Correlation and Path Coefficient Analysis between Seed Cotton Yield and its Attributing Characters in Intra Hirsutum Cotton Hybrids  

Yanal Alkuddsi1 , M.R. Gururaja Rao2 , S.S. Patil3 , Mukund Joshi4 , T.H. Gowda5
1. Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka, India
2. Professor, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Bangalore, Karnataka, India
3. Professor, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka, India
4. Associate Professor, Department of Agronomy, University of Agricultural Sciences, Bangalore
5. Professor of Genetics and Plant Breeding, Head of Agricultural Research Station, Bavikere, Therikere
Author    Correspondence author
Molecular Plant Breeding, 2013, Vol. 4, No. 26   doi: 10.5376/mpb.2013.04.0026
Received: 01 Jul., 2013    Accepted: 11 Jul., 2013    Published: 15 Jul., 2013
© 2013 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Alkuddsi et al., 2013, Correlation and Path Coefficient Analysis between Seed Cotton Yield and its Attributing Characters in Intra Hirsutum Cotton Hybrids, Molecular Plant Breeding, Vol.4, No.26 214-219 (doi: 10.5376/mpb.2013.04.0026)

Abstract

This study was carried out to determine direct and indirect effects of some characters ( Days to 50 per cent flowering, plant height (cm), Number of monopodial branches per plant, Number of Sympodia per plant, Number of bolls per plant, Mean boll weight (g), Ginning percentage, Seed index (g) and Lint index (g)) on seed cotton yield. The experiment was carried out with 48 F1 hybrids have produced through Line×Tester mating design using 6 hirsutum non Bt lines (RAH 318, RAH 243, RAH 128, RAH 146, RAH 97 and RAH 124) and 8 hirsutum non Bt testers ( SC 14, SC 18, SC 7, SC 68, RGR 32, RGR 24, RGR 58 and RGR 37)were sown in a randomized complete block design (RCBD) with two replications at the Agricultural Research Station, Bavikere, UAS, Bangalore. Seed cotton yield per plant was significantly and positively genotypic correlation with days to 50 per cent flowering (0.359), plant height (0.443), bolls per plant (0.590), mean boll weight (0.422) and ginning per cent (0.379) but significantly and negatively correlated with seed index at genotypic level. The phenotypic correlation values also revealed that seed cotton yield per plant had highly significant and positive phenotypic correlation with bolls per plant (0.624), significantly and positively phenotypic correlation found between seed cotton yield per plant and mean boll weight (0.327). Path coefficients were computed to estimate the contribution of individual characters to yield in cotton. The path analysis indicated high positive direct effect of monopodia per plant (0.619) and mean boll weight (0.321) on seed cotton yield. Sympodia per plant exhibited high positive indirect effect on seed cotton yield via seed index (0.374), the result of this mean boll weight had high positive correlation with seed cotton yield and sympodia per plant recorded low positive correlation with seed cotton yield. Key words: Yield, Path Coefficient, Correlation, Attributing Characters, Cotton

Keywords
Yield; Path coefficient; Correlation; Attributing characters; Cotton

Cotton is an important fiber yielding crop of global importance, which is grown in tropical and subtropical regions of more than 80 countriesthe world over. It provides livelihood to about sixty million people and is an important agricultural commodity providing remunerative income to million of farmers both in developed and developing countries. In India, inspite of severe competition from synthetic fibers in recent years, it is occupying the premiere position with 70 per cent share in the textile industry.

There have been many factors which effect the cotton yield, like all plants. With regard to plant breeding studies, it is important that locations where plants are grown and determination of some traits which effect morphological and physiological characters of plants. Therefore, determination of direct and indirect relations among the traits is important in order to determine aspect of plant selection criteria.
The main objective for a plant breeder is to evolve high yielding varieties. There are many factors on which the yield of cotton crop depends, such as plant height, number of fruiting branches, number of bolls per plant, boll weight, seed index, G.O.T % etc. It is desirable for plant breeder to know the extent of relationship between yield and its various components which will facilitate him in selecting plants of desirable characteristics. The knowledge of relationship among various yield components has been successfully exploited towards cotton improvement.
Correlation coefficient determines simple relations among the traits, so it doesn’t determine always decisive results about determination of plant selection criteria (Cakmakci et al., 1998). Path coefficient analysis as to correlation coefficient gives more detailed information on the relations so it is commonly used by researches in plant breeding to determine seed cotton yield and seed cotton yield criteria relations (Williams et al., 1990; Kang et al., 1993; Board et al., 1997).
Path coefficients have been used for complex characters in several crop species to provide information on interrelations of complex characters and to develop selection criteria (Kang et al., 1993; Gravois et al., 1991; Diz et al., 1994).
This present study was conducted with 48F1 cotton hybrids to provide information on interrelationships of seed cotton yield with some characters (Days to 50% flowering, plant height (cm), Number of monopodial branches per plant, Number of Sympodia per plant, Number of bolls per plant, Mean boll weight (g), Ginning percentage, Seed index (g) and Lint index (g)) and to partition the observed correlations into their direct and indirect effects.
1 Results and Discussion
1.1 Correlation Studies
The genotypic and phenotypic correlation co-efficients among all the character related to seed cotton yield per plant were estimated and the results are presented in Table 1 and Table 2, respectively.


Table 1 Genotypic correlation coefficients among kapas yield and its attributing characters in cotton hybrids (G. hirsutum L.)


Table 2 Phenotypic correlation coefficients among kapas yield and its attributing characters in cotton hybrids (G. hirsutum L.)

1.1.1 Association between seed cotton yield per plant and its component characters
Seed cotton yield per plant was significantly and positively genotypic correlation with days to 50 per cent flowering (0.359), plant height (0.443), bolls per plant (0.590), mean boll weight (0.422) and ginning per cent (0.379). This is in confirmity with the reports of Arshad et al, 1993. Significantly and negatively genotypic correlation was observed between seed cotton yield per plant and seed index (-0.352). Controversial reports regarding the negative significant association of seed index with yield are available in literature Salimath (1975). The phenotypic correlation values also revealed that seed cotton yield per plant had highly significant and positive phenotypic correlation with bolls per plant (0.624) (Shandhu et al, 1986). Significantly and positively phenotypic correlation found between seed cotton yield per plant and mean boll weight (0.327) (Griffee, Ligon and Brannon (1929)).
1.1.2 Association among seed cotton yield attributing characters
1.1.2.1 Genotypic association
Among yield attributing traits, highly positive genotypic and significant association of days to 50 per cent flowering was observed with monopodia per plant (0.621), highly negative and significant association recorded between days to 50 % and bolls per plant (-0.498) and ginning per cent (-0.808), but positive genotypic and significant association recorded between days to 50 % flowering and plant height (0.312).The high genotypic significant association recorded between plant height and monopodia per plant (0.389) and symposia per plant (0.887), but highly significant negative correlation recorded between plant height and seed index (-0.658), lint index (-0.474), significant positively and negatively association registered between plant height and bolls per plant (0.342), ginning per cent (-0.322). Monopodia per plant had high positive and significant correlation with mean boll weight (0.540).Contrary to this, sympodia per plant recorded high negative and significant correlation with mean boll weight (-0.529) and seed index (-0.401). Bolls per plant had high negative and significant correlation with lint index (-0.644) but negative significant with mean boll weight (-0.305). For other characters, mean boll weight, ginning per cent, seed index showed high positive and significant correlation with ginning per cent (0.454), seed index (0.387), lint index (0.647), respectively.
1.1.2.2 Phenotypic association
Highly positive and phenotypic significant association recorded between plant height and sympodia per plant (0.649), mean boll weight and seed index (0.529) and also mean boll weight had correlation with lint index (0.492), ginning per cent and lint index (0.731), seed index and lint index (0.623). On the other hand, positive and significant correlation showed between monopodia per plant and mean boll weight (0.281), mean boll weight and ginning per cent. Contrary to this, bolls per plant exhibited negative and significant correlation with seed index (-0.335).
1.2 Path Co-efficient Analysis
1.2.1 Direct and indirect effects of component characters on kapas yield
The relationship between yield and yield components may be negative or positive but it is the net result of direct effect of that particular trait and indirect effects via other traits. Hence, it is necessary to determine the path co-efficients which partition the observed correlation in to direct and indirect effects and also reveals the cause and effect relationship between yield and their related traits.
The path analysis indicated high positive direct effect of monopodia per plant (0.619) and mean boll weight (0.321) on seed cotton yield (Table 3 and Figure 1) (Afiah and Ghoneim (2000), Shazia Salahuddin et al., 2010). However, sympodia per plant (0.213) and lint index (0.181) showed low positive direct effect (Tomar and Singh (1992)). Sympodia per plant exhi- bitted high positive indirect effect on seed cotton yield via seed index (0.374) the result of this mean boll weight had high positive correlation with seed cotton yield and sympodia per plant recorded low positive correlation with seed cotton yield (Choudhary et al., 1988). On the other hand, seed index showed high negative direct effect on seed cotton yield (-0.933) and registered low positive indirect effect on seed cotton yield via lint index (0.117) resulting in high negative correlation with seed cotton yield (-0.351) and also plant height recorded high negative direct effect on seed cotton yield but exhibited high positive indirect effect on seed cotton yield via seed index (0.614), thereby resulting in high positive association with seed cotton yield (Alam and Islam (1991).


Table 3 Path analysis indicating direct and indirect effects of component characters on kapas yield in cotton hybrids (G. hirsutum L.)


Figure 1 Diagrammatic representation of direct effects and correlation coefficients of variable on dependent variable

2 Conclusion
Seed cotton yield being a complex polygenic character, direct selection based on this trait would not yield fruitful results without giving due importance to its genetic background. The association of yield and its component traits reflects the nature and degree of relationship between them. The correlation analysis helps in examining the possibility of improving yield through indirect selection of its component traits which are highly correlated.
3 Materials and Methods
This experiment was carried out during kharif 2008 at Agricultural Research Station, Bavikere, UAS, Bangalore, which is located at 130, 42” Nourth latitude and 750, 51’’ East longitude, . The experiment comprising of 48 experimental hybrids along with 3 checks (BUNNY Bt, RCH2 Bt, RAHH 95) (one repeated two times) was laid out in Randomized Complete Block Design (RCBD) with two replications.Each entry was sown in 3 row plots of 6 m length spaced at 90 cm with recommended dose of fertilizer and treatment of seeds with Imidochloprid were sown on 10-7-2008, 2-3 seeds were dibbled per spot in each row and thinning was attended to retain one healthy plant per hill at 25 days after sowing. All the recommended package of practices were followed to rise healthy crop.
Samples containing 20 bolls were hand-harvested from each plot prior to picking. The days to 50 per cent flowering recorded by the number of days taken from the date of sowing to the date when the first flower opens in 50% of the plants. The number of monopodia per plant are the number of branches on main stem which were lateral and axillary in position with vertical growth in acropetal succession was counted at maturity stage, avoiding small sprouts, but the number of sympodia per plant are branches which are extra-axillary in position and normally horizontal with zig-zag pattern of fruiting points were taken as sympodia. The number of such sympodia on main stem were counted at maturity stage. The boll samples were weighed to determine seed cotton weight per boll values, and ginned on a roller using laboratory gin for lint percentage (100×lint weight/seed cotton weight) and 100-seed weight calculations (seed index). The ginned lint from each plot was weighed and divided by the number of plants within each plot to determine lint yield per plant. Five plants were selected randomly from each genotype to find the boll number per plant.
Analysis of covariance was computed in a fashion similar to that of analysis of variance formula and these statistics were made use of in calculating, phenotypic and genotypic correlation coefficients.
3.1 Phenotypic correlation coefficient (rp)
The degree of phenotypic association amongst eleven characters was computed as per the formula given by Weber and Moorthy (1952).

  
Where,
rp=Phenotypic correlation coefficient
Cov P1.2=Phenotypic covariance between two traits (1 and 2)
σ P1=Phenotypic standard deviation of first trait (1)
σ P2=Phenotypic standard deviation of second trait (2)
3.2 Genotypic correlation coefficient (rg)
Genotypic covariance was obtained by deducting error covariance from phenotypic covariance and the genotypic correlation coefficients were calculated by these formula.


Where,
σg1.2=Genotypic covariance between two traits (1 and 2).
σg1=Genotypic standard deviation of first trait.
σg2=Genotypic standard deviation of second trait.
The significance of phenotypic and genotypic correlation coefficients were tested by referring to the table (r) value with (n-2) d.f given by Fisher and Yates (1963) at 5% and 1% level of degree freedom, where n=number of pairs of observations used in correlation analysis.
3.3 Path coefficient analysis
Path coefficient analysis was carried out using phenotypic correlation values of yield components on kapas yield as suggested by Wright (1921) and illustrated by Dewey and Lu (1959). Standard path coefficients which are the standardized partial regression coefficients were obtained using statistical software package called GENRES. These values were obtained by solving the following set of ‘P’ simul- taneous equations by using the above package.
P01+P02 r12 + -----------+P0P r1P=r 01
P01+P12 r02 + -----------+ P0P r2P=r 02
            
P01+r1P+P02 r2 P -----------+P0P=r 0P
Where P01, P02, ---------------------------P0P are the direct effects of variables 1,2, --------------p on the dependent variable 0 and r12, r13 ,-------r1P------ rP(P-1 ) are the possible correlation coefficients between various independent variables and r01, r02, r03 ------- r0P are the correlations between dependent and independent variables.   
The indirect effect of the ith variable via jth variable is attained as (Poj x rij). The contribution of remaining unknown factor is measured as the residual factor, which is calculated as given below.
P2ox=1-[P2 01 + 2P01 P02 r12 + 2 P01 P03 r13 + ----------+ P202 + 2P02 P03 r13 +……..+P20P]
Residual factor = (P2ox)½
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