Revista Científica UDO Agrícola Volumen 7.
Número 1. Año 2007. Páginas: 35-40
Variability studies in chickpea (Cicer arietunum L.) varieties grown in Isparta, Turkey
Estudios de variabilidad en variedades de garbanzo (Cicer arietunum L.)
cultivadas en Isparta, Turquía
Hasan VURAL
1 and
Abdullah KARASU2
1Faculty of Agriculture, University of Uludag,
Bursa, Turkey. 2Mustafakemalpaşa
Vocational School, University of Uludag, Bursa,
Turkey. E-mails: hvural@uludag.edu.tr and akarasu@uludag.edu.tr Corresponding
author
|
Received: 04/17/2007 |
First reviewing ending: 04/06/2007 |
First review received: 06/20/2007 |
|
Second reviewing ending: 08/06/2007 |
Second review received: 09/11/2007 |
Accepted: 09/15/2007 |
ABSTRACT
Chickpea is an
important field crop for less quality fields and enduring to drought. In Isparta ecology, province of Turkey, as a sowing duty
covers large area. This study aimed to investigate the variability of chickpea
varieties grown under the ecological conditions of Isparta.
Eleven varieties grown in Turkey were used in this two year
long study (between the years 1996 and 1997) which has been sowed in a
randomize block experimental design with four replications. Data were analyzed
by multivariate statistical methods. According to the two-year results, the
differences among varieties were found to be important in all components
observed. Differences between years were proved to be significant in all
components, except the number of pod per plant and the height of the first pod
from soil. In both years, anthracnose (Ascochyta rabiei. [Pass.] Lab.) was not found in all
varieties in natural conditions. It was found one principal component (PC1)
by factorial analyses. But, eleven examined varieties were separated in two
main groups and three subclusters by cluster
analyses.
Key words: Chickpea varieties, factor
analysis, cluster analysis
RESUMEN
El garbanzo es un cultivo de importancia para suelos
de baja fertilidad y es resistente a la sequía. En Isparta, provincia de
Turquía cubre una gran área de siembra. Esta investigación se realizó para
determinar la variabilidad de las variedades de garbanzo cultivadas bajo las
condiciones ecológicas de Isparta, Turkey. Se
emplearon once variedades cultivadas en Turquía en este estudio de dos años
(entre 1996 y 1997) los cuales se sembraron en un diseño de bloques al azar con
cuatro repeticiones. Los datos se analizaron mediante métodos estadísticos
multivariados. De acuerdo a los resultados de dos años, las diferencias entre
variedades fueron marcadas para todos los caracteres observados. Las
diferencias entre años fueron significativas en todos los caracteres, excepto
para el número de vainas por planta y la altura de la primera vaina. En ambos
años, no se encontró antracnosis (Ascochyta rabiei. [Pass.] Lab.) atacando las variedades bajo condiciones naturales. Se
determinó un componente principal (PC1) utilizando el análisis de
factores. Pero, las once variedades evaluadas se separaron en dos grupos
principales y tres subgrupos mediante el análisis de conglomerados.
Palabras
clave: Variedades de garbanzo,
análisis de factores, análisis de conglomerados
INTRODUCTION
In today’s world,
paralleling to population growth, nutrition problem is growing increasingly.
Especially production of high-range protein foods has been important for the
solving nutrition problem. For this reason, it is necessary growing the most
productive and high-quality varieties to the regions.
Growing of chickpea on the less quality fields
and enduring to drought, makes important to this products. Chickpea, which has
large market and entered to sowing duty with wheat pillar, is a demanded plant for
dry and salty areas (Şehirali, 1988). When
processed in the food industry, consumed as a roasted chickpea, if we look at
to roasted chickpea export, it is a necessary product (Anonymous, 1995).
In the Isparta, Turkey ecology, when chickpea duty in the drought
fields, cereal-chickpea, cereal-common vetch, cereal-lentil, cereal-fallow land
implementing as a sowing duty, covers an important area (Anonymous 1996). Some
researchers had carried out studies on the agronomical characteristics of some
Chickpea varieties (Doğangüzel, 1998; Karasu 1993; Engin, 1989; Samal and Jagadey 1989 and Khargade et al.
1985).
Factor analysis with principal component and
cluster analysis were used to determine the suitability of some features to characterize
the variation of the observations and to determine natural groups from the
varieties studied (Adam and Hwang 1999). In the first phase, factor analysis
has been used for identification of the number of principal component analysis
(PCA). In the second phase, cluster method has been used to determine
disparities and similarities. PCA is concerned with explaining the
variance-covariance structure through a few linear combinations of the original
variables. Its general objectives are (1) data reduction and (2)
interpretation. PCA method provides to form free new sets which are different
from the beginning set. Reflecting of the variables at ‘R’ is one of advantages
of the method. The usual objective of the analysis is to see if the first few
components account for most of the variation in the original data (Adam and
Hwang, 1999).
In this research,
multivariate statistical methods were used to obtain more results than those
from variance analysis. Rudimentary, exploratory procedures are often quite helpful
in understanding the complex nature of multivariate relationships. Analysis of
principal components is more of a means to an end rather than an end in them
because they frequently serve as intermediate steps in much larger
investigations. For example, principal components may be inputs to a multiple
regression or cluster analysis. Moreover, principal components are one
‘factoring’ of the covariance matrix for the factor analysis model (Johnson and
Wicherin, 1992).
Cluster analysis
when searching the data for a structure of ‘natural’ groupings is an important
exploratory technique. Grouping can provide an informal means for assessing
dimensionality, identifying-outliers and suggesting interesting hypotheses
concerning relationships (Johnson and Wicherin,
1992). The term of cluster analysis encompasses a large number of techniques
developed to identify groups of observations with similar characteristics. It
is based on the minimizing of the variance in the group and maximizing of the
variance among groups (Johnson and Wicherin, 1992). The
distance between two variants in which data have been standardized, can be
stated as the monotonic transformation of the correlation between the two
variables. This research has been done to investigate the variability of
chickpea varieties grown under the ecological conditions of Isparta
province in Turkey.
MATERIALS AND METHODS
This research has
been carried out in the 1996-1997 years, so as to determining suitable chickpea
varieties for Isparta ecological conditions. In the
research, assured from different agricultural institutions; Eser
87 (V1), Akçin 91 (V2), Canıtez
87 (V3), Diyar 95 (V4), ILC-482 (V5), AK-7112 (V6),
ICC-5566 (V7), Red roasted chickpea (ecotype) (V8), 4N-495/2 (V9), Spanish
Chickpea (ecotype growing in the region) (V10) and Aziziye
(V11), varieties have been used as a material.
While Atabey test area, which this research had been carried out
in 1996, is axle-clay, silt, not salty, a little bit alkaline with much limely, average in phosphorus and medium level in organic
matter, Çünür Kampus area
which this research had been carried out in 1997 is silt, slight alkaline, not
salty, mostly limely, average phosphorus and poor in
organic material (Anonymous, 1997a). The average precipitation of the years
1996-1997 was realized different from average long years (Anonymous, 1997b).
Study have been set
up every twice year, as randomize block experimental design with four
replications. Every twice year, sowing have been done in the middle of March.
Data about productive elements have been proved from counting and measurements
from ten plants which are taken from every plot before harvest. Seed yield has
been found from whole test field (
Principal component
analysis (PCA) is concerned with explaining the variance-covariance structure
through a few linear combinations of the original variables. Its general
objectives are (1) data reduction, and (2) interpretation. PCA method provides
to form free new sets which are different from the beginning set. Reflecting of
the variables at ‘R’ is one of advantages of the method. The usual objective of
the analysis is to see if the first few components account for most of the
variation in the original data (Adam and Hwang, 1999).
Clustering (or
grouping) is distinct from the classification methods. Cluster analysis is a
more primitive technique in that no assumptions are made concerning the number
of groups on the group structure. Grouping is done on the basis of similarities
or distances (dissimilarities). The theory behind clustering is an expected
positive relationship between the variables Euclidean distance and the
similarity of the observations (Johnson and Wicherin,
1992). As a result, cluster analysis is
driven by the trade-off between minimizing the Euclidean distance of
observations within a cluster, and maximizing the Euclidean distance between
clusters. Clustering can be conducted directly on the data set or as a two-step
procedure in combination with other statistical methods like factor analysis
and principal component analysis. The number of clusters is not a priori given, to decide which number of clusters to choose. It’s bared on
the aim of cluster analysis, which is maximizing the difference between the
clusters. There are a large number of different available how to conduct
cluster analysis.
The eleven
evaluated traits were: 1. Length of plant (cm), 2. Height from ground of first
pod (cm), 3. Number of main brunch, 4. Number of side brunch, 5. Pod number per plant, 6. Seed number per
plant, 7. 1000 seed weight (g), 8. Seed yield per plant (g), 9. Harvest index
(%), 10. Seed yield (kg/da, 1 da =
So as to find the natural
grouped between varieties and examining the changes in the data, principal
component factor analysis and cluster analysis as multivariate statistical
analysis methods have been used (Johnson and Wicherin,
1992; Adam and Hwangs, 1999).
RESULTS AND DISCUSSION
According to the two years analysis results
obtained from chickpea varieties, it is proved that in the whole examined
features, varieties differences are important (Table 1). Except the high of
first pod from soil and the number of pod per plant, it has been proved that
there are differences between years on the other features (data are not shown).
Except for thousand seed weight and unit field seed yield, year and variety
interaction have been important as statistically (data are not shown).
|
Table 1. Average values of
quantitative characteristics of 11 chickpea (Cicer arietinum L.) varieties grown in two
localities of Isparta, Turkey in 1996 (Atabey area) and 1997 (Çünür Kampus area). |
|||||||||||
|
|
Quantitative
characteristics † |
||||||||||
|
Varieties |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
|
Eser87 |
24.38 |
16.93 |
2.99 |
2.92 |
9.70 |
10.52 |
311.6 |
3.07 |
0.52 |
115.3 |
20.98 |
|
Akçin91 |
26.68 |
17.35 |
2.60 |
3.11 |
7.43 |
7.93 |
419.8 |
3.12 |
0.49 |
123.2 |
21.80 |
|
Canıtez87 |
23.87 |
15.52 |
2.79 |
3.31 |
7.22 |
7.60 |
516.4 |
3.59 |
0.49 |
110.9 |
19.08 |
|
Diyar95 |
25.38 |
17.80 |
2.84 |
3.30 |
5.53 |
5.95 |
449.6 |
2.67 |
0.49 |
114.6 |
19.63 |
|
ILC482 |
22.12 |
15.59 |
3.15 |
3.37 |
10.00 |
10.63 |
320.0 |
3.06 |
0.51 |
107.8 |
20.57 |
|
Ak7112 |
23.88 |
15.47 |
2.78 |
2.83 |
6.81 |
7.35 |
368.4 |
2.76 |
0.47 |
111.5 |
19.41 |
|
ICC5566 |
26.63 |
19.14 |
2.60 |
2.52 |
8.96 |
9.58 |
320.0 |
2.87 |
0.44 |
110.9 |
20.69 |
|
Kır.Nohut |
22.05 |
14.80 |
2.70 |
3.44 |
6.93 |
7.25 |
522.6 |
3.56 |
0.51 |
111.3 |
19.36 |
|
4N-495/2 |
25.39 |
16.95 |
2.90 |
3.43 |
6.94 |
7.34 |
510.8 |
3.36 |
0.50 |
104.6 |
18.64 |
|
İspany.No |
26.19 |
17.54 |
2.85 |
3.07 |
7.34 |
7.68 |
504.8 |
3.56 |
0.47 |
125.6 |
21.09 |
|
Aziziye
|
24.73 |
16.69 |
2.73 |
2.73 |
6.38 |
6.74 |
415.5 |
2.98 |
0.48 |
105.1 |
23.25 |
|
Average |
24.66 |
16.70 |
2.81 |
3.08 |
7.56 |
8.04 |
423.6 |
3.14 |
0.49 |
112.8 |
20.41 |
|
LSD(%5) |
0.543 |
0.4491 |
0.2169 |
0.3473 |
0.8838 |
0.88 |
6.173 |
1.090 |
1.852 |
6.89 |
0.49 |
|
† 1. Length of plant (cm),
2. Height from ground of first pod (cm), 3. Number of main brunch, 4. Number
of side brunch, 5. Pod number per
plant, 6. Seed number per plant, 7. 1000 seed weight (g), 8. Seed yield per
plant (g), 9. Harvest index (%), 10. Seed yield (kg/da,
1 da = |
|||||||||||
When Akçin-91
variety (
For number of side
brunch, Kırmızı Nohut
(3.44) has the most, ICC 5566 (2.52) has the least value (Table 1). Similar
results were reported by Singh & Tuwafe (1981)
who found values between 0.3 and 22.7 and for Eser et al. (1987) between 1.4 and 6.4.
ILC482 has the most
(10.00) and Diyar95 has the least (5.53) values of pod number per plant (Table
1). These results are near to researches of Singh & Tuwafe
(1981) who reported a range of 4-100, Eser et al. (1987) with 3-12, Samal and Jagadey (1989) with
8.5-21.8), but they are small than results of Dumbre
and Deshmuch (1984) who reported values between 14.4
- 67,0 and Khargade et al. (1985) with 53.5.
Mostly number of
main brunch in the plant from ILC-483 variety and the less one is obtained from
ICC-5566 and Akçin-91 (Table 1). Results have showed paralleling to the
findings of Tosun and Eser
(1975), Singh & Tuwafe (1981), Karasu (1993) and Eser et al. (1987).
When seed numbers
is analyzed, ILC-482 has the most; Diyar 95 has the
least values (Table 1). These results are near to the Singh and Tuwafe (1981), Eser et al. (1987) and Samal
& Jagadey (1989), but far from Dumbre and Deshmuch (1984) and Khargade et al.
(1985).
It was obtained
that Kırmızı Nohut
has high value (
Canıtez 87 variety has the
most seed yield value (
When giving
importance to seed yield, it has been noticed that with Spanish chickpea (125.6
kg/da, 1 da =
Protein ratios of
varieties were obtained for year 1997. Aziziye
variety has the most value (23.25 %); 4N-495/2 variety has the least value
(18.64 %) (Table 1). Similar values for this range had been reported for Karasu (1993) who informed a value of 16.44 % and Doğangüzel (1998) who reported values between 19.95
and 24.3 %.
According to the principal component
factor analysis results, one principal component (PC1) have been
obtained (it explained 99.45% of the total variance) (Table 2). For this
reason, ignorant information lost is low degree in research (% 0.55).
Communality values showed that, examined varieties have important degree of
similarity genetic feature, and data are reliable. When done ordering, the
varieties as their important degree (how can be act the group) they are
enumerated as; Akçin 91, Diyar
95, Aziziye, AK-7112 and Spanish Chickpea which are more important
varieties, and the least important variety is ICC-5566 which has the smallest
principal component coefficient (Table 2).
|
Table 2. Principal components and communalities
rates for 11 variables† of 11 chickpea (Cicer arietinum L.) varieties grown in two
localities of Isparta, Turkey in 1996 (Atabey area) and 1997 (Çünür Kampus area). |
|||
|
Varieties |
Principal
Component 1 |
Communalities ( hi2 ) |
Variance
matrix ( εi ,Ψ ) |
|
Eser87 |
0.9945 |
0.9890 |
0.0110 |
|
Akçin91 |
0.9998 |
0.9995 |
0.0005 |
|
Canıtez87 |
0.9967 |
0.9934 |
0.0066 |
|
Diyar95 |
0.9998 |
0.9996 |
0.0004 |
|
ILC482 |
0.9963 |
0.9926 |
0.0074 |
|
Ak7112 |
0.9986 |
0.9973 |
0.0027 |
|
ICC5566 |
0.9938 |
0.9876 |
0.0124 |
|
Kır.Nohut |
0.9958 |
0.9917 |
0.0083 |
|
4N-495/2 |
0.9962 |
0.9923 |
0.0077 |
|
İspany.No |
0.9986 |
0.9971 |
0.0029 |
|
Aziziye |
0.9996 |
0.9991 |
0.0009 |
|
† 1. Length of plant (cm),
2. Height from ground of first pod (cm), 3. Number of main brunch, 4. Number
of side brunch, 5. Pod number per
plant, 6. Seed number per plant, 7. 1000 seed weight (g), 8. Seed yield per
plant (g), 9. Harvest index (%), 10. Seed yield (kg/da,
1 da = |
|||
In this study,
multivariate statistical methods were used to classify a group of chickpea
varieties on the basis of their agronomic characteristics. Classifying of investigated
varieties into two basic groups which consist of eight groups has been
suggested according to the cluster analysis (Figure 1). When making of the
principal component values rotation, the most important varieties of the whole
group are in sequence, Diyar 95, Akçin
91 and Aziziye. While Eser
87 and Red roasted chickpea have the farthest and the most different features
(Euclidean distance 301), the nearest two varieties are Canıtez
87 and Red roasted chickpea (Euclidean distance 14) (Figure 1). It shows that,
similar varieties have easily used for the others. When adaptation applications
are done between varieties which are farthest from one another, so different
and new varieties will be obtained.
According to the dendogram results produced by cluster analysis, varieties
are separated to two main and three little groups (Figure 1). Beside, there are more different three main groups (3 sub
clusters) by cluster analysis. Eser 87, ILC-482,
ICC-5566 and AK-7112 varieties have formed the first population different from
the others and high similarities second main group which is formed by the other
separates to two little groups. The most similar ones among varieties are Red
roasted chickpea and Canıtez 87, Aziziye and Akçin 91 and ILC-482
and Eser 87. It has been noticed that, examined
varieties are divided thirdly groups. Similar varieties have importance for
preference richness of producer. While the representation variety of first
group is Diyar 95 (and Akçin
91), the most important of the second group is AK-7112 (Figure 1).
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