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GENETIC DIVERSITY AMONG Bt COTTON (GOSSYPIUM HIRSUTUM L.) GERMPLASM ASSESSED THROUGH MORPHOLOGICAL AND WITHIN-BOLL YIELD ATTRIBUTES.

Byline: B. Riaz, A. Saeed, S. Fiaz and A. Riaz

Keywords: ECV, GCV, genetic advance, heritability, PCA, PCV.

INTRODUCTION

Cotton (Gossypium hirsutum L.) is an important fiber crop and the second important oilseed crop after soybean (Freeland et al., 2006). Fiber quality in cotton has supreme importance as similar to yield, which involves growers, ginners and the textile industry simultaneously (Yaqoob et al., 2016). In Pakistan, cotton yield is low in comparison to average production of other cotton growing countries. This is due to the lack of resistant varieties against abiotic and biotic stresses such as high temperature, cotton leaf curl virus (CLCuV) disease, pest attack and the improper production technology (Panni et al., 2012). To overcome this situation, several possibilities can be employed, including increasing inputs, use of pesticides and genetic improvement of elite varieties. In future, sustainable production of cotton will depend upon the development of elite cotton varieties with high yield, better quality of seed cotton, and resistant to biotic and abiotic stresses (Ahmad et al., 2012).

Due to its importance, cotton crop has attracted plant breeders to improve the plant genetic architecture (Ahmad et al., 2016). These efforts led to the evolution of high yielding cultivars with better yield potential and fiber quality attributes (Khan et al., 2017).

For the successful breeding program, it is very important to have enough knowledge and understanding regarding genetic diversity present in the available crop germplasm, which will be helpful for plant breeders to select the best parental material that will be useful in generating high yielding cultivars (Esmail et al., 2008).

The aim of this study was to evaluate the genetic diversity in Bt germplasm of upland cotton using PCA. Genotypic, phenotypic, environmental variances, coefficients of variance, heritability and genetic advance were also calculated to determine heritable and non-heritable part of variability. The obtained results will be utilized in the improvement of existing cotton varieties or development of new cultivars.

MATERIALS AND METHODS

Sixty Bt cotton genotypes were evaluated under field condition during summer 2015-2016 (Table 1). Delinnted seeds of all genotypes were grown in RCBD design with two replications at the University of Agriculture, Faisalabad, Pakistan. Plant to plant and row to row distances were kept at 75 and 30 cm, respectively.

The crop was raised to maturity with standard production practices. On maturity, the data were recorded for morphological characters viz., node number of first fruiting branch, node number of first effective boll formation, node height up to first fruiting branch, plant height, monopodial branches per plant, sympodial branches per plant, bolls per plant, seed cotton yield per plant and within-boll yield components like boll weight, seeds per boll, lint mass per boll, lint percentage (%), seed cotton per seed and lint per seed, from randomly selected five plants.

Statistical Analysis: Replicated mean data of all the characters were subjected to the analysis of variance (ANOVA) technique as outlined by Steel et al. (1997). Data were further analyzed by PCA as described by Neyman and Pearson (1928) using statistical software Statistical.

The ANOVA was performed in Statistics 8.1 statistical software. The detailed procedure for all kinds of statistical data analysis has been given below:

Components of variances were calculated by the following formulas:

Genotypic variance (Ig) = Vg = (MSg - MSe)/r

Environmental variance (Ie) = Ve = (MSe)

Phenotypic variance (Ip) = Vp = Vg + Ve

The coefficients of variation on environmental, genotypic and phenotypic basis were determined as described by Burton (1952) and revealed by Singh and Narayanan (2000).

Genotypic coefficient of variance (GCV) = (Ig/trait mean) x 100

Environmental coefficient of variance (ECV) = (Ie/trait mean) x 100

Phenotypic coefficient of variance (PCV) = (Ip/trait mean) x 100

The PCV and GCV were classified as suggested by Sivasubramanian and Madhavamenon (1973) and are given below:

Low: less than 10%

Moderate: 10-20%

High: more than 20%

Broad-sense heritability was calculated by the formula given by Lush (1940).

h2 (b.s)% = (Vg/Vp) x 100

Categorization of broad-sense heritability was made according to Johnson et al. (1955) and has been given below:

Low: less than 30%

Moderate: 30-50%

High: more than 50%

Genetic advance and genetic advance as a percentage of the mean were calculated by the following formula given by Johnson et al. (1955).

Genetic advance (G.A.) = K x {Vg/(Vp)1/2}

Genetic advance (G.A.)% = (Genetic Advance/Trait Mean) x 100

Where;

(Vp)1/2 = phenotypic standard deviation

K = selection differential, and its value at selection intensity of 10% is 1.76 Falconer and Mackay (1996).

The classification of genetic advance as a percentage of mean was made as suggested by the Johnson et al. (1955) and is given below:

Low: less than 10%

Moderate: 10-20%

High: more than 20%

Table 1. Experimental material used in the study.

No.###Name of###No.###Name of

###accessions###accessions

1###C-26###31###VH-283

2###VH-282###32###VH-295

3###VH-259###33###AS-01

4###A.A-802###34###VH-329

5###MNH-886###35###FH-175

6###MNH-886###36###FH-187

7###IR-3071###37###FH-177

8###CRS-2007###38###CIM-599

9###FH-113###39###CIM-602

10###SB-149###40###CIM-598

11###NS-131###41###MG-6

12###KZ-181###42###MNH-586

13###KZ-189###43###FH-941

14###FH-169###44###VH-324

15###FH-172###45###VH-325

16###KZ-191###46###VH-333

17###A.A-703###47###VH-339

18###FH-154###48###VH-228

19###FH-142###49###FH-182

20###FH-170###50###FH-171

21###VH-148###51###FH-159

22###CRS-456###52###FH-183

23###FH-118###53###FH-158

24###NS-121###54###Lalazar

25###FH-114###55###VH-338

26###NIAB-820###56###VH-337

27###IR-3###57###VH-341

28###IR-901###58###VH-330

29###FH-4243###59###IUB-222

30###S-3###60###IUB-212

Table 2. Mean square of various morphological and within-boll yield traits in 60 cotton genotypes

Traits###Vg###GCV%###Vp###PCV%###Ve###ECV%###h2###GA###GA%

Node no. for the###0.6925###12.094###0.988###12.572###0.296###7.912###70.02###1.226###17.81

1st fruiting branch

Node height up###1.5258###9.332###2.772###12.578###1.246###8.435###55.03###1.604###12.11

to 1st fruiting

branch

Node 1st###1.5178###13.322###3.263###19.534###1.745###14.287###46.51###1.47###15.89

effective boll

formation

Monopodial###0.1687###31.555###0.276###40.337###1.107###25.12###61.19###0.562###43.19

branches

sympodial###6.8687###14.54###11.737###19.006###4.868###12.24###58.52###3.508###19.46

branches

Bolls per plant###175.95###31.459###34.098###34.373###210.05###13.85###83.77###21.246###50.38

Plant height###239.11###13.219###311.17###15.08###72.065###7.26###76.84###23.72###20.27

Seed cotton###414.29###22.31###517.54###24.945###103.24###11.14###80.05###31.86###34.94

Yield per plant

Boll weight###0.0839###12.957###0.118###15.381###0.034###8.287###70.96###0.427###19.1

Seeds per boll###3.3106###8.799###4.775###10.569###1.465###5.853###69.33###2.65###12.82

Lint mass per###0.0033###34.365###0.0045###40.267###0.001###20.99###72.84###0.086###51.33

boll

Seed cotton per###0.0002###13.324###0.0004###18.119###0.0002###12.281###54.065###0.019###17.14

seed

Lint %age###3.679###4.659###5.102###5.486###1.423###2.897###72.12###2.85###6.92

Lint per seed###0.0000001###14.8192###0.0000002###18.908###0.0000001###11.744###61.42###0.0005###20.32

Table 3. PCA for 14 characters in 60 Bt germplasm lines of G. hirsutum L.

Variables###PC-I###PC-II###PC-III###PC-IV###PC-V

Plant Height###-0.563455###-0.383145###0.118730###0.170069###0.347123

Sympodial branches###-0.673425###0.187292###0.225339###-0.078826###0.487360

monopodial branches###-0.609106###-0.031683###0.052797###0.129960###-0.619140

Bolls per plant###-0.736143###0.345000###0.171165###0.354958###-0.028165

Seed cotton yield per plant###-0.835743###-0.002981###0.272066###0.206863###0.041962

Node no. for 1st fruiting branch###0.434488###-0.572124###0.235541###0.361894###-0.035669

Node no. for 1st effective boll formation###0.340218###-0.525938###-0.15394###0.458341###0.134098

Node height up to 1st fruiting branch###0.119371###-0.648156###0.160286###0.464240###0.181972

Boll weight###-0.520593###-0.665693###0.147303###-0.344195###-0.120687

Lint %age###0.078984###0.100924###0.588373###0.354886###-0.400152

Seeds per boll###-0.133697###-0.355513###0.647483###-0.513656###0.071263

Seed cotton per seed###-0.598057###-0.435737###-0.449271###-0.053085###-0.205881

Lint per seed###-0.423162###-0.457144###-0.698234###-0.040712###-0.033370

Lint mass per boll###0.359515###-0.580759###0.243970###-0.382098###-0.138486

RESULTS AND DISCUSSION

ANOVA for each character showed that mean squares for studied genotypes were significant, suggesting differences among all 60 genotypes for all characters under investigation. Several researchers also reported similar results for all these characters (Siddique et al.,2007; Baraiya et al., 2011; Shakeel et al., 2012; Imran et al., 2012; Iqbal et al., 2013; Tang and Xiao, 2013; Baloch et al., 2014; Saeed et al., 2014). The variations among cotton genotypes for these traits influenced by genetic as well as environmental factors (Khodarahmpour et al., 2010). Therefore, the genotypic variance (Vg), genotypic coefficient of variance (GCV), phenotypic variance (Vp), phenotypic coefficient of variance (PCV), environmental variance (Ve), and environmental coefficient of variance (ECV), broad sense heritability (h2), genetic advance (GA) and genetic advance as a percentage of the mean (GA%) for all traits are calculated and given below in Table 2.

For a particular trait, if the phenotypic coefficient of variance is more than the genotypic coefficient of variance it shows that environmental influence is more than genetic component and vice versa. From Table 2 it is clear that the highest (34.37) genotypic coefficient of variance recorded for lint mass per boll while the highest (40.34) phenotypic coefficient of variance for the monopodial branches per plant. Estimation of heritability helps the breeders while making the selection. However, Johnson et al. (1955) reported that alone heritability estimates do not give the clear idea about expected gain in the next generation but only in conjunction with genetic advance. Genetic advance gives the magnitude of expected genetic gain obtained by one cycle of selection (Idahosa et al., 2010). The estimation of genetic components showed high heritability for seed cotton yield per plant (80%) and bolls per plant (83%).

The node number of first effective boll formation showed lower heritability about 46% as compared to other traits. Genetic advance for lint mass per boll was recorded as highest (51%) whereas the lint percentage exhibited lowest (6.9%).

PCA: The mean data were further analyzed by PCA using Statistica software. The data matrix of 14 x 60 was prepared for the analysis. Out of 14 PCs, first five PCs exhibited more than one Eigen value. The PC-I showed maximum variation (26.15%) which was mainly due to the sympodial branches, monopodial branches, bolls per plant and seed cotton yield per plant. In PC-II, node number of first fruiting branch, node number for first effective boll formation, node height sup to first fruiting branch, boll weight and the lint mass per boll were the most important traits contributing about 18.87% to the variation. The PC-III explained variation (12.94%) of the total variation mainly contributed by lint percentage, seeds per boll and lint per seed. In PC-IV, node number for first effective boll formation, node height up to the first fruiting branch and the seeds per boll were the most important traits contributing to the variation which amounted for about 10.25%.

Whereas, PC-V exhibited 6.94% of the total variation which was mainly caused by node number for first effective boll formation, node height up to the first fruiting branch and seeds per boll (Table 2).

Biplot analysis: A principal component biplot (Figure 1) showed that variables were super imposed on the plot as vectors. The distance of each variable with respect to PC-I and PC-II showed the contribution of this variable in the variation of germplasm. Different parameters including bolls per plant, seed cotton yield per plant, boll weight, node number of first fruit branch and node height up to first fruiting branch showed more differences (as represented in biplot) together in PC-I and PC-II, while lint percentage, sympodial branches, lint per seed and seeds per boll had minimum differences in PC-I and PC-II.

The distribution of morphological traits around the vectors and the angles of vectors in biplot display provides multi-trait selection associated with yield.The evaluation of genotypes for further improvement of cotton seed yield and its related components is essential in breeding programs for developing high yielding cotton varieties (McCarty et al., 2005).

The genetic variations among cotton genotypes for sympodial and monopodial branches, bolls per plant, seed cotton yield per plant, boll weight and lint percentage have also been reported in previous studies (Khan et al., 2017; Nazir et al., 2013). The genetic diversity studies for agronomic essential traits using principal component analysis ultimately lead to the identification of phenotypic variations in cotton germplasm (Li et al., 2008).

Ahmed et al. (2012) employed PCA to determine the extent of genetic diversity in cotton germplasm, which might be helpful for selection of parents for a successful breeding program. Mohammadi and Prasanna (2003) focused on the use of statistical tools and methods of genetic ranges. They analyzed that cluster analysis and PCA are the most frequently employed and seemed predominantly valuable.

Conclusion: The components of variances and PCA have revealed the valuable variations among cotton accessions for all the yield related traits. The germplasm used in this study also exhibited the high broad-sense heritability and moderate to high genetic advance. These findings indicate that germplasm exhibits potential for being utilized in the future breeding programs for the improvement of seed cotton yield by selecting the genotypes having desirable traits.

REFERENCES

Ahmad, M.Q., S.H. Khan and F.M. Azhar (2012). Decreasing level of genetic diversity in germplasm and cultivars of upland cotton (Gossypium hirsutum L.) in Pakistan. J. Agric. Soc. Sci. 8 (3): 92-96.

Ahmad, S., S. Fiaz., A. Riaz., I. Bashir and A. Zeb (2016). Correlation analysis of morphological and fiber quality traits in upland cotton (Gossypium hirsutum L.). Int. J. Biol. 9(4): 200-208.

Baraiya, B.R., S.K. Brade and R.K. Chhapare (2011). Screening of cotton genotypes for the drought tolerance in rain fed conditions. J. Cotton. Res. Dev. 25: 50-54.

Baloch, M.J., N.U. Khan, M.A. Rajput, W.A. Jatoi, S. Gul, I.H. Rind and N.F. Veesar (2014). Yield related morphological measures of short duration cotton genotypes. The J. Anim. Plant Sci. 24: 1198-1211.

Burton, G.W (1952). Quantitative inheritance in grasses. Proceedings of the 6th International Grassland Congress, 1: 227-283.

Esmail, R.M., J.F. Zhang and A.M. Abdel-Hamid (2008). Genetic diversity in the elite cotton germplasm lines using field performance and RAPDs markers. World J. Agri. Sci. 4: 15-25.

Falconer, D. S., and T.F.C. Mackay (1996). Introduction to Quantitative Genetics. Ed 4. Longmans Green, Harlow, Essex, UK.

Freeland, J.T.B., B. Pettigrew, P. Thaxton and G.L. Andrews (2006). Agro meteorology and cotton production. In: (3rd Ed). Guide to Agricultural Meteorological Practices, pp. 1-17.

Idahosa, D. O., J. E. Alikaand A. U. Omoregie (2010). Genetic variability, heritability and expected genetic advance as indices for yield and yield components selection in cowpea (Vigna unguiculataL.). Acad. Arena. 2(5): 22-26.

Imran, M., A. Shakeel, F.M. Azhar, J. Farooq, M.F. Saleem, A. Saeed, W. Nazeer, M. Naeem and A. Javaid (2012). Combining ability analysis for within-boll yield components in upland cotton (Gossypium hirsutum L.). Genet. Mol. Res. 11: 2790-2800.

Iqbal, M., M. Naeem, M. Rizwan, W. Nazeer, M.Q. Shahid, U. Aziz, T. Aslam, and M. Ijaz (2013). Studies of genetic variation for yield related traits in upland cotton. Am-Euras. J. Agric. Environ. Sci. 13: 611-618.

Johnson, H.W, H.F Robinson and R.E Comstock (1955). Estimation of genetic and environmental variability in soybeans. Agron. J. 47: 314-318.

Khan, A.M., S. Fiaz, I. Bashir, S. Ali, M. Afzal, K. Kettener, N. Mehmood and M. Manzoor (2017). Estimation genetic effects controlling different plant traits in cotton (Gossypium hirsutum L.) under CLCuV epidemic condition. Cer. Agro. Mol. 1(169): 47-58.

Khodarahmpour, Z., R. Choukan, M. Bihamta, and E. Majidi Hervan (2010). Determination of the best heat stress tolerance indices in maize (Zea mays L.) inbred lines and hybrids under Khuzestan province conditions. J. Agri. Sci. Tech. 13: 111-121.

Li, Z., X. Wang, Y. Zhang, G. Zhang, L. Wu, J. Chi, and Z. Ma (2008). Assessment of genetic diversity in glandless cotton germplasm resources by using agronomic traits and molecular markers. Front. Agri. China 2(3): 245-252.

Lush, J.L (1940). Intra-sire correlations or regressions of offspring on the dam as a method of estimating heritability of characteristics. Proc. Am. Soc. Anim. Prod. 33: 293-301.

McCarty J. C., J.N. Jenkins, and J. Wu (2005). Primitive accession derived germplasm by cultivar crosses as sources for cotton improvement. Crop Sci. 44:1231-1235.

Mohammadi, S.A. and B.M. Prasanna (2003). Analysis of genetic diversity in crop plants, salient statistical tools, and considerations. Crop Sci. 43: 1235-1248.

Nazir, A., F. Jehanzeb, M. Abid, S. Muhammad, and R. Muhammad (2013). Estimation of genetic diversity for CLCuV, earliness and fiber quality traits using various statistical procedures in different crosses of Gossypium hirsutum L. Vestnik Orel GAU. 4(43): 2-9.

Neyman, J. and E.S. Pearson (1928). The use and interpretation of certain test criteria for purpose of statistical inference. Biometrika 20: 175-240.

Panni, M.K., N.U. Khan, Fitmawati, S. Batool and M. Bibi (2012). Heterotic studies and inbreeding depression in F2 populations of upland cotton. Pakistan J. Bot. 44: 1013-1020.

Saeed, F., J. Farooq, A. Mahmood, T. Hussain, M. Riaz and S. Ahmad (2014). Genetic diversity in upland cotton for cotton leaf curl virus disease, earliness and fiber quality. Pakistan J. Agric. Res. 27: 226-236.

Shakeel, A., S. Ahmad, M. Naeem, T.A. Malik, M.F. Saleem and S. Fareed (2012). Assessment of best parents and superior cross combinations for earliness related traits in upland cotton. The J. Anim. Plant Sci. 22: 722-727.

Shaukat, S., T.M. Khan, A. Shakeel and S. Ijaz (2013). Estimation of best parents and superior cross combinations for yield and fiber quality related traits in upland cotton (Gossypium hirsutum L.). Sci. Tech. Dev. 32: 281-284.

Siddique, M.H., F.C Oda and U.A. Buriro (2007). Response of cotton cultivars to varying irrigation regimes. Asian J. Plant Sci. 6: 153-157.

Singh, P.and S.S. Narayanan (2000). Biometrical Techniques in plant breeding. 2nded. Kalyani Publ. Ludhiana, India.

Sivasubramanian, V. and P. Madhavamenon (1973). Path analysis of yield and yield components in rice. Madras Agric. J. 60: 1217-21.

Steel, R.G.D., J.H. Torrie and D.A. Dickey (1997). Principles and procedures of statistics. A biometrical approach. Singapore: 3rd Ed. McGraw Hill Book Co. Inc.

Tang, F. and W. Xiao (2013). Genetic effects and heterosis of within-boll yield components in upland cotton (Gossypium hirsutum L.). Euphytica 194: 41-51.

Yaqoob, M., S. Fiaz and B. Ijaz (2016). Correlation analysis for yield and fiber quality traits in upland cotton. Commun. Plant Sci. 6 (3): 55-60.
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