International Research Journal of Plant Science

Estimation of Different Genetic Parameters for Efficient Selection of Hybrids in Rice (Oryza sativa L.)

Saikiran Veldandi^*, L Krishna and Y Chandra Mohan Department of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad, India
^*Corresponding Author:
Saikiran Veldandi, Department of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad, India, Email: veldandisaikiran@gmail.com

Received: 05-May-2020, Manuscript No. IRJPS-23-10343; Editor assigned: 08-May-2020, Pre QC No. IRJPS-23-10343 (PQ); Reviewed: 22-May-2020, QC No. IRJPS-23-10343; Revised: 19-Jul-2023, Manuscript No. IRJPS-23-10343 (R); Published: 16-Aug-2023, DOI: http:/dx.doi.org/10.14303/irjps.2023.26

Introduction

Rice (Oryza sativa L.) is the most consumed cereal of the world and about 75 percent of the people of Asia consume rice as a staple food. In India rice is cultivated in an area of 43.8 million hectares with the production of 102.13 million tonnes. Despite the age-old human concerns about balancing population and food supplies.

The world population is expected to continue to grow, possibly reaching 9-11 billion times during 2030-2050. To meet the growing population demand we need to increase rice production through the development of high yielding varieties, which requires the knowledge of existing genetic variability (Akinwal e et al., 2011).

The large spectrum of genetic variability in the segregating population depends on the amount of the genetic variability present in the genotypes and offer better scope for efficient selection. The magnitude of heritable variation in the traits studied has immense value in understanding the worth of the genotype for a further breeding program. Assessment of variability for yield and its related characters become important before planning of an appropriate breeding strategy for genetic improvement. Genetic parameters such as the Genotypic Coefficient of Variation (GCV) and Phenotypic Coefficient of Variation (PCV) are useful in detecting the amount of variability present in the germplasm (Burton, 1952).

The genotypic coefficient of variation does not offer full scope to estimate the variation that is heritable or environmental and therefore the estimation of heritability becomes necessary to fulfill our selection efficiently. While estimating heritability, we always remember that narrow sense heritability is more effective than broad sense heritability because it considers an additive portion of the genotypes and hence more accurate towards selection (Falconer, 1981). Crop improvement could be studied on the magnitude of genetic variability transfer to progeny and the extent to which the desirable characters are heritable.

High heritability alone is not enough to make an efficient selection in segregating generation unless the required information is accompanied by a substantial amount of genetic advance. The information on heritability alone may be misleading when used in combination with genetic gain the utility of heritability estimate increases (Hasan, et al., 2015). The present investigation was undertaken to study the genetic parameters among the 48 rice genotypes for grain yield and yield contributing traits.

Materials and Methods

A field trial was conducted with four CMS lines having the WA cytoplasmic background viz., CMS 23A, CMS 59A, CMS 64A and JMS 13A used as females and eight promising rice varieties as males and thirty-two hybrids generated adopting line x tester mating design in Rabi, 2016-17. We used four local commercial checks to compare the performance of our hybrid (Table 1). All the forty-eight genotypes were sown in Randomized Block Design (RBD) with two replications at Rice Research Centre, Agricultural Research Institute, PJTSAU, Hyderabad during Kharif, 2017. Standard spacing of 30 cm × 15 cm was adopted for planting with a plot size of 1.2 m² each. Recommended packages of’ practices were followed during the crop growth period (Kole et al., 2008).

Table 1. List of successful lines, testers and checks (i.e., 2 varietal checks and 2 hybrid checks) used in the present study.

Observations were recorded for fourteen characters viz., days to 50% flowering, plant height (cm), panicle length (cm). number of productive tillers per plant, panicle weight (g), spikelet fertility%, 1000 grain weight (g), hulling percent, milling percent, head rice recovery (%), kernel length (mm), kernel breadth (mm), L/B ratio and grain yield per plant (g). Data on days to 50% flowering recorded on a plot basis at the flowering stage. Five representative plants for each genotype were randomly selected to record observations on the quantitative characters under study. Data on plant height, panicle length, number of productive tillers per plant were recorded at physiological maturity and number of grains per panicle, spikelet fertility, 100-grain weight, hulling percent, milling percent, head rice recovery, kernel length, kernel breadth, L/B ratio and grain yield per plant were post-harvest estimates. Genotypic and phenotypic coefficients of variance, heritability and genetic advance were estimated for all fourteen characters by following standard procedures. The genotypic and phenotypic coefficients of variation were calculated according to the formula given by Falconer. Heritability in the narrow sense refers to the proportion of additive variance to the total observed variance in the total population. Heritability (h²) in the narrow sense was calculated according to the formula given by Lush. Genetic advance refers to the expected gain or improvement in the next generation by selecting superior individuals under a certain amount of selection pressure. From the heritability estimates the genetic advance was estimated by the formula given by Burton (Lush, 1945).

Results and Discussion

Variability is a pre-requisite for the selection of superior genotypes from the population over the existing cultivars. To study the variation, a statistical component called analysis of variance is used. Here analysis of variance for different characters is presented in Table 2. The analysis of variance for grain yield and yield contributing characters revealed a significant difference between the treatments for all the characters viz., days to 50% flowering, plant height, panicle length, number of productive tillers per plant, panicle weight, spikelet fertility, 1000 grain weight, hulling percent, milling percent, head rice recovery, kernel length, kernel breadth, L/B ratio and grain yield per plant shown presence of sufficient genetic variability for these traits. In the case of replications, there was no significant difference in genotypes for all the characters). The significance of difference indicates that there was sufficient variability among the genotypes tested. The immensity of variation between genotypes was reflected by high values of mean and range for genotype traits studied (Table 3). The highest range variation was found in the case of plant height (48.50) followed by grain yield per plant (39.60), spikelet fertility (29.58) and days to 50% flowering (29.0) shown that simple selection was effective for these characters (Kencharahut et al., 2018).

Table 2. Analysis of variance for 14 different characters of 32 hybrids, parents and standard checks in rice.

Coefficients of variation

The extent of variability present in the genotypes was measured in terms of the Genotypic Coefficient of Variation (GCV) and Phenotypic Coefficient of Variation (PCV), it is shown in Table 3. The results revealed that the estimates of the Phenotypic Coefficient of Variation (PCV) were slightly higher than those of the Genotypic Coefficient of Variation (GCV) for all the traits studied. Large differences between GCV and PCV values reflect the high environmental influence on the expression of traits. In this study, slight differences indicated minimum environmental influence and consequently greater role of genetic factors on the expression of traits. Similar observations were also noted earlier in rice (Srinivas et al., 2016).

Table 3. Analysis of variance for 14 different characters of 32 hybrids.

In contrary to the present study which showed close values between GCV and PCV recorded wide differences (Seyoum et al., 2012). The GCV ranged from 2.457 for hulling percent to 25.359 for grain yield whereas PCV ranged from 3.106 for Hulling percent to 27.059 for grain yield. Next to grain yield per plant, characters of thousand grains weight (15.136, 15.790), number of productive tillers (12.135, 16.524), panicle weight (12.410, 14.071) and plant height (10.916, 11.175) also showed high GCV and PCV respectively.

The lowest GCV and PCV were observed on hulling percent (2.457, 3.106), milling percent (2.634, 1.023) and head rice recovery (3.607, 5.131). Similarly low GCV and PCV results found by Manjunath et al., 2018. Whereas Akinwake et al., 2011 and Ullah et al., 2011 reported moderate to low GCV and PCV estimates for different quantitative traits in rice.

Heritability

High heritability is not enough to make an efficient selection in segregating generation unless the information is accompanied by a substantial amount of genetic advance. Heritability and genetic advance, when calculated together, would prove more useful in predicting the resultant effect of selection on phenotypic expression, without genetic advance the estimation of heritability will not be of practical value and emphasized the concurrent use of genetic advance along with heritability (Mustafa et al., 2007).

High narrow sense heritability with high genetic advance shows a better selection of hybrids leading to the substantial improvement of the character (Rahman et al., 2014). In the present study, high heritability coupled with high genetic gain (>20%) as a percentage of mean was observed for 1000 grain weight (29.88%) and plant height (21.966%) (Table 4).

Table 4. Variability, heritability and genetic advance as percent mean on the performance of hybrids.

Whereas got the same results in the case of days to heading which gave a better response to selection. In the estimates of narrow-sense heritability were varied from 63.806 (kernel breadth) to 6.708 (head rice recovery). Low heritability was observed for traits of head rice recovery (6.708) (Tiwari et al., 2011). Panicle length (18.222), spikelet fertility (19.955), milling percent (23.996) and hulling percent (27.124). Lowest narrow-sense heritability (17.54%) was recorded for panicle length by Ganapathi et al., 2020 reported moderate heritability and high genetic advance for kernel length and grain yield per plant, which attributes that genetic advance is due to additive gene action and heritability was influenced by environmental factors thus the selection may be effective (Sadeghi, 2011). These results are in accordance with hulling percent and milling percent had low heritability and low genetic advance (<10%). Reported similar results for low heritability coupled with low genetic advance as percent of mean for hulling percent and milling percent (Shekhawat et al., 2015). Selection would be ineffective due to low expected gain or improvement in the next generation by these traits. Therefore, priority should be given to those traits which showed higher estimates of genetic advance as percent mean while deciding selection strategies and selection based on these characters may be useful in realizing better gain by selection (Shinde et al., 2015).

High heritability percent was observed for the characters like kernel breadth (63.806), test weight (58.831), plant height (53.516), days to 50% flowering (46.996), panicle weight (38.619) and kernel length (32.613) (Shivakumar et al., 2014). Similar results of high heritabilities were found for plant height, days to 50% flowering and test weight by Ganapathi et al.; Manjunath et al. and Seyoum et al.

Conclusion

Based on the present results it may be concluded that high heritability coupled with high genetic advance was recorded for plant height and 1000 grain weight indicating the major role of additive gene action in the inheritance of these characters. Thus, these characters could be improved through simple selection methods. The characters showing high heritability with low genetic advance indicated the presence of non-additive gene action. Hence selection could be postponed for these characters or these characters could be improved by intermating of superior genotypes of segregation population from recombination breeding.

References

1. Akinwale MG, Gregorio G, Nwilene BO, Akinyele SA, Ogunbayo AC, et al. (2011). Heritability and correlation coefficient analysis for yield and its components in rice (Oryza sativa L.). Afr J Plant Sci. 5: 207-212.

   [Google Scholar]

2. Burton GW (1952). Quantitative inheritance in grasses. Pro Int Gras Cong J. 1: 277-283.

   [Crossref] [Google Scholar]

3. Falconer DS (1981). Introduction to quantitative genetics. Longmann. 12: 294-300.

   [Google Scholar]

4. Hasan MJ, Kulsum MU, Emran H, Mustafizur RM, Niaz M, et al. (2015). Combining ability analysis for identifying elite parents for heterotic rice hybrids. Acad J Agr Res. 3: 070-075.

   [Google Scholar]

5. Kole PC, Chakraborty N, Bhat JS (2008). Analysis of variability, correlation and path coefficients in induced mutants of aromatic non-basmati rice. Trop Agric Res Exten. 113: 60-64.

   [Google Scholar]

6. Lush JL (1945). Intra-sire correlation on regression of off-spring on dams as method of   estimating heritability of characters. Am Soc Ani Prod. 33: 292-301.

   [Crossref] [Google Scholar]

7. Kencharahut M, Mohan C, Shankar G (2018). Assessment of genetic variability in newly developed rice (Oryza sativa L.) hybrids. Elect J Plant Breed. 9: 916-925.

   [Google Scholar]

8. Srinivas M, Thippeswamy B, Padmaja D (2016). Diversity and variability analysis for yield parameters in rice (Oryza sativa L.) genotypes. Ind J Agri Res. 50: 609-613.

   [Crossref] [Google Scholar]

9. Seyoum M, Alamerew S, Bantte K (2012). Genetic variability, heritability, correlation coefficient and path analysis for yield and yield related traits in upland rice (Oryza sativa L.). J Plant Sci. 7: 13-22.

   [Google Scholar]

10. Mustafa MA, Elsheikh M (2007). Variability, correlation and path coefficient analysis for yield and its components in rice. Afr Crop Sci J. 15: 183-189.

    [Google Scholar]

11. Rahman MA, Hossain MS, Matin C (2014). Variability study of advanced fine rice with correlation, path coefficient analysis of yield contributing characters. Int J App Sci Biotech. 2: 364-370.

    [Google Scholar]

12. Ganapati RK, Rasul G, Sarker U, Singha A, Muhiuddin B (2020). Gene action of yield and yield contributing traits of submergence    tolerant rice (Oryza sativa L.) in Bangladesh. Bull Nat Res Centre. 44: 8.

    [Crossref] [Google Scholar]

13. Sadeghi SM (2011). Heritability, phenotypic correlation and path  coefficient studies for some agronomic characters in landrace rice varieties. World Appied Sci J. 13: 1229-1233.

    [Google Scholar]

14. Shekhawat R, Harsh R, Singh VS, Yashpal Y (2015). Studies on variability, heritability and genetic advance in rice (Oryza sativa L.) germplasm lines. Ann Agri Bio Res. 20: 22-25.

    [Google Scholar]

15. Shinde SR, Kamlesh K, Pawar RM (2015). Genetic studies in upland rice (Oryza sativa L.). Int J Plant Sci. 10: 33-37.

    [Google Scholar]

16. Shivakumar N Kiran, Krishnappa K (2014). Genetic variability and heritability studies for grain yield and yield attributes in diverse rice (Oryza sativa L.) Inbred lines. Trends Biosci. 7: 958-960.

    [Google Scholar]

17. Tiwari DK, Pandey P, Tripathi S, Giri SP, Dwivedi JL, et al. (2011). Studies on genetic variability for yield components in rice (Oryza sativa L.). Bio Flux. 3: 76-81.

    [Google Scholar]

Citation: Veldandi S, et al. (2023). Estimation of Different Genetic Parameters for Efficient Selection of Hybrids in Rice (Oryza sativa L.). IRJPS. 14: 26.