ORI GIN AL PA PER

Spatial variability of soil solute and saturated hydraulicconductivity affected by undrained water tableconditions

Rifat Akıs

� Springer Science+Business Media New York 2014

Abstract Spatial information of soil solute and saturated hydraulic conductivity under

undrained water table conditions can provide explicit knowledge to better manage soil and

water than nonspatial management practices. This research was conducted to determine

spatial structure of soil saturated hydraulic conductivity and salt content, as influenced by

undrained water table conditions in the Amik Plain of Turkey. Using grid sampling, the

General Directorate of Turkish State Hydraulic Works sampled the Amik Plain soils at

approximately 1 600 locations, 254 of which were examined through undisturbed soil core

sampling for land drainage evaluation. Geostatistical analyses revealed that the 30–60 and

90–120 cm soil layers had a shift in the particle size and were exposed to two different

alluvial soil forming processes. Mean soil Ksat steadily decreased from 1.05 to

0.99 cm h-1 and mean salt content increased from 0.307 to 0.335 % below the 30 to 60-cm

layer. Correlation distance varied from 710 to 1 130 m for soil Ksat and 1 000–1 130 m

for soil salt content for horizontal variograms. Nugget values of the models for soil Ksat

ranged from 0.031 to 0.036, while the range of nugget was from 0.002 to 0.18 for soil salt

content. Sill variance was the highest for Ksat (0.201) from 30 to 60 cm layer and soil salts

(1.18) from 60 to 90 cm layer. Soil profile was moderately to heavily saline

(1.69–7.73 dS m-1). For the vertical variograms, correlation distance was approximately

75 cm for soil Ksat and 136 cm for soil salt content. Results showed that an

1 130 m 9 1 130 m subfield with 75 cm and/or deeper depth could be used for the layout

of drain tiles. Further studies of long-term spatial variability of these properties under

drained and undrained conditions with anisotropy are needed for sound surface and sub-

surface drainage system implementations in the Amik Plain.

Keywords Anisotropy � Asi river � Drainage � Horizontal variogram � Salinized profile �Vertical variogram � Amik Plain

R. Akıs (&)Soil Science and Plant Nutrition Department, College of Agriculture, Mustafa Kemal University,31034 Antakya, Hatay, Turkeye-mail: rakis@mku.edu.tr

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Precision AgricDOI 10.1007/s11119-014-9379-0

Introduction

A great environmental change in the Amik Plain Turkey due to high frequency floods,

shallow groundwater table, and sediment accumulations of about 60 m depth and resulted

in the change of the centers of main archeological sites in the region. Environmental

change based on the sediment history of the Amik (Plain) was studied by Friedman et al.

(1999). The Amik Plain is a highly irrigated and fertile Plain, the floor of which has been

occupied by several lakes and marshes at various times during the last 30 000 years. Water

resources were in the form of springs, marshes, perennial rivers, and episodic lakes for the

region’s agriculture. However, these water bodies were often excess water and sometimes

capable of limiting the amount of available arable land in the Amik Plain (Plain). As a

result of high-energy episodic floods, the Lake Amik deposited shaly silts and developed

between 3000 and 2500 BC. The growth of the surface area of the Amik Lake was because

of climatic fluctuation, human influences on the basin vegetation types, runoff waters, and

discharge of water from canals. The area of the lake was 22 000 hectares with an additional

9 000 ha of surrounding swampy lands (DSI 1962). Drainage problems of soil salinity,

shallow groundwater table, and surface runoff characterized crop production and tillage

practices in the Plain for long times. Therefore, this lake was drained through open drain

channels to the Mediterranean Sea and the area was opened to agricultural activities by the

year 1975. Since 1975, large areas of salt patches, water logging problems, and drainage of

needy areas have been the focus of field research studies and governmental construction

projects in the Plain. Over a million people inhabit the area and security of crop production

in the region can be enhanced by quantitatively mapping and managing soil salinity and

drainage problems. Spatial mapping of these problematic areas can serve a valuable tool to

decide when, how, and where to manage the abovementioned problems in the Plain. The

region is climatically semiarid and a center for very high value fruit and vegetable crop

production in the eastern Mediterranean part of Turkey. Late fall, winter, and spring

rainfalls require preventive measures for storm runoff, surface and subsurface field

drainage, and crop management strategies.

Soil salinity and its distributions generally negatively affect soil structural stability,

hydraulic conductivity, infiltration rate, and erosivity in arid and semi-arid climate (Juan

et al. 2011). Spatial position of a site (Manning et al. 2001), soil type, and rainfall (Juan

et al. 2011) can determine vulnerability of the site to soil salts. This risk can be quanti-

tatively mapped to facilitate management decisions and environmental remediation of

drainage and salinity problems (Florinsky et al. 2000). Evaluating and mapping of soil Ksat

and salt content can collectively help field managers in subfield-level management in

decision-making (Lesch et al. 2005; Li et al. 2007) and explaining crop yield variations

(Kitchen et al. 1999). Therefore, there is a great need for precise spatial variation maps of

soil salt content and Ksat to remedy soil salinity and degradation of drainage quality in

soils (Shi et al. 2003).

Non-spatial and conventional drainage system design models and uniform soil man-

agement practices do not differentiate sample positions from each other and treat the soil at

the same resolution to meet soil and crop drainage requirements (Agrawal et al. 1995;

Corwin et al. 1999), resulting in minimal benefit of the whole operations and redundancy

of management inputs to the field. On the other hand, information of spatial distribution of

soil properties as a result of decision rules in mapping process can delineate similar

subfields with certain characters (Corwin and Lesch 2003) to improve soil and crop

conditions, and management benefits of soil drainage practices. Spatial mapping of soil salt

content and Ksat can provide a cost-effective means for crop management in precision

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agriculture (Khosla et al. 2002; Franzen et al. 2002) and provide easily interpretable

(Corwin and Lesch 2003) reliable and predictable information over time (Doerge 1999).

Thus, any remedying enhancements in soil salt content can improve soil conditions, soil

water potential, soil Ksat, and thus soil drainage quality.

Soil drainage problems are costly to remediate, requiring determination of exact loca-

tions of drain tiles for the mechanical operations success and drainage efficiency in the

process of field installation. For the management of a drainage problem, smaller man-

ageable parcels or subfields are preferred over a large field (Kitchen et al. 2002). Through

its classification and interpolation capabilities, kriging is often preferred method for

management decision-making (Lark and Ferguson 2004) and is used in soil drainage

system solutions (Moustafa and Yomota 1998). Developing such spatial maps can be used

to remedy salinity hazard and low Ksat problems in subfields, which have been primarily

related to accumulation and movement of water as affected by soil, landscape (MacMillan

et al. 1998; Jaynes et al. 2003), and climate variations (Corwin et al. 1999; Sudduth et al.

1997).

The effects of both drained and undrained water table conditions on these properties are

still under scrutiny and data are very scarce in this type of research (Moustafa and Yomota

1998; Utset and Castellanos 1999; Moustafa 2000). In the Amik Plain, soils have expe-

rienced frequent flooding of storm waters, salinity, and shallow water table problems since

1975. These problems have catastrophically damaged crops, residential areas, soil, and

environment in the Plain. Geospatial modeling should be involved in soil drainage problem

remediation and water management plans. Understanding the patterns of spatial variability

of these properties is important step for effectively draining and washing the salts from the

Plain, designing sound drainage systems, and determining the size of drainage needy areas

for a better soil and water management plan implementations in the Plain.

The identification, characterization, and prioritization of problematic drainage areas in

the former Amik Lake floor and bound swamp soils are the concerns of this study. The

objectives of this study were to: (i) map the spatial variability of both soil properties, soil

Ksat and salt content, for drainage problem assessment, and (ii) evaluate the effect of

undrained water table conditions on the spatial variability of soil Ksat and salt content in

the study area.

Materials and methods

Study area

The study area (the whole Asi Basin, Hatay, Turkey) is situated between 36�120–37�020

North latitudes and 36�100–36�400 East longitudes. The Amanos Mountains are oriented in

northeast to southwest direction with 2 250 m elevation from the Mediterranean Sea in the

west side of the Amik Plain, while the east side of the Plain is bounded by the Kuseyr

Plateau (825 m). In between the two elevations lies the Amik Plain on the fault line of

Antakya-Kahraman Maras (80–250 m elevation above sea level) in the northeast and

southwest direction (Fig. 1). Sample posts of soil pits are illustrated in Fig. 2.

The slope of the landscape ranges between zero and two percent. The topography of the

83 000 ha of land is very suitable for irrigation agriculture (DSI 1962). Percentage dis-

tribution of the rains in the region is 35 % in winter, 29 % in spring, 12 % in summer, and

24 % in autumn (DSI 1962). For the last 50 years, mean yearly precipitation and evapo-

ration were recorded as 1 124 and 1 877 mm in the region, respectively (Kilic et al. 2008).

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Fig. 1 Locatıon of the study area and 3D view of the Amik Plain

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The winter is very rainy while the summer is hot and dry. The spring and fall seasons are

wet but less than winter levels in the Plain soils. Rare rains rarely fall on the Plain soils

during summer (between July and August). Finally, the wetter fall season starts by October

(Korkmaz 2005). Average annual precipitation and evapotranspiration is illustrated in

Fig. 3.

In earlier studies the depth of groundwater table from the surface in the Plain ranged

between 10 and 300 cm (DSI 1962), while in more recent times the water table depths have

ranged from 20 to 400 m in the current conditions of the Plain (Caliskan 2008) and 400 m

in an area of 75 000 ha in the Plain due to drought and climate change (DSI 2012). Salinity

classes of irrigation water were recorded as C4S1, C4S2, and C5S1 in the region (U.S.

Salinity Laboratory Staff 1954). All Amik Plain soils were drained through open ditch

drainage systems by the Turkish State Hydraulic Works (DSI 1962). Water ponds sur-

rounded by clay loam and clayey soils, with clay contents ranging from 73 to 97 % in some

of the soil profile depths of 0–30 and 30–120 cm, were prone to severe soil salinity

problems (DSI 1962).

The cropping pattern in the area includes winter-wheat (Triticum aestivum L.), cotton

(Gossypium hirsutum L.) for the first cropping period, corn (Zea Mays L.), and vegetables

(carrot (Daucus carota L.), pepper (Capsicum Annuum L.), onion (Allium Cepa L.), okra

(Abelmoschus esculentus L.), potato (Solanum Tuberosum L.), tomato (Lycopersicum

Esculentum L.)) for the second cropping period in the rotation. Two cropping periods exist

between middle of spring and late autumn seasons in the Amik Plain soils. Since flooding

is common in early spring and late fall seasons in the basin, crop rotation is a very common

soil and water conservation management practice in the region. Although small, subsurface

drainage practices are gaining popularity in the Plain, surface drain ditches basin-wide

have existed since 1975 to control storm runoff and improve drainage conditions.

Fig. 2 Geographical positions ofsoil pits in the soil survey bookby DSI (1962)

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Soils of the Amik Plain

The 73 000 ha area of Amik Plain soils were surveyed for crop suitability and land use

purposes (Kilic et al. 2008). A total of 55 soil series were classified into five soil orders,

consisting of 21 Entisols, 9 Vertisols, 12 Inceptisols, 10 Alfisols, and 3 Mollisols using

standard methods (Soil Survey Staff 1998). The soils have also been classified according to

the FAO (2014) system (Table 1).

According to the soil surveys of DSI (1962) and (Kilic et al. 2008), the Plains soils

predominantly consist of clayey texture because of the fluvially transported alluvial cal-

careous parent material accumulating in geologically young depressions in the Quaternary

time. Topsoils have grey, black, brown, dark brown, and red colors (according to Munsell

color chart), while subsoil layers are generally pale brown, grayish-brown, red, and red-

dish-brown colored. However, the dominant color is black and brown all over the Plains

soils. The soils are very low (\1 %) in lime content around Antakya-Kırıkhan Highway,

whereas the rest of the Plains have lime content above 5 %. The shallowest soils have a

depth of less than 100 cm, while the rest of the soil profiles are considered deep

(100–150 cm) and very deep ([150 cm) in both the soil surveys. The soils are generally

heavy clay in the flat foot lands. They gradually change to clay loam, sandy clay loam, and

loam as the landscape varies from flat to hill slopes. Since the Amik Lake existed until

1975, soils were swampy, mostly composed of peat and muck around the lake. Even today,

hemic and histic soil materials with a buried histic epipedon are very common in the area

between the Murat Pasa and Karasu Rivers which flowed into the Ex-Amik Lake (DSI

1962; Korkmaz 2005). However, soils surrounding the former Amik Lake are dominated

by Histic Inceptisols, rather than Histosols in the current time. The soils of the Plain are

classified by Kilic et al. (2008) as poorly and somewhat poorly drained Histic Inceptisols in

the surveys according to Soil Survey Staff (1998). The reason for Histosol to Histic

Inceptisol conversion has been slash and burn type agricultural practices, starting after

drainage of Amik Lake.

0

50

100

150

200

250

300

0

25

50

75

100

125

150

175

200

225

0 1 2 3 4 5 6 7 8 9 10 11 12

Mean

Mo

nth

ly Evap

otran

spratio

n, m

mMea

n M

on

thly

Pre

cip

itat

ion

, mm

Months

Precipitation Evapotranspration

Fig. 3 Average annual precipitation and evapotranspiration quantities of the last 50 years in the Amik Plain

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Table 1 Soil classification of the research area according to soil survey Staff (1998) and FAO/UNESCO(1990), adopted from Kilic et al. (2008)

Order Suborder Great Group Subgroup FAO/UNESCO Class

Entisol Fluvent Xerofluvent Aquic Xerofluvent Eutric Gleysol

Calcaric Fluvisol

Calcaric Fluvisol

Vertic Xerofluvent Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Oxyaquic Xerofluvent Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Typic Xerofluvent Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Calcaric Fluvisol

Vertisol Xerert Haploxerert Chromic Haploxerert Eutric Vertisol

Eutric Vertisol

Eutric Vertisol

Eutric Vertisol

Aquic Haploxerert Eutric Vertisol

Typic Haploxerert Eutric Vertisol

Eutric Vertisol

Calcixerert Chromic Calcixerert Eutric Vertisol

Aquert Epiaquert Xeric Epiaquert Eutric Vertisol

Inceptisol Xerept Calcixerept Typic Calcixerept Haplic Calcisol

Haplic Calcisol

Haplic Calcisol

Haplic Calcisol

Haplic Calcisol

Haplic Calcisol

Haplic Calcisol

Haplic Calcisol

Vertic Calcixerept Haplic Calcisol

Aquept Humaquept Histic Humaquept Haplic Phaeozem

Haplic Phaeozem

Halaquept Aeric Halaquept Haplic Calcisol

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Sampling description

Soil survey was conducted before drainage ditches were laid in the basin in 1962. A total of

1 600 soil pits of 150 cm depth were sampled with 30-cm depth intervals for different

purposes. The total number of soil core samples collected from study area was 8 000 (from

5 depths of 1 600 soil pits locations). Only 254 pits which are equivalent to 1 270 soil core

samples were used in spatial variability analysis of soil soluble salts and saturated

hydraulic conductivity (Ksat) in this study. The sampling design was a grid system

(2 9 2 km) with some extra random sampling points in the grid cells (Fig. 2). Soil satu-

rated hydraulic conductivity was measured in the soil core samples through constant head

permeameter in the drainage research laboratories of the General Directorate of Hydraulic

Works of Hatay Province, Turkey (DSI 1962). Soil salt as percentage (dissolved salts

(g) per 100 g soil) was measured in soil saturation paste by the U.S. Salinity Laboratory

Staff (1954) and Rhoades and Oster (1986). Soil salt content was classed according to

Wang et al. (1993). Soil core samples were taken from 0–30, 30–60, 60–90, 90–120,

120–150 cm depths of the soil profiles in the landscape by an undisturbed soil core sampler

(Klute and Dirksen 1986). The soil Ksat values were classed according to Schwab et al.

(1992).

Semivariogram analysis

The distribution of data points was evaluated. Outliers and anomalies of the data points

were analyzed by normality tests and data posting scattergrams, as well as cumulative

frequency analysis of the data. After data transformations, semivariogram modeling

principles were used to determine spatial structure of the data distribution in the soil.

Ordinary point kriging method was the master of cross-validation procedure used to

interpolate and estimate the observation points at unmeasured sample locations. Geosta-

tistical software GS? (Gamma Design Software, St. Plainwell, MI) was used to analyze

spatial structure of the soil data and semivariogram parameters. Semivariogram analysis

was based on a maximum lag distance of 40 km. No less than 32 pairs of points in a lag

class were utilized in the semivariance analysis.

A pair of sample points of soil variable Zi is measured at the positions of (xi ? h) and xi

in the landscape. Therefore, a pair of sample points of the soil variable is separated from

each other by a lag distance vector of h. According to Journel and Huijbregts (1978), each

Table 1 continued

Order Suborder Great Group Subgroup FAO/UNESCO Class

Alfisol Xeralf Haploxeralf Calcic Haploxeralf Calcic Luvisol

Calcic Luvisol

Calcic Luvisol

Vertic Haploxeralf Vertic Luvisol

Typic Haploxeralf Chromic Luvisol

Chromic Luvisol

Mollisol Xeroll Haploxeroll Fluventic Haploxeroll Haplic Phaeozem

Haplic Phaeozem

Calceric Phaeozem

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measurement of random variable Zi cannot be explicitly determined. Therefore, it is

assumed that each zi is a realization of the random variable Zi in the soil.

A semivariogram of a regionalized soil variable is denoted as c hð Þ and estimated by

Journel and Huijbregts (1978, p. 29) as the following.

c hð Þ ¼ 1

2NðhÞXNðhÞ

i¼1

Zi xi þ hð Þ � Zi xið Þ½ �2 ð1Þ

N(h) is the number of experimental pairs (Zi(xi, Zi(xi ? h))) of data separated by a vector h.

Isotropic variograms were computed from Eq. (1) at the azimuths 0�, 45�, 90�, 135�with angular bands of 22.5� in each direction in horizontal plain. On the other hand,

vertical variograms were computed based on a 90� azimuth with 90� angle tolerance and

0–150 cm soil thickness. A total of 1 270 undisturbed soil cores for soil Ksat from five

depths in the profile and disturbed soil samples for the soil salt content were vertically

interpolated to determine vertical variogram models for soil Ksat and salt content or total

soluble salts (TS). First of all, the active lag distance parameter for the model to be built in

vertical direction was input as 150 cm in the semivariance menu of the GS? software.

Then, through trial and error steps of determining search radii and the type of spatial

model, the search radius for the soil Ksat was 31 cm while the one for soil salt content was

29 cm, beyond which the produced model was only nugget effect.

Kriging technique and cross-validation for the soil variables

Spatial heterogeneity for a given soil property can be structured through kriging and

randomness of a soil variable can be evaluated through kriging variance. Therefore, kriging

is a well-known tool in soil variability to make optimal and unbiased estimations of a soil

variable at unsampled locations with minimum estimation variance. Kriging estimator is as

follows:

Z�OKðx0Þ ¼

Xn

i¼1

kiZðxiÞ ð2Þ

Z�OKðx0Þ is the ordinary kriging estimation value of the variable at an unsampled location, n

is the neighboring sample points used in estimation of the variable, ki is the weight applied

to the neighboring sample zðx0Þ. The weights are chosen so that z�OKðx0Þ is an unbiased

estimate:

E z�OKðx0Þ � zðx0Þ� �

¼ 0 ð3Þ

and the variance between z�OKðx0Þ and the true value of the soil property at point x0 is

minimized as the following:

var z�OKðx0Þ � zðx0Þ� �

¼ min ð4Þ

Total weights amounts to unity and the unique combination of the weights yield in kriging

system producing the minimum variance.

Xn

j¼1

kiC xi; xj

� �� l ¼ C xi; x0ð Þ for i ¼ 1; 2; 3; . . .. . .n;

Xkj ¼ 1 ð5Þ

Minimum variance or kriging variance is given by:

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r2ok ¼ Cð0Þ �

Xn

i¼1

kiC xi; x0ð Þ þ l ð6Þ

C xi; xj

� �and C xi; x0ð Þ are the covariance functions between observed locations xi and

estimated position x0, respectively, and l is Lagrange multiplier Journel and Huijbregts

(1978, p. 306).

Cross-validation employs kriging for the validity and robustness of the spatial models.

In addition, semivariogram calculation uses kriging to compute kriging weights. Based on

the kriging system, model selection is performed. In order to select a particular spatial

model out of many possible options of the same type, some criteria, such as descriptive

statistics, are considered.

In the cross-validation procedure, each observation point was estimated by using a

search radius and a best-fit theoretical model. Cross-validation produces an h-scatter-

gram and model descriptive statistics, illustrating estimated versus observed points with

a 45� line and a regression line fitted to the scattergram. The GS? package produced

cross-validation statistics such as linear regression intercept value, regression coefficient

of the linear fitting regression, standard error, standard error of prediction, and corre-

lation coefficient. Reduced mean error and reduced mean variance were calculated

separately using cross validation outputs because the software does not decompose

weighting factors and semivarinace values simultaneously. The reduced mean error was

produced by dividing the mean of the sum of differences between predicted and

observed values by kriging variance. This statistics is expected to be equal or close to

zero (Journel and Huijbregts 1978). On the other hand, the reduced variance was cal-

culated by dividing the mean of squared sum of the differences between predicted and

observed values by the kriging variance, which resulted values equal to 1 or approxi-

mately 1. No systematic error in the measurements is indicated by the reduced mean,

while consistency between kriging variance and residual sum of square (RSS) is indi-

cated by the reduced variance.

The cross-validation process temporarily deletes a sample point from the dataset and fits

the selected particular model to the data to estimate the intentionally deleted point. Then,

the removed sample point is put back in the original place in the dataset to continue the

validation process for the next sample point until the validation is finished for all the

sample points in the dataset.

Results and discussion

Descriptive statistics of soil Ksat and salt content

Large skewness and kurtosis addressed a non-normal distribution of soil variables in the

study. Kolmogorov–Smirnov testing (SPSS 17.0) showed the soil variables needed

transforming before implementing them in geostatistical interpolations (Yan et al. 2007;

Wang et al. 2013). Therefore, square root transformation was applied to guarantee

nonnegative values of small measurements of the variables and supposed to correct the

adverse effect of abnormality of the data in variogram modeling and interpretability

process. Statistical descriptive measures of the soil properties at different depths are

provided in Table 2. For the soil Ksat, standard deviations were practically the same,

except for the 30–60 cm depth, which was the highest of all Ksat values in the profile.

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The Ksat values of 120–150 cm layer frequently posed a permeability barrier in the soil

profile. With the exception of this layer, 0–30 cm depth produced the lowest mean Ksat

of all in the profile. Layers above and below the 30–60 cm depth had significantly lower

mean Ksat values (p value = 0.0001 \ 0.05) than the 30–60 cm depth and the mean

Ksat values steadily decreased below the 30–60 cm layer. The CV% values ranged

between 44 and 50 for the soil Ksat at all depths in the current study. General field-scale

spatial variability was fairly high because CV% greater than 45 occurred at all depths for

both the soil variables in this study. Considerably high CV% values for soil Ksat were

encountered in the research study by Moustafa and Yomota (1998) that observed

undrained alluvial clayey soils had Ksat of 0.11 m day-1 with CV% of 126 in their study

area. Soils above and below the 30–60 cm depth had significantly higher soil salt content

values than for the 30–60 cm depth. Mean salt contents below the 30–60 cm layer

steadily increased and were significantly higher than for 30 cm of top soil. In contrast to

this, a typical saline soil is described as having high salt concentration in the topsoil and

descending salt concentrations from top to down in the profile (Utset and Castellanos

1999).

The mean soil salt content of the 60–90 cm layer was significantly greater when

compared to the 30–60 cm layer and significantly less when compared to 120–150 cm

layer, while coefficient of variation, standard deviation, variance, minimum and maximum

statistics of salt content at the 60–90 cm layer was significantly greater when compared to

the layers just above and below the 60–90 cm layer (p-value = 0.0001\ 0.05). The

increase in soil salt content of surface layer is an indication of evapotranspiration and

upward transport of soil salts between the two cropping seasons in summer. Distribution of

soil salts in the profile can also be attributed to low soil permeability and poor drainage

quality.

Among the depths, the 30-cm depth of soil salt content had the highest skewness and

kurtosis values suggesting the highest non-normal distribution. In the current study, soil

salinity showed CV values ranging 48 through 54 % for all depths. Moustafa and Yomota

Table 2 Summary statistics for square-root transformed data

Variable Mean Stdev Variance Min. Max. CV% Skewness Kurtosis

Ksat (cm h-1)

0–30 (250) 1.014 0.493 0.243 0.20 3.46 48.6 1.40 3.05

30–60 (248) 1.049 0.518 0.268 0.20 3.56 49.4 1.34 2.94

60–90 (238) 1.027 0.492 0.242 0.24 3.43 47.9 1.22 2.37

90–120 (231) 1.017 0.451 0.204 0.28 2.57 44.4 0.87 0.46

120–150 (221) 0.987 0.491 0.241 0.20 2.89 49.8 1.17 1.45

Salt content (%)

0–30 (246) 0.312 0.150 0.023 0.14 1.07 48.1 2.54 7.30

30–60 (245) 0.307 0.164 0.027 0.14 1.20 53.4 2.80 9.13

60–90 (234) 0.313 0.157 0.025 0.14 1.21 50.2 2.60 8.92

90–120 (227) 0.323 0.173 0.030 0.10 1.21 53.6 2.45 7.32

120–150 (219) 0.335 0.171 0.029 0.17 1.01 51.1 1.85 3.67

Parenthesis denotes the number of samples used in calculations

Stdev standard deviation, Min minimum value, Max maximum value, CV% percentage of coefficient ofvariation, Ksat saturated hydraulic conductivity

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(1998) observed that undrained soil conditions developed higher salt concentrations in

25 cm and 50 cm depths with lower CV% (28 and 31 %, respectively) when compared to

post drainage time of salt concentrations.

The CV% values are very close to each other but are still high in the current study.

Fairly high CV values for soil salt content have been reported due to spatial distribution of

salts (Warrick and Nielsen 1980), irrigation applications and topography (Cetin and Kirda

2003), sampling distance and local variability of the experimental site (Corwin et al. 2006),

and groundwater and climate (Zheng et al. 2009). In the current study, large CV values,

skewness, and kurtosis in the data set were attributed to non-uniform irrigation and

undulating topography, geologic material, groundwater fluctuation, evapotranspration, and

alluviation processes in the Plain’s soil.

Cross-validation statistics

The reduced mean error and reduced variance in this study showed that ordinary point

kriging was appropriate to model soil Ksat and EC distributions in a heavy clay alluvial

soil and confirmed the criteria of Eqs. (3) and (4), respectively. Predicted standard errors of

mean were much higher than standard errors of mean for soil Ksat while the opposite

remains true for the soil soluble salts (Table 3). Correlation coefficients between the

observed and the predicted values ranged from 3.6 to 9.6 % for both variables. The

Y-intercept (threshold values) for the soil Ksat showed that moderate to slow Ksat values

predominated in the soil profile and the Ksat decreased in the soil profile with increasing

depth. On the other hand, the soil salt content values varied inconsistently over the profile

and were always greater than 0.02 %. A unit change in observed soil salt content value

caused the highest regression coefficient in the estimated soil salt content value in the

60–90 cm depth, whereas a unit change in the observed soil Ksat value turned in the

highest Ksat regression coefficient in the 0–30 cm depth. The behavior of regression

coefficient in estimation process for both variables can be attributed to the mean of the

variables (Table 4). The mean Ksat for 0–30 cm depth is the smallest of all Ksat means,

resulting in the highest reduced mean error and the lowest reduced variance among the

Ksat variables. On the other hand, soil salt content in 60–90 cm depth had the highest mean

value, resulting in the lowest reduced mean error and highest reduced variance (Table 3).

Table 3 Cross-validation statistics of soil variables

Variable Y-intercept

Regressioncoefficient

Standarderror (SE)

Standard error(SE)-predicted

Correlationcoefficient(r2)

Reducedmeanerror

Reducedvariance

Ksat-30 0.41 0.752 0.157 1.037 0.085 20.178 0.965

Ksat-60 0.76 0.439 0.170 1.063 0.03 20.123 1.054

Ksat-90 0.67 0.487 0.146 1.022 0.05 20.173 1.225

TS-30 0.05 0.515 0.167 0.078 0.046 20.008 1.041

TS-60 0.0045 0.632 0.222 0.111 0.036 20.013 1.064

TS-90 0.33 0.72 0.153 0.07 0.091 20.011 1.073

Ksat-30 saturated hydraulic conductivity in the layer 0–30 cm, Ksat-60 saturated hydraulic conductivity inthe layer 30–60 cm, Ksat-90 saturated hydraulic conductivity in the layer 60–90 cm, TS-30 soil electricalconductivity in the layer 0–30 cm, TS-60 soil electrical conductivity in the layer 30–60 cm, TS-90 soilelectrical conductivity in the layer 60–90 cm

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Horizontal semivariogram models of soil Ksat and salt content

The experimental variogram as a function of lag distance between the sample points was

calculated and h-scatter plots were produced by cross-validation. Allowed semivariogram

types were tried to obtain the best descriptive statistics for the model fitting. The model

with the best fitting statistics and smallest nugget value was accepted as the final model at

the end of the selection process (Goovaerts 1997). Both of the soil properties were

described spatially by exponential models for all depths (Table 4).

Webster (1985) described the nugget as the semivariance value as the lag distance is not

zero, but immeasurably close to zero. Nugget represents experimental error and field

variability within the minimum sampling distance (Wu et al. 2008). A greater amount of

variability is also counted for the partial sill, addition of which to the nugget variance

composes total sill variance. Total sill variance was 5.44, and 6.13 times greater than the

nugget variance of the models for Ksat, while it was 5.33, and 5.77 times greater than the

nugget variance of the models for the soil salt content, showing that short scale mea-

surements errors and natural variance between the sample points, and instrumental mea-

surement error variance, caused by field tillage and management practices (Rao and

Wagnet 1985), are significantly smaller than spatial variance for both of the soil properties.

Spatial variability was undetectable for both of the soil variables at 120–150 cm depth

layer and therefore, the nugget model was chosen for the depth for both variables. The

small nugget effect indicates small amount of erratic behavior or randomness of the

experimental study field, meaning the sample values existed in their locations by chance

without any describable distribution pattern and therefore, no spatial correlation existed

between them. The model nugget values decreased from 0.36 for Ksat at the 0–30 cm to

0.31 for Ksat at 60–90 cm depth, while the nugget values increased from 0.02 for the soil

salt content at the 0–30 cm layer to 0.18 for the salt content at the 60–90 cm depth

(Table 4). The nugget values for soil Ksat decreased longitudinally in the soil profile,

whereas nuggets for soil salt content increased in the soil profile. High nuggets for soil Ksat

indicated that the Ksat was more a short-scale variation property than soil salt content.

Relative structural heterogeneity was generally high among soil depths for both variables.

The sill values of semivariogram models differed from each other and ranged between

0.19 and 0.201 cm2 h-2 for soil Ksat values and 0.011 and 1.038 % for the soil salt

content. The layers of 0–30 cm and 30–60 cm are dominated by moderately saline con-

ditions (salt content 0.04–0.1 %), yielding a small amount of sill variances, which are

0.011 for the 0–30 cm and 0.016 for the 30–60 cm depths respectively. Saturated hydraulic

conductivity values of the 30–60 cm depth showed the greatest sill of all semivariogram

models of Ksat, while the 60–90 cm depth registered the smallest sill variance of all the

Ksat semivariogram models. The sill variance was in the ascending order for 0–90, 0–30,

and 30–60 cm depths for the soil Ksat. On the other hand, the sill variance of soil salt

content was in the order 0–30 [ 30–60 [ 60–90 cm depths. The salt content model sill

values increased from surface layer to the bottom of the soil profile depth.

The spatial continuity models for both variables at 60 to 90 cm depth were the same,

reached at the same range values with different sills, and had the same uniform distance

interval meaning equal number of points to interpolate the study area, indicating that these

variables can be co-regionalized to produce a better cross-variograms for estimation of the

soil variables from each other.

The range parameter of a semivariogram is a measure of soil homogeneity, spatial

dependence, and spatial continuity (Cohen 1994). The highest range of influence values

were observed for the soil salt content of 30–60 cm depth, soil Ksat at 0–30 cm depth, and

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soil Ksat at 30–60 cm depth. In considering the size of the Amik Plain (98 000 ha for this

study), 1 130 and 1 130 m range distances (correlation lengths) rather than sampling grid

size of 2 000 m 9 2 000 m are considered short ranges and show that spatial variability is

considerably high for both of the variables in the Amik Plain soils. For example, washing

the salts and increasing soil Ksat values by designing drain ditches in every 1 130 m and/or

shorter distances in the Amik Plain soils may not be economically and/or feasibly possible.

In this case, main collector drain lines with greater distances than 1 130 m can be laid

down in the field more economically. The costs of drainage system installation, building

hydraulic structures, and mechanized agricultural activities could be reduced in the Plain.

Range is the maximum distance over which two sample points are interrelated and

beyond which they are independent of each other. Therefore, the parameter is considered

as the area of similarity. Both soil variables showed similar spatial behaviors in terms of

range values, which were equal to and greater than 1 000 m, except Ksat at 30–60 cm

depth. The range parameters of Ksat models were 1 130 m above and below 30–60 cm

depth, indicating that similarity in soil Ksat values increased within both layers above and

below the 30–60 cm depth. On the other hand, soil salt content showed same correlation

distances of 1 000 m for the 0–30 and 30–60 cm depth and correlation distance increased

from 1 000 to 1 130 m from top layer to bottom layer. As a result, similarity of soil salt

content values within each layer in the soil profile increased. These range parameters are

relatively smaller than sampling distance (2 000 m), indicating that soil drainage man-

agement practices should have been planned for smaller similarity areas and distances.

Decreases in the correlation distances after drainage installation for soil salt content

(Agrawal et al. 1995) and for soil Ksat (Gallichand et al. 1992) have been reported. For

both of the soil properties, greater structural variation and behaviors of correlation dis-

tances at each depth may partially be attributed to sample variance values (Table 2).

Although mean Ksat steadily decreased between 60 and 150 cm, the variance of soil Ksat

fluctuated two times instead of constantly trending. A similar trend for soil salt content was

repeated in the profile, meaning that sampling design can reduce non-uniform and short

scale variations in the data, by assigning grid positions to locations and by modifying

sampling design in the current study area.

Spatial structure of the soil Ksat was dependent on the geological nature of the soil

because the correlation range was higher in the top layer that had relatively lower clay

content than the bottom layer. The similarity of correlation distances of soil Ksat and soil

salt content at 90 cm depth showed that drain tiles can be laid in 1 130 m 9 1 130 m

parcels in the Amik Plain. Drainage practices should reduce the problematic behaviors of

soil Ksat and EC in time and space based on the similar subfields. Drainage problem

management is likely to be practiced in short-scale variation areas which can increase the

success of the management practice, effectiveness, and easiness in smaller fields in the

Amik Plain because short-scale behavior (nugget values) of soil salt content increases

(Table 4). Although short-scale behavior of soil Ksat is much higher than soil salt con-

tent, The Ksat can be treated as large-scale drainage problem in the Amik Plain. The

large-scale Ksat based drainage problem is indicated by the partial sill fraction parameter

in Table 4.

A classification scheme of Trangmar et al. (1985) and Cambardella et al. (1994) was

shown as the strength of spatial continuity of a variable and it has gained popularity. They

proposed a proportion that enables comparison of the relative size of the nugget effect to

total sill variance Trangmar et al. (1985) and this rate was used as the strength of spatial

distribution of a soil variable in the landscape (Cambardella et al. 1994) and strength of

local spatial variability of a variable in soil (Wu et al. 2008). According to this

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classification, if the ratio was less than 25 %, the variable was considered strongly spatially

dependent; if the ratio fell in 25–75 %, the variable was considered moderately spatially

dependent; and if the ratio was above 75 %, the variable was considered weakly spatially

dependent.

The representation of structural variance over the total sill variance to explain the

strength of the structural continuity of soil variables is presented as given in Table 4. The

spatial models explained 81 and 84 % of the total variance, indicating that both soil

variables are strongly structural dependent in the Amik Plain soils. Strong spatial structural

dependency for soil variables may be affected by intrinsic variations in soil, such as soil

texture and mineralogy (Cambardella et al. 1994) and also climate, topography, and soil

matrix (Yao and Young 2010). As a result, unexplained variance in the models fitted to the

data is as small as 16–19 % in this study.

For the soil Ksat, the higher the strength of spatial dependency of a model, the longer

the range of influence occurred, indicating that soil spatial variability in Ksat was inherited

by soil parent material and geogenetical processes. Spatial heterogeneity (C/Co ? C) for

soil salt content is the lowest in the 30–60 cm depth while the longest range parameter and

spatial heterogeneity occurred in the 60–90 cm depth. In other words, salt dynamics in the

soil profile do not follow the same spatial variability trend as the Ksat follows. The soil

Ksat and salt content model results of the current study showed that model strengths above

80 % were due systematically to depth-based variations of soil properties. However,

surface soil salinization in the current study was not entirely because of groundwater

irrigation and leaching in the Plain’s soil. As a consequence, the 0–30 cm depth of the soil

was likely receiving extra salts from the surrounding areas as well as capillary rise and

leaching contributions of salts in dry summers and wet seasons, respectively. Sangun et al.

(2007) measured groundwater quality parameters in the Plain. They divided the Plain into

two well fields, wells above city center with average elevation of 85 m and the wells below

city center of Antakia with average elevation of 73 m. They found Ca and Mg carbonates

and bicarbonates were saturated in the groundwater wells and possessed a dangerous

situation (EC [ 2 dS m-1) for irrigational use in the Plain while the well-field on the 73 m

elevation contained lower levels of salts of the same species as in the well-field on 85 m

elevation. Friedman et al. (1999) determined two different sedimentation processes in the

Plain, especially in the lakebed area. In their study, 6.2–3.4 m thickness under the surface

soil experienced extremely high Ca carbonate concentrations due to soil forming processes

between 7000 and 3000 BC, while between 3000 and 2500 BC the Plain experienced a

very wet period with increasing potassium concentrations in the sediments between 3.4 and

1.2 m below the surface soil (Friedman et al. 1999). The wet period indicated that the lake

evolved, while Ca and Mg levels indicated that the lake dried. For the salt content in the

top layer of 0–30 cm depth, Odemis et al. (2007) reported that groundwater used in

irrigation caused to secondary salinization in a 3 073 ha area, 2 813 and 260 ha of which

are slightly salinized (EC around 2–4 dS m-1) and highly salinized (EC greater than

4 dS m-1) in the Plain, respectively. However, these salt content values are very small in

comparison to top layer soil salt content in the current study. Therefore, soil salts must be

leached and drained by a sound drainage system in the Plains soil.

In the topsoil, nugget and structural variance (C) are the lowest of all salt content

models evaluated in this study and may indicate that the topsoil salt content could be a

result of temporal runoff with considerable salt concentrations accumulating in the topsoil.

As a result, stochastic processes, such as fertilization and runoff, can temporally accu-

mulate high amount of salts in the topsoil.

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Mean soil salt content of bottom layer (120–150 cm) is significantly greater than the

content in the 30–60 cm depth. Variogram model parameters of soil salt content of

60–90 cm depth are greater than the ones for soil salt content model of the 30–60 cm layer.

Although mean salt content values of all depths showed different sequence in magnitude,

their variogram model parameters for each depth showed a consistent match of magnitude

sequence with data statistics of standard deviation, variance, CV%, skewness, and kurtosis

indicating that variogram shape is significantly affected by data statistics.

The 60–90 cm depth data statistics for Ksat are significantly smaller than the ones for

the 0–30 cm layer, except minimum and maximum statistics. The variogram parameters of

soil Ksat models were also smaller than those of the 0–30 cm depth model, while the 30 to

60-cm depth model’s structural variance and sill were always the highest against the rest of

the Ksat models. Soil Ksat in the topsoil was more variable than that of the 60–90 cm

depth (Table 2).

Both soil variables showed strong spatial dependency, indicating that spatial variance

was mainly due to structural factors such as soil topography and parent material in com-

parison to unstructured variance (the nugget variance) caused by soil tillage and man-

agement factors. In this current study, high nuggets corresponded to lower spatial

dependence or high heterogeneity for soil salt content (Table 4). Agricultural activities in

the Plain’s soils did not influence soil salt content significantly in the 30–60 cm depth

because of soil tillage and fertilization, but in the topsoil (Table 4). Similarly, soil Ksat was

spatially least heterogeneous at the 0–30 cm depth and most heterogeneous at the

60–90 cm depth of the soil profile indicating that soil parent material, topography, and

genetic processes in soil are structurally in effect at the 60–90 cm depth.

Spatial prediction maps of soil Ksat and salt content

Spatial variograms and distribution of soil saturated hydraulic conductivity

The origin of low Ksat values in Amik Plain soils was geologic material, two different

alluviation eras of dry and wet cycles during the formation of Lake Amik. Clay content in

all depths is the most dominant particle size group with varying thicknesses all over the

Plain. A uniform distance of 1 200 m, the search radius, was used to include enough

samples in the estimation and interpolation of an unsampled location at the 0–30 cm depth.

Correlation distance for the model was 1 130 m. Most of the spatial variability occurred

between the sample pairs falling in the first, second, and third lag classes, while the rest of

the lag classes did not affect the variation in the distances less than 1 130 m for variogram

computation at 0–30 cm layer. The scale of structural variation (C = 0.16) at the 0–30 cm

depth was higher than the one for Ksat model of 60–90 cm depth, showing that soil Ksat at

the 0–30 cm was more continuous than the Ksat values in the 60–90 cm depth over the

large landscape of Amik Plain. Most of the Ksat values (14 %) in the 0–30 cm depth were

less than 0.14 cm h-1 contributing to nugget effect (Fig. 4a). Low permeability values

(0.65 cm h-1 and lower) distributed in the north–south direction (Fig. 5a).

Soil Ksat at the 30–60 cm depth was interpolated with 21 lag classes containing sample

pairs of 172, with 1 200 m search radius. The scale of variation (sill-nugget = 0.201) was

the highest at the 30 to 60-cm depth for the soil Ksat when compared to the rest of the Ksat

models (Fig. 4b). Permeability barriers at the 30–60 cm soil depth are a result of this

spatial continuity of soil Ksat values and these barriers are very common at the 30–60 cm

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layer over the landscape of the Plain. The lower Ksat values (\0.65 cm h-1) distributed in

the north-east and south-west direction (Fig. 5b).

Spatial structure of soil Ksat at 60–90 cm depth showed that dissimilarity between the

sample points was high and all spatially correlated sample pairs fell into the lag classes that

occurred in the last one-third of the range of the model. The uniform distance between the

sample points was 1 300 m, above and below which the spatial structure model was pure

Fig. 4 Semivariograms of soil Ksat: a Ksat at 30 cm depth, b Ksat at 60 cm depth, c Ksat at 90 cm depth

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Fig. 5 Soil Ksat prediction maps: a Ksat at 30 cm depth, b Ksat at 60 cm depth, c Ksat at 90 cm depth

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nugget (Fig. 4c). While permeability barriers occurred in the north–south and east–west

directions, highly permeable areas occurred in the east–west direction in the Plain.

Regardless of depth, all of the low Ksat values in the in the soil fell into the Ex-Amik Lake

bed and the impermeable area of low Ksat patches increased as the depth increased in the

soil, especially in the north–south direction in the Plain. Although impermeable areas

occurred in the topsoil, these patches were not as many as those for the 30–60 cm and the

60–90 cm depths (Fig. 5a–c). The Ex-Amik Lake, probably, had a connection to some

other upstream lakes or water bodies that separated the Plain into two arable parts for

intensive agriculture (Fig. 5a), one in the east and the other in the west side of the Plain.

The southeast part had more permeable top layer than the northwest counterpart of the

Plain. In fact, Friedman et al. (1999) reported about some other lakes of drying and wetting

beside the ex-Amik Lake in the Plain.

Impermeable stratum in the 30–60 cm depth in the profile has larger area than the

topsoil (Fig. 5a, b) and high Ksat values occurred on the eastern part of the Amik Plain.

Moderately slow Ksat values (0.5–2.0 cm h-1) covered the east–west direction while

lower values than moderately slow Ksat values elongated in the north-east direction in the

Plain. The south-west part of the Plain has intrinsically a lower permeability than the north-

west section of the Plain that had moderately slow permeability (Fig. 5b). Although the

mean Ksat value is the highest in the 30–60 cm depth, a big part of the Plain for this layer

has a Ksat of 0.12 cm h-1 in the south direction. Moderately low Ksat values formed a

continuum in the east–west direction at this depth, and provided most of the characteris-

tically high mean Ksat values for the depth.

Slow Ksat values (0.1–0.5 cm h-1) and permeability barriers increased in the 60–90 cm

soil layer in the profile (Fig. 5c). Once again, high Ksat values ([0.5 cm h-1) increased in

the east–west direction while moderately slow and slower Ksat values covered the Plain

along the north–south direction. The most imperviousness is displayed by the 60–90 cm

depth of the profile because the lowest Ksat values were found in this layer and the area of

very slow permeability increased beyond the Ksat value for 30–60 cm depth (Fig. 5c).

Continuous highly-permeable zones occurred in the east–west direction for the 90 cm

depth, while very slow permeable (B0.1 cm h-1) areas were characteristically in the north

and in the farthest south part of the Plain. These intricate patterns of permeability distri-

butions in the soil profile are clear evidence of strong anisotropy in the Plain’s soil and

indications of previous lakes that had existed on the landscape of the Plain. Therefore, soil

drainage has been hampered and surface runoff has resulted in frequent catastrophic flood

events in the Plain till today.

Spatial variograms and distributions of soil salt content

Spatial variability of soil salt content at the 0–30 cm depth showed highly scattering

pattern of the soil salt concentrations due to soil tillage practices and undrained conditions

in the soil. A high undrained water table accumulates the salts in the surface layer of the

soil and tillage disperses it in the surface soil. Therefore, moderately and heavily salinated

areas occurred in the surface of the soil (Fig. 6a). Mean and natural variance between the

sample points for the layer are intrinsically small in comparison to spatial variance

(Tables 2, 4). On the other hand, scale of the spatial variation, the C component, is 4.3 and

4.8 times greater than the nugget, indicative of largely correlated spatial variability of soil

salt content in the surface soil. As the model reached the correlation distance of 1 130 m,

the greatest number of sample pairs were included in the lag class 1 and then lag class 3.

On the other hand, the lag class 2 had the highest 78 dissimilar pairs of points, with less

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than 1 130 m distance apart (Fig. 6a). These results show that a revision soil survey should

be done in the locations of those sample points and monitoring soil salinity in the rooting

depth of 30 cm is needed (Fig. 6a).

A uniform distance of 1 480 m separated the lag classes and yielded 1 000 m corre-

lation distance for the soil salt content, showing more variability in the distribution pattern

in the 30–60 cm depth in comparison to the rest of soil salt content models (Fig. 6b). The

variogram models for the 30–60 and 60–90 cm depth layers were almost the same in shape.

The only difference was with the model parameters (Table 4) indicating that the phe-

nomenon governing the salt concentration distribution in the soil depths were the same. As

Fig. 6 Semivariograms of soil salt content: a soil salt content at 30 cm depth, b soil salt content at 60 cmdepth, c soil salt content at 90 cm depth

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the soil depth increased, scatterness of the lag classes decreased, indicating that samples in

the 30–60 cm (Fig. 6b) are more similar to the samples of the 60–90 cm (Fig. 6c) than the

samples of the 90–120 cm depth of the soil, which are distributed as pure nugget model.

Based on the model strength soil salt content was more homogeneous or spatially

dependent than soil Ksat values in the soil. All variogram models showed that exponential

models reached the sill value faster with 2–3 lag-classes and the rest of the lag classes

became uncorrelated with the variography. Therefore, exponential models can be con-

sidered to reveal short scale variations for the dataset used. For example, there is a particle

size shift or a shift in alluvial processes in the parent material at the 30–60 cm depth of the

soil influencing both of the variables.

The origin of salts was different in topsoil and subsoil. Soil salts accumulated on the

slow permeable areas in the north-west part of the Plain. When Figs. 5a and 7a are

compared the salinity problem in the topsoil is predominantly a temporal situation

encountered in the Plain and was not of geologically originated from the substrata, but also

from the salts of different origin transported to the Plain by strong runoff and stream flow

events. Besides, capillary rise of salts contributed to salt content of the top layer in hot

summers (Fig. 7a, b; Table 2). On the other hand, the origin of salts in the profile below

30–60 cm depth was carbonate rich soil parent material and dissolved carbonates.

Therefore, salinity control is needed to prevent secondary salinization of soils which can be

leached by a conventional subsurface drainage system. Finally, the 60–90 cm layer of the

profile was the most salty layer with almost the same salt distribution characteristics as the

one from 30 to 60 cm layer. The most characteristic pattern of soluble salts distribution,

regardless of depth, predominated in the west part of the Plain along the north–south

transect for the 0–30 and 30–90 cm layers. The 30–60 cm layer in the soil showed soluble

salt distributions in the north–south and east–west directions (Fig. 7a–c).

Vertical variograms of soil properties

Vertical spatial distribution of both variables showed that Ksat was approximately 18 and

61 times greater than soil salt content in its nugget and sill variances, respectively (Fig. 8),

indicating that both intrinsic (parent material and topography) and extrinsic (soil and water

management practices) factors collectively affected these properties in the vertical direc-

tion. Soil profile depth below 60 cm practically acted as a permeability barrier and was

salty. The interpretability of the spatial structure and shape of vertical variograms was

reasonably improved after the number of lags and the increments of search radii had been

intentionally and skillfully chosen. The range parameters of the vertical variogram models

were approximately 136 cm with R2 of 83 % and 76 cm with R2 of 99 % for the variables

of soil salt content and the Ksat, respectively. The reduced mean error and reduced vari-

ance for Ksat and soil salt content was 0.0012 and 0.929, and 0.0001 and 1.068, respec-

tively. Model strengths were strong (86 %) for soil Ksat and moderate(57 %) for soil salt

content. In vertical direction soil salinity has longer range parameter (approximately

136 cm) than soil Ksat and therefore, soil salinity needs less number of depth-wise samples

than soil Ksat in soil sampling surveys. This is also another indication of soil salinity that

varies and increases over depths and smaller Ksat values are very common as the soil

deepens.

Fig. 7 Soil salt content prediction maps: a soil salt content at 30 cm depth, b soil salt content at 60 cmdepth, c soil salt content at 90 cm depth

c

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The vertical variogram map shows that soil depth-profile number plain was the coordinate

system used to map the soil in the Z direction. The map showed that there were slowly

permeable continuous vertical soil patches between soil surface and 150 cm depth in the

profile. The map also showed soil Ksat in the Z direction varied between 1.14 and

3.81 cm h-1. There existed continuous, somewhat slow (1.14 cm h-1) and moderately slow

(2.93 cm h-1) permeable areas in the Plain, acting as water conduits in the profile, repeating

approximately once in every 70 soil pits, located in 3.6 km 9 10.6 km grid cell equaling

approximately to 3 800 ha area in the Plain. Moderately slow and slow permeability class

values (Schwab et al. 1992) dominated horizontal and vertical direction over the Plain. The

most dominant pattern of Ksat in the profile was that the soil Ksat decreased as soil depth

increased. In the vertical direction, contiguous similar Ksat values between 2.03 and

2.93 cm h-1 formed permeability zones downward the profile. The Ksat values greater than

3.82 cm h-1 in the profile seem to be either erratic values or artifacts that never naturally

existed in their locations, which can be corrected by a revised soil survey in the field.

In contrast to Ksat, the salt content increased as soil depth increased, especially beneath

the 30–60 cm depth. Soil salt content distribution was more continuous than the soil Ksat

Fig. 8 Vertical variogram models of soil salt content (a) and Ksat (b), respectively

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distribution in the soil profile. Low values of soil salt content coherently followed the high

Ksat values from the top to the bottom layer of the soil. Continuous no-salty patches

(\%0.04) are also very common in the profile of the soil, indicating that salt distribution

was spatially affected by natural soil drainage and moderately high permeability zones. As

shown in the Fig. 9, overlapping of the two maps of Ksat and soil salt content reveals that

where Ksat was high and continuous longitudinally in the soil profile, the soil salt content

decreased in the profile. The vertical variogram maps revealed that the highest salt con-

centration in the soil profile was located in the lake-bed of the Ex-Amik Lake and

boundaries of the lake where Ksat was low. These are the locations requiring precise

drainage system design and drainage water management. These locations can guide where

to place drain tiles, laterals, and open ditches in the soil. As with permeability zones,

permeability barriers seem to be conterminous in the middle of the Plain, with Ksat values

less than 1.14 cm h-1 along the north–south direction.

Effect of undrained watertable conditions on soil Ksat and salt content

Soil Ksat values are dependent on texture, structure, and other soil properties. Schwab et al.

(1992) classed soil hydraulic conductivity values for drain depth and spacing designs of the

soils. According to their classification, soil textural classes were Clay, Clay Loam, Average

Loam, Fine Sandy Loam, and Sandy Loam with hydraulic conductivity classes very slow

(0.1 cm h-1), slow (0.1–0.5 cm h-1), moderately slow (0.5–2.0 cm h-1), moderate

(2.0–6.0 cm h-1), and moderately rapid (6.0–13.0 cm h-1), respectively. According to this

classification, mean Ksat values are generally above 0.9 cm h-1 at all depths (0–150 cm)

in the study area. The Amik Plain soils generally have soil saturated hydraulic conductivity

classes between moderately slow and very slow (Table 2), corresponding to textural

classes between Fine Sandy Loam and Clay, respectively. Because of the fact that floods

continue occurring, this is not a good characteristic value of all Ksat over the Plain.

Frequency distribution of Ksat values for all depths showed 57–68 % of the Ksat values

were below 0.4 cm h-1. As a result, a representative Ksat value still remains a problem to

be solved for the Amik Plain soils.

The most of the Plain has very low Ksat values (B0.14 cm h-1) to control downward

drainage in the profile. For instance, a 2 mm h-1 rainfall has a great potential to turn into a

flood event, especially if it continues around 4 consecutive days in the rainy period in the

Fig. 9 Vertical variogram maps of soil salt content and Ksat

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Plain. This amount of rainfall produces approximately 5 cm day-1 water height on the

landscape. Soil Ksat value of 0.14 cm h-1 can barely drain 3.36 cm day-1 out of

5 cm day-1 and remaining amount ponds in the soil, meaning hydraulic gradient is less

than 1 m m-1 in the soils of the Plain to drain the excess water. In other words, according

to Darcy’s equation, hydraulic head difference (DH = H2 - H1) over a unit distance in

the flow domain should be unity so that saturated hydraulic conductivity (Ksat) equals to

flux (q) which results in no water ponding in the field. Hydraulic head is known as the total

of pressure head and gravity head according to an arbitrary reference elevation in a flow

system such as soil or groundwater systems and is measured at arbitrary points 1 and 2 in

the flow domain of soil profile. Massive muddy soil structure may have restricted hydraulic

gradient in this case. In conclusion, low hydraulic gradient and low Ksat were reasoned for

poor drainage conditions in the Amik Plain soils. Both tile drainage and surface drain

ditches can collectively improve hydraulic gradient and drainage conditions to a better

level than the one measured in the Plain.

Half of the Plain’s Ksat values were between 1.25 and 0.44 cm h-1 for the surface layer,

which is in the slow permeability range when compared to very common 4 mm h-1 rainfall

intensities in the region. In the 30–60 cm soil depth, a strong anisotropy affected both soil

variables in two distinct directions with long correlation distances. The Ksat ranged from

1.99 to 0.14 cm h-1, the latter being the dominant value in the Plain. Since soil Ksat is

strongly related to soil pore size distribution, shape, and continuity, horizontal Ksat can be

different from vertical Ksat because the orientation of soil textural separates in both

directions makes Ksat directionally dependent and change the direction of flow in the soil

(Boumans 1976). The hydraulic conductivity of the subsoil was relatively low, given the

texture, and may be a result of fines blocking pore spaces and the lack of developed soil

structure. The field surveys also reported that the subsoil layers sometimes have had weak

blocky and weak prizmatic structures, Bw (cambic horizon), and a C horizon with massive

soil structure, which is very resistant to the soil water permeability in poorly drained sites of

the Plain. Besides, poor soil drainage conditions in the Plain caused massive soil structure

especially in deeply clay accumulated vertic soils. Clay lenses, lithic and paralithic contacts

in the soil profile on the east part of the Plain resulted in poor drainage conditions and the

east part of the plain is elevationally higher than the west part of the Plain, resulting in salt

movement and runoff flows into the center of the Plain (Figs. 5a–c, 7a–c).

Spatial mapping of soil Ksat and salt content provided homogeneity parameter (cor-

relation distance) as a reference size of similar subfield block in the Amik Plain. A drain

tile should be designed for depths of 60 or more cm in field (Schwab et al. 1992).

Therefore, drain tiles can be laid in a subfield of Ksat, with dimensions of 1 130 m

width 9 1 130 m length 9 0.75 m soil profile depth in the Amik Plain. These spatial

maps provided certain size and location of subfields in the Amik Plain and managing

drainage problem in these subfields using soil Ksat values can be more beneficial to

traditional drainage system installation in the field. Hydraulic conductivity is one of the

most important parameters for the system design as it affects drain spacing and hence the

drainage expenditures. Unlike conventional drainage system design, kriging interpolated

original grid nodes at a 1.2 m (in vertical direction) 9 350 m (in horizontal direction)

covering the entire soil profile of the field using validated kriged model and neighborhood

of 121 out of 254 soil pits.

Vertical variogram maps of soil properties showed extent and severity of drainage

problem in terms of soil Ksat and salt content in the basin soils. These areas indicated

special management zones for soil drainage water. Vertical subfields of Ksat are the

localities where deep drainage and recharge of groundwater can take place in flooding

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times in the Plain. These are the places to observe similar depths of infiltration and pore-

water pressures in a saturated condition of the soil. During a rainfall, it appears that the

depth of rainfall influence was within approximately first 60-cm depth, beyond which pore

water pressure relatively remained unaffected by the rainfall. The evidence of this was that

the soil became saltier and less permeable than the layer above 30–60 cm. As a result, soil

drainage can develop long time after each rainfall, indicating soils are slowly permeable in

these patches of subfields. These subfields are important because the most benefit would be

derived from drains located at the bottom of 90-cm layer and combining these drains with

open ditches in the upper part of the profile where water table is significantly close to the

soil surface.

Predominating soil Ksat values (a small portion of the map legends in Fig. 5a–c)

ranged between 1.96 and 0.94 for the 0–30 cm, 1.82 and 0.87 for the 30–60 cm, and

1.89 and 0.94 cm for the 60–90 cm depth, respectively. However, lower Ksat values

(\0.44 cm h-1) covered increasingly larger areas as the depth increased. In vertical

direction, the Ksat estimations ranged from 5.61 to 1.14 cm h-1. The Ksat values

between 1.44 and 3.73 cm h-1 were predominant for permeable areas and 0.44 and

lower values were dominant for the impermeable zones in the vertical direction in soil

(Fig. 9).

Salinity classes of soil by Wang et al. (1993) were reported for a coastal area of their

study to be heavily saline soils ([10.0), moderately salinized soils (4.0–10.0), lightly

salinized soils (2.0–4.0), and non-saline soils (\1.0 g kg-1). Kriging maps identified

similar regions with distinct soil salt content values for these reference areas for cropping

and drainage management.

The highest variance for the soil salt content was observed in 90–120 cm depth,

while the lowest variance was found in the 30–60 depth, as a result of genetical

particle size shift and anisotropy in the layer of the soil profile. The mean salt content

was in the descending order for the layers 120–150 greater than 90–120 greater than

60–90 greater than 0–30 greater than 30–60 for the soil layers in the profile (Table 2).

Except the 0–30 cm depth of soil layer, the soil salt content continuously increased as

the soil profile deepened. This trend is opposite to the soil Ksat values. The minimum

and maximum salt content values of soil layers ranged from 0.10 to 0.17 % and from

1.01 to 1.21 %, respectively, (Table 2). Horizontal variogram maps for soil salinity

showed that the surface soil (0–30 cm depth) was dominated by moderate salt content

(0.02–0.04) with the exception of some salty areas on the landscape of the Plain. Soil

salinity ranged from moderately to heavily saline conditions in the 30–60 cm depth.

Finally heavy saline conditions (TS [ 0.2) prevailed the 60–90 cm depth in comparison

to the upper soil layers. Soil salts ranged between 0.108 and 0.459 % in vertical

direction in the soil. Since there exists a linear relationship to a certain range between

total soluble salts (TS) and soil electrical conductivity (EC), which is given as

TS = 640 ppm 9 EC (dS m-1), soil EC ranged from 2.63 to 0.84 for the 0–30 cm,

3.8–0.98 for the 30–60 cm, and 2.28–0.77 dS m-1 in the horizontal direction. In the

vertical direction, this estimation was about 1.69 for the surface soil and 7.73 dS m-1

for the bottom layer (Fig. 9).

Soil salinity increased within the 60–150 cm depth of the soil Profile. Salinity showed a

large seasonal variation in the root zone of the plants (0–30 cm depth) and this variation

could be a result of seasonal rainfall recharge effect on the fluctuating groundwater table in

the profile because of recharge and capillary rise processes. A better understanding of soil

salt movement and water flow relations to soil Ksat requires a vadose zone hydrological

model study in the Amik Plain’s soils.

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Conclusions

In this study, the alluvial Amik Plain soils were found to be moderately limited for

agricultural use due to high water table and un-drained soil conditions during early winter

and late spring because of heavy clay texture, low Ksat, inadequate hydraulic gradient, and

moderate to heavy salt content in the soil profile. Spatial variability of soil salt content and

Ksat for un-drained water table conditions was evaluated in the Plain soils to assess

draining problematic areas for a better management plan. This study revealed that soils

above and below 30 to 60-cm layer in the profile depicted two different alluviation pro-

cesses and soil parent materials (Ca producing era of drying lake process and potassium

producing lake retreat process). Through comparing horizontal variograms for the depths,

soil Ksat and salt content variability were scale-dependent and indicated correlation dis-

tances of variogram models could be one option to implement a soil drainage management

plan. However, the range of influence of the variograms was small enough and therefore

non-practical for installing open drain ditches to wash the salts through these channels.

Therefore a combined drainage system (surface and subsurface) is needed in the Amik

Plain. A subfield of 1 130 m 9 1 130 m with a profile depth of 75 cm and deeper was

found a reasonably good reference for soil drainage system layout as a result of horizontal

and vertical variogram analyses in this study. Soil Ksat decreased and the salt content

increased as the soil profile deepened in the study area. The 60–90 cm depth showed

exactly the same model type, the same distance interval, and range of influence with

different sill values, indicating geometric anisotropy, research of which can offer to have a

better and representative soil Ksat and salt content values for an efficient soil and water

management in the Plain’s soils. Although no piezometers were driven into the soil profile

to measure hydraulic gradient for groundwater flow direction, an understanding of the

role of spatial anisotropy in the relationships between drainage and infiltration in the

upper 1.5 m depth is needed for understanding the importance of drain specifications and

locations in the subfields of the Plain.

Acknowledgments This work was supported by Grant funds of General Directorate of Turkish StateHydraulic Works (DSI). Many thanks go to the Department of Soil Survey and Planning team that collectedand analyzed field samples and to drainage research team working for the local branch of DSI in Hatayprovince of Turkey. Special thanks go to Mustafa Kemal University and its Scientific Research ProjectExecutive Office (BAP) for funding the Project BAP-1002M32 to revise soil survey at some criticallyimportant locations in the Plain.

References

Agrawal, O. P., Rao, K. V. G. K., Chauhan, H. S., & Khandelwal, M. K. (1995). Geostatistical analysis ofsoil salinity improvement with subsurface drainage system. Transactions of the American Society ofAgricultural Engineers, 38(5), 1427–1433.

Boumans, J. H. (1976). Drainage calculations in stratified soils using the anisotropic soil model to simulatehydraulic conductivity conditions. Wageningen: The International Drainage Workshop, ILRIPublication.

Caliskan, V. (2008). Human-induced wetland degradation: a case study of Lake Amik (Southern Turkey).BALWOIS, Ohrid, Republic of Macedonia.

Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., et al. (1994). Field-scalevariability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58,1501–1511.

Cetin, M., & Kirda, C. (2003). Spatial and temporal changes of soil salinity in a cotton field irrigated withlow-quality water. Journal of Hydrology, 272, 238–249.

Precision Agric

123

Cohen, W. B. (1994). GIS applications perspective: current research on remote sensing of forest structure(pp. 91–107). Washington, DC: Island Press.

Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture:Theory, principles and guidelines. Agronomy Journal, 95(3), 455–471.

Corwin, D. L., Lesch, T. S. M., Oster, J. D., & Kaffka, S. R. (2006). Monitoring management-inducedspatio-temporal changes in soil quality through soil sampling directed by apparent electrical con-ductivity. Geoderma, 131, 369–387.

Corwin, D. L., Loague, K., & Ellsworth, T. R. (1999). Assessing non-point source pollution in the vadosezone with advanced information technologies. In: D.L. Corwin, K. Loague, & T.R. Ellsworth (Ed.),Assessment of non-point source pollution in the vadose zone (pp. 1–20). Geophysical monographseries. 108. AGU, Washington, DC, USA.

Doerge, T. (1999). Defining management zones for precision farming. Crop Insights, 8(21), 1–5.DSI. (1962). The Amik Project: The report of land planning and classification of the Amik Plain. Ministry of

Public Works of the Republic of Turkey, General Directorate of State Hydraulic works, Department ofSurvey and Planning, Ankara, Turkey, Project No: 1901, Survey report No. 17104.

DSI. (2012). The construction of Reyhanli Dam being in full swing to meet the needs of the irrigation waterin the Amik Plain. Governmental document. The Ministry of Public Works of the Republic of Turkey.http://www.dsi.gov.tr/haberler/2012/08/15/reyhanlibaraji, Article on the web: 15 August 2012. Lastaccessed the web. 1 August 2014.

FAO. (2014). World reference base for soil resources. International soil classification system for namingsoils and creating legends for soil maps. World soil resource reports No. 106, (pp. 191), ISSN05320498, Rome.

Florinsky, I. V., Eilers, R. G., & Lelyk, G. W. (2000). Prediction of soil salinity risk by digital terrainmodelling in the Canadian prairies. Canadian Journal of Soil Science, 80, 455–463.

Franzen, D. W., Hopkins, D. H., Sweeney, I. D., Calmer, M. K., & Halvorson, A. D. (2002). Evaluation ofsoil survey scale for zone development of site-specific nitrogen management. Agronomy Journal, 94,381–389.

Friedman, E. S., Sato, Y., Alatas, A., Johnson, C.E., Wilkinson, T. J., et al. (1999). An X-ray fluorescencestudy of lake sediments from ancient Turkey using synchrotron radiation. Proceedings of the 47thannual Denver X-ray conference, Colorado Springs, CO.

Gallichand, J., Marcotte, D., Prasher, S. O., & Broughton, R. S. (1992). Optimal sampling density ofhydraulic conductivity for subsurface drainage in the Nile Delta. Agricultural Water Management, 20,299–312.

Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.Jaynes, D. B., Kasper, T. C., Colvin, T. S., & James, D. E. (2003). Cluster analysis of spatiotemporal corn

yield patterns in an Iowa field. Agronomy Journal, 95, 574–586.Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics (p. 600). New York: Academic Press.Juan, P., Mateu, J., Jordan, M. M., Mataix-Solera, J., Melendez-Pastor, I., et al. (2011). Geostatistical

methods to identify and map spatial variations of soil salinity. Journal of Geochemical Exploration,108, 62–72.

Khosla, R., Fleming, K., Delgado, J. A., Shaver, T., & Westfall, D. G. (2002). Use of site-specific man-agement zones to improve nitrogen management for precision agriculture. Journal of Soil and WaterConservation, 57(6), 513–518.

Kilic, S., Agca, N., Karanlık, S., Senol, S., Aydın, M., et al. (2008). The detailed soil surveys, soil fertility,and land use planning work of the Amik Plain. Project No: DPT2002K120480.

Kitchen, N. R., Snyder, C. J., Frazen, D. W., & Wiebold, W. J. (2002). Educational needs of precisionagriculture. Precision Agriculture, 3, 341–351.

Kitchen, N. R., Sudduth, K. A., & Drummond, S. T. (1999). Soil electrical conductivity as a crop pro-ductivity measure for claypan soils. Journal of Production Agriculture, 12(4), 607–617.

Klute, A., & Dirksen, C. (1986). Hydraulic conductivity and diffusivity: Laboratory Methods. In A. Klute(Ed.) Methods of soil analysis. Part 1: Physical and Mineralogical Methods. (pp. 687–732). Madison,WI: SSSA and ASA.

Korkmaz, H. (2005). Drying effects of the Lake Amik on local climate. Antakya, Hatay, Turkey, T.C.Mustafa Kemal University, Project No: 03F0701.

Lark, R. M., & Ferguson, R. B. (2004). Mapping risk of soil nutrient deficiency or excess by disjunctive andindicator kriging. Geoderma, 118, 39–53.

Lesch, S. M., Corwin, D. L., & Robinson, D. A. (2005). Apparent soil electrical conductivity mapping as anagricultural management tool in arid zone soils. Computers and Electronics in Agriculture, 46,351–378.

Precision Agric

123

Li, Y., Shi, Z., & Feng, L. (2007). Delineation of site-specific management zones based on temporal andspatial variability of soil electrical conductivity. Pedosphere, 7(2), 156–164.

MacMillan, R. A., Pettapiece, W. W., Watson, L. D., & Goddard, T. W. (1998). A landform segmentationmodel for precision farming. Fourth International Conference on Precision Agriculture, (pp.1335–1346), St. Paul, MN.

Manning, G., Fuller, L. G., Eilers, R. G., & Florinsky, I. (2001). Topographic influence on the variability ofsoil properties within an undulating Manitoba landscape. Canadian Journal of Soil Science, 81,439–447.

Moustafa, M. M. (2000). A geostatistical approach to optimize the determination of saturated hydraulicconductivity for large-scale subsurface drainage design in Egypt. Agricultural Water Management, 42,291–312.

Moustafa, M. M., & Yomota, A. (1998). Use of a covariance variogram to investigate influence of sub-surface drainage on spatial variability of soil–water properties. Agricicultural Water Management,37(1), 1–19.

Odemis, B., Sangun, M. K., & Buyuktas, D. (2007). Temporal variations in water quantity and quality ofOrontes River, Turkey. Asian Journal of Chemistry, 19(1), 711–723.

Rao, P. S. C., & Wagnet, R. J. (1985). Spatial variability of pesticides in field soils: Methods for dataanalysis and consequences. Weed Science, 33(2), 18–24.

Rhoades, J.D., & Oster, J.D. (1986). Solute content. In A. Klute (Ed.) Methods of soil analysis. Part 1, SSSABook Series. 5 (pp. 985–1006). Madison, WI: SSSA and ASA.

Sangun, M. K., Ozdilek, H. G., & Odemis, B. (2007). Determination of groundwater quality in HatayProvince, Turkey. Asian Journal of Chemistry, 19(1), 616–620.

Schwab, G. O., Fervert, R. K., Edminister, T. W., & Barens, K. K. (1992). Soil and water conservationengineering. New York: Wiley.

Shi, Z., Huang, M. X., & Li, Y. (2003). Physico-chemical properties and laboratory hyperspectral reflec-tance of coastal saline soil in Shangyu City of Zhejiang Province, China. Pedosphere, 13(3), 111–120.

Staff, U. S. S. L. (1954). Diagnosis and improvement of saline and alkali soils. Washington, DC: U.S.G.P.Office.

Staff, S. S. (1998). Keys to soil taxonomy. Natural Resources (Vol. 8, p. 326). Washington, DC: USDA.Sudduth, K. A., Drummond, S. T., Birrell, S. J., & Kitchen, N. R. (1997). Spatial modeling of crop yield

using soil and topographic data. The First European Conference on Precision Agriculture, 1:439–447,Warwick, UK.

Trangmar, B. B., Yost, R. S., & Uehara, G. (1985). Application of geostatistics to spatial studies of soilproperties. Advances in Agronomy, 38, 45–94.

Utset, A., & Castellanos, A. (1999). Drainage effects on spatial variability of soil electrical conductivity in avertisol. Agricultural Water Management, 38, 213–222.

Wang, K., Zhang, C., & Weidong, L. (2013). Predictive mapping of soil total nitrogen at a regional scale: Acomparison between geographically weighted regression and cokriging. Applied Geography, 42,73–85.

Wang, Z. Q., Zhu, S. Q., & Yu, R. P. (1993). In P. L. Chen (Ed.), China saline and sodic soils. China:Science Press.

Warrick, A. W., & Nielsen, D. R. (1980). Spatial variability of soil physical properties in the field. In D.Hillel (Ed.), Applications of soil physics (pp. 319–345). New York: Academic Press.

Webster, R. (1985). Quantitative spatial analysis of soil in the field. Advances in Soil Science, 3, 1–70.Wu, C., Wu, J., Lou, Y., Zhang, H., & Teng, Y. (2008). Statistical and geostatistical characterization of

heavy metal concentrations in a contaminated area taking into account soil map units. Geoderma, 144,171–179.

Yan, L., Zhou, S., Wu, C., Li, H., & Li, F. (2007). Improved prediction and reduction sampling density forsoil salinity by different geostatistical methods. Agricultural Sciences in China, 6(7), 832–841.

Yao, Y., & Young, J. (2010). Quantitative evaluation of soil salinity and its spatial distribution usingelectromagnetic induction method. Agricultural Water Management, 97, 1961–1970.

Zheng, Z., Zhang, F., Ma, F., Chai, X., Zhu, Z. Q., et al. (2009). Spatiotemporal changes in soil salinity in adrip-irrigated field. Geoderma, 149, 243–248.

Precision Agric

123