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Estimation of heritability for seed cotton yield in cotton based on regression approach.

Cotton is an important commercial crop grown for its fibre. Seed cotton yield determines the commercial value of cotton and the heritability value plays a key role in determining selection strategy for improvement. Apart from establishing half sib and full sib relations for determining components of variance and determining heritability of the trait, it is also possible to use information on consecutive generations to work out regression value for a trait and estimate heritability for a trait (Smith and Kinman, 1965 and Salimath and Patil, 1990). There are very limited studies focusing on determining heritability of yield per se based on regression approach. In this study [F.sub.5] and [F.sub.6] populations of a heterotic box representing opposite heterotic groups subjected to reciprocal selection were utilized.

MATERIALS AND METHODS

For determining heritability of yield per se [F.sub.5] and [F.sub.6] lines developed through reciprocal selection in cotton of a heterotic box involving elite lines of robust/stay green group (RSG) and RGR (high relative growth rate) were used. This heterotic box comprises of DSMR-10 line (of stay green group), DSG-3-5 line (of robust group) and two DRGR-32-100 and DRGR-24-178 lines (of RGR group). These lines were crossed (DSMR-10 X DSG-3-5) (DRGR-32-100 X DRGR-24-178) two give two [F.sub.1]. Resulting [F.sub.1]s were advanced to the [F.sub.4] and [F.sub.5] generation where recombinational variability for combining ability was evaluated. Here, regression of ten parental lines of [F.sub.6] generation over [F.sub.5] generation was carried out to determine the heritability of yield per se.

Regression Approach

The seed cotton yield values of [F.sub.5] and [F.sub.6] lines were utilized for determining regression values i.e., ([b.sub.F6F5]). Heritability ([h.sup.2.sub.NS]) of yield per se was calculated based on regression approach given by Smith and Kinman, (1965). [h.sup.2] = (b/[2r.sub.XY])

where, [h.sup.2] = Narrow sense heritability

b = Regression coefficient

[r.sub.XY] = Coefficient of parentage [which works out to be (31/32) for this situation]

The mean seed cotton yield of lines was used for regression of [F.sub.6] lines over [F.sub.5] lines, finally giving the regression value (bF6F5). The regression value ([b.sub.F6:F5]) was divided with the coefficient of parentage (31/32) depending upon the generations of the lines used in the analysis.

The set of [F.sub.5] and [F.sub.6] lines were evaluated in replicated block design and MSS values in the ANOVA for genotypes and error component were utilised in determining broad sense heritability ([h.sup.2] = [V.sub.g]/[V.sub.p]).

RESULTS AND DISCUSSION

Regression of seed cotton yield of [F.sub.6] lines over [F.sub.5] lines were carried out for RSG and RGR group, ANOVA of regression coefficient was presented in table 1 (a) and 1(b) respectively.

The regression value ([b.sub.F6:F5]) for the lines derived from (DSMR-10 x DSG-3-5) cross was (0.48) (Tab.2). Narrow sense heritability ([h.sup.2.sub.NS]) for yield per se of the lines derived from (DSMR-10 x DSG3-5) cross observed was 24.90 per cent. The regression value ([b.sub.F6:F5]) for the lines derived from (DRGR-24-178 x DRGR-32-100) cross was (0.41) (Tab.3). The heritability ([h.sup.2.sub.NS]) for the lines derived from (DRGR-24-178 x DRGR-32-100) cross observed was 21.21 per cent. Cahaner and Hillet (1980), Salimath and Patil (1990) and Sunderman et al. (1965) have also reported similar low narrow sense heritability values in different crops.

Broad sense heritability was estimated from the ANOVA of RBD obtained for RSG group (DSMR-10 x DSG-3-5) cross was 76.30per cent and RGR group (DRGR-24-178 x DRGR-32-100) cross was 90.00 per cent. Comparison of broad sense heritability obtained by RBD analysis and narrow sense heritability obtained by regression approach has been shown in tab. 4. In earlier studies wide range of broad sense heritability values were obtained for seed cotton yield by Naveed et al., 2004 (33%), Aziz et al., 2006 (74.10%), Desalegn et al., 2009 (44%), Alkuddsi et al., 2013 (43.74%), Reddy et al, 2014 (80%), Singh et al., 2014 (78.55%) and Ambedkar, 2015, (89.10%).

Yield per se is a trait which is highly influenced by environment. In this study, narrow sense heritability for RSG and RGR groups of lines was 24.90 and 21.21 per cent respectively. In present study considerable difference was obtained between broad sense heritability and narrow sense heritability values indicating role of both additive and non additive gene action. This is an indication that genotypic values for yield are determined by both breeding value and dominance deviation. (Falconer, 1981).

REFERENCES

(1.) Alkuddsi, Y.A., Rao, M.R.G., Patil, S.S., Joshi, M., Gowda, T.H. Estimation of genetic parameters among seed cotton yield and its attributing characters in upland cotton (Gossypium hirsutum L.). Int. J. Mol. Evol. Biod., 2013; 3(5): 1- 5.

(2.) Ambedkar, N. Variability, heritability and hereditary propel in upland cotton (Gossypium hirsutum L.). Int. J. Plant. Breed. Genet., 2015; 2 (2): 073-076.

(3.) Aziz, U., Afzal. J., Iqbal, M., Naeem, M., Khan, M.A., Nazeer, W., Aslam, T., Zahid, W. Selection response, heritability and genetic variability studies in upland cotton. J. Appl. Environ. Biol. Sci., 2006; 4(8): 400-412.

(4.) Cahaner, A., Hillet, J. Estimating heritability and genetic correlation between traits from generations F2 and F3 of self-fertilizing species: a comparison of three methods. Theor. Appl. Genet., 1980; 58(1):33-38.

(5.) Charnai, K., Mauro, A.O.D., Santiago, S., Vianna, V.F., Revolti, L.T.M., Costa, M.M. Estimates of genetic parameters in F4- F5 soybean populations resistant to Asian soybean rust. African J. Agril. Res., 2014; 9(15): 1200-1206.

(6.) Desalegn, Z., Ratanadilok, N., Kaveeta, R. Correlation and heritability for yield and fiber quality parameters of ethiopian cotton (Gossypium hirsutum L.) estimated from 15 (diallel) crosses. Kasetsart J. (Nat. Sci.)., 2009; 43: 1-11.

(7.) Falconer, D. S. 1981. Introduction of Quantitative Genetics. New York: Longman Inc. Ltd., 1981.

(8.) Hanson, C.H., Robinson, H.F., Comstock, R.E. Biometrical studies of yield in segregating populations of korean lespedeza. Agron. J., 1956; 48(6): 268-272.

(9.) Jogloy, C., Jaisil, C., Akkasaeng, C., Kesmala, T., Jogloy, S. Heritability and correlation for components of crop partitioning in advance generations of Peanut crosses. Asian J. Plant Sci., 2011; 10(1): 60-66.

(10.) Naveed, M., Azhar, F.M., Ali, A. Estimates of heritabilities and correlations among seed cotton yield and its components in Gossypium hirsutum L. Int. J. Agri. Biol., 2004; 6: 4.

(11.) Ntare, B.R. Early generation testing for yield and physiological components in groundnut (Arachis hypogea L.). Ephytica, 1999; 107: 141-147.

(12.) Reddy, Y.R., Sarma, A.S.R. Genetic variability for yield components and fibre characters in Interrelationships of Wheat protein content, flour sedimentation value, farinograph peak time and dough mixing and baking characteristics in the [F.sub.2] and [F.sub.3] generations of winter wheat, Triticum aestivum. Crop Sci., 1965; 5:537-540.

(18.) Yerimani, A.S., Mehetre, S., Kharde, M. N. Genetic variability for yield and yield component traits in advanced [F.sub.3] and [F.sub.4] generations of pigeonpea [Cajanus cajan (L.)]. Molecul. Plant Breed., 2013; 4(16): 136-140.

Girish Tantuway, Shreekant S. Patil, Hanamaraddi Kencharaddi, Aman Tigga and Vinayak Edke

Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad-580 005, India.

http://dx.doi.org/10.22207/JPAM.10.4.103

(Received: 13 June 2016; accepted: 19 September 2016)

* To whom all correspondence should be addressed.

Mob: +919455943822; E-mail: tantuwaygirish@gmail.com
Table 1(a). Analysis of variance for regression coefficient of RSG
group lines

Model        Unstandardized    Standardized   't' value   Significance
              Coefficients     Coefficients
               B       Std.        Beta
                      Error

(Constant)  1087.96   611.05                    1.78         0.11
[F.sub.5]   0.48 **   0.37         0.41         1.27         0.24

a. Dependent variable [F.sub.6]

Table 1(b). Analysis of variance for regression coefficient of
RGR group lines

Model         Unstandardized   Standardized    't'     Significance
              Coefficients     Coefficients   value
            B         Std.         Beta
                      Error

(Constant)  1065.38   326.73                  3.26        0.01
[F.sub.5]   0.41 **    0.20        0.58       2.04        0.07

a. Dependent variable [F.sub.6]

Table 2. Regression of [F.sub.6] lines over [F.sub.5] lines of RSG
group (DSMR-10 x DSG-3-5) cross for heritability of per se yield

Sl.   [F.sub.5] lines    Seed cotton    [F.sub.6] lines    Seed cotton
No.                       yield (kg                         yield (kg
                         [ha.sup.-1])                     [ha.sup.-1])

1     RSG [F.sub.5] 1    1620.47        RSG [F.sub.6] 1    2128.48
2     RSG [F.sub.5] 2    1804.54        RSG [F.sub.6] 2    2760.77
3     RSG [F.sub.5] 3    1984.95        RSG [F.sub.6] 3    2097.22
4     RSG [F.sub.5] 4    1891.55        RSG [F.sub.6] 4    1381.96
5     RSG [F.sub.5] 5    1445.01        RSG [F.sub.6] 5    1552.09
6     RSG [F.sub.5] 6    957.97         RSG [F.sub.6] 6    1741.90
7     RSG [F.sub.5] 7    1394.35        RSG [F.sub.6] 7    1320.61
8     RSG [F.sub.5] 8    998.85         RSG [F.sub.6] 8    1579.86
9     RSG [F.sub.5] 9    1814.59        RSG [F.sub.6] 9    1894.54
10    RSG [F.sub.5] 10   1826.13        RSG [F.sub.6] 10   2003.47

Regression of per se yield of [F.sub.6] lines over [F.sub.5] lines (b)
= 0.48 Heritability ([h.sup.2.sub.NS]) of per se yield = 24.90%

Table 3. Regression of [F.sub.6] lines over [F.sub.5] lines of RGR
group (DRGR-24-178 x DRGR-32-100) cross for heritability of per se
yield

Sl.   [F.sub.5] lines    Seed cotton    [F.sub.6] lines    Seed cotton
No.                       yield (kg                         yield (kg
                         [ha.sup.-1])                     [ha.sup.-1])

1     RGR [F.sub.5] 1    804.74         RGR [F.sub.6] 1    1728.50
2     RGR [F.sub.5] 2    1008.10        RGR [F.sub.6] 2    1263.68
3     RGR [F.sub.5] 3    1458.35        RGR [F.sub.6] 3    1406.25
4     RGR [F.sub.5] 4    1690.70        RGR [F.sub.6] 4    1748.62
5     RGR [F.sub.5] 5    1936.06        RGR [F.sub.6] 5    1763.90
6     RGR [F.sub.5] 6    1575.23        RGR [F.sub.6] 6    1499.20
7     RGR [F.sub.5] 7    1896.99        RGR [F.sub.6] 7    1899.55
8     RGR [F.sub.5] 8    1880.95        RGR [F.sub.6] 8    1664.13
9     RGR [F.sub.5] 9    2094.94        RGR [F.sub.6] 9    2332.23
10    RGR [F.sub.5] 10   1385.85        RGR [F.sub.6] 10   1819.01

Regression of per se yield of [F.sub.6] lines over [F.sub.5] lines (b)
= 0.41 Heritability ([h.sup.2.sub.NS]) of per se yield = 21.21%

Table 4. Comparison of broad sense heritability obtained by RBD
analysis and narrow sense heritability obtained by Regression approach

Sl.       Cross name          Broad sense         Narrow sense
No.
                            heritability (%)    heritability (%)
1.         RSG group             76.30               24.90
      (DSMR-10 x DSG-3-5)
2.         RGR group             90.00               21.21
        (DRGR-24-178 x
         DRGR-32-100)
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Author:Tantuway, Girish; Patil, Shreekant S.; Kencharaddi, Hanamaraddi; Tigga, Aman; Edke, Vinayak
Publication:Journal of Pure and Applied Microbiology
Article Type:Report
Date:Dec 1, 2016
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