Greg Rebetzke, CSIRO Agriculture and Food, Canberra ACT 2601

Transcription

1 Greg Rebetzke, CSIRO Agriculture and Food, Canberra ACT 2601

2 Background - Crop type, weed seed management Lambda (ryegrass) Pasture Oaten Hay Chemical Fallow Legume Barley Canola Wheat n= n=15 n=10 n=2 n=22 n=26 n=12 Wheat appears to be the weak link in the rotation Increasing herbicide resistance in multiple weeds No new actives in the pipeline

3 Phase 3, yr 1 (2013): Eurongilly - Nitrogen & Weeds Ryegrass control costs, ryegrass panicle numbers (m 2 ) and DM (t/ha), grain yield in Gross Margins Ryegrass Control Costs ($/ha) Ryegrass panicle No (m2) Ryegrass DM (t/ha) Grain Yield (t/ha) Wheat - low $ Lupin - Grain $ Field Pea - Brown manure $ NA TT Canola - low $ Wheat - high $ RR Canola - high $ RT Canola - high $ Fallow $ NA LOW INPUT WHEAT HIGH INPUT WHEAT (Source: Swan et al.)

4 Background - The cost of weeds to Grains Industry Total yield revenue loss $745 million Total expenditure $2,573 million Total cost of weeds >$3.4 billion The IWM package - non-herbicide, weed control tools Seed rate/sowing density East-West sowing Burning Pasture, 2-5 years Windrows (dry or burn) Bailing the crop Brown crop Row spacing Time of sowing Rotation (rotation rotation) Winter cleaning Harvest weed seeds into rows Crush weed seeds at harvest Crop competitiveness

5 Ryegrass seedbank(seeds/m 2 ) Long-term impact of competitive ability weed models Year/crop S1 S2 S3 S4 S5 S1 = herbicides + normal SR S2 = herbicides + high SR S3 = as per S2 + competitive cv. (+30% CA) S4 = as per S2 + competitive cv. (+50% CA) S5 = as per S4 + windrow burn Benefits of integrating competitive wheat cultivars with other IWM practices (in this case windrow burn) were also reflected in reduced soil seed-bank (Gurjeet Gill unpubl.)

6 Ryegrass seedbank(seeds/m 2 ) Long-term impact of competitive ability weed models Developing herbicide resistance S1 S2 S3 S4 S5 S1 = herbicides + normal SR S2 = herbicides + high SR S3 = as per S2 + competitive cv. (+30% CA) S4 = as per S2 + competitive cv. (+50% CA) S5 = as per S4 + windrow burn Year/crop Benefits of integrating competitive wheat cultivars with other IWM practices (in this case windrow burn) were also reflected in reduced soil seed-bank (Gurjeet Gill unpubl.)

7 Crop competitiveness Learnings from varieties US (GP - Challaiah et al. 1986) UK (Blackshaw 1994) Australia (Lemerle et al. 1996) Canada (Huel and Hucl 1996) Argentina (Acciaresi et al. 2001) US (PNW - Murphy et al. 2007)

8 Crop competitiveness Learnings from varieties US (GP - Challaiah et al. 1986) UK (Blackshaw 1994) Australia (Lemerle et al. 1996) Canada (Huel and Hucl 1996) Argentina (Acciaresi et al. 2001) US (PNW - Murphy et al. 2007) Limited opportunity in commercial varieties

9 Weed seed production (Seeds/m2) What do we know - Competitiveness differences between winter cereals Barley (7) Oats (5) Triticale (3) Wheat (3) Durum (1) Source: Davidson & Gill (unpublished)

10 Weed seed production (Seeds/m2) What do we know - Competitiveness differences between winter cereals Barley (7) Oats (5) Triticale (3) Wheat (3) Durum (1) Greater early vigour Source: Davidson & Gill (unpublished)

11 Learnings from weed competitive, genetic studies Reduced yield loss/ryegrass biomass - Wider seedling leaves - Greater biomass at stem elongation - Greater tiller number at stem elongation - Greater leaf area index at stem elongation - Mature plant height (.but reduced yield) W Frame (Source: Coleman, Gill & Rebetzke 2001)

12 Early vigour (leaf area) and water-use efficiency Soil evaporation Plant transpiration Soil evaporation Plant transpiration rapid early growth slow early growth

13 Learning from model species drivers of early vigour Wheat Barley

14 How do we genetically improve physiologically complex traits? Drivers of greater early vigour Embryo size Current cultivars New physiological types Leaf lamina thickness Seedling vigour Low vigour High vigour

15 New genetic sources of early vigour

16 New genetic sources of early vigour

17 How do you rapidly bring together many different genes? Recurrent selection - learning from maize AAbbccdd x aabbccdd aabbccdd x aabbccdd AABBccdd x aabbccdd AABBCCDD (positive genes at all four loci)

18 Recurrent selection for genetic gain (selecting and accumulating favourable genetic effects) Target value Trait value Existing value Intercross selections after progeny-testing Cycle of Selection

19 Selection for early vigour is simple at the level of plant

20 Selection for early vigour is simple at the level of plant

21 Phenotyping early growth across six recurrent selection cycles Sowing in tubes nitrogen treatment (above)

22 S1:2 recurrent selection in accumulating independent alleles for early vigour Sow 1 ( ), Sow 2 ( ), Sow 3 ( ), reduced nitrogen Sow 4 ( ) 10 9 Cultivar (Janz) Original parent Cycle 5 Cycle 5 Barley (Beecher) Mean leaf width (mm) 8 7 Comm. wheats 6 5 C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 Cycle

23 S1:2 recurrent selection in accumulating independent alleles for early vigour Sow 1 ( ), Sow 2 ( ), Sow 3 ( ), reduced nitrogen Sow 4 ( ) % vigour Cultivar (Janz) Original parent Cycle 5 Cycle 5 Barley (Beecher) Mean leaf width (mm) 8 7 Comm. wheats 6 5 Commercial variety Cycle 0 Cycle 6 C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 Cycle

24 Relationship between cycle and leaf width for leaves 1 ( ), 2 ( ) and 3 ( ) averaged across four environments Leaf width (mm) C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 Cycle

25 Relationship between cycle and leaf width for leaves 1 ( ), 2 ( ) and 3 ( ) averaged across four environments Leaf width (mm) Leaves get progressively larger with development 4 C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 Cycle

26 Relationship between cycle and leaf width for leaves 1 ( ), 2 ( ) and 3 ( ) averaged across four environments Leaf width (mm) Leaves get progressively larger with development 4 C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 Cycle

27 Correlated changes with selection for increased leaf width (vigour) Recurrent selection Commercial wheats Recurrent selection Commercial wheats Embryo width (mm) Specific leaf area (cm 2 /g) y = x (r 2 = 0.96) 360 y = x (r 2 = 0.90) C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 C Parental C 0 C 1 C 2 C 3 C 4 C 5 C 6 Cycle Cycle Janz Vigour 18 Cycle 5 selections

28 Early vigour in Canberra translates to vigour in the field Vigour in pots Translates to vigour in the field 28

29 DM (% of Yitpi) Greater early vigour in the first released breeding lines Line DM (% of Yitpi) NDVI at 56 DAS Wyalkatchem Yitpi WCD WCD WCD WCD WCD WCD R² = NDVI NDVI is a useful tool for selecting wheat lines for early vigour

30 Early vs. later vigour (large flag leaves) Mace W470201

31 Competition in the field (Western Australia) Wyalkatchem + ryegrass W ryegrass

32 Competition in the field (South Australia) Espada + ryegrass W ryegrass 32

33 Ryegrass (spikes/m 2 ) Competitive cultivars can provide improved ryegrass suppression

34 Ryegrass (spikes/m 2 ) Competitive cultivars can provide improved ryegrass suppression Around 50% reduction in ryegrass seed set

35 Weed seeds/m Plants/m2 200 Plants/m Gladius Scout WCD WCD WCD Wyalkatchem

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37 Competitive-wheat breeding lines Selected in collaboration with commercial breeding companies rows Adapted commercial backgrounds Scout EGA Gregory Magenta Espada Estoc Crusader Mace Cosmick Reduced height donor rht13 rht18 High vigour donor C6-1 Donor W W & 2019 Elite cultivars Scepter Suntop Trojan Beckom Coolah Cutlass Flanker Kittyhawk Lancer 7000 advanced breeding lines

38 Next generation of competitive germplasm Donor plant C6-2 Adapted parent cv. EGA Gregory Offspring, sister lines (C6-1/G18//G18)

39 From breeding nursery to breeders (2017)

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41 Screening and the challenge of natural weed populations - Seed dormancy - Variable seedling density - Variable seedling vigour - Dealing with weed seed W Frame

42 Consistency of competitive effects across different weed species and weed mimics Mustard seed per m Oat seed per m r 2 = 0.82 ** r 2 = 0.78 ** Ryegrass seed per m 2 Ryegrass seed per m 2 42

43 Genotype Species Scout + canola Scout - canola W canola W canola

44 Grain sorting from wheat-oat competition plots Separating oat from wheat - laser-based grain sorter (up to 30 plots/hour)

45 Aerial IR (infrared) thermography Wild type Mutant High res thermal camera Capture up to 25 images / second One pass of the field ~ 3 sec Virtually simultaneous measurements for large trials Paired plots +/- weeds Weed suppression linked to common canopy temperature Rapid, cheap and reliable

46 Phenomobile Lite Lightweight platform for field phenotyping Key features: Portable/Modular Cost-effective Easy and versatile operation 1 person ~ 1200 plots/hour LiDAR Canopy height Fractional ground cover Biomass index Greenness vertical distribution Weed differentiation

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48 The Secretome and allelochemicals 100s compounds Primary and secondary metabolites Few characterised Under genetic regulation 5 to 20% photosynthate M.Wat t 48

49 (Weston and Watt 2013) TaALMT1 Sorghum root Root hair WT Root tip 4 9 Sorgoleone Weed toxin m tyrosine Weed toxin

50 Modifying root architecture to increase belowground competitiveness 0.0 AUS33687 AUS33435 AUS Soil depth (m)

51 Modifying root architecture to increase belowground competitiveness 0.0 AUS33687 AUS33435 AUS Soil depth (m)

52 Take-home messages Key traits identified Early vigour and leaf width genetically understood for breeding Mace W New parental germplasm/new phenotyping methods Advanced breeding lines for delivery Key traits backcrossed and into commercial cultivars Delivery time optimised Close engagement with breeding companies

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