2015. Greg Rebetzke. Integration of new tools for improving genetic gain of grain yield in water...

Integration of new tools for improving genetic gain of grain yield in water-limited environments

CSIRO AGRICULTURE FLAGSHIP

Greg Rebetzke, Bill Bovill, David Deery, Jose Jimenez-Berni, Anton Wasson, Richard James and Lynne McIntyre

The connect and disconnect with trait delivery

1. Trait value? • Are the traits relevant to the target environment(s)? • What is the value proposition? • What are the trade-offs? • Can I scale up from cell, single-plant and row to canopy? • What do I give up in order to accommodate the new trait/germplasm? 2. Selection? • How do I phenotype? Is it quick, cheap and reliable? • Heritability and the correlation of phenotype with genotype? • Genetic complexity/QTL/markers? • Influence of genetic background/repeatable? • Quality of donor germplasm? • Correlated response? 3. Adoption? • How does the gene/trait fit in the target farming system?

The questions a breeder is asking are very different to those

being asked by researchers further upstream

Grain

Yield

= Water

Use

x Water-use

Efficiency

x Harvest

Index

Biomass

The model for productivity under water-limitation:

T/ET - rapid canopy growth to shade the soil surface and restrict evaporation

DM/T - transpiration efficiency (or leaf-level WUE)

HI - C partitioning to the growing spike (grain number) and then to the grain (grain

size) (balance water use before and after flowering to optimise harvest index)

T/ET x DM/T

Drought resistance? No!

Water use efficiency as a breeding target. Yes!

Rainfall amount and timing

Many potential traits to improve crop performance under

drought

New root architecture

Stem CHO for large grain

Genes for better emergence

Transpiration efficiency

C13

C12 Glaucousness

Vigorous early growth

Reduced tillering

Developmental genes

Which traits where? Trait dissection Trait delivery

Need for repeatable phenotyping - controlled ‘managed’

environments (Managed Environment Facilities – ‘MEF’)

In Australia - three sites with two-three irrigation regimes

Which traits where? Quality phenotyping – controlled field environments (Managed Environment Facilities – ‘MEF’)

Maximising water uptake by removing constraints to root

(and shoot) growth – mapping of soil conductivity

(Rick Graham NSWDPI)

Break crops to reduce root disease

Canola in the rotation

Wheat after Wheat Wheat after Canola

*** Reduce soil-borne diseases

(take-all, crown rot, CCN, root-

lesion nematodes)

(Kirkegaard CSIRO)

Random to selected lines – deriving greater benefit from

populations in selection of tails extreme for target trait

High-selected group Low-selected group

‘Traits’ germplasm

Germplasm Background(s) Number of lines

Canopy temperature Multiple 20 lines/tail

Development Single 13 near-isogenic pairs

Early vigour Multiple 10 lines/tail

Grain fertility Multiple 20 lines/tail

Grain size/screenings Single 10 lines/tail

Ear morphology Multiple 24 near-isogenic pairs

Reduced-tillering Multiple 20 near-isogenic pairs

Staygreen (leaf) Multiple 10 lines/tail

Stem carbohydrates Multiple 15 lines/tail

Transpiration efficiency Multiple 10 lines/tail

Box-plot of all entry means/variance for grain yield at each sampled MEF

(M = Merredin, N = Narrabri, Y = Yanco; Ir = irrigated, Rf = rainfed)

2014 contrasted genotype response at Narrabri with response at Yanco and Merredin (largely uncorrelated!)

-0.3 -0.2 -0.1 0.0 0.1

-0.3

-0.2

-0.1

0.0

0.1

Comp.1

Com

p.2

20-1-2NT

20-1-5NT

4-4-1NT

4-4-6NT

5-3-4NT

5-3-8NT

5216N

5216P

6072N

6072P

6184N

6184P

6266N

6266P

6336N

6336P2

6460N

6460P

6661N

6661P

7276N

7276PF

7566N

7566PUNI

7770N

7770P

8009N

8009P

Axe

B+

B++

B-

DH_R034

DH_R035

DH_R070

DH_R072

DH_R087

DH_R101

DH_R120

DH_R150

DH_R154

DH_R162

DH_R167

DH_R182

DH_R187

DH_R202

DH_R263

DH_R275

DH_R336

DH_R344

Drysdale

Excalibur

FA1+

FA1-

FA3+

FA3-

FA8+

FA8-

Gladius

Hartog

JA2+

JA2-

JA6+

JA6-

LRBC136LRBC156

LRBC181

LRBC187

LRBC193

LRBC24

LRBC243

LRBC27

LRBC271

LRBC285LRBC311LRBC327

LRBC386

LRBC388

LRBC392

LRBC409

LRBC466

LRBC62

Mace

QA144

QA175

QA177

QA178

QA179

QA183

QA193

QA223

QA227

QA236

QA268

QA321

QA35

QA69QA72

QA87

QA95

QA97

QC14

QC15QC18

QC19

QC2

QC20

QC25

QC27

QC28

QC29

QC30

QC7

QH181-7QH194-3

QH210-5

QH22

QH221-10

QH239-4

QH25

QH252-2R

QH32

QH5

QH52-2

QH56QH71-3r

QH71-4

QH71-9

QH74-10

QH74-2QW111

QW12

QW13

QW132

QW134

QW135

QW136

QW144

QW146

QW148

QW155

QW160

QW170

QW183

QW188

QW190

QW23

QW51

QW59

QW67

QW87

QW88

RAC875

SB002

SB012

SB017

SB021

SB023

SB025

SB026

SB027

SB035

SB037

SB049

SB051

SB053

SB070

SB071

SB091

SB095

SB1+

SB1-SB101

SB118

SB127

SB130

SB134

SB162

SB163

SB165

SB171

SB179

SB2+ SB2-

SB5-(B)

Sun595B

Sunstate+

Sunstate-

W010111

W030311

W040217

W050114

W050204W050306

W080205

W100109

W100209

W100402

W100504

W110511

W111402

W11B

W120216

W130102

W140910

W15A

W170310

W1A

W200118

W210203

W220416W260801

W280308

W3A

W4AW8A

WA11+

WA11-

WA3+

WA3-

WA8-

WB1+

WB1-

Weebill

Westonia

wj111

wj113wj115

wj119

wj171

wj22wj23

wj25

wj30

wj44wj84

Wyalkatchem

Yitpi

-30 -20 -10 0 10

-30

-20

-10

010

YIrrYRf

NIrrNRf

MIr

MRf

-0.2 -0.1 0.0 0.1 0.2

-0.2

-0.1

0.0

0.1

0.2

Comp.1

Com

p.2

20-1-2NT

20-1-5NT

4-4-1NT

4-4-6NT

5-3-4NT5-3-8NT

5216N

5216P

6072N

6072P

6184N

6184P

6266N

6266P

6336N

6336P2

6460N

6460P6661N

6661P

7276N

7276PF

7566N

7566PUNI

7770N

7770P

8009N 8009P

AxeB+

B++

B-

DH_R034

DH_R035

DH_R070

DH_R072

DH_R087

DH_R101

DH_R120

DH_R150

DH_R154

DH_R162

DH_R167

DH_R182

DH_R187

DH_R202

DH_R263

DH_R275

DH_R336

DH_R344

Drysdale

Excalibur

FA1+

FA1-

FA3+FA3-

FA8+

FA8-

Gladius

Hartog

JA2+

JA2-

JA6+

JA6-

LRBC136

LRBC156

LRBC181

LRBC187

LRBC193

LRBC24

LRBC243

LRBC27

LRBC271

LRBC285LRBC311

LRBC327

LRBC386

LRBC388

LRBC392

LRBC409LRBC466 LRBC62

Mace

QA144QA175

QA177

QA178

QA179

QA183

QA193

QA223

QA227

QA236QA268

QA321

QA35

QA69

QA72

QA87

QA95

QA97

QC14

QC15

QC18

QC19

QC2

QC20

QC25

QC27

QC28QC29

QC30

QC7

QH181-7

QH194-3

QH210-5

QH22QH221-10

QH239-4

QH25

QH252-2R

QH32QH5

QH52-2

QH56

QH71-3r

QH71-4

QH71-9

QH74-10

QH74-2

QW111

QW12

QW13

QW132

QW134QW135

QW136

QW144

QW146

QW148

QW155

QW160

QW170

QW183

QW188

QW190QW23QW51

QW59

QW67

QW87

QW88

RAC875

SB002

SB012SB017

SB021SB023

SB025

SB026SB027

SB035

SB037

SB049

SB051SB053

SB070

SB071

SB091SB095

SB1+SB1-SB101SB118SB127

SB130

SB134

SB162

SB163

SB165

SB171

SB179

SB2+

SB2-

SB5-(B)

Sun595B

Sunstate+

Sunstate-

W010111

W030311W040217W050114

W050204

W050306

W080205

W100109

W100209

W100402

W100504

W110511

W111402

W11BW120216

W130102

W140910W15A

W170310

W1A

W200118

W210203

W220416

W260801

W280308W3A

W4A

W8A

WA11+

WA11-

WA3+WA3-

WA8-

WB1+

WB1-

Weebill

Westonia

wj111wj113

wj115

wj119

wj171

wj22

wj23

wj25

wj30wj44

wj84

Wyalkatchem

Yitpi

-10 -5 0 5 10

-10

-50

510

YIrrYRf

NIrrNRf

MIr

MRf

Grain yield Harvest index

-0.2 -0.1 0.0 0.1 0.2

-0.2

-0.1

0.0

0.1

0.2

Comp.1

Com

p.2

20-1-2NT

20-1-5NT

4-4-1NT

4-4-6NT

5-3-4NT5-3-8NT

5216N

5216P

6072N

6072P

6184N

6184P

6266N

6266P

6336N

6336P2

6460N

6460P6661N

6661P

7276N

7276PF

7566N

7566PUNI

7770N

7770P

8009N

8009P

Axe

B+

B++

B-

DH_R034

DH_R035

DH_R070

DH_R072

DH_R087

DH_R101

DH_R120

DH_R150

DH_R154

DH_R162

DH_R167

DH_R182

DH_R187DH_R202DH_R263

DH_R275

DH_R336

DH_R344

Drysdale

Excalibur

FA1+FA1-

FA3+

FA3-

FA8+

FA8-

Gladius Hartog

JA2+

JA2-

JA6+

JA6-

LRBC136

LRBC156

LRBC181

LRBC187

LRBC193

LRBC24

LRBC243

LRBC27

LRBC271

LRBC285

LRBC311LRBC327

LRBC386

LRBC388LRBC392

LRBC409LRBC466 LRBC62

Mace

QA144QA175

QA177QA178

QA179QA183

QA193

QA223

QA227

QA236

QA268

QA321

QA35

QA69

QA72

QA87

QA95

QA97

QC14

QC15

QC18

QC19QC2

QC20QC25

QC27

QC28

QC29

QC30

QC7

QH181-7

QH194-3

QH210-5

QH22

QH221-10

QH239-4QH25

QH252-2R

QH32

QH5

QH52-2

QH56QH71-3rQH71-4

QH71-9

QH74-10

QH74-2

QW111

QW12

QW13

QW132

QW134

QW135

QW136

QW144

QW146

QW148

QW155

QW160 QW170

QW183

QW188

QW190

QW23

QW51

QW59

QW67 QW87

QW88

RAC875

SB002

SB012

SB017SB021

SB023

SB025

SB026

SB027

SB035

SB037

SB049

SB051

SB053

SB070

SB071

SB091

SB095

SB1+SB1-SB101SB118

SB127

SB130

SB134

SB162SB163

SB165

SB171

SB179

SB2+

SB2-

SB5-(B)

Sun595B

Sunstate+

Sunstate-

W010111

W030311W040217

W050114

W050204W050306

W080205

W100109

W100209

W100402

W100504

W110511

W111402

W11BW120216

W130102

W140910

W15A

W170310

W1A

W200118

W210203

W220416

W260801W280308

W3A

W4AW8A

WA11+

WA11-

WA3+

WA3-

WA8-

WB1+

WB1-

Weebill

Westonia

wj111

wj113

wj115

wj119

wj171

wj22

wj23

wj25

wj30wj44 wj84

WyalkatchemYitpi

-10 -5 0 5 10 15-1

0-5

05

10

15

YIrr

YRf

NIrrNRf

MIrMRf

Grain number

-0.1 0.0 0.1 0.2

-0.1

0.0

0.1

0.2

Comp.1

Com

p.2

20-1-2NT

20-1-5NT

4-4-1NT

4-4-6NT

5-3-4NT

5-3-8NT

5216N

5216P

6072N

6072P

6184N

6184P6266N

6266P

6336N

6336P2

6460N

6460P

6661N

6661P

7276N

7276PF

7566N

7566PUNI

7770N

7770P

8009N

8009P

Axe

B+

B++

B-

DH_R034

DH_R035

DH_R070 DH_R072

DH_R087

DH_R101

DH_R120

DH_R150

DH_R154

DH_R162

DH_R167

DH_R182

DH_R187

DH_R202

DH_R263

DH_R275

DH_R336

DH_R344

Drysdale

Excalibur

FA1+

FA1-

FA3+

FA3-

FA8+

FA8-

Gladius Hartog

JA2+

JA2-

JA6+

JA6-

LRBC136LRBC156

LRBC181

LRBC187

LRBC193

LRBC24

LRBC243

LRBC27LRBC271

LRBC285

LRBC311LRBC327

LRBC386

LRBC388

LRBC392

LRBC409

LRBC466

LRBC62

Mace

QA144

QA175

QA177

QA178

QA179

QA183

QA193

QA223

QA227

QA236

QA268

QA321

QA35

QA69QA72

QA87

QA95

QA97

QC14

QC15QC18

QC19

QC2

QC20QC25

QC27

QC28

QC29

QC30 QC7

QH181-7

QH194-3

QH210-5

QH22

QH221-10

QH239-4

QH25

QH252-2R

QH32

QH5

QH52-2

QH56

QH71-3r

QH71-4QH71-9

QH74-10

QH74-2

QW111QW12

QW13

QW132

QW134

QW135

QW136

QW144

QW146

QW148

QW155QW160

QW170

QW183

QW188

QW190QW23

QW51

QW59QW67

QW87

QW88

RAC875

SB002

SB012

SB017

SB021

SB023

SB025

SB026

SB027

SB035

SB037

SB049

SB051

SB053

SB070

SB071

SB091

SB095

SB1+

SB1-SB101

SB118

SB127

SB130

SB134

SB162

SB163

SB165

SB171

SB179

SB2+SB2-

SB5-(B)

Sun595B

Sunstate+

Sunstate-

W010111

W030311

W040217

W050114

W050204

W050306

W080205

W100109

W100209

W100402

W100504

W110511

W111402W11B

W120216

W130102

W140910

W15A

W170310

W1AW200118

W210203

W220416W260801

W280308W3A

W4A

W8A

WA11+

WA11-

WA3+

WA3-

WA8-

WB1+

WB1-

Weebill

Westonia

wj111

wj113

wj115

wj119

wj171

wj22

wj23

wj25

wj30

wj44

wj84

Wyalkatchem

Yitpi

-10 0 10 20

-10

010

20

YIrrYRf

NIrrNRf

MIr

MRf

Grain weight

Grain yield of trait germplasm is comparable to that of commercial checks at the MEF

Note that the traits are tested in germplasm developed in current or recent commercial wheat

backgrounds and that the trait lines used in the MEF are not selected for grain yield

Early ground cover (‘vigour’) E

arly g

round

cover

assessm

en

t (%

)

20

30

40

50

60

70

80

90

Drysdale - 41Mace - 40Yitpi - 47


YancoNarrabri

- Vigour + Vigour

Canopy temperature C

anop

y t

em

pe

ratu

re (

Z55)

(oC

)

14

16

18

20

22

24

26

Drysdale - 23.03Mace - 23.65Yitpi - 22.09


YancoNarrabri

Water-soluble carbohydrate concentration W

SC

con

centr

ation

(m

g/g

)

0

50

100

150

200

250

300

350



Yanco_Irr Merredin_Irr

Grain yield (multivariate – all sites and irrigation regimes)

-0.10 -0.05 0.00 0.05

-0.1

0-0

.05

0.0

00.0

5

Comp.1

Com

p.2

1

2

34

56

7

89

10

11

12

131415

16

17

18

19

20

21

22

23

24

25

26

2728

29

3031

32

3334

35

3637

38

39

40

414243

44

45

46

47

48

49

50

51

52

53

54

5556

57

58

59 60

61

62

6364

65

67

6869 70

71

7273

74

75

76

77

78

79

80

81

82

83

84

85

86

87

8889

90

91

92

93

94

95

9697

98

99100

101

102

103

104

105

106

107

108

109110

111

112

113114115

116117

118

119120

121

122

123

124

125

126

127128

129130

131132

133

134

135

136

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138

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142

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144

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146

147148149

150

151

152153

154155156

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166

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272

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412413414415416

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635 636

637638639

640

641642 643644

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736

737

738

739740

741

742

743

744

745746747

748 749

750751752

753

754755

756757

758

759760

761

762 763

764

765

766

767

768

769

770

771

772773

774

775

776

777

778

779

780

781

782

783

784

785786

787

788

789

790

791

792

793

794

795796

797798

799

800801

802803

804805

806

807

808

809

810

811

812

813

814

815816

817

818

819

820

821822

823824825

826

827

828

829

830

831

832

833

834

835

836837

838

839

840841842

843

844

845

846

847848

849

850

851

852

853

854 855

856857

858859860

861

862

863

864

865866

867868869

870

871

872873874

875

876

877

878

879

880

881882883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899900901

902

903904

905

906

907

908

909

910911

912

913

914

915

916917

918

919

920

921

922

923

924

925

926927928

929

930

931

932

933

934935

936

937938

939

940

941

942943

944

945

946947

948949

950

951

952

953954

955

956

957958

959

960961

962

963

964

965

966

967

968

969

970

971

972

973974

975976

977

978

979980

981

982983

984985

986

987988

989

990

991

992

993

994

995996

997

998999

10001001

1002

1003

1004

10051006

1007

1008

1009101010111012

1013

10141015

1016

1017

101810191020

10211022

1023

1024

1025

1026

1027

10281029

1030

1031

1032

1033

1034

1035

1036103710381039

1040

1041

1042

1043

1044

1045

1046

10471048

10491050

10511052

1053

1054

10551056

1057

105810591060

1061

10621063 10641065

10661067

10681069

1070

1071

1072

107310741075

1076

1077

1078

1079

1080

1081

1082

1083

1084

1085

10861087

1088

1089

1090

1091

1092

1093

109410951096

1097

10981099

1100

1101

1102

1103

11041105

1106

11071108

1109

11101111

1112

11131114

11151116

1117

1118

1119

1120

1121

112211241125

1126

1127

1128

1129

1130

1131

11321133

11341135

1136

1137

1138

1139

1140

1141

11421143

1144

1145

1146

11471148

1149

1150

11511152

11531154

1155

1156

1157

1158

1159 1160

1161

1162

116311641165

1166

1167

11681169

1170

1171

11721173

11741175

11761177

11781179

11801181

11821183

11841185

11861187

1188

118911901191

1192

1193

1195

1196

11971198

1199

1200

12011202

1203

1204

1205

1206

1207 12081209

12101211

1212

1213

1214

1215

12161217

1218

1219 1220

1221

12221223

1224

1225

12261227

1228

1229

12301231

1232

1233

1234123512361237

12381239

12401241

1242

12431244

12451246

1247

1248

12491250

1251

1252

1253

1254

12551256

1257

1258

1259

1260

12611262

1263

1264

1265

12661267

1268

1269

12701271

1272

1273

1274

1275

1276

1277

1278

1279

1280

1281

12821283

1284

12851286

1287

12881289

1290

1291

1292

1293

1294

1295

1296

1297

1298

1299

1300

13011302

1303

1304

1305

1306

1307

1308

1309 1310

1311

1312

1313

13141315

1316

13171318

1319

13201321

1322

13231324

13251327

1328

1329

13301331

1332

1333

13341335

1336

1337

1338

1339

13401341

1342

1343

1344

134513461347

13481349

1350

1351

1352

1353

13541355

13561357

1358

135913601361

1362

1363

1364

13651366

1367

13681369

1370

1371

1372

1373

1374

1375

1376

1377

1378

1379

13801381

1382

1383

1384

1385

13861387

13881389

13901391

1392

1393

1394

13951396

1397

1398

13991400 14011402

1403

1404

1405

1406

1407

1408

1409

141014111412

1413

1414

1415

1416

1417

1418

14191420

1421

1422

14231424

1425

1426

1427

14281429

1430

14311432 1433

1434

14351436

1437

1438

1439

14401441

1442

1443

1445

1446

1447

14481449

1450

1451

145214531454

1455

1456

1457

1458

1459

1460

1461

1462

14631464

1465

1466

1467

1468

1469

1470

14711472

1473

1474

1475

1476

14771478

1479

1480

1481

1482148314841485

1486

1487

1488

1489

14901491

1492

1493

1494

1495

14961497

1498

1499

1500

15011502

1503

1504

1505

1506

-30 -20 -10 0 10 20 30

-30

-20

-10

010

20

30

AnDM

Ayld

HI

Nospks

Seed wt

No.grain

Trait value for grain yield at each MEF (2010-14)

Trait value is calculated as the percentage change in mean yield of lines selected for the trait

relative to sister lines that lack the trait at each MEF location in one to four years of testing

Trait

Can

opy te

mpe

ratu

re

Car

bon

iso.

discr

iminat

ion

Ste

m c

arbo

hydr

ates

Pre

senc

e of

awns

Leaf

wax

ines

s

Leaf

-rollin

g

Ear

ly vigou

r

Red

uced

tille

ring

Tra

it (

yie

ld)

va

lue

(%

)

-10

0

10

20

MerredinNarrabriYanco

Trait delivery

1. Cooler canopies

2. Greater early leaf area

Dynamic phenotyping

Pt = G×Et (×M) with t: time

Time constant

Seconds Hours Days Season

Canopy

Temperature Canopy

Height

Crop

Yield

Phenomics from the pot to the paddock

Thermal Lidar /Colour 3-D Chlorophyll fluoresence Hyperspectral High spatial or temporal resolution

PM_run_stable_xvid.avi

Real-time / hourly continuous measurements of canopy temperature using ArduCrop

Wireless Sensor Network (US$300 per sensor)

Diurnal patterns of canopy temperature as a surrogate for stomatal behaviour. Yanco MEF 2014 (GRDC)

Above (well above) the canopy measuring canopy temperature/leaf area

FLIR SC645 thermal camera

Calibrated < 0.05 deg C sensitivity,

2% accuracy, 640x480 pixels, 0.7 kg

Linking phenotypes to traits and genetic architecture: Canopy

temperature and water use

“Old way” h2<0.1 “New way” h2>0.6

Using in irrigated trials for photosynthetic screens, and drought trials for water use

Wireless data logger and radio transmitter for monitoring soil moisture

and soil temperature

Canopy temperature

$21 m

Depth (cm)

25

45

65

85

115

145

Network of soil and leaf sensors

Knowledge on the go

(Severini, Wasson, Rich)

Trait delivery

1. Cooler canopies

2. Greater early leaf area

Early vigour (leaf area) and water use efficiency

Soil

evaporation

rapid early growth slow early growth

Soil

evaporation Plant

transpiration

Plant

transpiration

Genetic complexity - an example with early vigour - Partitioning of water use

Fertility

treatment

LAI

(lai.days)

Yield

(t/ha)

Water use

(mm)

Evaporation

(mm)

Transpiration

(mm)

High

63N, 20P 3.1 5.6 366 173 193

Low

8N, 10P 1.4 2.8 363 259 104

(David Hall, DAFWA)

Esperance 2001, 380 mm in-crop rainfall

Traits for Greater Early Vigour

Embryo size

Leaf lamina thickness

Seedling vigour

High vigour

New physiological types Current cultivars

Low vigour

Global Survey for Early Vigour

Entry Mean leaf width

(mm)

Leaf area

(cm2)

Jing Hong

(China)

6.3 14.3

Kharchia

(India)

6.2 14.2

V743/Oligo

(Israel)

5.9 / 6.3 11.1 / 14.6

Glenlea/Roblin

(Canada)

5.7 / 5.8 12.0 / 12.2

CC-CIMMYT

(Mexico)

5.6 13.9

Janz

(Australia)

4.5 7.4

Where available, pedigrees indicate coancestry among lines is low

Trait

value

0

Cycle of Selection 1 2 3 4

Recurrent selection for genetic gain (accumulating

favourable additive genetic effects)

Existing

value

Target

value

Genetic covariances and variances Cov (a,e) = 2ae2

A + 2ä+ë2D + (2äe+2aë) D1 + äë D2

Var (S0 families) = 2A + 2

D

Var (S0:1 families) = 2A + 0.25 2

D + 1 D1 + 0.125 D2

Var (S1:2 families) = 1.5 2A + 0.125 2

D + 2.5 D1 + 0.563 D2

Var (S families) = 2 2A + 0 2

D + 4 D1 + D2

Where 2

A are 2D are the additive and dominance genetic variances, D1 is the

covariance of an additive effect of an allele with its dominance deviation and D2 is the variance of homozygous dominance effects

Genotypic variation and covariation for early vigour

Parameter h2

ra_LFA RSG_LFA

(%)

Mean leaf

width

0.84 ± 0.11** 0.57 ± 0.10** 92

Mean leaf

length

0.67 ± 0.16**

0.43 ± 0.09**

64

Number of

leaves

0.39 ± 0.11**

-0.37 ± 0.16**

-10

+ Based on F2:4 - F2:6 parent-offspring covariance

Development of high vigour, parental germplasm through recurrent selection

Culling from 6000+ S0:1 to replicated testing of S1:2 progeny-

testing

Me

an le

af w

idth

(m

m)

5

6

7

8

9

10

Cycle

C0

CParental C

1C

3 C4

C5

C6C

2

Relationship between cycle number and mean leaf width measured in four

environments: Sow 1 (○), Sow 2 (●), Sow 3 (■), and the reduced N Sow 4 (▲)

(Zhang et al. 2015a)

Em

bry

o w

idth

(m

m)

1.60

1.65

1.70

1.75

1.80

1.85

1.90

1.95

Recurrent selection

Commercial wheats

Cycle

C0

CParental C

1C

3C

4C

5C

6C2

y = 1.59 + 0.05 x (r2 = 0.96)

Correlated changes with selection for increased vigour –

Sp

ecific

le

af a

rea

(cm

2/g

)

340

360

380

400

420

440

460

Recurrent selection

Commercial wheats

Cycle

C0

CParental C

1C

3 C4

C5

C6C

2

y = 354 + 12.2 x (r2 = 0.90)

Tim

e to

se

ed

ling

em

erg

ence

(oC

d)

200

205

210

215

220

225

Recurrent selection

Commercial wheats

Cycle

C0

CParental C

1C

3C

4C

5C

6C2

Ra

te o

f le

af e

long

atio

n (

mm

/da

y)

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

Recurrent selection

Commercial wheats

Cycle

C0

CParental C

1C

3 C4

C5

C6C

2

(Zhang et al. 2015b)

Higher vigour germplasm with greater leaf area.

Developed originally to improve WUE and weed competitiveness

Commercial

variety

Early vigour

selection

Advanced vigour

selection

….above ground differences – also reflected below ground!

Higher vigour germplasm with greater leaf area

Developed originally to improve WUE and weed competitiveness

Greater root biomass and root length of advanced vigour selections

Genotypes

Janz

Westo

nia

Wya

lkatch

em

Vigor18

50-437-6

38-19

92-11

Ro

ot

bio

ma

ss (

g/p

lan

t)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Commercial cultivars

vigor18

Recurrent selections

Genotypes

Janz

Westo

nia

Wya

lkatch

em

Vigor18

50-437-6

38-19

92-11

Root

length

(m

/pla

nt)

0

10

20

30

40

50

60

70

Commercial cultivars

vigor18

Recurrent selections

Commercial

cultivar Advanced vigour

selection

Advanced vigour

selections

50 – 90% greater

root biomass

30 – 60% greater

root length

Increased nutrient uptake/use-efficiency of root vigour selections results in greater biomass

Shoot biomass (g)

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

N u

pta

ke

(m

g)

10

20

30

40

50

60

70

80

Leaf area (cm2)

50 100 150 200 250 300

N u

pta

ke

(m

g)

10

20

30

40

50

60

70

80

(Jairo Palta unpub. data)

Advanced root vigour lines show greater P

uptake under P-limited conditions:

➙ 20% greater PAE (P acquisition efficiency)

Commercial

cultivars

Advanced

vigour

selections

Early vigour

selections

Phosphorus Nitrogen

➙ 10 - 40 % shoot growth advantage

Advanced root vigour selections

EGA Burke

Xioayan 54 .Vig#1

Vig#2Vig#3

Vig#4

Gro

wth

in P

-lim

itin

g c

ond

itio

ns (

5P

,%100

P)

0

20

40

60

80

Increased early vigour increases weed competitiveness

0

5000

10000

15000

20000

25000

30000

35000

Gladius Scout WCD2-320605 WCD2-390403 WCD2-470201 Wyalkatchem

We

ed

Se

ed

s p

rod

uce

d/m

2

400 Plants/m2

200 Plants/m2

- Vigour + Vigour Vigour selections

Vig 1 Vig 2 Vig 3

Phenomobile Lite

LiDAR • Canopy height

• Fractional ground cover

• Biomass index (subject to

validation)

• Greenness vertical distribution

GreenSeeker • NDVI (canopy density and

greenness)

High-res RGB • Plant counts

• Visual assessments

LiDAR intensity for ground cover

RGB camera vs LiDAR

LiDAR intensity for ground cover

RGB camera vs LiDAR

RGB (soil + crop)

LiDAR (crop only)

LiDAR Intensity Height image

Preliminary validation of LiDAR: canopy biomass (wheat)

Trait h2

Biomass (field) 0.72

NDVI 0.64

LiDAR Index 0.88

Sampling date 9/9/2014

Green biomass distribution across multiple genotypes

(MEF GRDC)

Green biomass profile across multiple genotypes (MEF)

GRDC)

The massive and complex wheat genome

Wheat

Human

Arabidopsis

Rice

Cotton

Barley

Huge

Polyploid

Repetitive

Geographic, phenotypic and genetic diversity combined in a

single genetic (mapping) population

Enhancing collaboration | ELP Team 3 | Page ‹#›

Genetic resolution

Bi-parental 4-way MAGIC 8-way MAGIC

2 cM 6 cM 9 cM

M3

M4

M1

M2

M3

M4

M1

M2 M2

M1

M3

M4

1807 bins 817 bins

Alternative dwarfing genes allow selection of reduced

height, long coleoptile progeny

Janz

HM14aS

134mm

81mm

1 2 3 4 5 6 7

Genetic dissection of coleoptile length† Integration of multi-population, multi-environment mapping

A B D

Group

(C/H = Cranbrook/Halberd, MAGIC = Baxter/Chara/Westonia/Yitpi)

Cranbrook/Halberd MAGIC (4-way)

† QTL common at two or soil temperatures

1 2 3 4 5 6 7

Genetic dissection of coleoptile length† Integration of multi-population, multi-environment mapping

A B D

Group

(C/H = Cranbrook/Halberd, MAGIC = Baxter/Chara/Westonia/Yitpi)

Cranbrook/Halberd MAGIC (4-way)

† QTL common at two or soil temperatures

Yitpi +13mm Baxter +5mm

Baxter +6mm Westonia +6mm

Chara +7mm

Yitpi +6mm

Westonia +7mm

1 2 3 4 5 6 7

Genetic dissection of early growth† Integration of multi-population, multi-environment mapping

A B D

Group

(4-way MAGIC = Baxter/Chara/Westonia/Yitpi)

Kukri/Janz MAGIC (4-way)

† QTL at two air temperatures

Yitpi +23mm Chara

+11mm Westonia

+15mm

Chara

+17mm

Chara

+13mm

Yitpi

+16mm

Yitpi

+15mm

Yitpi

+11mm

Westonia

+13mm Chara

+8mm

Character

Coleoptile

length

12°C

Coleoptile

length

20°C

Shoot

length

12°C

Shoot

length

20°C

Coleoptile length 12°C - 0.986 0.849 0.947

Coleoptile length 20°C 0.084 - 0.772 0.899

Shoot length 12°C 0.069 0.138 - 0.973

Shoot length 20°C 0.017 0.054 0.095 -

Genetic correlations (shaded) and residual correlations (lower diagonal) for coleoptile and shoot lengths measured at two soil temperatures on progeny in the MAGIC four-way population

Leaf and coleoptile genetic effects are strongly correlated

Coleoptile length allelic effect (mm)

-15 -10 -5 0 5 10 15

Sho

ot le

ng

th a

llelic

effe

ct (m

m)

-60

-40

-20

0

20

40

60

Baxter

Chara

Westonia

Yitpi+Rht-B1a

+Rht-B1a

+Rht-D1a+Rht-D1a

+Rht-D1b

+Rht-D1b

+Rht-B1b

+Rht-B1b

How do we overcome the constraint on seedling growth with the green revolution dwarfing genes?

240

260

280

300

320

0 1 2 3 4

Number of GA-insensitive Rht alleles

Cell

len

gth

(µ

m)

Tall Doubled-

dwarf

(Keyes et al. 1989)

(r2 = 0.99)

Trait x trait: Dwarfing gene (Rht) effects on coleoptile length

-5

-4

-3

-2

-1

0

1

Effect

on c

ole

optile

length

(cm

)

Rh

t4

Rh

t5

Rh

t7

Rh

t8

Rh

t9

Rh

t12

Rh

t13

Rh

t14

Rh

t1

Rh

t2

Rh

t17

GA-responsive

GA-insensitive

ns

ns

ns

***

ns

*

ns

ns

ns

*** ***

Tall Rht

Rh

t18

ns

Ludhiana, India at 15cm sowing depth

Indore, India at 15cm sowing depth

Rht5

Rht13

Critical need to better link whole crop physiology for delivery to commercial breeding programs:

1. Are traits relevant for the challenge being addressed?

2. How do we prioritise one trait over another (trait value - Managed Environments?)

3. Are there cheap, reliable, population-friendly high-throughput phenotyping methods?

4. Can phenotype be replaced with breeder-useful, linked markers in selection?

5. Can we deliver adapted germplasm containing key traits for crossing and validation?

6. In moving from traits singly, are there crop-gene models capable of assessing trait x trait

combinations?

In summary -

Thank you!

Carbon isotope discrimination C

arb

on

iso

top

e d

iscrim

ina

tio

n (

%)

16

17

18

19

20

21

22

23


Narrabri Yanco Merredin



110 mm

20 kg/ha/mm

French & Shultz (1984)

0

1

2

3

4

5

0 100 200 300 400 500

Water use (mm)

Gra

in y

ield

(t/

ha

)But first……

110 mm

20 kg/ha/mm

French & Shultz (1984)

0

1

2

3

4

5

0 100 200 300 400 500

Water use (mm)

Gra

in y

ield

(t/

ha

)

Genotype response is specific to each MEF (no two sites in any

given year are the same!)

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

-0.3

-0.2

-0.1

0.0

0.1

0.2

2010

Comp.1

Com

p.2

5447P

6184N

6184P

6266N

6460N

6460P

6768N

6768P

7276N

7276PF

7566N

7566PUNI

7750N

7750PF

B+B++

Cranbroo

K-K+

MagSilB7

MagSil-W

MSS13

QG11

QG24

QG49

QG53

QG56

QG58

QG6

QG62

QG76

QG8

QH10

QH12

QH13

QH15

QH19

QH22

QH25

QH32

QH35

QH5

QH6

QS1

QS14

QS29

QS41

QS42

QS44

QS45

QS6

QS8

SB002

SB012

SB017

SB021

SB023

SB025

SB026

SB028

SB033

SB035

SB037

SB049

SB051

SB053

SB057

SB064

SB070

SB071SB073

SB091

SB095

SB101

SB118

SB127

SB130

SB134

SB138

SB139

SB157

SB162

SB163

SB165

SB171

SB175

SB177

SB178

SB179

SB183

SB189

SB190

SB193

STin16

STin46

Sundor

Syn549

Syn604

wj111

wj113wj115

wj119

wj145wj171

wj22

wj23wj25

wj30

wj44

wj84

wj87

Y01_1

Y01_10

Y01_11

Y01_2

Y01_3

Y01_4

Y01_5

Y01_6

Y01_7

Y01_8

Y01_9

-15 -10 -5 0 5 10 15

-10

-50

510

M10Ir

M10Rf

N10IrN10Rf

Y10IrY10Rf

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

-0.1

0.0

0.1

0.2

0.3

2012

Comp.1

Com

p.2

13-4-4NT

13-4-5NT

14-4-4NT

14-4NT

16-6-3NT

16-6-4NT

20-1-2NT

20-1-5NT

22-2-3NT

22-2-4NT

23-5-5-N 23-5-7NT

2-4-3NT

2-4-5NT

27-1-3NT

27-1-5NT

3-1-8NT

34-1-6NT

4-3-6NT

4-4-1NT

4-4-6NT

5-3-4NT

5-3-8NT 5447N

5447P

6072N

6072P

6266N

6266P

6460N

6460P

6581N

7164N

7276PF

7566N

7566PUNI

9-5-3NT

9-5-6NT

AUS33681

Axe

B-

B+

B++

Drysdale

FA1-

FA1+

FA3-

FA3+

FA8-

FA8+

Hartog

JA6-

JA6+

Janz

JB1+

Mace

QG24

QG30

QG49

QG54

QG56

QG58

QG62

QG76

QG8

QH12

QH13

QH19

QH22

QH25

QH32

QH5

QH7

QS1

QS29

QS41

QS45

QS8

RAC875

SB002

SB017

SB025

SB026SB027

SB035

SB037

SB049

SB051 SB053SB071

SB091

SB095

SB101SB118

SB127

SB134

SB162

SB163

SB165SB171

SB178

SB179

SB189

SB2+

SB6+

Scout

STin46

Sundor

Suntop

Syn549

Syn604

W11B

W15A

W16A

W19A

W1A

W2A

W3A

W4A

W6A

W7A

WA1-WA1+

Weebill1

Westonia

wj111

wj113

wj115

wj119wj171

wj22

wj25

wj30

wj44

wj84

Wyalkatc

Yitpi

-20 -10 0 10 20

-10

-50

510

15

20

M12Ir

M12Rf

N12IrN12Rf

Y12Ir

Y12Rf

Phenomobile

• 3x LiDARs (Canopy Structure)

• 4x RGB cameras (Stereo reconstruction)

• 1x Thermal IR camera (Canopy temperature)

• 1x Hyperspectral line scanner (Canopy biochemistry)

• 1x Full range spectrometer (Canopy biochemistry)

• Removable light banks

2015. Greg Rebetzke. Integration of new tools for improving genetic gain of grain yield in water...

Science

Transcript of 2015. Greg Rebetzke. Integration of new tools for improving genetic gain of grain yield in water...