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Full Research Article
Genetic Variability, Correlation and Path Coefficient Analysis in Advanced Matromorphic Generations of Garden Pea (Pisum sativum L.)
Background: Garden pea is an important leguminous vegetable crop but the lack of high yielding and stable varieties is the major constraint. There is a need to explore genetic variability for the crop improvement programs. Considering this, the current investigation was aimed to study the genetic variability and relationship among the yield and its contributing traits in advanced matromorphic progenies for effective production.
Methods: A two year experiment was carried out at SKUAST- Chatha, Jammu during the cropping season of 2020-21 and 2021-22 to find out the genetic variability parameters, path coefficient and correlation studies for thirteen yield contributing traits in twenty two garden pea genotypes (twelve advanced matromorphic progenies and ten parental lines).
Result: The results revealed high variability among the tested genotypes. The values of phenotypic coefficient of variation (PCV) were greater as compared to the genotypic coefficient of variation (GCV) for all the traits. High heritability along with genetic advance (>20%) was observed for all the traits whereas pod length (PL) and shelling percentage (SP) were exceptions. Pod yield per plant ( PYPP) had positive genotypic and phenotypic correlation with node at which first flower appears (NFFA), plant height(PH), internodal distance (ID), no. of pods per node (NPPN), pod length (PL), no. of seeds per pod(NSPP), no. of pods per plant (NPP), days to first picking (DFF), average pod weight (APW) and shelling percentage (SP). Positive direct effect on pod yield per plant (PYPP) was exhibited by traits viz., days to 50 per cent flowering (DFPF), node at which first flower appears (NFFA), internodal distance (ID), no. of pods per node (NPPN), pod width (PW), no. of seeds per pod (NSPP), no. of pods per plant (NPPP), days to first picking (DFP) and average pod weight (APW) in path coefficient analysis.
MATERIALS AND METHODS
RESULTS AND DISCUSSION
Genetic Variability, heritability and genetic advance
For a successful selection programme of any crop, assessments of variability involving genetic characteristics such as genotypic variance, broad sense heritability (h2) and genetic advance are essential (Thapa et al., 2020). The higher PCV values with greater magnitude were recorded for all the studied traits as compared to GCV values indicating that the genetic material used was highly variable due to both genotypic effect and environmental influence (Table 2). However, there wasn’t much of a difference between GCV and PCV values, suggesting that character expression was more influenced by genetic factors than environmental ones. As a result, the selection might be made based on phenotypic performance, which would allow for crop improvement. The different PCV and GCV values of the traits due to environmental effect was also recorded by Bijalwan et al., (2018) and Azam et al., (2020).
The maximum PCV and GCV value of >20 per cent were found for PYPP (37.84 and 37.72%), NPP (31.23 and 31.07%), NPPN (29.95 and 29.84%), PH (24.21 and 24.07%) and PW (20.92 and 20.83%), respectively. As has been observed in the present study maximum PCV and GCV values of these traits was also reported by various workers (Katoch et al., 2016; Singh et al., 2019 and Thapa et al., 2020).
Furthermore, it might be feasible to ascertain the extent to which a character is passed on from parent to progeny by assessing heritability (Hanson et al.,1956). In the present investigation, broad sense heritability ranged from 55.88 per cent (SP) to 99.41 per cent (PYPP) and high heritability (> 60%) was exhibited by thirteen traits except for shelling percentage. Kassaye (2006) reported that high broad sense heritability (h2bs) along with GA would be a useful method for the selection of superior genotypes. High heritability (h2) and GAM (>20%) was noticed for all the traits except PL and SP showing the preponderance of additive gene effect. The maximum value of GAM (77.48) was shown by PYPP followed by NPP (63.65), NSP (61.26), PH (49.31), APW (42.72), DFF (33.87), NFFA (30.51), ID (29.41), DFP (28.69), NSPP (26.28), NPBP (25.07), PW (24.52), PL (15.89) and SP (14.35) except NPBP which showed maximum heritability (95.20%) but low GA (0.53) (Table 2). Out of all the traits under study, five traits viz., PYPP, NPPN, PW, NPP and PH recorded maximum values for heritability (h2bs), GCV and GAM% thus, depicting the effect of additive gene action on these traits and therefore, may be helpful for efficient selection. The findings of numerous studies are congruent with the current results (Georgieva et al., 2016; Singh et al., 2019; Thapa et al., 2020).
The correlation coefficient, which offers a balanced assessment of the extent of relationship among two traits, aids in determining the type and amount of association between yield and its components (Singh et al., 2018). The genotypic correlation coefficients between distinct traits were similar to the phenotypic correlation coefficients in the experiment in terms of sign and nature. However, genotypic correlations were larger than phenotypic correlations.
PYPP had highly significant and positive correlation with NFFA (rg= 0.424, rp= 0.399), PH (rg= 0.523, rp= 0.520), ID (rg= 0.565, rp= 0.549), NPPN (rg= 0.439, rp= 0.434), PL (rg= 0.529, rp= 0.445), NSPP (rg= 0.662, rp= 0.646), NPP (rg= 0.968, rp= 0.958), DFF (rg= 0.468, rp= 0.458), PW (rg= 0.710, rp= 0.706) and SP (rg= 0.672, rp= 0.495) both at genotypic and phenotypic levels (Table 3). DFF showed significant and positive correlation with NFFA, PH, NPBP, ID, PL and DFP. These traits were identified as most important component traits and were linked positively with PYPP implying that pod yield would increase by simultaneous selection for these traits. Earlier reports have also confirmed existence of strong positive correlation of PYPP with PH, NPPN, PL, NSPP and NPP (Kumar et al., 2019), days to first picking and shelling percentage (Rahman et al., 2019), average pod weight (Tiwari et al., 2020).
Path co-efficient analysis
Path coefficient analysis is an efficient approach to separate correlation coefficients into direct and indirect component effects since it assesses the direct impact of one variable on the other. This strategy is used to investigate the source and effect of a relationship between variables. Correlation studies give a greater understanding of the causes and effects of relationships between different pairs of component traits and the main trait when paired with path coefficient analysis (Verma et al., 2021). In the present study, PYPP was taken as a resultant (dependent) variable while 13 other traits were independent variables. The cause and effect relationship of PYPP and its related traits have been given in Table 4 (Fig 1). The results revealed that ID had maximum positive (1.713) and direct effect on PYPP followed by NSPP (1.124), APW (1.022), NFFA (0.682), DFF (0.483), NPPP (0.368), NPPN (0.291) and PW (0.234) suggesting that they are the major contributors to pod yield per plant and that if other characters remain constant, an increase or decrease in each of these characters will reflect in increase or decrease in pod yield. Positive and direct effect of NSPP, NPP, PW and DFF on PYPP was reported earlier also in field and garden pea (Tofiq et al., 2015; Kumar et al., 2019; Verma et al., 2021). PH had highest indirect effect (1.654) via ID on PYPP. The residual effect was 0.041 which inferred that the character under study contributed 95.90% to the pod yield.
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