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Full Research Article
Multivariate Analysis for Selection of High Yielding and Early Genotypes in Pigeonpea [Cajanus cajan (L.) Millsp.] for North Western Plain Zone of India
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First Online 27-03-2023|
Methods: The plant material comprised of thirty advanced pigeonpea genotypes along with three checks evaluated in small scale trial (SST) during in randomized complete block design with three replications.
Result: High GCV and PCV along with high heritability and GA as percent of mean was recorded for the characters PPP and SYPP indicating the preponderance of additive gene action for the expression of these traits. Seed yield per plot (SYPP) was found to be positively correlated with DF (0.30) and HSW (0.37). Path coefficient analysis revealed that PPP, DF and HSW had significant positive and direct effect on seed yield per plot. Principal Component Analysis further substantiated the variability of the population wherein three components explained 65.8% of variation. Cluster analysis classified the 33 genotypes into eight different clusters thereby highlighting sufficient divergence in the population. Cluster VI and VIII showed the highest distance, which is suggestive of prospective utilization of heterosis from their crosses. Two genotypes AH 20-10 and AH 20-23 were identified as the most genetically distant and hence could be forwarded in future breeding programme.
Pigeonpea being a long duration pulse crop takes 160-180 days to mature and hence finds low acceptance in different cropping systems in India. Hence identification of early maturing short duration genotypes, along with high seed yielding traits are desirable for crop intensification programmes. In this context, the current investigation highlights the study of different genetic variability parameters in pigeonpea genotypes evaluated in small scale trial to identify superior high yielding and early maturing pigeonpea lines which could be utilized in further crop improvement fitting in cropping systems suitable for the North West Plain zones of India.
MATERIALS AND METHODS
The observations regarding days to flowering, days to maturity, number of branches per plant, number of pods per plant, 100 seed weight, plant height and yield were recorded from five randomly collected plants in each replication for each genotype. The planting system consisted of four rows per genotype. The row to row and plant to plant spacing was 45 ×10 cm. The mean data obtained was subjected to analysis of variance as laid down by Panse and Sukhatme (1989). The estimation of genotypic and phenotypic correlation coefficients from the phenotypic and genotypic components of variances were carried out as per the methodologies put forward by Fisher (1954) and Al-Jibouri et al. (1958). Similarly, broad-sense heritability, genetic advance and genetic advance as percentage of mean, genotypic and phenotypic coefficient of variation (GCV and PCV) were also analyzed (Lush 1940; Burton 1952; Allard 1960 and Johnson et al., 1955). Path co-efficient analysis was worked out to estimate the direct and indirect effects of different traits on the yield (Dewey and Lu, 1959). Cluster analysis was carried out as per Tocher’s method (Rao, 1952). Group distances based on multiple characters was carried out as per Mahalanobis (1936) utilizing the D2 statistic. Data analyses were carried out in INDOSTAT software (https://www.indostat.org). Principle component analysis was carried out using PAST4.02 software package (Hammer et al., 2001).
RESULTS AND DISCUSSION
Genotypic coefficient of variation, phenotypic coefficient of variation, heritability and genetic advance
The estimates of heritability, genetic advance and the coefficients of variation enable prediction and assessment of gain under selection (Sahu et al., 2015). In the current investigation highest values for GCV and PCV were found in case of seed yield per plant (16.67, 17.40) followed by pods per plant (14.76, 16.95). Similar findings were reported by Patel et al., (2021) in seed yield and pods per plant having high GCV and PCV. For all the characters, values of PCV exceed the values of GCV slightly except for the trait branches per plant where sufficient difference between GCV and PCV is observed (Table 2).
This shows that the effect of environment in the manifestation of that character is high and plant selection on basis of this character would not be effective. For fruitful selection high heritability is desirable with high genetic advance (GA). The broad sense heritability for almost all the characters were high viz. plant height (PH) (90.8%), days to flowering (DF) (96.8%), days to maturity (DM) (90.8%), 100 seed weight (HSW) (92.3%) and seed yield per plant (SYPP) (91.7%). Correspondingly, high values of GA and genetic advance as percent of mean (GAM) was noticed for the characters PPP and SYPP. This is implicative of underlying additive gene action for the traits. This also means that the characters are fixable and hence selection for such traits would be effective. The character DM showed high heritability but low GAM (Table 2). This implies underlying non-additive gene action and hence scope for heterosis breeding. The lowest heritability was noticed in case of branches per plant (BPP) (40.3%), whereas pods per plant (PPP) depicted moderate heritability (75.8). Similar results for high heritability for PH, DF, HSW were found in the published results of Saroj et al., (2013); Vanniarajan et al., (2021).
Correlation study elucidates the direction and magnitude of association which reveal the relative contributions of the characters to yield and thereby facilitate the criteria for the selection of high yielding genotypes (Sharma et al., 2022). In the present study it was seen that the values of genotypic correlations were higher than phenotypic correlations. This means that environment does influence the character association negatively. Although the value reduction for the characters DF and HSW is low which justifies their degree of character stability and association with SYPP. Such results are in correspondence to the findings of Almeida et al., (2010) and Verma et al., (2018) who also testified the character correlations of DF and HSW with yield. Table 3 represents the values of correlation coefficients for the different characters with seed yield.
Positive and significant correlations were found for SYPP with DF (0.300) and HSW (0.365). This indicates that earliness to flowering would contribute to increase in seed yield. As the traits show high heritability along with genetic advance so these characters provide reliable factors for effective selection. Similarly, higher test weight of seeds indicates bold seeds which ensures better crop establishment. Negative correlations were seen in case BPP to SYPP at both genotypic and phenotypic levels. This is implicative environmental effects for this character expression is more and hence the character should be avoided for improved plant selections. Ranjani et al., (2018) also reported the negative correlation of BPP with seed yield.
Path coefficient analysis
Path coefficient analysis is carried out which divides the correlation into direct and indirect effects quantifying the relative importance of each character (Khan et al., 2016). With yield per plot being considered as the dependent variable and the other six characters as the independent variables path coefficient analysis was carried out. At genotypic level highest positive direct effect was observed in case of PPP (1.35) followed by DF (1.22) and HSW (0.34) (Fig 1).
Similar results of significant high positive direct effects of PPP on SYPP were mentioned by Bishnoi et al., (2019); Hemavathy et al., (2019); Devi et al., (2020). The residual effect of 0.191 pointed out that the independent characters played significant role in expression of the dependent character. PPP showed high positive indirect effect on seed yield through BPP. Similarly, DF exhibited positive indirect effect on DM. Although negative direct effect on seed yield was seen in case of BPP (-1.975), PH (-1.089) and DM (-0.469). The findings are in concurrence to the reports of Sharma et al., (2021) wherein it was emphasized that selection for yield enhancement could be made through direct selection for DF and indirect selection for BPP. The results are suggestive that the above morphological i.e. HSW and PPP and phenological characters i.e. DF are effective indicators for direct selection of high yielding genotypes in pigeonpea.
Principal component analysis
Principal component analysis (PCA) study was carried out to decipher the underlying factors contributing mostly to the variability in the population. The population under the present study is highly variable and hence the PCA analysis has revealed that 65.8% of variation could be explained by the first three principal components. Trait biplot (Fig 2) revealed that for SYPP had high correlation with HSW and DF. First principal component displayed high contribution to divergence for the characters DF, PH, HSW and SYPP. Correspondingly the genotypes AH 20-10 and AH 20-23 explicated the variability for SYPP and DF. Hence hybridization between these genotypes for the concerned characters could be rewarding.
PC2 substantiated the divergence for the characters BPP, PPP and DM. Such results also found in the published works of Hussain et al., (2021) wherein PC2 showed positive correlation with PPP and BPP. Thus the above characters were identified to be the key components of variability in the population and henceforth could be forwarded for in breeding programmes as significant factors of selection.
Mahalonobis D2 method is an effective tool enabling the classification of a population into clusters based upon their genetic divergence. Genotypes with diverse genetic architecture provide for increased chances of genetic recombination manifesting hybrid vigour. Cluster analysis of the population segregated the thirty-three genotypes under study into eight clusters indicating sufficient divergence in the population. The clustering pattern displayed that cluster I and II have the most number of genotypes (11), whereas cluster III, VI and VIII have only one genotype each (Table 4).
The genotypes in cluster VI, VII and VIII were found to be outstanding in terms genetic distance. Upon correlation with the yield values as shown in Table 1 genotypes AH 20-10 and AH20-23 from clusters VI and VIII showed highest yield compared to the checks. Hence, considering the genetic heterogeneity these clusters could be well utilized in hybridization programmes to develop superior recombinants. Among the recent investigations cluster analysis to decipher the diversity of pigeonpea populations were carried out by Hemavathy et al., (2019) and Sandeep et al., (2020).
CONCLUSION AND FUTURE PROSPECTS
Conflict of interest
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