







































 
 

 

1 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 6, No. 1, 1-8, 2019 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/journal.512.2019.61.1.8 

© 2019 by the authors; licensee Asian Online Journal Publishing Group 

    

 
 
 
Multivariate Analysis of Genetic Variation in Rapeseed (Brassica Napus L.) 

 
Elora Parvin1   
Firoz Mahmud2 
Md. Shahidur Rashid Bhuiyan3 

Md. Maksudul Haque4    

 

 
( Corresponding Author) 

 
1Scientific Officer, Bangladesh Institute of Research and Training on Applied Nutrition (BIRTAN), Dhaka, 
Bangladesh 

 
2,3Professor, Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka-1207, 
Bangladesh 

 
4Senior Scientific Officer, Bangladesh Institute of Research and Training on Applied Nutrition (BIRTAN), 
Dhaka, Bangladesh 

 

 
Abstract 

Designing breeding programs for rapeseed (Brassica napus L.) cultivars with developed seed and 
oil yields needs information about the genetic variability of characters. In this study, 40 rapeseed 
genotypes were evaluated for genetic variation and relationships between 12 agro-morphological 
characters. Brassica species represent a broad range of crops. This redirects the high degree of 
genetic diversity and allied phenotypic plasticity. Significant differences among the clusters were 
observed. The first two components with Eigen value were greater than unity contributed a total 
of 42.77% variation to the divergence. The genotypes were grouped into five clusters. Cluster V 
contained the maximum number of genotypes (11) and cluster III contained the lowest (4). The 
highest inter-cluster distance was found between cluster II and cluster V and the lowest between 
cluster II and cluster IV. The highest intra-cluster distance was noticed for cluster I and the 
lowest for cluster V. Considering diversity pattern and other agronomic performance lines Nap-
0837, Nap-0733-1, Nap-2066, Nap-9901, Nap-108 and BARI-8 could be considered apposite 
parents. This information is suitable for hybrid breeding and encouraging breeders to exchange 

their germplasm as to enlarge the genetic diversity of breeding accessions. 
 

Keywords: Multivariate analysis, Genetic variation, Rapeseed. 

 
Citation | Elora Parvin; Firoz Mahmud; Md. Shahidur Rashid 
Bhuiyan; Md. Maksudul Haque (2019). Multivariate Analysis of 
Genetic Variation in Rapeseed (Brassica Napus L.). Agriculture and 
Food Sciences Research, 6(1): 1-8. 
History:  
Received: 18 October 2018 
Revised: 23 November 2018 
Accepted: 4 January 2019 
Published: 2 February 2019 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Contribution/Acknowledgement: All authors contributed to the conception 
and design of the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ......................................................................................................................................................................................... 2 
2. Materials and Methods ...................................................................................................................................................................... 2 
3. Result and Discussion ........................................................................................................................................................................ 2 
4. Conclusion ............................................................................................................................................................................................ 7 
References ................................................................................................................................................................................................. 8 
 

 

 

 

http://crossmark.crossref.org/dialog/?doi=10.20448/journal.512.2019.61.1.8&domain=pdf&date_stamp=2017-01-14
http://crossmark.crossref.org/dialog/?doi=10.20448/journal.512.2019.61.1.8&domain=pdf&date_stamp=2017-01-14
http://creativecommons.org/licenses/by/3.0/
http://creativecommons.org/licenses/by/3.0/
http://www.asianonlinejournals.com/index.php/AESR/article/view/1667
https://orcid.org/0000-0003-0324-6834
https://orcid.org/0000-0001-9491-2753


Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

2 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

1. Introduction 
Rapeseed (Brassica napus L., 2n=38) is an important oil seed crop belonging to the family Cruciferae. The seeds 

of mustard and rapeseed contain 42% oil and 25% protein. Oilseed rape (Brassica napus L.) is third most important 
oil crop in the world. Today oilseed rape (B. napus ssp. napus) is the most important source of vegetable oil in 
Europe and the second most important oilseed crop in the world after soybean. However, its limited geographic 
range and intensive breeding has led to a fairly thin genetic basis in present breeding material. The gene pool of 
elite oilseed rape breeding material has been more eroded by an emphasis on specific oil and seed quality characters. 
As a consequence, genetic variability in this vital crop is restricted with regard to several characters of importance 
for breeding purposes. Oil and fat are not only the basis of energy but they also contain fat-soluble vitamins A, D, 
E and K. The oil cake holds proteins of high biological value and applicable quantities of calcium and phosphorus 
and is used as a very good animal feed as well as fertilizer for many crops. Rapeseed laterally with mustard is 
currently ranked as the world’s third vital edible oil crop in terms of area and production after soybean and cotton. 
Major producing areas include Canada, China, northern Europe, and the Indian sub-continent. The rapeseed and 
mustard grown in Bangladesh comprise three species viz. B. campestris, B. juncea, and the newly introduced B. napus. 
These crops have the largest zone and production among the oil crops grown in Bangladesh. Genetic diversity is 
vital to develop cultivars with improved yields, wider adaptation, desirable qualities, and pest and disease 
resistance. Inclusion of other diverse parents (within a limit) in hybridization is supposed to raise the chance of 
obtaining supreme heterosis and give a wide spectrum of variability in segregating groups. Variability and genetic 
diversity are the ultimate law of plant breeding which is a major tool being used in parent selection for effective 
hybridization program [1]. However, its partial geographic choice and intensive breeding has run to a moderately 
narrow genetic basis in present breeding material. The gene pool of best oilseed rape breeding material has been 
further worn by an emphasis on explicit oil and seed quality characters. As a consequence, genetic inconsistency in 
this vital crop is delimited with regard to many types of value for breeding purposes. Approximations of genetic 
diversity in a crop species can contribution in the valuation of germplasm collections as potential gene pools to 
improve the concert of cultivars. Genotyping accessions and assessing the level of genetic diversity inside a 
germplasm group can be of use to plant breeders in a number of ways. With this aim, an attempt was made in the 
present study to analyse genetic diversity among 22 advanced genotypes of B. napus. 
 

2. Materials and Methods 
Twenty two progressive genotypes of B. napus were grown in randomized complete block design with three 

replications.  The advanced lines of Brassica napus L. for the trial were collected from the Department of Genetics 
and Plant Breeding, Sher-e-Bangla Agricultural University (SAU) and three released varieties collected from 
Bangladesh Agricultural Research Institute (BARI) as presented in Table 1. The plot size was 3 m length with two 
rows. Row to row and plant to plant distances were 25 cm and 15 cm, respectively. Seeds were sown in lines in the 
trial plots. Observations were recorded on 10 randomly chosen plants from each plot. Data were collected on Days 
to 1st Flowering, Days to 50% Flowering, Days to 80% flowering, Days to maturity, Plant height (cm), Number of 
primary branches per plant, Number of secondary branches per plant, Siliqua/Plant, Siliqua/length (cm), Number 
of seeds per siliqua, 1000 seeds weight (g) and Seed yield per plant (g). Multivariate analysis viz., Principal 
Component Analysis (PCA), Principal Coordinate Analysis (PCO), Cluster Analysis (CLU) was done by using 
GENSTAT 5 Release 4.1 (PC/Windos NT) software program [2]. 
 

Table-1. List of the 40 Brassica napus L. genotypes used in the experiment with their sources 

Genotype No. Name/Acc No. Source Genotype No. Name/Acc No. Source 

G1. Nap-0717-2 BARI G21. Nap-10014 BARI 
G2. Nap-0733-1 BARI G22. Nap-10012 BARI 
G3. Nap-0762 BARI G23. Nap-0130 BARI 
G4. Nap-08-4 BARI G24. Nap-2012 BARI 
G5. Nap-0837 BARI G25. Nap-2013 BARI 
G6. Nap-0865 BARI G26. Nap-2022 BARI 
G7. Nap-0869 BARI G27. Nap-9906 BARI 
G8. Nap-0876 BARI G28. Nap-9908 BARI 
G9. Nap-0885 BARI G29. Nap-248 BARI 
G10. Nap-205 BARI G30. Nap-2001 BARI 
G11. BARI-8 BARI G31. Nap-9901 BARI 
G12. BARI-13 BARI G32. Nap-9904 BARI 
G13. Nap-10007 BARI G33. Nap-9905 BARI 
G14. Nap-10009 BARI G34. Nap-2057 BARI 
G15. Nap-10015 BARI G35. Nap-2037 BARI 
G16. Nap-10017 BARI G36. Nap-206 BARI 
G17. Nap-10019 BARI G37. Nap-2066 BARI 
G18. Nap-10020 BARI G38. Nap-179 BARI 
G19. Nap-1005 BARI G39. Nap-94006 BARI 
G20. Nap-1007 BARI G40. Nap-108 BARI 

 

3. Result and Discussion 
3.1. Diversity of the Brassica Napus L Lines 

Genetic diversity analysis involves several steps, i.e., estimation of distance between the varieties, clustering 
and analysis of inter-cluster distance. Therefore, more than one multivariate technique was required to represent 
the results more clearly and it was obvious from the results of many researchers Brassica napus L multivariate 
techniques were used.     
 



Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

3 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

3.2. Construction of Scatter Diagram 
Based on the values of principal component scores 2 and 1 obtained from the main component analysis, a two 

dimensional scatter figure (Z1-Z2) using component mark 1 as X-axis and component score 2 as Y-axis was 
constructed, which has been presented in Figure 1. The position of the genotypes in the scatter graph was 
apparently distributed into five groups, which designated that there existed considerable diversity among the 
genotypes. 
 

 
Figure-1. Scattered distribution of 40 Brassica napus L genotypes on principal component score superimposed with clustering 

 

3.3. Principal Component Analysis (PCA) 
D2 analysis through non-hierarchical grouping has taken care of simultaneous disparity in all the characters 

under study. The scattering of genotypes in different clusters of the D2 analysis has tailed similar trend of the Z1 
and Z2 vectors of the main component analysis. The D2 and principal component analysis were found to be 
another method in giving the information about the support of characters towards divergence of rapeseed and 
mustard. 

Generally genetic diversity is related with geographical diversity but the former is not essentially directly 
related with geographical circulation. The genotype within the same cluster while formed specific clusters but 
were collected or originated from different places which indicated the topographical distribution and genetic 
separation did not follow the same trend. In the current study pattern of clustering revealed that genotypes 
originating from the similar country did not form a single cluster. The genotypes originating from dissimilar 
countries were grouped in the similar cluster (Table 3). This indicates that topographical diversity was not related 
to genetic diversity, which might be due to nonstop exchange of genetic materials amongst the countries of the 
world. 

Most important components were computed from the correlation matrix and genotype scores obtained from 
first apparatus and ensuing mechanism with dormant roots greater than the unity donation of the different 
morphological characters towards divergence were discussed from the dormant vectors of the first two most 
important component. The major Component Analysis yielded Eigen values of each major Component axes with 
the first axes entirely bookkeeping for the variation among the genotypes, while three of these with Eigen values 
above unity accounted for 42.77%. The first two major axes accounted for 57.07% of the total variation among the 
12 characters recounting 40 lines (Table 2). Based on major Component axes I and II, a two dimensional chart (Z1-
Z2) of the cultivars are offered in (Figure 1). The scattered diagram discovered that apparently there were mainly 
five clusters.  

In vector I (ZI) obtained from PCA, the significant characters dependable for genetic divergence in the axis of 
discrimination were days to 50% flowering (5.137). Plant height (0.756), secondary branches/plant (0.578) and 
siliqua/plant (0.429). In vector II (Z2), the second axis of discrimination, primary branches per plant (0.711), 
Number of seeds per siliqua (0.031), Siliqua per plant (0.429) and thousand seeds weight were imperative because 
all these characters had optimistic signs. On the other hand days to maturity, primary branches per plant, siliqua 
length, seeds per siliqua, thousand seeds weight and seed yield per plant in the first axis of delineation and days to 
maturity, siliqua length and seed yield per plant in the second axis of delineation had a minor role in the genetic 
divergence because they had productive signs. Leaf length and siliqua per plant in both the vectors had 
constructive signs, which indicated they were the significant component characters having higher involvement to 
the genetic divergence among the materials calculated. It was distincted that three methods gave similar results. 
But factorial makes a distinction and Mahalanobis’s D2 distance methods mandatory collecting data plant by plant, 
while the PCA method obligatory taking data by plots. 
 
 
 
 
 
 
 
 



Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

4 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Table-2. Eigen values and percentage of variation in respect of twelve characters in Brassica napus L 

Principal component axis Eigen values % of total variation accounted for Cumulative percent 

I 5.132 42.77 42.77 
II 1.716 14.3 57.07 
III 1.452 12.1 69.17 
IV 0.965 8.04 77.21 
V 0.756 6.3 83.51 
VI 0.711 5.93 89.44 
VII 0.578 4.81 94.25 
VIII 0.429 3.58 97.83 
IX 0.228 1.9 99.73 
X 0.031 0.26 99.99 
XI 0 0 99.99 
XII 0 0 99.99 

 
Among five clusters, cluster I was unruffled of eight lines: 2,3,4,5,25,34,35  and 38. From the clustering mean 

value (Table 3), it was pragmatic that cluster I fashioned the highest mean for days to first flowering (58.54 days), 
days to 50% flowering (60.75 days), days to 80% flowering (63.50 days), Days to maturity (105.14 days), Plant 
height (107.99 cm), Number of primary branch per plant (3.49), Number of secondary branch per plant (3.49), 
Siliqua per plant (146.19), Siliqua per length (7.49 cm), seed per Siliqua (22.99), thousand seed weight (3.39 gm) 
and seed weight per plant (11.72 gm) (Table 3). Cluster II was composed of eight lines; 6,7,8,9,10,14,15 and 22. 
These genotypes formed the highest mean for seed weight per plant (12.35 gm). Cluster III was constituted of four 
lines; 1,17,19, and 27. The genotypes of this cluster formed the highest mean for Siliqua per length (7.62 cm), seed 
per Siliqua (24.27) and lowest value for Number of secondary branch per plant (3.93) and Siliqua per plant (130.25). 
Cluster IV constituted of nine genotypes, lines; 1,12,13,16,18,20,21,23 and 24. The line produced the highest mean 
for Siliqua per length (7.62 cm), yield per plant (11.89 gm) and lowest value for Plant height (92.55 cm). Cluster V 
constituted of eleven genotypes; line 26,28,29,30,31,32,33,36,37,39 and 40. The genotypes of this cluster produced 
the highest mean values for Plant height (118.63 cm), Number of secondary branch per plant (4.82), Siliqua per 
plant (181.39). Jagadev, et al. [3] showed utmost genetic distance in between cluster III and IV suggesting wide 
diversity in rapeseed and mustard. 
 

Table-3. Distribution of 40 Brassica napusL genotypes in five different clusters 

Cluster Total no. of line Genotype Number Genotype Designation 

I 8 2,3,4,5,25,34,35,38 
Nap-0733-1, Nap-0762, Nap-08-4, Nap-0837, Nap-
2013, Nap-2057, Nap-2037, Nap-179 

II 8 6,7,8,9,10,14,15,22 
Nap-0865, Nap-0869, Nap-0876, Nap-0885, Nap-
205,  Nap-10009, Nap-10015, Nap-10012 

III 4 1,17,19,27 Nap-0717-2, Nap-10019, Nap-1005, Nap-9906 

IV 9 11,12,13,16,18,20,21,23,24 
BARI-8, BARI-13, Nap-10007,Nap-10017,  Nap-
10020, Nap-1007, Nap-10014, Nap-0130, Nap-2012 

V 11 26,28,29,30,31,32,33,36,37,39,40 
Nap-2022, Nap-9908, Nap-248, Nap-2001, Nap-
9901, Nap-9904, Nap-9905, Nap-206, Nap-2066, 
Nap-94006, Nap-108 

 
Table-4. Cluster means for twelve characters of Brassica napus L 

Characters 
Cluster 

I II III IV V 

Days to 1st Flowering 58.54 32.58 39.58 42.89 57.82 
Days to 50% Flowering 60.75 34.58 41.58 44.89 59.82 
Days to 80% flowering 63.50 37.25 44.25 47.56 62.55 
Days to maturity 105.14 85.48 88.38 90.81 102.85 
Plant height(cm) 107.99 90.39 98.69 92.55 118.63 
No. of primary branch/plant 3.49 2.97 3.12 3.25 3.45 
No. of secondary branch/plant 4.20 3.96 3.93 4.16 4.82 
Siliqua/Plant 146.19 157.48 130.25 173.14 181.39 
Siliqua/length (cm) 7.49 7.43 7.62 7.62 7.49 
Seed/Siliqua 22.99 21.76 24.27 22.62 22.99 
Seed wt (1000) gm 3.39 3.33 3.46 3.63 3.53 
wt of plant seed (gm) 11.72 12.35 11.46 11.89 11.68 

 

3.4. Canonical Variety Analysis  
Canonical Variety Analysis was performed to calculate the inter-cluster Mahalanobis's values. Statistical 

distances indicate the index of genetic diversity in the midst of the clusters. The average intra and inter-cluster 
distance (D2) values were offered in Table 5. Consequences indicated that the highest inter-cluster distance was 
observed between II and V (8.145), followed by between I and II (7.767), III and V (7.434).The lowest inter-cluster 
distance was observed between the cluster II and IV (3.917) followed by I and V (3.958), II and III (5.308) 
suggesting a close relationship among these clusters (Figure 2). The inter-cluster distances were larger than the 
intra-cluster distances suggesting wider genetic diversity among the genotypes of dissimilar groups (Figure 2). 
Uddin [4] obtained superior inter-cluster distances in the multivariate analysis in mustard. Khan [5] reported 
alike results in raya. Subordinate intra-cluster distances in all the eight clusters indicated the genotypes within the 
same cluster were strongly allied. 

Conversely, the utmost inter-cluster distance was recorded between cluster II and V (8.145) followed by 
between I and II (7.767), III and V (7.434). Genotypes from these clusters if implicated in hybridization might 



Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

5 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

fabricate a wide range of segregating population, as genetic variation was very discrete among these groups. The 
inter-cluster divergence varied from 0.304 to 0.420 utmost being from cluster V which comprised of eleven 
cultivars of diverse origin, while the minimum distance was pragmatic in cluster III which comprised only four 
lines (Table 5). Alike results were obtained by Anand and Rawat [6] in brown mustard. Verma and Sachan [7] 
observed no parallelism between geographic and genetic diversity. Gupta, et al. [8] showed no correlation between 
geographic and genetic diversity. Mitra and Saini [9] reported no substantiation for any correlation between 
genetic divergence and geographic diversity. Chatterjee and Khare [10] studied a downbeat relationship between 
geographic and genetic diversity. 

Consequences obtained from different multivariate techniques were superimposed in Figure 1 from which it 
might be accomplished that all the techniques gave more or less similar results and one technique supplemented 
and deep-rooted the results of another one. The clustering blueprint of the lines bare that varieties/lines 
originating from the same places did not form a single cluster because of direct selection pressure. It has been 
observed that geographic diversity is not always allied to genetic diversity and therefore, it is not ample as an index 
of genetic diversity. Kumar, et al. [11] premeditated that genetic drift and selection in different environment could 
cause greater diversity than geographic distance. 

Additionally, there is a complimentary replace of seed material among different region as a upshot, the 
characters assemblage that might be associated with fastidious region in nature loose their individuality under 
human obstruction and however, in some cases effect of geographic origin predisposed clustering that is why 
geographic allocation was not the sole criterion of genetic diversity. The free cluster of the lines suggested 
confidence upon directional selection pressure practical for realizing maximum yield in different regions; the nicely 
evolved homeostatic campaign would favour fidelity of the associated characters. This would suggest that it was 
not necessary to choose diverse parents for diverse geographic regions for hybridization. 
 

Table-5. Average intra and inter-cluster distances (D2) for 40 Brassica napusL lines 

Cluster I II III IV V 

I 0.420         
II 7.767 0.384       
III 5.331 5.308 0.414     
IV 6.563 3.917 5.728 0.398   
V 3.958 8.145 7.434 6.116 0.304 

                                           Note: *Bold figures denotes intra-cluster distance. 

 

 
Figure-2. Diagram showing inter-cluster (outside the circle) and intra-cluster (inside the circle) distances of 40 genotypes of Brassica napus L 
 

Plate-1. Photograph showing seed type of the different genotypes of cluster I 

 



Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

6 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Plate-2. Photograph showing plant type of the different genotypes of cluster II 

 

 
Plate-3. Photograph showing plant type of the different genotypes of cluster III 

 

 
Plate-4. Photograph showing plant type of the genotype of cluster IV 

 



Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

7 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

 
Plate-5. Photograph showing plant type of the genotype of cluster V 

 

3.5. Non – Hierarchical Clustering 
The multiplication from co-variance matrix gave non-hierarchical clustering among 40 genotypes. By 

application of non- hierarchical clustering using covariance matrix, the 40 Brassica napus L genotypes were grouped 
into five different clusters. These results confirmed the clustering pattern of the lines according to the Principal 
Component Analysis. So, the results obtained through PCA were confirmed by non-hierarchical clustering. 
Compositions of unlike clusters with their corresponding lines integrated in cluster were presented in Table 3. 
Cluster V had utmost eleven lines followed by cluster I, II, IV, and III, which had eight, eight, nine and four 
genotype correspondingly. Cluster IV self-possessed of five lines; 1,17,19,27. Verma and Sachan [7] reported 12 
clusters; Gupta, et al. [8] five clusters; Khan [5] seven clusters and Srivastava and Singh [12] six clusters in 
rapeseed and mustard. These results deep-rooted the clustering pattern of the genotypes according to the major 
component analysis. 

From the clustering mean value (Table 4), pragmatic that cluster I produced the highest mean for days to first 
flowering (58.54 days), days to 50% flowering (60.75 days), days to 80% flowering (63.50 days), Days to maturity 
(105.14 days), Plant height (107.99 cm), Number of primary branch per plant (3.49), Number of secondary branch 
per plant (3.49), Siliqua per plant (146.19), Siliqua per length (7.49 cm), seed per Siliqua (22.99), thousand seed 
weight (3.39 gm) and seed weight per plant (11.72 gm). Photograph showing seed, Siliqua and plant of the different 
genotypes of this cluster has been offered in Plate 1.  

Cluster II was self-possessed of eight lines; 6,7,8,9,10,14,15 and 22. These genotypes fashioned the highest 
mean for seed weight per plant (12.35 gm). Photograph showing eed, Siliqua and plant of the different genotypes of 
this cluster has been presented in Plate 2. Cluster III was constituted of four lines; 1,17,19, and 27. The genotypes 
of this cluster fashioned the highest mean for Siliqua per length (7.62 cm), seed per Siliqua (24.27) and lowest value 
for Number of secondary branch per plant (3.93) and Siliqua per plant (130.25). Photograph screening seed, Siliqua 
and plant of the different genotypes of this cluster has been existing in Plate 3. Cluster IV constituted of nine 
genotypes, lines; 11,12,13,16,18,20,21,23 and 24. The line formed the highest mean for Siliqua per length (7.62 cm), 
yield per plant (11.89 gm) and lowest value for Plant height (92.55 cm).  Photograph screening seed, Siliqua and 
plant of the different genotypes of this cluster has been offered in Plate 4. Cluster V constituted of eleven 
genotypes; lines; 26,28,29,30,31,32,33,36,37,39 and 40. The genotypes of this cluster formed the highest mean 
values for Plant height (118.63 cm), Number of secondary branch per plant (4.82), Siliqua per plant (181.39). 
Photograph viewing seed, Siliqua and plant of the different genotypes of this cluster has been presented in Plate 5. 
From the class mean value it was observed that all the cluster mean values were more or less alike. The utmost 
range of variability was pragmatic for yield (11.46 g to 12.35 g) among all the characters in five clusters. Cluster II 
integrated mainly early flowering and early maturing genotypes with high yield. To develop high yielding 
varieties/lines, genotypes of this group could be used in hybridization program. 
 

3.6. Selection of Genotypes for Future Hybridization  
Genotypically far-away parents are competent to fabricate higher heterosis. Consequences of the present 

studies indicated noteworthy variation among the genotypes for all the characters studied. Number of Siliqua per 
plant, Siliqua length and seed weight per plant contributed maximum towards yield perfection. Forty Brassica napus 
L genotypes formed five different clusters. PCA and Cluster analysis gave alike results. By and large, diversity was 
influenced by the morphological characters, but not by the distribution of the genotypes, which indicated the 
consequence of consumer preference and growers aptness.  Considering diversity pattern and other agronomic 
performance lines Nap-0837, Nap-0733-1, Nap-2066, Nap-9901, Nap-108 and BARI-8 could be measured suitable 
genotypes for well-organized hybridization in future. Concerning of such diverse lines in crossing curriculum could 
produce enviable segregants. So, more or less divergent genotypes are suggested to use as parents in future 
hybridization program. 
 

4. Conclusion 
Assortment of genetically diverse parents is the most important assignment for any plant breeding actions. For 

that reason, taking into consideration the extent of genetic distance, contribution of character towards divergence, 
magnitude of cluster mean and agronomic performance the following genotypes were promising:  in view of 
diversity pattern and other agronomic performance lines Nap-0837, Nap-0733-1, Nap-2066, Nap-9901, Nap-108 
and BARI-8 might be recommended for future hybridization program. 



Agriculture and Food Sciences Research, 2019, 6(1): 1-8 

8 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 

 

 

References  
[1] G. Bhatt, "Comparison of various methods of selecting parents for hybridization in common bread wheat (Triticum Aestivum L.)," 

Australian Journal of Agricultural Research, vol. 24, pp. 457-464, 1973. Available at: https://doi.org/10.1071/ar9730457. 
[2] P. Digby, M. Galway, and P. Lane, GENSTAT. A second course. Oxford: Oxford Science Publications, 1989. 
[3] P. Jagadev, K. Samal, and D. Lenka, "Genetic divergence in rape mustard," The Indian Journal of Genetics and Plant Breeding, vol. 51, 

pp. 465-467, 1991. 
[4] M. J. Uddin, "Genetic divergence in mustard," Bangladesh Journal of Plant Breeding and Genetics, vol. 7, pp. 23-27, 1994. 
[5] M. N. Khan, "Multivariate analysis in Raya," Applied Biological Research, vol. 2, pp. 169-171, 2000. 
[6] I. Anand and D. Rawat, "Genetic diversity, combining ability and heterosis in brown mustard," The Indian Journal of Genetics and 

Plant Breeding, vol. 44, pp. 226-234, 1984. 
[7] S. Verma and J. Sachan, "Genetic divergence in Indian mustard (Brassica Juncea (L.) Czern & Coss.)," Crop Research (Hisar), vol. 19, 

pp. 271-276, 2000. 
[8] M. Gupta, K. S. Labana, and S. S. Badwal, "Correlation and path co¬efficient of metric traits contributing towards oil yield in 

Indian mustard," presented at the International Rapeseed Congress, Pozan, Poland, 107(En), 1987. 
[9] J. Mitra and H. Saini, "Genetic divergence for yield and its components in toria (Brassica Campestris var. Toria)," International 

Journal of Tropical Agriculture, vol. 16, pp. 243-246, 1998. 
[10] A. Chatterjee and D. Khare, "Multivariate analysis in Niger," Research and Development Reporter, vol. 8, pp. 111-114, 1991. 
[11] C. H. M. V. Kumar, V. Arunachalam, and P. S. K. Rao, "Ideotype and relationship between morpho-physiological characters and 

yield in Indian mustard (B. Juncea)," Indian Journal of Agricultural Science, vol. 66, pp. 14-17, 1996. 
[12] M. K. Srivastava and R. P. Singh, "Genetic divergence in Indian mustard," Crop Research Hisar, vol. 20, pp. 555-557, 2000. 

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. 
Any queries should be directed to the corresponding author of the article. 
 


