INTRODUCTION

Cowpea (Vigna unguiculata Walp.) is an important arid legume grown for its pods, leaves and grains (Ehlers and Hall, 1997, Boukar et al.,2011). It is a warm-season pulse crop mainly grown in the tropical and subtropical regions of the world (Timko et al., 2007). It belongs to the family Fabaceae and has the chromosome number of 2n=2x=22 (Chaudhary et al., 2021). Cowpea grains are the important source of protein (22–25%) and other essential nutrients (Boukaret al., 2011). Apart from its nutritional benefits, the crop has multiple advantages. It improves soil fertility by fixing the atmospheric nitrogen, and acts as a good cover crop or much crop and thus helps to check soil erosion. Cowpea can be grown as fodder crop, while its tender pods can be used as vegetables. However, it is predominantly grown for its dried grains (Lioi et al., 2019). It is highly drought tolerant and also thrives well in multiple cropping systems (Aliyu et al., 2022). However, despite its multiple uses and nutritional benefits, the crop is still under explored and the production is low (Goncalves et al., 2016). So, breeding for high yielding cowpea varieties is the prime objective of any cowpea breeding programme.

Knowledge on variation in genetic attributes is the foundation for crop improvement (Falconer and Mackay, 1989). The success of any plant breeding programme is dependent on the presence of sufficient variability in the breeding material (Rauf et al., 2016). Hybridization is a known method of creating variations by recombination of diverse alleles from the contrasting parents (Seehausen, 2004). Knowing the potential magnitude of genetic gain that could be achieved by selection of F1s in future generations is aided by studies on genetic variability. Estimation of genetic variability parameters reveal the information on the amount of variability present in a material, heritable portion of that variable and also the genetic advance that can be achieved by operating selection. Hence, estimation of coefficients of genetic variability, heritability and genetic advance are of much relevance while exercising selection to develop high yielding varieties (Mohammadi and Pourdad, 2009).

Yield being a complex trait, is highly influenced by environment and is often confounded by interaction of many component traits (Shi et al., 2009). Studying the association between the component traits of yield helps to choose the best selection criteria to operate indirect selection for yield. Correlation studies indicate the relative importance of characters on which greater emphasis should be made in selection for yield (Lu et al., 2011). However, correlation alone can only reveal the degree and direction of the association between any two characters. In order to split the correlation coefficients into measures of direct and indirect effects, the path coefficient analysis has to be done (Khan et al., 2022). Several genetic variability and correlation studies have been reported in cowpea by various researchers like Suganthi and Murugan (2008), Nehru et al. (2009), Manggoel et al. (2012), Vir and Singh (2014), Devi and Jayamani (2018), Mofokeng et al. (2020), Panchta et al. (2020), Vamshi et al. (2022) etc. However, continuous efforts on exploiting genetic variation and understanding the association among yield component traits are critical for breeding. So, assessing the genetic variability and understanding the association of different yield component traits is vital for breeding productive cultivars. The present investigation was therefore undertaken to elucidate the information on genetic variability, character associations and path coefficients in the cowpea genotypes to identify effective selection criteria for grain yield.

MATERIALS AND METHODS

The experiment was conducted during kharif 2021 at Botany Garden, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka, India. A total of 45 F₁ hybrids of cowpea derived from 10×10 half diallel crossing programme were evaluated for different yield and yield attributing traits along with their 10 parents. The experiment was laid out in Randomized Complete Block Design (RCBD) with two replications. Each entry was sown in 2 rows of 4m length and a spacing of 45×15 cm² was provided. All the necessary agronomic practices and plant protection measures were followed to ensure optimal growth of the plants.

Observations were recorded on seven traits viz., days to 50% flowering, days to maturity, number of pods plant^-1, pod length (cm), number of seeds pod^-1, 100 seed weight (g) and yield plant^-1 (g). The statistical analyses were carried out using “variability” package in R studio version 4.2.1.

RESULTS AND DISCUSSION

Analysis of Variance for different yield and yield attributing traits studied is presented in Table 1. The results revealed the presence of significant variation between the genotypes for all the studied characters. Thus, there is ample scope for selection of different quantitative characters in the experimental material.

Table 1: Analysis of variance for yield and yield attributing traits

3.1.  Genetic variability parameters for yield and yield related traits of 55 Cowpea genotypes

The data on mean, range, variability, heritability and genetic advance as per cent mean (GAM) are presented in Table 2. In the present study, days to 50% flowering ranged from 46.00–71.00 with the mean value of 54.11 days. Whereas, days to maturity varied from 75.00–105.00 with the mean of 105.00. The trait number of pods plant^-1 had the mean value of 18.50 and range varied from 8.00–34.00 in the studied genotypes. Similarly, the range of pod length (cm) and number of seeds pod^-1was 11.02–22.03 and 7.75–16.25 respectively while the mean was 16.75 cm and 13.15 respectively. The genotypes also displayed huge variation in 100 seed weight (g), that ranged from 8.12–25.30 g with the mean of 13.95 g. Grain yield plant^-1 on the other hand varied from 6.80–42.35 g and the mean yield was 24.53 g.

Table 2: Mean performance and parameters of genetic variation estimated for yield and yield attributing traits

Estimation of components of variability revealed that, the genotypic coefficient of variation (GCV) for all the characters studied were lesser than phenotypic coefficient of variation (PCV) indicating the modifying effect of the environment on their expression. High PCV coupled with high GCV was observed for number of pods plant^-1 (30.56 and 24.86, respectively) and grain yield plant^-1 (34.34 and 30.67, respectively) indicating the presence of higher magnitude of variability among the studied genotypes for these traits. However, moderate PCV and GCV were observed for pod length (12.50 and 10.68, respectively) and 100 seed weight (14.85 and 11.94, respectively) indicating the moderate amount of variability for these traits in the studied genotypes. Manggoel et al. (2012), Shankoet al. (2014), Devi and Jayamani (2018), Singhet al. (2020), Vinay et al. (2022) also reported similar results for pod length. The results on 100 seed weight are in accordance with Viswanatha and Yogeesh (2017).

Days to 50% flowering exhibited moderate PCV of 10.23% and low GCV of 7.98%. Similarly, number of seeds pod^-1 also had moderate PCV (12.09%) with a low GCV value (7.76%). This indicated that, the magnitude of variability present in the studied genotypes for the characters viz., days to 50% flowering and number of seeds pod^-1 is mainly due to environmental influence and has low genetic base. In contrast, the trait days to maturity showed low PCV and GCV of 7.58% and 5.94% respectively indicating that, the genotypes are not highly differing with respect to days to maturity. Similar findings are in conformity with Pandiyan et al. (2020) for days to 50% flowering. However, Devi and Jayamani (2018) reported low GCV and PCV for days to 50% flowering, while Shankoet al. (2014) reported moderate GCV and PCV for days to 50% flowering.

The heritability estimates were high for all the studied characters viz., days to 50% flowering (60.91%), days to maturity (61.30%), number of pods plant^-1 (66.14), pod length (73.08%), 100 seed weight (64.70%) and yield plant^-1 (79.79%) except number of seeds pod-1 (41.24%) which showed moderate heritability. However, the GAM was moderate for majority of the traits viz., days to 50% flowering (12.83%), pod length (18.82%), number of seeds pod^-1(10.27%) and 100 seed weight (19.79%). High GAM was observed for number of pods plant^-1 (41.64%) and grain yield plant^-1 (56.44%) while, the trait days to maturity exhibited low GAM of 9.58%. High heritability and high GAM were also reported by Devi and Jayamani (2018), Viswanatha and Yogeesh (2017) and Vinay et al. (2022) for number of pods plant^-1 and grain yield plant^-1.

The higher estimates of GCV, PCV, heritability (broad sense) and GAM for the traits number of pods plant^-1 and grain yield plant^-1 indicated the preponderance of additive gene actions in controlling these characters. Thus, selection for these characters among the studied genotypes may be rewarding.

3.2.  Correlation studies

Correlation analysis provides information on degree and direction of association between yield component traits, which is the key to the selection process in developing high-yielding cultivars. The fundamental information about the direct and indirect effects of various independent factors on the final dependent variable (grain yield), is provided by path coefficient analysis. The genotypic and phenotypic correlation coefficients between yield and yield components namely pods plant^-1, pod length, number of seeds pod^-1, 100 seed weight, were calculated and the results have been presented in Table 3.

Table 3: Phenotypic (above diagonal) and genotypic (below diagonal) correlation coefficients for yield and yield attributing traits

Correlation studies in the present experiment revealed that, day to 50% flowering showed significant and positive correlation with days to maturity both at phenotypic and genotypic levels indicating their strong association with one another. The results were in conformity with Romanus et al. (2008), Bhardu and Navale (2011) and Meena et al. (2015). However, both days to 50% flowering and days to maturity had significant negative association with 100-seed weight genotypically as well as phenotypically. But, at phenotypical level, the correlation of both days to 50% flowering and days to maturity with grain yield plant^-1 was significantly negative, while it was non-significant at genotypic level. This revealed that, the significant negative correlation at phenotypic level with grain yield is mainly due to the environmental influence and, thus the traits days to 50% flowering and days to maturity have no significant influence on grain yield genotypically.

There was significant and positive association of pods plant^-1 with pod length, number of seeds pod^-1, and yield plant^-1 both genotypically and phenotypically. However, the association of pods plant^-1 with 100 seeds weight was non-significant at both genotypic and phenotypic levels. Similar results were also earlier reported by Aliyu et al. (2022).

The phenotypic and genotypic correlation analysis revealed that, positive and significant correlation was observed in pod length with number of seeds pod^-1, 100 seed weight and yield plant^-1. Hence, selection for longer pods in the experimental set, leads to simultaneous selection of these associated traits.

Seeds pod^-1 was positively and significantly associated with 100- seed weight and yield plant^-1. Similarly,  100-seed weight also displayed significant positive association with yield plant^-1 at both genotypic and phenotypic levels.

The significant and positive association of grain yield plant^-1 was noted with pods plant^-1, pod length, number of seeds pod^-1 and 100-seed weight. The results are in accordance with the findings of Romanus et al. (2008), Tyagi et al. (2000), Manggoel et al. (2012), Ajayi et al. (2014), Meena et al. (2015), and Chaudhary et al. (2020).

3.3. Direct and indirect effects of different traits on grain yield plant^-1

The estimates of path coefficients are presented in table 4 and 5. In path analysis, at phenotypic level the direct positive effects were recorded highest for number of pods plant^-1 followed by 100 seed weight, and pod length. Results are in conformity with Shanko et al. (2014) for number of pods plant^-1 and Aliyu et al. (2022) for number of pods plant^-1, and pod length. The highest direct negative effects at phenotypic level were revealed by days to maturity implying that, increased maturity duration leads to low yield in cowpea. At genotypic level, number of pods plant^-1 had highest direct effect on yield plant^-1, followed by days to maturity and number of seeds pod^-1. However, days to 50% flowering recorded highest negative direct effect on yield plant^-1, followed by pod length. The results are in conformity with Manggoel et al. (2014) and Lal et al. (2018).

The positive indirect effect via number of pods plant^-1 was the main contributor to the correlation of pod length and number of seeds pod^-1with grain yield plant^-1 at both genotypic and phenotypic levels.   

Table 4: Phenotypic path coefficients estimated for yield and yield attributing traits

Table 5: Genotypic path coefficients estimated for yield and yield attributing traits

CONCLUSION

High PCV, GCV, h²_(bs) and GAM in number ofpods plant^-1 and yield plant^-1 indicated predominance of additive gene actions. Grain yield plant^-1 showed positive significant association with number of pods plant^-1, pod length, number of seeds pod^-1and 100-seed weight. Path analysis depicted number of pods plant^-1 as the best selection index in cowpea. Also pod length, number of seeds pod^-1 and 100-seed weight had significant contribution towards yield. Thus, selection for yield through these traits might be rewarding.

ACKNOWLEDGEMENT

We acknowledge Council of Scientific and Industrial Research, Ministry of Science and Technology, Govt. of India for providing financial assistance through CSIR-JRF fellowship to the Ph.D. Scholar.

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