alkalinity rates was
described using semivariograms, assuming stationar-
ity among similar lag increments (Isaaks and Srivas-
tava, 1989; Goovaerts, 1997). Geostatistical software
(GS+5.1, Gamma Design Software; 2001) was used to
analyze the spatial structure of the data and to define
the semivariograms. The hypothesis and parities used
to calculate semivariograms were described by Burgess
and Webster (1980). Semivariogram values at each lag
separation, γ (h), were computed as:
γ (h) = 1
2N (h)
{N (h)∑i=1
[Z (xi ) − Z (xi + h)]2
}(1)
Where N(h) is the number of pairs separated by a lag
distance of h, and Z is the regionalized variables, Z(xi )
and Z (xi + h) are measured sample values xi and xi +h locations.
Semivariograms were computed using the log-
transformation data in the measured data. The trend
of the computed semivariogram values was modeled
using one of the exponential, spherical or linear semi-
variogram models. The model selected to represent the
semivariogram values (nugget, sill and range of spatial
dependence) was chosen based on coefficient of varia-
tion (r2) and residual sum of squares (RSS). Semivari-
ograms were constructed for EC and ESP values.
3 Results and discussion
It was evident that the soil properties were quite
different in the three soil depths (Table 1 and 2). Mean
EC and ESP values were found higher at 60–90 cm
soil depth (3.95 dSm−1 and 5.92%) as compared to the
other soil depths (1.56 dSm−1 and 2.69% for 0–30 cm
soil depth, and 2.25 dSm−1 and 4.66% for 30–60 cm
soil depth), and the difference was significant among
the soil depths (p < 0.01). Application of poor
quality water or insufficient amounts of water to leach
salts would result in increase in EC or ESP. Salt
accumulation at the topsoil would probably occur
only in the years of low precipitation or insufficient
leaching. This result showed that soil salinity and
alkalinity risk existed in the 60–90 cm depth due to
high groundwater level and irrigation with low quality
of water, having 980 μS cm−1 EC and 1.28 SAR
values (Saltalı et al., 1999). Highly significant positive
correlation between soil salinity and water content in
a field of compacted clayey Entisols with poor water
infiltration was found (Miyamoto and Chacon, 2005).
Kachanoski et al. (1988) stated that EC was affected
by volumetric water content, and EC increased with
increasing water content when clay content was low.
The rate of exchangeable Na+ also increased sig-
nificantly (p < 0.01) while exchangeable Ca++ and
Mg++ decreased significantly (p < 0.01) with increas-
ing soil depth (Table 2). The pH of the topsoil was
8.25 compared with 8.61 at 30–60 cm depth and 8.81
at 60–90 cm depth, and the differences were statisti-
cally significant (p < 0.01).
Textural analysis showed that the topsoil (0–30 cm)
had the highest mean clay content (48.7%), but the sub-
soil (60–90 cm) had the lowest clay content (32.6%)
(Table 1). The clay content significantly decreased
while silt content significantly increased with depth
(30–60 cm and 60–90 cm depths) (Table 2). The most
important reason for lower clay content at the subsoil
in spite of the dense irrigation in the study area was
the intensive tillage practices which caused to the for-
mation of a plow pan at the depth of 20 cm in the clay
topsoil (Kılıc et al., 2004). The formation of a plow
pan could halt to clay movement through soil pores.
The claypan formed in the cultivated lands decrease
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60 Environ Monit Assess (2007) 127:55–65
Table 1 Descriptive statistics for studied soil properties
Soil property Mean S.D. Variance Skewness Kurtosis Min. Max. C.V.
0–30 cm depthCEC (me/100 g) 46.4 4.54 20.68 0.19 −0.55 36.81 55.79 9.79
Exchangeable Ca+Mg (me/100 g) 43.9 4.47 20.01 0.13 −0.61 34.34 52.98 10.17
Exchangeable K (me/100 g) 1.23 0.17 0.03 −0.70 0.48 0.73 1.54 14.46
Exchangeable Na (me/100 g) 1.24 0.18 0.03 0.21 −0.61 0.84 1.60 14.48
EC (dS/m) 1.56 0.11 0.01 0.92 1.38 1.31 1.89 7.59
pH 8.25 0.17 0.02 −0.26 2.38 7.68 8.67 2.07
CaCO3 (%) 5.25 0.79 0.62 −0.15 −0.40 3.22 6.86 15.05
O.M. (%) 1.63 0.28 0.08 0.40 −0.83 1.23 2.32 17.39
Clay (%) 40.6 2.49 6.21 −0.26 −0.16 34.10 45.90 6.13
Silt (%) 48.7 4.20 17.66 −0.21 −0.41 40.00 57.50 8.62
Sand (%) 10.6 3.54 12.59 0.83 −0.26 5.90 18.40 33.2
ESP (%) 2.69 0.42 0.17 0.35 −0.06 1.86 3.69 15.56
30–60 cm depthCEC (me/100 g) 34.1 3.75 14.25 −0.86 −0.24 25.30 39.68 11.06
Exchangeable Ca+Mg (me/100 g) 31.6 3.80 14.47 −0.87 −0.27 22.68 37.12 12.02
Exchangeable K (me/100 g) 0.92 0.13 0.01 0.04 −0.33 0.60 1.23 14.88
Exchangeable Na (me/100 g) 1.55 0.17 0.03 −0.75 0.36 1.08 1.85 11.44
EC (dS/m) 2.25 0.24 0.06 0.32 −0.53 1.87 2.81 11.05
pH 8.61 0.12 0.01 −0.27 1.02 8.27 8.92 1.46
CaCO3 (%) 8.37 0.86 0.74 0.01 −0.79 6.82 10.01 10.32
O.M. (%) 0.71 0.17 0.02 −0.04 −1.19 0.35 1.00 24.12
Clay (%) 35.3 2.11 4.48 −0.18 −0.32 30.00 38.40 5.99
Silt (%) 51.1 2.95 8.71 0.38 −0.004 45.00 57.50 5.78
Sand (%) 13.5 1.50 2.26 0.07 −0.15 10.90 16.60 11.11
ESP (%) 4.66 0.84 0.71 0.48 0.27 3.04 6.92 18.08
60–90 cm depthCEC (me/100 g) 30.2 4.01 16.10 −0.19 −0.72 21.28 36.81 13.26
Exchangeable Ca+Mg (me/100 g) 27.4 4.09 16.78 −0.17 −0.73 18.35 34.02 14.93
Exchangeable K (me/100 g) 1.06 0.15 0.02 −0.54 0.38 0.69 1.44 14.34
Exchangeable Na (me/100 g) 1.75 0.15 0.02 −0.54 −0.63 1.37 2.01 9.12
EC (dS/m) 3.95 0.46 0.21 −0.09 0.00 2.87 5.04 11.08
pH 8.81 0.17 0.02 −0.03 0.18 8.41 9.17 2.1
CaCO3 (%) 9.92 0.84 0.71 −0.24 −0.08 7.53 11.55 8.72
O.M. (%) 0.67 0.16 0.02 0.59 −0.93 0.47 0.99 24.19
Clay (%) 32.6 2.83 8.02 0.05 −0.86 28.40 38.40 8.69
Silt (%) 60.7 4.41 19.45 −0.22 −0.53 50.00 70.00 7.26
Sand (%) 6.6 2.96 8.79 −0.04 −0.71 1.60 11.60 44.6
ESP (%) 5.92 1.12 1.26 0.46 0.03 3.78 8.93 19.00
Table 2 The Changes in soil properties for soil depths (The different letter over the number indicates statistical difference at 0.01level)
Soil Exchangeable Exchangeable Exchangeable
depth CEC Ca+Mg K Na EC CaCO3 O.M. Clay Silt Sand ESP
(cm) (me/100g) (me/100g) (me/100g) (me/100g) (dS/m) pH (%) (%) (%) (%) (%) (%)
0–30 46,46c 43,98c 1,23c 1,25a 1,56a 8,25a 5,25a 1,63b 40,64c 48,70a 10,66b 2,70a
30–60 34,15b 31,67b 0,93a 1,56b 2,25b 8,61b 8,37b 0,72a 35,37b 51,10b 13,53c 4,67b
60–90 30,26a 27,44a 1,06b 1,75c 3,95c 8,81c 9,93c 0,68a 32,60a 60,75c 6,65a 5,92c
Springer
Environ Monit Assess (2007) 127:55–65 61
water movement through soil profile due to little pore
size (Rhoades et al., 1992).
CEC differed significantly in all the soil depths
(p < 0.01), with a mean of 46.46 me100 g−1 CEC
for 0–30 cm, 34.15 me100 g−1 CEC for 30–60 cm,
and 30.26 me100 g−1 CEC for 60–90 cm depth. CEC
decreased with soil depth due to the decreases of
clay content. OM content of 0–30 cm differed signifi-
cantly from those of 30–60 and 60–90 cm (p < 0.01;
Tables 1 and 2).
Soil pH CV had a lower variation as compared to
the other soil properties (Table 1). The OM had the
maximum variability with a mean of 1.63% at 0–30 cm
and 0.71% at 30–60 cm depths (Table 1). In general,
the CV for the soil properties increased with soil depth
except for Na+ and silt contents. The soil properties
had the highest CV values at the 60–90 cm depth. The
coefficients of skewness and kurtosis indicated that
most of the soil properties had normal distributions ex-
cept OM and sand content for 0–30 cm depth; CEC,
exchangeable Ca++, Mg++ and Na+ for 30–60 cm;
and exchangeable Na+ and OM for 60–90 cm
(Table 1). The values of salinity and alkalinity (EC and
ESP) had coefficients of skewness that was very close
to zero except for EC at 0–30 cm (0.92).
The linear correlation matrix between pairs of vari-
ables was shown in Table 3 for all depths. EC and ESP
values were much higher and significant at 60–90 cm
depth than the other depths. At the 30–60 cm depth,
the correlation coefficients between EC and CaCO3,
CEC and exchangeable Ca++ and Mg++ were signif-
icant (p < 0.05), while there was not significant rela-
tionship between EC and ESP and other soil properties
at the 0–30 cm depth. The relationships between ESP,
EC and K values were reciprocally associated (p < 0.01
and p < 0.05, respectively) at 60–90 cm depth; on the
other hand, a significant positive relationship occurred
between ESP and CaCO3 (p < 0.05). Significant re-
lationships were found between EC and Exchangeable
Ca++, Mg++ and K+ and CaCO3 contents at 60–90 cm
depth (p < 0.05), but these relationships were neg-
atively significant except for exchangeable K+. Van
Asten et al. (2003) pointed out that EC and alkalinity
increased when CEC, Mg++ and Ca++ decreased in
the soil solution.
Soil properties showed differences in spatial depen-
dence as showed by semivariance (Table 4). Exponen-
tial, spherical and linear isotropic models were used
to define the most of soil variables except for some
of the soil properties at 30–60 cm depth. Semivari-
ance increased with distance to a constant value at a
given separation distance for exponential, gaussian and
spherical models. The linear model has a slope that is
close to zero, and total variance was equal to the nugget
variance, and the variables are not spatially correlated
(Isaaks and Srivastava, 1989). In this study, soil vari-
ables for linear models were spatially correlated in the
distances greater than the minimum grid spacing at all
lag distances.
Nugget effect was generally higher at 60–90 cm
depth as compared to the other depths. This indicated
that soil properties had spatial variability at small dis-
tances at 60–90 cm depth (Table 4). Nugget effect is
related to the spatial variability at smaller distances
than the lowest separation distance between measure-
ments (Webster, 1985; Warrick et al., 1986). Nugget ef-
fect for EC increased considerably from soil surface to
60 cm depth, but it decreased below 60 cm depth. Utset
et al. (1998) reported a similar result in a hydromorpfic
Vertisol within a 500 by 600 m field. They showed that
nugget effects for EC values were higher at the soil sur-
face as compared to the subsurface. EC was variable
within short distances at 30–60 cm depth. The nugget
effect of ESP increased with soil depth, indicating that
ESP had high spatial variability at smaller distances
than the lowest separation distance at 60–90 cm depth.
Nevertheless, according to the difference between sill
and nuggets, a weaker spatial structure was found at
0–30 and 30–60 cm depths, with almost a pure nugget
effect and a stronger spatial structure at 60–90 cm depth
with a higher nugget effect (Table 4). Therefore, spatial
structure of ESP increased with depth.
The ranges of EC values ranged from 48 m to 169 m.
The EC values were spatially dependent over a dis-
tance of 48 m (Table 4). The geostatistical range of val-
ues calculated for EC in the present study was smaller
than that calculated by Utset et al. (1998). The EC
values at the topsoil might be dependent higher dis-
tances than maximum lag distance due to intensive irri-
gation and plow pan occurred about 20 cm depth (Kılıc
et al., 2004). On the contrary, the ranges of ESP values
increased with depth, between 62 m and 210 m. The
ranges of isotropic semivariograms for ESP (210 m)
were found higher for greater distances between mea-
surements at 60–90 cm depth, indicating that ESP val-
ues had a homogeneous variability in spite of higher
spatial variability at smaller distances than the lowest
separation distance and a spatially dependent over a
Springer
62 Environ Monit Assess (2007) 127:55–65
Tabl
e3
Co
rrel
atio
nb
etw
een
soil
pro
per
ties
and
EC
and
ES
Pfo
rea
chso
ild
epth
So
ilP
rop
erty
CE
CE
xch
ang
eab
leC
a+M
gE
xch
ang
eab
leK
Ex
chan
gea
ble
Na
EC
pH
CaC
O3
O.M
.C
lay
Sil
tS
and
0–30
cmde
pth
CE
C1
,00
0
Ca+
+ +M
g++
0,99
9∗∗
1,0
00
K+
0,1
67
0,1
30
1,0
00
Na+
0,28
4∗0
,24
80
,00
41
,00
0
EC
0,2
47
0,2
51
−0,0
12
0,0
01
1,0
00
pH
−0,5
22∗∗
−0,5
13∗∗
0,0
15
−0,4
74∗∗
−0,2
44
1,0
00
CaC
O3
0,0
82
0,0
79
0,0
38
0,0
81
0,1
87
−0,1
94
1,0
00
O.M
.0
,01
40
,01
50
,09
8−0
,11
4−0
,14
80
,13
5−0
,12
01
,00
0
Cla
y0
,00
4−0
,00
40
,14
40
,05
5−0
,04
40
,21
80
,05
0−0
,10
31
,00
0
Sil
t0
,02
30
,02
3−0
,07
70
,08
50
,12
8−0
,13
1−0
,07
00
,05
0−0
,539
∗∗1
,00
0
San
d−0
,03
0−0
,02
5−0
,01
0−0
,14
0−0
,12
10
,00
20
,04
80
,01
3−0
,06
5−0
,80
6∗∗
1,0
00
ES
P−0
,06
3−0
,07
80
,20
30
,14
60
,03
5−0
,00
5−0
,09
90
,21
8−0
,04
4−0
,02
00
,05
5
30–6
0cm
dept
hC
EC
1,0
00
Ca+
+ +M
g++
0,99
8∗∗
1,0
00
K+
0,0
62
0,0
13
1,0
00
Na+
−0,1
66
−0,2
21
0,2
69
1,0
00
EC
−0,1
20
−0,1
24
−0,0
35
0,1
27
1,0
00
pH
−0,2
36
−0,2
28
−0,1
19
−0,0
33
0,1
32
1,0
00
CaC
O3
−0,2
09
−0,2
02
−0,0
31
−0,1
04
0,35
1∗0
,21
71
,00
0
O.M
.−0
,03
0−0
,03
20
,10
0−0
,03
90
,05
80
,21
6−0
,04
31
,00
0
Cla
y−0
,22
0−0
,20
8−0
,12
3−0
,13
7−0
,00
70
,19
00
,01
80
,15
61
,00
0
Sil
t0,
291∗
0,28
4∗0
,03
10
,08
0−0
,01
6−0
,17
9−0
,08
4−0
,22
6−0
,875
∗∗1
,00
0
San
d−0
,26
1−0
,26
50
,11
30
,03
70
,04
00
,08
30
,13
90
,22
40,
308∗
−0,7
31∗∗
1,0
00
ES
P−0
,302
∗−0
,306
∗0
,03
50
,10
8−0
,05
80
,20
7−0
,07
1−0
,11
1−0
,05
30
,12
7−0
,17
4
60–9
0cm
dept
hC
EC
1,0
00
Ca+
+ +M
g++
0,99
8∗∗
1,0
00
K+
−0,1
65
−0,2
05
1,0
00
Na+
−0,3
33∗
−0,3
71∗∗
0,1
66
1,0
00
EC
−0,3
54∗
−0,3
61∗
0,33
7∗0
,02
21
,00
0
pH
−0,0
28
−0,0
50
0,2
61
0,34
6∗−0
,06
61
,00
0
CaC
O3
−0,0
62
−0,0
59
−0,2
06
0,1
69
−0,3
15∗
−0,2
10
1,0
00
O.M
.−0
,02
7−0
,02
4−0
,08
40
,01
10
,02
4−0
,10
60
,08
71
,00
0
Cla
y−0
,10
3−0
,10
60
,09
50
,04
0−0
,16
7−0
,05
40
,09
9−0
,24
41
,00
0
Sil
t0
,06
90
,07
5−0
,15
1−0
,04
30
,11
60
,04
2−0
,09
70
,25
7−0
,748
∗∗1
,00
0
San
d−0
,00
4−0
,01
00
,13
40
,02
5−0
,01
3−0
,01
00
,05
1−0
,14
80
,15
7−0
,773
∗∗1
,00
0
ES
P−0
,17
5−0
,16
3−0
,360
∗0
,13
4−0
,400
∗∗−0
,19
20,
322∗
0,1
11
0,1
26
−0,0
65
−0,0
24
Springer
Environ Monit Assess (2007) 127:55–65 63
Table 4 Geostatistical analysis of soil properties for each soil depth
Variable Model Nugget effect Sill Range (m) RSS r2
0–30 cm soil depthCEC Spherical 2.48 25.66 69 25.6 0.89
Ca+++Mg++ Spherical 2.08 24.68 65 24.4 0.89
K+ Linear 0.03 0.03 62 1 ∗ 10−4 0.20
Na+ Exponential 0.01 0.08 171 4 ∗ 10−5 0.80
EC Spherical 0.007 0.03 169 3.5 ∗ 10−6 0.95
pH Spherical 0.007 0.06 210 4.4 ∗ 10−5 0.87
Lime Linear 0.60 0.61 62 8.6 ∗ 10−3 0.007
OM Exponential 0.07 0.14 210 9 ∗ 10−4 0.03
Clay Linear 6.17 6.34 62 0.34 0.04
Silt Linear 17.37 17.37 62 14.4 0.20
Sand Linear 12.40 12.40 62 5.86 0.19
ESP Linear 0.15 0.15 62 3.5∗10−3 0.002
30–60 cm soil depthCEC Gaussian 10.31 34.87 166 2.06 0.77
Ca+++Mg++ Gaussian 12.30 44.22 158 0.95 0.94
K+ Linear 0.01 0.01 62 4 ∗ 10−5 0.002
Na+ Linear 0.03 0.03 62 4 ∗ 10−5 0.24
EC Exponential 0.04 0.11 150 3 ∗ 10−4 0.34
pH Gaussian 0.004 0.21 177 3 ∗ 10−5 0.92
Lime Exponential 0.63 1.26 180 0.05 0.16
OM Linear 0.02 0.02 62 1 ∗ 10−5 0.12
Clay Exponential 3.9 7.80 210 0.23 0.47
Silt Gaussian 8.42 16.85 174 1.36 0.32
Sand Linear 2.23 2.23 62 0.22 0.12
ESP Linear 0.65 0.70 62 0.03 0.03
60–90 cm soil depthCEC Exponential 12.82 27.81 156 18.6 0.36
Ca+++Mg++ Exponential 13.35 29.01 152 22.8 0.33
K+ Spherical 0.003 0.02 63 9 ∗ 10−7 0.99
Na+ Linear 0.02 0.02 62 5 ∗ 10−5 0.22
EC Spherical 0.0001 0.25 48 2 ∗ 10−3 0.88
pH Spherical 0.0001 0.03 36 2 ∗ 10−4 0.49
Lime Spherical 0.56 1.12 210 0.01 0.69
OM Linear 0.02 0.02 210 4 ∗ 10−5 0.02
Clay Linear 7.96 8.01 62 2.57 0.00
Silt Linear 19.63 19.63 62 3.52 0.13
Sand Linear 8.86 8.86 62 1.21 0.33
ESP Exponential 1.10 2.21 210 0.16 0.09
distance of 210 m. However, ESP showed anisotropic
spatial dependence, especially in northeast and north-
south directions (45◦ and 90◦). The most important rea-
son of directional spatial dependence of ESP could be
the disparities in the soil texture, as soils in study area
formed on Yesilirmak river terraces, deposited differ-
ent materials at the time of Holocene. The size arrange-
ments of soil particles were north-south direction and
reduced away from riverbed. Meanwhile, the values of
EC and ESP decreased away from riverbed. The spatial
dependence of soil properties may be controlled by in-
trinsic variations in soil characteristics such as texture
and mineralogy (Cambardella and Karlen, 1999). At
the same time, directional spatial dependence of ESP
could be high with the groundwater level and fluctuates.
Spatial variability in EC and ESP values reduced due
to systematic soil sampling on the same soil series in-
stead of arbitrary transects covering multiple soil types.
However, spatial dependence of ESP and EC showed
an important difference directionally among the soil
Springer
64 Environ Monit Assess (2007) 127:55–65
depths, especially in 60–90 cm. The variability in soil
properties at the soil series level is often caused by small
changes in topography that affect movement and stor-
age of water across and within the soil profile (Mulla
and McBratney, 2000). Because of the same landscape
positions of the study area, the main reason for dif-
ferences in spatial dependence could be the textural
difference and plow pan at 20 cm, which affects water
intake and percolation.
4 Conclusions
Spatial variability of salinity and alkalinity of a field
having salination risk was assessed. The observations
on spatial variability and correlation coefficients calcu-
lated among the variables indicated that the changes in
salinity and alkalinity varied depending on soil depths.
The results showed that topsoil did not have a risk of
salinity and alkalinity, but subsoil (60–90 cm depth)
had salinity and alkalinity problem. This study sug-
gested that spatial variability of salinity and alkalinity
had an important role for site-specific management.
Differences in EC and ESP were significant by soil
depth, and were higher for 60–90 cm. The semivari-
ograms fitted to the data showed that EC and ESP values
were spatially dependent over the geostatistical range
of distances from 48 to 210 m, and ESP had directional
dependence at 30–60 cm and 60–90 cm depths, with an
angle of 90. Consequently, soil sampling distance of
salinity and alkalinity for practical sampling purposes
could be taken as 48 m. Salinity data maps developed
using the kriging estimations showed that EC values at
60–90 cm depth slightly increased (EC ∼= 4 dS m−1).
The main reason of salinity in subsoil was upward
movement of salts during evaporation from high
groundwater due to recharged water from high lands
and irrigation with low-quality water diverted from
drainage channels. The most important reasons of low
salt concentration in topsoil could be the soil manage-
ment practices such as intensive irrigation, crop variety
(maize production) and soil tillage (a plow pan forma-
tion) applied in the study area.
The topsoil in study area occurred free of the risks
of salinity and alkalinity under current irrigation. How-
ever, if low-quality irrigation water is used it may cause
some problems of salinity and alkalinity. Meanwhile,
salts may rise until the plant root zone if dryland agri-
culture is practiced in these soils. The land should be
used for agriculture production with the rotation of ir-
rigated plants and non-irrigated plants. However, the
level of groundwater table should be under monitored
continuously to avoid high levels of groundwater.
Acknowledgements The authors thank the Research Funds Ac-countancy of Gaziosmanpasa University providing the financialsupport for this study (Project No: 2003/34). We also acknowl-edge the authorities at Kazova Farm for their permitting us un-restricted access to collect the soil samples.
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