6 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 Research on World Agricultural Economy https://journals.nasspublishing.com/index.php/rwae Copyright © 2023 by the author(s). Published by NanYang Academy of Sciences Pte. Ltd. This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/). DOI: http://dx.doi.org/10.36956/rwae.v4i2.814 Received: 18 February 2023; Received in revised form: 18 April 2023; Accepted: 27 April 2023; Published: 10 May 2023 Citation: Ghosh, S., Roy, A., Kundagrami, S., 2023. Screening of Elite Mungbean Genotypes (Vigna radiata (L.) Wilczek) through Multivariate Analysis for Food and Nutritional Security. Research on World Agricultural Economy. 4(2), 814. http://dx.doi.org/10.36956/rwae.v4i2.814 *Corresponding Author: Sanhita Ghosh, Department of Genetics and Plant Breeding, University of Calcutta, Kolkata, West Bengal, 70019, India; Email: sanhitaghosh91@gmail.com RESEARCH ARTICLE Screening of Elite Mungbean Genotypes (Vigna radiata (L.) Wilczek) through Multivariate Analysis for Food and Nutritional Security Sanhita Ghosh* Anindita Roy Sabyasachi Kundagrami Department of Genetics and Plant Breeding, University of Calcutta, Kolkata, West Bengal, 70019, India Abstract: The ever-increasing urbanization to accommodate the growing population reduces substantially the agricultural land but poses a threat to meeting the requirement of proper nutrition for human health. Mungbean [Vigna radiata (L.) Wilczek] is a unique gift bestowed by nature to mankind, which has the potency to make up the gap of protein shortage with an inexpensive cost, but due to its low level of production as well as productivity, which in a roundabout way influences the nutritional status of people resulting in malnutrition. Therefore, enhancement of the total area under mungbean cultivation is not permissible, and an increase in the total productivity per unit area is necessary. In this manner, screening and evaluation of improved genotypes for high yield are necessary to ensure food security. But at the same time seed yield being a complex character governed by several other contributing traits, selection for the characters proves to be quite challenging. As a prerequisite for any breeding program aimed at yield enhancement presence of significant genetic diversity in a given population is highly important. In the present investigation principal component analysis was performed and the results revealed two principal components contributing to the total variance in the population. While the PC1 was predominated by yield and its attributing traits, the PC2 was mainly comprised of growth-related traits. The hierarchical (UPGMA) cluster analysis using standardized data classified the fifty-two mungbean genotypes into 4 clusters, which showed 2 major, 1 minor and one outlier. Among them, cluster II is the most fascinating, as its individual had high seed yield plant–1 and related traits. The present work concluded that the identification of promising high-yielding mungbean genotypes through multivariate analysis has a good promise for future breeding programs with a view of food and nutritional security. Keywords: Mungbean; Screening; Multivariate analysis http://dx.doi.org/10.36956/rwae.v4i2.814 http://dx.doi.org/10.36956/rwae.v4i2.814 mailto:sanhitaghosh91@gmail.com https://orcid.org/0000-0002-3948-4087 7 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 1. Introduction The present unequivocal confirmation that the global population has already grown exponentially and predicted it will rise from the present to 9 billion in 2050 [1]. With rapid urbanization and unchecked population growth ensuring food and nutritional security for the population has proved to be quite difficult even with the support of advanced technology in the field of agricultural science [2,3]. In plant genetics resources, pulse crop species are the base subsistence of the world food security for a growing population. Mungbean [Vigna radiata (L.) Wilczek] is a unique gift presented by nature to mankind, which has the potency to make up the gap of protein shortage in view of its three-fold amount of protein as much as cereals [4]. Besides, this crop has not only the capability to enrich soil fertility with physical and biological properties of soil health through symbiotic nitrogen fixation but also plays an important role in the economy to increase the farmer’s income through the intercropping system [5]. India alone with grown area of 3.72 million hectares and production of 1.70 million tons with productivity of 406.98 kg/ha rep- resents around two third of global production [6]. Thus, the overall annual production of the crop has increased yet the crop productivity has plateaued due to the non-availability of high-yielding genotypes and lack of genetic variability as well as post-harvest losses because of bruchid attack [7]. Under the circumstances, enhancement of productivity is necessary for ensuring the food security of the population. So, there is a strong need for increasing the mungbean productivity but the current agricultural practices and the availability of sufficient land put a bar on it. Hence, an alternative approach is necessary to look for introducing improved high-yielding genotypes. The sound knowledge of genetic diversity in genetic resources is a crucial part for plant breeders to better comprehend the evolutionary and the hereditary connec- tions among accessions, to choose germplasm in a more organized and impressive way and to create convenient diversity in their plant breeding program [8]. From the very beginning of agriculture genetic variability within crop species to meet subsistence food requirements has been done and now it is being utilized to surplus food for ris- ing populations. The unavailability of stable high-yielding varieties potential is a major bottleneck for growing mungbean. Empirical selection for genotypes with high yield is difficult because of the yield complex nature con- trolled by polygenes. Yield is a complex trait, associated with many contributing traits which is highly influenced by the environment. Analysis of yield and related traits are also presented an intricate chain of relationships and picturized a reflection of their gene effects [6]. Multivariate analysis such as principal component analysis and cluster analysis are statistically eligible to experiment and ana- lyze a matrix of complicated values which can be utilized to think about the connection among traits and decide key properties and attributes that are involved in economic yield [9]. PCA makes it conceivable to transform a given set of traits, which are either associated or not into a new system while cluster analysis is a clear and easy method to group the investigated data through their similarities by a view of a two-dimensional vision [10,11]. Estimation of the genetic diversity can help in the identification of geneti- cally distant parents present in the population. Hybridiza- tion between such genetically distant parents can ensure a maximum number of recombinants expected in the segre- gating generation of such crosses. Keeping these factors in view, the present investigation was conducted to determine the nature and magnitude of genetic diversity among the fifty-two mungbean genotypes for yield and yield attributing traits through multivariate analysis, particularly principal component analysis. Such analysis can clarify the association among agro-morpholog- ical traits and cluster analysis provides valuable informa- tion to screen and identify the promising high-yielding elite mungbean genotypes for future food security. 2. Materials and Methods 2.1 Experimental Material The fifty-two mungbean genotypes were collected from different areas of India such as NBPGR (New Delhi); Pulse & Oil Seed Research Station (Berhampore); some local accessions of different districts of West Bengal and all genotypes listed in Table 1. 2.2 Experimental Site, Seasons and Cultivation The present study was carried out at the Department of Genetics and Plant Breeding at Institute of Agricultural Science, University of Calcutta and the experimental materials consisted of fifty-two mungbean genotypes that were evaluated at Experimental Farm of University of Calcutta, Baruipur, South 24 Parganas West Bengal, India (220 N, 88.260 E and 9.75 m above the sea level) during the period of mid-March to end May in three different Years. The experiment was laid out in a Random Block Design (RBD) using three replications with the experi- mental plot. There were rows per plot of each genotype spaced 30 cm apart. The length distance of each row was 3 m, with plant to plant distance of 10 cm within a row. Most of the cultural practices were performed according to Park, 1978 [12]. 8 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 2.3 Observed Traits Data were collected on five randomly selected healthy harvested plants from each replication and each genotype. Pods of each plant were kept separately in an envelope and dried. Threshing was done by hand was taken to avoid a mixture of seeds. The pre and post-harvesting observa- tions were recorded from five randomly selected plants from each replication on different parameters such as plant height (PH), branches plant–1 (BPP), pods plant–1 (PPP), pod length (PL), seeds pod–1 (SPP), 100 seed weight (HSW), harvest index (HI) and seed yield plant–1 (SYPP) which were determined on plot basis according to Moussa [13] and the mean values computed from the observations of both the seasons were used for statistical analysis. 2.4 Statistical Analysis To assess the overall variation attributed by yield attrib- uting traits in mungbean, the descriptive statistics includ- ing mean, standard error (SE) and range in standard unit were calculated using SPAR 2.0 software package and the Principal component analysis (PCA) and k-means cluster- ing (combined data over three seasons used for each trait) were done using IBM SPSS 20.0 while tree diagram (den- drogram) based on Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method with the Euclidean distance matrix [14] was constructed by Darwin version 6. The first two principal components were plotted against each other to find out the patterns of trait variability among the mungbean genotypes using SPSS version 20. 3. Results and Discussion The basic statistics for eight agro-morphological traits were analyzed and summarized in Table 2 exhibited a noticeable variation present in the experimental material. Pods plant–1, plant height, seed yield plant–1 and harvest index showed high to medium variation whereas the rest of the traits showed low variation. Screening is the first best step to selecting good geno- types for crop improvement. The hierarchical (UPGMA) cluster analysis constructed and classified the fifty-two mungbean genotypes into 4 clusters showing 2 major, 1 minor and one outlier in Figure 1. The genotypes were distributed in each cluster presented in Table 3 exhibited the result in a way that one genotype into cluster I con- tained the outlier (1.92%), 17 accessions were grouped into cluster II (32.69%), 2 genotypes made a small group into cluster III (3.85%) while 32 accessions grouped into transgressive cluster IV (61.54%). The K-Mean values were displayed in Table 4 and Figure 2 based on four clusters. Among them, cluster II constituted the most fas- cinating group because here each elite genotype had high seed yield as well as branches plant–1, pods plant–1, har- vest index whereas cluster IV showed intermediate yield potency. Cluster II showed lower values in all the traits except pod length and 100 seed weight while the outlier (cluster I) was showed distinct from the other cluster be- cause it demonstrated that the lowest seed yield plant–1 as well as low branches plant–1, pods plant–1, harvest index. The inter-cluster distance among four cluster range be- tween 10.57 to 28.60 based on Euclidean dissimilarity matrix presented in Table 5. The highest inter-cluster dis- tance was found between clusters I and IV (28.60) fol- lowed by clusters I and III (26.71), clusters I and II (14.41). The closer cluster distance appeared between clusters III and IV (10.57) followed by clusters II and III (14.39) and clusters II and IV (14.63). Kahraman et al. [11] and Darkwa et al. [15] present similar result in common beans. Eigen- values of eight principal components have been shown in the scree plot Figure 3. Principle component analysis (PCA) demonstrated that PC1 to PC2 had the Eigenvalues > 1 con- tributed traits variability 71.18% through PC1 and 28.81% Table 1. List of mungbean genotypes. Serial No. Genotype Name Serial No. Genotype Name Serial No. Genotype Name Serial No. Genotype Name 1 APDM-84 14 A-82 27 IPM-99-125 40 Sukumar 2 MH-98-1 15 PM-2 28 IPM-205-07 41 PDM-54 3 B1 16 TM-98-20 29 IPM-5-17 42 Sonamung 2 4 PS-16 17 HUM-8 30 KM-139 43 CUM1 5 PTM-11 18 Sonamung-1 31 PM-11-51 44 CUM2 6 SML-302 19 Panna 32 Pusa-1431 45 CUM3 7 ML-5 20 Baruipur local 33 SML-115 46 CUM4 8 APDM-116 21 Howrah local 34 PDML-13-11 47 CUM5 9 UPM-993 22 Purulia local 35 Pusa-1432 48 CUM6 10 MC-39 23 Bankura local 36 Samrat 49 CUS1 11 Pusa Baisakhi 24 Pant mung-5 37 HUM-16 50 CUS2 12 Pusa- 9632 25 VC-639 38 MH-909 51 CUS3 13 K-851 26 Pusa Vishal 39 WBM-045 52 CUS4 9 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 through PC2 in Table 6. Seed yield plant–1 and pods plant–1 with maximum values closer to unity within PC1 whereas plant height and seeds plant–1 close with PC2 illustrated in Figure 4. The positive and negative values in PCA represented correlation trend between the traits. These results were in trends with the findings of Pandiyan et al. Therefore, PC1 assists to select the traits such as branches plant–1 and seed yield plant–1 for yield improvement. PH-Plant Height, BPP-Branches per Plant, PPP-Pods Per Plant, PL-Pod Length, SPP-Seeds Per Pod, HSW- Hundred Seed Weight, HI-Harvest Index, SYPP-Seed Weight Per Plant. PH-Plant Height, BPP-Branches per Plant, PPP-Pods Per Plant, PL-Pod Length, SPP-Seeds Per Pod, HSW- Hundred Seed Weight, HI-Harvest Index, SYPP-Seed Weight Per Plant. Screening is a prerequisite strategy for breeding to improve productivity so that an important crop through breeding traits variation is a necessity. Significant vari- ation exists in the present study for yield contributing Table 2. Basic statistics for eight quantitative traits in fifty-two mungbean genotypes. Traits Pooled Mean ±Standard error Range Minimum Maximum PH (cm) 61.97±0.49 50.02 76.90 NBPP 3.88±0.04 2.50 4.97 NPPP 44.55±0.66 20.90 63.53 PL (cm) 7.55±0.06 6.67 9.37 NSPP 11.61±0.05 9.64 13.15 HSW (gm) 3.35±0.06 1.80 5.48 HI 24.89±0.36 17.23 32.64 SYPP (gm) 14.99±0.34 9.65 25.04 Note: PH-Plant Height, NBPP-No. of Branches per Plant, NPPP-No. of Pods Per Plant, PL-Pod Length, NSPP-No. of Seeds Per Pod, HSW-Hundred Seed Weight, HI-Harvest Index, SYPP-Seed Weight Per Plant, cm-centimeter, gm-gram. Figure 1. Dendrogram showing a cluster of 52 different mungbean genotypes. Table 3. Cluster analysis and classification with regard to agro morphological traits of mungbean. Cluster No of Genotypes Percentage of Contribution Name of Genotypes I 1 1.92 CUS4 II 17 32.69 Pusa Baishakhi, PS-16, MC-39, NDML-13-11, Panna, Sonamung-2, IPM-5-17, Howrah local, PM-11-51, HUM-16, Baruipur local, Pant mung-5, IPM-205-07, APDM-84, MH-909, B1, HUM-8. III 2 3.85 CUM4, Pusa-1432. IV 32 61.54 Sukumar, PM-2, PDM-54, UPM-993, CUS3, IPM-99-125, ML-5, CUM6, CUM1, Pusa-1431, CUS2, Sonali, K-851, CUM3, WBM-045, A-82, APDM-116, CUM2, CUS1, VC-639, SML-115, KM-139, Pusa- 9632, Purulia local, Pusa Vishal, Bankura local, PTM-11, MH-98-1, Samrat, TM-98-20, SML-302, Pusa 1432. 10 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 traits. Ghosh et al. [16] reported that adequate knowledge of trait variation is an imperative marker that provides a sign of the distinctive impacts which influence the aggregate variation of plant traits while variation alludes to detect- able contrasts among individuals for a specific trait. The knowledge of Multivariate analysis not only indicates the significant variance between average vectors but also pro- vides efficient utilization for securing the genetic resourc- es to forecast the potentiality of the breeding material by rapid authentication [11,17]. The nature of the distribution of the genotypes across four clusters observed in the cur- rent investigation suggested that the analysis successfully Table 4. K-Mean performance of agro-morphological traits of four different clusters in mungbean genotypes. Cluster PH (cm) BPP PPP PL (cm) SPP HSW (gm) HI SYPP (gm) I 56.23 ± 3.03 2.80 ± 0.46 20.90 ± 0.17 8.70 ± 0.80 12.00 ± 0.14 4.40 ± 0.17 27.42 ± 1.66 9.65 ± 0.22 II 62.37 ± 0.69 4.01 ± 0.05 51.73 ± 0.83 7.82 ± 0.10 11.50 ± 0.08 3.59 ± 0.11 28.28 ± 0.51 20.15 ± 0.39 III 74.23 ± 1.54 3.19 ± 0.37 48.46 ± 1.47 7.26 ± 0.19 11.74 ± 0.18 2.98 ± 0.11 23.43 ± 0.63 10.65 ± 0.18 IV 61.18 ± 0.62 3.89 ± 0.05 41.22 ± 0.58 7.38 ± 0.06 11.65 ± 0.07 3.22 ± 0.07 22.96 ± 0.42 12.68 ± 0.20 Note: cm-centimeter, gm-gram Figure 2. Means of eight quantitative traits of mungbean genotypes grouped into four clusters. Table 5. Inter cluster distance and mean performance of agro-morphological traits of four different clusters of mung- bean genotypes. Cluster II III IV I 14.41 26.71 28.60 II 14.39 14.63 III 10.57 Figure 3. Scree plot constructed for eight principal components. 11 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 classified the accessions based on their phenotypic per- formances. Similar observations were earlier reported by Basnet et al. [18]. Cluster II with seventeen elite genotypes presented the highest mean performance on seed yield plant-1 as well as pods plant-1 and high values for the rest of the traits and presumes that had special significance in diversification, conservation of natural resources, crop development and sustainability of production systems. Mohammad and Sharif [9] suggested that the selection of genotypes for hybridization must take into account the inter-cluster distances between different clusters as well as the intra-cluster distances among genotypes belonging to the same cluster to obtain optimum segregation during recombination. In addition to cluster analysis the princi- pal component analysis revealed that the first principal component designated at PC1 plays a conceivable role to identify the ideotype yield enhancement traits while PC2 differentiated factors that related to vegetative growth exclusively in regenerative advancement. Pandiyan et al. [19] reported that K-Mean values showed traits ho- mology, degree of genetic diversity and almost similar trends in principle component analysis. Hence, pods plant–1, branches plant–1, harvest index was considered as the most important yield attributing component which is directly reflected in the final yield and also selected seven- teen elite high-yielding mungbean genotypes from cluster II which transform new opportunity to surplus food and nutrition for the rising population. 4. Conclusions The current investigation successfully elucidated the magnitude of diversity existing within a given population of fifty-two mungbean germplasms. The study also helped in identifying seventeen germplasms distributed within the same cluster based on their high yield and promising morphological traits. Such information can be worthwhile to identify suitable parents for exploitation in future hy- bridization programs, and also aim for yield improvement along with other economically important traits. Author Contributions The first author as well as corresponding author San- hita Ghosh took the lead in analysis, interpretation as well as writing the manuscript while co-authors Sabyasachi Kundagrami provided suggestions on experiments and Anindita Roy helped during the analysis. Acknowledgements Authors highly acknowledge University Grant Com- mission (UGC) and University of Calcutta for the finan- cial support. Data Availability Data are available upon request to the corresponding author. Conflict of Interest The authors disclosed that they do not have any conflict of interest. References [1] Bahl, P.N., 2015. Climate change and pulses: Ap- proaches to combat its impact. Agricultural Research. 4(2), 103-108. Available from: https://link.springer. com/article/10.1007/s40003-015-0163-9 [2] Lipton, M., 2001. Reviving global poverty reduction: What role for genetically modified plants? Journal of International Development. 13(7), 823-846. DOI: https://doi.org/10.1002/jid.845 Table 6. Two principal components with eight agro-mor- phological traits of mungbean genotypes. Traits PC1 PC2 PH (cm) 0.303 0.953 BPP 0.849 –0.529 PPP 0.992 0.126 PL (cm) –0.933 0.359 SPP 0.777 0.629 HSW (gm) –0.882 –0.471 HI 0.805 –0.593 SYPP 0.998 –0.057 Eigen Values 5.695 2.305 % of Variance 71.189 28.811 Cumulative % 71.189 100.000 Figure 4. Scattered diagram of two principal components indicating a relationship between eight agro-morphologi- cal traits. https://link.springer.com/article/10.1007/s40003-015-0163-9 https://link.springer.com/article/10.1007/s40003-015-0163-9 12 Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023 [3] Thirtle, C., Lin, L., Piesse, J., 2003. The impact of research-led agricultural productivity growth on poverty reduction in Africa, Asia and Latin America. World Development, 31(12), 1959-1975. DOI: https://doi.org/10.1016/j.worlddev.2003.07.001 [4] Ghosh, S., Roy, A., Kundagrami, S., 2019. Character association studies on yield and attributing traits of fifty-two mungbean [Vigna radiata (L.) Wilczek] genotypes. International Journal of Current Research and Review. 11(12), 25-28. DOI: http://dx.doi.org/10.31782/IJCRR.2019.11125 [5] Perera, U.I.P., Chandika, K.K.J., Ratnasekera, D., 2017. Genetic variation, character association and evaluation of mungbean genotypes for agronom- ic and yield components. Journal of the National Science Foundation of Sri Lanka. 45(4), 347-353. Available from: https://jnsfsl.sljol.info/articles/ab- stract/10.4038/jnsfsr.v45i4.8228/ [6] Singh, B., Bains, T.S., 2014. Effective selection crite- ria for yield improvement in interspecific derivatives of Mungbean (Vigna radiata (L.) Wilczek). Indian Journal of Applied Research. 4(11), 1-3. Available from: https://www.worldwidejournals.com/indi- an-journal-of-applied-research-(IJAR)/fileview/No- vember_2014_1492779438__168.pdf [7] Ghosh, S., Roy, A., Kundagrami, S., 2022. Screening of mungbean [Vigna radiata (L.) Wilczek] genotypes against bruchid (Callosobruchus maculatus) attack to reduce postharvest losses. Legume Research. 45(8), 1019-1027. DOI: https://doi.org/10.18805/LR-4354 [8] Rahman, M.M., Munsur, M.A.Z.Al., 2009. Genetic divergence analysis of lime. Journal of Bangladesh Agricultural University. 7(1), 33-37. Available from: https://www.banglajol.info/index.php/JBAU/article/ view/4795 [9] Mohammad, G., Sharif, M.J., 2015. Study the re- sponses of mungbean genotypes to drought stress by multivariate analysis. International Journal of Agri- culture Innovation and Research. 3(4), 1198-1202. Available from: https://ijair.org/administrator/compo- nents/com_jresearch/files/publications/IJAIR-1151_ final.pdf [10] Mohsen, J., Zahra, M., Naser, S., 2014. Multivari- ate statistical analysis of some traits of bread wheat for breeding under rainfed conditions. Journal of Agricultural Sciences. 59(1), 1-14. Available from: https://www.semanticscholar.org/paper/Multivar- iate-statistical-analysis-of-some-traits-of-Janmo- hammadi-Movahedi/18c721ad492087a464fd1e- 01571c4a56cf83089d [11] Kahraman, A., Onder, M., Ceyhan, E., 2014. Clus- ter analysis in common bean genotypes (Phaseolus vulgaris L.). Turkish Journal of Agricultural and Natural Sciences. Special Issue(1), 1030-1035. Avail- able from: https://dergipark.org.tr/tr/download/arti- cle-file/142219 [12] Park, H.G., 1978. Suggested Cultural Practices for Mungbean [Internet]. Asian Vegetable Research and Development Center. Available from: https://avrdc. org/wpfb-file/culti_practices-pdf-3/ [13] Moussa, E.H., Millan, T., Moreno, M.T., et al., 2000. Genetic analysis of seed size, plant height, day to flower and seed per plant by using both morpholog- ical and molecular markers in chickpea. Annals of Applied Biology. 151(1), 34-42. DOI: https://doi.org/10.1111/j.1744-7348.2007.00152.x [14] Sneath, P.H.A., Sokal, R.R., 1973. Numerical taxon- omy: The principles and practice of numerical classi- fication. WF Freeman & Co.: San Francisco. pp. 573. [15] Darkwa, K., Ambachew, D., Mohammed, H., et al., 2016. Evaluation of common bean (Phaseolus vul- garis L.) genotypes for drought stress adaptation in Ethiopia. The Crop Journal. 4(5), 367-376. DOI: https://doi.org/10.1016/j.cj.2016.06.007 [16] Ghosh, S., Roy, A., Kundagrami, S., 2016. Genetic implication of quantitative traits and their interrela- tionship with seed yield in Mungbean (Vigna radiata L. Wilczek). Indian Agriculturist. 60(3&4), 247-254. [17] Iqbal, A., Shah, S., Nisar, M., et al., 2017. Mor- phological characterization and selection for high yielding and powdery mildew resistant Pea (Pisum sativum) lines. Sains Malaysiana. 46(10), 1727-1734. DOI: http://dx.doi.org/10.17576/jsm-2017-4610-08 [18] Basnet, K.M., Adhikari, N.R., Pandey, M.P., 2014. Multivariate analysis among the nepalese and exotic Mungbean (Vigna radiata L. Wilczek) genotypes based on the qualitative parameters. Universal Jour- nal of Agricultural Research. 2(5), 147-155. DOI: https://doi.org/10.13189/ujar.2014.020502 [19] Pandiyan, M., Senthil, N., Packiaraj, D., et al., 2012. Greengram germplasm for constituting of core col- lection. Wudpecker Journal of Agricultural Research. 1(6), 223-232. https://doi.org/10.1016/j.worlddev.2003.07.001 https://jnsfsl.sljol.info/articles/abstract/10.4038/jnsfsr.v45i4.8228/ https://jnsfsl.sljol.info/articles/abstract/10.4038/jnsfsr.v45i4.8228/ https://www.worldwidejournals.com/indian-journal-of-applied-research-(IJAR)/fileview/November_2014_1492779438__168.pdf https://www.worldwidejournals.com/indian-journal-of-applied-research-(IJAR)/fileview/November_2014_1492779438__168.pdf https://www.worldwidejournals.com/indian-journal-of-applied-research-(IJAR)/fileview/November_2014_1492779438__168.pdf https://www.banglajol.info/index.php/JBAU/article/view/4795 https://www.banglajol.info/index.php/JBAU/article/view/4795 https://ijair.org/administrator/components/com_jresearch/files/publications/IJAIR-1151_final.pdf https://ijair.org/administrator/components/com_jresearch/files/publications/IJAIR-1151_final.pdf https://ijair.org/administrator/components/com_jresearch/files/publications/IJAIR-1151_final.pdf https://www.semanticscholar.org/paper/Multivariate-statistical-analysis-of-some-traits-of-Janmohammadi-Movahedi/18c721ad492087a464fd1e01571c4a56cf83089d https://www.semanticscholar.org/paper/Multivariate-statistical-analysis-of-some-traits-of-Janmohammadi-Movahedi/18c721ad492087a464fd1e01571c4a56cf83089d https://www.semanticscholar.org/paper/Multivariate-statistical-analysis-of-some-traits-of-Janmohammadi-Movahedi/18c721ad492087a464fd1e01571c4a56cf83089d https://www.semanticscholar.org/paper/Multivariate-statistical-analysis-of-some-traits-of-Janmohammadi-Movahedi/18c721ad492087a464fd1e01571c4a56cf83089d https://dergipark.org.tr/tr/download/article-file/142219 https://dergipark.org.tr/tr/download/article-file/142219 https://avrdc.org/wpfb-file/culti_practices-pdf-3/ https://avrdc.org/wpfb-file/culti_practices-pdf-3/ https://doi.org/10.1016/j.cj.2016.06.007