Evaluation of genetic variability, heritability and genetic advance for yield traits in Indian mustard (Brassica juncea L.)

Introduction

Brassica juncea (L.), commonly known as Indian mustard, is the most predominant species among cultivated oilseed Brassica species and is a naturally occurring amphidiploid (2n = 36, AABB genome) derived from interspecific hybridization between B. rapa (AA) and B. nigra (BB) (Nagaharu, 1935; Yang et al., 2016) [19,39] . The genome size of Indian mustard is approximately 920 Mb, and the crop is primarily self-pollinated with a certain degree of cross-pollination, contributing genetic plasticity under diverse environments (Yang et al., 2016; Rakow, 2004) [39,27]. It is one of the most important oilseed crops in India, contributing significantly to nutritional, industrial, and economic sectors (Shekhawat et al., 2012; Chauhan et al., 2011) [32,4] . India is one of the leading producers of rapeseed-mustard globally. During 2023-2024, rapeseed and mustard production reached approximately 5.98 million tonnes, with Rajasthan contributing nearly 45.4% (2.71 million tonnes), followed by Uttar Pradesh and Madhya Pradesh. The national average productivity was about 1, 443 kg per hectare Major mustard-growing states include Rajasthan, Uttar Pradesh, Madhya Pradesh, Haryana, Bihar, Assam, Gujarat, Punjab, and West Bengal (Economic Survey of India, 2023-24) [9] . Rapeseed mustard oil serves as a primary cooking medium in many parts of India. Apart from edible oil extraction, seeds are utilized in condiments, pickles, salad dressings, and traditional preparations. The residual oil cake is an important source of cattle feed and organic manure, thereby enhancing its economic value (Shekhawat et al., 2012) [32] . Indian mustard is predominantly cultivated during the Rabi season under both irrigated and rainfed conditions. Seed yield is a complex quantitative trait governed by multiple component characters and significantly influenced by environmental factors. Therefore, understanding the association among yield and its contributing traits is essential for formulating effective selection criteria (Johnson et al., 1955; Falconer and Mackay, 1996) [12,10] . Since phenotypic expression is influenced by both genetic and environmental components, identifying heritable traits with stable performance improves breeding efficiency. Recent studies confirm abundant genetic variability in Indian mustard populations, indicating substantial scope for trait improvement. For instance, diversity assessments reported moderate to high genotypic and phenotypic coefficients of variation for key yield components such as seed yield per plant and secondary branches, along with high heritability and genetic advance, emphasizing the value of these traits in selection strategies. Genetic diversity is the foundation of crop improvement programs. The magnitude of genetic variation available in germplasm determines the effectiveness of selection and hybrid breeding strategies (Falconer and Mackay, 1996; Chand et al., 2023) [10,8] . Genetic variability is a fundamental requirement in crop improvement, as it creates a broad base for selecting superior genotypes. Understanding how seed yield is associated with other traits is essential for making effective selection decisions in breeding programs. Since seed yield is controlled by multiple genes, improving it can also influence several other characteristics, making it important to consider these relationships during selection. (Pal et al., 2019) [20] . In hybrid breeding, selection of parents with diverse genetic backgrounds is critical for achieving heterosis and broad variability. Therefore, the present study was to assess the extent of genetic variability and diversity among Indian mustard genotypes for yield and its contributing traits.

Materials and Methods

Experimental materials and site description

A Total 45 mustard genotypes were employed in the study (Table 1

Experimental design

The field design was in a randomized block design (RBD) with three replications. Each genotype was sown in five rows of 3 m row length with spacing of 30 cm between rows and 10 cm between plants. Recommended agronomic practices, including the application of fertilizers, manual weeding and need-based plant protection measures, were followed on regular intervals during the experiment.

Data collection

Twelve quantitative traits were recorded in the study. Five healthy plants from each genotype were randomly selected for data recording in each replication except DFF and DM, which were assessed on a population basis.

Statistical analysis

The mean data of 45 genotypes of different replications for 12 characters were subjected to standard procedure of analysis of variance (ANOVA) suggested by (Panse and Sukhatme, 1961) [22] . Component of Variance was calculated by the formula suggested by Burton and Devane (1953) [3] , Cockerham (1963) [6] . The coefficient of variation (genotypic (GCV), phenotypic (PCV), and environmental (ECV) 𝜎𝑒² = 𝑀𝑠𝑒 ; 𝜎𝑔² = 𝑀𝑆𝑔-𝑀𝑆𝑒

𝑟

; 𝜎𝑝² = 𝜎𝑔² + 𝜎𝑒², where, 𝑀𝑆𝑒, 𝑀𝑆𝑔, and r are the mean squares of error, mean squares of genotypes, and the number of blocks, respectively. (𝐺𝐶𝑉) = √𝜎 2 𝑔 × 100 ; (𝑃𝐶𝑉) = √𝜎 2 𝑝 × 100

, where X ̄ are the grand mean for each measured trait according to Sivasubramanjan and Menon (1973) [34] , the GCV and PCV values fall into three categories: low (0-10%), moderate (10-20%), and high (>20%). Heritability in broad sense is the ratio of genotypic variance to the total variance and is also calculated as

× 100, and it's percentage was categorized as low (0-30%), moderate (31-60%) and high (> 60%) by Robinson et al., (1949) [28] . It is also charactarized as the portion of total variability or phenotypic variability which is heritable and is due to genotype. It was calculated by the formula given by Lush (1949) [17] , Burton and Devane (1953) [3] . Genetic advance is the improvement in mean genotypic value of selected plants over the parental population and it was computed by the formula given by Lush (1949) [17] and Johnson et al. (1955) [12] . Genetic advances as a percentage of the mean (GAM) were also calculated by the formula suggested by Comstock and Robinson (1952) [7] , 𝐺𝐴𝑀 = 𝐺𝐴 × 100. The dataset was initially organized in MS Excel and subsequently analyzed in R using the RStudio platform, with genetic parameters across years estimated via. the variability package. A clustered column chart was prepared using MS Excel.

Results

Analysis of Varience (ANOVA)

The analysis of variance (ANOVA) for twelve traits revealed highly significant differences among the genotypes (Table 2), indicating the presence of substantial genetic variability.

Mean performance

The general mean, range, and mean performance of each of the forty-five genotypes for 12 traits are presented in Table 3. DFF ranged from 37.92 to 70.83 days (earliest: Jagannath; latest: CS-52), and DM from 103.33 to 148.33 days (earliest: Navgold; latest: Pusa Jaykisan). PH varied from 118.43 cm (Pusa Tarak) to 205.43 cm (PM-27). NPB ranged from 2.13 (Seeta) to 14.83 (PM-28), and NSB from 5.13 (Seeta) to 21.07 (Pusa Mahak). LMR varied between 34.75 cm (Pusa Tarak) and 78.75 cm (Lakshmi). NSP showed a wide range from 178.77 (Navgold) to 999.68 (DRMR IJ-31), while NSS ranged from 8.17 (PM-28) to 13.83 (Shivani). SY ranged from 3.53 g (Jagannath) to 39.51 g (PM-28). Test weight varied from 3.73 g (Jawahar Mustard-1) to 6.00 g (Ashirwad). Biological yield ranged from 44.30 g (Gujarat Mustard-1) to 141.70 g (PM-27), and harvest index from 4.48% (Jagannath) to 37.58% (PM-28). The box plot illustrates the mean performance of different quantitative traits among the studied genotypes (

Estimation of genetic parameters

The estimation of genetic parameters revealed the presence of considerable variability among the genotypes for the traits studied. The magnitude of genotypic and phenotypic coefficients of variation, along with heritability and genetic advance, provided insights into the nature and extent of genetic control governing these traits. These results indicate the potential effectiveness of selection for improvement of yield and its contributing characters. The results are presented as follows:

Estimation of Environmental Variance (σ²e), Genotypic Variance (σ²g), Phenotypic Variance (σ²p)

The estimates of environmental variance (σ²e), genotypic variance (σ²g), and phenotypic variance (σ²p) for all twelve traits are presented in Table 4. For all traits, the phenotypic variance was slightly higher than the corresponding genotypic variance, indicating a minor influence of environmental factors on trait expression. High genotypic and phenotypic variances were observed for NSP (77109.98 and 77212.36), PH (564.70 and 566.97), and BYP (714.25 and 715.96). Moderate variance was recorded for traits like LMR, DM, and SY, while relatively low variance was observed for NSS, TW, NPB, and NSB. The estimates of heritability broad sense (H²) and genetic advance as percent of mean (GA%) for all traits are presented in Table 4. High heritability values were observed for all traits, ranging from 99.37% (DM) to 99.87% (NSP), indicating that the traits are largely governed by genetic factors with minimal environmental influence. High genetic advance as percent of mean was recorded for NSP (115.24%), SY (100.05%), NPB (90.73%), and NSB (77.00%), suggesting the predominance of additive gene action and effectiveness of selection for these traits. Moderate GA% was observed for BYP (63.53%), HI (58.95%), and LMR (46.43%), indicating scope for improvement through selection. Relatively lower GA% was recorded for DM (18.77%), NSS (26.42%), TW (28.59%), PH (31.16%), and DFF (33.58%), suggesting comparatively lesser expected gain from selection despite high heritability.

Discussion

The ANOVA showed highly significant differences among genotypes for all traits, indicating substantial genetic variability and good scope for selection. Variation in DFF and DM suggests potential for early maturity, while plant height variability aids selection for lodging resistance (Kumar and Misra, 2007; Prajapati et al., 2025) [15,24] . Significant differences in yield traits (NPB, NSB, LMR, NSP, NSS, TW, BYP, HI, SY) highlight their importance in improving productivity (Sharma et al., 2025; Aslam and Qayyum, 2025; Sowmya et al., 2024) [30,2,35] . The coefficient of variation (CV) values were low (0.91-2.06), indicating high experimental precision and reliability. Traits with low CV were more stable for selection, while slightly higher CV suggested some environmental influence. PCV values were slightly higher than GCV for all traits, indicating that environmental influence was minimal and the observed variation was largely governed by genetic factors. This result aligns with Pant and Singh (2001) [23] , Rai et al.

(2005) [26] , Sharma et al. (2014) [29] , Akabari and Niranjana (2015) [1] and Yadav et al. (2022) [38] . High GCV and PCV for NSP, SY, NPB, and NSB suggest the presence of substantial variability and indicate that these traits are governed largely by additive gene action, making them suitable for effective selection. These characters have been reported as main yield contributing traits (Kardam and Singh 2005) [13] . Similar findings pertaining to presence of high genetic variability were reported for different traits including seed yield/plant (Singh, 2004) [33] . Moderate values observed for BYP, HI, and LMR indicate reasonable scope for improvement through selection. In contrast, lower GCV and PCV for DM and NSS suggest limited variability, indicating comparatively less scope for selection as same as the findings by (Jahan et al., 2025) [11] . Overall, the results highlight that traits with higher variability can be effectively utilized in breeding programmes for yield improvement. Phenotypic variance was slightly higher than genotypic variance for all traits, indicating low environmental influence (Choudhary et al., 2025) [5] . Small differences between σ²p and σ²g suggest that most variation is genetically controlled but with slight environmental effect. High variances were observed for NSP, BYP, PH, and LMR, indicating substantial genetic variability. Moderate variance was recorded for DFF, DM, and SY, while lower variance was observed for NSS and TW. Environmental variance was generally low, especially for TW and branch traits, indicating minimal environmental effects. Even where environmental variance was relatively higher, genetic variance remained predominant. The present findings corroborate the earlier report Sharma et al., (2014) [29] , Tiwari et al., (2017) [36] and Pal et al., (2019) [20] .

High heritability for different traits such as days to flowering, plant height, and number of siliquae per plant has also been reported earlier by Singh (2004) [33] and Kumar and Misra (2007) [15] , while high heritability for other traits was reported by Khulbe et al. (2000) [14] , Mahla et al. (2003) [18]

, Priyamedha et al. (2013) [25] , Pandey and Pandey (2014) [21] , Vanukuri and Pandey (2022) [37] , and Kumar et al. (2019) [16] . High genetic advance as percent of mean (>50%) observed for traits like NSP, SY, NPB, and NSB indicates the predominance of additive gene action and good scope for improvement through direct selection. Moderate values (20-50%) for traits such as BYP, HI, LMR, PH, DFF, NSS, and TW suggest the involvement of both additive and nonadditive gene effects, while lower values (<20%) as observed in DM indicate limited scope for selection. Similar findings were also reported by Sharma et al. (2025) [31] .

Conclusion

The present study revealed significant genetic variability among Indian mustard genotypes for yield and yield-related traits, indicating ample scope for selection and crop improvement. High genotypic and phenotypic coefficients of variation, along with very high heritability estimates, suggest that most of the traits are predominantly controlled by genetic factors with minimal environmental influence. The high genetic advance as percentage of mean observed for key traits such as number of siliquae per plant, seed yield per plant, and branch numbers indicates the predominance of additive gene action and highlights their potential for effective improvement through direct selection. Overall, these findings emphasize that selection based on these traits would be reliable and beneficial for developing highyielding and superior mustard genotypes.

References

1. Akabari VR, Niranjana M. Genetic variability and trait association studies in Indian mustard (Brassica juncea). International Journal of Agricultural Sciences. 2015;11(1):35-39.
2. Aslam HRMW, Qayyum A. Evaluation of Brassica genotypes for morphological and biochemical attributes. SABRAO Journal of Breeding & Genetics. 2025;57(3).
3. Burton GW, DeVane EH. Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material. Agronomy Journal. 1953;45(10):481-487.
4. Chauhan JS, Tyagi MK, Kumar A, Nashaat NI, Singh M, Singh NB, et al. Mustard breeding in India: Achievements and future directions. Indian Journal of Agricultural Sciences. 2011;81:1093-1109.
5. Choudhary R, Singh J, Barela A, Patel T, Yadav PK. Assessment of genetic variability and heritability in Indian mustard (Brassica juncea L.) genotypes. Journal of Advances in Biology & Biotechnology. 2025;28(7):436-443.
6. Cockerham CC. Estimation of genetic variances. In: Hanson WD, Robinson HF, editors. Statistical genetics and plant breeding. Washington, DC: NAS-NRC; 1963. p. 53-94.
7. Comstock RE, Robinson HF. Genetic parameters, their estimation and significance. 1952.
8. Chand SP, Debnath S, Rahimi M, Purakayastha S, Rout S. Genetic variability, correlation, diversity, path coefficients and principal component analysis in Indian mustard. Int J Comput Biol Drug Des. 2023;15(6):445462.
9. Economic Survey of India. Statistical appendix. Ministry of Finance, Government of India; 2023-2024.
10. Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Longman; 1996.
11. Jahan I, Rasul MG, Rashad MMI, Sarkar A, Farhan SMM. Genetic diversity analysis of mustard (Brassica rapa L.) in Bangladesh. SABRAO Journal of Breeding & Genetics. 2025;57(3):911-923.
12. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybeans. Agronomy Journal. 1955;47:314-318.
13. Kardam DK, Singh VV. Correlation and path analysis in Indian mustard (Brassica juncea (L.) Czern & Coss.) grown under rainfed condition. Journal of Spices and Aromatic Crops. 2005;14(1):56-60.
14. Khulbe RK, Pant DP, Saxena N. Variability, heritability and genetic advance in Indian mustard (Brassica juncea L. Czern & Coss.). Crop Research. 2000;20(3):551-552.
15. Kumar S, Misra MN. Study on genetic variability, heritability and genetic advance in Indian mustard (Brassica juncea L. Czern & Coss.). International Journal of Plant Sciences. 2007;2(1):188-190.
16. Kumar R, Kaur S, Bala K, Kaur S, Sharma L. Assessment of genetic variability, correlation and path analysis for yield traits in F1 hybrids of Indian mustard (Brassica juncea L.). Agriways. 2019;7(1):1-7.
17. Lush JL. Heritability of quantitative characters in farm animals. Hereditas. 1949;35:356-375.
18. Mahla HR, Jambhulkar SJ, Yadav DK, Sharma R. Genetic variability, correlation and path analysis in Indian mustard (Brassica juncea L. Czern and Coss.). Indian Journal of Genetics and Plant Breeding. 2003;63(2):171-172.
19. Nagaharu U. Genome analysis in Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilization. Japanese Journal of Botany. 1935;7:389-452.
20. Pal S, Dubey N, Avinashe H, Khan S, Reddy JP. Estimation of genetic variability, correlation and path analysis in Indian mustard (Brassica juncea L.). Journal of Pharmacognosy and Phytochemistry. 2019;8(1S):102-105.
21. Pandey SD, Pandey A. Genetic variability, heritability and genetic advance for some characters in mustard (Brassica juncea L. Czern & Coss.). Annals of Agricultural and Biological Research. 2014;19(3):467473.
22. Panse VG, Sukhatme PV. Statistical methods for agricultural workers. ICAR; 1954.
23. Pant SC, Singh P. Genetic variability in Indian mustard. Agricultural Science Digest. 2001;21(1):28-30.
24. Prajapati DK, Verma L, Tripathi A, Yadav AP, Roopa G, Kumar D. Studies on genetic variability and character association in Indian mustard (Brassica juncea L.). Journal of Advances in Biology & Biotechnology. 2025;28(5):245-252.
25. Priyamedha, Singh VV, Chauhan JS, Meena ML, Mishra DC. Correlation and path coefficient analysis in Indian mustard (Brassica juncea L.). Current Advances in Agricultural Sciences. 2013;5(1):37-40.
26. Rai SK, Verma A, Pandey DD. Genetic variability and character association in Indian mustard (Brassica juncea L.). Annals of Agricultural and Biological Research. 2005;10(1):29-34.
27. Rakow G. Species origin and economic importance of Brassica. In: Biology of Brassica coenospecies. Elsevier; 2004.
28. Robinson HF, Comstock RE, Harvey PH. Estimates of heritability and dominance in maize. Agron J. 1949;41(8):353-359.
29. Sharma R, Kumar M, Kumar BK, Chauhan MP. Variability analysis for yield and its component in Indian mustard (Brassica juncea L.). Trends Biosci. 2014;7(17):2539-2543.
30. Sharma A, Tripathi MK, Dangi D, Mishra R, Solanki R, Tomar SS, et al. Multivariate analysis of genetic diversity in Brassica species for yield and agronomical traits. J Exp Agric Int. 2025;47(8):334-350.
31. Sharma A, Tripathi MK, Dangi D, Mishra R, Solanki R, Tomar SS, et al. Genetic analysis for yield improvement in Brassica species: insights from variability and trait relationships. J Sci Rep. 2025;31(8):749-766.
32. Shekhawat K, Rathore SS, Premi OP, Kandpal BK, Chauhan JS. Advances in agronomic management of Indian mustard (Brassica juncea L.). Indian J Agron. 2012;57(3):1-10.
33. Singh B. Character association and path analysis in Indian mustard (Brassica juncea L.). Cruciferae Newsl. 2004;25:99-100.
34. Sivasubramanjan S, Menon M. Heterosis and inbreeding depression in rice. Adv Agron. 1973;47:85140.
35. Sowmya D, Delvadiya IR, Ginoya AV. Genetic variability, correlation, path coefficient and cluster analysis in Indian mustard (Brassica juncea L.). Electron J Plant Breed. 2024;15(1):201-208.
36. Tiwari AK, Singh SK, Tomar A, Singh M. Heritability, genetic advance and correlation analysis in Indian mustard (Brassica juncea L.). J Pharmacogn Phytochem. 2017;6(1):356-359.
37. Vanukuri AR, Pandey MK. Genetic variability and diversity studies in Indian mustard (Brassica juncea L.). Pharma Innov J. 2022;11(7):1357-1360.
38. Yadav D, Singh L, Jafri SKF, Singh A, Singh V. Genetic variability and principal component analysis in Indian mustard (Brassica juncea L.). Pharma Innov J. 2022;11(4):2166-2169.
39. Yang J, et al. The genome sequence of allopolyploid Brassica juncea. Nat Genet. 2016;48:1225-1232.