Genetic variability, heritability and gene action for yield and quality traits in ridge gourd [Luffa acutangula (L.) Roxb.] using half-diallel analysis

Introduction

Ridge gourd (Luffa acutangula (L.) Roxb. is an economically important cucurbitaceous vegetable crop widely cultivated in tropical and subtropical regions of India and South-East Asia. Belonging to the genus Luffa of the family Cucurbitaceae with a chromosome number of 2n = 26, it is commonly known as kalitori, angled gourd, angled loofah, Chinese okra, silky gourd, or ribbed gourd. Crop is popularly known by different names such as Kalitori, angled gourd, angled loofah and ribbed gourd and believed to have originated from Subtropical Asian region including India (Yadav et al., 2015; Swarup, 2016) [6,7] . Tender green fruits are extensively used in soups, curries, and as cooked vegetables, while dried fruits serve as bathing sponges, doormats, and cleaning utensils (Narasannavar et al., 2014) [21] . The nutritional, phytochemical, and medicinal importance of ridge gourd (Luffa acutangula L. Roxb.). The authors reported that ridge gourd is an important cucurbit vegetable rich in vitamins, minerals, flavonoids, and bioactive compounds. Yasmin et al. (2019) Ridge gourd acts as an appetizer and considered as a healthy food that contains 0.5 g of fiber, 0.5% of protein, 0.35% of carbohydrate, 37 mg of carotene, 5.0 mg of vitamin C, 18 mg of calcium and 0.5 mg of Iron in 100 g of the edible portion (Hazra and Som, 2005) [19] . They form a low-calorie diet, which is good for diabetes (Harshitha et al., 2019, and found to be helpful as a natural remedy for the treatment of jaundice (Panicker et al., 2019) [5] Ridge gourd (Luffa acutangula Roxb.) is an important warm season cucurbitaceous vegetable crop grown in diverse regions of India and tropical areas of Asia and Africa (Chaudhary et al., 2014; Haldhar et al., 2015) [4,14] for its tender fruits. It belongs to family Cucurbitaceae and bears a chromosome number 2n = 2x = 26 (Sen et al., 2023; Mithila et al., 2024) [12, 13] India is the second largest producer of vegetables in the world with an annual production of 185 million tonnes from an area of 10.20 million hectares (NHB, 2018-19) [22] , with ridge gourd contributing approximately 983 metric tonnes from 90 hectares. The total area under vegetable cultivation in India is approximately 11 million hectares. Total production stands at around 205-210 million tonnes annually. The average productivity of vegetables is about 19 tonnes per hectare. Vegetables account for nearly 60% of India's total horticultural output. (National Horticulture Board (NHB) and Ministry of Agriculture) 2023-24. Ridge gourd cultivation in India covers an area of 9920 ha generating a substantial production of about 3.17 lakh t with a productivity of 31.95 t ha -1 . India ranks second globally in vegetable production, occupying 11.35 million hectares with a total annual output of 219.67 million tonnes India ranks second globally in vegetable production, after China, contributing approximately 15-16% of global output (Anonymous, 2024-25) [2] . Cucurbits collectively represent a major share of this production, with India cultivating cucurbit crops over approximately 5.98 lakh hectares yielding 93.73 lakh tons annually. Ridge gourd fruits are nutritionally rich, containing Vitamin A, C, iron, carbohydrates, calcium (18 mg), phosphorus (26 mg), and carotene (33 g per 100 g edible portion), and are particularly recommended for individuals suffering from malaria and seasonal fevers due to their easy digestibility (Gautama et al., 2017) [17] . Evaluation and identification of superior genotypes among the existing germplasm is considered very imperative to utilize and improve the plant genetic resources in various crop improvement programmes (Terfa and Gurnu, 2020) [11] . Improvement of any crop depends upon the degree of genetic variability present and the level of heritability associated with economically significant traits. Saisupriya et al. (2022) [9] , The significant degree of natural variation or extensive variability in multiple characteristics across genotypes enhances greater opportunity of identifying new genotypes and suggests a strong potential for improvement in economic traits (Jonah et al., 2024) [8] . Being a monoecious and highly cross-pollinated crop, ridge gourd exhibits abundant heterozygosity and natural variability in populations. Most commercially released hybrids produce large-sized fruits that are not preferred by consumers, adversely affecting marketability (Narasannavar et al., 2014) [21] . Genetic variability is the foundation of any crop improvement programme because it provides the essential raw material for selection and hybridization. In vegetable crops, particularly cucurbits like ridge gourd (Luffa acutangula L. Roxb.), the assessment of genetic variability helps in understanding the extent of heritable variation available within the germplasm. Burton, G.W. DeVane, E.H. (1953) [15] . Rai et al. (2008) [23] studied the contribution of different quantitative traits toward yield improvement in ridge gourd through component analysis. The authors reported considerable variability among ridge gourd genotypes for important growth and yield characters. Traits such as fruit weight, number of fruits per plant, and fruit length showed positive association with yield and contributed significantly to total variation. Johnson et al. (1955) [20] reported that the combined study of genotypic coefficient of variation, heritability, and genetic advance provides reliable information regarding the nature and magnitude of gene action controlling quantitative traits. The progenitor of cultivated ridge gourd is considered to be Luffa graveolens, and South and South-East Asia are known centers of diversity for Luffa species (Yadav and Singh, 2022) [25] . When combined with diallel analysis for estimating general combining ability (GCA) and specific combining ability (SCA) variances, breeders can effectively identify the nature of gene actionadditive or nonadditivegoverning trait expression (Varalakshmi et al., 2016) [24] . The present study was therefore designed to estimate genetic parameters of variability and gene action for 24 yield and quality traits in ridge gourd using a halfdiallel mating design, with the objective of identifying traits amenable to direct selection and determining the most effective breeding strategies for crop improvement.

Materials and Methods

Experimental Location and Season

The experiment was conducted at the Vegetable Research Farm, Department of Vegetable Science, Kalyanpur, C.S. Azad University of Agriculture and Technology, Kanpur, Uttar Pradesh, India, during the summer (Zaid) Season 2024-2026. The experimental site is located in the Indo-Gangetic Plain, characterized by hot and semi-arid climate conditions suitable for cucurbit cultivation.

Genetic Material and Mating Design

The genetic material comprised 10 diverse parental lines of ridge gourd. A half-diallel mating design was employed, generating 45 F₁ hybrid combinations [n(n-1)/2 = 10×9/2 = 45 crosses]. All 45 crosses along with one standard commercial check variety, Kashi shivani, were evaluated in a Randomized Complete Block Design (RCBD) with three replications. The crop was planted in rows spaced at 3.0 meters apart with a Plant-Plant spacing of 0.60 meter.

Observations Recorded

Twenty-four yield and quality traits were recorded for each genotype across replications. Vine growth traits were measured at 30, 45, 60, and 75 days after sowing (DAS). All traits, their units, and abbreviations are summarized in Table 1.

Statistical Analysis

Genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were estimated following the formulae proposed by Burton and de Vane (1953) [15] . Broad-sense heritability (h²bs) was estimated as the ratio of genotypic variance to phenotypic variance and expressed as a percentage (Hanson et al., 1956) [18] . Genetic advance (GA) and genetic advance as percent of mean (GAM) were computed following the method of Johnson et al. (1955) [20] , assuming a selection intensity of 5% (k = 2.06). The GCV and PCV values were classified as low (<10%), moderate (10-20%), and high (>20%). Heritability values were categorized as low (<50%), moderate (50-75%), and high (>75%). Genetic advance as percentage of mean was classified as low (<10%), moderate (10-20%), and high (>20%). Diallel analysis was performed following Griffing's (1956) Method II, Model I (fixed effects), to estimate GCA and SCA variances. The ratio σ²GCA/σ²SCA was used to determine the relative importance of additive versus nonadditive gene action. A ratio less than 1.0 indicates the predominance of non-additive gene action (dominance and epistasis).

Results and Discussion

Coefficient of Variation (GCV and PCV)

The estimates of GCV and PCV for all 24 traits are presented in (Table 2. and figure 1.) The PCV was higher than GCV for all traits, indicating the influence of environmental factors on trait expression. However, the narrow difference between PCV and GCV for most traits suggested that genetic factors predominated over environmental effects in controlling these characters, consistent with earlier reports in cucurbit crops (Chandan et al., 2018) [16] . High GCV and PCV (>20%) were observed for vine length at flowering (GCV: 25.15%, PCV: 25.81%), number of primary branches (20.71%, 21.42%), and shelf life (20.85%, 21.02%). High variability for vine length at flowering suggests that this trait is highly responsive to selection pressure. Moderate GCV and PCV (10-20%) were recorded for fruit yield (13.06%, 14.38%), fruit length (10.88%, 13.27%), secondary branch number (13.48%, 13.67%), and flesh thickness (18.23%, 18.44%). Low GCV and PCV (<10%) were recorded for number of fruits per vine (3.97%, 4.71%), days to 50% flowering (4.22%, 6.88%), and days to first harvest (4.73%, 6.99%), indicating limited genetic variability and lesser scope for direct improvement through selection for these traits.

Heritability in Broad Sense

Heritability estimates ranged from 37.28% for vine length at seedling stage (VLSF) to 98.38% for shelf life, as presented in (Table 2 and figure 1) High heritability (>75%) was recorded for shelf life (98.38%), flesh thickness (97.68%), secondary branches (97.22%), vine length at flowering (94.99%), vine length at 30 DAS (94.99%), ascorbic acid (94.23%), dry matter (93.61%), number of primary branches (93.46%), total soluble solids (88.98%), nodes per branch (85.86%), and fruit yield (82.44%). High heritability for these traits indicated minimal environmental influence and suggested that phenotypic selection would be reliable and effective for their improvement (Johnson et al., 1955) [20] . Moderate heritability (50-75%) was recorded for fruit length (67.23%), sex ratio (66.47%), internodal length (65.73%), and days to first male flower initiation (56.23%), indicating moderate environmental influence on these traits. Low heritability (<50%) was recorded for vine length at seedling stage (37.28%), days to 50% flowering (37.65%), number of leaves per plant (40.56%), and days to first female flower initiation (47.18%), suggesting strong environmental control and limiting the effectiveness of direct selection for these traits.

Genetic Advance as Percentage of Mean

Genetic advance as percentage of mean (GAM) ranged from 5.33% for days to 50% flowering to 50.50% for vine length at flowering (Table 2 and figure 1). High genetic advance (>20%) was observed for vine length at flowering (50.50%), shelf life (42.60%), number of primary branches (41.24%), flesh thickness (37.11%), secondary branches (27.38%), fruit yield (24.43%), vine length at 30 DAS (23.12%), and ascorbic acid (19.78%, approaching high range). High GAM coupled with high heritability for traits such as shelf life, flesh thickness, vine length at flowering, and number of primary branches indicated the preponderance of additive gene effects and confirmed the effectiveness of direct phenotypic selection for improvement of these characters (Burton and de Vane, 1953) [15] . Moderate GAM (10-20%) was observed for fruit diameter (14.02%), fruit weight (15.66%), total soluble solids (14.67%), dry matter (19.22%), intermodal length (15.98%), sex ratio (12.80%), and nodes per branch (11.38%). Low GAM (<10%) was recorded for days to 50% flowering (5.33%), days to first female flower initiation (6.13%), number of fruits per vine (6.91%), and days to first harvest (6.61%), indicating limited genetic progress achievable through simple selection for these traits.

Gene Action: GCA and SCA Variance

The estimates of genetic components of variance from halfdiallel analysis are presented in (Table 3 and figure 2) For all 24 traits under study, the σ²SCA was higher in magnitude than σ²GCA, with the σ²GCA/σ²SCA ratio being less than unity across all characters. This unequivocally confirmed the predominance of non-additive gene actionincluding dominance and epistatic effectsin the inheritance of these traits in ridge gourd. For vine growth traits, σ²SCA far exceeded σ²GCA: vine length at 45 DAS (1605.422 vs 338.767), vine length at 60 DAS (867.256 vs. 123.710), and vine length at 30 DAS (113.036 vs 11.383). Similarly, for yield-contributing traits, fruit length showed σ²SCA = 4.848 versus σ²GCA = 0.272, and fruit weight exhibited σ²SCA = 78.054 versus σ²GCA = 11.837. Among quality traits, shelf life (σ²SCA: 0.991 vs. σ²GCA: 0.122) and flesh thickness (σ²SCA: 2.394 vs σ²GCA: 0.280) also reflected the dominance of non-additive gene effects. The lowest σ²GCA/σ²SCA ratio was observed for ascorbic acid (0.025), days to first harvest (0.053), and fruit length (0.056), indicating the strongest non-additive gene action for these traits. These findings are consistent with reports by Varalakshmi et al., (2016) [24] and Chandan et al., (2018) [16] in cucurbit crops, who similarly reported the predominance of dominance effects for fruit yield and quality characters. The predominance of non-additive gene action for most traits implies that exploitation of heterosis through hybrid breeding would be the most appropriate and effective strategy for genetic improvement of ridge gourd. The superior F₁ combinations identified in this study can be utilized for the development of high-yielding hybrids with improved fruit quality and shelf life.

Conclusions

The present study provides comprehensive insights into the genetic architecture of yield and quality traits in ridge gourd. High GCV and PCV coupled with high heritability and high genetic advance for traits such as vine length at flowering, shelf life, flesh thickness, and number of primary branches confirmed the preponderance of additive gene effects and suggested that these traits can be effectively improved through direct phenotypic selection. The predominance of non-additive gene action -as evidenced by σ²SCA > σ²GCA for all 24 traits in diallel analysisclearly indicated that hybrid breeding is the most promising approach for the genetic improvement of ridge gourd. Future research should focus on identifying superior specific cross combinations and evaluating their stability across environments for the development of high-yielding, qualityrich hybrids suitable for tropical and subtropical cultivation.

References

1. National Horticulture Board (NHB), Ministry of Agriculture and Farmers Welfare. Agricultural statistics at a glance. New Delhi: Government of India; 2023-24.
2. Anonymous. Area and production of vegetables in India. New Delhi: National Horticulture Board, Ministry of Agriculture and Farmers Welfare, Government of India; 2024-25.
3. Yasmin A, Bharathi RV, Radha R. Review article on Luffa acutangula (L.) Roxb. Res J Pharm Technol. 2019;12(5):2553-2558.
4. Choudhary BR, Kumar S, Sharma SK. Evaluation and correlation for growth, yield and quality traits of ridge gourd (Luffa acutangula) under arid conditions. Indian J Agric Sci. 2014;84(4):498-502.
5. Panicker PS, Vigneshwaram LV, Manjusha MP. Review on Luffa acutangula L.: Ethnobotany, phytochemistry, nutritional value and pharmacological properties. Pharma Sci Monit. 2019;10(3):152-159.
6. Yadav H, Maurya HK, Bhat L. SDS-PAGE based seed protein profiling and diversity assessment of indigenous genotypes of ridge gourd (Luffa acutangula (L.) Roxb.). Int J Agric Environ Biotechnol. 2015;8(3):501-510.
7. Swarup V. Vegetable science and technology in India. 2nd ed. New Delhi: Kalyani Publishers; 2016. p. 61.
8. Jonah PM, Hinekaron JA, Jadong AE. Genetic variability and heritability studies in cowpea genotypes (Vigna unguiculata L. Walp): A review. Ann Rev Res. 2024;11(1):555805. doi:10.19080/ARR.2024.11.555805.
9. Saisupriya P, Saidaiah P, Pandravada SR. Analysis of genetic variability, heritability and genetic advance for yield and yield related traits in chilli (Capsicum annuum L.). Int J Bioresour Stress Manag. 2022;13(4):387-393.
10. Harshitha S, Sood M, Indiresh KM. Variability and heritability studies for horticultural traits in ridge gourd Luffa acutangula (L.) Roxb. Int J Bioresour Stress Manag. 2019;10(4):335-339.
11. Terfa GN, Gurmu GN. Genetic variability, heritability and genetic advance in linseed (Linum usitatissimum L.) genotypes for seed yield and other agronomic traits. Oil Crop Sci. 2020;5:156-160. doi:10.1016/j.ocsci.2020.08.002.
12. Sen A, Khade SD, Das DS, Chatterjee R. Comparing effects of different trellis systems for organically grown ridge gourd (Luffa acutangula). Biol Agric Hortic. 2023. doi:10.1080/01448765.2023.2249855.
13. Mithila M, Prasad VM, Bahadur V, Topno SE. Evaluation of different genotypes of ridge gourd for better growth, yield and quality under Prayagraj agro climatic conditions. Int J Environ Clim Change. 2024;14(1):586-594.
14. Haldhar SM, Choudhary BR, Bhargava R, Gurjar K. Host plant resistance (HPR) traits of ridge gourd (Luffa acutangula (Roxb.) L.) against melon fruit fly (Bactrocera cucurbitae Coquillett) in hot arid region of India. Sci Hortic. 2015;194:168-174. doi:10.1016/j.scienta.2015.08.00.
15. Burton GW, de Vane EH. Estimating heritability in tall fescue Festuca arundinacea from replicated clonal material. Agron J. 1953;45:478-481.
16. Chandan A, Sharma A, Mehta DK. Genetic variability and character association in ridge gourd Luffa acutangula Roxb. Int J Pure Appl Biosci. 2018;6(1):1043-1049.
17. Gautama B, Bhardwaj M, Singh RK, Sharma A. Nutritional composition and medicinal properties of Luffa acutangula: A review. Int J Chem Stud. 2017;5(4):1183-1186.
18. Hanson CH, Robinson HF, Comstock RE. Biometrical studies of yield in segregating populations of Korean lespedeza. Agron J. 1956;48:268-272.
19. Hazra P. Vegetable science. 2nd ed. New Delhi: Kalyani Publishers; 2019. p. 345-362.
20. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybeans. Agron J. 1955;47:314-318.
21. National Horticulture Board (NHB). Indian horticulture database. New Delhi: Ministry of Agriculture and Farmers Welfare, Government of India; 2018-19.
22. Rai M, Pandey S, Kumar S, Singh M. Cucurbits research in India: Retrospect and prospects. Cucurbit Genet Coop Rep. 2008;31-32:1-17.
23. Varalakshmi B, Lakshmamma P, Rao MV, Pratap M. Variability studies in ridge gourd Luffa acutangula (L.) Roxb. for growth, yield and quality parameters. Int J Plant Sci. 2016;11(1):115-119.
24. Yadav RK, Singh DK. Genetic improvement of cucurbits in India. Indian J Hortic. 2022;79(1):1-15.