increasing yield potential and improving crop adaptation ...€¦ · average genetic gains at 556...

Increasing yield potential and improving crop adaptation to climate change:

Strategies and genetic gains

Matthew P. Reynolds, Gemma Molero, Maria Tattaris, C. Mariano Cossani, Sivakumar Sukumaran

EPPN PLANT PHENOTYPING SYMPOSIUM

November 12, 2015, Barcelona

International Wheat Improvement Network (IWIN) Coordinated by CIMMYT since 1960s

Latin

America

Africa Middle

East

South &

East Asia

CIMMYT distributes 1,000 new wheat genotypes annually targeted to a range of environments

Average genetic gains at 556 international sites: ~1% per year from 1996-2010

Manes et al. 2012 .Genetic yield gains of CIMMYT international semi-arid

wheat yield trials from 1994 to 2010. Crop Science 52:1543-1552.

Complementary strategies to increase

genetic gains

1) Identify crop characteristics conferring adaptation

2) Precision and high throughput phenotyping

3) Exploration of genetic resources for adaptive traits

4) Inter-specific hybridization to broaden the crop

genepool

5) Genomics to increase breeding efficiency

6) Strategic crossing to achieve cumulative gene action





genepool




genetic gains

Conceptual models

DROUGHT

YIELD = WU x WUE x HI

HEAT

YIELD = LI x RUE x HI

YIELD POTENTIAL

YIELD = LI x RUE x HI

Water Use (RUE)

•Roots match evaporative

demand

•Regulation of transpiration

(VPD; ABA)

Partitioning (HI)

•Spike fertility (meiosis, pollen, etc)

•Stress signaling (e.g. ethylene)

regulating

•senescence rate

•floret abortion

•Grain filling (starch synthase)

•Stem carbohydrate storage &

remobilization

Photo-Protection (RUE)

•Leaf morphology

(display, wax)

•Down regulation

•Pigment composition

• Chl a:b

• Carotenoids

•Antioxidants

Conceptual Model of Heat-Adaptive Traits YIELD = LI x RUE x HI

Efficient metabolism (RUE)

•CO2 fixation •CO2 conductance •Rubsico (>>)

•Canopy photosynthesis •spike photosynthesis

•Respiration

Light interception (LI)

•Rapid ground cover

•Functional stay-green

Cossani and Reynolds, 2012. Physiological traits for improving heat tolerance in wheat. Plant Physiology 160 1710-1718

G x E? G x G?





genepool




genetic gains

Plant selection tools

Visual selection ++

(Molecular markers) Spectral reflectance

Canopy temperature

Phenotyping is not just about tools!

►Design experimental

populations to avoid

confounding agronomic

traits

Seri/Babax

population

Representative phenotyping platforms (e.g. IWYP-PLAT)

Located at heart of high yield

wheat agro-ecosystem (Yaqui

Valley NW Mexico)

• Production >1 m tons

• Farm yields avg. 6.5 t/ha

• Maximum yields ~10 t/ha

• Research and breeding

conducted side by side,

encouraging maximum

accountability of both.

Canopy temperature shows consistent

association with yield under drought and heat

Flintham et al. 1997

GIDDINGS SOIL CORER

TO SAMPLE ROOTS

& MEASURE SOIL MOISTURE

Deeper roots under drought confer

stress adaptation

140

190

240

290

340

390

440

490

0 10 20 30 40 50 60 70

Root DW 60-120 cm (gm-2

)

Yie

ld (

gm

-2)

0

5

10

15

20

25

30

35

40

CT

gf (o

C)

CT=-0.20x+34.3, R2=0.88

Yield=2.07x+254.9, R2=0.35

Lopes MS and Reynolds MP, 2010. Partitioning of assimilates to deeper roots is associated with cooler

canopies and increased yield under drought in wheat. Functional Plant Biology 37:147-156

Aerial remote sensing

Ground v Airborne: UAV & Blimp

r=0.76 r_G=0.76

25

26

27

28

29

30

125 150 175 200

CT

GR

OU

ND

Thermal Index UAV

Thermal Index UAV VS CT Ground Heat_1

r=0.75 r_G=0.73

0.38

0.43

0.48

2.5 2.9 3.3 3.7

CT

Gro

un

d*

Thermal Index UAV*

Thermal Index UAV VS CT Ground Heat_2

r=0.84 r_G=0.93

0.50

0.60

0.70

0.80

0.50 0.60 0.70 0.80

ND

VI

GR

OU

ND

MSAVI BLIMP

MSAVI BLIMP VS NDVI Ground Drought_1

r=-0.86 r_G=0.86

0.50

0.60

0.70

0.80

0.10 0.20 0.30 0.40

ND

VI

GR

OU

ND

NCPI BLIMP

NCPI BLIMP VS NDVI Ground Drought_1

In most cases data from airborne platforms explains genetic

variation in yield etc. better than with ground based readings

Airborne UAV VS Yield, Biomass

Trial NDVI Ground

Yield (g/m2) Biomass (g/m2)

CIMCOG_H_1 NDVI UAV 0.85 0.77 0.79 NDVI GROUND 0.63 0.58

CIMCOG_H_2 NDVI UAV 0.89 0.79 0.72 NDVI GROUND 0.74 0.64

SEED_SEL NDVI UAV 0.82 0.67 - NDVI GROUND 0.43 -

DIVERSITY SET NDVI UAV 0.71 0.64 0.76 NDVI GROUND 0.63 0.66

FIGS NDVI UAV 0.90 0.60 0.69 NDVI GROUND 0.58 0.66

Trial CT Ground (oC)

Yield (g/m2) Biomass (g/m2)

Diversity_Set Thermal Index (UAV) 0.59 -0.57 -0.64

CT Ground (oC) -0.56 -0.60

CIMCOG_H_1 Thermal Index (UAV) 0.76 -0.73 -0.78 CT Ground (oC) -0.55 -0.61

CIMCOG_H_2 Thermal Index (UAV) 0.73 -0.74 -0.78

CT Ground (oC) -0.62 -0.67

Tattaris et al., unpublished

Additional Traits: Height

Fritz et al. 2013

Derivation of a 3D image (x, y, z coordinates) from overlapping RGB

images taken at an angle

• Important to obtain throughout the growing cycle, for

multiple years, across environments.

Things to think about…

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

50 60 70 80 90 100 110Co

rre

lati

on

Co

eff

icie

nt

Days after Emergence

NDVI VS YLD Irrigated

Ground VS YLD

UAV VS YLD

Average Days to

Anthesis ± STDEV

0

0.2

0.4

0.6

0.8

1

20 25 30 35 40 45 50 55 60 65 70

Co

rre

lati

on

Co

eff

icie

nt

Days after Emergence

NDVI VS YLD Heat

GROUND VSYLD

UAV VS YLD

Average Days to

Heading ± STDEV





genepool




genetic gains

Genetic resources:

~ 0.5 million

accessions of

wheat genetic

resources

in collections worldwide

The World

Wheat Collection at

CIMMYT has

~170,000

Wheat ‘landraces’ in Oaxaca

70,000

wheat genetic

resources screened

under drought and heat,

Sonora, Mexico, 2011-2013

FIGS drought set, Sonora, 2013 Focused Identification of Germplasm Strategy (http://www.figs.icarda.net/)

A

http://www.figs.icarda.net/

A B

FIGS drought set, Sonora, 2013 Focused Identification of Germplasm Strategy (http://www.figs.icarda.net/)

http://www.figs.icarda.net/





genepool




genetic gains

T. durum

AABB

T. tauschii

DD

Hexaploid synthetic

AABBDD

Wide crossing with close relatives

e.g. “Synthetics”

► Sources of disease

resistance

►Redistribution of

roots to deeper soil

profiles under water

stress

X =

0

5

10

15

20

25

30

35

Check

1,000 New primary synthetics screened for

biomass –heat environment-

# lines

Dry weight (g)





genepool




genetic gains

Canopy temp as a surrogate for root function

.

CTAMVEGCTPMVEGCTAMGFCTPMGF

0

50

100

150

200

250

300

350

400

450

500

18 20 22 24 26 28 30

y = -0 .003x + 21.54, r2 = 0.61y = -0 .004x + 25.904, r2 = 0.68y = -0 .005x + 24.545, r2 = 0.64y = -0 .006x + 27.98, r2 = 0.62

YIE

LD

(g/m

2)

CANOPY TEMPERATURE ( oC)

Figure1. Association of yield performance (g/m2) and canopy temperature ( oC)of Seri-Babax population under drought (cycle Y01/02).

CT is robustly associated with performance under

heat and drought stress

CANOPY TEMPERATURE (0C)

R2 = 0.47

200

250

300

350

400

450

27.0 28.0 29.0 30.0 31.0 32.0

CT-boot

Yiel

d

Drought stress

Heat stress

Olivares-Villegas et al, 2007. FPB

Cossani et al, unpublished

Consistent QTL identified in the Seri/Babax Population

1B-a.aac/caa-41B-a.wPt-14031B-a.wPt-52811B-a.aca/cac-51B-a.gwm2731B-a.wPt-01701B-a.aac/ctg-41B-a.wPt-75291B-a.agg/cat-41B-a.acc/cat-41B-a.act/ctc-71B-a.agg/cat-111B-a.barc0651B-a.gwm4131B-a.agg/ctg-51B-a.wPt-34651B-a.aac/cta-51B-a.agg/cat-181B-a.gwm1311B-a.agg/cac-31B-a.agc/cta-91B-a.agc/cta-21B-a.agc/cta-61B-a.agc/cag-51B-a.aag/ctg-141B-a.wPt-89301B-a.act/ctc-91B-a.aca/cta-91B-a.gwm5821B-a.gwm301b1B-a.wPt-17811B-a.aag/ctc-61B-a.wPt-20521B-a.aca/cac-21B-a.wPt-78331B-a.acc/ctc-41B-a.acg/cta-21B-a.act/ctc-51B-a.wPt-86161B-a.aca/cag-51B-a.aca/caa-31B-a.agg/ctg-31B-a.aac/ctc-6

Yie

ld

GM

2

ND

VIv

CT

v

CT

g

CH

Lg

1B-a

2B-a.wPt-96682B-a.aac/cta-12B-a.wPt-73202B-a.wPt-06152B-a.aag/ctc-32B-a.wPt-64772B-a.acc/ctc-22B-a.acc/ctg-42B-a.acc/ctc-102B-a.wPt-77502B-a.aag/ctg-52B-a.agg/cat-72B-a.agg/cac-102B-a.agc/cag-42B-a.aag/ctg-152B-a.agg/cac-52B-a.gwm3882B-a.acg/cta-12B-a.gwm191a2B-a.aca/ctg-12B-a.aag/ctg-122B-a.act/ctc-112B-a.wPt-56802B-a.wPt-97362B-a.aca/caa-42B-a.agg/cta-32B-a.agg/cac-132B-a.agg/ctg-22B-a.act/ctc-1

ND

VIg

CT

v

CT

g

2B-a

3B-b.wPt-82383B-b.aag/ctc-93B-b.gwm644

3B-b.aca/ctg-53B-b.gdm0083B-b.wPt-60473B-b.aac/cac-53B-b.wPt-19403B-b.aag/ctc-1

3B-b.agg/cta-63B-b.wPt-53583B-b.wPt-71863B-b.acc/ctg-53B-b.wPt-03843B-b.wPt-44283B-b.aca/cag-9

3B-b.wPt-1804

3B-b.wPt-00213B-b.gwm301e3B-b.aca/caa-93B-b.acc/ctg-113B-b.wPt-80213B-b.acc/ctc-8

3B-b.wPt-44123B-b.wPt-4370

Yie

ld

GM

2

CT

v

CT

g

3B-b

4A-a.gwm3974A-a.act/cag-54A-a.act/cag-34A-a.wmc048d4A-a.agg/cta-124A-a.aac/ctg-3

4A-a.wmc048c

Yie

ld GM

2

ND

VIg

CT

v

4A-a

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

Common QTL identified for heat and drought adaptation

Empty bars: Drought specific QTL

Lined bars: Stress QTL specific for DRT & HOT environments

Solid bars: Robust QTL identified under stress and irrigated environments

Pinto et al , 2010 . Heat and drought adaptive QTL in a

wheat population designed to minimize confounding agronomic effects.

TAG 121:1001–21

Root distribution in Seri/Babax ‘iso-QTL’ lines

0

40

80

120

160

200

COOL-Drt HOT-Drt COOL-Heat HOT-Heat

0-30 cm 30-60 cm 60-90 cm 90-120 cm

Root

s (g

/m2 )

46%

35%

16%

56%

33%

8%

79%

16%

5%

82%

13%

4%

T-tests for COOL v HOT genotypes: DROUGHT 30-120 cm (p=0.002) ; HEAT 30-90 cm (p=0.0025)

Pinto & Reynolds, 2015. TAG

QTLs for Spike Photosynthesis

Elite spring

bread wheat

spikes

intercept up

to 45% of

sun light

wPt_34350.0

wPt_498623.5wPt_7665 wPt_911635.8wPt_511836.9wPt_516843.4wPt_795944.3wPt_005446.1wPt_697158.2wPt_092770.7wPt_321387.9wPt_893195.7wPt_332996.9

wPt_3492 wPt_3083141.8wPt_3289165.3wPt_3012194.5wPt_8163195.1wPt_10193195.8wPt_7167199.9wPt_4936218.3wPt_6531 tPt_3719242.4wPt_3457243.0wPt_9300 wPt_3763wPt_5928

267.8

wPt_1733268.6wPt_2453269.6wPt_0103293.0wPt_2539300.6wPt_0929 wPt_1951310.0wPt_7848316.8wPt_4703325.3wPt_3461 wPt_7079325.9wPt_4736339.5tPt_8942340.2wPt_3085355.3wPt_5120 wPt_9569355.8wPt_0168356.1wPt_7029356.5wPt_9724361.7wPt_3055390.5wPt_6136391.1

qS

PC

GW

SP

5B

.1-Y

P-2

Yrs

qS

PC

GW

5B

.1-Y

P-2

Yrs

qS

PC

GW

5B

.1-Y

P-2

012

qS

PC

GW

SP

5B

.1-Y

P-2

012

qS

PC

GW

5B

.1-H

t-2013

qS

PC

GW

SP

5B

.1-H

t-2013

qS

PC

GW

5B

.1-H

t-2014

qD

TA

5B

.1-Y

P-2

012-1

4

qD

TM

5B

.1-Y

P-2

012-1

4

qP

H5B

.1-Y

P-2

012-1

4

qP

H5A

.1-H

t-2013-1

4

5B

wPt_97740.0wPt_51811.8wPt_580416.6wPt_179220.9wPt_801622.2

wPt_4408 wPt_566047.0

wPt_465862.5wPt_778472.5wPt_653080.2wPt_438492.6wPt_8797115.8wPt_9592121.3wPt_2465137.6wPt_6654148.9wPt_9897151.6wPt_7951157.1wPt_6941160.3wPt_5941162.5wPt_8172170.5wPt_8478172.6wPt_1720177.7wPt_5776190.2wPt_4735191.5wPt_4666198.0wPt_3870 wPt_1692199.3wPt_8770200.8wPt_9752202.4wPt_4652213.4wPt_4676215.9wPt_6993218.9

wPt_4361294.7wPt_8245 wPt_7746300.3wPt_2861302.7wPt_3571303.3

qS

PC

GW

SP

1A

.1-Y

P-2

Yrs

qS

PC

GW

1A

.2-4

En

vs

qS

PC

GW

1A

.1-Y

P-2

012

qS

PC

GW

SP

1A

.1-4

En

vs

qY

LD

1A

.1-Y

P2012-1

4q

SP

CG

W1A

.1-H

t2014

qS

PC

GW

SP

1A

.1-Y

P2012

qS

L1A

.1-H

t2013-1

4

qS

PC

GW

1A

.1-4

En

vs

qS

PC

GW

1A

.1-Y

P2014

qD

TA

1A

.1-Y

P2012-1

4

qS

L1A

.1-Y

P2012-1

4

qS

L1A

.1-4

En

vs qP

H1A

.1-4

En

vs

qY

LD

1A

.2-Y

P2012-1

4

qP

H1A

.1-H

t2013-1

4

1A

wPt_1225219.1wPt_8915234.6wPt_7478237.3wPt_4127240.7wPt_11160242.2wPt_6020272.4wPt_10514278.6wPt_7229279.2wPt_4597280.3wPt_5716281.5wPt_3078282.7wPt_6445285.8wPt_4364304.9wPt_2119332.9wPt_4782333.5wPt_11017 wPt_10341334.1wPt_6834338.5wPt_6961 wPt_2416340.3wPt_8386346.3wPt_7514346.9wPt_4483347.9wPt_10306349.7wPt_7614350.3wPt_0343350.4wPt_0668350.9wPt_8959353.5wPt_6299362.1wPt_0021 wPt_11082wPt_4194 wPt_5947

364.4

wPt_6000364.5wPt_8184366.4wPt_10057 wPt_11078368.5wPt_5452401.6wPt_3725402.7wPt_7968404.3wPt_9577405.1wPt_8056410.1wPt_11301 wPt_10785410.8wPt_7635411.4wPt_7145419.0wPt_0644423.6

qS

PC

GW

SP

3B

.1-Y

P-2

Yrs

qS

PC

GW

3B

.1-Y

P-2

Yrs q

TG

W3B

.1-4

En

vs

qS

PC

GW

3B

.1-Y

P2012

qS

PC

GW

SP

3B

.1-Y

P2012

qS

PC

GW

SP

3B

.1-Y

P2014

qS

PC

GW

SP

3B

.1-H

t2013

qT

GW

3B

.1-Y

P2012-1

4

qS

PC

GW

3B

.1-H

t2014

qD

TH

3B

.1-H

t2013-1

4

3B.2RILs Atil x T. Dicoccum

Molero et al., unpublished

QTLs for Spike Photosynthesis





genepool




genetic gains

Strategic crossing to achieve

cumulative gene action

WUE: Transpiration

Efficiency

•Efficient leaf

photosynthesis (CID)

Strategic Crossing to Combine Adaptive Traits

DROUGHT YIELD = WU x WUE x HI

Partitioning (HI) • Stem carbohydrate

storage

WUE: Photo-Protection • Leaf wax

• Pigments

Water Uptake (WU) •Ground cover

•Access to water by roots

First new generation of lines based on

physiological crosses & selection, (2007)

Yield distribution of 3 years mean drought trials

(Cd Obregon, Mexico)

0

5

10

15

20

25

30

35

40

45

85%

<= 9

0%

90%

<= <

95%

95%

<= <

100%

100%

<= <

105%

105%

<= <

110%

110%

<= <

115%

115%

<= <

120%

% of check

%

Conventionalcrosses

Physiologicaltrait crosses

Reynolds et al., 2009 AAB

12

16

24

30

22

41

20 20

5

0

5

10

15

20

25

30

35

40

45

83-89 90-94 95-99 100-104 105-109 110-114 115-119 120-129 130-133

Nu

mb

er

of

lin

es

Yield as % of drought adapted check Vorobey

70% of new lines

outyield the check Check 3.5 t/ha (Vorobey)

New lines based on physiological criteria, 2012

Yield traits considered in strategic crosses: YIELD = LI x RUE x HI

SINKS pre-grainfill:

•Spike fertility •grain number

•kernel weight potential •avoid floret abortion

•Development pattern

•long juvenile spike phase

SINK (grain-filling)

•Harvest Index (HI) •tiller survival •grain growth rate

SOURCE (pre-grainfill):

• Light interception (LI)

• Growth rate • Canopy temperature

SOURCE (grain-filling):

• Canopy photosynthesis (RUE/LI) •Leaf conductance

•Carbohydrate storage in stem •stay green

26 international

sites of the 2nd

WYCYT

35 new (PT) lines

7 elite checks

Abbreviation Site Country

BGLD J BARI Joydebpur Bangladesh

BGLD D BARI Dinjpur Bangladesh

BGLD R BARI Rajshahi Bangladesh

China L LAOMANCHENG China

Egypt A Assiut Egypt

India D Delhi India

India L Ludhiana India

India V Varanasi India

India K Karnal India

India H Dharwad India

India I Indore India

India U Ugar India

Iran D DARAB-HASSAN-ABAD Iran

Iran Z ZARGAN Iran

Iran SP SPII - KARAJ Iran

Iran S SAFIABAD AGRIC. RES. CENTER Iran

MEX Bajio INIFAP-Bajio Mexico

MEX CM CIMMYT CENEB Mexico

MEX BC INIFAP-Mexicali Baja California Mexico

MEX JAL INIFAP-Tepatitlan Jalisco Mexico

MEX SIN INIFAP-Valle del Fuerte, Sinaloa Mexico

MEX SON INIFAP_Valle del Yaqui Mexico

Nepal B Bhairahawa Nepal

PAK I Islamabad Pakistan

PAK F Faisalabad Pakistan

PAK P Pirsabak Pakistan

Mean yield of 7 elite checks: 2nd WYCYT, 2014 Y

ield

g/m

2

Mean yields of 35 new PT lines v 7 elite checks:

(average 7% advantage of new lines)

Yie

ld g

/m2

CROP

DESIGN

GENETIC RESOURCES

PHENO-

TYPING

GENETIC

ANALYSIS

BREEDING

DELIVERY

through

IWIN

Physiological Breeding Pipeline

INFORMATICS

Determine

traits/genes

needed to

adapt crops to

target

environments

•Landraces

•Wild

relatives

•Advanced

lines

•Transgenics

•High thru-put

remote sensing

•Precision

phenotyping

Strategic

crossing

Select best

progeny using

state-of-the-

art

phenotyping

/molecular

tools

QTL

identified

and MAS

systems

developed

Standard Phenotyping Protocols

http://libcatalog.cimmyt.org/download/cim/96140.pdf




Conclusions

• Investment in understanding the ‘phenome’ and trade-offs

between traits facilitate breeding decisions

• Genetic resources represent a vast and largely untapped

opportunity for crop improvement, if evaluated using

appropriate screens: – Aerial high throughput approaches on large numbers

– Precision phenotyping approaches on selected material

– Molecular markers (especially for hard to phenotype traits)

• Strategic trait-based crossing increases genetic gains compared

with crossing the best x best yielding lines

• Phenomic and genomic technologies can deliver genetic gains in farmers’ fields; sooner when integrated with proven

techniques

Acknowledgements

Carolina Saint Pierre, Alistair Pask, Ravi

Valluru, Marc Ellis, Yann Manes ,

Richard Trethowan…

Thank you

for your

interest!

increasing yield potential and improving crop adaptation ...€¦ · average genetic gains at 556...

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