A multivariate analysis approach for evaluating diverse germplasm of cotton (Gossypium hirsutum L.) for seed and fiber characteristics

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1 Basic Research Journal of Agricultural Science and Review ISSN Vol. 4(6) pp June 2015 Available online http// Copyright 2015 Basic Research Journal Full Length Research Paper A multivariate analysis approach for evaluating diverse germplasm of cotton (Gossypium hirsutum L.) for seed and fiber characteristics Rana Imtiaz Ahmad 1, Shoaib Liaqat 1 *, Etrat Noor 2, Abdul Qayyum 2, Ahsan Irshad 4, Altaf Hussain 1, Ghayour Ahmed 1, Abdul Karim 1 and Seema Mahmood 3 1 Cotton Research Station Multan, Pakistan. 2 Department of Plant Breeding and Genetics, Bahauddin Zakariya University, Multan, Pakistan. P.C Institute of Pure and Applied Biology, Bahauddin Zakariya University, Multan, Pakistan. P.C Regional Agricultural Research institute Bahawalpur, Pakistan. *Corresponding author shoaib87pk@hotmail.com Accepted 27 April, 2015 ABSTRACT Thirty nine genotypes of cultivated cotton (Gossypium hirsutum L.) were studied for total oil and protein content, seed index and seed density. Fiber cellulose content, fiber length, strength, fineness, ginning out turn (GOT) and lint index were also investigated. Data were subjected to analysis of variance and estimates were made for genetic advance, broad sense heritability and coefficient of variance for the traits. ANOVA revealed highly significant variability among genotypes for all characteristics studied except seed index and seed density. The heritability was higher for GOT% (0.99) and fiber length (0.91). The highest value (5.94) for genetic advance was observed for fiber length whereas this value was 0.14 for seed density. The principle component analysis (PCA) revealed significant differences between genotypes and the first three components with Eigen values greater than 1 contributed 65.95% of the variability among the genotypes. Keywords: Cotton, Seed, Fiber, Multivariate analysis. INTRODUCTION Among cotton growing countries, Pakistan ranks 5 th largest producer in the world and is grown over an area of about 780 million hectare producing 10.8 million bales during the year (Anonymous, 2012). It is mainly grown for oil, seed and fiber (Azhar et al., 2009). The amount of oil and protein in cottonseed are of considerable value from food and nutritional point of view (Ahmad et al., 2003; Qayyum et al., 2010). Cotton seed contains 20-40% of oil and 20-30% protein but some cultivars may yield up to 45-50% protein. A good proportion of protein is also found in seed cakes which are obtained after the extraction of oil. About 65% of the domestic supply of oil comes from the cotton because of its low tri-unsaturated fatty acids contents (Azhar and Khan, 2003). Modern textile industry and quality of many finished products are largely based on the eminence of cotton fiber as many key processes such as spinning, weaving and dying largely depend on fiber quality (Iqbal

2 Shoaib et al. 165 et al., 2003; Murtaza et al., 2004). Among fiber characteristics, length, strength and fineness are considered as important components of the fiber quality which in turn depends on the cellulosic content of the fiber, an attribute that determines the fiber quality (Hussain et al., 2010). Effective varietal selection has greatly been emphasized for the conservation of genetic resources as well as for consistent variability of desired characteristics (Shabbir and Cheema, 2011). Keeping in view the above aspects, in the present study thirty nine varieties of cotton were investigated for imperative seed and fiber characteristics using multivariate analysis. The prime objective of this study was to determine the genetic potential for the variability of characters through broad sense heritability, genetic advance and correlation, expressions and association of different traits in upland cotton cultivars. The study will be helpful in articulating efficient selection programmes for synthesis and development of new cotton genotypes with improved quality traits for seed oil and fiber having broad genetic basis. MATERIALS AND METHODS The seeds of thirty nine varieties were collected and sown in thirteen plots during June, In each plot, three varieties were sown singly in rows. Three seeds were sown at places but thinned out to one after twenty days of sowing when plants were about cm tall and single plant was maintained per hill. Recommended cultural practices were applied in a similar manner to all entries from sowing till harvesting. Plants were allowed to grow till maturity then picking was done during November-December, 2011 on single plant basis. Seed Characteristics Seed index The weight of 100 seeds was taken from the electronic weighing balance after delinting them. Seed density: It was determined by the following formula: following Furniss et al. (1989). For this purpose, 25 g of healthy and uniform seeds were taken and crushed in a clear mortar, care being taken that not a single grain or even a fraction of grain was lost. The crushed meal was transferred to a thimble of soxhlet apparatus with the help of a spatula. The last traces of cotton meal sticking to the pestle, mortar and spatula were transferred to the thimble with petroleum ether (60-80%). Two-thirds of the flask was filled with petroleum ether and the distillation was carried out for 15 h when the solvent started boiling. The solvent was then distilled off. The oil was then transferred to a tarred conical flask and the last traces sticking into the interior of the flask were removed with the solvent and the washings were transferred to the conical flask, which was then placed, on a boiling water bath for the expulsion of the petroleum ether and moisture till a constant weight was obtained. The contents were expressed as percentage of oil in seeds. Protein estimation Determination of crude protein was done using the micro Kjeldahl. The value of nitrogen obtained was multiplied by the general factor (6.25) following Black (1965) to get the percentage of total crude protein. Fiber characteristics Cellulose content 2 g fiber was put into the 450 ml flask with 350 ml of 95% ethanol in it and then extracted by soxhelt extractor for 6 hours with hot ethanol. After stop heating the fiber was then taken out by forceps and put it into the oven for drying, until the fiber weight was stable. The difference in weight is calculated as cellulose content. The fiber characteristics were estimated for fiber length, strength and fineness by using HVI Got % and lint index These two parameters were determine by the following formulae: GOT % = Weight of the lint sample 100 Weight of the seed cotton Seed density = Weight of 100 seeds Volume of 100 seeds Lint index = Seed index Lint% Lint% Oil content: Estimation of oil content in cotton seeds was carried out Statistical analysis Data were subjected to statistical analysis for all seed

3 166. Basic Res. J. Agric. Sci. Rev. Table 1. Cotton (Gossipium hirsutum L.) genotypes. S. No Genotype S. No Genotype S. No Genotype 1 VH NIAB CIM FH PB Bt A-1 3 NIBGE-2 16 Neelam CIM CIM MNH NIAB CRIS Neelam PBG-3 6 GOMAL FDH FVH-53 7 MNH FDH MNH F TARZAN-1 34 MARVI 9 SITARA FH CHANDI STONVILLE NIAB TD-1 11 COKER BH FH M CRIS FDH CIM NIAB FDH-228 Table 2. Analysis of variance (summary) for 10 traits among 39 genotypes of Gossypium hirsutum L. Characters MS(V) MS(R) MS(E) h 2 (%) GA CV (%) Oil Content 12.13** Protein Content 14.47** Cellulose Content 22.5** Fiber Length 49.26*** Fiber strength 55.70*** Fineness 1.66 N.S GOT 90.38*** Lint Index 8.3** Seed Index 2.94 N.S Seed Density 0.07 N.S MSV, MSR, MSE are mean squares for varieties, replicates and error where h 2 = heritability, GA= genetic advance and CV =coefficient of variance, respectively. fiber traits in order to determine estimate of variability. Analysis of variance for all the characteristics was carried by following Steel and Torrie (1980). Correlation analysis was carried out for mean values of each attribute. All recorded traits were analyzed by numerical taxonomic techniques using Principal Component Analysis (PCA). A cluster analysis using complete linkage method was also carried out. The data were standardized and transformed prior to PCA and cluster analysis using Z scores by MINITAB version RESULTS AND DISCUSSION Analysis of variance (Table 2) along with heritability (h 2 ) and genetic advance (GA) for 39 genotypes indicated significant differences for all the characters. Broad sense heritability, coefficient of variability and genetic advance which were computed for all ten characters indicated medium to high estimates. Heritability of characters was greater than 0.90 for all the parameters except for seed index and seed density which exhibited 0.72 and 0.63 heritability values, respectively. The highest estimate of heritability (0.99) was noticed for GOT (%). Heritable variation among genotypes which is in close agreement of Khan et al. (2010). The highest value of GA (7.32) was recorded for GOT (%) but for seed density the lowest GA (0.14) was observed. The genotypes showed a significant (P<0.01) variability for oil protein, cellulose content and for lint index. Similarly, the genotypes showed a marked contrast (P< 0.001) for fiber length, strength and GOT. Greater magnitude of broad sense heritability coupled with higher genetic advance in characters under study provided the evidence that these characters were under the control of additive genetic effects and is also supported by the findings of other workers (Ahmad et al., 2003; Naveed et al., 2004; Muhammad et al., 2012). The coefficient of variability (CV) ranged from for ten characters studied. Cellulose content had the maximum CV (22.7) followed by seed and lint index, and 10.42, respectively. Analysis of correlation The values for correlation coefficients (genotypic and

4 Shoaib et al. 167 Table 3. Genotypic and phenotypic correlation among Gossypium hirsutum L. genotypes. O.C PC CC FL FS FF GOT (%) LI SI SD O.C PC CC FL FS FF GOT (%) * * * P ** * * G ** * * * P G P * G * * * P G * * P G P * ** G * * P ** ** LI SI SD G G * P ** P G P **= Highly Significant, *= Significant Whereas: O.C= Oil content, P.C= protein content, C.C= Cellulose content, F.L= Fiber length, F.S= Fiber strength, F.F= Fiber finess, G.O.T= Ginning out turn, L.I= Lint index, S.I= Seed index, S.D= Seed density. phenotypic) between attributes were calculated and their significance was determined. Table 3 depicted a negative but non significant correlation between oil content and protein content, fiber strength and fiber length Thus, two types of seed reserves (oil and protein) did not show affirmative relationship with these fiber characteristics. However, positive and significant (P<0.05) genotypic correlations were observed between oil and cellulose content and between oil content, GOT (%) and lint index. Phenotypic correlation was found to be highly significant (P<0.001) for oil content and GOT (%). The positive phenotypic correlation was also significant (P<0.05) between oil content, seed density and lint index. Both genotypic and phenotypic correlation coefficients for protein and cellulose content, fiber fineness and seed density were negative and non significant. Genotypic correlation was found to be positive and significant (P<0.05) between protein and fiber length, fiber strength, lint and seed index. Both genotypic and phenotypic relationship between protein content and GOT (%) was positively correlated but non significant. Cellulose content and fiber length, fiber strength, fiber fineness, lint index, seed index and seed density were negatively correlated genotypically as well as phenotypically. However, negative association was significant (P<0.05) between cellulose content and fiber length only. The correlation coefficient were positive but non significant for cellulose content and GOT (%). These results are parallel to the findings of Irbil et al. (2006), Otoo et al. (2006) and Salahuddin et al. (2010). It is also evident from Table 3 that fiber length was positively correlated with fiber strength, GOT (%), lint index, seed

5 168. Basic Res. J. Agric. Sci. Rev. Table 4. Principle components (PCs) for 10 characters in 39 genotypes of cotton (Gossypium hirsutum L). PC1 PC2 PC3 PC4 Eigen values Proportion of variance Cumulative variance (%) Eigen Vectors Variables PC1 PC2 PC3 PC4 Oil content Protein content Cellulose content Fiber length Fiber strength Fiber fineness Ginning out turn Lint index Seed index Seed density index and seed density for genotypic and phenotypic correlation but out of these five correlated characters three were found to be statistically significant(p<0.05) for genotypic analysis. These results are in accordance with Ibo et al. (2003), Abuja et al. (2006), Hussain et al. (2009). The association between fiber length and fiber fineness was negative but non significant. For fiber strength and fineness and seed density, phenotypic and genotypic correlations were negative but significant (P<0.05) and positive were observed between fiber strength and ginning out turn, lint index and seed index genotypically however, phenotypes exhibited positive but non significant association between these attributes. The values for correlation coefficients between fiber fineness and GOT (%), lint and seed index and seed density were consistently negative both for genotypes and phenotypes. However, the negative association between fiber fineness and seed index was highly significant (P<0.001) phenotypically. Similarly, the negative correlation coefficient value was significant (P<0.05) between fiber fineness and lint index. The degree of association between GOT (%) and lint and seed index and seed density was positive and significant both genotypically (P<0.05) and phenotypically (P<0.001). The results of correlation analysis clearly indicated that several traits in these genotypes were positively correlated thus with the improvement of one character, the correlated attributes can also be improved. Thus correlation analysis appeared to satisfy selection criteria which should be based on a number of correlated traits rather than positive association between a few variables. Several other studies carried out for cotton germplasm also reported association as well as disassociation among various traits considered for correlation analysis thus our results are in close conformity to the finding of Naveed et al. (2004), Rauf et al. (2004), Salahuddin et al. (2010), Khan et al. (2010). Principal component analysis The mean values for various attributes of thirty nine cotton genotypes were analyzed by using principal component analysis which is presented in Table 4. the large dimensions of generated data were reduced to only four leading principal component (PC1, PC2, PC3 and PC4) which have extracted major share of variation in the data of selected traits for cotton. Three PCs exhibited more than 1 eigen value and accounted for to 65.95% cumulative variability as PC1 have 38.35%, PC2 showed 16.45% and PC3 exhibited 11.15% variability among the genotypes for the traits studied. The characteristics; oil content, cellulose content, protein content, fiber strength and seed index can be considered as important attributes of cotton cultivars that exhibited considerable variability. The first principal component was more associated to cellulose content and fiber fineness whereas the second principal component relates to protein content and fiber strength while the third one exhibited positive effects for cellulose content and seed index. The variability of these traits in cotton genotypes can be due to genetic as well as environmental variation. (Khodarahmpour et al., 2011). Cluster analysis Cluster analysis based on linkage distance using 10 traits of 39 cotton genotype is presented in Figure 1. In

6 Shoaib et al Linkage Distance Figure 1. Dendrogam resulting from cluster analysis in 39 genotypes of cotton (Gossypium hirsutum L).

7 170. Basic Res. J. Agric. Sci. Rev. 4 Projection of the cases on the factor-plane ( 1 x 2) Cases with sum of cosine square >= Factor 2: 16.45% Factor 1: 38.35% Active Figure 2. Scattered diagram of two principal components based on mean values of 39 genotypes of Gossypium hirsutum L. dendrogram, the V1 to V39 corresponds to genotypes at 190%. At 190% of level of similarity is indicated by 1 to 14 which shows dissimilarity to other all genotypes. Genotypes 1-5 show similarity at 18% of level while 6 to 14 genotypes show similarity at 21% of level while other fall in range 39% of level which are the maximum number of genotypes. Similarly range in 19% of similarity as indicated in Figure 1. Cluster analysis grouped together genotypes with greater genetic similarity and variability but the cluster did not essentially include all the cultivar from the same origin thus no association was established between traits and geographical origin of cultivars. Parallel finding have also been reported in diverse germplasm of other crops belonging to different geographical regions. The group with greater variability can be chosen for further breeding programmes. Scattered plot A principal component scattered plot group genotypes together with greater genetic similarity and variability based on the genotypes and ten variables studied. It is evident from the Figure 2, that the genotype number 32 and 35, 18 and 21, 10 and 20 exhibited a similar sort of variability while genotypes 37 and 38 did not show any similarity thus are spatially separated from other genotypes. Thus desirable genotypes may be selected from a particular group for future breeding programmes. (Maqbool et al., 2010) CONCLUSIONS The first three PC components have Eigen values greater than 1 contributed 65.95% of the variability among the genotypes. Broad sense heritability, genetic advance and coefficient of variability for all ten characters indicate medium to high estimates. Higher broad sense heritability coupled with higher genetic advance in characters under study signified that fiber and seed attributes due to additive genetic effects, indicating the possibility of prompt selection and genetic improvement of the germplasm. It is safe to conclude that germplasm used in this study can be exploited for future cotton breeding programmes of oil and fiber characteristics. REFERENCES Abuja LS, Dhayal LX, Parkash R (2006). A correlation and path coefficient analysis of components in G. hirsutum L. hybrids by usual and fiber quality grouping. Turk. J. Agric. For, 30,

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