Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.80

  • SJR 0.391

  • Impact Factor 0.8 (2023)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Legume Research, volume 45 issue 4 (april 2022) : 410-414

Study of Genetic Divergence for Yield and Quality Traits in Cowpea [Vigna unguiculata (L.) Walp.]

Sanjeev Vaggar1, Udit Kumar1,*, K. Prasad2, L.M. Yadav1, Pramila1, B.M. Sinha1
1Department of Horticulture, Dr. Rajendra Prasad Central Agricultural University, Pusa-848 125, Bihar, India.
2Department of Horticulture, Tirhut College of Agriculture, Dr. Rajendra Prasad Central Agricultural University, Dholi-843 121, Bihar, India.
  • Submitted10-12-2019|

  • Accepted15-04-2020|

  • First Online 10-09-2020|

  • doi 10.18805/LR-4297

Cite article:- Vaggar Sanjeev, Kumar Udit, Prasad K., Yadav L.M., Pramila, Sinha B.M. (2022). Study of Genetic Divergence for Yield and Quality Traits in Cowpea [Vigna unguiculata (L.) Walp.] . Legume Research. 45(4): 410-414. doi: 10.18805/LR-4297.
Background: The genetic divergence analysis was done using Mahalanobis D2 statistics. The mean intra-cluster and inter-cluster D2 values are compared for drawing conclusions on superiority of cowpea genotypes for various plant yield and quality attributes.

Methods: In the field of Vegetable Research Farm of Department of Horticulture, RPCAU, Pusa, Indiaduring 2018-19 (Rainy season) thirty-three cowpea genotypes were taken for genetic divergence analysis. The genotypes exhibited marked divergence and thus grouped into clusters following Tocher’s method of clustering. The mean intra-cluster and inter-cluster D2 values among the clusters were compared for their superiority of yield and quality attributes. 

Result: All the selected genotypes displayed marked divergence. Among the five clusters, cluster I consisted eight, cluster II had three, cluster III had eleven, cluster IV had nine and cluster V had two genotypes. The clustering pattern had no parallelism between genetic diversity and geographical distribution, suggesting that the selection of parental genotypes for hybridization will be more appropriate based on genetic diversity. The intra-cluster D2 values ranged from 0.00 in cluster II, cluster III, cluster IV and cluster V to 16.12 in cluster I. The inter-cluster D2 values revealed that highest inter-cluster generalized distance (44.50) was between cluster V and cluster III, while it was the lowest (20.12) between cluster II and cluster I. From this study it can be concluded that for selection and heterotic recombinants emphasis may be given to attributes such as seed germination percentage, peduncle length and average pod weight to enhance the yield of cowpea. Similarly, with respect to the clusters for obtaining the heterotic recombinants, genotypes placed in cluster V, cluster II and cluster III may be used in the crossing program. 
Cowpea [Vigna unguiculata (L.) Walp.] is an annual, autogamous leguminous crop belonging to family Leguminaceae (Mackie and Smith, 1935) with a diploid chromosome number of 2n=2x=22 (Darlington and Wylie, 1955). It is native to India (Vavilov, 1949) but tropical and central Africa is also considered as a secondary center of origin (Ng and Marechal, 1985). Cowpea is considered as one of the most important food legumes of the world, especially in countries of the tropics and sub-tropics, such as Africa, Asia, Central and South America (Singh et al., 1997; Anonymous, 2017). Due to high protein content, cowpea is called ‘Vegetable meat’ and has high grain and biological value on a dry weight basis. It is commercially grown throughout India and is used for multiple purposes such as long green pods, seeds as pulses, foliage as fodder for the milch animal, green manuring and cover crop (Meena et al., 2017). Cowpea cultivars grown for immature green pods are commonly referred to as southern pea, black eye pea, crowder pea, lobia, niebe, caupi or frijole (Dahiya et al., 2007; Mishra, 2007; Diouf, 2014). Globally India ranks second in vegetable production as it contributes 15.7% to the global vegetable area and 14.5% to global production i.e.187.47 million tonnes (Anonymous, 2020b).
       
As per the nutritional profile is concerned, cowpea grain contains 23.4% protein, 1.8% fat and 60.3% carbohydrates and it is a good source of vitamins and phosphorus (Venkatesan et al., 2003). In India and South East Asia, cowpea is well known for immature tender pods and dry seeds are considered as an economical source of protein (20.25%) (Salimath et al., 2007; Anonymous, 2020a). Cowpea is also valued for the fact that it is low in fat and high in fibers and the protein in pod legumes has been shown to reduce low-density lipoproteins that are implicated in heart diseases when harvested at appropriate maturity stage (Prasad et al., 2018b). Some recent uses of cowpea include vegetable mixture, baking powder of biscuits, vegetable milk and legume starch (Hall et al., 2003; Manggoel et al., 2012; Dovi, 2013). Cowpea is also well known for its postharvest value and responds very well to postharvest treatments such as edible coatings, packaging, etc. (Prasad et al., 2018a). It is a most versatile pulse crop because of its smothering nature, drought-tolerant character, soil-restoring properties and multi-purpose uses (Hall et al., 2003).
       
As a pulse crop, cowpea fits well into most of the cropping system (Singh et al., 2018) varieties of cowpea with a “persistent-green” grain have been developed by the breeding program in the USA that is a versatile product for frozen vegetable applications (Nwosu et al., 2013; Anonymous, 2020b). The variability of qualitative and quantitative characters of the breeding material will help to a breeder in its genetic improvement. Since the improvement in cowpea is concerned with the selection of superior genotypes for which the most suitable individuals from their phenotypic expression, estimates of genotypic and phenotypic variance for various quantitative characters and their heritability are necessary (Jogdhande et al., 2017).
       
The D2 statistics developed by Mahalanobis (1936) is a potential tool for obtaining quantitative estimates of divergence among biological populations and has extensively been utilized to assess diversity. Moreover, the relative contribution of different yield components to total divergence using Mahalanobis’s D2 analysis helps in the identification of selected parameters to be used as criteria for improvement in yield (Dalsaniya et al., 2009).
The experiment was conducted during 2018-19 (Rainy season) at Vegetable Research Farm of Department of Horticulture, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar. Pusa of state Bihar is located at a longitude of 85.67o E and latitude of 25.98o N. This place is situated at an altitude of 52.0 meters above mean sea level. The soil of Pusa (Samastipur) is mainly young alluvium and calcareous. Soil is deep, light to heavy in texture having CaCO3 more than 10 per cent and up to 30 per cent. This region has a subtropical climate with an extreme of summer and winter. The experimental materials consisted of thirty-three lines of cowpea genotypes including one check variety viz. Kashi Unnati. The genotypes were collected from IIVR-Varanasi, GBPUAT Pantnagar, CAZRI Jodhpur and from local. The experiment was conducted in a randomized block design with three replications at RPCAU, vegetable research farm, Pusa Samastipur. The seeds were sown on the 21st of August, during monsoon-season 2018 in plot size 378 m2. After the final preparation of the land, the field was divided into 99 plots of size 378 m2. The seeds of cowpea were dibbled in 60 × 45 cm apart. The observations were recorded from five randomly selected plants in each replication for all characters following viz., seed germination percentage, plant height (cm), number of branches per plant, days to first flowering, first flowering node, number of nodes on the main stem, peduncle length (cm), number of pods per cluster, day to first picking, pod length (cm), pod diameter (cm), average pod weight (g), number of pods per plant, number of seeds per pod,100 seed weight (g), pod yield per plant (g).
 
Statistical analysis 
 
The collected data were subjected to analysis of variance (ANOVA) using MS-Excel, OP-STAT software available from HAU, Hisar and presented in Table 1 and genetic divergence (D2) was worked out according to Mahalanobis (1936) using SPAR-1 software. A dendrogram was generated using the Wards method and Euclidean distance as a measure of similarity with the help of SPSS software version 16 and is presented in Fig 1.
 

Table 1: Analysis of variance for sixteen characters in cowpea.


 

Fig 1: Clustering pattern of thirty-three genotypes of cowpea based on D2 statistics by Tocher’s method.

The analysis of variance revealed highly significant differences among the genotypes for all the characters studied, indicating the existence of wide genetic divergence among them. Information on genetic diversity was also used to identify promising diverse genotypes, which may further be used in breeding programs.
       
In context to the performance of various traits, the thirty-three genotypes were grouped into five clusters based on the genetic divergence D2 values (Table 2). Cluster III had the largest number of eleven genotypes followed by nine genotypes in cluster IV, eight genotypes in cluster I, three genotypes in cluster II while only two genotypes in cluster V. Similar results were also reported by Venkatesan et al., (2004), Madheshia and Pandey (2005) and Brahmaiah et al., (2014).
 

Table 2: Clustering pattern of thirty-three genotypes of cowpea.


       
The cluster means of different traits shown the wider difference between the cluster means (Table 5). The highest cluster mean values for the first flowering node were maximum in cluster I, cluster II had maximum cluster mean value for seed germination percentage, 100 seed weight and pod yield per plant. Cluster III had a maximum cluster mean value for plant height and pod diameter. Cluster IV had a maximum cluster mean value for the number of branches per plant, pod length and average pod weight.
 

Table 5: Cluster mean value for sixteen characters in cow pea.


       
Cluster V had maximum cluster mean value for days to first flowering, number of nodes on the main stem, peduncle length, number of pods per cluster, days to first picking, number of pods per plant, number of seeds per pod, number of pods per cluster, number of nodes on main stem and number of seeds per pod. Genotypes namely EC-390237 and EC-725197 can be used further for enhancing the pod yield per plant. Whereas, the genotypes Kashi Unnati, Kashi Gouri and Kashi Kanchan may be used as one of the diverse parents to develop a more number of recombinants for yield improvement. Similar approaches as mentioned in the above discussion suggestion were given by Venkatesan et al., (2004), Dalsaniya et al., (2009), Kumari et al., (2010), Brahmaiah et al., (2014), Kumar and Kumar (2015).
       
The maximum inter-cluster distance was recorded between cluster V and cluster III followed by cluster V and cluster II (Table 3). The maximum mean value for the characters was recorded in cluster V viz, first flowering node, number of nodes on the main stem, peduncle length, number of pods per cluster, days to first picking, number of pods per plant and number of seeds per pod and followed by cluster II for the traits namely seed germination percentage, days to first flowering, 100 seed weight and pod yield per plant, suggesting that to have the heterotic recombinants for the improvement of above mentioned eleven traits. Genotypes from cluster V and cluster II may be used in crossing program as it is evident that high cluster means value recorded for seed germination percentage and peduncle length having the positive and high direct effect on pod yield per plant. Similar results were also obtained by Dalsaniya et al., (2009), Kumar and Kumar (2015), Jogdhande et al., (2016) and Singh and Srivastava (2017).
 

Table 3: Intra and Inter-cluster distance (D2) among five clusters in cowpea.


       
The contribution of different plant characters towards divergence is important for further selection and choice of the parents for hybridization (Table 4). The maximum character contribution towards divergence was recorded for 100 seed weight followed by plant height, average pod weight, pod yield per plant and very meager contributions were found for the rest of the traits. Similar results were obtained by Bakiyarani (2000), Borah and Khan (2002), Kumar and Kumar (2015) and Kwaga (2014) in legume crops.
 

Table 4: Contribution percentage of sixteen characters towards genetic divergence in cowpea.

Based on all these results we can conclude that during selection emphasis may be given to seed germination percentage, peduncle length and average pod weight to enhance the yield of cowpea. For obtaining the heterotic recombinant genotypes placed in cluster V, cluster II and cluster III may be used in the crossing program. Further heterotic recombinants may be obtained for enhancing peduncle length, average pod weight and seed germination percentage.

  1. Anonymous (2017). UN FAO Annual Report, Food and Agriculture Organization of the United States, Rome, Italy.

  2. Anonymous (2020a). Indian Horticulture Database, National Horticulture Board, Ministry of Agriculture, Government of India, Gurgaon, Haryana, India.

  3. Anonymous (2020b). Agricultural and Processed Food Products Export Development Authority, Ministry of Commerce, Government of India, New Delhi, India.

  4. Borah, H.K. and Khan, A.K.F. (2002). Genetic divergence in fodder cowpea [Vigna unguiculata (L.) Walp.]. Madras Agricultural Journal. 88: 625-628.

  5. Bakiyarani, S., Nadarajan, N., Rajendran, C. and Shanthi, S. (2000). Genetic divergence for physiological traits in cowpea [Vigna unguiculata (L.) Walp.]. Legume Research. 23: 114-117. 

  6. Brahmaiah, M., Jhansirani, K., Sunil, N. and Keshavulu, K. (2014). Genetic divergence in cowpea [Vigna unguiculata (L.) Walp.]. Electronic Journal of Plant Breeding. 5: 107-111. 

  7. Dahiya, O. P., Mishra, S. K. and Singh, D. (2007). Genetic variability in cowpea [Vigna unguiculata (L.) Walp.]. J. Arid Legumes. 4(2): 107-109. 

  8. Dalsaniya, S.B., Pansuriya, A.G. and Devada, B.K. (2009). Genetic divergence in cowpea [Vigna unguiculate (L.) walp.]. Legume Research. 32: 250-245.

  9. Darlington, C.D and Wylie, A.P. (1955).Chromosome Atlas of Flowering Plants. George Allen and Unwin Ltd London. The United Kingdom.

  10. Diouf, D. (2014). Recent advance in cowpea [Vigna ungiculata (L.) Walp.]. Omies Research for Genetic Improvement. Africa Journal of Biotechnology. FAO http//www.fao.stat.fao.org, 10: 2803-2810.

  11. Dovi, K.P. (2013). Whole grain sorghum and whole-grain cowpea biscuits as a complementary food for improved child nutrition, M.Sc. Food Science thesis. University of Pretoria, South Africa. 

  12. Hall, A. E, Cisse, N., Thiaw, S., Elawad, H.O.and Ehler, J.D. (2003). Development of cowpea cultivar and germplasm by Cowpea CRSP, Field Crop Research. 82: 103-134.

  13. Jogdhande, Srinivas, Vijay, S., Kale, P.K. and Nagre. (2017). Correlation and path analysis study in cowpea [Vigna unguiculata (L.) Walp.] genotypes. International Journal of Current Microbiology and Applied Sciences. 6: 3305-3313.

  14. Kumar, R. and Kumar, M. (2015). Estimation of genetic divergence in garden pea (Pisum sativum var. hortense L.) germplasm to facilitate the selection of potential parents for hybridization programme. Legume Research. 39: 709-712.

  15. Kumari, R.U., Usharani, K.S., Suguna, R. and Anadkumar, C.R. (2010). Relationship between the yield contributing characters in cowpea for grain purpose (Vigna unguiculata (L.) Walp.]. Electronic Journal of Plant Breeding. 1: 882-884.

  16. Kwaga, Y.M. (2014). Correlation coefficient between kernel yield of groundnut (Arachis hypogaea L.). American Journal of Research Communication. 2: 82-90.

  17. Mackie, W.W. and Smith, F.L. (1935).Evidence of field hybridization in beans. Journal American Society Agronomy. 27: 903-908.

  18. Madheshia, S.K. and Pandey, I.D. (2005). Studies on biodiversity in grain cowpea. Indian Journal of Plant Genetic Recourse. 18: 217-220.

  19. Mahalanobis, P.C. (1936). On the generalized distance in statistic. Proceedings of National Institute of Science, India. 2: 49-55.

  20. Manggoel, W., Uguru, M.I., Ndam, O.N. and Dasbak, M.A. (2012). Genetic variability, Correlation and path coefficient analysis of some yield component of ten cowpea [Vigna unguiculata (L.) Walp.]. Journal of Plant Breeding and Crop Science. 4: 80-86.

  21. Meena, B.L., Das, S.P., Kandpal, B.K. and Nagachan, S.V. (2017). Genetic estimates and character association studies in field pea (Pisum sativum L.). National Journal of Plant Improvement. 7: 169-174.

  22. Mishra, S.K. and Singh, D. (2007).Correlation and path coefficient analysis in cowpea [Vigna unguiculata (L.) Walp.]. Journal of Arid Legumes. 4: 127-129.

  23. Ng, N.Q. and Marechal, R. (1985) Cowpea taxonomy, origin and germplasm in Cowpea research, production and utilization. (Singh, S.R. and Rachie, K.O.) Wiley, New York, 11-21.

  24. Nwosu, D.J., Olatunbosun, B.D. and Adetiloye, I.S. (2013). Genetic variability, heritability and genetic advance in cowpea genotypes in two agro-ecological environments. Greener Journal of Biological Science. 3: 202-207.

  25. Prasad, K., Guarav, A., Preethi, P. and Neha, P. (2018a). Edible coating technology for extending market life of horticultural produce, Acta Scientific Agriculture. 2: 55-64.

  26. Prasad, K., Jacob, S. and Siddiqui, M.W. (2018b). Fruit Maturity, harvesting and quality standards. Preharvest modulation of postharvest fruit and vegetable quality. Elsevier publications. Academic Press. USA. 41-69.

  27. Salimath, P.M., Birander, S., Gowda, L. and Uma, M.S. (2007). Variability parameters in F2 and F3 populations of Cowpea involving determinate, semi-determinate and indeterminate. Karnataka Journal of Agricultural Sciences. 20: 255-256.

  28. Singh, B.B., Chambliss, O.L. and Shamar, B. (1997). Recent advances in cowpea breeding. IITAJIRCAS, Ibadan, Nigeria.

  29. Singh, O.V., Shekhawat, N., Singh, K. and Gowthami, R. (2018). Genetic divergence studies in cowpea [Vigna unguiculata (L.) Walp.]. germplasm using Mahalanobis D2 analysis. International Journal of Current Microbiology and Applied Science. 7: 2616-2624.

  30. Singh, S.C. and Srivastava, P. (2017). Field screening of green gram [Vigna radiate (L.) Wilczek]. Genotypes against spotted pod borer, Maruca vitrata (Fabricauas). Journal of Entomology and Zoology Studies. 5: 1161-1165.

  31. Vavilov, N.I. (1949). The origin, variation, immunity and breeding of cultivated plants. Chronica Botanica. 13: 1-54.

  32. Venkatesan, M., Prakash, M. and Ganesan, J. (2003). Correlation and path analysis in cowpea [Vigna unguiculata (L.) Walp.]. Legume Research. 26: 105-108.

  33. Venkatesan, M., Veeramani, N., Thangaveal, P. and Ganesan, J. (2004). Genetic divergence in cowpea [Vigna unguiculata (L.) Walp.]. Legume Research. 27: 223-225.

Editorial Board

View all (0)