Coping with drought in crop improvement -- a global perspective -- J-M Ribaut


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Keynote address at the InterDroughtIV Conference (2-6 Sep 2013) delivered on 2nd September 2013 by Jean-Marcel Ribaut, GCP Director, in Perth, Australia

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Coping with drought in crop improvement -- a global perspective -- J-M Ribaut

  1. 1. Coping with drought in crop improvement – a global perspective Jean-Marcel Ribaut Inter-Drought-IV, September 2-6, 2013, Perth, Australia
  2. 2. Climate change and impact on crop productivity
  3. 3. Global Climate Change …driving up the amount of water in the atmosphere… l  The world is warming… So the expectation is that future climate will be on average both warmer and wetter graph-that-should-be-_b_808747.html Willett K.M., Jones P.D., Thorne P.W., Gillett N.P. 2010. Environ. Res. Lett. 5 025210: 1-8.
  4. 4. Net impact of climate trends for 1980–2008 on crop yields Both temperature and precipitation affect crop productivity (median estimate, 5% to 95% confidence interval, bootstrap 500 replicates) Lobell et al. 2011. Science 333: 616-620,
  5. 5. The effect of higher temperature is magnified by drought Lobell D.B. et al. 2011. Nature Climate Change 1: 42-45 l  More than 20,000 maize trials, (80% WW, 20% WS), 1999-2007 l  Maize yields in Africa may gain from warming at relatively cool sites l  Sensitivity to heat is clearly exacerbated in drought conditions
  6. 6. Changes in rainfall seasonality (1930-2002) Crop seasonality is affected by both the intensity and the distribution of the rains over time and both are affected by climate change….. Feng et al. 2013. Nature Climate Change doi:10.1038/nclimate1907. Mean annual rainfall Seasonality index Changes in the seasonality index per year
  7. 7. What is drought?
  8. 8. Unpredictable l Can happen, or not happen l When it does happen, can be mild, intermediate or severe l Can happen at different developmental stages of the plant l Stress intensity is affected by soil composition and weather conditions l Stress intensity is affected by agriculture practices Moving target l Many different kinds of drought stress l As many ideal phenotypes as there are kinds of drought l Screening for drought tolerance under rain-fed conditions is always an unreplicable experiment Drought: A very complex, capricious and moody customer (1)
  9. 9. Difficult to phenotype l Proper drought trial management is challenging l Confounding effects of drought escape l GxE is exacerbated in drought conditions l Yield is a low heritability trait l A must to include secondary traits l Accurate trait measurement is required Genetically very complex l Gene effects can act in opposite directions depending on the nature of the stress and/or the target environment l Some gene interactions are highly dependent on the pattern of rainfall and other environmental conditions l Yield under drought conditions is one of the most, if not the most, polygenic trait Drought: A very complex, capricious and moody customer (2)
  10. 10. Breeding for drought tolerance
  11. 11. Nature’s way l To produce at least one seed, so that the whole life cycle is completed l Activate adaptive mechanisms as soon as stress occurs l Tolerance/survival generally based on a few mechanisms Breeder’s way l To produce as many seeds as possible l For the crop not to sense the stress too early l To pyramid multiple tolerance mechanisms So to breed for DT is not only to produce more (the situation under optimal conditions), but also to prevent the plant from producing less Breeding for drought tolerance is the opposite of Nature’s approach
  12. 12. Overall objective: l To stack favorable alleles for DT in elite germplasm Where to find these alleles: l Identifying them in breeding germplasm l  Genetic dissection of yield components and secondary traits l  The “omics” approach l Bringing new alleles l  Accessing the secondary genepool (landraces, CWRs) for adaptive alleles l “Creating” new alleles l  GM approach l  Mutagenesis Maintaining crop production in a warmer, drought-prone climate
  13. 13. Grain yield QTLs for drought tolerance
  14. 14. Wheat QTL for GY under drought: qYDH.3BL QTL identified in 3 Australian wheat populations cultivar Australia n region Drought tolerance Kukri south sensitive Excalibur south tolerant RAC875 south tolerant Gladius south tolerant Drysdale north tolerant Excalibur/Kukri   RAC875/Kukri   Gladius/Drysdale   barc77 cfb3200 gwm1564 gpw4143 wPt-0021 gwm1266 cfs6009 cfb43 wPt-8021 cfb539 gwm114 3.0 0.6 3.3 8.8 0.6 1.2 1.3 8.8 EK_DH cfb3200 barc77 gpw7108 wmm1966 cfp6016 cfb503 wmm1420 wPt-4401 wmm517 wmm480 wmm454 wmm274 wmm408 cfb528 cfb560 cfb515 gwm1266 wmm1758 gpw3233 cfs6009 cfb43 cfp6018 wmm448 wPt-9368 cfp6009 cfp6008 wPt-8021 cfb511 gwm299 barc290 cfp49 cfp1556 cfp1237 cfp50 cfp6029 wmc236 gwm114 1.7 3.2 0.9 2.8 0.9 2.5 0.3 2.7 0.6 2.9 0.3 0.2 0.4 0.3 0.3 3.1 6.1 RK_DH ccfb3200 wmm1966 gpw7108 wmm1420 wPt-0021 cfb512 wmm1758 wPt-1870 wmm448 cfb43 wPt-8021 cfb515 cfs6009 wPt-9368 wPt-2391 gwm299 cfb511 wmc236 3.8 0.2 0.9 6.8 0.3 1.2 0.2 0.3 0.6 0.9 3.0 COM_GD qYDH.3BL     Excalibur   RAC875   Drysdale   Edwards, PhD 2012; Bonneau, PhD 2012; Maphosa, PhD 2013 (Uni. of Adelaide) Courtesy P. Langridge and D. Fleury
  15. 15. Multi-Environment QTL analysis of RAC875/Kukri in 21 environments Contrasting allele effects depending on environmental conditions l  RAC875 allele contributes up to 15% in Mexican mid-yielding environment l  Kukri allele contributes up to 10% in South Australia “high” yielding environment (irrigated) qYDH.3BL expressed across environments Bonneau et al. 2013. TAG 126: 747-761 Courtesy P. Langridge and D. Fleury 2-­‐4  t/ha   1.5-­‐2  t/ha   0.5-­‐2  t/ha   Yielding  environments   Analysis  at  4  markers      è    
  16. 16. Rice QTL for GY under drought: qDTY12.1 Ecosystem Interval Peak marker LOD/ F value Additive effect R2 (%) Other traits affected Upland RM28048- RM511 RM28130 34.0 47.0** 33.0 DTF, PH, BIO, HI, DRI Lowland RM28099- RM28199 RM28166 48.8* 25.1** 23.8 DTF, PH, BIO, HI, LR, PAN DTF days to 50% flowering, PH plant height, BIO Biomass, HI harvest index, LR leaf rolling, PAN panicle number Bernier J et al. 2007 Crop Sci 47: 507-518 Mishra K.K et al. 2013. BMC Genetics 14: 6 Courtesy A. Kumar Upland cross: Vandna/Way Rarem / Lowland cross: IR 74371-46-1-1/Sabitri
  17. 17. qDTY1.1: a rice GY QTL expressed in multiple backgrounds l  qDTY12.1, qDTY1.1, qDTY3.2, qDTY3.1, qDTY2.2, qDTY6.1, qDTY2.3 are all detectable in multiple genetic backgrounds l  Effect of most of these QTLs (not qDTY6.1) validated by introgression into IR64 Courtesy A. Kumar
  18. 18. QTL for secondary traits
  19. 19. Transpiration Efficiency WUE of leaf photosynthesis •  low 12/13C discrimination Spike/awn photosynthesis Conceptual model of drought-adaptive traits YIELD = WU x WUE x HI Partitioning (HI) Partitioning to stem carbohydrates Signals (ethylene) Rht alleles Photo-Protection Leaf morphology •  wax/pubescence •  posture/rolling Pigments •  chl a:b •  carotenoids Antioxidants Water Uptake Rapid ground cover •  Leaf area (digital imagery) •  Coleoptile length/seed size Access to water by roots •  Ψ leaf (spectrometry) •  IR thermometry •  -osmotic adjustment- Reynolds M.P., Tuberosa R. 2008.. Current Opinion in Plant Biology 11: 171-179
  20. 20. • Homogeneous for height and phenology • Genetically polymorphic Canopy temperature in wheat Large populations easily phenotyped for CT using IR thermometer Seri/Babax RILs mapping Pop.: l  Common Rht allele l  Only 10d anthesis range Courtesy M. Reynolds Measurements associated with stomatal conductance, such as canopy temperature (CT), provide indirect indicators of water uptake (WU) by roots
  21. 21. . CTAMVEG CTPMVEG CTAMGF CTPMGF 0 50 100 150 200 250 300 350 400 450 500 18 20 22 24 26 28 30 y = -0.003x + 21.54, r2 = 0.61 y = -0.004x + 25.904, r2 = 0.68 y = -0.005x + 24.545, r2 = 0.64 y = -0.006x + 27.98, r2 = 0.62 YIELD(g/m2) CANOPY TEMPERATURE (oC) Figure1. Association of yield performance (g/m2) and canopy temperature (oC) of Seri-Babax population under drought (cycle Y01/02).Olivares-Villegas et al. 2007. Functional Plant Biology 34: 189-203 Courtesy M. Reynolds CANOPY TEMPERATURE (0C) CT is robustly associated with yield under stress CT is routinely used to screen for DT in wheat
  22. 22. “Stay-green” in Sorghum Stay-green Senescent Keeping leaves alive as long as possible is a fundamental strategy for increasing crop production, particularly under water-limited conditions. Stg2 fine-mapping population: with (right) and without (left) the Stg2 QTL (LG-03, 112 cM) Stg1 NIL (left) and Tx7000 (recurrent parent, right) Courtesy A. Borrell
  23. 23. Stay-green is much more than green leaves… Stay-green is a package of drought adaptation mechanisms l  Reduces canopy development: fewer tillers and smaller leaves (water savings impacting HI) l  Enhances root architecture: narrow root angle (Water Uptake) l  Modifies leaf anatomy: e.g. stomatal index and bundle sheath anatomy (WUE) l  Increases stem strength l  Produces larger grain l  Enhances grain yield At every QTL: Cluster of genes or single gene: hormone regulation? Courtesy A. Borrell
  24. 24. Stay-green improves grain yield Borrell et al. 1999, Int Sorghum Millets Newsl 40:31-34 Courtesy A. Borrell RIL population (QL39 x QL41, ICRISAT under severe terminal drought)
  25. 25. Stay-green and yield in sorghum breeding trials in Australia 2005-08 0 2 4 6 8 10 12 -­‐0.4 -­‐0.2 0 0.2 0.4 0.6 0.8 Grain  yied  t/ha Slope  of  the  linear  relationship  between    stay-­‐green  and  grain  yield  for  hybrids  based  on  specific  male  parents  at   a  particular  location R931945-­‐2-­‐2 R940386 R986087-­‐2-­‐4-­‐1 R993396 R995248 Trialmeanyieldt/ha Slope of the linear relationship between SG and GY for hybrids based on specific male parents at a particular location Jordan et al. 2012. Crop Sci. 52:1153–1161. Courtesy D. Jordan SG Males +++++ ++ ++++ +++ +
  26. 26. IR64 (paddy, shallow rooted) and KP (upland rice, deep rooted) alleles differ by 1bp The deletion induces a premature stop codon in the IR64 allele Positional cloning of the QTL DRO1 Nature Genetics 2013; doi:10.1038_ng.2725 NILs for DRO1 in an IR64 background The KP allele NIL induces deeper rooting (but not additional root biomass) Depth rooting in rice
  27. 27. Effect of DRO1 on field performance Soil water content under 3 drought regimes After 27 d of severe drought stress grain weight at maturity Nature Genetics 2013; doi:10.1038_ng.2725
  28. 28. Secondary traits in maize (ASI) Effect of selection for drought tolerance, carried out under drought conditions and based on selection for grain yield, ears per plant, ASI, senescence and leaf rolling DTP1 population (6 cycles of recurrent selection) Monneveux et al. 2006, Crop Sci. 46: 180-191.
  29. 29. Stacking the DT favorable alleles
  30. 30. Genetic dissection of drought tolerance GY ENO 0.77 0.42 0. 82 EW0D SW0D 3QTLs 2 QTLs 3QTLs Segregating phenotypes Drought Genes Sucrose (carbohydrates) ABA Proline (Stress response) -0.64 -0.57 cDNA array(900) 0/7D (S) T/S 20% Yield components Secondary traits Physiological parameters -0.51 3% 30% 12% 26% 6% TolerantSusceptible
  31. 31. Regulatory regions / QTL co-localization: Cluster of genes or pleiotropic effects? Chromosome 2 Chromosome 8
  32. 32. FIGURE 2 Digenic epistatic networks of FFLW 105 120 30 80 140 1 10 155 4 9 4 90 1 0 60 9 5 180 2 5 200 1 9 95 1 2 305 2 65 1 10 7 15 5 30 9 5 11 165 2 1 TL03BWW 2 TL03AIS 3 TL04BWW 4 TL04AIS 5 ZW03BIS 6 ZW03BSS 7 ZW04AWW 8 ZW04BIS 9 ZW04BSS Epistatic effects for female flowering in maize Jiankang Wang and Huihui Li ♦  Epistasis is very environment dependent ♦  Epistasis expressed up to 45% of the genetic variance ♦  Colocalization between loci expressing additive and epistasis effects was much trait dependant ♦  Even when linked, not always in phase ♦  10 positions for a total of 12 di-genic interactions across 6 environments
  33. 33. Advantages of Gene Blueprinting Technology: l  Enhances and improves understanding of gene function l  Provides invaluable exposure to predictable reliable alles, not just the right genes l  Harnesses numerous alleles to enable a broad-based response to stress factors Gene Blueprinting Technology: l  Identify and select multiple genes with distinctive modes of action l  Elite genes selected based on performance in target stress environments l  Uses multiple genes (vs. a single gene) to cover all stages of plant development Water Optimization tools: l  Testing sites with precision water stress management and in targeted stress environment l  Detailed plant phenotyping l  Genetic analysis and marker based breeding l  Crop modeling Science behind the Agrisure Artesian: gene blueprinting technology Native traits in elite germplasm: The Candidate gene approach Courtesy D. Benson
  34. 34. Agrisure Artesian™ Technology – 2012 Performance Summary1 1 Data are based on 2012 Syngenta on-farm strip trials Courtesy D. Benson
  35. 35. GM approach A number of transgenic events have been developed for DT l Bacterial RNA chaperones (cspB) (Castiglioni et al. 2008, Plant Physiol 147:446-455) •  Constitutive promoter •  Maintain protein structure and therefore function •  Effect on drought at both vegetative and reproductive stages •  CspB-Zm increases maize yield up to 20% (under stress condition of 50% yield reduction) •  No negative effects under optimal conditions DroughtGard from Monsanto contains the cspB l The release in 2012 was disappointing l Pleiotropic effects? l Non-specific promoter?
  36. 36. Tapping into the genebank pool
  37. 37. Root trait QTLs in two chickpea mapping populations TAA170 GA24 STMS11 ICCM0249 CaM0856 LG4: ICC 4958 x ICC 1882 RLD_06 RLD_08 RDW_06 RDW_08 RT DEPTH_06 RT DEPTH_08 SDW_06 SDW_08 RT VOL_06 RT VOL_08 RSA_06 RSA_08 RL_06 RL_08 STEM DW_06 LDW_06 R-T RATIO_06 LG4: ICC 283 x ICC 8261CAM1903 TA130 ICCM0249 TAA170 NC142 209
  38. 38. Root QTL introgression Marker-assisted backcross (MABC) Donors Cultivars JG 11 Chefe KAK 2
  39. 39. QTL introgression into JG 11 Varshney et al. 2013, The Plant Genome (in Press) JG 11 ICC 4958 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 ICCM ABCA-23 ICCM ABCA-24 ICCM ABCA-25 ICCM ABCA-26 ICCM ABCA-27 ICCM ABCA-28 ICCM ABCA-30 ICCM ABCA-31 ICCM ABCA-32 ICCM ABCA-33 ICCM ABCA-34 ICCM ABCA-35 ICCM ABCA-36 ICCM ABCA-37 ICCM ABCA-38 ICCM ABCA-39 ICCM ABCA-40 ICCM ABCA-41 ICCM ABCA-42 ICCM ABCA-43 ICCV 93954 ICC 4958 JAKI 9218ICCV 10 Yield(kg/ha) Irrigated Rainfed
  40. 40. Analysis based on an SSR (phi76) locus linked to a gene encoding catalase (cat3), an important enzyme for maintaining cellular function under oxidative stress conditions caused by high temperature Changes in allelic frequency over cycles of selection in maize PhD thesis, Claudia Bedoya Salazar Genetic effect must be tested in improved germplasm!
  41. 41. Conclusion
  42. 42. Conclusion (1) Better tools, more information l  Improved tools for measuring, storing and analysing environmental conditions (weather, soil, etc) l  Improved phenotyping methodology: •  More controlled stress conditions (drip irrigation) •  Better field design and analytical tools •  More sophisticated analysis (metabolites) •  Methodologies better adapted to routine and large scale screening (CT) l  Robust set of validated secondary traits now used routinely for DT breeding l  Large number of DT QTLs identified l  Numerous DT candidate genes have been confirmed via an association genetics approach l  Several regulatory genes identified as suitable for a GM approach l  Several models developed to allow improved prediction of performance in a given target environment
  43. 43. Conclusion (2) Different genetics for different crops l  Landraces and CWRs harbour novel alleles especially in crops where allelic diversity among cultivars is limited l  Validation of adaptive alleles in elite background can be a challenge, especially for crops with a long breeding history l  Major QTL/genes have been identified for GY components and secondary traits in crops with: •  a short DT breeding history, •  limited allelic diversity in cultivars or •  a large LD l  Such native gene effects do not exist in a crop like maize l  The genetic effect per se of any major gene, or cluster of genes will decrease over time with breeding effort l  Less usable in a predictive mode l  So an integrative breeding approach will be required sooner or later
  44. 44. Conclusion (3) Breeding perspectives l  Breeding for grain yield under normal conditions or under high density can be used as a substitute for DT selection l  Can be quite efficient particularly when phenotyping facilities are limited as long as there is still a large potential for genetic gain l  In the mid- to long-term, we will need to select under drought conditions and understand the DT mechanisms l  Linkage within clusters of DT genes must be broken l  How deeply we need to understand the mechanics of DT in order to breed effectively for DT continues to be an open question l  Probing too deeply may be a waste of resources considering the unpredictable nature of drought l  Breeding for drought is a numbers game aimed at pyramiding numerous favourable alleles to enable a broad-based response to drought conditions (timing and intensity)
  45. 45. Acknowledgements •  Tim Setter, Cornell University, USA •  Matthew Reynolds, CIMMYT •  Rajeev K Varshney, ICRISAT •  Andy Borrell, University of Queensland, Australia •  David Jordan, University of Queensland, Australia •  Arvind Kumar, IRRI •  Delphine Fleury, Australian Centre for Plant FunctionalGenomics •  François Tardieu, INRA •  Chris Zinselmeier, Science & Technology Research Fellow/ Technical Development Lead, Syngenta •  Dirk Benson, Head, Trait Project Management, Syngenta Many thanks to the following people, who provided slides and other invaluable input for the preparation of this presentation: Robert Koebner Antonia Okono Aida Martinez Gillian Summers
  46. 46. Vision A future where plant breeders have the tools to breed crops in marginal environments with greater efficiency and accuracy for the benefit of the resource- poor farmers and their families. Mission Using genetic diversity and advanced plant science to improve crops for greater food security in the developing world. The Integrated Breeding Platform (IBP), a one-stop shop providing access to modern tools applications, and services for integrated crop breeding with a focus on breeders in developing countries. /IntegratedBreedingPlatform /IBPlatform •  Downloadable online at: drought_phenotyping •  Also available in hard copy (limited edition). To request a copy please send an e-mail to: GCP’s phenotyping book Drought phenotyping in crops: from theory to practice – available on DVD at the GCP booth! The CGIAR Generation Challenge Programme (GCP)