ORIGINAL ARTICLE

Spatial distribution analysis of soil variables for agronomicdevelopment in El-Omayed Area, North-Coastal of Egypt

Kh. M. Darwish • Mohamed Rashad •

Sami Z. Mohamed • A. Gad

Received: 27 May 2014 / Accepted: 22 January 2015

� Springer-Verlag Berlin Heidelberg 2015

Abstract Geostatistical modeling has recently been de-

veloped for spatial prediction of environmental variables.

The current research aims to utilize geographic information

capabilities, remote sensing imagery and soil data, for de-

cision-making in natural resources management of El-O-

mayed area, N–W coast of Egypt. El-Omayed

geographically limited by latitudes 30�450N south and the

Mediterranean Sea at the north. The selected region repre-

sents an extended territory having an area of 102,35 ha, and

considers a promising location for future development,

keeping the ecological protectorate site as a reference. It

was possible to investigate the distribution of selected soil

variables and their relation with the actual properties to

carry out a detailed suitability evaluation for specific agri-

cultural uses. Spatial patterns of some chemical and phy-

sical soil properties were analysed. Data were investigated

both statistically and geostatistically based on the semi-

variogram analysis. Among the considered soil parameters,

the spatial distribution model varied and showed moderate

to strong spatial dependence. EC, gypsum and sand soil

texture were strongly spatially dependent. Using a

suitability model built in ALESarid software, the obtained

results indicate that the area currently lacks high suitability

classes. Following an improving soil properties program

may enhance the soil capability status. An area of 70,109 ha

(68.5 %) would become marginally suitable, then suit-

ability for different common crops land use (wheat, sun-

flower, maize, olive, fig, onion, tomato, potato and

watermelon) can elaborated. The usage of geospatial data,

distribution analysis and GIS proved to be suitable tools to

fulfill the needed large computational requirements.

Keywords Spatial distribution � Land use planning � Soil

sustainability � El-Omayed soils � Northern Coast of Egypt

Introduction

Food security and sustainable development are two fun-

damental and strategic goals in Egypt. The suitability of

soil leads to sustainable agricultural development. The

sustainable soil properties for efficient plant growth depend

mainly on the structural properties of the soil. The physical,

chemical and biological properties of soils are highly

variable over time and space (Ryel et al. 1996). The pre-

diction of soil properties and other properties such as pH,

EC, ESP, soil texture, etc. at unsampled locations creates

even more uncertainty. Spatial variability of soil properties

is somewhat inherent in nature because of variations in soil

parent materials and microclimate interface (Zhao et al.

2007). Efficiency in agriculture requires application of

farming principles based on the field variability, which

creates new requirements for estimating and mapping

spatial variability of soil properties.

Spatial distribution of soil properties is not uniform.

This uneven distribution of soil characteristics can reflect

Kh. M. Darwish � M. Rashad (&) � S. Z. Mohamed

Land and Water Technologies Department, Arid Land

Cultivation Research Institute, City of Scientific Research and

Technological Applications, Borg El-Arab, Alexandria, Egypt

e-mail: marashad2@gmail.com

Kh. M. Darwish

e-mail: kdarwish@hotmail.com

S. Z. Mohamed

e-mail: eng.sami87@gmail.com

A. Gad

Environmental Studies and Land Use Division, National

Authority for Remote Sensing and Space Sciences, Cairo, Egypt

e-mail: abdallagad@gmail.com

123

Environ Earth Sci

DOI 10.1007/s12665-015-4095-2

the processes that occur within the larger ecosystem

(Corstanje et al. 2006). To model spatial variability and

delineate spatial distribution of soil properties, geostatisti-

cal variogram analysis and kriging estimation, which pro-

vides the best linear unbiased prediction at unsampled

locations, have been widely used (Duffera et al. 2007;

Sauer and Meek 2003; Holmes et al. 2005; Huang et al.

2006; Liu et al. 2008; Cambardella et al. 1994; Askin and

Kizilkaya 2006).

Characterizing spatial variability of soil properties and

crop parameters by inventorying them is needed to evaluate

the effectiveness of their management. Also, characterizing

their temporal variability by monitoring them is important

to compare different management systems for sustain-

ability and environmental quality. In addition, an under-

standing of the spatial and/or temporal variability can

provide a framework for developing effective sampling

schemes for future site management and efficient ex-

perimental designs for research approaches (Douaik et al.

2011). Geostatistical methods have become popular in soil

science, as illustrated by the increasing number of studies

reported in the literature (Geypens et al. 1999). Concerning

the field of soils salinity mapping using in particular in-

terpolation techniques, Darwish et al. (2007) did apply

cokriging interpolation technique to estimate and identify

the spatial variability of soil salinity in Bahariya oasis,

western desert, Egypt. The results suggest that sampling

cost can be dramatically reduced and estimation can be

significantly improved using cokriging. It improved the

estimations greatly by reducing mean squared error and

kriging variance up to 70 % and increasing correlation of

estimates and measurements about 25 %. Darwish and El-

Khader (2008) investigated a methodology to determine

spatial variability in a dry onion field in EL-Saff agricul-

tural farming village, Giza Governorate, Egypt, and to

produce a management strategy, which is based on spatial

variability of yield and soil components. In addition,

Nawar et al. (2011) implemented geostatistical mapping

approach to generate an accurate soil salinity map as a base

for sustainability assessment of El-Tina plain at the

northwestern part of Sinai Peninsula, Egypt. They found

that the best model to generate soil salinity map was or-

dinary kriging with spherical semivariogram model (MPE

and MSPE close to 0, and RSSPE close to 1).

Nowadays, the Egyptian’s governance is directed more

attention towards its Northern coast zone for future devel-

opment. Therefore, studies on management of natural re-

sources in such region are considered of vital importance.

Geospatial techniques create great possibilities for fast in-

ventory, monitoring and updating the natural resources

status. Shendi et al. (2006) and Mahmoud et al. (2009)

studied the natural resources management and planning the

sustainable land use in El-Hammam area, North-Western

coast of Egypt using GIS, remote sensing and soil data. They

found that the study area lacked the soil high-capability and

moderate-capability classes. In addition, the results indicate

that, the area is susceptible for potential soil loss at different

severity levels. The combination of GIS and RS proved to be

effective in management and planning studies.

The objectives of this study were to: (1) examine the

spatial distribution of certain soil variables, e.g.; soil pH,

electrical conductivity (ECe), calcium carbonate CaCO3,

organic matter O.M. %, cation exchange capacity (CEC),

gypsum content and particle size distribution in El-Omayed

area northern coast of Egypt, (2) develop maps of these

characteristics and assess the spatial correlations among

them, (3) explore associations between these mapped

characteristics and the soil types present, (4) build up a

geographic soil attributes database for managing models

needed for decision makers, and (5) to assess soil suit-

ability to plan the most sustainable land use. This would

enable the identification of areas where rehabilitation is

needed to improve crop growth and development plans.

Materials and methods

Description of the study area

The investigated area, El-Omayed, was chosen in the

northern coast of Egypt limited by latitudes 30�450N at the

south and the Mediterranean Sea coast at the north. It is

bordered from the east by El-Hammam town and from the

west by El-Alamin town. The Khashm El-Eish ridge is the

southern extent of the study area. El-Omayed observatory

region is the core of the study area, where the UNESCO

biosphere reserve is a reference. El-Omayed observatory is

a part of the international ROSELT (Reseau d’observatoire

pour le Suivi Ecologique a long Terme) network, having an

area of about 5,000 ha and it belongs to the governorate of

Matrouh and the township of El-Hammam (Ayyad and Le

Floch 1983). It is representative of a more extended terri-

tory having an area of 102,350 ha. The extended territory

lies between latitudes 30�4403000 to 30�4904000 North and

longitudes 29�000 to 29�280 East. It is situated about 80 km

to the west of Alexandria city and about 230 km to the east

of Matrouh (Fig. 1).

Regarding the climatic conditions, the study area falls

within the Mediterranean coast of Egypt and experiences

long dry summers and very short rainy winters. The annual

rainfall varies considerably from month to month with mean

of 14.91 mm/month (it varies from 54.60 mm in January to

0.1 mm in June), and the dry season lasts from April to

September. The mean monthly temperature ranges from

13.70 �C in January to 26.55 �C in August (according to

data obtained from the metrological station of El-Dekhila).

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Geology of the Northwestern Coast

The area (Fig. 2) is formed in sedimentary rocks; belong to

the Tertiary and Quarterly geologic ages (60 million years

ago). The area, from the seacoast to the Libyan plateau, is

calcareous formation of the Pliocene and Pleistocene times,

but covered by recent sediments. The wind and fluvial

activities rework the sediments. The area is characterized

by the existence of elongated ridges parallel to the sea-

shore, but missed in some locations. The ridges consists of

white calcareous material, vary in their hardness, formed in

the Pleistocene period. The Libyan plateau is bordered

from the north by steep slope areas leading to flat and

depressions landscape. It extends southwards in the west-

ern desert, and is formed of the Middle Miocene and

Pliocene sediments, mixed with sandy stone, clay stones,

Oligocene and Eocene sediments (CAMELEO 1998).

Geomorphology

The northwestern coast of Egypt can be subdivided into the

following geomorphologic divisions:

Fig. 1 Study area El-Omayed, North-coast of Egypt

Fig. 2 Geological map of the study area (CAMELEO 1998)

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1. Coastal plain: It is bordered by the Mediterranean Sea

to the north and by the plateau to the south, taking an

orientation east west. The width of the coast varies

from one site to another, controlled by the geologic

formations.

2. Pedomental plain: It is the interference zone between

the coastal plain and the plateau. It appears clearly

between Ras El-Hekma and Ras Alam El-Room and in

El-Salloum area. This area has a good potentiality for

agriculture expansion if the water and land resources

are well managed.

3. Plateau: It is a rocky plateau, mostly covered by a thin

layer of soil (EGEO-SAI et al. 1999) (Fig. 3).

Concerning the soil conditions, the soils of the north-

western coast are highly calcareous. This is due to the

calcareous nature of the parent material from which these

soils were derived, and the insufficient and limited leach-

ing. The soils were formed from alluvial materials, Marine,

Aeolian and Lagoons origin on different elevations in the

study area (Rashad 2002).

Field work and sample analysis

Based on the pre-field work and ground truth data, inten-

sive testing auger samples was carried out to implement a

general reconnaissance survey throughout the study area,

then sampling method is applied to cross the different

mapping units, where 65 soil observations were tested and

sampled (Fig. 4).

Detailed morphological description was recorded for

each of the studied soil profiles on the bases outlined by

FAO (2006) guideline for soil profile description and

classified according to US Soil Conservation (2010) and

ISSS-ISRIC-FAO (1998). Reconnaissance soil survey was

done for purposes of physiographic and soil mapping, then

verification of the primary mapping units. Collected soil

samples were air dried; ground gently; and sieved through

2-mm sieve, where the main physical and chemical prop-

erties were determined.

Based on the profile morphological characteristics and

laboratory analysis of soil samples, and according to the

classification of soils, there are Typic Torripsamments and

Calcic Petrocalcids soil subgroup in the area (Soil Survey

Staff 2014). There are soil texture types such as sand,

sandy loam and loamy sand; sand and sandy loam texture

with gravel fragments. The majority of the soils (90 %) in

the region are low or very low organic matter, and 65 % of

the soils are rich in calcium carbonate (Rashad 2002).Fig. 3 Digital elevation model DEM of the Northern coast of Egypt

Fig. 4 Location of sample

points in the study area

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Data analysis

Measured results were used to produce maps (soil EC, pH,

CaCO3 %, O.M. % component maps, texture, etc.) to

demonstrate the spatial distribution and relationship be-

tween different soil properties. The maps were produced

using a methodology developed and published by Akdemir

and Blackmore (2004), Akdemir et al. (2005). Data ana-

lyses were carried out in three stages: (1) normal distri-

bution tests were applied for every variable with

logarithmic transformation; (2) distributions were analysed

by classical statistics (mean, standard deviation and coef-

ficient of variation (CV); (3) geostatistical interpolation

parameters, sill, range, nugget and nugget ratio values were

calculated for each variable, as a result of corresponding

semivariogram analysis. Values for unsampled locations

were interpolated by kriging analysis technique (Isaaks and

Srivastava 1989) and the results were plotted to build

spatial distribution map for each variable. GS ? geo-

statistics program (Robertson 2008) was applied to conduct

semi-variogram and spatial structure analysis for the in-

vestigated soil variables. The interpolated values of the

variables were imported into ArcGIS (ESRI 2013) to create

the maps. Correlation and regression analyses were con-

ducted to measure the relationships between the mapped

values of soil attributes. These relationships, among the

mapped variables, may be compared with those among the

data for the original 65 sites from which the maps were

derived.

Semivariance analysis

Semivariance analysis was implemented to estimate the

range over which samples of the soil variables was related.

The semivariance, c(h), of all samples separated by a

vector h were defined as:

c hð Þ ¼ 1

2N hð Þ

� �þXNðhÞ1¼1

½Zi � Ziþh�2 ð1Þ

where Zi and Zi?h are experimental measures of any two

points separated by the vector h, and N (h) is the number of

experimental pairs separated by h (Goovaerts 1999).

Interpolation technique

The interpolation technique is made for estimating values

for points not sampled. In this study, the inverse distance

weighting (IDW) was applied, in which interpolated esti-

mates made based on values at nearby locations are

weighted only by distance from the interpolation location.

IDW applies stronger weights to nearby points. The for-

mula used for IDW is:

Zestj ¼X½Zi= hij þ S

� �p� �X½1= hij þ S� �p� ð2Þ

Zestj = estimated value for location j, Zi = measured

sample value at point i, hij = distance between Zestj and Zi

S = smoothing factor, and p = weighting power

The weighting power p defines the rate at which weights

fall off with hij, the distance between the interpolated and

sample locations. A value of 1–5 is typical. The smoothing

factor S reduces the likelihood that any one sample value

will overly influence an estimated value for a given inter-

polation location. IDW is an exact interpolator, so where an

interpolation location coincides with a sample location

Zestj = z and a sharp ‘‘peak’’ or ‘‘valley’’ may result; set-

ting S [ 0 reduces this peaking effect when it occurs.

Land suitability assessment

A land suitability model was built using ALESarid soft-

ware to calculate the agricultural land capability indices, 12

crop suitability indices were introduced out of 27 land use

types (LUT) (field crops, vegetable and forage crops, and

fruit trees). The evaluation was based on crop suitability

affected by the environmental potential at the soil site, such

as physical, chemical and fertility soil characteristics, water

quality and climatic conditions. ALESarid-GIS designed

based on both decision trees and maximum limitation ta-

bles (Ismail et al. 2001).

Results

Descriptive parameters

Table 1 presents the descriptive statistical summary pa-

rameters of the soil properties in the study area. The mean

of pH and electrical conductivity EC values were 8.38 and

2.56 (dS/m). Probability distributions of the soil properties

were analysed and evaluated using skewness and kurtosis

indicators, and those not normally distributed were sub-

jected to logarithmic transformation for all soil properties.

Skewness and kurtosis coefficients are used to describe the

shape of the data distribution (Duffera et al. 2007). Nor-

mality of data sets was tested using the Kolmogorov–

Smirnov test and this test indicated that all variables were

significantly normal except (EC, OM, CEC, Gypsum and

clay contents).

Since the value distributions of soil parameters are

strongly positively skewed, logarithmic transformations (-

natural logarithm) were applied to the measured values to

eliminate the strongly positive skewness of distribution and

improve estimation. The EC values, O.M., CEC, gypsum

and clay values are the soil attributes exist with relatively

large skewness and kurtosis, showing that the distributions

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123

of these characteristics were far from normal distribution.

The quite large positive skewness for those soil attributes

reflects asymmetry in the distribution caused largely by a

number of relatively high values. A natural logarithmic Ln

transformation was applied to the data for EC, O.M%,

CEC, Gypsum and clay%. After transformation, the

skewness and kurtosis of those soil variables were greatly

reduced, providing a more normally distributed data. De-

spite skewness and kurtosis of the distribution of some soil

properties, the mean and median values were similar with

means being equal to or almost equal to the median; ex-

ceptions were EC, CEC and gypsum (Table 1).

Descriptive statistics of soil variables (Table 1) illus-

trated coefficients of variation CV ranging from 5.5 % for

pH values to 130.0 % for EC. CV for most of the soil

properties exceeded 20 % (except pH and sand) that indi-

cating significant spatial variability (Table 1). The pH and

sand had the lowest CV (5.49–8.00 %); CaCO3, clay and

silt had low medium CVs (26.53–13.06–36.77 %), while

OM and CEC had relatively high medium CVs

(75.00–74.84 %); and finally, EC and gypsum had the

highest CV (130.08–123.12 %), indicating that soil prop-

erties were ordinarily heterogeneous.

Semivariance analysis

Experimental semivariances of soil properties were

calculated and modeled using GS ? software with

consideration restricted to half the maximum lag dis-

tance (Journel and Huijbregts 1978). In this research,

isotropic semivariogram models were applied to the soil

parameters data. The investigated soil properties ex-

hibited spatial autocorrelative structures, indicating that

they responded to processes that occur throughout the

study area. Nevertheless, spatial dependencies differed

among the soil attributes, as illustrated by the nugget

and sill values, as well as the ranges (Table 2). The

range establishes the outer limit at which points in space

still interact spatially and the sill represents the total

priori variance (Webster and Oliver 2000). The exam-

ined soil properties displayed differences in their spatial

structures. The organic matter OM and CEC parameters

exhibited the largest range, indicating significant spatial

structure extending over 91.1 km. Gypsum content il-

lustrated the smallest range (2.7 km). These parameters

required a nested spatial model with large ranges for the

long-scale exponential model and small (2.7 km) for the

Table 1 The statistical analysis of soil properties in sampling sites (n = 65)

Soil property Min. Max. Mean Median SD Sample variance CV Skewness Kurtosis

pH 7.60 9.49 8.38 8.52 0.46 0.22 5.49 0.12 -0.64

EC (dS/m) 0.17 19.00 2.56 1.11 3.33 11.10 130.08 2.77 9.40

CaCO3 (%) 14.20 71.76 46.24 47.10 12.27 150.48 26.53 -0.21 0.22

O.M (%) 0.10 1.26 0.20 0.17 0.15 0.02 75.00 6.02 39.31

CEC meq/100 g soil 0.14 20.70 4.65 3.73 3.48 12.12 74.84 1.95 6.11

Gypsum meq/100 g soil 0.07 9.23 1.38 0.85 1.70 2.89 123.12 2.43 7.37

Clay (%) 4.20 19.50 7.96 7.85 1.04 1.09 13.06 0.29 0.89

Silt (%) 2.20 30.00 14.33 14.30 5.27 27.80 36.77 0.40 1.19

Sand (%) 59.50 88.20 76.58 77.25 6.13 37.60 8.00 -0.73 0.87

SD standard deviation, CV coefficient of variance (%)

Table 2 Parameters of fitted semivariograms of soil variables in the study area

Variables Theoretical model Nugget (Co) Sill (Co ? C) Nugget to sill ratio (%) Spatial dependenceb Range (km) R2

pH Gaussian 0.136 0.282 48.23 Moderate 18.36 0.84

ECa Spherical 0.069 1.254 5.50 Strong 6.43 0.49

CaCO3(%) Gaussian 104.9 209.90 49.98 Moderate 15.7 0.73

O.Ma Exponential 0.1113 0.297 37.47 Moderate 91.10 0.28

CECa Exponential 0.421 1.066 39.49 Moderate 81.09 0.53

Gypsuma Exponential 0.266 1.934 13.75 Strong 2.72 0.51

Claya Linear 0.079 0.085 92.94 Weak 36.4 0.50

Silt Exponential 10.72 28.71 37.34 Moderate 8.80 0.53

Sand Spherical 2.70 38.46 7.02 Strong 3.01 0.51

a Natural-logarithmic transformations were applied to some soil measured variablesb Spatial dependence was defined as strong, moderate, or weak for nugget to sill ratios \ 25, 25–75, or [ 75, respectively

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short-scale exponential model. Large nugget values

relative to the sill variance were found for clay per-

centage, which suggests that extrinsic factors associated

with human activities (e.g., soil management practices,

land-use activities, farm management practices, such as

fertilization and tillage) that influence the variability of

clay particle size significantly (Wei et al. 2009). Based

on the nugget-to-sill ratio, soil variables in El-Omayed

study area can be classified into three general cate-

gories. The first category includes EC, gypsum and sand

with strong spatial dependence, which may be affected

mainly by intrinsic factors (i.e., soil formation factors,

such as soil parent material and texture). The second

category consists of pH, CaCO3, OM, CEC and silt that

showed moderate spatial dependence. The third category

is for clay, which exhibited weak spatial dependence.

Table 2 shows that the fitted parameters for semi-var-

iograms of the studied soil variables. Obviously,

athematic natural-logarithmic transformation trends

significantly reduces the contributions of long-range

components to EC, OM, CEC, gypsum and clay, which

indicates that large-scale factors (i.e., parent material,

climate and human impact) may have predominant in-

fluences on the distributions of them. According to their

results, the isotropic variograms with theoretical models

of gaussian, spherical, linear and exponential functions

were fitted to variables (Fig. 5).

Discussion

Spatial distribution of soil variables

The statistical analysis can only explain the sample dif-

ference in volume and homogeneity, but it cannot describe

the spatial variability. Consequently, with implementing

these best fit theoretical geostatistics models and corre-

sponding semivariogram parameters, spatial variability

maps of soil attribute were created using IDW interpolation

method. IDW does not make assumptions about spatial

relationships except the basic assumption that nearby

points ought to be more closely related than distant points

to the value at the interpolate location. Figure 6 shows the

maps of soil properties generated from their semi-

variograms. Inverse distance weighted IDW interpola-

tion assumes that the characteristics of the targeted surface

are driven by local variation. IDW works better if the

sampling scheme and points are most evenly distributed

throughout the area and are not cluster distributed, which is

assumed to be found in this study area. Thus, IDW assumes

that each observed measured point has a local influence

that diminishes with real distance. It gives more weights to

the points closer to the prediction location greater than

those farther away (Webster and Oliver 2000; Childs

2004). IDW predictions were performed varying the

number of power (from 1 to 5) and the best consequence

Sem

ivar

ianc

e

pH

0.000

0.078

0.155

0.233

0.311

0.00 14302.70 28605.39 42908.09

EC

0.00

0.38

0.75

1.13

1.51

0.00 14302.70 28605.39 42908.09

CaCO 3

0.

57.

113.

170.

226.

0.00 8939.18 17878.37 26817.55

OM

0.000

0.063

0.125

0.188

0.250

0.00 14302.70 28605.39 42908. 09

CEC

0.000

0.191

0.382

0.572

0.763

0.00 14302.70 28605.39 42908.09

Gypsum

0.00

0.53

1.06

1.59

2.13

0.00 14302.70 28605.39 42908.09

Clay

0.0000

0.0234

0.0468

0.0702

0.0936

0.00 10727.02 21454.05 32181.07 42908.09

Silt

0.0

9.8

19.6

29.5

39.3

0.00 14302.70 28605.39 42908.09

Sand

0.0

11.7

23.3

35.0

46.7

0.00 14302.70 28605.39 42908.09

Separation Distance (h) (m)

Fig. 5 Best-fitted semivariogram models for some soil properties

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obtained using different radiuses and neighbors. In this

work, in all implementations of IDW was a good choice

among the others, which is possibly due to the relatively

inherent low skewness in all modeled soil properties, ex-

cept for organic matter content (%). In Fig. 6, maps of soil

properties displayed different patterns of ‘‘high’’, ‘‘middle’’

and ‘‘low’’ concentrations. Soil pH in the study area ranged

evenly between 7.6 and 9.49, which is reflect base to al-

kaline soil types with coefficient of variation equal to

5.5 % (Table 1). Soil pH varied widely in the most middle

northeast and west side of the study area.

Nevertheless, EC values were as high spot in the middle

northern part because this portion had a high groundwater

level. A similar trend to that of EC was observed for

gypsum and clay in the northern part of the study area.

With rising capillary movement in the soil and

evapotranspiration from plants and soil, salts rise to the

surface. Application of ground low-quality water would

result in increase in pH, EC, CaCO3 and gypsum values. A

highly significant positive correlation can be found be-

tween soil salinity (EC) in a field with Entisols soil order,

with high clay content, having low infiltration capacity

(Miyamoto and Chacon 2005).

Isotropic semivariogram exhibited higher differences in

spatial dependence based on distances and direction for all

soil properties (Table 2) and significant sampling direction

trend of spatial distribution. For all soil properties (physical

and chemical), the north, northeast and partly west direc-

tions had higher spatial variability than other directions.

The pH and CaCO3 attributes were defined by the Gaussian

semivariance model. The EC and sand fraction were de-

fined by the spherical model. The O.M, gypsum, clay and

silt variables were defined by the exponential model

(Table 2; Figs. 6, 7). Nugget effect was higher for soil

physical properties (silt, sand) compared to chemical

properties (pH, EC OM, CEC and gypsum) except for clay

and CaCO3 cases (Table 2). This showed that soil chemical

properties had spatial variability in small distances. Nugget

effect was related to the spatial variability in shorter dis-

tances than the lowest separation distance between mea-

surements (Webster 1985). All soil properties had a range

indicating the existence of a spatial structure for them.

Nevertheless, the large nugget effect (Co) suggested that

more sampling of these variables in larger numbers and at

smaller distances might be needed to detect spatial de-

pendence. In addition, a greater sampling density will

Fig. 6 Distribution maps of soil properties in study area

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result in more accurate spatially distributed maps. In case

of moderate or strong spatial correlation of the soil prop-

erties distribution, the mean range of these distributions can

be given by the geostatistical range of the fitted semi-

variogram. A larger range values indicates that observed

values of the soil property are influenced by other values of

this property over greater distances (Isaaks and Srivastava

1989).

The large nugget semivariance indicates that the lag

(h) evidently did not describe the spatial variation and that

an additional sampling of these variables at smaller lag

distances is needed to decrease uncertainty. Strong spatial

dependency of soil variables may be controlled by intrinsic

variations in soil characteristics (Cambardella et al. 1994).

Pearson’s correlations coefficient between all analysed

soil parameters the entire data set of 65 sampling sites are

shown in Table 2. The pH value was significantly

(p B 0.05) positively correlated with CEC (r = 0.331), but

negatively with clay (r = -0.292). Significant positive

correlation (p B 0.05) was found between CaCO3 and silt;

and negatively with sand content. Negative correlation

(p B 0.05) was found between sand and clay content. In

addition, EC was significant (p B 0.01) correlated with

characteristics such as CEC and gypsum. Nevertheless,

Fig. 7 Distribution maps of CEC, O.M and texture soil properties in study area

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there was significant (p B 0.01) negative correlation be-

tween sand and silt content (Table 3).

Soil physio-chemical properties map

A digital elevation model DEM map is one of the key data

layers for developing a robust global model, generating soil

map and evaluating land use types (Ahn et al. 1999; Gad

et al. 1999). In this research, the SRTM–DEM data layer

were utilized and indicated that the area is featured by

coastal plain landscape, which is subdivided into four relief

types, i.e., High Terraces, Moderately High Terraces,

Moderately Low Terraces and Low Terraces (Fig. 3). The

High Terraces relief unit represented the oldest sea beach.

The main recorded soil in this unit is classified to the sub-

great group as Calcic Petrocalcids (Soil Survey Staff 2014).

The Low Terraces relief unit is considered to be the

youngest landforms in the area and extend parallel to the

present sea shoreline. The main recorded soils are; Typic

Torripsamments and Calcic Petrocalcids. Finally, the area

was differentiated into different map units according to the

soil physio-chemical properties (e.g. soil salinity, depth and

texture) as indicated in Fig. 8. The soil depth varies be-

tween 60 and 130 cm; the electrical conductivity EC values

differ from 0.17 to 19.00 dS/m. The high values of salinity

were found in the Low Terraces relief unit (Hdh), while

low values characterized the High Terraces relief unit,

which at last represented the oldest sea beach with sand

sheet landform. The soil texture is varied between sand to

loamy sand and sandy loam. There are some gravel fraction

Table 3 The correlation

coefficient (r) between soil

properties in study area

a Pearson’s correlation is

significant at the 0.05 level

(2-tailed)b Pearson’s correlation is

significant at the 0.01 level

(2-tailed)

Variables pH EC CaCO3 OM CEC Gypsum Sand Silt Clay

pH 1

EC -0.076 1

CaCO3 (%) -0.168 -0.120 1

OM (%) -0.100 -0.022 0.071 1

CEC 0.331a 0.398b 0.255 -0.149 1

Gypsum 0.111 0.573b -0.164 -0.108 0.162 1

Sand (%) 0.176 0.040 -0.289a 0.188 -0.137 -0.002 1

Silt (%) -0.108 0.121 0.263a -0.146 0.256 0.137 -0.870b 1

Clay (%) -0.292a -0.103 0.241 -0.116 -0.183 -0.236 -0.259a 0.055 1

Fig. 8 Map of soil physio-

chemical properties of the study

area in north-coastal zone

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associated with the shallow soils of the moderately low

Terraces. Soil attributes of the different mapping units are

added from the analysis results of the modal soil profiles

representing the dominant main soil.

Land suitability assessment

Physical land suitability for twelve different LUTs was

evaluated in the capable soils using ALESarid model. The

results were reported as tabular data (Table 4). It is highly

observed that the study area suffers from a lack of highly

suitable soils for the different land use types presented in

the area due to the different existed soil limiting factors.

The results indicated that most suitable crops to grow in

the study area are Tomato, wheat, sunflower, maize, potato,

sunflower, olive and watermelon in the order indicated,

whereas fig is the most sustainable orchards there

(Table 4).

Obviously, (Mdh1) map unit showed marginal suit-

ability for most crops; except soybean, peanut, onion and

pea. Tomato crop reported moderate suitability in two

delineated map units (Ndh2 and Nsh). It is necessary to

mention that approximately, 68.5 % (70,109 ha) of the

investigated area was conditionally suitable (S4) to actually

unsuitable (NS2) for growing the evaluated crops. The

dominant limiting parameters for suitability of most crops

are the low levels of clay content (sandy soil texture), soil

depth, fertility and exchangeable sodium per cent (ESP).

ESP has a slight effect on wheat, and onion suitability.

Sandy soil texture and soil salinity are considerable limit-

ing parameters for onion. However, soil salinity has a slight

effect on wheat suitability, but water irrigation salinity is

considered a non-limiting parameter for them.

Conclusion

This research investigated the spatial distribution of se-

lected soil variables in El-omayed area north-coast of

Egypt. It demonstrated that how agricultural management

can affect the soil properties of geospatial patterns that are

spatially correlated as well as the range of correlation of

these properties in the area. Confidently, distribution pat-

tern of soil attributes would be used to develop indicator

maps in the study area according to their management and

rehabilitation requirements. For this purpose, identifying

the best method to determine the spatial variability of soil

chemical and physical properties is essential. In this re-

search, great variation exists in the soil properties of the

study area. The results indicate the importance of the geo-

information and identified the methodology for data col-

lection and interpretation in agricultural management. The

soil properties coefficient of variations are large, which

show that soil properties are not homogeneous in spatial

distribution because of the effect of geology, topography

and difference of land use and management measures in

this area. The majority of soil properties exhibited a strong

spatial dependency. It was inference that the strong spatial

dependency of soil properties would lead to salinity effect,

shallow depth, drainage and ground water level. The results

indicate that, the study area is susceptible for potential soil

loss at different severity levels. In the study area, pre-

dominantly applied limited low quality of irrigation water

(the most common cause of soil salinity) and inappropriate

drainage conditions deteriorated the salinity status. Modern

irrigation systems should be preferred to optimize water

productivity. Eventually, the results of this study showed

that spatial structure exists in the soil variables and is

Table 4 Crop suitability classes and their area percentage in the studied area

Map unit

Wheat Sunflower Maize Soybean Peanut Olive Fig Onion Pea Potato Tomato Water-melon

Areaha

Area%

Hdh S4 S4 NS1 NS1 NS1 NS2 NS2 NS1 NS1 NS1 NS1 NS1 3,790.7 3.7

Mdh1 S3 S3 S3 S4 S4 S3 S3 S4 S4 S3 S3 S3 3,973.6 3.9Mdh2 S4 S4 S4 NS1 NS1 S4 S4 S4 S4 S4 S4 S4 9,678.2 9.5Msh S4 S4 NS1 NS2 NS1 NS2 NS2 NS1 NS1 NS1 S4 NS1 6,101.2 6.0Nmsh S4 NS1 S4 NS1 S4 NS2 NS2 S4 S4 S4 S4 S4 16,870 16.5Ndh1 S4 S4 S4 S4 S4 NS2 NS2 S4 S4 S4 S4 S4 33,870.3 33.0Ndh2 S3 S3 S4 S4 S4 S4 S4 S4 S4 S4 S2 S4 5,669.8 5.6Nsh S3 S4 S4 NS1 S4 NS2 NS2 S3 S4 S4 S2 S4 22,396.3 21.8

102,350.1 100.0

Hdh [Highly saline deep soils with heterogeneous texture (Typic Torripsamments)], Mdh1 [Moderately saline deep soils with heterogeneous

texture (Typic Torripsamments)], Mdh2 [Moderately saline deep soils with homogenous texture (Calcic Petrocalcids)], Msh [Moderately saline

shallow soils with homogenous texture (Calcic Petrocalcids)], Nmsh [Non-to slightly saline moderate to shallow soils with heterogeneous texture

(Calcic Petrocalcids)], Ndh1 [Non-to slightly saline deep soils with heterogeneous texture (Typic Torripsamments)], Ndh2 [Non-to slightly saline

deep soils with homogenous texture (Calcic Petrocalcids)], Nsh [Non to slightly saline shallow soils with heterogeneous texture (Calcic

Petrocalcids)]

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important for depicting the effect of bed management

practices on soil quality parameters.

Furthermore, land suitability evaluation was performed

with the aid of field survey data and GIS. The results

showed that more than 70,109 ha (68.5 %) in the study

area are marginally to conditionally suitable for agriculture

purposes. The most suitability land uses recommended are

wheat, sunflower, maize and olive, while onion, potato,

tomato and watermelon as recommended vegetables,

whereas fig is the most sustainable orchard cultivation. On

the other hand, there are various limitations for agricultural

use. Therefore, proper soil management is required to in-

crease the soil suitability for different crops. Amelioration

of soil physical properties (i.e., soil texture) and adjustment

of soil pH rather than increased fertilization will greatly

improve the availability of soil nutrients and the agricul-

tural productivity of El-omayed because of the funda-

mental influences of calcareous and salinity soil conditions

and pH on their spatial variability.

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