physiological breeding: strategies & genetic gains matthew reynolds (cimmyt) contributions from:...

Physiological Breeding: strategies & genetic gains

Matthew Reynolds (CIMMYT)

Contributions from:Gemma Molero, Maria Tattaris

Carolina Saint Pierre, Mariano Cossani

Siva Sukumaran, Alistair Pask, Ravi Valluru

Marc Ellis, Yann Manes

Richard Trethowan

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

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 TraitsYIELD = 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

G x E?G x G?

Cossanni & Reynolds, 2012. Plant Physiology 160 1710-18

Complementary Strategies







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.

Plant selection tools

Visual selection ++

(Molecular markers)Spectral reflectance

Canopy temperature

Canopy temperature shows consistent association with yield under drought and heat

Deeper roots under drought confer stress adaptation

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-2)

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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

Pinto & Reynolds, 2015. Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution. TAG: 128

GIDDINGS SOIL CORER

TO SAMPLE ROOTS & MEASURE SOIL MOISTURE

Aerial remote sensing

Recently featured on BBC Horizons

Thermal Imagery: data processing

Removal of outlying pixels

Ground v Airborne: UAV & Blimp:

125 150 175 20025

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f(x) = 0.0340484014144242 x + 21.764091264697R² = 0.591929180109435

Thermal Index UAV VS CT GroundHeat_1

Thermal Index UAV

CT

GR

OU

ND

2.5 2.9 3.3 3.70.38

0.43

0.48

f(x) = 0.0593427427166828 x + 0.248722335188861R² = 0.568074593278231

Thermal Index UAV VS CT GroundHeat_2

Thermal Index UAV*

CT

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un

d*

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f(x) = 0.771202441869446 x + 0.131097474414508R² = 0.712717074376989

MSAVI BLIMP VS NDVI Ground Drought_1

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f(x) = − 0.741831729424865 x + 0.863742285002526R² = 0.73620187199727

NCPI BLIMP VS NDVI GroundDrought_1

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in most cases data from airborne platforms explains genetic variation in yield etc. better than with ground based readings

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

A

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


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

A B






4) Inter-specific hybridization to broaden the genepool



T. durumAABB

T. tauschiiDD

Hexaploid syntheticAABBDD

Wide crossing with close relatives

e.g. “Synthetics”

► Sources of disease resistance

► Redistribution of roots to deeper soil profiles under water stress

X =

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1,000 new primary synthetics screened for biomass –heat environment-

# lines

Dry weight (g)

Complementary Genetic Strategies







Canopy temp as a surrogate for root function

.

CTAMVEGCTPMVEGCTAMGFCTPMGF

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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

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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

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CT-boot

Yield

Drought stress

Heat stress

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

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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-93B-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

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Common QTL identified for heat and drought adaptation

Empty bars: Drought specific QTLLined bars: Stress QTL specific for DRT & HOT environmentsSolid 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

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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

Adaptation to plant density

Differences between inner and outer rows:

Sukumaran et al. Crop Sci. (2015)

Candidate gene: SET domain protein PI94960 for pollen abortion

GWAS candidate genes: A) ADi Yield, C) ADi KNO

QTL for spike photosynthesis

Complementary Genetic Strategies







Strategic crossing for cumulative gene action

WUE: Transpiration

Efficiency•Efficient leaf photosynthesis

(CID)

Strategic Crossing to Combine Adaptive TraitsDROUGHT YIELD = WU x WUE x HI

Partitioning (HI)• Stem carbohydrate

storage

WUE: Photo-Protection• Leaf wax• Pigments

Water Uptake •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)

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Physiologicaltrait crosses

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70% of new lines out-yielded the check, 2012

Check 3.5 t/ha (Vorobey)

New lines based on physiological trait (PT) criteria

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

•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) lines7 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

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Mean yields of 35 new PT lines v 7 elite checks:(average 7% advantage of new lines)

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/m2

CROPDESIGN

GENETICRESOURCES

PHENO-TYPING

GENETICANALYSIS

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

PT Heat + Parents (late sown Mexico)

PT CAL +PADs Y13-14 (march)Y13-14 YLD Gain BIOM NDVI Vg NDVI LLg

Cross Name g/m2 % BP g/m2

PASTOR//HXL7573/2*BAU/3/WBLL1 190 45% 395 0.675 0.472

PASTOR//HXL7573/2*BAU/3/WBLL1 179 37% 381 0.671 0.439SOKOLL/WBLL1 157 18% 457 0.690 0.550SOKOLL/WBLL1 142 7% 348 0.657 0.498SOKOLL/WBLL1 141 7% 330 0.658 0.488SOKOLL 132 315 0.659 0.469WEEBIL (CHECK) 131 294 0.671 0.448PASTOR//HXL7573/2*BAU 93 312 0.638 0.455MEAN 127.7 313 0.648 0.452r- yld 0.67 0.73 0.12LSD (5%) 33.5 96.2 0.07 0.05

PT CAL +PADs Y14-15 (march)Y14-15 YLD Gain BIOM NDVI Vg NDVI LLg

Cross Name g/m2 % BP g/m2

PASTOR//HXL7573/2*BAU/3/WBLL1 93 56% 196 0.262 0.335

PASTOR//HXL7573/2*BAU/3/WBLL1 78 30% 171 0.275 0.284SOKOLL/WBLL1 103 40% 253 0.323 0.418SOKOLL/WBLL1 118 61% 282 0.307 0.348SOKOLL/WBLL1 113 54% 265 0.363 0.356SOKOLL 73 176 0.255 0.317WEEBIL (CHECK) 60 140 0.307 0.279PASTOR//HXL7573/2*BAU 48 137 0.252 0.272MEAN 91.1 212 0.299 0.334r- yld 0.97 0.65 0.80LSD (5%) 37.1 84.2 0.12 0.09

physiological breeding: strategies & genetic gains matthew reynolds (cimmyt) contributions from:...

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