INTRUDUCTION
Agriculture expansion in Egypt especially in sandy and saline soils at
northern lakes depends mainly on irrigation. Land management is useful for
improving soil characteristics, and achieving the agriculture sustainability.
Therefore, better soil management and conservation are essential to improve
soil production and reduce soil degradation.
Many studies have been carried out to characterize lacustrine soils,
among them, Labib and Sys (1970), El-Husseiny et al. (1988) and Fayed
(2011). They pointed out that lacustrine soils are salt- affected, stratified, poorly
drained to water logged, heavy to light textured, poor in organic matter and
CaCO3, percent and underlain by shelly lagoonal or lake deposits.
The morphological characteristic of the lacustrine soils located south of
Lake Maruit were studied by Morgan (1976), Darwish (1977), El-Husseiny et al.
(1985), El-Attar et al. (1987), El-Zahaby et al (1999) and Fayed and ElJ.
Adv. Agric. Res. (Fac. Agric. Saba Basha)
157
Vol. 19 (1), 2014
Menshawy (2006). They found that these soils are stratified and salt affected in
some locations. Shells are abundant but irregularly distributed in both the
vertical and horizontal direction. The texture ranges from silty clay to clay loam.
The pedognic processes include the aggregation, salinization, alkalization, and
gleyzation in addition to the phenomena of slickensides.
The chemical properties of the deposits located south of Lake Maruit
were studied by many authors. El-Attar and Bakr (1963), Mahmoud (1978) and
El-Zahaby et al. (1999) reported that the high CaCO3 content of these soils may
be attributed to the frequent occurrence of shells, while the high content of
soluble Na+, Ca++, Cl- and SO4
-- is due to the saline nature of their parent
material. In another study, El-Husseiny et al. (1985) compared the chemical
properties of some soils in the lacustrine deposits of Lake Maruit and found that
the northern soils (at Abis) are moderate to non saline-affected as a result of
leaching by irrigation and perfect drainage, while the southern soils (at trouga)
are saline alkali with a surface crust formed by the upward movement of salts.
They also reported that the total carbonate of these soils is low to moderate
(1.0-11.6%) but greatly increase with depth in the layer of shell accumulations
(28-35%). El-Attar et al. (1987) studied the soils of El-Nahda project, which are
developed on calcareous lacustrine deposits. They found that these soils are
characterized by secondary salt accumulations due to the capillary mechanism
of soluble salts under the conditions of the dry hot climate, the presence of clay
layers and the shallow saline ground water.
A soil inherent erodibility, which is a major factor of erosion prediction
and land-use planning, is a complex property depends on its infiltration
capacity, and its capacity to resist detachment and transport by rainfall and
runoff. Soil erosion is the greatest hazard to the long-term maintenance of soil
fertility in most environments. It reduces soil depth and causes loss of topsoil
that has most nutrients, most organic matter and the best structure for root
growth. Moreover, erosion can reduce crop yields (Wild, 1996).
Erosion and sedimentation are land resource problems that lead to
significant economic, environmental, and social impacts (Morgan, 1996).Soil
erosion involves the detachment of sediment and soil from the soil surface by
raindrop impact, flowing water and winds (Bahr and Vogtle, 1999; Nikkami et
al., 2002; El-Hassanin et al. 2002 and Gomez et al., 2004). Egypt as located in
semi-arid region suffers from this problem. The rate of erosion depends on
erosive forces of rainfall and runoff (erosivity) and susceptibility of the soil for
detachment by these factors (erodibility). Accurate information on soil erodibility
is important for soil erosion prediction and control, as well as, for planning of
modern farming techniques. Soil erodibility factor (K) represents both
susceptibility of soil for erosion and the rate of runoff. Many attempts have been
made to devise a simple index for erodibility based either on the properties of
soil or on its response to rainfall and winds. Wischmeier and Mannering (1969)
showed that soils high in silt and low in clay and organic matter are the most
erodible.
Wischmeier et al. (1971) indicated that the organic matter content
reduces erodibility because it reduces the susceptibility of the soil to
detachment, and it increases infiltration, which reduce runoff and thus erosion.
Also, they revealed that (K) could be estimated if the grain size distribution,
organic content, structure index, and permeability of the soil are known.
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
158
Vol. 19 (1), 2014
Romkens et al. (1977) indicated that erodibility factor (K) could be predicted by
some soil properties [(Silt + v. f. s) x (silt + sand)], organic carbon, permeability,
and soil structure. Beasley et al. (1984) showed that, the flow of water causes
the movement of minerals, plant nutrients, soil particles, and other organic and
inorganic pollutants. Kukal et al. (1991) found that erodibility of some Indian
soils was strongly correlated (r = 0.98) with amounts of (silt + v. f. s). Bahnassy
(1992) estimated soil loss for wadi El-Haraka, North Western Coast, Egypt, and
found that it varied from 4 to 25 t/ha/yr. In Egypt, limited data are available for
the surface runoff, soil erodibility and erosivity, as will as, field practices which
control soil erosion.
Land evaluation has been defined as the process of assessing or
predicting the performance of land for specific purposes (FAO, 1976). Soil
attributes needed in land evaluation may be obtained from soil databases and
are typically used alone or in conjunction with other land characteristics to
derive the distribution of land suitability, limitations or potential ratings for
various land use types. The land evaluation could be performed using soil
interpretative groupings, productivity ratings or crop growth models.
Many investigators suggested that land evaluation should be made
according to most of the following parameters: soil texture, salinity,
exchangeable sodium percentage, pH, CaCO3, organic matter, level of water
table, salinity of water table, profile depth and necessity of drainage (Omar and
El-Kholie, 1970; El-Nahal et al., 1977; Mansour, 1979; El-Menshawy et al., 2005
and Abd El-Rahman et al., 2009). El-Fayoumy (1989) modified a system to
include soil fertility and irrigation water factors in addition to soil properties.
Each of the above mentioned factors was described as an index value to give
its status as a percentage. These indices were calculated using empirical
equations. In general, all land evaluation systems are very similar in the
concept, although there are some differences between them in the parameters,
which they are based on.
Agricultural Land Evaluation System for arid region (ALES-Arid) is a new
approach for land capability and suitability evaluation. The calculation of
capability index by ALES-Arid is an indication of land capability according to
multiplication method (Abdel Kawy et al., 2004). ALES-Arid evaluates the
suitability for different crops (field crops, vegetables, forage crops, and fruit
tress) to identify the optimum land use. Land suitability classes were identified
using the matching between standard crop requirements (FAO, 1985; Sys and
Verheye, 1974; and Sys et al., 1993) and actual land characteristics.
The objectives of the this work are to study and discuss soil
characterization, assessment the soil erodibility factor (K) and define the
important soil properties affecting erodibility, wind erosivity factor (C), rainfall
erosivity factor (R) and to find out the land capability and soil suitability
classification using reliable soil qualities to be helpful in better soil management
and land use in the lacustrine soils of Abis region at south Mariut Lake since a
very limited work was reported for this area.
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
159
Vol. 19 (1), 2014
MATERIALS AND METHODS
Study area:
The studied area is located at the North Western part of the Nile Delta,
which represent soils developed from lacustrine deposits at Abis region south of
Mariut Lake. It is located approximately between Latitudes 31o00\ and 31o 30\ N
and between Longitudes 29o30\ and 30o 30\ E. It situated in two main
governorates of Egypt with a total area about 40000 fed. About 52% of the total
area lies in the eastern part of Alexandria governorate while 48% in the western
part of El Behira governorate as shown in map (1). The reclamation strategy of
this area started at the late of sixties by drying part of Maruit Lake. This
reclamation was done in three stages; the third one named Abis extension and
covered about 8000 fed.
Map 1: General location of the study area
The study area is characterized by Mediterranean climate with relatively
cold and rainy winter and hot and dry summer. Table (1) shows the climatic
data for the studied area which obtained from Central Laboratory for Agriculture
Climate (2006).
The studied area is covered by Holocene formations (Said, 1962). The
soil of this area was derived from the lacustrine deposits of Mariut Lake with an
elevation of about 2.5m below sea level with almost flat surface.
The main source of irrigation water in the studied area is the Mahmodia
canal and the surface irrigation system is used for irrigation. The drainage
system is tile drains with moderate efficiency.
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
160
Vol. 19 (1), 2014
The cropping pattern in the studied area involves the plantation of field
crops, vegetables and fodder in addition to minor areas of fruit trees. The main
field crops in the area are maize and peanut in summer and fababean and
wheat in winter season. The dominant fodders are maize and alfalfa in summer
and clover and barley in winter season. The main vegetables in summer season
are watermelon, tomatoes and eggplant, while onion, potatoes and squash are
considered the main vegetables in winter season. The dominant fruits are
grapes, oranges, banana and guava.
Table 1: Meteorological data of Alexandria Region (2006)*
Month Temperature, oC Relative
Humidity
wind
Sp.
m/sec
Total
rain
(mm)
ETP
Max Min. Average (mm/day)
January 18.4 9.1 13.5 70 3.96 54.9 2.2
February 19.3 9.3 14.1 68 3.96 26.6 2.6
March 21.3 10.8 15.8 65 4.12 12.9 3.4
April 23.5 13.1 18.3 65 3.85 4.2 4.1
May 26.6 16.4 21.2 67 3.59 1.5 4.9
June 28.6 20.2 24.3 69 3.59 0.0 5.7
July 29.7 22.0 25.9 72 3.91 0.0 5.8
August 30.6 22.7 26.5 71 3.59 0.3 5.5
September 29.6 21.1 25.6 68 3.27 1.0 4.9
October 27.6 17.6 22.5 68 2.80 9.3 3.7
November 24.2 14.4 19.1 69 3.06 33.1 2.7
December 20.3 10.8 15.2 70 3.69 55.6 2.3
* Central Laboratory for Agriculture Climate
Map 2: Soil profiles location of the study area
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
161
Vol. 19 (1), 2014
Fieldwork:
Forty one soil profiles representing the study area were dug as shown in
map (2). The soil profiles were described in the field according to FAO (2006).
Soil samples were collected from the subsequent horizons according to
morphological variations. Water table samples were collected from some
profiles to characterize their chemical properties.
Laboratory analysis:
The collected soil samples were prepared to determine the electric
conductivity in dS/m and soluble cations and anions in saturation soil extract
and soil reaction (pH) in 1:2.5 suspension (Page et al., 1982). Sodium
adsorption ratio (SAR) was calculated according to Richards (1954). In addition,
total carbonates were determined by Collin,s calcimeter. Hydrometer method
(FAO, 1970) was used for the determination of silt and clay fractions, while wet
sieving was used for very fine sand fractions. The chemical characterizations of
water table samples were determined according to Page et al. (1982).
Soil erodibility factor (K):
Soil erodibility factor (K) represents both the susceptibility of soil to
erosion and the rate of runoff. It is estimated by the equation of Wischmeier and
Smith (1978) as follows:
K = 2.1* 10-6 (12-OM) (M 1.14) + 0.0325* (S-2) + 0.025*(P-3)
Where: OM= organic matter content M= (v. f .sand + silt) (100- clay)
S = structure index, which is coded as follows:-
(1) Very fine granular. (2) Fine granular.
(3) Medium to coarse granular. (4) Blocky, platy, or massive.
P= permeability class, which is coded as follows:-
(1) Rapid (Kh 50-16 cm/hr) (2) Rapid to Moderate (Kh 16-5 cm/hr)
(3) Moderate (Kh 5-1.5 cm/hr) (4) Moderate to slow ( Kh 1.5-0.5 cm/hr)
(5) Slow (Kh 0.5-0.16 cm/hr) (6) Very slow (Kh < 0.16 cm/hr)
Soil erodibility classified into three classes as follows:
(i) non erodible (< 0.10) (ii) moderately erodible (0.1-0.25)
(iii) sever erodible (0.25 - 0.40)
Wind erosivity factor (C):
Wind erosivity factor (C) refers to the effect of plants, soil cover, soil biomass,
and soil disturbing activities on wind erosion. It was calculated using the
equation adopted by FAO (1978):
C = V3 / 100 * (PET – P / PET)* n
Where: V= mean monthly wind speed at 2m height (m/sec)
P= precipitation (mm/day) PET= potential evapotranspiration (mm/day)
n= number of days in month
Rainfall erosivity factor (R):
Rainfall erosivity factor is the average of annual summation values in a
normal year's rain. It is calculated by the equation of Fournier (1960) as follows:
R = Pm
2 /P
where: Pm = mean monthly rainfall (mm) P =mean annual rainfall (mm)
Assessment of Land capability using ALES software:
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
162
Vol. 19 (1), 2014
Land capability was calculated by application of Agricultural Land
Evaluation System for arid region (ALES-Arid) as described by Abdel Kawy et
al. (2004). In this system, six soil classes (Table 2) were introduced, where four
factors (soil properties, environmental conditions, soil fertility and irrigation
water quality) were taken into consideration. Each factor is described as an
index value to give its status in the percentage form. These indices are
calculated using some empirical equations. The final index of land evaluation
(F.I.L.E) was calculated according to the following equation:
(F.I)
1
(W.I)
1
(E.I)
1
(S.I)
1
F.I.L.E 4
Where: S.I: soil index E.I: Environmental index
W.I: Water index F.I: Fertility index
Table 2: Land capability classes and ratings according to Storie (1964)
Class Degree of Capability Final value%
C1
C2
C3
C4
C5
C6
Excellent
Good
Fair (Moderate)
Poor (Marginal)
Very poor
Non-agricultural
100 – 80
79 – 60
59 – 40
39 – 20
19 – 10
< 10
RESULTS AND DISCUSSION
Soil characteristics:
Tables (3 and 4) show minimum, maximum and average values of main
chemical and physical properties and soil erodibility factor "K" in the surface and
subsurface horizons of the studied area. The data shows that the studied soils
are characterized by sandy loam to sandy clay loam texture in most profiles,
while, some profiles have a sandy clay texture. Concerning the surface
horizons, data in Table (3) reveal that sand, silt and clay contents vary from
47.80 to 82.50%, 2.8 to16.5% and 12.5 to 44.0% respectively. Data of total
soluble salts, as expressed by the electrical conductivity (dS/m), and
exchangeable sodium percentage (ESP) indicate that most of the studied soils
are characterized by their moderate to high EC and ESP values as shown in
Table (3). The EC values ranged between 0.77 and 10.12 dS/m, ESP values
being in the range of 1.73 to 18.26. pH values ranged between 7.51 and 8.33,
total carbonate content differ between 2.0 and 34.0%. Regarding the
subsurface horizons data in Table (4) show that, corresponding values were
47.7 to 80.7%, 2.8 to 27.5%, and 13.7 to 44.1% for sand, silt and clay
respectively, while it vary from 0.83 to 13.86 dS/m for EC, 1.02 to 36.32 for
ESP, 7.66 to 8.46 for pH and 1.0 to 46.0% for total carbonate. The high content
of soluble salts of these soils may be attributed to the saline nature of their
parent material, while the high CaCO3 content is due to the frequent occurrence
of shells. The results also, show that the water table depth ranged from 25cm to
120cm with mean value of about 79 cm. The coefficient of variation (C.V.) of the
soil depth (0.40) shows that the soil depth was low homogeneity in the study
area. For the surface layer the coefficient of variation show that pH and SP%,
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
163
Vol. 19 (1), 2014
were high homogeneity (CV=0.04 and 0.16 respectively). The lowest
homogeneity properties were for Ca and Cl (CV=0.92 and 0.94 respectively)
and for EC (CV=0.69) as shown in table (3). In the subsurface samples the
result shows that the high homogeneity was found for pH and SP% (CV=0.02
and 0.17 respectively) as in the surface layer. While, the highest values of
coefficient of variation (C.V.) accordingly lowest homogeneity were found for Na
and Cl (CV=0.91 and 0.98 respectively) as shown in table (4).
Table 3: The main the statistical parameters of soil characteristics and
Erodibility Factor K) for the surface samples
Min. Max. Range Medi. Mean S.D. Var. C.V
soil ◌
depth,cm
25.00 120.00 95.00 75.00 79.13 31.60 998.57 0.40
SP, % 43.30 95.00 51.70 73.30 73.26 11.93 142.30 0.16
pH 7.51 8.83 1.32 8.18 8.17 0.32 0.11 0.04
EC, dS/m 0.77 10.91 10.14 2.81 3.32 2.28 5.19 0.69
Ca, meq/L 1.00 34.00 33.00 5.00 7.44 6.83 46.64 0.92
Mg, meq/L 1.00 31.00 30.00 6.00 9.43 7.70 59.23 0.82
Na, meq/L 4.00 80.00 76.00 19.00 23.18 18.85 355.40 0.81
K, meq/L 0.12 1.20 1.08 0.40 0.46 0.28 0.08 0.61
HCO3,meq/L 1.00 10.00 9.00 2.50 3.66 2.80 7.84 0.77
Cl, meq/L 3.00 101.50 98.50 17.50 21.07 19.80 391.95 0.94
SO4, meq/L 0.30 56.66 56.36 13.66 15.57 13.62 185.46 0.87
ESP 1.73 18.26 16.53 8.12 8.74 4.90 24.04 0.56
CaCO3, % 2.00 34.00 32.00 18.50 15.60 9.90 98.00 0.64
O.M.% 0.45 1.82 1.37 0.36 0.52 0.48 0.23 0.92
Sand% 47.8 82.50 34.70 72.55 68.59 9.78 95.73 0.14
V.F. sand% 6.10 31.59 25.49 13.58 15.38 8.03 64.55 0.52
Silt% 2.80 16.500 13.70 8.20 8.81 3.10 9.60 0.35
Clay% 12.50 44.00 31.50 19.25 22.61 8.44 71.16 0.37
Erodibility(K 0.088 0.112 0.024 0.099 0.100 0.006 0.001 0.061
S.D=Standard deviation, C.V=Coefficient of variation=Standard deviation/Mean, var.= variance
Table 4: Soil characteristics and the main statistical parameters for the
subsurface samples
Min. Max. Range Medi. Mean S.D. Var. C.V
SP, % 49.95 98.00 48.05 72.05 73.23 12.13 147.12 0.17
pH 7.66 8.46 0.80 8.02 8.02 0.17 0.03 0.02
EC, dS/m 0.83 13.86 13.03 3.01 3.77 2.69 7.24 0.71
Ca, meq/L 2.00 25.00 23.00 4.50 7.27 6.17 38.03 0.85
Mg, meq/L 1.55 30.50 28.95 7.75 10.58 7.85 61.68 0.74
Na, meq/L 2.85 131.50 128.65 17.00 26.28 23.97 574.75 0.91
K, meq/L 0.11 1.33 1.22 0.47 0.52 0.29 0.08 0.55
HCO3,meq/L 1.00 6.00 5.00 2.50 2.71 1.53 2.33 0.56
Cl, meq/L 4.50 123.50 119.00 14.75 20.95 20.54 421.79 0.98
SO4, meq/L 2.58 77.60 75.03 14.97 21.51 19.02 361.82 0.88
ESP 1.02 36.32 35.21 7.69 8.75 6.23 38.86 0.71
CaCO3, % 1.00 46.00 45.00 18.50 16.85 12.54 157.20 0.74
O.M.% 0.29 0.62 0.33 0.32 0.39 0.30 0.65 0.76
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
164
Vol. 19 (1), 2014
Sand% 47.70 80.70 33.00 69.70 67.82 7.33 53.66 0.11
V.F. sand% 6.00 28.56 22.56 11.38 14.25 7.13 42.36 0.50
Silt% 2.80 27.50 24.70 11.00 11.66 4.90 23.98 0.42
Clay% 13.70 44.10 30.40 19.20 20.52 6.82 46.56 0.33
Water table analysis:
Data in table (5) shows the values of some chemical properties of water
table collected from the studied profiles at Abis region. These data indicate that
the ECw and SAR vary from 1.19 to 35.80 and from 3.16 to 47.18 respectively,
while the pH values ranged between 7.76 and 8.49.
Table 5: Some chemical properties for the water table in the study area
P. ECw pH
Cations (meq/L) Anions (meq/L)
No. dS/m Ca Mg Na K CO SAR 3 HCO3 Cl SO4
4 12.81 7.85 12.5 37.5 86.0 1.3 4.0 6.0 117.5 9.8 17.2
6 5.74 7.88 6.0 16.0 44.0 1.2 5.0 5.0 15.0 12.2 13.3
7 6.24 8.04 3.0 22.0 40.0 0.4 5.0 5.0 51.0 4.4 11.3
8 3.89 8.27 6.0 11.0 28.0 0.9 2.0 3.0 40.0 0.9 9.6
9 5.31 7.86 5.0 11.0 37.0 1.0 2.0 8.0 41.5 2.5 13.1
10 1.99 8.46 2.0 5.0 16.0 0.6 3.0 8.0 9.0 3.6 8.6
11 35.80 8.23 18.0 74.0 320 3.8 6.0 7.0 387 15.8 47.2
12 7.70 8.08 12.0 38.0 41.0 1.2 0.0 3.0 55.0 34.2 8.2
13 3.21 7.91 5.0 8.0 25.0 0.10 3.0 2.0 22.5 11.6 9.8
14 1.86 7.93 7.0 2.0 12.8 0.2 0.0 3.0 11.0 7.9 6.0
15 1.19 8.37 2.0 5.0 6.0 0.2 2.0 3.0 6.5 1.7 3.2
19 2.56 8.49 3.5 9.0 25.0 0.4 0.0 3.0 17.0 17.9 10.0
20 5.94 7.91 12.0 23.0 36.0 0.3 2.0 2.0 28.5 38.8 8.6
22 2.56 7.79 8.0 12.0 10.0 0.1 0.0 2.0 8.5 19.6 3.2
29 1.66 8.17 2.0 8.0 8.0 0.7 0.0 2.0 8.0 8.7 3.6
31 6.17 8.01 12.0 18.0 43.0 0.5 0.0 4.0 33.5 35.6 11.1
36 8.27 7.91 20.0 22.0 43.0 0.2 0.0 4.0 41.5 39.7 9.4
38 4.11 7.83 8.0 10.0 25.0 0.6 0.0 3.0 25.0 15.6 8.3
40 5.63 8.00 12.0 16.0 24.0 0.5 0.0 3.0 33.5 15.9 6.4
42 4.55 7.76 4.0 16.0 27.5 0.5 1.0 1.0 27.5 18.5 8.7
Soil Erodibility Factor (K):
Table (3) show summary of statistical parameter for soil erodibility factor
"K" in the studied soil. The values of soil erodibility factors "K" ranged between
0.088 - 0.112. Most of the studied profiles belong to class 2 which represent
soils that are moderate erodible (K values ranged between 0.1 and 0.25), while
some of the studied profiles belong to class 1 which represent soils that non
erodible (K values < 0.1). The coefficient of variation (C.V.) of the soil erodibility
factor "K" was 0.061 which reflect high homogeneity in the study area.
Relation between soil erodibility factor and soil properties:
Regarding the correlation coefficients matrix between soil erodibility
factor and soil properties are given in Table (6). The K- values are strongly
positively correlated with silt content (r =0.68). Soils having high silt content are
the most erodible one. They are easily detached; tend to crust and produce high
rates of runoff (Wischmeier et al., 1971). The positive correlation of K with silt
values are in agreement with those of Wischmeier and Mannering (1969),
Romkens et al. (1977), and El-Menshawy et al. (2005), but disagree with
findings of El-Asswad and Abufaied (1994) and El-Menshawy et al. (1997).
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
165
Vol. 19 (1), 2014
Data in table (6) also shows that the K- values are positively correlated
with very fine sand content (r=0.48) which agree with findings of Wischmeier et
al. (1971) Romkens et al. (1977), Wischmeier and Smith (1978), Kukal et al.
(1991) and El Menshawy et al. (1997). The positive correlation of K values with
silt and very fine sand data are attributed to the easiness of detaching of these
particles in comparison with clay and to their transferability in comparison with
the coarser particles. The K- values have moderate positive correlation with
sand percent (r=0.33). These results are in agreement with those obtained by
Wischmeier et al. (1971), Wischmeier and Smith (1978), Kukal et al. (1991) and
El Menshawy et al. (1997).
In contrast the K values are negatively correlated with clay content (r =
0.51) and this may be attributed to the hardness of detaching of these particles
due to aggregation. Also, the K values have low negative correlation with %
CaCO3 (r = 0.21) this may be attributed to the presence of calcium carbonate as
of cementing materials for soil particles which reduce the susceptibility of the
soil to detachment. Also, the K- values were negatively moderate correlated
with percent of organic matter (r=0.31) since increasing organic matter improves
the permeability of soil surface and decreases runoff. Also organic matter and
clay act as cohesion factors and resist erodibility (Wischmeier and Mannering,
1969). These results are in accordance with those obtained by Meyer and
Harmon (1984) and El-Asswad and Abufaied (1994). Generally, No significant
correlation was found between K values and pH, EC or ESP values.
The above discussions indicated that the soil becomes less erodible with
the decrease in silt fraction, regardless of the corresponding increase in the
sand fraction or the clay fraction. However, percentages of silt, clay and sand
must be considered in relation to other physical and chemical properties.
Table 6: Correlation matrix of different soil properties in the study area
pH ECe ESP CaCO3 O.M VFS. Sand Silt Clay K
pH 1.00
ECe -0.20 1.00
ESP 0.10 0.69 1.00
CaCO3 -0.13 -0.06 0.04 1.00
O.M 0.18 -0.43 -0.43 -0.58 1.00
V.F.S. 0.22 0.22 0.41 -0.08 -0.25 1.00
Clay 0.26 -0.25 -0.28 0.17 -0.51 0.56 1.00
Silt 0.05 -0.20 -0.10 0.04 0.64 -0.4 0.29 1.0
Sand -0.24 0.28 0.05 -0.16 0.06 -0.4 -0.95 -0.6 1.00
K -0.17 0.01 -0.06 -0.21 -0.31 0.48 0.33 0.68 -0.51 1.0
Wind Erosivity Factor "C":
Wind erosivity factor (C) varies greatly with location because it affects by
soil cover, soil biomass, and soil disturbing activities as well as velocity of wind.
The C monthly values in the studied areas ranged between 3.33 and 18.28 as
shown in Table (7). The annual value of C was 134.5, this relatively high value
may be attributed to that the increasing of wind velocity in this area as shown in
table (7). The highest values were observed in March through August, this may
be due to the absence of rainfall in these months. El-Menshawy et al. (2005)
studied the wind erosivity factor in some soils of southeast El-Manzala Lake and
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
166
Vol. 19 (1), 2014
reported that the total value of C was 127.05, this relatively high value may be
attributed to that most of these soils are non cultivated.
Rainfall Erosivity Factor (R):
Table (7) shows the variability of rainfall erosivity factor "R" throughout
2006 season. The annual rainfall erosivity factor "R" for the studied area is low
(41.035) due to the relative dry climate. The monthly rainfall erosivity factor "R"
is related to the amount of received rain for each month. Approximately 90
percent of the erosivity occurs in the months of January, February, November
and December in the studied area. The highest values were in January (15.11)
and December (15.50), whereas the lowest values appeared in June, July and
August.
Table 7: wind erosivity factor (C) and rainfall erosivity
factor (R) of the studied area
Month C R
January 3.637 15.115
February 11.808 3.548
March 18.283 0.835
April 16.571 0.088
May 13.726 0.011
June 13.868 0.000
July 17.872 0.000
August 13.843 0.000
September 10.440 0.005
October 6.034 0.434
November 5.089 5.495
December 3.331 15.503
total 134.500 41.035
Land evaluation and land use planning
Land capability classes:
The ALES Model (Agricultural Land Evalution System) provides
prediction for general land use capability for a broad series of possible uses
(Abdel Kawy et al., 2004). Evaluation results from the application of ALES
model on the study area indicate that the capability of most area belongs to
Class 2, which means soils are good for agriculture, regardless to the existing
management practices. Only C2aw, kh and C2aw are the land capability
subclasses which recognized in the study area which indicates that the main
limiting factors are available water and/or hydraulic conductivity. Limited areas
belong to class 3 which reflects fair or moderate capability. This class (C3)
divided to four land capability subclasses. These Subclasses are C3 aw, ece;
C3 aw, kh, sd; C3 aw, sd and C3 aw, sd, ece with available water (aw),
hydraulic conductivity (kh), soil depth (sd) and soil salinity (ece) as limiting
factor.
Soil suitability classes for specific uses:
The ALES model was used to predict soil suitability for some common
crops cultivated in the study area including: wheat, maize, alfalfa, cabbage,
J. Adv. Agric. Res. (Fac. Agric. Saba Basha)
167
Vol. 19 (1), 2014
cotton, fababean, onion, pea, rice, sorghum, tomato and watermelon. Data of
soil suitability class and subclasses are presented in Table (8). These data
indicate that most of the studied soils are suitable (class 2) for wheat, maize,
cabbage, cotton, fababean, pea, sorghum, tomato, watermelon. Some of the
studied soils are highly suitable (class 1) for alfalfa, onion and rice, regardless
to the existing management practices. However, limited areas belong to class 3
which reflect moderately degree of soil suitability or class 4 which reflect weak
or marginal degree of soil suitability for some of the traditional tested crops.
Regarding the subclasses, data show that the main limiting soil properties in
most of the studied soils are hydraulic conductivity (kh), salinity (ece), saturation
percent (sp), soil depth (sd) and exchangeable sodium percent (esp) as shown
in Table (8).
Table 8: Soil suitability classes for each crop of some selected profiles in
the study area
p. No wheat maize alfalfa fababean onion pea
5 S2 t kh S2 S2 kh S2 S2 S2
7 S2t kh S2 kh S2 kh S3 ece S3 ece kh S3 ece
10 S2 S2kh S2 kh S2 S2 S2
14 S2t kh S2 S2 kh S2 S2 kh S2
17 S2 S2 kh S1 S3 kh S1 S2 kh
22 S2t kh S2 S2 kh S2 S2 kh S2
25 S2 S2 kh S1 S3 kh S1 S2 kh
29 S2 t kh S2 S2 kh S2 S2 kh S2
34 S1 S2kh S1 S2 kh S1 S2 kh
37 S2 S2kh S1 S4 sd kh S2 S2
40 S2t kh S3 ece sp S2 kh S4 ece esp S4 ece kh S4 ece
42 S2 S2 kh S1 S4 sd kh S2 S2
Table 8: Cont.
p. No rice cotton sorghum watermel. cabbage tomato
5 S2 t S2 sd S2 S2 S2 S2
7 S2 t S4 sd S2 S2 S2 S2
10 S2 t S2 sd S2 S1 S1 S2
14 S2 t S2 sd S2 S2 S2 S2
17 S1 S2sd kh S2 kh S2 kh S2 kh S2 kh
22 S2 t S2 sd S2 S2 S2 S2
25 S1 S2 sd kh S2 kh S2 kh S2 kh S2 kh
29 S2 t S2 sd S2 S2 kh S2 S2
34 S1 S2 kh S2 kh S1 kh S2 kh S2 kh
37 S1 S2 kh S2 kh S2 kh S2 kh S2 kh
40 S3 ece t kh S4 sd S3 esp S3 ece esp S3 esp S3 esp
42 S1 S2 kh S2 kh S2 kh S2 kh S2 kh
View on SCiNiTO
