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2015. Greg Rebetzke. Integration of new tools for improving genetic gain of grain yield in water limited environments.
- 1. 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
- 2. The connect and disconnect with trait delivery
- 3. 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
- 4. 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!
- 6. Rainfall amount and timing
- 7. 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
- 8. Need for repeatable phenotyping - controlled ‘managed’ environments (Managed Environment Facilities – ‘MEF’) In Australia - three sites with two-three irrigation regimes
- 9. Which traits where? Quality phenotyping – controlled field environments (Managed Environment Facilities – ‘MEF’)
- 10. Maximising water uptake by removing constraints to root (and shoot) growth – mapping of soil conductivity (Rick Graham NSWDPI)
- 11. 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)
- 12. Random to selected lines – deriving greater benefit from populations in selection of tails extreme for target trait High-selected groupLow-selected group
- 13. ‘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
- 14. Box-plot of all entry means/variance for grain yield at each sampled MEF (M = Merredin, N = Narrabri, Y = Yanco; Ir = irrigated, Rf = rainfed)
- 15. 2014 contrasted genotype response at Narrabri with response at Yanco and Merredin (largely uncorrelated!) -0.3 -0.2 -0.1 0.0 0.1 -0.3-0.2-0.10.00.1 Comp.1 Comp.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- LRBC136 LRBC156 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 QA69 QA72 QA87 QA95 QA97 QC14 QC15 QC18 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 QH56 QH71-3r QH71-4 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 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 W111402 W11B W120216 W130102 W140910 W15A W170310 W1A W200118 W210203 W220416 W260801 W280308 W3A W4A W8A WA11+ WA11- WA3+ WA3- WA8- WB1+ WB1- Weebill Westonia wj111 wj113wj115 wj119 wj171 wj22wj23 wj25 wj30 wj44wj84 Wyalkatchem Yitpi -30 -20 -10 0 10 -30-20-10010 YIrr YRf NIrrNRf MIr MRf -0.2 -0.1 0.0 0.1 0.2 -0.2-0.10.00.10.2 Comp.1 Comp.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 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_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 LRBC285 LRBC311 LRBC327 LRBC386 LRBC388 LRBC392 LRBC409 LRBC466 LRBC62 Mace QA144 QA175 QA177 QA178 QA179 QA183 QA193 QA223 QA227 QA236QA268 QA321 QA35 QA69 QA72 QA87 QA95 QA97 QC14 QC15 QC18 QC19 QC2 QC20 QC25 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 QH71-4 QH71-9 QH74-10 QH74-2 QW111 QW12 QW13 QW132 QW134QW135 QW136 QW144 QW146 QW148 QW155 QW160 QW170 QW183 QW188 QW190 QW23QW51 QW59 QW67 QW87 QW88 RAC875 SB002 SB012 SB017 SB021SB023 SB025 SB026SB027 SB035 SB037 SB049 SB051 SB053 SB070 SB071 SB091SB095 SB1+SB1-SB101 SB118SB127 SB130 SB134 SB162 SB163 SB165 SB171 SB179 SB2+ SB2- SB5-(B) Sun595B Sunstate+ Sunstate- W010111 W030311 W040217W050114 W050204 W050306 W080205 W100109 W100209 W100402 W100504 W110511 W111402 W11BW120216 W130102 W140910W15A W170310 W1A W200118 W210203 W220416 W260801 W280308 W3A 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 -5 0 5 10 -10-50510 YIrr YRf NIrr NRf MIr MRf Grain yield Harvest index -0.2 -0.1 0.0 0.1 0.2 -0.2-0.10.00.10.2 Comp.1 Comp.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_R187DH_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 LRBC285 LRBC311 LRBC327 LRBC386 LRBC388 LRBC392 LRBC409LRBC466 LRBC62 Mace QA144QA175 QA177 QA178 QA179 QA183 QA193 QA223 QA227 QA236 QA268 QA321 QA35 QA69 QA72 QA87 QA95 QA97 QC14 QC15 QC18 QC19 QC2 QC20QC25 QC27 QC28 QC29 QC30 QC7 QH181-7 QH194-3 QH210-5 QH22 QH221-10 QH239-4QH25 QH252-2R QH32 QH5 QH52-2 QH56 QH71-3r QH71-4 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 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 W111402 W11B W120216 W130102 W140910 W15A W170310 W1A W200118 W210203 W220416 W260801 W280308 W3A 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 -5 0 5 10 15 -10-5051015 YIrr YRf NIrr NRf MIrMRf Grain number -0.1 0.0 0.1 0.2 -0.10.00.10.2 Comp.1 Comp.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 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 QC15 QC18 QC19 QC2 QC20 QC25 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 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 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 W111402 W11B W120216 W130102 W140910 W15A W170310 W1A W200118 W210203 W220416 W260801 W280308 W3A 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 -1001020 YIrr YRfNIrrNRf MIr MRf Grain weight
- 16. 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
- 17. Early ground cover (‘vigour’) Earlygroundcoverassessment(%) 20 30 40 50 60 70 80 90 Drysdale - 41 Mace - 40 Yitpi - 47 Drysdale - 50 Mace - 39 Yitpi - 48 YancoNarrabri - Vigour + Vigour
- 18. Canopy temperatureCanopytemperature(Z55)(oC) 14 16 18 20 22 24 26 Drysdale - 23.03 Mace - 23.65 Yitpi - 22.09 Drysdale - 18.74 Mace - 18.61 Yitpi - 18.65 YancoNarrabri
- 19. Water-soluble carbohydrate concentrationWSCconcentration(mg/g) 0 50 100 150 200 250 300 350 Drysdale - 135 Mace - 109 Yitpi - 115 Drysdale - 227 Mace - 251 Yitpi - 214 Yanco_Irr Merredin_Irr
- 20. Grain yield (multivariate – all sites and irrigation regimes) -0.10 -0.05 0.00 0.05 -0.10-0.050.000.05 Comp.1 Comp.2 1 2 3 4 56 7 8 9 10 11 12 1314 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 3637 38 39 40 41 4243 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 6364 65 67 6869 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 94 95 9697 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113114115 116 117 118 119120 121 122 123 124 125 126 127 128 129130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147148149 150 151 152153 154 155156 157 158 159 160 161 162163 164 165 166 167 168169 170 171 172 173174175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 200 201 202 203 204 205 206 207208209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231232 234 235 236 237238 239 240 241 242 243244 245 246 247 248 249 250 251 252 253 254 255 256 257 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- 21. 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 C anopy tem perature C arbon iso.discrim ination Stem carbohydratesPresence ofawns Leafwaxiness Leaf-rolling Early vigourR educed tillering Trait(yield)value(%) -10 0 10 20 Merredin Narrabri Yanco
- 22. 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 C anopy tem perature C arbon iso.discrim ination Stem carbohydratesPresence ofawns Leafwaxiness Leaf-rolling Early vigourR educed tillering Trait(yield)value(%) -10 0 10 20 Merredin Narrabri Yanco
- 23. Trait delivery 1. Cooler canopies 2. Greater early leaf area
- 24. Dynamic phenotyping Pt = G×Et (×M) with t: time Time constant Seconds Hours Days Season Canopy Temperature Canopy Height Crop Yield
- 25. Phenomics from the pot to the paddock Thermal Lidar /Colour 3-D Chlorophyll fluoresence Hyperspectral High spatial or temporal resolution
- 26. Real-time / hourly continuous measurements of canopy temperature using ArduCrop Wireless Sensor Network (US$300 per sensor)
- 27. Diurnal patterns of canopy temperature as a surrogate for stomatal behaviour. Yanco MEF 2014 (GRDC)
- 28. 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
- 29. 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
- 30. Wireless data logger and radio transmitter for monitoring soil moisture and soil temperature
- 31. 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)
- 32. Trait delivery 1. Cooler canopies 2. Greater early leaf area
- 33. Early vigour (leaf area) and water use efficiency Soil evaporation rapid early growth slow early growth Soil evaporation Plant transpiration Plant transpiration
- 34. 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
- 35. Traits for Greater Early Vigour Embryo size Leaf lamina thickness Seedling vigour High vigour New physiological typesCurrent cultivars Low vigour
- 36. 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
- 37. Trait value 0 Cycle of Selection 1 2 3 4 Recurrent selection for genetic gain (accumulating favourable additive genetic effects) Existing value Target value
- 38. Genetic covariances and variances Cov (a,e) = 2ae2 A + 2ä+ë2 D + (2äe+2aë) D1 + äë D2 Var (S0 families) = 2 A + 2 D Var (S0:1 families) = 2 A + 0.25 2 D + 1 D1 + 0.125 D2 Var (S1:2 families) = 1.5 2 A + 0.125 2 D + 2.5 D1 + 0.563 D2 Var (S families) = 2 2 A + 0 2 D + 4 D1 + D2 Where 2 A are 2 D 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
- 39. 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
- 40. Development of high vigour, parental germplasm through recurrent selection
- 41. Culling from 6000+ S0:1 to replicated testing of S1:2 progeny- testing
- 42. Meanleafwidth(mm) 5 6 7 8 9 10 Cycle C0 CParental C1 C3 C4 C5 C6C2 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)
- 43. Embryowidth(mm) 1.60 1.65 1.70 1.75 1.80 1.85 1.90 1.95 Recurrent selection Commercial wheats Cycle C0 CParental C1 C3 C4 C5 C6C2 y = 1.59 + 0.05 x (r2 = 0.96) Correlated changes with selection for increased vigour – Specificleafarea(cm 2 /g) 340 360 380 400 420 440 460 Recurrent selection Commercial wheats Cycle C0 CParental C1 C3 C4 C5 C6C2 y = 354 + 12.2 x (r2 = 0.90) Timetoseedlingemergence( o Cd) 200 205 210 215 220 225 Recurrent selection Commercial wheats Cycle C0 CParental C1 C3 C4 C5 C6C2 Rateofleafelongation(mm/day) 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 C1 C3 C4 C5 C6C2 (Zhang et al. 2015b)
- 44. 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
- 45. Greater root biomass and root length of advanced vigour selections Genotypes Janz W estonia W yalkatchem Vigor18 50-4 37-6 38-19 92-11 Rootbiomass(g/plant) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Commercial cultivars vigor18 Recurrent selections Genotypes Janz W estonia W yalkatchem Vigor18 50-4 37-6 38-19 92-11 Rootlength(m/plant) 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
- 46. 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 Nuptake(mg) 10 20 30 40 50 60 70 80 Leaf area (cm2) 50 100 150 200 250 300 Nuptake(mg) 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#2 Vig#3 Vig#4 GrowthinP-limitingconditions(5P,%100P) 0 20 40 60 80
- 47. Increased early vigour increases weed competitiveness 0 5000 10000 15000 20000 25000 30000 35000 Gladius Scout WCD2-320605WCD2-390403WCD2-470201Wyalkatchem WeedSeedsproduced/m2 400 Plants/m2 200 Plants/m2 - Vigour + Vigour Vigour selections Vig 1 Vig 2 Vig 3
- 48. 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
- 49. LiDAR intensity for ground cover RGB camera vs LiDAR
- 50. LiDAR intensity for ground cover RGB camera vs LiDAR RGB (soil + crop) LiDAR (crop only)
- 51. LiDAR Intensity Height image
- 52. 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
- 53. Green biomass distribution across multiple genotypes
- 54. Green biomass profile across multiple genotypes (MEF)
- 55. The massive and complex wheat genome Wheat Human Arabidopsis Rice Cotton Barley Huge Polyploid Repetitive
- 56. Geographic, phenotypic and genetic diversity combined in a single genetic (mapping) population
- 57. Enhancing collaboration | ELP Team 3 | Page ‹#›
- 58. 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 bins817 bins
- 59. Alternative dwarfing genes allow selection of reduced height, long coleoptile progeny Janz HM14aS 134mm 81mm
- 60. 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
- 61. 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
- 62. 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 +11mmWestonia +15mm Chara +17mm Chara +13mm Yitpi +16mm Yitpi +15mm Yitpi +11mm Westonia +13mm Chara +8mm
- 63. 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
- 64. Leaf and coleoptile genetic effects are strongly correlated Coleoptile length allelic effect (mm) -15 -10 -5 0 5 10 15 Shootlengthalleliceffect(mm) -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
- 65. 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 Celllength(µm) Tall Doubled- dwarf (Keyes et al. 1989) (r2 = 0.99)
- 66. Trait x trait: Dwarfing gene (Rht) effects on coleoptile length -5 -4 -3 -2 -1 0 1 Effectoncoleoptilelength(cm) Rht4 Rht5 Rht7 Rht8 Rht9 Rht12 Rht13 Rht14 Rht1 Rht2 Rht17 GA-responsive GA-insensitive ns ns ns *** ns * ns ns ns *** *** Tall Rht Rht18 ns
- 67. Ludhiana, India at 15cm sowing depth
- 68. Indore, India at 15cm sowing depth Rht5 Rht13
- 69. 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 -
- 70. Thank you!
- 71. Carbon isotope discriminationCarbonisotopediscrimination(%) 16 17 18 19 20 21 22 23 Drysdale - 19.71 Mace - 20.84 Yitpi - 20.07 Narrabri Yanco Merredin Drysdale - 18.31 Mace - 18.91 Yitpi - 18.43 Drysdale - 19.29 Mace - 21.02 Yitpi - 20.18
- 72. 110 mm 20 kg/ha/mm French & Shultz (1984) 0 1 2 3 4 5 0 100 200 300 400 500 Water use (mm) Grainyield(t/ha) But first……
- 73. 110 mm 20 kg/ha/mm French & Shultz (1984) 0 1 2 3 4 5 0 100 200 300 400 500 Water use (mm) Grainyield(t/ha)
- 74. 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 -0.3-0.2-0.10.00.10.2 2010 Comp.1 Comp.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 wj145 wj171 wj22 wj23 wj25 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 -10-50510 M10Ir M10Rf N10IrN10Rf Y10Ir Y10Rf -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 -0.10.00.10.20.3 2012 Comp.1 Comp.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 SB101 SB118 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 -10-505101520 M12Ir M12Rf N12Ir N12Rf Y12Ir Y12Rf
- 75. 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