Journal of Applied Science Studies - Ozean Publications

Volume 3, Issue 1
March 2010
Spectra out of Oxygen and Ozone in Dielectric Barrier
Discharge.
M. A. HASSOUBA and N. DAWOOD
Extension Mechanisms Influencing The Adoption Of Sprinkler
Irrigation System In Iran
SEYED JAMAL F.HOSSEINI, YOSRA KHORSAND and SHABALDEEN
SHOKRI
Geoelectric Assessment Of Groundwater Prospect And
Vulnerability Of Overburden Aquifers At Idanre, Southwestern
Nigeria
OMOSUYI, G.O.
Comparative vegetative and foliar epidermal features of three
Paspalum L. species in Edostate, Nigeria.
E.A OGIE-ODIA, A.I MOKWENYE, O. KEKERE and O. TIMOTHY
Digital Moulding of the Solicitations within the Dielectric of
the Transformers and the Evaluation of Life Cycle of the
Insulation Systems
MARIUS-CONSTANTIN POPESCU and CRISTINEL POPESCU
A disconnect congestion detection from TCP to improve the
robustness
ISSA KAMAR and SEIFEDDINE KADRY
Analysis Of Microwave Signal Reception Using Finite
Difference Implementation. (A Case Study Of Akure – Owo
Digital Microwave Link In South Western Nigeria)
OTASOWIE P.O and UBEKU E.U.
Estimation of C factor for soil erosion modeling using NDVI in
Buyukcekmece watershed
AHMET KARABURUN
Journal of
Applied Science
Physiological properties studies on essential oil of Jasminum
grandiflorum L. as affected by some vitamins
RAWIA.A.EID, LOBNA, S. TAHA and SOAD , M.M. IBRAHIM
Effect of zinc and / or iron foliar application on growth and
essential oil of sweet basil (Ocimum basilicum L.) under salt
stress
H.A.H. SAID-AL AHL and ABEER A. MAHMOUD
Growth and yield of Foeniculum vulgare var.azoricum as
influenced by some vitamins and amino acids
S.F. HENDAWY and AZZA A.EZZ EL-DIN
Effect of water stress and potassium humate on the
productivity of oregano plant using saline and fresh water
irrigation
H.A.H. SAID-AL AHL and M.S. HUSSEIN
Influence of Foliar Application of Pepton on Growth, Flowering
and Chemical Composition of Helichrysum bracteatum Plants
under Different Irrigation Intervals.
SOAD , M.M. IBRAHIM, LOBNA, S. TAHA and M.M. FARAHAT
Permeability and Porosity Prediction from Wireline logs Using
Neuro-Fuzzy Technique
WAFAA EL-SHAHAT AFIFY and ALAA H. IBRAHIM HASSAN
Response of vegetative growth and chemical constituents of
Schefflera arboricola L. plant to foliar application of inorganic
fertilizer (grow-more) and ammonium nitrate at Nubaria.
MONA, H. MAHGOUB, EL-QUESNI, FATMA E.M. and MAGDA,M.
KANDIL
Statistical Modelling For Outlier Factors
Ahmet KAYA

OZEAN JOURNAL of
APPLIED SCIENCE
A PEER REVIEVED INTERNATIONAL JOURNAL
----------------------------------------------------------------------------------------------------------------------------------------------
Volume 3, Issue 1, March 2010
ONLINE ISSN 1943-2542 PRINTED ISSN: 1943-2429
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Gerald S. Greenberg, Ohio State University, USA
Hakki Yazici, Afyon Kocatepe University, Turkey
Hayati Akyol, Gazi University, Turkey
Hayati Doganay, Ataturk University, Turkey
Laurie Katz, Ohio State University, USA
Lisandra Pedraza, University of Puerto Rico in
Rio Piedras, Puerto Rico
Lutfi Ozav, Usak University, Turkey
Managing Editor
Ali Ozel, Dumlupinar University
Publication Coordinator
Taskin Inan, Dumlupinar University
Editorial Board
Mihai Maxim, Bucharest University, Romania
Ibrahim Atalay, Dokuz Eylul University, Turkey
Ibrahim S. Rahim, National Research Center, Egypt
Janet Rivera, NOVA University, USA
Ramazan Ozey, Marmara University, Turkey
Samara Madrid, Northern Illinois University, USA
Samia Abdel Aziz-Ahmed Sayed, National Research
Center, Egypt
Web: http://www.ozelacademy.com E-mail: editorejes@gmail.com
Copyright © 2008 Ozean Publication, 2141 Baneberry Ct. 43235, Columbus, Ohio, USA

Journal of Applied Sciences 3(1), 2010
OZEAN JOURNAL of
APPLIED SCIENCE
A PEER REVIEVED INTERNATIONAL JOURNAL
---------------------------------------------------------------------------------------------------------------------------------
Volume 3, Issue 1, March 2010
ONLINE ISSN 1943-2542 PRINTED ISSN: 1943-2429
---------------------------------------------------------------------------------------------------------------------------------
Spectra out of Oxygen and Ozone in Dielectric Barrier Discharge.
M. A. HASSOUBA and N. DAWOOD
Extension Mechanisms Influencing The Adoption Of Sprinkler Irrigation System In Iran
SEYED JAMAL F.HOSSEINI, YOSRA KHORSAND and SHABALDEEN SHOKRI
Geoelectric Assessment Of Groundwater Prospect And Vulnerability Of Overburden Aquifers At
Idanre, Southwestern Nigeria
OMOSUYI, G.O.
Comparative vegetative and foliar epidermal features of three Paspalum L. species in Edostate,
Nigeria.
E.A OGIE-ODIA, A.I MOKWENYE, O. KEKERE and O. TIMOTHY
Digital Moulding of the Solicitations within the Dielectric of the Transformers and the Evaluation of
Life Cycle of the Insulation Systems
MARIUS-CONSTANTIN POPESCU and CRISTINEL POPESCU
A disconnect congestion detection from TCP to improve the robustness
ISSA KAMAR and SEIFEDDINE KADRY
Analysis Of Microwave Signal Reception Using Finite Difference Implementation. (A Case Study Of
Akure – Owo Digital Microwave Link In South Western Nigeria)
OTASOWIE P.O and UBEKU E.U.
Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed
AHMET KARABURUN
Physiological properties studies on essential oil of Jasminum grandiflorum L. as affected by some
vitamins
RAWIA.A.EID, LOBNA, S. TAHA and SOAD , M.M. IBRAHIM
Effect of zinc and / or iron foliar application on growth and essential oil of sweet basil (Ocimum
basilicum L.) under salt stress
H.A.H. SAID-AL AHL and ABEER A. MAHMOUD
Growth and yield of Foeniculum vulgare var.azoricum as influenced by some vitamins and amino
acids
S.F. HENDAWY and AZZA A.EZZ EL-DIN

Journal of Applied Sciences 3(1), 2010
Effect of water stress and potassium humate on the productivity of oregano plant using saline and
fresh water irrigation
H.A.H. SAID-AL AHL and M.S. HUSSEIN
Influence of Foliar Application of Pepton on Growth, Flowering and Chemical Composition of
Helichrysum bracteatum Plants under Different Irrigation Intervals.
SOAD , M.M. IBRAHIM, LOBNA, S. TAHA and M.M. FARAHAT
Permeability and Porosity Prediction from Wireline logs Using Neuro-Fuzzy Technique
WAFAA EL-SHAHAT AFIFY and ALAA H. IBRAHIM HASSAN
Response of vegetative growth and chemical constituents of Schefflera arboricola L. plant to foliar
application of inorganic fertilizer (grow-more) and ammonium nitrate at Nubaria.
MONA, H. MAHGOUB, EL-QUESNI, FATMA E.M. and MAGDA,M. KANDIL
Statistical Modelling For Outlier Factors
Ahmet Kaya
Web: http://www.ozelacademy.com E-mail: editorejes@gmail.com
Copyright © 2008 Ozean Publication, 2141 Baneberry Ct. 43235, Columbus, Ohio, USA
A peer revieved international journal
ONLINE ISSN 1943-2542 PRINTED ISSN: 1943-2429
http://ozelacademy.com/ojas.htm

Journal of Applied Sciences 3(1), 2010

Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Spectra out of Oxygen and Ozone
in Dielectric Barrier Discharge.
M. A. Hassouba* and N. Dawood
Applied Physics Dept., Faculty of Applied Sciences, Taibah Univ., KSA.
*E-mail address for correspondence: hassouba@yahoo.com
________________________________________________________________________________________
Abstract: Dielectric-barrier discharges (DBD) are very attractive for industrial applications because they can
provide nonequilibrium plasma conditions at about atmospheric pressure. DBD is an excellent source of ideal
energetic electrons with 1–10 eV and high density. Its unique advantageous is to generate low excited atomic
and molecular species, free radicals and excimers with several electron volt energy.Spectra out of ozone
synthesis system, in dielectric barrier discharge (DBD) using oxygen gas, have been detected in the range of 300
to 400 nm. The dependence of spectral intensity on the discharge voltage and the oxygen pressure has been
studied. The half-width of the detected lines was found to be within 20 A o approximately. Spectroscopic
technique is a well-known technique for the measurement of the mean electron temperature in the gas discharge.
The electron temperature within the microdischarge has been estimated by using the relative intensity of the line
to line ratio technique of the identified spectral lines. An average mean electron temperature of 3.6 eV was
obtained and it has been found to be insensitive to the gas pressure variation.
Key Words:- Dielectric Barrier Discharge, Ozone Spectra, Electron Temperature
measurements ,Plasma Spectroscopic Models.
__________________________________________________________________________________________
INTRODUCTION
Dielectric-barrier discharges (silent discharges) combine the ease of atmospheric pressure operation with
nonequilibrium plasma conditions suited for many plasma chemical processes. In most gases at this pressure the
discharge consists of a large number of randomly distributed short-lived microdischarges.. Traditionally mainly
used for industrial ozone production, dielectric-barrier discharges have found additional large volume
applications in surface treatment, high-power CO2 lasers, excimer ultraviolet lamps, pollution control and, most
recently, also in large-area flat plasma display panels. Future applications may include their use in greenhouse
gas control technologies [1].
Many articles [1-7] have been published about the optical emission from DBDs by using other gases such as Xe,
Cl, Ar, He. The dielectric barrier discharges (silent discharge) offer the possibility of building large area and
high intensity UV-sources, for industrial processing.
Burm [8] investigate two plasma sources, an air plasma torch and a nitrogen dielectric barrier discharge. Optical
emission spectroscopy is used to determine the heavy particles and the vibration (electron) temperatures for the
two plasma sources. The two temperatures are measured to obtain an estimation of the deviation from local
thermal equilibrium and to compare the two source configurations which are used for surface treatment.
The present work is originally directed toward the design of a cheap and simple DBD ozonizer [9]. The spectra
out from this system have been detected and the electron temperature of the discharge is determined using the
spectroscopic method.
1

The experiment setup consists of two parts
1- Discharge Cell
Ozean Journal of Applied Sciences 3(1), 2010
EXPERIMENTAL SETUP
In the present work, the cell of the discharge consists of coaxial electrodes. The inner electrode has been made of
brass rod with radii 0.5 cm. The outer electrode is made of graphite coated on the outer wall of a Silica glass
tube, which has been used as a dielectric material and as a window for the study output radiation of the system.
The gap space between the inner electrode and the inner wall of glass tube was 0.175 cm. The length of the
reactor region was 30 cm and the pressure of gas could be controlled by the needle valves, which enable
controlling the flow rate in the system. The pressure inside the discharge tube was measured by using manometer
and gauge model P200-H (RS-497-606) which enables the pressure to be measured in the range from 1 to 2.25
bars within 1 mbar experimental error. The flow rate of oxygen (98.7% purity) has been measured by using a
flow meter (Cole Parmer). Figure (1) shows the present DBD system.
The electric circuit of the discharge is also shown in Fig. (1) which consists of AC power supply (0�220 V , 50
Hz) connected to a high voltage transformer with a variable output, 0�20 kV. In order to measure the applied
potential across the discharge cell, a potential divider has been built, which consists of two resistors in the ratio
of (1/450) connected in parallel with the discharge cell. Only the potential difference across the lower part of the
divider is measured, and then the actual applied potential across the cell can be estimated.
2- The Spectroscopic Setup
Figure (2) shows the present spectroscopic setup, which is controlled by PC computer. The spectroscopic
devices of type Oriel, consists of mono-chromator 25 cm path length and has a diffraction grating of 1200
mm. The relative intensity of lines has been measured by using photo-multiplier (PMT) capable of measuring the
spectra in the range of 185–650 nm. PMT connect to readout system for reading the intensity of light, the readout
capable of current amplification up to 10 9 A. PC computer with interface and stepper motor driver has been used
to control in the motion of the grating, so that it enables to variation of the wavelength one angstrom by step.
1- Spectra of Oxygen in Silent Discharge
RESULTS AND DISCUSSION
The only detectable spectra have been found in the region from 300 to 400 nm. The absorption cross section of
ozone is known to be high in two ranges; Hartly band; 160 - 320 nm and Chappuis band; 420-730 nm [10].
Therefore, no spectra emission could be seen or measured in these two ranges.
Typical spectrums of DBD at different pressure and discharge voltages are shown in Figs. (3-a, b and c).
It is noticed that, the emitted lines could not be detected until the applied potential reached the onset potential
where the ozone starts to build up, which depends on the working gas pressure p, inner electrode distance d and
the type of the dielectric material and its thickness T.
e= e/d =
constant * V/p), where q is the electron charge, V is the applied voltage, d is the gap space distance between the
e is the mean free path of the electrons.
Figure (4) shows a typical graph for the present relation between (VB) and (p*d) in oxygen (right hand side of the
Paschen Curve) under the given conditions where VB is increasing linearly with (p*d). Also as a ozone
composition is a chemical reaction it is therefore the current is the main parameter which controls the number of
ozone reactions that take place in this system. Figure (5) shows the relation between the voltage and the current
flowing in the system at different gas pressures.
2

Ozean Journal of Applied Sciences 3(1), 2010
When the pressure is kept constant the mean free path is constant but if the applied voltage is increased, the
number of electrons capable on excitation and ionization increases. Therefore, it is expected in this case that the
intensity of the emitted lines will increase with the applied potential, [at p=1.25 bar]. Also, when the pressure
increases the collision frequency increases, therefore the intensity of the emitted lines increases.
It can be noticed that the half width of any of these lines is less than 2 nm, which could be, used more or less as a
monochromatic radiation [10]. Such radiation could be easily generated on a large scale for the purpose of
industrial applications [11].
2- Mean Electron Temperature.
Spectroscopic technique is a well-known technique for the measurement of the mean electron temperature in the
gas discharge.
In the present work not all the radiation lines has been identified, although pure oxygen of 98.7% purity has
been used and unfortunately only few lines of them has been identified and were therefore used for the deriving
of the electron temperature.
The line to line relative intensity ratio technique (for the same ionization stage) is used to determine the
mean electron temperature, Te, in silent discharge, according to the following equation [11]:-
Where: -
K is the Boltzmann`s constant, Te is the mean electron temperature, I, I’ are the relative intensity of the two
given lines, �, � ` are the wavelengths of two lines, g, g ` are the statistical weight of two lines, f, f ` are the
oscillator strength of two lines, E` is the excitation energy for the higher ionization stage and E is the excitation
energy for the lower ionization stage. The lines identified are of the wavelengths tabulated in Table (1), together
with their values of the above parameters [11]. The intensities of the identified lines were taken from the
measured discharge spectra in Fig. (3).
Wavelength (�)
A o
'
(E � E)
KTe
�
3 ' '
I�
g f
ln( )
' 3
I � gf
Table (1) The parameters of the selected lines used in the calculation.
Excitation
Energy (E)
Oscillator
Strength (f)
3
Statistical
Weight (g)
Product of
(f) x (g)
3709.5 45.07 eV 0.0747 1 0.0747
3754.7 36.29 eV 0.277 5 1.385
3911.96 28.71 eV 0.0326 4 0.130
3945 26.45 eV 0.113 4 0.452
In order to derive the mean electron temperature from the above equation two couples of lines have to be chosen
so that their wavelengths are very near to each other, and from the same stage of ionization.
The parameters of these couples of lines were substituted in the equation and hence the mean electron
temperature was calculated and has been tabulated below.
The results for the couple of lines of wavelengths 391.19 nm and 394.5 nm emitted from O + are shown in Table
(2). Also, the results of the couple of lines of wavelengths 370.95 nm and 375.47 nm, which emitted from O ++
are shown in Table (2).

Pressure
(bar)
Ozean Journal of Applied Sciences 3(1), 2010
Table (2) Shows the results for the couple of lines of wavelengths 391.19 nm
and 394.5 nm emitted from O + at different conditions.
Lines 391.19 nm and 394.5 nm Lines 370.9 nm and 375.47 nm
V (dis.)
18 kV
Electron
Temp.(Te)
V(dis.)
13.5 kV
Electron
Temp.(Te)
4
V (dis.)
18 kV
Electron
Temp. (Te)
V(dis.)
13.5 kV
Electron
Temp. (Te)
1.005 3.77 eV 3.6 eV 3.8 eV 3.4 eV
1.25 3.6 eV 3.7 eV 3.6 eV 3.2 eV
1.5 3.7 eV 3.7 eV 3.8 eV 3.4 eV
1.75 3.77 eV 3.8 eV 3.85 eV 3.4 eV
The mean electron temperature has been drawn as a function of gas pressure and applied voltage and is shown in
Fig. (6-a, b).
It can be noticed from Fig. (6-a, b) that the mean electron temperature in the dielectric barrier discharge is nearly
constant at a mean electron temperature is 3.6 eV and does not depend on the gas pressure.
CONCLUSION
The silent discharge is a non-equilibrium discharge which can be operated up to pressures of several bars. It is
industrially used on a large scale for the generation of ozone from air or oxygen. Ozone generators have a typical
power consumption ranging from some kilowatts to several megawatts. The main characteristic of the silent
discharge is that narrow discharge gaps of a few millimeter spacing are used and that at least one of the
electrodes is covered by an insulating layer. For this reason the silent discharge is also referred to as the
"dielectric-barrier discharge" (DBD).
The present work is originally directed toward the design of a cheap and simple DBD ozonizer.
It is noticed that, the emitted lines could not be detected until the applied potential reached the onset potential
where the ozone starts to build up, which depends on the working gas pressure p, inner electrode distance d and
the type of the dielectric material and its thickness T
Spectroscopic technique is a well-known technique for the measurement of the mean electron temperature in the
gas discharge.
It is concluded that, that the mean electron temperature in the dielectric barrier discharge is nearly constant at a
mean electron temperature is 3.6 eV and does not depend on the gas pressure.

Ozean Journal of Applied Sciences 3(1), 2010
FIGURES
Figure (1) shows the discharge Cell.
PC Computer Steeper Motor
P.S. for PMT
A/D Converter Readout Amp.
Figure (2) shows the spectroscopic setup.
5
PMT
Mono- Plasma
chromator Source

Intensity (Arb. Unit)
Silica
p=1.25 bar
V(dis.)=13.5 KV
300 320 340 360 380 400
Wavelength (nm)
Intensity (Arb. Unit)
Ozean Journal of Applied Sciences 3(1), 2010
3
2
2
1
1
CB
BA
0
300 320 340 360 380
Wavelength (nm)
400
6
B
Silica
p.=1.75 bar
V(dis)=18 kV
Figure (3-A, B and C) show the spectra of the discharge at different conditions and at T =0.15 cm, d
= 0.175 cm and L = 30 cm.

Ozean Journal of Applied Sciences 3(1), 2010
Figure (4) shows the relation between V (breakdown) and (pressure*gap space).
Figure (5) shows the relation between I(dis.) and V(dis.) at d=0.175 cm, L=30 cm and
T=0.15 cm
7

Ele.Temp.(eV)
Ele.Temp.(eV)
5
4
3
2
1
0
Ozean Journal of Applied Sciences 3(1), 2010
O(++)
3709.5 A
& 3754.7 A
0.75 1.00 1.25 1.50 1.75 2.00
Pressure (bar)
5
4
3
2
1 O(+)
3911.96
0
& 3954.7 A
0.75 1.00 1.25 1.50 1.75 2.00
Pressure (bar)
Figure-(6-A, B) show the Figure relation between (7-a, the b) electron show the temperature relation and between the pressure the at + electron 13.5 and temperature 18.0 KV an
at 13.5 and 18.0 KV
8
BA
AB

Ozean Journal of Applied Sciences 3(1), 2010
REFERENCES
A.A.Garamoon, F.F.Elakshar and A.M.Nossair, (1999), GEC 52 nd , Virginia, USA, 324.
A.Baulch, (1980), J. Phys. Chem. Ref. Data, 9, 296.
H.R.Griem, (1964), Plasma Spectroscopy , McGraw-Hill, New York., p.382..
Haile Lei, Yongjian Tang, Jun Li, and Jiangshan Luo, (2007), Appl. Phys. Lett. 91, 113119.
K.G. Kostov; R. Y. Honda; L.M.S. Alves and M.E. Kayama Braz, (2009), J. Phys., 39, 2.
K.T.A. Burm, (2005), Contrib. Plasma Phys., 45, 54.
M.P.Milden, (2001), J. of Phys. D: Appl. Phys. 34, L1.
Takaaki Tomai, Tsuyohito Ito and Kazuo Terashima, (2006), Thin Solid Films, 507 , 409.
U.Kogelschatz, (1997), J. de Physique IV, 7, C4-47 to C4-66.
U.Kogelschatz, (1997), ICPIG XXIII, Toulouse, France, 1.
Ulrich Kogelschatz², Baldur Eliasson and Walter Egli, (1999), Pure Appl. Chem., 71, 1819.
Young Sun Mok, (2005), XXVIIth ICPIG, Eindhoven, the Netherlands, 18-22 July, PP. 18.
9

Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Extension mechanisms influencing the adoption of sprinkler irrigation system in Iran
Seyed Jamal F.Hosseini*, Yosra Khorsand** and Shabaldeen Shokri***
*Islamic Azad University, Science and Research Branch Tehran, Iran
**Islamic Azad University, Birjand branch Birjand, Iran
***Department of Agricultural development Islamic Azad University, Science and Research Branch
Tehran, Iran
*Email address for correspondence: jamalfhosseini@yahoo.com
__________________________________________________________________________________________
Abstract: Horticultural producers were surveyed in order to explore their perception about the role of extension
mechanisms in adopting the sprinkler irrigation system in Iran. The methodology used in this study involved a
combination of descriptive and quantitative research. The total population for this study was 150 gardeners who
adopted the sprinkler irrigation system in Chenaran Township in Khorasan Razavi Province. Based on the
results of the mean score, respondents indicated that visiting extension agents in the service centers was the
most effective individual extension method main in helping them to adopt the sprinkler irrigation systems. It was
also reported from the findings of the study 45% of the variance in the perception of gardeners about the role of
extension mechanisms in adopting the sprinkler irrigation systems could be explained by visiting extension
agents in service centers, extension classes, visiting extension agents in the field and visiting sample farm.
Kewords: Extension Mechanisms, Sprinkler Irrigation System, Iran, Gardener.
__________________________________________________________________________________________
INTRODUCTION
World Bank predicted that by the year 2035, three billion people will live in the tough conditions because of
water shortage (World Bank, 2009). According to the Human Development Report, by the year 2080 climate
change would affect the life of many people throughout the world and more than 1.8 billion people would face
water shortages (UNDP, 2007, p.30).
Today, there are several major issues in connection with the water sector in developing and developed countries
which include: water cycle, quality of life, equality of water, sustainability and human rights (Sohail and Cavill,
2006). In Iran, the policy of government has been to increase agricultural production for various reasons, such as
price stability, improved per capita income and increased need for non-oil foreign exchange resources and this
trend has become an unavoidable reality for agricultural sector. Increasing agricultural production has resulted
in consumption of more water and there is no other way to change the amount of water used which is the
equivalent of 130 billion cubic meters a year unless to use water more efficiently and to adopt new methods of
irrigation.
Consumption of water by agriculture sector in Iran has always been an issue of concerns which caused by high
water losses in farm fields, farms inappropriate shape and size, lack of knowledge of farmers about making
optimum use of water, rapid destruction of water infrastructure, loss in quality of irrigation networks,
inappropriate methods of irrigation, irrigation efficiency and loss of water in irrigation systems (Keshavarz,
2000).
Omani et al (2009) citing Keshavarz, Heydari, and Ashrafi (2003) pointed out that the overall irrigation
efficiency in Iran ranges from 33 to 37%, which is lower than the average for both developing countries (45%)
and developed countries (60%).
11

Ozean Journal of Applied Sciences 3(1), 2010
Unfortunately, inefficient use of water in the past decades has nearly reduced more than 40 meter in
underground water level (Unit, 2005). Currently, the total water consumption is approximately 88.5 bm3, out of
which more than 93% is used in agriculture, while less than 7% is allocated to urban and industrial
consumption. Under the present situation 82.5 bm3 of water is utilized for irrigation on 7.5 million hectares of
land under irrigated agriculture (Ommani & Noorivandi, 2003).
In order to combat this problem, there is need for new technologies and methods to manage water more
efficiently especially in agricultural sector (Karami, Rezaei-Moghaddam, and Ebrahimi., 2006). On one hand a
more comprehensive water management is needed to achieve sustainable development and participatory
mechanism could accelerate this process (Guterstan, 2008). On the other hand the principle of sustainable
development is an essential imperative for the water industry which should be seen as an opportunity not a
limitation (Asheley et al. 2003).
Khorasan Razavi province is among regions in Iran with low rainfall. The amount of evaporation and
transpiration of rainfall in this province is very significant which is 2 to 3 times higher than the average in
country. According to the latest statistics, total volume of water consumption from surface water and
groundwater is 9261.8 million cubic meters and more than 8445 million cubic meters of this amount used in
agricultural sector.
The traditional methods of water management have many problems and the best option currently to use for
irrigating farms is sprinkler irrigation systems. The results of Study show that implementation of this irrigation
method resulted in decreasing rate of water consumption from 12,000 cubic meters in hectare to 6,200 cubic
meters (Vojdani, 2006). Despite, financial facilities which are allocated each year for farmers, the participation
of farmers has not reached to a satisfactory level.
Agricultural extension by its nature has an important role in promoting the adoption of new technologies and
innovations. Extension organizations have a key role in brokering between providers of technologies and
farmers. However, adopting is rarely instantaneous; the technology has to be taught and learned, adapted to
experience, and integrated into production. As is often the case with technological innovation, potential and
expectations can outpace reality (Bonati and Gelb, 2005).
Omani et al (2009) citing Evenson (1997) pointed out to this fact that agricultural extension and education as
achieving its highest economic impact and sustainability in agriculture by providing information to increase
farmers awareness, knowledge, adoption and productivity.
Therefore, understanding the extension mechanisms which would speed up the development and adoption of the
sprinkler irrigation system in the township of Chenaran in Khorasan Razavi Province was investigated in this
research.
MATERIAL AND METHODS
The methodology used in this study involved a combination of descriptive and quantitative research and
included the use of correlation, regression and descriptive analysis as data processing methods. The total
population for this study was 150 gardeners who adopted the sprinkler irrigation system. Data were collected by
using questionnaire and through interview schedules.
A series of in-depth interviews were conducted with some senior experts in the Department of Agriculture and
Power in the Khorasan Razavi Province to develop the questionnaire. The questionnaire included both openended
and fixed-choice questions. The open-ended questions were used to gather information not covered by the
fixed-choice questions and to encourage participants to provide feedback.
Content and face validity were established by a panel of experts consisting of faculty members at Islamic Azad
University, Science and Research Branch and some experts in the Departments of Agriculture and Power. A
pilot study was conducted with 25 specialists who had not been interviewed before the earlier exercise of
determining the reliability of the questionnaire for the study. Computed Cronbach’s Alpha score was 85.0%,
which indicated reliability of the questionnaire.
Independent variables in the study included extension mechanisms and personal characteristics of respondents.
The dependent variable in this research study were the adoption of the sprinkler irrigation system by gardeners...
For measurement of correlation between the independent variables and the dependent variable correlation
coefficients have been utilized and include spearman test of independence.
12

Ozean Journal of Applied Sciences 3(1), 2010
RESULTS
Table 1 summarizes the demographic profile and descriptive statistics of respondents. The results of descriptive
statistics indicated that average age of respondents was 46 years old and majority of respondents did not have
high school diploma. The study shows that average work experience was 19 years and the main occupation of
respondents was farming and gardening. Approximately 43 percent of respondents owned their lands and the
remainder either had a collective ownership or rented the land.
Respondents were asked to respond the question about role of water shortages in implementing sprinkler
irrigation system. As a result, 71 percent of respondents indicated that agricultural water shortage was the main
factor in the implementation of the irrigation system.
The perception of respondents about the sources which help them to acquire information about sprinkler
irrigation systems was displayed in Table 2. The highest mean refers to extension agents (mean=3.58) and the
lowest mean refers to experiment stations (mean=1.59).
The results of perception of respondents about the role of communication channels which would influence the
adoption of sprinkler irrigation systems by gardeners were displayed in Table 2. The results indicated that the
highest mean number refers to extension agents (mean=4.11) and the lowest mean number refers to relatives
(mean=1.67).
The respondents’ perception about the role of extension mechanisms in adopting the sprinkler irrigation systems
was displayed in Table 4. As can be seen from this table, the highest mean refers to visit by extension agents in
agricultural service centers (mean=3.18) and the lowest mean refers to extension workshops (mean=2.57).
Spearman coefficient was employed for measurement of relationships between perceptions of gardeners about
the role of extension mechanisms in adopting the sprinkler irrigation system as dependent variable. Table 5
displays the results which show that there was relationship between perception of respondents about the age,
visiting extension agents in the service centers, extension classes, visiting the sample farm and visiting extension
agents in the field and adopting the sprinkler irrigation system.
Table 6 shows the result for regression analysis by stepwise method. Independent variables that were
significantly related to perception of respondents about role of extension mechanisms in adopting the sprinkler
irrigation system were entered. The result indicates that 45% of the variance in the perception of gardeners
about the role of extension mechanisms in adopting the sprinkler irrigation systems could be explained by
visiting extension agents in service centers, extension classes, visiting extension agents in the field and visiting
sample farm.
CONCLUSION
The perception of gardeners about the role of extension mechanisms in adopting sprinkler irrigation system was
discussed in this article. As the regression analysis showed visiting sample farms, visiting extension agents in
the service centers and field and extension classes caused 45% of variance on the perception of respondents
regarding the role of extension mechanisms in adopting sprinkler irrigation systems. This result is consistent
with Okunade (2007) conclusion in which skill is better acquired through group contact methods. These
methods have the nature of practical demonstration which will help the clientele from desire stage through
conviction and probably into taking action. The individual contact method is considered to be important tool to
help farmers to adopt a new technology. This may be as a result of the nature of the methods of giving
information and deeper understanding of the innovation concerned.
Based on the results of the study by Chizari, etal. (1998) the majority of extension agents believed the result
demonstration were the most effective method for teaching their clientele. Result demonstrations are the
processes of showing farmers the impact of using a particular practice. The second most effective method
identified by extension agents was method demonstration. Method demonstrations typically occur after result
demonstrations.
Based on the results of the mean score, respondents indicated that visiting extension agents in the service centers
was the most effective individual extension method main in helping them to adopt the sprinkler irrigation
systems. The results demonstrated that respondents preferred individual teaching methods compared with group
13

Ozean Journal of Applied Sciences 3(1), 2010
and mass methods. Although all agents use a variety of teaching methods, agricultural agents generally tend to
use more individual methods than the other agents. Farm visits and on-farm demonstrations model the early
farm demonstration method of providing research-based recommendations to the local producer.
IMPLICATIONS
The perception of gardeners about the extension mechanisms in adopting the sprinkler irrigation system was
discussed in this article. The results demonstrated that visiting sample farms and face to face meetings with
extension agents in service center and farms are the most important mechanisms in helping gardeners in
adopting sprinkler irrigation systems. Successful adoption of this technology in Iran will depend on the
appropriate government support and the authorities should develop policies that would overcome the challenges
in adopting this method of irrigation.
In Iran like some of the developing countries, there is not a clear understanding about role of the new methods
of irrigation in sustainable water management in agriculture sector and policy makers have difficulty in
prioritizing the policies and strategies. In this regard, public involvement will enhance and accelerate the
adoption process.
REFERENCES
Ashley, R., Blackwood, D., Butler, D., Davies, J., Jowitt, P., & Smith, H. (2003). Sustainable decision making
for UK water industry. Engineering Sustainability, 1, 41-49.
Bonati, G., and Gelb, E. (2005) 'Evaluating internet for extension in agriculture.' In: gelb, B. And Offer, A.
(ed.), ICT in Agriculture: Perspectives of Technological Innovation, Paris: European Federation for
Information Technologies in Agriculture, Food and the Environment.
Chizari, M., Karbasioun, M., and Lindner, J.R. (1998). Obstacles facing extension agents in the development
and delivery of extension educational programs for adult farmers in the Province of Esfahan, Iran.
Journal of Agricultural Education, 1, 48-54.
Evenson, R. (1997). The economic contributions of agricultural extension to agricultural and rural development.
In Food and Agriculture Organization of the United Nations (eds.) Improving agricultural extension.
FAO. Rom. pp. 27–36.
Guterstam, B. (2008). Toward Sustainable Water Resource Management in Central Asia. [on-line]
Available:www.water.tkk.fi/English/wr/research/global/material/CA_chapters/02-CA_Waters-
Guterstam.pdf
Karami, E., Rezaei-Moghaddam, K., & Ebrahimi, H. (2006). Predicting sprinkler irrigation adoption:
Comparison of models. Journal of Science and Technology of Agricultural and Natural Resources, 1,
90–104.
Keshavarz, A. (2000). Recommendation on policies and programs about water and irrigation in Iran. Tehran:
Agricultural Extension Organization.
Keshavarz, A., Heydari, N., and Ashrafi, S. (2003). Management of agricultural water consumption, drought,
and supply of water for future demands. pp. 42–48. In: Proceedings of the Seventh International
Conference on the Development of Dryland, September 14–17, 2003, Tehran, Iran.
Okunade, E.O. (2007). Effectiveness of extension teaching methods in acquiring knowledge, skills and attitude
by women farmers in Osun State. Journal of Applied Science Research, 4, 282-286.
Ommani, A. R., Chizari, M., Salmanzadeh, C., & Hosaini, J. (2009). Predicting Adoption Behavior of Farmers
Regarding On-Farm Sustainable Water Resources Management (SWRM): Comparison of Models.
Journal of Sustainable Agriculture, 5, 595- 616.
Ommani, A.R., & Noorivandi, A. (2003). Water as food security resource (Crises and Strategies). Jihad Monthly
Scientific, Social and Economic Magazine, 255, 58–66.
14

Ozean Journal of Applied Sciences 3(1), 2010
Sohail, M., and Cavill, S. (2006). Ethics: making it the heart of water supply. Civil Engineering, 5, 11-15.
UNDP. (2007). Human Development Report 2007/2008. [on-line] Available: Http://hrd.undp.org
Vojdani, M. (2006). Assessing factors influencing the adoption of irrigation technologies by farmers in
Township of Bahar. Master Thesis in Agricultural Extension and Education, Tehran, Iran.
World Bank. (2009). Water Resource Management. [on-line] Available :
http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTWAT/0,,contentMDK:21630583~men
uPK:4602445~pagePK:148956~piPK:216618~theSitePK:4602123,00.html
Main Occupation Farming and Gardening (44%) Gardening (24.0%)
Age (year) Mean=46
Work Experience (Year) Mean=19
Educational level Secondary School (75%) Diploma (25%)
Amount of land owned (Hectares) Mean= 10.5
Table 1. Personal Characteristics of Respondents.
Sources Mean and Standard Deviation
15
Mean SD
Extension Agents 3.58 0.959
Rural Cooperatives 2.77 0.886
Agricultural Magazines 2.55 1.347
Agricultural Input suppliers 2.43 0.890
Local Leaders 2.31 1.056
Neighbors 2.13 0.824
Rural Service Centers 2.09 0.928
Relatives 1.67 0.864
Experiment Stations 1.59 1.783
Table 2. Means of respondents’ views about the sources which help them to acquire information about sprinkler
irrigation system (1=Too little; 5=Too much).

Ozean Journal of Applied Sciences 3(1), 2010
Communication Channels Mean and Standard Deviation
Extension Agents 3.80
Television 3.58
Visit the sample farm 3.09
16
Mean SD
1.069
0.959
0.963
Rural organizations 2.77 0.886
Printing Materials 2.63 1.178
Private Sector 2.41 0.881
Local Leaders 2.31 1.563
Radio 2.18 1.063
Neighbors 2.13 0.824
Researchers 2.08 0.900
Relatives 1.67
0.864
Table 3. Means of respondents’ views about the role communication channels which influence the adoption of
sprinkler irrigation systems by gardeners (1=strongly disagree; 5=strongly agree).
ٍ Extension Mechanisms Mean and Standard Deviation
Visiting extension agents in service centers 3.18
Extension Classes 3.17
Visit the sample farm 2.96
Mean SD
1.032
1.042
1.021
Extension films 2.81 1.024
Visiting extension agents in the field 2.78 1.041
Workshops 2.57 1.057
Table 4. Means of respondents’ views about the role of extension mechanisms in adopting sprinkler irrigation
systems by gardeners (1=strongly disagree; 5=strongly agree).
Independent variables Dependent variable Gardeners
r Sig.
Age Adoption of Sprinkler Irrigation System 0.535 0.000**
Workshop Adoption of Sprinkler Irrigation System 0.050 0.550
Visiting Extension Agents in service centers Adoption of Sprinkler Irrigation System 0.329 0.021*
Working Experience Adoption of Sprinkler Irrigation System 0.519 0.000**
Extension Classes Adoption of Sprinkler Irrigation System 0.315 0.025*
Visiting the Sample Farm Adoption of Sprinkler Irrigation System 0.327 0.020*
Extension films Adoption of Sprinkler Irrigation System 0.094 0.562
Visiting extension agents in the field Adoption of Sprinkler Irrigation System 0.306 0.031*
**p

Ozean Journal of Applied Sciences 3(1), 2010
B Beta T Sig.
Constant 0.402 ------- 0.792 0.434
Visiting extension agents in service
centers
0.414 0.386 4.327 0.000
Extension classes 0.226 0.284 3.086 0.004
Visiting extension agents in field 0.197 0.240 2.963 0.005
Visiting the sample farm 0.210 0.292 2.750 0.010
R 2 =0.45
Table 6. Multivariate Regression Analysis (adopting the sprinkler irrigation system as dependent variable).
17

Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Ozean Journal of Applied Sciences 3(1), 2010
GEOELECTRIC ASSESSMENT OF GROUNDWATER PROSPECT AND
VULNERABILITY OF OVERBURDEN AQUIFERS AT IDANRE, SOUTHWESTERN
NIGERIA
OMOSUYI, G.O.
Department of Applied Geophysics, Federal University of Technology,
P.M.B. 704, Akure, Nigeria
E-mail address for correspondence : droluomosuyi@yahoo.com
___________________________________________________________________________________________
Abstract: Idanre and environs, southwestern Nigeria, is characterized by extensive outcrops of crystalline
basement rocks, largely of granite gneiss petrology. Inadequate municipal water supply, coupled with
hydrogeologically difficult nature of the terrain, individuals and corporate bodies indiscriminately sink tube wells
and boreholes within the unconsolidated overburden materials, with glaring lack of concerns for the vulnerability
status of aquifers, and possible environmental risk. Sixty five (65) Schlumberger depth sounding data from the
area were interpreted in order to assess the groundwater prospect, focused on the thickness of the unconsolidated
materials overlying the crystalline bedrock. The resistivity parameter of the geoelectric topmost layer across the
area was also used to assess the vulnerability of the underlying aquifers to near-surface contaminants. The
thickness of the unconsolidated overburden varies from 0.5m to 15.8m, where about 81.5% falls within the 1-5.9m
brackets. This shows that unconsolidated materials are generally not significantly thick and hence of apparently
low groundwater prospect. The topmost geoelectric layer has resistivity mostly within the range of 1-100 Ohm-m
(77%) across the area. Resistivity values within these brackets tend to indicate silt or clay sequence, which can
constitute effective protective geologic barriers for the underlying aquifers. This suggests that aquifers within the
unconsolidated overburden at Idanre are mostly capped by impervious/semi-pervious materials, geologically
protecting the underlying aquifers from near-surface contaminants.
___________________________________________________________________________________________
INTRODUCTION
Groundwater has become immensely important for human water supply in urban and rural areas in developed and
developing nations alike. Despite its importance, there is gross inadequate supply of water at Idanre, the study
area.
Idanre lies within the Precambrian basement complex terrain of southwestern Nigeria (Rahaman, 1988).The
crystalline basement rocks are extensively exposed in the area (Ocan, 1991). In basement terrains, groundwater is
generally believed to occur within the overlying unconsolidated material derived from the in-situ weathering of
rocks, and the fractured/faulted bedrock (Clark, 1985; Jones, 1985; Acworth, 1987; Bala and Ike, 2001). Since the
intrinsic resistivity of the unconsolidated overburden and that of the crystalline basement differs by orders of
magnitude, geoelectric methods are suitable to map the thickness and extent of the overburden (1975; Koefoed,
19

1989; Parasnis, 1997). The electrical resistivity depth sounding is useful in locating areas of maximum aquifer
thickness and serves as a good predictive tool for estimation of borehole depth.
Aquifers in basement complex terrains often occur at shallow depths, thus exposing the water within to
environmental risks, that is, vulnerable to surface or near-surface contaminants. A recent study (Omosehin, 2009)
reveals that the people around Idanre abstracts water from the unconsolidated materials overlying the crystalline
basement through uncontrolled sinking of tube wells, with glaring lack of concern for aquifer vulnerability to
near-surface contaminants and quality status of the groundwater.
This work, in addition to assessing the groundwater prospect of the unconsolidated materials in the area, the
geoelecrtic parameters of the near-surface materials overlying the aquifers were also used to assess the
vulnerability of the near-surface aquifers to near-surface contaminants. The work is anticipated to upgrade our
knowledge on groundwater potential of the unconsolidated material in the area, and the vulnerability of the
aquifers within.
Geologic setting
Idanre area is underlain by the Precambrian basement complex of southwestern Nigeria (Ajibade and Fitches,
1988), where six major petrologic units has been identified and described by Rahaman (1988). The study area is
underlain by three of these six major petrologic units: the migmatite gneisses, members of the older granite suite
and charnockitic rocks (Ocan, 1991). Most of the outcrops observed in Idanre are melanocratics, therefore
possibly rich in biotite and/or hornblende. Field observation shows that granite rocks constitute extensive outcrops
in the entire area (Fig 1). Granite gneisses outcrops either occur alone or in association with other components.
Minerallogically, the granite gneiss around Idanre are composed of alkaline feldspar, quartz, plagioclase and
biotite (Ocan, 1991).
Materials and Methods of study
The vertical electrical soundings (VES) were conducted using the Schlumberger electrode array (Zhody et al.,
1974). The Ohmega Resistivity Meter was used for resistance measurement. The geoelectric survey comprised of
sixty five (65) depth soundings (Fig 2), with maximum current electrode spacing (AB) ranging from 130m to
200m (AB/2 = 65 - 100). The field curves were interpreted through partial curve matching (Koefoed, 1979),
engaging master curves and auxiliary point charts (Orellana and Mooney, 1966).
20

5 00' E
7 15' N
7 15' N
River Osun
River Apurare
River Owena
5 00' E
Owena
60
50
Ago - Moferere
Gberiwojo
Ago - Ireti
Aponmu Akore
Odoko Bekun Odoji
ALADE
IDANRE
Odo - Isa
Odo - Isa
21
Apefon
Igbo - Olokun
Igbo - Epo - Aiyemofewa
Omiwonja
Ajegunle - Arun
Iwanja - Moferere
Igbo - Epo
Igbo - Epo - Owomofewa
300 m
0
River Aponmu
River Esun
A
50
40
River Iwari
River Imoja
Porphyritic Granites
Massive charnockite
Granitic charnockite
Migmatite
Strike and dip of Foliation
Major fault
Idanrore
Tejugbala
Ajegunle - Iwonja
B
1 0 1 2 3 4 5 Km
0 2000 4000 6000 8000 10000 12000
LEGEND
ALADE River Arun
20
A
Cross Section Across Direction A - B
River Ogburugburu
Opa - Idanre
Kajola - Asoko
River Owena
Oposinle
River Otan
AKURE
Orientation Of feldspar in granite
Roads
Footpaths
Village
Fig. 1: Simplified Geological Map around Idanre (Ocan, 1991).
50
60
River
40
20
40
Italepo - Odo
Ododin Ipinlerere
B
30
30
Oda
Oke - Alafia
5 15' E
5 15' E
7 15' N
7 00' N

The manually derived geoelectric parameters were subjected to an inversion (Vander Velpen, 1988), which
successfully reduced the interpretation error to acceptable levels (Barker, 1989).
The electrical resistivity contrasts existing between lithological sequences in the subsurface (Dodds and Ivic, 1998;
Lashkaripour, 2003) were used in the delineation of geoelectric layers, identification of aquiferous materials
(Deming, 2002) and assessment of groundwater prospect of the area. Also, the resistivity parameter of the
uppermost geoelectric layer (topsoil) was used to evaluate, in quantitative terms, its permeability to surface/nearsurface
contaminants, and hence the vulnerability of the underlying aquifers, as demonstrated in Draskovits et al.
(1995).
7 06' 27.4''
7 06' 22.3'' 5 06' 15.0''
v54
o
T
A
e
r u
k
v53
v51
v52
v1
v3
v2
v7
v4
o
r v6 v5
l v8
v11 v12
a
d
v9
v10
v13
v37
v14
v33 v36 v19 v18
v15
v35
v32
v17
v16
v27
v26 v31 v34
v25 v30
v20
v23 v21
v28 v24
v29
v22
v38
v42
v41
v39
v40
v43 v44 v45 v46 v47
m
m
o
C
v49 v48
v50
e r c i a
T o A p e f o n
T o A b a B a b u b u
Scale
0 1000 2000
Fig. 2: Layout Map of Idanre showing VES positions (Inset: Map of Nigeria).
m
v59
22
v57
v58
v56
v60 v61 v62 v63
v65
v64
v55
T o
l
S c
h
l a
i c
n
T e c
h .
T o A p e t a n
5 08' 22.3''
Study Area
N
NIGERIA
v
LEGEND
Road / Major Street
Minor Street
VES Location
River
Scale
0 300 Km

RESULTS AND DISCUSSIONS
The Schlumberger depth soundings produced a short range of sounding curves: three-layer case of type A (41.5%),
H type (24.6%), and four-layer curves of type KH (15.4%) were mostly recorded. Typical curves are shown in Fig
3. Field curves often mirror-image (geoelectrically) the nature of the successive lithologic sequence in a place and
hence can be used, in qualitative sense, to assess the groundwater prospect of an area (Worthington, 1977). Type
H and KH curves are often associated with groundwater possibilities while type A may typify a rapid resistivity
progression, indicative of shallow, resistive bedrock.
Aquifer Delineation: Electrical resistivity contrasts exists across interfaces of lithologic units in the subsurface.
These contrasts are often adequate to delineate discrete geoelectric layers and identify aquiferous or nonaquiferous
layers (Schwarz, 1988). The geoelectric parameters of the aquifer units were determined from the
interpretation of the sounding curves. Resistivity of earth materials is strongly affected by water saturation and
water quality (Lucius et al., 2001). The resistivity parameter of a geoelectric layer is an important factor to
adjudge an aquifer or otherwise.
Cross sections of interpreted resistivity data from the area (Fig 4) show three to four geoelectric layers: the topsoil,
the lateritic or weathered layer, and the fractured/fresh bedrock. In the topsoil, resistivity values range from 20 to
260 Ohm-m, with layer thickness varying between 0.5 and 2.1m. The lateritic or weathered layer has resistivity in
the range of 35 to 600 Ohm-m, with most of the values (67%) less than 150 Ohm-m. Layer thickness ranges from
0.8 to 7.8m. In few areas however, the thickness gets up to over 20m, but about 85% of layer thickness obtained is
less than 6m. The presumed decomposed portion of the bedrock has resistivity in the range of 69 to 874 Ohm-m,
while the thickness ranges from 3.2 to 24m.
NORTH
DEPTH (m)
-1
-3
-5
-7
-9
-11
-13
-15
-17
(a)
VES 54
61
150Ohm-m
8121
Depth (m)
4
2
33
Scale
0
0 400
Distance (m)
VES 3
VES 11 VES 38
VES 8 VES 42
99
31
54694
22
197
2522
114
23
299
103
5683
59
27
599
73
54 Ohm-m
1199
LEGEND
VES 25 VES 29
79
26
9233
Topsoil
67
109
24
1175 Ohm-m
590
7251
Weathered layer
Fractured basement
Fresh basement
SOUTH

DEPTH (m)
WEST
0
-2
-4
-6
-8
-10
-12
-14
VES 43
88
VES 44
83 Ohm-m
8868
(b)
66
159
26840
Depth (m)
VES 45 VES 27 VES 26 VES 31
4
2
64
79
8284
Scale
0
0 300 600
Distance (m)
22
119
23
12742
24
60
38
351
1206
13
891
Fig 4(a) & (b): Cross Sections of Interpreted Resistivity data from Idanre.
Assessment of Groundwater Prospect
VES 34
26
3172 Ohm-m
221
1553
LEGEND
Topsoil
Weathered layer
Fractured basement
Fresh basement
VES 17
No acceptable framework has yet emerged, as to where exactly is the major focus for groundwater resources in a
typical crystalline basement terrain. Acworth (1987) reported successful completion of boreholes in shallow
weathered zones in a typical basement terrain. Fracture-zone aquifers in crystalline rocks are also believed to be
important sources of water for rural communities (Meju et al., 1999). Lenkey et al (2005) however believes that
the thickest layer above the basement constitute the main water-bearing layer.
The approach of Lenkey et al (2005) has been adapted for this study. Fig 5 is a contour map while figure 6 is the
numerical value distribution, showing the thickness of unconsolidated materials overlying the crystalline
basement in Idanre; the thickness ranges from 0.5m to 15.8m, with an average of 4.5m. Fig 5 shows that
overburden thickness of 1-5.9m in the area constitutes about 81.5%, thus suggesting that the water-bearing
horizon (Lenkey et al., 2005) across the area is generally not significantly thick.
Assessment of Aquifer Vulnerability
Due to shallow depth of occurrence, aquifers in crystalline basement terrains are often exposed to environmental
risks. An effective groundwater protection is given by protective geologic barriers with sufficient thickness
(Mundel et al., 2003) and low hydraulic conductivity. Laterite, silt or clay often constitutes protective geologic
barriers. When found above an aquifer they constitute its cover (Lenkey et al., 2005).
The resistivity parameters of the uppermost geoelectric layer in the study area have been used to assess the
vulnerability of the underlying aquifers. Fig 7 is a contour map of resistivity of the first layer while figure 8 shows
the numerical resistivity distribution across the first layer in the area.
35
56
12480
EAST

7 06' 27.4''
7 06' 22.3''
Frequency
v53
v54
v51
v52
v1
v3
v2
v7
v4
v8 v6 v5
v11v12 v9
v42
v10
v37
v13
v14
v15
v33 v36
v19v18
v35
v32
v17
v16
v27
v26 v31
v34
v25 v30
v20
v23 v21
v28 v24
v29
v22
v38
v49 v48
v50
v41
v39
v43 v46
v44v45
v47
v40
To Aba Babubu
5 06' 15.0''
To Akure
Commercial road
Scale
To Apefon
0 1000 2000 m
25
v65
v59
v58
v57
v64
v60v61v63
v62
v56
v55
To Technical Schl.
To Apetan
N
5 08' 22.3''
Fig. 5: Map of thickness of unconsolidated material overlying the Basement at Idanre.
30
25
20
15
10
5
0
1 - 2.9 3 - 5.9 6 - 8.9 9 - 11.9 12 - 14.9 15 -17.9
Overburden Thickness (m)
Fig 6: Distribution of thickness of unconsolidated material at Idanre.
v
Series1
22 m
19
16
13
10
7
4
1
LEGEND
Road / Major Street
Minor Street
VES Location

7 06' 27.4''
7 06' 22.3''
Frequency
v53
v54
v51
v52
v1
v3
v2
v7
v4
v8 v6 v5
v11v12 v9
v42
v10
v37
v13
v14
v15
v33 v36
v19v18
v35
v32
v17
v16
v27
v26 v31
v34
v25 v30
v20
v23 v21
v28 v24
v29
v22
v38
v49 v48
v50
v41
v39
v43 v46
v44v45
v47
v40
To Aba Babubu
5 06' 15.0''
60
50
40
30
20
10
0
To Akure
Commercial road
Scale
To Apefon
0 1000 2000
26
m
v65
v59
v58
v57
v64
v60v61v63
v62
v56
v55
To Technical Schl.
To Apetan
N
5 08' 22.3''
Fig. 7: Contour Map of Resistivity Distribution in the First Layer at Idanre.
1-100 101-200 201-300 301-400 401-500 501-600 601-700 701-800 801-900 901-
1000
Resistivity (Ohm-m)
Fig 8: Distribution of resistivity in the topmost geoelectric layer at Idanre
Series1
v
800 Ohm-m
750
700
650
600
550
500
450
400
350
300
250
200
150
100
50
0
LEGEND
Road / Major Street
Minor Street
VES Location

About 77% of the resistivity values of the topmost geoelectric layer fall within 1-100 Ohm-m range. In Nigerian
geological circumstances, this suggests considerable clayey or silt sequences (aquitard), with effective capacity to
constitute impervious/semi-impervious barriers.
CONCLUSIONS AND RECOMMENDATIONS
Due to rugged geologic terrain, the unconsolidated materials overlying the crystalline basement rocks around
Idanre constitute the major water-bearing horizon from which the inhabitants abstract water for domestic needs.
Geoelectric depth sounding around the area reveals that the thickness of the unconsolidated materials varies from
0.5m to 15.8m, where values within 1-5.9m brackets constitute about 81.5%. This indicates that the
unconsolidated material in the area is not significantly thick, thus suggesting that the groundwater potential is
apparently low.
About 77% of the resistivity values of the topmost geoelectric layer in the area fall within the range of 1-100
Ohm-m. Values of resistivity within this brackets suggest aquitard (silt or clay), which constitute effective,
impervious geologic barriers to infiltrating near-surface contaminants. Aquifers within the unconsolidated
materials at Idanre are therefore mostly capped by impervious/semi-pervious geologic materials, suggesting that
they are mostly non-vulnerable to near-surface contaminants.
Since decomposed bedrock in the crystalline basement terrain can house significant quantity of groundwater,
groundwater developers in the area may explore the bedrock for bedrock aquifers, to complement the aquifers
within the unconsolidated overburden.
ACKNOWLEDGEMENT
Messrs T. Omosehin, E. Faleye and M. Bawallah assisted in the data acquisition while Messrs O.Adegoke and A.
I. Adeyemo helped in preparing the figures. The author gratefully acknowledged these assistances.
REFERENCES
Acworth, R.I. (1987). The development of crystalline basement aquifers in a tropical environment. Q. J. Eng. Geol.
London. Vol. 20, pp. 265-272
Ajibade, A.C. and Fitches, W.R. (1988). The Nigerian Precambrian and the Pan- African Orogeny. In:
Precambrian Geology of Nigeria. A publication of the Geological Survey of Nigeria. Pp 329.
Barker, R.D. (1989). Depth of investigation of collinear symmentrical four-electrode arrays. Geophysics 54, 1031-
1037
Bala, A. E. and Ike, E. C. (2001). The Aquifer of the Crystalline Basement Rocks in Gusau Area, North-Western
Nigeria. Journal of Mining and Geology, Vol. 37, No. 2, pp. 177 – 184.
Clark, L. (1985). Groundwater abstraction from Basement Complex areas of Africa. Q. J. Eng. Geol. London. Vol.
18, pp 25-32.
Deming, D. (2002). Intrduction to Hydrogeology. McGraw Hill Company. Pp 468.
Dodds, A.R. and Ivic, D. (1988). Integrated geophysical methods used for groundwater studies in the Murray
Basin, South Australia. In Geotechnical and Environmental Studies Geophysics, Vol II: Soc. Explor
Geophys.,Tulsa, pp 303-310.
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Draskovits, P, Maayar, B and Pattantyus-A, M. (1995). Geophysical methods in groundwater prospecting and
environmental protection. Fisica de la Tierra, no 7, 53-86.
Lones, M.J. (1985). The weathered zone aquifers of the basement complex areas of Africa. Q. J. Eng. Geol.
London. Vol. 18, pp 35-46.
Koefeod, O. (1979). Geosounding Principles, 1. Resistivity sounding measurements. Elsevier Scientific
Publishing Comp., Amsterdam, pp 275.
Lenkey, L., Hamori, Z and Mihalffy, P. (2005). Investigating the hydrogeology of a water-supply area using
direct-current vertical electrical soundings. Geophy, Vol.70. no. 4, H1-H19
Lashkarripour, G.R. (2003). An investigation of groundwater condition by geoelectrical resistivity method: A case
study in Korin aquifer, southeast Iran. Journal of Spatial Hydrology, Vol.3, No. 1, pp1-5.
Lucius, J.E., Bisdort, R. J. and Abraham, J. (2001). Results of electrical survey near Red River, New Mexico.
USGS Open-File Report 01-331, pp24.
Meju, M.A., Fontes, S.L., Oliveira, M.F.B., Lima, J.P.R., Ulugerger, E.U. and Carrasquilla, A.A. (1999). Regional
aquifer mapping using combined VES-TEM-AMT?EMAP methods in the semiarid eastern margin of
Parnaiba Basin, Brazil. Geophysics, Vol. 64, No. 2. P. 337-356.
Mundel, J.A., Lother, L., Oliver, E.M. and Allen-Long, S. (2003). Aquifer vulnerability analysis for Water
Resources Protection. Indiana Department of Environmental Management (IDEM), ‘Source Water
Assessment Plan’, pp 25.
Ocan, T. (1990). Petrogenesis of the rock units of Idanre, southwestern Nigeria. Unpublished Ph.D thesis,
Obafemi Awolowo University, Ile Ife, Nigeria, pp 194.
Omosehin, T.B. (2008). Geoelectric delineation of aquifers and assessment of their vulnerability in Idanre,
Southwestern Nigeria. Unpublished M. Tech. thesis, Federal University of Tech., Akure, Nigeria. Pp 83.
Orellana, E. and Mooney, H.M. (1966). Master tables and curves for vertical electrical
sounding over layered structures. Inteciencis, Madrib, 34pp.
Parasnis, D.S. (1979). Principles of Applied Geophysics. Chapman and Hill, London. Pp 98 130.
Rahaman, M.A. (1988). Recent advances in the study of the Basement complex of Nigeria. Precambrian Geology
of Nigeria. A Publication of Geological Survey of Nigeria. Pp. 11-41.
Schwarz, S.D. (1988). Application of Geophysical Methods to Groundwater Exploration in the Tolt River Basin,
Washington State. In Geotechnical and Environmental Geophysics. Vol 1. Soc. Explor. Geophs, Tulsa,
pp 213-217.
Vander Velpen, B. P. A. (1988). Resist Version 1.0, M.Sc. Research Project, ITC. Delft, Netherlands.
Worthington, P. R. (1977). Geophysical investigations of groundwater resources in the Kalahari Basin.
Geophysics, Vol. 42, No 4, pp.838-849.
Zohdy, A. A. R., Eaton, G. P., and Mabey, D. R. (1974). Application of surface geophysics to groundwater
investigations: Techniques of water resources investigations of U.S. Geol. Survey: Book 2, Chapter DI,
U.S. Government Printing Office, Washington, pp.66.
28

Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Ozean Journal of Applied Sciences 3(1), 2010
Comparative vegetative and foliar epidermal features of three Paspalum L. species in
Edostate, Nigeria.
E.A Ogie-Odia*, A.I Mokwenye*, O. Kekere ** and O. Timothy ***
*Department of Botany, Ambrose Alli University, Ekpoma , Edo State.
** Department of Plant Science and Biotechnology, Adekunle Ajasin University, Akungba Akoko, Ondo State.
*** Department of Environmental Science, Western Delta University, Oghara, Delta State.
*Email address for correspondence: effexing@yahoo.com
___________________________________________________________________________________________
Abstract: Investigations into the vegetative morphology and epidermal features of three species of the genus
Paspalum L. (P. conjugatum Berg, P. scrobiculatum L. and P. vaginatum Sw.) was carried out. Pictorial
illustrations are presented. For the vegetative features, it was observed that there is variability of hairs on both the
margins of the lamina and leaf sheath of P. scrobiculatum. In the epidermal studies, macro-hairs were observed on
the margins of the lamina of two out of the three taxa; prickles were observed on the intercoastal zone of two out of
the three taxa; papillae was observed in the intercoastal zone of one of the three taxa; micro-hairs were observed in
two out of the three taxa. It was observed that the shape of the subsidiary cells of P. conjugatum varied from low
dome to triangular in the adaxial surface and mainly triangular shaped in the abaxial surface. P.scrobiculatum had
mainly triangular shaped subsidiary cells, while that of was low dome shaped in the adaxial surface and triangular
shaped in the abaxial surface. On the basis of the variations that exist in its morphological and epidermal features, a
taxonomic key has been produced for identification and separation of the various taxa.
Keywords: Vegetative, leaf epidermal, genus, Paspalum, Poaceae, systematic.
___________________________________________________________________________________________
INTRODUCTION
Poaceae which is the grass family includes approximately 10,000 species classified into 600 to 700 genera (Clayton
and Renvoize, 1986). The grasses are included with lilies, Orchids, Pineapples and Palms in the group known as
monocotyledons which includes all flowering plants with a single seed leaf (Kellogg, 2001). The members of this
group are present in all the conceivable habitats, suitable for growth of plant communities (Mitra and Mukherjee,
2005). The grass family Poaceae, is noted for its wide diversity and complexity and so has posed many problems to
the taxonomists using the traditional methods based on gross morphology (Strivastava, 1978).
Before the later part of the 19 th century, taxonomists were confined to the use of the features of reproductive organs
as floral characters were considered to provide the most valuable characters to taxonomic affinities (Nwokeocha,
1996). Of all the non-reproductive organs, the leaf is the most widely used in plant taxonomy (Stace, 1984).
Strivastava (1978) described the leaf epidermis as the second most important character after cytology for solving
taxonomic problems.
29

Ozean Journal of Applied Sciences 3(1), 2010
Paspalum L. is a member of the tribe Paniceae R. Br. Within the Paniceae, Paspalum is one of the most complex
genera containing over 400 species that are largely endemic to the tropics and subtropics of the world (Clayton and
Renvoize, 1986). The centre of diversity of this genus is South America (Fernandes et al, 1968). In Nigeria, Lowe
(1989) reported that the genus is represented by five species which are mostly straggling plants grown in damp open
places. The reported species are P. vaginatum, P. conjugatum, P. notatum, P. scrobiculatum and P. auriculatum.
The economic values of these species are many. For example, P. vaginatum provides for dune stabilization and
waterflow fodder under saline and fresh water environments respectively and can also be cultivated as a turfgrass for
soil stabilization. On the other hand, P. scrobiculatum has been domesticated as a cereal grain in Asia (Jarret et al.,
1998). The use of morphological and leaf epidermal features has been found to be of immense interest in taxonomy.
An excellent review of the application of morphological features in systematic studies is shown in the works of
Olowokudejo 1990, Mensah and Gill (1997), Edeoga and Ikem 2001, Gill and Mensah (2001) and Kharazian
(2007).The use of leaf epidermal features in systematics has become popular and distinctive and has been used as a
great taxonomic tool at the levels of family, genus and species.
This study is to give the morphological description of the three Paspalum species and also to compare/determine the
intraspecific relationship and patterns of variation associated with the epidermal features/characteristics among the
taxa studied.
METHODOLOGY
Fresh and matured leaves of Paspalum scrobiculum L. and Paspalum conjugatum Berg. were collected from
Beverly-Hills area, off Benin-Auchi express road, Ekpoma, while Paspalum vaginatum Sw. was collected close to
the bank of the Ikpoba river in Benin-city, Edo State. They were first boiled in water to restore to their normal
shape. The tissue above the epidermis was gradually scraped away with a safety razor blade and during this
operation the leaf was continuously irrigated with commercial Jik. The epidermal peels were then washed in water,
stained with 1% safranin solution in 50% alcohol and temporary mounts (under low and high power) viewed under
the microscope. Photo micrographs of the epidermal features were taken from the slides using a Light microscope
fitted with a Cannon digital camera (5.0 mega pixels). The terminologies for the epidermal morphology are that of
Metcalfe (1960) and Van Cottem (1973).
RESULTS
The morphological (vegetative) features of the three Paspalum species investigated are summarized in Table 1. The
descriptions of the leaf epidermal studies are presented in Tables 2; the epidermal and morphological Keys are also
presented while the epidermal slides are illustrated in Plates 1, 2 and 3. The vegetative results of the three taxa
studied showed that the habit of the three Paspalum species are perrenial herbs with P. scrobiculatum being a turfed
perennial herb. The heights are about the same (60cm) with P. scrobiculatum growing up to 100cm and while of the
habit and height of is perennial herb and respectively. Similarly the leaf shapes shows that they are all linear
although P. conjugatum is linear lanceolate while the the inflorescence is raceme for P. conjugatum and P.
vaginatum (terminal) and P. scobiculatum is digitate. The stem types varies with P. conjugatum having prostrate
stoloniferous stems, P. scrobiculatum with erect or decumbent stem and P. vaginatum with creeping stoloniferous
stem . Equally, all the spikelets in the three species have two rows with P. scrobiculatum and P. vaginatum being
hairless while P. conjugatum has hairs.
30

Ozean Journal of Applied Sciences 3(1), 2010
Table 1: Vegetative characters of the three Paspalum species studied
Characters Paspalum conjugatum Paspalum scrobiculatum Paspalum vaginatum
Habit Perennial herb Turfed perennial herb Perennial herb
Height 60cm 60-100cm 60cm
Stem type Prostrate stolon Erect or decumbent Creeping stolon
Leaf shape Linear or lanceolate Linear Linear
Leaf texture Smooth with hairy margins Soft with rough margins and
sometimes hairy
31
Smooth and hairless
Inflorescence Raceme Digitate Terminal raceme
Spikelet Two rows and hairy Two rows and hairless Two rows and hairless
Ligules Toothed Distinct and short Dense
Table 2: Summary of leaf epidermal features
Species Surface MIC MAH PRK PAP SSc OPB Distribution
of stomata
Paspalum conjugatum
Berg.
Paspalum
scrobiculatum L.
Paspalum vaginatum
Sw.
AD C
IC
AB C
IC
AD C
IC
AB C
IC
AD C
IC
AB C
IC
-
b
-
B
-
b
-
B
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
+
-
+
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
LD, T
T
T
T
LD
T
Db, S
-
S, Cr
-
Db, Cr
-
Db, Cr
-
HES
-
Db, Cr
-
AMP
AMP
AMP

Legends for Plates and Table 2
Ozean Journal of Applied Sciences 3(1), 2010
AB = Abaxial surface SHC = Short cell
AD = Adaxial surface C = Coastal zone
OPB = Opaline body LD = Low dome shaped
IC = Intercoastal zone S = Saddle shaped
MIC = Micro-hair Cr = Cross shaped
b = Bi-cellular Db = Dumb bell shaped
MAH = Macro-hair Amp = Amphistomatic
PRK = Prickle HES = Horizontally elongated surface
PAP = Papillae SSc = Subsidiary cell
LNC = Long cell T = Triangular
+ = Present - = Absent
Paspalum conjugatum Berg.
Leaf blade up to 9.5cm long and 6.1mm broad, with prickles and macro-hairs present on leaf margins.
a. Adaxial surface: Clearly separated into coastal and intercoastal zone.
Solitary short cells are present in the intercoastal zone. Long cell vary from slightly to straight anticlinal walls.
Prickles present in the intercoastal zone. Bi-cellular micro-hairs with distal cell having tapering apices present in the
intercoastal zone. Papillae and micro-hair were absent in the coastal and intercoastal zones. Subsidiary cells of the
stomata vary from low-dome to triangular shape. Opaline bodies of the coastal zone vary from dumbbell to rounded
or saddle shaped
b. Abaxial surface: Clearly separated into coastal and intercoastal zone
Long cells rectangular shaped with some tending to be square shaped with sinuous anticlinal walls. Long cells
observed in the coastal zone. Bi-cellular micro-hairs present in the coastal zone, distal cells with tapering apices.
Prickles present in the intercoastal zone. Macro-hairs and papillae absent, subsidiary cells of stomata are triangular
shaped. Opaline bodies mostly saddle shaped, occasionally cross shaped in the coastal zone
32

Plate 1a Adaxial surface of P. conjugatum
Plate 1b Abaxial surface of P. conjugatum
Paspalum scrobiculatum L.
Ozean Journal of Applied Sciences 3(1), 2010
Leaf blade up to 16cm long and 0.9mm broad with prickles and macro-hairs present on margins.
a. Adaxial surface: Clearly distinguished into coastal and intercoastal zone.
Long cell are rectangular shaped with some tending to be square with sinuous walls. Solitary short cells present in
the intercoastal zone. Long cells present in the coastal regions. Bi-cellular micro-hairs present in the intercoastal
zone, with distal cells having tapering apices. Prickles with hook shape present in the intercoastal zone. Papillae and
33
C
C
IC
PRK
IC
MIC
SSc
LNC
LNC
MIC
SSc

Ozean Journal of Applied Sciences 3(1), 2010
micro-hairs absent and stomata present with triangular shaped subsidiary cells. Opaline bodies of the coastal zone
are mainly cross and dumb bell shaped.
b. Abaxial surface: Coastal and intercoastal zone clearly distinct.
Long cells rectangular shaped with sinuous anticlinal walls. Long cells present in the coastal zone. Solitary short
cells present in the intercoastal zone. Hook shaped prickles present in the intercoastal zone. Papillae and macro-hair
absent, bi-cellular micro-hairs present in the intercoastal region and stomata with triangular shaped subsidiary cells.
Opaline bodies vary from cross to dumb bell shaped in the coastal zone.
Plate 2a Adaxial surface of P. scrobiculatum
Plate 2b Abaxial surface of P. scrobiculatum
Paspalum vaginatum Sw.
34
C
C
SSc
IC
MIC
IC
SSc
LNC
LNC
MIC

Ozean Journal of Applied Sciences 3(1), 2010
Leaf blade up to 6cm long and 2.5mm broad with prickles and macro-hairs present on margins.
a. Adaxial surface: Clearly seperated into coastal and intercoastal zone.
Long cell are rectangular shaped with some tending to be square with slightly sinuous to straight anticlinal walls.
Short cells is absent in the intercoastal zone. Macro-hairs and micro-hairs absent, papillae tend to overarch with
stomata in the intercoastal zone. Subsidiary cells of the stomata were low-dome shaped. Opaline bodies in the
coastal regions are horizontally elongated.
b. Abaxial surface: Clearly distinguished into coastal and intercoastal zone.
Long cells are rectangular shaped with sinuous anticlinal walls in the intercoastal zone. Long cells present in the
coastal zone. Short cells are paired, occasionally solitary in the intercoastal zone. Prickles, micro-hairs and macrohairs
are absent. Subsidiary cells of stomata are triangular shaped.
Opaline bodies vary from dumb bell to cross shaped in the coastal zone.
Plate 3a Adaxial surface of P. vaginatum
35
C
IC
PAP
SSc
LNC

Plate 3b Abaxial surface of P. vaginatum
Ozean Journal of Applied Sciences 3(1), 2010
Key to the three species of Paspalum studies based on their epidermal features
1. Prickles present on both surfaces………………………………………………2
1. Prickles absent on both surfaces……………………………………………….4
2. Macro-hairs variable on the leaf margins………………………..scrobiculatum
2. Macro-hairs constant on the leaf margins…………………….........conjugatum
3. Papillae present on adaxial surfaces…………………………………………...4
3. Papillae absent on both surfaces……………………………………………….2
4. Micro-hairs present on both surfaces…………………………………………..2
4. Micro-hairs absent on both surfaces………………………………....vaginatum
Key to the three species studies based on their morphological features
Inflorescence: Consisting of 2 long slender raceme between 8-15cm that has greenish yellow
spikelets which are almost circular lying flat with hairy fringe on their margin……..P. conjugatum
Inflorescence: Consisting of 2-10 racemes, each with two rows of overlapping swollen circular
spikelets…...................................................................................................................P. scrobiculatum
Leaves: Borne in two distinct ranks on either side of the culm
Inflorescence: Consisting of 1-3 racemes, sometimes a pair of terminal racemes with 2 rows of overlapping ovate
spikelets …………………………………………………..……..P. vaginatum
DISCUSSION
36
C
IC
SSc
OPB
SHC
LNC

Ozean Journal of Applied Sciences 3(1), 2010
The morphological (vegetative) features observed showed that there were little or slight differences in the features of
the three species of Paspalum studied and this suggests their differences in species level as they exhibited different
characters though some of the characters were quite identical.
Some differences in type of stem and the inflorescence and spikelets (Table 1) were observed, although the heights
were almost the same in the three species except for P.scrobiculatum which grows up to 100cm. The analysis of the
morphological structure of the three studied species has revealed characteristics, which correspond to those
mentioned by Lowe (1989) and Akobundu and Agwayaka (1998). The epidermal cells are arranged in horizontal
files. Leaf epidermis of the genus Paspalum is clearly distinguished into coastal and intercoastal zones. The coastal
zones are generally narrower while the intercoastal zones are broader. Studies carried out by Sharma and Salam
(1984); Sharma and Mittal (1986) have reported similar observations in other genera and tribes of Poaceae. The
epidermal cells possess sinuous, slightly to straight anticlinal walls. Metcalfe (1960) reported similar undulations in
various genera and tribes of Poaceae. Explanations have been given for the wavy nature of the anticlinal walls of the
epidermal cells. One of the explanations for this phenomenon relates the undulations to the development of stress
during the differentiation of the leaf (Avery, 1933). Another concept is that the waviness is caused by the method of
hardening of the differentiating cuticle (Watson, 1942). Furthermore, Linsbauer (1930) and Watson (1942) stated
that the waviness is also affected by environmental conditions prevailing during leaf development. Prickles with
hook shape have been observed in the adaxial and abaxial surfaces of P. conjugatum and P. scrobiculatum. Prickles
with angular spines were restricted to the margins of leaf lamina of the three species., various shapes of prickles in
tribes of Poaceae have been described by Sharma and Mittal (1986) Sharma and Salam (1984). Font Quer (1975)
defines papillae as the simplest of trichomes, characterized by wall projection followed by the protoplast of
epidermal cells. According to Ellis (1979), Poaceae papillae occur in long and short cells, especially in intercostal
zones, in numbers that may vary from one to many per cell. Papillae are absent in most of the three species studied
except on the adaxial surface of P. vaginatum where it was found to be present. Thus, the absence of papillae in this
last mentioned species can be interpreted as a taxonomic indicator. Micro-hairs observed are mainly bi-cellular with
tapering apices in the intercoastal zones of both the adaxial and abaxial surfaces of P. conjugatum and P.
scrobiculatum. Metcalfe (1960) have described bi-cellular hairs in a number of grass species. Macro-hairs were
absent in the coastal and intercoastal zones of the three species but were present on the margins of P. conjugatum. In
P. scrobiculatum, it varied (i.e it was present in some and absent in the others). The reason for this could be
probably due to the presence or absence of hairs on the leaf margins of the grass or other factors could be
responsible. The stomata are restricted to the intercoastal zones on both adaxial and abaxial surfaces. According to
Ellis (1979), the Poaceae stomata generally occur in well-defined bands in intercostal zones, and they may be
classified according to the shape of subsidiary cells. Subsidiary cells of the stomata varied from triangular to low
dome shape in the adaxial surface of P. conjugatum while it was the same in the abaxial surfaces of the remaining
two species. Mensah and Gill (1997) have reported similar observations in other genera and tribes of Poaceae like
Sporoboleae. Opaline bodies which are depositions of silica materials were mainly dumbbell, cross, saddle and
horizontally elongated in shape. The importance of opaline bodies as a systematic tool in grasses has been
overemphasized by many researchers including Metcalfe (1960).
Grasses are currently identified based on their floral characters, but a problem encountered with the system of
identification is that grasses do not flower for a greater part of their life cycle. Hence, a biosystematic approach
could be used in tackling this problem through gathering of data from various studies such as cytology, palynology,
epidemiology etc. The study of different epidermal characters such as macro-hairs, prickles, papillae, micro-hairs,
distribution of stomata, nature of long cells, subsidiary cells, opaline bodies and surface views of the leaf helps us
classify and identify grasses into their various tribes and genus and thus adds to our knowledge on the
biosystematics of grass species. It will also be beneficial as a research tool to cytologist, weed scientist and research
workers.
REFERENCES
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Avery G. S Jr (1933). Structure and development of the Tobacco leaf. Amer. Jour. Bot. 20: 565-592
37

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Clayton W. D and Renvoize, S. A (1986). Genera Graminae: grasses of the world. Royal Bot. Gard. Kew, London.
Kew Bulletin. Additional series XIII
Edeoga H.O and Ikem C.L (2001). Comparative Morphology of the leaf epidermis in three species of Boehavia L.
Nyctagininaceae. J.Pl. Anat Morph. 1:14-21.
Ellis R. P. (1979). A procedure for standardizing comparative leaf anatomy in the Poaceae II: the epidermis as seen
in surface view. Bothalia .12 (4): 641-671.
Font Quer P. (1975). Diccionario de Botanica. Labor., 1244p.
Gill L. S. and Mensah J. K. (2001). Epidermal and leaf anatomical studies of the tribe Eragrostideae (Poaceae) from
West Africa: J. Plant Anat. Morph. 25: 41 – 58.
Jarret R. L., Lui, Z. W and Webster R. W (1998). Genetic diversity among Paspalum spp. as determined by RPLPs.
Euphytica 104: 119-125
Kellogg E. A (2001). Evolutionary history of the grasses. Plant Physiol. 125: 1198-1205
Kharazian N. (2007). The Taxonomy and variation of leaf anatomcial characters in the genus Aegilops L. (Poaceae)
in Iran Turk J. Bot. 31: 1 – 9.
Linsbauer K. (1930). Die Epidermis. In: K. Linsbauer Handbuch der Pflanzenanatomie Band 4 Lief: 27.
Lowe J. (1989). The Flora of Nigeria grasses. Ibadan University Press, Ibadan. 326p.
Mensah J.K. and Gill L. S (1997).Cuticular and leaf blade anatomical studies of the tribe Sporoboleae (Poaceae)
from West Africa. J.Plant.Anat.Morph.7: 72-81.
Metcalfe C. R, (1960).Anatomy of the Monocotyledons I. Gramineae Clarendon Press Oxford. 731p.
Mitra S and Mukherjee S.K (2005). Ethnobotanical usages of grasses by the tribals of West Dinajpur district, West
Bengal. Indian J. Tradit. Knowledge 4(4): 396-402.
Nwokeocha,C. C, (1996). Foliar epidermal studies in Oryza punctata. Nig. J. Bot 9: 49 -58.
Olowokudejo J.D ( 1990). Comparative Morphology of leaf epidermis in the genus Annona (Annonaceae) in West
Africa. Phytomorphol 40: 407-422.
Sharma M. L. and Mittal H. R. (1986). Leaf epidermal studies in Gramineae V. Genus Eragrostic P. Beauv. Res.
Bull. Pangab Univ. 37: 29 – 35.
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in Punjab plains II Leaf epidermis Res. Bull. Pangab Univ. 35: 7 – 11.
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Strivastava A. K. (1978). Study of leaf epidermis in the genus (Gramineae). Journal of Indian Botanical society 37:
155 – 160.
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Watson R. W (1942). The effect of cuticular hardening on the form of epidermal cells. New Phytol. 42: 223-229
38

Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Digital Moulding of the Solicitations within the Dielectric of
the Transformers and the Evaluation of
Life Cycle of the Insulation Systems
MARIUS-CONSTANTIN POPESCU* and CRISTINEL POPESCU
Faculty of Electromechanical and Environmental Engineering, University of Craiova,
B-dul Decebal, nr.107, 200440-Craiova, Dolj, ROMÂNIA
*E-mail address for correspondence: mrpopescu@em.ucv.ro
_________________________________________________________________________________________
Abstract: This papers analyses the evaluation methods of the wear of transformers that occurs on their charging
with alternating load, taking into consideration mainly the thermal ageing of electroinsulation material,
especially of those of A class.
Key-Words: wear of transformer, thermal parameters.
_________________________________________________________________________________________
INTRODUCTION
In regular service conditions, the transformer is subject to added solicitations which influence the long-term
response of the transformer. Among the elements of the transformer, the insulation, which this chapter deals
with, is stressed in different conditions in use from those of the lab. In regular service conditions the insulation of
the transformers is under nominal voltage of the electric network, reaching the crest working voltage at the most.
In lab conditions, the insulation of the transformers is subject to the action of proof stress which has the value,
form and time period appropriate for the concepts and actual conditions of coordination of the insulation, applied
in transformer plants and distribution stations. As a result, the testing of the insulation of the transformers in lab
must guarantee the safe running of the transformers under most unfavorable conditions. The insulation of the
electrical power transformers is subject to different kinds of stress, both in real and in lab conditions. In standard
conditions, electrical (of nominal voltage and of overvoltage), mechanical (due to the short-circuit stresses),
chemical (due to deposits or chemical agents) and thermal (due to changes in temperature and in the atmosphere)
stresses are applied on the insulation. Due to these stresses, in the course of utilization, the properties of the
electroinsulation materials deteriorate, bringing about the ageing of these materials. On the whole, the ageing
process of the insulating materials is a complex process because of the multitude of different kinds of factors
which influence it. Among these stresses, those which influence the most the insulation are those due to internal
overvoltage, caused by the changes in the parameters of the electroenergetic system, and to external overvoltage
(atmospherical), caused by lightning, the temperature of the oil/paper insulation complex from the transformer
respectively. After the changes in the physical properties of the electroinsulation materials, changes occurred in
use, the marking of the state of an insulation can not be carried out taking into consideration a single property
due to the fact that the changes are the result of the action of a large number of external actions, the
combinations of which are random, their accurate reproduction being impossible. It is difficult to study the
transformers while they are working, because it would take a long time, namely decades. Shortening the length
of the trials could be carried out by introducing accelerated ageing procedures, but these methods do not
guarantee results that can be applied in real conditions. Therefore, the trial conditions must be created in a way
that they are as similar as possible to the real ones and in accordance to the purpose for which the results are
used later on. For this reason, the functional trials carried out on mock-ups are of great scientific value.
39

1.External stresses of the insulation of the electrical power transformers
In the case of the external stresses, the potential pulse that applies stresses on the insulation can be:
- full-wave impulse - of long standing, after which high voltage oscillations develop in the windings hence
resulting stresses both among the spires and between the winding and the earth point.
- chopped-wave impulse – the amplitude of which is not so great, but due to scarp cutting, it can bring about
voltage gradients that are dangerous to the insulation on entrance or at the neutral point of the transformer.
- steep-wave impulse – has great amplitude but it lasts little and as a consequence it produces the most stresses.
These can apply stress on the insulation at the table and the insulation among the entrance spires of the winding.
In lab conditions, the transformer is subject to a minute sinusoidal proof voltage, namely a proof voltage of
impulse. Each of the proof voltages is referred to maximum overvoltage in use, equating the overvoltage in use
to lab conditions.
The proof voltage of the internal insulation with full-wave is stated by the relation:
[Uinc]up=Kt[Ugp]up+0,5UN (1)
where: Kt is coefficient with the value 1.15 for the 6 -35 kV transformers and 1.10 for the 110 and 220 kV
transformers; UN is nominal voltage of the winding of the transformer.
The proof voltage of the internal insulation with the chopped-wave impulse is stated by the relation:
[Uinc]ut=1.15*1.25[Ugp]up (2)
where 1.15 takes into account the cumulative effect, and 1.25 takes into account the increase of voltage at the lug
of the transformer in relation with the lightning arrester.
The minute sinusoidal proof voltage is stated by the relation:
�U �
inc �
Ua�
�
� � K
si
c
where: βsi is equation coefficient of the Usi internal overvoltages in use with the Uα minute proof voltages in lab;
Kc ≈0.9 is the coefficient that takes into account the cumulative effect of repeated stresses in exploitation.
2. Factors that determine the state of the insulation
The insulation of the transformers is influenced by the following factors [1, 2, 4, 5]:
The alien substances in the internal insulation are: moisture left in the insulation after an inappropriate drying
procedure; residues of the coating varnish solvent that have not been removed when the windings were dried;
blisters or gaseous inclusions left in the insulation after the filling of the transformer with oil; dirt resulted from
an inappropriate operating process. The contamination of the external insulation, the thermal operating
conditions and the altitude at which the transformer works. Damping the insulation causes: an increase in
dielectric losses and a decrease in dielectric rigidity. The presence in oil of water in the form of emulsion (in
dispersed state) leads to a rapid decrease of rupturing voltage of the oil. The water molecularly dissolved in oil
does not influence the dielectric rigidity and losses. But in the presence of alien fields in the oil-water
molecularly dissolved changes into dispersed state leading to the decrease of dielectric rigidity. Variations in
temperature also cause changes in rupturing voltage. Thus, when the temperature increases from 20�C to 60�C,
at a frequency of 50 Hz, an important increase in rupturing voltage takes place. The electrical insulating oil,
commercially pure, contains an amount of moisture, dissolved gases and solid impurities (waste and grains).
When the temperature increases the dilution of water increases and the waters turns from the state of emulsion
into the state of dilution leading to the decrease of dielectric rigidity. The effect that the temperature variations
40
(3)

have on the insulation of the transformers in a long time cause an ageing of the insulation, which loses its
mechanical properties (it becomes fragile). The impurities and the dirt from the atmosphere reduce the value of
the rupturing voltage of the external insulation of the transformers, even under working stress. Gaseous
inclusions or blisters lead to partial discharges and thus to the decomposition of the structural insulation, having
negative effects on the dielectric rigidity as it decreases. Low atmospheric pressure in mountainous regions cause
the decrease of the voltage of the external insulation because of the decrease of the relative density of the air.
The quality of the impregnation process and especially the polymerization degree of the varnish highly influence
the condition of the insulation of the transformers, incomplete polymerization causing an increase in dielectric
losses, the decrease of the dielectric rigidity, the oxidation and ageing of the electrical insulating oil.
3. The testing of the insulation of transformers
The testing with direct voltage have as purpose the finding out of the dampness of the insulation of transformers,
if there are or not deficiencies able to cause partial discharges (gaseous inclusions, deficient junctions), if
changes in the response of the dielectric occur by applying stress for a long period of time. Based on the
variations from the initial state of the insulation, variations caused by the influence of dampness, of the ageing of
the insulation of the transformers or of an overcharge, it can be decided if the occurring deficiencies can be
remedied or if the transformer must be submitted to a general overhaul. Furthermore, the main tests on the
electrical power transformers are presented [4], [7], [10], [12], [14], [15], [16].
The measurement of the insulation resistance. The insulation resistance, through the absorption coefficient
R60/R15 allows the estimation of the degree of moistness of the insulation of transformers. For a dry enough
transformer the absorption coefficient must relate to the following: R60/R15≥1,5. When applying direct voltage,
in time, an alternative current is established through the dielectric which decreases and then settles at a value.
Initially the current has a high value and thus the insulation resistance has a high value due to the polarization
current and to the charging current. Compared to a dry dielectric, at a damp dielectric, to the polarization current,
displacement current, an orientation component is added and as a consequence the line current rises. The
insulation resistance of a dielectric is given by the relation between the applied direct voltage and the resulted
full current. The value of the insulation resistance is influenced by the following factors: the value of the direct
voltage which is applied, the length of applying the voltage, the electrostatic charge, the temperature of the
insulation and the dampness degree of the insulation. The state of the insulation of the transformer is determined
through the diagram I=f(U) by means of the position of the break which appears in the curve at a certain value of
the voltage. The higher the voltage at which the break appears and the gentler the transfer from a slope to another
is, the better the state of the insulation is. If the break appears below the value 2 * U max,
the insulation is
considered to be weakened and the transformer must be overhauled. If the current suddenly increases the
insulation is disrupted and the tests must be stopped. The state of the insulation of the transformer can also be
determined through the variation of the insulation resistance and of the current depending on the applied voltage.
After a while the insulation resistance, namely the through the insulation reach a set value. If the stabilization
takes place in a short period of time and also at a low value, it means that the conductance component IS is high
compared to the polarization component Ip and consequently the insulation is damp. On the other hand, the low
values of the insulation resistance due to the high content of water in the insulation do not mean that the
transformer is aged or permanently deteriorated. The insulation resistance is not standardized. It is to be
compared with the values of the measurements at the same temperature. If the previous measurements have been
carried out at different temperatures then their values are reduced to the temperature of the latest measurement.
The insulation resistance varies in inverse ratio to the temperature. As the measurements can not be carried out
all the time at the same temperature, recomputed values are used, recomputed through recomputation values of
the insulation resistance depending on the temperature. For each transformer two measurements are carried out
at temperatures between 20�C and 75�C. The relation to the same temperature is performed by the multiplication
or the division of the values of the insulation resistance with its variation coefficient by the temperature
difference K1 following the values presented in Table 1.
Δt
( 0 C)
Table 1. The values of the coefficient of variation K1
1 2 3 4 5 10 15 20 25 30 35 40 45 50
Val K1 1.05 1.07 1.12 1.16 1.23 1.4 1.74 2.23 2.65 3.35 4.05 5.15 6,1 7.5
41

The insulation resistance must not drop below 70% of the initial value of the insulation resistance. When
measuring, the following are taken into consideration:
- For the new transformers, when putting it to service, the R60 value must not be below 70% of the its value set in
the factory;
- For the transformers in use, R60 must not drop below the values shown in Table 2.
Un (kV)
Table 2. R60 values for different values of the nominal voltage.
R60
20 0 C 50 0 C
≤ 60 300 90
110 – 220 600 180
400 1000 300
In time, the insulation resistance of the transformer rises up to practically a set value. The variation of the
insulation resistance in relation with the time represents the absorption curve. The measurement of the insulation
resistance is carried out after 15, 60s respectively. The absorption coefficient R60/R15 tends to the value 1if the
insulation has a great content of dampness. The absorption coefficient provides information on the variation of
the insulation resistance in time, which is why the state of the insulation resistance is judged by absorption
curves and polarization curves. The absorption coefficient is used as one of the criteria to establish the dampness
of the curves, the shape of the absorption curve depending on the degree of dampness of the dielectric and its
build. The absorption curve shows the variation of the insulation resistance in time and of the current through the
insulation. The measurements are carried out in the specified thermal and dampness conditions, in fair weather,
at a relative dampness of the environment, of 80% at the most, taking into consideration the following
observations:
- When it starts working the Kabs value must not be over 5% below the value set in the factory;
- For the transformers in use the Kabs value is not standardized;
- The value of the absorption coefficient at 20�C is considered normal if: Kabs≥1,2 for power transformers with
U≤110kV; Kabs≥1,3 for power transformers with U≤110kV.
The dissipation factor. The tgδ dissipation factor is also a criterion for evaluating the state of the insulation. The
increase of the tgδ value is determined by the chemical degradation of the oil, its getting wet, the ageing of the
solid insulation affected by dampness, oxigen or by temperature. The measuring of tgδ is compulsory for all
transformers of over 110kV voltage and powers of over 10 MVA inclusive, respecting the following
observations:
- When putting into service tgδ the measuring must be carried out at the temperature recommended in the manual
provided by the factory (±5 0 C), not below 10 0 C .
- The values measured at the putting into function are compared to the values measured in the factory. The
values in use must be kept between the limits provided in the Table 3.
Table 3. Normal values for tgδ.
Un (kV) 20 0 C 50 0 C

In use, the values obtained for tgδ at one of the two reference temperatures is compared to the values measured
previously (in the factory, at putting into service). In the case of new transformers, the tgδ value must not
increase over the value set in factory with over 30%. If the previous measurement has been carried out at a
different temperature from the one of the latest measurement, it will relate to the temperature of the latest
measurement by division or multiplication by the coefficient of variation K2, depending on the difference of
temperature Δt ( 0 C).
Δt
( 0 C)
Table 4. The values of the coefficient K2
1 2 3 4 5 10 15 20 25 30 35 40 45 50 55 60
Val K2 1.04 1.07 1.10 1.11 1.16 1.26 1.52 1.65 2 2.4 2.55 3 3.4 4 4.5 5.4
For all transformers, the real values of the reporting coefficient is determined from the diagram obtained from
measuring the tgδ at the temperature of 50±5 0 C, after which the tgδ=f(t) line is used when the temperature has
dropped to 20±5 0 C. For measuring we employ a measuring bridge for MD – 16 capacity assembled after a
Scherine assemblage.
The measuring of the ohmic resistance of the curves. The purpose of the measuring of the ohmic resistance of
the curves is: to determine the real resistance of the curves; to check the welds or junctions from the conductors
of the windings; to track down the possible interruptions; to make evident the dead short circuits among the
spires or other deficiencies which are reflected in the value of the resistance. The measuring is carried out in
direct current at a value that exceeds with 20% the value of the non-load current, but it is not to exceed 0,1In.
While the ohmic measurements are carried out, the winding temperature is put down. The measured resistance is
related to another temperature by means of the relation:
T � t
R * 2
t � R
2 t1
T � t1
T = 235 both for the copper windings and for the aluminum windings.
Taking these into consideration, some special measurements have been carried out on some electrical power
transformers from Romania. The gathered data, recorded in the task report, have been synthesized in a tabular
and graphic form. Analyzing the result of the measurements, conclusions regarding the behaviour of the
transformers in use could be drawn. Fig’s 1 and 2 show the results of the measurements carried out on electrical
power transformers considered to be matters of study. The study has been completed for the main sizes that
characterize the behavior of the transformers, from 1998 to 2008. For each size the measured value has been
compared to the normalized value and to the value EPC set in the factory. From the obtained data and the
graphical representations it can be determined that only in the case of the transformer T11 (from a Station in
Romania) a negative variation can be observed both of the insulation resistance and of the tangent delta,
especially between 2004 and 2005. As a consequence, the increase of the insulation over the EPC values calls for
a further close surveillance of the insulation. The rest of the transformers can be considered suitable to be in
service.
Table 5. The result of the measurements TS – earth point performed on the T9 power transformer.
EPC
Tg la EPC 38 C
Tg EPC trans la 20C
R 15 EPC la 38 1200 TS-TI Trafo 11
R 15 EPCla 20 2472
R 60 EPCla 38 2400 Vn 20 C= 600 Mohm Vn 50 C=180 Mohm
R 60 EPCla 20 4944 Vn 20 C= 2,5% Vn 50 C= 7 %
R1-rez EPCla 38 1,373 An EPC-1986
t1-EPC 38 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Tg mas trans la 20 C 0,23 0,25 0,29 0,28 0,32 0,24 0,28 0,25 0,25 0,23 0,30
Tg masurata 0,35 0,38 0,4 0,42 0,45 0,4 0,42 0,4 0,38 0,4 0,42
%R60mas din R60EPC la 20gr 60 65 51 49 54 60 49 50 41 56 54
R60 transpus la 20 2980 3220 2527 2429 2652 2966 2429 2448 2024 2768 2652
R 60 masurata 1560 1750 1560 1320 1560 1440 1320 1230 1100 1230 1560
Roh plot 1-mas 1,332 1,326 1,312 1,325 1,313 1,345 1,326 1,338 1,325 1,353 1,316
t2-calc la mas 38,0 36,5 35,4 32,9 35,3 33,1 38,9 35,4 37,6 35,3 40,4 33,6
Delta t
kiz
18,0
2,06
16,5
1,91
15,4
1,84
12,9
1,62
15,3
43 1,84
13,1
1,7
18,9
2,06
15,4
1,84
17,6
1,99
15,3
1,84
20,4
2,25
13,6
1,7
k tg 1,65 1,55 1,51 1,38 1,51 1,42 1,65 1,51 1,6 1,51 1,75 1,42
R15 transpus la 20 2197 1748 1426 1838 1105 1813 1041 1325 1593 1766 1586
R15 masurat 1150 950 880 999 650 880 566 666 866 785 933
Kabs 1,36 1,84 1,77 1,32 2,40 1,64 2,33 1,85 1,27 1,57 1,67
(4)

Fig. 1. The variation of the insulation resistance of the T9 power transformer.
Table 6. The TS – TI measurement results performed on the T 11 power transformer.
EPC
Tg la EPC 38 C 0,49
Tg EPC trans la 20C 0,30
R 15 EPC la 38 2680 TS-masa Trafo 9 st Mare - 199999
R 15 EPCla 20 5521
R 60 EPCla 38 4930 Vn 20 C= 600 Mohm Vn 50 C=180 Mohm
R 60 EPCla 20 10156 Vn 20 C= 2,5% Vn 50 C= 7 %
R1-rez EPCla 38 1,353 An EPC-1986
t1-EPC 38 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Tg mas trans la 20 C 0,29 0,25 0,25 0,26 0,30 0,30 0,32 0,28 0,32 0,29 0,32 0,31
Tg masurata 0,45 0,38 0,35 0,4 0,43 0,5 0,48 0,45 0,48 0,5 0,45 0,45
%R60mas din R60EPC la 20gr 33 31 27 27 26 27 26 29 29 32 32 31
R60 transpus la 20 3343 3110 2722 2760 2635 2719 2668 2985 2944 3263 3230 3186
R 60 masurata 1750 1690 1680 1500 1550 1320 1450 1500 1600 1450 1900 1800
Roh plot 1-mas 1,332 1,326 1,312 1,325 1,313 1,345 1,326 1,338 1,325 1,353 1,316 1,322
t2-calc la mas 38,0 36,5 35,4 32,9 35,3 33,1 38,9 35,4 37,6 35,3 40,4 33,6 34,7
Delta t 18,0 16,5 15,4 12,9 15,3 13,1 18,9 15,4 17,6 15,3 20,4 13,6 14,71377
kiz 2,06 1,91 1,84 1,62 1,84 1,7 2,06 1,84 1,99 1,84 2,25 1,7 1,77
k tg 1,65 1,55 1,51 1,38 1,51 1,42 1,65 1,51 1,6 1,51 1,75 1,42 1,46
R15 transpus la 20 3362 3533 1944 2429 1870 3296 3165 3343 2263 2768 2584 2620
R15 masurat 1760 1920 1200 1320 1100 1600 1720 1680 1230 1230 1520 1480
Kabs 0,99 0,88 1,40 1,14 1,41 0,83 0,84 0,89 1,30 1,18 1,25 0,00
Fig. 2. The variation of the dissipation factor of the T9 power transformer.
Fig. 3. The variation of the insulation resistance of the T 11 power transformer.
44

Fig. 4. The variation of tangent delta of the T11 power transformer.
Table 7. The results of the TI – earth point measurements performed on the T11 power transformer.
EPC
Tg la EPC 38 C 0,25
Trafo 11
Tg EPC trans la 20C 0,15
R 15 EPC la 38 1000 TI-masa
R 15 EPCla 20 2060
R 60 EPCla 38 2200
Vn 20 C= 600 Mohm Vn 50 C=180 Mohm
R 60 EPCla 20 4532
Vn 20 C= 2,5% Vn 50 C= 7 %
R1-rez EPCla 38 1,373 An EPC-1986
t1-EPC 44 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Tg mas trans la 20 C 0,21 0,24 0,30 0,34 0,44 0,44 0,33 0,21 0,29 0,31 0,37
Tg masurata 0,33 0,36 0,42 0,52 0,62 0,72 0,5 0,33 0,44 0,55 0,52
%R60mas din R60EPC la 20gr 70 63 58 67 64 78 71 76 69 79 65
R60 transpus la 20 3171 2852 2608 3036 2890 3543 3220 3423 3128 3600 2924
R 60 masurata 1660 1550 1610 1650 1700 1720 1750 1720 1700 1600 1720
Roh plot 1-mas 1,332 1,326 1,312 1,325 1,313 1,345 1,326 1,338 1,325 1,353 1,316
t2-calc la mas 38,0 36,5 35,4 32,9 35,3 33,1 38,9 35,4 37,6 35,3 40,4 33,6
Delta t 18,0 16,5 15,4 12,9 15,3 13,1 18,9 15,4 17,6 15,3 20,4 13,6
kiz 2,06 1,91 1,84 1,62 1,84 1,7 2,06 1,84 1,99 1,84 2,25 1,7
k tg 1,65 1,55 1,51 1,38 1,51 1,42 1,65 1,51 1,6 1,51 1,75 1,42
R15 transpus la 20 1815 1693 2138 1582 1105 3193 2453 3483 1380 1575 1326
R15 masurat 950 920 1320 860 650 1550 1333 1750 750 700 780
Kabs 1,75 1,68 1,22 1,92 2,62 1,11 1,31 0,98 2,27 2,29 2,21
Fig. 5. The variation of the insulation resistance of the T11 power transformer.
45

Fig. 6. The variation of the dissipation factor of the T11 power transformer.
4.Thermal ageing of the electroinsulation materials
The lifetime of a transformer is influenced by a series of factors which determine the changes in physical
properties, properties of a mechanical, electrical, thermal or chemical nature.
- Alien substances in the internal insulation: moisture left in the insulation after an inappropriate drying
procedure; residues of the coating varnish solvent that have not been removed when the windings were dried;
blisters or gaseous inclusions left in the insulation after the filling of the transformer with oil; dirt resulted from
an inappropriate operating process. The damping of the insulation brings about an increase of the dielectric
losses as well as a decrease of the dielectric rigidity.
- The contamination of the external insulation, the thermal operating conditions and the altitude at which the
transformer works.
The ageing of the transformers occurs due to the alterations of the physical properties, ageing that brings about
the reduction of their lifetime. The way in which the external factors influence the transformers' lifetime is hard
to study as such a study includes some proof tests that take a great deal of time, that is decades. This is why the
study time must be reduced by reducing the period of testing and by settling the conditions of the testings. For
this purpose functional testings on models are employed. Consequently, mathematical laws have been
established to mirror as much as possible the ageing process of the transformers.
Concerning the electrical equipment, the most sensitive component is the insulation, the lifetime of which
actually determines the lifetime of the equipment. Concerning electrical transformers and static devices
generally, the ageing of the insulation is essential. For the lifetime, laws have been defined experimentally to
state the lifetime of the insulation. For example the Montsinger’s law in which the lifetime is expressed in
degrees Celsius, while in Bűssing’s law the lifetime is expressed in degrees Kelvin.
���
D � �*
e , (5)
V. M. Montsinger’s relation, valid for temperatures between 90� C and 110� C, namely
B
T
D � A*
e , (6)
W. Bűssing’s relation, valid for a large range of temperatures and for a great number of materials of different
classes, here: D - the lifetime of electroinsulant materials; θ, T – the temperature of the electroinsulant material
expressed in degrees Celsius, in degrees Kelvin respectively; α, β, A, B – constant specific to the electroinsulant
material.
The ageing method is based on Arhhenius’s equation, which expresses the degradation rate of an electroinsulant
material:
46

�E
�
� � A*
e KT
, (7)
Arhhenius’s relation, where: A is constant specific to the material; ΔE is activation energy; K is Boltzman’s
constant; T is the temperature of the electroinsulant material expressed in degrees Kelvin.
The law expressed by the relation (5) was established by V.M. Montsinger and it was proved to be valid for A
class electroinsulant materials, but for a relatively limited range of temperature, 90�C...110�C. The law given by
the relation (6) was demonstrated for the first time by W. Bűssing through the kinetic theory of chemical
reactions and it proved to be valid for a great deal of electroinsulant materials of different classes and for a wide
range of temperatures. The lifetime laws of electroinsulant materials take into account not only the temperature
as decisive factor, but also the other factors. The other factors are taken into account when choosing the
constants A and B, and α and β. The values of these constants are chosen in keeping with functional trials of
accelerated ageing. The factors which determine the ageing of the transformers determine their lifetime, deriving
from the lifetime equation of transformers. Thus, the ageing of transformers is marked by:
- The relative factor of thermal ageing, also named relative factor of thermal wear
- Relative thermal ageing, also named relative thermal wear.
The relative factor of thermal ageing is defined by:
� B B �
� � �
�TN
T � � � e
(8)
where: T is the absolute temperature expressed in degrees Kelvin; TN is the absolute temperature at which a
normal lifetime if obtained; B is a constant characteristic to the material.
Considering that the temperature varies in relation to time, the relative ageing factor will be, too, time function as
follows:
�(
t) � e
� B B
� �
�TN
T
The relative ageing factor p(t) marks the degradation rate of the insulating material and, thus, the extent of heat
density of the electroinsulant material. Relative thermal ageing is given by the average value of the relative
ageing factor in a certain period of time, namely:
Replacing the expression for p(t), we obtain:
1 �t
u(
t)
�
�t
0
47
�
� � � t �
��(
t)
dt
1
u(
t)
� � e
�t
� B B �
�t � � �
�TN
T ( t)
�
The two obtained expressions mark the thermal ageing of the electroinsulant material for a single time interval
taken into consideration. If there are several time intervals taken into consideration, the expression of relative
thermal ageing becomes:
0
(9)
(10)
(11)

n
�ui�ti
u(
t)
� i�1
n
(12)
� �ti
i�1
Considering the same range of validity for the relations (5) and (6) written for the temperatures θ şi θN, and T şi
TN, by equaling the following results:
where the following can be written:
and:
e
��
( � ��
)
� �
e
N � e
��(
�N
��)
48
B B
�
TN
T
1 �t
����
��
�
N ( t)
u
�
� e dt
�t
0
The two relations allow the evaluation of the ageing and wear degree of the insulation of electrical transformer.
Where transformers are concerned, temperature is the main factor that influences their behavior, affecting
especially the oil/paper insulation. In this case, dampness is another influencing factor, besides temperature. The
ageing state marked by the thermal ageing factor p, the relative thermal ageing u, respectively, does not allow a
different marking of the insulation considering temperature as main influencing factor. And that is because the
degradation rate of the insulation is influenced by dampness at any temperature, and the ageing p factor may be
considered as a ratio between two degradation rates, one of which the working temperature θ is expressed in
degrees Celsius, namely T expressed in degrees Kelvin, the other at standard temperature θN, TN respectively, the
relative ageing u being the average value of the ageing factor p in a certain period of time. thus, it may be
considered that, if the change in the dampness degree influences in the same way the degradation rate of the
oil/paper insulation, the ageing factor p can be regarded non dependent on dampness. The main component of
the ageing factor p is temperature, but at the same time, through the material constants α and β (actually θ şi β),
A and B respectively (actually TN and B), it depends on the other chemical, mechanical or electrical factors that
influence the ageing process of the oil/paper insulation from of the transformers.
5.Thermal wear of the transformers
In most cases the thermal wear of transformers in oil is determined by the wear of the paper impregnated with oil
insulation. It is usually exposed to the highest of temperatures of the transformer close to the curves. The starting
points of analyzing the wear of the transformer are two distinct cases: the temperature of the transformer varies
distortionless with time and the temperature varies exponentially with time. Each of the cases can be treated as
having as basis either Molntsinger’s law (5) or Bűssing’s law (6) [6], [9], [11], [14].
6.1. The case of distortionless variation of temperature in relation with time
The case in which the ageing of the transformer is reflected by Montsinger’s law (5). Taking into consideration
at the initial moment t=0, θ=θi, after a time t=tf the temperature θ=θf respectively, the distortion less variation of
the temperature can be written in relation with time, in degrees Celsius, after an expression as follows:
t
� ( t) � �i
� ��
�t
,
��
� �
f � �i
(13)
(14)
(15)
(16)

where: θi is the temperature at the beginning of the time period taken into consideration, expressed in degrees
Celsius; θf is the temperature at the end of the time period taken into consideration, expressed in degrees Celsius;
Δt is the period of time during which the temperature increases from θi to θf.; t is time as current variable.
The case in which the lifetime law of the transformer is expressed by the relation (6) given by Bűssing. In this
case, the temperature variation in relation with time is expressed in degrees Kelvin after an expression as the
following:
T( t)
� Ti
� �T
t
�t
There are the following relations between the two temperatures:
,
49
T T � � �
f i T
(17)
Ti=273+ θi; Tf=273+ θf, (18)
namely ΔT=Δθ (19)
The thermal ageing factor p as well as the relative thermal ageing u by means of their parameters link the relative
sizes that lead to the ageing of the materials and based on the variation of these factors in time the curves that
will characterise the ageing of the electroinsulant materials will be able to be marked off. This paper deals with
the evaluation of the relative thermal wear based on the Montsinger’s law employing the relation (5).
Thus, considering the distortionless variation of temperature the relation becomes:
By integrating the expression u(t), we bwill obtain:
� � t ��
�t ����N
���i
���*
�
1
�
t
u t � e � � � �
( ) � *
�dt
�t
0
1 � ��
u � * e
���
��
��N ��
f � ��
�
�� ��
e N i �� ��
The expression (21) allows us to determine by computing the relative thermal wear, applicable both for a cooling
process and in the case of one cooling process.
6.2 The case of exponential variation of temperature in relation with time
The exponential variation of temperature is deducted using the heating equation considering the parameter
constant Tt only in input condition with P=constant. When I= constant, Tt depends on I, and when U=constant it
depends on U and t. In these conditions we will continue to consider the hypothesis that P=constant and/or
I=constant.
In the case of exponential variation of temperature, temperatures θ and T become:
respectively:
t
�
T
� �t� � �
t
r � ��*
e
(20)
(21)
(22)

T
�t� � T � �T
* e
r
where: θr, Tr – stand for stationary regime temperatures expressed in degrees Celsius, and degrees Kelvin
respectively; Δθ, ΔT – stand for the difference between the stationary regime temperature and the temperature at
the beginning of the thermal process expressed in degrees Celsius, and degrees Kelvin respectively; Tt is time
constant with wich the temperature variation process takes place[s], having the following relation between the
temperatures:
50
t T
t
�
(23)
Tr=273+ θr; Δθ=ΔT (24)
The temperature differences Δθ, ΔT are of different symbols, according to the process, whether it is of cooling or
of heating. If we take into consideration the thermal wear based on Montsinger’s relation, the following results:
�t
1
u � � e
�t
0
� � t ��
� � � ��
����
���
���
T
N
��
� �
r * e t
��
� �
�
� �
��
�
Placing the independent of time factors before the integral, the following results:
��
u �
�� � � � �t
N r
�
�t
0
e
t
�
����*
e
Tt
Consequently, the wear variation u can be figured for different values of the material constat β and certain values
of the time value Tt.
In order to simplify the display, relative values can be thus used:
� � ��
*
;
With the above, the expression of thermal wear becomes:
e
u �
* t
t �
Tt
�
�� *
��
*
�
�
�
r N
� �t *
���
* �
*
e
t
* � e
�t
0
;
dt
dt
* dt
dt �
Tt
The value of the integral in the expression of the thermal wear depends on the parameters Δθ * and Δt * which
have a symbol for a cooling process and a different one for a heating process. Thus, in the case of the heating
process Δθ * is positive, and in the case of the cooling process Δθ * is negative. Consequently, the evaluation of
the thermal wear must be dealt with differently.
dt
*
(25)
(26)
(27)
(28)

6.2.1 The case of thermal heating process
In the case of the thermal heating process, Δθ * being positive, the following form of expressing the heating
integral Ii appears to be more convenient, employing the relation (28):
�t *
�e�
�ln ��
*
�t
*
�
�
� � � *
Ii
� e
dt
0
If lnΔθ * -t * is noted with x, dx=-dt * results, the expression of the integral Ii becoming:
The function
I
51
(29)
ln ��
*
�e
x
i � � e dx
ln ��
*
��t
*
(30)
y � e
is shown in the Fig. 7a the shaded area represents the value of the integral in the relation (10).
�e
a) b)
Fig. 7. The variation of function: a) y=f(x); b) f(x).
The value of the integral can be expressed as a difference between two values of the function f(x):
Considering the limits of integration:
x
� 0 x
f ( x)
x
* *
x ln�� � �t
the integral Ii can be expressed at heating as it follows:
� ,
� e
�e
x
dx
*
0 � ln�
x
(31)
(32)
(33)

� � � � *
* *
ln�� � �t
� ln��
Ii � f
f
(34)
The graphical representation of f(x) in relation to x is shown in Fig. 7b where the way of obtaining the integral Ii
is also explained. The graphical representation requires the setting of the limits of integration x0 and xi of the
heating integral Ii. The limit of integration x0 is chosen in a way that it accomplishes the condition lnΔθ *

where:
Ir � g
x
x
e �e 1�
�(
x)
� � �
x
0
53
dx
*
* * * *
�ln�� ��
���
g�ln��
��
��
t � � �t
� �t
a) b)
Fig. 8. The variation of the function y= e .
The graphical representation of the function � (x)
is shown in the Fig. 8b, illustration followed by the
explanation of how the integral Ir is obtained. The random limit of integration x0, similar to the case of the
heating process, depends on the sought accuracy in obtaining the value of the integral Ir. If a 1% error is
accepted, it can be considered that at cooling the curve y is identical with an asymptote to which it tends
(ordinate asymptote 1) when y reaches the value 1.01. The corresponding abscissa is a result of e �1,
01 as
being x0= - 4.6. Thus, for x

values of the constants β şi B. Fig. 9 [9] shows: curve 1 with dotted line – relation (14), corresponding to the “8
degree Celsius rule”, with β=0.08664 0 C -1 ; curve 2 with dotted line - relation (14), corresponding to the “6
degree rule”, with β=0.11552 0 C -1 ; curve 1 with solid line – relation (8), with B=11500 0 K; curve 2 with solid
line – relation (8), with B=14573 0 K; curve 3 with solid line – relation (8), with B=17184 0 K.
For all the cases the following were considered : θN=95 0 C, and TN=368 0 K, resulting ρ=1.
θN and TN represent the temperature at which the degradation rate of the electroinsulant material is considered to
be normal. These constants have been set starting from the hypothesis that the highest temperature admisible
must allow a 25000 hour working time of the transformer, that is at a temperature of 95�C about 30 years of
working time. Thus, taking into consideration the lifetime laws and all the above mentioned, θN is considered to
be ranging between 95 0 şi până la 98 0 C. Even 110�C is acceptable. The highest temperature admisible
determines the aspect of the curves that characterise the thermal wear of the transformers [11], [13]. Regarding
the transformers with oil its limit is 115�C
Fig. 9. The variation curves of the thermal ageing relative factor ρ in relation to the temperature.
Sometimes in cases of overvoltage conditions, this peak temperature can increase, without having negative
effects on the way the transformer works though. Experiments have revealed that in cases of overvoltage the oil
can reach 115�C, while the curves can reach even 200�C. This does not break the transformer if it lasts between
one and four hours, but it influences its ageing process. In case of shortcircuits the temperature limit of 250�C is
considered, in the hottest spot of the transformer.
6.2.3 The heating process case
The numerical computation of the function f(x) defined by the relation (31) allows the evaluation of the thermal
ageing of the transformer in case of a heating process, the limits of integration x0 and xi being set according to
the above.
Thus, the limit of integration x0 is obtained from the condition:
where lnΔθ*max is the peak value of Δθ* that can appear in use.
X0>lnΔθ*max (42)
The starting point is the hypothesis that the peak temperature of stationary regime that can appear in use is
Δrlim=200 0 C. In this case, the initial temperature can be lower than that which determines a minimum thermal
wear relative factor ρmin which must be taken into account. Therefore, when computing the function f(x) that
minimum value of Δθ is taken into account, value which at θr=200 0 C determines a temperature of which a
relative wear factor ρmin complies with. For higher values of Δθ, we will consider ρ=0 and so f(x), too. Taking all
these into consideration Δθ*max will be obtained from the condition:
54

� �� ���
���
��
� N r
then considering ρmin=0.1 and θr=200 0 C the following results:
e
55
max � �
min
(43)
Δθ*max=β(200-θN)+2.3 (44)
Taking into account the relation (43) in order to obtain the highest value of Δθ*max the lowest value possible
must be considered for θN and the highest for β. On the observations carried out previously we can consider the
extreme values θN=95 0 C, and β=0.126 0 C -1 . We thus obtain:
From the relations (41) and (44) the following results:
Δθ*max=0.126*(200-95)+2.3=15.53 (45)
X0=ln 15.53=2.75 (46)
When x has values over 2.75, we consider f(x)=0. The lower value of the variable x that is taken into
consideration is the one defined by the relation (31), namely xi= - 4.6. Fig. 10 [8] presents the variation curves
for f(x), and Δθ respectively, for different values of the constant β.
Fig. 10. The variation of the function f(x) and of temperature for different values of the material constant β.
When using the curve f(x) the wear corresponding to the wear factors below 0.1 is neglected only in the critical
case θr=θrlim. At other values of θr by using the curve f(x) the neglect of the wear appears in the case of wear
factors below a critical case of ρ than 0.1. This critical case can be determined with the relation:

considering x0=2.75.
56
x
�� ���
�e
0 ��
��
N r
� lim � e
(47)
As established, the constant ρlim depends on the parameters β, θr, θN, but it must always be ρlim≤0,1.
6.2.4 The cooling process case
The numerical computation of the function φ(x) defined by the relation (39) allows the evaluation of the thermal
ageing of the transformer in case of a cooling process, the limits of integration x0 and xi being set according to
the above. Thus, the value of the minimum limit is x0= - 4.6. The upper limit of integration results from the
condition:
xS
�� � �
� ln �
* max
considering –Δθ*max as the peak value for -Δθ* that can appear in use.
As shown previously, we take the hypothesis that the cooling temperature can not be higher than 140�C as
starting point. Exaggeratedly considering that the cooling takes place towards 0�C, the following results –
Δθ*max=140 0 C. The peak value of -Δθ* complies with the peak value of β=0.126 0 C -1 . Consequently we obtain:
(48)
xS=ln(0.126*140)=2.87 (49)
With the limits x0 şi xs thus delimited in the Fig. 11 the variation of the function φ(x) has been featured together
with that of Δθ with the help of which the evaluation of the ageing factor of the insulation of the transformer can
be carried out. The function φ(x) becomes equal with zero when x=-4.6. Because when x=-4.5, φ(x)=0.001
results, employing the curve in Fig. 11 [9] we will consider φ(x)=0 la x≤-4.5.
Fig. 11. The variation of the function φ(x) and of size Δθ for different material constants β.

6.3. Computing examples
As stated previously, the lifetime as well as the wear factor determines the state of ageing of the transformers.
For example, by using the Mathcad medium, the cases of some transformers from a transformer station from
Romania has been taken into consideration.
1) In the case of the first transformer, to compute the wear the following initial data have been used: the initial
temperature θi=78 0 C, the temperature is considered to vary exponentially with time, the stationary regime value
is θr=110 0 C, the time period of the thermal process is considered to be 2.25 hours, the time constant of the
thermal process is considered to be 1.5 hours. θN=95 0 C şi β=0.086643 0 C -1 are accepted, considering the “8
degree Celsius rule”. The stationary regime temperature being higher than the initial temperature, and so a
heating process takes place for which Δθ=110-78=32 0 C and as a consequence the f(x) curve will be used to
determine the values x1 and f(x1). Therefore, on the basis of the diagram in Fig. 10 for β=0.086643 0 C -1 , the
following values will be obtained: x1=1.02, f(1.02)=0.01745. Using relative values we obtain:
For the limit x2 the following is obtained x2=x1-Δt * =1.02-1.5=-0.48 with which f(-0.48)=0.4376 complies.
Consequently, considering the relations 28 and 33, the thermal wear will be:
e
u �
0.
086643
1.
5
�110�95� �0. 4376 � 0.
01745�
�1.
03
As the wear u is over unity, the thermal wear for the considered time period is lower than the normal wear. From
the Fig. 10, it can be established that, at the end of the time period, as x2=-0,48 complies with Δθ=7 0 C, the final
temperature will be θf=110-7=103 0 C.
2) In the case of the second transformer, the initial data are the following: the initial temperature θi=120 0 C, the
temperature varies exponentially with the time, the stationary regime temperature is θr=85 0 C, the period of time
for the thermal process is 4 hours, the time constant of the thermal process is 2 hours. θN=98 0 C şi β=0.11552 0 C -
1 0
are accepted, taking into consideration the six degrees rule. From the initial data, Δθ=85-120=-35 C, resulting
that we are dealing with a cooling process, the function φ(x) will be employed. From the diagram φ(x), for the
value β=0.11552 0 C -1 , the following results: –Δθ=35 0 * 4
C, x1=1.40, φ(1,40)=18.42; in relative values: �t � � 2 .
At x2=x1-Δt * =1.40-2=-0.60, in Fig. 11, we can establish φ (-0.60)=0.62. The thermal wear, computed on the
basis of the relations (28) and (41), will be:
0,
11552
e
u �
�85�98� 18,
42 � 0,
62 � 2
2
� � � 2,
21
Furthermore, with the help of the graphical representations in Fig. 11 the temperature at the end of the 4 hour
period can be established. Thus, from the diagram of the function φ (x), for x2=-0.6 –Δθ=5 0 C will result and so
the final temperature will be: θf=85+5=90 0 C.
3) The general case in which the variation range of the constant β can be considered with the following initial
data: the initial temperature θi=75 0 C, the temperature varies exponentially with time, the stationary regime
temperature is θr=110 0 C and θN=95 0 C. The graphical representation of the function f(x) by reproducing the
coordinates, is presented in Fig. 12.
57
.
�t
*
�
2,
25
1,
5
2
�
1,
5
.

Fig. 12. The variation of the function f(x).
The variation of the thermal wear with the variable parameter with the help of the Mathcad program is shown in
Fig. 13.
Fig. 13. The variation of the thermal wear with the material constant β.
Considering the same initial data we can observe the variation of the thermal wear with the temperature in Fig.
14.
58

Fig. 14. The variation of thermal wear with temperature.
CONCLUSIONS
As a result of the studies carried out, it can be stated that a number of the transformers from different transformer
stations are improperly employed, that is under their real potential. An estimation of the wear of the transformers
and of their ageing degree can be carried out considering the lifetime law by taking into consideration certain
grounds and specific particularities:
- the transformers are subject to different stresses, among which the thermal stress can be considered to be the
most significant;
- transformers can be considered to have a certain typical thermal behaviour;
- the evaluation of the behaviour is carried out by applying the lifetime law with certain values of the constants
that interfere with the law, most often making use of the rule of ”n” degrees. The most eloquent results have
been obtained by using the “8 degree Celsius rule”;
- certain load curves, of special shapes, are allowable.
The thermal behaviour of transformers can be understood by means of mathematical relations, simulated through
mathematical computation programme, such as the Mathcad programme. The results of the mathematical
simulation can be visualised using curves and tables in which a particularization of the calculus is needed having
in view: the behaviour of the transformers from a thermal point of view, the charging conditions of the
transformer, the characteristics of the insulation and the evaluation method of the thermal ageing of the
insulation.
REFERENCES
Ilie F., Bulucea C.A., Popescu M.C.,(2009), Simulations of Oil-filled Transformer Loss-of-Life Models,
Proceedings of the 11 th International Conference on Mathematical Methods and Computational
Techniques in Electrical Engineering (MMACTEE'09), Published by WSEAS Press, pp.195-202,
Vouliagmeni Beach, Greece.
Mastorakis N., Bulucea C.A., Manolea Gh., Popescu M.C., Perescu-Popescu L., (2009) Model for Predictive
Control of Temperature in Oil-filled Transformers, Proceedings of the 11 th WSEAS International
Conference on Automatic Control, Modelling and Simulation, pp.157-165, Istanbul, Turkey, May.
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Model Parameters of Oil-Filled Transformers, WSEAS Transactions on Circuits and Systems, Issue 6,
Vol.8, pp.475-486, Available: http://www.worldses.org/journals/circuits/circuits-2009.htm
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Mastorakis, N.. Bulucea, C.A., Popescu M.C., (2009) Transformer Electromagnetic and Thermal Models,
Proceedings of the 9 th WSEAS International Conference on Power Systems (PS`09): Advances in
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on Heat and Mass Transfer, Issue 4, Vol.4, pp. 87-97, October.
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Model Extension for Variation of Additional Losses with Frequency, Proceedings of the 11 th WSEAS
International Conference on Automatic Control, Modelling and Simulation, pp.166-171, Istanbul,
Turkey.
Popescu M.C., Manolea Gh., Perescu L., (2009), Parameters Modelling of Transformer, WSEAS Transactions
on Circuits and Systems, pp.661-675, Issue 8, Vol.8.
Available:http://www.worldses.org/journals/circuits/circuits-2009.htm
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Transformers Study, WSEAS Transactions on Circuits and Systems, Issue 6, Vol.8, pp.487-497.
Available: http://www.worldses.org/journals/circuits/circuits-2009.htm
Popescu M.C., Mastorakis N., Bulucea C.A., Popescu-Perescu L., (2009) Modelling of Oil-filled Transformer,
International Journal of Mathematical Models and Methods in Applied Sciences, Issue 4, Vol.3, pp.346-
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Transactions on Power Systems, Issue 6, Vol.4, pp.199- 209,
June.Available:http://www.worldses.org/journals/power/power-2009.htm
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Engieenering Research, pp.001-037.
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Ambient Temperature Profiles in Transformer, Proceedings of the 13 th WSEAS International
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Vouliagmeni Beach, Greece.
60

Ozean Journal of Applied Sciences 3(1), 2010
A disconnect congestion detection from
TCP to improve the robustness
Issa Kamar and Seifeddine Kadry
AUL university, Beirut, Lebanon
Lebanese University - Faculty of Science, Lebanon
E-mail: seifdine.kadry@aul.edu.lb, Issa.kamar@aul.edu.lb
Abstract – The Transmission Control Protocol (TCP) is the most popular transport layer protocol for the internet.
Congestion Control is used to increase the congestion window size if there is additional bandwidth on the network, and
decrease the congestion window size when there is congestion.This paper uses a classic TCP which we called Robust
TCP with an accurate algorithm of congestion detection in order to improve the performance of TCP. Our TCP Robust
only reacts when it receives an ECN (Explicit Congestion Notification) mark. The evaluation result shows a good
performance in the terms of drop ratio and throughput.
Keywords: Congestion Control, TCP, ECN, Implicit Congestion Notification.
________________________________________________________________________________________________
I. INTRODUCTION
TCP is a connection-oriented, end-to-end reliable protocol designed to fit into a layered hierarchy of protocols which
support multi-network applications. Congestion events in communication networks cause packet losses, and it's well
known that these losses occur in burst.TCP congestion control involves two tasks:
1. Detect congestion
2. Limit Transmission rate
To achieve good performance and obtain a Robust TCP, it is necessary and important to control network congestion, by
limiting the sending rate and regulating the size of congestion window (Cwnd) after the detection of congestion.TCP
congestion control operates in a closed loop that infers network conditions and reacts accordingly by means of losses. A
negative return is due to a loss of a segment which can be translated by decreasing the flow from the source through a
reduction in the size of window control.
TCP considers loss of a segment as a congestion in the network, the detection of this loss can be done in several ways:
Timeout, Three Duplicate ACKs (Fast retransmit) and by receiving a partial ACK.
The state is:
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
61

� If Packet Loss or congestion event =>TCP decreases Cwnd.
� All is well and no congestion in the network, i.e., TCP increases Cwnd.
At all cases, loss indication should be done with accuracy because it may lead to false indications like: Spurious
retransmission.
Spurious timeout occurs when a non lost packet is retransmitted due to a sudden RTT (Round Trip Time) increase (hand
over, high delay, variability, rerouting . .) which implies to an expiration of the retransmission timer set with a previous
and thus outdated RTT value.
This effect is known to be the root cause of spurious retransmission.
The function of the congestion control is an essential element to the stability of the internet.
Indeed, TCP congestion control reduces the flow when it detects a loss in the network. Therefore, it is important to be
accurate in the loss detection to improve the performance of TCP.
A congestion event (or loss event) corresponds to one or several losses (or in the context of ECN: at least one
acknowledgment path with an ECN-echo) occurring in one TCP window during one current RTT period, it means that a
congestion event begins when the first loss occurs and finishes one RTT later.
In this paper, we propose a congestion detection algorithm that is realized independently of the TCP code. To improve
the TCP by reducing the Cwnd, we aim to illustrate the feasibility of the concept by demonstrating that we can both
obtain similar performances and also improve the accuracy of the detection outside the TCP stack.
We implement the Implicit Congestion Notification (ICN) algorithm to better understand and investigate the problem of
congestion events estimation.
This paper is organized as follows: section 2 presents related works, section 3 shows the architecture of the congestion
detection, section 4 presents the detailed discussion for the Robust TCP with ICN congestion detection algorithm, and
section 5 presents an evaluation of the TCP Robust using simulations.
Finally, section 6 concludes this article and presents some perspectives.
II. RELATED WORKS:
Over the past few years, several solutions have been proposed to improve the performance of TCP. In [5] proposed
TCP-DCR modifications to TCP's congestion control mechanism to make it more robust to non-congestion events, this
is implemented by using the delay "tau" based on a timer.
Our mechanism is different; it relies on the accurate congestion detection algorithm (ICN) and uses the timestamp option
to detect spurious timeout which can more improve the reliability of the algorithm and leads to a real Robust TCP.
In Forward RTO-Recovery (F-RTO): the F-RTO algorithm of the TCP sender monitors the incoming
acknowledgments to determine whether the timeout was spurious.
TCP suffers from the inaccuracy of the congestion detection in the other TCP agents, for this reason we design an
accurate mechanism of congestion detection (ICN) that interacts with TCP robust.
Our study must prove the functionality of our TCP with ICN is better than other versions of TCP. For this point we have
to show that the mechanism of congestion detection for some TCP variants (New-Reno, Sack) doesn’t detect well when
there is congestion and doesn't not work well more than TCP Robust with ICN.
In [5], the idea or the solution proposed for the detection of congestion is the delay of the time to infer congestion by T,
and this value should be large to recover from non-congestion event, and should be small to avoid expensive RTO.
Our approach is different by using a classic TCP that responds only to an accurate algorithm of congestion detection.
62

III. STAND-ALONE TCP CONGESTION EVENTS ALGORITHMS
In this section we present the architecture of decorrelating congestion Detection from the Transport Layer (figure 1).The
main goal of this architecture is to simplify the task of kernel developers as well as improve TCP performances. This
scheme opens the door to another way to react to congestion by enabling ECN emulation at end-host. In this case ICN
emulates ECN marking to imply a congestion window reduction.
Figure. 1. Decorrelating Congestion Detecting from the Transport Layer
IV. ROBUST TCP ALGORITHM:
Our proposed algorithm which we called Robust TCP is to make the congestion detection reliable and to distinguish the
causes of losses in order to improve the flow control.
The main idea is to determine CE (i.e. the congestion detection) which impact on the TCP flow performance by
monitoring the TCP flow itself.
The principle is to obtain a detection system at the edge of a network or at the sender side which analyses the TCP
behavior through the observation of both data packets and acknowledgments paths.
So, the scenario is to make a new version of TCP (Robust TCP) without detection of congestion. Robust TCP doesn't
reacts (reducing of Cwnd) whenever it doesn't receive a notification ECN. Robust TCP must interact with ICN algorithm
through ECN. Once we have congestion indication and the congestion event is validated, in this case it must notify the
TCP we are exploring the functionality of Robust TCP and the ICN algorithm with the interaction between each other.
Robust TCP maintains all the functions of TCP Reno (slow start, Congestion avoidance, Fast retransmit and Fast
recovery) and modified by adding error control and limited transmit (like in New-Reno TCP) to avoid unnecessary
timeouts.
Robust TCP is a classic version of TCP but very sensitive to packet loss. It contains the major congestion control
phases:
1. Slow start and congestion avoidance (increase Window size).
2. Fast retransmit (Detection of congestion).
3. Fast recovery.
1. Slow start:
63

� When ACK received: cwnd++ which means for every ACK received, the sender sends two more segments.
� Exponential increase in the window (Every RTT: cwnd = 2*cwnd)
� Threshold (sstrhesh) controls the change to congestion avoidance.
2. Congestion avoidance
� When ACK received: cwnd+ = 1/cwnd.
� Linear increment of cwnd (every RTT: cwnd++) slow start is exists until cwnd is smaller or equal to ssthresh.
Later congestion avoidance takes over.
3. Fast retransmit:
TCP generates duplicate ACK when out-of-order segments are received. In this case Fast retransmit uses "duplicate
ACK" to trigger retransmission packets, so the sender does not wait until timeout for retransmission, sender retransmits
the missing packet after receiving 3
DUPACK.
4. Fast recovery:
TCP retransmits the missing packet that was signaled by three duplicate ACKs and waits for an acknowledgment of the
entire transmit window before returning to congestion avoidance. If there is no acknowledgment, TCP Robust
experiences a timeout and enters the
Slow-start state.
TCP recovers much faster from fast retransmit than from timeout. When congestion window is small, the sender may not
receive enough dupacks to trigger fast retransmit and has to wait for timer to expire but under
Limited transmit, sender will transmit a new segment after receiving 1 or 2 DUPACKs if allowed by receivers advertised
window to generate more dupacks.
Robust TCP is poor in performance without detection of congestion and worse than other TCP like TCP New-Reno and
Sack. It reacts only on the receiving of ECN notification.
Once it doesn't receive a notification that means there is no congestion control on TCP and the window keep increasing,
but in case of receiving ECN that will indicate the occurrence of congestion indication notified by ICN, than Robust TCP
reacts by limiting its sending rate and takes the full meaning of its name.
IV.1 ICN WITH TIMESTAMP
ICN (implicit congestion notification) is an algorithm for congestion detection implemented outside the TCP stack to
analyze TCP flow and to better understand the problem of congestion events and than to conclude if the congestion
occurs in the network or no and it is also more accurate in congestion detection than TCP.
The main goal of ICN is to determine the losses (i.e. the congestion detection) which impact on the TCP flow
performance by observing the flow itself which mean by looking at the losses occurring over an RTT period given.
ICN is a generic algorithm that doesn't depend on the TCP version used which implements a congestion control where a
negative feedback means a loss. It is important to note that ICN doesn't manage the error control which remains under
the responsibility of TCP
Starting from the observation of the data segments and the acknowledgments, we identify each TCP connection with a
state machine. This state machine indentifies the control congestion phase and classifies retransmission as spurious or
not.TCP congestion control reacts following binary notification feedbacks allowing assessing whether the network is
congested or not.
ICN algorithm consists of two states:
1. Normal state: which characterizes TCP connection without losses, in this state no congestion occurs and the sender
receive the ACK normally.
64

2. Congestion state: This state starts from the loss of the first window data segment. When a loss occurs ICN enters in
this state and waits to the congestion event to be validated to notify Robust TCP about this loss. When the top of the
window is acknowledged, ICN enters in the normal state.
To improve the performance of the congestion detection algorithm and especially against spurious timeout we added the
timestamp option, in order once the congestion happens ICN enter in this state and append a timestamp to let the sender
to compute the RTT estimate based on returned timestamp in ACK.
Time stamps used in this state to measure the round trip time (RTT) of a given TCP segment and including retransmitted
segment, this option also can help to eliminate the retransmission ambiguity ( due to false indication) and identifies when
retransmission is spurious or not.
Spurious Timeout are inevitable and not rare in data networks, for this reason and once the congestion event occurs, ICN
enter in the congestion event state, timestamp is added for each data segment. Timestamp can be considered as an
acknowledging mechanism in the time domain.
In the figure (2) shown below we will present the flowchart of TCP Robust with ICN mechanism:
IV.2 Robust TCP and ICN interaction
Figure 2: Robust TCP with ICN detection algorithm
ICN is an accurate congestion detection algorithm where after detecting a loss event in the congestion state, the
congestion event (CE) must be validated.
The validation of CE should lead to a congestion indication which is the principle responsible to inform the Robust TCP
about the congestion. The confirmation method due to a congestion indication is ECN (Explicit congestion notification),
which is the main fag in the ACK to notify the loss to the source TCP. Once the source is signaled by ECN notification it
reacts by reducing its window (Cwnd) and this time Robust TCP takes the full meaning of its name.
After reducing its window, we can notice very well the decreasing of the number of dropped packets (d) in the network
due to using of ICN congestion detector and our TCP becomes better in performance than others like
TCP New-Reno and Sack.
65

V. VALIDATIONS AND EVALUATIONS
In this section we evaluate the performance of Robust TCP with ICN algorithm. The main idea is to build an algorithm of
congestion detection outside the TCP stack that is responsible to detect the loss and notify it to Robust TCP.
The architecture of our tools is shown in the figure (3), which is mainly composed from the following components:
1. Network topology.
2. Traffic model.
3. Performance evaluation metrics.
After the simulation is done, a set of result statistics and graphs are generated.
V .1 Network topology
Figure 3: Architecture of our tools
To study our TCP and ICN behavior we built our Network and application model shown in figure (4), in which source
nodes and sink nodes connect to router 1 or router 2. The bandwidth between the two routers is much lower than the
other links, which causes the link between the routers to be a bottleneck. (Traffic can be either uni-directional or
bidirectional).
V.2 Traffic Model
Figure 4: Network topology
The tool attempts to apply the typical traffic settings. In our application include the FTP traffic that uses infinite, nonstop
file transmission, which begins at a random time and runs on the top of TCP. Implementation details and a
comparative analysis of TCP Tahoe, Reno, New-Reno, SACK and Vegas choices of TCP variant are decided by users.
V.3 Performance evaluation metrics
The metrics used in our simulations are Throughput and Drop ratio. Throughput is the total elapsed flow since the
beginning of simulation time. Throughput may also includes retransmitted traffic (repeated packets).Drop ratio is the
total rate of packet loss during the simulation time. To obtain network statistics, we measure also the drop ratio metric
that result in the failure of the receiver to decode the packet and simulation time is 100 seconds.
66

Robust TCP is poor in performance as a standalone TCP but after adding the ICN it becomes much better (see figure 5)
and accurate than TCP New-Reno as show in the figure (6). To evaluate our scenario, we compare TCP Robust with
other TCP variants (TCP New-Reno) by using different metrics that will show us clearly the improvement of our TCP
version compared to others. (Figure 6 and 7).
Figure 5: Comparison between TCP Robust before and after adding ICN algorithm.
The main difference between Robust and New-Reno TCP occurs in the reaction of each protocol. In the TCP New-Reno
the reaction will be whenever an error or congestion occurs on the network by slowdown the transmission without being
accurate if there is a congestion or not. In addition of that the main problem of New-Reno TCP that it suffers from the
fact that it takes one RTT to detect each packet loss. When the ACK for the first retransmitted segment is received only
then we can deduce which other segment was lost. This problem of inaccuracy in TCP New-Reno is solved by the ICN
algorithm that the ICN receive the packet and check the presence of congestion by using the normal and congestion
phase and by adding the timestamp option which can be make sure of the presence of congestion or no. The deduction of
congestion in TCP Robust is different from New-Reno, it will be deduced after signaling ECN from ICN to TCP robust,
and then the TCP reacts by decreasing the transmission. This accuracy in detection of congestion can be up to 24 % as
difference between the two protocols (Figure 6) before reaction of each one and starting slowdown retransmission.
Due the fast reaction of TCP robust, the transmission of TCP become less than in TCP New-Reno which means that the
throughput in the TCP robust must be less than in New-Reno, this is clear and deduced in the figure 7.
Figure 6: Comparison between TCP Robust and TCP New-Reno
67

In figure (6) represents that the drop ratio is less in Robust than in New-Reno due that TCP reacts only when receiving
ECN which make its reaction faster.
Figure 7: Comparison between TCP Robust and TCP New-Reno
In figure (7) Robust TCP algorithm reaction is faster than the Reaction of New-Reno, thus Throughput in New-Reno is
higher than when using Robust TCP. Congestion detection used by ICN algorithm is more accurate when using the
timestamp option for detecting a spurious timeout which improve more the performance of TCP.
The main difference between spurious timeout algorithms relies on the method how to detect spurious timeout by solving
the retransmission ambiguity in many circumstances. After clarifying this ambiguity TCP can tell whether the data is there
is spurious timeout has happened or not. DSACK, F-RTO and Robust TCP can see the problem of spurious timeout in
different aspects.
DSACK, an extension of TCP SACK, works it out in the sequence space. It requires the TCP receiver explicitly
acknowledging duplicate segments with duplicate SACK options. F-RTO algorithm is used for detecting spurious
retransmission timeouts with TCP. It is a TCP sender-only algorithm that does not require any TCP options to operate-
RTO delays the decision of loss recovery and waits further two ACK. If the first arrived ACK forwards the sender's
transmitting window, TCP concludes a spurious timeout and resume transmitting new data.
Our approach is different than other TCP by using an algorithm of congestion detection outside the TCP code, where it
can detect congestion and spurious timeout by using the timestamp option at the occurrence of loss or congestion event.
The main advantage of ICN with timestamp algorithm is that it can work with spurious timeouts and the others loss
events by detecting the congestion in the network immediately and then directly will be notified to Robust TCP in order
that TCP after this action will reduce its window, which can improve very well the performance of our TCP.
VI. CONCLUSION AND FUTURE WORK
This paper has proposed a new algorithm, which is implemented as a stand alone component and not inside a TCP stack.
This algorithm that interacts with a classic version of TCP is able to detect congestion and notify directly the loss to the
Robust TCP through the congestion notification (ECN) in order to reduce its window which leads to a Robust TCP
compared to other variants like New-Reno and SACK TCP. In our work we demonstrate that congestion event detection
can be realized independently of the TCP code in sake of better detecting congestion occurring in the network.
Following this work and the results obtained so far, we are currently planning to develop more the detection of
congestion by using the delay-based in the congestion detection algorithm (ICN) and the effect of fast reaction of TCP
robust in the Network.
68

REFERENCES
A Comparative Analysis of TCP Tahoe, Reno, New- Reno, SACK and Vegas.
Bhandarkar, S. and Reddy, A.L.N. (2004), Networking, May TCP-Dcr: Making TCP Robust to Non-Congestion Events.
K. Ramakrishnan, S. Floyd, and D. Black, (2001), The addition to explicit congestion notification (ECN) to ip. Request
for comments 3168,IETF.
P. Anelli and F. Harivelo, E. lochin. On TCP congestion events detection.
P.sarolahti and M. kojo. (2005), Forward rto-recovery (f-tro): An algorithm for detecting spurious retransmission
timeouts with tcp and the stream control transmission protocol (sctp).rfc 4138,IETF.
Reiner Ludwig and Randy H..Katz, (2000), The Eifel algorithm: making TCP robust against spurious retransmissions.
SIGCOMM Comput. Common. Rev., 30(1):30-36.
RFC 3649, (2003), High Speed TCP for Large Congestion Windows, S. Floyd.
RFC 793, (1981), Transmission Control Protocol, September .
S. Floyd, (2003), ICSI. RFC 3649 - High Speed TCP for Large Congestion Windows.
69

Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
ANALYSIS OF MICROWAVE SIGNAL RECEPTION USING FINITE DIFFERENCE
IMPLEMENTATION. (A CASE STUDY OF AKURE – OWO DIGITAL MICROWAVE
LINK IN SOUTH WESTERN NIGERIA)
OTASOWIE P.O* and UBEKU E.U.
Dept of Electrical /Electronic Engineering University of Benin, Benin City Nigeria.
*E-mail address for correspondence : potasowie@yahoo.com
______________________________________________________________________________________________
Abstract: In this work, the finite difference method have been used to model microwave signal propagation. The data
used for this analysis were gathered between January and December 2006 in Akure – Owo digital microwave line of
sight link in southwestern Nigeria.The data collected were analyzed using finite difference method and writing a
program in MATLAB 7.0 software program to obtain a model equation for the line of sight link. The results of the
work shows that the months of August, September, July and January have the poorest signal reception while the
months of February, march and April have the best signal reception in the link.The results of the predicted model
were validated by measured data and the results obtained showed that the developed model can be used to accurately
predict the link degradation parameters.
Keywords: Microwave link, finite difference method, average signal level, signal reception.
_____________________________________________________________________________________________
Justification for the work
INTRODUCTION
Microwave signal transmission and reception in Nigeria especially for telephone services is very poor basically
because of non-availability of data for planning and design of microwave links. There is a need for the build up of
such a database in Nigeria [1]
Microwave signal propagation
Microwave radio relay is a technology for transmitting digital and analog signals such as long – distance telephone
calls and the relay of television programs between two locations on a line of sight radio path [ 2, 3 ]
In a microwave radio relay, a line of sight link is required, therefore obstacles, the curvature of the earth, the
geography of the area are important issues to consider when planning radio links.
Microwave propagation hardly occur under ideal conditions, for most communication links, the analysis must be
modified to account for the presence of the earth, the ionosphere and atmosphere precipitates such as fog, raindrops,
snow and hail, for stations on the ground transmitting through the lower atmosphere is complicated by uncontrolled
variables associated with climate weather and path terrain. Signals are said to undergo fading which refers to the fact
71

Ozean Journal of Applied Sciences 3(1), 2010
that time – varying atmospheric processes influence the mechanisms of reflection, refraction and diffraction separately
or in combination, so as to cause signal losses at a receiving antenna [3 ].
Once a microwave signal is radiated by the antenna, it will propagate through space and will ultimately reach the
receiving antenna. As would be expected the energy level of the signal decreases rapidly as the distance from the
transmitting antenna is increased further.
Mathematical Modeling
The finite difference method is a full wave method of parabolic equation that directly solve a wave equation
numerically subject to a number of assumptions and simplifications. The finite difference method is based on
discretisation of the wave equation through the introduction of a rectangular gird and the evaluation of the various
derivative terms using centered finite differences [ 4,5,6,7,8 ].
The derivation of the parabolic equation normally start by reducing Maxwell’s equations to the Helmholtz equation.
However in this instance if we assume the presence of an atmosphere described by a complex refractive index:
n( r)
� �( r)
� �r
( r)
� j�
( r)
�� …………….(1.0)
0
which is a continuously varying function of position. Provided that � �ln n ��1,
the scalar Helmholtz equation
describes accurately each of the Cartesian components of the electric and magnetic fields.[,6,7]
2 2 2
� � � k n � � 0 ………………………(1.1)
0
The wave number in vacuo is now given by k 0 � 2� / �0
. Considering a two dimensional propagation problem
along the great circle path and making the approximation that the earth is flat over a short length for simplicity,
Equation 1.1 can be expanded in Carteisian co-ordinates as:
2 2
� � � �
� � k
2 2
�x
�z
2
0
2
n � � 0
……………….(1.2)
We now introduce the assumption that an a priori preferred direction of propagation exists and identify this with the
x axis. It is, therefore, reasonable that we can write the following form for the solution: [6,7]
( , ) ( , ) exp( 0 ) x jk z x u z x �
� � ………….(1.3)
where the reduced wave amplitude u( x,
z)
can now be assumed to vary slowly along the x direction on the scale of
a free-space wavelength, � . Substituting Equation 1.3 into 1.2 and discarding the common factor exp( 0 ) x jk � after
performing the differentiations yields the following equation for the reduced wave amplitude:
2
� u
� 2 jk 2
�x
0
2
�u
� u
� � k 2
�x
�z
2
0
( n
2
�1)
u � 0
……. (1.4)
Equation 1.4 describes waves propagating both along the positive and negative x directions. By analogy with the
one-dimensional wave equation
2
2
� w 1.
� w � � 1 � ��
� 1 � �
� � 0 �
� 0
2 2 2 � � ��
� �w
�x
c �t
� �x
c �t
��
�x
c �t
�
which has linearly independent solutions given as:
w � f ( x � ct)
and w � g(
x � ct)
……………………………. (1.5b)
72
0
……………… (1.5a)

Ozean Journal of Applied Sciences 3(1), 2010
These can be identified as forward and backward traveling waves corresponding to the two
differential operators in Equation 1.5a. If we factorise Equation 1.4 into forward and backward traveling wave
operators; then we have
…………………….(1.6)
�
�
�
�
� jk
� �x
�
� �
� jk
�
�
�x
0
0
�
�
jk
jk
0
0
1�
( n
1�
( n
2
2
1 �
�1)
� 2
k �z
1 �
�1)
� 2
k �z
73
0
0
2
2
2
�
�
�
�
�
�
�u
� 0
�
�
If you discard the backward traveling wave for consistency with Equation 1.3, then finally, we have,
�
� �
� jk
�
�
�x
0
�
jk
0
2
2 1 �
�n �1�
� �u
� 0
1� 2 2
k0
�z
�
�
�
……(1.7)
It is to be understood that the differential operator under the square root sign in Equation 1.7 can only be interpreted in
a formal sense. Its numerical evaluation can only be achieved by replacing the square root by a power series, or
rational fractions of operators.
Thus, we rewrite Equation 1.4 as:
�u
�
� jk �
�x
�
�
2
2 1 � �
1 � �n �1�
� �u
� jk �1 � 1�
Q(
x,
z)
�u ………….. (1.8)
2 2
k0
�z
�
�
0 1 �
0
The differential operator Q (x, z) must give a significantly smaller answer than the unity operator when operated on
u (x,z), since by assumption, the oscillatory variation of i (x,z) is predominantly along the x direction, perpendicular to
the z axis. Therefore, the z derivative on a scale of a wavelength (1/k0) is much smaller than the unity operator. For
the atmosphere, we also know that n (x,z) � 1, giving: [7]
Q( x,
z)
u(
x,
z)
�� u(
x,
z)
or formally ��1
Q ………………………. (1.9)
The simplest approximation for the square root term is given by the first two terms in its Taylor expansion, namely,
1 Q( x,
z)
� 1�
Q(
x,
z)
/ 2
� ……………….. (1.10)
which finally yields the narrow-angle parabolic equation:
�u
�
�x
2 jk
2 � � u
�
� � k 2
� �z
1 2
0
2
0
�
�n �1�u
�
�
�
………………(1.11)
Parabolic Equation – Finite difference Implementation
This method is based on a more direct discretisaton of Equation 1.11, through the introduction of a rectangular grid
and the evaluation of the various derivative terms using centered finite differences. The various terms appearing in
Equation 1.11 are evaluated at the centre point through their finite difference discrete approximations to yield; [7,8]

Ozean Journal of Applied Sciences 3(1), 2010
n n
u um
um
n
x
k
�
�1
� 1
( � ) �
…..…………………..(1.12)
� 2
METHODOLOGY
The line of sight microwave link used in this research work is situated between Akure located at latitude 071509.30N,
longitude 0051142.60E (transmitting end) and Owo located at latitude 071220.00N longitude 0053402.00E (receiving
end) over a path length of 41.42km. The Akure – Owo microwave link is owned and managed by NITEL – Nigeria
Telecommunication Ltd. The microwave signal data were gathered between January and December 2006. The
measurement was done with a data acquisition software PROCOMM PLUS 3.0 software program at the receiving
end twice a week over a 24- hour period. This software was installed in a computer system (laptop) type 3050 Acer
Aspire. The Laptop computer system was then connected to the NITEL equipment at Owo. The PROCOMM PLUS
3.0 software detects and captures the received signal values in the link. The system characteristics of the Akure –
Owo digital microwave link is given in Table 1.0.
Table 1.0 System characteristics of the Akure – Owo digital microwave link[9]
Characteristics Akure Owo
Elevation (M) 348 320
Latitude 071509.30N 071220.00N
Longitude 0051142.60E 0053402.00E
Antenna model VHP4 – VHP 4 –
71W
71W
Antenna Height 90.00 90.00
(M)
Antenna Gain
(dbi)
Frequency (MHz) - 7500
Polarization - Vertical
Path length (km) - 41.42
Radio Equipment model - MSM/H7 16E QPS
Transmitted power (dBm) - 25.00 to 28.00
Main received signal (dBm) - 45.03
Received Threshold level (dBm) - 85.50
36.60 36.60
RESULTS AND DISCUSSION
The recorded microwave signal data for the period (January to December 2006) were computed into monthly
averages as shown in Table 2.0
74

Ozean Journal of Applied Sciences 3(1), 2010
Table 2.0 Akure – Owo Digital Microwave link, Year 2006 Average monthly data
Month Received Signal Level (dBm)
January - 47.0
February - 36.0
March - 37.0
April - 36.0
May - 39.0
June - 39.0
July - 48.0
August - 50.0
September - 50.0
October - 46.0
November - 40.0
December - 47.0
(i) Variation of Average Signal level with months for Year 2006
The analysis of the results shows that the months of February, March and April have the best signal reception while
the months of August, September and July have the poorest signal reception.
(ii) Analysis of Daily signal reception using the Finite difference model
The figure 1.0 shows the plot of the finite difference implementation of daily received signal level with distance.
Predicted Received Signal Level for Year 2006
The model equation is
Y = 9x10 -32 x 8 -1.4x10 -26 x 7 + 8.8 x 10 -22
- 2.9 x 10 -17 x 5 + 5.4 x 10 -13
Figure 1.0: Daily microwave received signal level with distance
4
x – 5.5 x10 19
6
x
3
x
+ 2.8 x 10 -5 x 2 – 1.0x10 -5 x + 13 ………. (1.13)
If x (dBm) = Received signal level measured in equation (1.13) then Y (dBm) = predicted values.
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Ozean Journal of Applied Sciences 3(1), 2010
For example, on Mondays 15 th February 2006, the signal transmitted was 28dbm while the signal received was -
35dBm. This corresponds to the model deduced as shown in equation 1.13 that if x = 28dbm then y = -35dBm.
CONCLUSION
In this work microwave signal received level were measured on a monthly basis and the finite difference method was
used to develop a model that can predict microwave signal received level on a daily basis.
The result of the research work shows that the months of August, September, July and January have poor signal
reception while the months of February, march and April have good signal reception.
The model equation developed using the finite difference method is reasonably accurate.
REFERENCES
Akleman, F; Sevgi, L (2008) “A novel finite difference time – Domain wave propagation IEE transaction on
Antennas and propagation Vol 48 No 3.
Barclay, L. (2003) “Propagation of Radio waves “IEE London 2 nd Edition pp 169-177.
Bogucci, J; Wielowreyska, E. (2004) “Propagation reliability of line – of – sight radio systems” 17 th International
Symposium and Exhibition on Electromagnetic Compatibility Wroclaw.
Isaakidis, S.A; xenos, T.D. (2004) “Progress in Electromagnetic Research” Aristotle university of Thessaloniki
Greece.
Landstorfer, F.M. (1999) “wave propagation models for planning of mobile communication Networks” Proceedings
of the 29 th European Microwave Conference (EUMC) Vol 1
Matzler, C (2004) “Parabolic Equations for wave propagation and the advanced atmospheric effects prediction
systems” (AREPS) Literar seminar
Nigeria Telecommunication Ltd Journal (1992) Vol 2
Otasowie, P.O; Edeko, F.O. (2008) “An investigation of microwave link degradation due to Atmospheric Conditions”
(A case study of Akure – Owo Digital microwave link) Journal of Advances in materials research and
Systems Technologies II. Trans Tech publications Ltd Zurich Switzerland vol. 62 pp 159-165.
www. Answers.com (2007) “Microwave Radio Relay 15 th February 2007.
76

Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece
watershed
Ahmet Karaburun
Fatih University,
Department of Geography,
Buyukcekmece, Istanbul, 34500, Turkey
E-mail address for correspondence: akaraburun@fatih.edu.tr
__________________________________________________________________________________________
Abstract: In order to take measures in controlling soil erosion it is required to estimate soil loss over area of
interest. Soil loss due to soil erosion can be estimated using predictive models such as Universal Soil Loss
Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE). The accuracy of these models depends on
parameters that are used in equations. One of the most important parameters in equations used in both of
models is C factor that represents effects of vegetation and other land covers. Estimating land cover by
interpretation of remote sensing imagery involves Normalized Difference Vegetation Index (NDVI), an indicator
that shows vegetation cover. The aim of this study is estimate C factor values for Buyukcekmece watershed using
NDVI derived from 2007 Landsat 5 TM Image. The final C factor map was generated using the regression
equation in Spatial Analyst tool of ArcGIS 9.3 software. It is found that north part of watershed has higher C
factor values and almost 60% of watershed area has C factor classes between 0.2 and 0.4
Keywords: Erosion, RUSLE, USLE, C factor, Landsat, NDVI,
___________________________________________________________________________
INTRODUCTION
Sediment yield studies play key role for various soil and water conservation planning processes including
reservoir sedimentation analysis, studies on river morphology changes and river bed siltation, and agricultural
project planning. Erosion process result in soil loss from a watershed and it is difficult to estimate soil loss as it
arises from a complex interaction of various hydro-geological processes (Singh et al., 2008). Estimating the soil
loss risk and its spatial distribution are the one of the key factors for successful erosion assessment. Thus it can
be possible to develop and implement policies to reduce the effect of soil loss under varied geographical
conditions (Colombo et al., 2005). The accuracy of estimating soil risk depends on model and its factors.
Researchers have developed many predictive models that estimate soil loss and identify areas where
conservation measures will have the greatest impact on reducing soil loss for soil erosion assessments (Angima
et al., 2003).
Those models can be classified into three main categories as empirical, conceptual and physical based models
(Merrit et al.,2003). USLE and its modifications are the examples of empirical models and ANSWER,
CREAMS, and MODANSW are the samples of conceptual models. Examples for the first two groups comprise
the empirical USLE and its modifications, and some of the more comprehensive models like ANSWERS,
CREAMS, and MODANSW. ANSWERS and CREAMS are basically conceptual and eventbased. European Soil
Erosion Models, EUROSEM/KINEROS, EUROSEM/MIKE SHE and SHESED-UK are the physically-based
models that have been developed at catchment or small subbasin scales (Fistikoglu and Harmancioglu, 2002).
Universal Soil Loss Equation (USLE) was designed to predict longtime average soil losses in runoff from
specific field areas in specified cropping and management systems. The USLE (Wischmeier and Smith, 1978)
estimates the average annual soil loss from:
A = R.K.LS.C.P
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Ozean Journal of Applied Sciences 3(1), 2010
where A is the estimated soil loss per year R is the runoff factor, K is the soil erodibility factor, LS is the slope
length and steepness factor, C is the cover and management factor and P is the support practice factor
(Wischmeier and Smith, 1978). The R factor expresses the erosivity occurring from rainfall and runoff at a
particular location. An increase in the intensity and amount of rainfall results in an increase in the value of R.
The K factor expresses inherent erodibility of the soil or surface material. The value of "K" is defined as a
function of the particle-size distribution, organic-matter content, structure, and permeability of the soil or surface
material. The LS factor expresses the effect of topography, specifically hillslope length and steepness, on soil
erosion. An increase in hillslope length and steepness results in an increase in the LS factor. The C covermanagement
factor is used to express the effect of plants and soil cover. Plants can reduce the runoff velocity
and protect surface pores. The C-factor measures the combined effect of all interrelated cover and management
variables, and it is the factor that is most readily changed by human activities. The P factor is the support
practice factor. It expresses the effects of supporting conservation practices, such as contouring, buffer strips of
close-growing vegetation, and terracing on soil loss at a particular site. A good conservation practice will result
in reduced runoff volume, velocity and less soil erosion. The USLE concept has more recently been modified
and adapted by a large number of researchers by including additional data and incorporating research results.
Revised Universal Soil Loss Equation (RUSLE) was developed by integrating several recent techniques and
additional data that improves the accuracy of factors of USLE model (Renard and Freimund, 1994; Renard et al.
1997; Yoder and Lown, 1995). Thus RUSLE was extended to include forest, rangelands and disturbed areas
compared to USLE. The Revised Soil Loss Equation (RUSLE) followed the same formula as the USLE, but it
has a subfactor for evaluating the cover-management factor, a new equation for slope length and steepness, and
new conservation practice values. It is also applicable to non-agricultural conditions such as construction sites.
The RUSLE model is widely used as a predictive model for estimating soil erosion potential and effects of
different management practices for over 40 years (Renard et al., 1997).
One of the most important parameters in USLE and RUSLE equations is the cover management factor (C) that
represents effects of vegetation and other land covers. The C factor reflects the effect of cropping and
management practices on the soil erosion rate. The C factor indicates how conservation plans will affect the
average annual soil loss and how that soil-loss potential will be distributed in time during construction activities,
crop rotations, or other management schemes (Van der Knijff et al., 2000). Vegetation cover protects the soil by
dissipating the raindrop energy before reaching the soil surface. As such, soil erosion can be effectively limited
with proper management of vegetation, plant residue, and tillage (Lee, 2004). In both of USLE and RUSLE, the
C factor is computed using empirical equations that contain field measurements of ground cover. (Wischmeier
and Smith, 1978; Renard et al., 1997). Since the satellite image data provide up to date information on land
cover, the use of satellite images in the preparation of land cover maps is widely applied in natural resource
surveys (Deng et al., 2008; Serra et al.,2008; Yuan,2008).
The traditional method for spatial estimation of C factor is assigning values to land cover classes using classified
remotely sensed images of study areas. At the end of supervised or unsupervised classification, land cover
classes are derived from image for study area and then C factors that are obtained from USLE/RUSLE guide
tables or computed using field observation for each land cover classes are assigned to each pixel in land cover
class (Karaburun, 2009; Efe et al., 2008; Morgan, 1995; Folly et al., 1996; Juergens and Fander, 1993). Since all
pixels in a vegetation class have the same C factor value, those pixels can not represent variation of this
vegetation class over the study area (Wang et al., 2002). Researchers developed many methods to estimate C
factor using NDVI for soil loss assessment with USLE/RUSLE (De Jong 1994; De Jong et al., 1999; De Jong
and Riezebos, 1997; Wang et al., 2002; Lin et al. 2002). These methods employ regression model to make
correlation analysis between C factor values measured in field or obtained from guide tables and NDVI values
derived from remotely sensed images. The unknown C factor values of land cover classes can be estimated using
equation obtained from linear regression analyses. The aim of this study is to estimate C factor values of land
cover classes using NDVI values by regression analysis for erosion modeling in Buyukcekmece Watershed.
Study Area
Buyukcekmece watershed is located in the west of Istanbul and adjacent to the Marmara Sea (Fig.1) and it
contains most important water sources and a dam provides drinking water for Istanbul. The Buyukcekmece
watershed is one of the largest watersheds in Istanbul having an area of about 63000 ha and located 50
kilometers west of the center of Istanbul. Agriculture is one of the most dominant land use patterns in
Buyukcekmece watershed, occupying about 42000 hectares of land which makes around 67 % of the total
surface of watershed. Forest area of watershed is located in the west side and covers about 8000 hectares. The
upper side of watershed is forest and receives annual average rainfall about 750 mm with annual average
temperature 17 0 C. The climate of watershed is influenced by Mediterranean climate and Black Sea climate.
78

Fig.1 Study area
Normalized Difference Vegetation Index (NDVI)
Ozean Journal of Applied Sciences 3(1), 2010
METHODOLOGY
Remote sensing techniques are employed for monitoring and mapping condition of ecosystems of any part of
earth. Vegetation cover is the one of most important biophysical indicator to soil erosion. Vegetation cover can
be estimated using vegetation indices derived from satellite images. Vegetation indices allow us to delineate the
distribution of vegetation and soil based on the characteristic reflectance patterns of green vegetation. The
Normalized Difference Vegetation Index (NDVI), one of the vegetation indices, measures the amount of green
vegetation. The spectral reflectance difference between Near Infrared (NIR) and red is used to calculate NDVI.
The formula can be expressed as (Jensen, 2000);
NDVI = (NIR – red) / (NIR + red)
The NDVI has been used widely in remote sensing studies since its development (Jensen, 2005). NDVI values
range from -1.0 to 1.0, where higher values are for green vegetation and low values for other common surface
materials. Bare soil is represented with NDVI values which are closest to 0 and water bodies are represented
with negative NDVI values (Lillesand et al., 2004: Jasinski, 1990; Sader and Winne, 1992). More than 20
vegetation indices have been proposed and used at present. Since NDVI provides useful information for
detecting and interpreting vegetation land cover it has been widely used in remote sensing studies (Gao, 1996:
Myneni and Asrar, 1994; Sesnie et al., 2008).
A time-series of NDVI were derived from Landsat 5 TM images acquired on April, May, June and August 2007.
An average NDVI of watershed area was calculated using those NDVI images (Fig.2).
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C Factor Estimation
Ozean Journal of Applied Sciences 3(1), 2010
Fig.2 Average NDVI map of Buyukcekmece watershed
Soil loss is very sensitive to vegetation cover with slope steepness and length factor (Renard and Ferreira 1993;
Benkobi et al., 1994; Biesemans et al., 2000). Vegetation cover protects the soil by dissipating the raindrop
energy before reaching soil surface. The value of C depends on vegetation type, stage of growth and cover
percentage (Gitas et al., 2009). The C factor values vary between 0 and 1 based on types of land covers. Since
NDVI values have correlation with C factor (De Jong, 1994; Tweddales et al., 2000; De Jong et al., 1999; De
Jong and Riezebos, 1997). Many researchers used regression analysis to estimate C factor values for land cover
classes in erosion assessment (Lin et al., 2002; 2006; Symeonakis and Drake, 2004; Van der Knijff et al., 2002).
The goal of regression analysis is to estimate the unknown values of dependent variable based upon values of an
independent variable using a mathematical model. The linear or non-linear regression equations are constructed
using correlation analysis between NDVI values obtained from remotely sensed image and corresponding C
factor values obtained from USLE/RUSLE guide tables or computed using field observation.
Fig.3 Workflow of C factor estimating using NDVI
The study assumes that there exists a linear correlation between NDVI and C factor and uses bare soil and forest
NDVI values as reference values (Fig.3). Sample NDVI values were collected for bare soil and forest land cover
classes from average NDVI image. Since C factor values range from 0 for well-protected soil to 1 for bare soil
(Pierce et al.,1986; Vicenta et al., 2007) the C factor values for bare soil and forest land cover were set to 1 and
0, respectively in the regression analysis. Fig.4 shows the graph of the regression equation. The line in Fig.4 is
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Ozean Journal of Applied Sciences 3(1), 2010
the regression line that describes relationship between C and NDVI values and R shows the correlation
coefficient of regression analysis.
The regression equation was found as;
C factor = 1.02 – 1.21 * NDVI
The final C factor map was generated using the regression equation in Spatial Analyst tool of ArcGIS 9.3
software. The graphs of regression analysis and C factor are given in Fig. 4 and Fig.5 respectively.
RESULTS
As can be seen from Table 1, Buyukcekmece experienced lowest mean NDVI values in August. Since watershed
consists of agricultural areas the mean NDVI values of April, May and June are close to each other. Mean NVDI
values of August are lowest because there is no vegetation on agricultural areas.
Table 1 NDVI values of Landsat images
Image Date Max NDVI Min NDVI Mean NDVI
April 2007 1 -1 0,395
May 2007 1 -1 0,40
June 2007 1 -1 0,31
August 2007 0,66 -0,43 0,09
Fig.4 Linear regression of NDVI and C factor values
As seen from Fig.5 the north part of watershed is represented by 0-0.1 C factor class since it is occupied by
forest. Water bodies like Buyukcekmece Lake are represented by 0.9-1.0 C factor class. The agricultural areas of
watershed are represented by C factor classes from 0.2 to 0.4
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Ozean Journal of Applied Sciences 3(1), 2010
Fig.5 C factor map of Buyukcekmece Watershed
The estimated C factor values through regression equation were divided into ten categorical classes and those
classes vary between 0-0.1 and 1.0. The pixel numbers of those classes are shown in Fig.6. Since agricultural
areas cover almost 60% of watershed area the C factor classes 0.2-0.3 and 0.3-0.4 contain the highest pixel
number respectively. As shown in Fig.6, 0.2-0.3 class has about 28% of total pixels while 0.3-0.4 has about 15%
of total pixels. The 0.9-1.0 class has the lowest pixel number.
Fig.6 Pixel distribution of the C factor map based on NDVI
CONCLUSION
An attempt has been made to estimate C factor values of land cover classes using NDVI values for modeling soil
erosion using ArcGIS 9.3 software. A regression analysis was performed between NDVI and C factor using an
assumption. C factor values were assigned to pixels of NDVI image through regression equation. Based on an
assumption, the C factor map of Buyukcekmece watershed was produced to use in soil erosion methods such as
USLE and RUSLE based on an assumption that NDVI and C factor values are correlated with each other. The
results revealed that large parts of areas were assigned to C factor classes that vary between 0.2 and 0.4.
It should be noted that C factor values can be precisely estimated using empirical equations that contain field
measurements of land cover classes. However, this study shows that NDVI based regression method offers an
optimal method to estimate C factor values of land cover classes of large areas in a short time.
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Ozean Journal of Applied Sciences 3(1), 2010
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Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Physiological properties studies on essential oil of Jasminum grandiflorum L. as
affected by some vitamins
Rawia.A.Eid, Lobna, S. Taha*, and Soad , M.M. Ibrahim
Department of Ornamental Plants and Woody Trees,
National Research Centre, Dokki, Cairo, Egypt
*E-mail address for correspondence: lobnasalah82@yahoo.com
____________________________________________________________________________________
Abstract : A field experiment was conducted out during 2008 and 2009 seasons at Gezayh village, Imbaba
district, Giza Governorate, Egypt. The aim of this work is to study the effect of foliar application of various
concentrations (50, 100 and 150 ppm) of ascorbic acid, Thiamin and α –tocopherol separately or
collectively on some flower characters (flower yield and weight of flowers), oil pattern (essential oil
concrete percent, yield and some constituents of oil), physiochemical properties of oil and some chemical
composition of Jasminum grandiflorum L. Promoting results were obtained with foliar application of all
treatments, especially those of Ascorbic acid + Thiamin + α –tocopherol at 100 ppm of each or α –
tscopherol at 150 ppm alone on flower yield, weight of flowers, oil concrete percent and oil yield as well
as chemical constituents (soluble, non-soluble sugars and carbohydrates). Gas liquid chromatography of
the oil of control plants and those which showed increase in the oil percent revealed that the major
components, i.e. Benzyl Benzoate, benzyl acetate, eugenol, trans-methyl jasmonate and cis jasmone
pronouncedly increased depending on the applied vitamin (ascorbic acid, Thiamin and α –tocopherol)
which also showed a stimulatory effect on physiochemical properties of oil such as refractive index,
specific gravity, ester number and acid number as good conductor of oil quality.
Keywords: Ascorbic acid, Thiamin , α –tocopherol, jasmin
_____________________________________________________________________________________
INTRODUCTION
Jasmine (Jasminum grandiflorum L.) is an ornamental plant of Oleacae. It is semi-evergreen to deciduous
shrub reaching a length of 8 meters, often with pendulous branches. The leaves are odd-pinnate wit 7 to 9
leaflets and used medicinally in skin diseases, odontalgia, otalgia, wounds, etc. (Kulkarmi and Ansari,
2004; Sharma et al., 2005).
The flowers are white with faint, pinkish streaks, delightfully fragment, and borne in lax, terminal
inflorescences. These flowers are not only essential to the perfumery industry but also have been highly
appreciated by orientals since time immemorial. The pretty jasmine flower originated in the lower valleys
of the Himalayas of northern India (Braja et al ., 1990). The shrub is widely cultivated in the plains and
on the hills specially in Kashmir, Afghanistan, Persia, France, China, Japan and Egypt (Frank and Amelio,
1999).
Jasmine oil has great value for treating sever depression, respiratory tract, for muscle pain and for toning
the skin. This oil is expensive. It takes approximately 800 kilos of petals or 10000 flowers, to make 1 kilo
of concrete jasmine. Egypt is the main producer of jasmine oil.
Plants have a small molecule antioxidants (e.g. ascorbic acid, vitamin C), glutathion and tocopherol
(vitamin E) that they have signaling roles in plant development. In general, the energy metabolism
pathway could be affected by one or another of these substances (Robinson, 1973; Pallanca and
Smirnoff, 2000).
Ascorbate is the most abundant antioxidant in plants but little was known about its biosynthesis.
Smirrnoff et al., (2001) proposed a biosynthetic pathway and identified novel some enzymes. They also
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Ozean Journal of Applied Sciences 3(1), 2010
reported that ascorbate is synthesized from L-galactose via GDP-mannose and GDP-L-galactose. El-
Kobisy et a.,l (2005) stated that Ascorbic acid is synthesized in the higher plants and affects plant growth
and development, it is product of D-glucose metabolism which affects some nutritional cycles activity in
higher plants and play an important role in the electron transport system. Ascorbic acid (vitamin C) is
known as a growth regulating factor which influences many biological processes, Price (1966). Robinson
(1973) reported that Ascorbic acid acts as co-enzymatic reactions by which carbohydrates; proteins are
metabolized and involved in photosynthesis and respiration processes.
Ascorbic acid is an important antioxidant, which reacts not only with H2O2 but also with O2, OH and lipid
hydroperoxidase (CSIR, 1992; Jacobs et al., 2000). A high level of endogenous ascorbate is essential
effectively to maintain the antioxidant system that protects plants from oxidative damage (Cheruth, 2009).
Tarraf et al., (1999) on lemongrass (Cympapogom citrates L.) and Farahat et al (2007) on Cupressus
semperviren L. reported that foliar application of ascorbic acid caused pronounced increases in vegetative
growth and chemical constituents as well as essential oil percent, oil yield per plant.
Thiamin (vitamin B1) is a necessary ingredient for the biosynthesis of the coenzyme Thiamin
pyrophosphate, so it plays an important role in carbohydrate metabolism. It is an essential nutrient for both
plants and animals.
In plants, it is synthesized in the leaves and is transported to the roots where it controls growth. Thiamin is
an important cofactor for the translocation reactions of the pentose phosphate cycle, which provides
pentose phosphate for nucleotide synthsis and for the reduced NADP required for various synthetic
pathways (Kawasaki, 1992). Youssef and Talaat (2003) reported that pronounced increases in vegetative
growth and chemical constituents of rosemary plants by foliar application of thiamine.
Alpha-tocopherol (vitamin E) is low molecular weight lipophilic antioxidant which mainly protect
membrane from oxidative damage (Asada, 1999). Zhang et al., (2000) reported a positive correlation
between α-tocopherol and shoot or root growth in two grass species grown under drought. Tocopherols
were proposed to function in relation to their antioxidant properties being prominent in protection of
polysaturated fatty acids from lipid peroxidation (Bosch, 1995). Recently, tocopherol had an antioxigenic
property when added to green lubricating oil as rapeseed oil (Xiao et al., 2008).
The aim of the present study was to reveal the best level to apply of ascorbic, thiamin and α-tocopherol
which could improve the flower characters, chemical constituents and essential oil production of Jasminum
grandiflorum L.
Materials and methods
A field experiments were conducted out at Gezayh village, Imbaba district, Giza Governorate, Egypt during
two successive seasons of 2008 and 2009. The aim objective of this study was to investigate the effect of
foliar application of ascorbic acid, Thiamin and α –tocopherol on yield of flowers, oil pattern and chemical
constituents of Jasminum grandiflorum L. cv. The investigated soil characterized by coarse sand 54%,
fine sand 1%, silt 23 %, clay 22%, pH 7.5, EC 3.79 dSm -1 , and (N 35.3, P 22.6 and K 5.6 mg/100 g soil).
Thirty three years old trees of jasmine were planted 2X2 m apart (1000 tree /fed). The experimental area
was irrigated by flood irrigation system.
Trees were sprayed twice with freshly prepared solutions of ascorbic acid, thiaminand α-tocopherol each at
50, 100 and 150 ppm, and combination of the different concentrations of the three factors had been also
carried out, in addition to the untreated plants (control) which were sprayed with tap water. Foliar
application of ascorbic acid, thiamine and α-tocopherol carried out two times of 30 days intervals, starting
at one month after March at both seasons.
During the flowering period of each season, the following data were recorded: flowers yield/tree (kg) and
weight of 100 flowers (g). Jasmine concrete was extracted from the flowers using a solvent extraction
system of N-hexane according to Guenther (1961).
Flowers are placed in vessel and covered with the solvent such as hexan, it gently heated electrically while
the solvent extracts the fragments molecules of the plant. The fragment chemicals are transferred to the
alcohol which is removed by low heat distillation. The essential oil was dried over anhydrous sodium
sulfate and stored at 4-6 o C. The essential oil was subjected to Gc/Ms analysis and its components were
identified by matching their relative retention times in conjunction of discriminating Ms ions against a
computer library file of large number of data obtained under identical experimental conditions (Adams,
1995). Gc/Ms Analysis was carried out on finningan Mat SSQ 7000 mass sepctometer directly coupted to
a varion 3400 gas chromatography equipped with DB-5 (0.25 mm i.d. dX30m, 0.25 coating thickness,
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Ozean Journal of Applied Sciences 3(1), 2010
fused silica capillary column using helium as the carrier gas at flow rate of 1.017 ml/min injector
temperature, 220 o C transfer/line, 250 o C oven temperature programmed, 60 to 250 o C at 3 o C/min.
Some jasmine oil characters i.e. refractive index at 20 o C, specific gravity at 15 o C, acid number and ester
number were determined according to Guenther (1961). Also, total carbohydrates and soluble and non
soluble sugars % in the flowers were estimated according to Herbert et al (1971). The recorded data
(means of the two growing seasons) were statistically analyzed using the completely randomized design in
factorial arrangement according to the procedure of Snedecor and Cochran (1980), where the means of
the studied treatments were compared using LSD test at 0.05 of probability.
Flower characters
RESULTS AND DISCUSSION
Data presented in Table (1) reveled that all treatments of ascorbic acid, thiamine and α-tocopherol
separately or collectively significantly increased flower yield/tree and weight of flowers (100 flowers (gm))
of Jasminum grandiflorum L. trees compared with untreated plants. The highest increases in flower yield
and fresh weight of flowers were observed in plants treated with Asc. 100 ppm + thiamin 100 ppm + αtocopherol
100 ppm followed by α-tocopherol 150 ppm. The increments were 76.68 and 75.21 %, for
flower yield than the corresponding values of control plants, the fresh weight of f lowers increased by 56.69
and 53.42 % than the corresponding values of the control plants. Similar results were obtained by El-
Quesni et al (2009) who revealed that foliar application of Ascorbic acid and α-tocopherol on Hibiscus rosa
, Sineses L. plants significantly increased number of flower/plant and fresh weight of flowers gm/plant
compared with untreated plants. The stimulatory effects of ascorbic acid may be attributed to its role in the
regulation of cell division, differentiation and enhancement of leaf expansion (Noctor and Foyer, 1998).
The effect of thiamin was showed by Kawasaki (1992) who reported that thiamin (vitamin B1) is an
essential nutrient for plant growth. It is synthesized in the leaves and is translocated to the roots where it
controls growth. In addition, α-tocopherol interacts with the polyunsaturated acyl groups of lipids, stabilize
membranes, protect chloroplast from photooxidation and help to provide an optimal environment for
photosynthetic machinery (Jaleel et a.l, 2006 and Jaleel et al., 2007).
Essential oil concrete percent and oil yield
Data given in Table (1) show that the foliar application of Ascorbic acid , thiamin, α-tocopherol or the
combination of them significantly increased both jasmine concrete essential oil percent and oil yield as
gm/tree compared to control plants. The highest increment was obtained with treatments of Ascorbic acic
100 ppm + thiamin 100 ppm + α-tocopherol 100 ppm or α-tocopherol 150 ppm alone. It could be deduced
from the present results that both concrete essential oil percent and oil yield (g/tree) responded to foliar
application of vitamins. In support, Hasnaa et al (2009) on Pelargonium graveolens L. indicated that αtocopherol
treatments at 50 and 100 mg/l significantly increased concrete essential oil percent and yield.
This might be due to Alfa-tocopherol could cause a pronounced enhancement of both synthesis and
accumulation of oil. Also, Gamal El-Din (2005) on sunflower plants, reported that ascorbic acid
significantly increased oil percentage of seeds.
Essential oil constituents
Data represented in Table 2 indicates that oil of treated and control plant mainly consisted from benzyl
benzaoat as major constituent (20.1-26.6 %), followed by benzyl acetate (4.8-7.7%) , benzyl alcohol (1.3-
2.7%), eugenol (2.0-4.0%), trans-methyl Jasmonate (5.0-7.8%), cis jasmone (2.3-7.1%) and jasmine lactone
(3.1-8.5%). On the other hand, Gas liquid chromatography showed that oil consists of other constituents
such as n-acetyl and methyl anthranilate. Benzyl benzoate content was pronouncedly increased at
treatment (150 ppm) of Ascorbic acid, thiamin or α-tocopherol. The highest increment was obtained with
their combination treatment at concentration of 100 or 150 ppm. Similar trend was found in case of benzyl
acetate, benzyl alcohol, eugenol, trans-methyl jasmonate and cis jasmone contents. However, treatment of
Ascorbic acid 100+ Thiamin 100 ppm + α-tocopherol 100 ppm gave the highest content of n-acetyl.
Highest content of methyl anthranilate was obtained with α-tocopherol (100 ppm). These results were in
agreement with those obtained by Youssef and Iman (2003) on Rosmarinus officinals L. who found that oil
composition responded greatly to foliar spray of the vitamins nicotineamide, ascorbic and thiamin at
different rates of application. Hasnaa et al., (2009) reported that Gas-liquid chromatography of the oil of
Pelargonium graveolens L. revealed that the major components, i.e. citronellol and linalool pronouncedly
89

Ozean Journal of Applied Sciences 3(1), 2010
increased depending on the applied stigmasterol or α-tocopherol. Therefore, one can conclude the positive
response of jasmin oil constituents depended on the level of applied ascorbic acid, thiamin and αtocopherol.
Essential oil physiochemical properties
Results in Table 3 showed that refractive index of jasmine oil at 20 o C under treatments (ascorbic acid,
thiamin, α-tocopherol and their combination) ranged from 1.41 to 1.50 in comparison with 1.3 in the
control treatment. α-tocopherol (100 or 150 ppm) or Ascorbic acid + thiamin + α-tocopherol (50, 100 or
150 ppm) resulted in the highest refractive index value of jasmine oil. Refractive index of oil increases
with increase in the number of double bonds (iodine value). In general, the refractive indices of oils relate
to the degree of unsaturation in a linear way (Rudan-Tasic & Klofutar, 1999). Also, the specific gravity is
a good indicative of purity of oil and depends on the number of double bonds. At any given temperature,
specific gravity increases as the mean molecular weight decreases with increase in degree of unsaturation
(higher iodine value). In our study, we can notice that specific gravity of oil under treatments ranged from
0.93 to 0.99 in comparison with 0.82 in the control treatment (Table 4).
Results showed that the ester no. of jasmine oil were 50.0 to 55.8 with tested treatments, compared with
48.2 in the control. The acid no. of jasmine oil was 40.3 to 50.8 with various treatments, compared with
45.3 in the control. The acid value is an indirect measure of free fatty acid contents present in oil. Hence it
is not desirable, because they render unpleasant odor and deteriorate the quality of the product (Muhammad
et al., 1999). However, the fragrance of jasmine is characterized by "jasmonoid" compounds, whose
biosynthesis from unsaturated fatty acids. Under study, the results in Table 4, indicates that both Ascorbic
acid and thiamin at 50, 100 and 150 ppm reduced the acid number than that of control treatment. In
support, Ismail et al, 2007 indicated that refractive index of jasmine oil at 20 o C is 1.04, specific gravity of
oil at 15 o C is 0.4113, ester no. is 46.34 and the Acid no. of jasmine oil is 43.03 in untreated plants.
Chemical constituents
Data presented in Table 4 show that all treatments of Ascorbic acid, thiamin and α-tocopherol separately or
collectively significantly increased soluble sugars, nonsoluble sugars and total carbohydrate %. The
highest increment was observed in case of treatment Ascorbic acid + thiamin + α-tocopherol each at 100
ppm followed by α-tocopherol 150 ppm. The increments were 54.6 and 52.11 % for soluble sugars, 73.29
and 69.59 % for nonsoluble sugars and 48.84 and 40.78 % for total carbohydrates than the corresponding
values of the control plants., these results could be explained by the findings obtained by Price (1966) who
reported that ascorbic acid increased nucleic acid content, especially RNA and protein content of wheat
grains. It also influenced by synthesis of enzymes, and proteins, in addition, it acts as co-enzyme in
metabolic changes (Reda et al., 1977; Fadl et al, 1978 and Abdel-Halim, 1995). These results are also in
agreement with those obtained by Kawasaki (1992) who reported that thiamine (vitamine B1) is a
necessary ingredient for the biosynthesis of the co-enzyme thiamin pyrophosphate , in this latter form it
plays an important role in carbohydrate metabolism. El-Bassiouny et al., (2005) reported that foliar spray
with α-tocopherol on faba bean plant induced increments in yield components. Also , El-Quesni et al.,
(2009) indicated that foliar application of Asc. acid and α-tocopherol separately to hibiscus plants
significantly increased total soluble sugars through flowering stage.
90

Ozean Journal of Applied Sciences 3(1), 2010
Table 1: Flower characters, oil percent and oil yield (g/tree) of Jasminum grandiflorum L. plants as
affected by Ascorbic acid, thiamin and α-tocopherol. (Average of the two seasons)
Treatments
Conc.
ppm
Flower yield
kg/tree
91
Weight of 100
flowers (gm)
Concrete
(%)
Oil yield
(g/tree)
Control 0.87 6.8 0.13 1.13
Ascorbic acid 50 1.74 8.70 0.20 3.48
100 2.89 11.90 0.25 7.23
150 3.15 13.40 0.29 9.14
Thiamin 50 1.05 8.3 0.18 2.29
100 1.85 9.0 0.23 4.26
150 2.34 10.5 0.19 4.45
α-tocopherol 50 2.11 10.3 0.22 4.64
Ascorbic acid 50 + Thiamin 50 +
α-tocopherol 50 ppm
Ascorbic acid 100 + Thiamin 100 +
α-tocopherol 100 ppm
Ascorbic acid 150 + Thiamin 150 +
α-tocopherol 150 ppm
100 3.00 12.0 0.28 8.40
150 3.51 14.6 0.32 11.23
3.11 13.6 0.26 8.09
3.73 15.7 0.34 12.68
2.15 14.2 0.32 6.88
LSD 5% 0.31 0.25 0.01 0.05

Ozean Journal of Applied Sciences 3(1), 2010
Table 2: Major constituents of essential oil from Jasminum grandiflorum L. as affected by Ascorbic acid, thiamin and α-tocopherol.
Order Retention
time
1 0.532
(Average of the two seasons)
Treatments
Constituents
Benzyl
Benzoat&Photol
Control
Ascorbic acid Thiamin α-tocopherol
92
ppm
Asorbic acid+Thiamin +
α-tocopherol
50 100 150 50 100 150 50 100 150 50 100 150
20.1 21.2 21.5 23.0 21.5 21.5 23.0 21.5 21.8 23.4 22.5 26.5 26.6
2 0.729 Benzyl Acetate 4.8 5.2 5.9 6.8 5.0 5.8 6.8 5.8 6.4 6.8 6.8 7.5 7.7
3 0.838 Benzyl Alcohol 1.3 1.7 2.1 2.2 1.8 2.1 2.5 1.5 2.1 2.4 1.9 2.7 2.6
4 0.919 Eugenol 2.0 2.1 2.5 2.9 2.0 3.3 2.7 2.2 2.5 3.1 2.5 3.8 4.0
5 1.234
Trans-methyl
Jasmonates
5.0 5.8 6.5 7.1 5.8 6.8 7.0 5.7 6.6 7.6 5.9 7.8 7.6
6 1.641 Cis Jasmone 2.3 2.5 3.3 6.2 1.8 3.7 5.8 2.4 3.4 6.5 2.0 7.0 7.1
7 1.845 Jasmine Lactone 3.1 5.5 6.2 7.4 4.3 5.7 4.5 6.5 7.4 8.5 7.0 7.9 7.5
8 1.941 n-acetyl 0.81 1.0 1.5 1.8 1.1 1.3 1.6 1.4 1.8 2.2 2.2 2.5 1.9
9 1.958
Methyl
anthranilate
1.2 1.7 1.9 2.1 1.3 1.6 1.3 2.6 3.2 3.6 2.8 3.1 3.0

Ozean Journal of Applied Sciences 3(1), 2010
Table 3: Physiological properties of essential oil from Jasminum grandiflorum L. as affected by Ascorbic acid, thiamin and α-tocopherol.
Properties
(Average of the two seasons)
Treatments
Control
Ascorbic acid Thiamin α-tocopherol
93
ppm
Asc.+Thiamin+
α-tocopherol
50 100 150 50 100 150 50 100 150 50 100 150
Refractive index 20 o C 1.31 1.43 1.43 1.43 1.41 1.41 1.42 1.43 1.48 1.49 1.49 1.50 1.50 0.02
Specific gravity 0.82 0.94 0.94 0.94 0.93 0.94 0.94 0.96 0.97 0.97 0.99 0.99 0.99 0.02
Ester number 48.2 50.0 51.3 51.9 50.0 51.2 51.8 50.1 55.2 55.7 55.2 55.6 55.8 0.12
Acid number 45.3 44.2 44.5 44.8 40.7 40.9 40.3 49.2 50.1 50.7 50.2 50.8 50.5 0.022
LSD
5%

Ozean Journal of Applied Sciences 3(1), 2010
Table 4: Chemical constituents of Jasminum grandiflorum L.flowers (%) as affected by Ascorbic acid,
thiamin and α-tocopherol. (Average of the two seasons)
Treatments
Conc.
ppm
94
Soluble
sugars
Non-soluble
sugars
Total
carbohydrate
Control 10.2 2.11 13.2
Ascorbic acid 50 15.2 2.81 19.8
100 16.7 3.57 20.4
150 17.9 4.11 21.5
Thiamin 50 13.3 2.73 17.3
100 13.2 3.84 18.4
150 11.4 3.65 16.2
α-tocopherol 50 17.3 3.41 20.4
Ascorbic acid 50 + Thiamin 50 +
α-tocopherol 50 ppm
Ascorbic acid 100 + Thiamin 100 +
α-tocopherol 100 ppm
Ascorbic acid 150 + Thiamin 150 +
α-tocopherol 150 ppm
100 19.5 5.81 22.2
150 21.3 6.94 22.8
20.4 6.7 22.6
22.5 7.9 25.8
21.0 5.8 24.3
LSD 5% 1.82 0.23 0.13

Ozean Journal of Applied Sciences 3(1), 2010
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ISSN 1943-2429
© 2010 Ozean Publication
Ozean Journal of Applied Sciences 3(1), 2010
Effect of zinc and / or iron foliar application on growth and essential oil of sweet
basil (Ocimum basilicum L.) under salt stress
H.A.H. Said-Al Ahl* and Abeer A. Mahmoud **
* Department of Cultivation and Production of Medicinal and Aromatic Plants, National Research
Centre, Dokki, Giza, Egypt.
** Department of Botany (Plant Physiology Section), Faculty of Agriculture, Cairo University.
*Corresponding Author: saidalahl@yahoo.com
___________________________________________________________________________________
Abstract: The effect of salinity and Fe and/or Zn application on the vegetative growth, dry matter yield
and essential oil production and its constituents were studied at the farm station of the National
Research Centre, at Shalakan, Kalubia Governorate, Egypt on sweet basil (Ocimum basilicum L.)
during 2006 and 2007 seasons. The highest plant height, number of branches, fresh and dry matter
yield as well as essential oil yield was recorded in normal soil which decreased with the increase in the
salinity. Increasing the soil salinity increased essential oil %. The addition of micronutrients had an
active effect comparing with control, highest plant height and number of branches being with iron
application and zinc gave the highest value of fresh weight, whereas a mixture of iron + zinc gave the
highest values of dry matter and essential oil yield under normal soil condition. In contrast application
a mixture of iron + zinc gave the highest essential oil % under soil salinity condition. Concerning
essential oil constituents, linalool and methylchavicol were the major compounds. The concentration of
linalool and methylchavicol decreased with saline soil treatment. Addition of micronutrients decreased
linalool in normal soil; on the contrary there was an increase in linalool content by using soil salinity
treatment. Highest linalool content (52.14%) was recorded in saline soil with spraying mixture of
zinc+iron. Spraying plants with zinc and /or zinc+ iron increased the content of methylchavicol in
normal soil, and it's content (44.01%) was the highest in normal soil with zinc spraying. All the
spraying treatments except mixture of zinc+iron increased the content of methylchavicol in saline soil.
The highest decrease in linalool (25.687%) and methylchavicol (20.34%) was caused with zinc+iron in
normal and saline soils, respectively.
Key words: sweet basil, Ocimum basilicum L., foliar application, iron, zinc, salt stress, essential oil
________________________________________________________________________________
INTRODUCTION
The Ocimum genus, belonging to the Lamiaceae family, includes herbs and shrubs distributed in
tropical and subtropical regions of Asia, Africa and the Americas. The most important species of
Ocimum genus is O. basilicum L.; this species, usually named common basil or sweet basil, is
considered economically useful because of their basic natural characteristics as essential oil
producers (Lawrence, 1993). Sweet basil is a popular culinary herb used in food and oral care
products (La-Chowicz et al., 1996; Machale et al., 1997). The essential oil of the plant is also used as
perfumery. Also, basil is well known as a plant of a folk medicinal used as carminative, galactogogue,
stomachic and antispasmodic tonic and vermifugem, also, basil tea taken hot is good for treating
nausea, flatulance and dysentery (Ozcan and Chalchat, 2002; Sajjadi, 2006). Basil is used in pharmacy for
diuretic and stimulating properties, in perfumes and cosmetics for its smell; in fact, it is a part of many
fragrance compositions (Bariaux et al., 1992; Khatri et al., 1995). Antiviral and antimicrobial activities
of this plant have also been reported (Chiang et al., 2005). Available literature data indicates that
there is a great deal of diversity in growth characteristics and the composition of essential oil of the
genus Ocimum. Such observations have been attributed to the abundant cross-pollination that
occurs within this genus resulting in considerable degrees of variation in the genotypes
(Lawrence, 1988). The difference in the essential oil compositions in O. basilicum cultivated in
different geographical localities led to the classification of basil into chemotypes on the basis of the
97

Ozean Journal of Applied Sciences 3(1), 2010
prevalent chemical components (Lawrence, 1992) or components having composition greater than
20 percent (Grayer et al., 1996). There are usually considerable variations in the contents of the
major components within this species. In a study of essential oils of different geographical origins,
(Lawrence, 1988) found that the main constituents of the essential oil of basil are produced by two
different biochemical pathways, the phenylpropanoids (methyl chavicol, eugenol, methyleugenol and
methyl cinnamate) by the shikimic acid pathway, and the terpenes (linalool and geraniol) by the
mevalonic acid pathway. Other latter studies on the basils from other geographical regions have
added new chemotypes to that list based on the established classification scheme (Grayer,
1996; Lawrence, 1992).
Salinity is one of the major factors that affect plant growth; it is a serious problem in many areas of
world's causing considerable loss in agricultural production (Bray et al., 2000; Shao et al., 2008; Wu et
al., 2007). Soil salinity resulting from natural processes or from crop irrigation with saline water,
occurs in many arid and semi-arid regions of the world (Lauchli and Epstein, 1990). The deleterious
effects of salinity on plant growth are associated with (1) low osmotic potential of soil solution (water
stress), (2) nutritional imbalance, (3) specific ion effect (salt stress), or (4) a combination of these
factors (Yildirim and Taylor, 2005). Saline soil are generally dominated by sodium ions, with the
dominant anions being chloride and sulphate, they have a high sodium absorption rate with a high pH
and electrical conductivities (>4 dsm -1 ) (Flowers and Flowers, 2005).
In saline soils, the solubility of micronutrients is particularly low, and plants grown on such soil often
suffer from deficiencies in these elements. Soil salinity may reduce micronutrients uptake due to
stronger competition by salt cations at the root surface (Marschner and Romheld, 1994; Page et al.,
1990). Soluble ferrous fe tended to become oxidized to ferric oxide which was insoluble as well as the
limitation of iron uptake by root cell cytosol (Nikolic and Kastori, 2000) and inhibit iron transport to
shoots and its transfer from apoplasm to cytoplasm in shoot tissues (Nikolic and Romheld, 2002). Zinc
is necessary for root cell membrane integrity (Welch et al., 1982). As suggested by Parker et al. (1992),
root cell membrane permeability is increased under zinc deficiency which might be related to the
functions of zinc in cell membranes. From this point of view, external zinc concentrations could
mitigate the adverse effect of NaCL by inhibiting Na and /or Cl uptake or translocation. Alpaslan et al.
(1999) concluded that in the salt affected areas, zinc application could alleviate possible Na and Cl
injury in plants. Foliar spraying under these conditions could be much more efficient than any
application of nutrients to the soil (Horesh and Levy, 1981).
Iron (Fe) is a cofactor for approximately 140 enzymes that catalyze unique biochemical reactions
(Brittenham, 1994). Hence, iron fills many essential roles in plant growth and development, including
chlorophyll synthesis, thylakoid synthesis and chloroplast development (Miller et al., 1995). Iron is
required at several steps in the biosynthetic pathways. Zinc (Zn ) is an essential element for plant that
act as a metal component of various enzymes or as a functional structural or regulatory cofactor and for
protein synthesis, photosynthesis, the synthesis of auxin, cell division, the maintance of membrane
structure and function, and sexual fertilization (Marschner, 1995).
Moreover, little is known about salinity interaction with iron and zinc deprivation, the present study
aimed to decrease salinity stress is a main issue in this study to ensure increasing production. Also, the
present study describes the composition of the essential oils of sweet basil cultivated in Egypt.
MATERIALS AND METHODS
A field experiment was conducted at the farm station of the National Research Centre, at Shalakan,
Kalubia Governorate, Egypt during the two successive seasons of 2006 and 2007. The physical and
chemical properties of the soil sample were determined according to Jackson, 1973, Table 1.
Seeds of Ocimum basilicum L. were provided by the Medicinal and Aromatic Plants Division,
Horticultural Research Institute, Agricultural Research Center, Ministry of Agriculture, Egypt. The
seeds of basil were sown in the nursery on 1 st March of both seasons. After 45 days from seed sowing,
uniform seedlings were transplanted into plots 3x3.5m. on rows, with 60cm a part and 20 cm between
the seedlings. The experimental layout was split plot design in a complete randomized block design
with three replications. The main plots were devoted to the two levels of soil salinity (normal soil and
saline soil), while the sub ones were assigned for three sources of second factor (Fe, Zn, and a mixture
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Ozean Journal of Applied Sciences 3(1), 2010
of Fe + Zn). The experimental treatments consisted of 8 treatments, which represented all combinations
between soil salinity conditions (normal soil, S1 = 0.73 ppm and saline soil, S2 = 4.95 ppm) and foliar
application treatments (F0= control, F1= 250 ppm iron as a foliar spray of the chelated Fe-EDTA (Fe
8.5%), F2= 250 ppm Zn-EDTA (Zn16%) and F3= 250 ppm mixture of zinc and iron) were sprayed at
interval times of 45, 60, 120 and 150 days from transplanting.
Growth characters and chemical constituent's determinations were carried out at the first and second
cuts after 90 and 180 days from transplanting, respectively before flowering. The following data were
recorded. Plant height (cm), number of branches / plant, fresh and dry weights of herb (g / plant or ton /
feddan). The essential oil percentage was determined in the air dried herb using a modified Clevenger
apparatus according to Guenther (1961). Essential oil percentage was determined and expressed as (%),
while essential oil yield per plant was expressed as ml plant -1 . The essential oils of each treatment at
the first and second cuts were collected and dehydrated over anhydrous sodium sulphate and kept in a
refrigerator until GLC analysis. The GLC analysis of the essential oil samples was carried out in the
second season using gas chromatography instrument stands at the Central Laboratory of the National
Research Center with the following specifications. Instrument: Hewlett Packard 6890 series, Column:
HP (Carbwax 20M) 25m length × 0.32mm I.D, Film thickness: 0.3Mm, Sample size: 1µl, oven
temperature: 60-190 °C, Program: 60 °C/2min, 8 °C/min, 190 °C/25min. Injection port temperature:
240 °C, Detector temperature (FID): 280 °C, Carrier gas: nitrogen, Flow rate: N2 30 ml/min; H2 30
ml/min; air 300 ml/min. Main compounds of the essential oils were identified by matching their
retention times with those of the authentic samples injected under the same conditions. The relative
percentage of each compound was calculated from the area of the peak corresponding to each
compound.
Data were exposed to the proper statistical analysis of variance according to LeClerg et al. (1966) as
well as Snedecor and Cochran (1990). The means represented in the study following by the same
alphabetical letters were not significantly different at the probability level of 0.05, Least Significant
differences (L.S.D) were used compare between means according to Waller and Duncan (1969) at
probability 5%.
A. Growth characters and yield
Effect of soil salinity
RESULTS AND DISCUSSIONS
Tables (2-5) show the effect of soil salinity on plant height, number of branches, fresh and dry weights
of herb (g/plant or ton/fed.) of the basil plants. These growth characters decreased significantly with
soil salinity conditions in both cuts during two seasons. The inhibitory effect of salinity was also found
by (Abd El-Hady, 2007; Abd El-Wahab, 2006; Baghalian et al., 2008; Belaqziz et al., 2009; Ozturk et
al., 2004; Razmjoo et al., 2008; Shalan et al., 2006; Turhan and Eris, 2007). Saline conditions reduce
the ability of plants to absorb water causing rapid reductions in growth rate, and induce many
metabolic changes (Epstein, 1980). Also, salt stress with osmotic, nutritional and toxic effects prevents
growth in many plant species (Cheeseman, 1988; Hasegawa et al., 1986). Therefore, the reduction in
growth was explained by lower osmotic potential in the soil, which leads to decreased water uptake,
reduced transpiration, and closure of stomata, which is associated with the reduced growth (Ben-Asher
et al., 2006; Levitt, 1980). In general, the mechanisms of salinity effect on plant growth were reported
by Meiri and Shalhavet (1973) who attributed the effect of salinity to the following points: (a) the
distribution of salts within the plant cells may result in turgor reduction and growth retardations. Also,
salinity affects root and stomatal resistance to water flow, (b) the balance between root and shoot
hormones changes considerably under saline conditions, (c) salinity changes the structure of the
chloroplasts and mitochondria and such changes may interfere with normal metabolism and growth, (d)
salinity increases respiration and reduces photosynthesis products available for growth.
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Effect of micronutrients
Ozean Journal of Applied Sciences 3(1), 2010
Basil plants sprayed with zinc and/or iron under normal and saline soils conditions were superior if
compared without sprayed plants (tables, 2-5). Results also indicated that treated plants with iron were
much superior in plant height and number of branches compared with other treatments. However,
plants treated by zinc were produced fresh and dry weights of herb (g/plant or ton/fed.) greater than
those other treatments. While, plants treated with mixture of zinc and iron gave the heaviest dry weight
in the first cut, but treated plants with zinc or iron were produced dry weights of herb greater than those
of other treatments in the second cut during first and second seasons, respectively. The stimulatory
effect of zinc and/or iron were recorded by (Aziz and El- Sherbeny, 2004; Pande et al., 2007; Said-Al
Ahl, 2005; Said-Al Ahl and Omer, 2009).
The stimulation effects of applying zinc and iron on vegetative growth may be attributed to the well
known functions of zinc and iron in plant life, as described in the Introduction. Moreover, zinc is a
component of carbonic anhydrase, as well as several dehydrogenases and auxin production which in
turn enhanced the elongation processes, besides the function of zinc in CO2 assimilation. Consequently,
the fresh and dry weights of herb could be increased (Marschner, 1995). However, iron deficiency
inhibited leaf growth, cell number, size and cell division, as well as chlorophyll, protein, starch and
sugar content. Thus, the fresh and dry weights of herb could be decreased. Iron is necessary for the
biosynthesis of chlorophyll and cytochrome, besides the function of iron in the metabolism of
chloroplast RNA, leading to increase in the biosyntheses materials (produced and accumulated),
consequently, the growth was enhanced (Marschner, 1995).
Effect of interaction
The interaction between normal soil and saline soil conditions with zinc and/or iron application
resulted in a significant increment of plant height and number of branches in the two cuts for both
seasons (Tables, 2-5). Fresh weight of herb (g/plant or ton/fed.) was significantly increased in the
second cut and non significant in the first cut in both seasons, but dry weight of herb (g/plant or
ton/fed.) increased significantly in both cuts, except, this increment was insignificant in the second cut
at first season. The maximum plant height and number of branches mean values were recorded from
the combination of iron spraying and non soil salinity in all cuttings of both seasons. The highest fresh
and dry weights of herb (g/plant or ton/fed.) resulted from zinc spraying and non soil salinity, or
mixture (zinc and iron) spraying and non soil salinity, respectively in all cuttings of both seasons.
While, the minimum growth characters values were resulted from the soil salinity condition without
any treatment.
B. Essential oil production
Effect of soil salinity
Tables (4, 5) explain that soil salinity conditions significant increased essential oil %. The increase in
essential oil % due to salinity conditions was found by (Al-Amier and Craker, 2007; Baghalian et al.,
2008; Baher et al., 2002; Hendawy and Khalid, 2005; Prasad et al., 2006; Tabatabale and Zari, 2007).
In contrast, essential oil yield was significant decreased at the first and second cuts of both seasons.
The results were similar in both the two seasons. Similar results were found by (Abd El-Wahab, 2006;
Baghalian et al., 2008; Ozturk et al., 2004; Razmjoo et al., 2008). The reduction of essential oil yield
by salinity due to a decrease in growth characters.
The stimulation of essential oil production under salinity could be due to a higher oil gland density and
an increase in the absolute number of glands produced prior to leaf emergence (Charles et al., 1990).
Salt stress may also affect the essential oil accumulation indirectly through its effects on either net
assimilation or the partitioning of assimilate among growth and differentiation processes (Charles et
al., 1990).
Penka (1978) showed that the formation and accumulation of essential oil in plants was explained as
due to the action of environmental factors. It might be claimed that the formation and accumulation of
essential oil was directly dependent on perfect growth and development of the plants producing oils.
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Ozean Journal of Applied Sciences 3(1), 2010
The decrease in oil production might be due to the decrease in plant anabolism. Morales et al. (1993)
suggested that, an increase in oil content in some of the salt stressed plants might be attributed to
decline the primary metabolites due to the effects of salinity, causing intermediary products to become
available for secondary metabolites synthesis.
Effect of micronutrients
Data in Tables (4, 5) indicate that the essential oil (% or yield) of basil sprayed with zinc + iron was
higher than the other treatments, followed by plants treated with zinc, and then plants treated with iron,
whereas plants untreated (control) was lower in this respect in the two cuts during the two seasons. The
increase in essential oil due to zinc and /or iron was also found by (El-Sawi and Mohamed, 2002;
Maurya, 1990; Pande et al., 2007; Said-Al Ahl, 2005; Said-Al Ahl and Omer 2009; Subrahmanyam et
al., 1992). From previous studies, biosynthesis of secondary metabolites is not only controlled
genetically but it also is affected strongly by environmental influences (Naghdi Badi et al., 2004). In
line with the foregoing, environmental variables affect essential oil, Marschner (1995) found that iron
has important functions in plant metabolism, such as activating catalase enzymes associated with
superoxide dismutase, as well as in photorespiration and the glycolate pathway. Also, in iron-deficient
plants, the activities of some enzymes are impaired and may often be responsible for gross changes in
metabolic processes. Moreover, zinc is an essential micronutrient that acts either as a metal component
of various enzymes or as a functional, structural, or regulatory cofactor associated with saccharide
metabolism, photosynthesis, and protein synthesis (Marschner, 1995). Carbon dioxide and glucose are
precursors of monoterpene biosynthesis. Saccarides are also a source of energy and reducing power for
terpenoid synthesis. As zinc is involved in photosynthesis and saccaride metabolism, and as CO2 and
glucose is the most likely sources of carbon utilized in terpene biosynthesis, the role of zinc in
influencing essential oil accumulation seems particularly important (Srivastava et al., 1997).
Effect of interaction
From data in Tables (4, 5), it can be concluded that the interaction treatments between foliar spray with
micronutrients (Zn, Fe and mixture of Zn + Fe) and soil salinity significantly increased essential oil %
and essential oil yield in both cuts during two seasons, except essential oil % at first cut was increased
insignificantly of both seasons if comparing with none and soil salinity conditions. The highest value of
essential oil % was obtained by spraying with zinc + iron under soil salinity conditions. Whereas,
plants sprayed with zinc + iron under none soil salinity gave the highest value of essential oil yield in
both cuts during two seasons.
C. Chemical composition of essential oils
Table (6) show the data belonging to qualitative and quantitative constituents of essential oils distilled
from the basil herb before flowering stage collected from the first and second cuts during the season of
2007. The essential oil composition of 8 treatments, along with the quantitative data is listed in Table 6.
Twenty-four compounds were identified. Comparison of the analytical data of the oils revealed marked
differences in qualitative and quantitative composition. Considering the main components, of the all
treatments were characterized by high contents of linalool (38.28-52.14%) and methylchavicol (20.34-
27.67%), and moderate amounts of 1,8-cineol (6.04-9.22%), germacrene D (2.32-4.38%), and very
variable amounts of cis-bisabolene (0.00-2.53%), geraniol (0.59-2.02%), nerol (0.89-1.78%), cadinol
(0.00-1.75%) and 1,4 terpineol (0.66-1.70%), and a lower amounts of α-thujene, β-pinene, α-pinene,
camphene, sabinene, myrcene, ocimene, limonene, selinene, nerolidole, cadinene, caryophyllene,
eugenol, camphor and α-terpinene considered as traces.
The results in table (6) show that iron treatment gave the highest content of (methylchavicol,
germacrene D, α-pinene and α-thujene); zinc treatment gave the highest content of (1, 8-cineol, βpinene,
camphor, nerolidole and camphene) as well as iron + zinc treatment gave the highest content of
(linalool, sabinene, nerol, eugenol, selinene and cadinole). However, control treatment gave the same
result of (limonene, α-terpinene, 1,4 terpineol, caryophyllene, cis-bisabolene, cadinene and ocimene)
under the soil salinity conditions.
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Ozean Journal of Applied Sciences 3(1), 2010
According to major compounds it was obviously cleared that soil salinity treatment decreased both of
linalool and methylchavicol compared to that of control Also, linalool was decreased with spraying
treatments comparing to that of control, while there was an increase in methylchavicol by using zinc
and mixture of zinc + iron whereas iron treatment had the lowest in this regard comparing to that of
control in normal soil. However, under saline soil condition, the results indicate that linalool was
increased with spraying treatments comparing to that of control and mixture of iron + zinc followed by
zinc and then iron gave the highest content of linalool. Whereas, methyl chavicol was increased with
spraying of iron and zinc, but decreased by iron + zinc comparing to that of control and iron followed
by zinc and then control treatment gave the highest content of methylchavicol. The highest content of
linalool was obtained by using zinc + iron under saline soil compared to the other interaction ones and
the lowest value in this respect obtained in normal soil from the same treatment. However, zinc
treatment gave the highest content of methylchavicol in normal soil but, under soil salinity, zinc+iron
treatment gave the lowest value in this respect in all cases.
The present study was in accordance by EL-Keltawi and Croteau (1987) on spearmint and marjoram
who indicated that irrigation of both plants with saline solution consisting of calcium chloride and
sodium chloride reduced essential oil. They added that under salinity in spearmint the content of
limonene was increased and carvone was concomitantly decreased relative to controls irrigated with
water only. In case of marjoram, salt stress led to increase the content of sabinene which was
accompanied by a decrease in the content of sabinene hydrate. Hendawy and Khalid (2005) on salvia
officinalis reprted that treatment of 2500 ppm soil salinity increased α–thujone, camphor and 1,8-
cineol but it decreased the component of β–thujone compared with the control treatment.
Said-Al Ahl and Omer (2009) indicated that linalool was increased in the herb and seeds of coriander,
but dodecenal of herb was decreased with treatment of zinc and mixture of zinc + iron compared to
control. Also, Said-Al Ahl et al. (2010) indicated that soil salinity treatments at 1500 and 4500 ppm
levels increased the content of linalool and on the contrary there was decreased in eugenol content by
using 1500 and 4500 ppm of soil salinity in the Ocimum basilicum var. purpurascens.
The essential oil of basil in this study belonging to linalool and methylchavicol chemotype,
characterized by the simultaneous presence of linalool and methylchavicol, can be ascribed to those
plants in which essential oil constituents are produced by two different biosynthetic pathways. In fact,
methylchavicol has a common biosynthesis originating from the precursor (L-phenylalanine and
cinnamic acid), whereas linalool follows another biogenetic pathway from mevalonic acid via geranyl
pyrophosphate (Nikanen, 1989). It is known that climatic conditions and water available in the soil can
change the vegetal secondary metabolism and, consequently ,alter the composition of essential oils,
throughout the seasons of the year. Chemical variations in essential oils were associated with seasons
for Ocimum selloi (Moraes et al., 2002) and with time of day for Ocimum gratissimum (Vasconcelos
Silva et al., 1999(.
.
Table 1. The physical and chemical properties of the experimental soil.
Physical and chemical properties Normal soil Saline soil
Sand (%) 46.8 49.6
Silt (%) 28.2 26
Clay (%) 25.0 24.4
Soil texture Sandy loam Sandy loam
pH 8.12 8.45
E.C (m. mohs/cm) 0.73 4.95
Organic matter (%) 0.95 0.40
N (mg kg -1 ) 0.09 0.05
P (mg kg -1 ) 20.0 0.45
K (mg kg -1 ) 208.0 2.04
Zn (ppm) 1.2 0.65
Iron (ppm) 20.0 1.32
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Table 2. Effect of soil salinity, foliar spray with micronutruients and their interactions on plant height, branches number and herb fresh weight of Ocimum
basilicumL. plants at first cut in 2006 and 2007 seasons.
Treatments
Plant height (cm) Branches number plant -1 Fresh weight g plant -1
Season 1 Season 2 Season 1 Season 2 Season 1 Season 2
S1 55.43 a ± 7.831 55.61 a ± 7.972 11.91 a ± 2.188 11.24 a ± 1.898 110.44 a ± 9.481 110.24 a ± 10.077
S2 36.27 b ± 3.939 37.11 b ± 5.451 7.18 b ± 1.395 7.68 b ± 1.589 54.43 b ± 6.849 53.25 b ± 5.940
LSD at � 0.05 5.1220 0.6758 2.2470 1.0230 6.0970 7.8010
F0 38.24 d ± 6.538 37.45 d ± 7.113 7.68 c ± 2.891 7.23 c ± 2.285 71.25 b ± 28.708 69.83 b ± 28.388
F1 52.69 a ± 12.539 53.69 a ± 10.499 11.10 a ± 3.705 11.17 a ± 2.792 85.27 a ± 29.959 84.00 a ± 30.361
F2 44.15 c ± 11.738 44.04 c ± 11.247 9.29 b ± 1.385 9.46 b ± 0.499 88.43 a ± 33.338 87.52 a ± 33.179
F3 48.32 b ± 11.649 50.25 b ± 12.007 10.10 ab ± 3.188 9.99 b ± 2.357 84.80 a ± 31.976 85.63 a ± 33.696
LSD at � 0.05 1.8820 2.0190 1.0550 0.5440 6.9310 4.5900
S1xF0 44.07 d ± 2.001 43.87 c ± 0.998 10.15 c ± 1.550 9.29 cd ± 0.543 97.17 ± 4.908 95.50 ± 5.500
S1xF1 64.09 a ± 0.460 63.17 a ± 1.607 14.40 a ± 1.058 13.70 a ± 0.265 112.33 ± 4.646 111.50 ± 5.074
S1xP2 54.73 c ± 2.194 54.22 b ± 1.338 10.18 c ± 1.154 9.89 c ± 0.271 118.60 ± 5.769 117.63 ± 5.119
S1xF3 58.83 b ± 2.021 61.17 a ± 1.607 12.90 b ± 1.258 12.07 b ± 0.902 113.67 ± 5.508 116.33 ± 3.215
S2x F0 32.42 g ± 1.040 31.03 e ± 1.380 5.20 e ± 0.346 5.17 f ± 0.153 45.33 ± 4.619 44.17 ± 2.843
S2xF1 41.29 e ± 1.728 44.20 c ± 1.814 7.80 d ± 0.721 8.63 de ± 0.404 58.20 ± 4.952 56.50 ± 3.148
S2xF2 33.56 g ± 1.822 33.87 e ± 1.963 8.40 d ± 1.039 9.04 d ± 0.063 58.27 ± 3.900 57.41 ± 2.342
S2 xF3 37.80 f ± 1.836 39.33 d ± 0.577 7.30 d ± 0.520 7.90 e ± 0.173 55.93 ± 5.100 54.93 ± 0.902
LSD at � 0.05 2.6620 2.8550 1.4920 0.7693 N.S N.S
Means with different letters within each column are significant at 0.05 level and means in the same column with the same letters are not significant. Mean
±Sd (standard deviation)
Since: S = Soil salinity, i. e. S1 = 0.73 dsm -1 , S2 =4.95 dsm -1 ; F = Foliar spray with micronutrients, i. e. F0 = control, F1 = iron 250ppm, F2 = zinc 250ppm,
F3= mixture of iron + zinc
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Table 3. Effect of soil salinity, foliar spray with micronutruients and their interactions on plant height, branches number and herb fresh weight of Ocimum
basilicum L. plants in at second cut in 2006 2007 seasons.
Treatments
Plant height (cm) Branches number plant -1 Fresh weight g plant -1
Season 1 Season 2 Season 1 Season 2 Season 1 Season 2
S1 70.53 a ± 7.733 69.46 a ± 7.137 15.41 a ± 1.874 14.79 a ± 2.192 138.78 a ± 15.292 133.63 a ± 12.583
S2 51.08 b ± 5.595 51.75 b ± 5.683 11.03 b ± 2.116 11.17 b ± 2.248 67.75 b ± 9.697 71.65 b ± 11.697
LSD at � 0.05 3.3340 1.9350 1.3940 2.2600 5.3080 2.7990
F0 53.17 c ± 7.047 51.70 d ± 7.200 10.63 c ± 2.815 10.38 b ± 2.681 84.81 c ± 35.643 84.58 d ± 32.444
F1 69.32 a ± 10.901 68.02 a ± 8.634 14.44 a ± 3.247 14.18 a ± 2.717 106.50 b ± 35.681 107.30 b ± 30.040
F2 59.01 b ± 12.088 60.36 c ± 10.843 13.95 ab ± 0.580 13.48 a ± 0.949 116.50 a ± 45.873 114.67 a ± 34.373
F3 61.70 b ± 13.336 62.35 b ± 12.282 13.84 b ± 3.299 13.88 a ± 3.279 105.25 b ± 38.621 104.00 c ± 39.100
LSD at � 0.05 2.9820 1.2510 0.5352 0.9621 1.9870 2.6340
S1xF0 59.47 c ± 1.295 58.23 e ± 0.979 13.20 c ± 0.200 12.70 bc ± 1.082 117.29 c ± 2.350 114.17 d ± 1.443
S1xF1 79.01 a ± 2.968 75.87 a ± 0.594 17.39 a ± 0.344 16.50 a ± 1.323 139.00 b ± 1.000 134.67 c ± 2.517
S1xP2 69.89 b ± 1.723 70.22 c ± 1.235 14.23 b ± 0.521 13.09 bc ± 1.299 158.33 a ± 2.887 146.00 a ± 2.000
S1xF3 73.73 b ± 2.802 73.53 b ± 0.924 16.81 a ± 0.812 16.87 a ± 0.231 140.50 b ± 0.500 139.67 b ± 2.082
S2x F0 46.87 d ± 1.848 45.17 g ± 0.764 8.07 e ± 0.115 8.05 e ± 0.777 52.33 f ± 2.517 55.00 g ± 2.000
S2xF1 59.63 c ± 2.570 60.17 d ± 1.041 11.50 d ± 0.500 11.87 cd ± 0.777 74.00 d ± 3.606 79.93 e ± 1.675
S2xF2 48.13 d ± 2.721 50.50 f ± 0.866 13.67 bc ± 0.577 13.87 b ± 0.325 74.67 d ± 1.528 83.33 e ± 2.082
S2 xF3 49.67 d ± 1.528 51.17 f ± 1.041 10.87 d ± 0.231 10.90 d ± 0.361 70.00 e ± 1.000 68.33 f ± 1.155
LSD at � 0.05 4.2170 1.7690 0.7569 1.3610 2.8090 3.7260
Means with different letters within each column are significant at 0.05 level and means in the same column with the same letters are not significant. Mean ±Sd
(standard deviation)
Since: S = Soil salinity, i. e. S1 = 0.73 dsm -1 , S2 =4.95 dsm -1 ; F = Foliar spray with micronutrients, i. e. F0 = control, F1 = iron 250ppm, F2 = zinc 250ppm,
F3= mixture of iron + zinc
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Table 4. Effect of soil salinity, foliar spray with micronutruients and their interactions on plant height, branches number and herb fresh weight of Ocimum
basilicum L. plants at first cut in 2006 and 2007 seasons.
Treatments
Dry weight g plant -1 Essential oil % Essential oil yield plant -1
Season 1 Season 2 Season 1 Season 2 Season 1 Season 2
S1 30.16 a ± 5.739 26.49 a ± 3.998 0.70 b ± 0.089 0.69 b ± 0.083 0.21 a ± 0.064 0.19 a ± 0.046
S2 14.71 b ± 2.475 15.02 b ± 2.636 0.76 a ± 0.088 0.76 a ± 0.096 0.11 b ± 0.028 0.12 b ± 0.032
LSD at � 0.05 2.0730 1.1370 0.0095 0.0123 0.0145 0.0034
F0 16.63 d ± 6.445 16.30 c ± 6.123 0.60 d ± 0.043 0.59 c ± 0.033 0.10 d ± 0.033 0.10 d ± 0.033
F1 22.02 c ± 6.580 19.90 b ± 4.769 0.72 c ± 0.039 0.74 b ± 0.037 0.16 c ± 0.042 0.15 c ± 0.034
F2 24.67 b ± 9.694 22.85 a ± 6.791 0.76 b ± 0.048 0.75 b ± 0.064 0.18 b ± 0.062 0.17 b ± 0.039
F3 26.42 a ± 11.314 23.97 a ± 7.731 0.83 a ± 0.037 0.82 a ± 0.041 0.21 a ± 0.085 0.19 a ± 0.054
LSD at � 0.05 1.1730 1.2150 0.0325 0.0398 0.0080 0.0174
S1xF0 22.47 d ± 0.896 21.83 d ± 1.258 0.57 ± 0.025 0.57 ± 0.027 0.13 de ± 0.003 0.12 de ± 0.013
S1xF1 27.97 c ± 1.002 24.17 c ± 1.443 0.70 ± 0.040 0.72 ± 0.031 0.20 c ± 0.005 0.18 c ± 0.018
S1xP2 33.50 b ± 0.500 28.97 b ± 1.704 0.72 ± 0.006 0.70 ± 0.000 0.24 b ± 0.006 0.20 b ± 0.012
S1xF3 36.70 a ± 1.468 31.00 a ± 1.000 0.80 ± 0.021 0.78 ± 0.017 0.29 a ± 0.014 0.24 a ± 0.009
S2x F0 10.80 f ± 0.985 10.77 f ± 0.580 0.63 ± 0.025 0.62 ± 0.021 0.07 f ± 0.007 0.07 f ± 0.004
S2xF1 16.07 e ± 1.007 15.63 e ± 0.404 0.75 ± 0.025 0.75 ± 0.044 0.12 e ± 0.006 0.12 e ± 0.004
S2xF2 15.83 e ± 0.764 16.73 e ± 0.404 0.80 ± 0.006 0.81 ± 0.042 0.13 de ± 0.006 0.13 de ± 0.010
S2 xF3 16.13 e ± 0.709 16.93 e ± 0.058 0.85 ± 0.025 0.85 ± 0.017 0.14 d ± 0.002 0.14 d ± 0.004
LSD at � 0.05 1.6580 1.7180 1.4920 0.7693 N.S N.S
Means with different letters within each column are significant at 0.05 level and means in the same column with the same letters are not significant. Mean ±Sd
(standard deviation)
Since: S = Soil salinity, i. e. S1 = 0.73 dsm -1 , S2 =4.95 dsm -1 ; F = Foliar spray with micronutrients, i. e. F0 = control, F1 = iron 250ppm, F2 = zinc 250ppm,
F3= mixture of iron + zinc
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Table 5. Effect of soil salinity, foliar spray with micronutruients and their interactions on herb dry weight, essential oil % and essential oil yield of Ocimum
basilicum L. plants at second cut in 2006 and 2007 seasons.
Treatments
Dry weight g plant -1 Essential oil % Essential oil yield plant -1
Season 1 Season 2 Season 1 Season 2 Season 1 Season 2
S1 40.49 a ± 3.288 39.68 a ± 3.623 0.66 b ± 0.083 0.66 b ± 0.075 0.27 a ± 0.051 0.26 a ± 0.050
S2 21.40 b ± 2.424 21.39 b ± 2.220 0.73 a ± 0.083 0.73 a ± 0.080 0.16 b ± 0.030 0.16 b ± 0.030
LSD at � 0.05 1.6190 3.1310 0.0393 0.0156 0.0231 0.0271
F0 26.75 b ± 9.464 25.94 b ± 8.688 0.58 c ± 0.044 0.58 c ± 0.041 0.15 c ± 0.048 0.15 c ± 0.041
F1 32.58 a ± 10.394 32.23 a ± 10.213 0.71 b ± 0.026 0.70 b ± 0.025 0.23 b ± 0.069 0.22 b ± 0.068
F2 32.67 a ± 10.671 32.08 a ± 10.420 0.72 b ± 0.069 0.72 b ± 0.082 0.23 ab ± 0.055 0.22 b ± 0.050
F3 31.78 a ± 11.570 31.87 a ± 10.905 0.78 a ± 0.038 0.77 a ± 0.039 0.25 a ± 0.080 0.24 a ± 0.073
LSD at � 0.05 1.7420 0.8172 0.0303 0.0281 0.0164 0.0113
S1xF0 35.33 ± 1.528 33.83 b ± 0.764 0.55 g ± 0.045 0.55 e ± 0.027 0.19 c ± 0.021 0.19 d ± 0.006
S1xF1 42.00 ± 1.000 41.50 a ± 1.500 0.69 de ± 0.030 0.69 c ± 0.023 0.29 b ± 0.018 0.29 b ± 0.019
S1xP2 42.33 ± 0.577 41.57 a ± 0.751 0.66 e ± 0.010 0.65 d ± 0.021 0.28 b ± 0.008 0.27 c ± 0.011
S1xF3 42.30 ± 1.572 41.80 a ± 0.700 0.75 bc ± 0.031 0.74 b ± 0.017 0.32 a ± 0.016 0.31 a ± 0.012
S2x F0 18.17 ± 0.764 18.04 d ± 1.066 0.61 f ± 0.006 0.62 d ± 0.015 0.11 e ± 0.005 0.11 f ± 0.007
S2xF1 23.17 ± 1.756 22.97 c ± 0.950 0.72 cd ± 0.010 0.71 bc ± 0.023 0.17 d ± 0.012 0.16 e ± 0.009
S2xF2 23.00 ± 2.000 22.60 c ± 1.039 0.78 ab ± 0.015 0.79 a ± 0.012 0.18 cd ± 0.015 0.18 de ± 0.010
S2 xF3 21.27 ± 0.643 21.93 c ± 0.902 0.81 a ± 0.006 0.81 a ± 0.012 0.17 cd ± 0.004 0.18 de ± 0.010
LSD at � 0.05 N.S 1.1560 0.0428 0.0398 0.0232 0.0159
Means with different letters within each column are significant at 0.05 level and means in the same column with the same letters are not significant. Mean ±Sd
(standard deviation)
Since: S = Soil salinity, i. e. S1 = 0.73 dsm -1 , S2 = 4.95 dsm -1 ; F = Foliar spray with micronutrients, i. e. F0 = control, F1 = iron 250ppm, F2 = zinc 250ppm,
F3= mixture of iron + zinc
106

Ozean Journal of Applied Sciences 3(1), 2010
Table 6. Effect of soil salinity and foliar spray with micronutruients on essential oil constituents of Ocimum basilicum L. plants
in 2007 seasons.
Soil salinity
Compounds Saline soil (4.95 dsm )
-1
Normal soil (0.73 dsm )
-1
F0
F1
F2
F3
F0
F1
F2
α–thujene--
--
--
--
--
0.02
0.01
α-pinene
0.42
0.19
0.32
0.40
0.29
0.40
0.01
camphene--
--
--
--
--
0.02
0.46
sabinene--
--
0.08
0.07
--
0.01
0.35
β-pinene
0.86
0.94
0.93
1.21
0.75
1.18
1.69
myrcene--
--
--
--
0.45
0.84
1.33
limonene
0.37
0.70
0.76
0.97
0.64
0.10
0.11
1 ,8-cineol 7.79
9.40
6.36
7.99
6.63
8.39
9.22
α - terpinene 1.03
0.93
0.91
0.93
0.35
0.10
0.32
ocimene--
0.42
0.36
0.36
0.48
0.12
0.41
Linalool
39.85
32.42
27.68
25.68
38.28
46.54
49.33
camphor--
--
0.27
0.06
0.02
0.22
0.29
1,4-terpineol 1.05
1.34
0.78
0.73
1.70
0.66
0.89
methylchavicol 35.64
30.84
44.01
38.52
21.48
27.67
23.66
nerol
1.00
1.89
1.29
1.11
0.89
1.22
1.09
geroniol
0.75
1.23
1.13
1.18
2.02
0.85
0.59
eugenol--
--
--
--
--
--
--
caryophyllene-- 0.44
0.24
0.37
0.18
0.12
0.01
germacrene D 2.19
3.89
1.80
2.88
2.32
4.38
4.08
cadinene--
0.09
--
0.10
0.11
--
--
cis-bisabolene 1.71
1.98
1.74
2.04
2.53
--
--
nerolidole
0.08
0.41
0.60
0.25
0.44
0.43
0.45
selinene
0.42
0.60
0.05
0.86
0.01
0.09
0.74
t-cadinol
3.25
3.91
4.02
4.76
--
1.38
1.61
Identified
compounds
96.31
91.62
93.33
90.47
91.42
99.26
93.13
F = Foliar spray with micronutrients, i. e. F0 = control, F1 = iron 250ppm, F2 = zinc 250ppm, F3 = mixture of iron + zinc
107
F3
--
--
--
0.47
0.30
0.05
0.01
6.04
0.31
0.40
52.14
0.14
0.78
20.34
1.78
1.07
0.42
--
3.63
--
--
0.30
0.91
1.75
91.35

Ozean Journal of Applied Sciences 3(1), 2010
CONCLUSIONS
It may be concluded that Ocimum basilicum (linalool and methylchavicol chemo type) is tolerant to
soil salinity, thus we may recommend its cultivation in slain soil of Egypt. Foliar spraying with iron
and /or zinc under these conditions could be much more efficient than not application of nutrients. So,
we recommended that, foliar application of iron and /or zinc to raise the salt stress tolerance of sweet
basil (Ocimum basilicum L.).
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Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Growth and yield of Foeniculum vulgare var.azoricum as influenced by some
vitamins and amino acids
S.F. Hendawy and Azza A.Ezz El-Din*
Cultivation and Production of Medicinal and Aromatic Plants Dept.
National Research Centre, Dokki, Cairo -12622, Egypt
E-mail address for correspondence :azzaamin2001@hotmail.com
___________________________________________________________________________________
Abstract :This investigation was carried out in Saft El-Laban farm, Giza during two successive
seasons of 2006/2007 and 2007/2008 to study the effect of foliar application of ascorbic acid and
thiamine in a rate of 0, 25 , 50 and 75 mgL -1 . for each as well as amino acids i.e aspartic and phenyl
alanin in a rate of 0, 100, 200 and 300 ppm for each on growth, yield and chemical composition of
fennel plants. The obtained results could be summarized as follows: ascorbic acid at 75 mgL -1 .
recorded the best value of plant height, number of branches, number of flower heads and seed weight
per plant. No significant difference was shown between aspartic acid and ascorbic acid. Phenyl alanin
at 300 ppm resulted the highest essential oil percent (2.78%) compared with control (2.00%).
Key words: Foeniculum vulgare, ascorbic acid, aspartic, phenylalanine, thiamine, essential oil
__________________________________________________________________________________
INTRODUCTION
Foeniculum vulgare (Fennel) belonging to the family Apiaceae is a perennial herb native to the
Mediterranean Region. It is widely cultivated and extensively used as a culinary spice. The plant is
aromatic and is used as a pot herb. The leaves have diuretic properties and the roots are regarded as
purgatives. Dried fruits of fennel posses a pleasant aromatic taste and used for flavouring soups, meat
dishes and sauces. The fruits are considered to be usefull in treatment of diseases of the chest, spleen
and kidney (Singh and Kale 2008).
The anti-inflammatory, analgesic and antioxidant activities of the fruits of fennel have been reported by
(Choi and Hwang 2004).
The oil of fennel regulates the peristaltic functions of the gastrointestinal tract and relevies the spasms
of intestines (Fathy et al 2002).
Amino acids are fundamental ingredients in the process of protein synthesis. Glycine and Glutamic
acids play an important role in formation of vegetative tissue and chlorophyll. They also have a
chelating effect on micronutrients through making their absorption and transportation easier for the
plant. They are precursors or activators of phytohormones and growth substances. L. Methionine is a
precursor of ethylene and growth factors such as espermine and espermidine (Singh 1999).
Gamal El-Din et al (1997) found that foliar application of ornithine and phenylalanine 50, 100 mgL -1 .
on Cymbopgon citrates led to significantly increase in vegetative growth, number of leaves and tillers
as well as fresh and dry weight of herb. El-Sherbeny and Hassan (1987) reported that phenylalanine
or tryptophan at 200 or 250 mgL -1 significantly increased growth parameters and alkaloid content of
datura plants.
Moursy et al (1988) indicated that phenylalanine or ornithine increased the fresh and dry weight of
callus explants of Datura stramonium L. Refaat and Naguib (1998) found that spraying peppermint
plant with alpha-alanine at 25 and 50 ppm increased fresh and dry weight of the plant and essential oil
113

Ozean Journal of Applied Sciences 3(1), 2010
content. Harridy (1986) mentioned that the higher alkaloid percentage and yield of Catharanthus
roseus resulted from foliar application of some amino acids. Similar trend was observed by Awad
(1986), studying methionen on Hyoscyamus muticus. Talaat and Youssef (2002) found a pronounced
increase in vegetative growth of basil plant as a result of lysine and ornithine treatments.
Little information are available about the role of vitamins in regulating the biosynthesis of essential oil
in plants. Robinson (1973) reported that vitamin B complex and vitamin C act as co-enzmes in the
enzymatic reactions by which carbohydrates, fats and proteins are metabolized and involved in
photosynthesis and respiration. Taraf et al (1999) reported that foliar application of nicotine amide to
lemongrass plants significantly promoted vegetative growth as well as essential oil percent, oil yield
per plant, total carbohydrates and crude proteins.
Ascorbic acid (vitamin C) is known as a growth regulating factor which influences many biological
processes. Price (1966) reported that ascorbic acid (vit.C) increases nucleic acid content, especially
RNA. It also influenced the synthesis of enzymes, nucleic acids and protein, in addition it acts as coenzyme
in metabolic changes. Abd El-Halim (1995) reported that foliar application of ascorbic acid on
tomato plants significantly increased growth parameters (stem length, number of branches, leaves,
flowers and fruit set as well as dry weight of shoot per plant) in comparison with control plants.
Thiamine (Vitamin B1) is a necessary ingredient for biosynthesis of the coenzyme thiamine
pyrophosphate, in this latter form it plays an important role in carbohydrate metabolism. It is an
essential nutrient for both plant and animal. In plants, it is synthesized in the leaves and is transported
to the roots where it controls growth. Thiamine is an important cofactor for the transketolation reaction
of the pentose phosphate for nucleotide synthesis and for the reduced NADP required for various
synthetic pathways. It acts as co-enzyme oxidative carboxylation of � -keto acids (e.g. � ketoglutarate,
pyruvate, the � -keto analogs of the branched-chain amino acids: leucine isoleucine and
valine) Kawasaki (1992). In this concern, Reda et al (1977) reported that thiamine treatment
significantly increased the total yield of chromones as well as khellin and visnagin yield (mg plant -1 ) in
the fruits of Ammi visnaga. Ascorbic acid seemed to retard the growth of Ammi Visnaga but the yield
of different chromones in the fruits under the effect of ascorbic acid (50 and 100 mg/1) was about three
folds that of the corresponding control.
The aim of this study is to investigate the effect of some vitamins and amino acids on growth, yield and
oil composition of fennel plants.
Field experiments:
MATERIALS AND METHODS
Field experiments were carried out at Saft El-Laban farm, Giza, Egypt during two successive growing
seasons (2007 and 2008) to study foliar application of some vitamins i.e ascorbic acid and thiamine and
amino acids i.e aspartic and phenylalanine on growth, yield and chemical composition of fennel plants.
Seeds of Foeniculum vulgare var. azoricum were obtained from Sekem Group Company, Egypt.
The mechanical and chemical analysis of the soil were carried out according to the method of
Chapman and Pratt (1978), which were presented in Table (1).
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Ozean Journal of Applied Sciences 3(1), 2010
Table (1): Physical and chemical analysis of the experimental soil.
Physical properties Chemical parameters
Soil type loamy
Course sand 4.9%
Fine sand 30.7%
Silt 27.5%
Clay 27.5%
Organic matter 2.1%
115
pH
Available N
Available P2O5
Available K2O
Fe
Mn
Zn
Cu
Na
Mg
7.8
13.8 mg/100g
0.482 mg/100g
3.01 mg/100g
1.1 ppm
0.53 ppm
21 ppm
21 ppm
29 ppm
2.4 ppm
Seeds were sown directly in soil on 12 th of October 2006, and on the 2 nd of October 2007. The
experimental unit area was 4 m 2 and the treatments were arranged in completely randomized block
design with three replicates for each treatment. Each plot contained 3 rows of 60 cm apart and the
distance between plants was 30 cm. Calcium super phosphate (15.5% P2O5), at 100 kg/ feddan,
ammonium sulphate (20.5%N) at 150 kg/ feddan and potassium sulphate (48% K2O) at 100kg/feddan
were applied. Phosphorus fertilizer was added at the time of soil preparation while nitrogen fertilizer
was added at two separated side dressings, the first one on 15 th. December and the second on 15 th.
February in both seasons.
The treatments were as follows:
Treat. 1: Control (Sprayed with water)
Treat.2: Sprayed with aspartic acid at 100 ppm.
Treat.3: Sprayed with aspartic acid at 200 ppm.
Treat.4: Sprayed with aspartic acid at 300 ppm.
Treat.5: Sprayed with Phenyl alanin at 100 ppm.
Treat.6: Sprayed with Phenyl alanin at 200 ppm..
Treat.7: Sprayed with Phenyl alanin at 300 ppm.
Treat.8: Sprayed with thiamine at 25 mg/L.
Treat.9: Sprayed with thiamine at 50 mg/L.
Treat.10:Sprayed with thiamine at 75 mg/L.
Treat.11:Sprayed with ascorbic acid at 25 mg/L.
Treat.12:Sprayed with ascorbic acid at 50 mg/L.
Treat.13:Sprayed with ascorbic acid at 75 mg/L.
The plants were sprayed with vitamins or amino acids twice, the first one on 15 th December and the
second on 15 th February, early in the morning, while the control plants were sprayed with distilled
water.
Plant height (cm), plant fresh weight or (aerial parts)g plant -1 plant dry weight (aerial parts), g plant-1
flowers head number, no of branches, no. of suckers and seed weight per plant were recorded through
the two growth seasons. Essential oil percentage was determined in the fruits.

Chemical analysis:
Ozean Journal of Applied Sciences 3(1), 2010
Samples of dried seeds were subjected to water distillation for determination of their essential oil
content using clevenger's apparatus Marotti and Piccaglia (1992).
Oil samples were subjected to Gas Chromatographic Analysis to identify their oil composition adopting
the following condition:
Apparatus: Thermo Quest Trace GC 2000 Series-Finnegan
Standard material: Piano paraffines SUPELCO product.
Column: 60m x 0.32 mm SPB TM -5
Detector: FID; Operating mode Splitless; Base temp. 300 o C
Mobile phase: Nitrogen 30ml/min
Oven Program: Initial temp. 40 o C, rate of increase 3 o C/minute; Final temp. 240 o C; Hold time 10
minutes.
The results of all the parameters were statistically analysed adopted the method of (Snedecor and
Cochran 1980).
RESULTS AND DISCUSSION
Table (2&3) showed the effect of foliar application of vitamins i.e, ascorbic acid (vitamin C) and
thiamine (vitamin B1) as well as amino acids i.e, aspartic acid and phenylalanine on growth and yield
of Foeniculum vulgare var. azoricum during 2007 and 2008. The data indicate that application of
aspartic acid increased plant height (cm) up to
116

Ozean Journal of Applied Sciences 3(1), 2010
Table(2): Effect of foliar application of ascorbic acid and thiamine and amino acids, aspartic and phenylalanine on growth, yield and chemical composition
Foeniculum vulgare var. azoricum plants; First season (2007).
Treatments
Plant height
(cm)
Aerial parts
fresh weight
g plant -1
Aerial parts
dry weight
g plant -1
117
Number of
flower heads
plant -1
Number of
branches
plant -1
Number of
suckers plant -1
Seed weight
g plant -1
Control 0ppm 128.0 650.0 98.48 30.0 8.0 2.0 38.0 2.00
Aspartic
Phenylalanine
Thiamine
Ascorbic acid
100 ppm
200 ppm
300 ppm
Mean
L.S.D. (5%)
100 ppm
200 ppm
300 ppm
Mean
L.S.D. (5%)
25 mg L -1
50 mg L -1
75 mg L -1
Mean
L.S.D. (5%)
25 mg L -1
50 mg L -1
75 mg L -1
Mean
L.S.D. (5%)
158.0
173.0
178.0
169.7
5.12
145.0
155.0
158.0
152.7
4.60
167.0
185.0
184.0.
178.7
5.90
170.0
194.0
185.0
183.0
6.33
710.0
745.0
768.0
741.0
28.16
705.0
715.0
734.0
718.0
24.16
714.0
735.0
772.0
740.3
27.00
766.0
780.0
790.0
778.7
23.51
107.58
112.88
116.36
112.3
2.94
110.16
111.72
114.69
112.2
N.S.
105.00
108.09
113.53
108.9
3.10
117.85
120.00
121.54
119.8
2.80
L.S.D. (5%) for foliar appli. 6.12 21.60 2.34 2.18 0.90 0.36 2.85 0.074
38.0
42.0
48.0
42.7
2.41
35.0
39.0
42.0
38.7
2.38
35.0
42.0
48.0
41.7
2.21
38.0
45.0
50.0
44.3
2.26
10.0
12.0
12.0
11.3
0.90
12.0
14.0
15.0
13.7
1.10
10.0
12.0
13.0
11.7
0.80
11.0
13.0
15.5
13.0
1.11
3.0
3.0
5.0
3.7
0.40
3.0
4.0
4.0
3.7
N.S.
4.0
5.0
5.0
4.7
0.35
4.0
6.0
6.0
5.3
0.38
45.0
49.0
52.0
48.7
2.34
40.0
45.0
48.0
44.3
2.47
48.0
55.0
58.0
53.7
2.35
52.0
58.0
61.0
57.0
2.71
Oil%
2.20
2.32
2.45
2.3
0.090
2.43
2.51
2.58
2.5
0.092
2.44
2.48
2.53
2.5
0.075
2.48
2.55
2.67
2.6
0.070

Ozean Journal of Applied Sciences 3(1), 2010
Table(3). Effect of foliar application of ascorbic acid and thiamine and amino acids, aspartic and phenylalanine on growth, yield and chemical composition
Foeniculum vulgare var. azoricum plants; Second season (2008).
Treatments
Plant height
(cm)
Aerial parts
fresh weight
g plant -1
Aerial parts
dry weight
g plant -1
118
Number of
flower heads
plant -1
Number of
branches
plant -1
Number of
suckers plant -1
Seed weight
g plant -1
Control 0ppm 132.0 675.0 102.27 30.0 9.0 3.0 37.0 2.00
Aspartic
Phenylalanine
Thiamin
Ascorbic acid
100 ppm
200 ppm
300 ppm
Mean
L.S.D. (5%)
100 ppm
200 ppm
300 ppm
Mean
L.S.D. (5%)
25 mg L -1
50 mg L -1
75 mg L -1
Mean
L.S.D. (5%)
25 mg L -1
50 mg L -1
75 mg L -1
Mean
L.S.D. (5%)
155.0
170.0
174.0
166.3
5.05
148.0
156.0
160.0
154.7
4.63
165.0
182.0
189.0
178.7
5.82
170.0
194.0
185.0
183.0
6.12
714.0
733.0
756.0
734.3
26.00
724.0
733.0
754.0
737.0
21.32
722.0
730.0
775.0
742.3
25.14
755.0
784.0
788.0
775.7
23.51
108.18
111.06
114.55
111.3
2.88
113.13
114.53
117.81
115.2
2.56
106.18
107.35
113.97
109.2
2.94
116.15
120.62
121.23
119.3
2.14
L.S.D. (5%) for foliar appli. 6.08 22.30 2.55 2.01 0.85 0.24 2.78 0.078
36.0
43.0
48.0
42.3
2.11
36.0
41.0
45.0
40.7
2.08
37.0
44.0
49.0
43.3
2.03
38.0
48.0
52.0
46.0
2.06
10.0
13.0
14.0
12.3
0.75
13.0
14.0
15.0
14.0
0.84
13.0
14.0
14.0
13.7
0.70
12.0
15.0
15.0
14.0
0.90
3.0
5.0
5.0
4.3
N.S.
4.0
5.0
6.0
5.0
N.S.
4.0
6.0
6.0
5.3
0.25
5.0
6.0
7.0
6.0
0.22
43.0
49.0
53.0
48.3
2.28
42.0
47.0
50.0
46.3
2.40
48.0
53.0
57.0
52.7
2.25
54.0
60.0
63.0
59.0
2.65
Oil%
2.20
2.35
2.40
2.3
0.095
2.41
2.48
2.78
2.5
0.084
2.48
2.51
2.58
2.5
0.086
2.47
2.53
2.64
2.5
0.071

Ozean Journal of Applied Sciences 3(1), 2010
300 ppm, but this increment had no significant effect comparing with 200 ppm. The maximum value of
this character was obtained by applying ascorbic acid at the rate of 50 mgL -1 . This was true during both
seasons except thiamine and ascorbic acid in the second one, whereas the three applied doses were
significant up to 75 mgL -1 . Wahba et al 2002 reported that aspartic acid as foliar spray (25, 50 and 75
ppm) increased the vegetative growth, flowering parameters and yield of courms of Anthoglyza
aethiopica.
Fresh and dry weight of aerial parts significantly increased by increasing all treatments up to 300 ppm
for asp. and phenyl. and 75 mg for vit. B1 and vit. C during the 1 st . season. In the 2 nd . season, the higher
dose applied from vitamins and amino acids, the maximum fresh and dry weight more obtained. The
highest weights were recorded with spraying ascorbic acid at 75 mgL -1 . Gamal El-Din and Abd El-
Wahed (2005) stated that all amino acids treatments (ornithine, proline and phenlyalanin) significantly
increased plant height, number of branches, number of flower heads and fresh and dry weights of aerial
part of chamomile plant.
Applying ascorbic acid significantly increased number of flower heads in fennel plants up to the higher
dose. The same effect was observed when spraying aspartic acid, phenylalanine and thiamine.
Ascorbic acid is not only an important antioxidant, it also appears to link flowering time,
developmental senescence, programmed cell death and responses to pathogens (Pastori et al 2003;
Barth et al 2004 and Pavet et al 2005). Furthermore, it affects nutritional cycle's activity in higher
plants and plays an important role in the electron transport system Liu et al (1997). El-Banna et al
(2006) found stimulatory effects of vitamin C on potato. Golan-Goldhirsh et al (1995) indicated that
soybean treated with ascorbic acid increased photosynthesis process. Talaat (2003) detected that
ascorbic foliar application on sweet paper increased content of macro-nutrients (N, P and K).
Increasing aspartic acid doses significantly increased seed weight g plant -1 up to 300 ppm. Spraying
vitamin C at 75 mgL -1 . resulted in the maximum seed weight. Attoa et al (2002) found that aspartic
acid at 75 ppm increased both the fixed oil and total glucosinolate contents of Iberis amara. The
maximum oil % was achieved from 300 ppm of phenylalanine (2.78%) compared with control (2.00%).
Table (4) showed the effect of foliar application of ascorbic acid and thiamine and amino acids,
aspartic and phenylalanine on essential oil composition of Foeniculum vulgare var. azoricum. The
analysis of essential oil of fennel showed the presence of 7 componds as main constituents. The major
one was found to be anethol (trans-1- methoxy-4- (Prop-1- enly) benzene; C10H12O).
It ranged from 61.55 to 77.31%, followed by fenchone with values of 6.14 to 13.62%. Estragol
occupied the third place and recorded 4.21% to 9.54%. Braun and Franz (1999) found that anethole,
estragole, fenchone and Limonene are the major constituents of fennel essential oil. They represent
99% of herb oil and 93% of the fruits oil. Mahfouz and Sharaf El-Din (2007) reported that Anethole
was the main component in F.vulgare oil. It reached the highest value with half dose of N, P and K
(mineral fertilization 357 kg ammonium sulphate + 238 kg calcium super-phosphate +60 kg potassium
sulphate ha -1 ) and inoculation with Basillus megatherium).
119

Ozean Journal of Applied Sciences 3(1), 2010
Table(4). Effect of foliar application of ascorbic acid and thiamine and amino acids, aspartic and phenylalanine on oil composition of Foeniculum vulgare var. azoricum .
Treatments � -pinene � -pinene Myrcene Limonene Fenchone Estragol Anethol Un-Known
Total identified
compounds
Control 0 2.24 0.44 1.55 7.33 10.24 9.54 61.55 7.11 92.89
Aspartic 100 ppm 1.15 traces 2.11 6.54 9.15 8.71 62.14 10.2 89.8
200 ppm 1.36 0.16 0.06 6.65 9.13 8.82 64.21 9.61 90.39
300 ppm 1.34 0.21 0.18 7.02 9.24 7.16 65.02 9.83 90.17
Phenylalanine 100 ppm 1.44 0.33 0.16 9.14 10.38 6.23 62.34 9.98 90.02
200 ppm 1.45 0.25 0.95 7.25 12.45 6.31 68.11 3.23 96.77
300 ppm 1.48 0.24 0.38 7.93 12.36 6.01 68.42 3.76 96.24
Thiamine 25 mg L -1
50 mg L -1
75 mg L -1
1.01 0.29 0.36 6.54 10.14 6.42 64.32 9.53 90.47
1.06 0.41 0.41 6.72 11.21 6.13 65.16 9.08 90.92
1.18 0.45 0.45 6.72 10.25 6.02 66.17 8.76 91.24
Ascorbic 25 mg L -1
50 mg L -1
75 mg L -1
1.32 0.38 0.85 5.13 13.62 4.32 72.22 2.16 97.84
1.35 0.36 0.93 5.14 6.14 4.21 77.31 4.56 95.44
1.38 0.25 0.99 5.16 8.36 4.45 74.16 5.25 94.75
120

Ozean Journal of Applied Sciences 3(1), 2010
Khalil et al (2008) stated that the major components in fennel seed oil are anethole, more than 45%
and limonene, more than 12%. Abd El-Wahab and Mehasen (2009) reported that anethole content
recorded higher percentage in Indian fennel than local fennel in all sowing time (7, 15 and 21 Nov. )
and sowing locations (El-Minia, Assuit, Sohag and Qena governorates) under upper Egypt conditions.
CONCLUSION
We recommended that among all applied treatments, ascorbic acid at 75 mg L -1 resulted in the best
values of growth parameters, while phenylalanine at concentration of 300 ppm recorded the highest
essential oil percent compared with control.
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123

Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Ozean Journal of Applied Sciences 3(1), 2010
Effect of water stress and potassium humate on the productivity of oregano plant
using saline and fresh water irrigation
H.A.H. Said-Al Ahl* and M.S. Hussein
Department of Cultivation and Production of Medicinal and Aromatic Plants,
National Research Centre, Dokki, Giza, Egypt.
Postal code: 12622
*E-mail address for correspondence: saidalahl@yahoo.com
_________________________________________________________________________________
Abstract: To study the response of oregano (Origanum vulgare L.) plants to soil moisture regimes
using fresh and saline water irrigation and potassium humate fertilization, a pot experiment was
conducted during two successive seasons (2007/2008 and 2008/2009) under the natural conditions of
the greenhouse of the National Research Center, Dokki, Giza, Egypt. Herb fresh weight g plant -1 and
the content and yield ml plant -1 of essential oil were decreased significantly by using saline water
irrigation compared to fresh water irrigation. Herb fresh weight g plant -1 and essential oil yield ml
plant -1 of Origanum vulgare L were significantly decreased with the rise in water stress levels.
Whereas, there was significant increase in essential oil % by using lower level of available soil
moisture (30% ASM) followed by 90% ASM and then 60% ASM contained the highest values of
essential oil %. Fresh herb and essential oil production increased significantly with K-humate
application. The maximum of herb fresh and essential oil yields were obtained from plants irrigated
with 90% available soil moisture fresh water combined with k-humate fertilizer 1.5 g pot -1 . Essential
oil % recorded their maximum value from plants irrigated with 60% ASM fresh water combined with
1.5 g pot -1 K-hum ate. Totally, 20 compounds were identified in essential oils of three populations by
means of GLC. Carvacrol was the dominant compound (46.44–77.96%) for all essential oil samples,
followed by p-cymene (5.31–19.30%) and γ-terpinene (3.38–16.42%). The composition of essential oil
of oregano was affected by soil moisture regimes using fresh and saline water irrigation and potassium
humate fertilization.
Keywords: Origanum vulgare L., essential oil, potassium humate, soil moisture regime, saline
irrigation, carvacrol
_________________________________________________________________________________
INTRODUCTION
The genus Origanum belongs to the family of Lamiaceae (Labiatae) and includes many species that are
commonly found as wild plants in the Mediterranean areas (Skoula and Harborne, 2002). Because of
special compositions of essential oil the leaves of Origanum plants are widely used as a very popular
spice for food production. Origanum vulgare L. is the widest spread among all the species within the
genus which distributed all over, Europe, West and Central Asia up to Taiwan, North Africa, and
America (Goliaris et al., 2002; Ietswaart, 1980). Traditionally, leaves and flowers of oregano are used
in Lithuania mostly for their beneficial properties to cure cough, sore throats, relieve digestive
complaints, and probably stimulate the appetite (Ien et al., 2008). The volatile oil of oregano has been
used traditionally for respiratory disorders, indigestion, dental caries, rheumatoid arthritis and urinary
tract disorders (Ertas et al., 2005). The essential oil of oregano is composed of carvacrol as dominant
component, followed by p-cymene, γ- terpinene, germacrene D, and thymol (Azizi et al., 2009;
Dorman and Deans, 2004; Horne et al., 2001; Said-Al Ahl et al., 2009 a, b; Veldhuizen et al., 2007).
Recently, this spice plant has drawn more attention of consumers due to the antimicrobial, antifungal,
insecticidal and antioxidative effects of this herb on humanhealthy (Bakkali et al., 2008; Jaloszynski et
al., 2008; Kulisic et al., 2004; Lopez et al., 2007; Soylu et al., 2007).
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Ozean Journal of Applied Sciences 3(1), 2010
The commercial value of an aromatic and of a medicinal plant could be reflected by the composition of
their essential oils. The quality of oregano is determined mainly by the essential oil content and its
composition. Both parameters may vary considerably depending on genotypes and cultivation
conditions (D’antuono et al., 2000; Novak et al., 2003). In addition, the essential oil content of oregano
leaves and its components seem to be strongly influenced by environmental problems, especially
water/salt stress (Charles et al., 1990) and deficiency and inadequate mineral nutrients (Stutte, 2006).
Salinity is one of the major factors that affect plant growth; it is a serious problem in many areas of the
world causing considerable loss in agricultural production (Bray et al., 2000; Shao et al., 2008; Wu et
al., 2007). Soil salinity resulting from natural processes or from crop irrigation with saline water,
occurs in many arid and semi-arid regions of the world (Lauchli and Epstein, 1990). In Egypt, saline
water is used for irrigation in some areas. In the same time, under the arid climatic conditions
prevailing in Egypt and associated with the perennial irrigation practices, imperfect drainage system,
continuous increase of water–table level and the relatively high salinity levels of water sources
particularly in the new reclaimed land, the salinization of Egyptian soils rapidly going to be an acute
problem.
Similar to other Lamiaceae species, however, uniformity in essential oil content and composition and
consistency in growth and development are especially susceptibility to environmental stress due to
plant heterogeneity. Thus, crop yields and quality in major oregano production regions that are
frequently subject to dry periods can fluctuate (Al-Amier and Craker, 2007). The use of irrigation over
the past several years to promote crop growth has increased the salt content of the soil, frequently
forcing growers to apply 10% to 20% excess water to lower salt concentrations in the root zone (Arndt
et al., 2001; Mohamed et al., 2002; Takabayashi and Dick 1996; Takabayashi et al., 1994). Water
stress in plants from a lack of moisture or from drought induced by salt stress is associated with many
metabolic changes, including essential oil metabolites. In addition, the water supply is one of the most
determinative cultivation conditions which significantly affect the yield and essential oil content of
various spices and herb crops (Aziz et al., 2008; Mohamed et al., 2002; Singh and Ramesh, 2000;
Singh et al., 2000, 2002; Zehtab- Salmasi et al., 2001). In most cases Origanum plants must be
irrigated during the cultivation period to obtain a good yield. For example during cultivation of
Origanum dictamnus in Crete (Greece), irrigation was necessary for two harvests in 1 year (Skoula and
Kamenopoulos, 1997). Practically, the time at which the plants are irrigated is important for the
efficiency of irrigation. For example, appropriate irrigation strategies showed a great potential for
improvement of the yield of monoterpenes in field-grown spearmint and rosemary (Delfine et al.,
2005). Dunford and Vazquez (2005) reported that herb yield of Mexican oregano (Lippia Berlandieri
Schauer) increased significantly with increasing moisture and the amount of water received by the plant
did not have a significant effect on the thymol and carvacrol content of the oil.
The improvement of plant nutrition can contribute to increased resistance and production when the crop
is submitted to water stress. However, the content of essential oils and their composition are affected by
fertilization.
Humic acid (HA) is one of the major components of humus. Humates have long been used as a soil
conditioner, fertilizer and soil supplement (Albayrak and camas, 2005). Humic acid can be used as
growth regulate-hormone level improve plant growth and enhance stress tolerance (Albayrak and
Camas, 2005; Piccola et al., 1992; Tan and Nopamornbodi, 1979). Fortun et al., 1989 and Kononova,
1966 reported that humic acid improve soil structure and change physical properties of soil, promote
the chelation of many elements and make these available to plants, aid in correcting plant chlorisis,
enhancement of photosynthesis density and plant root respiration has resulted in greater plant growth
with humate application (Chen and Avid, 1990; Smidova, 1960). Increase the permeability of plant
membranes due to humate application resulted in improve growth of various groups of beneficial
microorganisms, accelerate cell division, increased root growth and all plant organs for a number of
horticultural crops and turfgrasses, as well as, the growth of some trees, Russo and Berlyn (1990),
Sanders et al. (1990) and Pioncelot (1993).
So far, there is no report on the yield and composition of the essential oil of Origanum vulgare L.
plants cultivated under this experimental condition in literature.
The aim of the present research was to investigate to evaluate oregano plants grown under osmotic
stress conditions and using organic fertilizer treatments to raise the tolerant of this plant to stress
conditions, and also to provide information on the composition of Origanum vulgare volatile oil and
its variability among osmotic stress conditions.
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Ozean Journal of Applied Sciences 3(1), 2010
MATERIALS AND METHODS
A pot trail study was carried out during the two successive seasons of 2007/2008 and 2008/2009 under
the natural conditions of the greenhouse of the National Research Center, Dokki, Giza, Egypt. The
physical and chemical analyses of the soil were determined according to Jackson, 1973. The soil
texture was sandy loam, having a physical composition as follows: 45.70% sand, 28.40% silt, 25.90%
clay and 0.85% organic matter. The results of soil chemical analysis were as follows: pH= 8.05; E.C
(dsm -1 ) = 0.81; and total nitrogen =0.09 %; available phosphorus =2.26mg/100gram; potassium= 18.85
mg/100gram; field capacity (F.C.) and wilting point (W.P.) were determined according to the pressure
membrane methods described by Black, 1965. Field capacity, permanent wilting point, available soil
moisture (A.S.M) and bulk density (B.D.), as means over the two seasons were 34.0 %, 16.0 % 18.0 %
and 1.36 g/cm 3 , respectively.
Seeds of oregano were obtained from Jellitto Standensamen Gmbh, Schwarmstedt, Germany. The
seeds were sown in the nursery on 15 th November during both seasons. The seedlings were transplanted
into pots (30 cm diameter, 50 cm depth) on the 15 th February of each season. Each pot contained three
seedlings and was placed in full sun light. Each pot was filled with 10 kg of air dried soil. The soil
related to the typic torrifluvents (based on USDA, 1996). Two levels of potassium humate (0.0 and 1.5
g pot -1 ) was applied to the soil with water irrigation application at three equal portions before each cut
in both seasons. Potassium humate which was used in this study is produced by Leili Agrochemistry
Co., LTD, China and its properties are shown in Table (1). Then after one month from transplanting,
irrigation treatments were applied to the oregano plants (90, 60 and 30% available soil moisture) equal
to 32.20., 26.80 and 21.40 soil moisture. The pots were separated into two sets, the first set irrigated
with tap water (0.40 dsm -1 ), and the second set irrigated with Nacl solution (4 dsm -1 ). Pots were
weighted daily and when soil moisture percentage reached the aforementioned points, pots were
irrigated to reach field capacity (34.0% soil moisture). The differences between the needed soil
moisture for the previous treatments and field capacity were calculated and added to the pots in the
different treatments. The experimental layout was factorial experiment in complete randomized design
(CRD) with three replications. Each replicate contained ten pots, while the pot contained three plants.
Herbal fresh weight (g plant -1 ) of each replicate was determined in the first, second and third cuts at
31 May, 31 July and 30 September, respectively before flowering stage in both seasons. Essential oil
content was determined by hydro-distillation for 3 hours by submitting fresh herb (100 g) for each
replicate at each cut in both seasons in modified Clevenger apparatus (Guenther, 1961). Essential oil
percentage of each replicate was determined and expressed as (%), while essential oil yield per plant
was expressed as ml plant -1 . The essential oils of each treatment were collected and dehydrated over
anhydrous sodium sulphate and kept in a refrigerator until gas-liquid chromatography (GLC) analyses.
The GLC analysis of the essential oil samples was carried out in the second season using a Hewlett
Packard gas chromatograph apparatus at the Central Laboratory of the National Research Center
(NRC) with the following specifications: instruments: Hewlett Packard 6890 series, column; HP
(Carbwax 20M, 25m length x 0.32mm I.D), film thickness: 0.3mm, sample size: 1µl, oven temperature:
60-190°C, Program temperature: 60°C/2min, 8°C/min, 190°C/25min, injection port
temperature:240°C, carrier gas: nitrogen, detector temperature (FID): 280°C, flow rate: N2 3ml/min.,
H2 3ml/min., air 300ml/min. Main compounds of the essential oil were identified by matching their
retention times with those of the authentic samples that were injected under the same conditions. The
relative percentage of each compound was calculated from the peak area corresponding to each
compound. Except for the constituents of the essential oils, the data of this experiment were statistically
analyzed according to Snedcor and Cochran (1981).
A.Herb fresh yield
RESULTS AND DISCUSSIONS
Effect of water stress using fresh or saline water irrigation
Data in Table (2) clear the effect of water stress and potassium humate on the productivity of oregano
plant using saline and fresh water irrigation. Under water stress, increase in available soil moisture
significantly enhanced the fresh herb yield in all cuts of both seasons. Increasing water amounts
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Ozean Journal of Applied Sciences 3(1), 2010
increased herb fresh weight. The highest mean values due to irrigation treatments were recorded with
plants that received the highest amounts of water. The pronounced effect of increased irrigation on
fresh herb yield may be attributed to the availability of sufficient moisture around the root concentrated
and thus a greater proliferation of root biomass resulting in the higher absorption of nutrients and water
leading to production of higher vegetative biomass (Singh et al., 1997). On the other hand, increasing
levels of water stress reduce growth and yield due to reduction in photosynthesis and plant biomass.
Under increasing water- stress levels photosynthesis was limited by low Co2 availability due to
reduced stomatal and mesophyll conductance. Drought stress is associated with stomatal closure and
thereby with decreased Co2 fixation. The superiority of the plants that received the highest rate of
irrigation treatments in producing the heaviest total plant fresh weight was in agreement with that of El-
Naggar et al. (2004); Moeini Alishah et al. (2006); Said-Al Ahl and Abdou (2009); Said-Al Ahl et al.
(2009 a, c) .
Data tabulated in Table (2) show that fresh weight of herb (g plant -1 ) decreased significantly with
increment of saline irrigation conditions in all cuts during both seasons. The inhibitory effect of
salinity was also found by (Abd El-Hady, 2007; Abd El-Wahab, 2006; Baghalian et al., 2008; Belaqziz
et al., 2009; Ozturk et al., 2004; Razmjool et al., 2008; Shalan et al., 2006; Turhan and Eris, 2007).
Saline conditions reduce the ability of plants to absorb water causing rapid reductions in growth rate,
and induce many metabolic changes (Epstein, 1980). Also, salt stress with osmotic, nutritional and
toxic effects prevents growth in many plant species (Cheeseman, 1988; Hasegawa et al., 1986).
Therefore, the reduction in growth was explained by lower osmotic potential in the soil, which leads to
decreased water uptake, reduced transpiration, and closure of stomata, which is associated with the
reduced growth (Ben-Asher et al., 2006; Levitt, 1980). In general, the mechanisms of salinity effect on
plant growth were reported by Meiri and Shalhavet (1973) who attributed the effect of salinity to the
following points: (a) the distribution of salts within the plant cells may result in turgor reduction and
growth retardations. Also, salinity affects root and stomatal resistance to water flow, (b) the balance
between root and shoot hormones changes considerably under saline conditions, (c) salinity changes
the structure of the chloroplasts and mitochondria and such changes may interfere with normal
metabolism and growth, (d) salinity increases respiration and reduces photosynthesis products available
for growth.
Effect of potassium humate application
Also, data reported in Table (2) showed that foliar application of humic acid caused significantly
positive trend in increasing herb fresh yield (g plant -1 ). Similar results were reported by Said-Al Ahl et
al. (2009, b) on Origanum vulgare and Zaghloul et al. (2009) on Thuja orientalis who indicated that
spraying the plants with potassium humate increased growth compared with control plants due to the
direct effect of humic acid on solubilization and transport of nutrients. These results are in accordance
with those obtained by Norman et al. (2004) on marigolds and peppers and number of fruits of
strawberries. Chen and Avaid (1990) added that humic substances have a very pronounced influence on
the growth of plant roots thought enhance root initiation which known as root stimulator. Humic acid
improve growth of plant foliage and roots where, Vaughan (1974) proposed that humic acids may
primarily increase root growth by increasing cell elongation or root cell membrane permeability,
therefore increased water uptake by increased plant roots, as well as it can produce root systems by
increasing branching and number of fine roots, as a result potentially increase nutrients uptake by
increase root surface area (Rauthan and Schnitzer, 1981).
Effect of interaction
There was a significant difference in most of interaction treatments between water or salt stress and
potassium humate application. Increment the available soil moisture using saline or fresh water
combined with potassium humate enhanced the fresh herb yield. The irrigation applied at 90%
available soil moisture using fresh water irrigation, combined with potassium humate gave the best
result of fresh herb yield in the all cuts during both seasons.
B. Essential oil production
Effect of water stress using fresh or saline water irrigation
In all cuts in both seasons, both water quantities using saline and fresh water irrigation and potassium
humate application and their interaction affected the content of essential oils in oregano (Tables 3, 4).
The mean values of essential oils due to water irrigation treatments showed that increasing water
supply from 30% to 60% available soil moisture increased essential oil percentage. Increasing water
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Ozean Journal of Applied Sciences 3(1), 2010
supply from 60% to 90% available soil moisture decreased percentage of essential oils. In other words,
the medium stress condition (60% available soil moisture treatment) accelerated the production of
essential oils, while the severe stress conditions due to water (40% available soil moisture treatment)
decreased the biosynthesis of the essential oils. On the contrary, essential oil yield increased with
increment of available soil moisture (Table 4).
Table (3) explains that salt condition significant decreased essential oil % at all cuts in both seasons.
With similar, essential oil yield also was significant decreased at all cuts of both seasons as a result of
salt stress (Table 4). The inhibitory effect of high level of salinity was also found by many investigators
(Abd El-Wahab, 2006; Baghalian et al., 2008; Ozturk et al., 2004; Razmjoo et al., 2008; Shalan et al.,
2006). The reduction of essential oil yield by salinity may be due to a decrease in growth characters
and/or essential oil %. Salt stress may also affect the essential oil accumulation indirectly through its
effects on either net assimilation or the partitioning of assimilate among growth and differentiation
processes (Charles et al., 1990).
Penka (1978) showed that the formation and accumulation of essential oil in plants was explained as
due to the action of environmental factors. It might be claimed that the formation and accumulation of
essential oil was directly dependent on perfect growth and development of the plants producing oils.
The decrease in oil production might be due to the decrease in plant anabolism.
Effect of potassium humate application
Essential oil percent and yield (ml plant -1 ) in oregano herb were significantly increased as a result of
foliar application with K-humate (Tables 3, 4). Said-Al Ahl et al. (2009, b) and Zaghloul et al. (2009)
reported that humate application lead to increase oil content in Origanum vulgare and Thuja orientalis,
respectively. From the above mentioned results, it could be concluded that foliar application of Khumate
promoted growth and possessed the best oil percentage and yield (ml plant -1 ) in oregano plant.
Effect of interaction
Generally, the maximum essential oil content was observed in the fresh herb of plants that irrigated
using fresh water at 60% ASM and sprayed with 1% K-humate in the all cuts during both seasons. In
addition, spraying oregano plants with K-humate caused an increase in the essential oil yield (Table 4).
Generally, the highest essential oil yield (ml plant -1 ) was obtained from plants irrigated using fresh
water at 90% ASM and sprayed at 1% K-humate in all cuts of both seasons. The increment of essential
oil yield may be obtained as a result of increment herb weight and/or essential oil %.
Essential oil composition of oregano
Totally, 20 constituents were identified for the oregano essential oil (Tables 5, 6). Carvacrol content
was the dominant constituent of the essential oil for all samples tested, ranging from 46.44% to
77.96%. The second major constituent was p-cymene (ranging from 5.31% to 19.30%) and the third
one was γ-terpinene (ranging from 3.38% to 16.42%). The other main constituents were α-pinene (0-
5.39%), α-terpinene (0-5.39%), α-thujene (0-5.05%), germacrene D (0-2.91%), thymol (0-2.76%),
caryophyllene (0-2.70%), terpineol-4-ol (0-1.56%) and β-pinene (0-1.19%). Other constituents such as
linalool, limonene, borneol, α-terpineol, bornyl acetate, carvacrol acetate, elemene, cadinene and
caryophyllene oxide were present in amount less the 1%.
In second cut of second season, among water and saline stress, the carvacrol percentage of essential oil
was increased by raising amount of fresh water irrigation and irrigation at 90% ASM gave a higher
value (77.96%), 60% ASM (67.17%) and 30% ASM gave a lower value (60.36%), but there was
decrease in this regard by raising amount of saline water irrigation and irrigation at 30% ASM gave a
higher content (58.02%), 90% ASM (57.79%) and 60% ASM gave a lower content (46.44%). Whereas,
p-cymene was decreased by raising amount of fresh and saline water irrigation and a higher content of
this component was resulted from irrigation at 30% ASM (15.63 and 16.94% using fresh and saline
water, respectively), 60% ASM fresh water and 90% ASM saline water (11.64 and 16.03%,
respectively) and a lower content (8.76 and 13.29%, resulted from 90% ASM fresh water and 60%
ASM saline water, respectively). Correspondingly, the percentage of γ-terpinene was decreased by
raising amount of fresh water irrigation and irrigation at 30% ASM gave a higher value (7.65%), 60%
ASM (6.61%) and 90% ASM gave a lower value (3.56%), but there was increase in this regard by
raising amount of saline water irrigation and irrigation at 90% ASM gave a higher content (12.94%),
30% ASM (11.63%) and 60% ASM gave a lower content (11.32%), Table (5). It is clear that saline
water irrigation increased the biosynthesis of p-cymene and γ-terpinene, while the apposite was true
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Ozean Journal of Applied Sciences 3(1), 2010
with carvacrol. It is well known p-cymene transforms to thymol or carvacrol and the environmental
conditions affect the rate of transformation (Aziz et al., 2008; Omer, 1999).
From Table (6) it can be observed the differences between three major constituents of essential oil of
oregano supplying with a water level of 90% available soil moisture and without / with 1.5 g K-humate
pot -1 in three cuttings, carvacrol was higher for plants irrigated with a fresh water alone in second cut
and compared to other treatments but, the plants irrigated with a saline water alone in first cut
contained a lower content. On the contrary p-cymene and γ-terpinene were the highest by using saline
water irrigation and with K-humate in first cut and the lowest content from the plants irrigated with
fresh water alone in third cut. Irrigation with 90% ASM with fresh water without potassium humate in
second cut gave the maximum value of carvacrol content (77.96%), while irrigation with 90% ASM
with fresh water without potassium humate in first cut gave the highest values for both p-cymene
(10.99%) and γ-terpinene (8.41%). Under saline water irrigation the maximum values for both pcymene
(19.30%) and γ-terpinene (16.42%) were obtained as a result of 90% ASM with potassium
humate in first cut, while 90% ASM with potassium humate in second cut gave the highest value of
carvacrol (63.17%).
Table (7) indicates that saline water irrigation decreased the mean value of carvacrol and on the
contrary there was increased in p-cymene and γ-terpinene mean values by using saline water irrigation.
Whereas, mean values of carvacrol, p-cymene and γ-terpinene were increased by application of Khumate.
Also, mean values of carvacrol, p-cymene and γ-terpinene were affected by cuttings. For
carvacrol, third cut recorded the highest mean value followed by second cut and then first cut.γterpinene
has adverse behavior, first cut resulted the highest mean value followed by second cut and
then third cut. However, the highest mean value of p-cymene resulted from second cut and third cut
recorded the lowest mean value.
Second cut was effective in raising the productivity of the essential oil yield. Table (8) show that saline
water irrigation decreased the mean value of carvacrol and on the contrary there was increased in pcymene
and γ-terpinene mean values by using saline water irrigation. Among soil moisure levels, the
carvacrol mean value of essential oil was the highest at 90% ASM and 60% ASM obtained the lowest
mean value. Also, the mean values of p-cymene and γ-terpinene were the highest for 30% ASM
followed by 60% ASM and then 90% ASM.
CONCLUSIONS
Our study showed that herbal production and essential oil content of Origanum vulgare L. can be
significantly affected by environmental and agronomical conditions including potassium humate
fertilization and soil moisture regime using fresh and saline water irrigation. Application of potassium
humate increase herb fresh yield and essential oil content of oregano herbage. Herbal fresh yield and
essential oil % and oil yield ml plant -1 were significant decreased by using a saline water irrigation
compared to fresh water. Supplying plants with a water level of 90% available soil moisture was
effective in raising the productivity of herb and yield of essential oil, but 60% available soil moisture
was effective in essential oil percentage, whereas 30% available soil moisture significantly decreased
herbal fresh yield and essential oil % and oil yield ml plant -1 .The interaction between 90% available
soil moisture of fresh water and with 1.5 g pot -1 k-hum ate gave the best results for herb and yield of
essential oil. Essential oil % recorded their maximum value from plants irrigated with 60% ASM of
fresh water combined with 1.5 g pot -1 K-humate. Whereas percentage of main compounds of essential
oil such as carvacrol, γ-terpinene and p-cymene affected by these treatments. Supplying plants with a
water level of 90% available soil moisture of fresh water alone in second cut contained the highest
value of carvacrol, but p-cymene and γ-terpinene recorded their maximum values by irrigating
Origanum vulgare with a saline water level of 90% available soil moisture and with k-humate in first
cut.
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Ozean Journal of Applied Sciences 3(1), 2010
TABLES
Table 1. Guaranteed analysis and physical data of Humic total
Guaranteed analysis
Humic acid 80%
Potassium (K2O) 10-12%
Zn, Fe, Mn, etc., 100ppm
Physical Data
Appearance Black powder
pH 9-10
Water solubility < 98%
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Ozean Journal of Applied Sciences 3(1), 2010
Table 2. Effect of water stress using saline and fresh water irrigation, K-humate fertilizer and their interaction treatments on the herb fresh weight of oregano
plants during the two seasons.
K-humate
132
First Season
Water
Herb fresh weight (g plant
and salt
stress
-1 )
Without
K-humate
First cut
K-humate Mean
Without
K-humate.
Second cut
K-humate. Mean
Without
K-humate
Third cut
K-humate Mean
S1 2.41 4.58 3.49 3.72 6.45 5.08 1.05 1.58 1.31
S2 4.18 7.77 5.97 7.50 10.07 8.78 3.13 5.02 4.08
S3 5.71 10.53 8.12 8.96 17.43 13.20 4.38 7.30 5.84
S4 1.58 2.02 1.80 2.03 2.48 2.25 0.61 0.75 0.68
S5 2.02 2.49 2-25 2.76 3.01 2.88 0.94 1.24 1.09
S6 2.72 3.21 2-96 3.59 4.42 4.00 1.83 2.02 1.92
Mean 3.10 5.10 4.76 7.31 6.03 1.99 2.98
L.S.D. at 5% K-humate =0.056
K-humate = 0.046
K-humate = 0.034
Stress = 0.098
Stress = 0.079
Stress = 0.059
Interaction =0.138
Interaction = 0.113
Interaction =0.083
S1 2.47 4.58 3.53 3.69
Second Season
6.45 5.07 1.03 5.03 3.03
S2 4.19 7.69 5.94 7.53 10.17 8.85 3.10 7.20 5.15
S3 5.70 10.75 8.23 8.88 17.28 13.08 4.44 0.58 2.51
S4 1.49 2.02 1.75 1.97 2.43 2.20 0.58 0.71 0.64
S5 2.05 2.42 2.23 2.72 3.05 2.88 0.94 1.10 1.02
S6 2.61 3.15 2.88 3.65 4.39 4.02 1.88 2.07 1.97
Mean 3.08 5.10 4.74 7.29 1.99 2.78
L.S.D. at 5% K-humate =0.050
K-humate = 0.042
K-humate = 0.039
Stress = 0.087
Stress = 0.073
Stress = 0.067
Interaction =0.123
Interaction =0.103
Interaction =0.055
S-stress; S1, S2, S3= irrigation of: 30, 60, 90% available soil moisture using fresh water; S4, S5, S6= irrigation of: 30, 60, 90% available soil moisture using
saline water

Ozean Journal of Applied Sciences 3(1), 2010
Table 3. Effect of water stress using saline and fresh water irrigation, K-humate fertilizer and their interaction treatments on the herb fresh volatile oil (%) of
oregano plants during the two seasons.
K-humate
133
First Season
Water
Volatile oil (%)
and salt
stress
Without
K-humate
First cut
K-humate Mean
Without
K-humate.
Second cut
K-humate. Mean
Without
K-humate
Third cut
K-humate Mean
S1 0.533 0.633 0.583 0.450 0.533 0.491 0.416 0.433 0.425
S2 0.650 0.750 0.700 0.533 0.633 0.583 0.500 0.583 0.541
S3 0.600 0.683 0.641 0.500 0.600 0.550 0.466 0.533 0.500
S4 0.366 0.383 0.375 0.316 0.366 0.341 0.233 0.266 0.250
S5 0.450 0.483 0.466 0.433 0.483 0.458 0.383 0.400 0.391
S6 0.433 0.466 0.450 0.433 0.466 0.450 0.350 0.383 0.366
Mean 0.505 0.566 0.444 0.513 0.391 0.433
L.S.D. at 5% K-humate = 0.0157
K-humate = 0.0143
K-humate = 0.0184
Stress = 0.0275
Stress = 0.0247
Stress = 0.0319
Interaction =N.S
Interaction = 0.0349
Interaction =0.0451
S1 0.516 0.633 0.575 0.433
Second Season
0.533 0.483 0.400 0.433 0.416
S2 0.633 0.766 0.700 0.533 0.616 0.575 0.500 0.583 0.541
S3 0.616 0.666 0.641 0.500 0.583 0.541 0.450 0.533 0.491
S4 0..383 0.416 0.400 0.300 0.383 0.341 0.250 0.316 0.283
S5 0.483 0.500 0.491 0.466 0.466 0.466 0.366 0.383 0.375
S6 0.433 0.450 0.441 0.416 0.433 0.425 0.366 0.383 0.375
Mean 0.511 0.572 0.541 0.441 0.502 0.388 0.438
L.S.D. at 5% K-humate = 0.0150
K-humate = 0.0190
K-humate = 0.0178
Stress = 0.0260
Stress = 0.0329
Stress = 0.0308
Interaction =0.0368
Interaction =0.0466
Interaction =0.0436
S-stress; S1, S2, S3= irrigation of: 30, 60, 90% available soil moisture using fresh water; S4, S5, S6= irrigation of: 30, 60, 90% available soil moisture using
saline water

Ozean Journal of Applied Sciences 3(1), 2010
Table 4. Effect of water stress using saline and fresh water irrigation, K-humate fertilizer and their interaction treatments on the volatile oil yield (ml plant -1 )
of oregano plants during the two seasons.
K-humate
134
First Season
Water
Oil yield (ml plant
and salt
stress
-1 )
Without
K-humate
First cut
K-humate Mean
Without
K-humate.
Second cut
K-humate. Mean
Without
K-humate
Third cut
K-humate Mean
S1 0.0128 0.0289 0.0209 0.0167 0.0353 0.0260 0.0044 0.0068 0.0056
S2 0.0271 0.0582 0.0427 0.0400 0.0528 0.0464 0.0153 0.0293 0.0223
S3 0.0342 0.0725 0.0534 0.0448 0.0916 0.0682 0.0204 0.0389 0.0296
S4 0.0058 0.0077 0.0068 0.0064 0.0091 0.0078 0.0014 0.0016 0.0015
S5 0.0091 0.0121 0.0106 0.0120 0.0146 0.0133 0.0036 0.0050 0.0043
S6 0.0118 0.0150 0.0134 0.0156 0.0206 0.0181 0.0064 0.0078 0.0071
Mean 0.0168 0.0324 0.0226 0.0373 0.0086 0.0149
L.S.D. at 5% K-humate = 0.00076
K-humate = 0.0048
K-humate = 0.0005
Stress = 0.00281
Stress = 0.0083
Stress = 0.0009
Interaction =0.00188
Interaction = 0.0118
Second Season
Interaction =0.0013
S1 0.0154 0.0290 0.0222 0.0160 0.0344 0.0252 0.0041 0.0065 0.0053
S2 0.0265 0.0589 0.0427 0.0387 0.0627 0.0507 0.0155 0.0294 0.0224
S3 0.0351 0.0717 0.0534 0.0444 0.1008 0.0726 0.0799 0.0384 0.0591
S4 0.0057 0.0337 0.0197 0.0059 0.0093 0.0076 0.0014 0.0023 0.0018
S5 0.0099 0.0121 0.0110 0.0127 0.0142 0.0134 0.0036 0.0046 0.0041
S6 0.0113 0.0142 0.0128 0.0152 0.0190 0.0171 0.0069 0.0079 0.0074
Mean 0.0173 0.0366 0.0270 0.0221 0.0401 0.0186 0.0148
L.S.D. at 5% K-humate = 0.0073
K-humate = 0.0012
K-humate = 0.0171
Stress = 0.0126
Stress = 0.0021
Stress = 0.0297
Interaction =0.0178
Interaction =0.0030
Interaction =0.0420
S-stress; S1, S2, S3= irrigation of: 30, 60, 90% available soil moisture using fresh water; S4, S5, S6= irrigation of: 30, 60, 90% available soil moisture using
saline water

Ozean Journal of Applied Sciences 3(1), 2010
Table 5. Essential oil composition (%) of Origanum vulgare L. plants grown under different levels of
Available soil moisture using fresh and saline water irrigation at second cut in 2009 season.
Compounds
135
Treatments
Irrigation with fresh water Irrigation with saline water
30% ASM 60% ASM 90% ASM 30% ASM 60% ASM 90% ASM
���thujene -- -- 0.33
���pinene -- 0.11 0.08
���pinene 0.56 0.61 0.11
��terpinene
0.45 0.28 0.38
P-cymene
15.63 11.64 8.76
limonene
γ- terpinene
linalool
borneol
Terpinene-4-ol
��terpineol
thymol
Bornyl acetate
carvacrol
Carvacrol acetate
elemene
0.70 0.46 0.30
7.65 6.61 3.56
0.64 0.35 0.54
0.90 0.76 0.03
0.85 0.51 0.40
0.19 0.20 0.27
1.87 1.55 0.98
0.16 0.19 0.06
60.36 67.17 77.96
0.59 0.41 0.59
0.29 0.21 0.36
�caryophyllene 0.14 0.45 0.40
germacrene D
cadinene
caryephyllene oxide
Identified compounds
0.59 1.61 0.83
0.05 0.09 0.01
-- -- 0.01
91.62 93.21 95.96
Irrigation of: 30, 60, 90% ASM– of available soil moisture
-- 5.05
-- 2.70
-- 0.75
1.62 5.39
16.94 13.29
-- 0.49
11.63 11.32
-- 0.41
-- 1.89
-- 1.56
-- --
-- 2.76
-- --
58.02 46.44
-- --
-- 0.45
1.27 2.57
1.52 1.66
-- --
-- 0.87
91.00 97.59
1.15
0.11
0.25
1.53
16.03
--
12.94
0.18
--
0.37
--
0.96
0.24
57.79
0.30
--
1.02
1.07
0.05
0.16
94.05

Compounds
���thujene
���pinene
���pinene
��terpinene
P-cymene
limonene
γ- terpinene
linalool
borneol
Terpinene-4ol
��terpineol
thymol
Ozean Journal of Applied Sciences 3(1), 2010
Table 6. Essential oil composition (%) of Origanum vulgare L. plants grown with 90% Available soil
moisture using fresh and saline water irrigation and/or k-humate application during three cuts in 2009
season.
Bornyl acetate
carvacrol
Carvacrol
acetate
elemene
�caryophyllene
germacrene D
cadinene
caryephyllene
oxide
Identified
compounds
Treatments
Irrigation with fresh water
Irrigation with saline water
Without K-humate With K-humate
Without K-humate With K-humate
Cut 1 Cut 2 Cut 3 Cut 1 Cut 2 Cut 3 Cut 1 Cut 2 Cut 3 Cut 1 Cut 2 Cut 3
0.21 0.33 0.33 0.31 0.31 1.32 1.50 1.15 1.45 0.77 0.37 1.07
0.23
0.81
0.46
10.99
0.90
8.41
0.40
0.45
0.41
0.20
1.51
0.21
66.04
0.21
0.14
0.36
0.66
0.07
--
92.67
0.08
0.11
0.38
8.76
0.30
3.56
0.54
0.03
0.40
0.27
0.98
0.06
77.96
0.59
0.36
0.40
0.83
0.01
0.01
95.96
0.18
1.04
0.58
5.31
0.53
3.38
0.24
0.23
0.52
--
0.91
0.18
77.04
0.28
0.23
0.22
2.45
0.31
0.19
94.15
0.39
0.65
0.49
9.21
0.36
7.33
0.31
0.40
0.61
0.27
1.27
0.45
67.51
0.81
0.45
0.70
0.48
--
--
91.90
0.31
--
--
10.73
--
5.33
0.36
--
0.43
0.63
1.01
0.30
74.54
0.89
0.45
0.35
0.47
0.02
--
96.13
136
1.74
0.66
0.56
5.41
--
5.00
0.63
0.39
0.68
--
1.33
0.37
71.74
0.51
0.43
0.90
2.00
0.80
0.43
94.90
1.51
1.19
1.27
13.08
--
13.56
--
--
0.71
--
1.52
--
52.41
--
--
2.70
0.79
0.73
--
91.97
0.11
0.25
1.53
16.03
--
12.94
0.18
--
0.37
--
0.96
0.24
57.79
0.30
--
1.02
1.07
0.05
0.16
94.05
0.18
0.41
--
11.95
--
11.41
--
0.12
0.40
0.08
1.03
--
60.11
0.19
--
1.15
2.91
0.30
0.11
91.50
0.88
--
1.64
19.30
--
16.42
--
--
--
--
1.61
--
54.29
--
--
1.77
--
--
--
96.68
0.40
--
0.15
17.40
--
9.58
--
--
0.65
--
1.45
--
63.17
0.65
0.11
0.75
1.60
--
0.01
96.29
0.60
0.11
0.34
12.08
0.41
10.52
0.18
0.11
0.25
0.19
1.18
0.18
61.29
0.29
0.18
1.71
2.45
0.28
0.17
93.59

Ozean Journal of Applied Sciences 3(1), 2010
Table 7. Important differences in three main compounds of Origanum vulgare L. essential oil. The
plants were grown with 90% Available soil moisture using fresh and saline water irrigation and/or k-
humate application during three cuts in 2009 season.
Compounds
137
Treatments
Water irrigation K-humate Cuttings
Mean of
Fresh
Mean of
Saline
Mean of
Without
Mean of
With
Mean of
Cut 1
Mean of
Cut 2
Mean of
Cut 3
carvacrol 72.47 58.13 65.18 65.42 60.22 63.36 67.79
P-cymene 8.40 14.37 11.02 12.35 13.14 13.23 8.68
γ- terpinene 5.50 12.40 8.87 9.03 11.43 7.85 7.57
Total 86.37 85.50 85.07 86.80 84.79 84.44 84.04
Table 8. Important differences in three main compounds of Origanum vulgare L. essential oil. The
plants were grown under different levels of Available soil moisture using fresh and saline water
irrigation at second cut in 2009 season.
Compounds
Mean of 30%
ASM
Treatments
Soil moisture Water irrigation
Mean of 60%
ASM
Mean of 90%
ASM
Mean of Fresh Mean of
Saline
carvacrol 59.19 56.80 64.87 68.49 54.08
P-cymene 16.28 12.46 12.39 12.01 15.42
γ- terpinene 9.64 8.96 8.25 5.94 11.96
Total 85.11 78.22 85.51 86.44 81.46

Ozean Journal of Applied Sciences 3(1), 2010
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141

Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Influence of Foliar Application of Pepton on Growth, Flowering and Chemical
Composition of Helichrysum bracteatum Plants under Different Irrigation
Intervals.
Soad , M.M. Ibrahim, Lobna, S. Taha* and M.M. Farahat
Department of Ornamental Plant and Woody Trees,
National Research Centre, Dokki, Cairo, Egypt
*E-mail address for correspondence: lonbasalah82@yahoo.com
___________________________________________________________________________________
Abstract: Two field experiments were carried out at Research and Production Station, Nubaria of
National Research Center, Egypt, during 2007 and 2008 seasons. The purpose of this study is to
investigate the influence of foliar spraying with peptone (0, 250, 500 and 1000 ppm) on growth,
flowering and chemical composition under three irrigation intervals (2, 4 and 6 days) on Helichrysum
bracteatum. Irrigation intervals treatments have a depressing effect on different growth characters
(plant height, number of branches/plant, leaf area and fresh and dry weight of leaves) by increasing
irrigation intervals. The same manner was observed and concerning flowering parameters and
chemical constituents (total soluble sugars, total soluble indoles and free amino acids). On the
contrary, three pigments content and total soluble phenols. Data also, showed that all growth
parameters and flowering parameters (number of flower/plant, flower diameter and fresh and dry
weights of flowers) were significantly promoted by increasing the concentration of pepton from 250 to
500 and 1000 ppm as well as chemical constituents. The most promising results were obtained from
plants treated with pepton 1000 ppm and irrigated every 2 days. These treatments may be
recommended for decreasing the hazard effect on growth of Helichrysum bracteatum under different
irrigation intervals.
Keywords: foliar pepton, irrigation intervals, growth parameters, chemical composition.
_________________________________________________________________________________
INTRODUCTION
Straw flower, Hardy Annual or Everlasting (Helichrysum bracteatum). Family Asteraceae is an easy
annual plant to grow with yellow, orange, pink, deep rose, red, wine, magento, purplor white blooms.
The true petals are found in the center of each flower and they are surrounded by colorful, straw like
bracts. The flowers bloom from summer to early autumn. Harvest flowers for drying before they open
fully. Seeds need light to germinate, plant in porous soil. It endemic to Austalia, growing in open
scrub and grassland areas. And using in Dried Arrangement, Border, Rock garden and Cutting Bed.
Water is the major component of the plant body. It constituents about 80 to 90 % of fresh weight of
most herbaceous plant organs and over 50 % of the fresh weight of woody parts. Water affects
markedly, either directly or indirectly, most plant physiological processes. Hence, with the exception
of some kinds of seeds, dehydration of plant tissues below some critical level is accompanied by
irreversible changes in structure and ultimately by plant death. The importance of water in living
organisms results from its unique physical and chemical properties, which also determine its functions
in plant physiology, water is a major constituent of the protoplasm, it acts as a solvent for many solid
and gaseous substances, forming a continuous liquid phase throughout the plant, it takes part in many
important physiological reactions, it maintains cell turgor, which exerts an impact on many
physiological processes. Several authors indicated the promotive effect of the high levels of wate
supply on growth parameters including, Farahat (1990) on Schinus molle, Schinus terebinthifolius and
Myoporum acuminatum, Sayed (2001) on Khaya senegalensis, Uday et al (2007), and Soad (2005) on
143

Simmodsia chinensis, Azza and Sahar (2006)on Melia azedarach and Azza et al (2007) on Bauhinia
variegata.
Amino acids as organic nitrogenous compounds are the building blocks in the synthesis of proteins,
Davies (1982). Amino acids are particularly important for stimulation cell growth. They act as buffers
which help to maintain favorable pH value within the plant cell, since they contain both acid and basic
groups; they remove the ammonia from the cell. This function is associated with amide formation, so
they protect the plants from ammonia toxicity. They can serve as a source of carbon and energy, as
well as protect the plants against pathogens. Amino acids also function in the synthesis of other
organic compounds, such as protein, amines, purines and pyrimidiens, alkaloids vitamins, enzymes,
terpenoids and others, Goss (1973) and Hass (1975), stated that the biosynthesis of cinamic acids
(which are the starting materials for the synthesis of phenols) are derived from phenylalanine and
tyrosine. Tyrosine is hydroxyl phenol amino acid that is used to build neurotran smitters and
hormones. Several other authors indicated that promotive effect of amino acids on ornamental and
medicinal plants including, Mohamed and Khalil (1992) on Antirrhinum majus, Matthiola incana and
Callistephus chinensis, Hussein et al (1992) on Datura metel, Mohamed and Whaba (1993) on
Rosmarinus officinalis, Abou Dahab and Nahed (2006) on Philodendron erubescens and Nahed and
Balbaa (2007) on Saliva farinacea.
Therefore, the present investigation was planned to explore the ability of helichrysum plants of
tolerating various degrees of drought, and possible alleviating of the harmful effects by the use of
pepton.
MATERIALS AND METHODS
Two field experiments were carried out at National Research Centre (Research and Production Station,
Nubaria), during two successive seasons of 2006/2007 and 2007/2008 to investigate the effect of
irrigation and foliar application of peptone on growth, flowering and chemical constituents of
Helichrysum bracteatum plants. Helichrysum seeds were supplied from Research and Production
Station, Nubaria. The soil is sand in texture with pH 8.0 , EC 0.92 dSm -1 (at 25 o C), organic carbon
0.89 %, and nutrients (N % 0.036, P% 0.012, K% 0.016 and Fe 265 ppm).seeds were sown on the 1 st
week of September, after 45 days from sowing uniform seedlings about 8 cm height with 2 pairs of
leaves were transplanted into the open field. The experiment was set up in a split plot design with three
replicates (each replicate contained 6 plants) containing three treatments of irrigation intervals (2, 4 and
6 days) occupied the main plots and three pepton concentrations (250, 500 and 1000 ppm) in addition
to the untreated plants (control) were assigned to the subplots. The seedlings were planted in row at 50
cm, distance. Drip irrigation system was applied in the experiment using drippers (4L/h) for two hours
every two days. The plants were fertilized with 4g ammonium nitrate (33% N), 2g potassium sulphate
(48 % K2O) and 4 g calcium super phosphate (15.5 % P2O5) / plant after 15 days from transplanting.
The grown plants received the normal cultural practices during the growth seasons.
Plants were sprayed with pepton (based on the energizing power of free amino acids, produced by
A.P.C. Europe Co. Avsan Julain-Spain). Plants were sprayed twice with pepton until run-off occurred;
the first spraying was in the second week of March. One month later the second spray was performed.
At the first week of May 2006/2007 and 2007/2008, the following data were recorded: plant height
(cm), number of branches /plant, leaf area (cm 2 ) fresh and dry weights of leaves (g), number of
flowers/ plant, flower diameter (cm) and fresh and dry weight of flower (g). total soluble sugars were
determined in the methanolic extract by using the phenol-sulphoric method according to Dubois et al
(1956), photosynthetic pigments including Chlorophyll (a+b) as well as carotenoids content were
determined in fresh leaves as mg/gm fresh weight, according to the procedure achieved by Saric et al
(1976). The total indoles were determined in the methanolic extract, using P-dimethyl
aminobenzaldhyed test " Erlic's reagent" according to Larsen et al (1962). Total soluble phenols were
determined calorimetrically by using Folin Ciocalte a reagent A.O.A.C. (1985). Free amino acid
content was determined according to Rosen (1957).
The data were statistically analyzed for each season and then a combined analysis of the two seasons
was carried out according to the procedure outlined by Steel and Torrie (1980).
144

Growth parameters:
RESULTS AND DISCUSSION
Data presented in Table (1) indicated that significant increasing in all growth parameters by reducing
the interval between irrigations. The highest values of plant height, number of branches/ plant, leaf
area and fresh and dry weights of leaves were obtained at the plants irrigated every 2 days, while the
lowest values occurred by irrigation at the longest intervals (6 days). Moreover, the differences
between each two successive irrigation intervals were significant. Numerically, plant height and fresh
weights of leaves were increased by (29.7 and 49.21%) and by (19.58 and 24.40 %) as a results of 2
and 4 irrigation intervals (days), respectively, in comparison with the long interval (6 days). According
to the previous results, El-Monayeri et al (1983) reported that, this may be due to the vital roles of
water supply at adequate amount for different physiological processes such as photosynthesis,
respiration, transpiration, translocation, enzyme reaction and cells turgidity occurs simultaneously.
Such reduction could be attributed to decrease in the activity of meristemic tissues responsible for
elongation. As well as the inhibition photosynthesis efficiency under efficient water condition
Siddique (1999). These results are in agreement with those obtained by Burman et al (1991) on
Azadirachta indica, Soad (2005) on Simmondsia chinensis and Azza et (2006) on Melia azedarach.
145

Table (1) Some growth parameters of Helichrysum bracteatum plants as affect with irrigation intervals and foliar application of pepton (average of two seasons).
Pepton
treatments
(ppm)
Plant height (cm) Number of branhes/plant Leaf area (cm2) Fresh weight of leaves (g) Dry weight of leaves (g)
Irrigation intervals days (A)
2 4 6 Mean 2 4 6 Mean 2 4 6 Mean 2 4 6 Mean 2 4 6 Mean
Control 48.07 43.13 37.83 43.01 20.67 17.33 14.00 17.33 5.26 4.99 3.75 4.67 17.63 14.51 11.87 14.67 3.52 2.9 2.37 2.93
P1 250 57.87 54.77 43.40 52.01 25.33 22.00 21.33 22.89 6.76 6.16 4.7 5.87 19.83 17.66 13.08 16.86 3.96 3.53 2.61 3.37
P2 500 62.83 58.87 47.50 56.40 29.00 24.33 23.00 25.44 7.65 6.58 5.29 6.50 24.04 18.83 15.00 19.29 4.81 3.76 3.00 3.86
P3 1000 68.7 62.10 54.30 61.70 33.33 26.33 26.33 28.66 7.72 6.70 6.18 6.87 25.82 21.80 18.59 22.07 5.16 4.36 3.71 4.41
Mean 59.37 54.72 45.76 27.08 22.50 21.17 6.85 6.11 4.98 21.83 18.20 14.64 4.36 3.64 2.93
LSD 5%
Irrigation
A
1.66
2.29 0.13 0.86
Pepton B 1.84 1.57 0.06 0.86 0.17
Interaction
AB
NS
NS 0.11 1.49
146
0.17
0.30

Concerning the effect of peptone on saga growth, data presented in Table (1) revealed that foliar
application of peptone significantly promoted plant height, number of branches/plant, leaf area and
fresh and dry weight of leaves. Increasing peptone concentration from 250 to 500 and 1000 pm to
Helichrysum bracteatum plants significantly increased all growth parameters over control plants. The
increments effect on plant height and number of branches/plant by (20.92, 13.13 and 43.00 %) and
32.1, 46.8 and 65.54%), respectively compared with control plants. The positive effect of amino acids
on yield may be due to the vital effect of these amino acids stimulation on the growth of plant cells.
The positive effect of amino acids on growth was stated by Goss (1973) who indicated that amino acids
can serve as a source of carbon and energy when carbohydrates become defficient in the plant, amino
acids are determinate, releasing the ammonia and organic acid form which the amino acid was
originally formed. The organic acids then enter the Kreb's cycle, to be broken down to release energy
through respiration. Thon et al (1981) pointed out that amino acids provide plant cells with an
immediately available source of nitrogen, which generally can be taken by the cells more rapidly than
in organic nitrogen. The results are characteristically accompanied by Youssef et al (2004) on lemon
basil, Gamal El-Din et al (1997) on lemon grass, Talaat Youssef (2002) on basil plant, El-Fawakhry
and El-Tayeb (2003) on chrysanthemum, Refaat and Naguib (1998) on peppermint plant, Youssef et al
(2004) on datura plant and Mona and Talaat (2005) on Pelargonium graveolens plant, the found that
amino acids significantly increased vegetative growth.
The interaction between different factors (irrigation and peptone ) was almost for all vegetative growth
parameters except plant height and number of branches/plant. The highest values due to the irrigation
regime and peptone were obtained due to irrigated every 2 days and concentration 1000 ppm of foliar
spray of pepeton. The lowest sensitivity of peptone –sufficient plants to drought stress is related to the
notion that some amino acids (e.g. phenylalanine, ornithine) can affect plant growth and development
through their influence on gibberelline biosynthesis Waller and Nawachi (1978). Amino acids acting
as the building blocks of proteins can serve in number of additional functions in regulation of
metabolism, transport and storage nitrogen Bidwell (1979) and Fowden (1973).
Flowering Characters:
Data presented in table (2) show that all decreasing irrigation intervals from 6 to 2 days significantly
increased flower diameter, number of flowers/plant and fresh and dry weights of flowers. The
increments on fresh and dry weights of flowers/plant by 26.93 and 31.02% respectively for the 2 days
compared with 6 days. Our results are computable with those obtained by Ruhi Bastug et al (2006) on
gladiolus plants, Kittas et al (2004) on Rose and Banker et al (2008) on wheat and stated that the high
level of irrigation lead to the increment of flowering parameters and quality.
Data presented in table (2) show that foliar spray of sage plants with pepton at 1000 ppm resulted in the
highest values flowering parameters. The maximum and values were observed for number of
flowers/plant and fresh and dry weights of flowers/plant by 38.45, 38.51 and 38.07 %, respectively
over control plants. These results are characteristically accompanied by Karima and Abd El-Wahed
(2005) who found that using amino acids led to significant increases in number of flowers, diameters of
flower and fresh and dry weights of flowers/plant of Matricaria chamomilla L. plant. Also, Nahed
and Balbaa (2007) on Saliva fFarincea stated that application of tyrosine 100 ppm significant
promotion in all flowering parameters at flowering stage.
Regarding the interaction effects, data in Table (2) show that flowering parameters were significantly
augmented. It is also clear from the obtained data that irrigation interval 2 days combined with foliar
spray of sage plants with 1000 ppm peptone resulted in the highest pronounced effects on all flowering
parameters.
Chemical Constituents:
Pigment content
Data in Table (3) recorded that, the content of three photosynthetic pigments (Chlorophyll a, b and
carotenoids) were increased by the gradual increasing in irrigation intervals. Accordingly it can be
stated that irrigation every 6 days was the most effective irrigation treatment for promoting the
synthesis and accumulation of the three photosynthetic pigments. In harmony with these results were
those obtained by Soad (2005) and Azza et al (2007).
147

The three photosynthetic pigments took similar trend in response to peptone levels. The three
concentrations used of peptone 250, 500 and 1000 ppm caused an increase in the contents of
Chlorophyll a, b and carotenoids in regard to those of untreated seedlings. Hussein et al (1992) found
that higher concentration of adenine and cytosine increased the photosynthetic pigments of datura
plants.
148

Table (2) Flowering parameters of Helichrysum bracteatum plants as affect with irrigation intervals and foliar application of pepton (average of two seasons).
Pepton
treatments
(ppm)
Number of flowers/plant Flower diameter (cm) Fresh weight of flowers (g) Dry weight of flowers (g)
Irrigation intervals days (A)
2 4 6 Mean 2 4 6 Mean 2 4 6 Mean 2 4 6 Mean
Control 11.83 11.30 8.83 10.65 3.23 3.13 2.17 2.84 27.20 25.96 20.32 24.49 8.70 7.79 6.09 7.53
P1 250 15.63 14.23 11.13 13.66 3.32 3.48 2.75 3.18 35.96 32.74 25.61 31.44 11.50 9.82 7.68 9.67
P2 500 16.60 16.00 12.50 15.03 3.76 3.56 2.87 3.40 38.18 36.54 28.75 34.49 12.21 11.06 8.63 10.63
P3 1000 20.50 16.77 14.70 17.32 4.82 3.54 2.91 3.76 47.15 38.53 33.81 39.83 14.78 11.56 10.14 12.16
Mean 16.14 14.58 11.79 3.78 3.43 2.68 37.12 33.44 27.12 11.80 10.06 8.14
LSD 5%
Irrigation
A
0.97 0.05 2.39 0.82
Pepton B 0.51 0.04 1.12 0.39
Interaction
AB
0.89 0.08 1.94 0.67
149

Table (3) Chemical constituents of Helichrysum bracteatum plants as affect with irrigation intervals and foliar application of pepton (average of two seasons).
Pepton
treatments
(ppm)
Chl a (mg/g F.W.) Chl b (mg/g F.W.) Chl a+b (mg/g F.W.) Carotenoids (mg/g F.W.)
Irrigation intervals days
2 4 6 Mean 2 4 6 Mean 2 4 6 Mean 2 4 6 Mean
Control 1.629 2.062 1.921 1.871 0.335 0.667 0.394 0.465 1.964 2.729 2.315 2.336 0.849 0.972 0.972 0.931
P1 250 1.782 2.126 2.372 2.093 0.455 0.935 0.583 0.658 2.237 2.581 2.955 2.591 1.079 1.186 1.027 1.097
P2 500 2.187 2.497 2.858 2.514 0.634 0.983 0.654 0.757 2.821 3.480 3.512 3.271 1.458 1.316 1.425 1.400
P3 1000 1.851 2.444 2.750 2.348 0.524 0.976 0.614 0.705 2.375 3.420 3.364 3.053 1.269 1.634 1.286 1.396
Mean 1.862 2.282 2.475 0.487 0.890 0.561 2.349 3.053 3.037 1.164 1.277 1.178
LSD 5%
Irrigation
A
0.036 0.018 0.054 0.110
Pepton B 0.031 0.010 0.041 0.010
Interactio
n AB
0.054 0.018 0.072 0.018
150

The present data are in agreement with the findings of Hussein (2003) on Foeniculum vulgare L. plants
and Nahed and Laila (2007) on Saliva farinacea plants, they reported that foliar application of amino
acids (Tryptophan) caused an increase in photosynthetic pigments contents. The accumulation of
photosynthetic pigments as a result of these nitrogen compounds may be due to the important role of
nitrogen in the biosynthesis of Chlorophyll molecules, Meyeret et al (1968).
In this respect, interaction between irrigation intervals and pepton applications, the data revealed that
the combination of both factors on Chlorophyll a, b and carotenoids was more effective than the effect
of each factors, all the interaction of used treatments increased significantly photosynthetic pigments in
the leaves of Helichrysum bracteatum plants.
Total soluble sugars content:
Data recorded in Table (4) indicated that total soluble sugars content as affected by different irrigation
intervals treatments, followed the same manner obtained previously on photosynthetic pigments, were
gradually decreased by increasing the intervals of irrigation. These results were in accordance with
those recorded by Azza et al (2007).
Pepton at all used concentration caused an increasing in total soluble sugars content as compared with
untreated seedlings. This result could be explained by the findings obtained by Refaat and Naguib
(1998) reported that application of all amino acids (alanine, cytosine, guanine, thiamine and L-tyrosine)
increased the total carbohydrates percentage in peppermint leaves. The promotive affected of the
amino acids on the total carbohydrates content may be due to their important role on the biosynthesis of
Chlorophyll molecules which in turn affected carbohydrate content.
As far the interaction between irrigation intervals and peptone applications the higher values were
provided when adding 1000 ppm peptone and irrigation every 4 days.
Total soluble indoles:
According to the data illustrated in Table (4) the total indoles levels which were determined in leaves
of Helichrysum bracteatum plants were increased by decreasing irrigation intervals.
Concerning pepton, total indoles levels were decreased by the increase in peptone levels. The highest
values of total indoles were obtained from interaction treated plants with peptone at 1000 ppm and
irrigated every 4 days.
151

Table (4) Chemical constituents of Helichrysum bracteatum plants as affect with irrigation intervals and foliar application of pepton (average of two seasons).
Pepton treatments
(ppm)
Total soluble sugars (mg/g F.W.) Total indoles (mg/g F.W.) Total phenoles (mg/g F.W.)
Irrigation intervals days
152
Total free amino acids (mg/g
F.W.)
2 4 6 Mean 2 4 6 Mean 2 4 6 Mean 2 4 6 Mean
Control 0.948 2.245 1.338 1.510 1.225 1.117 1.292 1.211 1.165 1.354 0.908 1.142 0.971 0.685 0.714 0.790
P1 250 1.659 3.143 1.511 2.104 0.816 0.718 0.635 0.723 1.216 1.659 1.510 1.462 1.132 0.835 0.742 0.903
P2 500 1.983 3.459 1.941 2.461 1.184 0.966 0.784 0.978 1.242 1.733 1.535 1.503 1.242 1.027 0.814 1.028
P3 1000 2.045 3.580 2.865 2.830 1.246 0.482 0.813 0.847 1.364 2.506 1.594 1.821 1.375 1.091 0.910 1.125
Mean 1.659 3.107 1.914 1.118 0.821 0.881 1.247 1.813 1.387 1.180 0.910 0.795
LSD 5%
Irrigation
A
0.018 0.009 0.008 0.007
Pepton B 0.013 0.008 0.010 0.008
Interaction
AB
0.022 0.014 0.018 0.013

Total soluble phenols:
The results in Table (4) emphasized that amounts of total soluble phenols were significantly increased
by increasing irrigation intervals. These results are in accordance with those obtained by Azza et al
(2006) on Taxodium distichum. Concerning peptone, total soluble phenols levels were increased by
increasing irrigation intervals and peptone concentration the highest values were provided when adding
1000 ppm and interval 4 days.
Total free amino acids:
From the given data in Table (4) it can be concluded that decreasing irrigation intervals caused an
increase of total free amino acids content. In regard to the effect of water stress on amino acids, it has
been indicated that generally total free amino acids increased under water stress, Simpson (1981). This
trend does not apply to Myoporum since long irrigation interval caused a decrease in amino acids.
However, many investigators reported that an increase in amino acids is associated with water stress,
Farahat (1990) on Schinus molle, Schinus terebinthifolius and Myoporum acuminatum). Data
presented in table (4) show that total free amino was significantly increased as a result of foliar spray of
peptone 500 and 1000 ppm. Our results are in agreement with those obtained by Karima and Abdel-
Wahed (2005) on Chamomile plants, Gamal ElDin et al (1997) on lemon grass, Mona and Iman (2005)
on Pelargonium graveolens L. and Nahed and Balbaa (2007) on Saliva fariacea plants. They reported
that application of amino acids significantly increased total amino acids.
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Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Permeability and Porosity Prediction from Wireline logs Using Neuro-Fuzzy Technique
Wafaa El-Shahat Afify* and Alaa H. Ibrahim Hassan**
*Lecturer of Applied Geophysics, Faculty of Science, Benha University
** Senior Reservoir Geologist (Bab Team)
Abu Dhabi Company for Onshore Oil Operations
*E-mail address for correspondence: w_afify@yahoo.com
____________________________________________________________________________________
Abstract: Petroleum reservoir characterization is a process for quantitatively describing various
reservoir properties in spatial variability using all the available field data. Porosity and permeability are the
two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its
ability to flow. These properties have a significant impact on petroleum fields operations and reservoir
management. In un-cored intervals and well of heterogeneous formation, porosity and permeability
estimation from conventional well logs has a difficult and complex problem to solve by statistical methods.
This paper suggests an intelligent technique using fuzzy logic and neural network to determine reservoir
properties from well logs. Fuzzy curve analysis based on fuzzy logic is used for selecting the best related
well logs with core porosity and permeability data. Neural network is used as a nonlinear regression
method to develop transformation between the selected well logs and core measurements. The technique is
demonstrated with an application to the well data in West July oil field, Gulf of Suez, Egypt for the
Miocene Upper Rudeis reservoirs (Asal and Hawara formations). The results show that the technique can
make more accurate and reliable reservoir properties estimation compared with conventional computing
methods. This intelligent technique can be utilized as a powerful tool for reservoir properties estimation
from well logs in oil and natural gas development projects.
____________________________________________________________________________________
INTRODUCTION
Reservoir characterization is a process of describing various reservoir characteristics using all the
available data to provide reliable reservoir models for accurate reservoir performance prediction. The
reservoir characteristics include permeability, porosity, pore and grain size distributions, facies distribution,
and depositional environment. The types of data needed for describing the characteristics are core data,
well logs, well tests, production data and seismic survey. Such information is essential to the determination
of the economic viability of a particular well or reservoir to be explored. A large number of techniques
have been introduced in order to establish an adequate interpretation model over the past fifty years.
Nevertheless, conventional derivation of a well log data analysis model normally falls into one of the two
main approaches: empirical and statistical. In the empirical approach, mathematical functions relating the
desired permeability based on several well log data inspired by theoretical concepts are used [Wyllie and
Rose, 1950, Kapadia and Menzie, 1985]. This approach has long been favored in the field and much effort
has been made to understand the underlying petroleum engineering principles. However, the unique
geophysical characteristic of each region prevents a single formula from being universally applicable.
Statistical techniques are viewed as more practical approaches [Wendt et al., 1986, and Hawkins, 1994].
The common statistical technique used is multiple regression analysis. The simplest form of regression
analysis is to find a relationship between the input logs and the petrophysical properties. The derived
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regression equations are then used for well log analysis. However, a number of initial assumptions of the
model need to be made. Assumptions must also be made as to the statistical characteristics of the log data.
Over the past decade, another technique that has emerged as an option for well log analysis is the Artificial
Neural Network (ANN). Research has shown that an ANN can provide an alternative approach to well log
analysis with improvement over the traditional methods [Osborne, 1992, Wong et al., 1995, Fung and
Wong, 1999]. Most of the ANN based well log analysis models have used the Multi-layer Neural Network
(MLNN) utilizing the backpropagation learning algorithm. Such networks are commonly known as
Backpropagation Neural Networks (BPNNs). A BPNN is suited to this application, as it resembles the
characteristics of regression analysis in statistical approaches. Fuzzy Logic (FL) that is capable to express
the underlying characteristics of a system in human understandable rules is also used. A fuzzy set allows
for the degree of membership of an item in a set to be any real number between 0 and 1. This allows human
observations, expressions and expertise to be modeled more closely. Once the fuzzy sets have been defined,
it is possible to use them in constructing rules for fuzzy expert systems and in performing fuzzy inference.
This approach seems to be suitable to well log analysis as it allows the incorporation of intelligent and
human knowledge to deal with each individual case. However, the extraction of fuzzy rules from the data
can be difficult for analysts with little experience. This could be a major drawback for use in well log
analysis. If a fuzzy rule extraction technique is made available, then fuzzy systems can still be used for well
log analysis [Wong et al., 1999 and Kuo et al., 1999]. With the emergence of intelligent techniques that
combine ANN and fuzzy together have been applied successfully in well log analysis [Huang et al., 2001,
Kadkhodaie Ilkhchi et al., 2008, Khaxar et al., 2007, Johanyák et al.2007]. These techniques used in
building the well log analysis model normally address the disadvantages encountered in ANN and fuzzy
system. This paper suggests an intelligent technique for reservoir characterization using fuzzy logic and
neural network to determine reservoir properties from well log data for the Miocene Upper Rudeis
reservoirs (Asal and Hawara formations), in West July oil field, Gulf of Suez, Egypt, Fig.1.
Back propagation neural networks (BPNN).
A neural network (NN) is an intelligent tool for solving complex problems. A BPNN is a supervised
training technique that sends the input values forward through the network then computes the difference
between calculated output and corresponding desired output from the training dataset. The error is then
propagated backward through the net and the weights are adjusted during a number of iterations, named
epochs. The training ceases when the calculated output values best approximate the desired values [Bhatt
and Helle, 2002].A flowchart of training procedure in a supervised NN is shown in Fig. 2.
Fuzzy logic (FL).
The basic theory of fuzzy sets was first introduced by Zadeh, 1965. In recent years, it has been shown that
uncertainty may be due to fuzziness (possibility) rather than probability. FL is considered to be appropriate
to deal with the nature of uncertainty in system and human errors, which were not considered in existing
reliability theories. Generally, geological data are not clear-cut and habitually are associated with
uncertainties. For example, prediction of core parameters from well log responses is difficult and is usually
associated with error [Nikravesh and Aminzadeh, 2003].FL derives useful information from this error and
applies it as a powerful parameter for increasing the accuracy of the predictions. A fuzzy inference system
(FIS) is a method to formulate inputs to an output using FL [Kadkhodaie Ilkhchi et al., 2006].
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Fig.2. A flow chart of training procedure in a supervised
neural network.
Fuzzy modeling technique can be classified into three categories, namely the linguistic (Mamdani-type), the
relational equation, and the Takagi, Sugeno and Kang (TSK). Takagi and Sugeno, 1985, is a FIS in which
output membership functions are constant or linear and are extracted by a clustering process. Each of these
clusters refers to a membership function. Each membership function generates a set of fuzzy if–then rules
for formulating inputs to outputs. A schematic diagram of FIS is shown in Fig.3.
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Neuro-fuzzy (NF) model.
Ozean Journal of Applied Sciences 3(1), 2010
Hybrid NF systems combine the advantages of fuzzy systems (which deal with explicit knowledge) with
those of NN (which deal with implicit knowledge). On the other hand, Fuzzy Logic (FL) enhances
generalization capability of a Neural Network (NN) system by providing more reliable output when
extrapolation is needed beyond the limits of the training data. A schematic diagram of information flows in
a NF system is shown in Fig.4. The architecture of the Neuro-Fuzzy classifier is slightly different from the
architecture used in function approximations [Tommi, 1994]. The two first layers have the identical
function with the approximation. Fig. 5 shows a system using the following fuzzy rules,
Rule 1: If x1 is A1 and x2 is B1, then class is 1.
Rule 2: If x1 is A2 and x2 is B2, then class is 2.
Rule 3: If x1 is A1 and x2 is B3, then class is 1.
Layer 3. Combination of firing strengths: If several fuzzy rules have the same consequence class, this layer
combines their firing strengths. Usually, the maximum connective (or operation) is used.
Layer 4. Fuzzy outputs: In this layer, the fuzzy values of the classes are available. The values describe how
well the input of the system matches to the classes.
Layer 5. Defuzzification: If the crisp classification is needed, the best-matching class for the input is chosen
as output class.
METHODS AND RESULTS
The data used for permeability and porosity determination are the open-hole wireline subsurface well log
data [gamma ray (GR), sonic (DT), density (ROHB), deep resistivity (RD), Neutron (PHIN) logs, water
saturation (SW)], and core data [core permeability and core porosity]. The work in the present research
proceeds as following;
• Removing erroneous and outliers from the raw well log data.
• Organizing data into input data sets including GR, DT, ROHB, RD, PHIN, SW and
output data sets including core permeability and core porosity.
• Normalization of input and output data sets (between the ranges 0-1) to renders the data
dimensionless and removes the effect of scaling.
• Dividing the data into: training, checking and testing data sets.
• Clustering the input and output data sets using fuzzy c-means (FCM), fuzzy k-means
(FKM) or subtractive clustering methods.
• Fuzzyfication, which involves the conversion of numeric data in real world domain to
fuzzy numbers in fuzzy domain, this takes place by building the fuzzy inference system
FIS, which involves setting the membership functions and establishment of fuzzy rules.
• Deffuzzification, which is optional, involves the conversion of the derived fuzzy
number to the numeric data in real world domain.
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Fig.3. Schematic diagram of FIS
Fig.4. Schematic diagram of information flow in a NF system
Fig.5. Neural architecture of the NF classifier.
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•Organizing data. The data for the neuro-fuzzy model come from one well SG-3105A at West July oil
field, Gulf of Suez, Egypt. The selection of this well is based on geological considerations; it contains
reasonably good core coverage of the Upper Rudeis Formation. Core-log calibration was carefully carried
out to compensate for differences in depth. Table (1), illustrates the statistics of the input and output data
sets used in NF modeling.
•Normalizing data. When processing the actual materials, due to the different dimensions of the source
rocks evaluation parameter, the volume level of actual data vary considerably. If we calculate by using the
raw data directly, the indicating role of the data which has a larger volume would become more
outstanding. While the indicator with a lower volume and a higher sensitivity will be underestimated. Thus,
we should preprocess and normalize the raw data. In this work normalizing data takes place by using the
maximum and minimum values of the data.
•Fuzzy clustering. It is necessary to classify the input and output datasets into groups using clustering
methods. In this study, a subtractive clustering method, which is a useful and effective way to FL modeling,
is used for extraction of clusters and fuzzy if–then rules. The details of subtractive clustering could be
found in Chiu [1994], Chen and Wang [1999], Jarrah and Halawani [2001].The important parameter in
subtractive clustering which controls number of clusters and fuzzy if–then rules is clustering radius. This
parameter could take values between the range of [0, 1]. Specifying a smaller cluster (say 0.1) radius will
usually yield more and smaller clusters in the data resulting in more rules. In contrast, a large cluster radius
(say 0.9) yields a few large clusters in the data resulting in few rules.
The effectiveness of a fuzzy model is related to the search for an optimal clustering radius, which is a
controlling parameter for determining the number of fuzzy if–then rules. Few rules could not cover the
entire domains, and more rules will complicate the system behavior and may lead to low performance of
the model. Regarding the permeability model, four centers result from clustering, thus the fuzzy model was
established by four fuzzy if-then rules and four membership functions for input and output data. Porosity
model, on the other hand, contains five centers (clusters), five rules and five membership functions. Figures
6 and 7 shows the subtractive clusters of permeability and porosity data.
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•Building the fuzzy inference system FIS. Fuzzy inference is the process of formulating the mapping from
a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can
be made, or patterns discerned. The process of fuzzy inference involves setting the membership functions
and establishment of fuzzy rules, [Matlab fuzzy logic user’s guide, and 2009].
1- Setting the Membership Functions (MF). A membership function (MF) is a curve that defines how each
point in the input space is mapped to a membership value (or degree of membership) between 0 and 1. The
input space is sometimes referred to as the universe of discourse, a fancy name for a simple concept. The
only condition a membership function must really satisfy is that it must vary between 0 and 1. The function
itself can be an arbitrary curve whose shape we can define as a function that suits us from the point of view
of simplicity, convenience, speed, and efficiency. There are many types of membership functions built
from several basic functions:
• Piece-wise linear functions
• The Gaussian distribution function
• The sigmoid curve
• Quadratic and cubic polynomial curves
In this study, a Gaussian distribution membership function is used to define the extracted input clusters. A
Gaussian function f (x) shows the normal distribution of data (x):
e
f ( X ) �
�
�(
x��
)
2 /
�
2�
2
Where µ and σ are the parameters of normal distribution showing the mean and standard deviation of data,
respectively. These Gaussian membership functions are constructed from mean and σ values of the clusters.
The mean represents the cluster centers and σ is derived from:
σ = (radii � (maximum data – minimum data))/sqrt. The input parameters of Gaussian membership
function for permeability and porosity are shown in tables 2A and 3A.
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In the FIS, output membership functions are linear equations constructed from inputs. For example, output
membership function number one (MF1), which is the consequent of rule no. 1, is constructed from six
petrophysical inputs as following:
Output MF1 = C1 � GR � C2
� DT � C3
� ROHB � C4
� RD � C5
� PHIN � C6
� SW � C7
In this equation, parameters C 1 , C2,
C3,
C4,
C5
and C 6 are coefficients corresponding to GR, DT,
ROHB, RD, PHIN and SW inputs, respectively. Parameter C 7 is constant in each equation. These
parameters are obtained by linear least-squares estimation. With these explanations there will be seven
parameters for each output membership function, which are shown in tables 2B and 3B for permeability
and porosity, respectively. Figures 8 and 9 represent the FIS generated Gaussian membership
functions of input data for permeability and porosity model, respectively.
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Moreover, Figure 10 A shows the FIS model generated for permeability and porosity, (Fig.10B).
2- Establishment of fuzzy rules. Fuzzy rule statements are used to formulate the conditional statements that
comprise fuzzy logic. A single fuzzy if-then rule assumes the form if x is A then y is B where A and B are
linguistic values defined by fuzzy sets on the ranges (universes of discourse) X and Y, respectively. The ifpart
of the rule “x is A” is called the antecedent or premise, while the then-part of the rule “y is B” is called
the consequent or conclusion.
The generated fuzzy if-then rules for formulating input petrophysical data to permeability are:
1. If (GR is in1mf1) and (DT is in2mf1) and (ROHB is in3mf1) and (RD is in4mf1) and (PHIN is in5mf1)
and (SW is in6mf1) then (K is out1mf1).
2. If (GR is in1mf2) and (DT is in2mf2) and (ROHB is in3mf2) and (RD is in4mf2) and (PHIN is in5mf2)
and (SW is in6mf2) then (K is out1mf2).
3. If (GR is in1mf3) and (DT is in2mf3) and (ROHB is in3mf3) and (RD is in4mf3) and (PHIN is in5mf3)
and (SW is in6mf3) then (K is out1mf3).
4. If (GR is in1mf4) and (DT is in2mf4) and (ROHB is in3mf4) and (RD is in4mf4) and (PHIN is in5mf4)
and (SW is in6mf4) then (K is out1mf4).
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The generated fuzzy if-then rules for formulating input petrophysical data to porosity are:
1. If (GR is in1mf1) and (DT is in2mf1) and (ROHB is in3mf1) and (RD is in4mf1) and (PHIN is in5mf1)
and (SW is in6mf1) then (PHI is out1mf1).
2. If (GR is in1mf2) and (DT is in2mf2) and (ROHB is in3mf2) and (RD is in4mf2) and (PHIN is in5mf2)
and (SW is in6mf2) then (PHI is out1mf2).
3. If (GR is in1mf3) and (DT is in2mf3) and (ROHB is in3mf3) and (RD is
in4mf3) and (PHIN is in5mf3) and (SW is in6mf3) then (PHI is out1mf3).
4. If (GR is in1mf4) and (DT is in2mf4) and (ROHB is in3mf4) and (RD is in4mf4) and (PHIN is in5mf4)
and (SW is in6mf4) then (PHI is out1mf4).
5. If (GR is in1mf5) and (DT is in2mf5) and (ROHB is in3mf5) and (RD is in4mf5) and (PHIN is in5mf5)
and (SW is in6mf5) then (PHI is out1mf5).
A graphical illustration showing steps to formulation of petrophysical data inputs to permeability using four
fuzzy if–then rules generated by FIS, is represented in Fig.11. The formulation of petrophysical data to
porosity using five fuzzy if-then rules generated by FIS are shown in Fig. 12. Each figure displays a
roadmap of the whole fuzzy inference process. The seven plots across the top of the figure represent the
antecedent and consequent of the first rule. Each rule is a row of plots, and each column is a variable. The
rule numbers are displayed on the left of each row.
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The structure of the NF model is now generated for permeability (Fig.13A) and porosity (Fig.13B). The
input is represented by the left-most node and the output by the right-most node. The node represents a
normalization factor for the rules.
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DISCUSSION
The NF technique is used to determine the porosity and permeability of the Upper Rudeis Formation using
the available well data, as well as core permeability and core porosity data, (Fig. 14). The Upper Rudeis
sand is the third most important reservoir in July oil field. The sand was supplied by fans draining the Red
Sea hills to the west of July field and deposited in a similar environment to the Lower Rudeis Formation,
Pivnik et al., (2003).
A total of 108 data points are used for training, 108 data points are used for checking and 60 data points are
used for testing the NF models of the permeability and porosity. The FIS is trained using the training data
set then checked and tested using checking data sets and testing data sets respectively. The testing data set
is used to check the generalization capability of the resulting fuzzy inference system. The idea behind using
a checking data set for model validation is that after a certain point in the training, the model begins over
fitting the training data set. In principle, the model error for the checking data set tends to decrease as the
training takes place up to the point that over fitting begins, and then the model error for the checking data
suddenly increases. Over fitting is accounted for by testing the FIS trained on the training data against the
checking data. Usually, these training and checking data sets are collected based on observations of the
target system and are then stored in separate files.
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Figure 15 shows the checking and the FIS output. On the other hand, Fig.16. shows testing data and FIS
output. The performance of the model is evaluated by the MSE of the data sets, as illustrated in Fig.17. and
table (4). The correlation coefficient between the measured and NF predicted K and PHI are 0.825 and
0.957, respectively. A comparison between measured and NF predicted K and PHI versus depth is shown in
Figs. 18 and 19.
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CONCLUSIONS
In this study, the NF intelligent technique is used to estimate reservoir porosity and permeability from
conventional well logs. Fuzzy curve analysis based on fuzzy logic can be used for selecting the
best related parameters with reservoir properties. The NF modeling approach presented in this paper has
been successfully applied for the prediction of petrophysical reservoir parameters. This modeling approach
has the significant advantage in that it does not require any previous assumption based on physical or
experimental considerations about the reservoir complexities to construct a reasonable and accurate model
from a set of measured data. Excellent correlation coefficients have been obtained for porosity 0.957, and
permeability 0.825, using NF models. The techniques can make more accurate and reliable reservoir
properties estimation and can be utilized a powerful tool for reservoir properties determination from well
logs in petroleum industry, and is applicable in different wells and oil fields.
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ACKNOWLEDGEMENTS
The authors would like to express their gratitude for the Gulf Of Suez Petroleum Company, (GUPCO),
Egypt for supplying the data needed for this work.
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Petroleum Technology, vol. 189, pp. 105-110.
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Figure 1 - Location map of July Oil Field.
Ozean Journal of Applied Sciences 3(1), 2010
FIGURES CAPTION
Figure 2 - A flow chart of training procedure in a supervised neural network.
Figure 3 - Schematic diagram of FIS.
Figure 4 - Schematic diagram of information flow in a NF system.
Figure 5 - Neural architecture of the NF classifier.
Figure 6 - Subtractive clustering of permeability fuzzy model.
Figure 7 - Subtractive clustering of porosity fuzzy model.
Figure 8 - Generated Gaussian membership functions for permeability model input data.
Figure 9 - Generated Gaussian membership functions for porosity model input data.
Figure 10 - Diagrams showing formulation of input petrophysical data to: (A)
permeability, K and (B) porosity, PHI using fuzzy modeling.
Figure 11 – Rule viewer of FIS permeability model.
Figure 12 - Rule viewer of FIS porosity model.
Figure 13 - Structure of Neuro-Fuzzy model for permeability (A) and porosity (B).
Figure 14 - Petrophysical and core data of SG-310-5A well.
Figure 15 - Showing checking data and FIS output, permeability (A) and porosity (B).
Figure 16 - Showing testing data and FIS output, permeability (A) and porosity (B).
Figure 17 - Mean square error (MSE) obtained during training the permeability
model (A) and the porosity model (B).
Figure 18 - Predicted and core permeability.
Figure 19 - Predicted and core porosity.
TABLES CAPTION
Table1 - Statistics of input and output data sets.
Table 2 - Showing input (a) and output (b) membership functions parameters derived by
FIS for permeability.
Table 3 - Showing input (a) and output (b) membership functions parameters derived by
FIS for porosity.
Table 4 - MSE of the different datasets.
175

Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
Response of vegetative growth and chemical constituents of Schefflera
arboricola L. plant to foliar application of inorganic fertilizer (grow-more)
and ammonium nitrate at Nubaria.
Mona, H. Mahgoub *, El-Quesni, Fatma E.M., and Magda,M. Kandil
Department of Ornamental plant and Woody trees,
National Research Centre, Dokki, Cairo, Egypt
*E-mail address for correspondence: azza856@yahoo.com
________________________________________________________________________________
Abstract: A pot experiment was carried out during 2007 and 2008 seasons at Research and Production
Station, Nubaria of National Research Centre, Dokki, Cairo, Egypt to study the response of Schefflera
plants to foliar fertilizer (Grow-more at the rates of 0.0, 1.0 cm 3 /L and 2.0 cm 3 /L) and ammonium
nitrate at the rate of (0, 100 and 200 kg) and their interaction on vegetative growth expressed as plant
height, stem diameter, number of leaves, leaf area, fresh and dry weight of (leaves, roots and stem) and
chemical composition significantly affected by application of the two factors which were used in this
study.Grow-more and nitrogen fertilizer promoted all morphological characters, photosynthetic
pigments, protein %, nitrogen, phosphorus and potassium.,
Keywords: Schefflera arboricola,Grow-more,ammonium nitrate
__________________________________________________________________________________
INTRODUCTION
Schefflera arboricola L. is flowering plant in the family araliaceae, native to Tiwan and Hainan. It is
also goes by the common name "Dwarf umbrella tree". It is an evergreen shrub growing to 3-4 m
height, often trailing stems scrambing over other vegetation. The leaves are palmately compound, with
7-9 leaflets, the leaflets 9-20 cm long and 4-10 cm broad (though often smaller in cultivation). The
flowers are produced in a 20 cm pancil of small umbels, each umbel 7-10 mm diameter with 5-10
flowers.
It is commonly grown as houseplant, popular for its tolerance of neglect and poor growing condition.
Numerous cultivars have been selected for variation in leaf colour and pattern, often variegated with
creamy-white to yellow edges or centers, and dwarf forms. Scheffleras are delicate tropical plants
often used to decorate public places, such as lobbies, shopping malls and waiting rooms. Smaller
Scheffleras are better studied for homes and small offices. Uphof(1959). Inorganic fertilizers are the
elements needed in small amounts, they are often refers to as micronutrients (Kohnke 1995) these
elements are chlorine (Cl), Iron (Fe), Manganese (Mg), Boron (B), Copper (Cu), Zinc (Zn),
Molybdenum (Mo), Nickel (Ni) and Cobalt (Co) most of these elements are derived from the soil and
organic sources (Brady and Weil 2000). Micronutrients are also essential for organization and rapid
alternation of nutrition compound within plant owing to their great importance in contribution to direct
the enzymes way in metabolism Massoud et al (2005). Therefore, both granular and fluid (liquid) NPK
fertilizers are commonly used as carriers of micronutrients including micronutrients with mixed
fertilizer which is a convenient method of application and allows more uniform distribution with
conventional application equipment. Micronutrients are essential for plant growth, but are required in
much smaller amounts than those of the primary nutrients Brady and Weil (2000).
Nitrate and ammonium are the major source of inorganic nitrogen taken up by the roots of higher
plants. Most of ammonium has to be incorporated into organic compounds in the roots whereas nitrate
177

Ozean Journal of Applied Sciences 3(1), 2010
is readily mobile in the xylem and can also be stored in the vacules of roots, shoots and storage organs.
Nitrate accumulation can be considerable importance for cation-anion balance, for osmoregulation,
particularly in so-called nitrophilie species such as Chenopodium album and Urtica dioica (Smirnoff
and Stewart, 1985). Dahiya et al (2001) mentioned that further increments in nitrogen level, up to 180
ppm, adversely affected growth and dry matter yield of tuberose. While Pal and Biswas (2000) found
that the lower doses of fertilizer produced poor quality plant and yield of flower and best results were
found when tuberose were fertilized N, P and K at the level of 15, 15, 20 g/m 2 , respectively. Also
Paradhan et al (2004) noticed that combined application of N at 40 g/m 2 and K at 30 g/m 2 gave the
highest values of plant height, number of leaves /plant, leaf area, spike length and number of
flowers/spike. Mahgoub et al (2006) studied the effect of the nitrogen levels 30, 40, 50 and 60 g/m 2 as
ammonium nitrate (33.5 % N) and the level of 25, 30, 35 and 40 g/m 2 as potassium sulfate (48 % K2O).
They found that Iris bulbs showed higher values for plant height, fresh and dry weight of leaves (40 g
N + 30 g K /m 2 ) N level up to 60 g/m 2 showed stimulatory effect on chlorophyll a, b and carotenoids,
60 g N/m 2 increased carbohydrate percentage in the presence of 30 g /m 2 K, (40 g N/m 2 + 25 g K /m 2 )
recorded high values of N, P and K in Iris leaves.
The aim of this work is to study the response of Schefflera arboricola plants to foliar fertilizer of
Grow-more and nitrogen fertilizer and their interactions on growth and some chemical composition.
MATERIALS AND METHODS
The experiments was conducted at Research and Production Station of National Research Centre at
Nubaria during two successive seasons 2007 and 2008 to investigate the response of Schefflera
arboricola plant to foliar fertilizer micro nutrients and nitrogen fertilizer (ammonium nitrate 33.5 %)
on growth and some chemical composition. On the third week of February 2007 and 2008 seasons,
vegetative uniform cutting (20-24 cm length) were taken from Schefflera arboricola plant, cutting
were treated for a minute with 1000 mg/L indole butric acid before planting in pots to enhance rooting.
Rooted cuttings were planted in black plastic pots (10 cm) in diameter (one plant /pot) and grown in
shaded green house media formulated by combination of peatmoss and sandy soil (1:1, v/v). The
seedling were transplanted on 20 th April 2007 and 2008 seasons, in plastic pot (30 cm) in diameter
filled with 10 kg of peatmoss and sandy soil (1:1, v/v) arranged in a complete randomized design with
three replicates. Each replicate consists of three plants. Water requirements were relative humidity
maintained between 45-65%, allow the surface of potting media to dry slightly before irrigation. Each
pot was fertilized twice with 1.5 g nitrogen as ammonium nitrate (33.5% N) and 1.0 g potassium
sulphate (48.5 % K2O). The fertilizers were applied at 30 and 60 days after transplanting. Phosphorus
as calcium superphosphate (15.5 % P2O5) was mixed with soil before transplanting at a rate of 3.0 g/
pot. Other agricultural processes were performed according to normal practice. Plants were sprayed
with different concentration of foliar fertilizer (Gropw-more) Table (1) which produced by Ajemco
International company at the rate of (0.0, 1.0 and 2.0 cm 3 /L). Nitrogen as ammonium nitrate was
fertilized with (0, 100 and 200 kg), interaction of the two factors had been also carried out, in addition
to untreated plants (control) which were sprayed with tap water. Foliar application of Grow-more and
nitrogen was carried out two times of 30 days intervals starting at 20 July at both seasons. The
experiments were sat in completely Randomized Design (CRD) with three replicates and two factors.
The following data were recorded on 1 st week of December 2007 and 2008 season, the recorded data
were plant height (cm), stem diameter (mm), No. of leaves, leaf area (cm 2 ) of 4 and 5 base leaves, fresh
and dry weight of plant organs (gm). Photosynthetic pigments i.e. chlorophyll (a, b and carotenoids)
were determined exactly 0.1 gm of fresh leaves of schefflera plant using the Spectrophotometric
method developments by Metzzner et al (1965). Total nitrogen was determined by Chapman and Pratt
(1961), while phosphorus determination carried out Colorimtrically according to King (1951). Potassium
was determined photometrically by flam photometer method as described by Brown and Lillan (1946).
Data obtained were subjected to standard analysis of variance procedure, the values of LSD were
obtained whenever F value were significantly as 5% levels reported by Snedecor and Cochran (1980).
178

Growth characters:
Ozean Journal of Applied Sciences 3(1), 2010
RESULTS AND DISCUSSION
Data in Table (2) show that foliar application of Grow-more at the concentration of 1.0 and 2.0 cm 3 /L
on schefflera plants significantly increased all growth parameters plant height (cm), No. of leaves, fresh
and dry weight of plant organs (gm), root and stem (gm), stem diameter and leaf area (cm 2 ) than the
untreated plants, the highest values of previous characters were found when plants treated with 2.0
cm3/L of grow-more followed by 1.0 cm 3 /L. These results are agreement with El-Fouly (2001) who
noticed that the number of leaves and leaf area of sunflower plants were increased by addition of Fe,
Mn, Zn, root size was increased by addition of Fe and Mn only. Rabie et al (2002) reported that foliar
fertilizer containing N, P, K, Fe, Mn and Zn pronounced increases in dry weight, macro and
micronutrients content of sorghum plants than control plants. Negm and Zahran (2001) found that,
foliar application of micronutrients had the significant effect on increasing wheat grain and straw
yields; yield attributes (plant height, spike length and 1000 grains weight). El -Quesni et al (2009)
mentioned that using inorganic fertilizers at the concentrations of 1.0 and 2.0 cm 3 /L on syngonium
plants increased plant height, stem diameter, No. of leaves/plant, leaf area and fresh and dry weight of
roots and leaves. These results may be due to micronutrient boron which helps transport vital sugars
through plant membranes and promotes proper cell division, cell wall formation and development, also
due to zinc which promotes seed/grain formation, plant maturity, acts as enzyme activator in protein,
hormone (i.e. IAA) and RNA / DNA synthesis and metabolism. Chlorine also indirectly affects plant
growth by stomatal regulation of water loss. Molybdenum has a significant effect on pollen formation,
so fruit and grain formation are affected by molybdenum –deficient plants.
With regard to the effect of nitrogen fertilizer on schefflera plants data in Table (2) illustrated that
using nitrogen fertilizer at the rate of 100 kg gave significant increases than control plants. 200 kg
nitrogen gave the highest values in all growth parameters under study compared with control plants.
These results are in agreement with Ramesh et al (2002) they mentioned that plant height increased
with increasing the rate of nitrogen. In this respect, Paradahan et al (2004) on gladiolus c.v. red
mention that 4 g/m2 N plus K fertilizer at the rate of 30 g/m2 recorded highest value of number, fresh
and dry weight of leaves as plant height . As regarding the interaction treatments, foliar application
micronutrients and nitrogen fertilizer, the data show that significantly increased all growth characters
under study. The highest values of growth characters were obtained by grow-more 2.0 cm 3 /L
combined with ammonium nitrate at the rate of 200 kg /fed followed by grow-more 2.0 cm3/L and N at
the rate of 200 kg/fed.
Data emphasized the interaction effect were significantly affected all growth parameters i.e. plant
height (cm), number of leaves, fresh and dry weight of (leaves, root and stem) stem diameter and leaf
area of schefflera arboricola L. plants. These results may be due to increasing the nitrogen levels
which delays senescence and stimulates growth and also changes plant morphology, particularly if the
nitrogen availably is high in the rooting medium during the early growth (Levin et al, 1989; Olsthoorn
et al 1991), it presumably related to nitrogen induced changes in the phytohormone balance (Sommer
and Six 1982).
Chemical constituents:
Synthetic Pigments:
Data in Table (3) indicated that spraying schefflera plants with grow-more at the rate of 1.0 cm3/L
increasing significantly in chl. a, b and total chlorophyll and decreased in total carotenoids content
whereas 2.0 cm3/L gave the highest values in the content of plants from Chl. a, Chl. b, total
Chlorophyll and total carotenoids content. These results were agreement with those obtained by
Ratanarat et al (1990), El-Quesni et al (2009) they found that the highest values of Chl. a, Chl. b and
total carotenoids in syngonium plants increases by increasing the concentration of grow-more up to 2.0
cm3/L. These results may be due to iron, manganese, which promote chlorophyll production and
photosynthesis process, copper which helps in chlorophyll formation. With regard to the effect of
nitrogen fertilizer on schefflera plants data in Table (3) showed that the two used concentration of N
fertilizer increasing Chl. a, b and total Chlorophyll whereas total carotenoids content gave significant
increased by using 200 kg N only. These results are agreement with Mahgoub et al (2006) on Iris
179

Ozean Journal of Applied Sciences 3(1), 2010
bulbs they mentioned that maximizing the rate of N up to 60 g/m2 showed the stimulatory effect on
chlorophyll a, b and carotenoids irrespective of the K fertilizer level.
Concerning the effect of interaction on photosynthetic pigments data show that the highest significant
values was found in plant treated with 2.0 cm2/L grow-more fertilizer plus 200 kg nitrogen fertilizer
followed by 2.0 cm3/L micronutrients and 100 N , respectively. These treatments may be due to
positive effect on growth parameters.
Mineral Ions content:
Data in Table (3) found that foliar application of grow-more at the concentration of 1.0 and 2.0 cm 3 /L
and nitrogen fertilizer at the rate of 100 and 200 kg increased the total amount of nitrogen, phosphorus
and potassium ions content on schefflera plants compared with control plants. These results were
agreement with those obtained by Sharma et al (2002) they found that application of organic material
either alone or in combination with chemical fertilizers caused substantial increase in total N, available
P, K as well as increased wheat and straw yield Mahgoub et al(2006) on Iris bulbs they found that
using 40 g/m2 N plus 25 g/m 2 K recorded high values of N, P and K in Iris leaves. With regard the
effect of interaction in mineral ions content data show that significantly increased N, P and K content
of schefflera plants were obtained by grow-more 2 cm 3 /L combined with 200 kg N followed by grow
more 2 cm 3 /L.
Data in Table (3) mentioned that total protein percentage increased by (6.46 and 10.53) when plants
treated with 1.0 and 2.0 cm 2 /L grow-more respectively compared with control plants which recorded
(8.68 %). Also nitrogen fertilizer 100 and 200 kg treatments in the total protein increased by (9.68 and
10.09 %) respectively than control plants (8.90 %).
The highest recorded data in total protein percentage were (11.21, 10.69 and 10.19) obtained from 2
cm 3 /L micronutrients plus 200 kg nitrogen fertilizer followed by 2 cm 3 /L and 200 kg N respectively.
These results are in line with those obtained by Negm and Zahran (2001) they mentioned that
micronutrients increased protein grain content in wheat plants and El-Quesni et al (2009) on
syngonium plants. These increments led to positive effect of growth parameters and enhancing effect
on plant metabolism which was regarded as a better indicator for foliage plants.
Table (1) Chemical properties of micronutrients fertilizer grow-more used in this study.
Growmore
content
N2 P2O5 K2O Fe Zn Mg Ca Cu S B Mo
% 11 6 8 0.15 0.15 0.14 0.02 0.20 0.02 0.01 0.01
180

Ozean Journal of Applied Sciences 3(1), 2010
Table (2) Effect of micronutrient and nitrogen fertilizer on vegetative growth of schefflera arboricola
L. plants. (means of two seasons 2007 and 2008).
Character
Treatments
Effect of micronutrient
Plant
height
Stem
diam
eter
Control 37.61 1.21
Micro 1 cm 3 /L 33.84 1.24
Micro 2 cm 3 /L 42.97 1.46
No.o
f
leave
s
181
Leaf
area
F.W
of
root
D.W
of
root
F.W
of
leave
s
cm mm cm 2 gm
18.6
2
19.9
0
22.2
7
8.87
9.56
11.6
6
39.6
2
41.4
0
45.0
8
17.5
3
36.6
7
41.3
7
75.6
8
76.9
2
84.6
3
D.W
of
leave
s
26.6
6
26.9
3
29.8
8
F.W
of
stem
35.7
6
36.6
7
41.3
7
LSD at 5% level 1.41 0.07 1.58 1.54 1.41 2.09 3.69 1.88 2.09 1.48
Effect of nitrogen
Control 39.91 1.23
N 100 kg 39.36 1.25
N 200 kg 41.10 1.42
19.8
6
19.5
0
21.4
2
9.10
9.80
11.1
8
41.2
2
41.7
9
43.0
9
18.0
5
19.2
0
20.3
8
77.0
3
78.0
6
82.1
4
26.2
0
27.0
5
29.2
1
30.9
1
37.4
0
39.5
0
LSD at 5% level 1.41 0.07 1.58 1.54 1.40 1.70 3.69 1.88 2.09 1.48
Effect of interaction
Control 32.00 1.11
Micro 1 cm 3 /L 41.38 1.28
Micro 2 cm 3 /L 43.34 1.32
N 100 kg 40.17 1.26
N 200 kg 40.67 1.27
Micro 1 cm 3 /L + N
100 kg
Micro 1 cm 3 /L + N
200 kg
Micro 2 cm 3 /L + N
100 kg
Micro 2 cm 3 /L + N
200 kg
38.67 1.24
36.42 1.20
39.25 1.25
46.33 1.80
14.7
3
22.0
2
22.8
3
19.8
3
21.2
8
19.0
0
18.6
6
19.6
7
24.3
2
6.33
10.2
9
10.6
8
10.0
7
10.2
0
9.39
9.00
9.95
14.3
4
33.5
4
43.7
4
46.3
8
42.0
0
43.3
3
41.5
5
38.9
0
41.8
3
47.3
0
12.5
7
20.3
1
21.2
7
19.8
5
20.1
7
18.1
5
17.0
7
19.6
0
23.9
0
67.0
7
81.6
8
82.3
3
80.3
0
79.6
7
76.2
5
72.8
3
77.6
3
93.9
3
21.1
2
28.1
7
29.3
3
27.7
8
28.0
7
26.2
1
25.4
2
27.1
7
33.1
5
28.1
3
40.8
7
41.4
2
39.3
3
39.8
0
35.7
8
33.3
7
37.0
9
45.3
2
LSD at 5% level 2.44 0.11 2.73 2.66 2.44 2.94 6.39 3.01 3.61 2.56
Micro= micronutrients, F.W.=fresh weight,D.W.=dry weight , N= nitrogen
D.W
of
stem
15.0
1
16.8
5
21.1
2
16.4
1
16.7
3
19.8
9
11.0
7
18.0
0
20.1
7
16.9
8
17.0
0
16.5
4
11.0
0
16.6
7
26.5
3

Ozean Journal of Applied Sciences 3(1), 2010
Table (3) Effect of micronutrient and nitrogen fertilizer on chemical constituents of schefflera
arboricola L. plants. (means of two seasons 2007 and 2008).
Treatments
Character
Effect of micronutrient
Chlorophylls Total
Chl. a Chl. b
Chl.
a+b
182
caroten
oids
Mineral ions contents
N P K
mg/g %
Total
protein
Control 0.99 0.30 1.29 0.17 1.47 0.25 3.23 8.68
Micro 1 cm 3 /L 1.12 0.33 1.45 0.18 1.52 0.27 3.26 9.46
Micro 2 cm 3 /L 1.29 0.41 1.70 0.25 1.62 0.33 4.12 10.53
LSD at 5% level 0.08 0.08 0.09 0.09 0.03 0.02 0.33 0.39
Effect of nitrogen
Control 1.03 0.33 1.36 0.19 1.51 0.27 3.32 8.90
N 100 kg 1.18 0.34 1.50 0.19 1.52 0.27 3.42 9.68
N 200 kg 1.19 0.38 1.57 0.22 1.59 0.30 3.87 10.09
LSD at 5% level 0.08 0.02 0.09 0.02 0.03 0.02 0.33 0.39
Effect of interaction
Control 0.52 0.17 0.69 0.09 1.19 0.15 2.50 5.83
Micro 1 cm 3 /L 1.26 0.40 1.66 0.23 1.63 0.32 3.46 10.17
Micro 2 cm 3 /L 1.31 0.43 1.75 0.26 1.67 0.35 4.00 10.69
N 100 kg 1.22 0.36 1.58 0.21 1.60 0.28 3.63 10.00
N 200 kg 1.24 0.37 1.61 0.22 1.65 0.30 3.55 10.19
Micro 1 cm 3 /L + N 100 kg 1.13 0.31 1.44 0.17 1.47 0.25 3.23
Micro 1 cm 3 /L + N 200 kg 0.96 0.29 1.25 0.13 1.43
9.35
0.22 3.10 8.80
Micro 2 cm 3 /L + N 100 kg 1.18 0.37 1.50 0.18 1.48 0.26 3.39 9.69
Micro 2 cm 3 /L + N 200 kg 1.37 0.47 1.83 0.30 1.70 0.39 4.96 11.21
LSD at 5% level 0.15 0.04 0.15 0.03 0.06 0.04 0.56 0.67
Micro= micronutrients, Chl. Chlorophyll, N= nitrogen

Ozean Journal of Applied Sciences 3(1), 2010
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Sommer, K.andR.six(1982).Ammonium als stickstoff qelle beim anbau von
Fttergerste.Landw.Forsch.38:151-161.
Uphof, J.C. Th. (1959). Dictionary of Economic plants, Weinheim. An excellent and very
comprehensive guide but it only gives very short descriptions of the uses without any details
of how to utilize the plants.
184

Ozean Journal of Applied Sciences 3(1), 2010
Ozean Journal of Applied Sciences 3(1), 2010
ISSN 1943-2429
© 2010 Ozean Publication
STATISTICAL MODELLING FOR OUTLIER FACTORS
Ahmet Kaya
Ege University, Tire Kutsan Vocational High School Computer Programming Department,
Tire-İzmir, Turkey.
e-mail address for correspondence: ahmet.kaya@ege.edu.tr
____________________________________________________________________________________
Abstract. Error in data is one of the facts that cause the parameter estimations to be subjective. If the
erroneous case is proved statistically, then these cases are called outliers. Outliers are defined as the few
observations or records which appear to be inconsistent with the rest of the group of the sample and more
effective on prediction values. Isolated outliers may also have positive impact on the results of data
analysis, data mining and estimated model. In this study, we are concerned with outliers in time series
which have two special cases, innovational outlier (IO) and additive outlier (AO). The occurence of AO
indicates that action is required, possibly to adjust the measuring instrument or mistake made by person
in observation or record. However, if IO occurs, no adjustment of the measurement operation is required.
Also in the study, a multi-factor ( 3 42
2
) modelling was done in order to fit the effects of model in data
analysis AR(1) coefficients, (0.5, 0.7, 0.9) outlier type (AO, IO), serie wideness (50, 100, 200, 500) and
criterion value sensibility (% 99 (C=3.00), % 95 (C=3.50), % 90 (C=4.00)) factors statistically by
making use of a simulation study. The results of the variance analysis on outlier factors were also
emphasized.
Key Words: ARMA, Outliers in Time Series, AO, IO, Modelling outlier factors.
___________________________________________________________________________________
INTRODUCTION
Real data and databases may often include some erroneous parts. These situations, which damage the
characteristics of data are called “abnormal condition”, and the values, which cause these “abnormal
condition” are called outliers. The outliers, which are really independent, are the situations that cause the
parameter estimation values in modelling to be subjective, they damage the processes even though they
are set properly, and it is an obligation to destroy or to eliminate the effects. They diminish the reliability
of the results. In this case, outliers is the name given to the data or data sets, which are inharmonious with
the rest of the serie, cause the parameter estimation values to be subjective, and damage the settled
processes.
The outliers are values which seem either too large or too small as compared to rest of the observations
(Gumbel, 1960).
An outlying observation, or outlier, is one that appears to deviate markedly from other members of the
sample in which it occurs (Grubbs, 1969).
The detection of influential subsets or multiple outliers is more difficult, owing to masking and swamping
problems. Masking occurs when one outlier is not detected because of the presence of others, while
swamping occurs when a non-outlier is wrongly identified owing to the effect of some hidden outliers
(Pena and Yohai, 1995).
Data analysis is done by adapting the data to a time series model which is composed of observations.
Outliers in time series were first studied by Fox in 1972. Fox has developed a criterion to fix the outliers
which is called likelihood ratio criteria, and defined the outliers as the first and second type outliers. The
simulations made have proved that the most effective method among these is the consecutive method,
185

Ozean Journal of Applied Sciences 3(1), 2010
developed by Chang(1982), Chang and Tiao(1983). Hillmer(1983) Tsay(1986), Pena(1987), Abraham
and Yatawara(1988), Bruce and Martin(1989) had some studies which contributed the theorical structure
of the consecutive method. Besides, Abraham and Yatawara(1988) have studied on lagrange multiplier
method or score based outlier tests. Pena(1987), Abraham and Chuang(1989) and Bruce and
Martin(1989) have studied about the tests depending on the elimination of the outlier values during
outlier detection and effective observations in time series (Ljung, 1993).
Isolating Outliers
The main reason for isolating outliers is associated with data quality assurance. The main exceptional
values are more likely to be incorrect. According to the definition given by Wand and Wang (1996),
unreliable data represents an unconformity between the state of the database and the state of the real
world. For a variety of database applications, the amount of erroneous data may reach to ten percent and
even more.
It is well known that outliers can seriously affect any inferences drawn if they are not treated
appropriately. Their detection and treatment, however, can lead to considerably greater computational
process. For that reason, removal of outliers effect can improve the quality of data used for statistical
inferences. Isolated outliers may also have positive impact on the results of data analysis and data mining.
Simple statistical estimates, like sample mean and standard deviation can be significantly biased by
individual outliers that are far away from the middle of the distribution.
Outlier Detection
The purpose of outlier detection is to discover the unusual data, whose behavior is very exceptional when
compared to the rest of the data set. Examining the extraordinary behavior of outliers helps to uncover the
valuable knowledge hidden behind them and to help the decision makers to make profit or improve the
service quality. Hence, mining aiming to detect outlier is an important data mining research with
numerous applications, which include credit card fraud detection, discovery of criminal activites in
electronic commerce, weather prediction, marketing, statistical applications and so on.
Detection methods are divided into two parts: univariate and multivariate methods. In univariate methods,
observations are examined individually and in multivariate methods, associations between variables in
the same dataset are taken into account.
Classical outlier detection methods are powerful when the data contain only one outlier. However, these
methods decrease drastically if more than one outliers are present in the data (Hadi, 1992).
Although manual inspection of scatter plots is the most common approach to outlier detection, this is not
the most effective method. Since the multidimensionality of databases provides a significant advantage to
automated perceptions of outliers over the manual analysis of visualized data. Unlike the case of human
decision-making, the parameters of the automated detection of outliers can be completely controlled,
making it an objective tool for data analysis. Outlier detection in a database is to enhance the performance
of data mining algorithms (Last and Kendal, 1995).
Before we address the issue of identifying these outliers, we must emphasize that not all are wrong
numbers. They may justifiably be part of the group and may lead to better understanding of the
phenomena being studied. When an outlier is detected, the analyst is faced with number of questions
(David,1978) :
� Is the measurement process out of control?
� Is the model wrong?
� Is some transformation required?
� Is there an identifiable subset of observations that is important in its different behavior?
OUTLIER MODEL
A mathematical definition of a stationary time series is that a time series is said to be stationary if there is
no systematic change in mean (no trend) if there is no systematic change in variance, and if strictly
periodic variations have been removed (Chatfield, 1991).
Now, Consider a stationary autoregressive moving average (ARMA) process of order ( p
, q)
186

t
Ozean Journal of Applied Sciences 3(1), 2010
�( B ) z � �(
B)
e
(1)
�(
B)
� 1�
� B � ... ��
B
1
q
� ( B)
� 1�
� B � ... ��
B ,
1
t
p
q
p
,
B
e t is a sequence of independent and identically distributed random variables with mean zero and
variance
2
� .
Model (2) can also be written as
t
t
k
187
z
t
� z
t�k
� ( B) z � e
(3)
2
where � ( B) � �(
B)
/ �(
B)
�1
��
B ��
B �...
.
1
When estimating the impact of an AO (4) and of an IO (5), respectively, in a hypothetical situation in
which all of time series parameters are known. Two types of outliers in time series introduced by Fox
(1972), are additive and innovational.
The additive outlier-AO model:
It is the type of outliers that affects a single observation and occurs as a result of a mistake made by
person in observation or record. This model, defined as “total outlier” in the literature, is shown as
follows:
y z � �x
t
t
t
2
� (4)
where y t is the observed value, � is the magnitude of outlier and
Innovational outlier-IO model:
x t
�1 t � T �
� �
�
�0
Otherwise�
It is the type of outliers that affects the subsequent observations starting from its position, in other words
that occurs as a result of natural randomness. The model, defined as “randomness outlier” in the literature,
is shown as follows:
� ( B)
yt ( et
� �xt
)
�(
B)
� (5)
Thus the AO case may be called a gross error model, since only the level of the T’th observation is
affected. On the other hand, an IO represents an extraordinary shock at time point T influencing
z T , zT
�1,...
through the dynamic system described by �( B) � �(
B)
/ �(
B)
(Chang, Tiao and Chen,
1988).
The occurence of AO indicates that action is required, possibly to adjust the measuring instrument or at
least to print an error massage on the database. However, if IO occurs no adjustment of the measurement
operation is required (Muirhead, 1986).
The existence of AO can seriously bias the estimates of the ARMA coefficients and variance, whereas IO
in general has much smaller effect (Chang Tiao and Chen, 1983).
Most often only AO’s are troublesome, whereas IO’s often have very little effect. This is understandable,
since an IO can be seen as an extreme disturbance, but an AO is always an observation separate from the
rest of data. For example think of the residual of an AR(1) model. An IO only affects residual, at the date
(2)

Ozean Journal of Applied Sciences 3(1), 2010
of outlier. An AO, on the other hand, affects the next residual as well, thus inflating two consecutive
residuals. This effect has several consequences for any further analysis of the residuals.
According to some researchers, if there exist an AO type outlier in an observation set, its effects must be
removed, however, in case an IO type outlier exists, it is accepted as it occured as a result of natural
randomness and its effects must not be removed. Since natural randomness is a situation, which already
exists in all phases of life, the notion of “these effects must be tolerated” is the valid thought.
Distinguishing an AO From an IO:
There is usually little information available in practice about what type the possible outlier might be
possible might be AO or IO is more appropriate for a given situation. When a test of an inappropriate type
is used, the detecting power the test could be substantially reduced. Furthermore, even if it is known that
an outlier has occurred at a particular point, the possibly adverse effect of the outlier may not be easy to
remove unless its nature is properly identified (Chang Tiao and Chen, 1988).
Outliers’ Characteristics:
In order to show the characteristics of and AO model, the data are “uncontrolled” concentration readings
of a chemical process recorded at every two-hour interval, titled “Box-Jenkins A Series” was used. (See,
Box-Jenkins, 1976).
In case of AO, only the level of the T’th observation is affected. (See the table-1, and the graph-1)
Moreover IO represents an extraordinary shock at time point T influcing z T , zT
�1,...
through the
dynamic system described by �( B) � �(
B)
/ �(
B)
. (See the table-2, and chart-2).
AO Description:
Table 1, Box-Jenkins A Series observations from 38 to 48.
1 2 3 4 5 AO 5 4 3 2 1
38 39 40 41 42 43 44 45 46 47 48
17.7 17.4 17.8 17.6 17.5 16.5 17.8 17.3 17.3 17.1 17.4
When Table-1 observations which is shown above have been investigated, it can be seen that finding
additive outlier don’t change the tendency of observations. Observations taken before T (occurence of
outlier) time same tendency as observations taken after. Thus, we conclude that this outlier is AO,
because only level of the T’th observation is affected. This conclusion can be seen from the chart-1which
is shown below.
Chart 1, Box-Jenkins A series observations from 38 to 48
188

IO Description:
Ozean Journal of Applied Sciences 3(1), 2010
Table 2, Box-Jenkins A series observations from 59 to 69.
1 2 3 4 5 IO 5 4 3 2 1
59 60 61 62 63 64 65 66 67 68 69
17.2 16.6 17.1 16.9 16.6 18.0 17.2 17.3 17.0 16.9 17.3
When Table-2 observations which is shown above have been investigated, it can be seen that finding
innovational outlier changes the tendency of observations. Observations taken from before T time
different from after, it can be seen that observations value has been changed. Thus, we conclude that this
outlier is IO, because at time point T influcing consecutive observations. z T , zT
�1,...
. This conclusion
can be seen from the chart-2 which is shown below.
Outlier Detection Algorithm:
Chart 2, Box-Jenkins A Series observation from 59 to 69
� Read observations from defined file.
� Read estimated ARMA parameters, using statistical package programs.
� Read calculated � j ’s from the estimated model.
� Read ê t ’s to use
2
ˆ � a and find outliers.
189

Ozean Journal of Applied Sciences 3(1), 2010
� Read critical values C which can be 3.00, 3.50 and 4.00.
� Do
1.
2
Calculate the ˆ � a from the ê t ’s.
2. Increase the index of the current value by one
3. Calculate � 1.
T , � 2.
T .
4. If � C then display T, IO.
1.
T �
2.
T �
5. If � C then display T, AO.
otherwise no outlier&stop
6. Calculate effect of IO or AO and update on new value in file.
7. Reallocate new ê T ’s
� End Do.
� Go Reading new observations from updated file and perform algoritm again.
SIMULATION PROCESS
A computer simulation is an attempt to model a real-life or hypothetical situation on a computer so that it
can be studied to see how the system works. By changing variables, predictions may be made about the
behaviour of the system Computer simulation has become a useful part of modelling many natural
systems in physics, chemistry and biology, and human systems in economics and social sciences as well
as in engineering to gain insight into the operation of those systems. Traditionally, the formal modeling of
systems has been via a mathematical model, which attempts to find analytical solutions enabling the
prediction of the behaviour of the system from a set of parameters and initial conditions. Computer
simulation is often used as an adjunct to, or substitution for, modeling systems for which simple closed
form analytic solutions are not possible. There are many different types of computer simulation, the
common feature they all share is the attempt to generate a sample of representative scenarios for a model
in which a complete enumeration of all possible states would be prohibitive or impossible
(http://en.wikipedia.org/wiki/Simulation# Computer_simulation)
In this study I conducted a simulation study to obtain some inferences about the different AR(1)
coefficients, series size, outlier types, and sensitivity coefficients. Table 1 shows the probability of
finding Additive Outlier-AO, and Innovational Outlier-IO, based on 10000 realizations of the AR(1) with
� � 0.
5,
0.
7,
0.
9 and the critical values are C � 3. 00,
C � 3.
50,
C � 4.
00 , For sizes
n � 50,
100,
200,
500 . The fact that critical value C � 3.
00 is close to the 1%, C � 3.
50 to the
5% and C � 4.
00 to the 10% significance level. The location of outliers is set in the middle of the
observational period, spesifically T � 26 when n � 50 , T � 51 when n �100
, T � 101 when
n � 200 , and T � 251 when n � 500 (Kaya, 2004).
190

Ozean Journal of Applied Sciences 3(1), 2010
Table 3. Finding AO and IO probability
based on 10000 replications of an AR(1) process.
� n
C=3.0
AO IO
C=3.5
AO IO
C=4.0
AO IO
50 0.34 0.38 0.33 0.37 0.322 0.35
1 2 4 0 4
100 0.44 0.48 0.42 0.46 0.431 0.46
2 2 8 6 4
0.5 200 0.57 0.61 0.55 0.59 0.540 0.57
7 0 9 6 9
500 0.62 0.66 0.61 0.65 0.609 0.64
2 2 4 0 1
50 0.52 0.56 0.52 0.55 0.512 0.55
9 2 5 5 1
100 0.62 0.66 0.61 0.65 0.607 0.64
2 0 9 3 3
0.7 200 0.65 0.68 0.63 0.66 0.622 0.65
1 8 9 8 6
500 0.75 0.78 0.74 0.77 0.723 0.75
0 7 3 6 7
50 0.64 0.67 0.63 0.67 0.621 0.66
2 6 8 2 1
100 0.69 0.72 0.68 0.72 0.681 0.71
1 8 8 1 5
0.9 200 0.79 0.83 0.79 0.83 0.782 0.81
5 6 1 0 5
500 0.90 0.94 0.89 0.93 0.875 0.90
1 5 2 2 7
STATISTICAL ANALYSIS
Statistics is an increasingly important subject which is useful in many types of scientific investigation. It
has become the science of collecting, analysing and interpreting data in the best possible way. Statistics is
particularly useful in situations where there is experimental uncertainty and is often defined as the science
of making decisions in the face of uncertainty. We begin with some scientific examples in which
experimental uncertainty is present. Most formal statistical models used in different area have been
linear, based on the principles of statistical inference, analysis of variance, regression and other models.
Formal models may be a variety of different forms described by equation models and implemented using
some special programming language which are Pascal, C, C+, C++, and also other special languages,
such as LISP and PROLOG (Kaya and İkiz,2001).
Models can be used for member of different purpose in research and management of production systems.
Some uses are (Bywater and Cacho, 1994);
� Classification of existing data
� Identification of type of data in research
� Taking results of experiments
� Constructing and testing hypothesis
� Estimating parameters
� Concluding experimental results
� Designing experimental production systems
� Determining best production systems
191

Ozean Journal of Applied Sciences 3(1), 2010
MODELLING
By using the simulation results a variance analysis study was made for putting forward how the factors
like Model AR(1) Coefficients (M), (0.5, 0.7, 0.9), Series Size (W), (50, 100, 200, 500), Critical value
sensitiveness (C), (C=3.00, C=3.50, C=4.00), Outlier Type (T) (AO, IO) are effective statistically in
detecting outliers. The factors to solve such problems are;
Model AR(1) coefficient (M)
Series sizes (W)
Critical value sensitiveness (C)
Outlier type (T)
The factor levels were selected properly and the model equation for multi-factored 3 42
2
experiment
layout was designed as given below:
Y � � M �W
� C �T
� �
ijkl
� (6)
i
i �1, 2,
3;
j �1,
2,
3,
4;
k �1,
2,
3;
l �1,
2
j
k
l
ijkl
In this model Y ijkl represents the probability of outlier detection, � represents general average, i M
represents AR(1) coefficients, j W represents the series sizes, C k represents critical value sensitiveness,
T l represents outlier type, and � ijkl represents the error term.
Table 4. Analysis of variance for Table 1.
SOURCE DF Sum
of
Square
s
AR(1) 2 0.8443
1
Series Size 3 0.6340
9
Critical 2 0.0054
Value
9
Outlier 1 0.0231
Type
1
Error 63 0.0324
6
Total 71 1.5394
8
192
Mean
Square
0.4221
6
0.2113
6
0.0027
5
0.0231
1
0.0005
2
F P
819. 0.000
86
410. 0.000
20
5.33 0.007
44.8
5
CONCLUDING REMARKS
0.000
From the results of this experiment, some main important conclusions were emerged:
� The data used in this study were obtained from the simulation experiments performed.
Simulation results were reorganized and remodeled under a suitable experimental design. The
type of factorial design was ( 3 4 2)
2
that is 2 factors each at 3 levels and two factors at 1 level
each. Different AR(1) (0.5, 0.7, 0.9), sensitivity coefficients (3.00, 3.50, 4.00), series size n (50,
100, 200, 500), outlier types (AO-IO) and were statistically significant.
� AR(1) coefficients are the values that show the strength of the dependence among the
observations. Therefore, it is easier to make outlier analysis on dependent observation sets. In
other words, outlier detection is easier in series having more dependency among the
observations.

Ozean Journal of Applied Sciences 3(1), 2010
� AO, known as the outlier value that occurs because of the user is detected more difficultly than
IO, the outlier value that occurs as a result of natural randomness. The presence of AO in data
causes loss of autocorrelation in outlier position. For that reason, finding IO is easier than
finding AO.
� For the sample lengths of n =50, 100, 200 and 500, the probability of detecting error is
considered important. So data analysis being easier for large data is detained as a result.
� Since the critical value sensitiveness is found as important and it is a factor showing the
tolerance bounderies of the outlier analysis in outlier detection phase, it must be emphasized
carefully.
� The proposed procedure is demonstrated to be useful for estimating time series parameters when
there is the possibility of outliers. It can be applied to all invertible models. Moreover, it is
flexible and easy to interpret, and it can be implemented with very few modifications to existing
software packages capable of dealing with ARMA and transfer function models. In practice, we
suggest that this procedure can be used in conjuction with other diagnostic tools for time series
analysis to produce even better results (Chang, Tiao and Chen, 1988).
Suggestions and Inferences:
1. Before making the statistical tests on an observation set, outlier analysis must be done. By this way,
test statistics can produce more healthy results.
2. There are two types of outliers, possible to encounter. The effect of first type of outliers, which are
caused by people, equipments and machines, must be eliminated. Since the second type of outliers
occurs because of the natural randomness, the elimination of the effects of them is optional.
3. The model, which is appropriate for the observation values must be used. If an inappropriate model is
applied, the values which are not outliers may be considered as outliers.
4. Outlier detection processes in time series are mutually attached with the autocorrelation powers of
the observations. For the observation sets, whose autocorrelation is low, the effect “hiding”, in which
the outlier is not considered as an outlier, and the effect “dragging”, in which a non-outlier
observation is considered as an outlier can be obtained.
5. Since outlier detection processes are also used in quality control processes, which are concerned with
finding error, their importance is increasing everyday.
6. While detecting an outlier, the following components of the process are determined as important:
a. Generosity of the error,
b. Series size,
c. Strong autocorrelation,
d. Criterion value sensitiveness.
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