Madras Agric. J. 92 (10-12) : 612 - 617 October-December - 2005

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GENETIC VARIABILITY AND TRAITS INTERRELATIONSHIP STUDIES IN INDUSTRIALLY UTILIZED OIL RICH CYMMIT LINES OF MAIZE (ZEA MAYS L) P.SUMATHI, A.NIRMALAKUMARI and K. MOHANRAJ Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, 641 003 Abstract : The genetic variability, correlations and path coefficients were studied in forty seven maize inbred lines, including seven high oil strains collected from CYMMIT, Mexico for ten characters. Close resemblance between GCV and PCV was observed for all the traits indicating that selection for these characters would be much effective. Heritability estimates in general were high for all the ten characters studied. The highest heritability coupled with high genetic advance as per cent of mean was observed for plant height, total number of kernels per ear, grain yield and oil content. Genotypic correlation studies indicated that ear weight, number of rows/ ear, number of kernels/row, and total number of kernels/ ear were positively associated with grain yield. Oil per cent exhibited negatively non-significant correlation with grain yield, whereas it showed positive association with number of rows/ ear only. Path coefficient analysis revealed that number of kernels per row showed high direct effect on grain yield followed by 100 seed weight, number of rows per ear and total number of kernels per plant. Key words: Maize, Heritability, Co-efficient of variation, Correlation, Path analysis.

Maize is one of the most important cereal crops in the world. It is having diversified products which has great potential for commercial exploitation. Maize oil is a by-product of importance, since it is a fine cooking medium when refined. The use of maize as a raw material in industrial sector has been increasing at a faster rate for starch and oil. It is expected that the demand for these two constituents will increase with increasing population, which in turn will require high yielding varieties having high starch and oil content. The first step in the success of any crop improvement programme depends on amount of genetic variability present in the population and extend to which it is heritable, which set the limit of progress that can be achieved through selection. The study of interrelationship of yield components along with oil per cent and path analysis of these traits on yield and oil content is imperative to enable the selection of inbred lines for the ultimate usages in commercial utilization of maize genotypes. Keeping in view the aforesaid objective, this investigation was taken up.

MATERIALS AND METHODS A set of 47 diverse genotypes (including seven high oil lines collected from CYMMIT, Mexico) were grown in a randomized block design with three replications during rabi 2001 - 2002 at Agricultural Research Station, Bhavanisagar. Each strain was raised in a single row of 5m length with a spacing of 60 cm between the rows and 20 cm between plants within the rows. Recommended package of practices were followed to raise the crop. Five plants were randomly selected from each line, replication-wise for recording observations on ten characters, viz., days to tasseling, days to silking, plant height, ear weight, number of rows per ear, number of kernels per row, total number of kernels per ear, 100 seed weight, single plant yield, and oil content. Standard statistical procedures were used for the analysis of variance, genotypic and phenotypic coefficients of variation (GCV and PCV), heritability (Lush, 1940) and genetic advance (Johnson et al, 1955). The genotypic correlation coefficients for yield, yield components and oil content were evaluated using the formula suggested by Al-jibouri et al, (1958). Further partitioning of

GENETIC VARIABILITY AND TRAITS INTERRELATIONSHIP STUDIES IN INDUSTRIALLY UTILIZED OIL RICH CYMMIT LINES OF MAIZE

correlations into direct and indirect effects by path coefficient analysis was estimated by using the procedure suggested by Dewey and Lu (1959). RESULTS AND DISCUSSION The analysis of variance for the ten characters revealed significant differences among the genotypes for all the traits indicating the very high variability within the genotype. The genotypic and phenotypic variances were highly significant for all the traits. Total number of kernels per ear showed the highest values followed by plant height, ear weight, and single plant yield (Table 1). A major portion of the total variation was accounted for by both the genotypic and phenotypic variances for all the traits. Though the phenotypic variance was greater than the genotypic variance, the difference between these two are very minor, this indicated that the traits were stable yet influenced by the environment a little. The genotypic and phenotypic coefficient of variability were high for as many as five traits namely, oil per cent, total number of kernels per ear, 100 seed weight, number of kernels per row, plant height and grain yield. Kabdal et al, (2003) reported high GCV for plant height and grain yield. Jha et al, (1998) reported high GCV and PCV for fodder yield in maize. In general, heritability estimates were high for all the ten characters studied. The highest heritability was observed for grain yield (Satyanarayanas and Sai Kumar, 1996 and Kabdal et al, 2003). This suggested that the greater effectiveness of selection and improvement to be expected for these traits in future breeding programmes, the genetic variance is mostly due to the additive gene action.Genetic advance was calculated to predict the net effect of selection (Ramanujam and Thirumalachari, 1967). Expected genetic advance was high for total number of kernels per ear, plant height, grain yield per plant and ear weight. High heritability accompanied with low genetic advance as per cent of mean in days to 50 per cent tasseling and days to 50 per cent silking indicated the non additive gene action governing the traits. The results on correlation coefficients for yield, yield components and oil per cent are

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presented in Table 2. Grain yield showed significant positive correlation with ear weight, number of rows/ear, number of kernels/row, and total number of kernels/ ear. Similar results for number of kernels per row and 100 seed weight were reported by Gautham et al., (1999) and Venugopal et al, (2003). The inter correlations study exhibited that the characters, which had significant correlation with grain yield were also highly positively inter correlated among themselves. Ear weight showed high positive correlation with number of rows/ear, number of kernels/row and total number of kernels /ear. Number of rows per ear showed positive correlation with number of kernels/row and total number of kernels/ ear and number of kernels/row showed significant positive association with total number of kernels/ear. Better understanding of direct and indirect influences of various characters on grain yield would however emerge from the path coefficient analysis. The values of residual path after deducing direct and indirect effects were not very high. All the direct effects were less than one, indicating the minimum inflation due to multi co-linearity (Gravois and Helms, 1992). The analysis of results indicated that the trait number of kernels per row showed high direct effect on grain yield followed by 100 seed weight, number of rows per ear and total number of kernels per plant. The direct effect of other characters on grain yield was negligible. The traits that had strong correlations with grain yield viz., ear weight, number of rows per ear, number of kernels per row and total number of kernels per ear exhibited positive contribution towards grain yield both directly and indirectly. The same results were reported earlier for number of kernels per row and total number of kernels per ear (Sharma and Kumar, 1987 and Alok Kumar et al., 1999). Oil per cent exhibited negative negligible direct effect and positive negligible indirect effect on grain yield for all the characters except, number of rows per ear and number of kernels per row. It can be concluded that the inbreds with high values of GCV, heritability and genetic advance as per cent of mean for grain yield and oil content can be utilized for maize improvement, through simple selection such as mass selection. The selection pressure can be applied on the positive side on ear

614 weight, number of rows per ear, number of kernels per row and total number of kernels per ear to improve grain yield per plant. Number of kernels

P.SUMATHI, A.NIRMALAKUMARI and K.MOHANRAJ

per row played a major role in improving the grain yield as revealed by path analysis and hence it can be used as an important selection criterion.

REFERENCES Al- jibouri, H.A., Miller, P.A. and Robinson, H.E. (1958). Genotypic and environmental variances and covariance in upland cotton cross of interspecific origin. Agron.J. 50: 63335. Alok Kumar, Gangashetti, M. G., Dahiya, Anju, Kumar, A. and Dahiya , A. (1999). Analysis of direct and indirect effects for quantitative traits in diallel crosses of maize(Zea mays L.). Ann. Biol. Ludhiana 15: 173-76. Dewey, D.R.and Lu, K.H. (1959). A correlation and path coefficient analysis of components of crested wheat grass seed production . Agron.J51: 515-18. Gautham, A.S., Mittal, R.K. and Bhandari, J.C. (1999). Correlation and path coefficient analysis in maize(Zea mays L.). Ann. Agri. Bio. Res. 4: 169-71. Gravois, K.A. and Helms, R.S. (1992). Path analysis of rice yield and yield components as affected by seeding rate. Agron.J. 84: 1-4. Jha P.B., Ghosh, J. and Nirala, R.B.P. (1998). Genetic variability and character association in fodder maize. J. of Res. 10 (2): 139-143.

Johnson, H.W., Robinson, H.F. and Comstock,R.E.(1955). Estimation of genetic and environmental variability in soybean. Agron . JA1: 314-318. Kabdal, M.K., Verma, S.S., Ahmad, N. and Panwar, U.B.S. (2003). Genetic variability and correlation studies of yield and its attributing characters in Maize (Zea mays L.) 23: (2) 137139. Lush, J.L. (1940). Intrasine correlation and regression of offspring on dams as a method of estimating heritability of characters. Proc. Amer. Soc. Anim. Nutr. 32: 293-301. Ramanujam, S. and Thirumalachari, O.K. (1967). Genetic variability of certain characters in red pepper (Capsicum annum L). Mysore J.agric.Sci.l: 30-36. Satyanarayanan, E. and Sai Kumar, R. (1996). Curr.Res. Univ. Agric. Sci., Bangalore 25: 10-11. Sharma, R.K. and Kumar. S. (1987). Association analysis for grain yield and some quantitative traits in popcorn. Crop Improv. 14: 201-04. Venugopal, M., Ansari, N.A.and Rajanikanth,T. (2003). Correlation and path analysis in maize (Zea mays L.). Crop Res. 25 (3) : 525-529.

Estimates of parameters of variability for yield and its components

Characters

Mean

Range

Genotypic Penotypic variance variance

Genotypic coefficient of variation

Phenotypic coefficient of variation

Heritability (%)

Genetic advance

Genetic advance as per cent

Days to 50% flowering

45.92

41.0-51.7

7.19

7.30

5.84

5.88

98.6

7.03

15.31

Days to 50% silking

50.42

44.7-56.3

8.25

8.37

5.69

5.74

98.5

7.52

14.92

Plant height

210.05

145.0-252.0

571.40

604.65

11.38

11.71

94.5

61.35

29.21

Ear weight

114.25

89.4-142.9

124.59

128.17

9.77

9.91

97.2

29.05

25.43

No. of rows/ear

12.91

10.9-15.5

1.16

1.29

8.35

8.83

89.38

2.69

20.84

No. of kernels/ear

24.48

20.7-33.6

11.38

11.73

13.78

13.99

97.01

8.77

35.83

Total No. of kernels/cob

246.74

176-37.6

2940.8

2976.99

21.98

22.11

98.79

142.29

57.67

100 seed weight

30.81

19.9-40.03

18.40

20.89

13.92

14.84

88.08

10.63

34.49

Grain yield

97.62

84.07-126.73

106.62

107.53

10.58

10.62

99.15

27.14

27.80

Oil per cent

4.16

3.21-7.12

0.989

1.002

23.86

24.01

98.7

2.61

62.57

GENETIC VARIABILITY AND TRAITS INTERRELATIONSHIP STUDIES IN INDUSTRIALLY UTILIZED OIL RICH CYMMIT LINES OF MAIZE

Table 1.

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Table 2. Genotypic correlation coefficients for ten characters in maize Days to Days to tasseling silking

Plant Ear height(cm) weight(g)

Number of rows per ear

Number of kernels per row

Total number of kernels per ear

100 seed Oil weight(g) content (%)

Grain yield / plant (g)

Days to tasseling

1.0000**

0.9612**

0.2912*

0.0233

-0.2924*

-0.3099*

-0.1617

0.4307**

-0.4336**

-0.1454

1.0000**

0.3216*

0.0629

-0.2264

-0.2825

-0.1444

0.3625*

-0.3649*

-0.1211

1.0000**

0.2608

-0.0121

-0.0944

0.0042

0.1156

-0.447**

0.0490

1.000**

0.5094**

0.6145**

0.6724

-0.4477** -0.0660

0.6912**

1.0000**

0.6393**

0.5715**

-0.8986** 0.2882*

0.53**

1.000**

0.8667**

-0.7457** 0.0384

0.8809**

1.000**

-0.5092** -0.0859

0.9731**

1.000**

-0.3336*

-0.4771**

1.000**

-0.1700

Days to silking Plant height (cm) Ear weight (g) Number of rows per ear Number of kernels per row Total number of kernels per ear 100 seed weight (g) Oil content (%)

* Significant at P = 0.05 ** Significant at P = 0.01

P.SUMATHI, A.NIRMALAKUMARI and K.MOHANRAJ

Characters

Direct and indirect effects of yield components on seed yield at genotypic level

Characters

Days to Days to tasseling silking

Plant Ear height(cm) weight(g)

Number of rows per ear

Number of kernels per row

Total number of kernels per ear

100 seed Oil weight(g) content (%)

Grain yield plant (g)

Days to tasseling

-0.3027

-0.2909

-0.0881

-0.0071

0.0885

0.0968

0.0489

-0.1304

0.1312

-0.1454

Days to silking

0.2305

0.2397

0.0771

0.0151

-0.0543

-0.0677

-0.0346

0.0869

-0.0875

-0.1211

Plant height(cm)

-0.0036

-0.0040

-0.0125

-0.0033

0.0002

0.0012

-0.0001

-0.0014

0.0055

0.0490

Ear weight (g)

0.0015

0.0041

0.0168

0.0646

0.0329

0.0397

0.0434

-0.0289

-0.0043

0.6912**

Number of rows per ear

-0.1260

-0.0975

-0.0052

0.2195

0.4308

0.2754

0.2462

-0.3872

0.1241

0.5300**

Number of kernels row

-0.2190

-0.1996

-0.0667

0.4342

0.4517

0.7067

0.6124

-0.5270

0.0272

0.8809**

Total number -0.0631 of kernels per ear

-0.0564

0.0016

0.2626

0.2232

0.3385

0.3905

-0.1989

-0.0335

0.9731**

100 seed weight(g)

0.2902

0.2442

0.0779

-0.3016

-0.6054

-0.5024

-0.3431

0.6737

-0.2247

-0.4771**

Oil content (%)

0.0469

0.0394

0.0481

0.0071

-0.0311

-0.0042

0.0093

0.0361

-0.1081

-0.1700

GENETIC VARIABILITY AND TRAITS INTERRELATIONSHIP STUDIES IN INDUSTRIALLY UTILIZED OIL RICH CYMMIT LINES OF MAIZE

Table 3.

Residual effect : 0.1015 Bold figures are direct effects

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