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

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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

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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

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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

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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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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

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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

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ICCM0249

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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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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: 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/