GENETIC VARIABILITY, HERITABILITY AND GENETIC GAIN POTENTIAL FOR YIELD TRAITS IN MAIZE (Zea mays L.)
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Authors
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P KIREETI VARMA
Department of Genetics and Plant Breeding, Agricultural College, Bapatla - 522101 Acharya N.G. Ranga Agricultural University, Lam, Guntur, Andhra Pradesh, India
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I SUDHIR KUMAR
Department of Genetics and Plant Breeding, Agricultural College, Bapatla - 522101 Acharya N.G. Ranga Agricultural University, Lam, Guntur, Andhra Pradesh, India
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K R YATHISH
Department of Genetics and Plant Breeding, Agricultural College, Bapatla - 522101 Acharya N.G. Ranga Agricultural University, Lam, Guntur, Andhra Pradesh, India
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P MUNIRATNAM
Department of Genetics and Plant Breeding, Agricultural College, Bapatla - 522101 Acharya N.G. Ranga Agricultural University, Lam, Guntur, Andhra Pradesh, India
Keywords:
Genetic Variability, GCV, Genetic Advance, Heribility, Maize, PCVAbstract
The present study was undertaken to evaluate the extent of genetic variability, heritability,
and genetic advance for yield and component traits in seventy maize (Zea mays L.) inbred lines.
The genotypes were assessed during rabi 2024–25 at two locations—ARS, Peddapuram and
WNC, ICAR-IIMR, Rajendranagar—using an alpha-lattice design with two replications. The analysis
of variance revealed highly significant differences among the inbred lines for all traits investigated,
confirming the presence of substantial genetic variability. In the pooled analysis, high genotypic
and phenotypic coefficients of variation were recorded for anthesis–silking interval, 100-kernel
weight and grain yield per plant, indicating a strong scope for selection and the involvement of
both additive and non-additive gene effects. Moderate variability was observed for plant height,
ear height, ear length and number of kernels per row, whereas the remaining traits expressed
comparatively low variability. Furthermore, high heritability coupled with high genetic advance as
a percentage of the mean for plant height, ear height, ear length, number of kernels per row,
100-kernel weight and grain yield per plant suggests the predominance of additive gene action,
thereby enabling effective improvement through direct selection. These findings provide a strong
foundation for identifying promising parental lines and advancing yield-oriented maize breeding
programmes.
References
1. Antony, B.J., Kachapur, R.M., Naidu, G.K.,
2. Talekar, S.C., Zerka, M. and Harlapur, S.I.
3. 2024. Genetic variability and character
4. association among maize (Zea mays L.)
5. inbred lines. Bangladesh Journal of
6. Botany,53 (1): 57-65.
7. Banakara, S., Lohithaswa, H.C., Biradar, S.,
8. Patil, K.G., Kashyap, G.S., Sowmya, M.S.,
9. Mugali, K.P., Meenakshi, J and
10. Likhithashree, T.R. 2024. Genetic
11. variability for per se grain yield potential
12. and its attributing traits among inbred
13. lines of maize (Zea mays L.). Plant
14. Archives,24 (2): 485-490.
15. Burton, G.W. 1952. Quantitative inheritance in
16. grasses. Proceedings of 6th International
17. Grassland Congress. 1 (2): 277-283.
18. Ghosh, A., Subba, V., Roy, A., Ghosh, A and
19. Kundagrami, S. 2014. Genetic variability
20. and character association of grain yield
21. components in some inbred lines of maize
22. (Zea mays L.). Journal of Agroecology
23. and Natural Resource Management,1 (2):
24. 34-39.
25. Gogoi, D., Bordoloi, D., Sarma, A and Barua,
26. N.S. 2025. Correlation and path analysis
27. of early inbred lines of maize (Zea mays
28. L.) for yield and yield related traits. Indian
29. Journal of Plant Genetic Resources, 38
30. (01): 123-130.
31. Johnson, H.W., Robinson, H.F and Comstock,
32. R.E. 1955. Estimates of genetic and
33. environmental variability in soybean.
34. Agronomy Journal, 47: 314-318.
35. Khan, S.A., Iqbal, A.M., Shikari, A.B., Dar, Z.A.,
36. Wani, M.A., Mahdi, S.S., Waza, S.,
37. Ahanger, M.A., Bangroo, S and Islam, N.
38. 2024. Diversity analysis of highland
39. temperate maize (Zea mays L.) inbred
40. lines. SKUAST Journal of Research, 26
41. (1): 38-45.
42. Panse, V. G and Sukhatme, P. V. 1978.
43. Statistical Methods for Agricultural
44. Workers, ICAR, New Delhi.
45. Rani, M., Anusha, C., Lavanya, G.R and Kiran,
46. M.S. 2022. Genetic variability and
47. interrelationship in maize (Zea mays L.)
48. genotypes for grain yield and yield
49. component characters. International
50. Journal of Environment and Climate
51. Change,12 (11): 3264-3271.
52. Samskriti, S., Sharma, D and Kandel, B.P.
53. 2025. Genetic parameters estimation of
54. promising maize hybrids in sundarbazar,
55. lamjung. Agricultura, 134 (1-2): 133-134.
56. Sondarava, P.M., Patel, M.B., Parmar, D.J.,
57. Borkhatariya, T.H., Vadodariya, J.M and
58. Akbari, K.M. 2025. Appraisal of genetic
59. variability, heritability and genetic
60. advance for quantitative traits in maize
61. (Zea mays L.). Horizon,3 (2): 22-29.
62. Tesfaye, D., Abakemal, D and Habte, E. 2021.
63. Genetic variability, heritability and
64. genetic advance estimation of highland
65. adapted maize (Zea mays L.) genotypes
66. in Ethiopia. Journal of Current Opinion
67. in Crop Science, 2 (2): 184-191.
68. Veeravishnu, S., Sudhir Kumar, I., Harish
69. Chanar S.R., Pushpalatha, G and
70. Krishnam Raju, K. 2023. Genetic
71. variability and association studies for
72. grain yield and its component traits in
73. maize (Zea mays L.) inbreds. The
74. Pharma Innovation Journal,12 (5): 295
75. 299
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