Paul K. K. Adu-Gyamfi BSc. Agriculture (Crop Science) A ...

THE PHYSIOLOGICAL BASIS OF DROUGHT TOLERANCE IDENTIFIED FROM

GENETIC ASSOCIATION ANALYSIS IN WHEAT (Triticum aestivum L.)

By

Paul K. K. Adu-Gyamfi

BSc. Agriculture (Crop Science)

A thesis submitted in fulfilment of the requirements for the degree of

THE DOCTOR OF PHILOSOPHY

Faculty of Agriculture and Environment

Plant Breeding Institute, Cobbitty

The University of Sydney

June, 2017

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Acknowledgements

All praise and thanks be to God the Father, His son Jesus Christ and the Holy Spirit for the gift of

life and strength throughout this study. I sincerely would like to express my profound gratitude to my

supervisor Professor Richard M. Trethowan, Director of the I. A. Watson Grains Research Centre-

Narrabri (Plant Breeding Institute- The University of Sydney) and associate supervisor Dr. Tariq M.

Chattha for their invaluable support, encouragement, guidance, suggestions and constructive criticism

which have resulted in the production of this thesis.

I will also like to extend a special thanks to the entire staff of the Plant Breeding Institute,

Narrabri for their support and assistance especially to Annette Treadea, Dr Mrs Angela Danette, Dr Phil

Davis, Mr. Graeme Rapp for their technical support in this work. My sincere thanks to Dr. Urmil K.

Bansal and Bosco Chemayeke for their assistance in providing greenhouse rooms for the development of

my top cross progenies. I would also like to thank Dr Vivi Arief from the University of Queensland for

assistance with the association analyses. I would also like to acknowledge Kate Rudd for her continuous

help throughout my studies at Plant Breeding Institute, Cobbitty. I am equally grateful to Mr. James Bell,

Operations Manager, Plant Breeding Institute, Cobbitty for his encouragement and his cooperation during

the course of my research. I am grateful to the Australian Government (Australian Development

Scholarship), The University of Sydney(Thomas Lawrance Pawlett Postgraduate Scholarship) and the

Ghana Cocoa Board for awarding the scholarship to support my studies.

I would also like to express my deep gratitude to my loving wife Eunice Adu-Gyamfi for her patience,

love, care and advice throughout this study. I wish to also extend my sincere gratitude to my son Gerald

and daughter Esther for putting smiles on my face every morning. My sincere appreciation are due my

parents, brothers, sisters and in-laws for all their prayers, support and patience during my studies.

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Abstract

Drought remains the single most important abiotic stress reducing wheat grain yield in the Australian

wheat belt. For this reason the development of cultivars with improved water use efficiency has become a

target of breeders. Trait based plant breeding is a strategy that allows the plant breeder to breakdown

water-use-efficiency into more heritable sub-components. This study aimed to elucidate the physiological

traits linked to genomic regions conferring higher yield under moisture stress identified in an earlier

genetic association analysis of multi-environment wheat trial data (Atta, 2013). Physiological traits were

tested to determine their possible association with grain yield in different environments under different

farming practices including plus and minus tillage and irrigation. Mixtures of genotypes were also made

based on complementary marker trait associations (MTAs) from the Atta (2013) analysis to test the

buffering effects of combined traits on yield. The genetic association analysis of Atta (2013) was also

validated by sowing the same materials in head-to-head comparisons in 2014 and 2015 and by developing

and testing new germplasm selected on the basis of significant MTAs for yield.

Five traits; days to heading, normalized difference vegetation index, leaf rolling, earliness to ground

cover and grain filling duration accounted for 87.8% of the observed variation in yield. These traits are

likely responsible in part or in combination for the observed significant marker trait associations. Water-

use-efficient genotypes were faster in ground cover, earlier to heading, developed larger photosynthetic

biomass, rolled their leaves to avoid damage while maintaining photosynthetic activity and had shorter

grain filling period. Their association with grain yield suggests that these traits could be utilized for

indirect selection of genotypes with higher yield and water-use-efficiency.

While significant year x genotype interaction for yield and water-use-efficiency were observed, tillage

practice was not a driver of this interaction. No-tillage rainfed environments produced higher mean grain

yield with better water-use-efficiency relative to conventional tillage. Mixtures of cultivars developed by

combining genotypes carrying complementary genomic regions based on the Atta (2013) genetic

association analysis produced higher yields relative to their pure components in some cases. However, the

selection of appropriate mixture components was critical in achieving higher yield under stress. When

progeny carrying accumulated markers for MTAs identified in the Atta (2013) multi-environment genetic

association study were assessed for yield no significant improvement in phenotype was noted. However,

when only those MTAs consistent between the Atta (2013) analysis and the head-to-head comparisons

conducted in 2014 and 2015 were selected an improvement in grain yield was observed with the

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accumulation of positive markers. In addition, several new MTAs for yield and associated traits were

identified in the head-to-head comparisons and these will need to be validated in further testing before

they are used to develop new germplasm.

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Certificate of originality

This thesis contains no material which has been accepted for the award of any degree or diploma in any

university and contains no material previously published except where due references are made in the text.

Paul Kwasi Krah Adu-Gyamfi

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TABLE OF CONTENTS

Acknowledgement i

Abstract ii

Certificate of originality iv

Table of contents v

List of Figures xi

List of Tables xii

Abbreviations/Acronyms xiv

1 INTRODUCTION 1

1.1 BACKGROUND 4

2 REVIEW OF LITERATURE 5

2.1 WHEAT 5

2.1.1 ECONOMIC IMPORTANCE OF WHEAT 5

2.1.2 ORIGIN, DOMESTICATION AND EVOLUTION OF WHEAT 5

2.1.3 DISTRIBUTION OF WHEAT CULTIVATION 6

2.1.4 WHEAT MORPHOLOGY, GROWTH AND DEVELOPMENT 7

2.2 DROUGHT 8

2.2.1 DROUGHT EFFECTS ON WHEAT PRODUCTION 8

2.2.2 DROUGHT RESISTANCE/TOLERANCE MECHANISMS 9

2.2.3 DROUGHT ESCAPE 9

2.2.4 DROUGHT AVOIDANCE OR WATER-USE-EFFICIENCY 9

2.2.5 DROUGHT TOLERANCE 9

2.2.6 BREEDING FOR DROUGHT TOLERANCE 10

2.2.7 GENOMIC REGIONS FOR DROUGHT TOLERANCE 11

2.2.8 ESTIMATION OF DROUGHT TOLERANCE 12

2.2.9 PHENOLOGY AND YIELD UNDER MOISTURE STRESS 12

2.2.10 PHYSIOLOGICAL TRAITS CONFERRING YIELD UNDER MOISTURE STRESS 14

2.2.10.1 Water soluble carbohydrates and yield under water stress 14

2.2.10.2 Canopy temperature depression and yield under water stress 14

2.2.10.3 Leaf chlorophyll content and yield under water stress 15

2.2.10.4 Stay green and yield under water stress 15

2.2.10.5 Stomatal conductance and yield under water stress 16

2.2.10.6 Carbon Isotope Discrimination, Transpiration efficiency, water use efficiency and yield under water

stress. 16

2.2.10.7 Leaf relative water content and yield under water stress 18

2.2.10.8 Epicuticular wax load and yield under water stress 18

2.2.10.9 Leaf rolling/curling and yield under water stress 18

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2.2.10.10 Seedling emergence/vigour and yield under water stress 19

2.2.10.11 Root morphology and yield under water stress 19

2.3 WATER USE EFFICIENCY 20

2.3.1 BREEDING FOR IMPROVED WATER-USE EFFICIENCY 21

2.3.2 GENOMIC REGIONS FOR WATER USE EFFICIENCY 23

2.4 CONSERVATION AGRICULTURE (CA) 26

2.4.1 CONSERVATION AGRICULTURE AND WATER-USE EFFICIENCY 26

2.4.2 CONSERVATION AGRICULTURE IMPACTS ON WHEAT GRAIN YIELD AND QUALITY 27

2.4.3 CONSERVATION AGRICULTURE AND SOIL STRUCTURE IMPROVEMENT 27

2.4.4 CONSERVATION AGRICULTURE AND THE PREVALENCE OF PESTS, DISEASES AND WEEDS 28

2.4.5 ADOPTION OF CONSERVATION AGRICULTURE AND THE CHOICE OF CULTIVAR 28

2.5 PLANT/CULTIVAR MIXTURES 29

2.5.1 PHYSIOLOGICAL CAUSES OF CULTIVAR MIXING EFFECTS 29

2.5.1.1 Complementary effects 29

2.5.1.2 Compensation effects 30

2.5.1.3 Facilitation 30

2.5.2 CULTIVAR MIXTURES AND WATER-USE EFFICIENCY 30

2.5.3 NUMBER OF CULTIVARS (COMPONENTS) IN MIXTURES AND YIELD PERFORMANCE 31

2.5.4 PLOT SIZE AND THE PERFORMANCE OF CULTIVAR MIXTURES 31

2.5.5 DIVERSITY IN CULTIVAR MIXTURES 32

2.5.6 CULTIVAR MIXTURES AND END-PRODUCT QUALITY 32

2.5.7 CULTIVAR MIXTURES AND THE CONTROL OF DISEASES AND PESTS PREVALENCE 32

2.6 ASSOCIATION MAPPING 33

2.6.1 FACTORS AFFECTING LINKAGE DISEQUILIBRIUM 34

2.6.2 TYPES OF ASSOCIATION MAPPING METHODS 34

2.6.3 APPLICATION OF ASSOCIATION MAPPING 35

2.7 DEVELOPING A WHEAT CROP IDEOTYPE ADAPTED TO NO TILLAGE WATER LIMITED ENVIRONMENTS AT NARRABRI IN THE

NORTHERN NSW GRAINS REGION. 36

3 EFFECT OF TILLAGE PRACTICE ON WHEAT PRODUCTIVITY UNDER WELL-

WATERED AND DROUGHT CONDITIONS 38

3.1 INTRODUCTION 38

3.2 MATERIALS AND METHODS 39

3.2.1 EXPERIMENTAL SITE 39

3.2.2 EXPERIMENTAL MATERIAL 39

3.2.3 FIELD LAYOUT 41

3.2.4 FERTILIZER AND CHEMICAL APPLICATIONS 43

3.2.5 IRRIGATION TREATMENT 43

3.2.6 WEATHER CONDITIONS 43

3.2.7 SOIL MOISTURE ASSESSMENT 46

3.2.8 ESTIMATION OF WATER USE AND WATER USE EFFICIENCY 46

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3.2.9 CROP MEASUREMENTS 47

3.2.9.1 Canopy Ground Cover (%) 47

3.2.9.2 Chlorophyll content (SPAD units) 47

3.2.9.3 Canopy temperature depression (ᵒC) 48

3.2.9.4 Biomass at Maturity(kg ha-1) 48

3.2.9.5 Light interception (%). 48

3.2.9.6 Grain yield (kg ha-1) 48

3.2.9.7 Leaf relative water content 48

3.2.9.8 Plant height(cm) 49

3.2.9.9 Number of days to heading(days) 49

3.2.9.10 Number of days to physiological maturity(days) 49

3.2.9.11 Protein content (%) 49

3.2.9.12 Flag leaf rolling 49

3.2.9.13 Normalized difference vegetation index (NDVI) 50

3.2.9.14 Climatic conditions 50

3.2.9.15 Soil water content 50

3.2.10 STATISTICAL ANALYSES 53

3.3 RESULTS 53

3.3.1 AGRO-PHYSIOLOGICAL TRAITS 53

3.3.1.1 Early ground cover (%) 53

3.3.1.2 Late ground cover (%) 55

3.3.1.3 Rate of ground cover (%) 56

3.3.1.4 Chlorophyll content 58

3.3.1.5 Harvest index 60

3.3.1.6 Biomass (kg ha-1) 62

3.3.1.7 Leaf rolling 63

3.3.1.8 Relative water content 65

3.3.1.9 Water use efficiency (kg ha-1 mm-1) 67

3.3.1.10 Canopy temperature(ᵒC) 69

3.3.1.11 Light interception(%) 70

3.3.1.12 NDVI at early heading 72

3.3.1.13 NDVI at late heading 74

3.3.1.14 NDVI at early grain filling 75

3.3.1.15 NDVI at late grain filling 77

3.3.2 PHENOLOGICAL TRAITS 79

3.3.2.1 Number of days to heading 79

3.3.2.2 Number of days to physiological maturity 81

3.3.2.3 Grain filling duration 83

3.3.2.4 Plant height (cm) 84

3.3.3 PRODUCTION TRAITS 86

3.3.3.1 Grain Yield(kg ha-1) 86

3.3.3.2 Thousand Kernel Weight (g) 87

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3.3.3.3 Protein content (%) 89

3.4 DISCUSSION 92

3.4.1 AGRO--PHYSIOLOGICAL TRAITS 92

3.4.1.1 Canopy ground cover 92

3.4.1.2 Chlorophyll content 94

3.4.1.3 Flag leaf rolling 94

3.4.1.4 Canopy Temperature (ᵒC) 95

3.4.1.5 Days to heading & Maturity 95

3.4.1.6 Grain filling duration 96

3.4.1.7 Biomass 97

3.4.1.8 Harvest Index 97

3.4.2 PRODUCTION TRAITS 98

3.4.2.1 Thousand Kernel weights 98

3.4.2.2 Protein content 98

3.4.2.3 Grain yield 99

3.4.3 IMPLICATIONS FOR WHEAT BREEDING 100

4 THE EFFICACY OF USING MIXTURES TO BUFFER YIELD UNDER STRESS 102

4.1 INTRODUCTION 102


4.3 STATISTICAL ANALYSIS 104

4.4 RESULTS 104

4.4.1 CANOPY TEMPERATURE (ᵒC) 104

4.4.2 CHLOROPHYLL CONTENT 106

4.4.3 HEADING TIME (DAYS FROM SOWING TO HEADING) 108

4.4.4 PHYSIOLOGICAL MATURITY (DAYS FROM SOWING TO MATURITY). 111

4.4.5 MATURITY TIME 2014 CROPPING SEASON 111

4.4.6 GRAIN FILLING DURATION (DAYS) 113

4.4.7 HARVEST INDEX 114

4.4.8 PLANT HEIGHT (CM) 116

4.4.9 LIGHT INTERCEPTION (IPAR) 118

4.4.10 NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) AT EARLY GRAIN FILLING. 120

4.4.11 NORMALIZED DIFFERENCE VEGETATION INDEX AT HEADING & ANTHESIS 122

4.4.12 NORMALIZED DIFFERENCE VEGETATION INDEX AT LATE GRAIN FILLING 124

4.4.13 PROTEIN CONTENT 126

4.4.14 RELATIVE WATER CONTENT (RWC) 128

4.4.15 THOUSAND KERNEL WEIGHT (G) 130

4.4.16 BIOMASS(KG HA-1) 132

4.4.17 GRAIN YIELD (KG HA-1) 134

4.5 DISCUSSION 136

4.6 CONCLUSIONS & IMPLICATIONS FOR BREEDING 138

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5 VALIDATION OF GENETIC ASSOCIATION MAPPING I 140



5.2.1 GERMPLASM 141

5.2.2 PHENOTYPIC DATA AND ANALYSIS 142

5.2.3 MOLECULAR DATA 142


5.4 RESULTS 144

5.4.1 ANALYSIS OF PHENOTYPIC DATA 144

5.4.2 ANALYSIS OF MOLECULAR DATA 148

5.4.3 ASSOCIATION ANALYSIS OF GRAIN YIELD 151

5.5 DISCUSSION 155

5.5.1 ASSOCIATION ANALYSIS OF GRAIN YIELD 155

5.5.2 ASSOCIATION ANALYSIS OF THOUSAND KERNEL WEIGHT (TKW) 158

5.5.3 ASSOCIATION ANALYSIS OF HEADING TIME 158

5.5.4 ASSOCIATION ANALYSIS OF MATURITY TIME 159

5.6 CONCLUSION 160

6 VALIDATION OF ASSOCIATION GENETIC MAPPING II 171



6.2.1 PLANT MATERIAL 172

6.2.2 DEVELOPMENT OF MATERIALS CARRYING PYRAMIDED MARKERS 172

6.2.3 DNA EXTRACTION AND GENOTYPING OF F2 TOPCROSS PROGENY 175

6.2.4 FIELD EXPERIMENT 178


6.4 RESULTS 178

6.4.1 PERFORMANCE OF THE 6 TOP CROSS POPULATIONS 178

6.4.2 VALIDATION OF MTAS FOR HIGHER GRAIN YIELD USING TARGETED MARKERS FROM THE EARLIER ATTA (2013) GENETIC

ASSOCIATION ANALYSIS 187

6.4.3 VALIDATION OF MTAS FOR HIGHER GRAIN YIELD USING TARGETED MARKERS FROM THE CURRENT GENETIC ASSOCIATION

ANALYSIS 188

6.5 DISCUSSION 189

6.6 CONCLUSION 190

7 GENERAL DISCUSSION 191

7.1 THE EFFECT OF TILLAGE AND MOISTURE ON GRAIN YIELD 191

7.2 THE EFFECTS OF GENETIC MIXTURES ON WATER USE EFFICIENCY 192

7.3 PHYSIOLOGICAL TRAITS LINKED TO GENOMIC REGIONS 192

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7.4 PRODUCTION TRAITS 195

7.5 WHEAT IDEOTYPES ADAPTED TO ZERO-TILLAGE RAINFED ENVIRONMENTS IN NORTHWESTERN NSW, AUSTRALIA. 196

7.6 ASSOCIATION ANALYSIS 196

7.7 CONCLUSIONS 197

8 REFERENCES 199

9 APPENDIX I (CHAPTER 3&4) 231

10 APPENDIX II (CHAPTER 5) 232

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List of Figures

FIGURE 2.1 WHEAT GROWING REGIONS IN AUSTRALIA. ADAPTED FROM ABARE (2012) ............................................................... 7 FIGURE 2.2 CONCEPTUAL MODEL FOR TRAITS ASSOCIATED WITH ADAPTATION TO DROUGHT PRONE-ENVIRONMENTS ADAPTED

FROM REYNOLDS AND TUBEROSA (REYNOLDS AND TUBEROSA 2008). ................................................................................ 11 FIGURE 3.1 THE EXPERIMENTAL AREA: ZERO TILLAGE (LEFT HAND SIDE) AND CONVENTIONAL TILLAGE ENVIRONMENTS IN 2013

............................................................................................................................................................................................. 41

FIGURE 3.2 THE EXPERIMENTAL AREA: ZERO TILLAGE ENVIRONMENTS WITH STUBBLE FROM THE PREVIOUS CROP (2013). ......... 42

FIGURE 3.3 THE EXPERIMENTAL AREA: CONVENTIONAL TILLAGE ENVIRONMENT WITH LOOSENED SOIL SURFACE (2013). ........... 42

FIGURE 3.4 CROPPING SEASON RAINFALL AND TEMPERATURE IN 2013 AT NARRABRI................................................................... 44

FIGURE 3.5 CROPPING SEASON RAINFALL AND TEMPERATURE IN 2014 AT NARRABRI................................................................... 44

FIGURE 3.6 IN SEASON RELATIVE HUMIDITY AND SOLAR RADIATION IN 2013 AT NARRABRI ......................................................... 45

FIGURE 3.7 IN-SEASON RELATIVE HUMIDITY AND SOLAR RADIATION IN 2014 AT NARRABRI. ....................................................... 45

FIGURE 3.8 SOIL WATER CONTENT UNDER CONVENTIONAL TILLAGE AND ZERO TILLAGE RAINFED ENVIRONMENTS IN 2013. ....... 51

FIGURE 3.9 SOIL WATER CONTENT UNDER CONVENTIONAL TILLAGE AND ZERO TILLAGE IN IRRIGATED ENVIRONMENTS IN 2013. 51

FIGURE 3.10 SOIL WATER CONTENT UNDER CONVENTIONAL TILLAGE AND ZERO TILLAGE IRRIGATED ENVIRONMENTS IN 2014. . 52

FIGURE 3.11 SOIL WATER CONTENT UNDER CONVENTIONAL TILLAGE AND ZERO TILLAGE RAINFED ENVIRONMENTS IN 2014. ..... 52

FIGURE 5.1 LINKAGE DISEQUILIBRIUM HEAT MAP OF THE GENOME OF THE WHEAT GENOTYPES TESTED ..................................... 145 FIGURE 5.2 PATTERN ANALYSIS OF GRAIN YIELD, NUMBER OF DAYS TO HEADING (DH)/MATURITY (DM), AND THOUSAND KERNEL

WEIGHTS (TKW) OF GENOTYPES TESTED IN 2014 AND 2015 AT NARRABRI. (A) OPTIMIZED DENDROGRAMS (B) BIPLOTS OF

1ST, 2ND AND 3RD PRINCIPAL COMPONENTS. THE BLUE AND RED COLOURS INDICATE GENOTYPES THAT ARE ALLOCATED TO

THE SAME GROUP. ............................................................................................................................................................... 147 FIGURE 5.3 COMBINED PATTERN ANALYSIS OF GENOTYPES TESTED IN 2014 AND 2015 AT NARRABRI USING MARKER DATA. (A)

OPTIMIZED DENDROGRAMS (B) A PLOT OF 1ST , 2ND, AND 3RD PRINCIPAL COMPONENTS. THE BLUE AND RED COLOURS

INDICATE GENOTYPES BELONGING TO THE SAME GROUP. .................................................................................................... 150 FIGURE 5.4 POLYMORPHIC AND NON-POLYMOPHIC MARKERS/REGIONS ACROSS GENOTYPES TESTED IN 2014 AND 2015. THE BLUE

COLOUR INDICATES POLYMORPHIC REGIONS WHILST THE BROWN REPRESENTS NON-POLYMORPHIC REGIONS. ................... 153 FIGURE 5.5 MARKER TRAIT ASSOCIATIONS FOR GRAIN YIELD (YLD), THOUSAND KERNEL WEIGH T(TKW), DAYS TO HEADING

(DH), AND DAYS TO PHYSIOLOGICAL MATURITY (DPM) BASED ON DATA FROM 2014 AND 2015. NEGATIVE ASSOCIATIONS

ARE SHOWN IN RED AND POSITIVE ASSOCIATIONS ARE SHOWN IN BLUE. INCREASING COLOUR INTENSITY SHOWS THE

STRENGTH (-LOG10 PROBABILITY) OF ASSOCIATION (ARIEF 2010). .................................................................................... 154 FIGURE 5.6 MAP OF SIGNIFICANT DART MARKERS ASSOCIATED WITH GRAIN YIELD (YLD), NUMBER OF DAYS TO HEADING

(DHD), NUMBER OF DAYS TO PHYSIOLOGICAL MATURITY (DPM) AND THOUSAND KERNEL WEIGHT (TKW) IN THE CURRENT

ASSOCIATION ANALYSIS. ..................................................................................................................................................... 161 FIGURE 6.1 PRINCIPLE COMPONENT PLOT OF THE FIELD PERFORMANCE OF PROGENIES AND PARENTS ON THE BASIS OF AGRONOMIC

TRAITS (GRAIN YIELD, HEADING TIME, MATURITY TIME, THOUSAND KERNEL WEIGHT AND BIOMASS). ............................... 189

Supplementary figures-Appendix

FIGURE 1 EXPERIMENTAL FIELD LAYOUT IN 2013 CROPPING SEASON. ....................................................................................... 231

FIGURE 2 EXPERIMENTAL FIELD LAYOUT IN 2014 CROPPING SEASON. ....................................................................................... 231

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List of Tables

TABLE 3.1 LIST OF GERMPLASM EVALUATED IN FIELD STUDIES IN 2013 AND 2014 ....................................................................... 40

TABLE 3.2 MEANS FOR EARLY GROUND COVER (%) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ................. 54

TABLE 3.3 MEANS FOR LATE GROUND COVER (%) IN 2013 AND 2014 CROPPING YEAR. ................................................................ 56

TABLE 3.4 MEANS FOR RATE OF GROUND COVER (%) IN 2013 AND 2014. ..................................................................................... 57 TABLE 3.5 MEANS FOR CHLOROPHYLL CONTENT (SPAD UNITS) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014

............................................................................................................................................................................................. 59

TABLE 3.6 MEANS FOR HARVEST INDEX IN 2013 AND 2014. .......................................................................................................... 61

TABLE 3.7 MEANS FOR BIOMASS (KG/HA) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ................................. 62

TABLE 3.8 MEANS FOR LEAF ROLLING UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ...................................... 64

TABLE 3.9 MEANS FOR RELATIVE WATER CONTENT (%) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014. .......... 66 TABLE 3.10 MEANS FOR WATER USE EFFICIENCY (KG HA

-1 MM

-1) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014

............................................................................................................................................................................................. 68

TABLE 3.11 MEANS FOR CANOPY TEMPERATURE (°C) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 .............. 70

TABLE 3.12 MEANS FOR LIGHT INTERCEPTION (%) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ................... 71

TABLE 3.13 MEANS FOR NDVI AT EARLY HEADING IN 2013 AND 2014 ........................................................................................ 73

TABLE 3.14 MEANS FOR NDVI AT LATE HEADING UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 .................... 74

TABLE 3.15 MEANS FOR NDVI AT EARLY GRAIN FILLING UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ........ 76

TABLE 3.16 MEANS FOR NDVI AT LATE GRAIN FILLING UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ........... 78

TABLE 3.17 MEANS FOR NUMBER OF DAYS TO HEADING UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 .......... 80

TABLE 3.18 MEANS FOR DAYS TO PHYSIOLOGICAL MATURITY IN THE 2013 AND 2014 CROPPING SEASONS. ................................. 82

TABLE 3.19 MEANS FOR GRAIN FILLING DURATION (DAYS) IN 2013 AND 2014............................................................................. 83

TABLE 3.20. MEANS FOR PLANT HEIGHT (CM) IN 2013 AND 2014 .................................................................................................. 85

TABLE 3.21 MEANS FOR GRAIN YIELD (KG/HA) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ......................... 86

TABLE 3.22 MEANS FOR THOUSAND KERNEL WEIGHT (G) UNDER DIFFERENT MANAGEMENT PRACTICES IN 2013 AND 2014 ......... 88

TABLE 3.23 MEANS FOR PROTEIN CONTENT (%) IN 2013 AND 2014 .............................................................................................. 90 TABLE 3.24 PHENOTYPIC CORRELATION OF COMBINED MEANS OF PHYSIOLOGICAL CHARACTERISTICS POSSIBLY ASSOCIATED WITH

GRAIN YIELD AND QUALITY TRAITS IN 2013-2014. ............................................................................................................... 91 TABLE 4.1 CHARACTERISTICS OF MIXTURE COMPONENTS AND THEIR CORRESPONDING NUMBERS OF POSITIVE MARKER/TRAITS

ASSOCIATIONS FOR GRAIN YIELD IDENTIFIED IN THE EARLIER ASSOCIATION ANALYSIS (ATTA 2013) ................................. 103 TABLE 4.2 COMBINED MEANS AND ANALYSIS OF CANOPY TEMPERATURE (°C) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE

MIXTURES VERSUS THEIR PURE STANDS IN 2013 AND 2014. ................................................................................................ 105 TABLE 4.3 MEANS AND ANALYSIS OF CANOPY TEMPERATURE (°C) IN MIXTURES VERSUS THEIR PURE LINE COMPONENTS IN 2014.

........................................................................................................................................................................................... 106 TABLE 4.4 COMBINED MEANS AND ANALYSIS OF CHLOROPHYLL CONTENT (SPAD UNITS) OF LINCOLN+LANG AND

SUNSTATE+STAMPEDE MIXTURES AND THEIR PURE STAND IN 2013 AND 2014. .................................................................. 107 TABLE 4.5 MEANS AND ANALYSIS OF CHLOROPHYLL CONTENT (SPAD UNITS) OF MIXTURES VERSUS THEIR PURE STANDS IN 2014.

........................................................................................................................................................................................... 108 TABLE 4.6 COMBINED MEANS AND ANALYSIS OF HEADING TIME (DAYS FROM SOWING) OF LINCOLN+LANG AND

SUNSTATE+STAMPEDE MIXTURES AND THEIR PURE STANDS IN 2013 AND 2014.................................................................. 109

TABLE 4.7 MEANS AND ANALYSIS OF HEADING TIME (DAYS FROM SOWING) OF MIXTURES VERSUS PURE STANDS IN 2014. ........ 110 TABLE 4.8 COMBINED MEANS AND ANALYSIS OF PHYSIOLOGICAL MATURITY (DAYS FROM SOWING) OF LINCOLN+LANG AND

SUNSTATE+STAMPEDE MIXTURES AND THEIR PURE STANDS IN 2013 AND 2014. ................................................................ 111

TABLE 4.9 MEANS AND ANALYSIS OF MATURITY TIME (DAYS FROM SOWING) OF MIXTURES VERSUS PURE STANDS IN 2014. ...... 112 TABLE 4.10 COMBINED MEANS AND ANALYSIS OF GRAIN FILLING DURATION (DAYS) OF LINCOLN+LANG AND


TABLE 4.11 MEANS AND ANALYSIS OF GRAIN FILLING DURATION (DAYS) OF MIXTURES VERSUS PURE STANDS IN 2014. ............ 114

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TABLE 4.12 COMBINED MEANS AND ANALYSIS OF HARVEST INDEX OF LINCOLN+LANG AND SUNSTATE+STAMPEDE MIXTURES

AND THEIR PURE STANDS IN 2013 AND 2014. ...................................................................................................................... 115

TABLE 4.13 MEANS AND ANALYSIS OF HARVEST INDEX OF MIXTURES VERSUS PURE STANDS IN 2014. ....................................... 116 TABLE 4.14 COMBINED MEANS AND ANALYSIS OF PLANT HEIGHT (CM) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE MIXTURES

AND THEIR PURE STANDS IN 2013 AND 2014. ..................................................................................................................... 117

TABLE 4.15 MEANS AND ANALYSIS OF PLANT HEIGHT (CM) OF MIXTURES VERSUS PURE STANDS IN 2014 .................................. 118 TABLE 4.16 COMBINED MEANS AND ANALYSIS OF LIGHT INTERCEPTION (%) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE

MIXTURES AND THEIR PURE STANDS IN 2013 AND 2014. ..................................................................................................... 119

TABLE 4.17 MEANS AND ANALYSIS OF LIGHT INTERCEPTION (%) OF MIXTURES VERSUS PURE STANDS IN 2014 . ........................ 120 TABLE 4.18 COMBINED MEANS AND ANALYSIS OF NDVI AT EARLY GRAIN FILLING STAGE OF LINCOLN+LANG AND


TABLE 4.19 MEANS AND ANALYSIS OF NDVI AT EARLY GRAIN FILLING OF MIXTURES VERSUS PURE STANDS IN 2014 .............. 122 TABLE 4.20 COMBINED MEANS AND ANALYSIS OF NDVI AT HEADING AND ANTHESIS OF LINCOLN+LANG AND


TABLE 4.21 MEANS AND ANALYSIS OF NDVI AT HEADING AND ANTHESIS OF MIXTURES VERSUS PURE STANDS IN 2014. .......... 124 TABLE 4.22 COMBINED MEANS AND ANALYSIS NDVI AT LATE GRAIN FILLING OF LINCOLN+LANG AND SUNSTATE+STAMPEDE

MIXTURES AND THEIR PURE STANDS IN 2013 AND 2014. ..................................................................................................... 125

TABLE 4.23 MEANS AND ANALYSIS OF NDVI AT LATE GRAIN FILLING OF MIXTURES VERSUS PURE STANDS IN 2014 ................. 126 TABLE 4.24 COMBINED MEANS AND ANALYSIS OF PROTEIN CONTENT (%) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE

MIXTURES AND THEIR PURE STANDS IN 2013 AND 2014 ...................................................................................................... 127

TABLE 4.25 MEANS AND ANALYSIS OF PROTEIN CONTENT (%) OF MIXTURES VERSUS PURE STANDS IN 2014 .............................. 128 TABLE 4.26 COMBINED MEANS AND ANALYSIS OF RWC (%) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE MIXTURES AGAINST

THEIR PURE STANDS IN 2013 AND 2014. .............................................................................................................................. 129

TABLE 4.27 MEANS AND ANALYSIS OF RWC (%) OF MIXTURES VERSUS PURE STANDS IN 2014. ................................................. 130 TABLE 4.28 COMBINED MEANS AND ANALYSIS OF THOUSAND KERNEL WEIGHTS (G) OF LINCOLN+LANG AND

SUNSTATE+STAMPEDE MIXTURES AND THEIR PURE STANDS IN 2013 AND 2014. ............................................................... 131

TABLE 4.29 MEANS AND ANALYSIS OF THOUSAND KERNEL WEIGHTS (G) OF MIXTURES VERSUS PURE STANDS IN 2014 .............. 132 TABLE 4.30 COMBINED MEANS AND ANALYSIS OF BIOMASS (KG HA

-1) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE MIXTURES

AND THEIR PURE STANDS IN 2013 AND 2014. ...................................................................................................................... 133

TABLE 4.31 MEANS AND ANALYSIS OF BIOMASS (KG HA-1

) OF MIXTURES VERSUS PURE STANDS IN 2014 .................................... 134 TABLE 4.32 COMBINED MEANS AND ANALYSIS OF GRAIN YIELD (KG/HA) OF LINCOLN+LANG AND SUNSTATE+STAMPEDE

MIXTURES AND THEIR PURE STAND IN 2013 AND 2014 ........................................................................................................ 135

TABLE 4.33 MEANS AND ANALYSIS OF GRAIN YIELD (KG HA-1

) OF MIXTURES VERSUS PURE STANDS IN 2014.............................. 136 TABLE 5.1 SUMMARIES OF NUMBER OF DETECTED MARKER TRAIT ASSOCIATIONS IN THE CURRENT AND EARLIER ASSOCIATION

ANALYSIS FOR GRAIN YIELD, NUMBER OF DAYS TO HEADING, NUMBER OF DAYS TO PHYSIOLOGICAL MATURITY AND

THOUSAND KERNEL WEIGHT. .............................................................................................................................................. 158

TABLE 6.1 LIST OF GENOTYPES HYBRIDIZED TO PRODUCE TOP CROSS PROGENIES AND THEIR CORRESPONDING NUMBERS OF

POSITIVE MARKERS DETECTED IN THE EARLIER ASSOCIATION ANALYSIS CONDUCTED BY ATTA 2013. ............................... 173

TABLE 6.2 SIMILARITY MATRIX OF THE 10 PARENTS IN TABLE 6.1 BASED ON DART MARKER PROFILES. ..................................... 173

TABLE 6.3 LIST OF HYBRIDIZATIONS MADE TO GENERATE F1 AND F2 TOP CROSS POPULATIONS. ................................................. 174 TABLE 6.4 LIST OF SIGNIFICANT DART MARKERS ASSOCIATED WITH HIGHER GRAIN YIELD UNDER MOISTURE STRESS IDENTIFIED

FROM THE CHAPTER 5 HEAD-TO-HEAD ASSOCIATION ANALYSIS. ........................................................................................ 174 TABLE 6.5 LIST OF PARENTS WITH THEIR CORRESPONDING TARGET DARTSEQ MARKERS UTILIZED IN THE HYBRIDIZATION

PROGRAM. ........................................................................................................................................................................... 177 TABLE 6.6 MEANS OF THE TOP CROSS POPULATION (PBI-100 X PBI-11) X PBI-94 FOR DAYS TO HEADING(DHD), DAYS TO

PHYSIOLOGICAL MATURITY(DPM), CANOPY GROUND COVER(GC), HARVEST INDEX(HI), GRAIN FILLING DURATION(GFD),

NORMALIZED DIFFERENCE VEGETATION INDEX(NDVI), THOUSAND KERNEL WEIGHT(TKW), BIOMASS(BMAS) AND GRAIN

YIELD(YLD). ...................................................................................................................................................................... 179

xiv

TABLE 6.7 MEANS OF THE TOP CROSS POPULATION (PBI-11 X PBI-10) X PBI-1 FOR DAYS TO HEADING (DHD), DAYS TO

PHYSIOLOGICAL MATURITY(DPM), CANOPY GROUND COVER (GC), HARVEST INDEX( HI), GRAIN FILLING DURATION(GFD),

NORMALIZED DIFFERENCE VEGETATION INDEX(NDVI), THOUSAND KERNEL WEIGHT (TKW), BIOMASS(BMAS) AND GRAIN

YIELD(YLD) KG/HA. ........................................................................................................................................................... 181 TABLE 6.8 MEANS OF THE TOP CROSS POPULATION (PBI-11 X PBI-10) X PBI-45 TOGETHER WITH PARENTS AND CONTROLS FOR

HARVEST INDEX (HI), GRAIN FILLING DURATION (GFD), THOUSAND KERNEL WEIGHT (TKW), BIOMASS (BMAS) AND GRAIN

YIELD (YLD). ..................................................................................................................................................................... 182 TABLE 6.9 MEANS OF THE TOP CROSS POPULATION (PBI-100 X PBI-11) X PBI-98 TOGETHER WITH PARENTS AND CONTROLS FOR

HARVEST INDEX (HI), GRAIN FILLING DURATION (GFD), THOUSAND KERNEL WEIGHT (TKW), BIOMASS (BMAS) AND GRAIN

YIELD (YLD). ..................................................................................................................................................................... 183 TABLE 6.10 MEANS OF THE TOP CROSS POPULATION (PBI-151 X PBI-45) X PBI-273 TOGETHER WITH PARENTS AND CONTROLS

FOR HARVEST INDEX(HI), GRAIN FILLING DURATION(GFD), THOUSAND KERNEL WEIGHT(TKW), BIOMASS(BMAS) AND

GRAIN YIELD(YLD). ........................................................................................................................................................... 184 TABLE 6.11 MEANS OF THE TOP CROSS POPULATION (PBI-100 X PBI-11) X PBI-94 TOGETHER WITH PARENTS AND CONTROLS

FOR HARVEST INDEX (HI), GRAIN FILLING DURATION (GFD), THOUSAND KERNEL WEIGHT (TKW), BIOMASS (BMAS) AND

GRAIN YIELD (YLD). .......................................................................................................................................................... 186 TABLE 6.12 MEANS OF GRAIN YIELD AND OTHER TRAITS AND NUMBER OF ACCUMULATED MARKERS BASED ON THE EARLIER

ATTA (2013) ASSOCIATION ANALYSIS ................................................................................................................................ 187 TABLE 6.13 MEANS OF GRAIN YIELD AND OTHER TRAITS AND NUMBER OF ACCUMULATED MARKERS BASED ON THE ON MARKERS

CONFIRMED IN BOTH THE ATTA (2013) STUDY AND THE HEAD-TO-HEAD ANALYSIS OF CHAPTER 5 .................................... 188

Supplementary Tables-Appendix

TABLE 1 MEANS FOR NUMBER OF DAYS TO HEADING (DHD) OF 215 GENOTYPES EVALUATED HEAD-TO-HEAD IN ASSOCIATION

ANALYSIS IN 2014 AND 2015 AT NARRABRI. ...................................................................................................................... 232 TABLE 2 MEANS FOR NUMBER OF DAYS TO PHYSIOLOGICAL MATURITY (DPM) OF 215 GENOTYPES EVALUATED HEAD-TO-HEAD IN

ASSOCIATION ANALYSIS IN 2014 AND 2015 AT NARRABRI. ................................................................................................. 238 TABLE 3 YIELD MEANS OF 215 GENOTYPES EVALUATED HEAD-TO-HEAD IN ASSOCIATION ANALYSIS IN 2014 AND 2015 AT

NARRABRI. ......................................................................................................................................................................... 244 TABLE 4 MEANS FOR THOUSAND KERNEL WEIGHT (TKW) OF 215 GENOTYPES EVALUATED HEAD-TO-HEAD FOR ASSOCIATION

ANALYSIS IN 2014 AND 2015 AT NARRABRI. ....................................................................................................................... 250

xv

Abbreviations / Acronyms

ANOVA = Analysis of variance

CID = Carbon isotope discrimination (Δ)

CIMMYT = International Maize And Wheat Improvement Centre

cM = centiMorgan

CTD = Canopy temperature depression (Tair - Tcanopy)

DArT = Diversity arrays technology

DAS = Days after sowing

DSI = Drought susceptibility index

FAO = Food and Agricultural Organization of United Nations

MTA = Marker trait association

NDVI = Normalized difference vegetation index

NSW = New South Wales

QTL = Quantitative trait loci

QWRI = Queensland Wheat Research Institute, Toowoomba

TE = Transpiration efficiency (μmol mol-1)

WUE = Water use efficiency (kg ha-1mm

-1)

WUEGrain = Water use efficiency for grain yield (kg ha-1

mm-1

)

1

1 Introduction

Wheat is an important cereal globally supplying more nourishment to humans than any other food

source (Curtis et al. 2002). It is a staple food for over 35% of the world's population and its cultivation

extends over 220 million hectares world-wide making it the largest crop in terms of area under

cultivation (Rajaram 2001; Ogbonnaya et al. 2007; Ashraf 2010; Morris et al. 2015). Wheat is

Australia's prominent crop and a prime agricultural commodity with gross production valued at over

AU$ 7 billion dollars (Ogbonnaya et al. 2007; Statistics 2014).

Wheat is successfully grown within the latitudes of 30° and 60°N and 27° and 40°S (Nuttonson

1955; Ecocrop 2011) even stretching beyond these limits to within the Arctic Circle and higher

elevations close to the equator (Ecocrop 2011). Even though moisture stress limits wheat production,

studies by the International Maize and Wheat Improvement Center (CIMMYT) suggested the

feasibility of wheat production in much warmer regions (Saunders and Hettel 1994).

World-wide, wheat is grown under both irrigated and rainfed conditions, however in Australia,

wheat cultivation is primarily rainfed in variable agro-ecological zones known as the Wheat belt. The

wheat belt extends from central Queensland through New South Wales, Victoria, South Australia and to

South-Western Australia. Inadequate supply of moisture at critical growth stages characterizes the

Australian wheat belt. Projected climate change will increase production variability and research is

vital if food production is to be sustained (Ogbonnaya et al. 2007; Semenov and Halford 2009).

Subsequently, global production of wheat needs to be doubled by 2050 to meet the projected demands

from rising population, diet shifts, and increasing biofuels consumption (Ray et al. 2013b).

In many environments, wheat is low yielding compared to rice and maize (Langridge et al. 2001).

In rain-fed systems, average wheat yields are often well below 2 t/ha (Langridge et al. 2001). However,

some estimate that wheat yield could be as high as 15 t/ha under irrigated conditions (Langridge et al.

2001). This suggests that 80%–90% of the yield potential is lost because of moisture stress and other

factors including poor agronomy. The majority of wheat produced globally tends to be cultivated in

environments where water is severely limiting (Langridge 2013). In Australia, changed management,

improved agronomy, better genetics and a synergy among these elements has significantly improved

rainfed wheat yields (Richards et al. 2014). Yield improvement from breeding will therefore require

2

understanding the genetic basis of morphophysiological traits that control yield under moisture stress

(Araus et al., 2002; Salekdeh et al., 2009).

Drought is a complex trait under polygenic control with low heritability and high genotype x

environment interaction effects (Blum 2011; Khakwani et al. 2011). Although, varieties with improved

water use efficiencies have been found to be associated with earliness to flowering, faster canopy

development and increased harvest index. Currently, advances in genomics has facilitated the selection

and identification of chromosomal regions controlling key agronomic and yield traits in crops

(Tuberosa and Salvi, 2006; Collins et al., 2008; Cooper et al., 2009) and association mapping has been

proven to be efficient over traditional linkage mapping because it utilizes linkage disequilibrium (LD)

in natural populations to identify significant marker trait associations (Ochieng et al., 2007).

Genetic association analysis has also proved to be effective in identifying loci for traits with low

heritability, particularly yield and its components (Breseghello and Sorrells 2006a; Neumann et al.

2011c). These techniques have detected genomic regions of agronomically important traits in crops

such as maize (Buckler et al. 2009; Liu et al. 2011), wheat (Reif et al. 2011; Kulwal et al. 2012; Kollers

et al. 2013), barley (Wang et al. 2012; Berger et al. 2013; Zhou and Steffenson 2013) and sugar beet

(Stich et al. 2008; Würschum et al. 2011).

In wheat, progress has been slower and while genetic association analysis has identified genomic

regions linked to complex traits, there is little evidence of targeted marker pyramiding to improve trait

expression. Moreover, the physiological traits associated with chromosomal regions identified for yield

are generally unknown. Reports of identified genomic regions to date have tended to be based on

individual small to moderately sized mapping populations screened with relatively few markers

affording a relatively low resolution of marker–trait association (Xu 2002; Salvi and Tuberosa 2005).

Therefore a limited number of genomic regions reported have found their way into Marker Assisted

Selection (MAS) in plant breeding (Xu and Crouch 2008) mainly due low heritability, environmental

effects and the high associated costs (Collard and Mackill 2008). Marker Trait Associations (MTAs)

must therefore be validated in representative parental lines, breeding populations and phenotypic

extremes before they can be used for routine MAS (Xu and Crouch 2008). In most cases, markers will

lose their selective power during this validation, therefore a plausible approach to identify new markers

in the genomic regions around the target locus to find MTAs that are shared across different breeding

populations and environments is needed (Xu and Crouch 2008). Parents of any breeding population

3

must therefore be monomorphic thus affording breeders the opportunity to track the alleles donated

from each parent using MAS throughout the breeding process (Xu and Crouch 2008).

Genetic diversity deployed in cereal mixtures based on complementary genomic regions could

increase yield by buffering the effects of drought. Plant ecology theory predicts that seed mixtures of

varieties (genotype mixtures) may increase grain yield compared to the average of the component

varieties in pure stands (Kiær et al. 2009). Previous studies reported increased resistance to biotic and

abiotic stresses and stochastic events through increased genotypic diversity in plant communities

(Hughes and John 2004; Reusch et al. 2005; Hughes et al. 2008). A 30% increase in biomass was

reported in eelgrass when a mixture of six genotypes was evaluated under high temperature, suggesting

that more genotypically diverse populations maintain productivity better than monocultures under

abiotic stress (Ehlers et al. 2005; Tooker et al. 2012). Mixtures therefore induce beneficial interaction

(compensatory, complementary and facilitation) effects among component varieties (Stützel and

Aufhammer 1990; Faraji 2011).

Soil structural modification affects water loss from the soil and conservation agriculture

practices can minimize the effects of drought through, (a) less disturbance of the soil, i.e. reduced

tillage or no-tillage (b) provision of soil cover, i.e. crop residue, cover crops, relay crops or intercrops

to mitigate soil erosion and to improve soil fertility and soil functions and (c) crop rotation to control

weeds, pests and diseases (Derpsch et al. 2001). The ability of roots to grow and explore the soil for

water and nutrients is a key determinant of plant growth rates (Clark et al. 2003) and structural

modifications of the soil caused by tillage affects crop available water, nutrient uptake, stomatal

conductance, evapotranspiration, canopy development and subsequently leaf area index (Lo Cascio and

Casa 1997). Therefore, the choice of tillage system is crucial to optimizing moisture availability at

critical wheat growth stages.

4

1.1 Background

In the earlier Atta (2013) study the following findings were made:

a) The water use efficient wheat ideotype for northwestern NSW should have higher normalized

difference vegetation index (NDVI), higher transpiration rates resulting in cooler canopies,

higher biomass at anthesis and maturity, greater plant height, superior harvest index and

thousand grain weight, better grain yield, superior WUEDM-Maturity and WUEGrain.

b) The genotypes with higher gas exchange parameters were positively associated with biomass at

maturity, WUE and grain yield.

c) Significant MTAs for grain yield under moisture stress were also identified on all wheat

chromosomes whereas for other traits the MTAs were found on specific chromosomes. A

number of MTAs were also identified in genomic regions reported previously and many new

regions were identified for grain yield, stripe rust, leaf rust and crown rot. It was observed that

each trait is affected by many markers and each MTA affects multiple traits.

In this study, the genomic regions linked to yield from a previous association analysis conducted by

Atta (2013) of a commercial wheat breeding program were examined:

1. To identify physio-genetic traits possibly linked to genomic regions conferring high yield under

moisture stress.

2. To test the performance of selected wheat mixtures of complementary genomic regions against

their pure stand components in different environments that varied for tillage and irrigation

regimes.

3. To assess genotype x environment effects on the performance of these advance wheat lines on

grain yield.

4. To test the validity of the results of genetic association analysis by more intensive evaluation of

materials in head-to-head comparisons and by combining significant markers into a single

genotype.

5

2 REVIEW OF LITERATURE

2.1 Wheat

Wheat (Triticum spp.) is one of the earliest domesticated cereal crops belonging to the class

Liliopsida, tribe Triticeae (Hordeae), family Poaceae (Grasses) and sub-family Pooideae which

evolved some 20-70 million years ago (Kellogg 2001; Huang et al. 2002).

2.1.1 Economic importance of wheat

The world's food security relies heavily on the cultivation of three cereals, wheat, rice (Oryza

sativa L.) and maize (Zea mays L.) with wheat ranking first (Peng et al. 2011). It provides over 20% of

calories and protein to the world's populace (Reynolds et al. 2012), straw as a source of carbon for fuel

production using bioethanol yeasts (Murphy and Power 2008; Petrik et al. 2013), feed for livestock

(Nagarajan 2005) and a non-wood pulp for the paper making industry (Zhang et al. 2012).

2.1.2 Origin, domestication and evolution of wheat

Notwithstanding that, the center of origin of wheat has received varied opinions substantiated

by both archeo-botanical and molecular evidence. In the 19th century, bio-geographical studies

identified the Fertile Crescent, particularly in the areas that surround the fertile alluvial plains of the

Tigris and Euphrates rivers to be the centre of origin of wheat. This has been validated by

archaeological evidence from the fossilized remains of crops (Braidwood et al. 1969; Vavilov and

Dorofeev 1992; Saunders and Hettel 1994; Willcox 2005; Kuijt and Finlayson 2009). Recent studies on

the relationship between cultivated and wild wheat accessions at the molecular level suggested that

they originated from the Karacadag Mountains, validating the location of the cradle of agriculture in

the Middle-East (Manfred et al. 1997; Lev-Yadun et al. 2000; Kilian et al. 2007).

Prior to the domestication of wheat, there were extensive polyploidization and duplication of

events mainly from species of the Triticum and Aegilops genera resulting in the classification of the

genus into three main groups, diploids having 14 (n=7), tetraploids 28 (n=2x=14) and hexaploids

(n=3x=21) with 42 chromosomes. The first cultivated forms were diploid (einkorn) and tetraploid

(emmer) (genome AABB) wheat and with the later appearance of hexaploid (AABBDD) bread wheat

when farming extended to the Near East by about 7000 years BC (Feldman 2001).

Gill et al. (2006) opined that hexaploid wheat (Triticum aestivum L.genome AABBDD) was

developed from the rare hybridization between tetraploid wheat (Triticum turgidum L., AABB) and

wild wheat relatives (A. tauschii Coss., DD) that occurred fairly recently (~8000 years ago) in farmers’

6

fields in the West Caspian region of Iran. Some major events that accompanied the domestication of

wheat included loss of shattering of the spike at maturity (Nalam et al. 2006) and the appearance of

hulled forms, which is controlled by a mutation at the Q locus (Jantasuriyarat et al. 2004; Simons et al.

2006; Dubcovsky and Dvorak 2007).

2.1.3 Distribution of wheat cultivation

The cultivation of wheat (Triticum spp.) extends from its center of origin through Iran into

central Asia, China to Africa and eventually in Australia and Mexico, where it was introduced by

European colonization (Feldman 2001). Wheat is adapted to a wide range of environments from 65°N

to 45°S (Lantican et al. 2005) and its cultivation is technologically feasible in warmer areas within

temperature ranges of 3 - 32°C (Briggle 1980). It is tolerant to a range of moisture conditions from

xerophytic to littoral and it can be grown in most locations where precipitation ranges from 250 to 1750

mm (Martin 1963).

In Australia, wheat cultivation began with a small plot of 8 acres at Farm Cove (currently

Sydney Botanical Gardens) under Governor Phillip in 1788 (Simmonds 1989). Yields were very low

due to lack of adaptability and the cultivation of wheat only became viable with the eventual

development of cultivars adapted to dry conditions. Currently, wheat is grown in all states in Australia

except the Northern Territory (Fig 2.1). Bread, durum, emmer and spelt wheat are cultivated in

Australia and yields are also limited by poorer soils with low water holding capacities. Intermittent

rainfall is one of the critical factors influencing changes in yields in Australia (Simmonds 1989).

Cultivation under rain fed conditions and flowering in mid-September produces the highest yields with

the least frost damage (Cooper 1992).

Current national average yields have fallen due to changes in climatic conditions and the

movement of wheat to more marginal environments, displaced by higher value crops, and to meet

current demands from an increasing world population (Ray et al. 2013a). Contemporary strategies

emphasize the development and deployment of high yielding wheat varieties under moisture stress and

resistance to major insect pest and fungal diseases (Anwar et al. 2007) .

7

Figure 2.1 Wheat growing regions in Australia. Adapted from ABARE (2012)

2.1.4 Wheat morphology, growth and development

Physiologically, wheat growth and development can be partitioned into: germination to

emergence and double ridge to maturity (Acevedo et al. 2006). Physiological maturity refers to the

period when both the flag leaf, spikes and peduncle turn yellow (Hanft and Wych 1982) and this varies

with genotype, temperature, day length and sowing date (Acevedo et al. 2006). An important trait

conferring adaptation of wheat to cold winters is the requirement of long exposure to low temperatures

(vernalization) to accelerate flowering. This regulates initiation of the transition between the vegetative

and reproductive apices (seedling to flowering). However, the period from flowering to maturity is

regulated by day length or photoperiod response.

Depending on the response to vernalization wheat is classified into two phenological groups;

spring and winter wheat (Flood and Halloran 1986). After vernalization wheat varieties which are

sensitive to photoperiod require certain day length to induce flowering. Both processes control the

adaptation of wheat to various environments, therefore genetic manipulation of these responses could

improve adaptation and yield (Acevedo et al. 2006). Photoperiod sensitive wheat genotypes require

long days for induction of flowering while photoperiod insensitive genotypes flower independent of

daylength. Photoperiod insensitive genotypes are cultivated in equatorial regions where shorter days

delay reproductive growth for photoperiod sensitive genotypes. Anthesis occurs about three to ten days

after the ear emerges and a minimum temperature of 9.5 °C and an optimum temperature between 18

8

°C to 24°C is ideal (Macdowall 1973; Slafer and Savin 1991). However temperatures below 9°C and

beyond 31°C may be lethal (Macdowall 1973). Optimal temperature for grain development lies

between 19.3 °C and 22.1 °C with a maximum between 33.4 °C and 37.4°C (Porter and Gawith 1999).

2.2 Drought

The definition of drought reflects many disciplinary perspectives including the meteorologist

who views it as the lowest amount of annual precipitation and the agronomist who assesses yield loss

attributable to water deficit. Farmers attempt to minimize the impact of drought, particularly at

flowering, by integrating best crop management practices with drought resistant cultivars (Passioura

2007).

2.2.1 Drought effects on wheat production

Drought always results in yield decline (Saini and Westgate 2000; Mahajan and Tuteja 2005)

and wheat is one of the principal staple food crops affected by drought globally with about 32 million

ha under wheat cultivation experiencing periodic water stress in developing countries and 60 million

ha in developed countries (Rajaram 2001; Ashraf 2010; Sareen et al. 2014). An estimated higher future

demand for food by an increased populace coupled with limited water supply is expected to worsen the

effects of drought (Somerville and Briscoe 2001). Its effects are evident at all the phenological stages

of plant growth (Harris D. et al. 2002; Kaya et al. 2006). In wheat growing areas, drought negatively

impacts yield (Lott N et al. 2011; Semenov and Shewry 2011) with the reproductive stages being most

susceptible (French and Schultz 1984; Passioura 2006b). It decreases grain set (Dorion and Lalonde

1996; Saini and Westgate 2000; Ji et al. 2010), grain filling duration, grain filling rate and grain weight

(Wardlaw and Willenbrink 2000). In barley (Hordeum vulgare), drought stress reduces grain yield by

reducing the number of tillers, spikes, grains per plant and individual grain weight. Post-anthesis

drought stress is detrimental to grain yield regardless of the stress intensity (Samarah 2005). Yield

improvement in cereals therefore, requires consideration of the whole developmental process with

appropriate strategies, from grain to grain, to target the various developmental stages (Triboi and

Triboi-Blondel 2002).

9

2.2.2 Drought resistance/tolerance mechanisms

Drought tolerant plants have evolved defensive mechanisms against water loss (Chaves and

Oliveira 2004) and Mitra (2001) has opined that tolerance mechanisms could be categorized into (a)

drought escape (b) drought avoidance and (c) drought tolerance.

2.2.3 Drought escape

This mechanism operates when plants complete their life cycle before the onset of

drought/severe water stress. This could be achieved when phenological development is appropriately

harmonized with defined periods of soil moisture availability or where the growing season is shorter

and terminal drought stress predominates (Araus et al. 2002). Short-duration varieties have proven to

be effective in minimizing yield losses from terminal drought, as early maturing types escape the

period of stress (Kumar and Abbo 2001). However, a yield penalty may arise due to reduced length of

crop duration (Turner et al. 2001).

2.2.4 Drought avoidance or water-use-efficiency

This mechanism refers to the plant’s ability to maintain tissue water potential through

increasing uptake of available water and reducing transpiration (Izanloo et al. 2008; Agbicodo et al.

2009). Traits such as root biomass, length, density and depth which are helpful in extracting water from

greater depths are the major traits linked to drought avoidance (Subbarao et al. 2000; Turner et al.

2001; Kavar et al. 2008).

2.2.5 Drought tolerance

Drought tolerance mechanisms evolve when plants manage conditions of water deficiency

through the manipulation of biochemical and physiological parameters to evade the injurious effects of

drought (Jones, 2004). Blum (2005) opined that crops adapted to dry environments employ drought

avoidance mechanisms rather than drought tolerance and that osmotic adjustment is the fundamental

trait to assess the performance of crops under drought conditions. Evaluation of three Australian bread

wheat (Triticum aestivum L.) cultivars; Excalibur, Kukri, and RAC875, has confirmed osmotic

adjustment to be the main physiological attribute associated with tolerance under cyclic water stress

(Izanloo et al. 2008). Traits that contribute to improved drought tolerance include reduced leaf area

(Schuppler 1998).

10

2.2.6 Breeding for drought tolerance

Genetic gains in wheat yields in sub-optimal environments has been slower (Richards et al.

2001). This is mainly due to the complexity of drought (Khan and Iqbal 2011). Under water stress, a

myriad of genes are induced and these in part determine how the plant copes with the stress and

edaphic factors, mainly composition and structure, affect the balance of these different stresses

(Whitmore and Whalley 2009). Various biochemical mechanisms may have opposing effects under

different stresses; therefore tackling tolerance to one stress may lead to sensitivity to another. Osmo-

protectants such as the amino acid proline, have a toxic effect under heat stress and their accumulation

may not be an acceptable tolerance mechanism in field conditions when heat and drought stresses are

merged (Rizhsky et al. 2004; Salekdeh et al. 2009). Variation in rainfall patterns over a crop cycle and

traits for drought adaptation to specific environments also exist and these factors complicate selection

for drought tolerance.

In molecular breeding, little progress has been made due to the polygenic control of many

drought tolerance traits. Drought induces many changes in gene expression, therefore the identification

of potential candidate genes that express in drought stress conditions is crucial, however microarray

technology has had limited practical application (Umezawa et al. 2006). Zhang et al. (2005b) reported

on two dwarf mutants of wheat showing a remarkably low rate of transpiration with higher water use

efficiency as compared to the Chinese wheat cultivar Jingdong 6. The two mutant lines were identified

through haploid breeding and mutagenesis which appeared to be a promising drought tolerance

breeding strategy (Khan et al. 2001). Likewise, Njau et al. (2009) developed drought resistant wheat

varieties in the marginal areas of Kenya, using mutation and the double haploid techniques. Although

the success of molecular-based approaches in developing drought tolerant cultivars has not been fully

realized (Zhao et al. 2008), the technology holds promise and can ultimately help identify genomic

regions for drought tolerance that can be recombined in breeding (Tuberosa and Salvi 2006).

To increase productivity under drought through breeding, Reynolds et al. (2005) proposed a

conceptual model (Fig 2.2) elucidating how physiological traits could be targeted to improve yield

under drought stress. They include traits related to pre-anthesis growth (G1): Early ground cover

portends good crop establishment that suppresses weeds and reduces water loss from the soil surface.

Therefore the selection of genotypes that provide early ground cover, especially in the Mediterranean

regions of southern Australia where rainfall is available in the early part of the season, is essential to

avoiding peaks of pre-anthesis moisture stress. However, in the northern region where water is limiting

throughout the growing season, early ground cover may not offer an advantage. Selection based on

11

pre-anthesis biomass and partitioning of assimilates to stem reserves is also essential for higher yield

under moisture stress because water soluble carbohydrates can be later remobilized to fill grains when

the grain filling period is affected by moisture stress (Gebbing et al. 1999; Shearman et al. 2005b).

Traits related to water access (G2): Root systems that will ensure maximum soil moisture capture from

deep in the soil profile will be an added advantage under drought stress. Also stomata aperture related

traits such as stomatal conductance, leaf water content, canopy temperature etc affecting plant water

status are essential under moisture stress. Traits related to water use efficiency(G3): Although difficult

to measure, carbon isotope discrimination has been accepted as an indirect measure of water use

efficiency. Genes regulating higher harvest indices are essential as are traits related to photo protection

(G4). Traits include accumulation of sugars, mainly sucrose and raffinose which maintain membrane

integrity and increases desiccation tolerance. G4 also includes growth regulators such as ABA and

proline (Sansberro et al. 2004; Bayoumi et al. 2008; Hussain et al. 2008).

Figure 2.2 Conceptual model for traits associated with adaptation to drought prone-environments

adapted from Reynolds and Tuberosa (Reynolds and Tuberosa 2008).

2.2.7 Genomic regions for drought tolerance

Genomic regions controlling drought tolerance in wheat and barley have been identified through

yield and yield component measurements under water-limited conditions (Maccaferri et al. 2008a;

12

Mathews et al. 2008; McIntyre et al. 2010a). Regions linked to floral infertility resulting from water

deficit, root traits under controlled conditions and nitrogen deficiency have been identified in wheat

(Laperche et al. 2006; Passioura 2007). Nevertheless, despite this substantial research, only a limited

number of molecular markers associated with high-yield under moisture stress have been used in plant

breeding programs (Gupta et al. 2010b), although many markers linked to other traits such as rust

resistance, grain quality, dormancy, dwarfing and vernalization are routinely utilized. The limited

success of the physiological and molecular breeding approaches suggests a careful re-evaluation of

strategies to dissect and breed for drought tolerance is needed (Fleury et al. 2010).

2.2.8 Estimation of drought tolerance

Drought tolerance assessment entails measuring grain yield under drought (Acevedo et al. 2006)

and a number of models have been developed to assess genotypes with significant dependence on yield

potential and crop phenology. These models exhibit high genotype x environment interaction effects

(Acevedo 1991) and include (a) the yield stability index (YSI) across environments (Finlay 1963;

Eberhart and Russell 1966) and (b) the drought susceptibility index (DSI) (Fischer and Maurer 1978).

As a measure to neutralize these genotype x environment interaction effects, Bidinger (1987)

propounded (c) a drought resistance index (DRI) equal to the residual effect of yield under stress once

the effects of yield potential, phenology and experimental error is eliminated. This criterion is utilized

to select drought resistant genotypes or genotypic traits related to drought resistance that could be

manipulated as independent genetic traits (Acevedo and Ceccarelli 1989). Morphophysiological traits

that confer adaptation to drought can be categorized into their relationship to water uptake and water

loss from the crop. Traits that enhance water absorption include root growth, osmotic adjustment,

accumulated solutes and membrane stability (Emundo Acevedo et al. 1998a). Whereas those traits that

control transpiration/water loss include leaf colour (Van Oosterom and Acevedo 1992), leaf water

movements, epicuticular waxes and trichomes on leaf surfaces (Upadhyaya and Furness 1994),

stomatal regulation (Venora and Calcagno 1991), transpiration efficiency (Farquhar and Richards 1984;

Austin et al. 1990) and air to canopy temperature difference (Blum 1988b; Rees et al. 1993).

2.2.9 Phenology and yield under moisture stress

Wheat is a long day plant and its cultivation is confined to temperate and semi-temperate agro-

climates due the sensitivity of both the vegetative and productive stages to elevated temperatures

(Kumar et al. 2012). Its growth, development and adaptation is influenced by environmental factors

such as vernalization temperatures and photoperiod (González et al. 2002). Therefore an appropriate

13

flowering time is crucial to maximization of yield and adaptation to drier environments (Richards

1991). Variation in response to photoperiod is controlled by a homologous series of genes Ppd-A1,

Ppd-B1 and Ppd-D1 (Dubcovsky et al. 2006; Yang et al. 2009; Bentley et al. 2011) and the dominant

alleles Ppd-D1confer insensitivity (Worland et al. 1998). Ppd-D1 confers early flowering and maturity

thus avoiding peaks of moisture stress (Worland 1996). Ppd-D1 is also associated with fewer spikelets,

reduced plant height and reduced tillering (Worland et al. 1998). The pleotropic effects of Ppd-D1 in

wheat suggest that summer stress avoidance associated with early flowering promotes a yield

advantage of about 30% in southern Europe (Borner et al. 1993; Law et al. 1994). The effects of Ppd-

D1 are neutral in winter wheat averaged over years in the UK (Borner et al. 1993; Law et al. 1994),

although in dry years yield increases have been observed suggesting earliness confers summer drought

avoidance (Worland 1996). Photoperiodic sensitivity effects on different developmental phases is

independent from each other and varies with genotypes (Slafer and Rawson 1994), therefore Slafer et

al. (2001) suggested that manipulation of the vegetative phase and the reproductive phases could be

done separately to improve wheat yield. Recognition of these genes has facilitated the manipulation of

days to ear emergence to reduce the impact from abiotic stress during critical growth stages of

flowering and grain fill (Bennett et al 2012).

Vernalization also induces flowering competence in winter wheat crops (Flood et al. 1984;

Goncharov 2004; Trevaskis et al. 2007) and is controlled by major genes such as Vrn-A1, Vrn-B1, Vrn-

D1 and Vrn-D4 (Eagles et al. 2009; Shimada et al. 2009; Rousset et al. 2011). They function in the

establishment and maintenance of floral meristem identity at tiller apex in wheat plants (Danyluk and

Sarhan 2003; Preston and Kellogg 2008).

A third class of genes, called the earliness per se genes, fine-tune the flowering time of wheat to

regional conditions whilst the major Vrn-1 and Ppd genes govern the gross adaptation to environments

(Hoogendoorn 1985; Båga et al. 2009; Rousset et al. 2011).

Significant interaction effects between vernalization and photoperiodic genes have been

reported (González et al. 2002) and the presence of Ppd- D1b in fully vernalized winter wheat plants

reduced the time to flowering by up to 24 days (González et al. 2005; White et al. 2008; Bespalova et

al. 2010), whereas those carrying an active allele Vrn-A1 was at least 30 days earlier (González et al.

2005; Bespalova et al. 2010). Ppd-D1a in the presence of one or more active Vrn-1 allele(s) advanced

flowering time by up to 12 days (White et al. 2008; Eagles et al. 2010). Flowering time was found to

be advanced by 11.8 days when Vrn-A1, Vrn-B1 and Vrn-D1 were present simultaneously and only by

14

3.7 days when Vrn-B1 alone was present in addition to Ppd-D1a (Eagles et al. 2010). The Vrn

genotypes were observed to be marginally early flowering time in the following order Vrn-A1 Vrn-B1

Vrn-D1 < Vrn-A1 Vrn-B1, Vrn-A1 VrnD1 and Vrn-A1 < Vrn-B1 and Vrn-D1 (Iqbal et al. 2007a, b;

Eagles et al. 2009). Dubcovsky and Dvorak (2007) therefore indicated that the natural and conscious

movement of the active alleles of Vrn and Ppd genes across landraces and cultivars may have governed

the cultivation of bread wheat over wider environments. The occurrence of Vrn and Ppd alleles

singularly or in combination has imparted considerable phenological plasticity to wheat (Dubcovsky

and Dvorak 2007; Zhang et al. 2008; Eagles et al. 2009). Manipulating the genetics of flowering time

could facilitate the designing of superior wheat genotypes for the new and conventional locations,

sowing times, considering climate change (Mathews et al. 2007).

2.2.10 Physiological traits conferring yield under moisture stress

2.2.10.1 Water soluble carbohydrates and yield under water stress

Most cereal crops have the capacity to store water soluble carbohydrate in their stems and later

remobilise these to increase grain yield. In wheat, varieties that are capable of synthesizing and storing

significant amounts of water soluble carbohydrates in their stems prior to anthesis are likely to produce

higher yields under water stress conditions (Conocono 2002). This trait has been accepted as an

adaptive trait for drought tolerance (Reynolds et al. 2009) and its contribution to yield varies with

genotype, environment, and growing conditions ranging from 10 to 20% under non-stressed conditions

(Gebbing et al. 1999; Shearman et al. 2005b). Due to its high heritability, breeding for high WSC

should be feasible (Ruuska et al. 2006) although it appears to be controlled by many genes (Rebetzke

GJ et al. 2008). Genomic regions responsible for water soluble carbohydrate remobilization have been

reported (Salem et al. 2007; Snape et al. 2007; Yang et al. 2007a).

2.2.10.2 Canopy temperature depression and yield under water stress

Canopy temperature can indicate overall plant water status (Blum et al. 1982; Idso 1982) and

has been utilised to evaluate plant response to drought (Blum et al. 1989; Royo et al. 2002). Strong

negative correlations have been noted between canopy temperature and grain yield in wheat under

irrigated conditions when measured between 12:00 to 4:00pm with no effects from stage of

development (booting, heading or grain filling) or time relative to irrigation (Amani et al. 1996). Also,

strong genotypic variation for canopy temperature measurements in wheat were reported (Blum et al.

1989; Reynolds et al. 1994; Ayeneh et al. 2002) and genotypes with high canopy temperature

15

depression (cooler canopies) have corresponding yield increases (Fischer et al. 1998). Canopies with

higher water content are indicative of genotypes with higher biomass resulting from larger rates of

carbon fixation associated with greater stomatal conductance and therefore, cooler canopies (Babar et

al. 2006). Thus drought-susceptible genotypes may have warmer canopies than tolerant cultivars

(Reynolds et al. 2001). However, canopy temperature depression measurements could be misleading

when yield is highly dependent on limited amounts of soil stored water (Royo et al. 2002; Balota et al.

2007) or when affected by both biological and environmental factors mainly, vapour pressure deficits,

soil water status, wind, evapo-transpiration, cloudiness, conduction systems, plant metabolism, air

temperature, relative humidity and continuous radiation (Reynolds et al. 2001). Hence measurements

are preferably taken in high temperatures and low relative humidity to minimize both environmental

and biological effects (Amani et al. 1996). In wheat, Rebetzke et al. (2013a) has identified 16 genomic

regions regulating canopy temperature and these regions could be targeted for selection to improve

drought tolerance.

2.2.10.3 Leaf chlorophyll content and yield under water stress

Limited water supply decreases chlorophyll formation (Begum and Paul 1993), chlorophyll

content (Beltrano and Ronco 2008; Nikolaeva et al. 2010), plant growth and yield by accelerating leaf

senescence (Sionit et al. 1980; Ashraf et al. 1994). Variation in chlorophyll concentration among

genotypes is controlled mainly by genes acting additively (Hervé et al. 2001; Juenger et al. 2005). Four

additive QTLs controlling chlorophyll content in wheat were mapped (Yang et al. 2007b).

2.2.10.4 Stay green and yield under water stress

The stay green trait allows plants to retain leaves in an active photosynthetic state when

subjected to stress conditions (Rosenow et al. 1983). It contributes to a long grain-filling period and

stable yield even when the plant is stressed (Vijayalakshmi et al. 2010). Delayed leaf senescence has

been associated with higher grain yield especially under water stress during the grain filling period,

when water supply is inadequate to support potential transpiration (Borrell et al. 1999; Borrell et al.

2000a). In sorghum, hybrids that retain greenness produced 47 % more post-anthesis biomass than their

counterparts under terminal moisture deficit conditions (Borrell et al. 2000b). Moreover, in wheat, four

green-retaining lines had corresponding higher yields than their parents under drought (Spano et al.

2003). Strong positive relationships with radiation use efficiency, nutrient remobilisation (Gregersen et

al. 2008) and water use efficiency during the grain formation of wheat have been reported (Gorny et al.

16

2006). However, there are limited reports on wheat providing evidence of substantial variation for the

staygreen trait (Joshi et al. 2007; ur Rehman et al. 2009). Visual rating of staygreen is quick to perform

in the field on a plot basis using a 0–9 scale and as such, it represents an important tool for use by plant

breeders to screening large numbers of progenies (Xu et al. 2000).

2.2.10.5 Stomatal conductance and yield under water stress

Stomatal conductance and photosynthetic rate reflects the ability of the plant to take up CO2 or

lose water through transpiration. Genotypic variation for stomatal conductance and a positive

correlation with yield were found in wheat (Fischer et al. 1998; Rebetzke et al. 2003; Bijanzadeh and

Emam 2010) and increased stomatal conductance has resulted to greater radiation-use efficiency among

some Australian wheat varieties (Sadras and Lawson 2011). Rebetzke et al. (2003) has observed that

variation in stomatal conductance is repeatable and could be targeted for improving adaptation to

specific environments. A reduced transpiration rate can slow water use and increase transpiration

efficiency for wheat crops growing under limited water supply (Condon et al. 1990; Pinter et al. 1990;

Morgan and Lecain 1991). Alternatively, greater transpiration and photosynthetic rates may be

associated with increased grain yield in irrigated environments where water is plentiful (Fischer et al.

1998; Condon et al. 2007). James et al. (2002) indicated that stomatal conductance has an advantage

over measuring photosynthesis, as the former is often more sensitive to water deficit on a per unit area

basis. Strong associations between stomatal conductance and canopy temperature depression have been

reported (Pinter et al. 1990; Amani et al. 1996; Fischer et al. 1998). However, not all studies have

demonstrated correlated benefits of canopy temperature depression and grain yield in wheat (Winter et

al. 1988; Royo et al. 2002). Genomic regions controlling leaf porosity, a surrogate for stomatal

conductance were also reported (Rebetzke et al. 2013a). However, both traits are difficult to measure.

2.2.10.6 Carbon Isotope Discrimination, Transpiration efficiency, water use efficiency and yield

under water stress.

Carbon isotope discrimination determines the ratio of stable isotopes of carbon 13

C/12

C in plant

dry matter relative to the value of the 13

C/12

C ratio in the air that plants use in photosynthesis. Its

measurement provides an indirect estimate of variation in transpiration efficiency and water-use

efficiency of dry matter production (Farquhar et al. 1982; Farquhar and Richards 1984) and negative

correlations between 13

C and plant water-use efficiency have been reported in many species (Farquhar

et al. 1989; Condon A. G and E. 1997). 13

C is a highly heritable trait (Condon and Richards 1992;

17

Rebetzke et al. 2002) and therefore amenable to breeding to increase water-use efficiency through

greater transpiration efficiency (Condon et al. 2004). Carbon isotope discrimination was found to

correlate with yield in crops under water stressed conditions (Leidi et al. 1999; Cabuslay et al. 2002;

Shaheen and Hood-Nowotny 2005) and has therefore been utilised to evaluate genotypes for grain yield

and water use efficiency under field water stress conditions. In wheat, grain yield was found to be

positively correlated to carbon isotope discrimination in environments with characteristic post-anthesis

water deficit such as South Australia (Condon et al. 1987), California, USA (Ehdaie et al. 1991),

Morocco and Spain (Araus et al. 1998), southern France (Merah et al. 2001) and Greece (Tsialtas et al.

2001) although there were exceptions. The relationship between grain yield and carbon isotope

discrimination therefore varies with the environment, with the correlation being weaker and less stable

under pre-anthesis water stress (Xu et al. 2007) and under residual soil moisture conditions (Misra et

al. 2006). Therefore the association largely depends on the amount of water stored in the soil before

sowing and on the evaporative demand during the growth cycle (Monneveux et al. 2005). Genomic

regions for carbon isotope discrimination have been reported across a range of species including barley

Hordeum vulgare (Saranga et al. 1999) and rice (Orzya indica) (Xu et al. 2009). A wheat breeding

program in Australia released two high water use efficient wheat cultivars, “Drysdale" (released in

2002) and “Rees" (released in 2003), through indirect selection of carbon isotope discrimination

(Rebetzke et al. 2002; Condon et al. 2004) although these are not commonly cultivated in recent years.

Transpiration efficiency at the leaf level is the ratio of leaf CO2 exchange rate to stomatal conductance

(Gutierrez-Rodriguez et al. 2000; Xue et al. 2002). The presence of an impermeable cuticle in crop

plants might indicate drought tolerance (Clarke and Richards 1988; Jamaux et al. 1997) thus plant

varieties with low cuticluar transpiration rates can conserve higher amounts of water under water-

deficient conditions, indicating their drought tolerance. In wheat, variation in transpiration efficiency

was found to be correlated with variation in the ratio of biomass production to water transpired of

potted plants, suggesting that leaf gas exchange techniques may provide an accurate assessment of the

transpiration efficiency of the whole plant (Heitholt 1989). In several plant species, carbon isotope

discrimination is negatively correlated with transpiration efficiency (Shangguan et al. 2000; Cabuslay

et al. 2002; Xue et al. 2002). Carbon isotope discrimination may therefore permit an integrated

measure of transpiration efficiency.

18

2.2.10.7 Leaf relative water content and yield under water stress

Leaf relative water content indicates the water status of plants relative to their fully turgid state

(Moayedi et al. 2011). Genotypes that maintain high levels of leaf water under water deficit conditions

are less affected by stress and are able to maintain normal growth and yield (Beltrano et al. 2006). In

wheat, water balance among genotypes is disrupted when relative water content decreases in leaves

under water deficit conditions (Molnar et al. 2004; Dulai et al. 2006) and a positive correlation between

grain yield and leaf relative water content has been observed (Schonfeld et al. 1988; Tahara et al. 1990;

Merah 2001). The selection of leaf relative water content traits for breeding under drought stress

conditions has therefore been emphasised (Schonfeld et al. 1988). Teulat et al. (2003) identified nine

chromosomal regions linked to leaf relative water content in field grown barley.

2.2.10.8 Epicuticular wax load and yield under water stress

The epicuticular waxes covering the aerial parts of plants play an important role in the control

of water flow across the cuticle (Eigenbrode and Espelie 1995). They help leaves retain water (Jordan

et al. 1984) by minimizing cuticular transpiration (Premachandra et al. 1992b; Jefferson 1994). They

also shield plants from high radiation and UV light damage by providing the leaves with greater

reflectance (Grant et al. 1995). Its role in reducing cuticular transpiration and improving drought

resistance is evident in sorghum and wheat (Blum 1988b) and genotypes with low cuticular

transpiration rates usually have a functional advantage during water deficit due to more efficient water

use (Paje et al. 1988). QTLs for wheat leaf epicuticular wax load were reported on chromosomes 2B,

3B, 5A, 5B, 6B, 7A, and 7B.

2.2.10.9 Leaf rolling/curling and yield under water stress

Water deficit induces leaf rolling in cereal crops such as rice, maize, wheat and sorghum (Kadioglu and

Terzi 2007) and this has been noted as a stress avoidance mechanism that moderates abiotic stresses

such as light interception, irradiation, drought and high temperature (Kadioglu and Terzi 2007). In

wheat genotypes with this trait, leaves roll up to prevent photo damage (Kadioglu and Terzi 2007). The

correlation between leaf rolling and photosynthesis is not clear because leaf rolling during stress

reduces leaf surface exposure to sun light energy and transpiration leading to closure of stomata, thus

CO2 entry into cells and photosynthesis decreases (Allah 2009). Subashri et al. (2009) observed a

negative relationship between leaf rolling and chlorophyll content because leaf rolling reduced the leaf

area exposed to light, thus reducing the activity of chlorophyll and the process of photosynthesis. On

19

the other hand, leaf rolling protected the photosynthetic apparatus in Grey maranta (Ctenanthe setosa

EichL.) from photodamage (Nar et al. 2009) and the function of leaf rolling in maximising

photosynthesis with a complimentary increased grain yield by decreasing water loss from plants under

stress was reported (Richards et al. 2002; Zhang et al. 2009a). This trait, induced under abiotic stress, is

controlled by major genes in rice (Yan et al. 2008; Luo et al. 2009) and maize (Nelson et al. 2002;

Juarez et al. 2004).

2.2.10.10 Seedling emergence/vigour and yield under water stress

Emergence is the ability of a seedling to emerge its aerial parts from the soil and is considered

one of the most important traits of seedling vigour (Basra et al. 2003). High seedling vigour indicate

good growth and yield, and it is necessary for effective crop establishment, particularly under sub-

optimal growing conditions (Landjeva et al. 2010). Poor germination and uneven crop stand are the

main constraints of a good crop and survival after desiccation has been considered as a valid and

suitable technique for screening large populations (Winter et al. 1988). Landjeva et al. (2010) mapped

twenty QTL's in wheat controlling germination, seedling vigour, longevity, and early seedling growth.

2.2.10.11 Root morphology and yield under water stress

The ability to develop roots capable of exploiting soil moisture deep in the soil profile is

considered an important drought adaptive trait. In turf grass, drought resistance has been associated

with deep root penetration (Carrow 1996; Huang et al. 1997) whilst in rice, strong root growth is the

major factor governing drought tolerance (Yoshida S and S 1982; Ray et al. 1996). A larger root

system increases water and nutrient uptake from the soil (Ehdaie et al. 2003) and in rice, lines with

large deep root systems tend to have high leaf water potential and delayed leaf death during drought

(Mambani and Lal 1983; Cruz and O'Toole 1984). Root thickness, dry weight, volume and density

have high heritabilities showing positive association with drought tolerance (Qu et al. 2008) Variation

in root development among wheat genotypes in response to drought has been observed (Ehdaie et al.

2001) and this phenotypic plasticity depends on the genotype, year to year variation in growing

conditions and drought timing and intensity (Ehdaie et al. 2001; Kano et al. 2011). Variation in root

growth angle and the number of seminal roots among 27 Australian wheat genotypes have been also

observed (Manschadi et al. 2008). Satisfactory grain yield can be achieved under terminal drought

stress when soil moisture is captured by a vigorous and deep root system when deep soil moisture is

available (Kirkegaard et al. 2007; Blum 2009). Therefore, genotypes with an efficient root system are

20

crucial for improving productivity in rain fed environments (Yadav et al. 1997; Price et al. 2002b; Li et

al. 2003). Genomic regions for different root traits have been identified in rice (Champoux et al. 1995;

Price and Tomos 1997), maize (Lebreton et al. 1995) and wheat (Sharma et al. 2011; Bai et al. 2013).

2.3 Water use efficiency

Water use efficiency (WUE) determines the plant response to water stress with respect to the final

performance (Condon et al. 2004). The agronomist or breeder perceives it as the ratio of aerial biomass

yield to water utilised during the life cycle of a crop (Condon et al. 2004; Tuberosa et al. 2007), whilst

the crop physiologist recognizes it as the ratio of net photosynthesis to transpiration over a period of

seconds or minutes (Farquhar et al. 1989; Condon et al. 2002; Bousba et al. 2009). At the leaf level,

instantaneous WUE is measured as the net amount of carbon assimilated per unit of water transpired

(transpiration rate) during the same period (Farquhar et al. 1989; Condon et al. 2002; Polley 2002).

Intrinsic water use efficiency is independent of specific environmental conditions and is defined as the

ratio of the amount of carbon assimilated and stomatal conductance. Therefore water use efficiency is

defined as:

WUE = Y / ET (1)

Where Y is total harvestable biomass or marketed yield and ET is evapo-transpiration. For agronomists

and plant breeders,

WUE = TE/1+Es/T (2)

Where TE is the transpiration efficiency (aboveground dry matter/transpiration), Es is the water lost by

evaporation from the soil surface whilst T is water lost through transpiration by the crop.

Intrinsic water use efficiency could also be expressed as:

WUEins or TE = A/T= [Gc(Ca-Ci)] ∕ [Gw(Wi-Wa)] (3)

Where A is the product of stomatal conductance to CO2 (Gc) and the gradient in concentration of CO2

between the outside (Ca) and inside (Ci) of the leaf whilst T is the product of stomatal conductance and

water vapour (Gw), the gradient in concentration of water vapour from the inside (Wi) to the outside

(Wa) of the leaf.

Richards et al. (2002) indicated that crop water loss is controlled by the gradient in water

vapour concentration between the crop canopy and the atmosphere with the least being in cool, humid

regions and during the coolest months of the year. It could be inferred from equation (2) that WUE

21

could be improved either by increasing TE or minimize water lost by evaporation from soil surface (Es)

through agronomic management practices (Gregory et al. 1997; Condon et al. 2002; Richards et al.

2002; Passioura 2006a).

WUE calculated as grain yield divided by water supply may be underestimated because it

assumes that the same amount of water is transpired by each genotype, therefore, Blum (2009) has

advocated the application of effective use of water (EUW) for genetic improvements/breeding, since

the former resulted in the selection of genotypes with reduced yield and reduced drought resistance.

Thus, water use efficiency could be expressed as the following equation (Hatfield et al. 2001) :

Y = B x HI, (4)

B = mT/Eo (5)

Therefore WUE = mT/Eo x HI/ET or mT x ET/Eo x HI (6)

where Y is yield, B is the crop biomass, m is a crop constant, HI is the harvest index, T is the crop

transpiration and Eo is free water (potential) evaporation.

2.3.1 Breeding for improved water-use efficiency

Water/yield relationships are the basis of efficient water management (Siahpoosh et al. 2011)

and the need to improve food security in many regions necessitates the development of crops with

improved yield and high water-use efficiency (Hamdy et al. 2003; Parry and Reynolds 2007). Passioura

(2006a) proposed a model for grain yield under limited water environments as a partial function of

water use efficiency:

Y= WU x WUE x HI (1)

Where yield (Y) and WU is described as being a function of the amount of water used or

actually transpired by the crop, the transpiration efficiency of biomass production (WUE), i.e. how

much biomass is produced per millimetre of water transpired, and, lastly, how effectively the achieved

biomass is partitioned into the harvested product, i.e. the ratio of grain yield to standing biomass

termed the harvest index (HI).

From the above model, three key processes could be exploited in breeding for high water-use

efficiency (i) moving more of the available water through the crop rather than wasting it through

evaporation from the soil surface or drainage beyond the root zone or being left behind in the root zone

at harvest (ii) acquiring more carbon/biomass in exchange for the water transpired by the crop i.e.

22

improving crop transpiration efficiency (iii) partitioning more of the achieved biomass into the

harvested product. None of the components of this yield model are truly independent of each other

(Condon et al. 1993; Tambussi et al. 2007b) but each could be considered a target for genetic

improvement. In addition, Richards et al. (2002) recognized that a deep root system increases water

uptake from the soil and there improves performance under drought. It has therefore been suggested

that transpiration can be directly related to root system size and leaf area (Cinnirella et al. 2002; Cooper

et al. 2003) and that large root systems supply more water (Kramer 1969). Improvement of biomass

production for higher harvest index and grain yield stability under drought stress can be achieved by

maximizing soil water capture while diverting the largest part of the available soil moisture toward

stomatal transpiration during grain filling (Blum 2009). This has been confirmed by modeling where

each additional millimeter of water extracted by the root system during grain filling is estimated to

generate an extra 55 kg ha-1 of grain in Australian dry environments (Manschadi et al. 2006). Greater

root production under drought is advantageous only when plant-available water is enough to support

grain production (Ehdaie et al. 2012).

The Mediterranean climates are characterized by frequent rainfall during vegetative growth and

terminal drought during grain filling with evaporation from the soil surface accounting for as much as

50% from the growing season rainfall (Cooper et al. 1987). Thus, a reduction in soil evaporation or the

average evaporative gradient during the crop cycle could be beneficial (Richards et al. 2002).

Increasing rooting depth, distribution and ground cover of crops to reduce soil evaporation in these

regions could be accomplished through early sowing, rapid seedling establishment and good canopy

cover combined with higher specific leaf area (SLA) (Richards et al. 2002; Tambussi et al. 2007a).

Early vigorous genotypes develop deep rooting systems and exhibit greater soil water extraction

capacity (Turner and Asseng 2005), which is a remarkable trait for the effective use of water under

most drought conditions (Blum 2009).

Under temperate climates, crops rely on stored soil water, which is usually depleted at maturity

(Cooper et al. 1987; Loss and Siddique 1994), a restricted leaf area therefore reduces transpiration and

conserves soil moisture thereby contributing more water for late grain filling (Rebetzke et al. 2002).

Transpiration efficiency could be improved genetically by increasing surface reflectance

(Richards et al. 2002) and is achieved through selection for glaucousness, pubescence, and awns with

photosynthetic capacity (Richards 1986). Cuticular loss during the day can be important and significant

genotypic variation has been found in wheat (Clarke and McCaig 1982). Selection of genotypes with

23

higher photosynthetic capacity has also been noted to improve transpiration efficiency by increasing

CO2 concentration in plants (Farquhar et al. 1989).

Higher harvest index is generally linked to higher yield (Cattivelli et al. 2008). According to

Richards et al. (2002), the genetic manipulation of HI in rain fed environments is probable but not

simple because crops that experience drought have two factors, drought independent and drought

dependent traits, that determine HI. These can be genetically manipulated to maximise HI to achieve

higher grain yields (Richards 1991). The latter is largely a function of post anthesis water use mainly

during the grain filling period and other factors such as pre-anthesis period during partitioning between

structural and soluble carbohydrate (Richards et al. 2002). It has therefore been noted that large post

anthesis water use relative to total water use increases HI. Improvement in yields among cereals have

therefore been achieved by selecting for a higher drought independent HI through the reduction of plant

height and earlier flowering (Slafer and Rawson 1994; Richards et al. 2002; Zhang 2007).

Nevertheless, the upper limit for genetic increases in crop yields based on harvest index is close to

maximised (Mann 1999; Richards et al. 2002).

Although Blum (2005) opined that selection for high water use efficiency (WUE), will result in

smaller or early flowering plants without increasing yield when targeting net reduction in the total

amount of water used, other research suggests that breeding involving the selection of genotypes with

high water use efficiency is promising, since water use efficiency is one of the mechanisms by which

wheat plants may adapt to lack of water with association to high productivity under drought (Chaves

and Oliveira 2004; Condon et al. 2004; Rensink and Buell 2004; Munns 2005; Shao et al. 2006).

2.3.2 Genomic regions for water use efficiency

Recent advances in molecular markers and computer software have facilitated understanding of

quantitative trait inheritance, improved genetic improvement of yield in drier environments and

provided a basis for the identification of chromosomal regions controlling adaptive traits (Zhang 2007).

Farquhar and Richards (1984) found that carbon isotope discrimination measurements during growth

can be an excellent surrogate for direct measurement of water use efficiency, therefore identification of

chromosomal regions for carbon isotope discrimination could indicate regions conferring high water

use efficiency. Similar relationships between carbon isotope discrimination, leaf characters and other

physiological traits have been demonstrated in several plant species, including wheat, forage, knotweed

and cotton (Condon et al. 1990; Geber and Dawson 1997; Saranga et al. 1999). The first genomic

region responsible for carbon isotope discrimination was reported by Martin et al. (1989) in tomato

24

(Lycopersicon esculentum and L. pennellii). Similarly, genomic regions responsible for carbon isotope

discrimination in soybean (Specht et al. 2001), barley (Teulat et al. 2002; Diab et al. 2004), cotton

(Gossypium hirsutum G. barbadense) (Saranga et al. 2004) and rice (Takai et al. 2006; Xu et al. 2009)

have been found.

In wheat, genomic regions controlling water use efficiency through carbon isotope discrimination

in both rain fed and irrigation systems have been identified. Peleg et al. (2009a) identified 12 QTLs

under well-watered and water-limited conditions using recombinant inbred lines whilst Rebetzke et al.

(2008b) identified 9-12 QTLs using double haploid lines under rain fed and irrigated conditions. A

number of genomic regions are reported to co-locate and overlap with some morphophysiological

traits.

In barley, Teulat et al. (2002) identified eight QTLs for carbon isotope discrimination that were

co-located with QTLs for physiological traits related to plant water status and/or osmotic adjustment,

and/or for agronomic traits previously measured on the same population. One genomic region known

to be associated with thousand-grain weight (Teulat et al. 2001), located on chromosome 2H, mapped

near EBmac0684 and was linked to QTLs for carbon isotope discrimination. Four genomic regions

harboring genes responsible for carbon isotope discrimination also co-located with regions for

physiological traits related to plant water status measured under controlled conditions (Teulat et al.

2001). In rice, Diab et al. (2008b) indicated that the genomic region for carbon isotope discrimination

and transpiration were located near the same marker (gwm389) and that these regions are of interest in

terms of plant breeding as they control both important drought-adaptive traits for cereals and yield

components (Teulat et al. 2002).

25

Summary of some wheat traits and their identified QTLs together with their corresponding chromosomes.

Trait

No. of

QTLs Location Heritability Populations Experimental System

Root Morphology 31

2A, 2D, 3A, 3B, 4D, 5A, 5BS,

6A LOW Near Isogenic lines Green house, Field Experiments

Seedling Vigour/Ground Cover 20 1D, 2D, 4D, 5D, 7D, 1A, 2A MODERATE Lines carrying D genome Field experiment

Epicuticular Wax Load 10 2B, 3B, 5A, 5B, 6B, 7A, 7B MODERATE Recombinant Inbred lines Field experiment

Carbon Isotope

Discrimination/WUE 24

2B, 4A, 5A, 7B, 1BS, 2BS,

3BS, 4AS, 4BS, 5AS,

7AS,7BS LOW Double haploid Field Experiment

Chlorophyll Content 8 1A, 3B, 5A, 7A LOW Double haploid Field experiment

Canopy Temperature 16 3B HIGH Double haploid Field experiment

Water soluble carbohydrate 10 1A, 1D, 2B, 2D, 4B, 5B, 6B HIGH Double haploid Field experiment

Heading date 2

1A, 2B, 6D, 7AL, 7B, 2DS,

4AS MODERATE Recombinant Inbred lines Field experiment

Maturity date 2 1B , 3AS MODERATE Recombinant Inbred lines Field experiment

Biomass 2 4B, 4D LOW Double haploid Field experiment

Stay green 3 1AS, 3BS, 7DS HIGH Recombinant Inbred lines Field experiment

Thousand Kernel Weight 10 1A, 1D, 2B, 2D, 4B, 5B, 6B LOW Double haploid Field experiment

Grain filling duration 6 1A, 3B, 5D, 6D LOW Double haploid Field experiment

26

2.4 Conservation agriculture (CA)

Conservation agriculture utilises three modules concurrently: (1) reduced disturbance of the soil

(2) soil cover, i.e. crop residue, cover crops, relay crops and (3) crop rotation (Derpsch et al. 2001).

Major benefits include: increased soil organic matter, improved water and nutrient use efficiency,

reduced erosion of topsoil, soil surface temperatures, fuel, labor, and overall cost associated with crop

establishment and timely operations (Hobbs et al. 2008).

2.4.1 Conservation agriculture and water-use efficiency

The ability of roots to grow and explore the soil for water and nutrients influences plant growth

rates (Clark et al. 2003) and soil structural modifications resulting from tillage affects root

water/nutrient uptake, stomatal conductance, evapotranspiration, canopy development and

subsequently leaf area index (Lo Cascio and Casa 1997). It is estimated that wheat yield could be as

high as 15 t/ha (Langridge et al. 2001) suggesting that 80 – 90% of the yield potential is lost because

of moisture stress and other factors including poor agronomy. However, conservation agriculture with

its characteristic larger biopores constructed by activities of earthworms, mainly anecic and epigeic

(Lo Cascio and Venezia 1986; Peigné et al. 2009; Pelosi et al. 2009) provides good drainage of water,

improved root penetration of the soil profile and improved water use (Turner 2004).

The pores favours percolation of incident rainfall and ensures that a greater proportion of

received water is held at plant available tensions (Shaxson 2009), therefore soils have higher moisture

content with reduced evapotranspiration in the upper soil layers (Josa and Hereter 2005) and a 30–80%

greater saturated hydraulic conductivity than soils tilled with moldboard and chisel plows (Chan and

Mead 1989; Benjamin 1993). Under sub-optimal rainfall conditions, the availability of water

accounted for the differences in the performance of genotypes under no tillage and conventional tillage

(Hall and Cholick, 1989; Cox, 1991; Carr et al., 2003a), because conservation agriculture lowers soil

evaporation, increases infiltration, conductibility, reduces runoff, mainly due to the presence of crop

residues on the soil surface and soil structure modifications (Blevins et al. 1971; Unger 1990; Hatfield

et al. 2001; Karamanos et al. 2004).

In wheat, no-tillage systems support higher yields by providing greater water storage in the

soil profile, mostly in drier regions, enhancing water use-efficiency and saving energy (Su et al. 2007;

Muñoz-Romero et al. 2010). An increase in grain yield was realised when precipitation increased,

indicating that conservation of soil water was a critical factor explaining yield differences between no

27

tillage and conventional tillage (Herrera et al. 2013). Variation in yield due to increased soil moisture

and nutrient availability in CA compared to full-tillage was reported in the range of 20–120 % in dry

Mediterranean climates (Mrabet 2000; Fernández-Ugalde et al. 2009b).

Long-term CA adoption results in improved nutrient availability, biological activity, fertility

restoration and soil water balance due to organic matter build-up at the soil surface (Kassam et al.

2012) and increased nitrogen availability which improves water use efficiency (Casa 2007; Di Paolo

and Rinaldi 2008). CA also impedes rapid oxidation of soil organic matter to CO2 which is induced by

tillage (Nelson et al. 2009), reduces losses of phosphorus and nitrate in run-off/ leaching and even

reverses the emission of CO2 and other greenhouse gases (Lal 2002; Lal 2004; Soane et al. 2012).

Overall, CA positively increases the grain yield of winter wheat in drier conditions (Li et al. 2006).

2.4.2 Conservation agriculture impacts on wheat grain yield and quality

The yield potential of crops under CA in rainfed systems is often higher than conventional

tillage systems especially in dry environments where sub-optimal rainfall limits yield (Farooq et al.

2011) even though the yield advantage has not always been positive in all experiments. Crop residue

control in no-tillage is the most critical factor influencing crop yield (Li et al. 2011) and yield

performance could be comparatively higher when water stress is present (Huang et al. 2008b). Under

extreme arid conditions in northern Spain, barley yields under no tillage were sometimes twice those

of conventional tillage (Fernández-Ugalde et al. 2009a). In addition grain water content at harvest was

28 and 27% for no-tillage and 19 and 21% in conventional tillage, requiring an extra cost for grain

drying from no-tillage crops (Soane et al. 2012). Wheat yield under no tillage was 18-42% higher than

conventional tillage mainly due to water availability (Hemmat and Eskandari 2006; Huang et al.

2008a) even though some authors have reported higher yields under conventional tillage than

conservation tillage (Thompson et al., 1987; Hwu and Allan, 1992; Weisz and Bowman, 1999; Kharub

et al., 2008; Kumudini et al., 2008) with similar results observed in maize (Karlen and Sojka 1985;

Newhouse and Crosbie 1986; Graven and Carter 1991). Trethowan et al. (2012) in an earlier study

concluded that genotypes used for some of those studies were primarily developed under conventional

tillage and that accounted for their reduced adaptability to CA.

2.4.3 Conservation agriculture and soil structure improvement

Tillage modifies soil factors such as bulk density, penetrance, pore continuity, soil temperature

and root impedance (Huwe B. 2003; Hamza and Anderson 2005; Johnson et al. 2006). CA improves

soil structure through accumulation of organic matter and soil organic carbon (Stockfisch et al. 1999;

28

Horacek et al. 2001) resulting in carbon vertical stratification affecting top soil physical properties

(Josa and Hereter 2005; Moreno et al. 2006). Soil organic carbon accumulation of up to 11 t/ha after 9

years was reported under CA (Baker and Saxton 2007). CA also improves soil aggregate stability,

reduces erosion, soil surface sealing, increases infiltration, soil water content, water use efficiency and

reduces runoff (Tebrügge and Düring 1999; Hernanz et al. 2002; Hobbs et al. 2008; Farooq et al.

2011). Erosion control is achieved through the combined effects of soil cover, topsoil aggregates

stability, accumulation of organic carbon and the water infiltration rate which are associated to soil

organic matter, soil organic carbon and earthworm activity (Puget et al. 1995; Hernanz et al. 2002).

Hansen et al. (2012) obtained up to 25% reduction in soil erosion in a wheat summer fallow system.

However, Blevins and Frye (1993) cautioned that the use of no-till in fine-textured (clayey) soils may,

over time may increase impedance and density in the topmost 30 cm, thus hindering root penetration.

In addition, CA it increases soil bulk density, which results in reduction of soil nutrient and water

absorption by plant roots (Fabrizzi et al. 2005; Su et al. 2007) unless sub-soiling tillage is adopted as a

control (Borghei et al. 2008).

2.4.4 Conservation agriculture and the prevalence of pests, diseases and weeds

The adoption of conservation agriculture encourages crop infestation by necrotrophic

pathogens such as tan spot, the Septorias and crown rot caused by Fusarium. However, the use of

tolerant cultivars in combination with crop management practices such as rotation, timely sowing,

irrigation and fungicide application can afford effective control (Duveiller et al. 2007). The prevalence

of perennial weeds poses a challenge but an integrated application of pre and post emergence

herbicides in combination with crop residues, timely sowing and rotation can control their emergence

and subsequent growth, although weed resistance to herbicides in the long term is a danger (Chauhan

et al. 2006; Farooq et al. 2011; Johansen et al. 2012)

2.4.5 Adoption of conservation agriculture and the choice of cultivar

Kumudini et al. (2008) indicated that seven out of twelve wheat studies (58%) had significant

genotype by tillage interaction effects even though other authors found none in winter wheat (Dao and

Nguyen 1989; Weisz and Bowman 1999; Carr et al. 2003a, 2003b). This could be possibly linked to

the relatively small number of genotypes were evaluated (Carr et al. 2003a; Kumudini et al. 2008;

Zamir et al. 2010). Therefore, the variation in grain yield observed among genotypes in no-till systems

compared to conventional tillage suggests that better adapted genotypes could be developed (Herrera et

al. 2013). Trethowan et al. (2012) also reported significant genotype x tillage interaction effects and

29

QTLs for grain yield under no-till in a wheat mapping population of 150 entries involving two parents

Berkut and Krichauff. It is therefore essential that the correct parents be selected when breeding for

adaption to tillage management (Herrera et al. 2013). A contemporary approach necessitates the

development of genotypes under no till and the transfer of research on genotype performance under no

till systems to defined ecologies with reduced input use and subsequent evaluation of factors that

discriminate conservation agriculture from conventional practice (Herrera et al. 2013).

2.5 Plant/cultivar mixtures

Cultivar mixtures involve the cultivation of a mix of crop varieties differing in many traits

including disease resistance but have sufficient resemblance to be grown together simultaneously on

the same parcel of land, with no attempt to breed for phenotypic uniformity (Wolfe 1985b; Mundt

2002). It ensures effective utilisation of total environmental space, greater stability (homeostasis)

relative to a single genotype under environmental stress (drought) and provides the possibility of

greater overall resistance to diseases and insect pests (Clay and Allard 1969; Barrett 1981; Lammerts

van Bueren et al. 2008). Cultivar mixtures assume that genetic, physiological, structural and

phenological diversity among component varieties induce beneficial interactions thus increasing yield

and yield stability across environments (Kiær et al. 2009) .

2.5.1 Physiological causes of cultivar mixing effects

The principle assumes that a cultivar which produces less in a particular environment utilises

less of the available resources than two cultivars deployed in a mixture. This advantage of the mixture

over the pure line components is due to compensatory or mixing effects (Stützel and Aufhammer

1990). Therefore, among mixture components, three types of interaction are evident, complimentary,

compensatory and facilitatory effects, which are difficult to quantify (Faraji 2011).

2.5.1.1 Complementary effects

These effects are evident when genotypes vary in their ability to protect themselves and exploit

available resources resulting in higher yield fluctuations which is a function of complementary

resource use above and below ground (Willey 1979). Variation in growth duration due to the demand

on resources occurring at different times (Fukai and Trenbath 1993), growth habit and shading are

complementary and are responsible for the increased grain yield of mixtures (Gallandt et al. 2001).

30

2.5.1.2 Compensation effects

Effects exist among varieties differing in their competitive abilities (Willey 1979). Bowden et

al. (2001) observed that a disease tolerant variety may balance a weak, susceptible or injured variety

by producing more tillers when disease occurs early in the season, thus producing bigger heads, and/or

heavier kernels at maturity . The mechanism functions only between neighboring plants and mixing

varieties that vary in their genetic backgrounds may increase the chances of compensation effects

(Faraji 2011). Compensation effects are expressed when components vary in plant height and when the

yield of one component improves whilst the other does not improve without affecting the overall

performance of the mixture (Khalifa and Qualset 1974).

2.5.1.3 Facilitation

The facilitation mechanism is expressed as a benefit offered by a plant on the establishment or

growth of other plants (Garcia-Barrios 2003). They occur when a cultivar supports another directly by

improving the microclimate, or providing physical support such as windbreaks thus ameliorating harsh

environmental conditions. They can also indirectly provide protection from other pests and diseases

thus improving the water-holding capacity of the mixture (Callaway 1995; Garcia-Barrios 2003).

2.5.2 Cultivar mixtures and water-use efficiency

Mixed plant populations afford improved utilisation of available resources (light, CO2, H20,

nutrients) compared to pure stands (Stützel and Aufhammer 1990). Under sub-optimal conditions,

wheat mixtures conserve more soil water prior to stem elongation ensuring the availability and

efficient utilisation of resources; this translates into higher water use efficiency and yield than pure

stands (Naudin et al. 2011; Fang et al. 2014b).

In natural ecosystems, functional diversity portends higher stability (Petchey and Gaston 2002)

and could be achieved by using cultivar mixtures (Wolfe 1985b; Finckh et al. 2000). Mixtures of

several varieties of the same crop species could have higher yields than monocultures of single

varieties (Wolfe 1985a) and varieties with differing characteristics have the means to increase as well

as stabilise crop yield over environments compared to pure stands (Smithson and Lenne 1996; Finckh

et al. 2000; Kiær et al. 2009). Kiær et al. (2009) conducted an extensive review of the literature on

grain yield of cereal varietal mixtures and reported higher yield when varieties are grown in mixtures.

Tratwal A et al. (2007) observed a 1–15% yield increase in barley mixtures compared to pure stands.

In wheat, mixtures of genetically modified wheat lines outperformed monocultures (Zeller et al. 2012),

indicating that mixtures could be formulated to meet the specific production requirements (Gallandt et

31

al. 2001; Cowger and Weisz 2008). Smithson and Lenne (1996) assessed genotype x environment

interaction effects and concluded that yields from mixtures varied less across environments. Likewise,

Østergård and Jensen (2005) also concluded that yield from spring barley (Hordeum vulgare L.)

mixtures were stable across 17 environments. Mixtures improved productivity under suboptimal

conditions in oats (Frey and Maldonado 1967), wheat (Sarandon and Sarandon 1995) and maize

(Tilahun 1995).

Varietal mixtures tolerant to drought and flood do not only increase productivity, but prevent

soil erosion, desertification, increase soil organic matter and help stabilise slopes (Hajjar et al. 2008).

However, the performance of cultivar mixtures has not always been positive, some studies reported

negative mixing effects (Baker and Briggs 1984; Finckh and Mundt 1996). Therefore, favorable cereal

variety mixtures need to be tailored for various growing conditions vis-a-vis the recognition of specific

environmental and varietal characteristics (Cowger and Weisz 2008; Newton and Guy 2009).

2.5.3 Number of cultivars (components) in mixtures and yield performance

Cultivar mixtures are formulated in components of two (biblends) or three (triblends) and the

influence of component number on yield improvement is not clear (Smithson and Lenne 1996). In

studies involving small grains, yield increases were observed in plots with more than two component

mixtures (Stuke and Fehrmann 1987; Newton et al. 1997; Kiær et al. 2009). Likewise, Nitzsche and

Hesselbach (1983) also observed a yield increased in barley when the number of component of

mixtures increased from two to six. Smithson and Lenne (1996) also claimed that yield stability of

field crops increased with increasing numbers of component mixtures in about 50% of the data sets

examined.

2.5.4 Plot size and the performance of cultivar mixtures

The effects of plot size on the yield performance of mixtures was studied and the performance

of wheat cultivar mixtures in small plots (1.83×0.31) m was lower compared to larger plots (6.1×3.1)

m (Cowger and Weisz 2008). The benefits obtained from mixtures are at larger spatial scales as host-

diversity effects on disease increases over vast areas (Garrett and Mundt 1999). The effects of the

contributing mechanisms, mainly disease reduction and compensation are lesser in smaller plots than

larger ones and this observation has been supported (Zhu et al. 2000; Mille et al. 2006).

32

2.5.5 Diversity in cultivar mixtures

An essential component to the choice of cultivars to compose mixtures is the diversity of the

cultivars used in terms of agronomic traits (Faraji 2011), although the correlation between component

diversity and blend advantage is not clear (Smithson and Lenne 1996). Soybean mixtures varying in

yield, plant height or maturity have shown some positive mixing effects and component diversity

(Smithson and Lenne 1996). In contrast, others have shown no advantages in mixture (Patterson et al.

1963; Gizlice et al. 1989). Mixing advantages have been obtained among wheat cultivars differing in

height which creates a wavy canopy affording a uniform distribution of leaves, more penetration of

light due to decreased shading of adjacent plants. Therefore, increased light use efficiency,

photosynthesis and dry matter production and reduced evaporation from the soil surface due to reduced

incident amount of solar radiation reaching the soil surface helps conserve the soil moisture necessary

for the production of higher yields especially under sub-optimal conditions (Faraji 2011).

2.5.6 Cultivar mixtures and end-product quality

Wheat grain protein content influences bread, noodle, and tortilla making performance

(Qarooni et al. 1994; Lang et al. 1998; Ambalamaatil et al. 2002) and protein content is influenced by

environmental conditions (Huang and Varriano-Marston 1980; McGuire et al. 1994; Lang et al. 1998).

Improved quality equivalent to a higher quality component was obtained by blending a high and a low

bread quality wheat when grown under low soil fertility (Sarandon and Sarandon 1995). In addition,

Bean et al. (1990) obtained a synergistic improvement in bread quality by blending low protein

‘Klasic’ and low protein ‘Anza’ flours in equal portion.

2.5.7 Cultivar mixtures and the control of diseases and pests prevalence

Cultivar mixtures help control diseases and pests due to increased distances between

susceptible cultivars (Browning and Frey 1969; Chin and Wolfe 1984) thereby decreasing the chance

of inoculum produced from an infected or susceptible cultivar landing on another susceptible cultivar

(Araus et al. 2002; Faraji 2011). The presence of a resistant cultivar in a mixture presents a physical

barrier that restricts the movement of inoculum from the susceptible cultivar (Browning and Frey

1969).

In wheat and barley, increased distance between plants of the same genotype in cultivar

mixtures was the most important mechanism of control of powdery mildew (Chin and Wolfe 1984;

Manthey and Fehrmann 1993). Mixtures also foster the chance of interactions and competition

between pathogen races (Garrett and Mundt 1999) thus preventing the dominance of one pathotype

33

over another and reducing the likelihood of eroding host resistance through mutation in cultivar

mixtures (Faraji 2011).

Mixtures induce resistance when races that are non-virulent on a cultivar induce the plant

defense response mechanisms resulting in the elimination of infection when any virulent race invades

exactly the same area of the plant (Chin and Wolfe 1984). Calonnec et al. (1996) found that up to one

third of the reduction in infection by Puccinia striiformis in wheat mixtures was due to induced

resistance.

The outstanding performance of wheat yield in mixtures over pure stands over a range of

environments has been emphasised suggesting that mixtures are capable of producing repeatable,

worthwhile yield improvements over their components in monoculture as long as appropriate

combinations can be identified (Frey and Maldonado 1967; Clay and Allard 1969; Fehr and Rodriguez

1974) and that yield losses due to various biotic and abiotic factors of environment, i.e., diseases, late

sowing, drought could be compensated by such mixtures (Asghar et al. 2011).

2.6 Association mapping

Association mapping analyses statistical associations between genotypes, usually SNPs or SNP

haplotypes, determined in a collection of individuals and the traits (phenotype) of the same individuals.

It therefore verifies marker alleles occurring at significantly different frequencies in individuals

carrying a particular phenotype (trait) compared to a control (Painter et al. 2011). Association genetic

studies were used to identify the genetic causes of several diseases in humans (Pritchard and

Przeworski 2001; Slatkin 2008), and was later applied to crops such as wheat, maize, barley, rice,

sorghum, soybean, grape and forest tree species (Abdurakhmonov and Abdukarimov 2008).

Association mapping is closely related to quantitative trait loci mapping (Paterson 1998) and

this technique, based on linkage disequilibrium (LD), was introduced as an alternative approach to

mapping QTLs in bi-parental populations (Reich et al. 2001; Weiss and Clark 2002). Linkage

disequilibrium (LD) refers to a condition where a combination of alleles at loci located close together

in the genome occur more frequently than by chance because of previous population history (Painter et

al. 2011). In a sense it refers to a nonrandom association of alleles at different loci, describing the

condition with non-equal (increased or reduced) frequency of the haplotypes in a population at random

combinations at different loci (Abdurakhmonov and Abdukarimov 2008). Linkage disequilibrium is

not synonymous with linkage, although tight linkage may generate high levels of linkage

disequilibrium between alleles (Abdurakhmonov and Abdukarimov 2008).

34

Association mapping has some advantages over traditional QTL mapping because it does not

require the development mapping populations such as back cross, doubled haploids, recombinant

inbred lines, near isogenic lines and the multi environment evaluation of these populations to generate

robust phenotypic data (Hansen et al. 2001; Stella and Boettcher 2004; Gupta et al. 2005). In addition,

high resolution results are achieved from individuals from germplasm collections or natural

populations because accumulated meioses are used in the breeding history and past phenotypic data on

cultivars can be mapped without the need to establish new trials (Zhao et al. 2009). The most ideal LD

quantification measure required for association mapping is r2 (square of the correlation coefficient

between the two loci) that is also indicative of marker-trait correlations (Abdallah et al. 2003; Gupta et

al. 2005).

2.6.1 Factors affecting linkage disequilibrium

Linkage disequilibrium is influenced by both genetic and demographic factors shaping the

haplotypic LD blocks in a genome (Stich et al. 2005; Stich et al. 2006; Stich et al. 2007). Factors

increasing LD include mutation, mating system (self-pollination), genetic isolation, population

structure, relatedness (kinship), small founder population size or genetic drift, admixture, selection

(natural, artificial, and balancing), epistasis, and genomic rearrangements (Gupta et al. 2005).

However, LD is decreased by high recombination and mutation rates, recurrent mutations, out-crossing

and gene conversions (Gupta et al. 2005). Another salient factor influencing linkage disequilibrium is

“ascertainment bias” and its relationship with an assayed sample and data characteristics

(Abdurakhmonov and Abdukarimov 2008). In addition, the presence of minor alleles can cause

inaccurate estimates in datasets. These alleles tend to inflate linkage disequilibrium values (Mohlke et

al. 2001; McRae et al. 2002; Maccaferri et al. 2005).

2.6.2 Types of association mapping methods

Association mapping is accomplished by two main methods, Candidate Gene Association and

Whole Genome Scan, also known as Genome-Wide Association Study. In candidate gene association,

one investigates the possibility of correlation between DNA polymorphisms of a gene and the trait of

interest; the technique normally employs a sound understanding genetics, biochemistry or biochemical

pathways and regulatory genes. In the absence of such skills or information, a whole genome scan

technique is adopted (Rafalski 2010) and this technique entails analysing associations of most of the

segments of the genome, by genotyping densely distributed genetic marker loci covering all

35

chromosomes. It is based on the principle that ‘one (or more) of the genetic loci being considered is

either causal for the trait or in linkage disequilibrium with the causal locus’(Rafalski 2010).

2.6.3 Application of association mapping

The development and effective utilisation of association studies to unravel the genetic cause of

several diseases began with humans (Pritchard and Przeworski 2001; Slatkin 2008). In plants,

association mapping commenced with Arabidopsis, and the technique has now been applied to crops

such as maize, barley, durum wheat, spring wheat, rice, sorghum, sugarcane, sugar beet, soybean, and

grape and forest tree species (Abdurakhmonov and Abdukarimov 2008). Its application remained

impracticable in cereals for some time owing to the genotyping of large numbers of entries at the

required number of marker loci (Neumann et al. 2011b). However, high throughput systems such as

diversity array technology (DArT) (Jaccoud et al. 2001), SNP and GBS have overcome this difficulty,

thus providing rapid and cost-effective genome-wide genotyping (Wenzl et al. 2006). DArT markers

are bi-allelic dominant anonymous markers obtained by cloning random fragments of genomic

representations and analysis of sequencing these markers revealed most of them to be derived from the

genespace (Wenzl et al. 2006). In wheat, Crossa et al. (2007c) conducted the first genome wide

association mapping using DArT markers and identified several marker-trait associations on historic

data from a multi-locational field data for grain yield and disease resistance. Neumann et al. (2011b)

also identified several markertrait associations in bread wheat of which several coincided with known

major QTL identified in traditional QTL mapping, whilst the remaining were detected in regions where

no known QTL have been detected to date. Likewise, Dodig et al. (2012) also identified a number of

marker-trait associations with developmental and agronomic traits highly correlated with grain yield

under drought in wheat, even though no marker related to yield under drought stress was observed.

Recently, association mapping was used to dissect the genetic bases of seed dormancy and pre harvest

sprouting in a panel of 96 diverse winter wheat cultivars (Rehman Arif et al. 2012). Le Gouis et al.

(2012a) identified 62 markers individually associated with earliness components corresponding to 33

chromosomal regions in wheat.

The output of association mapping analysis is usually applied in genotype-based selection of

superior individuals in plant breeding, or as a step toward positional cloning (Rafalski 2010).

However, the efficacy of using these predictions in this way in applied breeding has not been

established. The independent validation of the associations in different genetic backgrounds remains

essential and is a neglected aspect of this technology (Rafalski 2010). The results of association

36

mapping could therefore be useful in marker assisted selection (MAS) and development of elite

genotypes when genotypes that carry complementary regions are hybridized to produce superior types.

2.7 Developing a wheat crop ideotype adapted to no tillage water limited environments at

Narrabri in the northern NSW grains region.

The phenotype is a product of the genotype interaction with the prevailing environment and it is

dynamic and conditional, ever changing with the environment in response to both endogenous and

exogenous signals over the development and life history of the organism, thus plants are continually

sensing their environment and modifying their growth response through signaling (Davies et al. 2005;

Brenner et al. 2006; Cobb et al. 2013). Phenotyping of physiological traits has been utilised to assess

variation and increase rates of genetic gains through (a) strategic trait-based crossing to combine

complementary traits in progeny; (b) enriching desirable alleles in intermediate generations; and (c)

exploration of genetic resources to broaden the genetic base for hybridisation (Reynolds et al. 2009).

Strategic trait-based hybridisation aims to accumulate traits that will be complementary for a

given target environment (target-traits).Therefore, under water stressed conditions, traits that improve

water uptake, water use efficiency and partitioning to yield, may work synergistically to improve

productivity in the target environment (Passioura 1977). It is assumed that a physiological trait strategy

employing crosses between parents with different but potentially complementary physiological trait

expression will realise cumulative gene action in selected progeny with the caution that a trait may

show interaction with genetic background and environment (Reynolds and Tuberosa 2008).

A conceptual model for drought tolerance in wheat was proposed by Reynolds et al. (2001b). It

included seed size and coleoptile length facilitating early crop establishment, early ground cover and

pre-anthesis biomass which decreased loss of soil moisture, stem reserves/remobilisation and spike

photosynthesis supporting grain filling during post-anthesis stress, stomatal conductance indicative of

roots capable of extracting deep soil water, osmotic adjustment which maintains cell functions at low

water potential, accumulation of abscisic acid which pre-adapts cells to stress, heat tolerance (heat

stress may be due to low leaf transpiration rates under drought), leaf traits e.g. waxiness, pubescence,

rolling, thickness reduce danger of photo-inhibition, high tiller survival and stay green traits. These are

all easily observable traits indicative of good drought tolerance.

More recently these traits are placed in four different groups (Figure 2.2) such that the

physiological effects between groups are relatively independent and when contrasting parents in trait-

groups are crossed, drought-adaptive genes are more likely to be pyramided (Reynolds et al. 2005a).

37

Under extreme drought conditions, the wheat ideotype as proposed by Reynolds et al. (2001b) could

realize full expression of the genetic potential when conservation agriculture/no tillage systems which

improves soil fertility, structure, soil water balance, soil nitrogen, reduces soil erosion, soil organic

carbon is adopted.

38

3 Effect of tillage practice on wheat productivity under well-watered and

drought conditions

3.1 Introduction

Moisture stress limits wheat productivity with the period between anthesis and maturity crucial

for grain yield as about 80% of the dry matter deposited in grains is derived from photosynthesis

during this period (Zheng et al. 2008).

Tillage is a basic factor affecting soil quality, crop performance and the sustainability of

cropping systems (Munkholm et al. 2013). Conventional tillage results in increased soil erosion, higher

energy consumption and low economic returns (De Vita et al. 2007b) whereas conservation tillage

(defined as reduced soil disturbance and maximized soil cover by residues) offers sustainable and

economically viable production with little impact on the environment (Trethowan et al. 2012). The

approach preserves soil and soil moisture when zero and minimum tillage are utilized (El Titi 2003;

Trethowan et al. 2012). Currently, many crop improvement programs globally only select materials

under conventional tillage (Liebman and Davis 2000; Cook 2006) thus masking the genotype x tillage

practice effects and the subsequent identification of genotypes amenable to conservation agriculture

(Mahmood et al. 2009). Most studies examining genotype x tillage practice interactions for yield

reported no significant interaction in a range of crops including wheat and barley etc (Ullrich and Muir

1986; Cox 1991). However, Trethowan et al. (2012) observed significant genotype x tillage practice

interactions for many traits. This study included a larger number of genetically diverse materials which

may have influenced the interaction. Trethowan et al. (2005a) suggested that genotypes developed in

conventional or full tillage systems may not be amenable to the changing cropping environment, thus

necessitating the development of specifically adapted genotypes to conservation agriculture.

Physiological traits are drivers of yield under moisture stress and some recent studies

emphasize that future wheat genetic yield gains must come from improving physiological

characteristics (Donmez et al. 2001; Shearman et al. 2005a; Foulkes et al. 2011) as evidenced through

biomass increases biomass, harvest index etc (Sayre et al. 1997; Calderini and Slafer 1999)

The aim of the research presented in this chapter was to assess the genotype x management

interaction effects on the performance of a set of released wheat varieties. These environments

included adjacent and contrasting tillage and irrigation treatments. The genotypes evaluated were

identified from association analysis as carrying different genomic regions potentially linked to grain

39

yield. The study aimed to identify key traits conferring adaptation to specific crop management

environments.

3.2 Materials and Methods

3.2.1 Experimental site

The experiments were conducted at the I.A. Watson Grains Research Centre, Narrabri, NSW

(30° 20΄S, 149° 45΄E) on a self-mulching black soil classified as Ug 5.2 (KH 1965) during the 2013

and 2014 growing seasons (May-October) under both rainfed and irrigated conditions. Narrabri has a

predominantly variable summer rainfall with an annual average rainfall of 580 mm (2001-2014); 212

mm falls in the winter cropping season and 368 mm in summer (November-April), making it ideal for

drought studies during winter. The zero-tillage treatments were established 8-years ago. The zero-

tillage treatments were maintained without tillage except a single pass of seeding equipment at sowing.

The conventional tillage treatment was maintained by regular disc ploughing and harrowing. The

treatments were established in 2007 and maintained as strips in a three year rotation of wheat, barley

and field pea. The genotypes were sown in plots of 12 m2 and later reduced to 8m

2 for harvesting.

3.2.2 Experimental material

Eighteen released wheat genotypes were evaluated in 2013 and 2014 (Table 3.1). These

represented a subset of two hundred and sixteen advanced wheat lines evaluated in an earlier multi

environment genetic association study (Atta 2013). These advanced wheat lines originated from five

commercial programs. Entries 1, 4 and 20 originated from LongReach Plant Breeders; 2, 9, 10, 11, 15

and 18 from the now defunct University of Sydney program at Narrabri; 5, 6, 7 and 19 from Australian

Grains Technologies; 3 and 13 from the now defunct Enterprise Grains Australia and 8 and 12 from

the Queensland Department of Primary Industries. Six mixtures (formulated based on complementary

genomic regions from the results of the earlier association analysis) were evaluated across

environments varying for tillage and irrigation practices. They consisted of two two-way mixtures of

Lincoln+Lang and Sunstate+Stampede evaluated in 2013 and 2014 and an additional set of two-way

mixtures (EGA Gregory+Ventura and Suntop+Spitfire) and two sets of three-way mixtures

(Sunstate+Stampede+Janz and Lincoln+Lang+Sunstate) were included in 2014.

40

Table 3.1 List of germplasm evaluated in field studies in 2013 and 2014

ENTRY VARIETY PEDIGREE

1 CRUSADER SUNBROOK/H45

2 SUNSTATE HARTOG*4//COOK*5/VPM 1

3 EGA STAMPEDE SERI-M 82/SUNPICT/GENARO81/BATAVIA/HARTOG/SUN-

290-B/JANZ/SUNVALE/QT-4646/11-IBWSN-50[000];

4 LINCOLN LEVELHEAD//BLOCKHEAD/KING'S JUBILEE

5 LIVINGSTON SUN129A/SUNVALE

6 SUNVEX CNT3/4*3765//2*CHM/3/2*SUNVAL

7 MERINDA VICAM S 71/3*SUNECA//SUN 231 A

8 LANG IFIFE//VNSS/INDIAN G

9 SUNVALE COOK*2/VPM1//3*COOK

10 VENTURA SUNVALE/ROWAN

11 ELLISON VICAM S 71/3*SUNECA//SUN 231 A

12 JANZ 3AG#3/4*CONDOR//COOK

13 CUNNINGHAM QT2826

14 EGA GREGORY PELSART/2*BATAVIA

15 SUNCO COOK*3/WW15/4SUN9E-27/3AG14

16 SUNSTATE+STAMPEDE MIXTURE 1

17 LICOLN+LANG MIXTURE 2

18 SUNLIN SUNELG*2//SUNECA*3/VPM 1

19 SUNTOP SUNCO/2*PASTOR//SUN-436-E[4172]

20 SPITFIRE DRYSDALE/KUKRI

21 SUNSTATE+STAMPEDE+JANZ SUNSTATE+ STAMPEDE+JANZ

22 LICOLN+LANG+SUNSTATE LICOLN+LANG+SUNSTATE

23 SUNTOP+SPITFIRE SUNTOP+SPITFIRE

24 EGA GREGORY+VENTURA EGA GREGORY+VENTURA

Source: Queensland 2013 wheat varieties (nvtonline.com.au).

41

3.2.3 Field layout

The experiments were laid out in a randomized complete block design with 3 replications.

Tillage regimes (zero-tillage and conventional tillage) were laid out in parallel strips established in

2007 and irrigation treatments were established at right angles to the tillage treatments (Appendix I).

This created four different managed environments which were repeated in both years. These managed

environments were classified as (i) conventional-tillage irrigated; (ii) zero-tillage irrigated; (iii)

conventional-tillage no-irrigation; (iv) zero-tillage no-irrigation (Figures 3.1, 3.2 and 3.3). Irrigation

was managed to generate differential soil moisture conditions across tillage treatments and the amount

of water applied varied depending on seasonal rainfall.

Figure 3.1 The experimental area: Zero tillage (left hand side) and conventional tillage environments

in 2013

42

Figure 3.2 The experimental area: zero tillage environments with stubble from the previous crop

(2013).

Figure 3.3 The experimental area: conventional tillage environment with loosened soil surface (2013).

43

3.2.4 Fertilizer and chemical applications

Fertilizer treatments followed recommended practices for this region of NSW. Following pre-

sowing soil nutrient analysis, 100kg/ha of urea and DAP 50kg/ha were applied at sowing in 2013 and

2014 to facilitate establishment. In season herbicides were applied as required. These were Hotshot

and MCPA LVE @ 750 ml ha-1 during tillering to control weeds such as milk thistle, wild turnip,

volunteer field pea and wild mustard. In 2014, fungicide was sprayed to control rust in the late grain

filling stage (Zadok, Z25 – Z31).

3.2.5 Irrigation Treatment

The irrigated experimental plots were watered with an overhead irrigator at the rate of 35 mm

per irrigation using ground water. Two irrigation applications were made to the high moisture

environments during 2013 and 2014. The first irrigation was applied 60 DAS (days after sowing) and

the second at 115 DAS during grain filling. In total 70mm was applied in both 2013 and 2014 on

addition to rainfall.

3.2.6 Weather conditions

Total rainfall and rainfall distribution varied across the 2013 and 2014 cropping seasons. Total

seasonal rainfall was higher in 2014 (125.2mm) than 2013 (96.6mm). The 2013 season was

characterized by an initial higher total rainfall in June that later declined producing relatively long

periods of drought. The 2014 cropping season was characterized by low initial June rainfall, that later

increased through anthesis; therefore post-anthesis moisture stress was minimal. Mean temperatures

were also relatively higher in 2013 compared to 2014 (Figures 3.4 and 3.5). In season relative

humidity was initially higher in 2013 but declined sharply in June, whereas 2014 had a higher relative

humidity until September after which it declined (Figures 3.6 and 3.7).

44

Sowing Anthesis Harvesting

Figure 3.4 Cropping season rainfall and temperature in 2013 at Narrabri

Sowing Anthesis Harvesting

Figure 3.5 Cropping season rainfall and temperature in 2014 at Narrabri

45

Figure 3.6 In season relative humidity and solar radiation in 2013 at Narrabri

.

Figure 3.7 In-season relative humidity and solar radiation in 2014 at Narrabri.

46

3.2.7 Soil moisture Assessment

Aluminium tubes of 1.5m were inserted immediately after sowing in the centre of control plots

(selected genotypes) using a hydraulic corer mounted on a tractor. Tubes were inserted in three and

two replications in 2013 and 2014, respectively. Soil moisture content (counts) was determined using a

neutron moisture meter (CPN® 503DR Hydroprobe, Boart Longyear, California, USA) with the

individual reading time set at 16 seconds. Moisture readings were taken throughout the cropping

season at 10, 20, 40, 60, 70, 80, 90, 100 and 120 cm depths in the root zone at two weekly intervals. A

calibration (Eq. 1) previously developed on the same soil type (grey vertosol) was used to convert the

neutron probe counts to volumetric water content (Personal Communication, Rafiq Islam).

θ = 0.0117 + 0.00003*Counts................................................. Eq. 1

Where θ = volumetric water content, cm3 cm

-3; Counts = neutron probe counts; 0.0117 and 0.00003

are the coefficients of the calibration equation.

3.2.8 Estimation of water use and water use efficiency

Total seasonal water utilized by each plot was estimated during the cropping season.

Evapotranspiration (ETo) was assumed equal to the moisture loss from a bare soil surface over the

period preceding the first day of measurement from sowing. The total water use was estimated using

the equation developed by Zhang and Oweis (1999) and described by Zhang et al. (2005a):

TWU = P + I + ΔW – R – D + CR…………………..Eq. 2

Where TWU = total water use during crop growth (mm), P = precipitation (recorded from a weather

station) (mm), I = irrigation (mm), ΔW = change in soil water content in the profile up to 50 cm depth

from sowing to anthesis or maturity (mm), R = runoff (mm), D = drainage from the root zone (mm)

and CR = capillary rise to the root zone (mm). Surface runoff, drainage and capillary rise on these soils

was considered negligible, thus the following equation was used as reported by Khan MK . (1999) on

the same site:

TWU = P + I + ΔW………………………………Eq. 3

Water use efficiency for grain yield was calculated following Zhang et al. (2005) and Siddique et al.

(2001):

47

WUEGrain = Y/TWU …………………………Eq. 4

Where WUE = water use efficiency kg ha-1

mm-1

; Y = grain yield kg ha-1

; DM = dry matter kg ha-1

;

TWU = total water use (mm).

3.2.9 Crop measurements

Data were recorded on crop phenology as described by Zadoks (1974.), biomass at maturity (kg

ha-1

), chlorophyll content, NDVI, canopy temperature (°C), number of days to physiological

maturity(days), number of days to heading(days), canopy ground cover (%), light interception (%),

relative water content (%), NIR (protein content) and grain yield (kg ha-1

) during the two years of

experiments. Heading days were classified as early at Zadokes score of 55 and late at a score of 59.

3.2.9.1 Canopy Ground Cover (%)

Canopy cover (%) was recorded at early (3 to 4 leaf) and mid-tillering stages of development.

The protocol utilized is outlined at http://www.pi.csiro.au/canopy_cover/ which states that:

Percentage crop canopy is estimated by taking a photo with a camera pointed perpendicularly

(90°) to the centre of the plot at a height of 1m above the crop canopy.

The image captured (data) was uploaded and processed on a computer using software

downloaded from http://www.pi.csiro.au/canopy_cover/canopycover setup.zip. The software generated

an estimate of the percentage ground cover. Canopy ground cover data collection was carried out

before the application of irrigation treatment so only the effects of zero-tillage and conventional tillage

treatments were determined. Data on percentage canopy cover was discontinued at stem elongation

when 100% cover was achieved and no bare soil was visible in any of the genotypes under study. Data

on canopy ground cover were taken at two stages (a) early ground cover (Zadok score 21) (b) late

ground cover (Zadok score 29). The rate of ground cover (c) was then calculated as difference between

early and late ground cover.

3.2.9.2 Chlorophyll content (SPAD units)

Chlorophyll content was measured twice during heading and anthesis on fully expanded flag

leaves of 10 random plants from each plot using a hand held chlorophyll meter (SPAD 502 Plus,

Konica Minolta Sensing Inc. Japan).

http://www.pi.csiro.au/canopy_cover/canopycover%20setup.zip

48

3.2.9.3 Canopy temperature depression (ᵒC)

Canopy temperatures for the various genotypes (including spikes, peduncle and leaf) were

measured at the grain filling stage using a hand held infrared gun on sunny, calm and clear days

between 11:00h and 14:00h. To avoid confounding results, measurements were taken within 60-90

minutes on plots that had achieved approximately 100% soil cover. Infrared guns were held at 0.5

meters from the edge of the plot and 1m above the canopy at an angle of approximately 30° to the

horizontal. Canopy temperature readings were taken twice on each plot in all replicates per genotype

and the mean calculated for each plot.

3.2.9.4 Biomass at Maturity(kg ha-1

)

Random 1 m2 sections of each plot were cut at maturity using a quadrat. Sections were

harvested at ground level avoiding border rows. Samples were dried in a dehydrator (Hurricane,

Forced Air heating, WESSBERG & Tulander Sydney, Australia) and the dry tissue weighed to

determine total dry weight.Harvest Index

Harvest indices were calculated by threshing the biomass cuts to obtain the grain yield. Harvest

indices were obtained as HI = grain yield (g) per square meter / biomass (g) per square meter.

3.2.9.5 Light interception (%).

Light interception was evaluated using a potable ceptometer held in the middle row of each

plot. Measurements were recorded 7 days after anthesis and three readings were taken above and

within each plot. Mean were calculated and Light interception (%) = ((A-B) - C) / (A-B) × 100.

Where: A = above-canopy PAR; B = reflected PAR; and C = below canopy PAR.

3.2.9.6 Grain yield (kg ha-1

)

At maturity the first and last meter of each plot was removed and an area of 8m2

was harvested

with a combine harvester to determined grain yield. Grains obtained from each plot were subsequently

weighed and converted to grain yield in kg/ha-1

.

3.2.9.7 Leaf relative water content

The most fully expanded flag leaves receiving sunlight were selected randomly from each plot.

The bottom and the tops were cut off including any dead tissue to avoid confounding results. Leaves

were placed immediately into pre-weighed sealed tubes and immediately placed into cooled boxes.

The fresh weight was calculated (FW). Sample tubes were weighed and 5mL of distilled water was

added to each sample and refrigerated in darkness for 24hrs. After this period leaves were blot dried

49

and weighed to determine their turgor weight (TW). Blotted leaves were placed in envelopes and dried

in a dehydrator (Hurricane, Forced Air heating) to a constant mass to obtain the dry weight (DW). Leaf

RWC was calculated as:

RWC = ((FW-DW) / (TW-DW)) x 100…………………Eq. 5

3.2.9.8 Plant height(cm)

Plant height was assessed at maturity using a 1m meter ruler. Three random readings per plot

were taken by measuring the distance from ground level to the top of the spike and the average used to

determine the height of each plot.

3.2.9.9 Number of days to heading(days)

The number of days to heading was determined as the number of days from sowing to when

approximately 50% of the tillers extruded awns out of the flag leaves. Heading days were classified as

early at Zadokes score of 55 and late at a score of 59.

3.2.9.10 Number of days to physiological maturity(days)

The number of days to physiological maturity was calculated as the number of days from

sowing to the period when the 80% of plants lost green colour in their peduncle. This was deemed

physiological maturity.

3.2.9.11 Protein content (%)

Harvested grains were cleaned and protein content was measured using an Infrared Grain

Analyzer-Foss Grain Analyzer (FOSS, InfratecTM

1241, Sweden).

3.2.9.12 Flag leaf rolling

Observations for leaf rolling were conducted in the mornings 10 am in the afternoon between

13:00 and 16:00. Observations were made on the most recent fully expanded flag leaf. The extent of

leaf rolling (%) was scored on entire plot basis on a scale of 0 - 4 where 0 = all flag leaves not rolled,

1= 75% of flag leaf lamina exposed, 2 = 50% of flag leaf lamina exposed, 3 = 25% of flag leaf lamina

exposed, 4 = 0 % of flag leaf lamina exposed-tube-like appearance.

50

3.2.9.13 Normalized difference vegetation index (NDVI)

NDVI was recorded at anthesis and grain filling using a hand held Green SeekerR (Ntech

Industries, Canada) held 20-50 cm above the crop canopy. Measurements were recorded between 11

am and 2pm on bright sunny days.

3.2.9.14 Climatic conditions

Meteorological data for the 2013 and 2014 cropping seasons were obtained from a weather

station located 4km away at Narrabri Airport AWS (Climate data online, Bureau of Meteorology).

Data included humidity, temperature, rainfall and global solar radiation data measurements. The ETo

values were collected online from the Myall vale weather station for the entire duration of the study.

Source:

Narrabri Weather Portal (http://www.weatherdata.net.au/narrabri/)

Narrabri Airport AWS (54038) (http://www.bom.gov.au/climate/data/)

Narrabri West Post Office (53030) (http://www.gov.au/climate/data/)

3.2.9.15 Soil water content

Soil water content was initially higher under zero tillage environments than conventional tillage

although not significant at sowing each year (Figures 3.8, 3.9, 3.10 and 3.11). The difference between

zero-tillage and conventional tillage was greater when moisture was limiting (3.8 and 3.90). However,

soil water differences between tillage regimes were minimized under irrigation (3.9 and 3.10).

51

Figure 3.8 Soil water content under conventional tillage and zero tillage rainfed environments in 2013.

Figure 3.9 Soil water content under conventional tillage and zero tillage in irrigated environments in

2013.

52

Figure 3.10 Soil water content under conventional tillage and zero tillage irrigated environments in

2014.

Figure 3.11 Soil water content under conventional tillage and zero tillage rainfed environments in

2014.

53

3.2.10 Statistical analyses

GenStat statistical software, version 14.1 (Payne RW 2011), was used to analyze data and to

test differences among the four contrasting management practices using the REML function. Years,

genotypes and management were considered fixed effects and range/row coordinates within years as

random effects.

The model utilized in the analysis was as follows:

(a) Fixed model: Constant + Year + Management + Genotype + Year. Management + Year.

Genotype + Management. Genotype + Year. Management. Genotype

(b) Random model: Year. Range. Row.

Management refers to the four specific irrigation/tillage practice regimes. The adjusted means

derived from the mixed model analysis were used for all subsequent comparisons. Significant

differences among genotype means were calculated using Fisher's protected least significant difference

test at P < 0.05. The strength of the simple linear relationship between pairs of variables, based on the

phenotypic data, was assessed using Pearson's correlation coefficient.

3.3 Results

3.3.1 Agro-physiological traits

3.3.1.1 Early ground cover (%)

The effect of year, genotype, year x genotype and management x genotype interaction

significantly influenced earliness to ground cover (Table 3.2). Earliness to ground cover varied across

years and was 43% higher in 2013 compared to 2014. This could possibly be attributed to the higher

initial total rainfall from May-July in 2013. In the two cropping seasons, the management x genotype

interaction effects revealed that Crusader, Lang and Lincoln produced significantly more early-ground

cover in zero tillage, whilst Ventura produced better ground cover in conventional tillage. The

remaining genotypes were stable across the two management regimes. Early ground cover was

positively correlated with yield (r = 0.35), plant height (r = 0.36) and NDVI at heading (r = 0.65) and

NDVI at grain filling (r = 0.66) and negatively correlated with RWC (r = - 0.42) (Table 3.24).

54

Table 3.2 Means for early ground cover (%) under different management practices in 2013 and 2014

Management

Year Genotype ZT CT

2013 CRUSADER 58.3 54.3

SUNSTATE 64.7 59.7

EGA STAMPEDE 55.0 57.0

LINCOLN 55.3 56.7

LIVINGSTON 63.7 58.7

SUNVEX 56.7 58.0

MERINDA 57.3 63.3

LANG 65.3 59.3

SUNVALE 60.3 60.3

VENTURA 61.3 68.3

ELLISON 52.7 52.0

JANZ 62.7 59.3

CUNNINGHAM 66.0 65.7

EGA GREGORY 58.0 59.7

SUNCO 55.3 62.0

SUNSTATE+STAMPEDE 58.0 56.3

LICOLN+LANG 58.3 62.3

SUNLIN 63.3 65.3

SUNTOP 58.0 56.7

SPITFIRE 55.3 58.0

2014 CRUSADER 22.3 16.3

SUNSTATE 19.7 16.6


LINCOLN 25.7 13.3

LIVINGSTON 9.0 11.7

SUNVEX 7.0 14.7

MERINDA 20.3 17.7

LANG 22.0 17.3

SUNVALE 14.3 15.0

VENTURA 9.7 14.3

ELLISON 14.3 14.0

JANZ 19.7 16.7



SUNCO 14.0 15.7



SUNLIN 17.7 13.7

SUNTOP 18.7 14.3

55

SPITFIRE 17.0 17.3

SUNSTATE+STAMPEDE+JANZ 20.3 15.0

LICOLN+LANG+SUNSTATE 20.3 16.0

SUNTOP+SPITFIRE 18.3 19.7

EGA GREGORY+VENTURA 14.7 15.7

Mean

36.5 35.7

Lsd (5%) M x G

4.6

Wald statistic

Y

7697.39***

M

2.63

G

82.68***

Y x M

4.7*

Y x G

70.9***

M x G

41.32**

Y x M x G 21.36

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. M- Management , G - Genotype, Y-Year

3.3.1.2 Late ground cover (%)

The influence of year, genotype and year x genotype interaction effects were significant on late

ground cover (Table 3.3). Late ground cover in 2013 was 32% higher than in 2014. The year x

genotype interaction ranked Cunningham, Lang and Janz among the top 3 genotypes with significantly

higher late cover, whilst in 2014 the genotypes Crusader, Lincoln+Lang and Merinda were highest.

This interaction could partly be driven by the higher stored soil moisture prior to sowing in 2013

coupled with higher initial rainfall from May-July in 2013. Late ground cover positively correlated

with grain filling duration (r = 0.70) and yield (r = 0.64) but negatively correlated with the number of

days to heading (r = - 0.75) and number of days to physiological maturity (r = - 0.61) (Table 3.24).

56

Table 3.3 Means for late ground cover (%) in 2013 and 2014 cropping year.

Cropping Year

Genotype 2013 2014

CRUSADER 90.2 42.2

SUNSTATE 91.0 35.3


LINCOLN 90.5 34.5


SUNVEX 91.3 20.8

MERINDA 91.0 40.5

LANG 92.3 34.0

SUNVALE 91.8 30.2

VENTURA 91.0 18.7

ELLISON 87.7 32.8

JANZ 92.3 30.3



SUNCO 90.8 34.2



SUNLIN 90.5 30.3

SUNTOP 90.7 26.5

SPITFIRE 91.0 37.5

SUNSTATE+STAMPEDE+JANZ - 36.7

LICOLN+LANG+SUNSTATE - 26.7

SUNTOP+SPITFIRE - 30.8

EGA GREGORY+VENTURA - 25.0

Mean 91.0 31.6

Lsd (5%) Genotype x Year 20.5

Wald statistic

Y 3535.5***

E 0.11

G 44.55**

Y 1.48

Yx G 37.27*

E x G 24.92

Y x E x G 22.14

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, Y-Year, M-Management

3.3.1.3 Rate of ground cover (%)

The rate of ground cover development was significantly influenced by year, genotype and year

x genotype interaction effects (Table 3.4). All tested genotypes had significantly higher rate of ground

cover in 2014 cropping season with the exception of Livingston, Sunvex, Ventura, EGA Gregory and

Suntop which appeared to be stable across the two cropping seasons. In 2013 the top three genotypes

57

with significantly higher rate of ground cover included Ellison, EGA Gregory and Lincoln whereas

Cunningham, Lang and Crusader were higher in 2014.

Table 3.4 Means for rate of ground cover (%) in 2013 and 2014.

Cropping Year

Genotype 2013 2014

CRUSADER 33.8 64.2

SUNSTATE 28.8 58.2


LINCOLN 34.5 56.8


SUNVEX 34.0 40.3

MERINDA 30.7 62.7

LANG 30.0 64.3

SUNVALE 31.5 59.3

VENTURA 26.2 38.7

ELLISON 35.3 58.2

JANZ 31.3 59.8



SUNCO 32.2 61.0



SUNLIN 26.2 61.0

SUNTOP 33.3 47.3

SPITFIRE 34.3 60.3





Mean 31.5 56.6

Lsd(p< 5%) YxG 20.1

wald statistic

Y 471.86***

E 0.42

G 54.66***

Y x E 1.19

Y x G 50.54***

E x G 20.22

Y x E x G 12.41

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively G-Genotype, Y-Year, M-Management

58

3.3.1.4 Chlorophyll content

The effects of year, management, genotype, genotype x management and year x genotype

interaction significantly influenced the chlorophyll content of the genotypes tested (Table 3.5).

Chlorophyll content varied across years and was 29.3% higher in 2014 compared to 2013. In rainfed

conditions, Sunstate, EGA Stampede, Lang, Ventura, Sunco Sunstate+Stampede, Suntop and Spitfire

had significantly higher chlorophyll content under conventional tillage management whilst in zero

tillage Ventura produced the highest chlorophyll content. In irrigated conditions, Crusader, Sunstate,

EGA Stampede, Janz, Cunningham, Lincoln, Lang, Suntop had significantly higher chlorophyll

contents in zero tillage management with Sunlin producing the highest chlorophyll content in

conventional tillage. Chlorophyll content correlated positively though weakly with yield (r = 0.17) and

negatively with days to heading (r = - 0.51), maturity (r = - 0.42) and grain filling duration (r = - 0.42)

(Table 3.24). These negative correlations could be due to the drought escape strategy employed by the

early heading/maturity genotypes which develops rapidly and loses their chlorophyll prior to their

measurement.

59

Table 3.5 Means for chlorophyll content (SPAD units) under different management practices in 2013

and 2014

Management

Rainfed Irrigated

Year Genotype ZT CT ZT CT

2013 CRUSADER 27.3 28.4 37.6 31.2

SUNSTATE 25.3 21.8 31.5 39.3

EGA STAMPEDE 28.8 25.5 35.8 39.6

LINCOLN 36.5 32.7 26.3 22.3

LIVINGSTON 38.3 32.8 26.4 34.7

SUNVEX 35.1 40.0 37.8 35.7

MERINDA 33.2 28.4 30.3 33.1

LANG 27.0 42.6 37.0 43.3

SUNVALE 22.8 28.3 29.1 26.6

VENTURA 26.3 33.7 33.8 44.7

ELLISON 31.7 26.9 28.6 33.8

JANZ 29.0 21.8 36.3 35.8

CUNNINGHAM 31.7 37.9 45.2 39.3

EGA GREGORY 41.8 36.7 38.7 41.8

SUNCO 23.6 32.3 29.1 33.8

SUNSTATE+STAMPEDE 21.5 27.1 29.8 39.0

LICOLN+LANG 32.6 20.4 30.3 33.5

SUNLIN 32.0 34.9 25.4 34.3

SUNTOP 27.1 28.9 39.4 35.3

SPITFIRE 26.2 23.3 34.5 33.9

2014 CRUSADER 53.9 50.6 51.8 56.1

SUNSTATE 52.6 49.5 51.9 55.7

EGA STAMPEDE 50.8 50.1 54.4 54.4

LINCOLN 54.1 52.4 52.6 55.6

LIVINGSTON 51.4 45.5 52.2 54.1

SUNVEX 53.6 49.4 53.0 53.0

MERINDA 50.8 51.5 55.0 52.8

LANG 53.9 51.7 51.6 53.2

SUNVALE 51.5 53.7 53.0 53.5

VENTURA 54.7 46.4 50.2 55.7

ELLISON 52.2 51.1 52.8 52.7

JANZ 50.9 49.9 51.3 51.3

CUNNINGHAM 54.2 52.3 52.9 55.3

EGA GREGORY 53.8 50.8 50.6 53.7

SUNCO 48.7 53.3 51.7 52.3

SUNSTATE+STAMPEDE 53.8 52.8 50.5 51.8

LICOLN+LANG 53.0 53.1 48.4 54.0

SUNLIN 53.2 53.3 50.5 57.4

SUNTOP 52.2 52.3 58.5 52.8

SPITFIRE 54.9 47.7 51.8 53.8

SUNSTATE+STAMPEDE+JANZ 51.8 49.7 53.9 50.7

LICOLN+LANG+SUNSTATE 52.9 51.3 50.6 56.0

SUNTOP+SPITFIRE 55.2 52.7 49.5 55.3

60

EGA GREGORY+VENTURA 54.2 51.5 55.9 53.1

Mean

42.4 41.5 43.5 45.5

Lsd (p< 5%) Yx E x G

7.1

Wald

statistic

Y

1183.37***

E

15.25**

G

50.79**

Y x E

74.88**

Y x G

81.85**

E x G

110.1*

Y x E x G 117.51*

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively Y-Year, E-Environment, G-Genotype.

3.3.1.5 Harvest index

Harvest indices were significantly influenced by year, management, genotype, year x management and

year x genotype interaction effects (Table 3.6). Genotypes with stable harvest indices in the two

cropping seasons included EGA Stampede, Lincoln, Livingston, Sunvex, Merinda, Sunvale, Ventura,

EGA Gregory, Sunlin, Spitfire, Sunstate+Stampede whilst the rest expressed significant year x

genotype interaction effects. The relatively lower harvest indices in 2014 are likely due to the higher

and more evenly distributed rainfall that resulted in higher biomass production. Harvest indices were

4.8% higher in 2013 compared to 2014. Under rainfed environments, harvest indices were 2.4% higher

in zero tillage compared to conventional tillage. This advantage reduced to 1.2% under irrigation

61

Table 3.6 Means for harvest index in 2013 and 2014.

Cropping Year

Genotype 2013 2014

CRUSADER 0.44 0.41

SUNSTATE 0.43 0.38


LINCOLN 0.43 0.42


SUNVEX 0.37 0.38

MERINDA 0.40 0.40

LANG 0.44 0.39

SUNVALE 0.41 0.38

VENTURA 0.43 0.42

ELLISON 0.39 0.44

JANZ 0.46 0.37



SUNCO 0.44 0.37



SUNLIN 0.40 0.37

SUNTOP 0.43 0.39

SPITFIRE 0.43 0.43





Mean 0.42 0.4

LSD(p <5%) Y x G 0.04

Wald statistic

Y 35.89***

M 14.67**

G 63.34***

Y x M 8.33*

Y x G 46.96***

M x G 59.66

Y x M x G 64.4


62

3.3.1.6 Biomass (kg ha-1

)

The influence of management, year and genotype on biomass was highly significant and

significant year x management and year x genotype interactions were observed (Table 3.7). Biomass

was 4.9% higher in zero tillage than conventional tillage under rainfed conditions. A similar and

stronger trend was observed in irrigated conditions, with zero tillage biomass 10.9% higher. Biomass

across years was 51% higher in 2014 compared to 2013. Genotypes with higher biomass included

Suntop, Lincoln and Lincoln+Lang in 2013 and Lang, Sunstate+Stampede+Janz and Crusader in 2014.

Biomass was positively correlated with TKW (r = 0.59) and yield (r= 0.26) (Table 3.24).

Table 3.7 Means for biomass (kg/ha) under different management practices in 2013 and 2014

Management

Rainfed

Irrigated


2013 CRUSADER 6262 7186

7416 7291

SUNSTATE 6289 6849

7086 7070

EGA STAMPEDE 5301 7197

6962 7045

LINCOLN 6496 6341

8789 7644

LIVINGSTON 5861 7091

6767 6531

SUNVEX 4403 5935

5994 5344

MERINDA 6522 6962

7321 6840

LANG 5789 6916

6372 6507

SUNVALE 4391 6816

4969 5149

VENTURA 6520 6930

7706 7537

ELLISON 5640 6271

7155 6665

JANZ 6292 6939

6953 6358

CUNNINGHAM 5678 6812

6127 6991

EGA GREGORY 6049 7157

6858 7024

SUNCO 5186 6479

5904 6389

SUNSTATE+STAMPEDE 5572 6373

6861 6459

LICOLN+LANG 6520 7360

7585 7600

SUNLIN 6505 5981

6725 6802

SUNTOP 6311 7189

7736 8169

SPITFIRE 6486 6639

7306 6814

2014 CRUSADER 11949 9167

11662 9987

SUNSTATE 9497 8876

11501 9097

EGA STAMPEDE 9834 9453

11694 11180

LINCOLN 10031 10225

11872 8348

LIVINGSTON 10277 8108

10329 10313

SUNVEX 8619 7745

10349 8640

MERINDA 9142 9520

11919 10055

63

LANG 11000 10216

12536 10472

SUNVALE 10386 8778

10720 9742

VENTURA 9552 7971

10653 8277

ELLISON 9081 8437

10498 9679

JANZ 10971 7717

10645 10362

CUNNINGHAM 9963 7946

11768 11156

EGA GREGORY 10148 7843

11928 9474

SUNCO 11202 7504

10758 9655

SUNSTATE+STAMPEDE 10153 8459

12980 10304

LICOLN+LANG 9391 7820

12205 11757

SUNLIN 10024 7671

10911 8812

SUNTOP 9190 8478

12082 10314

SPITFIRE 10205 8710

11075 10472

SUNSTATE+STAMPEDE+JANZ 10211 9588

13907 9423

LICOLN+LANG+SUNSTATE 9204 8438

11002 11018

SUNTOP+SPITFIRE 8819 8832

11876 9705

EGA GREGORY+VENTURA 10446 7880

11875 9798

Grand Mean

8122 7745

9439 8506

Lsd (p< 5%) MxG

1155

Wald statistic

Y

1336.9***

M

219.6***

G

92.1***

Y x M

149.9***

YxG

45.9***

MxG

88.2

YxMxG 61.7

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively M-Management, G-Genotype, Y-Year

3.3.1.7 Leaf rolling

The extent of leaf rolling was significantly influenced by year, management, genotype, year x

management and year x genotype interaction effects (Table 3.8). The extent of leaf rolling was 8%

higher in conventional tillage compared to zero tillage under rainfed conditions, whereas in irrigated

conditions, zero tillage had 0.32 % more leaf rolling than conventional tillage. The extent of leaf

rolling was 32% higher in 2014 compared to 2013. Across the environments evaluated the extent of

leaf rolling increased in the order of conventional tillage/irrigated < zero tillage/irrigated < zero

tillage/rainfed < conventional tillage/rainfed. Genotypes which rolled their leaves exposing only 25%

64

of their lamina included Crusader, Livinsgton, Merinda and Suntop Leaf rolling was positively

associated with yield ( r = 0.499) (Table 3.24).

Table 3.8 Means for leaf rolling under different management practices in 2013 and 2014

Management

Rainfed

Irrigated


2013 CRUSADER 4.0 4.0

4.0 4.0

SUNSTATE 0.0 0.0

0.0 0.0


3.0 2.3

LINCOLN 0.3 1.0

0.0 0.3

LIVINGSTON 2.0 3.3

3.0 3.0

SUNVEX 1.3 0.7

0.0 0.0

MERINDA 4.0 4.0

4.0 4.0

LANG 0.0 0.0

0.0 0.0

SUNVALE 3.3 2.7

0.7 1.0

VENTURA 0.0 0.0

0.0 0.0

ELLISON 1.0 0.7

0.3 0.0

JANZ 2.7 3.0

1.7 0.7

CUNNINGHAM 0.3 0.0

0.0 0.0

EGA GREGORY 0.3 1.0

0.3 0.0

SUNCO 0.7 1.7

0.3 0.3


0.3 1.3

LICOLN+LANG 0.7 1.7

0.0 0.7

SUNLIN 0.0 0.0

0.0 0.0

SUNTOP 3.7 4.0

3.7 4.0

SPITFIRE 0.0 0.0

0.0 0.0

2014 CRUSADER 4.7 4.7

4.0 3.0

SUNSTATE 3.0 3.0

2.3 2.3


3.0 1.7

LINCOLN 2.3 3.0

1.7 1.7

LIVINGSTON 2.3 3.7

2.3 2.7

SUNVEX 2.7 2.0

1.0 1.0

MERINDA 5.0 5.0

4.7 4.3

LANG 2.7 3.0

2.0 1.3

SUNVALE 3.3 3.0

1.3 1.0

VENTURA 3.3 3.3

2.0 1.0

ELLISON 2.0 2.7

1.0 1.0

JANZ 3.7 3.3

1.7 2.0

CUNNINGHAM 2.3 3.3

1.7 1.7

EGA GREGORY 1.7 3.3

2.0 1.7

SUNCO 3.0 3.3

1.0 1.3

65


2.3 2.3

LICOLN+LANG 2.7 2.7

2.0 1.3

SUNLIN 1.7 1.0

1.3 1.0

SUNTOP 2.0 4.0

3.0 2.0

SPITFIRE 2.7 3.7

1.7 1.3


2.3 2.0


2.0 1.7


2.3 2.7


1.0 2.0

Grand Mean

2.1 2.5

1.6 1.5

Lsd(p< 5%) ExG

1.13

Wald statistic

Y

381.96***

M

161.43***

G

1003.04***

Y x M

31.71***

Y x G

215***

M x G

126.72***

Y x M x G 46.86

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively M-Management, G-Genotype, Y-Year

3.3.1.8 Relative water content

Relative water content was significantly influenced by year, genotype and management and all

observed interactions were significant (Table 3.9). RWC was 1.6% higher in zero tillage compared to

conventional tillage under rainfed conditions. However under irrigation RWC was 5.1% higher in zero

tillage. RWC was 5% higher in 2013 than 2014. Genotypes with higher relative water content (>45)

included Crusader, Sunvex, Ventura, Ellison, Sunlin, Suntop and Spitfire. RWC was positively

associated with yield (r = 0.48) (Table 3.24).

66

Table 3.9 Means for relative water content (%) under different management practices in 2013 and

2014.

Management

Rainfed

Irrigated


2013 CRUSADER 42.48 43.98

50.56 56.94

SUNSTATE 42.01 42.69

41.69 50.00


46.35 55.55

LINCOLN 46.52 29.60

43.93 60.93


42.25 58.02

SUNVEX 46.35 42.37

58.13 53.33

MERINDA 41.36 38.26

40.38 60.19

LANG 40.11 38.83

49.58 54.04

SUNVALE 51.55 37.54

48.62 45.05

VENTURA 45.47 52.34

49.69 59.69

ELLISON 51.34 41.40

42.03 60.42

JANZ 48.84 47.86

41.98 56.96


47.78 56.13


46.05 54.51

SUNCO 45.62 35.81

42.66 57.04


31.60 57.27


41.52 56.93

SUNLIN 40.97 45.46

41.91 55.05

SUNTOP 49.18 42.76

46.91 60.44

SPITFIRE 35.42 34.17

50.45 60.78

2014 CRUSADER 37.64 38.65

51.53 41.20


42.60 42.69


44.83 42.76

LINCOLN 45.19 40.54

47.59 37.67


51.09 39.01

SUNVEX 37.99 41.28

50.03 45.47

MERINDA 37.88 43.72

42.80 41.00

LANG 25.85 37.50

32.84 45.59

SUNVALE 36.66 34.24

53.16 41.50

VENTURA 58.33 44.20

43.47 42.58

ELLISON 57.63 42.17

46.04 43.23

JANZ 30.68 35.39

35.07 36.45


38.91 41.30


40.17 46.25

SUNCO 39.29 27.65

37.75 40.72


40.96 36.49


34.67 43.95

67

SUNLIN 58.78 43.38

41.02 48.64

SUNTOP 40.56 42.32

43.28 45.02


49.84 50.86


51.46 39.98


44.74 40.47


50.36 37.36

GREGORY+VENTURA 26.18 39.14

49.54 71.04

Mean

41.55 40.23

44.72 49.33

Lsd (p<0.05) YxGxE

11.36

Wald statistic

Y

64.15***

M

129.68***

G

73.28***

Y x M

60.48***

Y x G

41.93**

M x G

174.07***

Y x M x G 82.22*

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively G-Genotype, Y-Year, E-Environment

3.3.1.9 Water use efficiency (kg ha-1

mm-1

)

WUE was highly influenced by year, management, genotype and significant genotype x

management interaction effects were observed (Table 3.10). WUE was 2.4% higher in 2014 than 2013.

WUE was 5.1% and 8,7% higher in zero tillage compared to conventional tillage under rainfed and

irrigated conditions, respectively. WUE increased in environments in the order conventional

tillage/rainfed < conventional tillage/irrigated < zero tillage/rainfed < zero tillage/irrigated. The

genotypes Crusader, Janz, Sunstate, Merinda and Sunstate+EGA Stampede tended to have higher

WUE in rainfed environments, whilst Crusader, Lincoln, Lincoln+Lang and Lincoln+Lang, Suntop

and EGA Stampede had higher WUE under irrigation.

68

Table 3.10 Means for water use efficiency (kg ha-1

mm-1

) under different management practices in

2013 and 2014

Management

Rainfed

Irrigated


2013 CRUSADER 17.59 17.92

19.6 17.59

SUNSTATE 16.9 15.17

16.76 16.81


18.04 17.47

LINCOLN 16.46 16.36

20.36 15.97


17.32 15.29

SUNVEX 12.74 13.56

15.59 12.34

MERINDA 18.04 17.94

17.62 15.15

LANG 16.17 16.97

17.62 15.63

SUNVALE 12.35 14.74

11.68 12.41

VENTURA 16.01 17.63

17.32 15.57

ELLISON 14.56 13.99

17.63 13.19

JANZ 17.48 17.26

18.95 16.41


15.41 16.46


15.3 15.27

SUNCO 14.53 15.98

16.75 16.8


17.94 15.51


18.97 17.36

SUNLIN 15.55 14.87

13.08 12.05

SUNTOP 16.46 16.86

19.09 19.43


18.18 14.99

2014 CRUSADER 20.47 17.2

26.19 18.93


20.22 18.08


23.01 19.2

LINCOLN 18.49 15.08

24.14 16.61


19.28 12.98

SUNVEX 10.71 8.08

13.32 9.2

MERINDA 17.09 15.45

21.91 17.05

LANG 17.9 13.64

20.68 17.34

SUNVALE 17.13 12.63

20.03 14.53

VENTURA 14.32 10.57

16.81 13.83

ELLISON 15.4 11.76

19.27 16.39

JANZ 18.94 14.56

22.27 15.52


22.58 18.23


21.43 15.62

SUNCO 17.42 11.91

19.9 15.96


23.46 18.85


23.54 20.55

69

SUNLIN 13.24 11.08

18.76 14.49

SUNTOP 18.17 14.33

22.38 17.76


21.25 17.78


24.28 18.13


20.95 18.13


22.76 17.86


19.99 16.31

Mean

16.4 14.7

19.35 16.15

Lsd (p<0.05) M x G

0.26

Wald statistic

Y

28.86***

M

535.57***

G

569.25***

Y x M

279.54***

Y x G

136.15***

M x G

79.22*

Y x M x G 64.56

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively G-Genotype, M-Management, Y-Year

3.3.1.10 Canopy temperature(ᵒC)

Observed canopy temperatures were significantly influenced by year, management and year x

management interaction effects (Table 3.11). Zero tillage managed environment were significantly

cooler than conventional tillage in both rainfed and irrigated conditions. Canopy temperature

correlated negatively with yield (r = - 0.35) (Table 3.24).

70

Table 3.11 Means for canopy temperature (°C) under different management practices in 2013 and

2014

Management

Rainfed

Irrigated

Year ZT CT ZT CT

2013 22.87 24.89

20.15 21.78

2014 25.95 26.26

21.21 21.33

Mean 24.41 25.57

20.68 21.55

Lsd (p < 5%) Yx M 0.82

Wald statistic

Y

24.35***

M

1368.46***

G

22.4

Y x M

67.8***

Y x G

24.34

M x G

58.92

Y x M x G 64.03

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively M-Management, Y-Year, G-Genotype

3.3.1.11 Light interception(%)

The amount of solar radiation intercepted varied significantly with year, management,

genotype and year x management interaction effects (Table 3.12). Light interception was 0.4% higher

under zero tillage in rainfed environments compared to conventional tillage, whilst under irrigation

interception was 1.9% higher in conventional tillage. Intercepted solar radiation was 7.4% higher in

2013 than 2014. Light interception increased in different environments in the order conventional

tillage/rainfed < zero tillage/rainfed < zero tillage/irrigated < conventional tillage/irrigated. Light

interception was 6.4% higher in zero tillage/irrigated compared to zero tillage/rainfed environments

and was 8.2% higher in conventional tillage under irrigated conditions. The genotypes Lang, Ventura

and EGA Gregory generally had higher light interception (>83%).

71

Table 3.12 Means for light interception (%) under different management practices in 2013 and 2014

Management

Rainfed

Irrigated


2013 CRUSADER 93.79 93.10

80.37 81.88


76.06 82.67


86.70 88.24

LINCOLN 91.86 91.26

81.53 83.33


79.52 85.39

SUNVEX 93.59 94.84

90.77 82.64

MERINDA 92.24 93.86

86.48 82.39

LANG 96.06 95.73

81.85 86.03

SUNVALE 94.61 95.89

71.63 83.9

VENTURA 96.32 95.84

85.47 81.53

ELLISON 96.09 95.98

84.41 84.2

JANZ 95.20 94.30

85.44 89.08


77.01 83.85


88.77 90.91

SUNCO 91.43 92.88

86.35 89.76


72.75 85.62


80.40 85.53

SUNLIN 93.80 96.29

79.79 84.98

SUNTOP 94.95 94.09

86.90 90.85


72.20 78.71

2014 CRUSADER 66.61 65.59

88.88 93.05


85.00 92.2


90.94 94.3

LINCOLN 57.17 64.25

86.41 91.34


87.03 90.2

SUNVEX 64.50 52.59

79.53 82.05

MERINDA 63.96 53.95

89.97 91.68

LANG 64.00 66.55

94.04 94.24

SUNVALE 64.66 64.54

94.50 95.49

VENTURA 68.67 64.72

91.27 91.84

ELLISON 66.60 55.47

90.83 92.78

JANZ 43.67 55.74

93.09 92.63


92.38 94.76


93.66 93.52

SUNCO 54.25 62.21

94.49 95.01


93.00 92.3


91.34 94.95

SUNLIN 62.71 63.91

93.44 95.01

72

SUNTOP 63.75 63.10

88.00 86.91


89.08 92.05


95.42 92.64


90.34 93.57


89.60 93.92


93.82 95.53

Grand Mean

76.18 75.64

86.60 89.91

Lsd(p< 5%)YxM

11.20

Wald statistic

Y

402.53***

M

397.77***

G

41.28**

Y x M

1127.93***

Y x G

28.97

M x G

52.35

Y x M x G 46.41

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively M-Management, Y-Year, G-Genotype

3.3.1.12 NDVI at early heading

NDVI measurements at early anthesis were significantly influenced by year, genotype,

management, year x management and year x genotype interactions effects (Table 3.13). Whilst the

majority of the genotypes had a relatively stable NDVI at early heading, significant year x genotype

interaction effects were observed and Livingston, Sunvex, Lang, Ellison, Sunco, Sunstate+Stampede,

Lincoln+Lang, Sunlin, Suntop and Spitfire all had higher NDVI in at early heading in 2013. NDVI at

early heading was positively associated with yield (r = 0.47) (Table 3.24).

73

Table 3.13 Means for NDVI at early heading in 2013 and 2014

Cropping Year

Genotype 2013 2014

CRUSADER 0.735 0.717



LINCOLN 0.734 0.696


SUNVEX 0.747 0.540

MERINDA 0.741 0.635

LANG 0.756 0.678

SUNVALE 0.737 0.693

VENTURA 0.726 0.582

ELLISON 0.756 0.688

JANZ 0.744 0.697



SUNCO 0.728 0.638



SUNLIN 0.747 0.677

SUNTOP 0.740 0.666






Mean 0.739 0.664

LSD(5%) Y x G 0.06

Wald statistic

Y 144.03***

M 54.05***

G 88.41***

Y x M 17.73***

Y x G 60.2***

M x G 86.75

Y x M x G 81.95 Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively G-Genotype, Y-Year, M-Management

74

3.3.1.13 NDVI at late heading

Later anthesis NDVI measurements were significantly influenced by year, genotype and

management and genotype x management interaction effects were observed (Table 3.14).

Measurements were 6.7% higher in 2013 compared to 2014. NDVI across the environments tested

increased in the order of zero tillage/irrigated < zero tillage/rainfed < conventional tillage/ rainfed <

conventional tillage/irrigated. Genotypes with higher (>0.75) NDVI values included Cunningham,

EGA Gregory and Sunlin.

Table 3.14 Means for NDVI at late heading under different management practices in 2013 and 2014

Management

Rainfed

Irrigated


2013 CRUSADER 0.777 0.756

0.779 0.805


0.782 0.791


0.776 0.801

LINCOLN 0.769 0.739

0.767 0.802


0.796 0.788

SUNVEX 0.824 0.830

0.838 0.784

MERINDA 0.803 0.792

0.797 0.804

LANG 0.812 0.814

0.824 0.788

SUNVALE 0.787 0.819

0.822 0.803

VENTURA 0.769 0.766

0.777 0.780

ELLISON 0.830 0.816

0.834 0.794

JANZ 0.819 0.830

0.843 0.801


0.832 0.809


0.826 0.799

SUNCO 0.774 0.802

0.829 0.780


0.773 0.791


0.818 0.790

SUNLIN 0.813 0.804

0.822 0.792

SUNTOP 0.808 0.780

0.805 0.760


0.826 0.772

2014 CRUSADER 0.686 0.672

0.744 0.673


0.623 0.689


0.739 0.605

LINCOLN 0.696 0.710

0.793 0.706


0.773 0.758

SUNVEX 0.546 0.527

0.715 0.637

MERINDA 0.669 0.720

0.768 0.650

LANG 0.735 0.739

0.667 0.646

75

SUNVALE 0.671 0.719

0.712 0.718

VENTURA 0.634 0.574

0.774 0.689

ELLISON 0.708 0.661

0.708 0.513

JANZ 0.722 0.697

0.757 0.545


0.726 0.737


0.723 0.689

SUNCO 0.695 0.709

0.709 0.599


0.783 0.667


0.758 0.654

SUNLIN 0.734 0.693

0.793 0.699

SUNTOP 0.693 0.696

0.788 0.645


0.741 0.627


0.766 0.582


0.748 0.665


0.713 0.684


0.753 0.673

Mean

0.732 0.732

0.771 0.718

Lsd(<0.05) MxG

0.089

Wald

statistic

Y

467.06***

M

67.29***

G

59.32***

Y x M

24.92***

Y x G

52.27***

E x G

116.76***

Y x M x G 88.4*

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, E-Environment, Y-Year

3.3.1.14 NDVI at early grain filling

NDVI measured at initial grain filling was significantly influenced by year, genotype and management

and all the observed interactions were significant (Table 3.15). NDVI was higher in 2013 compared to

2014. NDVI increased in the order of conventional tillage/rainfed < zero tillage/rainfed < zero

tillage/irrigated < conventional tillage/irrigated. Generally, the top three outstanding genotypes with

higher NDVI were Janz, Cunningham and Lincoln+Lang. NDVI at early grain filling was positively

associated with yield (r = 0.46) (Table.3.24).

76

Table 3.15 Means for NDVI at early grain filling under different management practices in 2013 and

2014

Management

Rainfed

Irrigated


2013 CRUSADER 0.797 0.807

0.814 0.797


0.808 0.799


0.829 0.785

LINCOLN 0.787 0.832

0.778 0.821


0.781 0.800

SUNVEX 0.799 0.784

0.806 0.782

MERINDA 0.782 0.791

0.806 0.801

LANG 0.758 0.799

0.826 0.785

SUNVALE 0.792 0.790

0.797 0.806

VENTURA 0.836 0.797

0.765 0.799

ELLISON 0.779 0.789

0.821 0.798

JANZ 0.808 0.783

0.793 0.805


0.812 0.810


0.838 0.787

SUNCO 0.785 0.794

0.828 0.787


0.783 0.794


0.839 0.786

SUNLIN 0.787 0.780

0.811 0.784

SUNTOP 0.818 0.809

0.781 0.783


0.798 0.768

2014 CRUSADER 0.637 0.619

0.691 0.693


0.679 0.701


0.702 0.590

LINCOLN 0.666 0.652

0.702 0.647


0.724 0.705

SUNVEX 0.591 0.574

0.625 0.647

MERINDA 0.630 0.688

0.694 0.719

LANG 0.712 0.734

0.732 0.633

SUNVALE 0.685 0.736

0.620 0.686

VENTURA 0.636 0.526

0.699 0.583

ELLISON 0.622 0.618

0.672 0.621

JANZ 0.707 0.725

0.718 0.718


0.688 0.687


0.687 0.692

SUNCO 0.671 0.708

0.674 0.715


0.687 0.702


0.712 0.689

77

SUNLIN 0.666 0.696

0.710 0.661

SUNTOP 0.646 0.666

0.687 0.677


0.713 0.685


0.663 0.628


0.683 0.706


0.658 0.699


0.643 0.732

Mean

0.072 0.073

0.059 0.059

Lsd(p< 5%)YxExG

0.069

Wald statistic

Y

1211***

M

33.52***

G

71.52***

Y x M

10.07**

Y x G

56.86***

E x G

114.97**

Y x E x G 103.39***

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, Y-Year, E-Environment

3.3.1.15 NDVI at late grain filling

Late NDVI measurements were similarly influenced significantly by year, genotype and

management and all observed two-way interactions were significant (Table 3.16). NDVI was 6.4%

higher in zero tillage compared to conventional tillage under rainfed conditions. Similarly, in irrigated

environments, NDVI was 2.64% higher in zero tillage compared to conventional tillage. However,

NDVI did not vary significantly across years. Higher NDVI (>0.550) was obtained from the genotypes

Lang, Sunvale, EGA Gregory and Sunlin. NDVI at late grain filling was negatively associated with

thousand kernel weight (r = - 0.58) (Table 3.24).

78

Table 3.16 Means for NDVI at late grain filling under different management practices in 2013 and

2014

Management

Rainfed

Irrigated


2013 CRUSADER 0.468 0.433

0.588 0.596


0.541 0.576


0.532 0.585

LINCOLN 0.416 0.404

0.556 0.592


0.505 0.553

SUNVEX 0.531 0.487

0.620 0.607

MERINDA 0.495 0.454

0.591 0.573

LANG 0.495 0.515

0.593 0.559

SUNVALE 0.457 0.481

0.581 0.577

VENTURA 0.464 0.396

0.557 0.565

ELLISON 0.503 0.483

0.630 0.597

JANZ 0.517 0.474

0.579 0.571


0.563 0.585


0.621 0.592

SUNCO 0.471 0.515

0.573 0.571


0.534 0.552


0.590 0.575

SUNLIN 0.530 0.511

0.597 0.589

SUNTOP 0.522 0.517

0.638 0.565


0.582 0.540

2014 CRUSADER 0.524 0.436

0.644 0.583


0.673 0.672


0.654 0.577

LINCOLN 0.362 0.445

0.692 0.550


0.692 0.669

SUNVEX 0.521 0.373

0.570 0.597

MERINDA 0.393 0.422

0.630 0.581

LANG 0.576 0.517

0.685 0.538

SUNVALE 0.592 0.515

0.658 0.659

VENTURA 0.473 0.486

0.630 0.579

ELLISON 0.446 0.436

0.670 0.601

JANZ 0.472 0.431

0.531 0.507


0.643 0.553


0.644 0.646

SUNCO 0.529 0.370

0.492 0.636


0.554 0.684


0.684 0.601

79

SUNLIN 0.550 0.377

0.657 0.621

SUNTOP 0.484 0.355

0.635 0.559


0.556 0.627


0.633 0.551


0.570 0.640


0.708 0.653


0.627 0.622

Grand Mean

0.467 0.437

0.607 0.591

Lsd (p< 5%)ExG

Wald statistic

Y

0.32

M

682.08***

G

58.52***

Y x M

49.81***

Y x G

46.09***

M x G

115.62***

Y x M x G 71.26

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, E-Environment, Y-Year

3.3.2 Phenological Traits

3.3.2.1 Number of days to heading

The number of days to heading was significantly influenced by the effects of years,

management, genotype, year x genotype and management x genotype interaction (Table 3.17). The

numbers of days to heading were generally shorter by approximately 2 days in zero tillage compared to

conventional tillage under rainfed conditions and 1 day shorter under irrigation. Drought therefore

reduced the number of heading days to heading by approximately 0.7%. The number of days to

heading varied with year and was 103 days in 2013 and 108 days in 2014. In irrigated condition,

Livingston and Sunvex were significantly early to heading in conventional tillage management whilst

in rainfed condition EGA Stampede and Lincoln had significantly shorter days to heading under zero

tillage management. The number of days to heading was negatively associated with thousand kernel

weight (r = -0.74) and yield (r = -0.68) (Table 3.24).

80

Table 3.17 Means for number of days to heading under different management practices in 2013 and

2014

Management

Rainfed

Irrigated


2013 CRUSADER 98.7 99.0

99.0 98.7


101.3 101.0


100.3 98.7

LINCOLN 99.7 99.7

100.0 99.7


100.7 98.7

SUNVEX 108.0 107.7

110.0 109.0

MERINDA 101.0 102.0

102.7 101.3

LANG 102.0 106.3

106.7 106.0

SUNVALE 106.7 106.7

108.0 108.0

VENTURA 103.0 101.3

102.3 101.3

ELLISON 106.3 109.7

109.7 109.0

JANZ 102.7 102.7

103.0 103.7


106.0 105.3


109.7 108.0

SUNCO 101.7 102.7

102.7 102.0


101.3 100.7


102.3 100.3

SUNLIN 106.0 106.3

106.3 107.3

SUNTOP 101.0 100.7

102.3 101.0

SPITFIRE 98.3 99.7

98.7 98.3

2014 CRUSADER 98.0 103.0

99.3 97.3


108.0 111.3


101.3 105.3

LINCOLN 96.3 104.0

97.0 96.3


105.7 100.0

SUNVEX 119.0 121.3

118.0 111.0

MERINDA 102.7 102.7

106.3 101.7

LANG 113.7 112.7

115.3 113.7

SUNVALE 116.3 117.3

118.3 118.7

VENTURA 108.3 104.3

112.3 111.0

ELLISON 106.0 109.3

112.7 112.3

JANZ 108.0 109.3

112.7 112.7


112.0 111.7


117.3 118.7

SUNCO 113.7 112.3

115.3 115.0


104.7 110.0


107.0 105.3

81

SUNLIN 112.3 116.7

117.7 115.0

SUNTOP 104.0 104.7

101.7 109.7

SPITFIRE 99.3 99.3

103.0 100.3


107.7 107.3


108.3 105.3


105.0 108.3


116.0 113.7

Grand Mean

104.3 105.7

106.7 106.1

Lsd(p< 5%)ExG

3.4

Wald statistic

Y

391.04***

M

51.97***

G

1388.48***

Y x M

12.66**

Y x G

183.66***

M x G

98.95***

Y x M x G 71.87

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, E-Environment

3.3.2.2 Number of days to physiological maturity

The number of days to physiological maturity was significantly influenced by the effects of

year, management, genotype, year x management and year x genotype interaction (Table 3.18). The

top three genotypes which had significantly shorter days to maturity in 2013 included Sunlin, Sunvex

and EGA Gregory whereas in 2014, Crusader, Lincoln and Spitfire were the earliest maturing. Ellison

and Cunningham were stable across the two cropping seasons. The number of days to physiological

maturity was 3.8 days longer in 2014 and this could be attributed to the higher amount and more even

distribution of rainfall in 2014. The number of days to maturity was negatively correlated with TKW (r

= -0.67) and yield (r = -0.78) (Table 3.24).

82

Table 3.18 Means for days to physiological maturity in the 2013 and 2014 cropping seasons.

Cropping Year

Genotype 2013 2014




LINCOLN 143.3 146.7


SUNVEX 151.0 157.5

MERINDA 145.4 147.5

LANG 147.2 150.8

SUNVALE 147.7 151.0

VENTURA 143.2 149.5

ELLISON 150.1 150.7

JANZ 147.0 149.8



SUNCO 146.7 151.5



SUNLIN 150.7 155.4

SUNTOP 145.8 149.7


SUNSTATE+STAMPEDE+JANZ * 148.9

LICOLN+LANG+SUNSTATE * 147.7

SUNTOP+SPITFIRE * 148.8

EGA GREGORY+VENTURA * 153.0

Mean 146.1 149.9

LSD(5%) G x Y 1.86

Wald statistic

Y 353.07***

E 563.47***

G 672.25***

Y x E 27.57***

Yx G 60.34***

M x G 63.27

Yx Mx G 53.97


83

3.3.2.3 Grain filling duration

Grain filling duration was significantly influenced by year, management, genotype and year x

genotype interaction effects (Table 3.19). The genotypes Sunstate, Sunvex, Merinda, Ventura, Ellison,

Sunstate+Stampede, Lincoln+Lang, Suntop and Spitfire had a relatively stable grain filling duration

across the two cropping seasons. Janz, Cunningham, EGA Gregory, Sunco, Sunvale and Sunlin had a

relatively longer grain filling duration in 2013 whilst EGA Stampede, Lincoln and Livingston had the

longest grain filling duration in 2014. The significantly longer grain filling duration in 2014 is again a

function of the milder and wetter season. Grain filling duration correlated positively with TKW (r =

0.61) and yield (r = 0.41) (Table 3.24).

Table 3.19 Means for grain filling duration (days) in 2013 and 2014

Cropping Year

Genotype 2013 2014

CRUSADER 43.6 46.2

SUNSTATE 42.1 41.5


LINCOLN 43.6 48.3


SUNVEX 42.3 40.2

MERINDA 43.7 44.2

LANG 41.9 36.9

SUNVALE 40.4 33.3

VENTURA 41.3 40.5

ELLISON 41.4 40.6

JANZ 44.0 39.2



SUNCO 44.4 37.4



SUNLIN 44.2 40.0

SUNTOP 44.6 44.7

SPITFIRE 45.7 46.9





Mean 43.2 41.9

LSD (p < 5%) G x Y 2.8

Wald statistic

Y 17.98***

84

M 140.31***

G 356.08***

Y x M 7.03

Y x G 130.32***

E x G 75.32

Y x E x G 70.54

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, Y-Year, M-management

3.3.2.4 Plant height (cm)

Plant height was significantly influenced by year, management, genotype and year x genotype

interaction effects (Table 3.21). All the genotypes had a relatively stable plant height across the two

cropping seasons with the exception of Sunstate, Sunvex and Ventura which were significantly taller

in 2013 and EGA Stampede and Sunvale which were taller in 2014. Plant height was generally greater

(by approximately 4cm) in irrigated compared to rain fed environments. Under zero tillage

management plants were on average 2 cm higher than under conventional tillage when irrigated (Table

3.10). This difference reduced to 1.34cm under rainfed conditions. Mean plant heights were 0.44%

shorter in 2013 compared to 2014. Plant height was positively correlated with TKW (r = 0.45) and

yield (r = 0.54) (Table 3.24).

85

Table 3.20. Means for plant height (cm) in 2013 and 2014

Cropping Year

Genotype 2013 2014

CRUSADER 86.2 88.3

SUNSTATE 94.0 90.2


LINCOLN 86.4 86.5


SUNVEX 73.5 70.9

MERINDA 86.7 86.1

LANG 85.6 87.4

SUNVALE 81.4 84.0

VENTURA 93.5 91.2

ELLISON 85.0 84.5

JANZ 82.4 81.9



SUNCO 83.5 85.4



SUNLIN 83.6 84.0

SUNTOP 89.7 88.9

SPITFIRE 83.7 83.0





Mean 86 86.7

LSD (p < 5%) YxG 2.3

Y 8.75**

M 229.48***

G 1138.27***

Y x M 116.87***

Y x G 41.49**

M x G 99.72

Yx Mx G 73.17


86

3.3.3 Production traits

3.3.3.1 Grain Yield(kg ha-1

)

Yield was significantly influenced by year, management, genotype, year x management and year x

genotype interaction effects (Table 3.22). Ventura and Livingston displayed stable yield across the two

cropping seasons whilst the remaining genotypes showed higher year x genotype interaction effects

with higher yields in 2014. These interactions could possibly be driven by the higher available

moisture from evenly distribution rainfall in 2014. A yield advantage of 5.9% and 8.6% was observed

in zero tillage relative to conventional tillage under rainfed and irrigated conditions, respectively.

Table 3.21 Means for grain yield (kg/ha) under different management practices in 2013 and 2014

Genotype Cropping Year

2013 2014

CRUSADER 3228

4018

SUNSTATE 2883

3358

EGA STAMPEDE 2968

3805

LINCOLN 3069

3603

LIVINGSTON 2860

2899

SUNVEX 2406

2009

MERINDA 3049

3487

LANG 2947

3394

SUNVALE 2272

3139

VENTURA 2953

2727

ELLISON 2634

3067

JANZ 3111

3479

CUNNINGHAM 2831

3618

EGA GREGORY 2688

3277

SUNCO 2847

3168

SUNSTATE+STAMPEDE 2885

3670

LICOLN+LANG 3068

3682

SUNLIN 2463

2824

SUNTOP 3147

3545

SPITFIRE 2937

3387

SUNSTATE+STAMPEDE+JANZ -

3820

LICOLN+LANG+SUNSTATE -

3391

SUNTOP+SPITFIRE -

3513

EGA GREGORY+VENTURA -

3096

Mean 2862.3

3332.3

Lsd(5%) G xY 244.1

Wald statistic

87

Y 321.22***

M 635.29***

G 585.54***

Y x M 333.67***

Y x G 139.64***

E x G 77.57

Yx M x G 60.09

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively G-Genotype, Y-Year

3.3.3.2 Thousand Kernel Weight (g)

TKW was significantly influenced by year, management, genotype and year x genotype interaction

effects (Table 3.23). Genotypes that had stable TKW across the two cropping seasons included

Crusader, EGA Stampede, Livingston, Merinda, Sunvale, Lang, Ellison, Cunningham,

Sunstate+Stampede and Sunlin whilst the rest displayed significant year x genotype interaction with

generally higher TKW in 2013 than 2014. TKW was 1.6 % higher in zero tillage compared to

conventional tillage under rainfed conditions. A similar trend was observed under irrigation and TKW

was 2.9% higher in 2013 than 2014. TKW was positively associated with yield (r = 0.52) (Table 3.24).

88

Table 3.22 Means for thousand kernel weight (g) under different management practices in 2013 and

2014

Cropping Year

Genotype 2013 2014

CRUSADER 32.3 30.2

SUNSTATE 34.6 30.9


LINCOLN 35.3 33.0


SUNVEX 27.4 29.6

MERINDA 33.1 32.8

LANG 30.6 29.3

SUNVALE 29.7 28.9

VENTURA 35.0 32.4

ELLISON 32.8 31.7

JANZ 33.4 29.0



SUNCO 32.1 28.8



SUNLIN 29.9 28.7

SUNTOP 34.8 32.2

SPITFIRE 37.6 34.4





Mean 32.9 31

LSD(5%)YxG 2.3

Wald statistic

Y

59.35***

M

63.03***

G

227.58***

Y x M

58.87***

Y x G

44.88***

M x G

85.62

Y x M x G 51.29

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. G-Genotype, Y-Year, Management

89

3.3.3.3 Protein content (%)

Grain protein content was significantly influenced by management, year, genotype and year x

genotype interaction effects (Table 3.24). While the majority of the genotypes were stable, Sunstate,

EGA Stampede, Lincoln, Livingston, Merinda, Janz, EGA Gregory, Sunstate+Stampede, Sunlin, and

Suntop displayed significant year x genotype interaction effects. Protein was 2.5% higher in

conventional tillage compared to zero tillage practices under rainfed conditions, whilst little difference

was observed under irrigation. Protein content in 2014 was 2.6% higher than in 2013. This was

surprising given the higher yield in 2014. Nevertheless, protein content was strongly negatively

correlated with yield (r = - 0.74) (Table 3.24).

90

Table 3.23 Means for protein content (%) in 2013 and 2014

Cropping Year

Genotype 2013 2014




LINCOLN 12.67 13.38


SUNVEX 14.18 14.38

MERINDA 12.31 14.25

LANG 13.20 13.58

SUNVALE 13.85 13.76

VENTURA 12.85 13.40

ELLISON 14.37 14.01

JANZ 12.63 14.13



SUNCO 13.35 13.57



SUNLIN 13.52 14.30

SUNTOP 12.25 13.44






Mean 13.09 13.81

LSD (p <5%) YxG 0.62

Wald statistic

Y 113.2***

M 166.5***

G 188.55***

Y x M 159.28***

Y x G 72.16***

M x G 81.32

Y x M x G 67.3


91

Table 3.24 Phenotypic correlation of combined means of physiological characteristics possibly associated with grain yield and quality traits in 2013-2014.

BMAS CHL CT DH DM GC1 GF GC2 GCR HI HT IPAR LFRL ND1HA ND2GF ND2HA ND1GF PROT% RWC TKW

CHL -0.16 -

CT -0.52 -0.10 -

DH -0.53 -0.52 0.18 -

DM -0.43 -0.49 0.22 0.87 -

GC1 -0.02 0.26 -0.11 0.06 -0.17 -

GF 0.50 -0.42 -0.10 -0.88 -0.53 -0.27 -

GC2 0.44 -0.19 -0.17 -0.75 -0.61 0.50 0.70 -

GRC -0.31 -0.38 0.03 -0.25 -0.31 0.28 0.12 0.32 -

HI 0.37 -0.51 -0.08 -0.76 -0.80 -0.13 0.53 0.38 0.27 -

HT 0.56 0.08 -0.50 -0.32 -0.54 0.36 0.02 0.29 0.00 0.30 -

IPAR 0.10 0.60 -0.58 0.49 0.33 0.23 -0.52 -0.3 -0.14 -0.28 0.34 -

LFRL 0.09 -0.21 -0.18 -0.50 -0.47 -0.06 0.41 0.32 0.23 0.21 0.13 -0.07 -

ND1HA -0.32 0.23 -0.33 0.16 -0.06 0.41 -0.33 0.00 0.47 0.05 0.28 0.39 -0.07 -

ND2GF -0.33 0.59 -0.35 0.74 0.62 0.10 -0.67 -0.53 -0.06 -0.55 -0.11 0.74 -0.19 0.43 -

ND2HA -0.27 0.37 -0.26 0.36 0.20 0.65 -0.43 0.10 0.37 -0.28 0.13 0.50 -0.12 0.75 0.46 -

ND1GF -0.28 0.11 -0.24 0.19 0.02 0.66 -0.32 0.20 0.53 -0.10 0.10 0.43 0.09 0.67 0.31 0.82 -

PROT% -0.51 0.08 0.42 0.53 0.64 -0.24 -0.29 -0.44 0.00 -0.50 -0.62 -0.17 -0.42 -0.14 0.26 -0.05 -0.27 -

RWC 0.25 0.18 -0.15 0.06 0.22 -0.42 0.12 -0.20 -0.52 -0.12 -0.11 0.04 -0.13 -0.22 0.14 -0.28 -0.61 0.42 -

TKW 0.59 -0.25 -0.25 -0.74 -0.67 -0.06 0.61 0.51 0.01 0.64 0.45 -0.28 0.16 0.00 -0.58 -0.19 -0.23 -0.35 0.22 -

YIELD 0.26 0.17 -0.35 -0.68 -0.78 0.35 0.41 0.64 0.53 0.63 0.54 0.02 0.49 0.47 -0.32 0.24 0.46 -0.74 0.48 0.52

Note: BMAS: Biomass, CHL: Chlorophyll content, CT: Canopy temperature, DH: number of days to heading, DM: number of days to physiological maturity, GC1: early ground cover, GF: grain filling duration, GC2: Late ground cover, GRC: Rate of ground cover, HI: harvest index, HT: plant height, TKW: Thousand kernel weight, Yield: Grain yield, RWC: relative water content, P%: protein content,

NDIGF: NDVI at early grain filling stage, ND2HA: NDVI at late heading and Anthesis, ND2GF: NDVI at late grain filling stage, ND1HA: NDVI at early heading, LFRL:Leaf rolling, IPAR light

interception

92

3.4 Discussion

The design of the experiment in this chapter and the following chapter replicated genotypes

within four management practices (representing combinations of irrigation and tillage practices).

However, these management practices were replicated across years, not within years. This was done to

maximize the number of genotypes assessed given the finite resources available. Similar treatment

structures have been used in many other experiments (Olivares-Villegas et al. 2007; Reynolds et al.

2007; Ramya et al. 2016). Water-use-efficient wheat varieties with appropriate phenology for the

target environment are paramount to sustainable agriculture in water deficit environments. The current

study looked at a range of traits in a set of genotypes selected on the basis of contrasting markers,

identified through a previous association analysis, linked to yield in multi-environment trials. A

number of traits were identified that were linked to yield and adaptation to both moisture stress and

tillage practice. However, no tillage practice x genotype interaction for yield was observed indicating

that materials do not need to be bred under specific tillage regimes for adaptation to those regimes.

This observation agrees with previous findings for a range of crops(Ullrich and Muir 1986; Cox 2010)

and contrasts with the conclusions of Trethowan et al (Trethowan et al. 2005b; Trethowan et al. 2012)

who reported a significant genotype x tillage practice interaction. Zero-tillage was included in this

study as it is reported to conserve soil moisture ((Tebrügge and Düring 1999; Hernanz et al. 2002;

Hobbs et al. 2008; Farooq et al. 2011; Soane et al. 2012) and this was confirmed. In addition,

significant year x genotype interaction effects on yield and other traits were observed which could be

possibly driven by greater moisture availability in 2014 due to higher and more evenly distributed

rainfall. The selection of more stable genotypes would be an insurance against erratic rainfall patterns.

3.4.1 Agro--physiological traits

3.4.1.1 Canopy ground cover

Greater early season leaf area development results in faster ground cover which influences

yield by reducing soil evaporation from the soil surface in certain environments (Rebetzke et al. 2004).

In the current study, earliness to ground cover did not vary significantly among the environments

tested. The coverage was initially higher under zero tillage; however the difference between the zero

and conventional tillage quickly disappeared. This could be attributed to the initially higher (but non-

significant) stored soil moisture in the zero-tillage environments. Nevertheless, this trend makes the

trait a possible target for breeding and selection to improve adaptation to zero-tillage. Evidence

suggests that reduced seedling vigor occurs in zero-tillage systems despite the many structural and

93

biological advantages offered by conservation agriculture in Australia (Chan et al. 1987;

Simpfendorfer et al. 2002; Watt et al. 2006) and worldwide (Lekberg and Koide 2005). Therefore a

more vigorous plant should be an advantage in these systems. The higher early ground cover observed

in 2013 could be due to the higher total precipitation in June of that year and higher initial soil water

content. Excluding the occurrence of diseases and pests, reduced seedling vigour has been attributed to

slower root tip growth in harder undisturbed soil which induces a proliferation of inhibitory bacteria

which release toxins thus reducing leaf growth (Watt et al. 2003; Watt et al. 2006). Nevertheless, the

significant year x genotype interaction observed for both late ground cover and the rate of increase in

ground cover could be driven by the initially higher stored soil moisture and initially higher rainfall in

May-June of 2013. Therefore, selection of stable genotypes for these characters such as Livingston,

Sunvex, Ventura, EGA Gregory and Suntop could provide security against erratic rainfall and soil

moisture availability.

The strongly positively correlation observed between ground cover and various agronomic

traits and grain protein indicates that faster leaf area development captures enough sunlight to advance

heading time therefore avoiding later season moisture stress. These adaptations result in higher yields,

higher thousand kernel weight but potentially lower protein percentage. The ground cover trait may

therefore offer an avenue for indirect selection for improved water use efficiency.

A number of genomic regions were identified that influence seedling traits including

emergence, early vegetative vigour, nutrient uptake, nodal root number, and root hair length and

density (Liu et al. 2013a; Zhang et al. 2014a). Furthermore, these morpho-physiological traits were

found to co-locate with regions influencing grain yield in many studies (Venuprasad et al. 2002;

Gautami et al. 2012).

Li et al. (2014a) found thirty-seven genomic regions in wheat responsible for seedling

related traits, it is therefore possible that earliness to ground cover is linked to genomic regions which

confer high yield under moisture stress identified in the earlier association study of these genotypes.

Several markers identified in the earlier association analysis (Atta 2013) that were associated with

yield coincided with QTLs regions found by Li et al. (2014a). QTLs QGCw-caas-1A.1, QGCw-caas-

2A.2, TQGCw-caas-1A.2 at 6.1, 6.7 and 3.0 cM respectively, coincided with the markers wPt-6564,

wPt 8242, wPt 8455 and wPt 1945 on chromosomes 1A, 2A, 1A at similar distances. These regions

and their associated markers could be used to improve yield and related traits in marker assisted

selection.

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3.4.1.2 Chlorophyll content

Chlorophyll content influences photosynthetic efficiency and significant correlations with grain

yield and their corresponding QTLs have been found in wheat (Cao et al. 2003; Quarrie et al. 2006;

Yang et al. 2007b; Grzesiak et al. 2009; Zhang et al. 2009b; Zhang et al. 2010b). In the current study,

chlorophyll content was significantly influenced by environment. Although chlorophyll content

weakly correlated with yield (r = 0.17), the significantly higher chlorophyll contents in zero tillage

relative to conventional tillage in rainfed environments and their corresponding yield differences

suggest that moisture stress, which was greater under conventional tillage, limits the photosynthetic

apparatus of plants and eventually leads to lower yield. The correlation of chlorophyll content with

IPAR, phenology and yield suggests that genotypes that maintained their photosynthetic apparatus

(chlorophyll content) were more efficient at capturing sunlight for photosynthesis and therefore

developed faster thus avoiding peak periods of moisture stress. Chlorophyll was not assessed in the

association analysis so co-location of significant marker trait association with previously identified

QTLs cannot be made. Nevertheless, some yield related marker trait associations are located on the

same chromosome arms as previously identified QTLs including 7A which was confirmed by Quarrie

et al. (2006). These authors indicated that flag leaf chlorophyll content and leaf width at tillering were

associated with a QTL on chromosome 7A. In addition, Graziani et al. (2014) identified QTLs for leaf

greenness on chromosome 3B at 6.9 cM, which overlapped with a QTL for TKW. The earlier

association studies coincidentally found marker wPt-7907 on chromosome 3B to be associated with

higher grain yield (Atta 2013). These results suggest that chlorophyll content could be associated with

genomic regions conferring higher yield under moistures stress.

3.4.1.3 Flag leaf rolling

Attaining an optimum level of photosynthesis under moisture stress could be augmented by

changes in leaf morphology such as rolling of the leaf blade. In the current study, leaf rolling was

highly influenced by the environments tested and was more pronounced in rainfed than irrigated

conditions which agrees with previous findings that leaf rolling is more pronounced under moisture

stress(Blum 1988a; Kadioglu et al. 2012). It was therefore expected that zero tillage under rainfed

conditions; the environment with the highest soil moisture, will have the less rolling of leaves. This

was observed in the current study. Rolling clearly increases the efficiency of water metabolism in the

flag leaves of wheat. Others have concluded that leaf rolling ability is an adaptive trait for drought

tolerance(Peleg et al. 2009b; Sarieva et al. 2010). Selection of cultivars with this character has been

95

achieved in rice, maize, sorghum and wheat (Dingkuhn et al. 1989; Premachandra et al. 1992a; Corlett

et al. 1994; Price et al. 2002a). Results suggest that genotypes with ability to roll their leaves avoided

photo damage, reduced transpiration loss under stress and therefore were more efficient in capturing

sunlight resulting in higher yield.

3.4.1.4 Canopy Temperature (ᵒC)

Canopy temperature has been reported as an integrative trait that is correlated with yield that

can be used to select superior genotypes or segregating bulks (Reynolds et al. 1997; Gutiérrez-

Rodrıguez et al. 2000). The trait is routinely used at CIMMYT in early generations to change gene

frequency in favour of lines with cooler canopies (Reynolds and Trethowan 2007; Trethowan and

Reynolds 2007). In the current study, canopy temperature was significantly influenced by the

environments tested. The higher canopy temperatures observed in the rainfed environments indicate

that the trait is affected by moisture status of the environment. Results suggested that genotypes with

cooler canopies had improved plant water status through access of water from deeper in the soil

profile. This produced taller plants with better light interception that subsequently produced higher

yields. The lack of significant differences among the genotypes tested for canopy temperature likely

reflects a lack of diversity for this trait and should not preclude targeted selection for this character in

future crop improvement. In other studies the trait was reported to have high heritability with over

60% of genetic variation in yield explained in some studies and QTLs have been detected for use in

breeding and selection (Olivares-Villegas et al. 2007; Lopes and Reynolds 2010; Pinto et al. 2010;

Mason et al. 2013). Bennett et al. (2012) identified a QTL located on chromosome 3B which had a

large effect on canopy temperature and grain yield. This coincided with marker trait associations

identified on the same chromosome for yield in the earlier association analysis (Atta 2013).

3.4.1.5 Days to heading & Maturity

The duration of wheat developmental is significantly influenced by sensitivity to vernalization,

photoperiod and earliness per se genes. Earliness per se controls flowering time independent of

environmental signals and therefore helps to fine tune the time of flowering and maturity (Kamran et

al. 2013). In the present study, genotype phenology varied significantly across the four environments

tested. While no significant differences were noted between tillage regimes, shorter days to flowering

and maturity were observed in the rainfed environments relative to the irrigated environments. A

significant year x genotype interaction effects for days to physiological maturity could be influenced

96

by the higher rainfall amount and distribution in 2014. This is simply a function of reduced moisture

and greater stress. However, some genotypes were earlier flowering and thus avoided most of the

impacts of terminal drought stress (Nagesh and Kumar 2006). Later flowering genotypes may have

had greater seed abortion under rainfed conditions, as observed earlier by Passioura (2006), however

this could not be confirmed as seed number per spike was not recorded. The strong negative

correlation between heading date and productivity traits including yield and the positive correlation

with protein percentage observed under both moisture conditions indicates that high temperature as

well as moisture stress impacted later flowering materials. Others have observed similar responses

(Kamran et al. 2013). A number of genomic regions regulating earliness per se gene effects have been

identified in wheat and other crops (Kuchel et al. 2006; Båga et al. 2009; Griffiths et al. 2012; Le

Gouis et al. 2012b). In the set of 20 genotypes assembled for this study, several carried significant

marker trait associations in regions already reported as influencing phenology. Coincidentally, MTAs

identified on chromosome 1AL at 57.96, 68.25, 117.16, 125.40, 134.61 and 135.6 cM in the earlier

association analysis (Atta 2013) coincided with the findings of Bullrich et al. (2002) who mapped a

QTL for earliness per se controlling heading time in the distal region of chromosome 1AL in wheat.

Kuchel et al. (2006) identified additional QTLs controlling time to heading on chromosomes

1A, 2A, 2B, 6D, 7A and 7B which coincided with MTAs on the same chromosomes identified in the

earlier association study (Atta 2013). Undoubtedly, higher grain yield could be achieved by avoiding

critical periods of moisture stress through early flowering under rainfed conditions.

3.4.1.6 Grain filling duration

Grain filling duration and rate influences final grain yield (Takai et al. 2005). As expected, the

current study indicated that grain filling duration was much shorter under rainfed environments which

resulted in correspondingly lower yields. The significantly longer grain filling duration observed in

zero tillage compared with conventional tillage in rainfed environments suggest that although zero

tillage impacts grain filling duration effect are driven by soil moisture levels. This has been

confirmed(Liu et al. 2013b) Tillage did not impact grain fill. Our results confirm many previous

reports that terminal drought shortens the grain-filling period and reduces yield (Aggarwal and Sinha

1984; Kobata et al. 1992; Zhang et al. 1998). Few genetic studies have been conducted on grain filling

because of its complexity and the strong influence of environment (Takai et al. 2005). The observed

significant year x genotype interaction effects are likely driven by higher rainfall in 2014 and the

selection of stable genotypes could provide insurance against moisture stress.

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In the current study, longer grain filling duration increased important agronomic characters

including yield and harvest index. Clearly, genotypes that flower early tend to have longer grain filling

duration and ultimately higher yield, WUE and higher grain weight. However, these genotype

responses are greatly influenced by the environment. For this reason TKW, which is highly heritable

and more stable than yield (Giura and Saulescu 1996) could be targeted for the improvement of yield

and water use efficiency in wheat. Six QTL on chromosomes 1A, 3B, 5D and 6D that influence TKW

and grain filling rate have been identified in wheat across two environments (Wang et al. 2009) and

several of these chromosome locations coincide the earlier association study (Atta 2013). Larger

kernels indicate greater seedling vigor and increased yield and TKW can be targeted in breeding for

wheat adaptation to moisture stress.

3.4.1.7 Biomass

Biomass is a key driver of grain yield in rainfed environments (Shepherd et al. 1987; Royo et

al. 1999; Slafer 2003; Slafer et al. 2005). Zero-tillage biomass was greater than conventional tillage

under both moisture conditions. This difference possibly reflects differences in soil moisture content

and additional nitrogen from plant residues in the zero-tillage environments. Biomass correlated

positively with yield and productivity traits as expected but also with early ground cover. This

however implies that genotypes with good early ground cover tend to produce higher biomass and in

turn higher yield and kernel weights under stress. QTLs controlling total biomass have been reported

on chromosomes 4B and 4D (Li et al. 2014b).

3.4.1.8 Harvest Index

Harvest index defined as the ratio of reproductive yield to total plant biomass, is a measure of

the efficiency of partitioning assimilated photosynthates to harvestable product. Thus the historic

genetic improvement of yield has been achieved by increasing harvest index and reducing plant height

(Zheng et al. 2011; Xiao et al. 2012). In the current study, harvest indices were greatly influenced by

year x genotype interaction effects. EGA Stampede, Lincoln, Livingston, Sunvex, Merinda, Sunvale,

Ventura, EGA Gregory, Sunlin, Spitfire, Sunstate+Stampede appeared to be stable in the two cropping

seasons. Harvest indices were higher in 2013 compared to 2014 where rainfall was higher the

distribution more even. This resulted in higher biomass in 2014 with low harvest indices suggesting

that as a drought escape and survival strategy, assimilates were remobilized from the stems to fill the

grains in the drier year. By classifying wheat varieties into old, modern and semi-dwarf types in

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Australia, Perry and d'Antuono (1989) reported that harvest index contributed 400 kg ha–1

and

aboveground biomass only 60 kg ha–1

to increased grain yield in Western Australia. This report is

supported by studies in higher yielding environments (Sayre et al. 1997; Shearman et al. 2005a). These

observations indicate that the overall WUE of biomass accumulation has not increased greatly through

breeding, whereas WUE for grain yield has increased sharply because of improvements in harvest

index (Richards et al. 2014). In the current study the effects of year x genotype interaction on harvest

index were significant, suggesting that the trait was driven by moisture availability, therefore selection

of stable genotypes could provide insurance against moisture stress.

3.4.2 Production traits

3.4.2.1 Thousand Kernel weights

In the current study, TKW was significantly influenced by year x genotype interaction effects

and the selection of stable genotypes could help buffer the effects of stress. TKW was highest under

zero tillage compared with the conventional tillage environment, suggesting that moisture stress

impacts grain weights. The current study supports the findings of (Gevrek and Atasoy 2012) who

reported a reduction in thousand kernel weights of 5.4% following post anthesis moisture stress. TKW

is also an integrated trait that was correlated with many other traits including yield. It is therefore a

good target for selection given its relatively high heritability.

3.4.2.2 Protein content

Grain protein content is determined by complex interactions between N, soil water, yield and

temperature. These factors generally hinder the investigation of genetic responses (De Vita et al.

2007a). The current study revealed that grain protein content is influenced by year x genotype

interaction effects and that this interaction is likely driven by differences in rainfall and rainfall

distribution between the cropping seasons. A few studies analyzed wheat grain protein content as a

function of tillage system, reporting no significant differences (Bassett et al. 1989; Cox and Shelton

1992). In contrast, De Vita et al. (2007a) reported higher grain protein content under conventional

tillage compared to zero tillage. However, in the current study, grain protein content was higher in zero

tillage environments regardless of soil moisture suggesting that grain protein content was significantly

influenced by the interaction of N from residues with soil moisture. Nevertheless, a negative

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correlation with yield regardless of environment type affirms the frequently reported observation that

increased grain yield comes at the expense of grain protein.

3.4.2.3 Grain yield

Grain yield is the key agronomic trait targeted by wheat breeders for improvement. The current

study revealed that yields in zero-tillage were significantly higher than conventional tillage regardless

of soil moisture. These observations support the findings of others, particularly under moisture stress

(Cantero‐Martínez et al. 2007; Karrou 2013). Clearly, the absence of soil disturbance reduced moisture

loss thus improving yield. The advantage of zero-tillage under sub-optimal rainfall conditions and the

link with increased water availability has been reported earlier (Hall and Cholick, 1989; Cox, 1991;

Carr et al., 2003a). Thus zero-tillage lowers soil evaporation, increases infiltration, enhances

conductibility, reduces runoff and enhances soil structure (Blevins et al. 1971; Unger 1990; Hatfield et

al. 2001; Karamanos et al. 2004). The results presented here show the superiority of zero tillage over

conventional tillage in maintaining higher grain yield and improving WUE.

Assessing the extent to which factors like genotype and genotype x interaction influence yield

is crucial. This allows breeders and farmers to make better use of the potential of genotypes. However,

in the current study, no significant genotype x environment interaction effect on yield was observed

instead significant year x genotype interaction effects were observed It may be that the germplasm

utilized were stable across all the environments tested or simply did not carry the vital genetic

variability related to adaptation to zero-tillage under either moisture regime. The development of new

varieties specifically adapted to zero-tillage systems is crucial if these interactions are to be exploited

in future (Duvick et al. 1990; Trethowan et al. 2005b). The significant year x genotype interaction

effects could be driven by the luxuriant supply of moisture in 2014, its therefore underscores the

selection of stable genotypes against unforeseen weather conditions.

Reynolds et al. (2005) emphasized the significance of and utilization of physiological traits to

increase wheat productivity under drought through breeding. The association of physiological traits

with identified genomic regions that confer higher yield under moisture stress could provide a cheaper

and faster avenue to improve genetic gains in wheat breeding. In this study, a wide range of traits

including biomass at maturity, canopy temperature, chlorophyll content, phenology, NDVI, plant

height, leaf rolling, relative water content, WUE, harvest index, TKW and earliness to ground cover in

combination accounted for 96.5% of the phenotypic variance associated with yield. However, just 5

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traits (days to heading, NDVI at early grain filling, leaf rolling, earliness to ground cover and grain

filling duration) accounted for 87.8% of the observed phenotypic variance in yield. These traits are

likely responsible in part or in combination for the observed significant marker trait associations in the

current study. Thus the ideal water use efficient genotypes should be: faster in providing ground cover,

early to heading to avoid post-anthesis moisture stress, develop larger photosynthetic biomass to

capture sunlight, roll their leaves to avoid damage while maintaining photosynthetic activity and have

longer grain filling periods.

Previous studies have identified QTLs on 1B, 4A and 5B associated with heading time

(Kamran et al. 2013; Zanke et al. (2014)). For instance, Kamran et al. (2013) reported 3 QTLs

(QEps.dms-1B1 and QEps.dms-1B2, QEps.dms-5B) regulating earliness per se and flowering time on

chromosomes 1B, 5B and 4A, respectively. The QTL (QEps.dms-1B1) found on chromosome 1B

mapped at 31.8cM and resided approximately in the same region as MTAs found in the earlier Atta

(2013) association analysis (markers, wPt-2597, wPt-3819, wPt-5899, wPt-8682, wPt-1684, wPt-1684

were found at distances of 29.4, 29.4, 30.9, 28.6 and 34.6 cM, respectively). The QTL (QEps.dms-5B)

on chromosome 5B directly coincided with an MTA (wPt 6135) mapped at 76.1 cM.. QTLs for NDVI

mainly, QNDVIs-caas-5B (Li et al. 2014a) located on chromosome 5B at 1.0 cM coincided with an

MTA for grain yield (wPt 1302) that is 0.2 cM distant. In addition QNDVIw-caas-2A.2 located at 4.0

cM coincided with marker wPt-8464 which was associated with yield in the earlier association

analysis. A number of QTLs have also been identified for earliness to ground cover. QTLs QGCw-

caas-1A.1, QGCw-caas-2A.2, TQGCw-caas-1A.2 at 6.1, 6.7 and 3.0 cM respectively (Li et al. 2014a)

coincided with markers identified in the earlier association analysis, notably wPt-6564, wPt 8242, wPt

8455 and wPt 1945 on chromosomes 1A, 2A, 1A at distances of 6.1, 6.7 and 3.0 cM, respectively.

Grain filling duration QTLs have also been identified in wheat. These QTLs include QGfd.nfcri-1A,

QGfd.nfcri-3B, QGfd.nfcri-5D, QGfd.nfcri-6D on chromosomes 1A, 3B, 5D, and 6D, respectively.

Leaf rolling is an adaptive trait that enables the plant to manage drought. Although little research on

QTLs for leaf rolling in wheat has been published, QTLs have been identified in rice (Price et al.

1997).

3.4.3 Implications for wheat breeding

Genetic gains in wheat yield under moisture stress have been limited (Richards et al. 2001) and

progress has been slow mainly due to the complexity of drought tolerance (Khan and Iqbal 2011). The

complexity of drought tolerance therefore demands a multifaceted and integrative genetic approach.

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The current study showed that superior yield with improved water use efficiency can be obtained under

zero tillage in both irrigated and rainfed environments. The advantage of zero tillage over conventional

tillage for yield, WUE, TKW, canopy temperature, intercepted photosynthetic active radiation, NDVI

and harvest index under low rainfall or high evaporative demand environments was confirmed. Higher

and more stable yield can be achieved by farmers when water use efficient genotypes are deployed in

zero tillage environments regardless of soil moisture status.

Although no genotype x environment interaction effects were observed, genotypes such as

Crusader, EGA Stampede, Lincoln, Sunvex and Janz tended to be adapted to zero tillage and therefore

could carry the potential genetic variability required to develop varieties better adapted to zero tillage.

The significant year x genotype interaction effects observed revealed that the selection of stable

genotypes, mainly Ventura and Livingston, could provide buffering against moisture stress.

Current molecular approaches have identified a number of chromosomal regions controlling

yield in wheat. However, very few of these genomic regions have been used for Marker Assisted

Selection (MAS) in plant breeding. The genomic regions/Marker Trait Association must be validated

in representative parental lines, breeding populations and phenotypic extremes before they can be

targeted by breeders. However, the 5 key traits identified could be targeted to efficiently improve grain

yield of wheat under moisture stress at relatively low cost.

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4 The efficacy of using mixtures to buffer yield under stress

4.1 Introduction

Varietal mixtures (genotype mixtures) may offer yield, disease and pest tolerance advantages

compared to their pure stands (Kiær et al. 2009). This phenomenon is based on the premise that

biodiversity has been found to increase ecological stability to sustain production (Brush 1991; Gómez

et al. 2000). Improved adaptability and buffering effects in the presence of abiotic and biotic stresses

have been reported in wheat (Sarandon and Sarandon 1995; Kaut et al. 2009; Zhou et al. 2014), maize

(Tilahun 1995), oats (Peltonensainio and Makela 1995) and barley (Tratwal A et al. 2007). Wheat

mixtures have been found to increase water-use-efficiency and produce higher yield than pure stands

(Naudin et al. 2011; Fang et al. 2014b).

Mixing effects are not always positive (Finckh and Mundt 1996; Jedel et al. 1998; Kaut et al.

2008a; Dai et al. 2012) and the selection of genotypes that show complementarity for specific traits is

crucial (Mille et al. 2006). Therefore, combining genetic modification with mixtures has the potential

to increase sustainability and productivity whilst avoiding the cost of stacking transgenes in individual

plants (Zeller et al. 2012).

Reports from most studies on varietal mixtures are limited since evaluation was conducted

mostly in a single environment or management condition. It is therefore difficult to extrapolate to other

environments. Consequently, contemporary mixtures research is geared towards designing varietal

mixtures for specific environments to maximize productivity while minimizing the impact on the

environmental (Cowger and Weisz 2008; Kaut et al. 2008b).

In order to optimise varietal mixtures for real environmental situations, it is necessary to

understand how their performance is affected by relatively different tillage/management practices. This

study evaluated the performance of a set of wheat varietal mixtures formulated on the basis of

complementary genomic regions linked to high yield under moisture stress identified from an earlier

association analysis. The objective was to determine the efficacy of mixtures in buffering yield against

moisture stress in four contrasting environments/management conditions.

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4.2 Materials and methods

Two and six genotype mixtures were evaluated alongside their pure stands in 2013 and 2014,

respectively. Each genotype represented an equivalent proportion of the mixture. The mixtures were

formulated on the basis of complementary markers/genomic regions identified in an earlier association

analysis (Atta 2013) together with other agronomic and genetic traits (Tables 4.1). It was assumed that

since the mixture components varied for the targeted markers, that complementary and compensatory

effects could further increase yield. The experiment involved four two-way mixtures:

Sunstate+Stampede, Lincoln+Lang, EGA Gregory+Ventura and Suntop+Spitfire and two three-way

mixtures: Sunstate+Stampede+Janz and Lincoln+Lang+Sunstate. The mixtures were evaluated in four

management types as indicated in Chapter 3: (i) zero-tillage/rainfed (ii) conventional-tillage/rainfed

(iii) zero tillage/irrigated and (iv) conventional tillage/irrigated.

The trial was laid out in 4 contiguous randomized complete block designs of three replicates

each as described in the previous chapter (see Appendix I for a description of the field layout). The

following data was collected using the procedures described in Chapter 3: canopy ground cover (GC),

number of days to heading (DHD), number of days to maturity (DPM), harvest index (HI), relative

water content (RWC), grain yield (GYD), thousand kernel weight (TKW), light interception (IPAR),

canopy temperature (CT), normalized difference vegetation index (NDVI) and plant height (HIGT).

Table 4.1 Characteristics of mixture components and their corresponding numbers of positive

marker/traits associations for grain yield identified in the earlier association analysis (Atta 2013)

GENOTYPE

NO. OF POSITIVE

MARKERS BREEDING METHOD MATURITY LODGING

JANZ 27 SELECTION FROM F4 BULKS INTERMEDIATE GD

LANG 30 SELECTION FROM BULKS INTERMEDIATE GD

LINCOLN 20 DOUBLE HAPLOID LINE INTERMEDIATE GD

SPITFIRE 22 DOUBLE HAPLOID LINE EARLY MS

EGA STAMPEDE 40 RECURENT SELECTION EARLY GD

SUNSTATE 43 SINGLE PLANT SELECTION EARLY GD

SUNTOP 23 RECURRENT SELECTION EARLY GD

VENTURA 31 RECURRENT SELECTION INTERMEDIATE GD

EGA GREGORY 28 DOUBLE HAPLOID LINE EARLY GD

Key GD-Good, MS-Moderately Susceptible

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4.3 Statistical analysis

GenStat statistical software, version 14.1 (Payne RW 2011), was used to test differences among the

four contrasting management practices using REML. The same treatment structure outlined in Chapter

3 on page 52 was used. Each varietal mixture and each pure line component were considered as

separate genotypes for the purposes of analysis. Treatments were decomposed by linear contrast

analysis to test differences between mixtures and their pure stands at a significance of 5% (Clewer and

Scarisbrick 2013). Year, management and genotype were considered fixed effects and range/row

within years as random effects as in Chapter 3.

4.4 RESULTS

4.4.1 Canopy temperature (ᵒC)

Generally, canopy temperatures were significantly influenced by management and means

ranged from 20.6°C in the conventional tillage irrigated management to 25.2°C in the conventional

tillage rainfed management. Comparatively, canopy temperatures were 1°C lower in zero tillage

compared to conventional tillage in both rainfed and irrigated conditions. No significant differences

among genotypes were observed and average canopy temperatures ranged from 22.7°C for the

Lincoln+Lang mixture to 23.50 °C for Sunstate+Stampede. No significant mixing effects on canopy

temperature were observed between mixtures and their corresponding pure stands (Table 4.2).

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Table 4.2 Combined means and analysis of canopy temperature (°C) of Lincoln+Lang and

Sunstate+Stampede mixtures versus their pure stands in 2013 and 2014.

Management

Genotype Zero till Irrigated Zero till rainfed Conv till Irrigated Conv. till Rainfed

LANG 21.37a 24.62a 21.77a 25.05a

LINCOLN 21.87a 23.93a 20.86a 24.27a

LINCOLN+LANG 20.46a 24.41a 19.87a 26.18a

EGA STAMPEDE 21.55a 24.40a 20.30a 24.96a

SUNSTATE 21.52a 24.65a 20.63a 25.71a

SUNSTATE+

EGASTAMPEDE 21.98a 26.45a 20.50a 25.06a

Mean 21.46 24.74 20.66 25.21

SED 0.63

Wald statistic

Year 10.43***

Management 117.04***

Genotype 2.42

Year x Management 17.25***

Year x Genotype 6.93

Management x Genotype 9.4

Year x Management x Genotype 5.65

Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. Conv-conventional. Means followed by the

same letter in columns within each mixture/pure line contrast are not significantly different at P<0.05.

In 2014, when the number of genotypes tested was increased, the management similarly

significantly influenced canopy temperature (Table 4.3). Canopy temperatures ranged from 21.0°C in

the conventional tillage irrigated management to 26.2°C in the conventional tillage rainfed

management. Temperatures in zero tillage rainfed managements were significantly cooler by 1.5°C

than in conventional tillage rainfed conditions. In irrigated managements no significant differences

were evident and tillage practice did not impact canopy temperatures. Similarly, no significant effects

of mixtures on canopy temperatures were observed. Canopy temperatures ranged from 23.3°C in Lang

to 24.1°C in Ventura as a pure stands.

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Table 4.3 Means and analysis of canopy temperature (°C) in mixtures versus their pure line

components in 2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv till

Irrigated

Conv. till

Rainfed

EGA GREGORY 20.72a 25.27a 20.84b 26.58a

VENTURA 21.85a 25.55a 22.42a 26.48a

EGA GREGORY+VENTURA 20.97a 25.53a 20.91ab 25.70a

LANG 21.07a 25.71bc 20.98a 25.26ab

LINCOLN 21.47a 26.22ac 21.18a 24.90b


LANG 21.07a 25.71a 20.98a 25.26bc

LINCOLN 21.47a 26.22a 21.18a 24.90bc

SUNSTATE 21.51a 27.07a 20.94a 26.26ac

LINCOLN+LANG+SUNSTATE 21.34a 26.84a 20.96a 27.02a

SUNSTATE 21.51a 27.07a 20.94a 26.26ac


SUNSTATE+EGA STAMPEDE 21.12a 26.38a 20.56a 25.26bc

SPITFIRE 21.31a 25.14a 20.99a 26.66a

SUNTOP 21.34a 26.61a 21.24a 25.50a

SUNTOP+SPITFIRE 21.33a 25.53a 20.64a 26.01a

SUNSTATE 21.51a 27.07a 20.94a 26.26a

EGA STAMPEDE 21.33a 26.60ac 20.94a 26.78a

JANZ 20.62a 25.60ac 21.20a 26.27a

SUNSTATE+EGA STAMPEDE+JANZ 21.13a 24.57bc 20.97a 27.22a

Mean 21.18 25.99 21.03 26.16

SED 0.38

Wald statistic Management 1261.89***

Genotype 15.69


Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. Means followed by the same letter in

columns within each mixture/pure line contrast are not significantly different at P<0.05.

4.4.2 Chlorophyll content

The amount of chlorophyll observed was significantly influenced by management, genotype

and genotype x management interaction effects (Table 4.4). Chlorophyll contents ranged from 37.3 in

conventional tillage rainfed managements to 41.9 in the conventional tillage irrigated management.

Comparatively, Lang as a pure stand had a significantly higher chlorophyll content than the

107

Lincoln+Lang mixture and EGA Stampede had the highest chlorophyll content in the conventional

tillage irrigated management. The mixtures were generally intermediate to the two parents.

Table 4.4 Combined means and analysis of chlorophyll content (SPAD units) of Lincoln+Lang and

Sunstate+Stampede mixtures and their pure stand in 2013 and 2014.

Management

Genotype Zero till Irrigated Zero till rainfed Conv. till Irrigated Conv. till Rainfed

LANG 42.95a 39.02a 43.83a 45.17a

LINCOLN 38.20b 40.75a 36.07b 35.77b

LINCOLN+LANG 39.07b 40.55a 41.08a 36.60b


SUNSTATE 38.47b 37.25a 42.32a 36.60a

SUNSTATE+ EGA STAMPEDE 39.37b 36.37a 43.65a 32.62b

Mean 40.13 38.27 41.86 37.29

SED 1.42

Wald statistic

Year 852.39***

Management 18.27***

Genotype 16.64**


Year x Genotype 22.09***

Management x Genotype 27.93*




In 2014 the chlorophyll contents were similarly significantly influenced by management.

Chlorophyll content ranged from 50.1 in the conventional tillage irrigated management to 52.8 in the

conventional tillage rainfed management (Table 4.5). There was no advantage of mixtures over pure

stands in chlorophyll content across all four managements. Conventional tillage and zero tillage

rainfed managements produced higher chlorophyll content in EGA Stampede and Ventura pure stands,

whilst EGA Stampede had higher chlorophyll in the zero tillage irrigated management. Although no

significant genotypic differences occurred, positive mixing effects on chlorophyll contents were

observed in the EGA Gregory+Ventura mixture compared to its components.

108

Table 4.5 Means and analysis of chlorophyll content (SPAD units) of mixtures versus their pure stands

in 2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 50.1b 53.0a 50.6a 53.5b

VENTURA 49.9b 53.8a 46.3b 55.2a

EGA GREGORY+VENTURA 55.5a 54.0a 51.2a 52.1b

LANG 50.2ac 52.4a 48.6b 53.4a

LINCOLN 51.9a 49.9b 50.5a 54.4a

LINCOLN+LANG 49.5bc 51.3a 51.4a 54.1a

LANG 50.2a 52.4a 48.6a 53.4a

LINCOLN 51.9a 49.9bc 50.5a 54.4a

SUNSTATE 50.9a 51.7ac 49.7a 54.9a

LINCOLN+LANG+SUNSTATE 51.7a 50.1bc 50.4a 54.8a

EGA STAMPEDE 52.2a 47.2c 50.3b 52.2b

SUNSTATE 50.9a 51.7b 49.7b 54.9a

SUNSTATE+ EGA STAMPEDE 51.4a 53.0a 52.4a 49.2b

SPITFIRE 50.9b 53.3a 48.4b 53.4a

SUNTOP 55.7a 51.0b 52.2a 52.6a

SUNTOP+SPITFIRE 49.2b 53.3a 51.2a 53.7a

JANZ 51.1a 49.4c 48.9a 48.3d

EGA STAMPEDE 52.2a 47.2b 50.3a 52.2b

SUNSTATE 50.9a 51.7a 49.7a 54.9a

SUNSTATE+EGA STAMPEDE+JANZ 52.7a 47.9bc 49.9a 50.2c

Mean 51.5 51.4 50.1 52.8

SED 1.05

Wald statistic

Management 24.06***

Genotype 22.01




4.4.3 Heading time (days from sowing to heading)

Heading time was significantly influenced by management and genotype. Heading time ranged

from 101.2 days in zero tillage rainfed to 104 days in conventional tillage irrigated managements

(Table 4.6). Among the genotypes tested, heading time ranged from 99 days in Lincoln to 109.5 days

109

in Lang. The effects of mixing were intermediate for heading time compared to the component pure

stands for both mixtures.

Table 4.6 Combined means and analysis of heading time (days from sowing) of Lincoln+Lang and

Sunstate+stampede mixtures and their pure stands in 2013 and 2014.

Management

Genotype Zero till Irrigated Zero till Rainfed Conv. till Irrigated Conv. till Rainfed

LANG 111.0b 107.8a 109.8a 109.5a

LINCOLN 98.5c 98.0c 98.0b 101.8c

LINCOLN+LANG 104.7a 100.8b 102.8c 104.0b

EGA STAMPEDE 100.81c 99.3a 102.0b 103.0a

SUNSTATE 104.7a 100.8a 106.2a 102.7a

SUNSTATE+ EGA STAMPEDE 103.0b 100.5a 105.3a 101.8b

Mean 103.8 101.2 104 103.8

SED 0.80

Wald statistic

Year 67.51***

Management 24.69***

Genotype 190.36***

Year x Management 8.44*



Year x Management x Genotype 27.01*



In 2014, the numbers of mixtures were increased and heading time was influenced significantly

by genotype, management and genotype x management interaction (Table 4.7). Heading time was

significantly earlier in zero tillage compared to the conventional tillage (by 2 days) in rainfed

managements. No mixing effects were observed and all mixtures tended to be intermediate for heading

time.

110

Table 4.7 Means and analysis of heading time (days from sowing) of mixtures versus pure stands in

2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 117.3a 115.3a 118.7a 117.0a

VENTURA 112.3b 108.3b 111.0c 104.3b

EGA GREGORY+VENTURA 116.0a 114.0a 113.7b 114.7b

LANG 115.3a 113.7a 113.7a 112.7a

LINCOLN 97.0c 96.3c 96.3c 104.0c

LINCOLN+LANG 107.0b 102.0b 105.3b 106.7b

LANG 115.3a 113.7a 113.7a 112.7a

LINCOLN 97.0c 96.3c 96.3d 104.0b

SUNSTATE 108.0b 101.7b 111.3b 104.3b

LINCOLN+LANG+SUNSTATE 108.3b 102.0b 105.3c 105.7b

EGA STAMPEDE 101.3c 99.0b 105.3b 102.3a

SUNSTATE 108.0a 101.7a 111.3a 104.3a

SUNSTATE+ EGASTAMPEDE 104.7b 101.3ab 110.0a 103.3a

JANZ 112.7a 108.0a 112.7a 109.3a

EGA STAMPEDE 101.3c 99.0c 105.3b 102.3c

SUNSTATE 108.0b 101.7b 111.3a 104.3bc

SUNSTATE+EGA STAMPEDE+JANZ 107.7b 100.0bc 107.3b 105.7b

SPITFIRE 103.0a 99.3b 100.3b 99.3b

SUNTOP 101.7b 104.0a 109.7a 104.7a

SUNTOP+SPITFIRE 105.0a 97.7b 108.3a 101.0b

Mean 107.8 104.2 108.6 106.3

SED 1.24

Wald statistic

Management 54.87***

Genotype

512.01**

*

Management x Genotype 75.75***



111

4.4.4 Physiological maturity (days from sowing to maturity).

Earliness to physiological maturity varied significantly with year, genotype and management

(Table 4.8). However, no significant genotype x management interaction was observed. Maturity time

was earlier by 4 days in 2013 compared to 2014. Among the four managements tested, heading time

was significantly earlier by 4 days in the rainfed than irrigated managements. Although significant

differences among genotypes were observed, no significant maturity time advantage was observed

between mixtures and all their pure stands. The mixtures tended to be intermediate to their pure line

components.

Table 4.8 Combined means and analysis of physiological maturity (days from sowing) of

Lincoln+Lang and Sunstate+Stampede mixtures and their pure stands in 2013 and 2014.

Management


LANG 153.8a 145.0a 150.8a 146.2a

LINCOLN 146.8c 142.7b 146.7b 143.8b

LINCOLN+LANG 151.2b 144.8a 149.7c 144.3b


SUNSTATE 147.3b 143.0a 149.0a 142.2b

SUNSTATE+EGA STAMPEDE 148.3b 143.8a 148.3a 143.7a

Means 149.3 143.8 148.9 144.0

SED 0.54

Wald statistic

Year 161.68***


Genotype 73.12***

Year x Management 13.3**






4.4.5 Maturity time 2014 cropping season

In 2014, significant genotypic and management influence on maturity time was also observed

(Table 4.9). However no significant genotype x management interaction effect was observed. Maturity

112

time was significantly earlier by 1 day in zero tillage compared with conventional tillage irrigated

managements. However, no differences were noted in rainfed managements. Although significant

differences among genotype were observed, no advantage in maturity time of mixtures over pure

stands were observed. Again, mixing generally produced an intermediate maturity compared to the

pure line component genotypes.

Table 4.9 Means and analysis of maturity time (days from sowing) of mixtures versus pure stands in

2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 155.3a 151.7a 155.3a 150.0a

VENTURA 154.0b 147.3c 148.3b 148.3b

EGA GREGORY+VENTURA 156.0a 149.7b 156.0a 150.3a

LANG 157.3a 146.0a 152.7a 147.0a

LINCOLN 148.3c 143.7b 148.7c 146.0a

LINCOLN+LANG 154.0b 145.7a 150.7b 145.3a

LANG 157.3a 146.0a 152.7a 147.0a

LINCOLN 148.3c 143.7b 148.7c 146.0a

SUNSTATE 151.0b 144.7a 152.0a 143.7b

LINCOLN+LANG+SUNSTATE 149.3c 145.0a 151.0b 145.3a


SUNSTATE 151.0a 144.7a 152.0a 143.7a

SUNSTATE+ EGA STAMPEDE 151.0a 146.0a 152.0a 145.0a

EGA STAMPEDE 150.3b 145.7ac 152.3a 145.3bc

SUNSTATE 151.0b 144.7c 152.0a 143.7b

JANZ 154.0a 147.3a 151.3ab 146.7ac

SUNSTATE+EGASTAMPEDE+JANZ 153.0a 145.3bc 150.0b 147.3a

SPITFIRE 150.7c 144.7b 150.3b 144.0b

SUNTOP 152.3b 147.3a 152.7a 146.3a

SUNTOP+SPITFIRE 154.0a 144.7b 152.3a 144.3b

Mean 152.7 146.3 151.7 146.3

SED 1.02

Wald statistic


Genotype 91.74***




113

4.4.6 Grain filling duration (days)

Grain filling duration was significantly affected by management and genotype in 2013 and

2014 (Table 4.10). In rainfed managements, grain filling duration was significantly shorter (by 2 days)

in zero tillage compared to conventional tillage. However, no differences we noted under irrigation.

No advantage of mixtures over pure stand components was observed.

Table 4.10 Combined means and analysis of grain filling duration (days) of Lincoln+Lang and

Sunstate+Stampede mixtures and their pure stands in 2013 and 2014.

Management


LANG 42.83c 37.17c 41.00c 36.67c

LINCOLN 48.33a 44.67a 48.67a 42.00a

LINCOLN+LANG 46.50b 44.00a 46.83b 40.33b

EGA STAMPEDE 47.67a 44.33a 47.00a 40.67ac

SUNSTATE 42.67c 42.17b 42.83c 39.50bc

SUNSTATE+ EGA STAMPEDE 45.33b 43.33a 43.00b 41.83a

Mean 45.6 42.6 44.9 40.2

SED 0.83

Wald statistic

Year 0.16

Management 77.54***

Genotype 81.04***

Year x Management 5.3




Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. Means followed by the same letter

in columns within each mixture/pure line contrast are not significantly different at P<0.05.

In 2014, only management and genotype significantly influenced grain filling duration (Table

4.11). Grain filling duration was similarly significantly shorter in rainfed compared to irrigated

conditions and longer in conventional tillage compared to zero tillage. Generally, no advantage of

mixtures over all pure stands was observed. Mixing effects for grain-filling were again intermediate.

114

Table 4.11 Means and analysis of grain filling duration (days) of mixtures versus pure stands in 2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 38.0b 36.3b 36.7b 33.0b

VENTURA 41.7a 39.0a 37.3b 44.0a

EGA GREGORY+VENTURA 40.0a 35.7b 42.3a 35.7b

LANG 42.0b 32.3c 39.0c 34.3c

LINCOLN 51.3a 47.3a 52.3a 42.0a

LINCOLN+LANG 47.0b 43.7b 45.3b 38.7b

LANG 42.0b 32.3c 39.0c 34.3b

LINCOLN 51.3a 47.3a 52.3a 42.0a

SUNSTATE 43.0b 43.0b 40.7c 39.3a

LINCOLN+LANG+SUNSTATE 41.0b 43.0b 45.7b 39.7a


SUNSTATE 43.0c 43.0b 40.7b 39.3bc

SUNSTATE+ EGA STAMPEDE 46.3b 44.7b 42.0b 41.7ac


SUNSTATE 43.0bc 43.0b 40.7bc 39.3bc

JANZ 41.3c 39.3c 38.7c 37.3b

SUNSTATE+ EGA STAMPEDE+JANZ 45.3b 45.3b 42.7b 41.7ac

SPITFIRE 47.7b 45.3ac 50.0a 44.7a

SUNTOP 50.7a 43.3bc 43.0b 41.7bc

SUNTOP+SPITFIRE 49.0b 47.0a 44.0b 43.3ac

Mean 45.1 42.2 43.3 39.9

SED 1.47

Wald statistic

Management 43.21***

Genotype 179.59***




4.4.7 Harvest index

Harvest indices were significantly influenced by management and year. Harvest indices were

5.7% higher in 2013 than 2014 (Table 4.12). Harvest indices were also higher in zero tillage compared

to conventional tillage in rainfed managements; however in the irrigated managements differences

115

were not significantly different. No advantage of mixtures over pure stands was observed for harvest

index.

Table 4.12 Combined means and analysis of harvest index of Lincoln+Lang and Sunstate+Stampede

mixtures and their pure stands in 2013 and 2014.

Management


LANG 0.40a 0.45a 0.40a 0.40a

LINCOLN 0.46a 0.42a 0.41a 0.42a



SUNSTATE 0.40a 0.43a 0.40a 0.39a

SUNSTATE+EGASTAMPEDE 0.45a 0.42a 0.41a 0.40a

Mean 0.43 0.43 0.41 0.41

SED 0.01

Wald statistic

Year 13.88***

Management 11.55**

Genotype 11.04


Year x Genotype 15.4*


Year x Management x

Genotype 20.14



In 2014, harvest indices were significantly influenced by genotype when the number of

genotypes (mixtures) tested was increased (Table 4.13). However, no advantages of higher harvest

indices were observed in mixtures over their pure stands. Harvest indices were again intermediate in

mixtures compared to the component genotypes in pure stand.

116

Table 4.13 Means and analysis of harvest index of mixtures versus pure stands in 2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 0.34b 0.36b 0.36b 0.38b

VENTURA 0.39a 0.41a 0.45a 0.41a

EGA GREGORY+VENTURA 0.37a 0.35b 0.35b 0.36b

LANG 0.37b 0.44a 0.35b 0.39a

LINCOLN 0.46a 0.42a 0.41a 0.40a

LINCOLN+LANG 0.39b 0.39b 0.38c 0.40a

LANG 0.37c 0.44a 0.35c 0.39a

LINCOLN 0.46a 0.42ad 0.41a 0.40a

SUNSTATE 0.36c 0.40bd 0.38b 0.40a

LINCOLN+LANG+SUNSTATE 0.40b 0.37c 0.35c 0.39a

EGA STAMPEDE 0.44b 0.45a 0.45a 0.42a

SUNSTATE 0.36c 0.40b 0.38c 0.40a

SUNSTATE+EGA STAMPEDE 0.47a 0.41b 0.42b 0.40a

EGA STAMPEDE 0.44a 0.45a 0.45a 0.42ac

SUNSTATE 0.36c 0.40b 0.38c 0.40bc

JANZ 0.38c 0.38b 0.34d 0.39bd

SUNSTATE+ EGA STAMPEDE+JANZ 0.41b 0.40b 0.40b 0.43a

SPITFIRE 0.42a 0.43a 0.43a 0.43a

SUNTOP 0.39b 0.40b 0.37b 0.39b

SUNTOP+SPITFIRE 0.41a 0.40b 0.39b 0.42a

Mean 0.40 0.40 0.39 0.40

SED 0.02

Wald statistic

Management 3.01

Genotype 72.5***




4.4.8 Plant height (cm)

Plant height was significantly influenced by genotype and management. In rainfed

managements the differences in plant height were not significant, however under irrigation plants were

on average 2cm taller in zero tillage. Lincoln+Lang mixtures were significantly shorter by 1.49cm

against pure stands whilst the Sunstate+Stampede mixture was intermediate to its components in pure

stands (Table 4.14).

117

Table 4.14 Combined means and analysis of plant height (cm) of Lincoln+Lang and


Management


LANG 89.4a 86.3a 87.2a 83.0a

LINCOLN 88.6a 85.6ac 87.5a 84.1a

LINCOLN+LANG 89.2a 84.8bc 84.3b 81.6b

EGA STAMPEDE 89.6c 87.9b 88.8c 86.5b

SUNSTATE 95.1a 91.3a 91.5a 90.6a

SUNSTATE+ EGA STAMPEDE 91.8b 86.4c 90.0b 90.2a

Mean 90.6 87.1 88.2 86

SED 0.73

Wald statistic

Year 0.84

Management 66.21***

Genotype 125.33***




Year x Management x Genotype 23.85 Note: *, **, *** indicates significance at P≤0.05, 0.01 and 0.001, respectively. Means followed by the same letter in


In 2014, plant heights were similarly significantly influenced by genotype and management. In

rainfed and irrigated conditions plants were significantly higher under zero tillage (Table 4.15). No

significant mixing effect on plant height was observed although there was a trend to taller mixtures in

the EGA GREGORY+VENTURA combination compared it the component lines in pure stand.

118

Table 4.15 Means and analysis of plant height (cm) of mixtures versus pure stands in 2014

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 99.2a 91.2a 92.1a 83.2b

VENTURA 99.4a 89.1a 89.1b 87.0a

EGA GREGORY+VENTURA 99.4a 90.4a 94.4a 88.0a

LANG 92.6a 87.8a 89.1a 80.2b

LINCOLN 91.1a 85.1a 86.8b 83.1a

LINCOLN+LANG 91.7a 84.2b 84.4b 80.1b

LANG 92.6a 87.8b 89.1b 80.2c

LINCOLN 91.1a 85.1c 86.8c 83.1b

SUNSTATE 93.3a 90.1a 88.3bc 89.1a

LINCOLN+LANG+SUNSTATE 91.3a 87.7b 93a 82.9b

EGA STAMPEDE 93.3a 90.6a 88.6a 86.3b

SUNSTATE 93.3a 90.1a 88.3a 89.1a

SUNSTATE+EGA STAMPEDE 93.7a 89.2a 88.9a 88.2ab

EGA STAMPEDE 93.3a 90.6a 88.6b 86.3b

SUNSTATE 93.3a 90.1a 88.3b 89.1a

JANZ 86.7b 82.4b 79.4c 79.0c

SUNSTATE+EGA STAMPEDE+JANZ 93.3a 88.7a 90.6a 86.8b

SPITFIRE 85.1b 83.3b 83.2b 80.3c

SUNTOP 92.4a 89.0a 86.7a 87.3a

SUNTOP+SPITFIRE 91.7a 89.9a 88.9a 84.6b

Mean 93 87.9 88.2 84.4

SED 1.17

Wald statistic


Genotype 197.31***




4.4.9 Light interception (IPAR)

There was a significant influence of management, genotype and year on the amount of

intercepted light recorded (Table 4.16). Light interception was significantly higher in conventional

tillage than zero tillage under irrigation; however in rainfed managements the differences were not

significant. No advantage of light interception in mixtures over pure stands was observed.

119

Table 4.16 Combined means and analysis of light interception (%) of Lincoln+Lang and


Management


LANG 87.94a 80.03a 90.14a 81.14a

LINCOLN 83.97b 74.51b 87.34b 77.76bc

LINCOLN+LANG 85.87a 77.21a 90.24a 78.18ac


SUNSTATE 80.53b 76.91a 87.43b 72.48b

SUNSTATE+EGA STAMPEDE 82.87b 75.53a 88.96b 73.34b

MEAN 85.0 76.66 89.23 76.72

SED 1.88

Wald statistic

Year 109.49**

Management 99.24***

Genotype 12.34*







In 2014, light interception was significantly affected by management and genotype (Table

4.17). In irrigated managements, conventional tillage had significantly higher light interception than

zero tillage. However, in rainfed managements these differences were not significant. No light

interception advantage was observed in mixtures compared to their pure stands.

120

Table 4.17 Means and analysis of light interception (%) of mixtures versus pure stands in 2014 .

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 93.66a 69.87a 93.52a 59.68a

VENTURA 91.27a 68.67a 91.84a 64.72a

EGA GREGORY+VENTURA 93.82a 60.73b 95.53a 57.71b

LANG 94.04a 64.00a 94.24a 66.55a

LINCOLN 86.41b 57.17bc 91.34a 64.25a

LINCOLN+LANG 91.34a 59.47ac 94.95a 62.84a

LANG 94.04a 64.00ab 94.24a 66.55a

LINCOLN 86.41b 57.17c 91.34a 64.25ac

SUNSTATE 85.0b 60.7bc 92.20a 57.85b

LINCOLN+LANG+SUNSTATE 90.34ab 66.73a 93.57a 60.32bc

EGA STAMPEDE 90.94ab 63.90a 94.30a 60.81a

SUNSTATE 85.0b 60.70a 92.20a 57.85ac

SUNSTATE+STAMPEDE 93.0a 57.44b 92.30a 53.86bc

EGA STAMPEDE 90.94ab 63.90a 94.30a 60.81ac

SUNSTATE 85.0b 60.70a 92.20a 57.85bc

JANZ 93.09a 43.67b 92.63a 55.74bc

SUNSTATE+EGA STAMPEDE+JANZ 95.42a 61.73a 92.64a 66.5a

SPITFIRE 89.08a 57.05a 92.05ab 58.07a

SUNTOP 88.0a 63.75a 86.91b 63.10a

SUNTOP+SPITFIRE 89.6a 56.04b 93.92a 60.32a

Mean 91 60.73 92.8 60.82

SED 3.11

Wald statistic


Genotype 17.61***




4.4.10 Normalized Difference Vegetation Index (NDVI) at early grain filling.

NDVI was significantly influenced by year, management and genotype. NDVI at early grain

filling was significantly higher in 2013 than 2014 (Table 4.18). NDVI at early grain filling was

significantly higher in conventional tillage compared to zero tillage in rainfed managements. However

121

in irrigated conditions, zero tillage had higher NDVI than conventional tillage. Mixtures did not

demonstrate any advantage of NDVI over pure stands.

Table 4.18 Combined means and analysis of NDVI at early grain filling stage of Lincoln+Lang and


Management


LANG 0.779a 0.735a 0.709a 0.766a

LINCOLN 0.740c 0.727a 0.734b 0.742bc

LINCOLN+LANG 0.775b 0.736a 0.737b 0.744bc

EGA STAMPEDE 0.765a 0.732a 0.688b 0.720a

SUNSTATE 0.744ac 0.680b 0.750a 0.695b

SUNSTATE+ EGA STAMPEDE 0.735bc 0.676b 0.748a 0.729a

Mean 0.756 0.714 0.727 0.733

SED 0.011

Wald statistic

Year 325.42***

Management 19.81***

Genotype 12.27*







At early grain filling stage in 2014, NDVI was significantly influenced by genotype,

management and genotype x management interaction effects (Table 4.19). NDVI was significantly

higher in zero tillage irrigated managements than under conventional tillage. However, no differences

were observed under rainfed conditions. Significant positive mixing effects were observed in EGA

Gregory+Ventura, Suntop+Stampede mixtures whilst, Lincoln+Lang+Sunstate and

Sunstate+Stampede+Janz mixtures were intermediate. Although positive mixing effects were observed

in the Sunstate+Stampede mixture, these were not significant.

122

Table 4.19 Means and analysis of NDVI at early grain filling of mixtures versus pure stands in 2014 .

Environment

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 0.687a 0.657a 0.692b 0.645a

VENTURA 0.699a 0.636a 0.583c 0.526b

EGA GREGORY+VENTURA 0.643b 0.609b 0.732a 0.616a

LANG 0.732a 0.712a 0.633b 0.734a

LINCOLN 0.702a 0.666b 0.647b 0.652b

LINCOLN+LANG 0.712a 0.683a 0.689a 0.692b

LANG 0.732a 0.712a 0.633b 0.734a

LINCOLN 0.702ab 0.666b 0.647b 0.652c

SUNSTATE 0.679b 0.626c 0.701a 0.607d

LINCOLN+LANG+SUNSTATE 0.683b 0.674b 0.706a 0.679b


SUNSTATE 0.679a 0.626ac 0.701a 0.607b

SUNSTATE+STAMPEDE 0.687a 0.601bc 0.702a 0.644ab

EGA STAMPEDE 0.702ab 0.658bc 0.590b 0.653b

SUNSTATE 0.679b 0.626b 0.701a 0.607c

JANZ 0.718a 0.707a 0.718a 0.725a

SUNSTATE+STAMPEDE+JANZ 0.663b 0.677ac 0.628b 0.674b

SPITFIRE 0.713a 0.588b 0.685a 0.606b

SUNTOP 0.687ab 0.646a 0.677a 0.666a


MEANS 0.691 0.654 0.672 0.652

SED 0.019

Wald statistic

Management 20.2***

Genotype 50.28***

Management x Genotype 75.34**



4.4.11 Normalized Difference Vegetation Index at heading & anthesis

NDVI at early anthesis was similarly influenced significantly by year, genotype and genotype x

management interaction effects (Table 4.20). NDVI at early anthesis was significantly higher in zero

tillage in both irrigated and rainfed conditions. NDVI was also higher in 2013 than 2014. No NDVI

advantage was observed in mixtures over pure stands across the managements tested. However,

123

negative mixing effects were observed between Sunstate+Stampede and Lincoln+Lang mixtures

compared to their pure stands.

Table 4.20 Combined means and analysis of NDVI at heading and anthesis of Lincoln+Lang and


Management


LANG 0.721a 0.753a 0.728a 0.665a

LINCOLN 0.750a 0.692b 0.726a 0.692a

LINCOLN+LANG 0.671b 0.732a 0.715a 0.682a


SUNSTATE 0.784a 0.687b 0.685a 0.662a

SUNSTATE+EGA STAMPEDE 0.730b 0.632c 0.679a 0.687a

Mean 0.734 0.701 0.709 0.674

SED 0.022

Wald statistic

Year 325.42***

Management 19.81***

Genotype 12.27*



Environment x Genotype 29.68*

Year x Environment x Genotype 18.71



In 2014, the effect of management on NDVI were significant. NDVI was significantly higher in

zero tillage under both irrigated and rainfed conditions (Table 4.21). However, no advantage of NDVI

was observed in mixtures compared to pure stands.

124

Table 4.21 Means and analysis of NDVI at heading and anthesis of mixtures versus pure stands in

2014.

Management

Genotype

Zero till

Irrigated

Zero till

Rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 0.718a 0.745a 0.723a 0.660a

VENTURA 0.634a 0.555a 0.627a 0.510a

EGA GREGORY+VENTURA 0.735a 0.623a 0.656a 0.620a

LANG 0.669a 0.764a 0.698a 0.581a

LINCOLN 0.742a 0.702a 0.687a 0.654a


LANG 0.669a 0.764a 0.698a 0.581a

LINCOLN 0.742a 0.702a 0.687a 0.654a

SUNSTATE 0.817a 0.701a 0.631a 0.610a

LINCOLN+LANG+SUNSTATE 0.608a 0.716a 0.744a 0.646a


SUNSTATE 0.817a 0.701a 0.631a 0.610a

SUNSTATE+EGASTAMPEDE 0.697a 0.566a 0.623a 0.639a


SUNSTATE 0.817a 0.701a 0.631a 0.610a

JANZ 0.727a 0.770a 0.671a 0.620a

SUNSTATE+EGA STAMPEDE+JANZ 0.724a 0.758a 0.684a 0.636a

SPITFIRE 0.722a 0.659a 0.730a 0.561a

SUNTOP 0.613a 0.740a 0.684a 0.628a


MEANS 0.701 0.699 0.683 0.616

SED 0.041

Wald statistic

Management 20.2***

Genotype 50.28




4.4.12 Normalized Difference Vegetation Index at late grain filling

NDVI at late grain filling was significantly influenced by year, management and genotype

(Table 4.22). Significantly higher NDVI was observed in 2014 than 2013. Measurements were also

significantly higher in zero tillage irrigated managements than conventional tillage irrigated

125

managements. However, no significant influences were observed among managements under rainfed

conditions. No significant influence of mixtures over pure stands was observed for NDVI at late grain

filling.

Table 4.22 Combined means and analysis NDVI at late grain filling of Lincoln+Lang and


Management

Genotype Zero till irrigated Zero till rainfed Conv. till Irrigated Conv. till Rainfed

LANG 0.639a 0.535a 0.549b 0.516a

LINCOLN 0.624a 0.389c 0.571a 0.424b

LINCOLN+LANG 0.637a 0.478b 0.588a 0.436b

EGA STAMPEDE 0.593b 0.456a 0.581b 0.478a

SUNSTATE 0.607a 0.398b 0.624a 0.414b

SUNSTATE+EGA STAMPEDE 0.544c 0.394b 0.618a 0.417b

Mean 0.607 0.442 0.588 0.448

SED 0.017

Wald statistic

Year 9.84***


Genotype 19.34***







Similarly, in 2014, NDVI at late the grain filling stage was significantly affected by

management and genotype (Table 4.23). NDVI was significantly higher in zero tillage than

conventional tillage in both rainfed and irrigated conditions. Positive mixing effects were evident in

the Suntop+Spitfire, mixture whilst negative effects were observed in Sunstate+Stampede, EGA

Gregory+Ventura and Lincoln+Lang+Sunstate mixtures.

126

Table 4.23 Means and analysis of NDVI at late grain filling of mixtures versus pure stands in 2014

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 0.644a 0.488a 0.646a 0.378b

VENTURA 0.630a 0.473a 0.579b 0.486a

EGA GREGORY+VENTURA 0.627a 0.462a 0.622ab 0.352b

LANG 0.685a 0.576a 0.538bc 0.517a

LINCOLN 0.692a 0.362c 0.550ac 0.445b

LINCOLN+LANG 0.684a 0.491b 0.601a 0.418b

LANG 0.685a 0.576a 0.538b 0.517a

LINCOLN 0.692a 0.362b 0.550b 0.445b

SUNSTATE 0.673a 0.365b 0.672a 0.398c

LINCOLN+LANG+SUNSTATE 0.570b 0.379b 0.640a 0.387c


SUNSTATE 0.673a 0.365b 0.672a 0.398b

SUNSTATE+EGA STAMPEDE 0.554b 0.348b 0.684a 0.429a


SUNSTATE 0.673a 0.365b 0.672a 0.398b

JANZ 0.531b 0.472a 0.507c 0.431ab

SUNSTATE+EGA STAMPEDE+JANZ 0.633a 0.471a 0.551bc 0.429ab

SPITFIRE 0.556c 0.414a 0.627ac 0.347a

SUNTOP 0.635b 0.484a 0.559c 0.355a


Means 0.632 0.445 0.60 0.41

SED 0.029

Wald statistic


Genotype 22.02*




4.4.13 Protein content

Grain protein content was significantly influenced by year, management and genotype. Protein

contents were 6% higher in 2014 than 2013 (Table 4.24). Protein was also significantly higher in

conventional tillage than zero tillage in both rainfed and irrigated managements. No significantly

higher protein content of mixtures over pure stands was observed.

127

Table 4.24 Combined means and analysis of protein content (%) of Lincoln+Lang and

Sunstate+Stampede mixtures and their pure stands in 2013 and 2014

Management


LANG 12.67a 13.22a 13.73a 13.93a

LINCOLN 12.29a 13.01a 13.40a 13.40b

LINCOLN+LANG 12.67a 13.27a 13.18b 13.49b

EGA STAMPEDE 12.43ac 11.95a 13.16b 12.60b

SUNSTATE 12.80a 12.98a 14.26a 13.53a

SUNSTATE+EGA STAMPEDE 12.16bc 12.25b 13.71c 12.79c

Mean 12.5 12.78 13.57 13.29

SED 0.23

Wald statistic

Year 32.88***

Management 39.69***

Genotype 23.01***




Year x Mangement x Genotype 19.24



In 2014, protein contents were significantly influenced by genotype and management (Table

4.25). Similarly, protein contents were significantly higher in conventional tillage than zero tillage

management under both irrigated and rainfed conditions. In addition, higher grain protein content was

observed in Suntop+Spitfire than its pure stand components whilst negative mixing effects were

evident in Lincoln+Lang.

128

Table 4.25 Means and analysis of protein content (%) of mixtures versus pure stands in 2014

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 13.05a 14.19a 14.82b 14.71a

VENTURA 12.20b 13.41ab 14.06b 13.92b

EGA GREGORY+VENTURA 12.62ab 13.12b 15.83a 14.88a

LANG 12.10a 13.36a 14.04a 14.79a

LINCOLN 12.06a 13.03a 14.05a 14.36a


LANG 12.10a 13.36a 14.04b 14.79ab

LINCOLN 12.06a 13.03a 14.05b 14.36b

SUNSTATE 12.73a 13.11a 15.41a 14.43b

LINCOLN+LANG+SUNSTATE 12.50a 13.47a 14.43b 15.50a

EGA STAMPEDE 12.98a 11.26b 14.61b 13.60b

SUNSTATE 12.73ab 13.11a 15.41a 14.43a

SUNSTATE+EGA STAMPEDE 12.08b 11.97b 14.92a 13.62b

EGA STAMPEDE 12.98a 11.26b 14.61b 13.60b

SUNSTATE 12.73a 13.11a 15.41a 14.43a

JANZ 12.75a 13.56a 15.55a 14.65a

SUNSTATE+EGA STAMPEDE+JANZ 12.85a 13.02ab 14.28b 14.90a

SPITFIRE 14.05a 13.64a 14.06b 13.78b

SUNTOP 13.35ab 12.52b 13.94b 13.97b


Mean 12.72 13.12 14.56 14.51

SED 0.41

Wald statistic


Genotype 28.3**




4.4.14 Relative water content (RWC)

RWC among the genotypes tested was significantly influenced by year and management (Table

4.26). RWC was higher by 10% in 2013 than 2014. RWC was significantly higher in irrigated

conditions under conventional tillage compared to zero tillage. No significant effects of genotype on

RWC were observed.

129

Table 4.26 Combined means and analysis of RWC (%) of Lincoln+Lang and Sunstate+Stampede

mixtures against their pure stands in 2013 and 2014.

Management


LANG 41.21b 32.98b 49.82a 38.16ac

LINCOLN 45.76a 45.86a 49.30a 35.07bc

LINCOLN+LANG 38.1c 42.79a 50.44a 39.41a

EGA STAMPEDE 45.59a 37.74a 49.15a 35.03b

SUNSTATE 42.14b 39.92a 46.35a 43.01a

SUNSTATE+EGA STAMPEDE 36.28c 34.94b 46.88a 43.30a

Mean 41.51 39.04 48.66 39

SED 1.70

Wald statistic

Year 38.67***

Management 64.53***

Genotype 6.92


Year x Genotype 14.43*


Year x Management x Genotype 35.81**



The management and genotype significantly influenced RWC in 2014. RWC in conventional

tillage was significantly higher than zero tillage in rainfed managements (Table 4.27). However in

irrigated managements differences were not significant. The Sunstate+Stampede+Janz mixture had

significantly higher RWC than its pure line components whilst effects among the other mixtures were

not significant.

130

Table 4.27 Means and analysis of RWC (%) of mixtures versus pure stands in 2014.

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 40.17b 43.17b 46.25b 40.89ab

VENTURA 43.47b 58.33a 42.58b 44.20a

EGA GREGORY+VENTURA 49.54a 26.18c 71.04a 39.14b

LANG 32.84b 25.85c 45.59a 37.50a

LINCOLN 47.59a 45.19a 37.67b 40.54a

LINCOLN+LANG 34.67b 37.34b 43.95a 30.56b

LANG 32.84c 25.85d 45.59a 37.50a

LINCOLN 47.59a 45.19a 37.67c 40.54a

SUNSTATE 42.60b 37.82b 42.69ab 43.33a

LINCOLN+LANG+SUNSTATE 44.74a 31.35c 40.47bc 40.59a

EGA STAMPEDE 44.83a 34.64b 42.76a 34.66b

SUNSTATE 42.60a 37.82a 42.69a 43.33a

SUNSTATE+EGA STAMPEDE 40.96a 33.35c 36.49b 42.45a

EGA STAMPEDE 44.83b 34.64bc 42.76a 34.66b

SUNSTATE 42.6b 37.82b 42.69a 43.33a

JANZ 35.07c 30.68c 36.45b 35.39b

SUNSTATE+EGA STAMPEDE+JANZ 51.46a 44.08a 39.98ab 38.43ab

SPITFIRE 49.84a 45.43a 50.86a 49.80a

SUNTOP 43.28b 40.56a 45.02b 42.32b

SUNTOP+SPITFIRE 50.36a 35.35b 37.36c 39.97b

Mean 43.43 37.95 43.94 39.98

SED 2.56

Wald statistic

Management 16.71***

Genotype 72.14***




4.4.15 Thousand Kernel Weight (g)

TKW was significantly influenced year, management and genotype (Table 4.28). TKW were

significantly higher by approximately 6% in 2013 than 2014. TKW was also significantly higher in

zero tillage irrigated managements than conventional tillage managements. However, no difference

131

between managements was observed under rainfed conditions. There was no significant advantage of

mixtures over their pure line components for TKW observed.

Table 4.28 Combined means and analysis of thousand kernel weights (g) of Lincoln+Lang and


Management


LANG 31.85b 29.34b 31.19b 27.34b

LINCOLN 37.31a 33.27a 33.83a 32.08a

LINCOLN+LANG 33.53b 32.36a 33.72a 31.47a


SUNSTATE 34.48a 31.85b 32.28a 32.30a

SUNSTATE+STAMPEDE 30.91b 31.22b 32.49a 32.15a

Mean 33.66 31.89 32.8 31.34

SED 0.72

Wald statistic

Year 20.28***

Management 18.01***

Genotype 40.53***







In 2014, genotype and management significantly influenced TKW (Table 4.29). TKW was

significantly higher in zero tillage conditions in both irrigated and rainfed managements. Mixtures did

not significantly improve TKW over the pure line components.

132

Table 4.29 Means and analysis of thousand kernel weights (g) of mixtures versus pure stands in 2014

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

VENTURA 33.80a 32.40a 31.00ac 32.40a

EGA GREGORY 32.13a 30.25ab 31.70a 28.20b

EGA GREGORY+VENTURA 30.60a 28.50b 29.20bc 22.10c

LANG 30.80b 29.80b 29.30a 27.20b

LINCOLN 34.20a 35.20a 31.40a 31.10a

LINCOLN+LANG 31.30b 31.70b 30.50a 27.80b

LANG 30.80b 29.80b 29.30ab 27.20b

LINCOLN 34.20a 35.20a 31.40a 31.10a

SUNSTATE 32.70ab 31.60b 28.80b 30.50a

LINCOLN+LANG+SUNSTATE 31.50b 30.00b 30.30ab 29.70a


SUNSTATE 32.70a 31.60b 28.80b 30.50a

SUNSTATE+EGA STAMPEDE 30.10b 32.20c 33.00a 31.10a


SUNSTATE 32.70a 31.6b 28.80b 30.50ab

JANZ 31.30a 29.9b 26.30c 28.30b

SUNSTATE+EGA STAMPEDE+JANZ 32.60a 31.7b 29.90b 31.60a

SUNTOP 31.00b 35.10a 29.90b 32.80b

SPITFIRE 34.40a 35.00a 33.90a 34.30a


Mean 32.3 32.3 30.8 30.2

SED 1.21

Wald statistic

Management 16.71***

Genotype 72.14***




4.4.16 Biomass(kg ha-1

)

Biomass was significantly influenced by year, management, genotype, year x management and year x

genotype interaction (Table 4.30). The year effect on total biomass obtained across years could be

driven by the in the higher amount and distribution of rainfall in 2014.

133

Table 4.30 Combined means and analysis of biomass (kg ha-1

) of Lincoln+Lang and


Management

Genotype Zero till Irrigated Zero till Rainfed Conv. till Irrigated Conv. till Rainfed

LANG 9405a 8459a 8554b 8564a

LINCOLN 10307a 8274a 7976b 8295a

LINCOLN+LANG 9962a 7979a 9696a 7674a

EGA STAMPEDE 9317b 7564a 9065a 8317a

SUNSTATE 9329b 7946a 8073a 7800a

SUNSTATE+STAMPEDE 9938a 7762a 8323a 7436a

Mean 9709 7997 8614 8014

SED 663.8

Wald statistic

Year 1455***

Management 167.5***

Genotype 95.1***




Year x Management x

Genotype 68.6



Biomass was up to 21% higher under irrigation in the combined data across 2013 and 14. Biomass was also

significantly higher (12%) in zero tillage than conventional tillage under irrigation. However, no differences in

management were observed under rainfed conditions. The mixtures Sunstate+Stampede under zero-till irrigated

conditions and Lincoln+Lang under conventional tillage irrigated conditions produced significantly more

biomass than their pure line components.

However, when mixtures grown in 2014 were compared similar differences in biomass were noted between

irrigated and rainfed managements (Table 4.31). The irrigated treatments produced on average 20% higher

biomass. Zero-tillage was 14% higher than conventional tillage under rainfed conditions and no difference was

observed under irrigation. The three-way mixtures Sunstate+Stampede+Janz and Lincoln+Lang+Sunstate

produced significantly higher biomass than their pure stands under irrigation in zero-tillage and conventional

tillage, respectively.

134

Table 4.31 Means and analysis of biomass (kg ha-1

) of mixtures versus pure stands in 2014

Management

Genotype

Zero till

Irrigated

Zero till

Rainfed

Conv. till

Irrigated

Conv. till

Rainfed

VENTURA 10653a 9552a 8377a 7971a

EGA GREGORY 11928a 10148a 9574a 7843a

EGA GREGORY+VENTURA 11875a 10446a 9898a 7880a

LINCOLN 11872a 10031a 8348b 10225a

LANG 12536a 11000a 10472a 10216a

LINCOLN+LANG 12205a 9391a 11757a 7820b

SUNSTATE 11501a 9497a 9097a 8876a

EGA STAMPEDE 11694a 9834a 11180a 9453a

SUNSTATE+EGA STAMPEDE 12980a 10153a 10304a 8459a

SUNSTATE 11501b 9497a 9097b 8876a

EGA STAMPEDE 11694b 9834a 11180a 9453a

JANZ 10645b 10971a 10362a 7717a

SUNSTATE+EGA STAMPEDE+JANZ 13907a 10211a 9423a 9588a

LINCOLN 11872a 10031a 8348b 10225a

LANG 12536a 11000a 9072b 10216a

SUNSTATE 11501a 9497a 9097b 8876a

LINCOLN+LANG+SUNSTATE 11002a 9204a 11018a 8438a

SPITFIRE 11075a 10205a 10472a 8710a

SUNTOP 12082a 9190a 10314a 8478a

SUNTOP+SPITFIRE 11876a 8819a 9705a 8832a

Mean 11855 9910 9859.75 8700

SED 10034

Wald statistic

Management 211.3***

Genotype 43.4**

Management x Genotype 75



4.4.17 Grain yield (kg ha-1

)

Grain yield was significantly influenced by year, management and genotype (Table 4.32).

Average yields ranged from 2845kg/ha under conventional tillage rainfed managements to 3811 kg/ha

in zero tillage irrigated managements. Yields were 317kg/ha higher under zero tillage in rainfed

managements and 649kg/ha higher in irrigated conditions. Positive mixing effects on grain yield were

135

observed in Sunstate+Stampede and Lincoln+Lang mixtures in zero tillage irrigated and conventional

tillage irrigated managements, respectively.

Table 4.32 Combined means and analysis of grain yield (kg/ha) of Lincoln+Lang and

Sunstate+Stampede mixtures and their pure stand in 2013 and 2014

Management


LANG 3579b 3157a 3134b 2811a

LINCOLN 4138a 3241a 3073b 2891a

LINCOLN+LANG 3962c 3147a 3579a 2810a

EGA STAMPEDE 3846a 3283a 3485a 2930a

SUNSTATE 3459b 3142ac 3221b 2658b

SUNSTATE+EGA STAMPEDE 3880a 3000bc 3261b 2970a

Mean 3811 3162 3292 2845

SED 88.19

Wald statistic

Year 146.11***

Environment 187.07***

Genotype 15.65**

Year x Environment 77.95**


Environment x Genotype 24.34

Year x Environment x Genotype 8.52



In 2014, yield was similarly influenced by management and genotype (Table 4.33). Yield

ranged from 3093 kg/ha in the conventional tillage rainfed management to 4405.9 kg/ha in zero tillage

irrigated conditions. Genotype yield ranged from 3450 kg/ha in EGA Gregory to 3979 kg/ha in the

Sunstate+Stampede mixture. Significant positive mixing effects on grain yield were observed only in

the Lincoln+Lang mixture. However, positive but non-significant mixing effects were also observed in

Sunstate+Stampede+Janz, Sunstate+Stampede and Suntop+Spitfire mixtures.

136

Table 4.33 Means and analysis of grain yield (kg ha-1

) of mixtures versus pure stands in 2014

Management

Genotype

Zero till

Irrigated

Zero till

rainfed

Conv. till

Irrigated

Conv. till

Rainfed

EGA GREGORY 4303a 3520a 3252a 2726b

VENTURA 3749b 3528a 3558a 3078a

EGA GREGORY+VENTURA 4104a 3500a 3390a 2967ab

LANG 4160b 3570a 3442b 2842a

LINCOLN 4794a 3769a 3266b 3124a

LINCOLN+LANG 4731a 3733a 4123a 2994a

LANG 4160b 3570a 3442ab 2842a

LINCOLN 4794a 3769a 3266b 3124a

SUNSTATE 4044b 3643a 3752a 3104a

LINCOLN+LANG+SUNSTATE 4226b 3374a 3613a 2938a

EGA STAMPEDE 4615a 4099a 3861a 3277ab

SUNSTATE 4044b 3643b 3752a 3104b

SUNSTATE+EGA STAMPEDE 4703a 3884ab 3885a 3442a

EGA STAMPEDE 4615ab 4099a 3861a 3277a

SUNSTATE 4044c 3643b 3752a 3104a

JANZ 4443b 3824ab 3051b 3230a

SUNSTATE+EGA STAMPEDE+JANZ 4856a 3918ab 3594a 3280a

SPITFIRE 4267a 3610b 3635a 3009a

SUNTOP 4513a 4161a 3527a 3138a

SUNTOP+SPITFIRE 4581a 3605b 3676a 3246a

Mean 4405.9 3715.9 3575 3093

SED 160.7

Wald statistic


Genotype 37.84**




4.5 Discussion

The effectiveness of varietal mixtures, relative to traditional gene pyramiding, in reducing the

impacts of abiotic stress through more efficient resource use has been reviewed (Adu-Gyamfi et al.

2015). In the current study, the performance of varietal mixtures in four contrasting managements

based on varying tillage practice and moisture level was evaluated. Significant differences in the

137

performance of mixtures in the different managements were observed and mixtures outperformed their

component lines for some traits in some managements.

In this study, mixtures were constructed based on complementary genomic regions or markers

linked to yield in rainfed multi-management trials identified in an earlier association study A major

assumption was that the marker effects estimated in the earlier study were real and not false positives.

Not all mixtures produced a positive result indicating that the components of any mixture must be

carefully selected and tested before deployment. In addition, the four managements represented in this

study may not have been the most appropriate to elicit a mixture response. Nevertheless, the

Sunstate+Stampede, Sunstate+Stampede+Janz and Suntop+Spitfire mixtures were observed to increase

yield in conventional-tillage rainfed and zero tillage irrigated managements whilst the Lincoln+Lang

mixture increased yield in the conventional tillage irrigated management (Tables 4.30 and 4.31). This

observation supports earlier findings (Marshall and Brown 1973; Fang et al. 2014a). These authors

observed increased yield in mixtures particularly under moderate drought. The performance of the

Sunstate+Stampede, Sunstate+Stampede+Janz and Suntop+Spitfire mixtures supports the notion that

mixtures buffer yield under moisture stress (Finckh et al. 2000; Kiær et al. 2012). However, while

yield advantages of mixtures have been reported, some authors also found negative mixing effects

(Treder et al. 2008; Lithourgidis et al. 2011). In the current study, the EGA Gregory+Ventura,

Suntop+Spitfire and Lincoln+Lang+Sunstate mixtures appeared to show a negative response

compared their component lines under drought stress in zero tillage supporting the notion that mixtures

can have both positive and negative responses.

In the current study, it was evident that some mixture yields could be increased by adding more

components. For example, the yield of the 3-way mixture Sunstate+Stampede+Janz increased when

tested in the 2014 cropping season under zero tillage. Others made similar observations when mixtures

of more than two components were developed (Stuke and Fehrmann 1987; Newton et al. 1997; Kiær et

al. 2009). Nitzsche and Hesselbach (1983) reported yield increases in barley when the number of

components of mixtures were increased from two to six. Smithson and Lenne (1996) also claimed that

the yield stability of field crops increased with increasing numbers of component mixtures in about

50% of the data sets they examined. While a positive yield response to mixing was observed in some

instances, it was heavily influenced by management. Thousand kernel weights were not significantly

positively influenced by mixing, rather a negative mixing effect was observed in EGA

Gregory+Ventura across all managements.

138

The increased biomass production of Lincoln+Lang+Sunstate mixtures compared to

component lines under rainfed conditions is also likely a function of increased tiller production

resulting from interplant competition among mixture components. Similarly, the positive mixing

effects on plant height obtained from EGA Gregory+Ventura could also result from competition for

light and other resources. The significant mixing effects on protein content in the Suntop+Spifire

mixture suggests more efficient utilization of soil nitrogen. The relatively significant mixing effects on

relative water content observed in the Sunstate+Stampede+Janz mixture indicates a possible

synergistic interaction of the root systems allowing deeper penetration of the soil profile to access

moisture. The higher NDVI observed in Suntop+Spitfire at late grain filling suggests that early

establishment resulted in the development of larger photosynthetic biomass; a prerequisite for light

capture for photosynthesis, and earlier ground cover thus avoiding soil moisture loss and eventually

producing higher yield.

Early heading is a plant drought avoidance strategy. However, in the current study the mixtures

were intermediate for grain filling duration, maturity and heading date compared to their pure stands.

Mixing effects therefore appeared to have little impact on the phenology of the different components

and this is not surprising as similar plant height and maturity were considerations, along with

complementary markers linked to yield, when forming the mixtures. Canopy temperature is an

integrative trait that can be used to select superior genotypes or segregating bulks and is positively

correlated with yield and root depth (Reynolds and Trethowan 2007; Trethowan and Reynolds 2007).

However, little difference was noted among mixtures and component lines for this trait in the current

study. This is somewhat surprising as differences in correlated traits such as yield and biomass were

observed. However, at Narrabri there was no correlation between canopy temperature and yield (r = -

0.02) which likely explains the lack of significance for this trait.

4.6 Conclusions & Implications for breeding

The positive mixing effects observed in Sunstate+Stampede, Sunstate+Stampede+Janz ,

Suntop+Spitfire and Lincoln+Lang mixtures in some managements suggests that intra-component

competition for resources, both above and below ground, induced a positive effect on yield. Mixtures

may offer a competitive advantage over pure line components through (i) development of larger

photosynthetic biomass (NDVI) through rapid early ground cover that increases light interception and

reduces soil moisture loss through evaporation, and (ii) higher biomass and improved plant water

139

status through deeper roots accessing water deeper in soil profiles resulting in improved relative water

content. It could be concluded from these results that mixtures offer a buffering effect against moisture

stress; however, the mixtures tested varied in their adaptability to specific managements. Thus the

performance of mixtures in a particular management may not produce a corresponding effect in a

different management. It is therefore imperative to design mixtures comprised of components that

match defined ecologies.

140

5 Validation of genetic association mapping I

5.1 Introduction

Association mapping could be an improvement over traditional linkage mapping as it is based

on the correlation of genotypes with phenotypes in germplasm collections or natural populations

(Neumann et al. 2011c). It assumes that linkage disequilibrium (LD) is maintained over many

generations between loci which are genetically linked to one another. and could be utilized to identify

markers with significant allelic frequencies (Ochieng et al. 2007).

Genetic association analysis has proved to be effective in identifying loci for traits with low

heritability, particularly yield and its components (Breseghello and Sorrells 2006a; Neumann et al.

2011c) and has been applied successfully in crops such as maize (Buckler et al. 2009; Liu et al. 2011),

wheat (Reif et al. 2011; Kulwal et al. 2012; Kollers et al. 2013), barley (Wang et al. 2012; Berger et al.

2013; Zhou and Steffenson 2013) and sugar beet (Stich et al. 2008; Würschum et al. 2011) to detect

genomic regions linked to agronomically important traits. Crossa et al. (2007a) conducted the first

genome-wide association mapping study in wheat using DArt technology (www.diversityarrays.com. ;

Shewry 2009) on historical multilocational wheat yield and disease resistance data and reported many

marker-trait associations.

Despite a growing number of published association studies, much less attention has been given

to the validation of these regions in plant breeding; either through recombination or the effectiveness

of marker trait associations in other genetic backgrounds (Price et al. 2002c; Holland 2007; Yue et al.

2008). The simplest validation of genomic regions is to test their effectiveness in other populations

(Langridge et al. 2001).

This study utilized the results of an earlier genetic association study (Atta 2013) based on

DArTs which identified significant marker trait associations for grain yield in an Australian wheat

breeding program. The study evaluated different genotypes over a range of locations and across

different years and identified both confirmed and unique genomic regions linked to yield.

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The earlier genetic association analysis revealed:

(i) Seventeen significant marker traits associations (MTAs) with grain yield observed on

chromosome 1BS, 16 on 2BS, 5BL and 6BS, 15 on 3BL, 14 on 3BS, 4AL and 4BS, 12 on 1BL, 2BL,

6AL and 7BL and 11 on 5BS and 7DS across 27 multi-environment trial locations. However, no

significant associations with grain yield across the 27 tested locations were found on chromosomes

2DS, 3AS, 4DS, 5DS and 7BS.

(ii) The MTAs for grain yield were scattered over the whole wheat genome with the maximum

number identified on the B genome (specifically 1B, 2B, 3B and 6B), whereas only a single marker

each was found on 4D and 5D which is consistent with Crossa et al. (2007) who also reported few

significant markers on both of these chromosomes.

(iii) Several MTA blocks were identified for grain yield including 53 markers covering 61.7-86.5

cM on 2BS, 43 markers covering 2.8-40.5 cM on 1BS, 7 markers covering 0.0-10.7 cM on 2AS; 24

markers covering 95.8-114.0 cM in 2DL whilst 17 markers covering 20.3-29.8 cM were found on 3BS.

Similar observations were made on other chromosomes with the exception of 4D and 5D.

In this study, we hypothesize that the previously mapped genomic regions will be detected and

effective in the same materials when sown in head-to-head yield trials at the same location across two

years. The previous study was highly unbalanced and examined subsets of materials tested across

several years in multi-environment trials. This study aimed to validate the significant marker trait

associations identified earlier in the same environment in two different cropping seasons.

5.2 Materials and methods

5.2.1 Germplasm

The wheat genotypes evaluated were the same as those tested in the earlier multi-environment

genetic association analysis (Atta 2013). The earlier study focused on commercial breeding program

germplasm based on the premise that genomic regions associated with key traits would be of relevance

to the Narrabri based wheat breeding program. A total of 215 genotypes were evaluated for yield and

other traits in 2014 and 2015. These genotypes are listed in Appendix II. The experiments were

conducted at the IA Watson Grains Research Centre during the 2014 and 2015 cropping seasons. Each

experiment was sown following field pea (Pisium sativum) in the previous season.

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5.2.2 Phenotypic data and analysis

The traits studied included grain yield, thousand kernel weight, days to heading and days to

physiological maturity. The trials were arranged in randomized complete block designs with 2

replications. The procedures and methods used to capture physiological data are described in Chapter

3. A standard fertilizer treatment, mainly Urea 100 kg ha-1

and DAP 50 kg ha-1

was applied at sowing

to enhance plant establishment, vigour and robustness. Experiments were harvested at maturity with a

combine harvester and grain yield was recorded and expressed as kg ha-1

. GenStat statistical software,

version 14.1 (Payne RW 2011), was used to analyze data obtained from both the 2014 and 2015

cropping seasons using REML. Year and genotype were considered fixed effects and range/row

coordinates within years as random effects.

Significant effects among genotype means were separated by the Fisher's protected least

significant difference test at P < 0.05.

5.2.3 Molecular data

This study utilized the molecular data generated from the same genotypes in the earlier genetic

association studies of Atta (2013). These were DArT (Diversity™ Arrays Technology) markers.

Details of DArT technology can be found at (www.diversityarrays.com.). In summary, DArT are

biallelic dominant markers. The markers were scored as 0, 1 and –, where “0” indicates absent, “1”

present and “–” missing data. In the earlier study 2199 markers were applied on 215 wheat genotypes.

Of these, 364 markers were not associated with any chromosome and were hence discarded from all

subsequent analyses.


The trials were arranged as randomised complete block designs with two replications in each

year. These were analysed using a linear mixed model in the REML function of Genstat (v 14.1) where

years and genotypes were considered fixed effects and rows and columns within years as random

effects. The pattern and association analyses were carried out on genotype means using R version

2.13.1 (Team 2014) and the following R package ‘gclus’ (Hurley and Hurley 2012), ‘rpart’ (Therneau

et al. 2010) 2012), ‘maptree’ (White 2009) and ‘sparcl’ (Witten and Tibshirani).

Pattern analysis, the combined use of clustering and ordination techniques, was then used to

derive separate structures for genotypes based on phenotypic and marker data. Principal coordinate

analysis based on spectral decomposition was used for ordination (Gower 1966, 1967) and the method

of (Ward 1963) was used as the clustering strategy for hierarchical clustering. Optimized dendrograms

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(Arief et al. 2017) were generated following Gruvaeus and Wainer (1972), where adjacent entities in

the dendrograms were considered the most similar. Clustering was also used to group locations

(environment structure) based on phenotypic data.

A two way table of genotypes (as rows) x traits (as columns) was prepared and columns (traits)

standardized prior to calculating the squared Euclidean distances among the genotypes. Principal

coordinate analysis was conducted on the complementary similarity measure to standardize squared

Euclidean distance (SED) and the results were presented using biplots with symmetrical scaling

(Gabriel 1971). Hence the phenotypic data was presented as; (a) optimized dendrograms based on SED

as the dissimilarity measure and Ward’s method as the clustering strategy, (b) biplots for the 1st and

2nd principal components obtained from principal coordinate analysis using the complementary

similarity of the SED, and (c) biplots for the 1st and 3rd principal components. Different colours

indicate genotypes that are allocated to the same group.

The molecular marker data was presented as; (a) optimized dendrograms based on the

complementary dissimilarity of the Czekanowski method as the dissimilarity measure and Ward’s

method as the clustering strategy, (b) scatter plots of the 1st and 2nd principal components obtained

from principal coordinate analysis using the Czekanowski similarity and (c) scatter plots of the 1st and

3rd principal components. The different colours indicate genotypes that are allocated to the same

group.

An association analysis of DArT markers and traits was then performed. These analyses

included the complete dataset using population structure for genotypes and individual analyses within

each environmental group. However, in the interest of brevity and to maintain sufficient scale, the

association analysis of the complete data set is elaborated here. Association analysis was conducted

following Bansal et al. (2010). For each marker a t-test was conducted to check if the absence or

presence of that marker significantly affected the phenotypic performance of the genotypes. The

probability (P) of the differentiation among marker classes (i.e. absent and present) was converted into

a log score (i.e. –log10P). A log score of three was used as a threshold to declare an association

significant. The marker was considered to be positively associated with the trait if presence of the

marker contributed to better phenotypic performance and vice versa for negative associations. The

gametic phase disequilibrium was evaluated using the two most commonly used measures D’

(Lewontin 1964) and r2

(Hill and Robertson 1968). The Linkage disequilibrium (LD) parameter r2

was

144

estimated for loci on the same chromosome and plotted against genetic distance measured in

centimorgans.

The chromosome arms were determined following Crossa et al. (2007a) and the significant

markers were then assigned to the specific chromosome arms. Where the chromosome arms were not

identified in the cited literature but the distance was given in centimorgans for the reported

genes/markers the arms were assigned following Crossa et al. (2007). Chromosome maps were drawn

with MapChart 2.2 (Voorrips 2002).

5.4 Results

Gametic phase disequilibrium existed among the 1835 markers within the population and the

LD (r2) estimated for loci on the same chromosome and plotted against genetic distance revealed that

LD decreased with genetic distance between marker loci (Figure 5.1).

5.4.1 Analysis of phenotypic data

The genotypes clustered into two main groups on the basis of yield, days to heading, days to

maturity and thousand kernel weights. The first group comprised genotypes which were low yielding

with lower thousand kernel weight and longer days to maturity, whilst the other group consisted of

higher yielding lines with higher thousand kernel weight and shorter days to heading and maturity. The

first group was related to the cultivar Cook whilst the other group was comprised of non-Cook types.

Phenotypic data were summarized in dendrograms (Figure 5.2a). While the genotype groups varied

significantly on the basis of yield, thousand kernel weight (TKW), days to heading and maturity, no

main effect of year on yield was observed (Appendix chapter 5). However, the number of days to

heading and maturity were approximately 3 days longer in 2014 than 2015. The year x genotype

interaction effects were significant for yield and crosses SUNCO/2*PASTOR//SUN436E,

SUN500B/Carinya, and UN421T/SUN399C.22.3 were the top 3 outstanding genotypes in 2014 whilst

CHARA/SUN434A, RAC1192/Ventura and LPB05-1164 were the top 3 genotypes in 2015. The first

three principal components of the phenotypic data including yield, days to heading and days to

maturity explained 45.6% of the total variation (Figure 5.2b). Yield in both years was similar as was

the size of the GEI. Days to heading and maturity appeared to be unassociated with yield based on the

PC1 versus PC2 biplot. The PC1/PC3 biplot indicated that TKW assessed in 2015 was negatively

associated with days to maturity.

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Figure 5.1 Linkage disequilibrium heat map of the genome of the wheat genotypes tested

146

a)

147

(b)

Figure 5.2 Pattern analysis of grain yield, number of days to heading (DH)/maturity (DM), and thousand kernel weights (TKW) of

genotypes tested in 2014 and 2015 at Narrabri. (a) Optimized dendrograms (b) Biplots of 1st, 2nd and 3rd principal components. The blue

and red colours indicate genotypes that are allocated to the same group.

148

5.4.2 Analysis of molecular data

Two primary groups of genotypes were observed at the molecular level. The first three

principal components of the ordination explained 29.5% of the total variation based on marker data

(Figure 5.3a). Both the pattern analysis and the ordination analysis allocated genotypes into two main

groups (Figures 5.3a and b). Group one was dominated by Cook based materials (a key progenitor of

most northern Australian prime hard wheat cultivars) such as Sunco, Sunvex Sunvale, Sun431 and

Lang whilst the second group consisted of materials with non-Cook backgrounds such as Crusader,

Merinder, Livingston and EGA Gregory (Figure 5.3b).

149

a)

150

(b)

Figure 5.3 Combined pattern analysis of genotypes tested in 2014 and 2015 at Narrabri using marker data. (a) Optimized dendrograms (b)

A plot of 1st , 2nd, and 3rd principal components. The blue and red colours indicate genotypes belonging to the same group.

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5.4.3 Association analysis of grain yield

Population structure was assessed by dividing genotypes into groups based on yield, days to

heading, days to physiological maturity and TKW. Although population structure was presented based

on Figure 5.3, the analysis was conducted across all 215 lines to ensure sufficient degrees of freedom.

Association analysis revealed significant Marker Trait Associations (MTA) for grain yield, days to

heading, days to maturity, chlorophyll content, canopy ground cover, Normalized Difference

Vegetation index (NDVI), grain filling duration, harvest index, plant height, biomass, canopy

temperature and TKW within and across years using the set of polymorphic markers (Figure 5.4). The

association analysis results of the complete dataset are presented graphically (Figure 5.5) and the

significant markers for grain yield and other traits are presented on the heat map. Summaries of the

association analysis results together with information on chromosome, marker name and marker

position (cM) are presented in the (Figure 5.6). The colour intensity denotes the degree of strength of

the association with a deeper colour indicating a stronger association. MTAs for grain yield were

scattered across the genome. There were 19 MTAs for grain yield spanning 7 linkage groups; 1A, 6A,

1B, 2B, 4B, 6B and 5D in the combined analysis of grain yield across 2014 and 2015. Fourteen DArT

markers were positively associated with grain yield; wPt-740654, wPt-2859, wPt-1637, wPt-5562,

wPt-5385, tPt-5080, wPt-8930, wPt-7359, wPt-7905, wPt-2075, wPt-3561, wPt-741515, wPt-8814,

wPt-5256 and five were negatively associated including rPt-3825, wPt-666266, wPt-667461, wPt-

666615 and wPt-732892.

DArT markers were associated 9 times with grain yield on chromosome 1BS, once on each of

1A, 6A, and 5D and 4 times on an unknown chromosome. MTAs for grain yield; notably wPt-1637,

wPt-5562, wPt-5385, were located at the same position on chromosome 1B at 31.64, 30.96, 30.96 cM,

respectively. Similarly, tPt-5080 and wPt-8930 and wPt-7905 and wPt-2075 were also co-located on

chromosome 1B at distances of 9.16 and 14.96 cM, respectively. Similarly, the significant markers

wPt-741515, wPt-8814, wPt-5256 located at respective distances of 25.78, 25.77, 25.76 cM, clearly

represent the same locus on chromosome 6B. MTAs on chromosome 6A, 2B, 4B, 1A and 1B occurred

at distances of 41, 51.42, 114.9, 5.75 and 5.75 cM, respectively. When years were analyzed

individually, a higher number of MTAs were observed in 2014 compared to 2015. However, these did

not represent the same regions as the combined cross year analysis. Notably, wPt-1409(67.69), wPt-

731442(38.01), wPt-666266(41), wPt-734107(12.47), wPt-5834 on chromosomes, 5B, 6A, 6A, 1A, 6A

were present in 2014 and wPt-667461, wPt-666615, and wPt-732892 were assigned to an unknown

chromosome. In 2015, the significant MTAs were associated with wPt-740654 (114.91), wPt-6594

152

(14.94) and rPt-3825 (77.5) on chromosomes 4B, 6B and 5D, respectively and wPt-669388 was

assigned to an unidentified chromosome. These results indicate a strong genotype x year interaction as

significant markers in one year are not necessarily associated in the following year. These associations

may also of course be artefactual and this will be discussed in the next section. Only two significant

MTAs for TKW were observed; wPt-0751 located on chromosome 3B and tPt-2920 on an unknown

chromosome.

Several significant MTAs were identified for days to heading and days to maturity. Two

hundred and fifty-three markers were associated with days to heading; of these 54 were positively

associated (linked to later heading) whilst the remaining were negatively associated (linked to earlier

heading). The highest number of markers found on chromosomes 3B, 5B and 7B. MTAs for days to

heading were located across the genome with 2 noted on chromosome 1A, 22 on 1B, 3 on 1D, 2 on 2A

and 2D, 8 on 2B, 3 on 3A, 44 on 3B, 13 on 3D, one on 4A and 5D, 10 on 4B, 32 on 5B, 11 on 6A, 16

on 6B, 10 on 7A, 32 on 7B and 7 on 7D. The majority of these markers assigned to chromosomes

tended to co-locate at the same region.

In contrast, 507 MTAs for days to physiological maturity were detected. One hundred and seven of

these were positive (associated with longer maturity) whilst the rest associated negatively (shorter

maturity) with a larger proportion of the markers occurring on chromosomes 1B, 6B and 7B. Ten

MTAs were found on 1A and 7D, 8 on 2A and 3A, 14 on 4B and 7A, 35 on 1B, 17 on 1D, 89 on 2B,

70 on 3B, 19 on 3D, 24 on 4A, 5 on 2D, one 4D, 33 on 5B, 3 on 5D, 13 on 6A, and 34 on 6B and 7B.

Clusters of markers were observed in MTAs for grain maturity and clusters were observed at 2.7, 51,

132, 158, 175, 176, 209 and 225.59 cM respectively. Many markers assigned to chromosomes co-

located for days to maturity.

.

153

Figure 5.4 Polymorphic and non-polymophic markers/regions across genotypes tested in 2014 and

2015. The blue colour indicates polymorphic regions whilst the brown represents non-polymorphic

regions.

154

Figure 5.5 Marker trait associations for grain yield (YLD), thousand kernel weigh t(TKW), days to

heading (DH), and days to physiological maturity (DPM) based on data from 2014 and 2015. Negative

associations are shown in red and positive associations are shown in blue. Increasing colour intensity

shows the strength (-log10 probability) of association (Arief 2010).

155

5.5 Discussion

The identification and selection of functional or linked markers assists the development of

superior drought tolerant/high yielding varieties. This study evaluated 215 wheat cultivars, previously

tested in unbalanced multi-environment trials, head-to-head in a single location over 2 years for grain

yield, to identify and confirm marker trait associations conferring high yield in rainfed environments.

In addition, traits possibly linked to yield mainly; heading time, maturity time and TKW which were

not measured in the earlier association analysis were assessed. These genotypes had never been

evaluated head-to-head and the two year trial at Narrabri attempted to confirm the earlier associations

(Atta, 2013). The relationships among the genotypes tested were generated by pattern analysis utilizing

both phenotypic and marker data which produced two primary groups giving rise to population

structure. While Arief (2010) emphasized that avoiding population structure is crucial for avoiding

spurious associations in gene discovery, the relatively small number of genotypes (215) and even

smaller sub-groups made individual analyses difficult to interpret. Hence the analysis of the full set is

presented as was the case in the earlier study by (Atta 2013). In the earlier Atta (2013) association

study, 1835 high throughput Diversity Arrays Technology (DArT) markers were polymorphic in the

germplasm evaluated. Among these, 1402 were identified on the final map, with 473, 650 and 279

markers located on genomes A, B and D, respectively. The significant markers on the various

chromosomes ranged from 1 each on 4D and 5D to 152 on 3B.

In the current study, genome wide association analysis was conducted utilizing combined

phenotypic data from the 2014 and 2015 cropping seasons and the same DArT marker data used by

(Atta 2013). The MTAs detected in this analysis (Figure 5.6) were then compared to the results of the

(Atta 2013) analysis of unbalanced multi-environment trial data (Table 5.1).

5.5.1 Association analysis of grain yield

The current study found 19 DArT markers (representing 1% of all markers) to be significantly

associated with grain yield. Of these, 68.4% were located on the B genome, 10.5% on the A genome

and 0.1% on the D genome. Nine of the 19 markers were also reported by Atta (2013) (Table 5.1).

This result was consistent with most studies using DArT or micro-satellite markers (Crossa et al.

2007a; Edae et al. 2014). Since this time SNP technology has improved the coverage of the D-genome

(Tiwari et al. 2014; Wang et al. 2015) and higher numbers of associations would now be expected.

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Chromosomes 1A, 1B and 1D

The current study observed only a single marker wPt-2859 at a distance of 5.75cM to be

associated with grain yield on chromosome 1A. This marker was also identified in the earlier

association analysis conducted by (Atta 2013). Comparatively, the previous Atta (2013) study found

many markers associated with grain yield in addition to wPt 2859 (5.75cM) and this MTA coincided

with QTLs reported by (Carter et al. 2011) and (Fang 2011). This marker is also 4.25cM distant from a

QTL QY ld.wak-1A (between Xgdm33 and Xpsp2999) previously identified for grain yield (Carter et

al. 2011). A number of markers (wPt-2859, wPt-1637, wPt-5562, wPt-5385, tPt-5080, wPt-8930, wPt-

7359, wPt-7905, wPt-2075) were associated with grain yield on chromosome 1B in the current study

and these coincided with markers linked to grain yield reported by Atta (2013), with the exception of

wPt-7905, wPt-2075 and wPt-2859, which appeared to cluster at the same locus at a distance of 14.96

cM on chromosome 1B. A QTL within the same region on 1B for grain yield QTL Q.Gy.ui-1B.2 was

previously reported (Zhang et al. 2014b). Others reported grain yield and harvest index MTAs in the

same region on the short-arm of chromosome 1B at 21.6 cM. (Edae et al. 2014).


The Atta (2013) association analysis identified MTAs on 2B at multiple locations,

including those at a distance (cM) of 6.19 (tPt.1663 and wPt.8918), 14.59 (wPt.3949), 51.42

(wPt.3561), 74.64, 81.34, 89.18 (cluster of markers) and 119.17 (wPt.1454). However, in the current

study only one significant MTA located on chromosome 2B (markers wPt-3561at a distance of 51.42)

was confirmed. No MTAs were found on chromosomes 2A and 2D. The MTA on 2B that coincided

with the Atta (2013) study is new and previously unreported. However, the chromosome 4B MTA was

previously reported to be associated with grain yield.


The earlier study conducted by Atta (2013) found a significant marker trait association on

chromosome 3B and 3D which was confirmed by other researchers (Kuchel et al. 2007; Maccaferri et

al. 2008b; KunPu et al. 2009). However, no MTAs were found in the current study on any group 3

chromosomes.

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Chromosome 4A, 4B and 4D

No MTAs were found on chromosomes 4A and 4D. However, a marker (wPt-740654 at 114.9

cM) found on chromosome 4B was previously reported as associated with grain yield (Marza et al.

2006; Crossa et al. 2007a; Neumann et al. 2011a).


In the current analysis an MTA was found on chromosome 5D (wPt-3825 at 77.5cM) that was

associated with grain yield. In the Atta (2013) analysis, a number of MTAs were identified yet none

coincided with the current study. There is no published evidence of previous MTAs in this region on

5D; therefore wPt-3825 appears to be a new MTA.


In the current study the chromosome 6B markers wPt- wPt-741515, wPt- 8814 and wPt-

5256 at distances of 25.78, 25.78, 25.78 cM, respectively, were associated with grain yield. These

markers are located in the same location and therefore represent the same MTA. This region on 6B

was also reported in the (Atta 2013) analysis. In addition, a single MTA for grain yield reported by

Atta (2013) was confirmed on chromosome 6A with marker wPt-666266 (41cM). Sukumaran et al.

(2015a) also found a loci on chromosome 6A that was associated with grain yield and maturity. This

chromosome 6A locus, located in the same region as the current study, was associated with grain yield

under drought, heat and irrigated conditions (Edae et al. 2013; Lopes et al. 2015a). No MTAs were

found on chromosome 6D.


The earlier Atta study identified new MTAs on chromosome 7A, 7B and 7D. However, in the

current study no significant MTAs were found on any of these group 7 chromosomes.

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Table 5.1 Summaries of number of detected marker trait associations in the current and earlier

association analysis for grain yield, number of days to heading, number of days to physiological

maturity and thousand kernel weight.

Trait

No. of MTA's detected

in the current study

No. of MTA's detected in

the earlier Atta (2013)

association study

No. of MTA's

detected in both

studies

Grain yield 19 192 9

TKW 2 Not assessed -

No. of days to heading. 228 Not assessed -

No. of days to maturity 414 Not assessed -

5.5.2 Association analysis of thousand kernel weight (TKW)

Two MTAs for TKW were identified in the current study; one on chromosome 3B (wPt-0751)

at 129.2cM and the other (tPt-2920) on an unknown chromosome. This trait was not assessed in the

earlier association analysis conducted by (Atta 2013). However, Huang et al. (2004) identified a QTL

on chromosome 3B for TKW at a distance of 21cM and Li et al (2015) reported QTKW.caas-3B on the

same chromosome, however no coincidences were observed with the current analysis. Other reported

QTLs that influence TKW include QTKW.caas-4B and QTKW.caas-5B.1 (Li et al. 2015). However,

none of these were found in the current study. It is possible that wPt-0751 at 129.2cM on chromosome

3B is a new MTA.

5.5.3 Association analysis of heading time

Heading date (HD) is one of the pivotal traits regulating adaptation of bread wheat (Triticum

aestivum L.) to diverse climatic environments and earliness adaption has often been emphasized as an

escape strategy to avoid drought and heat stress (Debaeke 2004). The previous Atta (2013) study did

not determine MTAs for heading. In the current study, MTAs for earliness to heading were distributed

throughout the whole genome. This observation supports the findings of (Griffiths et al. 2009) who

indicated that QTLs for flowering are scattered over the whole wheat genome. However, a larger

proportion of the MTAs were found on chromosomes 1B, 3B, 5B, 7B. This observation is consistent

with earlier reports ((Zanke et al. (2014)). Blocks of markers on 7B between 200.72 - 218.05 cM and

associated with days to heading were identified. Kamran et al. (2013) reported 3 QTLs (QEps.dms-

1B1 and QEps.dms-1B2, QEps.dms-5B) regulating earliness per se and flowering time on

chromosomes 1B, 5B and 4A, respectively. In the current study, seven clusters of markers associated

159

with heading time were observed: three clusters at 56.53, 125.15, 136.87 cM on chromosome 1B and

one cluster each on 3D, 6A, 6B and 7B at 151.80, 107.78, 14.25 and 219.79 cM, respectively. The

QTL (QEps.dms-1B1) found on chromosome 1B mapped at 31.8cM and was located in the same

region as MTAs found in the current analysis linked to markers, wPt-2597, wPt-3819, wPt-5899, wPt-

8682, and wPt-1684, wPt-1684 at distances of 29.4, 29.4, 30.9, 28, 6 and 34.6 cM respectively. The

QTL QEps.dms-5B on chromosome 5B directly coincided with an MTA at wPt-6135 and mapped at

76.1 cM. Earliness per se genes have also been found on chromosomes 1A and 3A (Gawroński and

Schnurbusch 2012). Many of the identified MTAs coincided with regions already reported (Griffiths et

al. 2009). However, new and previously unreported MTAs were found on chromosome 7B at 219.9 (a

marker cluster), on chromosome 4B between 4.29 and 68.08 cM and on chromosome 6A between

86.48 and 108.986 cM.

5.5.4 Association analysis of maturity time

Several markers associated with grain maturity have been reported on chromosomes 2B, 3B,

4B, 4D and 6A (Pinto et al. 2010; Kamran et al. 2013). In the current analysis 12 major clusters of

markers associated with maturity were found on chromosomes 1A, 2B, 3B, 3D, 4B, 6B and 7D.

However, a number of markers (wPt-5906, wPt-8096, wPt-11400, wPt-3107, wPt-11278) located

between 81.17 and 83.02 cM on 3B co-located with a previously reported QTL for grain maturity

identified by the SNP marker Kukri_rep_c112440_422 (Sukumaran et al. 2015b). On chromosome 4D

the previously reported SNP marker wsnp_Ex_rep_c107564_91144523 was only 2.5cM distant from

the significant markers wPt-5809 and wPt-3058 in the current association analysis (Sukumaran et al.

2015a). In addition, on chromosome 4B the markers wPt-6209, wPt 6016 wPt,7412 and wPt 8905 at

68.88, 68.78, 77.88, 77.88cM co-located with the SNP marker BS00034371_51 reported by (Kamran

et al. 2013). On chromosome 6A wPt-1695, wPt- 4925, wPt-5550 were only 10cM distant from the

reported SNP BobWhite_c30930_192 associated with maturity (Sukumaran et al. 2015b). New

previously unreported MTAs were found on chromosome 4B between 4.29 and 68.08cM. Several

other unreported MTAs were identified including wPt-2923 and wPt-8164 at 160.155 and 166.42cM

respectively on 3D; wPt-1400 and wPt-5505 both at 85.34 cM on chromosome 5D and a cluster of

markers between 170.56 and 174.18 cM on 7D.

Higher grain yield is associated with dwarfing genes and shorter stature (Trethowan et al.

2005a), however Rht8 and Rht-2 were not detected in this study whereas the MTA found on 4BS

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likely explains the presence of the Rht-1 gene. The significance of photoperiod insensitivity (Ppd),

vernalization (Vrn) and earliness per se (Eps) genes on grain yield have also been recognized and

MTAs reported (Crossa et al. 2007b).

5.6 Conclusion

The discovery and validation of markers linked to grain yield, thousand grain weight, number

of days to heading and number of days to maturity in hexaploid wheat is a first step to better

understanding of the genetic mechanisms regulating yield and adaptation. Association analysis based

on multi-environment trial data conducted earlier by Atta (2013) identified several MTAs across the

wheat genome for high grain yield. Some of these markers were confirmed in the current association

analysis including regions on chromosomes 1A, 1B, 2B, 4B and 6B. Others previously unreported

included the markers wPt-7905, wPt-2075, wPt-2859 and wPt-3825, however, these MTAs need to be

confirmed. Several MTAs were found for heading time scattered across the whole genome; many of

these confirm previously reported associations on chromosomes 1BS, 2BS, 2BL, 3B and, 7DL (Wang

et al. 2009; Ain et al. 2015). In general, the MTAs for grain yield that confirm the Atta (2013) study

are potentially useful for wheat breeding. However, the new MTAs identified in the current study will

likewise require confirmation before their utility can assessed.

161

wPt-2859(YLD)5.8

wPt-734027(DPM)14.8

wPt-666723(DPM)wPt-666723(DHD)

26.2

wPt-667172(DHD)35.9

wPt-8016(DPM)125.4wPt-1786(DPM)135.3wPt-664593(DPM)wPt-664703(DPM)

135.5

wPt-5160(DPM)wPt-6005(DPM)wPt-7147(DPM)

135.6

wPt-732616(DPM)136.0

1A

wPt-1782(DPM) wPt-1782(DHD)wPt-4497(DHD)

0.0

wPt-741297(DPM) wPt-8320(DPM)wPt-8320(DHD)

4.8

wPt-1560(DPM) wPt-1560(DHD)5.3wPt-744437(DPM)7.3tPt-5080(YLD) wPt-8930(YLD)9.2wPt-7359(YLD)11.2wPt-3465(DPM)14.3wPt-2230(DPM) wPt-9639(DPM)14.6wPt-2859(YLD) wPt-7905(YLD)wPt-2075(YLD)

15.0

wPt-4343(DPM) wPt-5740(DPM)17.3wPt-8240(DPM) wPt-8240(DHD)22.4wPt-8682(DPM) wPt-8682(DHD)28.7wPt-3819(DHD) wPt-2597(DHD)29.5wPt-5899(DPM) wPt-5899(DHD)30.9wPt-5562(YLD) wPt-5385(YLD)31.0wPt-1637(YLD)31.6wPt-1684(DHD)34.7wPt-6012(DPM) wPt-6012(DHD)34.8wPt-3266(DPM) wPt-3266(DHD)35.7wPt-9973(DPM) wPt-9973(DHD)38.6wPt-668027(DPM) wPt-668027(DHD)40.3wPt-8168(DPM) wPt-8168(DHD)41.1wPt-1573(DPM) wPt-1573(DHD)42.4wPt-2389(DPM) wPt-2389(DHD)43.0wPt-3451(DPM) wPt-3451(DHD)45.6wPt-2315(DPM) wPt-2315(DHD)57.2rPt-7906(DPM)65.8wPt-7160(DPM) wPt-7160(DHD)73.5wPt-1403(DPM) wPt-1403(DHD)75.7wPt-7066(DPM) wPt-7066(DHD)76.5

wPt-5034(DPM) wPt-5061(DPM)83.8

wPt-2526(DPM)88.3wPt-5281(DPM)91.5rPt-2940(DPM)93.2wPt-664593(DPM) wPt-664703(DPM)95.9

wPt-1786(DPM)111.2

1B

Figure 5.6 Map of significant DArT markers associated with grain yield (YLD), number of days to

heading (DHD), number of days to physiological maturity (DPM) and thousand kernel weight (TKW)

in the current association analysis.

162

wPt-3855(DPM) wPt-666719(DPM)wPt-667287(DPM) wPt-5915(DHD)

0.0

wPt-4497(DHD)21.4

wPt-4497(DHD)83.1wPt-8866(DHD)84.7

1D

wPt-6361(DHD)69.6

wPt-665330(DHD)70.9

2A

Figure 1 continued

163

wPt-2568(DHD) wPt-3995(DHD)0.0tPt-1663(DHD) tPt-1663(DPM)wPt-8918(DPM)

6.2

wPt-1813(DHD) wPt-1813(DPM)22.1tPt-4627(DPM)29.3wPt-5556(DPM)51.4wPt-6199(DPM)60.6wPt-4125(DPM)61.7wPt-5672(DPM) wPt-7757(DPM)wPt-1992(DPM)

63.2

wPt-1992(DHD) wPt-1127(DPM)70.1wPt-2907(DPM) wPt-5287(DPM)wPt-7747(DPM)

74.6

wPt-7747(DHD) wPt-2327(DPM)76.1wPt-9350(DPM)76.2wPt-4889(DPM)76.6wPt-2249(DPM)76.9wPt-3569(DPM) wPt-6576(DPM)wPt-741721(DPM)

79.1

wPt-1882(DPM)80.5wPt-733929(DPM) rPt-3823(DPM)80.6rPt-5747(DPM) tPt-5296(DPM)wPt-2082(DPM) wPt-2832(DPM)wPt-3906(DPM) wPt-6144(DPM)wPt-6321(DPM) wPt-731385(DPM)wPt-733161(DPM) wPt-734147(DPM)wPt-742618(DPM) wPt-744324(DPM)wPt-7512(DPM) wPt-7990(DPM)wPt-2854(DPM)

81.3

wPt-5440(DPM) wPt-2120(DPM)wPt-8077(DPM) wPt-5044(DPM)

82.1

wPt-3109(DPM)84.9tPt-5897(DPM)85.9wPt-2528(DPM) wPt-3023(DPM)wPt-8693(DPM) wPt-9812(DPM)wPt-665645(DPM)

89.2

wPt-9257(DPM)89.7wPt-5128(DPM)90.8wPt-667945(DPM)96.6wPt-7004(DPM)101.6wPt-9190(DHD) wPt-7004(DHD)wPt-666931(DPM)

101.7

wPt-7161(DPM)102.1wPt-3632(DPM)105.1wPt-4426(DPM) wPt-1454(DPM)116.7wPt-4368(DPM)119.2wPt-3561(YLD)125.4

2B

wPt-3692(DPM) wPt-666518(DPM)70.9

wPt-667584(DPM) wPt-731148(DPM)wPt-666518(DHD) wPt-3692(DHD)

114.1

2D

Figure 1 continued

164

wPt-6012(DPM) wPt-6012(DPM)0.0

wPt-6012(DHD)34.8wPt-743723(DPM)35.1

wPt-10311(DPM)50.5

wPt-9422(DPM)166.7

wPt-1036(DPM)190.2wPt-1036(DHD)190.6wPt-5476(DHD) wPt-5476(DPM)206.9wPt-6234(DPM)210.1wPt-8741(DPM)211.7

3A

Figure 1 continued

165

wPt-1162(DPM)12.0wPt-742222(DPM) wPt-3609(DPM)16.2wPt-3761(DPM) wPt-3921(DPM)wPt-666738(DPM) tPt-9267(DPM)

17.1

wPt-1081(DPM) wPt-10311(DPM)19.0wPt-2045(DPM)24.6wPt-6211(DPM) wPt-6718(DPM)wPt-734244(DPM) wPt-7668(DPM)wPt-798970(DPM) wPt-2757(DPM)wPt-800213(DPM) wPt-743847(DPM)wPt-10585(DPM) wPt-7132(DPM)

24.9

wPt-6239(DPM)25.1wPt-10130(DPM)25.4wPt-1159(DPM)26.7tPt-1366(DPM)27.6wPt-5105(DPM)29.8wPt-6945(DPM)33.8

wPt-7015(DPM) wPt-9170(DPM)47.7wPt-10196(DPM)49.0wPt-11295(DPM)52.7wPt-4608(DPM) wPt-6047(DPM)wPt-11218(DPM) wPt-3078(DPM)

53.2

wPt-9443(DPM) wPt-4364(DPM)wPt-731910(DPM) wPt-10546(DPM)wPt-6785(DPM) wPt-5769(DPM)wPt-664981(DPM) wPt-672088(DPM)wPt-732501(DPM)

56.5

wPt-2119(DPM)57.8wPt-731500(DPM) wPt-5295(DPM)58.4wPt-10349(DPM) wPt-3342(DPM)63.2

3B [1]

wPt-6856(DPM) wPt-8959(DPM)wPt-3760(DPM)

65.2

wPt-2685(DPM)67.6wPt-1834(DPM)68.6

wPt-7514(DPM)70.8

wPt-742982(DPM)79.0

wPt-3165(DPM)85.9wPt-1311(DPM) wPt-10407(DPM)86.1wPt-0751(TKW) wPt-742222(DHD)wPt-3761(DHD) wPt-666738(DHD)

87.3

wPt-6211(DHD)97.5

wPt-7668(DHD)117.6wPt-734244(DHD) wPt-6802(DHD)120.8wPt-0672(DHD)122.1wPt-0371(DHD) wPt-1159(DHD)122.9wPt-10130(DHD) wPt-6945(DHD)wPt-7015(DHD)

123.3

wPt-9170(DHD)123.6wPt-5105(DHD) wPt-6047(DHD)wPt-0695(DHD) wPt-5769(DHD)wPt-672088(DHD) wPt-664981(DHD)wPt-667746(DHD) wPt-2119(DHD)

125.2

wPt-5261(DHD) wPt-731500(DHD)wPt-0401(DHD)

126.8

wPt-5295(DHD) rPt-0996(DHD)127.0

3B [2]

wPt-8959(DHD) wPt-3342(DHD)wPt-10349(DHD) wPt-6856(DHD)

130.8

wPt-0668(DHD) wPt-3760(DHD)131.4wPt-2685(DHD)132.9wPt-7514(DHD)135.5wPt-1834(DHD) wPt-742982(DHD)wPt-7956(DHD) wPt-8352(DHD)wPt-8396(DHD) wPt-0995(DHD)

136.9

wPt-10407(DHD)139.9

3B [3]

Figure 1 continued

166

wPt-3815(DPM)11.0

wPt-7535(DHD) wPt-9389(DHD)150.1wPt-665320(DPM) wPt-665392(DPM)wPt-665570(DPM) wPt-666115(DPM)wPt-666659(DPM) wPt-671598(DPM)wPt-730421(DPM) wPt-730995(DPM)wPt-731402(DPM) wPt-732466(DPM)wPt-730421(DHD) wPt-665320(DHD)wPt-744794(DHD) wPt-666907(DHD)wPt-671603(DHD) wPt-6107(DHD)wPt-668020(DHD)

151.8

wPt-731144(DHD)153.9wPt-2923(DPM) wPt-667368(DHD)wPt-669412(DHD) wPt-666955(DHD)

160.2

wPt-8164(DPM)166.4

3D

wPt-2788(DPM)22.2

wPt-7939(DPM)50.3

wPt-730387(DPM) wPt-800147(DPM)62.1wPt-800509(DPM)62.7wPt-7924(DPM)62.9wPt-5951(DPM)64.3tPt-9400(DPM)64.6wPt-2903(DPM)68.1

wPt-3449(DPM)83.1

wPt-2951(DPM)96.9wPt-744256(DPM) wPt-8967(DPM)99.8wPt-5172(DPM)101.6wPt-3845(DPM)104.6wPt-4680(DPM)106.2wPt-7919(DPM)106.9wPt-730387(DHD)108.4

4A

Figure 1 continued

167

wPt-1931(DPM) wPt-1931(DHD)4.3

wPt-3439(DPM) wPt-6869(DPM)wPt-6869(DHD) wPt-3439(DHD)

15.5

wPt-5559(DPM) wPt-734310(DPM)wPt-5559(DHD)

16.6

wPt-4607(DPM) wPt-667593(DPM)wPt-8650(DPM) wPt-4607(DHD)wPt-8650(DHD)

22.6

wPt-7569(DPM) wPt-7569(DHD)63.2wPt-7062(DPM) wPt-9067(DPM)wPt-9067(DHD) wPt-7062(DHD)

68.1

wPt-6209(DPM) wPt-733038(DPM)68.8

wPt-740654(YLD)114.9

4B

rPt-3825(YLD)77.5

wPt-1400(DPM) wPt-5505(DPM)85.3

5D

Figure 1 continued

168

wPt-741630(DPM) wPt-9205(DPM)3.2

wPt-4681(DPM)21.0wPt-665036(DPM)21.1wPt-9113(DPM) wPt-734068(DPM)22.8wPt-667618(DPM)28.4

wPt-7063(DPM)41.0wPt-734140(DPM)41.8wPt-666494(DPM)43.2


86.5

wPt-666266(YLD) wPt-741630(DHD)94.9wPt-734140(DHD) wPt-8954(DHD)wPt-666494(DHD)

104.1

wPt-9976(DHD) wPt-667607(DHD)wPt-4229(DHD) wPt-9474(DHD)wPt-8124(DHD) wPt-3247(DHD)

107.8

wPt-732328(DHD)109.0

6A

tPt-2055(DPM)4.7wPt-6293(DPM)5.5wPt-5971(DPM)9.2wPt-7711(DPM)11.8wPt-4564(DPM) wPt-3526(DPM)12.8wPt-4142(DPM) wPt-665017(DPM)wPt-6667(DPM) wPt-745074(DPM)wPt-6594(DPM) wPt-4900(DPM)wPt-4930(DPM) wPt-665036(DPM)wPt-8721(DPM) wPt-729979(DPM)

14.3

wPt-2000(DPM) wPt-6160(DPM)14.9wPt-8560(DPM) wPt-2424(DPM)wPt-9231(DPM) wPt-3168(DPM)wPt-743099(DPM)

15.9

tPt-8161(DPM) wPt-5211(DPM)wPt-6208(DPM)

25.8

wPt-4164(DPM)38.5wPt-6116(DPM)50.6wPt-9952(DPM)51.9wPt-8059(DPM) wPt-4662(DPM)wPt-741515(YLD)

54.3

wPt-8814(YLD)58.1wPt-5256(YLD)61.3wPt-2095(DHD)65.7wPt-1437(DHD) wPt-8059(DHD)wPt-4164(DHD)

65.8

wPt-0406(DHD)66.0wPt-9952(DHD) wPt-6116(DHD)111.9wPt-4142(DHD) wPt-4564(DHD)113.3wPt-6594(DHD) wPt-4930(DHD)113.9wPt-745074(DHD) wPt-6667(DHD)wPt-665017(DHD)

117.2

wPt-3526(DHD)127.9wPt-0137(DHD)134.5

6B

Figure 1 continued

169

wPt-1931(DPM)4.3wPt-1179(DPM)10.8wPt-8473(DPM)13.6wPt-3992(DPM) wPt-7299(DPM)13.7


56.5

wPt-2501(DPM) wPt-1931(DHD)85.9wPt-8473(DHD)87.1wPt-7015(DHD)90.0

wPt-9170(DHD)125.1

wPt-5105(DHD)150.7wPt-3992(DHD)158.4wPt-0205(DHD)164.5wPt-3226(DHD)168.7

wPt-7108(DHD)179.9

wPt-1179(DHD)217.4

7A

wPt-4230(DPM) wPt-4863(DPM)15.5wPt-2737(DPM)38.8wPt-1853(DPM)67.6wPt-4025(DPM)106.8wPt-2305(DPM) wPt-4600(DPM)134.5wPt-4814(DPM)136.6wPt-4038(DPM)137.6wPt-3190(DPM)144.3wPt-5462(DPM)146.4wPt-7108(DPM) wPt-8387(DPM)149.9wPt-2592(DPM) wPt-2449(DPM)wPt-733669(DPM) tPt-3700(DPM)

200.7

wPt-2878(DPM) wPt-6320(DPM)211.0wPt-7251(DPM) wPt-6869(DPM)wPt-800156(DPM)

212.6

wPt-3439(DPM)213.1wPt-9813(DPM)214.2wPt-0318(DHD) wPt-7108(DHD)214.8wPt-0137(DHD) wPt-3439(DHD)wPt-6869(DHD) wPt-0695(DHD)

217.4

wPt-7318(DHD)217.5wPt-2305(DHD) wPt-4600(DHD)218.1wPt-4814(DHD)219.4wPt-4038(DHD)219.7wPt-3190(DHD) wPt-3004(DHD)219.8wPt-744987(DHD) wPt-0600(DHD)wPt-5462(DHD) wPt-8387(DHD)

220.0

wPt-2449(DHD) wPt-0504(DHD)wPt-0884(DHD) wPt-733669(DHD)tPt-3700(DHD)

224.0

wPt-2878(DHD)227.6wPt-6320(DHD) wPt-7251(DHD)228.5wPt-800156(DHD)234.8

7B

Figure 1 continued

170

wPt-1100(DPM) wPt-2592(DPM)wPt-3328(DPM) wPt-3923(DPM)

37.3

wPt-4315(DPM) wPt-664384(DPM)wPt-731810(DPM) wPt-7842(DPM)wPt-8343(DPM)

170.6

wPt-8422(DPM)170.8wPt-664384(DHD) wPt-1100(DHD)172.0wPt-3328(DHD)174.2

7D

Figure 1 continued

171

6 Validation of association genetic mapping II

6.1 Introduction

Wheat yield gains have reduced in recent years due to varying climatic and precipitations

patterns (Olesen et al. 2011). To facilitate the development of varieties adapted to varying climatic

conditions, positive alleles conferring high-yield under changing conditions must be identified in

current elite germplasm, as well as in new genetic pools (Lopes et al. 2015b).

Risch and Merikangas (1996) suggest that association mapping or genome-wide association

analysis (GWAS) may be a suitable complementary approach to bi-parental populations for identifying

genetic markers associated with traits of interest. An extensive study of marker-trait associations

(MTAs) in bread wheat has enabled the identification of MTAs for a large number of traits including

yield under moisture stress (Atta 2013), high molecular weight (HMW) glutenins (Ravel et al.2006),

milling quality, seed size/seed and shape (Breseghello and Sorrells 2006b), Stago-nospora nodorum

blotch resistance (Tommasini et al. 2007) and resistance against stem, leaf, and stripe rust (Crossa et

al. 2007a). However, more complex traits such as tolerance to drought and salinity have limited

application to marker assisted selection programs (Gupta et al. 2008).

A significant number of genomic regions/QTLs associated with drought response in wheat

have been reported (Kirigwi et al. 2007; Yang et al. 2007a; Rebetzke et al. 2008c; McIntyre et al.

2010b); (Rebetzke et al. 2013b); (Rebetzke et al. 2008a); (Diab et al. 2008a). However, few genomic

regions/QTLs have been targeted to improve breeding because of the following factors (i) lack of

validation in different genetic backgrounds and environments (Price et al. 2002c; Yue et al. 2008), (ii)

significant QTL x QTL interaction, (iii) significant QTL x genetic background interaction, (iv) QTL x

environment interaction, and (v) the degree of linkage between markers and associated QTL (Gupta et

al. 2010a).

Although it has been difficult to identify consistent genetic markers associated with yield under

stress environments (particularly drought) in either wheat or maize (MacCaferri et al. 2011; Dodig et

al. 2012; Lu et al. 2012), the validity and usefulness of these regions, and their applicability to wheat

breeding needs to be verified (Bentley et al. 2014). In this study, the hypothesis that hybridization of

advanced wheat lines carrying complementary genomic regions identified from an earlier genetic

association analysis (Atta 2013) and confirmed in a subsequent head-to-head cross-year comparisons

of the same lines (see Chapter 5) would accumulate positive alleles in the recombinants that

172

outperform their parents. This chapter aimed to confirm the yield expression of these selected genomic

regions/markers when pyramided in a single genotype.

6.2 Materials and Methods

6.2.1 Plant Material

The experiments were conducted at the Plant Breeding Institute, Cobbitty and the IA Watson

Grains Research Centre, Narrabri during the 2015 cropping season. The study evaluated 10 wheat

advance lines which included PBI 1, PBI 100, PBI 273, PBI 10, PBI 11, PBI 151, PBI 45, PBI 94, PBI

98 and PBI 299. These lines carried the highest numbers of positive markers based on the earlier

association analysis carried out by Atta (2013). The pedigree and description of each line is listed in

Table 6.1. The genetic similarity of these 10 parents based on the DArT genotype profiles ranged from

0.96 (for PBI-273 and PBI-151) to 0.45 (PBI-11 and PBI-45) (Table 6.2).

6.2.2 Development of materials carrying pyramided markers

Hybridization were carried out on the basis of the Atta (2013) associations in the green house

to produce F1 populations mainly: (a) PBI-100 x PBI-273 (b) PB1-11 x PBI-10 (c) PBI-100 x PBI-

11(d) PBI-11 x PBI-10, (e) PBI-11 x PBI-10 (f) PBI-151 x PB1-45 and (g) PBI-100 x PBI-11

combinations(Table 6.3). The ensuing F1 populations were then top crossed with lines PBI-299, PBI-

45, PBI-94, PBI-1 PBI-273 and PBI-98 respectively, in the green house to produce six top cross F2

populations. Each F2 top cross consisted of 30 to 100 plants which were then multiplied to produce F3

progeny for evaluation. The list of targeted DArT markers for each parent from the Atta (2013)

analysis is given in Table 6.5 However, subsequent association analysis of the same materials in head-

to-head comparisons in 2014 and 2015 confirmed Marker Trait Associations on chromosomes 1A, 1B,

2B, 4B and 6B (Table 6.4). These confirmed MTAs subsequently became the targets for validation in

the derived topcross progeny.

173

Table 6.1 List of genotypes hybridized to produce top cross progenies and their corresponding

numbers of positive markers detected in the earlier association analysis conducted by Atta 2013.

Genotype Pedigree No. of positive markers

PBI-1 Crusader 49

PBI-10 EGA Stampede 43

PBI-11 Sunstate 40

PBI-45 SUN344 E/VPMB36020 42

PBI-94 SUN434A/SUN436E.116.4 42

PBI-98 EGA Bonnie Rock/SUN436F 41

PBI-100 CHARA/B409C//SUN498E 47

PBI-151 SUN344 E/Ventura 50

PBI-273 WA-1-21005/2*SUN426B 52

PBI- 299 SUN445C/QT10776 43

Table 6.2 Similarity matrix of the 10 parents in Table 6.1 based on DArT marker profiles.

PBI-1 PBI-10 PBI-11 PBI-45 PBI-94 PBI-98 PBI-100 PBI-151 PBI-273 PBI-299

PBI-1 -

PBI-10 0.77 -

PBI-11 0.71 0.64 -

PBI-45 0.75 0.61 0.45 -

PBI-94 0.75 0.71 0.59 0.56 -

PBI-98 0.76 0.86 0.63 0.67 0.74 -

PBI-100 0.89 0.75 0.62 0.78 0.69 0.75 -

PBI-151 0.91 0.86 0.77 0.63 0.79 0.84 0.81 -

PBI-273 0.91 0.82 0.77 0.65 0.79 0.82 0.84 0.96 -

PBI-299 0.86 0.73 0.54 0.78 0.64 0.68 0.88 0.72 0.72 -

174

Table 6.3 List of hybridizations made to generate F1 and F2 top cross populations.

No.

F1 Population

F2(Top Crosses) Population

1 PBI-100 X PBI-273 (PBI-100 X PBI-273) X PBI 299

2 PB1-11 X PBI-10 (PB1-11 X PBI-10) X PBI-45

3 PBI-100 X PBI-11 (PBI-100 X PBI-11) X PBI-94

4 PBI-11 X PBI-10 (PBI-11 X PBI-10) X PBI-1

5 PBI-151 X PB1-45 (PBI-151 X PB1-45) X PBI-273

6 PBI-100 X PBI-11 (PBI-100 X PBI-11) X PB1-98

Table 6.4 List of significant DArT markers associated with higher grain yield under moisture stress

identified from the Chapter 5 head-to-head association analysis.

Marker Name Chromosome Position (cM)

wPt-2859 1A 5.75

tPt-5080 1B 9.16

wPt-8930 1B 9.16

wPt-7359 1B 11.2

wPt-2859 1B 14.96

wPt-7905 1B 14.96

wPt-2075 1B 14.96

wPt-5562 1B 30.96

wPt-5385 1B 30.96

wPt-1637 1B 31.64

wPt-3561 2B 51.42

wPt-4930 6B 15.41

wPt-740654 4B 114.9

wPt-741515 6B 25.79

wPt-1637 6B 15.41

wPt-5256 6B 25.79

wPt-6594 6B 14.94

rPt-3825 5D 77.5

wPt-666266 6A 41

.

175

6.2.3 DNA extraction and genotyping of F2 topcross progeny

DNA was extracted from each seedling top cross F2 progeny prior to multiplication utilizing a

modified CTAB method Doyle JJ (1990). This is described below:

(a) Four fresh leaves of 2cm length were collected from each two week old seedling (top cross F2)

and put in the 20 ml labelled tubes containing silica gel to dry and preserve the leaf samples for

transportation.

(b) Upon drying, leaves were transferred into 2ml tubes containing two stainless steel beads. The

material was grounded into a fine powder in a mixer mill (MM300 Retsch, Haan, Germany) for

two minutes at 20 rpm. Beads were removed and 800-900 μl of warmed CTAB buffer was

added to each tube and mixed thoroughly.

(c) Grounded samples were incubated at 65 O

C for 40 minutes with intermittent gentle swirling

(every 10 minutes). Samples are allowed to cool down at room temperature and 600 μl of

Chloroform: phenol (24:1 v:v) was added and mixed by gently inversion for about 2 minutes

until the two layers homogenized.

(d) Samples were centrifuged at 3600 rpm at room temperature for 20 minutes and the aqueous

phase transferred to a clean tube 1.5 ml and 700 μl of cold isopropanol added and gently mixed

to precipitate the nucleic acids. Tubes were kept at -20 0C for 20 minutes and then centrifuged

at 10,000 rpm for 10 minutes. The supernatant was removed and DNA was dried until

isopropanol evaporated.

(e) DNA obtained was washed with 500 μl of CTAB buffer and centrifuged at 7000 rpm for 10

minutes to remove the wash buffer.

(f) Sample tubes were kept open overnight in an upright position to allow the buffer if any to

evaporate. Then 100 μl of TE (pH-8) with RNAase (1 μl per 100 μl ofTE) was added and the

samples maintained at 37O

C in oven for 1-2 hours.

(g) The DNA quantification (NanoDrop® ND-1000 Spectrophotometer, NanoDrop Technologies

Inc., USA) was to assess DNA purity at an absorbance of 260nm and 280nm using 2 μl of

DNA sample. A ratio of 1.8 was accepted as pure.

The DNA plates were sent to the Diversity™ Arrays Technology (DArT™) Pty Ltd, ACT2600,

Australia for genotyping (www.diversityarrays.com). Target DArt markers in chromosomal regions

linked to yield from the Atta (2013) study were matched with a consensus map (unpublished) that

http://www.diversityarrays.com/

176

integrated 5,000 array markers with 70,000 DArTseq markers. The DArTseq marker profiles of the

parents that correspond to the MTAs in the Atta (2013) analysis are given in Table 6.4.

177

Table 6.5 List of parents with their corresponding target DArtseq markers utilized in the hybridization

program.

CloneID Chrom PBI-1 PBI-10 PBI-11 PBI-45 PBI-94 PBI-98 PBI-100 PBI-151 PBI-273 PBI-299

1123955 1B 0 0 1 0 1 1 0 1 0 0

1163780 1B 1 1 0 1 0 0 0 0 1 1 1101991 1B 1 1 1 1 1 1 1 1 1 0

1106888 2A 1 0 - 0 1 0 0 0 0 0

6020282 2B 1 1 1 1 0 0 1 1 1 0 2351065 2B 0 0 0 0 0 0 0 0 0 1

1203755 2B 0 0 0 0 1 1 0 0 0 1

1232931 2B 0 0 0 0 0 0 0 0 0 0 1234491 2B 0 0 0 0 1 1 0 0 0 0

1242436 2B 0 0 0 0 1 1 0 0 0 0

1230660 2B 0 0 0 0 1 1 0 0 0 0 2299734 2B 0 0 0 0 0 1 0 0 0 1

1716898 2B 0 0 0 0 1 0 0 0 0 0

3027838 2B 1 1 1 1 1 0 0 1 1 0 1258412 2B 0 0 0 1 0 0 0 0 0 0

1116476 2D 1 1 1 1 0 0 0 0 1 1

1127261 2D 1 1 1 1 1 1 1 1 1 1 1093304 2D 0 0 0 0 1 0 0 0 0 0

1397590 2D 0 1 1 0 0 1 - 0 - -

1151200 3B 0 0 0 0 1 0 0 0 0 0 3026577 3B 0 0 1 0 0 0 0 0 0 1

3027186 3B 1 0 1 0 0 0 1 0 0 1

1219810 3B 1 1 0 1 1 1 1 1 1 1 1013770 3B 0 0 0 0 1 0 0 0 0 0

1002107 3B 0 1 1 1 0 1 0 1 1 0

1109327 3B 0 1 1 1 0 1 0 1 1 0 1137819 3B 0 0 0 1 0 1 0 1 1 0

1092850 3B 1 1 1 0 1 0 1 0 0 1

1095941 3B 1 1 1 0 1 0 1 0 0 1 3022971 3B 1 1 1 0 1 0 1 0 0 1

1000283 3B 1 1 1 0 1 0 1 0 0 1

992140 3B 1 1 1 0 1 0 1 0 0 1 1057473 3B 1 1 1 0 1 0 1 0 0 1

984103 3B 1 1 1 0 1 0 1 0 0 1 1048368 3B 1 1 1 0 1 0 1 0 0 1

3533736 3B 0 0 0 1 0 1 0 1 1 0

3029150 3B 1 1 1 0 1 0 1 0 0 1 6027166 3D 1 1 0 0 0 1 1 0 0 1

1220326 3D 0 1 1 1 1 1 0 1 0 1

3020835 3D 0 1 1 1 1 1 0 1 0 1 1076731 3D 1 1 0 1 0 0 0 1 1 1

1075145 3D 0 0 1 1 0 1 0 1 0 1

1673946 3D 1 0 0 0 0 0 1 0 1 0 1675351 3D 1 0 0 0 0 0 1 0 1 0

2279884 4B 0 1 0 0 1 1 0 - 0 -

1150837 4D 1 1 1 1 0 1 - 1 1 0 3027580 5B 0 0 0 0 0 0 0 0 0 0

1091141 5B 0 0 0 0 0 0 0 0 0 0

1204542 5B 1 0 1 1 0 0 1 1 1 1 1052428 5B 1 0 1 1 1 0 1 1 1 1

1094527 5B 0 1 0 0 0 0 0 0 0 0

1107756 5B 1 1 1 1 0 0 1 1 1 1 1093400 6B 1 0 1 1 0 0 0 1 1 0

1121615 6B 1 1 1 1 0 0 0 1 1 0

1100767 6B 0 1 0 0 1 1 1 0 0 0 1229248 6B 1 0 1 1 0 0 0 1 1 0

1058967 6B 0 - 0 0 1 - 1 0 0 1

989612 7A 1 1 1 1 1 0 1 0 1 0 1131893 7A 0 0 0 0 1 0 0 0 0 0

Total 16 17 17 16 16 13 13 15 16 12

Key: Present = 1, Absent = 0

178

6.2.4 Field experiment

The resultant F3 seeds obtained from each topcross F2 progeny were sown at a rate of 15g/plot

in an alpha lattice design in a bird cage and partially replicated in 1 x 2m plots. The agronomic traits

assessed included days to heading, days to physiological maturity, TKW, total biomass, harvest index

and grain yield. All agronomic practices were conducted as outlined in Chapter 3 of this thesis.


Data were analysed to test differences among progenies from the various populations using a

linear mixed model and the REML function of Genstat (version 14.1). Genotypes were considered

fixed effects and ranges and rows as random effects.

Significant effects among genotype means were separated by the Fisher's protected least

significant difference test at P < 0.05. Progenies were grouped based on markers associated with grain

yield detected in the earlier Atta (2013) association analysis. Regression analysis was conducted to

group means. The materials were then re-classification based on those markers confirmed in Chapter 5

as effective in both the Atta (2013) study and the head-to-head comparison of the same materials in

2014 and 2015. The differences among genotypes based on the numbers of markers present were the

compared using REML. These target markers were wPt 4930, wPt 1637, wPt 8682 and wPt 6594.

Principal component analysis was carried out to determine the relationship among topcross progenies

on the basis of their field performance and number of target markers accumulated.

6.4 Results

6.4.1 Performance of the 6 top cross populations

The phenotypes of the 6 top cross populations; (PBI-100 X PBI-11) X PBI-94, (PBI-11 X PBI-10)

XPBI-1, (PBI-11 X PBI-10) X PBI-45, (PBI-100 X PBI-11) X PBI-98, (PBI-151 X PBI-45) X PBI-

273 and (PBI-100 X PBI-11) X PBI-94 are presented in Tables 6.6, 6.7, 6.8, 6.9, 6.10 and 6.11,

respectively. The progeny of these populations varied, sometimes significantly, from each other, their

parental lines and the commercial check cultivars.

179

Table 6.6 Means of the top cross population (PBI-100 X PBI-11) x PBI-94 for days to heading(DHD), days to physiological maturity(DPM), canopy

ground cover(GC), harvest index(HI), grain filling duration(GFD), normalized difference vegetation index(NDVI), thousand kernel weight(TKW),

biomass(BMAS) and grain yield(YLD).

Genotype DHD DPM GC (%) GFD HI NDVI TKW(g) BMAS YLD(kg/ha)

A1 89.6 137.1 41.76 47.23 0.32 0.761 33.32 1512 2408

A2 87.1 130.9 46.24 43.94 0.32 0.716 34.06 1574 2416

A3 90.1 135.6 25.88 45.49 0.30 0.750 27.28 1128 1798

A4 89.3 136.5 34.78 46.88 0.32 0.729 35.54 1492 2351

A5 91.2 136.5 34.59 44.47 0.37 0.838 29.1 1199 2213

A6 90.9 137.3 39.73 46.11 0.27 0.811 29.39 1405 1901

A7 92.1 135.7 24.07 44.32 0.38 0.816 36.62 1468 2733

A8 90.1 136.1 30.82 45.48 0.31 0.662 30.95 1376 2175

A9 92.1 136.4 39.41 44.63 0.33 0.740 33.39 1308 2164

A10 91.5 134.5 17.72 44.64 0.24 0.802 27.37 1098 1529

A11 89.4 135.8 35.72 46.07 0.27 0.794 32.16 1613 2274

A12 86.4 136.2 32.92 48.98 0.28 0.714 37.94 1893 2835

A13 90.8 137.0 38.37 46.15 0.31 0.722 32.76 1622 2547

A14 90.2 135.8 30.34 44.85 0.35 0.745 33.55 1411 2522

A15 92.4 135.8 22.76 43.22 0.29 0.768 33.53 1492 2150

A16 87.3 136.2 37.73 48.58 0.35 0.756 35.68 2023 3492

A17 89.3 136.4 28.86 47.04 0.31 0.718 30.76 1776 2721

A18 91.4 135.6 37.44 45.49 0.33 0.719 34.12 1683 2786

A19 92.5 137.5 30.13 44.52 0.31 0.769 33.65 1486 2139

A20 88.9 137.7 31.2 48.16 0.33 0.754 29.72 1439 2360

A21 88.2 135.8 36.53 47.96 0.31 0.750 36.28 1608 2497

A22 87.5 136.1 32.03 48.39 0.39 0.737 36.81 1713 3340

A23 93.8 136.6 23.82 42.29 0.27 0.744 28.48 1364 1951

A24 94.7 136.4 34.67 41.59 0.34 0.749 30.22 1556 2684

A25 93.3 136.2 26.16 43.35 0.33 0.755 27.98 1265 2032

A26 94.7 135.6 22.71 40.89 0.27 0.776 29.64 1287 1761

A27 103.0 136.6 37.94 33.33 0.26 0.700 31.94 1382 1834

A28 87.7 136.9 32.03 50.68 0.34 0.684 30.56 1547 2578

A29 90.4 135.7 29.88 45.28 0.30 0.735 33.31 1420 2102

A30 88.4 136.5 38.85 48.10 0.27 0.709 32.33 1469 1962

A31 89.7 136.2 39.08 46.62 0.43 0.795 34.09 1331 2613

A32 94.3 135.9 39.59 41.24 0.27 0.760 32.41 1217 1452

A33 87.8 134.4 25.87 46.87 0.26 0.696 29.54 1630 2180

A34 86.7 133.9 54.81 46.89 0.29 0.751 31.83 1799 2546

A35 93.7 136.5 21.24 41.89 0.30 0.759 34.34 1097 1586

A36 88.6 136.4 31.65 47.40 0.33 0.752 33.01 1398 2352

180

A37 88.3 135.8 32.71 47.23 0.30 0.661 35.74 1624 2470

A38 86.1 136.0 29.62 49.30 0.22 0.788 31.61 982 1009

A39 92.6 137.7 28.52 44.39 0.33 0.859 30.19 1545 2592

Parents

PBI-100 85.68 135.1 31.39 48.87 0.35 0.755 34.14 1564 2761

PBI-273 87.22 135.6 28.50 47.99 0.29 0.748 29.4 1524 2215

PBI-299 92.36 137.1 37.65 44.70 0.32 0.796 31.59 1426 2241

Control

SPITFIRE 91.02 136.1 43.38 44.66 0.29 0.783 29.63 1809 2658

SUNTOP 90.68 136.4 48.4 45.73 0.30 0.795 32.28 1461 2178

Mean 90.43 136.0 33.35 45.49 0.31 0.753 32.23 1477.63 2297.9

Lsd 3.62 2.7 24.62 3.82 0.92 0.076 7.82 325.2 725.8

181

Table 6.7 Means of the top cross population (PBI-11 X PBI-10) x PBI-1 for days to heading (DHD), days to physiological maturity(DPM), canopy ground

cover (GC), harvest index( HI), grain filling duration(GFD), normalized difference vegetation index(NDVI), thousand kernel weight (TKW),

biomass(BMAS) and grain yield(YLD) kg/ha.

Genotype DHD DPM GC GFD HI NDVI TKW(g) BMAS YLD(kg/ha)

B1 92.03 135.8 30.02 43.53 0.25 0.787 28.80 1264 1561

B2 91.28 135.2 26.52 43.62 0.27 0.728 30.58 1588 2254

B3 92.04 135.9 34.89 43.06 0.27 0.773 26.82 1246 1757

B4 91.6 137.5 46.16 46.11 0.25 0.700 34.68 1526 2002

B5 94.19 135.3 14.96 41.62 0.25 0.696 28.93 1116 1405

B6 90.74 135.8 30.44 44.87 0.28 0.724 30.31 1369 1917

B7 92.65 136.2 37.37 43.15 0.25 0.816 25.95 1090 1382

B8 85.12 135.7 33.54 50.45 0.25 0.806 31.33 1360 1735

B9 93.61 136.2 30.21 42.18 0.31 0.751 28.12 1385 2163

B10 90.38 135.7 21.68 44.83 0.25 0.772 27.14 1248 1636

B11 88.16 135.1 31.14 46.48 0.28 0.754 30.26 1474 2137

B12 91.88 135.9 29.95 43.95 0.26 0.773 31.80 1511 1959

B13 86.92 135.8 31.03 48.62 0.31 0.748 30.19 1298 1932

B14 93.75 136.1 35.55 41.77 0.25 0.820 29.96 1365 1756

B15 90.58 132.4 37.24 40.28 0.30 0.750 24.87 1397 2078

B16 91.38 136.1 24.22 44.51 0.27 0.722 33.89 1206 1678

B17 98.41 135.5 30.22 37.27 0.31 0.774 27.64 1335 2049

B18 92.15 135.5 33.83 43.4 0.27 0.739 32.74 1618 2186

B19 93.12 135.8 33.76 42.38 0.28 0.774 30.60 1270 1815

B20 88.12 135.1 39.42 47.32 0.28 0.738 31.11 1669 2333

PBI-1 86.55 132.3 37.87 45.4 0.34 0.710 27.67 1858 3139

PBI-10 91.07 135.7 39.46 44.32 0.30 0.736 33.92 1501 2339

PBI-11 91.06 135.1 43.95 43.89 0.25 0.806 26.91 1432 1816

SPITFIRE 91.02 136.1 43.38 44.66 0.29 0.783 29.63 1809 2658

SUNTOP 90.68 136.4 48.4 45.73 0.30 0.795 32.28 1461 2178

Mean 91.14 135.52 33.8 44.14 0.28 0.759 29.85 1415.84 1994.6

Lsd 3.62 2.7 24.62 3.82 0.92 0.076 7.82 325.2 725.8

182

Table 6.8 Means of the top cross population (PBI-11 X PBI-10) X PBI-45 together with parents and controls for harvest index (HI), grain filling duration

(GFD), thousand kernel weight (TKW), biomass (BMAS) and grain yield (YLD).

Genotype DHD DPM GC (%) GFD HI NDVI TKW(g) BMASS YLD(kg/ha)

C1 88.8 136.1 31.51 46.54 0.30 0.803 32.92 1507 2349

C2 92.7 135.9 29.74 42.77 0.27 0.771 27.83 1770 2369

C3 89.0 136.0 40.27 46.49 0.29 0.748 29.52 1546 2281

C4 90.4 135.8 38.68 45.98 0.32 0.752 37.75 1671 2812

Parents

PBI-45 95.2 136.0 36.56 40.02 0.26 0.775 28.07 1603 2112

PBI-10 91.1 135.7 39.46 44.32 0.30 0.736 33.92 1501 2339

PBI-11 91.1 135.1 43.95 43.89 0.25 0.806 26.91 1432 1816

Control

SPITFIRE 91.0 136.1 43.38 44.66 0.29 0.783 29.63 1809 2658

SUNTOP 90.7 136.4 48.40 45.73 0.30 0.795 32.28 1461 2178

Mean 91.14 135.52 33.8 44.14 0.28 0.774 30.98 1589 2324

Lsd 3.62 2.7 24.62 3.82 0.92 0.076 7.82 325.2 725.8

183

Table 6.9 Means of the top cross population (PBI-100 X PBI-11) X PBI-98 together with parents and controls for harvest index (HI), grain filling duration

(GFD), thousand kernel weight (TKW), biomass (BMAS) and grain yield (YLD).

Genotype DHD DPM GC (%) GFD HI NDVI TKW(g) BMAS YLD(kg/ha)

D1 85.8 135.4 29.80 49.4 0.31 0.787 28.18 1186 1877

D2 89.5 135.8 14.91 45.8 0.27 0.742 31.92 1055 1393

D3 84.5 134.7 30.87 50.3 0.29 0.736 32.18 1548 2229

D4 85.8 134.7 44.20 49.4 0.31 0.764 33.44 1648 2460

D5 87.3 136.1 31.08 49.1 0.24 0.810 26.06 1286 1665

D6 85.6 132.9 37.38 47.4 0.28 0.759 32.32 1348 1919

D7 87.8 133.9 20.50 47.0 0.26 0.675 27.59 1406 1872

D8 96.0 134.9 23.33 39.4 0.30 0.892 25.66 1193 1804

D9 92.6 135.3 35.53 42.9 0.24 0.814 36.19 1224 1437

D10 92.0 130.9 43.66 38.4 0.31 0.753 30.76 1101 1616

D11 86.9 136.9 34.78 49.2 0.35 0.803 31.78 1360 2363

D12 89.0 134.8 40.98 45.6 0.31 0.764 28.78 1356 2119

D13 85.9 133.6 31.87 47.3 0.22 0.734 31.44 1516 1706

D14 85.9 135.2 27.46 49.3 0.33 0.761 31.67 1294 2263

D15 88.2 136 27.77 47.0 0.29 0.756 28.4 1399 2059

D16 90.8 136.3 38.17 44.4 0.31 0.800 27.78 1389 2146

D17 87.5 134.2 44.52 46.7 0.29 0.734 35.64 1639 2378

D18 90.3 135.5 29.91 45.7 0.30 0.741 27.2 1382 2076

D19 96.2 136.4 21.34 40.2 0.23 0.707 27.36 670 620

D20 87.4 135.6 24.37 47.7 0.29 0.769 30.47 1336 1940

D21 87.2 135.6 33.10 47.9 0.29 0.769 31.72 1368 1873

D22 88.7 135.7 28.96 47.4 0.31 0.764 31.99 1367 2132

D23 87.7 134.7 26.63 46.8 0.27 0.765 31.42 1285 1657

D24 86.7 135.7 27.72 48.8 0.29 0.767 26.81 1188 1774

D25 92.5 135.8 34.35 43.1 0.26 0.807 24.94 1135 1497

D26 89.0 135.1 29.57 45.9 0.29 0.731 31.36 1470 2106

D27 92.7 135.2 25.13 42.4 0.31 0.725 30.86 1208 1952

D28 90.9 135.6 38.83 44.0 0.25 0.776 31.16 1439 2038

D29 90.5 135.5 47.46 44.8 0.29 0.776 30.1 1646 2440

Parents

PBI-100 85.7 135.1 31.39 48.9 0.3545 0.755 34.14 1564 2761

PBI-98 92.8 136.6 34.87 43.5 0.2882 0.818 31.01 1412 2032

PBI-11 91.1 135.1 43.95 43.9 0.25 0.806 26.91 1432 1816

Control

SPITFIRE 91.0 136.1 43.38 44.7 0.29 0.783 29.63 1809 2658

SUNTOP 90.7 136.4 48.40 45.7 0.30 0.795 32.28 1461 2178

Mean 91.1 135.52 33.80 44.1 0.28 0.774 30.98 1589 2324

Lsd 3.6 2.7 24.62 3.8 0.92 0.076 7.82 325.2 725.8

184

Table 6.10 Means of the top cross population (PBI-151 X PBI-45) X PBI-273 together with parents and controls for harvest index(HI), grain filling

duration(GFD), thousand kernel weight(TKW), biomass(BMAS) and grain yield(YLD).

Genotype DHD DPM GC (%) GFD HI NDVI TKW(g) BMASS YIELD(kg/ha)

E1 89.27 136.2 25.31 46.22 0.2877 0.6152 32.55 849 1088

E2 88.21 136.5 22.09 47.55 0.3029 0.7547 30.28 1351 1977

E3 91.72 135.2 31.3 43.9 0.2399 0.7297 32.76 1259 1587

E4 88.26 135.7 41.63 47.43 0.2743 0.8083 35.09 1351 1861

E5 94.59 131.9 41.95 36.13 0.0629 0.7888 24.04 1069 200

E6 90.16 136 39.65 44.76 0.1695 0.7737 20.65 1037 757

E7 91.7 137.1 29.38 45.61 0.277 0.6708 35.83 1115 1459

E8 93.03 135.6 25.88 41.76 0.262 0.7134 32.75 1205 1579

E9 91.53 135.5 17.14 43.69 0.3043 0.8046 28.01 1127 1671

E10 90.54 135.4 33.36 44.56 0.2712 0.7824 30.18 1164 1603

E11 91.1 134.4 29.49 42.93 0.2718 0.7712 32.19 1257 1585

E12 92.28 135.5 25.25 42.77 0.2408 0.7996 29.79 964 1189

E13 93.65 135.3 30 41.4 0.2524 0.7758 26.25 1075 1297

E14 90.79 136.8 27.41 45.73 0.2585 0.747 29.85 1112 1411

E15 90.54 137.6 43.41 46.83 0.1582 0.8524 27.87 1021 635

E16 91.24 135.6 30.81 43.43 0.2167 0.7447 32.91 1363 1446

E17 89.01 135.1 28.08 45.65 0.2557 0.7592 31.46 1190 1505

E18 89.13 133.6 37.78 44.64 0.2904 0.7351 26.27 1171 1546

E19 89.04 136.7 34.8 47.52 0.2643 0.7679 30.11 1234 1601

E20 93.68 136 31.49 41.92 0.2844 0.8061 32.56 1399 1948

E21 88.4 135.3 36.49 46.2 0.2868 0.7258 31.42 1381 1961

E22 88.05 136.5 20.7 48.34 0.2725 0.7485 35.47 1637 2325

E23 91.7 135.2 22.14 43.04 0.2531 0.7586 23.78 1228 1551

E24 93.69 137.2 15.18 44.33 0.2861 0.7051 32.55 1001 1387

E25 94.05 136.1 23.75 41.88 0.1324 0.7756 33.03 1568 1132

E26 89.09 135.8 19.08 46.15 0.3048 0.7331 30.74 1417 2301

E27 87.39 137.5 29.13 49.58 0.2861 0.7483 28.65 1458 2072

E28 87.02 135.5 34.68 48.28 0.2708 0.7219 32.14 1419 1880

E29 88.86 135 25.33 44.98 0.2782 0.7001 31.52 1429 1991

E30 90.57 135.8 39.19 45.32 0.2676 0.7626 31.38 1200 1604

E31 90.7 134.1 25.09 44.01 0.2475 0.7566 28.91 1249 1492

E32 93.95 135.6 17.57 42.82 0.2549 0.6486 28.1 1239 1479

E33 88.29 136.4 43.79 48.19 0.2858 0.6922 31.99 1441 2054

E34 94.31 135.2 35.75 40.75 0.347 0.7757 27.56 1356 2282

E35 91.85 136 24.35 43.76 0.277 0.7484 31.88 1414 1923

E36 89.4 135.9 33.46 46.18 0.3221 0.7442 32.11 1392 2230

185

Parents

PBI-151 89.54 135.3 37.4 45.45 0.3394 0.7729 33.09 1370 2363

PBI-273 87.22 135.6 28.5 47.99 0.2887 0.7484 29.4 1524 2215

PBI-45 95.22 136 36.56 40.02 0.2648 0.7752 28.07 1603 2112

Control

SPITFIRE 91.0 136.1 43.38 44.7 0.29 0.783 29.63 1809 2658

SUNTOP 90.7 136.4 48.40 45.7 0.30 0.795 32.28 1461 2178

Mean 90.7 135.7 30.88 44.7 0.26 0.752 30.36 1290.46 1686.23

Lsd 3.6 2.7 24.62 3.8 0.92 0.076 7.82 325.2 725.8

186

Table 6.11 Means of the top cross population (PBI-100 X PBI-11) X PBI-94 together with parents and controls for harvest index (HI), grain filling

duration (GFD), thousand kernel weight (TKW), biomass (BMAS) and grain yield (YLD).

Genotype DHD DPM GC(%) GFD HI NDVI TKW(g) BMAS YLD(kg/ha)

F1 92.8 135.3 24.1 42.6 0.31 0.797 31.69 1323 2110

F2 88.0 135.0 25.6 46.4 0.26 0.762 30.91 1315 1664

F3 92.7 136.0 33.5 43.0 0.30 0.747 34.46 1418 2045

F4 93.3 135.1 37.3 41.4 0.26 0.776 26.64 1264 1609

F5 91.9 135.5 20.9 43.6 0.26 0.784 29.81 1041 1639

F6 93.1 135.6 30.6 42.8 0.29 0.782 30.47 1363 2001

F7 91.7 135.1 29.8 44.0 0.32 0.732 32.21 1178 1843

F8 90.1 135.6 30.4 45.1 0.34 0.724 27.82 1380 2340

F9 89.4 136.1 23.7 46.0 0.27 0.750 27.6 1376 1954

F10 94.2 136.8 25.2 42.8 0.30 0.704 31.04 1201 1869

F11 94.1 136.2 33.7 41.0 0.32 0.774 29.99 1470 2442

F12 94.0 135.1 29.0 40.9 0.26 0.731 33.56 1525 1964

F13 94.4 136.0 24.3 41.1 0.29 0.719 33.95 1225 1788

F14 89.7 135.7 28.5 46.3 0.32 0.777 32.45 1422 2205

F15 92.9 135.9 33.4 42.5 0.34 0.762 31.67 1542 2780

F16 91.2 135.7 32.8 44.1 0.32 0.786 35.35 1350 2135

F17 91.9 135.4 41.4 43.6 0.24 0.710 26.49 1548 1930

F18 91.1 135.7 40.4 44.3 0.34 0.777 35.47 1311 2244

F19 90.3 136.0 26.5 44.9 0.31 0.712 36.23 1504 2462

F20 96.4 136.1 28.5 40.1 0.27 0.777 24.46 1066 1282

F21 91.2 136.6 24.3 45.9 0.32 0.765 34.43 1777 2873

F22 89.7 134.0 30.4 45.6 0.31 0.803 25.69 1253 1962

F23 91.9 135.6 32.8 43.3 0.33 0.769 36.24 1329 2210

F24 90.4 135.3 20.1 44.1 0.27 0.751 32.04 1409 1706

F25 96.1 134.0 18.4 38.6 0.24 0.694 32.52 1187 1446

Parents

PBI-100 85.7 135.1 31.4 48.9 0.35 0.755 34.14 1564 2761

PBI-11 91.1 135.1 44.0 43.9 0.25 0.806 26.91 1432 1816

PBI-94 88.3 135.2 29.4 46.8 0.26 0.733 26.98 1456 1898

Control

SPITFIRE 91.0 136.1 43.4 44.7 0.29 0.783 29.63 1809 2658

SUNTOP 90.7 136.4 48.4 45.7 0.30 0.795 32.28 1461 2178

Mean 91.6 135.6 29.0 43.8 0.29 0.757 31.10 1383.3 2060.46

Lsd 3.6 2.7 24.6 3.8 0.92 0.076 7.82 325.2 725.8

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6.4.2 Validation of MTAs for higher grain yield using targeted markers from the earlier Atta

(2013) genetic association analysis

Progenies resulting from the hybridization of selected parental materials varied significantly in the

number of accumulated targeted markers based on the earlier association analysis of Atta (2013). The

number of accumulated markers ranged from 11-18. However, differences in mean yield based on the

number of accumulated markers were not significant (Table 6.12). Similarly, differences observed for

thousand kernel weight (TKW), harvest index and maturity were not also significantly different.

However, significant differences were observed in biomass between progeny groups. Those materials

with higher numbers of pyramided markers tended to have higher biomass.

Table 6.12 Means of grain yield and other traits and number of accumulated markers based on the

earlier Atta (2013) association analysis

MARKER#

Yield

Biomass

DHD

DPM

GC

GF

HI

NDVI

TKW

11 1715a 1263b 89.0b 135.0c 32.10d 45.71d 0.267e 0.78f 30.30g

12 1884a 1258b 91.9b 135.3c 28.92d 43.58d 0.300e 0.75f 31.30g

13 1750a 1303b 90.5b 135.5c 31.22d 44.86d 0.268e 0.76f 30.71g

14 2126a 1449a 90.1b 135.8c 33.74d 45.55d 0.289e 0.76f 31.02g

15 1869a 1329b 90.7b 135.6c 29.79d 44.78d 0.280e 0.75f 30.17g

16 2057a 1417a 90.4b 135.8c 31.84d 45.14d 0.289e 0.76f 32.04g

17 2039a 1382ab 91.2b 135.6c 31.99d 44.05d 0.290e 0.76f 30.03g

18 2002a 1444a 91.0b 135.6c 28.32d 44.23d 0.272e 0.75f 28.94g

Mean 1930 1356 90.6 135.5 30.99 44.7375 0.282 0.76 30.56

SED 49.3 20.5 0.3 0.11 0.75 0.3 0.004 0.003 0.323

Note: Means bearing different letters are significantly different ( p< 0.05). DHD, DPM, GC, GF, HI, TKW is days to

heading, days to maturity, ground cover, days of grain-filling, harvest index and thousand kernel weight, respectively.

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6.4.3 Validation of MTAs for higher grain yield using targeted markers from the current

genetic association analysis

The progenies were then re-classified based on those markers confirmed in both the Atta (2013) multi-

environment study and the head-to-head comparisons of 2014 and 2015 at Narrabri. Grain yield was

generally positively associated with increasing accumulation of targeted markers (Table 6.13).

Progenies that accumulated 3 or more targeted markers were the highest yielding. Similarly,

significant differences were observed among progeny groups for thousand kernel weight (TKW),

heading time, biomass and grain filling duration with accumulation of markers producing higher traits

expression. No significant differences in maturity time, NDVI, canopy ground cover and harvest index

were observed.

Table 6.13 Means of grain yield and other traits and number of accumulated markers based on the on

markers confirmed in both the Atta (2013) study and the head-to-head analysis of Chapter 5

MARKER Yield Biomass DHD DPM GC GF HI NDVI TKW

0 1617c 1181a 93.23b 135.0a 27.75a 41.86b 0.267a 0.750a 29.7b

1 1908bc 1330b 89.98a 135.4a 31.37a 45.11a 0.288a 0.757a 31.68ab

2 1981b 1380b 90.62a 135.7a 31.57a 44.86a 0.284a 0.758a 30.55b

3 2235a 1473a 90.06a 135.6a 32.72a 45.48a 0.303a 0.751a 32.25a

Mean 1935 1341 90.97 135.4 30.85 44.3 0.28 0.773 31.04

SED 52.6 21.5 0.32 0.12 0.83 0.32 0.004 0.004 0.35

Note: Means bearing different letters in the same column are significantly different at p < 0.05. DHD, DPM, GC, GF, HI,

TKW is days to heading, days to maturity, ground cover, days of grain-filling, harvest index and thousand kernel weight,

respectively

The principal component analysis displayed a clear separation of some progeny from their donor

parents based on field performance for key agronomic traits (Fig 6.1). Although many showed similar

adaptation to the parents, others such as A27, A38, A16, B8, D3, D19, E5, E6, E15, F20 and F25

showed more extreme performance and were generally higher yielding than the donor parent.

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SUNTOPF10

B17

D7

B18

PBI-98B19

PBI-45

B20

PBI-273

C1PBI-11

C2

PBI-10

C3

F25C4

F23

D1

F21

D2

F19

D3

F17

D4

F15

D5

F13

D6

F11

A2

D8

A4D9

A6

D10

A8

D11 A10

D12

A12

D13

A14

D14

A16

D15

A18

D16

A20

D17

A22

D18

A24

D19

A26

D20

A28D21

A30D22

A32

D23

A34

D24

A36

D25

A38

D26 B1

D27

B3

D28B5

D29

B7

E1

B9

E2

B11

E3

B13

E4

B15

E5

SPITFIRE

E6

PBI-299

E7PBI-100

E8

F24

E9

F20

E10

F16

E11

F12

E12A3

E13

A7

E14

A11

E15

A15

E16

A19

E17

A23

E18

A27

E19

A31

E20

A35

E21

A39

E22

B4

E23

B8

E24

B12E25

B16E26PBI-151

E27F22E28

F14E29 A5

E30

A13

E31

A21E32

A29

E33

A37

E34

B6

E35B14

E36

PBI-1F1

A1

F2

A17

F3

A33

F4

B10F5

F18 F6

A25

PBI-94

A9B2

F7F8

F9

-4

-4

0

-2

4

0

2

4

2-2

PC

2 (

26

.3%

)

PC1 (54.74%)

Figure 6.1 Principle component plot of the field performance of progenies and parents on the basis of

agronomic traits (grain yield, heading time, maturity time, thousand kernel weight and biomass).

6.5 Discussion

The polygenic nature of yield in rainfed conditions coupled with the low heritability of yield

under stress limits the development and release of improved cultivars. The era of genomics could

facilitate the process but functional validation of MTAs or genomic regions associated with key

agronomic traits is crucial for successful marker assisted selection (MAS). This is an essential step

because MTAs identified in one population or group of materials may not be applicable to all breeding

programs (Gupta et al. 2010a). Accumulation or pyramiding of favourable genomic regions/QTLs into

single plants has resulted in additive effects in many studies including wheat leaf rust (Singh et al.

2004), Fusarium head blight in wheat (Shi et al. 2008) and drought tolerance in rice (Shamsudin et al.

2016). Likewise Ribaut and Ragot (2007) recently reviewed a study to improve drought adaptation of

maize by marker assisted backcrossing (MABC) and the introgression of positive alleles for five target

regions involved in the expression of yield components under water limited conditions. The grain yield

of MABC progenies had higher yields than control hybrids under severe moisture stress conditions.

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However in barley (Zhu et al. 1999) attempted to accumulate genomic regions conferring higher yield

but concluded that epistasis and QTL x environment interaction reduced the impact of the favorable

alleles. Similarly, when progenies were evaluated in the current study using targeted markers identified

in the Atta (2013) multi-environment study there was no significant yield advantage of those progeny

carrying accumulated markers. However, when only those markers confirmed in both the Atta (2013)

study and the subsequent head-to-head evaluation of the same materials in 2014 and 2015 were used, a

significant improvement in the yield of the progeny was observed with increased accumulation of

much smaller numbers of MTAs. Others reported that accumulation of genomic regions does not

always result in additive effects (Dalton et al. 2013). However, it is important to validate these MTAs

to remove false positives and to identify regions that consistently contribute to phenotype.

Xu and Crouch (2008), suggested that in some cases, markers will lose their selective power during

validation and that the most promising approach is to continually identify new markers associated with

the target trait across different breeding populations. For this reason the re-classification of the

progenies in the current study based on confirmed MTAs produced discernible improvements in yield

and some yield related characters.

6.6 Conclusion

The current findings suggest that association analysis can be used in wheat breeding to improve

complex traits such as yield once the repeatability of MTAs are confirmed. The impact of marker x

location interaction is significant and MTAs should continually be tested and refined and new

associations identified if this strategy is to be successfully implemented in wheat breeding. Some

genotypes mainly: A16, A22, F21, A12, C4, A18, F15, A7, A17, A24, A31, A39, A28, A13, A34,

A14, appeared to have combined favourable alleles that could be further evaluated and introgressed

into existing cultivars to develop drought tolerant varieties. However, the phenotypic evaluation was

conducted for one season only and further testing of the recombinant materials is required before firm

conclusions can be drawn.

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7 General Discussion

A multifaceted approach that combines technologies is required to increase yield gains in

wheat. Grain yield under stress has low heritability and high genotype x environment interaction

confounds results leading to limited genetic gains. Reduced wheat yield gains over the past 30 years

have been attributed to increasingly erratic climatic conditions (Olesen et al. 2011). Therefore water

use efficiency has become the trait of interest to many breeders.

This study aimed to validate MTAs from a previous association genetics study in various ways.

In the process genotype x environment interaction effects were explored as was the efficacy of

combining MTAs in mixtures and through targeted marker pyramiding. The effectiveness of MTAs

identified through association analysis in both mixtures and marker pyramided lines was apparent after

extensive confirmation of important and repeatable MTAs.

7.1 The effect of tillage and moisture on grain yield

Winter wheat cultivation in northern NSW relies heavily on stored soil moisture. Soil surface

structural modification (or tillage), mulching and residue cover alter the water-use-efficiency of the

farming system.

While no significant genotype x tillage interaction effects for yield were observed in the current

study, some genotypes such as Crusader, Janz, EGA Stampede Lincoln performed better under zero-

tillage whilst others were clearly less well adapted including Lang, Sunvale and Ventura. The lack of

genotype x tillage practice interaction has been reported in earlier studies. However, the report of

Trethowan et al (2012) is an exception and the significant responses reported are likely germplasm and

environment specific. Nevertheless, zero-tillage significantly increased grain yield under both rainfed

and irrigated environments, with a pronounced yield advantage of 6% in rainfed conditions. Clearly,

the absence of soil disturbance and maintenance of crop residues on the surface reduced moisture loss

thus improving yield. This observation confirms previous reports that zero-tillage reduces evaporation,

increases water infiltration and reduces runoff (Hall and Cholick, 1989; Cox, 1991; Carr et al., 2003a).

These reports suggest that higher grain yield can be achieved under moisture stress when higher

yielding cultivars are deployed in zero-tillage systems. Comparatively, the year to year variation in

yields and the significant year x genotype interaction effects which could be explained by the amount

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and distribution of in season rainfall suggests that critical periods of crop development (post-anthesis)

when moisture is limiting could significantly reduce yield.

7.2 The effects of genetic mixtures on water use efficiency

Genetic mixtures can contribute to the sustainability of grain yield under moisture stress through more

efficient utilization of resources including water, nutrients and CO2. It was assumed that mixture

components, assembled based on complementary genomic regions from the Atta (2013) study, would

confer higher grain yield as the combined traits would compensate and complement each other under

stress. However, the performance of genotypes in mixtures was heavily influenced by the genotypes

selected and the environment. A few mixtures including Sunstate+Stampede, Sunstate+Stampede+Janz

and Suntop+Spitfire outperformed their components for grain yield under rainfed conditions only, thus

supporting the notion that mixtures buffer yield under stress (Finckh et al. 2000; Kiær et al. 2012).

However, the Lincoln+Lang mixture appeared to show a negative response compared to its component

lines under drought stress indicating that mixtures can also suppress plant responses; an observation

also noted by others (Treder et al. 2008; Lithourgidis et al. 2011). There was also a significant year

effect as the 3-way mixture Sunstate+Stampede+Janz was superior to its components only when

evaluated in 2014. The current results indicate that mixtures vary in their adaptability to tillage and

irrigation systems and that mixture components must be carefully selected and tested before

deployment. The performance (mixing effects) of most mixtures was intermediate compared to their

pure stands and this could be attributed to high marker x environment interaction effects as markers

were the basis of their composition. The high level of unrepeatable MTAs observed in later analysis

(see Chapter 5) may also have played a role. It is possible that new mixtures, based only on those

MTAs that were positive and significant across the 2014 and 2015 head-to-head evaluation, will show

greater responses to mixing.

7.3 Physiological traits linked to genomic regions

Trait based plant breeding could improve genetic gains in grain yield relative to conventional

breeding strategies (Atta 2013). Significant variation for physiological traits was recorded among the

materials tested (see Chapter 3 for details). All trait means were higher in 2014 which is partly

attributed to the higher in season rainfall. The increased moisture produced greater biomass, faster

ground cover, higher NDVI and higher grain yield.

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Earliness to ground cover significantly influences yield by reducing water loss from the soil

surface in certain environments (Rebetzke et al. 2004). The current results indicated that late ground

cover accounted for 39% of the observed genetic variance in yield and was higher under zero-tillage

systems and therefore selection for higher early ground cover could improve crop adaptation to this

farming practice. Part of this response can be explained by the positive correlation of earliness with

NDVI, biomass and water use efficiency. Several markers identified in the earlier association analysis

(Atta 2013) used to assemble the mixtures were associated with yield and these co-located with QTLs

reported by Li et al. (2014a). These QTLs, QGCw-caas-1A.1, QGCw-caas-2A.2, and TQGCw-caas-

1A.2, located at 6.1, 6.7 and 3.0 cM, respectively, coincided with wPt-6564, wPt 8242, wPt 8455 and

wPt 1945 on the same chromosomes at the same positions. It appears that earliness to ground cover

has both a physiological and molecular basis and could be targeted, through indirect selection, to

improve water use efficiency under moisture stress.

Crop phenology is one of the pivotal traits regulating adaptation of bread wheat (Triticum

aestivum L.) to diverse climatic environments and earliness has often been emphasized as an escape

strategy to avoid peak periods of drought and heat stress (Debaeke 2004). In northern NSW, post-

anthesis moisture stress is prevalent and early heading genotypes tend to escape drought and

subsequently produce higher yields. In the current study, heading and maturity positively associated

with yield. Coincidentally, yield MTAs identified on chromosome 1AL at 125.40, 134.61 and 135.6

cM in the earlier association analysis of (Atta 2013) coincided with the findings of Bullrich et al.

(2002) who mapped a QTL for earliness per se controlling heading time in the distal region of the

same chromosome in wheat. In addition, Kuchel et al. (2006) identified QTLs controlling time to

heading on chromosomes 1A, 2A, 2B, 6D, 7A and 7B which coincided with yield MTAs on the same

chromosomes identified in the earlier association study (Atta 2013). However, of the previously

reported MTAs for heading date (Bullrich et al., 2002; Kuchel et al., 2006), only locations on 6B and

7B were consistent with the current study. In the current study the only significant MTA coinciding

with a known photoperiod or vernalization locus was on 5B, which co-located with Vrn-B1. Heading

and maturity clearly have a physiological and molecular basis in these materials and can be targeted to

optimize crop development and yield in this environment.

Spectral reflectance indices assessed using NDVI measured photosynthetic biomass and

chlorophyll content and these were positively associated with yield. Quarrie et al. (2006), reported that

flag leaf chlorophyll content and leaf width at tillering were associated with a QTL on chromosome

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7A. In addition, Graziani et al. (2014) identified QTLs for leaf greenness on chromosome 3B at 6.9

cM, which overlapped with a QTL for TKW. The earlier association study of Atta (2013) found a

marker (wPt-7907) on chromosome 3B in a similar position to the Graziani study to be associated with

higher grain yield. Chlorophyll content could therefore be associated with higher yield under moistures

stress and could be targeted for selection.

Biomass is a key driver of grain yield in rainfed environments (Shepherd et al. 1987; Royo et

al. 1999; Slafer 2003; Slafer et al. 2005). Total biomass was higher under conventional-tillage than

zero-tillage environments, even though the latter had higher moisture storage capacity. This difference

could possibly be attributed to differences in the way nitrogen is utilized in both tillage regimes. There

is some evidence to suggest that N use is less efficient in zero-tillage, largely due to less efficient

placement of fertilizer (Ken Sayre, personal communication). Biomass has both a physiological and

molecular basis with QTLs reported on chromosomes 4B and 4D (Li et al. 2014b). These

chromosomes coincide with the earlier Atta (2013) study although the map positions for yield do not

overlap with the reported biomass QTLs. In the current study harvest index was higher in zero-tillage,

reflecting the reduced total biomass in this tillage regime and equivalent or higher grain yield.

However, the MTAs for yield in the Atta (2013) study when pyramided did produce lines with

significantly greater biomass, even though the effect on yield was non-significant, suggesting that

biomass has a higher heritability.

Bennett et al. (2012), identified a QTL located on chromosome 3B which had a large effect on

canopy temperature and grain yield. This also coincided with MTAs identified on the same

chromosome for yield in the earlier association analysis of (Atta 2013). However, significant

differences among genotypes were not observed in the current study, even though canopy temperatures

were significantly lower in zero-tillage treatments.

Flag leaf rolling ability is an adaptive trait for drought tolerance and the selection of cultivars

with this character has been achieved in rice, maize, sorghum and wheat (Dingkuhn et al. 1989;

PREMACHANDRA et al. 1992c; Corlett et al. 1994; Price et al. 2002a). The current study found this

trait to be positively correlated with yield (r = 0.57) and water use efficiency (r = 0.52) under moisture

stress. The result suggests that water use efficient genotypes roll their leaves as part of the water

conservation strategy. Although no QTLs have been found controlling this trait in wheat, its strong

positive association with yield indicates a possible contribution to the observed marker trait

associations for yield. It could therefore be selected indirectly to improve grain yield and water use

efficiency.

195

The improvement of grain yield under moisture stress is a function of improved crop water use-

efficiency which has been targeted in Australia (Kirkegaard et al. 2014) and internationally (Grassini

et al. 2009; Grassini et al. 2011; Zhang et al. 2014c). In the current study, water-use-efficiency varied

across irrigation and tillage regimes with the greatest water use efficiency observed in full-tillage

rainfed environments. Water-use-efficiency correlated significantly with yield (r = 0.99), leaf rolling

(r=0.52), days to heading (r= - 0.62), days to physiological maturity (r= -0.80), plant height (r= 0.57)

and ground cover (r= 0.64) suggesting that high yielding drought tolerant genotypes were good early

ground coverers, early heading, early maturing and rolled their leaves to avoid moisture loss. These

results support the findings of (Zhang and Oweis 1999) and (Zhang et al. 2010a) who concluded that

higher yielding cultivars have the potential to improve water-use-efficiency, thereby saving water.

7.4 Production traits

Grain yield is the key agronomic trait targeted by wheat breeders for improvement. The current

study revealed that zero-tillage significantly increased grain yield under both rainfed and irrigated

environments, with a pronounced yield advantage in rainfed conditions. However, the lack of a

genotype x tillage environment interaction suggests that the germplasm utilized did not carry vital

genetic variability related to zero-tillage adaptation and the development of new varieties specifically

adapted to zero-tillage systems is crucial (Duvick et al. 1990; Trethowan et al. 2005b). To increase

wheat productivity under drought through breeding Reynolds et al. (2005) emphasized the significance

of and utilization of physiological traits. Five traits in the current study (days to heading, NDVI, leaf

rolling, earliness to ground cover and grain filling duration) accounted for 87.8% of the observed

phenotypic variance in yield. These traits are likely responsible in part or in combination for the

observed significant marker trait associations in the current study. Thus high yielding and water use

efficient genotypes were faster in providing ground cover, earlier heading to avoid post-anthesis

moisture stress, developed larger photosynthetic biomass, rolled their leaves to avoid damage while

maintaining photosynthetic activity and had shorter grain filling periods.

The current results show that some yield related MTAs from the Atta (2013) study are in fact

linked to different phenological and physiological traits. Previous studies identified QTLs on 1B

(QEps.dms-1B1 and QEps.dms-1B2) and 5B (QEps.dms-5B1) that were associated with heading time

(Kamran et al. 2013; Zanke et al. (2014)). The QTL (QEps.dms-1B1) found on chromosome 1B

mapped at 31.8cM in approximately the same region as MTAs identified in the Atta (2013) study.

Similarly, the QTL (QEps.dms-5B1) directly coincided with an MTA (wPt-6135) on the same

196

chromosome. Other QTLs for various traits including NDVI (QNDVIs-caas-5B) (Li et al. 2014a), and

earliness to ground cover (QGCw-caas-1A.1, QGCw-caas-2A.2, TQGCw-caas-1A.2) (Li et al. 2014a)

coincided with markers linked to yield in the Atta (2013) study. Thousand kernel weights also varied

significantly among genotypes but no significant genotype x tillage interaction was observed. It was

clear that thousand kernel weight correlated with grain yield and as this trait has a high heritability it

could be targeted indirectly for yield improvement.

In the current study no genotype x environment interaction effects were observed for grain

protein. However, the positive correlation with maturity, days to heading and NDVI at grain filling and

negative correlation with yield simply confirms earlier findings (Blanco et al. 2012; Noorka et al.

2013). Austin et al. (1980), suggested that N is diluted by higher carbohydrate assimilation resulting

in lower grain protein in higher yielding materials. The slightly higher protein under conventional

tillage environments compared to zero-tillage in the current study probably reflects the difficulty of

placing N accurately in zero-tillage systems. The results suggest that early heading, shorter and earlier

maturing genotypes tended to have higher protein content. Genotypes with the highest protein included

the prime hard cultivars Sunvex, Ellison, Spitfire, Sunlin and Sunvale.

7.5 Wheat ideotypes adapted to zero-tillage rainfed environments in northwestern NSW,

Australia.

The identification and selection of water-use-efficient genotypes adapted to zero-tillage rainfed

systems is the goal of breeding programs in northwestern NSW. In the current study, the cultivars

Crusader, Janz, Merinda, EGA Stampede and Lincoln were the most water-use-efficient whilst

Ventura, Sunvale, Ellison, Sunlin and Sunvex were the least. This superior performance could be

attributed to early canopy ground cover to reduce moisture loss through evaporation, faster canopy

development to capture sunlight resulting in earliness to heading to avoid peak periods of moisture

stress especially at anthesis, higher eventual biomass, higher harvest indices, cooler canopies (probably

a reflection of deeper rooting) and higher photosynthetic and stomatal conductance rates.

7.6 Association Analysis

This study confirmed that genetic association analysis was effective in developing higher

yielding progeny through selection and recombination based on MTAs with a caveat. The caveat is

that MTAs must be confirmed through additional testing as many of the earlier MTAs identified in the

Atta (2013) study were not repeatable and were either artefactual or limited by significant marker x

197

environment interactions. These results are therefore preliminary and would require further evaluation

of materials for confirmation.

When the smaller number of consistent markers that influenced grain yield found on

chromosomes 1A, 1B, 2B, 4B and 6B were targeted, the resulting progeny tended to have higher grain

yield. These consistent high value markers located on chromosome 1B between 30.96 and 31.64 cM

and on 6B at 15.41 cM. However, the head-to-head analysis of the same materials evaluated in the

multi-environment Atta (2013) study revealed several new MTAs for yield and other traits. These new

MTAs likewise must be validated before broad scale selection in breeding to ensure populations are

improved for alleles that actually contribute to phenotype.

In the current study, highest numbers of DArT markers significantly associated with grain yield were

found on the B genome with the least on D genome; an observation similar to Crossa et al. (2007b).

New MTAs for grain yield from the head-to-head study were found on chromosome 1A (wPt-3825),

1B (wPt-7905 and wPt-2075) and 5D (wPt-2859). Several previously unreported MTAs for heading

date were found on chromosomes 7B and 4B whilst new MTAs for maturity were identified on

chromosomes 3D, 4D, 5D and 7D.

7.7 Conclusions

1. The results of this study indicated that 5 key physiological traits, mainly days to heading,

NDVI, leaf rolling, earliness to ground cover and grain filling duration are likely responsible in

part or in combination for the observed significant marker trait associations in the current study

and could be targeted indirectly to develop cultivars with superior drought tolerance (Chapter

3).

2. Higher yield could be achieved under drought when water-use-efficient cultivars are deployed

in zero-tillage systems (Chapter 3).

3. Higher yield with improved water use efficiency could be achieved under moisture stress when

high yielding cultivars are carefully combined mechanically in mixtures based on significant

and repeatable MTAs and deployed in zero-tillage systems (Chapter 4).

4. Comparatively few MTAs identified in the earlier Atta (2013) analysis were confirmed in the

head-to-head association analyses in 2014 and 2015 (Chapter 5).

198

5. Recombination of targeted markers associated with grain yield into progenies resulted in

additive effects revealing superior yield (Chapter 6). Association analysis could therefore be

validly applied in developing cultivars with improved yield in rainfed conditions.

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231

9 Appendix I (chapter 3&4)

Zero till Irrigated

Conventional till Irrigated

3 16 1 12 8 17 4 11 2 15 7 19

3 16 1 12 8 17 4 11 2 15 7 19

5 6 20 14 7 10 14 2 4 20 10 11

5 6 20 14 7 10 14 2 4 20 10 11

13 2 15 8 16 12 19 15 12 13 17 1

13 2 15 8 16 12 19 15 12 13 17 1

4 10 19 7 9 1 18 6 18 8 3 5

4 10 19 7 9 1 18 6 18 8 3 5

9 18 17 11 20 13 5 3 16 6 14 9

9 18 17 11 20 13 5 3 16 6 14 9

Rep 1 Rep 2 Rep 3

Rep 1 Rep 2 Rep 3

16 2 11 14 1 7 9 18 19 4 17 9

5 2 18 4 12 17 14 3 1 18 10 4

9 20 19 10 6 16 13 12 18 10 3 5

15 17 7 12 2 18 6 5 2 6 9 19

6 8 7 12 10 14 11 19 13 14 6 12

11 19 1 13 7 20 13 4 7 20 17 16

3 15 1 13 20 15 4 17 2 15 20 11

8 14 9 16 1 16 11 19 3 14 12 8

5 17 4 18 5 8 2 3 7 8 16 1

20 6 3 10 15 8 9 10 15 11 13 5

Zero till Rainfed

Conventional till Rainfed

Figure.1 Experimental Field layout in 2013 cropping season.

Conventional till rainfed

Zero till Rainfed

22 17 16 15 5 14 9 21 12 24 6 23

Rep3

2 16 9 22 5 11 21 8 17 7 14 20

4 3 11 19 10 7 20 18 2 8 1 13 3 19 4 10 18 24 6 23 12 15 1 13

2 23 24 19 16 22 11 1 10 8 18 9

Rep2

21 18 9 5 20 7 2 1 15 23 13 8

20 21 14 5 6 13 3 12 4 17 7 15 4 10 3 19 11 16 17 24 14 22 6 12

4 18 14 7 19 11 13 21 20 6 24 8

Rep1

1 7 6 23 5 3 20 17 11 22 13 15

22 23 2 12 5 3 17 9 15 10 16 1 8 10 16 12 21 4 18 24 19 14 9 2

2 1 21 22 15 9 11 3 10 24 8 13

Rep3

22 15 24 12 9 23 11 2 18 14 7 3

16 4 6 18 23 14 20 5 7 12 19 17 13 19 16 10 5 17 21 1 4 8 6 20

24 2 19 16 3 22 10 9 21 8 14 20

Rep2

22 7 10 15 8 2 3 21 19 13 17 24

18 13 17 6 11 23 1 5 15 4 12 7 6 20 9 5 14 23 18 16 1 4 12 11

5 24 2 11 9 16 13 6 4 12 19 1

Rep1

16 15 8 21 5 10 3 11 9 4 2 19

23 15 20 8 17 18 7 14 10 21 3 22 7 20 12 18 17 1 13 6 24 22 14 23

Conventional till Irrigated

Zero till Irrigated

Figure 2 Experimental Field layout in 2014 cropping season.

232

10 Appendix II (Chapter 5)

Table 1 Means for number of days to heading (DHD) of 215 genotypes evaluated head-to-head in

association analysis in 2014 and 2015 at Narrabri.

Genotype No. Pedigree Cropping year means (days to heading)

2014 2015

1 CRUSADER 110.5

105.0

2 SUNSTATE 115.0

113.0

3 EGA STAMPEDE 113.0

107.5

4 LINCOLN 110.5

108.0

5 LIVINGSTON 110.0

104.5

6 SUNVEX 122.0

119.5

7 MERINDA 107.5

110.5

8 LANG 118.0

115.0

9 SUNVALE 121.0

116.0

10 VENTURA 114.0

112.0

11 ELLISON 117.0

115.5

12 JANZ 116.0

116.0

13 CUNNINGHAM 117.5

114.0

14 EGA GREGORY 121.0

118.0

15 SUNCO 116.5

116.5

16 SUNLIN 120.5

118.5

17 SUNTOP 113.5

113.0

18 SPITFIRE 109.0

117.5


113.0

20 GILES/Carinya 120.5

106.5

21 DM5637*B8/H45//SUN498D 106.5

108.0

22 Wentworth/SUN434A 114.5

110.0

23 Ellison/Ventura 116.5

112.5

24 LPB05-1157 112.0

102.0

25 DM5637*B8/H45//SUN485A 109.0

107.0


119.0

27 Carinya/Sunpict 117.5

115.0

28 SUN325K/SUN434A 109.0

110.5


113.0

30 DM5637*B8/H45//SUN498D 116.5

104.5

31 DM5637*B8/H45//SUN498D 98.0

116.0

32 CROC_1/AE.SQUARROSA (224)/ 104.5

103.5

33 DM5637*B8/H45//SUN485A 110.5

106.0

34 LPB05-2148 116.0

115.5

35 Sunco/SUN431A 118.0

113.0

233


2014 2015

36 CUNNINGHAM 2007 117.5

117.0

37 Sunco*2/Kukri 116.0

113.5

38 CPI133814/SUN421H//SUN421E 118.0

115.0

39 SUN325K/SUN434A 115.5

94.5

40 Crusader 114.5

111.5

41 SUNCO/2*PASTOR//SUN436E 109.0

103.5

42 RAC892/98ZHB03//RAC892 113.0

115.5

43 DM5637*B8/H45//SUN498D 101.0

120.0


101.0


116.0


118.5

47 SUNSTATE/CHARA 117.0

116.5

48 SUN500B/Carinya 116.0

115.5

49 SUN445C/QT10776 116.0

114.5

50

CROC_1/AE.SQUARROSA

(224)//2*OPATA 110.0

106.5

51 SUNSTATE/VO1225//SUN429E 116.0

115.0

52 Lincoln 114.0

115.0

53 SUN344 E/VPMB36020 113.5

112.0


114.5


111.5

56 GILES 2002 118.5

117.0

57 Ega Bonnie Rock 121.5

118.5


111.5

59 Yr15Yr24 2*399C.1 113.0

112.5

60 Lang/SUN431B 117.5

114.0

61 SUN431A/SUN492A 111.0

109.5

62 SUN421T/QT10401 117.5

119.5

63 QT6581/4/PASTOR//SITE/MO/3/CHEN/ 102.5

100.5

64 SUN434A/LANG 107.5

105.5


117.0

66 DM5637*B8/H45//SUN485A 119.0

115.5


117.0

68 SUNVALE/VPMB36020 110.0

107.0

69 Carinya/Cascade 115.0

113.0


108.0


111.0

72 Ruby 115.0

109.0

73 Lang/SUN431B 119.0

115.5

74 ZWC06 19 116.5

108.0

75 SUNCO/2*PASTOR 117.5

114.0

234


2014 2015


114.0

77 SUN498F/SUN485A 112.0

112.5

78 SUN431A 118.0

116.5


115.0

80 Sunvale*2/VO1225 119.5

118.0

81 Chara 118.5

115.5

82 QT10580/SUN431A.34.1 120.5

118.0

83 Chara/4*Sun376G 121.0

118.0

84 LANG/SUN366A 110.5

93.0

85 IRAQ43 116.5

103.0

86 SOKOLL 109.0

109.5

87 SUN344 E/VPMB36020 115.0

115.0

88 Sunvale*2/VO1225 120.0

118.5

89 SUN429E/Ruby 114.0

111.5

90 URES/JUN//KAUZE/3/2*SUNVALE 118.0

116.0

91 CHARA/SUN434A 119.0

112.0

92 SUN421T/SUN399C.22.3 115.0

110.0


118.5


100.0

95 Sunvale/Janz 120.0

119.5

96 LANG/SUN366A 113.0

111.5

97 SUN431A/Lang.25.2 116.0

112.5

98 BAXTER/SUN421N 118.0

117.5

99 SYN 1.57.2 111.0

109.0

100 RAC1192/Ventura 117.0

114.0


115.0


119.5

103 SUN429E/Ruby 116.5

114.5

104 Sunco/SUN431A 115.0

113.0

105 SYN 1.57.3 114.0

114.0

106 Janz 115.0

111.0

107 Lang/Ellison 117.5

115.5

108 SUN434A/Lang.67.2 111.0

106.0

109 SUN325K/SUN434A 109.0

116.0

110 QT10580/SUN431A.4.3 119.0

115.0

111 SUN429E/Ruby 114.0

113.0

112 QT10580 (Wentworth) 116.0

118.0

113 Carinya 117.0

116.0

235


2014 2015

114 SUN429E/Ruby 116.0

115.5

115 SUN498F/SUN485A 109.0

107.0

116 SUN421T/QT10401 115.0

113.0

117 SUN429E/Ruby 115.0

112.0

118 Sunvale*2/VO1225 121.0

115.0

119 ELLISON/Sunbri 119.0

114.5


115.5

121 SUN325K/SUN434A*2 116.0

104.0

122 Sunvale*2/VO1225 121.0

119.5

123 SUN434A/SUN436E 118.0

113.0

124 SUN434A/SUN436E.116.4 109.0

108.5

125 98ZHB03/2*Lang 121.0

115.5


115.5

127 SUNVALE//OPATA*2/WULP 113.0

115.5

128 Sunstate/Ellison 117.0

111.5

129 SUN429E/Ruby 115.0

107.0

130 SUN421T/QT10401 117.0

107.5


112.5

132 SUN431A/SUN492A 109.0

107.0

133 Yr15,24,2*399C.102 112.0

109.5

134 D67.2/P66.270//AE.SQUARROSA (320)/3 111.0

131.0

135 KUKRI/SUNSTATE 111.0

100.5


110.5


113.0

138 DM5637*B8/H45//SUN485A 111.0

111.0


107.5

140 30Ibwsn008 123.0

113.5

141 SUN434A/SUN436E.46.4 118.0

111.0

142 WA-1-21005/2*SUN426B 117.0

110.0

143 Lang/3*Sun376J.1.2.1 115.0

106.5

144 Sunstate/QT8733 117.0

116.5

145 syn6.47.4 117.0

110.0


115.0

147 SUN431A/Lang.21.1 117.0

113.0


113.0

149 SUN372D.1.3 115.0

114.5

150 SUN429E/Ruby 117.0

116.0

151 SUN421T/QT10401 115.0

109.5

236


2014 2015

152 Cascade 109.0

113.0

153 SUN431A/Lang.21.2 117.0

115.5

154 SUN434A 117.0

114.0

155 SYN 3.74.2 98.0

100.0

156 LPB05-1164 109.0

109.5


110.0

158 15HRWSN112 115.0

114.5

159 SUN429E/Ruby 117.0

114.0


109.5

161 Sunco/SUN431A 118.0

114.0


106.5

163 SUN431A/SUN492A 115.0

109.0

164 Yr15,24,2*399C.85 111.0

110.0

165 Sunvale*2/VO1225 119.0

119.5


111.5

167 B409C/SUN420A//SUN498E 100.0

102.0

168 SUN434A/LANG 100.0

118.0


115.5

170 SUN429E/Ruby 116.0

109.0

171 WA-1-21005/2*SUN426B 109.0

105.0

172 SUN434A/SUN436E.188.3 114.0

113.5

173 RAC 892 116.0

115.0

174 Sunco/SUN431A 119.0

117.5

175 4zwf04.1.3 112.0

105.5

176 SUN434A/Lang.40.1 111.0

107.0

177 4zwf04.1.4 111.0

106.0


102.5

179 QT10580/SUN431A.34.3 121.0

118.5


108.5

181 SUN434A/SUN436E 112.0

112.0

182 SUN429E/Ruby 115.0

114.0

183 DM5637*B8/H45//SUN485A 121.0

118.0

184 SUN431A/SUN492A 109.0

113.0

185 SUNVALE//SERIISO-6/SUNVALE 117.0

110.5

186 98ZHB16/SUN399H//SUN399C 121.0

119.0

187 Yr15,24,2*399C.76 115.0

107.5

188 MILAN/KAUZ/5/CNDO/R143//ENTE/MEXI 112.0

101.0

189 QT8733 115.0

109.5

237


2014 2015

190 QT10580/SUN431A.10.3 117.0

114.0


115.0

192 WA-5-21-020/Lang*2 121.0

115.5

193 AMSEL/2*HTG/3/Sunstate 115.0

113.0

194 URES/KAUZ//2*Janz/3/SUN421E 121.0

118.5

195 Giles 121.0

118.0


108.0


117.5

198 SUN434B/Sunco 112.0

104.5

199 SUN434A/CHARA 117.0

119.0

200 Sunco/SUN431A 119.0

113.0


113.0


103.5

203 EGA Bonnie Rock/SUN436F 99.0

100.5

204 DM5637*B8/H45//SUN498D 101.0

106.5

205 SUN434A/SUN436E.142.1 112.0

110.0

206 SUN436F 116.0

113.5


109.5

208 Isr614.23/2*Sunvale 121.0

119.5

209 SUN434A/Lang.67.1 114.0

109.5

210 Sunco/SUN431A 112.0

93.0

211 CHARA/SUN434A 118.0

112.5

212 Yr15,24,2*399C.103 117.0

110.5

213 KUKRI/SUNLIN 118.0

114.5

214 SUN498E/Sunco 117.0

114.0

215 CHARA/SUN434A 117.0

121.5

Mean

114.6

112.1

SED

5.22

Wald Statistic

Year

45.2

Genotype

563.05***


Note: ns indicates non-significance and *** significance at P<0.001

238

Table 2 Means for number of days to physiological maturity (DPM) of 215 genotypes evaluated head-

to-head in association analysis in 2014 and 2015 at Narrabri.

Genotype No. Genotype Cropping year means (days to maturity)

2014 2015

1 CRUSADER 152.0

157.5

2 SUNSTATE 158.0

156.5


154.0

4 LINCOLN 153.0

152.0

5 LIVINGSTON 159.0

155.5

6 SUNVEX 162.0

155.0

7 MERINDA 148.0

153.0

8 LANG 158.5

158.5

9 SUNVALE 161.5

154.0

10 VENTURA 157.5

155.0

11 ELLISON 157.5

155.0

12 JANZ 159.0

155.5

13 CUNNINGHAM 160.0

155.5


154.0

15 SUNCO 159.0

153.0

16 SUNLIN 161.5

153.5

17 SUNTOP 159.0

154.0

18 SPITFIRE 155.0

159.0


156.0


154.5

21 DM5637*B8/H45//SUN498D 155.5

156.0


154.0


153.0

24 LPB05-1157 156.0

156.0

25 DM5637*B8/H45//SUN485A 156.5

154.5


153.5


155.0

28 SUN325K/SUN434A 157.5

155.5


156.0

30 DM5637*B8/H45//SUN498D 155.0

155.0

31 DM5637*B8/H45//SUN498D 147.5

156.0


156.0

33 DM5637*B8/H45//SUN485A 157.0

156.5

34 LPB05-2148 157.0

155.5

239


2014 2015


154.0


156.5


155.5

38 CPI133814/SUN421H//SUN421E 160.0

156.0

39 SUN325K/SUN434A 156.0

156.0

40 Crusader 154.0

153.5


154.5

42 RAC892/98ZHB03//RAC892 157.5

146.0

43 DM5637*B8/H45//SUN498D 152.0

157.5


155.0


157.0


153.0


154.5


153.5

49 SUN445C/QT10776 157.0

154.5

50 CR_1/AE.SQUARROSA (224)//2*OPATA 149.5

153.5


156.0

52 Lincoln 156.5

154.0

53 SUN344 E/VPMB36020 159.0

153.0


156.0


152.0

56 GILES 2002 158.5

155.0


156.0


153.5

59 Yr15Yr24 2*399C.1 153.0

156.0

60 Lang/SUN431B 160.5

154.5

61 SUN431A/SUN492A 158.0

152.0

62 SUN421T/QT10401 160.0

159.0


153.0

64 SUN434A/LANG 155.0

153.0


153.5

66 DM5637*B8/H45//SUN485A 159.0

155.0


155.0


153.5


155.0


154.0

240


2014 2015


152.0

72 Ruby 158.5

153.0

73 Lang/SUN431B 156.0

157.0

74 ZWC06 19 158.0

155.5


152.5


157.5

77 SUN498F/SUN485A 158.5

153.0

78 SUN431A 158.5

156.0


155.0

80 Sunvale*2/VO1225 160.0

153.0

81 Chara 159.0

155.0

82 QT10580/SUN431A.34.1 160.5

153.0

83 Chara/4*Sun376G 157.5

154.0

84 LANG/SUN366A 155.5

155.5

85 IRAQ43 153.5

155.5

86 SOKOLL 154.0

157.0

87 SUN344 E/VPMB36020 156.5

154.0

88 Sunvale*2/VO1225 157.0

157.5

89 SUN429E/Ruby 155.0

155.5


154.0


155.0

92 SUN421T/SUN399C.22.3 159.0

158.5


155.5


154.0


155.5

96 LANG/SUN366A 162.0

153.5

97 SUN431A/Lang.25.2 159.0

157.0


154.5

99 SYN 1.57.2 157.0

156.5


153.5


154.0


154.0

103 SUN429E/Ruby 157.0

152.0

104 Sunco/SUN431A 159.0

152.5

105 SYN 1.57.3 157.0

157.0

106 Janz 159.0

155.5


154.0

108 SUN434A/Lang.67.2 155.0

155.0

241


2014 2015

109 SUN325K/SUN434A 151.0

155.5

110 QT10580/SUN431A.4.3 160.0

155.0

111 SUN429E/Ruby 157.0

150.5

112 QT10580 (Wentworth) 152.5

154.0

113 Carinya 162.0

153.0

114 SUN429E/Ruby 156.5

155.5

115 SUN498F/SUN485A 153.5

158.0

116 SUN421T/QT10401 160.0

154.0

117 SUN429E/Ruby 158.5

153.5

118 Sunvale*2/VO1225 162.0

153.0


153.0


157.0

121 SUN325K/SUN434A*2 155.0

155.0

122 Sunvale*2/VO1225 160.0

153.0

123 SUN434A/SUN436E 163.0

156.0

124 SUN434A/SUN436E.116.4 155.0

156.0

125 98ZHB03/2*Lang 161.0

156.5


154.5


155.5


153.5

129 SUN429E/Ruby 155.0

153.0

130 SUN421T/QT10401 158.0

154.5


153.0

132 SUN431A/SUN492A 155.0

153.5

133 Yr15,24,2*399C.102 157.0

157.5

134 D67.2/P66.270//AE.SQUARROSA (320)/3 152.0

155.0


155.0


153.0


155.0

138 DM5637*B8/H45//SUN485A 151.0

153.0


153.0

140 30Ibwsn008 159.0

152.5

141 SUN434A/SUN436E.46.4 158.0

157.0

142 WA-1-21005/2*SUN426B 155.0

153.0

143 Lang/3*Sun376J.1.2.1 155.0

154.0


155.5

145 syn6.47.4 158.0

157.5


156.0

147 SUN431A/Lang.21.1 160.0

154.5

242


2014 2015


154.5

149 SUN372D.1.3 158.0

153.0

150 SUN429E/Ruby 160.0

154.5

151 SUN421T/QT10401 161.0

156.5

152 Cascade 158.0

155.5

153 SUN431A/Lang.21.2 158.0

156.0

154 SUN434A 159.0

154.5

155 SYN 3.74.2 152.0

153.0

156 LPB05-1164 155.0

155.0


155.0

158 15HRWSN112 155.0

157.0

159 SUN429E/Ruby 160.0

153.5


154.0

161 Sunco/SUN431A 160.0

154.0


156.5

163 SUN431A/SUN492A 155.0

155.0

164 Yr15,24,2*399C.85 152.0

153.0

165 Sunvale*2/VO1225 159.0

154.0


156.5

167 B409C/SUN420A//SUN498E 158.0

154.5

168 SUN434A/LANG 152.0

155.0


155.0

170 SUN429E/Ruby 158.0

149.0

171 WA-1-21005/2*SUN426B 157.0

154.0

172 SUN434A/SUN436E.188.3 157.0

155.0

173 RAC 892 157.0

155.5

174 Sunco/SUN431A 154.0

156.0

175 4zwf04.1.3 147.0

154.0

176 SUN434A/Lang.40.1 153.0

156.0

177 4zwf04.1.4 147.0

156.0


153.0

179 QT10580/SUN431A.34.3 158.0

155.0


152.0

181 SUN434A/SUN436E 155.0

154.0

182 SUN429E/Ruby 158.0

157.5

183 DM5637*B8/H45//SUN485A 153.0

154.5

184 SUN431A/SUN492A 154.0

155.0

243


2014 2015


156.0

186 98ZHB16/SUN399H//SUN399C 161.0

154.0

187 Yr15,24,2*399C.76 155.0

155.0


157.0

189 QT8733 155.0

156.5

190 QT10580/SUN431A.10.3 160.0

153.5


154.5

192 WA-5-21-020/Lang*2 161.0

157.5


156.0


154.0

195 Giles 162.0

155.0


155.0


153.5

198 SUN434B/Sunco 158.0

153.5

199 SUN434A/CHARA 158.0

153.0

200 Sunco/SUN431A 162.0

154.5


155.5


156.0


154.5

204 DM5637*B8/H45//SUN498D 160.0

156.0

205 SUN434A/SUN436E.142.1 157.0

152.5

206 SUN436F 155.0

154.0


156.0

208 Isr614.23/2*Sunvale 154.0

155.5

209 SUN434A/Lang.67.1 157.0

153.0

210 Sunco/SUN431A 158.0

155.5

211 CHARA/SUN434A 161.0

154.0

212 Yr15,24,2*399C.103 158.0

153.0


156.5

214 SUN498E/Sunco 158.0

153.0

215 CHARA/SUN434A 160.0

155.5

Mean

157.2

154.7

SED

2.73

Wald statistics

Year

158.44***

Genotype

277.82***

Year x Genotype 352.71**

Note: ns indicates non-signficance and *** significance at P<0.001

244

Table 3 Yield means of 215 genotypes evaluated head-to-head in association analysis in 2014 and

2015 at Narrabri.

Genotype No. Genotype/Crosses Cropping year mean yield (kg ha-1

)

2014 2015

1 CRUSADER 4444

4531

2 SUNSTATE 4478

4091

3 EGA STAMPEDE 4356

4069

4 LINCOLN 4484

4409

5 LIVINGSTON 4700

4547

6 SUNVEX 4316

4122

7 MERINDA 4569

4647

8 LANG 3725

4150

9 SUNVALE 3925

3866

10 VENTURA 4597

4931

11 ELLISON 4306

3703

12 JANZ 4966

4472

13 CUNNINGHAM 4537

4453

14 EGA GREGORY 4412

4147

15 SUNCO 3528

4141

16 SUNLIN 4181

3644

17 SUNTOP 5028

4909

18 SPITFIRE 4747

3409

19 EGA GREGORY 4200

4956

20 GILES/Carinya 4787

4078

21 DM5637*B8/H45//SUN498D 4184

4544

22 Wentworth/SUN434A 4197

4622

23 Ellison/Ventura 4522

4687

24 LPB05-1157 4744

4059

25 DM5637*B8/H45//SUN485A 4466

4353


4425

27 Carinya/Sunpict 4147

4291

28 SUN325K/SUN434A 3956

4431


4537

30 DM5637*B8/H45//SUN498D 4109

4272

31 DM5637*B8/H45//SUN498D 4181

4337

32 CROC_1/AE.SQUARROSA (224)/ 4050

4375

33 DM5637*B8/H45//SUN485A 4675

4494

34 LPB05-2148 4653

4041

245


)

2014 2015

35 Sunco/SUN431A 4434

4556

36 CUNNINGHAM 2007 3416

4172

37 Sunco*2/Kukri 4697

3684

38 CPI133814/SUN421H//SUN421E 4547

4687

39 SUN325K/SUN434A 2937

3722

40 Crusader 4547

4737

41 SUNCO/2*PASTOR//SUN436E 5034

5199

42 RAC892/98ZHB03//RAC892 4919

4078

43 DM5637*B8/H45//SUN498D 3912

4300


4984


4569


4591

47 SUNSTATE/CHARA 4712

4794

48 SUN500B/Carinya 5291

5187

49 SUN445C/QT10776 4444

4531

50

CROC_1/AE.SQUARROSA

(224)//2*OPATA 3778

4103

51 SUNSTATE/VO1225//SUN429E 4778

4778

52 Lincoln 4347

4487

53 SUN344 E/VPMB36020 4666

4391


4672


4319

56 GILES 2002 4947

3897

57 Ega Bonnie Rock 4125

4519


3947

59 Yr15Yr24 2*399C.1 4266

4944

60 Lang/SUN431B 4878

4559

61 SUN431A/SUN492A 4884

4431

62 SUN421T/QT10401 4537

4222

63 QT6581/4/PASTOR//SITE/MO/3/CHEN/ 4253

4387

64 SUN434A/LANG 5209

4791


4306

66 DM5637*B8/H45//SUN485A 4244

4278


5125

68 SUNVALE/VPMB36020 4259

4066

69 Carinya/Cascade 4550

3662


4419


4766

246


)

2014 2015

72 Ruby 4466

4762

73 Lang/SUN431B 3256

3856

74 ZWC06 19 3737

4781

75 SUNCO/2*PASTOR 3466

4797


4444

77 SUN498F/SUN485A 4859

4550

78 SUN431A 4616

4106


3812

80 Sunvale*2/VO1225 4256

4456

81 Chara 4656

4575

82 QT10580/SUN431A.34.1 4434

4166

83 Chara/4*Sun376G 4506

4275

84 LANG/SUN366A 4369

4028

85 IRAQ43 2409

3509

86 SOKOLL 3719

4678

87 SUN344 E/VPMB36020 4603

4716

88 Sunvale*2/VO1225 4481

4197

89 SUN429E/Ruby 4672

4234

90 URES/JUN//KAUZE/3/2*SUNVALE 4316

4650

91 CHARA/SUN434A 4631

3875

92 SUN421T/SUN399C.22.3 5222

4544


4212


4719

95 Sunvale/Janz 3228

4444

96 LANG/SUN366A 4100

3887

97 SUN431A/Lang.25.2 4103

3919

98 BAXTER/SUN421N 4575

4122

99 SYN 1.57.2 4356

4091

100 RAC1192/Ventura 4472

4912


4215


3997

103 SUN429E/Ruby 4978

4900


4487

105 SYN 1.57.3 4750

4525

106 Janz 4944

4184

107 Lang/Ellison 4153

4109

108 SUN434A/Lang.67.2 4631

3731

109 SUN325K/SUN434A 3431

4412

247


)

2014 2015

110 QT10580/SUN431A.4.3 4369

4375

111 SUN429E/Ruby 4622

4716

112 QT10580 (Wentworth) 4744

4481

113 Carinya 5119

4266

114 SUN429E/Ruby 4256

4706

115 SUN498F/SUN485A 4447

3334

116 SUN421T/QT10401 4825

4387

117 SUN429E/Ruby 4253

4384

118 Sunvale*2/VO1225 4925

4025

119 ELLISON/Sunbri 4116

4316


4653

121 SUN325K/SUN434A*2 4200

4369

122 Sunvale*2/VO1225 4825

4597

123 SUN434A/SUN436E 5075

3987

124 SUN434A/SUN436E.116.4 5081

3966

125 98ZHB03/2*Lang 4206

4169


3887

127 SUNVALE//OPATA*2/WULP 2112

3691

128 Sunstate/Ellison 4012

3772

129 SUN429E/Ruby 4581

4697

130 SUN421T/QT10401 4637

4653


4366

132 SUN431A/SUN492A 4906

4456

133 Yr15,24,2*399C.102 3462

4728

134 D67.2/P66.270//AE.SQUARROSA (320)/3 3875

4137

135 KUKRI/SUNSTATE 3744

4066

136 SUNVALE/VPMB36020 4956

4503


4237

138 DM5637*B8/H45//SUN485A 4256

4566


4222

140 30Ibwsn008 3612

4112

141 SUN434A/SUN436E.46.4 4800

4631

142 WA-1-21005/2*SUN426B 4119

4147

143 Lang/3*Sun376J.1.2.1 4669

4412

144 Sunstate/QT8733 4644

4375

145 syn6.47.4 2787

4619


4537

248


)

2014 2015

147 SUN431A/Lang.21.1 4150

4475


3756

149 SUN372D.1.3 4562

4681

150 SUN429E/Ruby 4087

3650

151 SUN421T/QT10401 5081

4316

152 Cascade 4312

3800

153 SUN431A/Lang.21.2 4512

4994

154 SUN434A 4075

3706

155 SYN 3.74.2 1800

3622

156 LPB05-1164 3887

5256

157 Wentworth/SUN434A 3969

4556

158 15HRWSN112 4469

4575

159 SUN429E/Ruby 4512

4800


4834


4809


5144

163 SUN431A/SUN492A 4475

3722

164 Yr15,24,2*399C.85 4744

4650

165 Sunvale*2/VO1225 4300

4578


4425

167 B409C/SUN420A//SUN498E 4094

3819

168 SUN434A/LANG 4594

4078


4284

170 SUN429E/Ruby 4437

4000

171 WA-1-21005/2*SUN426B 4300

3981

172 SUN434A/SUN436E.188.3 4981

4794

173 RAC 892 4594

3875


4262

175 4zwf04.1.3 4856

4750

176 SUN434A/Lang.40.1 3337

4891

177 4zwf04.1.4 4656

4566


5269

179 QT10580/SUN431A.34.3 4681

4141


4728

181 SUN434A/SUN436E 5087

4681

182 SUN429E/Ruby 4750

4731

183 DM5637*B8/H45//SUN485A 4412

3912

249


)

2014 2015

184 SUN431A/SUN492A 4737

4247

185 SUNVALE//SERIISO-6/SUNVALE 4244

4997

186 98ZHB16/SUN399H//SUN399C 3456

3869

187 Yr15,24,2*399C.76 4481

4159

188 MILAN/KAUZ/5/CNDO/R143//ENTE/MEXI 4031

4322

189 QT8733 4281

4116

190 QT10580/SUN431A.10.3 4506

4769


4131

192 WA-5-21-020/Lang*2 3744

4356

193 AMSEL/2*HTG/3/Sunstate 3944

4387

194 URES/KAUZ//2*Janz/3/SUN421E 4925

3847

195 Giles 4625

4359

196 URES/KAUZ//2*Janz/3/SUN421E 4531

4250


3775

198 SUN434B/Sunco 4794

4262

199 SUN434A/CHARA 2306

2812


4204

201 SUNVALE//SERIISO-6/SUNVALE 4781

4237


4687

203 EGA Bonnie Rock/SUN436F 4619

4169

204 DM5637*B8/H45//SUN498D 4731

4600

205 SUN434A/SUN436E.142.1 4506

4103

206 SUN436F 4612

4662


4359

208 Isr614.23/2*Sunvale 3850

3444

209 SUN434A/Lang.67.1 4612

3994


4462


4931

212 Yr15,24,2*399C.103 4462

4597

213 KUKRI/SUNLIN 4106

4362

214 SUN498E/Sunco 4131

4100


5412

Mean

4352.94

4349.38

SED

369.8

Wald Statistic

Year

0.42ns

Genotype

767.03***

Year x Genotype

398.04*


250

Table 4 Means for thousand kernel weight (TKW) of 215 genotypes evaluated head-to-head for

association analysis in 2014 and 2015 at Narrabri.

Genotype No. Genotype Cropping year mean TKW (g)

2014 2015

1 CRUSADER 33.55

36.70

2 SUNSTATE 32.35

35.17


39.67

4 LINCOLN 37.55

31.47

5 LIVINGSTON 36.10

38.02

6 SUNVEX 35.75

37.10

7 MERINDA 35.85

35.72

8 LANG 33.15

36.10

9 SUNVALE 34.50

33.70

10 VENTURA 37.10

37.42

11 ELLISON 34.80

29.45

12 JANZ 35.00

31.15

13 CUNNINGHAM 36.85

37.05


36.65

15 SUNCO 35.45

36.75

16 SUNLIN 34.95

35.25

17 SUNTOP 36.95

35.42

18 SPITFIRE 35.15

32.75


34.90


35.00

21 DM5637*B8/H45//SUN498D 32.65

38.35


34.40


34.20

24 LPB05-1157 37.15

33.35

25 DM5637*B8/H45//SUN485A 36.50

34.02


35.70


32.30

28 SUN325K/SUN434A 35.05

36.37


36.37

30 DM5637*B8/H45//SUN498D 41.15

41.12

31 DM5637*B8/H45//SUN498D 37.95

36.60


37.10

33 DM5637*B8/H45//SUN485A 43.35

33.07

251


2014 2015

34 LPB05-2148 37.35

32.15


34.82


34.12


37.00

38 CPI133814/SUN421H//SUN421E 37.10

34.10

39 SUN325K/SUN434A 34.00

38.42

40 Crusader 33.20

32.85


34.90

42 RAC892/98ZHB03//RAC892 33.55

37.48

43 DM5637*B8/H45//SUN498D 39.30

35.62


36.72


31.60


31.08


35.67


35.95

49 SUN445C/QT10776 28.95

32.35

50

CROC_1/AE.SQUARROSA

(224)//2*OPATA 34.70

34.10


36.05

52 Lincoln 39.50

29.77

53 SUN344 E/VPMB36020 34.90

35.77


37.55


36.95

56 GILES 2002 33.45

37.25


38.08


35.23

59 Yr15Yr24 2*399C.1 35.75

33.80

60 Lang/SUN431B 36.40

33.52

61 SUN431A/SUN492A 36.20

31.40

62 SUN421T/QT10401 35.55

33.85


33.15

64 SUN434A/LANG 40.20

31.60


31.95

66 DM5637*B8/H45//SUN485A 34.15

32.32


37.85


36.40


42.92


39.10


38.97

72 Ruby 35.40

36.40

73 Lang/SUN431B 35.85

32.52

252


2014 2015

74 ZWC06 19 33.20

39.10


35.22


35.40

77 SUN498F/SUN485A 32.70

28.55

78 SUN431A 37.00

32.97


35.20

80 Sunvale*2/VO1225 32.20

33.17

81 Chara 34.50

33.60

82 QT10580/SUN431A.34.1 33.05

38.25

83 Chara/4*Sun376G 32.45

37.23

84 LANG/SUN366A 35.80

33.42

85 IRAQ43 38.20

38.62

86 SOKOLL 43.55

31.02

87 SUN344 E/VPMB36020 38.40

30.47

88 Sunvale*2/VO1225 34.30

34.92

89 SUN429E/Ruby 37.40

30.05


37.62


33.87

92 SUN421T/SUN399C.22.3 37.70

30.87


29.40


38.30


31.20

96 LANG/SUN366A 36.65

39.05

97 SUN431A/Lang.25.2 31.70

37.13


34.60

99 SYN 1.57.2 34.45

33.22


34.90


35.95


37.90

103 SUN429E/Ruby 34.80

35.45

104 Sunco/SUN431A 36.10

29.87

105 SYN 1.57.3 33.90

32.45

106 Janz 35.90

28.70


34.00

108 SUN434A/Lang.67.2 35.40

41.58

109 SUN325K/SUN434A 35.50

39.58

110 QT10580/SUN431A.4.3 39.10

38.27

111 SUN429E/Ruby 35.55

35.95

112 QT10580 (Wentworth) 36.15

34.95

113 Carinya 36.20

35.83

253


2014 2015

114 SUN429E/Ruby 31.30

37.92

115 SUN498F/SUN485A 33.25

36.35

116 SUN421T/QT10401 35.20

32.85

117 SUN429E/Ruby 34.15

31.83

118 Sunvale*2/VO1225 35.20

34.40


34.55


29.85

121 SUN325K/SUN434A*2 44.70

33.20

122 Sunvale*2/VO1225 38.90

38.78

123 SUN434A/SUN436E 30.40

37.55

124 SUN434A/SUN436E.116.4 35.00

38.05

125 98ZHB03/2*Lang 33.30

39.17


37.55


37.02


34.67

129 SUN429E/Ruby 36.60

29.72

130 SUN421T/QT10401 37.80

35.82


35.05

132 SUN431A/SUN492A 41.50

31.82

133 Yr15,24,2*399C.102 36.70

30.25

134 D67.2/P66.270//AE.SQUARROSA (320)/3 41.50

32.27


34.45


33.33


33.62

138 DM5637*B8/H45//SUN485A 36.20

35.30


34.15

140 30Ibwsn008 32.50

34.67

141 SUN434A/SUN436E.46.4 34.00

32.88

142 WA-1-21005/2*SUN426B 31.70

35.67

143 Lang/3*Sun376J.1.2.1 38.90

33.82


35.88

145 syn6.47.4 35.60

32.35


27.82

147 SUN431A/Lang.21.1 35.20

32.23


30.77

149 SUN372D.1.3 30.30

32.83

150 SUN429E/Ruby 30.80

35.58

151 SUN421T/QT10401 34.20

37.02

152 Cascade 33.00

32.25

153 SUN431A/Lang.21.2 33.60

33.57

254


2014 2015

154 SUN434A 46.50

34.35

155 SYN 3.74.2 38.90

30.12

156 LPB05-1164 39.10

30.40


37.37

158 15HRWSN112 40.80

34.62

159 SUN429E/Ruby 30.50

31.37


33.65

161 Sunco/SUN431A 31.20

32.80


33.73

163 SUN431A/SUN492A 36.60

34.97

164 Yr15,24,2*399C.85 33.00

36.00

165 Sunvale*2/VO1225 36.40

32.73


34.22

167 B409C/SUN420A//SUN498E 40.60

34.62

168 SUN434A/LANG 37.70

33.80


31.85

170 SUN429E/Ruby 35.10

37.60

171 WA-1-21005/2*SUN426B 38.30

32.48

172 SUN434A/SUN436E.188.3 34.10

32.70

173 RAC 892 33.00

33.37

174 Sunco/SUN431A 33.30

33.50

175 4zwf04.1.3 30.90

33.52

176 SUN434A/Lang.40.1 40.80

32.55

177 4zwf04.1.4 31.70

32.60


36.38

179 QT10580/SUN431A.34.3 33.50

34.50


32.98

181 SUN434A/SUN436E 29.20

36.33

182 SUN429E/Ruby 34.90

34.53

183 DM5637*B8/H45//SUN485A 35.30

32.35

184 SUN431A/SUN492A 31.50

30.73


30.63

186 98ZHB16/SUN399H//SUN399C 36.00

32.27

187 Yr15,24,2*399C.76 39.50

28.77


35.07

189 QT8733 35.80

34.80

190 QT10580/SUN431A.10.3 34.00

41.50


32.60

192 WA-5-21-020/Lang*2 31.40

34.30


34.70

255


2014 2015


37.05

195 Giles 34.20

34.22


33.97


31.17

198 SUN434B/Sunco 39.00

32.20

199 SUN434A/CHARA 35.10

33.67

200 Sunco/SUN431A 35.20

30.40


32.02


30.02


35.00

204 DM5637*B8/H45//SUN498D 40.40

34.70

205 SUN434A/SUN436E.142.1 32.60

33.40

206 SUN436F 39.10

30.32


31.83

208 Isr614.23/2*Sunvale 34.10

32.10

209 SUN434A/Lang.67.1 33.40

30.43

210 Sunco/SUN431A 36.40

34.92

211 CHARA/SUN434A 33.40

34.70

212 Yr15,24,2*399C.103 41.30

34.00


33.20

214 SUN498E/Sunco 36.20

31.07

215 CHARA/SUN434A 32.10

33.85

Mean

35.77

34.4

SED

3.19

Wald Statistic

Year

34.06**

Genotype

265.69**

Year x Genotype 322.21**


Paul K. K. Adu-Gyamfi BSc. Agriculture (Crop Science) A ...

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Transcript of Paul K. K. Adu-Gyamfi BSc. Agriculture (Crop Science) A ...