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Research Article

Legume Research, volume 44 issue 6 (june 2021) : 615-620

Devising selection criteria based on variability and association studies in segregating populations derived from an interspecific cross between Cajanus scarabaeoides × C. cajan

Gurjeet Singh1,*, Inderjit Singh1, Pankaj Sharma1, Mamta Gupta1, Sarvjeet Singh1
1Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana-141 004, Punjab, India.

  • Submitted25-03-2019|

  • Accepted10-07-2019|

  • First Online 04-10-2019|

  • doi 10.18805/LR-4137

Cite article:- Singh Gurjeet, Singh Inderjit, Sharma Pankaj, Gupta Mamta, Singh Sarvjeet (2019). Devising selection criteria based on variability and association studies in segregating populations derived from an interspecific cross between Cajanus scarabaeoides × C. cajan . Legume Research. 44(6): 615-620. doi: 10.18805/LR-4137.

ABSTRACT

Segregating populations (BC1F3 and F3:4) from an interspecific cross (C. scarabaeoides × C. cajan) were evaluated for variability and association studies for yield and its component traits. In both the generations, high values of PCV and GCV were obtained for pods per plant, seed yield per plant and fruiting branches per plant. High estimates of heritability as well as genetic advance were observed for fruiting branches per plant followed by pods per plant and seed yield per plant. High heritability coupled with high genetic advance revealed the presence of less environmental influence and prevalence of additive gene action. Seed yield per plant had highly significant and positive association with pods per plant, fruiting branches and 100-seed weight. Path coefficient analysis revealed that pods per plant, fruiting branches and 100-seed weight contributing maximum towards seed yield per plant. The study revealed that fruiting branches and pods per plant could be used as selection criteria for improving yield.

INTRODUCTION

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop in semi-arid tropical regions of the world and globally grown on 4.70 million ha area with a total production of 3.69 million tonnes in the world. India is the largest producer by contributing almost 80 per cent of the global area and production (Anonymous, 2018). Pigeonpea grains are a good source of protein (21%), carbohydrate, vitamins, lipids and certain minerals (Sodavadiya et al., 2009). Being an often cross pollinated, pigeonpea has moderate genetic variability but low yield potential due to multiplicative interactions of various yield attributing traits affected by environmental factors (Sharma et al., 2018). Exploitation of the genetic resources for improvement of pigeonpea is very limited and majority of diversity existing in the germplasm remained unexplored (Majumder and Singh, 2005). For overcoming these problems, introgression breeding is the most important strategy for creation of new genetic variability for yield and its component traits (Sharma and Upadhyaya, 2016; Sharma, 2017). Successful interspecific hybridization between cultivated pigeonpea and its wild relative C. scarabaeoides has been frequently reported (Pundir and Singh, 1986; Singh and Bajpai, 2005; Sandhu et al., 2009; Singh et al., 2018), but conclusive information related to extent of variability generated and association of yield contributing traits with yield is scanty in interspecific populations.
 
For conducting dynamic plant breeding programme, it is essential to study nature and magnitude of variability and heritability of various yield enhancing traits. The estimation of various parameters of genetic variability like phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) give an idea about the magnitude of genotypic and phenotypic variability present in a population. Further, it is advisable to split the overall variability into heritable and non-heritable components to give an idea about the effect of selection. Additionally, genetic advance analysis gives an idea regarding the genetic improvement of new population over original population of the selected plants. Association studies helps to understand the relationship between traits of economic importance that helps to devise selection criteria. Limited information is available on these aspects in the interspecific populations derived from cross of cultivated pigeonpea with wild species, C. scarabaeoides. Therefore, the present study was conducted to generate information on variability and association among yield and its component traits in the segregating generations of an interspecific cross involving cultivated pigeonpea and its wild relative for devising selection criteria for yield improvement.

MATERIALS AND METHODS

An inter-specific cross between wild species C. scarabaeoides (ICP 15683) and C. cajan (ICPL 20329) was generated during kharif 2013. Successively, F1 plants were selfed and backcrossed to produce F2 (2014) and BC1F1 (2014) populations. These progenies were further selfed for two generations to get F3:4 (2016) and BC1F3 (2016) progenies. Twenty progenies of selected promising plants from each F3:4 and BC1F3 generations were raised along with parents in randomized complete block design with two replications during kharif 2017 in the experimental field area of Pulses Section, Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana. Each progeny was accommodated in a single row plot of four meter length with row to row and plant to plant spacing of 50 and 25 cm, respectively. All the recommended cultural practices were followed to raise a healthy crop. Five desirable plants were selected on the basis of phenotypic superiority from each progeny to record the data on eight morphological traits viz; days to 50 per cent flowering, days to maturity, plant height (cm), number of fruiting branches per plant, number of pods per plant, number of seeds per pod, 100-seed weight and seed yield per plant. Statistically analyses were employed for estimation of various genetic parameters viz; phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) (Burton and Devane, 1953; Johnson et al., 1955), heritability in broad sense (Allard, 1960), genetic advance (Miller et al., 1958), correlation coefficients (Al-Jibouri et al., 1958) and path coefficients analysis (Dewey and Lu, 1959).

RESULTS AND DISCUSSION

Yield is a complex trait as many component traits contribute towards it. Being a quantitative trait in nature it is affected by genotype (G) × environment (E) interactions. Hence to bring change in yield, deep understanding of extent of variability present in a population and also interrelationship among yield and yield attributing characters is necessary. The analysis of variance (ANOVA) carried out for BC1F3 and F3:4 generations indicated significant genotypic variation for all the traits in both the generations (Table 1).
 

Table 1: Analysis of variance (ANOVA) for different traits in BC1F3 and F3:4 generations of an interspecific cross in pigeonpea.


 
Genetic variability
 
Phenotypic coefficient of variation (PCV) estimates was higher than the respective genotypic coefficient of variance (GCV) for all the traits under study, indicating the influence of environment in the expression of these traits. Wide range of phenotypic variation was observed in BC1F3 and F3:4 generations (Table 2).  In BC1F3, the highest value of PCV was observed for pods per plant (33.63%) followed by seed yield per plant (33.12%) and fruiting branches per plant (30.72%). The highest value of GCV was observed for pods per plant (31.25%) followed by seed yield per plant (30.26%) and fruiting branches per plant (28.61%). The low GCV was observed for traits like 100-seed weight (8.37%), plant height (6.16%), days to 50 per cent flowering (5.52%), days to maturity (4.89%) and seeds per pod (3.98%) in BC1F3. While in F3:4 generation, the highest value of PCV was observed for seed yield per plant (51.34%) followed by pods per plant (44.47%) and fruiting branches per plant (37.05%). The highest value of GCV was observed for seed yield per plant (47.40%) followed by pods per plant (40.83%) and fruiting branches per plant (33.47%) in F3:4 generation. It was interesting to note that extent of variability was reduced in BC1F3 as compared to F3:4 generation, which was probably due to reduction of wild genome as a result of one backcross. Generation of abundant variability for these traits is generally expected in the inter-specific crosses in pigeonpea as earlier reported by Bohra et al., (2015). Presence of variability for different traits was also reported by Bhadru (2010), Sharma et al., (2012), Rangare et al., (2013), Singh et al., (2014), Ram et al., (2016), Mallesh et al., (2017), Reddy and Jayamani (2019) and Watsal (2019) in cultivated pigeonpea. Above results suggested that due to higher values of PCV and GCV for pods per plant, fruiting branches per plant and seed yield per plant in both the generations, the selection will be more effective for these traits in subsequent generations for improving yield.
 

Table 2: Parameters of genetic variability for different traits in BC1F3 and F3:4 generations of an interspecific cross in pigeonpea.


 
Heritability and genetic advance
 
Perusal of Table 2 indicated high estimates of heritability for fruiting branches per plant (86.76%), pods per plant (86.35%) and seed yield per plant (83.48%), whereas moderate heritability was recorded for days to maturity (76.52%), days to 50 per cent flowering (74.46%) and plant height (66.89%) in BC1F3 generation. The highest heritability in F3:4 was recorded for days to 50 per cent flowering (87.36%) followed by seed yield per plant (85.24%), days to maturity (84.82%), pods per plant (83.17%) and number of fruiting branches per plant (81.59%). Whereas, for plant height (63.27%), seeds per pod (62.50%) and 100-seed weight (53.00%), moderate heritability was recorded. Similar findings were reported by Sharma et al., (2012), Rangare et al., (2013), Nagy et al., (2013), Reddy and Jayamani (2019)  in cultivated pigeonpea and by Bohra et al., (2015) in interspecific crosses in pigeonpea.
 
The highest genetic advance was recorded in BC1F3 generation (Table 2) for pods per plant (59.82%) followed by seed yield per plant (56.96%), while the lowest genetic advance was recorded for seeds per pod (5.37%). However, in F3:4 generation, the highest estimate of genetic advance was recorded for seed yield per plant (90.11%) followed by pods per plant (76.70%), while seeds per pod recorded the lowest genetic advance (12.30%). Present results were in confirmation with results obtained by Patel et al., (2011), Prasad et al., (2013), Rao et al., (2013), Shunyu et al., (2013), Meena et al., (2017) and Watsal (2019) who also obtained high genetic advance for pods per plant and seed yield per plant in cultivated pigeonpea.
 
In both the generations, high heritability coupled with high genetic advance for fruiting branches per plant and pods per plant revealed that these two traits are important yield components and selection should be focused on these for yield improvement. These results revealed the presence of low environmental influence and prevalence of additive gene action in the expression of these traits. Moderate or low heritability estimates coupled with low genetic advance were recorded for seeds per pod and 100-seed weight indicating that these traits were governed by non-additive gene action and highly influenced by environment so direct selection would not be effective for improving yield.
 
Association studies (correlation and path analysis)
 
Correlation analysis clearly revealed that the phenotypic and genotypic correlations have similar trend in direction but the magnitude of genotypic correlations was higher than the phenotypic correlations. Low phenotypic correlation could be the result of masking and modifying effect of the environment on the association of traits. Correlation coefficients among all the traits in BC1F3 generation presented in Table 3. Seed yield per plant had significantly high and positive correlation with pods per plant (0.92) followed by fruiting branches per plant (0.85), 100-seed weight (0.52) and seeds per pod (0.32). Sinha and Singh (2005), Baskaran and Muthiah (2007), Prasad et al., (2013), Pandey et al., (2016), Pushpavalli et al., (2017) and Baldaniya et al., (2018) also found significant and positive correlations between seed yield per plant and number of pods per plant. In F3:4 generation, results also showed highly significant and positive correlation of seed yield per plant with pods per plant (0.88) followed by fruiting branches per plant (0.85) and seeds per pod (0.41). Similar, results were reported by Singh et al., (2018) in BC1F2 and F2:3 generations of the same cross for fruiting branches and pods per plant which indicated that correlations of traits remained consistent in different generations. An overall observation of correlation coefficient analysis revealed that fruiting branches per plant and pods per plant showed positive correlation with seed yield per plant. Hence, direct selection for these traits will be quite effective to improve yield in pigeonpea. It was interesting to note that days to 50% flowering showed significant negative correlation with fruiting branches, pods per plant, 100-seed weight and seed yield per plant in BC1F3 population, while in F3:4 generation, these correlation were very low or non-significant. Similarly, days to maturity showed significant negative correlation with seed yield per plant in BC1F3 generation, while in F3:4 these correlations were very low or non-significant. This indicated that selection for high yield should be focused on early flowering and early maturity plants in segregating progenies where one backcross has been performed while in F3:4, selection for both early and late maturity can be done without sacrificing yield.
 

Table 3: Correlation coefficients among all possible combinations of traits in BC1F3 and F3:4 generation.


 
Correlation simply indicates the types of relationship among the characters but does not provide information on extent of relationship (direct or indirect effect). Path coefficient analysis was carried out to understand the extent of relationship by taking seed yield per plant as dependent variables and rest of the traits as independent variables. At phenotypic level in BC1F3 generation (Table 4), the highest direct effect on seed yield per plant was exerted by pods per plant (0.482) followed by fruiting branches per plant (0.344) and 100-seed weight (0.318). Whereas, the plant height (-0.082) and days to maturity (-0.065) showed negative direct effect on the seed yield per plant. Indirect positive effects on seed yield per plant were revealed by number of fruiting branches per plant (0.463), 100-seed weight (0.131) and seeds per pod (0.063) via pods per plant. In F3:4 generation, pods per plant (0.818) exhibited the highest direct effect on seed yield per plant followed by 100-seed weight (0.285) and seeds per pod (0.195), While fruiting branches per plant (0.783), plant height (0.204), seeds per pod (0.186), days to 50 per cent flowering (0.131) and days to maturity (0.120) exerted indirect positive effect on grain yield per plant via pods per plant. Similar findings were reported by Thanki and Sawargaonkar (2010), Devi et al., (2012), Chaithanya et al., (2014), Vijayalakshmi et al., (2013), Chandana et al., (2014), Kothimbire et al., (2016), Ram et al., (2016), Kumar (2017) and Ranjani et al., (2018) in pigeonpea. Hence, in both generations direct selection for the pods per plant and fruiting branches per plant could be effective in developing high yielding genotypes in pigeonpea.
 

Table 4: Direct and indirect effects of various traits on seed yield per plant in BC1F3 and F3:4 generations of an interspecific cross in pigeonpea.

CONCLUSION

Results of variability, heritability, genetic advance, correlation and path analysis revealed that pods per plant and fruiting branches per plant are the main yield components and selection for these traits should be practiced for the improvement of grain yield in interspecific populations in pigeonpea.

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