Estimation of genetic variability in china aster (Callistephus chinensis) genotypes at Raipur Chhattisgarh

Introduction

China aster (Callistephus chinensis) is one of the most commonly cultivated annual flowering plants worldwide. The genus name derives from the Greek words 'kalistos' meaning "most beautiful" and 'stephos' meaning "a crown", referencing the flower's striking appearance. It belongs to the family Asteraceae and consists of only one species, chinensis, which is native to China. China aster is classified as a half-hardy annual, meaning it thrives in moderate climates but is sensitive to frost. The plant exhibits an upright growth habit, with rough, hairy branches and alternate leaves that are broad, elliptical or ovate in shape, and distinctly serrated. The flowers vary in form ranging from solitary, single, semi-double, to fully double bloomsand are commonly found in shades of indigo-blue, lavender, rose and white. China aster serves both commercial and ornamental purposes, cultivated for cut and loose flowers. (Arora, 2006). Its blooms are extensively used for religious offerings, garlandmaking, home décor, and floral arrangements. (Singh, 2022) [3] . Owing to its adaptability, it grows successfully in a range of agro-climatic regions, including India, France, Germany, the Netherlands, the UK, Russia, Japan, North America, Switzerland and other parts of Europe. In India, the flower is predominantly grown by small and marginal farmers, particularly in the states of Karnataka, Tamil Nadu, Maharashtra, Andhra Pradesh and West Bengal. In the year 2023-24, floriculture was practiced on about 2.85 lakh hectares of land in India. The total production included around 22.84 lakh tonnes of loose flowers and 9.47 lakh tonnes of cut flowers. In Chhattisgarh, flower crops are cultivated over an area of 13,638 lakh hectares, with a total production of 3,34,672 metric tonnes (MT). (Directorate of Horticulture, 2022-23). The evaluation of genetic variability is a critical initial step in any crop improvement program. Variability observed within a population result from combined influence of genetic (G) factors, environmental (E) influences and their interaction (G × E). As emphasized by Johnson et al., (1955) [14] , the phenotypic variation in a segregating population arises from both inheritable and noninheritable components, with the inheritable portion defined as genetic variance. Therefore, the estimation of genotypic variance, heritability, and genetic advance for various quantitative traits becomes essential in breeding programs designed to develop improved cultivars. Variability refers to the extent of differences among individuals in a population, which may be due to inherent genetic differences or variations in the environmental conditions under which the plants are grown. The magnitude of this variability plays a pivotal role in the selection process, as it forms the basis for identifying and selecting genetically superior genotypes. Moreover, the presence of substantial genetic variation is vital not only for enhancing tolerance to biotic and abiotic stresses but also for improving adaptability across diverse agro-climatic zones, ultimately contributing to yield stability and crop resilience. Variance, which measures the degree of deviation among individuals, was first partitioned into its components by Fisher (1935) [11] , who classified phenotypic variance into genotypic and environmental variance. Additionally, he further subdivided genotypic variance into additive, dominance and epistatic components, with only the additive (genetic) portion being transmissible to the next generation. The effectiveness of selection is largely governed by the magnitude of this genetic variability, as greater diversity enhances the probability of isolating elite genotypes. Consequently, for breeders, genetic variability represents the cornerstone of any effective breeding strategy. The importance of assessing variability for formulating efficient and targeted selection approaches. In this context, the genotypic coefficient of variation emerges as a crucial parameter that allows for comparative assessment of genetic diversity across various quantitative traits, thus aiding in the identification of traits with the most promising genetic potential.

Materials and Methods

The Field experiment was carried out at Horticultural Research cum Instructional Farm, Department of Floriculture and Landscape Architecture, College of Agriculture, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh during 2022-23 and 2023-24. The location of experiment i.e., Raipur, is situated in the central part of Chhattisgarh at 21 0 16' N latitude, 81 0 36' E longitude and at an altitude of 286.56 m from mean sea level. The experiment was laid out in a Completely Randomized Design with three replications. Totally 10 genotypes were collected from different sources and evaluated for growth and yield parameters. Observations were recorded on various growth parameters, plant height(cm), number of branches and plant spread(cm), floral traits days to first flower opening, flower head diameter (cm), disc diameter(cm), number of ray florets and yield parameters (number of flowers per plant, duration of flowering and flower yield per plant (g)). Analysis of variance (Fisher and Yates, 1963) [12] ; Coefficient of variation and Heritability Broad Sense (Burton and De Vane, 1953) [6] and Genetic advance (Johanson et al.)

Results and Discussion

Analysis of variance

An analysis of variance (ANOVA) was conducted to assess the mean sum of squares for morphological and floral traits of China aster across two consecutive Rabi seasons (2022-23 and 2023-24), including the pooled data. ANOVA results indicated that the differences among the 10 genotypes were highly significant (p < 0.05) for all traits studied. This signifies the presence of considerable genetic variability within the evaluated China aster genotypes. Such variability is essential in breeding programs, as it provides a strong basis for effective selection and potential genetic enhancement of desirable traits in future cultivar development. Hence, the findings justify further genetic studies and selection efforts for crop improvement.

Genotypic and phenotypic coefficients of variance

Genotypic and phenotypic coefficients of variance. The analysis and tabulation of genetic variability parameters for yield traits. These parameters include mean, range, genotypic and phenotypic coefficients of variance (percent), heritability in the broad sense (percent), genetic advance and genetic advance as a % of mean. The value of yield and its components for genotypic coefficients of variance and phenotypic coefficients of variance ranged low to high. For all the character genotypic coefficients of variance is smaller then phenotypic coefficients of variance, it presented the environment have masking effect on the expression on genetic variability presented in Table-1. Among the yield attributes, Flower yield per plant (g) had highest genotypic coefficients of variance (76.37) and phenotypic coefficients of variance (76.40) value followed by Number of leaves per plant [90 DAT] genotypic coefficients of variance (75. 19) and phenotypic coefficients of variance (75.20) and Number of ray florets/flower head genotypic coefficients of variance (52.39) and phenotypic coefficients of variance (52.47). In contrast Seed germination (%) genotypic coefficients of variance (9.19) and phenotypic coefficients of variance (10.60) showed relatively low genotypic coefficient of vaiance and phenotypic coefficient of variation, suggesting that this trait is more strongly affected by the environment than by genetic factors, which may limit the effectiveness of selection. The comparison between genotypic coefficient of variation and phenotypic coefficient of variation for traits in China aster revealed that phenotypic coefficient of variation consistently exceeded genotypic coefficient of variation for all traits across pooled mean indicating the influence of environmental factors on the expression of these traits. These results are consistent with the findings of Mugali et al. in China aster, Savadi et al.

Heritability

Heritability estimates along with genetic advance is a useful criterion in selecting an individual, high heritability estimates along with high genetic advance as percent of mean. In case of pooled mean Heritability was attained in all characters of cultivars ranges from Number of leaves per plant [90 DAT] (99.99) followed by Plant height (cm) [90 DAT] (99.97), Flower yield per plant (g) (99.94), Weight of flower/plant (g) (97.89), Flower stalk length (cm) (99.87), Days to first flower opening (99.74), Number of branches per plant and Number of ray florets/flower head (99.72), Plant spread (cm) [E-W] (99.58), Number of flowers per plant (99.52), Plant spread (cm) [N-S] and Duration of flowering (days) (99.39), Flower head diameter (cm) (97.70), Disc diameter (cm) (97.20) and Seed germination (%) (75.03) respectively. This indicates the character were less influenced by environment. This intrinsic genetic potential is further measured through heritability, which evaluates the extent of genetic correlation between parental traits and those of their progeny and is frequently used to forecast the probability of trait transmission across generations (Bharathi et al., 2023) [4] .

Genetic Advance

High genetic advance (>20) was recorded for several key traits, indicating the predominance of additive gene action and the possibility of substantial improvement through selection. The highest genetic advance was observed in flower yield per plant (167.50), followed by number of leaves per plant at 90 DAT (158.61), number of ray florets per flower head (109.67), plant height at 90 DAT (38.15) and number of flowers per plant (25.89), days to first flower opening (20.48) and Flower stalk length (cm) (20.76). These traits hold significant promise for achieving genetic gains under selection pressure. Moderate genetic advance (10-20%) was noted for number of branches per plant (14.51), Seed germination (%) (13.9), duration of flowering (12.13) plant spread [E-W] (11.69) and plant spread [N-S] (10.21), suggesting reasonable potential for improvement. In contrast, low genetic advance (<10) was observed for weight of flower per plant (2.60), flower head diameter (2.30) and disc diameter (0.90). Such low values point to the predominance of non-additive gene effects and greater environmental influence, limiting the scope for improvement through direct phenotypic selection in these traits. Genetic advance gives an idea about the expected genetic changes and for efficient selection, high heritability along with high genetic advance can be used Similar results were obtained by (Johnson et al., 1955) [14] .

Genetic advance as % of mean

The genetic advance as a percentage of the mean for pooled mean ranged from 16.39% to 157.28%, indicating the expected improvement in trait performance through selection. High genetic advance as percent of mean (>20%) was observed for all characters except seed germination, reflecting substantial genetic variability and strong potential for genetic gain. The highest genetic advance as percent of mean was recorded for flower yield per plant (157.28%), followed by number of leaves per plant at 90 DAT (154.89), number of ray florets per flower head (107.78%), number of branches per plant (104.67%), number of flowers per plant (90.04%), flower stalk length (82.95%), weight of flower per plant (80.95%), plant height at 90 DAT (75.90%), plant spread [E-W] (48.89%), plant spread [N-S] (48.62%), duration of flowering (47.00%) and disc diameter (45.06%). These traits suggest the predominance of additive gene action and indicate that selection would be highly effective in improving these characters. Moderately high genetic advance as percent of mean (20-40%) was observed for flower head diameter (37.53%) and days to first flower opening (31.87%), which also demonstrate good potential for genetic improvement through selection. The only trait exhibiting moderate genetic advance as percent of mean (10-20%) was seed germination (16.39%), implying a relatively lower scope for genetic gain, likely due to greater environmental influence or non-additive gene effects. Additionally, traits like plant height, number of flowers per plant, weight of flower per plant and flower stalk length showed moderately high to high genetic advance values along with high heritability, suggesting that selection based on phenotypic performance could be highly effective for these traits. Similarly, moderate genetic advance combined with high heritability was observed for traits such as days to flower opening and plant spread, indicating that although genetic control exists, environmental influence may also play a role. In contrast, traits like seed germination, flower head diameter, and disc diameter consistently exhibited low genetic advance despite moderate to high heritability, reflecting the involvement of non-additive gene action and a higher environmental influence on their expression. This reduces the efficiency of direct selection and implies that such traits may require alternate breeding strategies, such as recurrent selection or heterosis breeding, to realize genetic gains. These patterns highlight the importance of understanding genetic variability and inheritance to guide targeted improvement efforts in China aster.

Acknowledgement

AcknowledgementThe author are thankful to my committee for their continued support and encouragement and to conduct the whole research programme to obtain its significant findings from Department of Floriculture and Landscape Architecture, IGKV, Raipur, Chhattisgarh, India.

References

1. Al-Jibouri HA, Miller HA, Robinson HF. Genotypic and environmental variances and covariances in upland cotton crosses of interspecific origin. Journal of Agronomy. 1958;50:633-636.
2. Allard RW. Relationship between genetic diversity and consistency of performance in different environments. Crop Science. 1960;1:127-132.
3. Aman, Singh AK. Study of genetic variability, character association and path analysis in urdbean (Vigna mungo L.) under irrigated conditions of Jammu region. Annals of Agricultural Research, New Series. 2022;43(3):272-280.
4. Bharathi D, Reddy KH, Reddy DM, Latha P, Reddy BR, Devi MS. Heterosis for yield and quality traits in blackgram (Vigna mungo L.). The Pharma Innovation Journal. 2023;12(2):525-529.
5. Burton GW. Quantitative inheritance in grasses. Proceedings of the 6th International Grassland Congress. 1952;1:227-283.
6. Burton GW, Devane EM. Estimation of heritability in tall fescue (Festuca arundinacea). Agronomy Journal. 1953;45:478-480.
7. Comstock RR, Robinson HF. Genetic parameters, their estimation and significance. Proceedings of the Sixth International Grassland Congress. 1952;1:248-251.
8. Deekshith KS, Venkanna V, Devi KR, Naik DS, Sameer Kumar CV. Genetic variability, heritability and genetic advance in blackgram (Vigna mungo L.). Biological Forum -An International Journal. 2022;14(2A):583-588.
9. Dewey DR, Lu KH. A correlation and path coefficient analysis of components of crested wheat grass production. Agronomy Journal. 1959;51:179-188.
10. Falconer DS. Introduction to quantitative genetics. 3rd ed. New York (USA): Longman; 1989. p. 1-386.
11. Fisher RA. The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh. 1935;52(2):399-433.
12. Fisher RA, Yates F. Statistical tables for biological, agricultural and medical research. London: Oliver and Boyd; 1963. p. 1-143.
13. Griffing B, Lindstrom EW. A study of combining abilities of corn inbreds having varying proportions of corn-belt and non-corn belt germplasm. Agronomy Journal. 1954;46(12):545-552.
14. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybean. Agronomy Journal. 1955;47:413-418.
15. Joshi DP, Parmar LD, Meena RK, Chaudhary GK. Estimation of genetic diversity in mungbean genotypes grown in Gujarat. Legume Research -An International Journal. 2022;45(7):828-833.
16. Lush JL. Heritability of quantitative characters in farm animals. Proceedings of the 8th International Congress of Genetics. Hereditas (Supplement). 1949;3(2):356375.
17. Mahalanobis PC. On the generalized distance in statistics. Proceedings of the National Institute of Science of India. 1936;2:49-55.
18. Panse VG, Sukhatme PV. Statistical methods for agricultural workers. New Delhi: ICAR; 1985. p. 1-357.
19. Pushpa RY, Rao KY, Satish Y, Babu SJ. Estimates of genetic parameters and path analysis in blackgram (Vigna mungo (L.) Hepper). International Journal of Plant, Animal and Environmental Sciences. 2013;3(4):231-234.
20. Rao CR. Advanced statistical methods in biometrical research. New York: John Wiley and Sons Inc.; 1952. p. 1-383.
21. Reeve EGR. The variance of genetic correlation coefficient. Biometrics. 1955;11:357-374.
22. Sagar K, Vastrad DC, Sathish SM, Mallappa BR, Basavaraj T, Nagajyothi GN. Germplasm conservation, evaluation, characterization and improvement of genetic resources of fruit crops in India: preserving fruit crops for the future. International Journal of Advanced Biochemistry Research. 2024;SP-8(1):271-276.
23. Searle SR. Phenotypic, genotypic, and environmental correlations. Biometrics. 1961;17:474-480.
24. Singh RK, Chaudhary BD. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani Publishers; 1985. p. 1-318.
25. Steel RGD, Torrie JH. Principles and procedures of statistics. New York: McGraw-Hill Book Company; 1960. p. 1-481.
26. Wright S. Correlation and causation. Journal of Agricultural Research. 1921;20:557-585.