Genetic Variability and Association Analysis for Various Quantitative Traits in Lentil (Lens culinaris Medik. ssp. culinaris)

 
The estimates of correlation coefficients alone may be misleading due to the mutual cancellation of component characters. So, the study of correlation coupled with a path analysis is a more effective tool in the study of yield contributing characters (Chavan et al., 2011). Thus, the current study was carried to assess genetic variability, trait association between seed yield and its attributing components and to assess the direct and indirect effects of various attributing components on seed yield.

Hundred lentil genotypes were evaluated infield trials with three checks viz., WBL 77, Kota Masoor 2 and L 4717 for eleven quantitative traits. The genotypes were grown in an augmented design across two environments during rabi 2022-23, at Research farm of Pulses Section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar. Two sowing dates, namely timely sowing (E₁; November 22, 2022) and late sowing (E₂; December 15, 2022), were used to generate the required environments. The data was observed for eleven quantitative traits viz., days to flowering, days to maturity, plant height (cm), number of primary branches, number of secondary branches, number of pods per plant, seeds per pod, 100 seed weight (g), biological yield per plant (g), seed yield per plant (g) and harvest index (%). R Studio (2020) was used to conduct all the statistical analyses of the data.

Analysis of variance was performed for augmented design with 100 lentil genotypes and 3 checks were sown under timely (E₁) and late (E₂) sown condition (Table 1). The analysis of variance revealed significant differences among the test genotypes for all the traits in both the environments. Estimation of genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) was done to compare the relative magnitude of genotypic and phenotypic variance present for all the traits studied which revealed about the impact of environment on the particular trait. In all the characters studied, the PCV were slightly higher than their corresponding GCV signifying some influence of environmental factor in causing variations for all the traits. Reddy et al., (2016), Meena et al., (2020), Kishor et al., (2020) and Khatun et al., (2022) also observed higher PCV value than GCV value for all the traits.The values of coefficient of variation, heritability, genetic advance and genetic advance as per cent of mean for different traits are presented in Table 2.




 
In the investigation, high PCV and GCV coupled with high heritability and high genetic advance (as per cent of mean) was observed for seed yield per plant, biological yield per plant, 100 seed weight and number of pods per plant in E₁ whereas, in E₂ high PCV, GCV, heritability and genetic advance (as per cent of mean) was observed for 100 seed weight and seed yield per plant suggesting that these traits are genetically controlled by additive gene action and can be improved by direct selection. These results are in agreement with Singh et al., (2014), Reddy et al., (2016) and Meena et al., (2020).
 
The identification of important yield components and their association with yield and also with each other is very useful for selecting superior genotypes for evolving high yielding varieties. Therefore, the correlation coefficients were estimated for eleven quantitative traits in lentil grown under timely sown (E₁) and late sown (E₂) conditions and the results are presented in Table 3. Positive correlation was observed among most of the yield attributing traits in both the environments. Seed yield per plant exhibited positive and significant (p<0.01) correlation with days to flowering, days to maturity, plant height, number of primary branches, number of secondary branches, number of pods per plant, seeds per pod, biological yield per plant and harvest index in both the environments. Seed yield per plant and biological yield per plant exhibited strongest correlation (r = 0.808 in E₁ and r = 0.637 in E₂. The results obtained in the present study were in consonance with the findings of Ghimire and Mandal (2019), Sakthivel et al., (2019), Maurya et al., (2020), Pawar et al., (2022) and Sharma et al., (2022). Path coefficient analysis was carried out by taking seed yield per plant as dependent variable and all other traits as independent variable. The direct and indirect effects of various traits on seed yield per plant are given in Table 4. In timely sown (E₁) environment, the highest direct positive direct effect on seed yield per plant was exerted by biological yield per plant (0.819) followed by harvest index (0.568), number of primary branches (0.037), seeds per pod (0.022), plant height (0.018), days to flowering (0.010) and number of secondary branches (0.006). Similar results were presented by Dalbeer et al., (2013) and Khanam et al. (2021).  However, number of pods per plant (-0.053), 100 seed weight (-0.010) and days to maturity (-0.007) had negative direct effect on seed yield per plant. Sakthivel et al., (2019) and Kumar et al., (2020) also observed negative direct effect of plant height and number of pods per plant on seed yield per plant. Latif et al., (2020) also found negative direct effect of 100 seed weight on seed yield per plant. Number of pods per plant exhibited positive indirect effect via days to flowering, plant height, number of primary branches, number of secondary branches, seeds per pod, biological yield and harvest index. Residual effect was 0.017, which showed that 98.3% of the variability in seed yield per plant was explained by the component factors. In both the environments maximum direct positive effect on seed yield per plant was of biological yield per plant(0.819 in E₁ and 0.775 in E₂) followed by harvest index (0.568 in E₁ and 0.722 in E₂). Akter et al., (2020); Kumar et al., (2020); Meena et al., (2020) and Sharma et al., (2022) also found similar results. In general, from path analysis, it can be deduced that biological yield, harvest index, seeds per pod and number of primary branches had direct positive effects on seed yield per plant; hence the selection for these traits should be done for enhancing the seed yield in lentil.The traits seed yield per plant, biological yield per plant and harvest index have relatively high coefficient of variation along with high heritability and genetic advance as percent of mean, thus selection for these traits could bring about significant genetic gain. Also, these traits have positive and significant correlation with seed yield per plant along with positive direct effect. Thus, the traits seed yield, biological yield and harvest index may be used as selection indices for improvement of lentil. Also, these traits have positive and significant correlation with seed yield per plant along with positive direct effect. Thus, the traits seed yield, biological yield and harvest index may be used as selection indices for improvement of lentil. In timely sown environment, the genotype, LH 09-27 (16.2 g) had maximum biological yield per plant followed by LH 18-42 (14.9 g), PL 105 (14.7g), IPL 406 (14.3) and LH 18-49 (13.1 g). Similarly, the genotypes PL 105 and LH 09-27 (6.02 g) produced maximum seed yield per plant followed by LH 18-03 (5.5 g), IPL 406 (5.5 g) and PL 01 (5.4 g). The performance of the genotypes was highly affected by the environment, hence the genotypes which performed better for biological yield per plant under the late sown conditions are viz., LH 09-26 (6.6 g), LH 09-22 (6.4 g), LH 18-34 (6.0 g), LH 18-16 (5.9 g) LH 18-53 (5.9 g). Similarly, the genotypes PL 01 and LH 18-03 (2.4 g) had maximum seed yield per plant followed by DPL 58 (2.3 g), LH 09-22 (4.3 g), IPL 315 (2.3 g) and Kota Masoor 2 (2.3 g).

This study clearly showed that for selecting high yielding genotypes, the breeder should give more emphasis to the genotypes with higher seed yield per plant, higher number of pods per plant and plants with high biomass. Based on the observations it was found that, genotypes PL 01, PL 105, LH 18-03, IPL 406, LH 18-38, LH 18-42, LH 18-45 and LH 18-46 were found to be better performing under timely sown conditions. Similarly, genotypes PL 01, LH 09-18, LH 09-19, LH 09-20, LH 09-26, LH 09-27, WBL 77 and Kota Masoor 2 were found superior under late sown conditions and genotype PL 01 showed good seed yield per plant under both the conditions. Hence, these genotypes can be used as suitable breeding materials for further genetic improvement of lentil based on their performance.

 The authors declare that there is no conflict of interest.