Statistical analysis of variances studies in segregating populations of okra (Abelmoschus esculentus L. Moench)

Introduction

Okra (Abelmoschus esculentus L. Moench) is an important vegetable crop grown widely in tropical and subtropical regions for its nutritional and medicinal benefits. It is a rich source of vitamins, minerals, and dietary fibre, and plays a vital role in food security and nutrition (Kumar et al., 2021) [4] . Despite its significance, okra breeding has faced challenges such as low genetic diversity, susceptibility to diseases, and yield constraints (Patel et al., 2020) [5] . Therefore, exploring genetic variability in segregating populations is crucial for the development of improved varieties with enhanced yield, early maturity, and superior fruit quality. The use of variance analysis techniques, such as the augmented block design and randomized complete block design (RCBD), allows breeders to assess the genetic diversity and environmental stability of key agronomic traits (Verma et al., 2022) [12] . Statistical analysis of variance was performed on fourteen key traits, including days to flowering, plant height, fruit dimensions, number of fruits, and total yield per plant, to assess treatment effects and determine the extent of genetic variation within the populations. Previous studies have emphasized the importance of such analyses in the identification of superior segregants for further selection and advancement in breeding programs (Sharma et al., 2018; Gupta et al., 2020) [10,3] . By identifying significant genetic differences among the segregating populations, this study aims to contribute to the ongoing efforts to enhance the productivity and quality of okra through targeted breeding strategies. The findings will provide valuable insights for breeders in selecting high-performing genotypes for the development of new okra cultivars with improved agronomic traits.

Materials and Methods

The present investigation was carried out in the Department of Biotechnology and Crop Improvement, College of Horticulture, Bengaluru, to asses the statistical analysis of variance in the F3 and F4 populations of OK-201-010 x Pusa Sawani and OK-2017-010 x Varsha Upahr. The F3 consist of the 25 families from the both the cross along with the check Varsha Uphar, Pusa A4, Prabhani Kranti, Khasi Pragati and Arka Anamika along with the parents were analysed in Augmented block design the statistical analysis was done by using the R-STUDIO 4.4.1 (AUGMENTEDRBD) packages was used. The F4 consist of the eleven segregant in cross OK-2017-010 x Pusa Sawani, and twelve segregants from OK-2017-010 x Varsha Uphar along with the checks Prabhani Kranti, Arka Anamika, Pusa A4 and parents analysed in Randomized Complete Block Design, the statistical analysis was done by using the GRAPES 1.0 from the Kerala Agriculture University. The observations were recorded from all the individual plants.

Results and Discussion Statistical Assessment of Variance in F3 Populations

The statistical of variance in the F3 populations derived from the crosses OK-2017-010 × Pusa Sawani and OK-2017-010 × Varsha Uphar reveals significant differences for all the fourteen traits under investigation. The application of the augmented block design ensured robust analysis and precise identification of the genetic variation within these populations. This variation is critical for the progress of okra breeding programs aimed at developing superior cultivars with enhanced agronomic traits.

Cross OK-2017-010 × Pusa Sawani

In the F3 populations of OK-2017-010 × Pusa Sawani, significant variation was observed among the treatments across all fourteen characters, including key traits such as days to first flowering, days to 50% flowering, plant height, number of branches per plant, fruit size (length and diameter), number of fruits per plant, and total yield per plant presented in Table 1. This suggests that these traits are influenced by genetic factors rather than environmental variability, making this population a valuable resource for selection. The genetic variability for earliness and yield components, particularly the number of fruits and fruit size, highlights the potential of specific segregants in contributing to early maturity and higher yields, which are critical breeding targets in okra improvement programs. The presence of significant differences across traits related to plant architecture (e.g., plant height, number of branches, and stem girth) and reproductive attributes (e.g., number of fruits and seeds per plant) suggests that the population contains considerable genetic diversity similar findings with the Prakash et al. (2022) [6] , Akotar et al. (2010) [2] , and Singh et al. (2017) [9] . This diversity offers opportunities for breeders to select desirable genotypes that combine favourable traits, ultimately leading to the development of high-yielding okra varieties with superior agronomic performance.

Cross OK-2017-010 × Varsha Uphar

Similarly, the analysis of variance for the F3 populations of OK-2017-010 × Varsha Uphar revealed significant differences for all fourteen characters, further emphasizing the presence of genetic variation within this cross presented in Table 2. Traits such as plant height, days to flowering, fruit dimensions, and yield-related parameters exhibited substantial variability among the treatment diversity, similar to the findings of Prakash et al. (2022) [6] , Akotar et al. (2010) [2] , and Singh et al. (2017) [9] . This variability is crucial for identifying superior segregants that can be advanced to the next generation. The significant differences observed in traits such as fruit length, diameter, and average fruit weight are particularly important for okra breeding, as these characteristics directly influence marketability and consumer preference. Additionally, the variation in total yield per plant across the segregants indicates the potential for selecting high-yielding lines from this population.

Implications for Breeding Programs

The significant variation in both crosses reflects the genetic diversity present in the F3 populations, which is essential for effective selection in early segregating generations. The ability to identify superior genotypes based on key traits such as earliness, fruit quality, and yield potential highlights the effectiveness of the augmented block design in assessing genetic variability while controlling environmental effects. For okra breeders, this analysis provides valuable insights into the potential of specific crosses to contribute to the development of improved okra cultivars. The identification of significant treatment effects across multiple traits demonstrates that these populations harbour sufficient genetic variability for further selection and advancement to subsequent generations. This variability forms the foundation for selecting superior lines that exhibit a combination of desirable traits, such as early maturity, increased yield, and enhanced fruit quality.

Statistical Analysis of Variance in F4 Populations

The analysis of variance (ANOVA) conducted for the F4 populations of both crosses OK-2017-010 × Pusa Sawani and OK-2017-010 × Varsha Uphar revealed highly significant differences among the treatment groups for all fourteen parameters studied. This result indicates substantial genetic variation among the evaluated populations, which is crucial for selecting superior lines in the breeding program.

Cross OK-2017-010 × Pusa Sawani

In the case of the cross OK-2017-010 × Pusa Sawani, the significant differences observed among the treatments, including the segregating F4 populations, the parents, and the four checks, suggest considerable genetic diversity is present within the population in Table 3. This diversity offers a strong foundation for selecting desirable traits related to yield and its attributing factors. Highly significant treatment effects typically reflect the influence of genetic factors over environmental conditions, which reinforces the potential for identifying superior genotypes within these similar results found in the studies of Adeolua and Kehinde (2011) [1] , Simon et al. (2013) [8] and Reddy et al. (2012) [11] . The results also suggest that specific segregants in the F4 generation are likely to exhibit promising agronomic traits, enhancing the potential for further selection in subsequent generations.

Cross OK-2017-010 × Varsha Uphar

Similarly, the variance analysis for the F4 populations derived from the cross OK-2017-010 × Varsha Uphar showed significant differences across all fourteen parameters Furnished in Table 4. This indicates the presence of considerable genetic variability within this population as well, which is essential for effective selection similar results were found in the studies of Adeolua and Kehinde (2011) [1] , Simon et al. (2013) [8] and Reddy et al. (2012) [11] . The significant variation observed among the treatments, including the segregants, parents, and checks, points to the genetic potential of this population to contribute to okra improvement programs, especially for traits related to yield, disease resistance, and plant vigour.

General Implications for Breeding Programs

The identification of highly significant differences among the treatments across both crosses underscores the effectiveness of the Randomized Complete Block Design (RCBD) in controlling environmental variability and accurately estimating genetic variation. This, in turn, provides breeders with robust data for the selection of superior genotypes. The genetic variability observed in these F4 populations is crucial for enhancing important traits in okra, including yield and resilience to environmental stressors. These findings suggest that there is ample scope for selecting highperforming lines that could contribute to the development of superior okra cultivars with improved yield potential and other agronomically valuable traits.

Conclusion

Statistical variance analysis of the F3 populations of both crosses, OK-2017-010 × Pusa Sawani and OK-2017-010 × Varsha Uphar reveals substantial genetic variation across key agronomic traits. Similarly, the significant treatment effects across both crosses demonstrate the presence of meaningful genetic variability in the F4 populations, which is a critical requirement for the continued progress of okra breeding programs. This provides a strong basis for the selection of promising segregants that can be advanced in future breeding efforts aimed at developing superior okra varieties.

References

1. Adeoluwa OO, Kehinde OB. Genetic variability studies in West African okra (Abelmoschus caillei). Agric. Biol. J North Am. 2011;2(10):1326-1335.
2. Akotkar PK, De DK, Pal AK. Genetic variability and diversity in okra (Abelmoschus esculentus L. Moench). Electron J Plant Breed. 2010;1(4):393-398.
3. Gupta R, Patel S, Mehta N. Genetic variability and correlation studies in okra (Abelmoschus esculentus L. Moench) for yield and quality traits. J Veg Sci. 2020;47(2):78-85.
4. Kumar A, Singh R, Mishra P. Nutritional value and health benefits of okra (Abelmoschus esculentus L.): A review. Int. J Agric. Food Sci. 2021;11(3):156-162.
5. Patel D, Singh S, Kaur M. Genetic improvement in okra (Abelmoschus esculentus L. Moench): Challenges and opportunities. Plant Breed Crop Sci. J. 2020;28(1):44-52.
6. Prakash G, Halesh GK, Jagadeesha RC, Ravishankar KV, Pitchaimuthu M, Shankarappa KS, et al. Studies on genetic variability and character association in okra (Abelmoschus esculentus L.) for yield and its contributing traits. J Pharm Innov. 2022;11(12):3639-3643.
7. Sharma V, Verma S, Singh J. Role of segregating populations in improving yield and quality traits in okra. Hortic. Res. Rev. 2018;39(4):92-98.
8. Simon SY, Gashua IB, Musa I. Genetic variability and trait correlation studies in okra (Abelmoschus esculentus (L.) Moench). Agric Biol J North Am. 2013;4(5):532538.
9. Singh N, Singh DK, Sati UC, Rawat M, Pandey P. Genetic analysis studies in okra (Abelmoschus esculentus (L.) Moench). Int J Pure Appl Biosci. 2017;5(4):361-367.
10. Singh P, Rai N. Genetic analysis and variability studies in okra (Abelmoschus esculentus L. Moench) for yield traits. Indian J Hortic. 2019;76(1):53-59.
11. Reddy TM, Babu HK, Ganesh M, Reddy CK, Begum H, Reddy PB, et al. Genetic variability analysis for the selection of elite genotypes based on pod yield and quality from the germplasm of okra (Abelmoschus esculentus L. Moench). J Agric. Technol. 2012;8(2):639655.
12. Verma P, Gupta S, Bhattacharya A. Advances in statistical methods for analysing genetic variability in crop plants. Biometr. Genet J. 2022;45(1):23-34.