INHERITANCE OF MORPHO-YIELD AND SEED QUALITY TRAITS IN ...prr.hec.gov.pk › jspui › bitstream › 123456789 › 7859 › 1 › Ibni Amin kh… · INHERITANCE OF MORPHO-YIELD AND

INHERITANCE OF MORPHO-YIELD AND SEED QUALITY

TRAITS IN BRASSICA NAPUS UNDER IRRIGATED AND RAINFED

CONDITIONS

BY

IBNI AMIN KHALIL

A dissertation submitted to the University of Agriculture Peshawar in partial fulfillment of the

requirements for the degree of

DOCTOR OF PHILOSOPHY IN AGRICULTURE

(PLANT BREEDING AND GENETICS)

DEPARTMENT OF PLANT BREEDING AND GENETICS

FACULTY OF CROP PRODUCTION SCIENCES

THE UNIVERSITY OF AGRICULTURE, PESHAWAR

KHYBER PAKHTUNKHWA-PAKISTAN

JANUARY, 2016

ACKNOWLEDGEMENT

I have no words to express the deepest sense of gratitude to the Almighty "ALLAH",

the most merciful, the most beneficent and the source of all knowledge and wisdom endowed

to mankind, who enable me to complete this research and to contribute to the Noble field of

knowledge. Countless salutations to be upon the Holly Prophet "Hazrat Muhammad"(SAW),

the most perfect among and ever born on the earth, who is forever a torch of guidance and

knowledge for humanity as a whole.

I am also grateful to my supervisor, Prof. Dr. Raziuddin Department of Plant Breeding

and Genetics, the University of Agriculture Peshawar for his constant encouragement, helpful

suggestions and guidance during my scholastic life. His critical insight, consistent advises,

constructive criticism, personal interest and supervision, generated the vigor in me to complete

this task.

I am also highly indebted to Meritorious Prof. Dr. Hidayat ur Rahman, Chairman,

Department of PBG, my teachers and all members of the department and PBG field staff for

their cooperation and encouragement throughout my research.

I would like to extend my deepest gratitude and profound regards to Prof. Dr. S. Safdar

Hussain Shah, Institute of Biotechnology and Genetic Engineering for his help during my PhD

research.

The present work was a part of PhD research which was financially supported by Higher

Education Commission of Pakistan under Indigenous PhD fellowship program which is highly

acknowledged.

I am much thankful to Dr. Wikai Yan of Agriculture and Agri-Food Canada for

providing GGE biplot software for data analysis and his informative publications on

interpreting the biplot that helped me a lot.

At last I am very much thankful to Gul Ghani Afridi, Fahim Ullah, M. Ali, Khilwat

Afridi, M. Ishaq, Sheraz khan, and all other friends and colleagues for their support in any

respect during the completion of this task.

Finally, I would like to thank my parents, sister, brothers and my wife for their sacrifices,

understanding and being constant source of prayers and inspiration which enabled me to

complete this research successfully.

Ibni Amin Khalil

TABLE OF CONTENTS

Chapter No. Title Page No.

List of Tables 6

List of Figures 19

Abstract 21

I. INTRODUCTION 1

II. REVIEW OF LITERATURE 5

2.1. Combining Ability Studies in Brassica 5

2.2. Generation mean analysis 10

2.3. Drought tolerance 12

2.4. Inheritance studies 14

III. MATERIALS AND METHODS 19

3.1. COMBINING ABILITY STUDIES IN PARENTS AND F1 19 CROSSES

3.1.1. Genetic Materials 19

3.1.2. Development of F1 crosses 19

3.1.3. Evaluation of F1 generation 20

3.1.4. Data recorded on morpho-yield and seed quality traits 21

3.1.5. Statistical analysis of parental and F1 data 22

3.1.6. Combining ability analysis 22

3.2. GENERATION MEAN ANALYSIS 23

3.2.1. Development of F2 and back cross (BC11 & BC12) generations 23

3.2.2. Evaluation of generations under rainout shelter 23

3.2.3. Physiological Traits 24

3.2.4. Evaluation of generations under field condition 25

3.2.5. Data recording on various traits 25

3.2.6. Statistical analysis of various generations 26

3.2.7. Generation Means Analysis 26

3.2.8. Correlation among traits 27

IV. RESULT AND DISCUSSION 28

4.1. Analysis of variance, mean performance and combining 28

ability

4.2. Generation Mean Analysis 55

4.2.1. Inheritance of drought stress related traits at seedling stage 55

4.2.2. Correlation among RWC, Proline and Chlorophyll content 66

4.2.3. Inheritance of morpho-yield traits under field condition 75

4.2.4. Relationship among various traits 102

V. SUMMARY 126

Conclusions 132

LITERATURE CITED 133

6

I. LIST OF TABLES

Table No. Title Page No.

3.1. Major characteristics of parental genotypes used in combining ability

2

0

studies………………………………………………………...............

3.2. Analysis of variance format for parents and F1 crosses evaluated

during 2011-12………………………………………………………... 22

3.3. Schematic representation of back cross generations development…… 23

3.4. Major characteristics of experimental site and screening season.….. 25

3.5. Analysis of variance format for combined analysis across two

2

6

environments………………………………………………………..…

3.6. Analysis of variance format for generations evaluated in individual

2

6

environment…………………………………………………………...

4.1. Mean squares for various morphological and yield associated traits in

7

4

5

parents and F1 crosses evaluated during 2011-12……………………

4.2. Mean squares for seed yield and oil quality traits in parents and F1 45 crosses

evaluated during 2011-12……………………………………..

4.3. Mean values for various morpho-yield and oil quality traits of

4

6

parental genotypes evaluated during 2011-12………………………..

4.4. Mean values for various traits in F1 crosses evaluated during 2011- 47

12……………………………………………………………………...

4.5. Mean values for various traits in F1 crosses evaluated during 2011- 48

12……………………………………………………………………...

4.6. Analysis of variance for 20 brassica generations evaluated for relative

67 w

ater content across two different environments……………………

4.7. Mean values for relative water content and percent reduction of 20

6

7

genotypes across two different environments………………………..

4.8. Combine analysis of variance for relative water content of various

generations derived from four crosses evaluated across irrigated and

6

8

8

rainfed conditions……………………………………………………..

4.9. Mean squares from analysis of variance for relative water content of

68 v

arious generations evaluated under two different environments…..

4.10. Mean performance of generations derived from four crosses for

68

relative water content under irrigated and rainfed conditions……….

4.11. Estimates of genetic effects for relative water content in different

6

9

crosses under different environments………………………………..

4.12. Analysis of variance for 20 brassica generations evaluated for proline

6

9

content across two different environments…………………………..

4.13. Mean values for proline content (µMol g-1) and percent increase of 20

7

0

genotypes across two different environments…………………….

4.14. Combine analysis of variance for proline content of various


7

0

9


4.15. Mean squares from analysis of variance for proline content of various

71

generations evaluated under irrigated and rainfed conditions……….


71 p

roline content under irrigated and rainfed conditions………………

4.17. Estimates of genetic effects for proline content in different crosses

7

1

under different environments…………………………………………

4.18. Analysis of variance for 20 brassica generations evaluated for

72 C

hlorophyll content across two different environments…………….

4.19. Mean values for Chlorophyll content (mg cm-2) and percent increase

72 o

f 20 genotypes across two different environments………………… .

4.20. Combine analysis of variance for Chlorophyll content of various


7

3


4.21. Mean squares from analysis of variance for Chlorophyll content of

various generations evaluated under irrigated and rainfed

10

7

3

conditions……………………………………………………………..


73 C

hlorophyll content under irrigated and rainfed conditions…………

4.23. Estimates of genetic effects for Chlorophyll content in different

crosses under different environments and pooled over

7

4

environments…………………………………………………………..

4.24. Analysis of variance for days to 50% flowering of 20 brassica

generations evaluated across irrigated and rainfed

104

conditions……………………………………………………………...

4.25. Combine analysis of variance for days to 50 % flowering of various

generations derived from four crosses evaluated across two different

1

04

environments…………………………………………………………

4.26. Mean squares from analysis of variance for days to 50 % flowering

regarding various generations evaluated under irrigated and rainfed

1

04

11

conditions……………………………………………………………...

4.27. Mean values for days to 50% flowering of various generations

derived from four crosses under irrigated and rainfed

1

05

conditions……………………………………………………………...

4.28. Estimates of genetic effects for days to 50% flowering in different

1

05

crosses under irrigated and rainfed conditions……………………….

4.29. Analysis of variance for plant height of 20 brassica generations

1

06

evaluated across two different environments………………………..

4.30. Combine analysis of variance for plant height of various generations

derived from four crosses evaluated across two different

1

06

environments…………………………………………………………..

4.31. Mean squares from analysis of variance for plant height of various

106 g

enerations evaluated under irrigated and rainfed conditions……….

4.32. Mean values for plant height of various generations derived from four

107 c

rosses under irrigated and rainfed conditions…………………

12

4.33. Estimates of genetic effects for plant height in different crosses under

1

07

irrigated and rainfed conditions………………………………………

4.34. Analysis of variance for primary branches plant-1 of 20 brassica

108 g

enerations evaluated across two different environments……………

4.35. Combine analysis of variance for primary branches plant-1 of various


1

08

environments…………………………………………………………..

4.36. Mean squares from analysis of variance for primary branches plant-1

of various generations evaluated under irrigated and rainfed

1

08

conditions……………………………………………………………...

4.37. Mean values for primary branches plant-1 of various generations

derived from four crosses under irrigated and rainfed

1

09

conditions……………………………………………………………..

4.38. Estimates of genetic effects for primary branches plant-1 in different

1

09

13


4.39. Analysis of variance for pods on main raceme of 20 brassica

110 g

enotypes evaluated across two different environments…………….

4.40. Combine analysis of variance for pods on main raceme of various


1

10


4.41. Mean squares from analysis of variance for pods on main raceme of


1

10

conditions……………………………………………………………...

4.42. Mean values regarding pods on main raceme of various generations of

111 f

our crosses under irrigated and rainfed conditions……………....

4.43. Estimates of genetic effects for pods on main raceme in different

1

11


4.44. Analysis of variance for pod length of 20 brassica genotypes

112 e

valuated for across two different environments…………………….

4.45. Combine analysis of variance for pod length of various generations

derived from four crosses evaluated across irrigated and rainfed

14

1

12

conditions……………………………………………………………...

4.46. Mean squares from analysis of variance for pod length of various

112 g


4.47. Mean values for pod length of various generations derived from four

1

13


4.48. Estimates of genetic effects for pod length in different crosses under

113 d

ifferent environments and pooled over environments……………..

4.49. Analysis of variance for for seed pod-1 of 20 brassica genotypes

1

14

evaluated across two different environments………………………..

4.50. Combine analysis of variance for seed pod-1 of various generations


1

14

conditions………………………………………………………….......

4.51. Mean squares from analysis of variance for seed pod-1 of various

114 g


4.52. Mean values for seed pod-1 of various generations derived from four

15

1

15


4.53. Estimates of genetic effects for seed pod-1 in different crosses under

115 d

ifferent environments and pooled over environments……………….

4.54. Analysis of variance for 1000-seed weight of 20 brassica genotypes

1

16

evaluated across two different environments………………………….

4.55. Combine analysis of variance for 1000-seed weight of various


1

16


4.56. Mean squares from analysis of variance for 1000-seed weight of


1

16

conditions……………………………………………………………...

4.57. Mean values for 1000-seed weight of various generations derived

117

from four crosses under irrigated and rainfed conditions……………..

4.58. Estimates of genetic effects for 1000 seed weight in different crosses

16

117

under different environments and pooled over environments………...

4.59. Analysis of variance for seed yield plant-1 of 20 brassica generations

1

18


4.60. Combine analysis of variance for seed yield plant-1 of various


1

18


4.61. Mean squares from analysis of variance for seed yield plant-1 of


1

18

conditions……………………………………………………………...

4.62. Mean values for seed yield plant-1 of various generations derived

119

from four crosses under irrigated and rainfed conditions……………..

4.63. Estimates of genetic effects for seed yield plant-1 in different crosses

119

under different environments and pooled over environments………...

17

4.64. Analysis of variance for 20 brassica generations evaluated for oil

1

20

content across two different environments……………………………

4.65. Combine analysis of variance for oil content in various generations


1

20

conditions………………………………………………………………

4.66. Mean values for oil content of various generations derived from four

1

20

crosses under irrigated and rainfed conditions………………………

4.67. Estimates of genetic effects for oil content in different crosses pooled

1

21

over environments……………………………………………………..

4.68. Analysis of variance for 20 brassica generations evaluated for

121 g

lucosinolate content across two different environments……………...

4.69. Combine analysis of variance for glucosinolate content of various


18

1

21

rainfed conditions……………………………………………………...

4.70. Mean squares from analysis of variance for glucosinolate content in various

generations evaluated under two different

1

22

environments…………………………………………………………..

4.71. Mean values for glucosinolate content of various generations derived

122 f

rom four crosses under irrigated and rainfed conditions……………

4.72. Estimates of genetic effects for glucosinolate content in different

1

22

crosses under different environments………………………………….

4.73. Analysis of variance for erucic acid in 20 brassica genotypes

1

23


4.74. Combine analysis of variance for erucic acid of various generations


1

23

conditions……………………………………………………………....

4.75. Mean values for erucic acid in various generations derived from four

19

123 c

rosses under irrigated and rainfed conditions………………………...

4.76. Estimates of genetic effects regarding erucic acid in different crosses

1

24

pooled over environments……………………………………………..

II. LIST OF FIGURES

Figure No. Title Page No.

4.1. Biplots based on days to 50% flowering data explaining combining ability

and specific cross combinations in brassica

genotypes………………………………………………………….... 49

4.2. Biplots based on plant height data explaining combining ability and specific

cross combination in brassica genotypes……………... 49

4.3. Biplots based on primary branches per plant data explaining combining ability and

specific cross combination in brassica

genotypes…………………………………………………………… 50

4.4. Biplots based on pods on main raceme data explaining combining ability and

specific cross combination in brassica genotypes…….. 50

4.5. Biplots based on pod length data explaining combining ability and specific cross

combination in brassica genotypes………………….. 51

4.6. Biplots based on seeds per pod data explaining combining ability and specific

cross combination in brassica genotypes……………. 51

20

4.7. Biplots based on 1000 seed weight data explaining combining ability and specific

cross combination in brassica genotypes…….. 52

4.8. Biplots based on seed yield per plant data explaining combining ability and

specific cross combination in brassica genotypes…….. 52

4.9. Biplots based on oil content data explaining combining ability and specific cross

combination in brassica genotypes……………. 53

4.10. Biplots based on glucosinolates data explaining combining ability and specific

cross combination in brassica genotypes……………. 53

4.11. Biplots based on erucic acid data explaining combining ability and specific cross

combination in brassica genotypes………………….. 54

4.12. Genotype by trait biplot for relationship among Relative water content (RWC),

Proline content (Pro) and Chlorophyll content (Chl) under irrigated (I) and

drought stress (D)…………………... 74

4.13. Biplot for genetic correlation among various morpho-yield, oil quality and

physiological traits under irrigated condition………… 124

4.14 Biplot for genetic correlation among various morpho-yield, oil quality and

physiological traits under rainfed condition…………. 125 INHERITANCE

OF MORPHO-YIELD AND SEED QUALITY TRAITS IN

BRASSICA NAPUS UNDER IRRIGATED AND RAINFED CONDITIONS

Ibni Amin Khalil and Raziuddin

Department of Plant Breeding and Genetics

Faculty of Crop Production Sciences

The University of Agriculture, Peshawar-Pakistan

January, 2016

21

III. ABSTRACT

Pakistan has made tremendous progress in majority of the food crops however

country is suffering from deficit of quality edible oil due to unavailability of high yielding

cultivars and deficit of irrigation water. This situation demands the development of high

yielding and drought tolerant oilseed cultivars. During a breeding program for improved

cultivars, the knowledge of combining ability and gene action is important. Therefore,

this study was undertaken to examine combining ability and inheritance pattern of

essential characters in Brassica napus under irrigated as well as rainfed conditions at the

University of Agriculture Peshawar, Pakistan. During crop season 2010-11, eleven

Brassica napus advance lines were crossed with four genotypes following line × tester

matting design. The resultant 44 F1 crosses were planted in the field along with their

parental genotypes for evaluation during crop season 2011-12. Data obtained regarding

morpho-yield and oil quality traits were graphically analyzed for combining ability

among genotypes following GGE-biplot methodology to identify best combiners. On the

basis of performance, two testers and two lines along with their four crosses were selected

and forwarded to develop their F2s, BC11 and BC12 during 2012-13. The resultant

generations were evaluated under irrigated and water deficit conditions under rainout

shelter as well as field condition during 2013-14. Inheritance pattern of various important

traits via generation mean analysis was studied.

The results obtained from parental and F1 crosses data indicated that GCA effects

were comparatively higher than SCA effects for days to 50% flowering, primary braches

plant-1, number of pods on main raceme, 1000 seed weight, seed yield plant-1, erucic acid

and glucosinolate content, indicated the importance of additive type of gene action for

the expression of these traits in the present set of genotypes. Desirable negative GCA

was depicted by L-6, T-2 and T-3 for days to flowering. Both GCA and SCA were found

important with predominant role of SCA for plant height where parental lines; L-3, L-4,

L-6, L-7 and L-8 and tester T-4 showed positive GCA effects while cross combinations

(L-8 × T-2), L-3 × (T-4 and T-1) and (L-7 and L-6) × T-3 were identified outstanding.

For primary branches per plant L-6, L-7, T-1 and T-3 were found best general combiners.

Regarding number of pods on main raceme line L-4 and L-7 produced good combinations

22

with testers T-1 and T-2 whereas, L-6 and L-8 resulted in superior hybrids with tester T-

3 and T-4. For 1000-seed weight, L-6, L-7, L8, T-1 and T-3 showed maximum positive

general combining ability.

For seed yield plant-1 lines L-6 and L-7 were identified as best specific combiners

with T-3 and T-4 respectively. Additive genetic control mechanism was found more

important in controlling oil content in the present set of genotypes. Among parents, L-6,

L-7, L-4, T-1 and T-4 were best general combiners for oil content. For erucic acid and

glucosinolate content, parental genotypes (L-6, L-7, L-5 and L-8) depicted desirable

negative GCA. These lines also produced the most desirable cross combinations

especially with tester T-1 and T-2. Based on the results obtained from combining ability

studies of important traits, two lines, L-6 and L-7 and two testers, T1 and T-3 were

identified as the most promising parental genotypes. Therefore, these four parents and

their resultant four F1 crosses were used in the following season (201213) to develop four

F2, four BC11 and four BC12 generations. The resultant generations were evaluated under

irrigated and rainfed conditions for inheritance studies via generation mean analysis

approach at seedling and whole plant stage during crop season 2013-14.

Inheritance studies at seedling stage explored both additive and non-additive type

of gene action along with non-allelic interaction for relative water content. Minimum

reduction in relative water content due to drought stress was observed in parental

genotype L-7 and T-3. Additive type of gene action under irrigated as well as rainfed

conditions was observed for proline content. Maximum increase in proline content in

response to drought stress was observed in parental genotypes L-7 and T-1 and their

segregating generations. Overall, dominance type of gene action along with dominance

× dominance epistasis was involved in controlling chlorophyll content. Least reduction

in chlorophyll content was observed in parental genotype T-3 and in segregating

generation of L-7 × T-3. The Genotype × trait biplot explored strong and positive

relationship of proline and chlorophyll content was observed with seed yield and

associated traits under irrigated as well as drought stress.

23

Inheritance study under field conditions for morpho-yield and oil quality traits

revealed that additive type of gene actions along with epistasis were involved in the

expression of seed yield plant-1, glucosinolates and erucic acid content. Dominance type

of gene action along with epistasis was mostly involved in controlling days to flowering,

Pod length and seed pod-1, 1000 seed weight and oil content. Dominance type of gene

action was found for primary branches palnt-1 except two L-6 × T-1 and L7 × T-3 under

rainfed condition. For pods on main raceme, in most of the crosses dominance type of

gene action was observed except L-7 × T-1 under rainfed condition, which depicted

additive type of gene action. Simple selection in early generation would be effective for

traits controlled by additive types of genes whereas selection should be delayed till

advance generation for traits controlled by dominance type of genes. Moreover a change

in magnitude of gene action was observed for plant height with a change from normal to

rainfed condition. Under such circumstance separate selection criteria should be followed

for each environmental condition.

Regarding high seed yield per plant and low erucic acid the F2 generation of L-7

× T-1 might be used for selection of potential segregants. For low erucic acid and

glucosinolates having additive type of gene action, the segregating generations of cross

combination L-6 × T-1 might have potential segregants for early generation selection.

For incorporation of drought tolerance and high seed yield both proline and chlorophyll

content can be used as a selection criterion.

24

1 INTRODUCTION

Since its inception, Pakistan has made significant improvement in all sectors of

life including agriculture. Pakistan‟s economy is predominantly agriculture based

however it still imports a huge amount of edible oil to fulfill domestic requirements. The

import bill of edible oil during 2013-14 was more than 2 billion US$ (Pak. Econ. Survey

2014-15). This bill is continuously increasing with rise in population and changes in

dietary habits.

In Pakistan, brassica is the second most important source of oil after cotton.

Rapeseed and mustard group is one of the major contributors among traditional oilseed

crops used in the country (Ali and Mirza, 2005). Brassica was cultivated on an area of

198 thousand hectares which produced 183 thousand tons of seed. The national average

seed yield was 924 kg ha-1. Total edible oil availability from all sources in the country

during 2014-15 was 2.3 million tons. In which, only 0.55 million tons were produced

domestically from all other oilseed sources, whereas the remaining was imported (Pak.

Econ. Survey, 2014-15). In the province Khyber Pakhtunkhwa, brassica was planted on

an area of 18 thousand hectares which produced a total of about 07 tons seed with an

average seed yield of 389 kg ha-1 (Develop. Stat. KP, 2014-15).

Most of the varieties from the rapeseed and mustard group are having high content

of erucic acid in the seed oil and glucosinolates in the seed cake which impose negative

impact on human as well as animal health. However, rapessed/canola type of cultivars

has a benefit over other vegetable oils because they contain a very less quantity of

saturated fatty acids. Moreover, they contain poly-unsaturated fatty acids in a moderated

quantity. Generally, canola type of brassicas produces seed which contain < 2% erucic

acid in oil whereas < 30 μMol g-1 glucosinolates in meal. Overall they contain lower level

(only 6%) of saturated fats as compare to other oilseed crops (Kaushik 1998). Moreover,

canola oil also contains a high quantity of un-saturated fats, which is mostly comprised

of both type of fatty acids (i.e. mono and poly-unsaturated), and as a result this makes

canola a preferred cooking oil. In this regard researchers are trying to improve the

25

situation by manipulating genetics of Brassica species for oil and meal consumption.

Incorporation of the other desirable features such as, large seeds, more seed per pod, early

maturity and shattering resistance are also of prime importance.

Seed yield of Brassica in Pakistan is lower than developed countries because this

crop is mostly grown on marginal lands. Other major limiting factors for low seed and

oil yield of Brassica in the country are, ever increasing population, expanding

urbanization, biotic and abiotic factors (Dutta et al., 2005; Grover and Pental 2003; Ullah

et al., 2012). Prolonged and irregular drought stress and unavailability of high yielding

drought tolerant genotypes are also responsible for lower rapeseed yields. Further,

drought is a serious problem which negatively affects all crops performance including

rapeseed mostly grown in dry land regions.

Plants under field conditions are always exposed to numerous environmental

factors. Increase or decrease in these factors from the optimal levels exerts adverse effects

on plant performance. Seed yield being an important trait is highly vulnerable to drought

stress. Crop exposed to drought stress during the reproductive stage even for shorter

period of time can adversely affect the seed yield (Ahmadi and Bahrani, 2009). Drought

stress is the cumulative effect resulted from the interaction of genotype with duration and

intensity of drought stress (Robertson and Holland, 2004). Moreover, the weather

condition and growth stage of the crop also play important role during drought stress

period. Shortage of water has adverse effect on different stages of plant growth especially

during flowering and seed setting. Insufficient availability of water causes disturbance in

metabolic and physiological functions of plant as well as it reduces the chlorophyll

contents (Din et al. 2011). Drought stress coupled with high temperature also reduces

crop yield because these two factors negatively affect both source and sink for assimilates

(Mendham and Salisbury, 1995).

Plants can cope with drought stress through genetic and adaptive mechanisms

such as escape, avoidance and tolerance to drought. Drought escape is the competency of

the plant to complete its life span before drought becomes a serious limiting factor

(Arraudeau, 1989). Earliness and maturity are not true resistance mechanisms but help

26

crops to escape from drought. Since, drought stress is a major problem of most arid

regions therefore tolerance in the crop plants against drought stress has always been given

great importance. Moreover, tolerance is always considered as important breeding

strategy for coping stresses (Talebi 2009).

During a breeding program for improved cultivars, the knowledge of combining

ability and gene action is important. General combining ability (GCA) is the average

performance of a parental line in a series of cross combinations, whereas specific

combining ability (SCA) is the performance of parental genotypes in a specific cross

combination (Sprague and Tatum, 1942). Both general combining ability (GCA) and

specific combining ability (SCA) variances are related to different gene action involved

in the expression of certain trait. The GCA variance includes the additive component of

the total variance, whereas SCA includes the non-additive component which further

includes dominance and epistatic components (Malik et al., 2004).

For estimation of GCA and SCA components, Line × tester design is commonly

used. During line by tester analysis, the total variation is distributed into components i.e.

variation among male parents, variation among female parents and variation due to

interaction of male and female parents (Singh and Narayanan, 1993). Using this line by

tester scheme, and other genetic designs like diallel analysis for GCA and SCA effects

(Griffing 1956) of various seed yield and other yield associated traits has been already

reported in rapeseed (Wang et al., 2007). Various other breeders have used line × tester

analysis for the genetic analysis of morphological traits, estimation of GCA and SCA,

evaluation of gene action and heterosis in Brassica napus (Leon 1991; Thakur and

Sagwal, 1997; and Rameeh 2012), Sunflower (Khan et al., 2009), wheat (Saeed et al.,

2001), cotton (Panhwar et al., 2008), Sorghum (Mohanraj et al., 2006), and in pea

(Ceyhan et al., 2008).

Conventional line × tester design was proposed by Kempthorne (1957) which

provides information concerning general combining ability of parental genotypes and

specific combining ability of their cross combinations. However, Yan and Hunt (2002)

developed new GGE biplot software for the analysis of line × tester data. This GGEbiplot

27

graphically demonstrates the combining ability, heterotic studies and correlation among

genotypes. The GGE biplot methodology is applicable to all sorts of two-way data that

assume a line by tester data structure (Yan and Hunt, 2002). In comparison with the

Griffing approach of combining ability analysis, the biplot approach has two advantages.

Firstly, the graphical presentation of the data enhances the ability to understand the

patterns of the data and secondly it is more informational. Similarly, the generation means

analysis approach proposed by Hayman (1958) is also used for inheritance studies.

During this approach, the total genetic variance is distributed into various genetic

components i.e. additive, dominance and epistatic variance (Suzuki et al., 1981).

Following the generation mean approach, inheritance pattern of yield and yield associated

traits has been widely studied in Brassica (Taj and Khan, 2000; Prasad et al., 2001; Ghosh

et al., 2002; Rishipal and Kumar, 1993; Cheema and Sadaqat, 2004).

The demand for edible oil is increasing due to increase in population and changes

in nutritional behaviors. Since, Pakistan is suffering from deficit of quality edible oil and

irrigation water therefore there is a dire need to develop high yielding drought tolerant

canola type of Brassica napus. Keeping in view the current scenario, the present study

was carried out with the objectives to:

i. study combining ability among local and introduced genotypes of Brassica

napus.

ii. study the pattern of inheritance for important traits in Brassica napus under

normal and rainfed conditions.

iii. identify traits related to drought tolerance in Brassica napus.

iv. identify potential segregants in various generations for drought tolerance and

seed quality traits.

28

IV. REVIEW OF LITERATURE

To develop high yielding genotypes with its suitability to perform better under both

irrigated and rainfed environments, it is essential to have higher genetic diversity

available in rapeseed germplasm. Moreover, the knowledge about variability, combining

ability and mode of inheritance of traits are pre requisites for designing efficient rapeseed

breeding program. In the present study, the genetic studies were concluded from

combining ability analysis from line × tester data via GGE-biplot approach and

generation mean analysis approach. A review of literature on various aspects is given

below.

2.1. Combining Ability Studies in Brassica

Sprague and Tatum (1942) for the first time established the perception of combining

ability. They partitioned the combining ability into two type‟s i.e. general combining

ability (GCA) and specific combining ability (SCA). Furthermore, they described GCA

as the average performance of a line in a series of cross combinations whereas, SCA as

the performance of genotypes in a specific cross combinations. For inbred lines to be

tested in hybrid combination, both GCA and SCA effects are of prime importance. GCA

effects are primarily due to addítive and additíve × additivé variances whereas SCA

effects are attributed to variances due to dominance and epistatic deviations (Falconer

1981). For evaluation of combining ability of a large number of lines, Kempthorne (1957)

suggested the of line × tester analysis which is equivalent to Design II of Comstock et al.

(1949) where the covariance of half and full sibs were related to the variances due to

general and specific combining abilities.

Muhammad et al. (2014) evaluated (4 × 4) full diallel crosses of Brassica napus for

combining ability and heritability studies. Significant differences were observed for

height of plant, main shoot length, siliquae length and days to flowering. Parental

genotype G-6 depicted desirable GCA effects for days to flowering, plant height and pod

length. Overall, importance of both additive and non-additive.types of genetic effects

were revealed. Moreover, genotype G-9 exhibited good GCA effects for main raceme

29

length. Cross G-2 × G-4 showed good SCA effects for plant height and pod length. Broad

sense heritability estimates for days taken to blooming, length of the raceme, plant height

and pod length were 0.26, 0.52, 0.65 and 0.73, respectively. They concluded that high

GCA and high heritability estimates indicated the usefulness of selection for the

improvement of various traits.

Nasim and Farhatullah (2013) investigated combining ability in complete diallel crosses

(6 × 6) of Brassica rapa. They observed significant differences for oleíc acid and oil

content in the tested exotic elite genotypes. Moreover, they also found that mean squares

due to GCA effects were non-significant for oil, oleic acid, protein and glucosinolate

content. However, the SCA and RCA components of variation were significant for

protein and glucosinolate content. Both SCA and RCA effects were nonsignificant for

oleic acid and oil content. They concluded that non-additive genetic control is

accountable for the quality expression of protein and glucosinolates; moreover oleic acid

content was primarily controlled by maternal effects.

Rameeh (2012) performed combining ability analysis, heterosis (high parent) and

heritability (narrow sense) for plant height, seed yield and yield components using

line×tester mating design in spring rapeseed (Brassica napus) cultivars. As a result the

line×tester effect for pods/ plant and seed yield was found significant. It indicated that

non additive genetic effects played important role in the expression of these economically

important traits. Significant mean squares regarding parental germplasm vs hybrids

combinations indicated that average heterosis were also significant for all the studied

traits except seeds per pod. High heritability (narrow sense) estimates for all the traits

except seeds/ pod exhibited additive genetic mechanism was of a prim importance for

these traits except seeds per siliquae. The author concluded that for majority of the traits

for determining better cross combinations the parent heterosis effect was more effective

than SCA effect except pods per plant.

Patel et al. (2012) evaluated ten diverse elite parental lines and their 45 hybrid developed

via half diallel mating system for nine quantitative and quality traits in Indian mustard.

They determined combining ability, heterosis (mid parent and high parent) for the traits

30

studied. On average performance basis, for seed yield per plant the hybrid (RK-

9501×GM-2) and its first parental genotype (RK-9501) exhibited outstaning

performance. For seed yield/plant, two crosses showed a considerable degree of desirable

and significant heterosis over mid parent (MP) and better parent (BP). Both general

combining ability (GCA) and specific combining ability (SCA) for all traits studied were

found significant. Since both GCA and SCA effects were significant and higher in

magnitude therefore indicated both additive and non-addítive gene interactions for the

inheritance of various studied traits. They concluded that for seed yield plant-1 hybrid

breeding might be used as an suitable methodology since nonadditive gene action was

predominant for this trait. Moreover, they mentioned that both additive and non-additive

gene actions were found during the present research, therefore a bi-parental mating

among appropriate genotypes using reciprocal recurrent selection method may be

employed for cultivar development.

Azizinia (2012) carried out combining ability studies in brassica. For this purpose

a set of 56 diallel F1 hybrids (Direct and reciprocal crosses) with their parents were

evaluated. Data were recorded for several agronomic and yield associated traits i.e. plant

height, number of lateral branches, number of pod per main branch, number of seed pod-

1, 1000 seed weight, seed yield and oil content. All the genotypes varied significantly for

all of the studied traits except for seed number per plant for which nonsignificant

differences among the genotypes were observed. Moreover both GCA and SCA

components were found significant for oil content, 1000 seed weight and seed yield. For

oil content effects of reciprocal were also found significant.

Muhammed (2011) estimated general, specific combining ability variances and

potential heterosis. For this purpose seven parental lines and their resultant 21 F1 crosses

of Brassica carinata were evaluated. As a result of the study, standard heterosis ranged

from -8.22% (harvest index) to 191.57% (number of pods per plant), whereas for seed

yield per plant trait it ranged (-16.64 to 66.09%). Moreover, both type of gene actions

(additive and non-additive) were found responsible for the expression of maturity trait

(days to 50% flowering), morphological traits (plant height, length of main shoot), yield

31

and yield associated traits (pod length, number of primary and secondary branches, seed

yield per plant, biological yield, harvest index), and percent oil content. Furthermore,

days to 90% maturity, seeds per pod and thousand seeds weight were found to be

controlled by additive type of genes whereas pods/ plant was identified to be controlled

by non-additive type of genes actions.

Turi et al. (2011) evaluated 8×8 full diallel crosses in Brassica juncea L., genotypes.

They determined combining ability for seed yield and its associated traits. As a result of

data analysis, they found that general combining ability (GCA) mean squares were

significant for seed yield plant-1 and 1000 seed weight, whereas for pods plant-1, pod

length and seeds pod-1 GCA was non-significant. Moreover, SCA and RCA mean squares

were found significant for majority of the studied traits except seeds pod1. However, both

SCA and RCA were smaller in magnitude as compared to GCA effects for pods plant-1,

seed yield plant-1 and pod length. This further designated that additive type of gene action

was responsible for the expression of these traits. Likewise, for seed pod -1 and 1000 seed

weight RCA effects were proved to be greater in magnitude as compare to GCA and

SCA. This also confirmed that maternal effects were actively involved in the expression

of these traits. Therefore these traits need due attention during the selection process.

Since, their results revealed the importance of additive as well as non-additive genetic

variability hence, suggested the use of a joint breeding strategy for efficient utilization of

both additive as well as non-additive genetic components of variations.

Lohia (2008) studied combining ability while evaluating seven parents and their 21

direct F1 hybrid developed through diallel mating in Indian mustard. Data were recorded

regarding maturity traits (days to flower initiation, days to maturity), morpholocial traits

(secondary branches, plant stature, length of main raceme), yield associated traits (1000

kernel weight, number of pods/plant, seed yield/plant) and oil content. Analysis of the

data revealed that both GCA and SCA effects were significant. It was also found that all

the studied traits were under the control of both additive and non-additive type of gene

action. The author suggested that, the identified ten crosses which showed advantageous

32

specific combining ability could be utilized for the enhancement of certain traits

following hybrid breeding in these Indian mustard genotypes.

Jeromela et al. (2007) studied general and specific combining ability estimated in a set

of five rapeseed genotypes. The mode of inheritance of several morphological traits (i.e.

plant height, height of first lateral branch, number of. lateral branches) and seed

yield/plant was also studied. For plant height positive heterosis was found in five cross

combinations. For height of first lateral branch positive heterosis was observed in two

combinations. Likewise, for number of lateral branches positive heterosis was found in

only one cross combination whereas for seed yield three cross combinations identified to

be positive heterotic.

Singh and Dixit (2007) determined combining ability using 9 × 9 diallel cross of Indian

mustard. For two generations (F1 and F2), they studied yield, yield related attributes and

oil content. In most of the crosses, for majority of the traits SCA was higher in F1 than in

F2 generation. Regarding various traits, both general and specific combining ability

effects were found significant for parents and crosses respectively. This indicated that

both (additive and non-additive) type of gene actions were actively involved in governing

the studied traits. They concluded that selection in later generation world be rewarding

as non-additive component were higher than additive effects.

Cheema and Sadaqat (2004) studied heterosis (mid-parent and better-parent) in crosses

of four Brassica napus genotypes. Of the total genotypes, two (Ester and Rainbow) were

drought sensitive and two (Range and Shiralee) were drought tolerant. The experiment

was conducted under two irrigation levels (normal and drought) for seedling,

physiological and morphological traits. For almost all studied traits they observed

significant heterosis in all crosses under both irrigation levels. Furthermore, the direction

and magnitude of heterosis varied with plant character, cross combination and irrigation

level. Under normal and drought stress condition, mid-parent and betterparent heterosis

was observed for shoot length and fresh root weight. For water potential under drought,

highest positive and significant better parent heterosis was observed in cross combination

T × S. For chlorophyll „a‟ heterosis over mid parent was observed in T × T under both

33

normal and drought condition. Similarly, for oil content highest positive heterosis (mid

and better parent) was found in S × S and T × S under normal and drought. Under drought

conditions T × T showed very high heterosis over mid and better parent. For seed yield

under normal condition cross combination (T × T) exhibited very high heterosis over mid

parent.

Singh and Lallu (2004) estimated GCA and SCA effects in Indian mustard genotypes.

For this purpose nine genotypes were crossed in a half diallel fashion. All the parents and

hybrid combinations were evaluated in the field. Data was recorded on plant height,

number of branches, number of pods on main raceme, 1000-seed weight, seed yield, oil

and protein content. As a result it was found that both GCA and SCA effects were

significant for majority of the studied traits except 1000-seed weight for which these

effects were found non-significant. For various parental cultivars and crosses, both

general and specific combining ability effects respectively, were found significant for

seed yield and yield contributing traits. They also observed that hybrids with significant

SCA effects also showed significant heterosis. Therefore, they suggested that these

crosses could be exploited in heterosis breeding.

2.2. Generation mean analysis

Most of the economically important traits are quantitatively inherited. They are

controlled by many gens, each with small effects and show continues variation. They are

also influenced by environment. To investigate the pattern of inheritance of quantitative

traits, the most common method used by the plant breeder‟s is the generation mean

analysis approach. On generation mean analysis relevant literature in rapeseed is as

follows.

Kemparaju et al. (2009) studied the six generations (P1, P2, F1, F2, B1 and B2) of eleven

primary cross combinations of Indian mustard. During the study data was recorded on

four characters i.e. days to 50% flowering, days to maturity, seed yield per plant and

harvest index. To the mean of six generations scaling test was applied for estimation of

epistasis and genetic parameters (m, d, h, i, j and l). It was found that all the studied traits

34

were under the control of both additive and non-additive type of gene action. In response

to these results, and due to important role played by duplicate epistasis as compare to

complementary epistasis the authors suggested that reciprocal recurrent selection might

be used for development of improved cultivars.

Singh et al. (2008) determine gene actions (additive, dominance and epistatic component

of variation) while, evaluating thirty-three families of Indian mustard. For all of the

characters studied, epitasis‟ was evidenced. For all the studied traits additive × additive

type of non-allelic interaction was found important, except for primary branches,

secondary branches and seeds per pod. The j and l types interactions were prominent for

all the characters. In genetic control of the characters, they found that both additive and

dominance type of gene actions played important role.

Sing et al. (2007) evaluated three crosses through generation mean analysis approach

using six generations (P1, P2, F1, F2, BC1 and BC2). They determined the comparative

importance and involvement of the genetic components i.e. additive (d), dominance (h)

and epistatic (i, j and l) for yield and yield associated parameters. Epistatic genetic effects

for majority of the characters except one cross (T-59 × Pusa Bold) for days to flowering

was observed. A joint scaling test was applied for fitness of the model. Chi-square values

were significant therefore six parameter model was used. This model discovered that

among the main effects, higher magnitudes of dominant gene effect (h) were observed

for all the studied traits except for plant height in one cross. This clarified the role of

dominant genes was more important than the additive genes. Similarly, among the

epistatic effects, the magnitude of dominant × dominant (l) and additive × additive (i)

components were high and more important as compared to additive × dominant (j) for

most of the traits. Furthermore, complementary type of nonallelic interaction was

observed in the expression of number of primary branches, number of secondary

branches, length of main raceme, seed yield plant-1 and oil content plant-1. Out of total

crosses, this appeared desirable in two crosses and hence it might be helpful in further

improvement of these traits. Moreover, a duplicate type of non-allelic gene interaction

35

was observed because both h and i estimates were found with opposite signs for plant

height, siliquae number on main raceme, siliquae length and 1000-seed weight.

Cheema (2004) used generation means analysis (P1, P2, F1, F2, BC1 and BC2) of three

crosses of Brassica napus. To determined the nature of gene action governing seedling

traits generations were evaluated under irrigated and drought conditions Results indicated

that type of gene action varied with the traits, crosses and treatments. Number of

components of generations also varied with crosses and treatments. Under drought

conditions, majority of the traits in cross combination Range × Ester were found to be

controlled by additive type of genes. Likewise, under normal conditions in the same cross

combination shoot/root length, fresh shoot weight, dry root weight and water content

were under the control of additive type of genes however other than these traits were

under the control of non-additive type of gene. Therefore, the author concluded that to

improve different traits in different conditions in canola different selection methods

should be practiced.

Varsha et al. (1999) studied six generations namely, P1, P2, F1, F2, BC1 and BC2 of B.

napus. They determined gene actions comprised of allelic interaction i.e. additive, and

dominance and non-allelic interactions i.e. additive × dominance and dominance ×

dominance gene effects in two crosses (i.e. ABU × GS-63 and ABU × IRMA). As a result

it was observed that for days to flowering, plant height, siliqua number, seed weight and

seed yield additive and dominance gene effects were prominent. Moreover, it was also

found that all the three types of epistasis were involved in the expression of seed yield in

cross (ABU × GS-63) and for plant stature in cross (ABU × IRMA).

2.3. Drought tolerance

Cowley and Luckett (2011) evaluated nine canola genotypes in a rain-out shelter,

where three water regimes i.e. wet, dry and very dry. Chlorophyll fluorescence was

measured on three occasions with four weeks interval. Significant differences were

observed among genotypes and between moisture regimes. Likewise, genotype by water

regimes interaction was also found significant. Chlorophyll florescence in one genotype

36

was found sensitive to drought stress. It was further stated that chlorophyll fluorescence

is a measure of photosynthetic performance and is widely used by plant physiologists.

The changes occurring during the process of photosynthesis are not obvious to the naked

eye. For measuring these changes, the authors suggested chlorophyll fluorescence a

potentially useful tool to assess drought tolerance.

Khan et al. (2010) evaluated three canola type cultivars (Hyola-42, Con-III and

Shiralee (Check) and two mutants of Rainbow (Rainbow-1 (R-75/1) and Rainbow-2 (R-

100/6) using physiological indices under four irrigation levels i.e., W-1 (300 mm

irrigation in three splits); W-2 (200 mm irrigation in two splits); W-3 (100 mm single

irrigation) and W-0 (no irrigation) except soaking one. As a result, decrease in the relative

water contents (RWC), osmotic potential (OP) and potassium contents were observed.

As compared to control treatment, total greenness (Spad value) and proline contents were

increased under various irrigation levels. They concluded that based on comparison with

all other genotypes, these two genotypes (Con-III and Rainbow-2 (R100/6) were found

to be tolerant to drought stress condition.

Kauser et al. (2006) provided water stress to two canola (Brassica napus L.) cultivars

under hydroponics for investigation of their differential morpho-physiological responses.

Three weeks old canola seedlings were planted at 0 MPa (control) or -0.6 MPa (PEG

18.2%) in artificial nutrient solution for a period of another three weeks under stress

condition. As result it was observed that water stress negatively affected the growth of

both canola cultivars. However, under water stress conditions cultivar Dunkeld was

identified to be more tolerant to drought stress contions. This cultivar showed higher

values for almost all the growth parameters (in shoot, root biomass and leaf area).

Moreover, it was also observed that growth performance of Dunkeld was good as

compare to cultivar Cyclone. The leaf chlorophyll content „a‟, carotenoids and quantum

yield of PS-II (photo-system II) was also negatively affected due to water deficit

conditions. However, all these traits were less affected under water stress condition in

drought tolerant genotype (Dunkeld). In both canola cultivars water deficit caused a

considerable decrease in photosynthetic rate but they did not differ significantly in net

37

CO2 assimilation rate under drought stress conditions. They suggested that variation in

performance regarding drought tolerance in these canola cultivars was related to leaf area

and root growth. In canola cultivars they found no relationship between growth and

osmotic adjustment. Hence, concluded that leaf chlorophyll „a‟ and quantum yield of PS-

II might be used as a probable selection criterion while breeding for drought tolerance in

canola cultivars.

Wright et al. (1997) studied leaf turgor in two species of Brassica i.e. B. juncea

and B. napus. It was found that B. juncea maintained higher leaf turgor than B. napus

under drought stress condition which in turn resulted in higher seed yield in B. juncea

under stress environment. At zero turgor the leaf water potential was found lower in B.

juncea as compare to B. napus. This difference was due to an extreme decrease in osmotic

potential of leaves with the decrease i water potential in B. juncea rather. B. juncea also

showed high solute accumulation indicated its capacity to osmoregulate than B. napus.

Regarding the variation in osmoregulation, the other differences in plant water relations

were consistent. The predicted relative water content of leaves for B. napus at an osmotic

potential of -2.5 MPa was 0.43 whereas for for B. juncea was 0.61. It was therefore

concluded that high capacity to accumulate solutes B. juncea is a major factor for its high

seed yield performance under drought stress condition.

Champolivier and Merrien (1996) conducted experiment on oilseed rape in pots

under controlled conditions to study the effect of water stress. It was found that from

flowering to the end of seed set majority of the yield and yield associated traits were

adversely affected by scarcity of irrigation water. The highest reduction of 48% was

noticed when only 37% of total required water was provided to the plant during the above

mention growth stage. The main yield associated trait which was affected the most was

number of seeds plant-1. However, some restoration was occurred when the water was re

supplied to the plants. Similarly, the seed weight was only adversely affected when

irrigation water was stopped from the stage when the pods were swollen till the stage

when the seeds got colored. It was therefore concluded that when water deficit started

from flowering stage till maturity of the crop a noticeable decrease in oil content was

38

found. In their investigation the most important findings was the increase in the

concentration of glucosinolates (up to 60%) when water stress was applied during the

above mention stage.

2.4 Inheritance studies

For a plant breeder to devise suitable breeding strategy for the improvement of any crop

the knowledge of inheritance mechanism of agronomic traits is very important.

Generation mean analysis is commonly used to study inheritance mechanism of plant

traits.

Pandey et al. (2013) investigated number of genes controlling inheritance of erucic acid

in Brassica juncea using one zero erucic acid line PRQ-9701-46 crossed with JM-1

(46.29%), a high erucic acid cultivar including reciprocals. The level of erucic acid

content in F1‟s and their reciprocals was intermediate between the parents. It indicated

that erucic acid was embryonically controlled and there were no maternal effects involved

in the inheritance of this trait in B. juncea. Erucic acid content in F2 generation was

segregated into 5 classes (<2%, 10-22%, 22-34%, 34-46% and 46% erucic acid) with a

ratio of 1:4:6:4:1 Backcross seeds of BC1 generation segregated into three classes (<2%,

10-22%, 22-34% erucic acid) with a ratio of 1:2:1 and backcross seeds of BC2 generation

segregated into three classes 22-34%, 34-46% and >46% erucic acid) with a ratio of 1:2:1.

From the pattern of segregation it is concluded that inheritance of erucic acid content in

B. juncea was governed by two genes with additive effects.

Arifullah et al. (2013) investigated the nature of gene action in eight promising B.

Juncea genotypes crossed in (8 × 8) complete diallel systems for seed yield and yield

attributes. Results from the genetic analysis revealed that both additive and dominance

genes were involved in the expression of all the traits. Though, these genetic estimates

also confirmed that for seeds/ siliqua and 1000-seed weight only additive effects were

found more important. It is concluded for the improvement of these traits early generation

selection would be fruitful. On the other hand non-fixable (Dominance effects) which

were more prominent with the presence of over-dominance for majority of the parameters

39

(primary branches per plant, plant stature, total siliqua per plant, length of siliqua and

seed yield per plot). It is therefore suggested that selection in latter generation or advance

generation could be rewarding. Only on trait, length of siliqua exhibited the presence of

directional dominance. However, the asymmetrical distribution of dominant genes

among the parents was identified for all the traits.

Ullah (2012) studied the effects of Salicylic acid (SA) and Putrescine (Put) on growth

and oil quality attributes of canola under drought stress conditions. Two canola cultivars

(Rainbow and Dunkeld) were grown under natural environmental conditions. Drought

stress was implied for 10d during flowering (90 days after sowing) until the soil moisture

content decreased from 22 % - 9 %. After that the gr0wth regulat0rs i.e. salicylic acid and

Putrescine were applied @ 10-5mol/L as foliar spray three days after dr0ught induction.

It was f0und that dr0ught stress significantly reduced b0th chl a, chl b, car0tenoids, s0luble

pr0teins and leaf relative water c0ntent (LRWC), h0wever an increase was 0bserved in the

leaf pr0line, seed gluc0sin0lates and 0il erucic acid c0ntents. These gr0wth regulators were

found highly effective in reducing the adverse effects pr0duced due to drought stress on

both the can0la cultivars. The applied gr0wth regulat0rs maintained the water requirement

of can0la plants, amplified the increase of 0sm0lyte proline and pr0tected photosynthetic

pigments from opposing effects of drought stress. The SA was effective to reduce the

dr0ught induced accumulation of glucosinolates and erucic acid in canola oil and both the

growth regulators overcame the drought induced decrease in 0leic acid (C18:1). It is

inferred that SA is economical and environment friendly alternative and can be

implicated to impr0ve the plant growth and 0il quality of can0la in current scenari0 of

dr0ught and climate change.

Iqbal et al. (2008) c0mpared the perf0rmance 0f ten gen0types each 0f tw0 brassica

species (B. napus L. and B. juncea L.) f0r m0rph0l0gical, maturity, yield and yield

ass0ciated traits. B. juncea pr0duced significantly greater yield than B. napus gen0types.

Seed 0il c0ntent was higher in B. napus while, the levels 0f erucic acid and gluc0sin0lates

were l0wer in B. napus than B. juncea. M0re0ver, significant variability f0r 0il,

40

gluc0sin0lates and erucic acid c0ntent in B. napus gen0types sh0wed their p0tential f0r

utilizati0n in breeding pr0grams f0ll0wing intra- and inter-specific hybridizati0n.

Hill et al. (2003) investigated inheritance 0f pr0g0itrin and t0tal aliphatic

gluc0sin0late c0ncentrati0ns in 0ilseed rape, using parental, F1, F2 and first backcr0ss

generati0ns, derived fr0m a cr0ss between resynthesized spring rape and a d0uble-l0w

spring rape cultivar. Pr0g0itrin and t0tal aliphatic gluc0sin0late c0ncentrati0ns were

measured in mature seeds 0f single plants fr0m these generati0ns, using micellar

electr0kinetic capillary chr0mat0graphy. F0r pr0g0itrin, an additive/d0minance m0del 0f

gene acti0n adequately explained the variati0n am0ng the generati0n means, but f0r t0tal

aliphatic gluc0sin0late c0ncentrati0n, n0n-allelic interacti0ns were als0 detected.

Predicti0ns based 0n estimates 0f the genetic parameters indicated that rec0mbinant inbred

lines, rather than sec0nd cycle hybrids, appeared t0 0ffer a better pr0spect 0f reducing

gluc0sin0late c0ncentrati0ns in this material. Estimates 0f the minimum number 0f genes

c0ntr0lling these tw0 characters were br0adly in line with the number required f0r the

kn0wn stages 0f their bi0synthesis.

Li, et al. (2001) determined inheritance 0f three maj0r genes in segregating p0pulati0ns

0f Brassica 0leracea L inv0lved in synthesis 0f aliphatic gluc0sin0lates (GSL). The

p0pulati0ns used in for this study were produced fr0m three cr0sses: br0cc0li

× caulifl0wer, c0llard × br0cc0li, and c0llard × caulifl0wer. Tw0 0f these genes (GSLPR0)

and (GSL-EL0NG) regulate side chain length. The acti0n 0f the first gene outcomes in

three-carb0n GSL.. Whereas, the acti0n 0f the second gene creates f0urcarb0n GSL. It was

also found during the investigation that in B. 0leracea these tw0 genes express and

segregate independently fr0m each 0ther. Moreover, the d0uble recessive gen0type

resulted in 0nly trace am0unts 0f aliphatic GSL. The third gene, GSL-ALK c0ntr0ls the

side chain desaturati0n. This has been also 0bserved in Arabid0psis thaliana (L.) Heynh.

It was also f0und that this gene c0-segregates with a f0urth gene which is GSL-0H, and is

resp0nsible f0r side chain hydr0xylati0n. Elucidati0n 0f the inheritance 0f maj0r genes

c0ntr0lling bi0synthesis 0f GSL all0w f0r manipulati0n 0f these genes and facilitate

devel0pment 0f lines with specific GSL pr0files.

41

Chen and Heneen (1989) studied the inheritance 0f fatty acid c0mp0siti0n 0f 0il 0f f0ur

synthetically reproduced rapeseed (Brassica napus L.) lines. These line were produced

fr0m cr0sses between B. campestris L. and B. alb0glabra Bailey. Results regarding

genetic analysis revealed that partially epistatic 0ver high f0r l0w palmitic acid c0ntent was

0bserved. Depending 0n the erucic acid c0ntents 0f the parental species, high 0leic acid

c0ntent c0uld be either partially hyp0static t0 0r transgressively epistatic 0ver l0w c0ntent

0f this fatty acid. In tw0 cr0sses, epistasis was 0bserved f0r l0w lin0leic acid c0ntent.

Whereas, in the 0ther tw0 cr0sses additive gene acti0n was sh0wn f0r the same fatty acid.

Moreover, partial 0r transgressive epistatic effect was 0bserved f0r l0w lin0lenic acid

c0ntent. It was also found that high eic0sen0ic acid c0ntent generally showed an epistatic

effect 0ver l0w eicosenoic acid. Furthermore, in other three cr0sses partial epistatic effect

was found f0r high erucic acid c0ntent. H0wever, 0ne cr0ss c0mbinati0n sh0wed hyp0stasis

effect.

Krzymanski and D0wney (1969) carried 0ut fatty acid analysis 0f F2 seed fr0m the cr0ss

zer0 × l0w (7%) erucic acid winter rapeseed parents. It was found that, in these genotypes,

0ne gene pair g0verns the level 0f erucic acid. Moreover, it was also confirmed that each

allele c0ntributes appr0ximately 3.5% erucic and 6% eic0sen0ic acid t0 the seed 0il. In this

series the gene acti0n is similar t0 0ther alleles, in such a way that the genes display n0

d0minance and behave in an additive manner. The l0ng chain fatty acids, erucic and

eic0sen0ic, were each significantly negatively c0rrelated with the 18 carb0n fatty acids,

0leic and lin0leic. C0rrelati0n c0efficients between 0leic and lin0leic were als0 negative

and significant within each 0f the three F2 gen0types.

Sabaghnia et al. (2013) carried 0ut path c0efficient analysis f0r interrelati0nships

am0ng seed yield and ass0ciated traits. F0r this purp0se 49 can0la gen0types were

evaluated irrigated and dr0ught c0nditi0n. Path analysis revealed p0sitive relati0nship 0f

seed yield with all parameters studied except stem diameter and days t0 fl0wering under

irrigated c0nditi0n. 0n the 0ther hand under dr0ught stress c0nditi0n, seed yield exhibited

p0sitive ass0ciati0n with all characters except height 0f first p0d, height first lateral branch,

branches, p0ds per plant and stem diameter. Path analysis sh0wed that 1000seed weight

42

and main stem length directly influenced seed yield under n0rmal c0nditi0n h0wever under

dr0ught c0nditi0n, plant height and 1000-seed weight were f0und m0re imp0rtant traits that

influenced seed yield. They suggested 1000-seed weight as a selecti0n criteri0n f0r

increased seed yield in can0la under b0th irrigated and dr0ught c0nditi0ns.

Cheema and Sadaqat (2004) studied relati0nship am0ng vari0us seedling and

m0rph0-yield traits 0f Brassica napus under dr0ught c0nditi0n. All the three seedling traits

i.e. r00t length, r00t fresh and dry weight exhibited str0ng ass0ciati0n with seed yield.

Similarly, p0sitive relati0nship was 0bserved between r00t weight traits and sec0ndary

branches per plant. Plant height sh0wed p0sitive c0rrelati0n with seed yield and its

ass0ciated traits and 0il c0ntent. Relati0nship 0f water c0ntent with maturity traits was als0

p0sitive. Negative ass0ciati0n 0f seed yield was 0bserved with repr0ductive peri0d. The

auth0rs suggested that days t0 fl0wering and maturity traits may help in inc0rp0rati0n 0f

av0idance mechanism against dr0ught stress.

V. MATERIALS AND METHODS

The present study was carried out in two phases at the University of Agriculture

Peshawar Pakistan. During the first phase combining ability studies were carried out

based on which selection was made for best parents and crosses. Subsequently in the

second phase, selected parents and crosses were used for inheritance studies via

generation mean analysis. The step wise presentation of methodology used during the

study is as follows.

3.1 COMBINING ABILITY STUDIES IN PARENTS AND F1 CROSSES

43

3.1.1 Genetic Materials

Genetic material for the present study was c0mprised 0f a set of 15 Brassica napus

gen0types. Each gen0type was having distinct genetic make-up. Relative information

regarding their major attributes is given in Table 3.1. Out of total 15 genotypes, eleven

(L-1, L-2, L-3, L-4, L-5, L-6, L-7, L-8, L-9, L-10 and L-11; introduced form China) were

used as lines (female parents) whereas, four genotypes (T-1 = Concord, T-2 = Ac-elect,

T-3 = Shiralee, and T-4 = Hoyla-43) collected form Plant Genetic Resources Institute,

National Agriculture Research Centre, Islamabad were used as testers.

3.1.2 Development of F1 crosses

Initially, during the first season (2010-11), all parental genotypes (i.e. lines and testers)

were planted in crossing blocks for hybridization. Eleven female lines were cr0ssed with

f0ur male testers following a line × tester matting design t0 pr0duce 44 F1 hybrid

c0mbinati0ns. Manual emasculation and pollination was done during hybridization

program. For emasculation process, juvenile buds were selected. In order to uncover the

anthers the petal whorl was removed with the help of forceps. After that, young and

unripe anthers were removed with the help of forceps. The emasculated buds were

shielded with butter paper bags to avoid adulteration by foreign pollens. Those branches

on which emasculated buds were located, were properly labeled with necessary

information like gen0type name, date of emasculati0n etc. During the same day in the

evening buds from the male parents were also covered with butter paper bags for

collection next day pollen collection. The following morning, newly bloomed flowers

from the male parents were picked in petri dish and placed in sunlight for few minutes.

These opened flowers with fresh pollens were used to p0llinate the emasculated buds 0f

the female parents. After p0llinati0n the buds were re-c0vered with the same butter paper

bags t0 av0id c0ntaminati0n. At the time of maturity, all the p0ds resulted fr0m cr0ssing f0r

each c0mbinati0n were separately harvested. The harvested pods were properly sundried

and subsequently threshed and stored to be used in next growing season.

44

Table 3.1 Major characteristics of parental genotypes used in combining ability

studies.

Seed quality traits

S. No. Genotype/source Codes EA GLU Oil content Seed yield

(%) (µMg-1) (%)

Lines

1 1136/7/7 L-1 High High Medium Low

2 1140/1/144 L-2 High Medium Low Medium

3 1141/1/135 L-3 High High Low Medium

4 1143/1/137 L-4 High High Medium High

5 1147/1/129 L-5 High High Low High

6 R-Y/1/107 L-6 High Medium Very high Medium

7 11480/10/92 L-7 High High Medium High

8 ATC900/1/115 L-8 High High Low Medium

9 H-J-1/114 L-9 High Medium Medium Low

10 H-Z-5/1/111 L-10 High High Low Low

11 Z-758/1/109 L-11 High High Medium Low

Testers

12 Concord T-1 Low Low Very high Low

13 Ac-elect T-2 Low Medium Medium Low

14 Shiralee T-3 Low Medium High High

15 Hoyla-43 T-4 Low Low Very high High

EA= Erucic acid GLU= Glucosinolate

3.1.3 Evaluation of parents and F1 hybrids

During the second growing season (2011-12) all the 44 F1 crosses developed

during previous season were evaluated along with 15 parental genotypes under natural

conditions in a replicated trial. Each genotype was planted in two rows of five meter

length with plant spacing of 50 and 15cm between and within row, respectively. All crop

management practices were carried out as recommended for raising a successful brassica

crop.

3.1.4 Data recorded on morpho-yield and seed quality traits

i) Days to 50% flowering

45

Data on days to 50% flowering were recorded as the difference between (number

of days) date of sowing and the date when 50% plants produced flowers in each plot.

ii) Plant height

Plant stature was measured fr0m the gr0und level t0 the tip 0f the plant with the

help 0f a measuring r0d 0n rand0mly selected plants in each generation/genotype.

iii) Primary branches plant-1

Primary branches are those which arise fr0m the main stem were c0unted 0n

rand0mly selected plants in each generation/gen0type.

iv) Pods main raceme-1

Main raceme is the main stem that terminates int0 infl0rescence; p0ds present 0n

main raceme were c0unted 0n rand0mly selected plants.

v) Pod length

Average pod length was recorded in centimeters on a random sample from each

of the randomly selected plants.

vi) Seed pod-1

Data regarding average number of seed pod-1 were recorded on the sample drawn

for pod length measurement.

vii) 1000 seed weight

A rand0m sample of 1000 seeds fr0m each of the rand0mly selected plants was

drawn using a seed c0unter. Weight was rec0rded in grams by weighing a sample of 1000

seeds plant-1 thr0ugh an electrical balance.

viii) Seed yield plant-1

46

Seed yield is the final result of vari0us yield c0ntributing traits. Seed yield plant1

was rec0rded in grams by weighing the threshed seeds fr0m rand0m sample in each

generati0n.

ix) Oil and quality traits

To determine 0il content, erucic acid and gluc0sin0late, seed sample fr0m each

selected plant was scanned 0n Near Infra-Red (NIR) spectr0sc0py at bi0chemical lab0rat0ry

Nuclear Institute f0r F00d and Agriculture (NIFA) Peshawar (Ali et al., 2012).

3.1.5 Statistical analysis of parental and F1 data

Data obtained from F1 generation was subjected to analysis of variance technique

(Table 3.2) following a standard procedure for line by tester data as described by Singh

and Chaudhary (1985).

Table 3.2 Analysis of variance format for parents and F1 crosses evaluated

during 2011-12.

Sources of variance df Mean Squares F-values

Replication (r) [r-1] MSR MSR/MSE

Genotype (g) [g-1] MSG MSG/MSE

Parents (P) (p-1) MSP MSP/MSE

F1s (F1) (F1-1) MSF1 MSF1/MSE

P vs F1 1 MS P vs F1 MS P vs F1/MSE

Lines (L) (L-1) MSL MSL/MSLT

Testers (T) (T-1) MST MST/MSLT

L×T (L-1)(T-1) MSLT MSLT/MSE

Error [(g-1)(r-1)] MSE

3.1.6 Combining ability analysis

Upon significant (L×T) effect in ANOVA the data were further subjected t0 c0mbining

ability analysis acc0rding t0 the meth0d of Yan and Hunt (2002) and Bertoia et al., (2006).

For combining ability studies a graphical approach was used with the help of GGE-biplot

software which is a window based program that generates biplots on two way data set

(Yan, 2001). This step was performed to identify and select potential parents and their

47

cross combinations based on their combining abilities for the development of F2 and back

cross generations in the next cropping season.

The biplot model used for line by tester data was:

Yij- βj = λ1ξi1 ηj1 + λ2ξi2ηj2 + εij

3.2 GENERATION MEAN ANALYSIS

3.2.1 Development of F2 and back cross (BC11 & BC12) generations

Best parental genotypes and their F1 crosses identified during combining ability studies

were selected and forwarded to develop various generations to fulfill the requirement of

generation mean analysis approach for further inheritance studies. During the third

cropping season (2012-13) part of the seed from four selected parental genotypes along

with their four resultant F1 crosses were planted t0 pr0duce F2, BC11 and BC12 generati0ns.

The F2 generati0n was pr0duced by self-p0llinating F1 plants. BC11 and BC12 generati0ns

were pr0duced by cr0ssing back each F1 hybrid with its respective first and sec0nd parents

as given in Table 3.3. All the generations (4 parents, 4 F1, 4 F2, 4 BC11, and 4 BC12)

developed were properly labeled and stored to use in evaluation trial in the following

season.

Table 3.3 Schematic representation of back cross generations development.

S. No. BC11 BC12

♀ × ♂ ♀ × ♂

1 (L-6 × T-1) × L-6 (L-6 × T-1) × T-1

2 (L-6 × T-3) × L-6 (L-6 × T-3) × T-3

3 (L-7 × T-1) × L-7 (L-7 × T-1) × T-1

4 (L-7 × T-3) × L-7 (L-7 × T-3) × T-3

3.2.2 Evaluation of generations under rainout shelter

48

During the fourth cropping season (2013-14), 20 entries generated from the above

crossed and selfed combinations were evaluated in a replicated trial under irrigated and

drought stress conditions at University of Agriculture Peshawar. All genotypes were

planted in pots under rainout shelter. Initially five seeds from each genotype were planted

in each p0t which were later 0n reduced t0 0ne plant p0t-1. After establishment of seedlings,

plants under irrigated condition were applied with continuous irrigation water whereas

under drought stress no irrigation was applied.

After 30 days of the treatment, uniform leaf samples were drawn from each generation

to determine various drought stress related physiological traits.

3.2.3 Physiological Traits

i) Relative water content (%)

Samples of fresh top most leaves were collected and immediately put in clean

glass vials already weighed. Fresh weight of the sample was recoded and then distilled

water was added to keep the leaves submerged for 2 hours. After that water was drawn

and the leaves surface water was also dried and turgid weight was rec0rded. Finally the

samples were dried at 70˚C in 0ven for 48 hours thereafter dried weight was recorded.

Relative water content (RWC) was calculated according to Dhopte and Manuel (2002).

ii) Chlorophyll content (mg cm-2)

Leaf chlorophyll content was determined in top most leaves with the help of

chlorophyll content meter (atLEAF+) of FT Green LLC, 1000 N.West St. Suite 1200 #

638 Wilmington, DE 19801-USA. AtLEAF+ is a p0werful, handheld, easy t0 use device

f0r n0ninvasively measuring the relative chl0r0phyll c0ntent (mg/cm2) of green leaf plants

(Gekas et al., 2013).

iii) Proline content (µMol g-1)

49

The proline content was analyzed acc0rding t0 the meth0ds of Bates et al. (1973).

Leaf sample (1g) was extracted with 3 % sulphosalicylic acid. The resultant extracts (2

ml) was held f0r 0ne h0ur in b0iling water by adding 2 ml ninhydrin and 2 ml glacial acetic

acid. After this 4 ml of c0ld t0luene was added. Pr0line c0ntent was measured by a

spectr0ph0t0meter (Shimadzu UV 265) at 520 nm and calculated as µMol g-1 weight of

sample.

3.2.4 Evaluation of generations under field condition

Experimental site:

Peshawar has a warm t0 h0t, semi-arid, sub-tropical climate with mean annual

rainfall 0f ab0ut 360 mm. S0il of the experimental site is deficient in N, P and available

Zn, but has adequate K. For irrigation purpose canal water is available (Harris et al.,

2007). Major characteristics of experimental site and season are given in Table 3.4.

Table 3.4 Major characteristics of experimental site and screening season.

Parameter Month Mean temperature ˚C Average rainfall (mm)

Min Max

Season (2013-14)

October 16.4 November 7.0

December 3.0

January 1.6

February 4.4

32.1 26.6

19.3

18.6

19.3

0.0 4.3

2.4

2.0

4.4

March 9.7 22.0 8.9

April 13.7 28.3 11.9

Latitude and Longitude Lat. 34° 01' 10.37 N'

Long. 71° 28'01.69 E'

Elevation. 365m

Soil type Silt loam/alkaline pH 8.2 to 8.3

50

All the genotypes were evaluated in a rand0mized c0mplete bl0ck (RCB) under irrigated

and rainfed conditions at the University of Agriculture Peshawar. Experimental pl0t was

c0mprised 0f tw0 r0ws f0r n0n-segregating P1, P2 and F1 generati0ns, f0ur r0ws f0r BC11

and BC12 generati0ns and six r0ws f0r F2 generation of each cr0ss. Seeds were planted in

five meter l0ng r0ws. Plant spacing was maintained at 15 cm within r0w whereas 50 cm

between r0ws. All the cultural practices required were als0 applied thr0ugh0ut the growing

peri0d. For data collection in parents and F1 generations, ten plants were selected at

random from each plot in each replication whereas 20 and 30 plants were selected from

back cross and F2 generations respectively, to record data on individual plant basis.

3.2.5 Data recording on various traits

Data regarding various morphological, yield and yield associated traits, oil

content and oil quality traits were recorded on uniform random samples in each

replication. Methodology used for measuring each trait is given in the previous section

under combining ability studies.

3.2.6 Statistical analysis of various generations

Combined analysis over environments (Table 3.5) were carried out following the

methodology proposed by McIntosh (1983). Analysis of variance procedure for

Randomized Complete Block (RCB) design in individual environments (Table 3.6) was

performed following the procedure of Cochran and Cox (1960).

Table 3.5 Analysis of variance f0rmat f0r c0mbined analysis across two

environments.

S0urce of Variation df Mean Squares F-test

Environments (E) e-1 MSE MSE/MS Error

Reps (within E) e(r-1)

Genotypes (G) g-1 MSG MSG/MSGE

G × E (g-1)(e-1) MSGE MSGE/MSE

Error e(g-1)(r-1) MS Error

Total reg-1

51

df = Degrees of freedom

Table 3.6 Analysis of variance for

environment. mat for generation s evaluated in individual

Source of Variation df Mean Squares F-test

Replication (r) r-1 MSR MSI/MSE

Generation (Gen) Gen-1 MSGen MSGen/MSE

Error (Gen-1)(r-1) MSE

Total rGen-1


3.2.7 Generation Means Analysis

Generation means analysis was carried out to determine the inheritance pattern of

marpho-yield and oil quality traits in Brassica napus.

The joint scaling test (Cavalli, 1952) was used to detect epistasis. By 0bserving a

significant chi-square value then six parameters m0del was used. Under six parameter

m0del additive (d), dominance (h) effects along with n0n-allelic interaction c0mp0nents

(i, j and l) of generation means were estimated t0 explain inheritance 0f various traits

following Hayman (1958) and Mather and Jinks (1982). When the chi-square values

under joint scaling test were found non-significant then a three parameter m0del als0

known as additive-dominance m0del was used f0r interpretation of inheritance following

Jinks and Jones (1958).

a) Three Parameters model have:

m = the mid-parent value of F2 means.

d = the additive genetic effect.

h = the dominance genetic effect.

b) In addition to m, a, and d, the six parameters model have:

i = the additive × additive epistatic

effects. j = the additive × dominance

52

epistasis. l = the dominance × dominance

epistasis.

3.2.8 Correlation among traits

For estimation of genetic correlation among traits a Genotype × trait biplot was

constructed using GGE biplot methodology (Yan, 2001). Relationship between any two

traits was estimated from the angle between their vectors, in such a way that when the

angle between their vect0rs was less than 90 degree, they were c0nsidered as positively

correlated however they were negatively correlated when the angle between their vectors

was greater than 90 degree (Yan and Hunt, 2002).

The model for the trait correlation biplot was as:

Yij = OEj cosα ij OGi = OEj OPij

Where Yij is the value 0f the trait i f0r gen0type j. OGi is the distance fr0m the bipl0t 0rigin

t0 the marker 0f the trait i, OEj is abs0lute distance fr0m the bipl0t 0rigin t0 the marker 0f

the genotype j, α ij is the angle between the vect0rs OGi and OEj and OPij (OPij = cosα

ijOGi) is the pr0jection 0f the marker 0f trait i t0 the vect0r of genotype j (Yan, 2001).

VI. RESULTS AND DISCUSSION

The step wise presentation of results and discussion regarding combining ability and

inheritance studies are given in the following section. The data obtained from parents and

F1 generation (line × tester) was subjected to analysis of variance to find out differences

among the genotypes and combining ability (GCA and SCA) among genotypes.

The results obtained regarding various traits from the analysis of variance and biplot

figures for combining ability studies are given below to describe various features of the

experimental material.

4.1 Analysis of variance, mean performance and combining ability

53

4.1.1 Days to 50% flowering

Genotypes varied significantly (P<0.01) for days to flowering indicated variability

among themselves. Partitioning of the genotypes sum of square into parents and F1

crosses also exhibited significant differences among themselves. Sum of square for F1

was further partitioned into line, tester and line × tester which clearly demonstrated

significant effects (Table 4.1). Days to mid flowering is considered as one of the

important attribute in determining the length of maturity peri0d in cr0p plants. Generally,

genotypes getting less days t0 fl0wering c0upled with high seed yield are desired. The

genetic variability among the parental genotypes and their hybrid combination used in

the present study is a h0pe f0r effective selection. Among the parental lines, L-6 t00k

minimum days (102) for mid flowering, whereas maximum days (150) were taken by the

parental line L-10, followed by L-11 which took 147 days to bloom 50% flowers. Am0ng

the testers, minimum days t0 mid fl0wering (117) were recorded for T-3 whereas

maximum (150) were recorded for T-1 (Table 4.3). Similarly among the crosses

minimum days to flowering (115) were noted for L-6 × T-3 followed by L-8 × T-3 (120

days) whereas maximum days to flowering 157 and 156 were recorded for hybrid

combinations L-11 × T-2 and L-10 × T-2 respectively (Table

4.4).

Significant variation among crosses necessitated further genetic analysis of this

trait. Therefore, biplot approach was used to determine GCA, SCA and heterotic studies.

GCA effects of the entries were estimated by their pr0jecti0ns 0nt0 the ATC xaxis (Fig

4.1a). Parallel lines perpendicular t0 the ATC x-axis help in ranking 0f entries in terms of

GCA. The pr0jections 0f the entries 0nt0 ATC y-axis exhibited SCA effects which den0te

the trend of the entries t0 result in superior hybrids (Rastogi et al., 2011).

Biplot regarding the trait days to mid flowering explained 87.2% (70.7% and 16.5% by

PC1 and PC2 respectively) 0f the t0tal variati0n. This maximum explanation 0f the variati0n

showed that the line × tester data were proficiently analyzed by the bipl0t and the

instability 0f extra c0mp0nents was insignificant. The dispersed placement 0f the lines 0n

the ATC x-axis (Fig 4.1a) described significant GCA effects (Bertoia et al., 2006). The

54

lines L-10 and L-11 showed maximum positive general combining ability as they were

placed at long distance fr0m the bipl0t 0rigin in the directi0n of single arr0w headed line.

On the other hand L-6 being placed in the opposite direction showed significant negative

GCA effect. Among the testers, T-2 and T-3 0ccupied farthest p0siti0n 0n the ATC x-axis

therefore were c0nsidered as g00d general c0mbiner. Similarly, the SCA effect was als0

found significant, since the lines sh0wed different pr0jecti0ns 0n the ATC y-axis (Bertoia

et al., 2006). In addition, the p0lyg0n view 0f the bipl0t (Fig 4.1b) validated best and w0rst

hybrid c0mbinati0ns (Rastogi et al., 2011). As gen0types taking relatively fewer days t0

fl0wer are desired in m0st 0f the cr0ps. Theref0re, the lines L-6 and L-8 pr0duced desirable

cr0ss c0mbinati0ns especially with tester T-3 (Fig 4.1b). The greater pr0p0rti0n 0f the sum

of squares (56.0 %) explained by cr0sses (Table 4.1) als0 depicted high heter0sis in the

hybrid c0mbinati0ns (Rastogi et al., 2011). The desirable negative heter0sis could be

attributed t0 differences in genetic makeup of gen0types used in hybridization pr0gram

which is c0nfirmed by the significant differences am0ng the parental gen0types (Table

4.1). From the present study it is clear that both GCA and SCA played important role in

controlling this trait in this set of brassica genotypes. However the greater proportion of

percent contribution by both lines and testers (42.9 + 34.0 = 76.9%) as compare to L × T

(23.1%) confirmed the predominance of additive type of gene action. It is also clear from

the variegated placement of genotypes on ATC x-axis of the biplot that GCA effects were

relatively greater than SCA effects which indicated that in the expression of days to mid

flowering additive type of genetic mechanism was more involved. Maurya et al. (2012)

also stated both additive and dominance type of gene effects were important for

controlling days to flowering in brassica. During combining ability studies in rapeseed

brassica Huang et al. (2010) reported that for days to flowering and maturity both additive

and non-additive type of gene actions were significant but the additive gene action was

more prominent for these traits. Similar findings were also reported by Singh et al. (2008)

who were of the opinion that additive type of gene effect was predominant in controlling

days to flowering in brassica. Sarkar and Singh (2001) discovered that both GCA and

SCA played important role in the expression of days to 50 % flowering in Indian mustard

cultivars. In contrast, the study reported by Parmar et al. (2005) indicated that non-

additive type of gene action was prevailing in the inheritance of days to flowering trait in

55

diallel crosses of Indian mustard. Oghan et al. (2009) investigated parents and crosses for

combining ability. They found that both additive and non-additive genetic effects were

responsible for the expression of flowering trait with a predominant role of additive type

of gene action.

4.1.2 Plant height

As a result of data analysis for plant height trait, significant differences (P<0.01) were

f0und am0ng the gen0types. Furthermore, parents and F1 hybrids also depicted significant

variability. Similarly, in line × tester analysis, the tester‟s main effects and the interaction

effect (L × T) were also found significant however the lines main effect was found non-

significant for plant height (Table 4.1). Significant differences among parents and crosses

in a line by tester matting were also observed by (Rameeh 2012) in brassica. Genotypes

with intermediate plant height coupled with balance positioning of primary branches can

be considered best against lodging; however, taller plants can perform better under

rainfed conditions, therefore are given due consideration during selection. Among the

parental lines L-4 attained maximum plant height (188 cm), whereas minimum plant

height (146 cm) was attained by line L8. Among the testers maximum plant height of 208

cm was recorded for T-3 followed by T-1 (193 cm), whereas minimum plant height (175

cm) was recorded for both T-2 and T-4 (Table 4.3). Among the crosses maximum plant

height (201 cm) was attained by L-3×T-4 followed by L-7 × T-3 (194 cm) whereas

minimum plant height (129 cm) was recorded for L-5 × T-2 (Table 4.4).

The first two components of the biplot explained 72.7% (44.9 and 27.8% by PC1

and PC2 respectively) 0f the t0tal variati0n; whereas the remaining pr0p0rti0n 0f the t0tal

variati0n was n0t acc0unted by bipl0t analysis which might be due t0 much c0mplexity in

genetics inv0lved in this trait am0ng the parents (Rastogi, et al., 2011). The average tester

coordinate (ATC) of the biplot demonstrated that among the parental lines, L-7, L-6, L-

4, L-2, L-11, L-5, and L-3 showed positive GCA effects whereas the remaining four lines

showed negative GCA effects (Fig 2a). Similarly, the tester T-3 was the best general

combiner since it is occupied its position at a longer distance from the place of origin in

the direction of single arrow headed line (Fig 4.2b). The dispersion of the parental

56

genotypes over both ATC x-axis and ATC y-axis dem0nstrated that b0th GCA and SCA

effects played a significant r0le in the inheritance 0f plant height. Significance of both

GCA and SCA effects illustrated that b0th additive and n0n-additive types of gene actions

were resp0nsible f0r the expression of this character (Farshadfar et al., 2013). Imp0rtance

0f b0th additive and n0n-additive type of gene acti0n f0r the inheritance of plant height in

brassica has been also rep0rted by Maurya, et al. (2012). Similarly, Singh et al. (2004)

also reported that in brassica b0th additive and n0n-additive type of gene acti0n played

imp0rtant r0le in the expression of plant height. The polygon view of the biplot explained

best and worst hybrid combinations (Fig 4.2b). The well-defined combinations were

identified in the biplot i.e. L-7 × T-3, L-10 × T-2, L-3 × T-4 and L-6 × T-1.

4.1.3 Primary branches plant-1

Analysis of variance revealed significant (P<0.01) difference among genotypes

for primary branches per plant. Further partitioning of the genotype sum squares into

parents and F1 crosses also exhibited significant differences among themselves. In line

by tester analysis, the main effects for lines and testers and their interaction effect were

also found significant (Table 4.1). Among the parental lines L-7 produced maximum (11)

primary branches per plant, whereas minimum primary branches (5) were produced by

the parental line L-10. Among the testers maximum primary branches per plant (11) were

recorded for tester (T-1), whereas the remaining three testers produced minimum (8)

primary braches per plant (Table 4.3). Similarly among the crosses L-7 ×

T-1 and L-6 × T-3 produced maximum primary branches per plant (11), followed by L7

× T-4 and L-8 × T-3 which produced 10 primary branches per plant whereas minimum

primary branches (5) were produced by L-10 × T-2 (Table 4.4). Primary branches have a

direct positive effect on seed yield and can be considered as an affective seed yield

component in rape seed (Jeromela, et al., 2007). The significant variation among

genotypes and crosses demanded further genetic analysis of this trait via biplot approach

to determine GCA, SCA and heterotic studies.

57

The biplot for primary braches per plant explained 93.4% (75.3% and 18.1% by

PC1 and PC2, respectively) of the total variation. This also clarified that the line × tester

data were well analyzed by the biplot and the variation explained by other components

was negligible. The variegated placement of the genotypes on the ATC x-axis (Fig 4.3a)

clearly described significant GCA effects (Bertoia, et al., 2006). The greater proportion

of percent contribution by lines (66.4 %) in total confirmed major role of GCA than SCA

for this trait. The lines L-6 and L-7 showed maximum positive general combining ability

because they occupied position far away from the origin in the direction of single arrow

headed line whereas L-11, L-10 and L-9 depicted negative GCA being placed in the

opposite direction. The present study revealed that GCA effects were much higher as

compared to SCA effects therefore, indicated that additive type of gene action was

actively involved in the expression of primary branches per plant in the present set of

brassica genotypes. Since, the lines showed different projections on the ATC y-axis

therefore it is clear that SCA effect was also significant (Bertoia, et al., 2006).

The biplot (Fig 4.3b) also demonstrated best and worst hybrid combinations in

the polygon view (Rastogi, et al., 2011). Genotypes having more primary branches per

plant coupled with maximum number of pods per plant are desired in rapeseed. Therefore,

line L-6 produced desirable cross combination with tester T-3. On the other had L-7

resulted in good cross combination especially with T-1 (Fig 4.3b). The desirable positive

cross combinations could be credited to the genetic variability among the genotypes used

in the present investigation (Table 4.1). Since, GCA effects were comparatively higher

than SCA, thus signified the role of additive type of gene effect for the inheritance of this

trait in the present set of genotypes. Singh, et al. (2004) also stated that both additive and

non-additive type gene action played important role in controlling plant height trait.

Studding heterosis and combining ability in brassica, Sabaghnia et al. (2010) also

reported important role of both GCA and SCA for primary branches per plant. However,

Maurya et al. (2012) reported the predominance of nonadditive type of gene action for

plant height in brassica.

4.1.4 Pods on main raceme

58

Results of the data regarding pods on main raceme indicated significant (P<0.01)

differences among genotypes. Parental genotypes and F1 crosses also exhibited

significant differences among themselves. Furthermore, the variance due to lines, testers

and lines × tester were also found significant (Table 4.1). The genetic variability for pods

on main raceme among these genotypes is a hope for effective future breeding program.

Genotypes having more pods on main raceme coupled with more primary branches are

desired for high seed yield. Among the parental lines maximum pods on main raceme

(71) were produced by L-7, whereas minimum (39) pods on main raceme were produced

by L-10. Among the testers maximum pods on main raceme (85) were recorded for T-1

followed by T-3 (61) whereas minimum pods on main raceme (45) were produced by T-

4 followed by T-1 with 47 pods on main raceme (Table 4.3). Similarly among the crosses

maximum were observed in the hybrid combinations L-7 × T-1 which produced 92 pods

on main raceme followed by L-6 × T3 (88) whereas minimum pods on main raceme (41)

were produced by L-10 × T-4 and

L-11 × T-3 with 42 pods on main raceme and both remain statistically at par (Table

4.4).

The significant variation among genotypes and crosses necessitated further

genetic analysis of this trait. The biplot for pods on main raceme explained 93.1% (74.9%

and 18.2% by PC1 and PC2, respectively) of the total variation. Moreover, this maximum

explanation of the variation also confirmed that the line × tester data were competently

investigated by the biplot and the variability accountable for other components was

negligible. The dispersion of the lines on the ATC x-axis (Fig 4.4a) depicted significant

GCA effects (Bertoia, et al., 2006). Since, the lines L-6 and L-7 ranked top in term of

positive general combining ability since they occupied position far away from the origin.

Moreover, the line L-10, L-1, L-11 and L-9 depicted negative GCA effects by occupying

position in the opposite direction. Since, all the testers occupied somewhat same position

on the ATC x-axis hence considered as not good general combiner. This statement is

supported by the greater proportion of the sum of square (68.8%) by the Lines as compare

to testers (7.3%) (Table 4.1). Similarly, the SCA effect was significant which is

confirmed by their differences in projections on the ATC y-axis (Bertoia, et al., 2006).

59

The biplot approach also revealed that GCA effects were larger in magnitudes as compare

to SCA effects which clarified the important role of additive type of gene action in

expression of pods on main raceme in genotypes under study. Moreover, the polygon

view of the biplot (Fig 4.4b) helped in identification of best and worst hybrid

combinations (Rastogi, et al., 2011). As genotypes having more pods on main raceme are

important in brassica, therefore two well defined groups of best hybrid combinations

were identified by the biplot. In the first group the line L-7 and L-4 produced good

combinations with testers T-1 and T-2, whereas in the second group L-6 and L-8 resulted

in superior hybrids with tester T-3 and T-4 (Fig 4.4b). As the lines L-6 and L-7 were the

vertex genotypes in the biplot therefore they showed maximum heterosis in combination

with T-3 and T-1 respectively.

4.1.5 Pod length

Statistical analysis of the data regarding pod length revealed significant difference

among the genotypes (P<0.01) which indicated genetic variability among these

genotypes. The partitioning of the genotypes sum of square into parents and F1 crosses

also revealed significant results. Furthermore, the sum of square for F1 was partitioned

into lines, tester and lines × tester components which showed significant results (Table

4.1). Mean performance showed that among the parental lines L-7 produced longer pods

of about 10.9 cm followed by L-6 (8.2 cm) whereas L-9 and L-11 produced shorter pods

of 4.7 and 4.8 cm, respectively. Among the testers T-1 and T-3 produced longer pods of

7.4 and 6.7 cm, respectively whereas T-2 produced shorter pods (4.5 cm) followed by T-

4 with pod length of 5.6 cm (Table 4.3). Similarly among the crosses L-7×T-1 outclassed

all other crosses by producing lengthy pods of 10.3 cm whereas shorter pods (4.4 cm)

were observed in L-11× T-2 (Table 4.4).

Since significant variation among genotypes and crosses were observed hence

data were further subjected to estimate combining ability studies (Rastogi, et al., 2011).

The biplot for pod length data elucidated 95.2% (76.8 and 18.4% by PC1 and PC2,

respectively) of the total variation. Since all the lines occupied variegated placement on

the ATC x-axis (Fig 4.5a) hence depicted significant GCA effects (Bertoia, et al., 2006).

60

Among the lines, L-7 ranked top regarding positive general combining ability followed

by L-8 and L-6 being placed far away from the origin, whereas L-9, L-11 and L-1

depicted negative GCA as they are placed in the opposite direction on the biplot.

Likewise among the testers, T-3 and T-1 showed good GCA as compare to T-2 and T4.

In a similar way, the SCA effects were also found significant which is confirmed from

the projections of the lines on the ATC y-axis (Bertoia, et al., 2006). On the other hand,

as a result of the polygon view of the biplot (Fig 4.5b) two groups of potential crosses

were identified. In the first group, lines L-6 and L-7 resulted in superior cross

combinations especially with tester T-3, T-1 and T-2 whereas in the second group only

one line L-8 resulted in best hybrid combination with T-4 (Fig 4.5b). The biplot analysis

also clarified that GCA effects were comparatively higher than the SCA effects which in

turn made clear that additive type of gene action was predominant for controlling pod

length trait in brassica genotypes under investigation. The results reported by Ali et al.

(2014) are in close agreement with our findings who also found significant GCA and

SCA effects for pod length in Brassica napus. Our results are also in line with those

reported by Rameeh (2010) who is of the opinion that both GCA and SCA played

important role in controlling pod length in Brassica napus. Maurya et al. (2012) reported

significant GCA effects for pod length in Brassica juncea. In contrast Sabaghnia et al.

(2010) reported the predominance of SCA effects for pod length in brassica. Similarly,

Arifullah et al. (2011) also reported significant SCA effects for pod length in Brassica

juncea.

4.1.6 Seeds pod-1

As a result of data analysis regarding seeds per pod, variance for genotype, F1 crosses,

lines, testers and L × T were found significant (P<0.01) (Table 4.1). These significant

effects could be attributed to the diversity existed among the parents used in this study

and also their ability to inherit this into their F1 hybrids. Among the parental genotypes

maximum seeds per pod (26) were observed in L-6 whereas minimum seeds per pod (13)

were noted in three lines i.e. L-1, L-9 and L-11. Similarly, among the testers T-1

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outclassed all other testers by producing maximum seeds per pod (21) whereas T-2

produced minimum (12) seeds per pod (Table 4.3). The hybrid observed with maximum

seed per pod was L-6×T-3 (27) followed by L-8×T-3 (25), whereas minimum seed per

pod (11) were noted in four hybrid combinations i.e. L-1 × T-2, L-4

× T-2, L-9 × T-2 and L-11 × T-2 (Table 4.4).

The biplot analysis of seeds per pod data explained 98.8% of the total variation in

such a way that 82.7 and 12.1% were explained by PC1 and PC2, respectively. The

dispersion of the parents on ATC x-axis and y-axis indicated the significance of both

GCA and SCA effects however the GCA effects were comparatively higher than SCA

effects in the biplot (Fig. 4.6a). It is also clear form the major portion of the variation

explained by lines and testers (60.7+24.7%) as compare to that explained by L × T

(14.6%) in Table 4.1. Among the lines, L-6 proved to be good general combiner, followed

by L-8, L-5 and L-7, whereas the remaining lines showed negative GCA with all the

testers. Among the testers T-1 was found to be good general combiner being highly

discriminating and representative (Fig 4.6a). The biplot identified two groups of lines

which exhibited good specific combining ability with testers (Fig 4.6b). In the first group

L-6 and L-8 were identified as good specific combiner with tester T-3, whereas in the

second group L-5 and L-2 produced superior cross combinations with tester T-1 and T-

4. The remaining lines being present in other sectors where no tester is present therefore

were declared as poor combiner with any of the testers used in this study. The placement

of parental genotypes onto the ATC abscissa was significantly variable thus clarified the

importance of GCA as compared to SCA for controlling seeds per pod trait in the present

set of brassica genotypes. Number of seeds per pod is one of the important yield

contributing traits therefore, was given due consideration. In addition to high heterotic

responses, positively general and specific combining ability effects were also considered

as essential aspects for increasing seed yield by Sincik et al. (2011). Since, GCA effects

provide an estimate for additive type of gene action (Sprague and Tatum 1942), thus an

additive genetic control mechanism was found more important in controlling the trait

under investigation. Our results are in good agreement with the findings of those reported

62

by Farshadfar et al. (2013) who also found significant GCA effects and non-significant

SCA effects for seeds per pod in brassica.

4.1.7 1000-seed weight

Genotypes varied significantly (P<0.01) for 1000-seed weight. The partitioning

of the genotypes sum square into parents and F1 crosses also revealed significant

differences among themselves. Variances for lines, tester and lines × tester were also

found significant (Table 4.2). Among the parental lines, L-7 and L-8 produced heavier

seeds of 5.8 and 5.5 g, respectively whereas L-4, L-2, L-10 and L-3 attained minimum

seed weight of 2.4, 2.6, 2.7 and 2.8 g, respectively. Among the testers T-3 showed

maximum 1000-seed weight of 5.0 g whereas T-2 attained minimum 1000-seed weight

of 3.2 g (Table 4.3). Similarly, among the crosses L-7×T-1 attained maximum seed

weight of 5.3 g followed by L-6×T-3 (4.8 g) whereas L-10 × T-2 and L-10 × T-1 attained

minimum 1000-seed weight of 2.3 and 2.4 g, respectively (Table 4.5).

The significant variation among genotypes and crosses suggested combining

ability analysis. Therefore, biplot approach was used to determine GCA and SCA. The

biplot based on 1000-seed weight data described 93.2% (80.3 and 12.9% by first and

second principal components, respectively) of the total variation. Since, maximum

variation is explained by the biplot hence confirmed that L × T data was efficiently

analyzed. The variegated placement of the lines on the ATC x-axis (Fig 4.7a) showed

significant GCA effects. Among the lines, L-7 was identified as the most promising

genotype with maximum positive general combining ability. The lines L-6 and L-8 also

showed good general combining ability whereas the genotypes placed in opposite

direction far away from the origin showed negative GCA effects. Since, the tester T-1

occupied position far away from the origin in positive direction on ATC x-axis hence

considered as good general combiner. The variegated projections of the lines on ATC y-

axis also depicted significant SCA effects. Moreover, the polygon view of the biplot (Fig

4.7b) played an important role in identification of best hybrid combinations (Rastogi, et

al., 2011). The biplot identified two promising cross combinations i.e. L-6 × T-3 and L-

7 × T-1 (Fig 4.7b). The biplot also clarified that GCA effects were relatively higher in

63

magnitude as compare to SCA effects which indicated that additive type of gene action

was more important for controlling 1000-seed weight in genotypes under investigation.

Similar findings were also reported by Farshadfar et al. (2013) who found the importance

of both additive and non-additive type of gene action with predominance of additive type

for seeds per pod in brassica. Several other studies reported earlier (Sabaghnia et al.,

2010; Singh et al., 2010; Delourme et al., 2006) have suggested the predominance of

additive type of gene action responsible for controlling seeds per pod in brassica.

4.1.8 Seed yield plant-1

Results of the data regarding seed yield per plant exhibited significant differences

(P<0.01) among genotypes, parents and F1 hybrids. In line by tester ANOVA, the

variances for lines, testers and interaction (L×T) effect were also found significant

(P<0.01) (Table 4.2). These significant effects clarified enormous variability among

parental genotypes and their capability to transfer this into their hybrids. Seed yield is the

important trait and is obtained from the cumulative response of different yield associated

components. Therefore, the crop species planted for their final product (seeds) are always

looked-for to have cultivars with high seed yield potential. During the present study,

among the parental lines, L-5 attained maximum seed yield per plant (28.2 g) and remain

statistically at par with L-7 (27.7 g) and L-4 (27.6 g). Low yielding line was L-11 which

attained minimum 12.7 g of seed yield per plant. Simillarly, among the testers, T-4

produced maximum seed yield of 28 g which was followed by T-3 by producing 21 g of

seed yield per plant (Table 4.3). Likewise, among the hybrids highest seed yield per plant

of 43.9 g was recorded for L-6 × T-3 followed by L-8 × T-3 (40.0 g) and L-7 × T-1 (38.5

g). Minimum seed yield per plant was observed for four hybrids i.e. L-9 × T-2 (10.8 g),

L-2 × T-2 (11.0 g), L-10 × T-2 (11.3 g) and L-11× T-2 (11.5 g) and they remain

statistically at par (Table 4.5). Interesting cross combinations were observed which

yielded high quantity even their parental genotypes were not high yielding themselves.

This might be due to positive heterosis and good combining abilities which is further

explained in the following section.

64

The biplot regarding combining ability of the parents for seed yield explained

88.1% of the total variation in such a way that PC1 and PC2 accounted for 72.4 and 15.7%

variation, respectively. The projection of the markers of parental genotypes onto ATC x-

axis depicted significant GCA effects. Among the parental genotypes (lines) L7 was

identified as best general combiner, followed by L-6, L-8, L-3 and L-5. Maximum GCA

effects of the above parental lines were confirmed by their placement far away from the

origin and their positive interaction with all the testers. Poor general combining ability

was exhibited by remaining six lines i.e. L-11, L-10, L-9, L-1, L-2 and L-4 since they

interacted negatively with all the testers. Similarly, among the testers, T-1 was identified

as best general combiner since it showed highly discriminating and representative in

nature by occupying position far away from the origin (Fig 4.8a). The polygon view of

the biplot (Fig. 4.8b) identified best hybrid combinations. Among the lines, L-6 and L-7

were identified as best specific combiners since they showed potential to produce

superior and heterotic cross combinations especially with tester T-3 and T-1,

respectively. The poorest specific cross combinations were produced by L-11, L-10 and

L-9, since these lines could not interact positively with any of the testers used in this

study. The placement of parental genotypes on both ATC x-axis and y-axis varied

significantly which clarified that both GCA and SCA effects involved in the expression

of seed yield per plant. Since, GCA and SCA provides estimates for additive and non-

additive genetic effects therefore it can be concluded that both additive and non-additive

gene actions were involved in the inheritance of seed yield per plant trait. However the

magnitude of GCA was comparatively more than the magnitude of SCA. Significant

GCA and SCA effects for yield and its associated components were also reported for

spring and winter types of rapeseed (Rameeh 2010) and (Huang et al. 2010; Sabaghnia

et al., 2010) respectively. Though, these researchers reported that non-additive genetic

effects were more involved in the expression of yield and yield associated traits.

However, in the present study additive gene action was found more important. This might

be due to the differences in the genetic makeup of genotypes used in both studies.

Furthermore, the variation explained by lines and testers collectively

(58.0+15.1% = 73.1%) is much larger than 26.8% which was explained by L × T in

65

ANOVA also confirmed that additive genetic control mechanism was more important as

compared to non-additive for seed yield per plant in the present study. Similar findings

were reported by Farshadfar et al. (2013) who were of the opinion that additive type of

gene action was more important in rapeseed line by tester crosses. Huang et al (2009)

suggested that additive genetic effects were predominant in controlling seed yield per

plant in brassica. The results reported by Ghosh et al. (2002) revealed that both additive

and non-additive type of gene actions were involved in controlling seed yield per plant

in Indian musterd. In contrast, Cheema and Sadaqat (2004) reported involvement of non-

additive genetic effects for seed yield per plant in brassica. It is important to mention,

that the non-additive genetic variance which was significant is also an important component.

This can be utilized in heterosis breeding for the development of high yielding hybrids. High

heterosis coupled with positive general and specific combining ability effects can be

considered as important factors for increasing seed yield (Sincik et al., 2011). This was

further explained by Kadkol et al. (1984) that heterotic performance of a hybrid

combination depends upon the combining abilities of its parents.

4.1.9 Oil content (%)

Analysis of variance regarding oil content revealed significant difference among

genotypes, parents and F1 crosses. Moreover, the main effect for lines and testers and

their interaction effect (L × T) were also found significant (P<0.01) (Table 4.2). These

significant effects indicated diversity of the selected parents and their ability to inherit

this into their cross combinations. Mean values regarding oil content in parental

genotypes are presented in Table 4.3. Among the lines, L-6 showed high oil content

(52.3%), followed by L-7 (44.5%) whereas low oil content was found in L-2 (32.8%).

Among the testers, high oil content (50.9%) was found in T-1 followed by T-4 (48.9%)

whereas low oil content (42.1%) was found in T-2. Mean values regarding oil content in

F1 crosses are presented in Table 4.5. Among the hybrids high oil content (52.6%) was

observed in L-6 × T-1, followed by L-6 × T-4 (48.9%) and L-6 × T-3 (48.7%) whereas

low oil content (36.7%) was found in L-10 × T-2. Moreover, the L-7 in combination with

T-1, T-3 and T-4 also resulted in high oil content.

66

Significant lines, testers and L × T effect suggested further data analysis for

combining ability of parents and F1 crosses. The (L × T) two-way data set was subjected

to biplot analysis for GCA and SCA effects (Yan and Hunt, 2002). The biplot analysis of

oil content data explained 96.6% of the total variation in such a way that 91.5 and 5.1%

variation was counted for the first and second principle components, respectively.

Dispersion of genotypes on ATC x-axis was high as compare to y-axis thereby, advocated

high GCA effects as compare to SCA effects. Among the female parents (lines), L-6

outclassed all other lines in term of GCA effects. High GCA effect of this line was

confirmed by their positive interaction with all the testers. Poor general combining ability

was depicted by L-10, L-2, L-3 and L-8. These four lines interacted negatively with all

the testers. Among the testers, T-1 and T-4 were identified best general combiners since

they showed highly discriminating ability and were representative in nature (Fig 4.9a).

The polygon divided the biplot into four well defined sectors (Fig 4.9b). Line L-6 being

the vertex genotype interacted positively with all testers but resulted in superior cross

combination especially with tester T-1. The worst specific combiners were L-10 and L-

2, since they showed negative interaction with all the testers used in the study. The

placement of parental genotypes onto the ATC abscissa and its ordinate significantly

variegated which pointed towards the importance of both GCA and SCA effects for oil

content with predominance of GCA effects. Thus, additive genetic control mechanism

was counted more important in controlling oil content in the present set of genotypes.

Furthermore, the percent variation explained by both lines and testers collectively (74.1

+ 18.5 = 92.6%) was much larger than 7.4% variance which was explained by L×T (Table

4.2). This also confirmed that additive gene action was more involved as compared to

non-additive gene action for oil content trait in the present study. Similar finding

regarding prevalence of GCA effects for oil content was also reported for winter types of

rapeseed (Sabaghnia et al., 2010; Singh, et al. 2010 and Qian et al. 2007). Moreover,

Farshadfar et al. (2013) also reported prevalence of GCA effects for oil content in

rapeseed line by tester crosses. In earlier study reported by Cheema and Sadaqat (2004)

found both GCA and SCA effects important for the inheritance of oil content in Brassica

napus. On the other hand, Azizinia (2012) reported high SCA effects as compare to GCA

67

effects thus suggested the role of non-additive gene action in control of oil content trait

in Brassica napus.

4.1.10 Glucosinolates

Genotypes varied significantly (P<0.01) for glucosinolates in their seeds. After

further partitioning of genotype sum of square into various components, significant

differences were observed among parents and F1 crosses. Sum of square for F1 was further

partitioned into lines, tester and lines × tester interaction effect. Variances for lines and

L × T were found significant whereas the tester effect was found nonsignificant (Table

4.2). Glucosinolate is considered as one of the prime seed quality trait in oil seed rape.

Canola quality cultivars in brassica are well known internationally due to having <30

µM/g of glucosinolates. After oil extraction the glucosinolates remains in the seed cakes

and if are >30 µM/g then the seed cakes are undesirable for animal feeding. Therefore

genotypes with low glucosinolates are desired for quality rapeseed production. Mean

values for glucosinolates in parental genotypes are presented in Table 4.3. Among the

parental lines, L-2 attained low glucosinolates of 43.8 µM g-1 followed by L-6 (46.4 µM

g-1) whereas high glucosinolate content was found in L-7 (111.8 µM g-1). Among the

testers low glucosinolates (28.8 µM g-1) were found in tester T-1, followed by T-4 (29.0

µM g-1) whereas high glucosinolates were found in tester T-3 (42.9 µM g-1). Mean values

for glucosinolates in F1 crosses are given in Table 4.5. Among the crosses low level of

glucosinolates were found in L-6 × T-4 (34.3 µM g-1) followed by L-6 × T-1 (38.9 µM

g-1) whereas high level of glucosinolates were observed in L-7 × T-3 (86.0 µM g-1).

The significant variation among genotypes and crosses necessitated further

genetic analysis of this trait using biplot approach to determine GCA and SCA effects.

GCA effects of the entries were estimated by their projections onto the ATC x-axis (Fig

4.10a). The projections of the entries onto ATC y-axis exhibited SCA effects which

denote the trend of the entries to result in superior hybrids (Rastogi, et al., 2011).

The biplot for this trait explained 93.8% (81.7 and 12.1% by PC1 and PC2,

respectively) of the total variation, which in turn confirmed efficient analysis of the line

68

× tester data by the biplot. This also verified that the involvement of extra components

was non-significant. The variegated placement of the lines on the ATC x-axis (Fig 4.10a)

revealed significant GCA effects (Bertoia, et al., 2006). Since, low glucosinolates are

required therefore genotypes with negative GCA effects were selected as desirable one.

Among the lines, L-6 showed negative desirable GCA effects since it was placed far away

from the origin in negative direction. Some other lines (L9, L-2, L-10 and L-11) also

exhibited desirable negative GCA effects. On the other hand, L-7 and L-5 occupied

position far away from the origin in positive direction revealed high positive general

combining ability. Other positive good general combiners were L-3, L-4 and L-8.

Likewise among the testers, T-3 and T-4 occupied position far away from the origin on

ATC x-axis thereby considered as good general combiners. Furthermore, the polygon

view of the biplot (Fig 4.10b) also discovered best and worst hybrid combinations

(Rastogi, et al., 2011). Since, genotypes with low glucosinolates are desired therefore,

the line L-6 produced desirable cross combinations especially with tester T-4 both being

opposite to each other (Fig 4.10b). The desirable negative heterotic groups identified by

the biplot were (L-6, L-9) × T-4, (L-6, L-2) × (T-1, T-2, T-3).

It is clear from the biplot analysis that GCA effects were comparatively higher

than the SCA effects which indicated that additive type of gene action was responsible

for the expression of this trait in the present set of genotypes. It is also confirmed from

the high contribution of lines and testers collectively (77.4 + 4.0 = 81.4%) as compare to

7.4% by L×T in total variance (Table 4.2) that additive genetic control mechanism was

predominant for glucosinolate content in the present set of genotypes under investigation.

Sodhi et al. (2002) demonstrated the importance of both additive and non-additive gene

action in the inheritance of glucosinolates in brassica. The findings of Alemayehu and

Becker, (2005) also signified the role of additive, dominance and cytoplasmic effects

with the prevalence of partial dominance in governing total glucosinolates with some

level of over-dominance in some cases.

4.1.11 Erucic acid

69

Analysis of the data regarding erucic acid content revealed significant difference

(P<0.01) among genotypes. After further partitioning of genotype sum of square into

parents and F1 crosses also demonstrated significant results. Sum of square for F1 was

further partitioned into lines, tester and lines × tester which clearly depicted significant

effects (Table 4.2). Erucic acid in oil content is considered as one of the important oil

quality trait in brassica. Low erucic acid in brassica oil is desirable. Brassica oil with high

erucic acid is not preferred for edible use since it not only deteriorates the oil quality but

also impose serious health concerns (Pandey, et al. 2013). Mean values for erucic acid in

parental genotypes are presented in Table 4.3.

Among the parental lines, L-6 and L-8 attained minimum erucic acid of 27.8 and 27.4 %,

respectively whereas high erucic acid content (68.6 %) was found in L-11. Most of the

testers exhibited low erucic acid content especially T-1 with 9.1 % followed by T-2

(12.4%). Mean values for erucic acid in F1 crosses are presented in Table 4.5. Among the

crosses low erucic acid content was found in L-6 × T-1 (11.4 %), L-6 × T-4 (14.8 %), L-

8 × T-4 (18.2 %), L-6 × T-2 (18.7 %) and L-7 × T-4 (19.0 %).

Significant differences among genotypes and crosses necessitated further

inheritance studies of the trait. Therefore, biplot methodology was used to estimate

general and specific combining ability of the parents and crosses, respectively. The biplot

for erucic acid content elucidated 96.6 % of the total variation in such a way that PC1 and

PC2 explained 92.7 and 3.9%, respectively. The dispersion of the lines on the ATC x-axis

(Fig 4.11a) illustrated significant GCA effects (Bertoia, et al., 2006). Among the lines,

L-11 showed high positive GCA effects followed by L-1 and L-10, whereas L-6 being

placed in opposite direction far away from the origin showed negative GCA effects. The

remaining lines were found in a cluster near to the origin therefore were considered as

poor general combiners. Since low erucic acid content is desirable therefore lines with

negative GCA especially L-6, L-7 and L-5 were considered the best. Likewise among the

testers, T-1 and T-3 occupied position far away from the origin on the ATC x-axis thereby

considered as good general combiners as compare to the remaining testers. Similarly,

lines showed different projections on the ATC y-axis therefore indicated that SCA was

also significant (Bertoia, et al., 2006). Since, genotypes having low erucic acid are desired

70

for quality oil production hence the line L-6 being the most opposite in direction produced

the most desirable cross combinations especially with tester T-1 and T-2, respectively

(Fig 4.11b).The biplot analysis also revealed that GCA effects were relatively higher in

magnitude than the SCA effects which clarified that additive type of gene action was

more involved in the expression of erucic acid content in these genotypes. Furthermore,

the greater proportion of the percent sum of square (71.6 + 18.1 = 89.7%) explained by

lines and testers collectively vs 10.3% by L × T (Table 4.2) also confirmed the

predominance of additive type of gene action. Pandey, et al. (2013) also reported that

inheritance of erucic acid was governed by two genes with additive effects.

Table 4.1 Mean squares for various morphological and yield associated traits in parents and F1 crosses evaluated during 2011-12.

Source of

variance df

Days to 50%

Flowering

Plant height (cm) Primary

branches plant-1

Pods on main

raceme

Pod length (cm) Seeds pod-1

Mean

Squares

% of

SS

Mean

Squares

% of

SS

Mean

Squares

% of

SS

Mean

Squares

% of

SS

Mean

Squares

% of

SS

Mean

Squares

% of

SS

Replication 2 19.5 - 4.7 - 0.5 - 1.6 - 0.15 - 0.5 -

Genotype 58 303.3** - 746.2** - 5.3** - 571.0** - 6.1** - 46.2** -

Parents 14 494.9** - 903.9** - 7.0** - 575.7** - 8.3** - 45.3** -

F1s 43 229.3** 56.0 699.5** 69.5 4.9** 67.9 541.8** 70.3 5.5** 66.6 47.0** 75.5

parents vs F1s 1 803.6** - 542.6** - 0.6ns - 1762.6** - 1.8** - 22.8** -

Lines 10 422.7** 42.9 493.2ns 16.4 13.9** 66.4 1601.7** 68.8 14.0** 59.3 122.6** 60.7

Testers 3 1118.1** 34.0 2877.6** 28.7 7.4** 10.6 566.2* 7.3 16.2** 20.7 166.8** 24.7

L × T 30 76.0** 23.1 550.5** 54.9 1.6** 23.0 186.0** 24.0 1.6** 20.0 9.8** 14.6

Error 116 7.7 - 35.9 - 0.5 - 29.2 - 0.3 - 1.7 -

Table 4.2 Mean squares for seed yield and oil quality traits in parents and F1 crosses evaluated during 2011-12.

Source of

variance df

1000-seed weight (g) Seed yield plant-1

(g) Oil content

(%)

Glucosinolate (µMol

g-1

)

Erucic acid (%)

Mean

Squares

% of SS Mean

Squares

% of SS Mean

Squares

% of SS Mean

Squares

% of SS Mean

Squares

% of SS

Replication 2 0.1 - 0.6 - 0.3 - 26.8 - 15.9 -

Genotype 58 1.6** - 192.0** - 48.0** - 992.7** - 315.4** -

Parents 14 3.2** - 98.3** - 104.2** - 2091.4** - 712.9** -

F1s 43 1.1** 50.1 216.6** 83.7 29.7** 45.8 631.6** 47.2 172.0** 40.4

parents vs F1s 1 1.4** - 444.4** - 51.5** - 1137.4** - 917.4** -

Lines 10 2.9** 61.7 540.7** 58.0 94.5** 74.1 2111.1** 77.7 529.1** 71.6

Testers 3 2.5** 16.0 470.0** 15.1 78.8** 18.5 359.7ns 4.0 446.6** 18.1

L × T 30 0.3** 22.3 83.3** 26.8 3.1** 7.4 165.6** 18.3 25.5** 10.3

Error 116 0.03 - 1.7 - 0.1 - 26.2 - 4.4 -

*,** = Significant at 5 and 1% level of Probability, respectively.

45

Table 4.3

Genotypes Mean values for various morpho-yield and oil quality traits of parental genotypes evaluated during 2011-12.

Days to

flowering Plant height

(cm) Primary

Branches Pods main

raceme-1

Pod length

(cm) Seeds

Pod-1

1000 seed

wt. (g) Seed yield

Plant-1

(g) Oil

(%) Glucosinolate

(µMol g-1

) Erucic

acid (%)

L-1 144 163 7 45 5.2 13 3.4 15.7 40.1 79.3 49.3

L-2 134 161 8 44 6.4 19 2.6 20.9 32.8 43.8 35.7

L-3 136 184 7 63 5.5 14 2.8 24.1 36.2 87.2 38.1

L-4 130 188 8 68 5.5 14 2.4 27.6 42.8 78.5 38.9

L-5 133 159 8 44 6.2 20 3.4 28.2 39.3 93.3 31.5

L-6 102 178 9 67 8.2 26 3.7 20.1 52.3 46.4 27.8

L-7 132 162 11 71 10.9 17 5.8 27.7 44.5 111.8 38.9

L-8 120 146 10 63 7.5 18 5.5 20.3 36.5 94.6 27.4

L-9 130 159 7 42 4.7 13 3.3 15.3 40.3 57.2 37.2

L-10 150 150 5 39 6.3 15 2.7 14.3 33.3 64.6 44.6

L-11 147 156 6 42 4.8 13 3.1 12.7 43.2 88.6 68.6

Mean of

Lines 132 164 8 53 6.5 16 3.5 20.6 40.1 76.8 39.8

T-1 150 193 11 85 7.4 21 3.9 16.3 50.9 28.8 9.1

T-2 135 175 8 47 4.5 12 3.2 12.7 42.1 42.1 12.4

T-3 117 208 8 61 6.7 17 5.0 21.0 44.9 42.9 16.9

T-4 137 175 8 45 5.6 19 3.9 28.0 48.9 29.0 18.0

Mean of

Testers 135 188 9 59 6.1 17 4.0 19.5 46.7 35.7 14.1

LSD0.05 2.6 5.6 0.7 5.0 0.5 1.2 0.2 1.2 0.4 4.8 2.0

46

74

Table 4.4 Mean values for various traits in F1 crosses evaluated during 2011-12.

Crosses Days to

flowering

Plant height

(cm)

Primary

branches

Pods main

raceme

Pod

length

Seeds

Pod-1

L-1 × T-1 151 178 8 46 6.3 15

L-2 × T-1 149 172 9 62 7.6 22

L-3 × T-1 144 181 8 53 6.6 17

L-4 × T-1 143 188 9 74 6.4 16

L-5 × T-1 138 169 8 64 7.4 23

L-6 × T-1 141 191 9 81 8.7 23

L-7 × T-1 137 171 11 92 10.3 23

L-8 × T-1 138 162 9 70 7.5 23

L-9 × T-1 140 172 7 61 6.0 13

L-10×T-1 150 148 6 48 7.7 18

L-11×T-1 151 173 7 55 6.1 13

L-1 × T-2 141 160 7 46 4.9 11

L-2 × T-2 137 159 8 52 5.2 17

L-3 × T-2 142 141 7 50 4.6 13

L-4 × T-2 139 148 8 64 5.0 11

L-5 × T-2 135 129 7 54 5.4 17

L-6 × T-2 125 135 9 71 6.7 19

L-7 × T-2 132 166 9 82 8.7 13

L-8 × T-2 146 177 8 60 7.1 19

L-9 × T-2 147 155 6 55 4.6 11

L-10×T-2 156 158 5 38 6.1 14

L-11×T-2 157 159 6 45 4.4 11

L-1 × T-3 130 145 8 53 5.6 15

L-2 × T-3 133 176 8 67 5.8 17

L-3 × T-3 138 156 7 76 6.7 17

L-4 × T-3 133 173 8 70 5.7 19

L-5 × T-3 135 164 8 71 5.7 18

L-6 × T-3 115 168 11 88 7.7 27

L-7 × T-3 135 194 9 63 8.7 22

L-8 × T-3 120 162 10 73 6.8 25

L-9 × T-3 132 158 6 52 5.8 15

L-10×T-3 139 152 9 59 6.5 19

L-11×T-3 137 176 7 42 6.2 17

L-1 × T-4 140 173 8 45 5.4 16

L-2 × T-4 132 157 7 65 8.5 19

L-3 × T-4 134 201 7 72 7.5 17

L-4 × T-4 132 177 8 71 7.3 15

L-5 × T-4 131 176 9 75 7.2 21

L-6 × T-4 121 183 9 73 6.7 21

L-7 × T-4 138 171 10 71 8.4 21

L-8 × T-4 133 188 8 84 8.9 21

L-9 × T-4 134 164 8 48 5.1 15

L-10×T-4 143 155 6 41 6.0 17

L-11×T-4 142 161 7 55 5.2 13

LSD0.05 2.6 5.6 0.7 5.1 0.5 1.2

75

Table 4.5 Mean values for various traits in F1 crosses evaluated during 2011-12.

Crosses 1000-seed wt.

(g)

Seed yield

plant-1

(g)

Oil

(%)

Glucosinolate

(µMol g-1

)

Erucic acid

(%)

L-1 × T-1 3.2 14.7 44.2 54.1 36.1

L-2 × T-1 3.4 17.0 40.0 41.7 24.6

L-3 × T-1 3.1 22.2 42.7 60.1 25.5

L-4 × T-1 3.2 24.3 46.4 54.3 25.9

L-5 × T-1 3.1 23.7 44.2 76.2 22.2

L-6 × T-1 3.6 30.4 52.6 38.9 11.4

L-7 × T-1 5.3 38.5 46.6 79.2 20.9

L-8 × T-1 4.5 30.1 43.0 59.5 25.1

L-9 × T-1 3.6 15.8 45.3 43.0 25.0

L-10×T-1 2.4 15.3 42.2 46.7 28.7

L-11×T-1 3.1 14.5 46.4 58.7 46.7

L-1 × T-2 3.3 14.2 43.0 60.7 41.7

L-2 × T-2 3.0 11.0 38.0 47.7 31.1

L-3 × T-2 3.1 31.3 39.9 54.1 30.1

L-4 × T-2 2.8 18.3 42.2 59.3 32.3

L-5 × T-2 3.2 18.7 40.8 80.2 26.8

L-6 × T-2 3.2 27.4 45.5 41.9 18.7

L-7 × T-2 4.2 32.5 44.2 85.2 25.5

L-8 × T-2 3.0 26.1 39.9 63.5 24.8

L-9 × T-2 3.1 10.8 41.1 48.0 29.7

L-10×T-2 2.3 11.3 36.7 50.7 33.4

L-11×T-2 2.4 11.5 41.3 61.7 47.1

L-1 × T-3 3.5 23.2 42.5 49.2 29.8

L-2 × T-3 3.9 23.7 40.9 45.0 31.7

L-3 × T-3 3.8 34.7 41.1 83.4 29.2

L-4 × T-3 3.1 22.9 44.1 74.0 29.6

L-5 × T-3 3.6 34.0 41.2 85.2 25.9

L-6 × T-3 4.8 43.9 48.7 43.1 23.5

L-7 × T-3 4.2 25.9 46.8 86.0 24.6

L-8 × T-3 3.8 40.0 41.9 57.0 28.8

L-9 × T-3 3.5 19.3 42.3 50.5 33.7

L-10×T-3 2.7 16.7 38.4 59.0 32.4

L-11×T-3 3.1 18.7 43.8 58.9 44.5

L-1 × T-4 3.1 21.8 43.8 54.2 27.1

L-2 × T-4 3.6 33.4 40.9 66.0 19.5

L-3 × T-4 3.7 24.0 41.6 82.4 23.6

L-4 × T-4 3.2 25.0 46.0 77.9 23.9

L-5 × T-4 3.5 37.4 44.9 72.8 20.2

L-6 × T-4 4.1 23.5 48.9 34.3 14.8

L-7 × T-4 4.3 37.1 46.8 73.7 19.0

L-8 × T-4 3.7 28.3 41.9 72.8 18.2

L-9 × T-4 3.6 21.7 42.7 43.1 25.3

L-10×T-4 3.3 21.2 38.4 46.8 29.6

L-11×T-4 3.5 18.4 42.7 61.7 32.6

LSD0.05 0.15 1.21 0.36 4.77 1.96

76

4.1a 4.1b

Fig. 4.1 Biplots based on days to 50% flowering data explaining combining ability

(4.1a) and specific cross combinations (4.1b) in brassica genotypes.

4.2a 4.2b

Fig. 4.2 Biplots based on plant height data explaining combining ability (4.2a) and

specific cross combination (4.2b) in brassica genotypes.

77

4.3b

4.3a

Fig. 4.3 Biplots based on primary branches per plant data explaining combining

ability (4.3a) and specific cross combination (4.3b) in brassica genotypes.

4.4a 4.4b

Fig. 4.4 Biplots based on pods on main raceme data explaining combining ability

(4.4a) and specific cross combination (4.4b) in brassica genotypes.

78

4.5a 4.5b

Fig. 4.5 Biplots based on pod length data explaining combining ability (4.5a) and specific

cross combination (4.5b) in brassica genotypes.

4.6a 4.6b

Fig. 4.6 Biplots based on seeds per pod data explaining combining ability (4.6a) and


79

4.7b

4.7a

Fig. 4.7 Biplots based on 1000 seed weight data explaining combining ability (4.7a)

and specific cross combination (4.7b) in brassica genotypes.

4.8b

4.8a

Fig. 4.8 Biplots based on seed yield per plant data explaining combining ability (4.8a) and


80

4.9a 4.9b

Fig. 4.9 Biplots based on oil content data explaining combining ability (4.9a) and


4.10a 4.10b

Fig. 4.10 Biplots based on glucosinolates data explaining combining ability (4.10a) and specific

cross combination (4.10b) in brassica genotypes.

81

4.11a 4.11b

Fig. 4.11 Biplots based on erucic acid data explaining combining ability (4.11a) and


4.2 Generation Mean Analysis

Combining ability studies revealed four crosses viz., (L-6 × T-1, L-6 × T-3, L-7

× T-1 and L-7 × T-3) performed better for majority of the traits, therefore these crosses

and their four parents (L-6, L-7, T-1 and T-3) were selected for further inheritance studies

via generation mean analysis. The data obtained from various generations (P1, P2, F1, F2,

BC11 and BC12) under two different environments (irrigated and rainfed) were subjected

to combined analysis of variance approach for testing significance of environments and

genotypes main effect and genotype by environment interaction effect. After that

generation mean analysis for inheritance pattern of various important traits were carried

out to formulate suitable breeding scheme and isolate potential segregants for

manipulation in future breeding programme.

82

The results obtained from statistical analysis of the studied characters are presented

below to describe several features of the experimental material. 4.2.1 Inheritance of

drought stress related traits at seedling stage

i) Relative water content (%)

Analysis of variance revealed significant (P<0.05) differences among the

genotypes and between the environments for relative water content (RWC). Likewise, G

× E interaction effect was also found significant (Table 4.6). Of the total variation,

maximum was explained by environment main effect (55.4 %) followed by genotype

main effect (32.4 %) whereas the G × E contributed 10.1 % in the total variation. Mean

values for RWC of various 20 genotypes under irrigated as well as rainfed environment

and percent reduction under drought are presented in Table 4.7. Mean RWC under

irrigated environment was 67 % whereas under drought it was reduced to 55 %. Similarly,

for genotypes the mean values for RWC ranged from 52 % (BC11 of L-7 × T3) to 72 %

(T-3). In majority of the genotypes, marked reduction in RWC was observed due to

drought stress condition. Maximum reduction in relative water content (30 %) due to

drought stress was observed in BC11 generation of L-6 × T-3, followed by F2 generation

of the same cross combination and its first parental genotype (L-6) with 28 % reduction

in RWC. However, the reduction was slight in parental genotypes L-7 (6 %) and T-3 (8

%).

Significant effect for genotypes necessitated further analysis of the data for

variability among the generations of each cross combination across environments (Table

4.8). The environment main and Gen × E effects were found significant for all four

crosses. However, the generation main effect was found significant for only L-7 × T-1

and L-7 × T-3. Since Gen × E effect was significant hence data for the generations of

each cross was further analyzed under each environment i.e. irrigated and rainfed (Table

4.9). Significant differences were observed among the generations of all four cross

combinations under irrigated as well as rainfed conditions.

Mean values regarding relative water content of various generations of each cross

combination under irrigated and rainfed conditions along with percent reduction in RWC

due to drought stress condition are presented in Table 4.10. Under irrigated condition

83

among the six generations of L-6 × T-1, maximum RWC (75%) was found in F1

generation whereas minimum RWC (70%) was observed in BC11. Likewise, under

rainfed condition maximum RWC (61%) was found in F1 generation whereas minimum

(46 %) was recorded for P1. Reduction in RWC due to drought stress was minimum in

P2 (16 %), followed by BC12 (18 %) and F1 (19 %). Among the six generations of L-6 ×

T-3 under irrigated condition BC12 generation showed minimum RWC (66 %) whereas

P2 exhibited maximum (74 %) and remain statistically at par with P1 and F2 generations

of the same cross combination. Similarly, under rainfed condition P1 of the same cross

combination showed minimum RWC (46 %), followed by BC11 (47 %) whereas P2

generation showed maximum (57 %) of RWC. Reduction in RWC among the generations

of this cross combination was minimum in P2 (7 %). Among various generations of L-7

× T-1 under irrigated condition P1 generation attained minimum (56 %) RWC whereas,

P2 generation showed maximum RWC (71 %). Likewise under rainfed condition P1 and

BC11 generations of the same cross combination attained minimum RWC (53 %) whereas

P2 exhibited maximum RWC (59 %). All generations of this cross combination showed

a slight reduction in RWC, especially P1 generation with minimum reduction of 6 %.

Among the six generations of L-7 × T-3 under irrigated condition RWC ranged from 56

% (P1 and BC11) to 74 % (P2), whereas under rainfed condition it ranged from 49 %

(BC11) to 57 % (P2). The reduction in RWC in various generations of this cross

combination due to drought stress was comparatively very low, especially in parental

genotypes P1 and P2 which showed minimum reduction of 6 and 7 %, respectively.

Since, Gen × E effect in all crosses was significant therefore genetic analysis was

carried out for each cross combination under each environment. For inheritance studies,

generation mean analysis (Hayman, 1958) was performed to estimate various parameters

i.e. mean (m), additive effect (d), dominance effect (h), additive × additive effect (i),

additive × dominance effect (j) and dominance × dominance effect (l). Estimates of

genetic effects in six parameter model regarding relative water content along with chi-

square values of joint scaling test under irrigated as well as rainfed condition are given

in Table 4.11. The joint scaling test revealed significant chi-square values for all cross

combinations, thereby indicated the adequacy of six parameter model for the

interpretation of genetic pattern including epistasis for the trait under investigation.

84

Analysis of the data regarding genetic estimates exhibited significant additive

genetic effects for all crosses under irrigated as well as rainfed conditions. Likewise, the

dominance component was also found significant for most of the crosses except L-6 ×

T-1 under both irrigated and rainfed conditions and for L-7×T-1 under irrigated

condition. The magnitude of dominance effect was greater in L-6 × T-3 and L-7 × T-3

under both environments, thereby indicated the importance of dominance type of gene

action playing role in the inheritance of RWC in these two genotypes. However, in the

rest of the crosses involvement of both additive and dominance gene action was revealed.

Cross combination (L-6 × T-1) exhibited significant additive and nonsignificant

dominance component, thereby indicated the major role of additive type of gene action

responsible for the expression RWC in this specific cross.

Involvement of additive × additive type of epistasis (i type) in inheritance of RWC

was evidenced in all crosses except L-6 × T-1 under both the environments. The j type

of epistasis (additive × dominance) was also found significant in all of the crosses under

irrigated as well as rainfed conditions. The l type of epistasis (dominance × dominance)

in most of the crosses was greater and significant under both environments except L-7 ×

T-1 which showed consistently non-significant l component under irrigated as well as

rainfed condition. Significant and greater magnitude of these nonallelic interactions in

most of the crosses indicated the complex pattern of inheritance for RWC in these

genotypes.

In cross (L-6 × T-1), where only additive gene action was evidenced, simple

selection in early generation for the improvement of RWC might be performed for

fruitful results. However, in L-6 × T-3 and L-7 × T-3 due to presence of higher magnitude

of dominance effects along with dominance × dominance type of non-allelic interaction,

delayed selection till advance generation would be effective. Cross combination L-7 ×

T-1 exhibited different genetic effects under irrigated and rainfed conditions, therefore

specific selection criteria should be followed under each environment. Such that under

irrigated condition with significant additive effect selection in early generation might

give good results whereas under rainfed condition with significant additive as well as

non-additive effects delayed selection would be fruitful.

85

Significant variation among the generations of all four cross combinations under

irrigated as well as rainfed conditions indicated presence of genetic variability for water

potential in seedling stage. Such variation among genotypes is of a prime importance for

breeding work and a success to develop cultivars for specific environment. The

predominance of non-additive type of gene action with differential performance

regarding RWC in brassica seedlings under water stress has been already reported by

Cheema and Sadaqat (2004). Leaf RWC is considered as one of the best biochemical

indices for drought stress (Alizade 2002). Minimum reduction in RWC under drought

stress condition of parental genotypes (L-7 and T-3) signified their potential to withhold

water during stress condition and provided opportunity to be used as potential parents for

development of drought tolerant cultivar. Similarly, the segregating generations of L-7 ×

T-1 and L-7 × T-3 also possessed slight reduction in RWC under drought condition

therefore they might have potential segregants for selection and development of drought

tolerant cultivars. To carryout selection for segregants with water holding ability at

seedling stage under controlled environment is highly hopeful, moreover it not only save

the time and resources but minimize the environmental error as well. Positive association

between selection at seedling under controlled environment and field condition has also

been reported by Nagarajan and Rane (2000) in wheat crop.

ii) Proline content

Analysis of variance for proline content revealed significant (P<0.05) differences

among genotypes and environments. Likewise, the G × E interaction effect was also

found significant (Table 4.12). Maximum variation was explained by environment main

effect (76.8 %) followed by genotype main effect (17.42%) whereas the G × E

contributed 5.5% in the total variation. Mean values for relative water content of various

20 genotypes under irrigated as well as rainfed environment and percent change in

proline content due to drought are presented in Table 4.13. Overall the proline content

under irrigated environment was 49 %, however under drought this was increased up to

82 %. Similarly, for genotypes mean proline content ranged from 54 % (L-6) to 75 % (F1

and F2 of L-7 × T-1). In most of the genotypes significant increase in proline content was

detected due to drought stress. Maximum increase in proline content (91 %) was observed

in parental genotype L-7, followed by BC11 generation of (L-7 × T-3) with 87 % increase

86

in proline content however, minimum increase (43 %) was observed in F1 (L-6 × T-1)

followed by the first parent of the same cross combination (L-6) with 46 % increase.

Significant effect for genotypes necessitated further analysis of the data for

variability among the generations of each cross combination across environments (Table

4.14). The environment main effect and Gen × E effect were found significant for all four

crosses. However, the generation main effect was found significant for only two crosses

(L-6 × T-1 and L-7 × T-3). Upon significant Gen × E effect further analysis of the data

for generations of each cross was carried out under each environment i.e. irrigated and

rainfed (Table 4.15). As a result significant differences were observed among the

generations of all four cross combinations under irrigated as well as rainfed condition

except L-6 × T-3 and L-7 × T-1 under irrigated condition.

Mean values regarding proline content of various generations of each cross

combination under irrigated and rainfed condition along with percent increase in proline

content due to drought stress are presented in Table 4.16. Under irrigated condition

among the six generations of L-6 × T-1, minimum proline content (44 µMol g-1) was

observed in P1 whereas maximum (55 µmol g-1) was found in P2 generation. Likewise,

under rainfed condition minimum proline content (65 µMol g-1) was recorded for P1

generation whereas maximum (87 µmol g-1) was found in P2 generation. Increase in

proline content due to water stress was maximum in P2 (57 %) followed by BC12 (50

µMol g-1). Among generations of L-6 × T-3 under irrigated condition non-significant

difference were observed. However, under rainfed condition P1 and BC11 generations of

the same cross combination showed minimum proline content 65 and 68 µMol g-1,

respectively, whereas P2 generation showed maximum (74 µMol g-1) proline content.

Increase in proline content among the generations of this cross combination was high in

P2 (70 %). Among various generations of L-7 × T-1 under irrigated condition non-

significant differences were observed. However under rainfed condition P2 generation of

L-7 × T-1 attained minimum proline content (87 µMol g-1) whereas P1 exhibited

maximum proline content (105 µMol g-1). Maximum increase in proline content was

observed in P1 generation (91 %). Among various generations of L-7 × T-3 under

irrigated condition proline content ranged from 44 µMol g-1 (P2) to 55 µMol g-1 (P1),

whereas under rainfed condition it ranged from 74 µMol g-1 (P2) to 105 µMol g-1(P1).

87

Increase in proline content due to drought stress in generations of this cross combination

was maximum in P1 (91%).

Since, Gen × E effect in all of the crosses was found significant hence genetic

analysis was carried out for each cross combination under each environment. Estimates

of genetic effects in six parameter model regarding proline content along with chisquare

values under irrigated as well as rainfed condition are given in Table 4.17. The joint

scaling test (Cavalli, 1952) revealed significant chi-square values for all cross


interpretation of genetic pattern including epistasis for the trait under study.


genetic effects for all crosses under irrigated as well as rainfed conditions. Likewise, the

non-additive component was found significant for two crosses (L-6 × T-3 and L-7 × T-

3) under irrigated condition. The magnitude of additive component in all crosses

increased under rainfed conditions. Significant and greater magnitude of additive effects

under rainfed condition clarified the major role of additive type of gene action

responsible for the expression this trait. However under irrigated condition the significant

and greater magnitude of non-additive effect for two crosses (L-6 × T-3 and L-7 × T-3)

revealed the importance of dominance gene action for the inheritance of this trait in these

cross combinations.

Involvement of additive × additive type of epistasis (i type) in inheritance of this trait

was evidenced in most of the crosses under irrigated condition except L-6 × T-1. Whereas

under rainfed condition all of the crosses exhibited non-significant i type of epistasis.

Likewise, the j type of epistasis (additive × dominance) was found significant in all of

the crosses under rainfed condition and in two crosses (L-6 × T-1 and L-7 × T3). The l

type of epistasis (dominance × dominance) was found significant in most of the crosses

under irrigated condition except L-7 × T-1. Similarly under rainfed condition only one

cross (L-6 × T-3) exhibited significant j type of epistasis.

Proline is widely known as a protectant biochemical in various plants under stress

condition which protects subcellular components and macromolecules under osmotic

stress condition (Ali et al. 2013; Szabados and Savoure, 2009). The biosynthesis of

88

proline in plants is triggered by drought stress along with other component involved in

the proline synthetic pathway (Ueda et al. 2001). Significant variation among the

generations of all four cross combinations under both environments especially under

rainfed conditions indicated presence of genetic variability for proline content in these

genotypes. Such variation among genotypes is of a prime importance for breeding work

and a success to develop cultivars for specific environment. Role of proline against

various abiotic stresses has been reported in different crops, for drought tolerance in rice

(Choudhary et al. 2005), salt and cold tolerance in Arabidopsis (Liu and Zhu, 1997; Xin,

and Browse, 1998), drought stress in Matricaria chamomilla (Pirzad et al. 2011), and for

drought tolerance in wheat (Farshadfar et al. 2015) and in Faba bean (Ali et al. 2013).

In the present study maximum increase in proline content due to drought stress

was observed in parental genotypes (L-7 and T-1) which signified their potential to cope

drought stress and provided opportunity to be used as potential parents for development

of drought tolerant cultivar. Similarly, the segregating generation of L-7 × T-1 also

possessed increase in proline under rainfed condition therefore they might have potential

segregants for selection and development of drought tolerant cultivars. To carryout

selection for segregants with high proline content at seedling stage under controlled

environment is highly hopeful. Positive association between selection at seedling under

controlled environment and field condition has also been reported by

Nagarajan and Rane (2000) in wheat crop. Moreover, the cross (L-7 × T-1) along with (L-6 × T-

1) clearly exhibited significant additive effects under both environments, therefore simple

selection in early generation for the improvement of proline content might be performed for

fruitful results. Similarly, under rainfed condition results regarding additive component were

much clarified in all crosses, therefore under drought stress selection can be made for high

proline segregants in early generation of all crosses. Genetic analysis of proline content has been

reported in wheat (Farshadfar, et al. 2015) who demonstrated that additive type of gene action

was responsible for the inheritance of proline content in wheat. It has been reported by Xue, et

al (2009) that during drought stress biosynthesis of proline is activated and its catabolism is

repressed, however upon availability of water this biosynthetic pathway is regulated in opposite

direction. They further stated that two genes are responsible for biosynthesis of proline.

iii) Chlorophyll content

89

Results of the data regarding relative chlorophyll content revealed significant

(P<0.05) differences among the genotypes and between the environments. Likewise, the

G × E interaction effect was also found significant (Table 4.18). Of the total, maximum

variation was explained by genotype main effect (65.4 %), followed by environment

main effect (28.4 %) whereas the G × E contributed 2.71 % in the total variation. Mean

values for chlorophyll content of various genotypes under irrigated as well as rainfed

environment and percent reduction due to drought stress are presented in Table 4.19.

Mean relative chlorophyll content under irrigated environment was 51 mg cm-2 whereas

under drought it was reduced to 46 mg cm-2. Similarly, among the genotypes mean

minimum chlorophyll content (42 mg cm-2) was found in parental genotype L-6 and BC11

of L-6 × T-3. In most of the genotypes noticeable change in chlorophyll content was

observed when shifted from irrigated to drought condition. Maximum change in

chlorophyll content (-19 %) due to drought stress was observed in BC11 generation of L-

6 × T-1, followed by its first parental genotype (L-6) with -17 % change. However, the

reduction was slight (-6 %) in parental genotype T-3. Moreover, the parental genotypes

L-7 and T-3, along with their non-segregating and segregating generations consistently

showed slight reduction in chlorophyll content.

Significant effect for genotypes necessitated further analysis of the data for variability

among the generations of each cross combination across environments (Table 4.20). The

environment and generation main effects were found significant for all four crosses. However,

the Gen × E interaction effect was found significant for only two crosses (L-6 × T-1 and L-6 ×

T-3). Therefore, data for the generations of these cross combinations was further analyzed under

each environment i.e. irrigated and rainfed (Table 4.21). As a result significant differences were

observed among the generations of these cross combinations under irrigated as well as rainfed

condition.

Mean values regarding relative chlorophyll content of various generations of each

cross combination under irrigated and rainfed condition along with percent reduction in

chlorophyll content due to water deficit are presented in Table 4.22. Under irrigated

condition among six generations of L-6 × T-1, low chlorophyll content (46 mg cm-2) was

observed in P1 whereas maximum (53 mg cm-2) was found in P2 generation. Likewise,

under rainfed condition minimum chlorophyll content (38 mg cm-2) was recorded for P1

followed by BC11 generations (39 mg cm-2) whereas maximum (48 mg cm-2) was found

90

in P2 generation followed by F1 (47 mg cm-2). Reduction in chlorophyll due to drought

stress was minimum (-8 %) in F1 and BC12 generations. Among the generations of L-6 ×

T-3 under irrigated condition BC11 showed minimum chlorophyll content (45 mg cm-2)

and remain statistically at par with P1 and BC12 both with 46 mg cm-2 whereas P2

exhibited maximum (49 mg cm-2) and remain statistically at par with F1 (48 mg cm-2) of

the same cross combination. Similarly, under rainfed condition P1 generation of the same

cross combination showed minimum chlorophyll content (38 mg cm-2), whereas P2

generation showed maximum (46 mg cm-2) chlorophyll content. Reduction in chlorophyll

content among the generations of this cross combination was minimum in P2 (-6 %)

followed by BC12 (-9 %). Among various generations of L-7×T-1 reduction in

chlorophyll content due to drought stress was low in P2 and BC11 (-9 %). Similarly,

among the six generations of L-7 × T-3, the change in chlorophyll content was

comparatively very low (-6 %) especially in parental genotypes P2 and all three

segregating generations.

Since, Gen × E effect in two out of four crosses (L-6 × T-1 and L-6 × T-3) was

found significant therefore genetic analysis for these crosses was carried out under each

environment, whereas for the remaining two crosses the pooled data was used for

estimation of genetic effects. Estimates of genetic effects in six parameter model

regarding chlorophyll content along with chi-square values under irrigated as well as

rainfed condition are given in Table 4.23. The joint scaling test revealed significant

chisquare values for all cross combinations, thereby indicated the adequacy of six

parameter model for the interpretation of genetic pattern including epistasis for the trait

under study.


genetic effects for all crosses in pooled data. Since Gen × E for L-6 × T-1 and L-6 × T3

was significant hence the genetics estimates for these crosses under each environment

are presented. Additive effects for these two crosses were significant under irrigated as

well as rainfed condition except for L-6 × T-3 under irrigated. Moreover, the magnitude

of additive effects was high under rainfed as compare to irrigated condition. Likewise,

the non-additive component was also found significant for crosses under pooled analysis

and for two crosses under irrigated as well as rainfed condition. The magnitude of non-

additive component was greater as compare to additive component, thereby indicated the

91

importance of dominance type of gene action in the inheritance of chlorophyll content in

these genotypes. In one cross (L-6 × T-1) under rainfed condition both additive and

dominance effects played equal role in the inheritance of chlorophyll content. For crosses

(L-7 × T-1 and L-7 × T-3) under pooled data both additive and non-additive effects were

significant with predominance of non-additive effects. Moreover, the non-additive

effects were in negative direction.

Involvement of additive × additive type of epistasis (i type) in inheritance of this

trait was evidenced in all the crosses except L-6 × T-1 under both the environments. The

j type of epistasis (additive × dominance) was also found significant in one cross L-7 ×

T-1. Moreover, under irrigated and rainfed conditions both the crosses exhibited

significant j type of epistasis except L-6 × T-1 under irrigated condition. The l type of

epistasis (dominance × dominance) in most of the crosses was greater and significant

except L-6 × T-1 under irrigated condition. Significant and greater magnitude of these

non-allelic interactions in most of the crosses indicated the complex pattern of

inheritance for chlorophyll content in these genotypes. Since both additive as well as

non-additive effects were significant with predominance of nonadditive type of gene

action along with epistatic effects suggested delayed selection for the improvement of

this trait in these genotypes.

Among the various abiotic stresses (heat, salinity and freezing etc) drought stress

is of a more importance that limits growth and productivity of crop plants (Shinozaki et

al., 2002). Drought is widely spread problem which not only reduce the yield and quality

of crop plants but affects plant physiological and biochemical processes as well

(Moghadam et al., 2011). It is a fact that carbon is the basic essential component for plant

growth and development and it affects the process of photosynthesis. The metabolism of

carbon has a direct influence on photosynthate and in turn on yield and quality attributes

in plants. Moreover the process of photosynthesis is directly related to chlorophyll

content in plants (Wang et al 2013). Like other secondary traits chlorophyll content is

also genetically associated with seed yield under drought stress and can be used one of

the criteria for drought tolerance. Chlorophyll content has been used as one the selection

criteria for drought tolerance in maize by Monneveux et al. (2008) and Campos et al.

(2004). It has been reported by Cowley and Luckett (2011) that the reaction sites of

photo-system II are more sensitive to heat and drought stress. Therefore, high chlorophyll

92

content under drought stress condition can be used as one of the criteria for drought

tolerance in plants.

Minimum reduction in chlorophyll under water stress condition of parental

genotypes (T-1 and T-3) signified their potential to stay green during stress condition and

provided opportunity to be used as potential parents for development of drought tolerant

cultivar. Similarly, the segregating generations of L-7×T-3 also possessed a slight

reduction in chlorophyll under rainfed condition therefore they might have potential

segregants for selection and development of drought tolerant cultivars. To carryout

selection for segregants with stay green ability under controlled environment at seedling

stage is highly hopeful. Positive association between selection at seedling under

controlled environment and field condition has also been reported by Nagarajan and Rane

(2000) in wheat crop. Regarding inheritance of chlorophyll content in the present set of

crosses, it has been revealed that both additive and non-additive type of gene actions

along with additive × additive and dominance × dominance type of epistasis were

involved in controlling this trait. During joint segregation analysis Wang, et al. (2013)

observed that chlorophyll content is controlled by two major genes with additive,

dominance effects and interaction effects in maize crop. However, Fadshadfar et al.

(2011) reported only additive type of gene action responsible for the inheritance of

chlorophyll content in brassica. Similarly, in other crops like rice and cucumber Li et al.

(2009) reported that total chlorophyll content was controlled by many genes with minor

effect.

4.2.2 Correlation among RWC, Proline and Chlorophyll content

Genetic association among the above traits was carried out following GGEbiplot

technique (Yan 2001). The results regarding correlation among these traits are presented

in Figure 12. Since, the angles between the vectors of proline and chlorophyll content

were found smaller than 90˚ hence indicated strong and positive relationship among these

two traits under irrigated as well as drought stress conditions. Moreover, collectively

these two traits showed no or negative relationship with RWC. In response to water stress

condition decrease in chlorophyll content and increase in proline accumulation usually

take place (Gibon et al. 2000). Since proline is widely accepted as a protectant

biochemical in various plants under stress condition (Ali et al. 2013; Szabados and

93

Savoure, 2009) therefore positive association of proline with chlorophyll might be due

to the protectant properties of proline.

Table 4.

94

6 Analysis of variance for 20 brassica genotypes evaluated for relative water

content across two different environments.

Sources of variance df Mean Squares % of total SS

Environment(E) 1 4349** 55.4

Rep (E) 4 2.8 0.14

Genotype (G) 19 133.7** 32.4

G×E 19 41.8** 10.1

Error 76 2.1 1.99

**=significant at 1% level of probability.

Table 4.7 Mean values for relative wa genotypes across

two different environments.

ter content and perc

ent reduction of 20

S. No. Generation Genotypes Irrigated Rainfed Mean Reduction

(%)

1

2

3 Parents

L-6

L-7

T-1

73

56

71

53

53

59

63

55

65

-28 -

6

-16

4 T-3 74 69 72 -7

5

6

7 F1

L-6×T-1

L-6×T-3

L-7×T-1

75

72

65

61

55

56

68

63

60

-19

-24

-13

8 L-7×T-3 59 53 56 -11

9

10

11 F2

L-6×T-1

L-6×T-3

L-7×T-1

72

73

62

57

52

53

65

62

57

-21

-28

-15

12 L-7×T-3 63 54 58 -14

13

14

15

16

17 Back

crosses

(L-6×T-1)×L-6

(L-6×T-1)×T-1

(L-6×T-3)×L-6

(L-6×T-3)×T-3

(L-7×T-1)×L-7

70

72

70

66

62

53

60

49

51

53

62

66

59

59

57

-24

-18

-30

-22

-16

18 (L-7×T-1)×T-1 67 57 62 -14

19 (L-7×T-3)×L-7 56 49 52 -13

20 (L-7×T-3)×T-3 61 52 57 -13

Mean 67 55 -18

LSD0.05 for Environment = 2.20

LSD0.05 for Genotypes = 6.37

LSD 0.05 for G × E = 1.35

Table 4.

95

8 Combine analysis of variance for relative water content of various


rainfed conditions.

Sources of variances df Mean squares

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment (E) 1 2426.9** 3743.8** 569.4** 636.5**

Reps (E) 4 0.6 1.0 0.7 0.6

Generations (Gen) 5 55.3NS 47.6NS 78.7* 128.0*

Gen × E 5 49.0** 31.7** 14.2** 29.7**

Pooled error 20 1.5 1.5 1.8 2.0

CV % 1.90 2.03 2.26 2.49

*,** significant at 5 and 1 % level of probability respectively, ns= non-significant, df= degrees of

freedom

Table 4.9 Mean squares from analysis of variance for relative water content of

various generations evaluated under two different environments.

Rainfed Rainfed Rep 2 0.3 1.0 1.20 0.7 1.06 0.30 0.69 0.49

Generations 5 8.6** 95.8** 28.01** 51.2** 71.82** 21.07** 134.58** 23.08**

Error 10 1.54 1.40 1.65 1.4 2.04 1.64 2.16 1.91

CV % 1.72 2.12 1.80 2.33 2.24 2.29 2.39 2.60


freedom, SOV=source of variance

Table 4.10 Mean performance of generations derived from four crosses for relative water

content under irrigated and rainfed conditions.

Mean values

Gen. L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

% % % % Irrigated Rainfed Red. Irrigated Rainfed Red. Irrigated Rainfed Red. Irrigated Rainfed Red.

P1 73 46 -28 73 46 -28 56 53 -6 56 53 -6 P2 71 59 -16 74 57 -7 71 59 -16 74 57 -7

F1 75 61 -19 72 51 -24 65 56 -13 59 53 -11

F2 72 57 -21 73 52 -28 62 57 -15 63 54 -14

BC11 70 53 -24 70 47 -30 62 53 -16 56 49 -13

BC12 72 60 -18 66 51 -22 67 57 -14 61 52 -13

LSD0.05 1.31 1.24 1.35 1.24 1.50 1.34 1.54 1.45

SOV df

Mean squares

L - 6 ×T - 1 L - 6 ×T - 3 L - ×T 7 - 1 L - 7 ×T - 3

Irrigated Rainfed Irrigated Rainfed Irrigated Irrigated

Table 4.

96

Gen. = Generations

11 Estimates of genetic effects for relative water content in different crosses

under different environments.

Non-allelic Crosses

Irrigated interaction

L-6×T-1 72.35** -2.30** -1.27 NS -4.23 NS -3.46** 12.69** 37.5** -

L-6×T-3 72.52** 3.99** -21.10** -19.34** 4.49** 39.43** 53.9** Duplicate

L-7×T-1 62.25** -4.69** 3.43NS 9.51* -4.69** 2.90 NS 37.6** -

L-7×T-3 62.82** -4.70** -24.28** -18.30** 4.18** 34.27** 65.7** Duplicate

Rainfed

L-6×T-1 56.85** -6.47** 2.40 NS -1.98 NS -3.24* 9.88* 12.7** -

L-6×T-3 51.98** -2.87** -14.14** -7.90* 5.13** 39.02** 94.6** Duplicate

L-7×T-1 52.68** -4.64** 5.93* 9.09** -4.64** 2.29 NS 72.6** -

L-7×T-3 54.15** -3.58** -22.22** -14.01** 4.41** 38.73** 95.2** Duplicate

m= mean, d= additive, h= dominance, i= additive × additive, j= additive × dominance, l=

dominance × dominance, 2= Chi square *,** significant at 5 and 1 % level of probability

respectively, NS= non-significant.

Table 4.12 Analysis of variance for 20 brassica generations evaluated for proline content

across two different environments.


Environment(E) 1 32547** 76.81

Rep (E) 4 0.42 0.00

Genotype (G) 19 388.55** 17.42

G × E 19 123.27** 5.53

Error 76 1.32 0.24


m d h i j l 2

Table 4.

97

13 Mean values for proline content (µMol g-1) and percent increase of 20

genotypes across two different environments.

S. No. Generation Genotypes Irrigated Rainfed Mean Increase (%)

1

2

Parents 3

L-6

L-7

T-1

44

55

55

65

105

87

54

80

71

46

91

57

4 T-3 44 74 59 70

5

6

7 1 F

L-6×T-1

L-6×T-3

L-7×T-1

51

44

53

72

71

97

61

57

75

43

61

83

8 L-7×T-3 51 89 70 77

9

10

11 2 F

L-6×T-1

L-6×T-3

L-7×T-1

50

44

54

74

70

97

62

57

75

48

59

80

12 L-7×T-3 49 88 69 78

13

14

15

16 Back

17 crosses

(L-6×T-1)×L-6

(L-6×T-1)×T-1

(L-6×T-3)×L-6

(L-6×T-3)×T-3

(L-7×T-1)×L-7

47

51

43

42

54

69

77

68

70

98

58

64

55

56

76

49

50

57

66

80

18 (L-7×T-1)×T-1 55 92 73 66

19 (L-7×T-3)×L-7 49 92 70 87

20 (L-7×T-3)×T-3 46 86 66 86

Mean 49 82 67

LSD0.05 for Environment = 0.85 LSD0.05 for Genotypes = 10.95 LSD 0.05 for G × E = 1.08

Table 4.14 Combine analysis of variance for proline content of various generations


conditions.

Sources of variance df Mean squares

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Table 4.

98

Environment (E) 1 5332.2** 6094.4** 15489.0** 14424.4**

Reps (E) 4 0.3 0.2 0.2 0.8

Generations (G) 5 190.6** 16.1NS 57.3NS 289.1*

Gen × E 5 23.0** 17.0** 63.1** 61.4**

Pooled error 20 1.5 1.4 1.4 1.3

CV % 1.99 2.06 1.56 1.63


freedom

15 Mean squares from analysis of variance for proline content of various

generations evaluated under irrigated and rainfed conditions.

SOV

Mean squares

df L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Irrigated Rainfed Irrigated Rainfed Irrigated Rainfed Irrigated Rainfed

Rep 2 0.1 0.4 0.06 0.4 0.09 0.39 1.18 0.39

Generations 5 44.2** 169.4** 1.81NS 31.3* 2.93NS 117.52** 46.74** 303.78**

Error 10 1.6 1.4 1.34 1.4 1.35 1.39 1.15 1.39

CV % 2.57 1.59 2.66 1.69 2.13 1.23 2.19 1.32



Table 4.16 Mean performance of generations derived from four crosses for proline


Gen.

Mean values

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

% % % % Irrigated Rainfed Change Irrigated Rainfed Change Irrigated Rainfed Change Irrigated Rainfed Change

P1 44 65 46 44 65 46 55 105 91 55 105 91 P2 55 87 57 44 74 70 55 87 57 44 74 70

F1 51 72 43 44 71 61 53 97 83 51 89 77

F2 50 74 48 44 70 59 54 97 80 49 88 78

BC11 47 69 49 43 68 57 54 98 80 49 92 87

BC12 51 77 50 42 70 66 55 92 66 46 86 86

LSD0.05 1.34 1.24 1.22 1.24 1.22 1.24 1.13 1.24

Table 4.

99

Gen. = Generations

Table 4.17 Estimates of genetic effects for proline content in different crosses under

different environments.




18 Analysis of variance for 20 brassica generations evaluated for Chlorophyll

content across two different environments.


Environment(E) 1 771.06** 28.4

Rep (E) 4 0.22 0.03

Genotype (G) 19 93.46** 65.4

G × E 19 3.87** 2.71

Error 76 1.25 3.51


Table 4.19 Mean values for Chlorophyll content (mg cm-2) and percent increase of 20

genotypes across two different environments.

S. No. Generation Genotypes Irrigated Rainfed Mean Increase %)

1

2

3 Parents

L-6

L-7

T-1

46

58

53

38

52

48

42

55

51

-17

-10

-9

4 T-3 49 46 48 -6

Irrigated

L-6×T-1 49.96** -4.45** -3.25 NS -4.07 NS 1.12* 8.73** 8.6* -

L-6×T-3 44.25** 1.07** -6.04** -6.11** 0.89 NS 11.15** 39.9** Duplicate

L-7×T-1 53.56** -0.89* 1.93 NS 4.25** -0.89 NS -6.33 NS 9.6* -

L-7×T-3 49.45** 2.85** -6.70** -7.67** -2.91** 17.60** 39.1** Duplicate

Rainfed

L-6×T-1 73.86** -7.19** -6.45 NS -3.15 NS 3.91** 7.05 NS 13.3** -

L-6×T-3 70.33** -2.30** -4.39 NS -5.89 NS 2.50** 11.05** 27.3** -

L-7×T-1 96.62** 5.97** 2.45 NS -7.83 NS 5.97** -3.07 NS 50.4** -

L-7×T-3 87.97** 5.84** 2.53 NS 3.11 NS -9.76** -0.07 NS 28.1** -

Crosses m d h i j l

2

N on - allelic interaction

Table 4.

100

5

6

7 F1

L-6×T-1

L-6×T-3

L-7×T-1

51

48

55

47

42

49

49

45

52

-8

-12

-11

8 L-7×T-3 54 50 52 -7

9

10

11 F2

L-6×T-1

L-6×T-3

L-7×T-1

48

47

56

42

42

50

45

45

53

-12

-11

-11

12 L-7×T-3 54 50 52 -6

13

14

15

16

17

Back

crosses

(L-6×T-1)×L-6

(L-6×T-1)×T-1

(L-6×T-3)×L-6

(L-6×T-3)×T-3

(L-7×T-1)×L-7

48

50

45

46

54

39

46

39

42

49

44

48

42

44

52

-19

-8

-13

-9

-9

18 (L-7×T-1)×T-1 53 47 50 -11

19 (L-7×T-3)×L-7 53 50 52 -6

20 (L-7×T-3)×T-3 50 47 48 -6

Mean 51 46 -10

LSD0.05 for Environment = 0.68 LSD0.05 for Genotypes = 2.10

LSD 0.05 for G × E = 1.08

20 Combine analysis of variance for Chlorophyll content of various


rainfed conditions.

Sources of variance df Mean squares

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment (E) 1 313.9** 249.1** 277.7** 122.2**

Reps (E) 4 0.3 0.2 0.3 0.3

Generations (Gen) 5 71.6** 26.6* 15.6** 43.2**

Gen × E 5 6.7** 4.4* 0.5NS 1.8NS

Pooled error 20 1.4 1.35 1.25 1.24

CV % 2.51 2.62 2.15 2.18


freedom

Table 4.21 Mean squares from analysis of variance for Chlorophyll content of various


Table 4.

101

SOV df

Mean squares

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


Rep 2 0.39 0.27 0.39 0.03 - - - -

Generations 5 20.5** 57.8** 6.34* 24.6** - - - -

Error 10 1.4 1.3 1.39 1.3 - - - -

CV % 2.39 2.65 2.51 2.75 - - - -



Table 4.22 Mean performance of generations derived from four crosses for Chlorophyll


Change Change Change Change

Gen. = Generations

23 Estimates of genetic effects for Chlorophyll content in different crosses under

different environments and pooled over environments.

2 Pooled

L-6×T-1 - - - - - - - -

L-6×T-3 - - - - - - - -

L-7×T-1 53.04** 1.20** -6.62** -8.25** 1.20* 10.23** 21.6** Duplicate

L-7×T-3 52.14** 3.53** -6.69** -7.95** -0.01NS 14.49** 18.8** Duplicate

Irrigated

P1 46 38 -17 46 38 -17 58 52 -10 58 52 -10 P2 53 48 -9 49 46 -6 53 48 -9 49 46 -6

F1 51 47 -8 48 42 -12 55 49 -11 54 50 -7

F2 48 42 -12 47 42 -11 56 50 -11 54 50 -6

BC11 48 39 -19 45 39 -13 54 49 -9 53 50 -6

BC12 50 46 -8 46 42 -9 53 47 -11 50 47 -6

LSD0.05 1.24 1.21 1.24 1.20 NS NS NS NS

Gen.

Mean values

L - 6 ×T - 1 L - 6 ×T - 3 L - 7 ×T - 1 L - 7 ×T - 3

Irrigated Rainfed %

Irrigated Rainfed %

Irrigated Rainfed %

Irrigated Rainfed %

Crosses m d h i j l

N on - allelic interaction

Table 4.

102

L-6×T-1 48.05** -1.54** 6.61** 4.51NS 2.14** 0.09NS 28.5** -

L-6×T-3 47.35** -0.41NS -6.06** -6.67** 1.31** 14.43** 67.4** Duplicate

L-7×T-1 - - - - - - - -

L-7×T-3 - - - - - - - -

Rainfed

L-6×T-1 42.05** -6.54** 6.83* 2.51NS -1.24* 7.66* 59.0** Complimentary

L-6×T-3 42.13** -2.41** -5.47* -5.77** 1.80** 11.55** 48.0** Duplicate

L-7×T-1 - - - - - - - -

L-7×T-3 - - - - - - - -




Fig. 4.12 Genotype by trait biplot for relationship among Relative water content

(RWC), Proline content (Pro) and Chlorophyll content (Chl) under

irrigated (I) and drought stress (D).

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4.2.3 Inheritance of morpho-yield and oil quality traits

i) Days to 50% flowering

Results from combine analysis of variance regarding days to 50% flowering of

20 genotypes comprising four parental genotypes, their resultant four F1, four F2, four

BC11 and four BC12 generations are presented in Table 4.24. Analysis of variance across

environments exhibited significant differences (P<0.05) among brassica genotypes for

days to 50% flowering. The environments main effect and genotype by environment

interaction effects were also found significant (P<0.01). Among the total variation

maximum (48.9%) was explained by genotype main effect, followed by genotype by

environment interaction effect (21.7%) and environment main effect (18.0%).

Since genotype main effect was found significant, therefore data was further

analyzed to test the variability among the generations of each cross combination across

environments. As a result the generation main effect was found significant for cross L-6

× T-1 whereas the rest of the crosses showed non-significant differences among their

generations. However, significant differences were exhibited between environments

except L-6 × T-3 for which the environment effect was non-significant. Similarly, the

generation by environment interaction for days to 50% flowering was also found

significant (Table 4.25). Upon significant Gen × E effect, the data was reanalyzed under

each environment i.e. irrigated and rainfed for each cross combination. Significant

differences were observed among generations under irrigated as well as rainfed

environment (Table 4.26).

Mean values regarding days to 50% flowering for all the six generations of four

different cross combinations under irrigated and rainfed environment are presented in

Table 4.27. Under irrigated environment among the six generation of first cross

combination (L-6 × T-1) minimum days (102) to bloom 50% flowers were observed for

P1 whereas maximum of 140 days were recorded for P2. Similarly, under rainfed

environment P1 of the same cross combination took minimum (96) days to bloom 50%

flowers whereas maximum were taken by P2 (116) which remain statistically at par with

BC12 (115). Among the various generations of cross combination (L-6 × T-3) P1 took

minimum days to mid flowering under both irrigated (102) and rainfed (96) whereas

104

maximum were recorded for P2 (116 days) under irrigated and BC12 (115 days) under

rainfed condition. Among generations of cross combination (L-7 × T-1) under irrigated

environment minimum days (120) were recorded for BC11 followed by P1 (121 days),

whereas maximum days (140) were recorded for P2. Likewise under rainfed P1 took

minimum days (92) to bloom 50% flowers whereas F2 generation of the same cross took

maximum days (118) which remain statistically at par with F1 (117 days) and P2 (116

days). In the 4th cross combination (L-7 × T-3) under irrigated BC12 generation took

minimum days (110) for mid flowering whereas maximum were taken by P1 (121 days).

Similarly, under rainfed condition P1 took minimum days (92) whereas BC12 generation

took maximum days (114) to produce 50% flowers. On overall basis early flowering was

observed in most of the genotypes under rainfed environment. This shortening of time to

flowering or maturity undoubtedly acted as an escape from drought stress in brassica

genotypes. Similar results regarding reduction in flowering time under drought stress

conditions were also reported by Moghadam et al. (2011) in canola cultivars.

Estimates of genetic effects in six parameter model regarding days to 50%

flowering along with chi-square values are presented in (Table 4.28). Following the joint

scaling test all the chi-square values were found significant for all four cross

combinations under irrigated and rainfed environments, thereby indicated the adequacy

of six parameter model for the elucidation of genetic pattern including epistasis for the

trait under study. This also indicated the presence of more than two genes governing days

to flowering trait working in a complex fashion (Cheema and Sadaqat, 2004).

Analysis for estimate of genetic effects exhibited significant additive genetic

effects under irrigated as well as rainfed condition for all four cross combinations except

L-6 × T-3 under rainfed condition for which additive effect was found nonsignificant.

Overall the additive component was higher in magnitude under irrigated as compare to

rainfed environment. Similarly, the dominance component was also found significant

under both environments. Moreover, under irrigated as well as rainfed condition L-7 ×

T-1 showed consistent high values for dominance effects as compare to other crosses

which exhibited contrasting results such that negative values were turned positive with a

change from irrigated to rainfed condition.

105

Involvement of epistasis (i, j or l type) in inheritance of flowering trait was

evidenced in all the crosses used in the present study. Overall i type of epistasis (additive

× additive) was found significant in all crosses except L-7 × T-3 under rainfed condition.

Moreover under irrigated condition the magnitude of i was high as compare to that under

rainfed condition. Cross combination L-7 × T-1 showed consistent i type of epistasis

across both environment, whereas all other crosses exhibited contrasting i effects.

The j component (additive × dominance) was significant for L-6 × T-1 and L-7 ×

T-3 under irrigated condition whereas for rest of the crosses it was non-significant. Under

irrigated and rainfed condition all of crosses revealed significant l type of epistasis. The

values for l type under irrigated were positive whereas under rainfed condition negative

values were observed except for L-7 × T-1. A duplicate type of nonallelic interaction was

observed for days to mid flowering in all crosses.

Days to 50% flowering is considered as one of the criteria for development of short

duration crop which in turn results in escape before the onset stress condition.

Development of Brassica napus cultivars with potential to complete flowering and life

cycle in optimum number of days might perform better in rainfed area to cope drought

stress. To accomplish such goal considerable information on the pattern of inheritance of

the trait is a prerequisite (Naveed et al., 2009). In the present study, both additive and

non-additive types of gene actions along with some type of epistasis were revealed.

Significant and greater magnitudes of estimates for dominance and dominance ×

dominance type of non-allelic interactions in most of the crosses revealed that the role of

dominance type of gene action was largely involved in the inheritance of this trait (Babu

et al., 2012). Moreover, the cross combination L-7 × T-1 showed significant dominance

genetic estimates under rainfed environments which indicated the importance of

dominance component for the control of flowering in this cross. From the present set of

genetic material used in this study it can be justified that both additive and non-additive

with predominance of non-additive type of gene action are playing important role in the

inheritance of the trait under study. Babu et al. (2012) and Kant and Gulati (2001) were

also of the opinion that the involvement of both additive and non-additive gene effects

played important role in the expression of days to 50% flowering in brassica. Kemparaju

et al. (2009) investigated various segregating and non-segregating generations of Indian

mustard and found that both additive and nonadditive type of gene actions were involved

106

in controlling days to flowering trait. While working with generations of Brassica napus,

Cheema and Sadaqat (2004) also reported both additive and non-additive type of gene

actions governing days to flowering trait with predominance of non-additive type in some

crosses under irrigated as well as rainfed conditions. Significant and higher magnitude of

non-fixable components for the trait under investigation demanded delayed selection till

advance generation.

ii) Plant Height

Results of the data regarding plant height of 20 genotypes evaluated across

irrigated and rainfed conditions exhibited significant (P<0.05) differences for plant

height. Both the environments also varied significantly for plant height of genotypes.

Likewise, the G × E effect was also found significant (P<0.01). Among these three

sources of variation, maximum contribution of 64.5 % was shown by genotype main

effect, followed by environment main effect (24.7 %) whereas their interaction effect

contributed only 10.2 % to the total variation (Table 4.29).

Significant genotype main effect necessitated further analysis of the data to

investigate variability among the generations under irrigated and rainfed environments.

As a result significant differences were demonstrated by environment, generations, and

generation by environment interaction for plant height (Table 4.30). Subsequently, the

generations of all four crosses exhibited significant difference under irrigated as well as

rainfed conditions (Table 4.31).

Among the generations of cross L-6 × T-1 under irrigated environment minimum

plant height of 174 cm was observed for P1 whereas maximum plant height (194 cm) was

recorded for P2. Similarly, under rainfed environment P1 of the same cross combination

attained minimum plant height (169 cm) whereas maximum plant height of 189 cm was

observed for P2. Among the generations of cross (L-6 × T-3) under both irrigated and

rainfed conditions P1 attained minimum plant height of 174 and 169 cm respectively,

whereas maximum plant height was recorded for P2 under irrigated and rainfed as 211

cm and 193 cm respectively. Among generations of cross (L-7 × T-1) the P1 showed

minimum plant height of 163 cm and 161 cm under irrigated and rainfed conditions

respectively, whereas taller plants were observed in P2 under irrigated (194 cm) and

107

rainfed (189 cm) condition. In the 4th cross combination (L-7 × T-3) the generation P1

attained minimum plant height under both irrigated (163 cm) and rainfed (161 cm)

whereas, maximum plant height was recorded for P2 under both irrigated and rainfed

conditions as 211 and 193 cm, respectively (Table 4.32).

As a result of joint scaling test all the chi-square values were found significant for

all the crosses under irrigated and rainfed environments, thereby indicated the adequacy

of six parameter model for the explanation of allelic and non-allelic gene interaction

responsible for the expression of plant height (Table 4.33).

Genetic analysis of the data revealed significant additive effects under both

irrigated and rainfed conditions for all four crosses except L-6 × T-1 under rainfed

condition, where the additive component was found non-significant. Overall the additive

component was reduced in magnitude under rainfed as compared to irrigated

environment. However the reduction in this component was comparatively small in L-6

× T-3 than the rest of the crosses where a drastic change was observed. Similarly, the

dominance effect under irrigated condition was found significant for only one cross (L6

× T-3) as compare to other crosses which showed non-significant dominance effects. On

the other hand, under rainfed condition all of the crosses showed significant dominance

effect.

Non-allelic gene interactions were also involved in the inheritance of plant height in all

crosses under the present investigation. Under irrigated as well as rainfed all of the

crosses showed significant additive × additive epistasis except L-6 × T-1 under irrigated.

Likewise the additive × dominance component was found significant for most of the

crosses both under irrigated and rainfed environments except L-6 × T-1 under irrigated

condition. Moreover, the dominance × dominance component was also found significant

for all crosses except L-7 × T-3 under irrigated condition. Most of the crosses showed

duplicate type of non-allelic interaction especially under rainfed condition.

Plant height is considered as one of the criteria for development of drought

tolerant cultivar. Development of Brassica napus cultivars with taller plants might

perform better under rainfed conditions to cope the drought stress. To accomplish such

goal information regarding gene expression of the trait is important (Naveed et al., 2009).

108

Overall the genetic components for plant height exhibited the major role of additive type

of gene action however in some cases both additive and non-additive types played

important role especially under rainfed conditions the magnitude of nonadditive was

greater than additive. Sing (2004) reported the importance of additive gene action in the

expression of plant height in brassica, which is in line with the finding of the present

study. Sundari et al. (2012) reported that both additive and nonadditive type of gene

actions important for the inheritance of plant height in brassica. However, Parkash et al.

(1998) and Cheema and Sadaqat (2004) reported dominance gene action for the control

of plant height in brassica. The difference in finding of Parkash et al. (1998) and Cheema

and Sadaqat (2004) and those found during the present investigation might be due to the

genetic differences in the breeding material. Moreover, the differences in results obtained

regarding genetic components under different environmental conditions suggested that

specific selection criteria should be followed for specific environmental condition.

Delayed selection under rainfed condition might be fruitful whereas under irrigated

condition selection in segregating generation is suggested.

iii) Primary branches per plant

Combine analysis of variance regarding primary branches per plant revealed

significant (P<0.01) differences among genotype. Likewise, the environment main effect

and the interaction (G × E) effects were also found significant (P<0.01). Of the total,

maximum variation of 58.8 % was explained by genotype main effect, followed by

environment main effect (29.7 %) whereas the interaction effect explained 7.0 % of the

variation (Table 4.34).

Analysis of primary branches per plant of generations of all cross combinations

across irrigated and rainfed conditions exhibited significant effects for environment,

generations, and generation by environment interaction, however one cross (L-7 × T-1)

showed non-significant generation main effect (Table 4.35). Since, generation by

environment interaction effect was significant for all the crosses hence data was further

analyzed under each environment i.e. irrigated and rainfed for each cross combination

(Table 4.36). Significant differences were observed among generations for primary

branches per plant under both irrigated and rainfed environment.

109

Among the generations of cross L-6 × T-1 under irrigated environment minimum

primary branches (7) were observed in P1 whereas maximum (12) were recorded for P2.

However, under rainfed environment BC11 of the same cross combination produced

minimum primary branches (7) whereas maximum (9) were recorded for both P2 and

BC12. Among the generations of cross (L-6 × T-3) P1 produced minimum primary

branches under both irrigated and rainfed conditions as 7 and 8 respectively, whereas

maximum primary branches were recorded for P2 under irrigated and rainfed as 16 and

12 respectively. Among generations of cross (L-7 × T-1) the plants in F2 generation

showed minimum primary branches of 9 and 8 under irrigated and rainfed environment

respectively, whereas maximum primary branches (12) were observed in both P2 and

BC12 in irrigated and 10 primary branches in F1 under rainfed condition. In the 4th cross

combination (L-7 × T-3) the generation P1 attained minimum primary branches under

both irrigated (10) and rainfed (8) whereas, maximum primary branches were recorded

for P2 under irrigated (16) and rainfed condition (12). Moreover F1 under irrigated remain

statistically at par with P2 generation (Table 4.37).

Estimates of genetic effects in six parameter model for primary branches per plant along

with chi-square values are presented in (Table 4.38). Following the joint scaling test all

the chi-square values were found significant for all the crosses under irrigated and rainfed

environments except for L-6 × T-1 for which chi-square value was non-significant under

irrigated condition and for L-7 × T-3 under rainfed condition. Hence, three parameter

model was adequate for L-6 × T-1 under irrigated and L-7 × T3 under rainfed for the

description of only additive and dominance genetic components. For the rest of the

crosses the chi square value was significant therefore six parameter model was found

adequate for the description of allelic and non-allelic gene interaction liable for the

expression of primary branches plant-1 in brassica.

Both additive and non-additive component of genetic effects were found

significant in most of the crosses. The additive component was reduced in magnitude

under rainfed as compare to irrigated environment in most of the crosses. Moreover, the

additive effect for L-7 × T-1 under rainfed was found non-significant. The dominance

component under both irrigated and a rainfed condition was found significant in most of

the crosses except L-6 × T-1 and L-7 × T-3 under rainfed. The dominance component in

110

irrigated was greater in magnitude as compare to that in rainfed condition in most of the

crosses.

Involvement of non-allelic interaction (i, j or l type) in inheritance of primary branches

per plant was evidenced in some crosses used in this study. The i type of nonallelic

interaction in all the three cases was found significant for L-6 × T-3 and L-7 × T1 cross

combinations. Under pooled data the j component was found significant for all the crosses

except L-6 × T-1 for which it was found non-significant. Similarly, under irrigated

condition j component was significant for L-6 × T-3 and L-7 × T-3, whereas under rainfed

environment j was found significant for L-6 × T-1 and L-6 × T-3. The l component was

mostly non-significant for all the crosses in all the three cases except L7 × T-1 for which

it was significant under pooled and irrigated condition.

Primary branches per plant is an important yield associated trait in brassica.

Strong and positive direct as well as indirect association of this trait has been previously

reported by Meena et al. (2010) in brassica napus genotypes. Increase in primary branches

plant-1 significantly increased the pods plant-1 and subsequently resulted in increased seed

yield plant-1 (Khan et al., 2013). Development of cultivars with increased primary

branches might perform better for high seed yield production. Overall the genetic

components for primary branches exhibited the major role of nonadditive type of gene

action however in some cases additive type of gene action was found responsible for the

expression of primary branches plant-1. Parkash et al. (1998) and Cheema and Sadaqat

(2004) are also in agreement with the present findings who reported dominance gene

action for the control of primary branches per plant in brassica. Sundari et al. (2012)

reported that both additive and non-additive types of gene actions are important for the

inheritance of primary branches per plant in brassica. The negative sign (-) of l component

in cross L-7 × T-1 revealed duplicate type of nonallelic interaction. Moreover the higher

values of dominance component for the same cross also indicated its potential in heterosis

breeding. For the improvement of primary branches plant-1 in this combination selection

should be delayed till advance generation.

iv) Pods on main raceme

111

Results obtained for combine analysis of variance exhibited significant main

effects for genotypes and environments (P<0.05). Likewise, the interaction effect (G ×

E) was also found significant (P<0.01). Among these three sources of variances,

maximum variation of 46.1 % was enlightened by genotype main effect, followed by

environment main effect (36.3 %) whereas the interaction effect explained 16.8 % of the

variation (Table 4.39).

Further analysis of the data under both irrigated and rainfed conditions for each

cross combination revealed significant effects for environment, generations, and

generation by environment interaction in all crosses, however the generation main effect

for two crosses (L-6×T-1 and L-6×T-3) was found non-significant (Table 4.40). Since,

generation by environment interaction effect was significant in all four crosses hence data

was reanalyzed under irrigated and rainfed conditions for each cross combination (Table

4.41). Significant differences were observed among generations for pods on main raceme

under both irrigated and rainfed environment.

Mean values regarding pods on main raceme for all six generations derived from

four different crosses under two different environments are given in Table 4.42. Among

the generations of cross L-6 × T-1, under irrigated environment maximum pods on main

raceme (71) were recorded for F1 whereas minimum pods on main raceme were observed

in P1 (45) and F2 generation (46). Likewise, under rainfed environment maximum of 52

pods on main raceme were recorded for P2 whereas P1 of the same cross combination

produced minimum pods on main raceme (38). Among the generations of cross (L-6 ×

T-3) under irrigated conditions maximum pods on main raceme (76) were recorded for

BC12 whereas F1 and P1 produced minimum pods of 44 and 45 on main raceme,

respectively. Similarly, under rainfed condition maximum 43 pods were produced by P2

whereas F2 and P1 produced minimum 37 and 38 pods on main raceme, respectively.

Among generations of cross (L-7 × T-1), maximum pods on main raceme were observed

in P2 under irrigated (66) and under rainfed condition (52) whereas the plants in P1

generation produced minimum pods on main raceme under irrigated (35) and rainfed (34)

environments. In the 4th cross combination (L-7 × T-3) maximum pods on main raceme

were recorded for F1 under irrigated (58) and under rainfed condition (47) whereas the

generation P1 attained minimum pods on main raceme under both irrigated (35) and

112

rainfed (34). Moreover, the performance of BC12 under irrigated was statistically at par

with F1.

Scaling test revealed significant chi-square values for all crosses under both

environments, thereby suggested six parameter model for the description of allelic and

non-allelic gene interaction liable for the expression of pods on main raceme. Under

rainfed condition, chi-square value for L-7 × T-1 was found non-significant hence

suggested three parameter model for the explanation of allelic gene interaction

responsible for the inheritance of this trait in this specific cross (Table 4.43).

Both additive and dominance genetic effects were found significant in most of the

crosses. The additive component was found non-significant for L-6 × T-1 under irrigated

and L-7 × T-3 under rainfed conditions. Moreover the dominance component was found

non-significant for L-7 × T-1 under rainfed condition. Overall the magnitude of

dominance effects was high as compare to additive effects under both environments. The

additive component was reduced in magnitude under rainfed as compare to irrigated

environment in most of the crosses. The dominance component under irrigated

environment was greater in magnitude for most of the crosses as compare to those under

rainfed condition. Only one cross L-7 × T-1 under irrigated showed negative values for

dominance effect whereas the rest of the crosses exhibited positive values.

Presence of non-allelic interaction (i, j or l type) in inheritance of pods on main

raceme was evidenced in most of the crosses used in this study. The i type of nonallelic

interaction in all was found significant for all the crosses except L-7 × T-1 for which it

was non-significant under rainfed environment. Likewise, the j component under

irrigated was also found significant in most of the crosses except L-7 × T-1 however

under rainfed environment it was found non-significant for most of the crosses except L-

7 × T-3. The l component under irrigated was mostly significant for all the crosses except

L-6 × T-1, whereas under rainfed environment it was found significant for L-6 × T-1 and

L-6 × T-3. Mostly duplicate type of non-allelic interaction was evidenced for this trait in

most of the crosses.

Number of pods on main raceme is an important yield associated trait in brassica.

Positive genetic association of this trait with seed yield in brassica has been previously

113

reported by Khan et al. (2013). Therefore, improvement of this trait might improve seed

yield indirectly. For improvement of such important yield associated trait information

regarding its gene expression play a key role. Overall the genetic components for pods

on main raceme exhibited the role of additive and dominance type of gene action along

with non-allelic interactions. Moreover, L-6 × T-1 under irrigated revealed only

dominance type of gene action responsible for the expression of this trait. The cross

combination (L-7 × T-1) under rainfed condition exhibited significant additive effect and

non-significant dominance and non-allelic interactions, thereby indicated the

involvement of only additive type of gene action for this trait. Somewhat similar results

have been reported by Babu et al. (2012) and Parkash et al. (1998) regarding the

involvement of both additive and non-additive type of gene actions for the inheritance of

pods on main raceme in brassica however, Anand, et al. (1987) reported non-additive

type of gene action for the control of pods on main raceme in brassica. In three crosses

i.e. L-6 × T-1, L-6 × T-3 and L-7 × T-3 under both irrigated and rainfed the magnitude

of dominance was greater than additive component which indicated the importance of

non-additive type of gene action responsible for the expression of this trait in these cross

combinations. Since dominance type of gene action is predominant hence selection for

the improvement of this trait in these crosses would be effective in advance generation

(Cheema and Sadaqat, 2004). Moreover with additive type of genetic estimates, the early

segregating generation of L-7 × T-1 under rainfed condition might have potential

segregants for the improvement of plant height.

v) Pod length

Results from combine analysis of variance regarding pod length of 20 genotypes

comprising various generations are presented in Table 4.44. Analysis of variance across

environments exhibited significant differences (P<0.01) among brassica genotypes for

pod length. Similarly, the environment main effect and genotype × environment

interaction effect were also found significant (P<0.01). Of the total variation, maximum

(65.1 %) was explained by genotype main effect, followed by environment main effect

(16.9 %) and genotype by environment interaction effect (7.4 %).

Since genotype main effect was found significant therefore data was further

analyzed for testing variability among the generations of each and every cross

114

combination across environments. As a result environment and generation main effects

and their interaction effect were found significant (P>0.05) for two crosses i.e. L-7 × T1

and L-7 × T-3. The generation main effect for L-6 × T-1 was significant whereas, the

environment main effect and G×E effects were found non-significant. For L-6 × T-3 the

environment main effect was significant whereas, the generation main effect and G × E

effect were found non-significant (Table 4.45). Since generation by environment

interaction effect in two crosses (L-7 × T-1 and L-7 × T-3) was found significant

therefore, further analysis of the data was carried out for these two crosses under each

environment i.e. irrigated and rainfed. Significant differences were observed among

generations under both irrigated and rainfed environments (Table 4.46).

Under irrigated environment among the generations of first cross combination (L-

6 × T-1) mean maximum pod length (7.9 cm) was recorded for BC11 and remain

statistically at par with P1 and BC12 (7.3 cm) whereas, minimum pod length (6.3 cm) was

recorded for P2. Among various generations of 2nd cross combination (L-6 × T-3) average

over environments, lengthy pods (8.0 cm) were produced by BC11 and BC12 generations

whereas F2 generation produced shorter pods (6.9 cm). Among generations of 3rd cross

combination (L-7 × T-1) under irrigated environment maximum pod length (11.2 cm)

was recorded for F1 and remain statistically at par with P1 and BC11 whereas, minimum

pod length (6.4 cm) was recorded for P2. Likewise, under rainfed maximum (9.4 cm) was

observed in both P1 and F1 followed by BC11 with 9.1 cm pod length whereas minimum

pod length (6.2 cm) was observed in P2. In the 4th cross combination (L-7 × T-3) under

irrigated condition longer pods (11 cm) were observed in P1 whereas, shorter pods (8.3

cm) were observed in P2. Likewise, under rainfed condition F1 produce shorter (7.3 cm)

pods and remain statistically at par with P2 (7.5 cm) and BC12 (7.4 cm) whereas longer

pods (9.4 cm) were produced by P1 (Table 4.47).

Estimates of genetic effects in six parameter model regarding pod length along

with chi-square values are presented in (Table 4.48). Following the joint scaling test all

the chi-square values were found significant for all four cross combinations, thereby

indicated the adequacy of six parameter model for the explanation of genetic pattern

including epistasis for the trait under study. Moreover, under rainfed condition both L-7

× T-1 and L-7 × T-3 showed non-significant chi-square values, thereby suggested three

parameter model for interpretation of genetic pattern.

115

Overall both additive and dominance components were found significant for the

expression of pod length in all of the crosses except L-6×T-3 which showed

nonsignificant additive effect, however the magnitude of dominance component was

larger as compare to additive component. Similarly under rainfed and irrigated both

additive and dominance components were found significant for two crosses except L-

7×T-1 for which the dominance component under rainfed condition was found non-

significant. The estimates for both additive and non-additive genetic effects were found

positive in direction except L-7×T-3 under rainfed conditions for which the dominance

component was negative.

Various types of epistasis (i, j or l type) were also found working in the expression

of pod length trait in all crosses. Under pooled data as well as irrigated condition, i type

of epistasis was significant whereas, under rainfed condition it was found non-significant.

Likewise, the j component was found significant for only one cross (L-7 × T-1) under

irrigated condition. Significant and higher magnitude for l type of epistasis was evidenced

under pooled data and irrigated condition whereas under rainfed condition it was non-

significant.

Pod length is one of the important yield contributing traits in brassica. Yield

improvement in Brassica might be accomplished through introgression of genes from

longer podded genotypes into desired cultivars coupled with increase in seed weight and

minimum reduction in number of pods plant-1. In the present study both additive and

dominance components were found responsible for the expression of pod length, however

the greater magnitudes of estimates for dominance in most of the crosses revealed that

dominance gene action might have largely been involved in the inheritance of this trait.

Overall duplicate type of non-allelic interaction was observed in most of the crosses used

in this study. From the present set of genetic material used in this study it can be justified

that non-additive type of gene action is pre-dominant for the inheritance of the trait under

study. Furthermore, one cross i.e. L-7 × T-3 under rainfed environment exhibited only

additive type of gene action for pod length. Sabaghnia et al. (2010) also reported the

predominant role of non-additive genetic effects for pod length in brassica. The study

reported by Arifullah et al. (2011) is also in close agreement with the present findings,

who observed significant non-additive genetic effects for pod length in Brassica juncea.

Moreover, Rameeh (2010) was of the opinion that both GCA and SCA played important

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role in controlling pod length in Brassica napus. In contrast Maurya, et al. (2012) reported

additive type of gene action for the control of pod length in Brassica juncea. The

differences in results obtained in the present study and those of Maurya, et al. (2012)

might be due to the difference in genotypes and crop specie. For those crosses where

magnitude of dominance gene action was exhibited, selection in advance generations for

the improvement of this trait would be more effective (Cheema and Sadaqat, 2004). For

improvement of pod length with additive type of gene action observed under rainfed

condition, the segregating generation of L-7 × T-3 might provide potential segregants.

vi) Seed pod-1

Analysis of variance for seed per pod data of 20 genotypes evaluated across

environments exhibited significant (P<0.01) results for genotype and environment main

effects and genotype by environment interaction effect. Of the total variation, maximum

(66.7 %) was explained by genotype main effect, followed by environment main effect

(18.4 %) whereas genotype by environment interaction effect contributed 5.7 % (Table

4.49)

As a result of reanalysis of the data for variability among the generations across

environments, the main effects for environments and generations was found significant

(P>0.01) for all four cross combinations. Moreover, the generation by environment

interaction effect was found non-significant for most of the crosses except L-7 × T-1 for

which the interaction effect was found significant (Table 4.50). Since generation by

environment interaction for only L-7 × T-1 was significant therefore the data for this

specific cross was further analyzed and genetic effects were also estimated via generation

mean approach under each environment i.e. irrigated and rainfed. For the rest of the

crosses which showed non-significant generation by environment effect, mean data was

used for genetic analysis. Significant differences were observed among generations of

cross L-7 × T-1 under both irrigated and rainfed environments (Table

4.51).

Mean values regarding glucosinulates for all the six generations of four different

cross combinations under irrigated and rainfed environments are presented in Table 4.52.

On overall mean performance, among the six generations of first cross combination (L-6

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× T-1) minimum seed per pod average over environments were observed in F2 generation

(16) whereas, maximum (25) seeds per pod were produced by P1. Similarly, among

various generations of 2nd cross combination (L-6 × T-3) under irrigated condition,

maximum of 26 seeds per pod were found in P1 generation whereas, minimum seeds per

pod (18) were found in P2 generation. Likewise, under rainfed condition P1 showed

maximum seeds per pod (23) whereas, F2 generation showed minimum seeds per pod

(16). Among the generations of 3rd cross combination (L-7 × T-1) mean maximum seed

per pod were recorded for P2 (21) and BC12 (20) whereas, mean minimum seeds per pod

(16) were found in P1. Based on overall mean performance among the generations of 4 th

cross combination (L-7 × T-3), maximum seeds per pod (18) were recorded for P2 and

BC12 generations whereas, minimum seeds per pod (16) were observed in P1 generation

which remain statistically at par with F1, F2 and BC11 generations.

Genetic components of six parameter model for seeds per pod along with

chisquare values are presented in (Table 4.53). The joint scaling test revealed significant

results for most of the crosses, thus indicated the adequacy of six parameter model

however, under rainfed condition L-7 × T-1 exhibited non-significant chi-square value

thus three parameter model was used for the explanation of inheritance pattern.

Pooled analysis for estimates of genetic effects revealed significance of both

additive and dominance gene action for controlling seed per pod in all of the crosses.

Since, only one cross combination (L-7 × T-1) depicted significant generation ×

environment effect therefore genetic analysis for this cross was also carried under each

environment. Under irrigated and rainfed conditions both additive and dominance

components were found significant for this specific cross combinations. The relative

magnitude of non-additive component was found greater than additive component in all

of the crosses.

During the present study contribution of epistasis (i, j or l type) in inheritance of

seed per pod was also observed in most of the crosses. Overall for cross L-6 × T-1, the i

and l type of non-allelic interactions were found significant. For cross L-7 × T-1 all three

types (i, j and l) were found significant however, for cross L-7 × T-1 non-allelic

interactions were non-significant. For cross L-6 × T-3, the i component was significant

under irrigated as well as rainfed conditions. Similarly, the j type of epistasis was

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significant under irrigated and the l type under rainfed condition. Overall duplicate type

of non-allelic interaction was evidenced in all of the crosses used in the present study.

Estimates for additive and dominance in most of the crosses revealed that both

type of gene action played important role in the inheritance of this trait however the

higher magnitude of dominance effects as compare to additive effects clarified the

predominant role of dominance gene action for the inheritance of this trait. Babu et al.

(2012) and Sriram (1990) reported significance of additive, dominance and epistatic

effects in governing seeds pod-1 in different crosses of brassica. Similarly, Maurya et al.

(2012) were also of the opinion that both additive and non-additive gene actions played

important role in the inheritance of seeds pod-1 in Brassica juncea. On the other hand

Singh (2004) reported the involvement of additive gene action in the inheritance of seeds

pod-1 in brassica. The differences in results might be due to the differences in genetic

material used. In the present study most of the crosses exhibited greater magnitude of

non-additive component therefore selection in advance generation might be fruitful for

the improvement of this trait.

vii) 1000-seed weight

Results from the combine ANOVA regarding 1000-seed weight are presented in

Table 4.54. Significant difference (P<0.01) were exhibited by 20 genotypes for 1000 seed

weight evaluated across irrigated and rainfed condition. Likewise the environment main

effect and interaction (G × E) effect were also found significant (P<0.01). Of the total

variation, maximum was explained by genotype main effect (94.2 %), followed by

interaction effect (2.6 %) whereas the environment main effect explained 1.7 % of the

variation.

Significant results regarding genotype main effect demanded further analysis of

data for variability among various generations of each cross combination across

environments (Table 4.55). As a result, environment and generation main effects were

found significant for all of the four crosses except L-6 × T-3 for which the environment

main effect was found non-significant. Similarly, Gen × E interaction effect was also

found significant (P<0.01) for all crosses except L-6 × T-1 and L-7 × T-1. Significant

Gen×E interaction effect for two crosses demanded further analysis of the data under

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each environment i.e. irrigated and rainfed for each cross combination (Table 4.56).

Significant differences were observed among generations of two crosses for 1000 seed

weight under both irrigated and rainfed environment.

Since Gen × E was non-significant for L-6×T-1 hence overall the values for 1000

seed weight ranged from 3.1 g (F2) to 3.6 g (P2). Among the generations of cross (L-6 ×

T-3), P2 attained maximum seed weight of 5 and 4.8 g under irrigated and rainfed

environments, respectively whereas, P1 attained minimum 1000 seed weight of 3.7 and

3.2 g under irrigated and rainfed conditions, respectively. The cross (L-7 × T-1) also

exhibited non-significant Gen × E effect therefore mean values are presented in such a

way that mean maximum seed weight of 5.7 was recorded for P1 whereas minimum (3.6

g) was attained by P2. In the 4th cross combination (L-7 × T-3) under irrigated maximum

seed weight was recorded for P1 (5.8 g) whereas, the generation BC12 attained minimum

seed weight of 4.8 g. Similarly, under rainfed condition maximum 1000 seed weight

(5.5g) was recorded for P1 whereas, minimum 4.7 g was recorded for F2 generation (Table

4.57).

All the genetic effects estimated in six parameter model for 1000 seed weight

along with chi-square values are presented in Table 4.58. Joint scaling test revealed

significant chi-square values for all the crosses under irrigated and rainfed environments

thus suggested six parameter model adequate for the description of allelic and non-allelic

gene interaction. However for L-7 × T-1 under irrigated, chi-square value was found non-

significant therefore three parameter model was used for the interpretation of inheritance

pattern in this specific cross combination.

Additive effects were found significant for one cross (L-7 × T-1) under pooled

data and for two crosses (L-6 × T-3 and L-7 × T-3) under irrigated condition. Under

rainfed condition this component was found non-significant. The additive component

was reduced in magnitude under rainfed as compare to that under irrigated environment.

The dominance component was mostly found significant except L-7 × T1 under pooled

data. Contribution of only two types of non-allelic interaction (i and j) in the expression

of 1000 seed weight was evidenced in most of the crosses used in this study. Under

irrigated condition both the crosses exhibited significant i component. On the other hand

under rainfed it was found significant in L-7 × T-3. Likewise, the j component under

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irrigated condition was found significant for L-7 × T-3 whereas, under rainfed condition

it was significant for both the crosses.

Weight of 1000 seeds is one of the important criteria for high seed yield in most

of the crops. Development of cultivars with maximum 1000 seed weight might help in

increasing seed yield of brassica. Overall the genetic components for 1000 seed weight

exhibited the role of both additive and non-additive type of gene action in most of the

studied crosses however one cross (L-7 × T-1) showed only additive type of gene action

responsible for the expression of this trait. Babu, et al. (2012) reported both additive and

dominance type of gene action important for the inheritance of 1000 seed weight in

brassica. Several other studies (Nagendra et al., 2012; Sabaghnia et al., 2010; Singh et

al., 2010) reported only additive gene action responsible for the inheritance of 1000 seed

weight in brassica. In contrast, other researchers (Azizinia 2012; and Yadav and Yadava,

1996) reported the importance of dominance type of gene action for the control of this

trait in brassica. Overall the segregating generation of cross L-7 × T-1 with the significant

additive type of gene action for 1000 seed weight might have potential segregants for

simple selection. On the other hand in those crosses where nonadditive type of gene

action was found predominant, delayed selection in advance generation will be efficient.

viii) Seed yield plant-1

Analysis of variance resulted in significant differences (P<0.05) among brassica

genotypes for seed yield per plant. Likewise, the environments main effect and genotype

by environment interaction effects were also found significant (P<0.01). Maximum (50.7

%) was elucidated by genotype main effect, followed by environment main effect (41.8

%) whereas the genotype by environment interaction effect contributed only 6.0 % in the

total variation (Table 4.59).

Since genotype main effect was found significant therefore data was further

analyzed for generations of each cross combination across environments. Generations of

all crosses varied significantly for seed yield per plant except L-6 × T-3 for which non-

significant differences were observed among the generations. Likewise, the environment

main effect was also found significant for all the crosses however, the generation by

environment interaction effect was found significant for only two crosses i.e. L-6 × T-3

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and L-7 × T-3 (Table 4.60). Upon significant generation by environment interaction the

data for only these two crosses (L-6 × T-3 and L-7 × T-3) was further analyzed for

generation mean under each environment i.e. irrigated and rainfed. Since the generation

by environment interaction effect for L-6 × T-1 and L-7 × T-1 was nonsignificant

therefore mean data over environment was used for genetic analysis.

Significant differences were observed among generations of L-6 × T-3 and L-7 × T-3

under both irrigated and rainfed environments (Table 4.61).

Mean values regarding seed yield per plant for all the six generations of four

different cross combinations under irrigated and rainfed environment is presented in

Table 4.62. Among the six generations of cross combination (L-6×T-1), mean maximum

(29 g) was recorded for BC11 which remain statistically at par with P2 (28 g) whereas,

minimum seed yield per plant (22 g) was recorded for P1 followed by BC12 with 23 g.

Among various generations of cross combination (L-6×T-3), under irrigated condition

maximum seed yield per plant was produced by BC11 (29 g) and P1 (28 g) whereas F2

generation attained minimum seed yield per plant (22 g) which remain statistically at par

with P2 (23 g) and BC12 (23 g). Similarly, under rainfed environment maximum seed

yield per plant (21 g) was recorded for both P2 and BC11 followed by BC12 (20 g) whereas,

minimum seed yield (16 g) was produced by F2 generation of the same cross combination

and remain statistically at par with P1 (17 g). Among generations of cross combination

(L-7 × T-1) mean maximum seed yield was recorded for BC11 (38 g) and P1 (37 g)

whereas, minimum seed yield per plant (28 g) was recorded for P2 and BC12. In the cross

combination (L-7 × T-3) under irrigated condition P1 produced maximum seed yield per

plant (43 g) whereas, P2 generation produced minimum seed yield per plant (23 g).

Likewise, under rainfed condition BC11 produced maximum seed yield per plant (31 g)

which remain statistically at par with P1 (30 g) whereas P2 produced minimum seed yield

per plant (21 g).

The scaling revealed significant chi-square values for all cross combinations

under irrigated and rainfed environments thereby indicated the adequacy of six parameter

model for the interpretation of genetic pattern including epistasis for the trait under study.

Estimates of genetic effects in six parameter model regarding seed yield per plant along

with chi-square values are given in (Table 4.63).

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Pooled analysis exhibited significant estimates of genetic effects for both the

crosses, thus indicated the importance of both additive and dominance gene action for the

inheritance of seed yield per plant. Overall in L-6 × T-1 both additive and dominance

components contributed equally in the inheritance of this trait. The dominance

component for L-6 × T-3 under irrigated as well as rainfed conditions was found greater

in magnitude as compare to additive component. For cross L-7 × T-1 overall the additive

component was greater in magnitude as compare to dominance component. Similarly, in

cross L-7 × T-3 under irrigated condition the additive component was larger than

dominance whereas under rainfed condition the dominance component was greater than

additive component.

All the three types of epistasis (i, j and l) in inheritance of seed yield per plant

were evidenced in all crosses. Genetic estimates for both i and l type of non-allelic

interactions were found greater in magnitude in two crosses (L-6 × T-3 and L-7 × T-3)

under irrigated as well as rainfed conditions. The j component was found significant and

greater for two crosses i.e. L-6 × T-1 and L-7 × T-1. Overall a duplicated type of non-

allelic interaction was revealed in all crosses for seed yield per plant.

Seed yield per plant is the final outcome of various morphological and yield

associated traits. The development of Brassica napus cultivars with high yield potential

especially under rainfed environment is a prime objective. To accomplish such goal

considerable information regarding the pattern of inheritance of the trait is a prerequisite.

During this study significant decrease was observed in seed yield per plant in most of the

genotypes under rainfed condition. The decrease in yield and yield associated traits, due

to shortage of irrigation water has also been reported in canola cultivars by Moghadam

et al. (2011). With regards to genetic studies in the present set of crosses significant and

greater magnitudes of estimates for additive gene action along with additive × additive

type of epistasis in cross (L-7 × T-1) revealed that additive or additive type of epistasis

might have been involved in the inheritance of this trait in this specific cross combination.

Simple selection in early segregating generation for the improvement of seed yield per

plant in this cross will be effective (Cheema and Sadaqat, 2004). It has been also reported

by Kant and Gulati (2001) that for the inheritance of seed yield per plant in brassica

additive type of gene action more important. Similar results were reported by Maurya et

al. (2012) who observed additive gene action for the expression of seed yield in Brassica

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juncea. On the other hand cross L-6 × T-3 consistently exhibited significant dominance

along with i component under both irrigated and rainfed conditions, which clarify the

important role of dominance and additive × dominance type of epistasis in the inheritance

of this trait in this specific cross. Fixation of genes in the subsequent generations will be

difficult therefore delayed selection would be fruitfull. Similar findings were reported by

Singh (2004) and Kumar, et al. (2004) who stated that non-additive type of gene action

played important role in the expression of seed yield in brassica. During the investigation

of inheritance of seed yield in oil seed rape both additive and dominance gene effects

have been also observed to be responsible (Yadev et al. 2005). Another important finding

of this study was the change in magnitude of gene effects of L-7 × T-3 with the change

in environment. Although both additive and dominance components were significant,

however under irrigated condition the magnitude of additive component was high

whereas under rainfed the magnitude of dominance was high. Under such circumstances,

different selection criteria should followed under different environments. Such that under

irrigated condition where magnitude of additive gene effect is high selection in

segregating generation would be effective, whereas under rainfed condition delayed

selection in advance generations would be fruitful (Cheema and Sadaqat, 2004).

ix) Oil content

Significant difference (P<0.01) were observed among genotypes evaluated for oil

content under irrigated and rainfed conditions. Likewise, the environment main effect

was also found significant however the interaction effect (G×E) was found

nonsignificant. Maximum contribution in the total variation was described by genotype

main effect (68.6 %), followed by environment main effect which contributed about 18.9

%, whereas the interaction effect elucidated only 2.5 % of the total variation

(Table 4.64)

Since significant results regarding genotype main effect were obtained therefore

further analysis of the data was carried out across environments (Table 4.65). Results of

the data indicated that main effects for environment and generation were found

significant for all crosses except L-7 × T-3 for which the generation main effect was

found non-significant. Similarly, Gen × E interaction effect was found non-significant

(P<0.05) for all the four crosses, thereby suggested genetic analysis on mean data.

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Mean values regarding oil content of all the six generations of four different

crosses in two different environments are given in Table 4.66. Among the generations of

cross (L-6 × T-1), overall maximum oil content (51 %) was observed in P1 generation

whereas, minimum oil content (47 %) was observed in F2 generation.

Among the generations of cross (L-6 × T-3), maximum oil content was observed in P1

generation (51 %) whereas, minimum oil content was found in P2 generation (44 %).

Among generations of cross (L-7 × T-1), maximum oil content (49 %) was recorded for

P2 whereas, minimum oil content was recorded for P1 (44 %) which remain statistically

at par with F2. Differences for oil content in the generations of cross combination (L-7 ×

T-3) were found non-significant. In most of the genotypes decrease in oil content was

observed under rainfed condition. The results obtained by Champolivier and Merrien

(1996) also demonstrated a clear reduction in oil content under water deficit condition.

Following joint scaling test all the chi-square values were found significant for all

the crosses thereby suggested six parameter model adequate for the explanation of allelic

and non-allelic gene interaction liable for the expression of oil content. All the genetic

effects estimated in six parameter model for oil content along with chi-square values are

presented in Table 4.67. Since Gen × E effect was found non-significant for all the crosses

therefore genetic analysis based on pooled data are presented. Estimates for genetic

effects revealed significant additive type of gene action in two out of four crosses i.e. L-

6 × T-3 and L-7 × T-1. On the other hand the dominance component was found significant

in all four crosses. Moreover, the magnitude of dominance effects was comparatively

greater than additive effects in all crosses. The contribution of nonallelic interaction (i, j

or l type) in the expression of oil content was also evidenced in all of the crosses under

investigation. Both i and l type of non-allelic interactions were found significant however,

the j type of epistasis was found significant in only one cross i.e. L-7 × T-1. In all crosses

a duplicate type of non-allelic interaction was revealed to be taking part in the expression

of oil content.

Development of cultivars with high oil content is one of the prime objectives in

brassica breeding programs. To overcome this important objective sufficient knowledge

is required about the inheritance pattern of this trait. In the present study both additive

and non-additive type of gene action were found responsible for the expression of this

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trait in Brassica napus. However, the magnitude of dominance was greater in most of the

cases as compare to additive component which indicated the major role of dominance

gene action for the expression of oil content in these genotypes. Wang et al. (2010) also

reported that dominance gene action is playing major role in the inheritance of oil content

in brassica. These results are also in close agreement with those reported by Singh et al.

(2007) who observed higher magnitude of dominance genetic effects for oil content in

various generations derived from three different cross of brassica. However, the results

obtained by Cheema and Sadaqat (2004) elucidated the importance of both additive and

dominance type of gene action in the expression of oil content in brassica. They further

specified that the component of generation mean can change with change in genotypes

and environments. Similar finding were also reported by Babu et al. (2012) who

explained the importance of both additive and dominance type of gene action for the

inheritance of oil content in brassica. For the improvement of oil content in the present

set of crosses where non-additive type of gene action was predominant, selection would

be carried out in advance generations for fruitful results (Cheema and Sadaqat, 2004).

x) Glucosinolate content

Results from combine analysis of variance regarding glucosinulates of 20

genotypes comprising four parental genotypes, their resultant four F1, four F2, four BC11

and four BC12 generations are presented in Table 4.68. Analysis of variance exhibited

significant differences (P<0.01) among brassica genotypes for glucosinolates. Likewise,

the environments main effect and genotype by environment interaction effects were also

found significant (P<0.01). Of the total variation, maximum (95.7 %) was explained by

genotype main effect, followed by environment main effect (2.1 %) and genotype by

environment interaction effect (1.6 %).

Significance of genotype main effect necessitated further analysis of the data for

each cross combination across environments. As a result, the environment main effect

was found significant for all crosses. The generation effect was found significant in two

out of four crosses i.e. L-7 × T-1 and L-7 × T-3. Likewise, the Gen × E effect was found

significant (P>0.01) for all four cross combinations (Table 4.69) which demanded

reanalysis of the data under each environment. The genetic analysis through generation

mean approach was also carried out under irrigated and rainfed conditions for these two

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cross combination. As a result, significant differences were observed among generations

of two crosses under irrigated as well as rainfed conditions except L-6 × T-3 for which

the generation effect was found non-significant (Table 4.70).

Mean values regarding glucosinolates for all the six generations of four different

cross combinations under irrigated and rainfed environments are presented in Table 4.71.

Among the six generations of first cross combination (L-6 × T-1) low level of

glucosinolates were found in P2 under irrigated (30 µM/g) and rainfed conditions (34

µM/g) whereas, high level of glucosinolates were observed in P1 as 44 and 70 µM/g under

irrigated and rainfed conditions, respectively. Among various generations of 2nd cross

combination (L-6 × T-3), under irrigated condition non-significant differences were

observed glucosinolate content however, under rainfed condition low level of

glucosinolates (43 µM/g) were found in F1 generation whereas, high level of 70 µM/g

were found in P1. Among generations of 3rd cross combination (L-7 × T-1) low

glucosinolates under irrigated (30 µM/g) and rainfed condition (34 µM/g) were found in

P2, whereas high level of glucosinolates were recorded for P1 under irrigated (113 µM/g)

and rainfed condition (119 µM/g). In the 4th cross combination (L-7 × T-3) under irrigated

and rainfed conditions low glucosinolates of 44 and 47 µM/g respectively were found in

P2 whereas, high level of glucosinolates were found in P1 under irrigated (113 µM/g) and

rainfed condition (119 µM/g). Overall an increase in glucosinolate content was observed

in most of the genotypes under rainfed condition. It is widely accepted that glucosinolate

is responsive to various environmental factors, which includes climatic conditions,

nutrition and agronomic practices. An increase in glucosinolates was also observed under

drought stress condition by Moghadam et al. (2011) in canola cultivars.

Estimates of genetic effects in six parameter model regarding glucosinolates

content along with chi-square values are presented in (Table 4.72). Under irrigated

condition only two crosses i.e. L-6 × T-1 and L-6 × T-3 whereas, under rainfed condition

one cross i.e. L-7 × T-1 exhibited non-significant chi-square values. For these three

crosses six parameter model was inadequate therefore three parameter model was used

for interpretation of inheritance pattern. Under irrigated as well as rainfed conditions, the

additive genetic effects were found significant for all four cross combinations. Similarly,

the dominance effects under irrigated condition were found significant for only one cross

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(L-7 × T-1) whereas, under rainfed condition all the crosses showed significant

dominance effects except L-7 × T-1.

Involvement of epistasis (i, j or l type) in inheritance of glucosinolate content was

also evidenced in most of the crosses used in this study. Under irrigated condition, cross

L-7 × T-1 exhibited i type of epistasis whereas, under rainfed two crosses L-6 × T-3 and

L-7 × T-3 revealed significant i component. The j of non-allelic effect under irrigated

condition was found significant in two crosses i.e. L-7 × T-1 and L-7 × T-3 whereas,

under rainfed condition all the crosses exhibited significant j type of epistasis except L-7

× T-1. Similarly, the l component under irrigated condition was found significant in two

out of four crosses i.e. L-7 × T-1 and L-7 × T-3 whereas, under rainfed condition it was

significant in most of the crosses except L-7 × T-1.

Canola quality cultivars in brassica are well known internationally due to having

<30 µM/g of glucosinulates. After oil extraction the glucosinulates remains in the seed

cakes and if are >30 µM/g then the seed cakes are undesirable for animal feeding.

Development of Brassica napus cultivars with low glucosinulates is one of the prime

objectives. To fulfill this objective considerable knowledge about the inheritance pattern

of the trait is essential. During the present study significant estimates for additive and

dominance in most of the crosses revealed that both type of gene action might have

largely been involved in the inheritance of this trait. Moreover, the magnitude of additive

genetic effects was higher than dominance under irrigated condition, whereas under

rainfed condition the dominance effects were higher than additive effects except L-7 ×

T-3. Hence it can be justified that both additive and nonadditive type of gene actions

along with non-allelic interactions are playing important role in the inheritance of this

trait. These results are in agreement with those of Alemayehu and Becker, (2005) who

reported significance of additive, dominance and cytoplasmic effect with the prevalence

of partial dominance in governing total glucosinolates with some level of over-

dominance in some cases. Likewise, Sodhi, et al. (2002) also demonstrated the

importance of both additive and non-additive gene action in the inheritance of

glucosinolates in brassica. They further explained that 6-7 genes are involved in

controlling expression of this trait in Brassica juncea. Paisan and Thitiporn (2012) found

three major genes with one or more types of epistatic gene effects important for the

inheritance of glucosinolate content in brassica napus. In the present set of genotypes

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only two crosses (L-6 × T-1 and L-6 × T-3) under irrigated condition with additive

genetic effects and lower level of glucosinolates might provide chances for selection in

early segregating generation. Under rainfed condition the cross combination (L-7 × T-1)

showed additive type of gene action coupled with high level of glucosinolates in F2

generation might have desirable segregants due to the involvement of low parent (T-1).

xi) Erucic Acid

Analysis of variance across environments resulted in significant differences

(P<0.01) among brassica genotypes for erucic acid content. Likewise, the environment

main effect was also found significant however, genotype by environment interaction

effect was found non-significant (P>0.05). Maximum variation (95.7 %) was explained

by genotype main effect, followed by environment main effect which explained only (2.4

%) variation (Table 4.73)

Since, genotype main effect was found significant therefore data was further

analyzed for variability among the generations of each cross combination across

environments. As a result the generations of all crosses varied significantly for erucic

acid content. Likewise the environment main effect was also found significant for all

crosses however, the generation by environment interaction effect was found

nonsignificant (Table 4.74). Since the generation by environment interaction effect for

all the crosses was non-significant therefore only mean data over environment was used

for genetic analysis via generation mean analysis approach.

Mean values (averaged over environments) regarding erucic acid content for six

generations derived from four different cross combinations are presented in Table 4.75.

Among the six generations of first cross combination (L-6 × T-1), low erucic acid (10 %)

was observed in P2 whereas, maximum (29 %) was observed in P1. Among various

generations of cross combination (L-6 × T-3), P2 exhibited low erucic acid (17 %)

whereas, P1 showed high erucic acid (29 %). Among generations derived from cross (L-

7 × T-1), minimum erucic acid (10 %) was recorded for P2, whereas maximum was

recorded for P1 (39 %). In cross combination (L-7 × T-3), P2 generation indicated

minimum erucic acid (17 %) whereas, P1 produced maximum erucic acid (39 %).

129

The joint scaling test revealed significant chi-square values for all cross


interpretation of genetic pattern including epistasis for the trait under study. Estimates of

genetic effects in six parameter model regarding erucic acid along with chi-square values

are given in Table 4.76. Pooled analysis exhibited significant estimates for additive

genetic effects in all four crosses whereas dominance effects were found nonsignificant,

thus indicated the importance of additive gene action for the inheritance of erucic acid

content. The magnitude of additive component was greater in L-7 × T-1 and L-6 × T-1

as compare to L-6 × T-3 and L-7 × T-3. Involvement of only additive × dominance type

of epistasis (j type) in inheritance of this trait was evidenced in all four crosses. The other

two types of epistasis (j and l type) were found non-significant.

The level of erucic acid content in oil seed predicts the quality of oil. Brassica oil

with high erucic acid is not preferred for edible purpose. Since, it not only deteriorates

the oil quality but also impose serious health concerns (Pandey et al. 2013) thus, there is

a dire need to develop Brassica napus cultivars with low level of erucic acid. To

accomplish such goal considerable information regarding the mode of inheritance of the

trait is important for the choice of effective breeding program. During the present study

significant estimates for additive and additive × dominance in all of the four crosses

revealed that additive or additive type of epistasis might have been involved in the

inheritance of erucic acid in these cross combinations. Similar finding were reported in

Brassica juncea by Pandey et al. (2013) who found that inheritance of erucic acid was

governed by two genes with additive effects. In earlier studies by Geninet et al. (1997) it

has been found that in amphidiploid species of brassica like Brassica napus the erucic

acid content of the oil is controlled by two additive genes. This was more elaborated by

Bhat et al. (2002) that of these two genes in amphidiploid brassica, one occupy position

in each respective genome. In another investigation made by Chauhan and Tyagi (2002)

found that erucic acid content in brassica was controlled by partial dominant genes. They

further explained that high erucic acid content was found partially dominant over low

erucic acid content. The study reported by Shufen et al. (2008) indicated that inheritance

of erucic acid content in brassica was controlled by both additive and dominance genetic

effects.

130

Overall the effect of water stress on erucic acid content was also revealed from

the significant environment effect. Significant decrease in erucic acid was evidenced in

most of the genotypes tested under rainfed condition. It might be due to a shorter growing

period coupled with reduced availability of carbohydrates for synthesis of erucic acid

under drought stress environment. Similar finding were reported by Moghadam et al.

(2011) who also observed reduction in concentration of various fatty acids in brassica

cultivars evaluated under water stress conditions. The segregating generations of all four

crosses might provide opportunities for selection of desirable segregants. Especially the

F2 generation of L-6 × T-1 with low level of erucic acid content would provide potential

segregants for improvement of this trait.

4.2.4 Relationship among various traits

Genetic correlation among various important traits under irrigated and rainfed

condition are presented in Fig. 4.13 and 4.14, respectively. Relationship among seedling

traits and yield associated traits might be useful for selection of drought tolerant and high

yielding genotypes at early developmental stage. Association of seedling traits with seed

yield and its contributing traits has been also reported by Cheema and Sadaqat (2004).

Under irrigated condition the biplot demonstrated three groups of traits based on the basis

of angles between their vectors. In first group, eight traits i.e. seed yield per plant (SY),

Proline content (PROL), Chlorophyll content (CHL), erucic acid content (EA),

glucosinolates (GSL), Pod length (PL), and 1000-seed weight (1000-swt) and Days to

flowering (DF) in such a way that their vertices depicted angles less than 90˚ therefore,

indicated strong and position relationship among themselves. Somewhat similar

relationship among the traits of first group was observed under rainfed condition except

that days to flowering should slight negative relationship with the rest of the traits in first

group (Fig. 4.14).

Similarly in the second group under irrigated condition, primary branches per

plant (PB), Plant height (PH) and pods on main raceme (PMR) showed strong and

positive relationship among themselves. Under rainfed condition, both DF and RWC

showed positive and strong relationship with the three traits in second group (Fig. 4.14).

In the third group under irrigated condition, oil content (OIL) and seed per pod (SPP) and

Relative water content (RWC) showed strong positive correlation whereas, under rainfed

131

condition only two traits i.e. SPP and OIL showed positive correlation. It is also

evidenced from the biplot that the traits in first group showed negative relationship with

the traits in the third group. One interesting finding was that the oil content showed

negative relationship with seed yield. Significant and positive relationship of pod length

and 1000-seed weight with seed yield per plant was also observed by Khan et al (2005)

in brassica juncea. Positive and direct effect of 1000seed weight on seed yield per plant

has been also reported by Khan et al. (2013) in brassica napus.

It is clear that increase in plant height resulted in increase in primary branches per

plant. Significant and positive relationship of plant height with primary branches per

plant and pods on main raceme has been reported by Ali et al. (2013) in brassica. Khan,

et al (2005) reported non-significant correlation among seed yield and oil content in

Brassica juncea. Negative correlation among seed yield and oil content has been also

reported by Singh and Choudhury (1983) in Brassica juncea. The present association

study under irrigated and rainfed condition indicated that pod length and 1000-seed

weight can be used as indirect selection criteria for the improvement of seed yield per

plant. Both the drought stress related traits i.e. proline content and chlorophyll content

consistently showed strong relationship with seed yield and associated traits therefore,

these traits as indirect selection criterion might be used at seedling stage for the

improvement of seed yield per plant under rainfed condition.

Table 4. Analysis of variance

132

24 for days to 50% flowering of 20 brassica generations evaluated across

irrigated and rainfed conditions.


Environment (E) 1 2253.6** 18.1

Rep (E) 4 100.4 3.2

Genotype (G) 19 321.3* 48.9

G × E 19 143.0** 21.8

Error 76 13.24 8.1

df= Degree of freedom

Table 4.25 Combine analysis of variance for days to 50 % flowering of various


environments.

Sources of variance df Mean Squares

L-6 × T-1 L-6 × T-3 L-7 × T-1 L-7 × T-3

Environment (E) 1 1409.6** 0.3NS 2703.4** 810.0**

Reps (E) 4 19.1 62.3 0.6 32.8

Generations (Gen) 5 663.2* 175.2NS 372.9NS 40.0NS

Gen × E 5 135.9** 50.4* 95.6** 181.0**

Pooled error 20 18.8 18.2 10.9 3.0

CV % 3.82 3.98 2.78 1.55

*,** = Significant at 5 and 1% level of probability respectively, NS=Non-significant, df=

Degree of freedom.

Table 4.26 Mean squares from analysis of variance for days to 50 % flowering

regarding various generations evaluated under irrigated and rainfed

conditions.

SOV df

Mean Squares

L-6 × T-1 L-6 × T-3 L-7 × T-1 L-7 × T-3

Irrigated

0.6

Rainfed

37.5

Irrigated

1.15

Rainfed

123.5

Irrigated Rainfed Irrigated Rainfed

Rep 2 0.85 0.42 0.65 65.00

Generations 5 639.8** 159.2* 84.13** 141.5* 168.67** 299.85** 42.93** 178.12**

Error 10 1.3 36.4 1.48 34.9 1.36 20.54 1.25 4.81

CV % 0.93 5.61 1.13 5.52 0.91 4.11 0.96 2.05

*,** = Significant at 5 and 1% level of probability respectively, NS=Non-significant, SOV=

Source of variance, df= Degree of freedom.

Table 4. Mean values

133

27 for days to 50% flowering of various generations derived from four

crosses under irrigated and rainfed conditions.

Mean values

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Gen. Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean P1 102 96 99 102 96 99 121 92 107 121 92 107

P2 140 116 128 116 111 114 140 116 128 116 111 114

F1 117 104 111 104 103 103 125 117 121 116 109 113

F2 127 107 117 109 105 107 131 118 125 117 109 113

BC11 106 107 106 103 112 107 120 106 113 119 108 113

BC12 128 115 122 110 115 112 129 113 121 110 114 112

LSD0.05 1.17 6.34 14.12 1.28 6.21 8.60 1.23 4.76 11.85 1.17 2.30 16.30

Table 4.28 Estimates of genetic effects for days to 50% flowering in different crosses

under irrigated and rainfed conditions.

Non-allelic


L-6×T-1 126.90** -23.05** -39.02** -35.17** -3.90* 39.37** 35.1** Duplicate

L-6×T-3 109.29** -6.62** -15.84** -10.99** 0.23NS 10.26** 13.8** Duplicate

L-7×T-1 130.92** -10.48** -33.32** -27.72** -1.08NS 43.89** 33.87** Duplicate

L-7×T-3 117.40** 10.10** -16.27** -14.07** 7.20** 28.27** 46.81** Duplicate

Rainfed

L-6×T-1 106.52** -9.28** 15.51** 17.34** 0.45NS -40.44** 22.4** Duplicate

L-6×T-3 105.39** -4.05NS 26.39** 27.21** 3.50NS -62.34** 45.9** Duplicate

L-7×T-1 118.18** -8.48** -22.79** -36.08** 3.33NS 41.68** 45.80** Duplicate

L-7×T-3 108.84** -5.90** 14.02** 6.96NS 3.73NS -28.22** 27.13** Duplicate

Crosses m d h i j l

2


134

m= mean, d= additive, h= dominance, i= additive× additive, j= additive × dominance, l=



29 for plant height of 20 brassica generations evaluated across two different

environments.

Source of variance df Mean Squares % of total SS

Environment (E) 1 4348.8** 24.73

Rep (E) 4 9.83 0.22

Genotype (G) 19 596.8** 64.48

G × E 19 94.3** 10.18

Error 76 0.89 0.39


Table 4.30 Combine analysis of variance for plant height of various generations

derived from four crosses evaluated across two

different environments.

Source of variance df Mean Squares

L-6 × T-1 L-6 × T-3 L-7 × T-1 L-7 × T-3

Environment (E) 1 369.9** 1843.3** 830.4** 1950.7**

Reps (E) 4 1.2 1.5 1.4 1.1

Generations (Gen) 5 264.2** 749.6* 676.5* 1190.4**

Gen × E 5 16.7** 105.7** 105.2** 107.3**

Pooled error 20 1.0 1.4 1.6 1.4

CV % 0.55 0.64 0.71 0.62

*,**= Significant at 5 and 1% level of probability respectively, NS= Non-significant, df=

Degree of freedom.

Table 4.31 Mean squares from analysis of variance for plant height of various


SOV df

Mean squares

L-6 × T-1 L-6 × T-3 L-7 × T-1 L-7 × T-3


Rep 2 0.6 1.7 1.15 1.8 0.85 1.91 0.65 1.62


135

Generations 5 159.3** 121.7** 624.01** 231.3** 532.35** 249.45** 916.85** 380.83**

Error 10 1.3 0.8 1.48 1.4 1.36 1.78 1.25 1.47

CV % 0.61 0.49 0.62 0.66 0.64 0.78 0.57 0.67


Degree of freedom

32 for plant height of various generations derived from four crosses under


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-1 Gen. Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean P1 174 169 172 174 169 172 163 161 162 163 161 162

P2 194 189 191 211 193 202 194 189 191 211 193 202

F1 181 178 179 203 182 193 191 172 182 197 186 191

F2 184 175 179 198 175 186 182 172 177 203 186 194

BC11 181 177 179 179 176 178 168 167 168 189 175 182

BC12 191 179 185 202 186 194 192 172 182 208 180 194

LSD0.05 1.17 0.91 4.96 1.28 1.25 12.5 1.23 1.40 12.4 1.17 1.27 12.5

Table 4.33 Estimates of genetic effects for plant height in different crosses under


Non-allelic


L-6×T-1 183.93** -8.35** 4.50NS 7.70NS 1.75NS -20.60** 15.1** -

L-6×T-3 198.06** -21.87** -19.96** -30.96** -3.67* 61.02** 43.8** Duplicate

L-7×T-1 182.22** -22.43** 3.28NS -9.22* -6.73** 29.09** 67.63** -

L-7×T-3 202.63** -18.30** -10.10NS -20.20** 5.50* -3.00NS 26.76** -

Rainfed

L-6×T-1 174.80** -0.75NS 12.28** 13.43** 9.00** -12.83** 64.8** Duplicate

L-6×T-3 174.56** -13.87** 27.39** 26.84** -1.72* -25.68** 29.1** Duplicate

Mean values

Crosses m d h i j l

2


136

L-7×T-1 171.79** -3.68** -13.09** -10.19** 10.02** 27.16** 54.16** Duplicate

L-7×T-3 186.27** -3.90* -27.73** -36.33** 12.20** 54.13** 41.83** Duplicate




Analysis of variance

137

Table 4.34 for primary branches plant-1 of 20 brassica

generations evaluated across two different environments.


Environment (E) 1 173.9** 29.72

Rep (E) 4 1.01 0.69

Genotype (G) 19 18.1** 58.79

G × E 19 2.16** 7.02

Error 76 0.29 3.78


Table 4.35 Combine analysis of variance for primary branches plant-1 of various


environments.

Source of variance df Mean squares

L-6 × T-1 L-6 × T-3 L-7 × T-1 L-7 × T-3

Environment (E) 1 26.8** 49.1** 34.9** 109.0**

Reps (E) 4 0.7 0.2 0.2 0.1

Generations (Gen) 5 9.1* 25.8* 4.6NS 17.6**

Gen × E 5 1.8** 3.5** 1.1** 1.5**

Pooled error 20 0.3 0.4 0.2 0.3

CV % 5.69 5.72 5.00 4.72

*,**= Significant at 5 and 1% level of probability respectively, NS= Non-significant, df= Degree

of freedom

Table 4.36 Mean squares from analysis of variance for primary branches plant-1 of


conditions.

SOV df

Mean squares

L-6 ×T-1 L-6 ×T-3 L-7 ×T-1 L-7 ×T-3


Rep 2 1.1 0.334 0.23 0.1 0.29 0.19 0.13 0.09

Generations 5 9.1** 1.8* 22.96** 6.3** 4.64** 1.11** 14.52** 4.56**

Error 10 0.1 0.4 0.40 0.3 0.35 0.14 0.47 0.19

CV % 3.44 7.85 5.37 6.15 5.41 4.26 4.93 4.17

*,**= Significant at 5 and 1% level of probability respectively, NS= Non-significant, df= Degree

of freedom

Table 4.

138

37 for primary branches plant-1 of various generations derived from four


Gen.

Mean values

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean

Table 4.38 Estimates of genetic effects for primary branches plant -1 in different


Non-allelic


L-6 × T-1 9.59** -2.45** 2.04* 1.74NS -0.02NS -1.64NS 4.1NS -

L-6 × T-3 11.32** -3.05** 3.86** 3.48** 1.03** -5.28NS 16.4** - L-7 × T-1 8.91** -0.53* 10.56**

11.02** 0.57NS -13.69** 77.40** Duplicate

L-7 × T-3 13.62** -1.72** 3.19** -0.19NS 1.03** 4.19NS 21.87** -

Rainfed

L-6 × T-1 8.41** -1.62** -1.94NS -1.54NS -0.75* 2.38NS 8.6* -

L-6 × T-3 8.18** -0.77** 6.14** 5.76** 1.22** -5.26NS 69.9** -

L-7 × T-1 8.12** -0.32NS 4.43** 3.61** 0.20NS -2.94NS 24.18** - L-7 × T-3 10.41** -1.37** 1.12NS -0.44NS 0.27NS 1.98NS 5.09NS -




39 Analysis of variance for pods on main raceme of 20 brassica genotypes

evaluated across two different environments.


P1 7 8 8 7 8 8 10 8 9 10 8 9 P2 12 9 11 16 12 14 12 9 11 16 12 14

F1 10 8 9 12 10 11 11 10 10 16 11 14

F2 10 8 9 11 8 10 9 8 9 14 10 12

BC11 9 7 8 11 9 10 11 9 10 13 10 11

BC12 11 9 10 14 10 12 12 9 11 15 11 13

LSD0.05 0.4 0.7 1.6 0.7 0.6 2.3 0.6 0.4 1.3 0.7 0.5 1.5

Crosses m d h i j l

2


139

Environment(E) 1 4003.2** 36.26

Rep (E) 4 8.80 0.32

Genotype (G) 19 267.8* 46.08

G × E 19 97.37** 16.76

Error 76 0.85 0.59


Table 4.40 Combine analysis of variance for pods on main raceme of various


rainfed conditions.


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment 1 1175.3** 1704.3** 764.5** 750.8**

Reps / E 4 0.6 1.1 0.8 0.6

Generations 5 316.5NS 266.7NS 461.7* 236.7*

Gen x E 5 86.2** 191.3** 60.2** 47.6**

Pooled error 20 1.3 1.5 1.4 1.3

CV % 2.21 2.63 2.51 2.48


Degree of freedom

Table 4.41 Mean squares from analysis of variance for pods on main raceme of


conditions.

SOV df

Mean squares

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


Rep 2 0.6 0.6 1.15 1.1 0.85 0.85 0.65 0.65

Generations 5 319.2** 83.5** 443.54** 14.5** 405.19** 116.64** 226.85** 57.38**

Error 10 1.3 1.3 1.48 1.5 1.36 1.36 1.25 1.25

CV % 1.98 2.49 2.29 3.09 2.28 2.78 2.26 2.76

*,**= Significant at 5 and 1% level of probability respectively, NS= Non-significant, SOV= source

of variance, df= Degree of freedom.

Table 4.

140

42 regarding pods on main raceme of various generations of four crosses


Gen.

Mean values

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


P1 45 38 42 45 38 42 35 34 35 35 34 35

P2 66 52 59 51 43 47 66 52 59 51 43 47

F1 71 47 59 44 39 41 47 42 44 58 47 52

F2 46 42 44 47 37 42 58 41 49 44 39 41

BC11 56 41 49 56 39 47 42 38 40 52 40 46

BC12 54 49 52 76 41 59 59 46 52 58 40 49

LSD0.05 1.17 1.17 NS 1.28 1.28 NS 1.23 1.23 1.15 1.17 1.17 1.10

Gen. = Generations

Table 4.43 Estimates of genetic effects for pods on main raceme in different crosses


Non-allelic

Crosses Irrigated

interaction

L-6×T-1 46.27** 1.20NS 51.92** 36.87** 11.75** -6.77NS 109.7** -

L-6×T-3 46.89** -20.37** 73.81** 77.71** -17.47** -159.4** 1009.4** Duplicate

L-7×T-1 57.56** -16.58** -31.21** -27.86** -1.23NS 21.52** 61.12** Duplicate

L-7×T-3 43.77** -5.00** 60.37** 45.67** 2.70* -64.87** 56.17** Duplicate

Rainfed

L-6×T-1 42.07** -8.10** 14.22** 12.27** -1.25NS -8.97* 36.3** Duplicate

L-6×T-3 36.82** -2.82** 10.83** 12.68** -0.47NS -14.41** 39.5** Duplicate

L-7×T-1 40.69** -7.58** 2.31NS 3.61NS 1.32NS -0.84NS 5.65NS -

L-7×T-3 38.50** -0.30NS 15.63** 6.93** 4.10** 3.27NS 35.91** -

m d h i j l 2


141


dominance × dominance, 2= Chi square *,** Significant at 5 and 1 % level of probability

respectively, NS= Non-significant.

Table 4. 20 brassica genotypes

142

44 Analysis of variance for pod length of

evaluated for across two different environments.


Environment (E) 1 38.95** 16.93

Rep (E) 4 0.42 0.73

Genotype (G) 19 7.88** 65.10

G × E 19 0.90** 7.43

Error 76 0.30 9.81

df = Degree of freedom

Table 4.45

Combine analysis of variance for pod length of various generations

derived from f

conditions.

our crosses evaluated across irrigated and rainfed


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment (E) 1 2.7NS 6.3* 16.7** 19.9**

Reps (E) 4 0.6 0.9 0.1 0.0

Generations (Gen) 5 1.8* 1.1NS 13.7** 4.9*

Gen × E 5 0.4NS 0.6NS 1.3** 0.8*

Pooled error 20 0.3 0.3 0.3 0.2

CV % 7.35 7.64 5.50 5.71


Degree of freedom

Table 4.46 Mean squares from analysis of variance for pod length of various


SOV

Mean squares

df L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


Rep 2 - - - - 0.07 0.11 0.00 0.01

Generations 5 - - - - 10.29** 4.65** 3.71** 1.99**

Error 10 - - - - 0.25 0.25 0.18 0.32

CV % - - - - 5.10 5.97 4.48 7.05


143

*,**= Significant at 5 and 1% level of probability respectively, NS= Non-significant, SOV=

source of variance, df= Degree of freedom.

47 for pod length of various generations derived from four crosses under


Gen.

Mean Values

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


P1 7.5 7.1 7.3 7.5 7.1 7.3 11.0 9.4 10.2 11.0 9.4 10.2

P2 6.4 6.2 6.3 8.3 7.5 7.9 6.4 6.2 6.3 8.3 7.5 7.9

F1 7.5 6.0 6.8 8.3 6.7 7.5 11.2 9.4 10.3 8.6 7.3 7.9

F2 7.3 6.8 7.0 7.3 6.6 6.9 9.2 8.9 9.0 8.8 8.3 8.5

BC11 8.1 7.8 7.9 8.0 8.0 8.0 11.1 9.1 10.1 10.6 8.2 9.4

BC12 7.5 7.2 7.3 8.7 7.2 8.0 10.0 7.8 8.9 9.5 7.4 8.4

LSD0.05 NS NS 0.75 NS NS NS 0.53 0.53 1.36 0.45 0.59 1.10

Gen= Generations

Table 4.48 Estimates of genetic effects for pod length in different crosses under different

environments and pooled over environments.

Pooled

L-6×T-1 4.78** 0.59** 11.30** 11.35** 0.10NS -14.69** 83.1** Duplicate

L-6×T-3 4.63** 0.01NS 13.20** 13.32** 0.31NS -14.94** 84.3** Duplicate

L-7×T-1 - - - - - - - - L-7×T-3 - - - - - - - -

Irrigated

L-6×T-1 - - - - - - - -

L-6×T-3 - - - - - - - -

L-7×T-1 9.17** 1.07** 8.07** 5.53** -1.23** -7.87* 51.74** Duplicate

L-7×T-3 8.81** 1.12** 3.99** 5.06** -0.22NS -8.82** 44.18** Duplicate

Rainfed


144

Non-allelic

interaction




49 Analysis of variance for for seed pod-1 of evaluated across

two different environments.


Environment (E) 1 201.2** 18.39

Rep (E) 4 7.99 2.92

Genotype (G) 19 38.4** 66.74

G × E 19 3.30** 5.72

Error 76 0.90 6.22


Table 4.50 Combine analysis of variance for seed pod-1 of various generations


conditions.

L-6×T-1 - - - - - - - -

L-6×T-3 - - - - - - - -

L-7×T-1 8.91** 1.30** -0.50NS -2.04NS -0.29NS 2.83NS 8.87NS -

L-7×T-3 8.27** 0.80** -3.20** -2.00NS -0.16NS 2.37NS 5.51NS -

Crosses m d h i j l

2


145


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment (E) 1 73.2** 68.6** 91.7** 11.4**

Reps (E) 4 0.7 0.9 0.7 0.6

Generations (Gen) 5 57.2** 34.7* 20.4** 4.5*

Gen × E 5 1.7NS 4.9* 2.3NS 1.0NS

Pooled error 20 1.3 1.4 1.4 1.3

CV % 5.42 5.76 6.30 6.54


Degree of freedom

Table 4.51 Mean squares from analysis of variance for seed pod-1 of various


SOV

Mean Squares

df L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


Rep 2 - - 1.15 0.7 - - - -

Generations 5 - - 19.88** 19.7** - - - -

Error 10 - - 1.48 1.4 - - - -

CV % - - 5.48 6.08 - - - -



52 for seed pod-1 of various generations derived from four crosses under


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3 Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean P1 26 23 25 26 23 25 17 15 16 17 15 16

P2 22 19 21 18 18 18 22 19 21 18 18 18

F1 23 22 23 23 19 21 21 17 19 18 17 17

Gen.

Mean values


146

F2 18 14 16 21 16 19 18 17 17 17 16 17

BC11 24 22 23 23 21 22 21 17 19 17 16 17

BC12 23 19 21 21 19 20 23 18 20 19 17 18

LSD0.05 - -

Gen.= Generations

1.60 1.28 1.24 2.67 - - 1.84 - - 1.19

Table 4.53 Estimates of genetic effects for seed pod-1 in different crosses under different

environments and pooled over environments.

Non-allelic

Pooled interaction

L-6×T-1 15.74** 2.00** 25.89** 25.96** -0.05NS -24.29** 373.9** Duplicate

L-6×T-3 - - - - - - - -

L-7×T-1 17.25** -1.21** 10.12** 9.12** 1.19* -12.30** 36.97** Duplicate

L-7×T-3 16.75** -1.23** 2.98* 2.78NS -0.03NS -4.03NS 2.84NS Duplicate

Irrigated

L-6×T-1 L-6×T-3 21.32** 1.73** 5.38** 4.58* -2.17** -4.31NS 14.0** -

L-7×T-1 - - - - - - - -

L-7×T-3 - - - - - - - -

Rainfed

L-6×T-1 - - - - - - - -

L-6×T-3 15.79** 1.83** 14.64** 16.11** -0.77NS -14.98** 100.3** Duplicate

L-7×T-1 - - - - - - - -

L-7×T-3 - - - - - - - -

Crosses m d h i j l

2


147




54 Analysis of variance for 1000-seed weight of evaluated



Environment(E) 1 1.19** 1.68

Rep (E) 4 0.05 0.31

Genotype (G) 19 3.52** 94.18

G × E 19 0.10** 2.62

Error 76 0.01 1.21

df= Degrees of freedom

Table 4.55 Combine analysis of variance for 1000-seed weight of various


rainfed conditions.


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment 1 1.65** 0.01NS 0.36** 0.43**

Reps / E 4 0.01 0.004 0.01 0.01

Generations 5 0.13** 1.53** 3.18** 0.53*

Gen × E 5 0.01NS 0.12** 0.04NS 0.09**

Pooled error 20 0.02 0.02 0.01 0.01

CV % 4.16 3.07 2.45 2.15


Degree of freedom

Table 4.56 Mean squares from analysis of variance for 1000-seed weight of various


SOV Mean squares


148

df L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3


Rep 2 - - 0.01 0.002 - - 0.00 0.01

Generations 5 - - 0.72** 0.93** - - 0.39** 0.24**

Error 10 - - 0.02 0.02 - - 0.01 0.01

CV % - - 3.05 3.08 - - 1.98 2.33



57 for 1000-seed weight of various generations derived from four crosses under


Gen.

Mean values

L-6×T-1 L-6×T-3

L-7×T-1 L-7×T-3


P1 3.7 3.2 3.4 3.7 3.2 3.4 5.8 5.5 5.7 5.8 5.5 5.7

P2 3.8 3.4 3.6 5.0 4.8 4.9 3.8 3.4 3.6 5.0 4.8 4.9

F1 3.6 3.3 3.4 4.6 4.6 4.6 5.1 4.9 5.0 5.2 4.9 5.0

F2 3.4 2.9 3.1 4.0 4.4 4.2 5.0 5.0 5.0 5.2 4.7 5.0

BC11 3.6 3.2 3.4 4.2 4.4 4.3 5.5 5.3 5.4 5.1 5.0 5.1

BC12 3.7 3.2 3.4 4.8 4.5 4.6 4.7 4.6 4.7 4.8 4.9 4.9

LSD0.05 - - 0.12 0.14 0.14 0.43 - - 0.23 0.11 0.12 0.37

Gen. = Generations

Table 4.58 Estimates of genetic effects for 1000 seed weight in different crosses under


L-6×T-1 3.14** -0.05NS 1.09** 1.17** 0.02NS -0.98NS 33.2** - L-6×T-3 - - - - - - - -

L-7×T-1 4.98** 0.76** 0.33NS -0.08NS -0.28** -0.47NS 11.08** -

L-7×T-3 - - - - - - - -

Irrigated

L-6×T-1 - - - - - - - -


149

Pooled

m = mean, d = additive, h = dominance, i = additive × additive, j = additive × dominance, l

= dominance × dominance, 2 = Chi square *,** = Significant at 5 and 1 % level of probability

respectively, NS = non-significant.

L-6×T-3 4.05** -0.52** 2.16** 1.87** 0.13NS -2.14NS 26.2** -

L-7×T-1 - - - - - - - -

L-7×T-3 5.25** 0.26** -1.32** -1.11** -0.19** 2.42NS 37.90** -

Rainfed

L-6×T-1 - - - - - - - -

L-6×T-3 4.41** -0.12NS 0.86** 0.30NS 0.68** -0.97NS 46.0** -

L-7×T-1 - - - - - - - -

L-7×T-3 4.67** 0.01NS 0.76** 1.03** -0.32** -0.62NS 58.32** -

Crosses m d h i j l

2 Non - allelic

interaction

Table 4.

150

59 Analysis of variance for seed yield plant -1 of 20 brassica generations

evaluated across two different environments.


Environment(E) 1 2719.5** 41.78

Rep (E) 4 8.30 0.51

Genotype (G) 19 173.7** 50.71

G × E 19 20.6** 6.00

Error 76 0.85 1.00


Table 4.60 Combine analysis of variance for seed yield plant-1 of various generations


conditions.


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment(E) 1 1266.0** 316.4** 1482.4** 524.2**

Reps (E) 4 0.6 0.9 0.8 0.7

Generations (Gen) 5 41.1** 18.5NS 106.8** 202.4**

Gen × E 5 2.5NS 13.2** 3.4NS 21.4**

Pooled error 20 1.2 1.4 1.4 1.3

CV % 4.35 5.43 3.59 3.84


Degree of freedom

Table 4.61 Mean squares from analysis of variance for seed yield plant-1 of various


SOV

Mean squares

df L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Irrigated Rainfed Irrigated Rainfed Irrigated Rainfed Irrigated

Rainfed

Rep 2 - - 1.00 0.8 - - 0.71 0.65

Generations 5 - - 21.12** 10.6** - - 170.55** 53.28**

Error 10 - - 1.45 1.4 - - 1.30 1.26

CV % - - 4.82 6.24 - - 3.43 4.37


151



62 for seed yield plant-1 of various generations derived from four crosses


Gen.

Mean values

L-6×T-1 L-6×T-3

L-7×T-1 L-7×T-3 Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean

P1 28 17 22 28 17 22 43 30 37 43 30 37

P2 35 22 28 23 21 22 35 22 28 23 21 22

F1 31 19 25 24 19 22 39 26 32 31 26 28

F2 31 18 25 22 16 19 38 25 32 32 23 27

BC11 35 23 29 29 21 25 43 33 38 41 31 36

BC12 28 18 23 23 20 21 35 21 28 29 23 26

LSD0.05 - - 1.90

Gen. = Generations

1.26 1.25 - - - 2.24 1.20 1.18 5.61

Table 4.63 Estimates of genetic effects for seed yield plant -1 in different crosses under


L-6×T-1 24.70** 5.93** 4.60* 5.05* 8.88** -8.17* 97.6** Duplicate

L-6×T-3 - - - - - - - -

L-7×T-1 31.68** 11.23** 4.96** 5.07** 7.09** -7.10* 56.4** Duplicate

L-7×T-3 - - - - - - - -

Irrigated

L-6×T-1 - - - - - - - -

L-6×T-3 22.50** 6.31** 14.46** 15.50** 4.18** -20.92** 29.0** Duplicate

L-7×T-1 - - - - - - - -

L-7×T-3 31.98** 12.72** 9.23** 11.44** 2.85** -22.13** 29.5** Duplicate

Rainfed

Table 4.

152

Pooled




64 Analysis of variance for 20 brassica generations evaluated for oil content



Environment (E) 1 191.6** 18.90

Rep (E) 4 8.98 3.54

Genotype (G) 19 36.6** 68.57

G × E 19 1.35NS 2.53

Error 76 0.86 6.47


Table 4.65 Combine analysis of variance for oil content in various generations


conditions.


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

L-6×T-1 - - - - - - - -

L-6×T-3 16.33** 0.37NS 15.21** 15.16** 2.12** -19.60** 73.6** Duplicate

L-7×T-1 - - - - - - - -

L-7×T-3 22.77** 7.38** 19.06** 18.78** 2.68** -26.32** 70.0** Duplicate

Crosses m d h i j l

2 Non - allelic

interaction


153

Environment (E) 1 62.6** 35.8** 49.8** 92.2**

Reps (E) 4 0.7 1.2 0.8 0.7

Generations (Gen) 5 9.3** 40.4** 21.0** 4.7NS

Gen × E 5 0.4NS 1.0NS 1.4NS 2.3NS

Pooled error 20 1.3 1.5 1.4 1.3

CV % 2.29 2.60 2.53 2.57

*,** = Significant at 5 and 1% level of probability respectively, NS = Non-significant, df=

Degree of freedom

Table 4.66 Mean values for oil content of various generations derived from four


Mean Mean Mean Mean

Gen. = Generations

P1 53 49 51 53 49 51 46 42 44 46 42 44 P2 50 48 49 45 43 44 50 48 49 45 43 44

F1 51 48 50 48 46 47 47 44 46 46 42 44

F2 49 46 47 47 44 45 46 44 45 45 40 42

BC11 52 49 50 49 48 49 46 45 46 46 43 45

BC12 50 48 49 46 45 46 48 46 47 45 43 44

LSD0.05 - - 0.81 - - 1.24 - - 1.44 - - -

Gen.

Mean values L - ×T 6 - 1 L - 6 ×T - 3 L - ×T 7 - 1 L - ×T 7 - 3


Table 4.

154

67 Estimates of genetic effects for oil content in different crosses pooled over

environments.

Non-allelic

Pooled interaction


L-6×T-3 45.14** 3.29** 7.69** 8.29** -0.24NS -8.43** 25.4** Duplicate

L-7×T-1 44.93** -1.25** 4.51** 5.58** 1.40** -6.43* 16.95** Duplicate





Table 4.68 Analysis of variance for 20 brassica generations evaluated for

glucosinolate content across two different environments.


Environment (E) 1 1202.6** 2.07

Rep (E) 4 17.80 0.12

Genotype (G) 19 2923.8** 95.75

G × E 19 47.4** 1.55

Error 76 3.87 0.51


Table 4.69 Combine analysis of variance for glucosinolate content of various


rainfed conditions.


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment (E) 1 559.9** 529.2** 216.8** 486.6**

Reps (E) 4 0.7 14.6 0.8 0.6

Generations (Gen) 5 460.5NS 156.4NS 5057.2** 3343.5**

Gen × E 5 117.1** 136.6** 4.0* 28.7**

Pooled error 20 1.3 12.0 1.4 1.3

CV % 2.74 7.25 1.60 1.44

*,** = Significant at 5 and 1% level of probability respectively, NS = Non-significant, df=

Degree of freedom

Crosses m d h i j l

2

Table 4.

155

70 Mean squares from analysis of variance for glucosinolate content in

various generations evaluated under two different environments.

SOV df

Mean squares

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Irrigated Rainfed Irrigated Rainfed 1.15 28.0

Irrigated Rainfed Irrigated Rainfed

Rep 2 0.6 0.8 0.85 0.85 0.65 0.65

Generations 5 78.4** 499.2** 1.24NS 291.8** 2457.44** 2603.73** 1575.61** 1796.58**

Error 10 1.3 1.3 1.48 22.5 1.36 1.36 1.25 1.25

CV % 2.99 2.53 2.76 9.20 1.65 1.55 1.51 1.37

*,** = Significant at 5 and 1% level of probability respectively, NS = Non-significant, SOV

= source of variance, df = Degree of freedom.

Table 4.71 Mean values for glucosinolate content of various generations derived

from four crosses under irrigated and rainfed conditions.

Gen.

Mean values

L-6×T-1 L-6×T-3

L-7×T-1 L-7×T-3 Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean Irrigated Rainfed Mean

P1 44 70 57 44 70 57 113 119 116 113 119 116 P2 30 34 32 44 47 45 30 34 32 44 47 45

F1 38 42 40 43 43 43 72 76 74 75 90 83

F2 38 42 40 44 54 49 67 74 70 71 82 76

BC11 41 45 43 45 49 47 89 93 91 81 86 84

BC12 34 38 36 43 47 45 54 57 55 61 65 63

LSD0.05 1.17 1.20 NS NS 4.99 NS 1.23 1.23 2.42 1.17 1.17 6.49

Gen. = Generations

Table 4.72 Estimates of genetic effects for glucosinolate content in different crosses

under different environments.

Irrigated

L-6×T-1 37.57** 6.41** 0.64NS 0.12NS -1.04NS -1.46NS 1.9NS -

L-6×T-3 44.03** 1.57** -0.52ns 0.13NS 1.16NS -1.50NS 2.9NS -

L-7×T-1 66.71** 35.62** 18.14** 17.67* -5.85** -17.10* 10.32* Duplicate

L-7×T-3 71.05** 18.07** -2.62NS 0.07NS -16.37** 22.53** 42.14** -

Rainfed

Crosses m d h i j l

2 N on - allelic

interaction

Table 4.

156

L-6×T-1 42.04** 6.55** -11.02** -1.03NS -11.80** 22.01** 148.7** Duplicate

L-6×T-3 53.66** 1.54** -38.38** -22.26** -10.17** 32.30** 197.6** Duplicate

L-7×T-1 73.76** 38.45** 6.03NS 6.92NS -4.41NS -4.47NS 5.39NS -

L-7×T-3 81.77** 17.78** -22.63* -29.19** -18.45** 77.15** 78.50** Duplicate




73 Analysis of variance for erucic acid in 20 brassica genotypes evaluated


Source of variance df Mean Squares

%

of total SS

Environment (E) 1 128.7** 2.42

Rep (E) 4 8.46 0.64

Genotype (G) 19 267.5** 95.67

G × E 19 0.21NS 0.08

Error 76 0.83 1.19


Table 4.74 Combine analysis of variance for erucic acid of various generations


conditions.


L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Environment (E) 1 41.7** 39.6** 48.9** 34.7**

Reps (E) 4 0.8 1.0 1.2 0.7

Generations (Gen) 5 259.5** 95.5** 583.0** 307.5**

Gen × E 5 0.2NS 0.03NS 0.3NS 0.5NS

Pooled error 20 1.1 1.4 1.3 1.3

CV % 6.25 5.31 4.88 4.20

*,** = Significant at 5 and 1% level of probability respectively, NS = Non-significant, df

= Degree of freedom

Table 4.75 Mean values for erucic acid in various generations derived from four


Gen.

Mean values

L-6×T-1 L-6×T-3 L-7×T-1 L-7×T-3

Table 4.

157


P1 30 28 29 30 28 29 40 38 39 40 38 39 P2 11 8 10 18 16 17 11 8 10 18 16 17

F1 16 14 15 23 21 22 21 19 20 27 25 26

F2 17 15 16 22 20 21 25 24 25 27 26 27

BC11 20 18 19 24 22 23 30 28 29 29 27 28

BC12 14 12 13 21 19 20 21 19 20 26 24 25

LSD0.05 - - 0.58 - - 0.21 - - 0.63 - - 0.83

Gen. = Generations

76 Estimates of genetic effects regarding erucic acid in different crosses

pooled over environments.

Pooled




L-6×T-1 16.07** 5.82** -5.91NS -1.42NS -3.65** 7.23NS 12.9** -

L-6×T-3 21.43** 2.89** -0.47NS 0.52NS -3.16** 2.59NS 15.2** -

L-7×T-1 24.54** 9.66** -5.19NS -0.66NS -4.88** -7.99NS 30.97** -

L-7×T-3 26.54** 3.21* -1.50NS 0.84NS -7.91** -0.98NS 22.83** -

Crosses m d h i j l

2 N on - allelic

interaction

Table 4.

158

Fig. 4.13 Biplot for genetic correlation among various morpho-yield and oil quality

and physiological traits under irrigated condition.

159

Fig. 4.14 Biplot for genetic correlation among various morpho-yield and oil quality

and physiological traits under rainfed condition.

160

VII. SUMMARY

The present study was carried out in two phases at the Department of Plant

Breeding and Genetics, The University of Agriculture Peshawar Khyber Pakhtunkhwa

Pakistan during 2010-2014. During the first phase combining ability studies were carried

out based on which selection was made for best parents and crosses. Subsequently in the

second phase, selected parents and crosses were used for inheritance studies via

generation mean analysis.

Parental material for this experiment was comprised of a set of 15 Brassica napus

genotypes. Out of total, 11 lines (L-1, L -2, L -3, L -4, L -5, L -6, L -7, L -8, L 9, L -10

and L -11) were introduced from China and four testers (Concord = T-1, Acelect = T-2,

Shiralee = T-3, and Hoyla-43 = T-4) were procured form PGRI, NARC Islamabad. All

lines were crossed in line × tester fashion to develop 44 F1 hybrids. The resultant hybrids

along with parental genotypes were evaluated for morph-yield and oil quality traits under

field condition. The data obtained from parents and F1 generation (line × tester) was

subjected to analysis of variance (ANOVA) to find out differences among the genotypes.

Upon significant line × tester effect the data was further subjected to GGE biplot

methodology for combining ability (GCA and SCA) and heterotic studies to identify

potential parents and promising crosses for further genetic studies.

The results obtained from the biplot approach revealed that both GCA and SCA

played important role in controlling majority of the traits however, GCA effects were

higher than SCA effects for days to flowering, primary branches plant-1, pods on main

raceme, pod length, seeds pod-1, 1000-seed weight and seed yield plant-1 indicating the

predominant role of additive type of gene action for these trait in this set of brassica

genotypes. For plant height both GCA and SCA were significant with predominance of

SCA effects thereby indicated that dominance type of gene action played important role

in controlling this trait. Overall for most of traits, desirable GCA was depicted by parental

lines, L-7, L-6, L-4, L-3, L-8, T-1, T-2, T-3 and T-4.

Additive genetic control mechanism was found more important in controlling oil

content and erucic acid in the present set of genotypes. Among the female parents (lines)

L-6 L-7, L-4, L-5 and L-8 and testers T-4, T-2 and T-1 were best general combiners. The

161

line L-6 was identified as best specific combiner having potential to produce heterotic

hybrid with specific tester i.e. T-1. Data regarding glucosinolates showed both GCA and

SCA effects significant with predominance of GCA effects. Among the lines, L-6, L-9,

L-2, L-10 and L-1 depicted desirable negative GCA whereas among the testers, T-3 and

T-4 were found having good GCA. Since, genotypes with low glucosinolates are desired

therefore the line L-6 produced desirable cross combinations especially with tester T-4.

Based on the results obtained from combining ability studies of various important

traits, the most promising genotypes among the lines were identified as L-6 and L-7

whereas among the testers as T-1 and T-3. Therefore, these parental genotypes along with

their F1 crosses were selected and further used to develop various segregating generations

(F2, BC11 and BC12) for each cross combination. As a result 20 genotypes comprising four

parents (L-6, L-7, T-1 and T-3), their resultant four F1 (L-6

× T-1, L-6 × T-3, L-7 × T-1, L-7 × T-3), four F2, four BC11 and four BC12 were

evaluated both in glass house and field conditions under irrigated as well as rainfed

environments. The experiment in glass house was carried out for screening of generations

and inheritance of relative water content, proline content and chlorophyll content under

irrigated as well as drought stress conditions. Whereas, the experiment under field

condition was carried out for screening of various generations for morphological, yield

and oil quality traits and the genetic analysis of these traits under irrigated and rainfed

condition. The data obtained from both experiments were subjected to generation mean

analysis approach to understand inheritance pattern of various important traits under both

environments.

Genetic analysis for RWC indicated that additive type of gene action was

predominant in cross (L-6 × T-1) under irrigated as well as rainfed conditions and in cross

L-7 × T-1 under irrigated condition. Likewise, dominance gene action was found more

important in L-6 × T-3 and L-7 × T-3. Minimum reduction in RWC under drought stress

condition was observed in parental genotypes (L-7 and T-3), which indicated their

potential to withhold water during drought stress condition. Moreover, they can be used

as potential parents for development of drought tolerance in future breeding programmes.

Similarly, the segregating generations of L-7 × T-1 and L-7 × T3 also possessed slight

reduction in RWC under rainfed condition with dominance type of gene action may have

162

potential segregants in latter generations for selection and development of drought

tolerant cultivars. The F2 generation of L-6 × T-1 with high mean values for RWC along

with additive type of gene action would probably provide desirable segregants for

selection.

For proline content overall the additive genetic effects were found more important

in most of the crosses under irrigated as well as rainfed conditions. The dominance

component was found significant for L-6 × T-3 and L-7 × T-3 under irrigated condition

whereas, under rainfed condition the dominance component was found non-significant

for all crosses. The magnitude of dominance component was greater than additive

component in two crosses i.e. L-6 × T-3 and L-7 × T-3 under irrigated condition.

Maximum increase in proline content due to water stress was observed in parental

genotypes (L-7 and T-1) which suggested their potential to cope drought stress and

provided opportunity to be used as potential parents for development of drought tolerant

cultivar. Similarly, the segregating generation of L-7 × T-1 also showed an increase in

proline under rainfed condition, with high mean performance in F2 generation along with

additive type of gene action may provide potential segregants for future breeding

programmes.

Regarding inheritance of chlorophyll content genetic estimates exhibited

significant additive genetic effects for all crosses under irrigated and rainfed condition

except L-6 × T-3 and L-7 × T-1 under irrigated condition. Likewise, the non-additive

component was also found significant for all four crosses under both environments. The

magnitude of non-additive component was greater as compare to additive component

thereby, indicated the importance of dominance type of gene action playing role in the

inheritance of chlorophyll content in these genotypes. In one cross (L-6 × T-1) under

rainfed condition, both additive and dominance effects played equal role in the inheritance

of chlorophyll content in this specific cross. Involvement of additive × additive type of

epistasis (i) in inheritance of this trait was evidenced in all the crosses except L-6 × T-1

under both the environments. The j type of epistasis (additive × dominance) was also

found significant in all of the crosses under pooled data except L6 × T-1 and L-7 × T-3.

Moreover, under irrigated and rainfed conditions both L-6 × T-1 and L-7 × T-3 exhibited

significant j type of epistasis. The l type of epistasis (dominance × dominance) in most of

the crosses was greater and significant except L-6

163

× T-1 under irrigated condition. Significant and greater magnitude of these non-

allelic interactions in most of the crosses indicated the complex pattern of inheritance for

chlorophyll content in these genotypes. Since both additive as well as non-additive effects

were significant with predominance of non-additive type of gene action along with

epistatic effects suggested delayed selection for the improvement of this trait in these

genotypes. Strong and positive relationship among proline and chlorophyll content was

observed under irrigated as well as drought stress conditions.

The results of field experiment for genetic components revealed that for days to

50 % flowering, primary branches plant-1, main raceme length, pods on main raceme, pod

length, 1000 seed weight, oil content and glucosinolates content both additive and non-

additive type of gene actions along with some type of epistasis were effective in the

inheritance of these traits. However for erucic acid content additive genetic effects were

found significant for all crosses whereas dominance component was found nonsignificant

thus indicated additive gene action involved in the inheritance of this trait.

Additive and additive × additive type gene action in L-6 × T-1 and L-7 × T-1 was

found responsible in the expression of flowering. However, the cross L-7 × T-1 showed

consistent non-additive genetic estimates under both irrigated and rainfed environments

which clarified the importance of dominance component for the control of flowering trait

in this cross combination. Overall i type of epistasis (additive × additive) was found

significant in all of the crosses except L-7 × T-3 under rainfed condition. Cross L-7 × T-

1 showed consistent i type of epistasis across both environment, whereas the rest of

crosses exhibited contrasting i effects. The j component (additive × dominance) was

mostly non-significant in most of the crosses except L-6 × T-1 and L-7 × T-3 under

irrigated condition. Under irrigated and rainfed condition all of the crosses revealed

significant l type of epistasis.

For plant height, overall the estimates of genetic effects exhibited significant

negative additive component in all the four crosses under both irrigated and rainfed

conditions however, under rainfed the additive component for L-6 × T-1 was found non-

significant. Under irrigated condition dominance component was significant for only one

cross (L-6 × T-3) as compare to other crosses which showed non-significant dominance

effects however, under rainfed condition all of the crosses showed significant dominance

164

effect. Under irrigated as well as rainfed all of the crosses showed significant additive ×

additive epistasis except L-6 × T-1 under irrigated. The additive × dominance component

was found significant for most of the crosses both under irrigated and rainfed

environments except L-6 × T-1. Likewise, the dominance × dominance component was

also found significant except L-7 × T-3 under irrigated.

For primary branches plant-1 the additive component was reduced in magnitude

under rainfed as compare to irrigated environment in most of the crosses. Moreover the

additive effect for L-7 × T-1 under rainfed was found non-significant. The dominance

component was also found significant in most of the crosses except L-6 × T-1 and L-7 ×

T-3 under rainfed. The dominance component in irrigated was greater in magnitude as

compare to that in rainfed condition in most of the crosses. The i type of non-allelic

interaction in all the three cases was found significant for L-6 × T-3 and L-7 × T-1 cross

combinations. Similarly, under irrigated condition j component was significant for L-6 ×

T-3 and L-7 × T-3, whereas under rainfed environment j was found significant for L-6 ×

T-1 and L-6 × T-3. The l component was mostly non-significant for all the crosses in all

the three cases except L-7 × T-1 for which it was significant under pooled and irrigated

condition.

The magnitude of dominance was greater than additive component for pods on

main raceme and pod length indicating that dominance might have largely been involved

in the inheritance of these traits. Crosses L-6 × T-1, L-6 × T-3 and L-7 × T-3 under both

environments signified the importance of non-additive type of gene action for the

expression of pods on main raceme in these cross combinations. For pod length cross L-

7 × T-3 exhibited additive type of gene action under rainfed environment. With regards

to genetic studies for seed yield plant-1in the present set of crosses significant and greater

magnitudes of estimates for additive along with additive × additive in one cross (L-7 × T-

1) revealed that additive or additive type of epistasis might have been involved in the

inheritance of this trait in this specific cross combination. On the other hand cross L-6 ×

T-3 consistently exhibited significant dominance along with i component under both

irrigated and rainfed conditions, which clarify the important role of dominance and

additive type of epistasis in the inheritance of this trait in this specific cross. Another

important finding of this study was the change in magnitude of genetic effects of L-7 ×

T-1 with the change in environment. Although both additive and dominance components

165

were significant however, under irrigated condition the magnitude of additive component

was high whereas under rainfed the magnitude of dominance was high. Under such

conditions different selection criteria should followed under different environments.

Regarding oil content both additive and non-additive types of gene action were

found significant however the magnitude of dominance was greater as compare to additive

component which indicated the major role of dominance gene action for the expression

oil content in these genotypes. For glucosinolates under irrigated condition, cross

combinations (L-6 × T-1 and L-6 × T-3) with low level of glucosinolates in F2 population

and additive type of gene action might provide opportunities for selection of desirable

segregants. Under rainfed condition most of the crosses depicted significant high

dominance effect, thereby suggested delayed selection. For erucic acid content, additive

genetic effects were found significant for all crosses whereas dominance component was

found non-significant, thus indicated the importance of additive gene action for the

inheritance of erucic acid. Low level of erucic acid in F2 generation of L6 × T-1 with high

additive genetic effects can be used for desirable segregants in future breeding

programme.

Genetic association among traits was carried out using genotype × trait biplot

methodology. The biplot demonstrated three groups of traits based on the angles between

the vectors of traits. In first group eight traits i.e. seed yield per plant, proline content,

Chlorophyll content, days to flowering, erucic acid content, glucosinolates, Pod length

and 1000-seed weight showed strong and positive relationship in such a way that their

vertices depicted angles less than 90˚. Similarly in the second group primary branches per

plant, plant height and pods on main raceme showed positive relationship. It was found

that increase in plant height resulted in increase in primary branches per plant. In the third

group oil content and seeds per pod showed strong positive correlation. It was also

evidenced from the biplot the oil content showed negative relationship with seed yield.

Moreover, the seedling traits especially proline and chlorophyll content consistently

showed strong positive correlation with seed yield and its associated traits.

Conclusions:

166

Based on the results obtained, the following conclusions were drawn.

1. The current biplot approach revealed that both GCA and SCA played important

role in controlling majority of the traits however GCA effects were predominant

for yield and yield associated traits except plant height for which SCA effects

were higher.

2. Among the lines, (L-6 and L-7) whereas among the testers, (T-1 and T-3) were

found good general combiner. The outstanding cross combinations were (L-8 ×

T-2), L-3 × (T-4, T-1), (L-7, L-6) × T-3, L-8 × T-4, L-6 × T-1, L-7 × T-1 for most

of the traits. For majority of the traits cross combinations [L-6 and L-7] × [T-1

and T-3] were found outstanding.

3. Under rainfed condition for RWC, both additive and dominance and for proline

content only additive gene action were predominant whereas, for chlorophyll

content dominance gene action was found more important. Segregating

generations of L-7 × T-1 and L-7 × T-3 showed increase in proline and slight

reduction in chlorophyll content due to drought stress therefore they might have

potential segregants for development of drought tolerant genotype.

4. Field experiment revealed that for majority of the traits both additive and

nonadditive types of gene actions along with some type of epistasis were involved

in their inheritance.

5. For seed yield per plant both additive and dominance types of genetic effects were

significant, whereas for oil content mostly dominance type of gene action

involved. For erucic acid content additive gene action was found responsible for

expression. Regarding high seed yield per plant and low erucic acid the F2

generation of L-7 × T-1 might be used for selection of potential segregants.

6. For low erucic acid and low glucosinolates having additive type of gene action the

segregating generations of cross combination L-6 × T-1 might have potential

segregants for early generation selection.

7. For incorporation of drought tolerance and high seed yield both proline and

chlorophyll content can be used as a criterion for selection.

167

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