Hafiz Saad Bin Mustafa1 RETRACTED - doiSerbia · CLUSTER AND PRINCIPLE COMPONENT ANALYSES OF MAIZE ACCESSIONS UNDER NORMAL AND WATER STRESS CONDITIONS . Hafiz Saad Bin Mustafa1, Jehanzeb

Journal of Agricultural Sciences

Vol. 60, No. 1, 2015

Pages 33-48

DOI: 10.2298/JAS1501033M

UDC: 633.15:575.22

Original scientific paper

CLUSTER AND PRINCIPLE COMPONENT ANALYSES OF MAIZE

ACCESSIONS UNDER NORMAL AND WATER STRESS CONDITIONS

Hafiz Saad Bin Mustafa1, Jehanzeb Farooq

2*, Ejaz-ul-Hasan

1,

Tahira Bibi1 and Tariq Mahmood

1

1Oilseeds Research Institute, AARI, Faisalabad, Pakistan 2Cotton Research Institute, AARI, Faisalabad, Pakistan

Abstract: In the current set of an experiment, forty maize genotypes were

assessed for drought associated traits. For evaluation of these traits, PC and

correlation analyses were employed to obtain suitable parents that can be further

exploited in future breeding programmes. Correlation analysis revealed some

important associations among the traits studied. Fresh root length had positive and

significant associations, but leaf temperature had a significant negative correlation

with root density at both 40% and 100% moisture levels while root density had

negative association at 100% and positive correlation at 40% moisture level with

chlorophyll content. The positive correlation among these yield contributing traits

suggested that these characters are important for direct selection of drought tolerant

high yielding genotypes. Principal component (PC) analysis showed first 4 PCs

having Eigen value >1 explaining 86.7% and 88.4% of the total variation at 40%

and 100% moisture levels respectively with different drought related traits. Cluster

analysis classified 40 accessions into four divergent groups. The members of

clusters 1 and 2 may be combined in future breeding programmes to obtain

genotypes/hybrids that can perform well under drought stress conditions. Members

of cluster 3 may be selected on the basis of root density, leaf temperature, dry root

weight and root shoot ratio by weight and can be combined with members of

cluster 4 due to higher leaf temperature and root shoot ratio by length. The results

showed that the germplasm having a wide genetic diversity can be thus utilized for

future breeding programme to obtain drought tolerant maize genotypes/ hybrids for

adaptation to water scarce areas.

Key words: Zea mays, cluster analysis, drought, genotypes, principle

component analysis.

*Corresponding author: e-mail: [email protected]

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Hafiz Saad Bin Mustafa et al.

34

Introduction

Maize is a very important crop of Pakistan and ranks third among cereal crops

after wheat and rice. In Pakistan, maize is grown in an area of 1,083 thousand

hectares with total production of 3,990 thousand tons (Anonymous, 2011 12). It is

consumed as food by humans and as feed for the livestock and poultry. It is also

used as raw material in different food, medicine and textile industries, which

finally manufacture corn oil, corn flakes, dextrose, and textile dyes etc. Maize has

high nutritive values as it contains 72 % starch, 10 % protein, 4.80 % oil, 8.50 %

fibre, 3.0 % sugar, and 1.70 % ash (Mustafa et al., 2014).

Among different abiotic stresses, drought stress or water deficit is an

inevitable and recurring feature of world agriculture. It has been estimated that

about one third of the world’s potentially arable land suffers water shortage and

crop yields are often reduced by drought (Khan et al., 2004). Drought can reduce

crop yield and pasture deterioration by increasing poisonous elements and causing

death of livestock. It strongly affects the production of cereals, and poses a serious

threat to the food security of households. World food security is dependent on

continuous crop improvement. The development of crops with increased tolerance

to abiotic stresses especially drought and salinity is the need of the hour throughout

the world (Denby and Gehring, 2005). Bänziger et al. (2000) observed that the full-

sib family selection scheme was the most extensively used at CIMMYT to improve

maize populations for drought and low-N tolerance. For high production in maize,

the precise selection of elite genotypes is very important for any area

(AshoftehBeiragi et al., 2010). Knowledge of the associations among genotypes

would help to identify a set of genotypes that have maximal diversity for the

analysis of the effects of genetic background.

To get precise information on nature and extent of genetic variation depends

upon the various techniques used for its estimation, like plant characterization based

on agronomical, morphological and physiological traits (Bajracharya et al., 2006).

Multivariate analysis based on Mahalonobis’s D2 statistics (MDS), principal

component analysis (PCA) and principal coordinate analysis (PCoA) are mostly used

to evaluate the magnitude of genetic diversity among the germplasm (Brown-

Guedira, 2000). Several authors suggested first principal component (PC) scores as

input variables for the clustering process (Mujaju and Chakuya, 2008). Hierarchical

cluster analysis has been suggested for classifying entries of germplasm collections

based on degree of similarity and dissimilarity (Van Hintum, 1995). Similarly, a

combination of cluster analysis and principal component analysis has been used to

classify maize (Zea mays L.) accessions (Crossa et al., 1995). Several studies have

reported comparisons of cluster analysis algorithms, including those used for

classification of germplasm collections (Peeters and Martinelli, 1989), and

classification of maize inbreds (Mummand and Dudley, 1994).

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Cluster and principle component analyses of maize accessions

35

Among these biometrical procedures, the main edge of principal component

analysis (PCA) is that each genotype can be assigned to only one group and it also

reflects the significance of the largest contributor to the total variability at each axis

of differentiation (Sharma, 1998). Genetic variation for morphological traits has

been estimated using principal component analysis, which has led to the

recognition of phenotypic variability in cotton (Sarvanan et al., 2006; Esmail et al.,

2008; Li et al., 2008).

The purpose of the present study was to evaluate the genetic diversity among

maize genotypes specifically for drought tolerance to select the best genotypes at

seedling stage by using PCA and cluster analysis that can be exploited in future

maize breeding programme.

Material and Methods

The investigation presented here was carried out in the glasshouse of the

Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad,

Pakistan during 2010. The experimental material consisted of 40 accessions (inbred

lines) namely: EV-1098, EV-5098, EV-6098, SWL-2002, Agaiti-2002, Sadaf, Pak

Afgoyee, TLOZA, TLOOB-341, TLOOB-343, F-153, F-204, F-220, F-115, F-186,

F-135, F-202, F-206, F-209, F-211, F-210, F-192, F-114, B-54, AF-02B, F-109, F-

110, F-163, F-208, F-189, F-219, F-158, F-160, F-191, PR-98, 8288, 8441, 6525,

32B33 and 33H25. These accessions were seeded in polythene bags (18 × 9 cm)

filled with sandy loam soil (pH 7.8 and EC 1.7 dS m-1) using completely

randomized design with three replications. Field capacity of the soil was determined

before seeding. Two seeds per polythene bag were sown and thinned up to one

healthy seedling after emergence. All the recommended agronomic and cultural

practices were carried out. The moisture level was maintained by volume on alternate

days by using a moisture meter (ΔT-NH2, Cambridge, England). The average

temperature throughout the experimental period was 420C. At five-leaf stage, five

plants were selected randomly from each genotype and data was recorded for the leaf

temperature (LT), fresh root length (FRL), fresh shoot length (FSL), root-to-shoot

length ratio (RSR/L), dry root weight (DRW), dry shoot weight (DSW), root-to-

shoot weight ratio (RSR/W), root density (RD) and chlorophyll contents (CC) at

normal (100%) and 40% moisture levels of field capacity.

The data was subjected to basic statistics, correlation analysis, cluster analysis

and principal component analysis (PCA) using statistical software packages of

SPSS version 19 and Statistica version 5.0 (Sneath and Sokal, 1973). Cluster

analysis was performed using K-means clustering while tree diagram based on

eucladian distances was developed by Ward’s method. The first two principal

components were plotted against each other to find out the patterns of variability

among genotypes using SPSS version 19.

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36

Results and Discussion

Normal conditions (100% moisture level)

The basic statistics of various traits studied under normal conditions (100%

moisture level) have demonstrated considerable variability among 40 maize

genotypes (Table 1). Simple correlation coefficients revealed 10 significant

associations among traits (Table 2). FRL had a positive and significant association

with RSR/L, FSL, DSW. LT had a negative and significant association with RD.

FSL exhibited a positive correlation with DRW and DSW, but a negative and

significant association with RSR/L. DRW had a positive and significant association

with DSW and RSR/W. RD showed a positive and significant association with CC.

Table 1. Basic statistics for various traits of 40 maize genotypes under normal

conditions (100% moisture level).

Variable Mean Mean SE SD Variance CV Minimum Maximum Range

FRL 20.492 0.709 4.481 20.080 21.87 12.333 31.667 19.333

LT 31.167 0.165 1.046 1.094 3.36 28.500 33.667 5.167

FSL 51.26 1.45 9.14 83.55 17.83 31.33 69.00 37.67

DRW 0.6183 0.0292 0.1847 0.0341 29.88 0.2000 1.1667 0.9667

DSW 0.7075 0.0243 0.1539 0.0237 21.75 0.2333 1.0667 0.8333

RD 4.067 0.300 1.898 3.602 46.67 1.667 8.000 6.333

RSR L 0.4046 0.0132 0.0834 0.0070 20.62 0.2375 0.6640 0.4265

RSR W 0.8683 0.0204 0.1289 0.0166 14.85 0.6667 1.1852 0.5185

CC 0.3480 0.0719 0.4547 0.2068 130.69 0.0130 1.1240 1.1110

SE = Standard error, SD = Standard deviation, CV = Coefficient of variation, FRL = Fresh root

length, LT = Leaf temperature, FSL = Fresh shoot length, DRW = Dry root weight, DSW = Dry shoot

weight, RD = Root density, RSR/L = Root shoot ratio length, RSR/W = Root shoot ratio weight,

CC = Chlorophyll content.

Table 2. Simple correlation coefficients of various physiological traits in maize

under normal conditions (100% moisture level).

Traits FRL LT FSL DRW DSW RD RSR/L RSR/W

LT -0.037

FSL 0.523** -0.118

DRW 0.379 -0.044 0.526**

DSW 0.456** -0.084 0.663** 0.847**

RD -0.074 -0.625** 0.065 -0.084 -0.035

RSR/L 0.623** 0.076 -0.333* -0.070 -0.114 -0.125

RSR/W 0.094 0.016 0.087 0.686** 0.207 -0.104 0.025

CC 0.041 -0.520 0.149 -0.036 0.028 0.877** -0.079 -0.098

FRL = Fresh root length, LT = Leaf temperature, FSL = Fresh shoot length, DRW = Dry root weight,

DSW = Dry shoot weight, RD = Root density, RSR/L = Root shoot ratio length, RSR/W = Root shoot

ratio weight, CC = Chlorophyll content.

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37

In this study, out of total l9, four principal components (PCs) extracted had

Eigen value >1. These four PCs contributed 88.4% of the total variability amongst

the maize genotypes assessed for drought related traits (Table 3). However, the

remaining components contributed only 11.6% towards the total diversity for this

set of maize genotypes. The PC I contributed maximum towards the variability

(32.3%) followed by PC II (27.0%), PC III (16.9%) and PC IV (12.2%). The traits

like DRW, DSW, FSL and FRL showed considerable positive factor loadings on

PC I while LT and RSR/L showed negative loadings (Table 4).

Table 3. Principle component analysis of different physiological traits in maize

under normal conditions (100% moisture level).

PC 1 PC 2 PC 3 PC 4

Eigen value 2.9038 2.4278 1.5219 1.1008

% of total variance 32.3 27.0 16.9 12.2

Cumulative variance % 32.3 59.2 76.2 88.4

Table 4. Factor loadings by various traits (100% moisture level).

Variable PC I PC II PC III PC IV

FRL 0.370 0.075 -0.581 -0.193

LT -0.085 0.487 0.077 -0.153

FSL 0.457 -0.092 0.123 -0.456

DRW 0.537 0.086 0.143 0.273

DSW 0.523 0.013 0.088 -0.181

RD 0.006 -0.604 -0.067 0.123

RSR/L -0.012 0.162 -0.759 0.207

RSR/W 0.286 0.135 0.139 0.750

CC 0.053 -0.576 -0.117 0.060




The 2nd PC was related to diversity among maize genotypes due to LT with

their positive loadings and RD and CC with negative loadings. The PC III was

explained by variation among genotypes due to DRW with their positive loadings

and negative loadings exhibited by RSR/L and FRL. The PC IV was elucidated by

diversity among the genotypes for FSL with negative loadings and RSR/W had a

positive value. A PC biplot in Figure1 showed that variables and genotypes are

super imposed on the plot as vectors. The distance of each variable with respect to

PC-1 and PC-2 showed the contribution of these variables in the variation of

genotypes used. The biplot showed that DRW, RD, CC and LT as a whole

contributed maximum towards variability in maize germplasms.

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Figure 1. Biplot between PCs 1 and 2 showing contribution of various traits in

variability of germplasms under normal conditions (100% moisture level).

Forty maize genotypes were grouped into 4 clusters based on various drought

related traits (Table 5). Cluster analysis showed that cluster 1 was comprised of 19

genotypes, cluster 2 of 5 while cluster 3 had 8 and cluster 4 contained 8 genotypes

(Table 6). The genotypes in cluster 1 showed higher values of FRL, RSR/L and

RSR/W. Similarly, the 2nd cluster was comprised of genotypes having the highest

value and reasonable values of LT. The members of the 3rd cluster were

characterized by higher value of RD. The cluster 4 is characterized by maximum

FSL, DRW, DSW and CC.

Table 5. Cluster analysis of various traits in maize under normal conditions (100%

moisture level).

Variable Cluster 1 Cluster 2 Cluster 3 Cluster 4

FRL 31.67 12.67 12.33 24.00

LT 30.80 33.10 31.33 31.00

FSL 53.33 53.33 31.33 69.00

DRW 0.57 0.43 0.20 0.67

DSW 0.63 0.53 0.23 0.87

RD 3.00 2.67 3.67 3.00

RSR/L 0.59 0.24 0.39 0.35

RSR/W 0.89 0.81 0.86 0.77

CC 0.03 0.04 0.04 0.08




5.02.50.0-2.5-5.0

3

2

1

0

-1

-2

-3

-4

First Component

Se

co

nd

Co

mp

on

en

t

CC

RSR WRSR L

RD

DSWDRW

FSL

LT

FRL

Biplot of FRL, ..., CC

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39

Table 6. Cluster membership of various maize genotypes under normal conditions

(100% moisture level).

Cluster Name No. of genotypes Name of genotypes in each cluster

Cluster 1 19

EV-1098, EV-5098, EV-6098, Sadaf, Pak Afgoyee, TLOZA,

TLOOB-341, F-206, F-109, F-110, F-114, F-186, F-160,

F-211, B-54, AF-02B, 8288, 8441, 6525

Cluster 2 5 SWL-2002, Agaiti-2002, TLOOB-343, F-135, F-209,

Cluster 3 8 F-202, F-153, F-204, F-208, F-189, F-219, F-210, 33H25

Cluster 4 8 F-220, F-115,F-191, F-163, F-158, F-192, PR-98, 32B33

The tree diagram showed more or less similar results comprising two main

groups A and B each of which is further subdivided into two clusters (Figure 2).

Figure 2. Tree diagram of 40 maize genotypes based on different physiological

traits under normal conditions (100% moisture level).

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40

Drought stress conditions (40% moisture level)

The basic statistics of various traits studied under drought stress conditions

(40% moisture level) has demonstrated considerable variability among 40 maize

genotypes (Table 7). Simple correlation coefficients revealed 9 significant

associations among traits (Table 8). FRL had a positive and significant association

with FSL and RSR/L. LT had a negative and significant association with RD and

positive with CC. FSL exhibited a positive correlation with DRW and DSW, but a

negative and significant association with RSR/L. DRW had a positive association

with DSW and RSR/W, but DSW showed a negative correlation with RSR/W. RD

showed a positive association with CC.

Table 7. Basic statistics for various traits of 40 maize genotypes under drought

stress conditions (40% moisture level).

Variable Mean Mean SE SD Variance CV Minimum Maximum Range

FRL 20.275 0.686 4.341 18.843 21.41 12.333 28.000 15.667

LT 34.103 0.121 0.764 0.583 2.24 31.867 35.033 3.167

FSL 28.542 0.865 5.468 29.901 19.16 16.333 46.000 29.667

DRW 0.5123 0.0212 0.1343 0.0180 26.22 0.2000 0.9000 0.7000

DSW 0.3917 0.0151 0.0957 0.0092 24.44 0.2000 0.5667 0.3667

RD 3.258 0.188 1.190 1.416 36.52 1.667 5.667 4.000

RSR L 0.7231 0.0244 0.1544 0.0238 21.35 0.4262 1.0800 0.6538

RSR W 1.3368 0.0501 0.3169 0.1004 23.70 0.7692 2.2500 1.4808

CC 0.3382 0.0722 0.4565 0.2084 134.98 0.0113 1.1210 1.1097

SE = Standard error, SD = Standard deviation, CV = Coefficient of variation, FRL = Fresh root

length, LT = Leaf temperature, FSL = Fresh shoot length, DRW = Dry root weight, DSW = Dry shoot

weight, RD = Root density, RSR/L = Root shoot ratio length, RSR/W = Root shoot ratio weight,

CC = Chlorophyll content.

Table 8. Simple correlation coefficients of various physiological traits in maize

under drought stress conditions (40% moisture level).

Traits FRL LT FSL DRW DSW RD RSR/L RSR/W

LT -0.110

FSL 0.355 0.087

DRW 0.044 0.145 0.351*

DSW 0.255 0.122 0.484** 0.595**

RD 0.301 -0.716** 0.093 0.195 0.197

RSR/L 0.662** -0.152 -0.442* -0.268 -0.189 0.191

RSR/W -0.221 0.070 -0.149 0.496** -0.381 -0.027 -0.078

CC 0.169 -0.606** 0.027 0.048 0.165 0.805** 0.143 -0.124




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41

In this study, out of total 9, four principal components (PCs) extracted had

Eigen value >1. These four PCs contributed 86.7% of the total variability amongst

the maize genotypes assessed for drought related traits (Table 9). However, the

remaining components contributed only 13.3% towards the total diversity for this

set of maize genotypes. The PC I contributed maximum towards the variability

(29.9%) followed by PC II (24.9%), PC III (17.9%) and PC IV (14.1%). The traits

like RD and CC showed considerable positive factor loadings on PC I while LT

showed negative loadings (Table 10). The 2nd PC was related to diversity among

maize genotypes due to FSL, DRW and DSW with their positive loadings and

RSR/L with negative loadings. The PC III was explained by variation among

genotypes due to FRL with their positive loadings and negative loadings exhibited

by RSR/W. The PC IV was elucidated by diversity among the genotypes for FRL,

DRW, RSR/L and RSR/W with negative loadings. A PC biplot in Figure3 showed

that variables and genotypes are super imposed on the plot as vectors. The distance

of each variable with respect to PC-1 and PC-2 showed the contribution of these

variables in the variation of genotypes used. The biplot showed that as a whole CC,

RD and LT contributed maximum towards variability in maize germplasms.

Table 9. Principle component analysis of different physiological traits in maize

under drought stress conditions (40% moisture level).

PC I PC II PC III PC IV

Eigen value 2.6901 2.2366 1.6110 1.2663

% of total variance 29.9 24.9 17.9 14.1

Cumulative variance % 29.9 54.7 72.6 86.7

Table 10. Factor loadings by various traits (40% moisture level).

Variable PC I PC II PC III PC IV

FRL 0.340 0.040 0.501 -0.411

LT - 0.440 0.278 -0.191 0.213

FSL 0.111 0.513 0.168 0.120

DRW 0.060 0.513 -0.280 -0.418

DSW 0.196 0.525 0.210 0.070

RD 0.552 0.001 -0.246 -0.004

RSR L 0.225 -0.386 0.347 -0.491

RSR W -0.155 0.016 -0.536 -0.580

CC 0.507 -0.043 -0.230 0.150




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Figure 3. Biplot between PCs 1 and 2 showing contribution of various traits in

variability of germplasms under drought stress conditions (40% moisture level).

Forty maize genotypes were grouped into 4 clusters based on various drought

related traits (Table 11). Cluster analysis showed that cluster 1 was comprised of 11

genotypes, cluster 2 of 16 while cluster 3 had 7 and cluster 4 contained 6 genotypes

(Table 12). The genotypes in cluster 1 showed higher values of FSL, DSW and RD.

Similarly, the 2nd cluster was comprised of genotypes having the highest value and

reasonable values of RD. The members of the 3rd cluster were characterized by higher

value of DRW and RD.

Table 11. Cluster analysis of various traits in maize under drought stress conditions


Variable Cluster 1 Cluster 2 Cluster 3 Cluster 4

FRL 23.00 27.00 15.33 13.67

LT 34.20 34.27 34.53 35.03

FSL 46.00 25.00 29.33 16.33

DRW 0.73 0.37 0.90 0.60

DSW 0.53 0.27 0.43 0.27

RD 3.67 2.67 3.67 1.67

RSR L 0.50 1.08 0.52 0.84

RSR W 1.38 1.38 2.08 2.25

CC 0.06 0.07 0.23 0.02




43210-1-2-3

4

3

2

1

0

-1

-2

-3

-4

First Component

Se

co

nd

Co

mp

on

en

t

CCRSR W

RSR L

RD

DSWDRWFSL

LT

FRL

Biplot of FRL, ..., CC

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Table 12. Cluster membership of various genotypes under drought stress conditions


Cluster Name No. of genotypes Name of genotypes in each cluster

Cluster 1 11 EV-1098, EV-6098, Pak Afgoyee, F-202, F-115, F-186,

F-158, F-210, F-192, F-211, B-54

Cluster 2 16 EV-5098, TLOZA, TLOOB-341, F-135 , F-209, F-153, F-220,

F-163, F-208, F-219, AF-02B, PR-98, 8288,8441, 6525, 32B33

Cluster 3 7 SWL-2002, Sadaf, TLOOB-343, F-114, F-204, F-160, 33H25

Cluster 4 6 Agaiti-2002, F-206, F-109, F-110, F-191, F-189

The cluster 4 is characterized by maximum LT and RSR/W. The tree diagram

showed more or less similar results comprising two main groups A and B each of

which is further subdivided into two clusters (Figure 4). Multivariate data analysis

assists a graphic display of the underlying latent factors and an interface between

individual samples and variables (Nielsen and Munck, 2003). Principal component

analysis (PCA) is usually used in plant sciences for the reduction of variables and

grouping of genotypes. Kamara et al. (2003) used PCA to categorize traits of maize

(Zea mays L.) that accounted for most of the variance in the data. Granati et al. (2003)

used PCA to scrutinize the relationship among Lathyrus accessions. Žáková and

Benková (2006) recognized traits that were the main sources of variation of genetic

diversity among 106 Slovakian barley accessions. Cartea et al. (2002) and Salih et al.

(2006) used PCA and cluster analysis to group kale populations and winter wheat

genotypes, respectively. The classification of diversity among the genotypes into

groups with similar traits can be used to design a collection strategy (Ariyo, 1993).

When dissimilarity between a pair of a variety is defined on a multivariate

criterion, it is useful to be able to determine the specific plant characters which cause

the dissimilarity and the relative contributions that the various characters make to the

total variability in the germplasm (Ariyo, 1993). The information regarding association

among various traits is an important part for the initiation of any breeding programme

as it provides an opportunity for the selection of genotypes having desirable traits

simultaneously (Ali et al., 2009). In the present set of the experiment, results of

correlation analysis revealed some important associations among the traits studied.

FRL had positive and significant associations at both 40% and 100% moisture levels,

but LT had a significant negative correlation with RD at both 40% and 100% moisture

levels while RD had a negative correlation at 100% and a positive correlation at 40%

moisture level with CC. FSL had positive significant associations with DSW and DRW

while negative with RSR/L both at 40% and 100% moisture levels.

DRW had a positive significant correlation with DSW and RSR/W both at 40%

and 100% moisture levels. DSW had showed a significant negative correlation with

RSR/W at 40% moisture level while its positive but non significant association was

found at 100% moisture level. RD had showed a positive and significant correlation

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with CC both at 40% and 100% moisture levels. The positive correlation among these

yield contributing traits suggested that these characters are important for direct

selection of high yielding genotypes. The maintenance and exploitation of genetic

resources could be made by partitioning the total variance into its components. It also

provides a chance for utilization of appropriate germplasm in crop improvement for

particular plant traits (Sneath and Sokal, 1973; Pecetti et al., 1996). The principal

component (PC) analysis divides the total variance into different factors. The Principal

Component Analysis is a powerful tool to obtain parental lines for a successful

breeding programme (Akter et al., 2009).

Figure 4. Tree diagram of 40 maize genotypes based on different physiological

traits under drought stress conditions (40% moisture level).

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In this experiment, the PC analysis partitioned the total variance into 4 PCs

contributing maximum to the total diversity among the genotypes due to the study

of various traits. Chozin (2007), Mujaju and Chakuya (2008), and Ali et al. (2011)

reported important contribution of the first PCs in total variability while studying

different traits. In our research, the 1st PC was mainly due to variations in CC, RD,

FRL and LT at 40% moisture level while variations were found for DRW, DSW

and FSL at 100% moisture level. These results are in agreement with the results of

association analysis which showed a positive association among these traits. The

2nd PC was related to diversity among cotton genotypes due to DSW, FSL and

DRW at 40% moisture level while variations were found for LT and RD, CC at

100% moisture level. The PC III was explained by variation among genotypes due

to FRL, RSR/L and RSR/W at 40% moisture level while diversity was found for

traits like FRL and RSR/L at 100% moisture level. Similarly, PC IV was explicated

by variation in RSR/W, RSR/L, DRW and FRL with their considerable negative

factor loadings at 40% moisture level while at 100% moisture level negative

estimates were found for traits like RSR/W and FSL. PC analysis ultimately

confirmed the amount of variation for the traits among the materials in hand which

could be utilized in designing a breeding programme aimed at improving drought

tolerance and ultimately grain yield as it is generally assumed that maximum

variation yields maximum heterotic effects.

While comparing the fresh root length and leaf temperature, the genotypes in

cluster 2 showed desirable results under stress and in cluster 1, the traits like FRL,

FSL, DSW, RD and RSR/W showed promising results. Thus, members of clusters 1

and 2 may be combined in future breeding programmes to obtain genotypes/hybrids

that can perform well under drought stress conditions. The members of cluster 3 may

be selected on the bases of RD, LT, DRW and RSR/W and they may be combined

with the members of cluster 4 due to higher leaf temperature and RSR/L. The

germplasms exploited to obtain maximum diversity in this experiment yield excellent

results. Amurrio et al. (1995) and Rabbani et al. (1998) reported a lack of relationship

between various clusters based on agronomic traits and origins of genotype in peas

(Pisumsativum) and mustard (Brassica juncea) respectively. The tree diagram was

comprised of group of genotypes showing considerable tolerance to drought stress.

The occurrence of this wide variation between the clusters is of great genetic value in

providing materials aimed at maize selection for adaptation to water scarce areas. A

similar kind of results related to germplasm grouping has been reported by Ayana

and Bekele (1999) and Grenier et al. (2001).

Conclusion

PC analysis, cluster analysis and correlation coefficient in this present set of

the experiment provided facilitation in the classification of genotypes and

identification of the subset of core genotypes having tolerance to drought stress.

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46

Various useful correlations and aforementioned information extracted from cluster

and PC analysis will be helpful in designing breeding programmes to obtain

drought tolerant maize genotypes/hybrids.

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Received: July 15, 2014

Accepted: October 10, 2014

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KLASTER ANALIZA I ANALIZA GLAVNIH KOMPONENTI GENOTIPOVA

KUKURUZA U NORMALNIM USLOVIMA I U USLOVIMA

VODNOG STRESA

Hafiz Saad Bin Mustafa1, Jehanzeb Farooq

2*, Ejaz-ul-Hasan

1,

Tahira Bibi1 i Tariq Mahmood

1

1Institut za seme uljarica, AARI, Fejsalabad, Pakistan

2Institut za pamuk, AARI, Fejsalabad, Pakistan

R e z i m e

U ovom ogledu, četrdeset genotipova kukuruza su procenjeni u pogledu

osobina povezanih sa sušom. Za evaluaciju ovih osobina korišćene su analiza

glavnih komponenti i korelaciona analiza, kako bi se preporučili odgovarajući

roditelji, koji se mogu koristiti u budućim programima oplemenjivanja.

Korelaciona analiza je pokazala da postoji značajna povezanost meĎu nekim

ispitivanim osobinama. Dužina svežeg korena je bila pozitivno i značajno

korelisana, a temperatura lista takoĎe značajno, ali negativno korelisana sa

gustinom korena kako pri 40% tako i pri 100% nivou vlažnosti. Nasuprot tome,

korelacija izmeĎu gustine korena i sadržaja hlorofila je bila negativna pri 100%

nivou vlažnosti, a pozitivna pri 40% nivou vlažnosti. Pozitivna korelacija meĎu

nekim osobinama koje doprinose prinosu ukazuje da su ove osobine važne za

direktnu selekciju visokoprinosnih genotipova tolerantnih na sušu. Analizom

glavnih komponenti izdvojene su četiri glavne komponente Eigen vrednosti veće

od 1 koje objašnjavaju 86,7% i 88,4% ukupne varijabilnosti pri 40% odnosno

100% nivou vlažnosti. Klaster analizom 40 genotipova kukuruza je klasifikovano u

četiri divergentne grupe. Članovi klastera 1 i 2 se mogu kombinovati u budućim

programima oplemenjivanja radi dobijanja genotipova/hibrida tolerantnih na

uslove stresa izazvane sušom. Članovi klastera 3 svrstani na osnovu gustine

korena, temperature lista, mase suvog korena i odnosa mase korena i izdanka mogu

se kombinovati sa članovima klastera 4, koje karakteriše viša temperatura lista i

odnos dužine korena i izdanka. Rezultati su pokazali da germplazma, koja ima

široku genetsku raznovrsnost, može biti iskorišćena u budućim programima

oplemenjivanja, radi dobijanja genotipova/hibrida kukuruza tolerantnh na sušu,

adaptiranih za područja koja oskudevaju vodom.

Ključne reči: Zea mays, klaster analiza, suša, genotipovi, analiza glavnih

komponenti.

Primljeno: 15. jula 2014.

Odobreno: 10. oktobra 2014.

*Autor za kontakt: e-mail: [email protected]

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Documents

Hafiz Saad Bin Mustafa1 RETRACTED - doiSerbia · CLUSTER AND PRINCIPLE COMPONENT ANALYSES OF MAIZE ACCESSIONS UNDER NORMAL AND WATER STRESS CONDITIONS . Hafiz Saad Bin Mustafa1, Jehanzeb