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Research Article

Legume Research, volume 44 issue 8 (august 2021) : 900-905

Genetic Analysis for Yield and Yield Attributing Traits in Cowpea (Vignaunguiculata L. Walp)

Reshmi Rani Das1,*, Goutam Das1, PranabTalukdar1, Seuji Bora Neog1
1Department of Plant Breeding and Genetics, Assam Agricultural University Jorhat-785 013, Assam, India.

  • Submitted28-06-2019|

  • Accepted26-08-2019|

  • First Online 03-12-2019|

  • doi 10.18805/LR-4186

Cite article:- Das Rani Reshmi, Das Goutam, PranabTalukdar, Neog Bora Seuji (2021). Genetic Analysis for Yield and Yield Attributing Traits in Cowpea (Vignaunguiculata L. Walp) . Legume Research. 44(8): 900-905. doi: 10.18805/LR-4186.

ABSTRACT

The present investigation was conducted comprising of the parental lines and F1 progenies derived from a 6 x 6 diallel cross among cowpea varieties, excluding reciprocals. Analysis of variance revealed presence of sufficient variation among the genotypes for all the characters studied. For seed yield both GCA and SCA variances were significant, while GCA variance was significant for pods per cluster and SCA variance was significant for plant height, number of primary branches and pod length. Variety JCC-4 followed by UPC-622 were good general combiner for yield and yield attributing characters. Maximum SCA effect for seed yield per plant was observed with cross JCC-1 x JCC-4 followed by JCC-3 x JCC-4. Genetic analysis revealed that both additive (D) and dominance (H1 and H2) components were involved in controlling most of the characters. The predominant role of non-additive gene action was evident from relatively higher magnitude of dominance components, including H1, H2 and h2.

INTRODUCTION

Cowpea [Vigna unguiculata (L.) Walp.] is one of the most important pulse crops native to Central Africa, which is also considered as vegetable meat due to its high amount of protein with better biological value. Apart from this, cowpea forms excellent forage with its heavy vegetative growth and covers the ground and checks the soil erosion, weed growth and moisture loss. It is a most versatile pulse crop because of its smothering nature, drought tolerance, soil restoration properties and multipurpose uses. Cowpea is the most economically important indigenous African legume crop and has a wide variety of uses as a nutritious component in the human diet as well as nutritious livestock feed (Langyintuo et al., 2003). With >25% protein in dry seeds as well as in young leaves (dry weight basis), cowpea is a major source of protein, minerals and vitamins in daily diets and is equally important as nutritious fodder for livestock (Singh et al., 2003).
       
The NE India including Assam is particularly deficient in pulse crop production and is dependent on importing different pulse crops from other states of India. There is scope to popularize cowpea in Assam and N.E. India as a seed crop or as a dual-purpose crop. The major pulse crops of the state are blackgram, greengram, arhar, lentil and pea. Along with increased production there is also a need to diversify pulse crops with crops like cowpea and Lathyrus. Cowpea could be a potential pulse crop as it could be used both as seed crop as well as green pod vegetable in addition to its use as fodder crop. This will further encourage much needed diversification of pulse crops and there by accelerate growth in pulse production. So, it is important to develop improved varieties adaptable to this region and suitable to fit into the existing cropping system of the region. Development of cultivars with early maturity, acceptable grain quality, resistance to some important diseases and pest has significantly increased the yield and cultivated area of cowpea (Ehlers and Hall, 1996). An understanding of the variability existing in a crop is necessary to formulate and accelerate breeding program (Johnson et al., 1955). For this it is important to understand the genetics in reference to available germplasm resources. Combining ability analysis and genetic analysis help not only to identify potential parents and crosses but also the relative importance of additive and non-additive components controlling various traits.Thus, the aim of the study was to study the extent of genetic variation in cowpea genotypes for seed yield and yield attributing characters and the combining ability of parents and crosses.

MATERIALS AND METHODS

Experimental site and materials
 
Field experiment was conducted at Assam Agricultural University Research Farm, located in Jorhat, India (26°44’N and 94°10’E, 86.6 masl). Experimental materials comprised of 6 parents and 15 crosses. Cowpea varieties used as parents were two improved cultivar, UPC-622 and UPC-287 from GBPUAT, Pantnagar along with four local cultivars, JCC 1 to 4 from Manipur. Parents and their F1 progenies were sown during 1st week of October in a randomized block design with two replications.
 
Data collection and analysis
 
Data on 10 randomly selected representative plants from each plot to records various traits, including - plant height (PH), number of primary branches per plant (NPB), clusters of pod per plant (CPP), pods per cluster (PPC), pod length (PL), seeds per pod (SPP), seed yield per plant (SYP) and test weight (TW). Replicated data for each character was subjected to analysis of variance. Partitioning of variance into different components was done in accordance with the following model:
 
Pij= m+ gi+ eij,
 
Where,
Pij is the ijth observation of the ith genotype; m is the general mean; gi is the effect of ith genotype; eij is the random error associated with ijth observation. Genetic and phenotypic variances were computed as the formula suggested by Burton (1952).
       
Heritability in broad-sense, i.e. ratio of genetic variance (s2g) to the phenotypic variance (s2p) expressed in percentage (Allard, 1960) and expected genetic advance for each character was calculated by using the formula suggested by Hanson et al., (1956). Combining ability analysis was done following Griffing (1956) method-2 (using parents and F1 s is excluding reciprocals) Model-1 (Fixed effect model). Data were also analysed following Hayman’s (1954a and 1954b) approach for the components.

RESULTS AND DISCUSSION

Analysis of variance
 
Analysis of variance for yield and yield attributing characters is presented in Table 1. A significant variation among the genotypes for all the characters under study was obtained. Significant genotypic variability for yield and yield attributing characters in cowpea was also reported in previous studies (Das et al., 2018; Yalcin, 2007). On further partitioning, it is observed that the both mean squares due to parents and crosses showed significant variation for most of the traits, except PH, NPB, PPC, SPP and PL. Parents versus crosses variation was significant for all the characters except NPB, PPC and SPP. Further, a comparison for range of mean performance of parents and those of resultant progenies indicated transgressive segregation for almost all the characters. This has revealed considerable scope to exploit desirable recombination for genetic improvement.
 

Table 1: Analysis of variance for yield and yield attributing characters.


 
Estimation of genetic variance and related parameters
 
Genotypic and phenotypic coefficients of variation were estimated along with heritability and genetic advance are presented in Table 2. In the present study, all those estimates clearly revealed not only presence of enough variation at genotypic level but transmissibility of the variation to the progenies. The study also focussed those characters that were governed predominantly by additive genes.
 

Table 2: Estimates of genetic variance and related parameters for yield and yield attributing characters.

  
 
For all the characters studied, estimates of phenotypic coefficients of variation (PCV’s) were higher than genotypic coefficients of variation (GCV’s) indicating effect of environment on the expression of characters. However, a close examination of these estimates revealed greater difference between these two estimates for PL, SPP, PPC, and PH, which indicated considerable effect of environment for these characters. This was also reflected in the lower heritability (h2) estimates of these characters. On the other hand, TW, SYP, CPP and NPB in that order revealed very close PCV and GCV estimates indicating non-significant role of environment on these traits. Their respective high h2 estimates corroborated the findings. These results suggested that these characters are highly heritable and therefore can be easily transferred from parent to offspring. Genotypic coefficient of variation together with heritability could perhaps be a better index of extent of advance that can be expected from gene selection scheme (Burton, 1952). Expected genetic advance (GA) was found high for PH (32.06%), NPB (29.46%), SYP (21.69%) and CPP (21.51%). Similar high GA was reported by Idahosa et al., (2010) for yield attributing traits in cowpea.
       
In the present study high genotypic coefficient of variation followed by high heritability and genetic advance was obtained for NPB, CPP and SYP. Johnson et al., (1955) reported that heritability estimates together with genetic advance are more important than heritability alone to select the best individuals. Heritability combined with genetic advance is a more reliable index for selections of traits (Ubi et al., 2001). According to Ansari et al., (2004), high heritability percentage reflects large heritable variance, which may offer possibility of improvement. Therefore, besides SYP phenotypic selection for increased NPB and increased CPP might accumulate favourable additive genes controlling these characters.
 
Combining ability analysis
 
Comparison of GCA effects of the parents (Table 3) revealed that JCC-4 was good general combiner for SYP. This parent could be utilized in hybridization programs to exploit heterosis. JCC-4 also exhibited significant GCA effect for CPP. UPC-622 possessed good GCA effects for reduced PH, maximum PPC, PL and SYP. JCC-3 demonstrated good GCA effects for CPP and minimum PPC. Though, parents without high GCA could also be used to exploit dominance gene effects (Arunga et al., 2010). Vigna unguiculata is a self-pollinated crop and autogamous crop plants are homozygous and as a consequent, do not make use of the dominance gene effects at individual loci (Moreno-Gonzaled and Cubero, 1993). Therefore, crosses involving genotypes with greater magnitude of GCA should be potentially superior in advanced generations (Franco et al., 2001). Carvalho et al., (2012) found genotypes with high pod weight, pod length, 100-grain weight, and number of beans per pod should be used to improve seed yield in cowpea.
 

Table 3: General Combining Ability effects of the parents of a diallel cross for different characters.


 
Significant specific combining ability (SCA) effect for yield was shown by most of the crosses (Table 4). JCC-1 × JCC-4 showed the highest SCA effects for yield followed by JCC-3 × JCC-4, JCC-2 × JCC-3, UPC-287 × JCC-2 and UPC-287 × JCC-1. Further, these crosses exhibited high SCA for other characters as well. The cross JCC-3 × JCC-1 exhibited higher SCA for shorter PH, UPC-287 × JCC-1 for increased NPB, JCC-2× JCC-3 for CPP, UPC-622 × UPC-287 for PPC, JCC-3 x JCC-4 for SPP, UPC-622 × JCC-1 for reduced PL and UPC-622 × JCC-2 for TW and decreased NPB. The crosses JCC-1 × JCC-4 and JCC-3 × JCC-4 with high SCA effect from high × high combining parents might produce useful transgressive segregates to use pedigree method of selection and could be exploited successfully in cowpea varietal improvement programs. High SCA effects of such crosses might be attributed to additive × additive type of gene interaction and high yield potential of this category of cross can be fixed in subsequent generations. On the other hand, high SCA effects of the crosses JCC-2 × JCC-3 and UPC-287 × JCC-1 results from high × low combining parents are attributed to additive × dominance type of gene action. High yields from such crosses would be unfixable in subsequent generation and therefore cannot be exploited by standard selection procedure. However, the cross would produce desirable transgressive segregates in later generation if efforts are made to modify the conventional breeding methodologies to capitalize on both additive and non-additive genetic effects. In view of this, it is suggested that a breeding procedure which may take care of fixable gene effects and at the same time maintains considerable heterozygosity for exploiting the dominance effects may prove most efficient for yield improvement. In this regard, recurrent selection appears to be the most effective selection procedure. However, in self-pollinated crops, recurrent selection in true sense is difficult to practice. Therefore, biparental mating in early generation might be practiced ensuring utilization of both additive and non-additive gene actions. High SCA effects of cross combination UPC-287 × JCC-2 involving low ´ low combiners could be due to overdominance and dominance x dominance type of gene action. Such specific crosses can be exploited for heterosis breeding. However, highly significant SCA effects suggests that non-additive gene action could play a vital role in the improvement of cowpea for the traits of interest. Further results showed that none of the parents or specific cross was best for all the characters. Similar results were observed by others (Pandey and Singh, 2010 and Ayo-Vaughan et al., 2013).
 

Table 4: Estimates of specific combining ability effects of crosses for yield and yield attributing characters.


 
Genetic analysis
 
Both additive (D) and dominance (H1 and H2) effects were involved in controlling most of the characters. Predominant role of non- additive gene action was observed from relatively higher magnitude of dominance components H1, H2 and h2 (Table 5). Traits such as PH, NPB, PPC, PL and SYP were found to be predominantly controlled by dominant component, whereas additive effect (D) was found to be significant for CPP, SPP and TW. Role of dominance gene effects for PH has also been reported in snap bean (Rodriguez et al., 1998). Existence of both additive and dominance gene effects detected in the genetic control of the characters in set of genotypes studied implies that both gene effects should be considered in developing strategies for selection of superior lines (Skoric et al., 2000). F-value was negatively significant for PPC and TW, indicating recessive alleles were more frequent rather than dominant alleles in parents of these characters. KD/KR estimates further confirmed excess of recessive genes for these characters. Estimates of degree of dominance were found to be more than unity for all the characters indicating prevalence of overdominance for the characters. Similarly, Ikram and Saleem (2005) observed over-dominance for most of the characters studied.
 

Table 5: Genetic components of variation for yield and yield attributing characters.


 
The proportion of alleles in parents with positive and negative effects (H2/4H1) were less than expected value of 0.25 for all the characters except PL. This indicated unequal allelic frequencies for the characters. Therefore, the loci exhibiting positive and negative genes were unequally distributed in the parents for these traits. The value of h2/H2 indicated that nearly one group of genes might be involved in the control of PH, CPP, PPC, SPP, PL, SYP and TW. A very low value for this parameter did not reflect any inferences regarding the number of gene group controlling a character revealing their polygenic inheritance as is characteristic of quantitative traits. The negative correlation between the mean value of the parents Yr and parental order of dominance (Wr+Vr) for characters indicated that dominant genes were associated with high mean expression. A high mean associated with dominant characters were found in PH and NPB. A high mean associated with recessive genes for other characters viz., CPP, PPC, SPP, PL, SYP and TW. This information could assist a breeder to exploit desirable alleles of specific traits in homozygous condition thereby effecting genetic improvement of these traits. Narrow sense heritability h2(ns) was found to be high for TW and moderate for PPC, whereas it was low for CPP, SPP, PL and SYP. TW also exhibited high GCA compared to SCA variance, highest heritability in broad sense for such a character, selection even without progeny testing would bring desirable improvement. This is due to preponderance of additive genetic variance and a relatively smaller contribution of the environment to the phenotype. In contrast, the characters with low narrow sense heritability and lower estimates of the dominance components compared with those of the additive (D), selection may be considerably difficult or virtually impractical due to the masking effect of environments on genotypic effects. Similar result was reported by Ayo-Vaughan et al., (2013) in cowpea.

CONCLUSION

Analysis of variance revealed highly significant variation among the genotypes for yield and yield attributing characters. Parents, crosses as well as parents vs. crosses showed significant variations for most of the characters studied. Among the parents, JCC-4 and among the crosses, JCC-1 × JCC-4 showed the highest mean performance for seed yield. Heritability was highest for TW and expected genetic advance was found highest for PH. JCC-4 was found to be the best general combiner for seed yield. It also exhibited positive and significant effect for CPP. A high, positive and significant specific combining ability (SCA) effect for yield was shown by most of the crosses. JCC-1 × JCC-4 showed the highest SCA effects for yield. Other crosses which exhibited high SCA effects for yield were JCC-3 × JCC-4, JCC-2 × JCC-3, UPC-287 × JCC-2 and UPC-287 × JCC-1. Further, these crosses exhibited high SCA for other characters as well. Component analysis indicated the importance of both additive and dominance gene action for most of the characters studied. From the genetic analysis it was found that both additive and dominance effects were involved in controlling most of the characters. Predominant role of non-additive gene action was observed from relatively higher magnitude of dominance components H1, H2 and h2.

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