IJAS_1st Issue-2014_2

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Volume 5, Issue 1, 2014 ISSN : 0976-450X
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International Journal on Agricultural SciencesISSN NO. 0976-450X
Volume - V Issue : 1st 2014
Editor in Chief :
Padma Bhushan Dr. S.Z. Qasim
206 Raj Tower-1, Alaknanda Comm. Centre, New Delhi - 110 019
Editor:
Prof. Javed Ahmad, General Secretar, NESA
Dean, Faculty of Science, Jamia Hamdard, New Delhi-110062
Members of the Board
Dr. A. Arunachalam Dr. Sonam Tashi
FNIE, FTE, FNRS, FISCA, FIIRM, A-NAAS College of Natural Resources
Principal Scientific Officer, Royal University of Bhutan, Lobesa, Punakha
Office of the Secretary, DARE & Director-General, ICAR
Krishi Bhavan, New Delhi 110001 Prof. M.O. Aremu
Department of Chemical Sciences,
Dr. Elsayed Elsayed Hafez Federal University Wukari, PMB 1020, Taraba State, Nigeria
City of Scientific Research and Technology Applications,
Arid Lands Cultivation Research Institute (ALCRI), Dr. D. Prantik Chakraborty
PlantProtection and Biomolecular diagnosis Department, AE-248 Sector-I, Salt Lake, Kolkata
New Borg El Arab City, 21934, Alexandria, Egypt.
Prof Smita Mazumder
Dr. Muhammad Asif Dept of Economics, Surendranaah College for Women
Agricultural, Food and Nutritional Science M G Road, Kolkata-700009 WB India
4-10 Agriculture/Forestry Centre, Univ. of Alberta
Edmonton, AB T6G 2P5 Dr. Onosemuode Christopher
Dept. of Environmental Science, College of Science
Dr. Gunjan Mukherjee, Ph.D. Federal University of Petroleum Resources
Scientist, Biotechnology, Agharkar Research Institute (ARI) Effurun-Delta State, Nigeria
(Autonomous Research institute of
Department of Science & Technology, Dr. Akbar Masood
Government of India), Pune, MS HOD, Biochemistry, University of Kashmir, Sri Nagar, J&K
Dr. Ratnabali Sengupta Dr. Valentin Bartra Abensur
Department of Zoology Profesor de Legislación Ambiental
West Bengal State University , Barasat, WB India Univesidad Nacional Mayor de San Marcos, Lima, Peru
Dr. Sudip Datta Banik Prof. A.K. Gupta
Somatology Laboratory of Department of Biotechnology, Maharishi Markandeshwar University,
Human Ecology in Cinvestav-IPN, Merida, Mexico. Mullana, Ambala-133207 (Haryana) India
Dr. R. S. Fougat Dr. Saikat Kumar Basu
Professor & Head & Unit Officer Department of Biological Sciences, University of Lethbridge
Department of Ag. Biotechnology Lethbridge AB Canada T1K 3M4
Anand Agricultural University, ANAND, Gujarat
Dr. R.A. Balikai
Dr. William Cetzal-Ix Professor & Head, University of Agricultural Sciences, Dharwad
Research fellow, Herbarium CICY, College of Agri. & Regional Agril. Research Station, Bijapur
Centro de Investigación Científica de Yucatán, México.
Dr. K. Sivakumar
Dr. Peiman Zandi Department of Soil Science and Agricultural Chemistry,
Department of Agronomy Faculty of Agriculture, Annamalai University,
Takestan Branch, Islamic Azad University, Iran Annamalainagar-608002
Dr. Xianping Li, Ph.D. Ms. Pallav Mukhopadhyay,
Director of Potato Research Center, Industrial Crops Research Institute, Assistant Professor, Department of Journalism & Mass Communication,
Yunnan Academy of Agricultural Sciences Kunming, Yunnan Province, China West Bengal State University, West Bengal, India
Prof. Lucindo José Quintans Júnior Dr. Onosemuode Christopher
The University of Iowa, Roy J. and Lucille A. Carver Department of Environmental Science, College of Science, Federal University of
College of Medicine, Neurobiology of Pain Laboratory Petroleum Resources, Effurun-Delta State, Nigeria
375 Newton Road, Iowa City, IA, US
Mr. I. Gerarh Umaru, PhD
Dr. Xiuhua Wu Department of Economics, Faculty of Social Sciences,
Inner Mongolia Academy of Forestry, 288, Xinjian East Street, Nasarawa State University, Keffi-Nigeria
Saihan District Hohhot, Inner Mongolia, P.R. China P.O.Box 8414, Wuse-Abuja, Nigeria
ISSN NO. 0976-450X
ISSN NO. 0976-450X
International Journal on Agricultural Sciences
Volume - V Issue : 1st 2014
CONTENTS
Editor in Chief : 1. COMPARISON OF DIFFERENT METHODS 5-13
Dr. S.Z. Qasim TO THE DEVELOPMENT OF PEDOTRANSFER
206, Raj Tower -I,
FUNCTIONS FOR WATER-RETENTION CURVES
Alaknanda Comm. Centre
K. Balathandayutham and M. Krishnaveni
New Delhi -110019
2. SEAWEED DIVERSITY AND LIFE FORM 15-26
OF MANDAPAM COAST, INDIA DURING
Editor: PREMONSOON SEASON
Prof. Javed Ahmad Arunjit Mayanglambam and Dinabandhu Sahoo
HoD, Botany, Jamia Hamdard
New Delhi - 110 062
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General Secretary, NESA
INDUCED OXIDATIVE STRESS AND ANTIOXIDANT
Co-Editor DEFENCE MECHANISM IN GLYCINE MAX (L.) MERR.
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SEED AND OIL YIELD OF SUNFLOWER
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ISSN NO. 0976-450X
International Journal on Agricultural Sciences
Volume - V Issue : 1st 2014
CONTENTS
9. PESTICIDAL ACTIVITES OF COMMERCIAL 73-80
BLEACHING POWDER IN PISCICULTURE
Mamata Kumari, Rashmi Prabha and Navin Kumar
10. SEAWEEDS DIVERSITY OF GULF OF KUTCH, 81-89
PORT OKHA, GUJARAT, INDIA DURING
POSTMONSOON PERIOD
Priyanka Verma and Dinabandhu Sahoo
11. GENETIC VARIABILITY IN MORPHOLOGICAL 91-98
AND FODDER QUALITY TRAITS OF
ANOGEISSUS LATIFOLIA WALL.
IN HIMACHAL PRADESH
Hari Paul Sankhyan and Naresh Bahadur Singh
12. SEASONAL AND ANNUAL VARIABILITY 99-102
OF MAJOR CLIMATIC PARAMETERS
IN EASTERN UTTAR PRADESH
A.N. Mishra, Arvind Kumar, A.K. Singh
and Padmakar Tripathi
13. FARM DIVERSIFICATION PRACTICES - 103-107
A CASE STUDY OF MONPA TRIBES
IN ARUNACHAL PRADESH, INDIA
Kuldip Gosai, A. Arunachalam,
Prasanna Kumar G.V. and Chandan Owary
14. STUDYING THE ADOPTION BEHAVIOR AN 109-116
CONSTRAINTS FACED BY BT COTTON
GROWERS IN INDIA
M. Venkatachalam, B.S. Hansra,
M.J. Chandre Gowda and A. Arunachalam
15. EFFECT OF DICHLOROVAS ON CARBOHYDRATE 117-120
RESERVE CONTENT IN HAEMOLYMPH AND
FATBODY OF THE SILKWORM, BOMBYX MORI
G. Md. Ameen, N.S. Hallikhed
and Md. Bashamohideen
International Journal on Agricultural Sciences Vol. V (Issue 1) pp. 5-13, 2014 ISSN NO. 0976-450X
COMPARISON OF DIFFERENT METHODS TO THE
DEVELOPMENT OF PEDOTRANSFER FUNCTIONS
FOR WATER-RETENTION CURVES
K. Balathandayutham1 and M. Krishnaveni2
1Research Scholar, Department of Soil And Water Conservation Engineering,
Agricultural Engineering College and Research Institute, Tamilnadu Agricultural University,
2Assistant Professors, Centre for Water Resources, Department of Civil Engineering,
College of Engineering Guindy, Anna University Chennai
Research Paper
Received on: 03.02.2013 Revised on: 07.02.2014 Accepted on: 18.03.2014
ABSTRACT
Modelling water flow and solute transport in vadose zone requires knowledge of soil
hydraulic properties, which are water retention and hydraulic conductivity curves. As an
alternative to direct measurement, indirect determination of these functions from basic soil
properties using Pedotransfer functions (PTFs) has attracted the attention of researchers in a
variety of fields. In this study, PTFs for point and parametric (Van Genuchten parameters)
estimation of soil hydraulic parameters from basic soil properties such as particle-size
distribution, bulk density, and three different pore sizes were developed and validated
using artificial neural network (ANN) and multiple-linear regression methods and the
predictive capabilities of the two methods was compared using some evaluation criteria.
These PTFs indirect method datasets were compared with moisture retention lab
measurement data set. All the two PTFs showed the best performance when applied to soils
of the watershed from where the PTFs were developed. A significant result of this study is
that robust PTFs may be developed from a limited number of soil samples provided there is
sufficient variability in soil properties.
No. of Pages: 9 No. of Tables: 2 No. of Figs.: 4 References: 18
Keywords: Pedo-Transfer Functions, soil hydraulic parameters, ANN method, soil water
retention.
INTRODUCTION relates the conductivity (K) to the soil water
The hydraulic properties include the soil water pressure head (h) or the water content. When
retention curve (SWRC), which presents the the temporal and spatial variability of the region
relationship between the volumetric water is considered, the required measurements of
content (θ) and the soil water pressure head (h), unsaturated soil hydraulic properties are
and the hydraulic conductivity curve, which tremendous, time-consuming, and very
Corresponding author: [email protected]
International Journal on Agricultural Sciences Vol. V (Issue 1) pp. 5-13, 2014 ISSN NO. 0976-450X
expensive. Therefore, it is necessary to develop Vereecken et al., 1989, 1990; Minasny et al.,
a set of so called pedo-transfer functions (PTFs) 1999;). The third method is widely used to
to estimate the unsaturated soil hydraulic directly predict hydraulic model parameters for
properties from more easily measured or basic describing soil water retention and hydraulic
soil properties in the attribute database of a conductivity properties. PTFs are usually
digital soil survey map, in which soil hydraulic expressed as linear or nonlinear regression
properties are not always available. equations or, more recently, distributed as
computer codes resulting from artificial
Many attempts have been made to determine neutron network analysis (Schaap and Leij,
the water retention curve indirectly from easily 1998; Minasny et al., 1999; Schaap et al., 2001;
measured properties or properties available Nemes et al., 2003).
from routine soil survey data. Bouma (1989),
introduced the term Pedo Transfer Function If van Genuchten models (van Genuchten,
(PTF), which he described as translating data we 1980) for soil water retention and soil hydraulic
have into what we need, i.e., predictive conductivity, based on the statistical pore-size
functions of certain soil properties from other distribution model of Mualem (1976), are
easily, routinely, or cheaply measured applied in modelling, the parameters
properties. In developing PTFs, soil texture representing the soil hydraulic conductivity
(including sand, silt and clay contents), bulk curve can be the same or directly derived from
density and organic matter content are the most the soil water retention parameters, except for
used predictors in the literatures, and
the saturated soil hydraulic conductivity (Ks).
additional factors (soil particle size and
This eliminates the need for the direct
distribution indices) are rarely applied because
measurement or indirect estimation of the
of lack of availability in the soil databases
hydraulic conductivity curve if Ks are known.
(Wösten et al., 2001). Furthermore, as
Hence, the van Genuchten models of soil water
summarized by Nemes et al. (2003), most of
retention and unsaturated soil hydraulic
PTFs are developed to estimate the soil water
conductivity are considered in this study. The
retention (points at a series of matric potentials
objectives of this study are (1) the investigation
or parameters of analytical water retention
of soil hydraulic parameters θs, θr, α, n and Ks
equations) and saturated hydraulic
obtained from three different methods, and (2)
conductivity. A small number of PTFs were
the evaluation of these methods in Moisture
proposed for the estimation of unsaturated
retention lab measurement of soil water
hydraulic conductivity, e.g. Wagner et al.
retention curve.
(2001). Methods for predicting soil hydraulic
characteristic using PTFs are grouped by Tietje
MATERIALS AND METHODS
and Tapkenhinrichs (1993) into three types: (i)
Experiment field site
estimation of the water contents at certain The surface water storage bodies termed as
matric potentials (Husz, 1967; Renger, 1971; tanks are commonly adopted in the Tamilnadu
Gupta and Larson, 1979), (ii) estimation of soil state located in the south eastern part of India.
water retention relation with a Sindapalli Uppodai sub basin, situated in
physical–conceptual model approach (Arya and Tamilnadu, consists of many tanks forming
Paris, 1981; Haverkamp and Parlange, 1986), cascade type and some are isolated. The
and (iii) estimation of parameters of algebraic maximum amount of rainfall is collected and
retention functions for describing θ(h) and K(θ) stored in these 15 tanks and utilized for the
or K(h) (Wösten and van Genuchten, 1988; irrigation and drinking water demands through
IJAS 2014 • 6
International Journal on Agricultural Sciences Vol. V (Issue 1) pp. 5-13, 2014 ISSN NO. 0976-450X
directly as well as by recharging ground water Darcy's law and the principle of mass
aquifers. In the sub basin, tank irrigation is conservation (Richards, 1931). The pressure
followed in the vicinity of tanks and well head form of the equation for the one-
irrigation is practiced in other areas. dimensional vertical flow is
Sindapalli Uppodai sub basin of Vaippar river (1)
basin, receives drainage from its own
catchment. It originates from the plain terrain Where C (h) is the differential water capacity
near by Duraiswamypuram village of Sivakasi (sθ/sh) (1/L), h is the volumetric water content
taluk, runs for a distance of 26 km and it joins in (L3/ L3), h is the soil water pressure head (matrix)
Arjunanadhi at the downstream of Allampatti (L), t is the time (T), z is the vertical coordinate
Village. The location of the basin is at latitude of (L), K is the isotropic hydraulic conductivity (L
9° 25'00”N to 9° 30'00” N and longitude 77° /T), and S is the sink term which represents the
45'00”E to 77° 55'00”E situated in taluks of root water extraction (L3 /L3 T1). Retention
Sivakasi and Sattur in Virudhunagar District of function is the most widely used because it is
Tamilnadu. continuous over the entire range of pressure
head which leads to stable numerical solutions
Normally subtropical climate prevails over for Eq. (2). Van Genuchten also derived the K (h)
district without any sharp variation. The relationship using the capillary-based
temperature rise slowly to maximum in summer unsaturated hydraulic conductivity prediction
months up to may and after which it drops model developed by Mualem (1976). The soil
slowly. The mean maximum temperature is hydraulic functions are
33.95 ºC to the mean minimum temperature is
(2)
23.78ºC. The seventy years average annual
rainfall is 799.8 mm from three distinct seasons
that is South West monsoon, North East monsoon
(3)
and transitional period. There are seven rain
gauge stations spread over the district and Where θ and θ are the saturated and the
s r
maintained by different organisation. In this, residual water content, respectively (L3/ L3), α is
Sindapalli Uppodai is influenced by 3 rain gauge approximately the inverse of bubbling pressure
stations namely Vembakottai, Sathur and head (1/L), n is the pore size distribution index
Sivakasi. The average annual rainfall values are (–), m is 1-1/n (–), K is the saturated hydraulic
s
828.1, 665.3and 694.8 in mm respectively. Paddy conductivity (L /T), and l is a parameter (–)
is the main crop in both Kharif and Rabi seasons, usually chosen to be 0.5. The solution in
whereas vegetables are grown in few patches in equation (1) requires the definition of the
summer season. On an average, three irrigations appropriate constitutive relationships of water
are provided in each cropping season. Data on retention curve and unsaturated hydraulic
various aspects of the watershed viz. topography, conductivity curve at three depths obtained by
substituting pedo transfer functions using soil
geology, soils, crops, groundwater and
hydraulic parameters and different metric
meteorology are obtained from PWD,
section in equation (2) and equation (3)
Virudhunagar.
respectively. The soil hydraulic parameters θs,
θr, α, n and Ks are estimated from three methods
Methodology
The governing equation for water movement in are used (i) Moisture retention lab measurement
unsaturated soil derived by is a combination of (ii) Prediction via PTF using field texture
IJAS 2014 • 7
International Journal on Agricultural Sciences Vol. V (Issue 1) pp. 5-13, 2014 ISSN NO. 0976-450X
measurement (iii) Prediction through Artificial residual water content (θ), α (bubbling pressure
r
neural networks approach using field head), and n (pore size distribution index) were
measurement. Parameters are estimated using substituted by linear equations relating these
various methods. parameters with soil properties in a physically
meaningful way: saturated water content (θ) = f
s
These methods have been termed as Methods A,
(porosity, clay), residual water content (θ) = f
B and C respectively, and are executed for each r
(clay, organic C), bubbling pressure head (α) = f
soil data set. In class PTFs, soil horizons are
(d), and n = f (1/σ). That is, the particle-size
grouped into taxonomic classes with associated g g
distribution parameters d and σ, were assumed
average hydraulic properties, whereas g g
to be related to the pore-size distribution
continuous PTFs are obtained from regression
parameters α and n.
equations that relate hydraulic parameters to
basic soil properties, e.g., particle size, bulk
The parameters of retention function were
density, etc.
evaluated using pedo transfer functions. The
Method A - Prediction through Artificial neural pedo transfer functions proposed by Vereecken
networks approach using field measurement. et al. (1989) are based on a four-parameter
The major disadvantage of the regression retention function of the above Van Genuchten
equation is that a priori relations between textural model. The following regression equations were
data and hydraulic characteristics need to be proposed by Vereecken et al. (1989).
described by well-defined models to estimate soil
hydraulic parameters. In contrast to the (4)
regression models, a neural network is an
adaptable model that can learn the relations
(5)
between the input and output data. Schaap and
Leij (1998) used a bootstrap- neural network
(6)
analysis to develop a hierarchical approach for
predicting the unknown soil hydraulic
parameters. In this study, ROSETTA, developed (7)
by United States Salinity Laboratory (USSL), a
neural networks model was executed to obtain (8)
soil hydraulic parameter. ROSETTA allows for
obtaining continuous and class PTFs. Where ρ is the bulk density (g /cm3), C the
b
Continuous PTFs are obtained using average of carbon content (%), Cl the clay content (%), and
sand, silt and clay percentages along with bulk Sa the sand content (%), Si is the silt content
density. PTFs were also determined using (%). Vereecken et al. (1989) assumed that
artificial neural networks. Average soil hydraulic parameter m was equal to 1, unlike the original
parameters were also available in the database for Van Genuchten model with m=1–1/n.
12 different USDA soil textural classes. Soil
Method C- Moisture retention lab
texture class, as discussed in Method B, is entered
measurement
in to the software to obtain PTFs.
Soil samples were collected at different depth 0-
Method B- Prediction via PTF Using Field 30 cm, 30-60 cm, and 60-90 cm by using auger
Texture Measurement hole at different locations. Soil samples were
Therefore, the parameters of a Van Genuchten- dried by air dry method and the soil samples
type function, saturated water content (θ), were sieved manually by 2 mm sieve. The
s
IJAS 2014 • 8