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

1. Coping with drought in crop
improvement – a global perspective
Jean-Marcel Ribaut
Inter-Drought-IV,
September 2-6, 2013, Perth, Australia

2. Climate change and impact on
crop productivity

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
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.

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. 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. 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. What is drought?

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. 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. Breeding for drought tolerance

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. 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. Grain yield QTLs for drought
tolerance

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. 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. 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. 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. QTL for secondary traits

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. • 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. .
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. “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. 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. 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. 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. 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. 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. 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. Stacking the DT favorable alleles

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. Regulatory regions / QTL co-localization:
Cluster of genes or pleiotropic effects?
Chromosome 2 Chromosome 8

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. 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. Agrisure Artesian™ Technology –
2012 Performance Summary1
1 Data are based on 2012 Syngenta on-farm strip trials
Courtesy D. Benson

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. Tapping into the genebank pool

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. Root QTL introgression
Marker-assisted backcross (MABC)
Donors
Cultivars
JG 11 Chefe KAK 2

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. 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. Conclusion

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. 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. 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. 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. 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:
books@generationcp.org
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)
http://www.generationcp.org/