Coping with drought in crop improvement – a global perspective
Jean-Marcel Ribaut Inter-Drought-IV,
September 2-6, 2013, Perth, Australia
Climate change and impact on crop productivity
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
http://www.huffingtonpost.com/peter-h-gleick/the-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.
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,
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
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
What is drought?
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)
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)
Breeding for drought tolerance
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
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
Grain yield QTLs for drought tolerance
Wheat QTL for GY under drought: qYDH.3BL
QTL identified in 3 Australian wheat populations
cultivar Australian 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 gwm1564gpw4143
wPt-0021
gwm1266cfs6009cfb43wPt-8021 cfb539
gwm114
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0.6
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8.8
0.61.2
1.3
8.8
EK_DH
cfb3200
barc77
gpw7108 wmm1966cfp6016
cfb503 wmm1420wPt-4401
wmm517 wmm480 wmm454wmm274 wmm408cfb528cfb560cfb515gwm1266 wmm1758 gpw3233cfs6009cfb43 cfp6018wmm448wPt-9368 cfp6009 cfp6008wPt-8021cfb511gwm299 barc290 cfp49cfp1556 cfp1237 cfp50cfp6029wmc236
gwm114
1.7
3.2
0.9
2.8
0.9
2.5
0.3
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0.6
2.90.30.20.40.30.33.1
6.1
RK_DH
ccfb3200
wmm1966gpw7108wmm1420 wPt-0021 cfb512
wmm1758wPt-1870 wmm448cfb43 wPt-8021cfb515cfs6009 wPt-9368 wPt-2391gwm299cfb511
wmc236
3.8
0.20.9
6.8
0.31.20.20.30.60.9
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
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
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
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
QTL for secondary traits
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
• 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
.
CTAMVEGCTPMVEGCTAMGFCTPMGF
0
50
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18 20 22 24 26 28 30
y = -0.003x + 21.54, r2 = 0.61y = -0.004x + 25.904, r2 = 0.68y = -0.005x + 24.545, r2 = 0.64y = -0.006x + 27.98, r2 = 0.62
YIE
LD
(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
“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
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
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)
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-‐2R940386R986087-‐2-‐4-‐1R993396R995248
Tria
l mea
n yi
eld
t/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 +++++ ++ ++++ +++ +
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
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
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.
Stacking the DT favorable alleles
Genetic dissection of drought tolerance
G Y E N O 0 . 7 7 0 . 4 2 0 . 8 2
E W 0 D S W 0 D
3 Q T L s 2 Q T L s 3 Q T L s
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%
Tolerant Susceptible
Regulatory regions / QTL co-localization: Cluster of genes or pleiotropic effects?
Chromosome 2 Chromosome 8
FIGURE 2 Digenic epistatic networks of FFLW
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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
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
Agrisure Artesian™ Technology – 2012 Performance Summary1
1 Data are based on 2012 Syngenta on-farm strip trials
Courtesy D. Benson
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?
Tapping into the genebank pool
Root trait QTLs in two chickpea mapping populations
TAA
170
GA
24 STM
S11
ICC
M0249
CaM
0856
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 8261
CAM
1903
TA130
ICCM0249
TAA170
NC142
209
Root QTL introgression Marker-assisted backcross (MABC)
Donors
Cultivars
JG 11 Chefe KAK 2
QTL introgression into JG 11
Varshney et al. 2013, The Plant Genome (in Press)
JG 11
ICC 4958
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ABCA
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ABCA
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ICCV 93954
ICC 4958
JAKI 9218
ICCV 10
Yield (kg
/ha)
Irrigated
Rainfed
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!
Conclusion
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
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
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)
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
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.
www.integratedbreeding.net /IntegratedBreedingPlatform /IBPlatform
• Downloadable online at: www.generationcp.org/ drought_phenotyping
• Also available in hard copy (limited edition). To request a copy please send an e-mail to: [email protected]
GCP’s phenotyping book Drought phenotyping in crops: from theory to practice – available on DVD at the GCP booth!
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http://www.generationcp.org/