ABR-2013-4-8-201-204

8/13/2019 ABR-2013-4-8-201-204

1/4

www.scholarsresearchlibrary.comtAvailable online a

Scholars Research Library

Annals of Biological Research, 2013, 4 (8):201-204

(http://scholarsresearchlibrary.com/archive.html) ISSN 0976-1233

CODEN (USA): ABRNBW

201


Study of yield and yield components Cultivars Promising Grain Sorghum using cluster

analysis and Factor analysis

Ahad Jahangiri Ajirlou1, Shahrooz Aghaei

1, and Shamsali Darvishi

1*

1Department of Agriculture, Parsabad, Moghan, Islamic Azad University, Parsabad Branch, Iran

____________________________________________________________________________________________

ABSTRACT

20 varieties of sorghum were evaluated in a randomized complete block design with four replications in 2007 in the

region of the Moghan.Analysis of variance of traits showed that there is a significant difference among the

varieties except for stamen leaf area. Using cluster analysis of Ward's method based on standardized data and all

traits the Iranian varieties and BISC-11 from India were ranked superior clusters. This grouping was confirmed by

the detection function. In factor analysis, five factors were determined which explained 86.24% of the total

variation. The first main factor explained 33.890 % of the total variation was introduced as a performance factor.

Keywords:Promising Varieties, Factor Analysis, Cluster Analysis, Sorghum, Yield

____________________________________________________________________________________________

INTRODUCTION

Sorghum is one of the most important cereal crops in arid and semi-arid. The plant in the world after wheat, rice,

maize and barley is in the fifth place [1]. This plant is used for providing protein for many people in Asia and

Africa [2], malt production of non-alcoholic drinks, flour production and animal feed [3]. Cultivation of sorghum in

the world in 2007 was nearly 47 million hectares of which 90% of the cultivated area is dedicated for Grain

sorghum varieties. Therefore, sorghum is considered as the world's primary cereal. India, with about 9 million

hectares under cultivation in first place and the USA with 3 million hectares under cultivation have the greatest

production in the world [4]. The success of the breeders depends on the choice of appropriate materials and

diversity. Breeding those traits which have high heritability is more important. It is notable that the evaluation and

application of the results have a significant role in Agricultural Sciences [5]. The purpose of principalcomponent

analysis is to reduce the volume of data. In this method examining the correlation between variable we are able to

realize the relationship between the traits. In component analysis, the correlation between lots of dependentvariables is expressed by a few independent components. The role of each trait is determined in variation of each

trait. In addition, the principal component analysis is used for genotype classification [6]. Several reports of

increasing genetic distance between genotypes of a species increases the likelihood of heterosis in cross-linkage

programs. The genotype classification based on genetic distance, is effective in corrective programs when such

traits simultaneously are examined. Therefore, in order to determine genetic variation standard, genotype

classifications and genetic distance among them the Cluster analysis is done [7].

MATERIALS AND METHODS

The study examined 20 varieties of sorghum, of which the first 17 variety were Iranian, two Indian and one Thai.

The land under the test immediately plowed after wheat harvest in May 2007. The amount and type of fertilizer

according to soil samples were added to the land. Other operations for Seedbed preparation were performed. Stacks

were constructed within 60 cm of each other and planting was done. Each cultivar in each plot was planted in four

8/13/2019 ABR-2013-4-8-201-204

2/4

Shamsali Darvishiet al Annals of Biological Research, 2013, 4 (8):201-204

_____________________________________________________________________________

202


rows of seven meters in length.After emergence, the distance between they were narrowed to six centimeter apart

in the phase of 6-4 leaves. Therefore, on each seven meters line remained 118 plants. Plants at a height of about 40

cm were given 150 kg of Serk nitrogen fertilizer on the banks of the stacks in the form of ribbon. Then permeable

irrigation was done. The irrigation period determined once every 7-10 days. In order to classify varieties based on

the entire traits and standardized data of Ward's method cluster analysis using squared Euclidean distance based on

the mean standardized data were carried out. To determine the appropriate location for cutting the dendrogram,

discriminant function analysis was used. To obtain more information on the relationship between variouscharacteristics and profound understanding of data structures, factor analysis was used. Data analysis was

performed using SPSS-16 software.

RESULTS AND DISCUSSION

Cluster Analysis

Cluster analysis of all varieties of seeding sorghum based on all traits was performed using Ward's method with

standardized data. Based on the results of the discriminant function analysis on various sections of the cut, the

maximum difference amongst the group was observed in two clusters (Table 1 and Figure 1). The first cluster

includes varieties KGS-29, KGS-3, KGS-11, KGS-17, KGS-24, KGS-15, KGS-31, KGS-9, KGS-12, KGS-19,

KGS-32, BICS-11, KGS-23, KGS-33, KGS-25, KGS-5, KGS-27 and KGS-36 and the second cluster included

genotypes ICSV-274 and UT-378B, respectively. Variety classification of the experiments showed the good nature

of the classification in geographical distribution due to exposure of foreign varieties groups in similar groups. Todemonstrate the effectiveness of each trait in distinction of each cluster, the mean of each cluster, and the average

deviation from total mean and total mean were calculated for each of the traits (Table 2).

In the first cluster, the value of yield, harvest index, plant biomass, seed weight, number of grains per panicle,

number of branches per panicle, panicle length and stem diameter was greater than the mean of the whole group. In

the second cluster, number of tillers, plant height and stamen leaf area had a value greater than the mean of theentire group.

Figure 1. The dendrogram of sorghum cultivars resulting from cluster analysis using Ward Method based on

standardized data of all traits

TABLE 1. Discriminant function analysis to determine the cut-off point of dendrogram resulting from

cluster analysis based on all standardized traits

Number of groups Eigenvalues Percent of variance Canonical Correlation Wilkes Lambda Probability

2 140.220 94.6 0.995 0.001 0.0003 5.910 5.4 0.925 0.145 0.05

8/13/2019 ABR-2013-4-8-201-204

3/4


_____________________________________________________________________________

203


Table 2. Groups average and their deviation percentage based on all traits of 20 cultivars of sorghum.

Group

Group Average Deviation of totalmean

Group Average Deviation of totalmean total mean

1 2

Performance 5.15 5.94 2.765 -53.47 4.868

Harvest index 51.44 5.56 24.305 050.12 48.729

Plant biomass 77.62 4.9 37541 -44.0 73.99Seed weight 29.19 3.43 28.8 -1.2 29.15

Number of grainsper panicle

1431 8.2 346.75 -73.78 1322.85

Branches per

panicle

84.85 0.46 80.95 -4.15 84.46

Panicle Length 27.97 0.61 25.25 -9.17 27.87

Days to emergence 65.61 -0.06 66.0 0.53 65.65

Number of tillers 0.26 -49.0 2.77 443.0 0.51Stem diameter 7.241 0.85 6.605 -8.0 7.177

Plant height 124.9 -4.6 185 41.32 130.9

Stamen leaf area 146.3 -0.85 158.35 25.65 147.505

FACTOR ANALYSIS

In factor analysis, five factors which accounted for a total of 86.244% of the total variation were selected (Table 3).

In this analysis, factors with eigenvalues greater than one were selected. Validity of the factor selection wasconfirmed byScree graph (Figure 1). In this study, the first main factor which explained 33.890% of the variation

had high correlation with traits such as yield, harvest index, biomass, plant height, number of tillers, and number ofgrains per panicle. Therefore the first factor can be introduced as a performance factor. It is noteworthy that this

factor had a high negative correlation with Plant height and Number of tillers. The second factor accounted for

16.232% variation, had a high positive correlation with stem diameter and number of days to panicle emergence.

Therefore the second factor can be introduced as an effective factor for growth. The third factor explained 13.999%

along with the second and first factor with 64.120% of total variance having a high positive correlation with grain

weight and biomass. And other traits in selection of cultivars through this factor were less important factors. Thefourth factor with 13.612 percent variation (along with the first three, 77.732 percent variation) accounted for which

had a high positive correlation with panicle length and panicle branches. And other traits in selection of cultivars

through this factor were less important factors. The fifth and the first four factors explained 8.512% and 86.244% of

the total variance, respectively. This factor had high positive correlation with stamen leaf area. Other traits were

less important factor in selecting varieties.

Table 3 - Factor Coefficients, Eigenvalues and Cumulative Changes Principal Factors with Varimax

Rotation.

Traits Eigenvalues Vectors

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

Stamen leaf area -0.049 -0.111 0.021 0.112 0.933Plant height -0.885 -0.102 -0.237 -0.058 0.235

Stem diameter 0.360 0.708 0.427 -0.033 -0.109

Number of tillers -0.847 -0.249 -0.076 -0.027 -0.132

Days to panicle

emergence

0.031 0.903 -0.064 0.185 0.111

Panicle Length 0.233 0.115 -0.036 0.923 0.026Branches per

panicle

-0.030 -0.053 -0.019 0.957 0.096

Number of grainsper panicle

0.900 -0.187 -0.155 0.158 -0.135

Seed weight -0.196 0.216 0.819 -0.138 0.111

Biomass 0.749 -0.046 0.619 -0.100 -0.065Harvest index 0.899 -0.064 0.026 0.112 -0.108

Performance 0.790 0.009 0.109 -0.034 0.327

Eigenvalues 4.745 2.272 1.960 1.906 1.192Cumulative

variation

33.890 50.122 64.120 77.732 88.244

8/13/2019 ABR-2013-4-8-201-204

4/4


_____________________________________________________________________________

204


Figure 1 - Scree graph obtained from the Factor analysis

CONCLUSION

There was a significant difference between experimented cultivars for most of studied traits indicating the

difference between seeding sorghum varieties. So this provides selection, breeding and introduction of high

yielding cultivars in different regions. In cluster analysis using Ward's method with standardized data varieties

classified in two clusters. Varieties of cluster one were superior in terms of yield. Variety classification of theexperiments showed the good nature of the classification in geographical distribution due to exposure of foreign

varieties groups in similar groups. In factor analysis the first factor was introduced as the performance factor with

33.890% of the total variation.

REFERENCES

[1] Almodares, A., Taheri, R., and Safavi, V. 2008. Sorghum. Isfahan Jahad. Daneshgahi Press First Edition.

263pp.

[ 2] Belton, D. S., and Taylor, J. R. N. 2004. Trends in Food Science and Technology. 15: 94-98.

[3] Defoor, D. J., Cole, N. A., Galgean, M. L., and Jones, O. R. 2001.Journal of Animal Science. 79: 19-25.

[4] Anonymous, Food and Agricultural Organization. 2007. Crops production, sorghum harvesting area, Retrieved

November, 15, 2009, from http: //www.fao.org/crops production.[5] Borojevic, S., 1990. crop Sci. 17:145-152.

[6] Jafari, AS., Nosrati Nygjh, M.. Heidari Sharif Abad, H., 2003. Quarterly Journal of genetics and plant breeding

for forestry Iran (11): 63-103, published by Research Institute of Forests and Rangelands, Tehran.

[7] Farshadfar, AS., 1998. Application of Quantitative Genetics in Plant Breeding (Volume I), publications, Tagh

Bostan, Razi University.

1413121110987654321

Component Number

6

5

4

3

2

1

0

Eigenvalue

Scree Plot

ABR-2013-4-8-201-204

Documents

Transcript of ABR-2013-4-8-201-204