DROPS

DROught-tolerant yielding PlantS

DROPS

EU funded project (2010-2015)

Coordinated by François Tardieu (INRA)

PhenoDays2011 12-14 September 2011, Wageningen

DROPS

- 8.7 million euros

- 10 public organisations

- 11 countries

- 15 partners

- 5 companies

- 4 continents

DROPS

WP2 Leader: Alain Charcosset Identification of genes and QTLs for drought tolerance

WP3 Leader: Graeme Hammer

Comparative advantages of alleles and traits on crop performance

WP4 Leader: Bjorn Usadel

Data collection, database, statistic and bioinformatic tool

WP5 Leader: Roberto Tuberosa

Dissemination and technology transfer

WP6 Leader: Olga Mackre

Project management

WP1 Leader: Xavier Draye From phenotyping platforms to dry fields: development of new methods

Coordinator: Francois Tardieu, INRA, France

DROPS

CO2 H2O H2O

CO2

Water for CO2

Water flux through plants

A common ground from the very beginning

1. Drought tolerance is driven and limited by physics

Le

af

tem

pe

ratu

re (°

C)

time of day

low

35

25

15

0 0 12

high transpiration

Le

af

tem

pe

ratu

re (°

C)

time of day

high transpiration

35

25

15

0 0 12

low transpiration

Water

for heat

Courtesy of F. Tardieu

DROPS

A common ground from the very beginning

2. Any trait or QTL can have positive, negative or no consequence

on yield (Collins et al., 2008, Plant Phys 147: 469-486).

"IT DEPENDS" on the drought scenario (G x E x M)

Consequence for the project:

we want to explore a large number of scenarios

- Network of experiments (field + platforms)

- Modelling (simulation in 100s scenarios)

Courtesy of F. Tardieu

DROPS

A common ground from the very beginning

3. It is worth exploring the natural genetic variability?

Evolution/natural selection vs. modern agriculture

Consequence for the project:

exploring allelic effects

• panels for association mapping

• biparental crosses

• introgression lines

Courtesy of F. Tardieu

DROPS

Plant Accelerator

ACPFG

Adelaide

DROPS A common ground from the very beginning

4. Dissection + modelling, a key method

Yield is too complex – particularly under different drought scenarios – for

a direct association mapping study approach

Need for targeting under controlled conditions less complex processes

and traits genetically related to yield

Consequence for the project:

Genetic variability of

- Processes: hydraulics, metabolism, transpiration, growth

- Traits: leaf growth/architecture, root architecture,

seed abortion, water use efficiency

- Yield, components

Processes assembled via models (statistical + functional)

Courtesy of F. Tardieu

DROPS

Objectives Develop methods that increase the efficiency of breeding under water deficit -Novel indicators: “Identity cards” of genotypes: heritable traits genetically related to yield -Explore the natural variation: identify genomic regions that control key traits; assess the effects of a large allelic diversity under a wide range of scenarios -Develop models for estimating the comparative advantages of alleles and traits in fields with contrasting drought scenarios Courtesy of F. Tardieu

DROPS

Three crops

• Maize

• Durum wheat

• Bread wheat

Comparative approaches:

- common mechanisms?

- common models?

- common causal polymorphisms / QTLs?

Courtesy of F. Tardieu

DROPS

CO2 H2O

Four traits

1. Leaf growth / architecture

- Genetic variability of growth response

to water deficit?

- Genetic variability of plant architecture

and its change with water deficit?

- Consequence of allelic diversity on

yield depending on drought scenarios

- METHODS Courtesy of F. Tardieu

DROPS Four traits

2. Root architecture

• Genetic variability of architectural traits

(not biomass)

• Consequence of allelic diversity on

water uptake and yield depending on

drought scenarios

• METHODS

Courtesy of F. Tardieu

DROPS

Horizontal root spread (cm)

120 90 60 30 0 30 60 90 120

Ro

oti

ng

dep

th (

cm

)

0.0

22.5

45.0

67.5

90.0

112.5

Hartog

SeriM82

Mackay

Varieties differ in RSA – Seri root system more compact

Wheat Root System Architecture

Courtesy of G. Hammer

DROPS

Horizontal root spread (cm)

120 90 60 30 0 30 60 90 120

Ro

oti

ng

dep

th (

cm

)

0.0

22.5

45.0

67.5

90.0

112.5

Hartog

SeriM82

Mackay

17 22

18 23

19 24

20 25

Consequences of RSA differences on water extraction at depth

G-to-P Modelling as the missing link

Courtesy of G. Hammer

DROPS

Kofa Lloyd 1 cm

Sanguineti et al. (2007). Ann Appl Biol 151, 291–305

DROPS

Sanguineti et al. (2007). Ann Appl Biol 151, 291–305

NILs for Root-yield-1.06 (Landi et al., 2010, J. Exp. Bot. 61: 3553-62)

Lower yield Higher yield

+ / +

ABA - / -

ABA

Lower yield Higher yield

NILs for Root-ABA1 Landi et al., 2007, J. Exp. Bot. 58: 319

DROPS

Four traits

3. Seed abortion

Main source of progress in recurrent

selection for yield in maize at CIMMYT

(Tuxpeno Sequia)

A main cause of yield loss in wheat

METHODS

Courtesy of F. Tardieu

DROPS

Four traits

4. Water use efficiency

A success story in wheat

H2O

CO2

Rainfall (mm)

Wheat genotypes with high WUE.

Positive effect in very dry environments

only (avoidance)

Rebetzke et al. 2002

Yie

ld g

ain

(%

)

Courtesy of F. Tardieu

DROPS Approach for phenotyping D

issection :

genetic v

ariabili

ty?

Field

Phenotyping platform

+ modelling: target

more heritable traits

Genetic analysis

of heritable traits

Experim

ents + sim

ulation

agronomic value of alleles in clim

atic scenarios?

Tardieu & Tuberosa 2010, Current Opinion in Plant Biology

DROPS Dissection

Phenotyping platform: identify heritable traits of genotypes

- amenable to genetic analysis

- usable in modelling for predicting genotype performance

in diverse climatic scenarios

(NOT a means to measure yield and yield component,

not reliable in pot experiments)

Courtesy of F. Tardieu

DROPS Dissection: genetic variability of plant architecture

Architecture: which variables for a genetic and G x E analysis? Digitizing

Biomass = Incident light * % intercepted * Radiation Use Efficiency (RUE) Biomass = Incident light * % intercepted * Radiation Use Efficiency (RUE) t

0 0

Genetic / environmental

analyses of parameters I II III IV V

QTL analysis

DROPS

- Daily increase in leaf area at plant level

- (tentative) daily increase in leaf length, response to water deficit

and evaporative demand

Dissection: genetic variability of leaf area/growth

Biomass = Incident light * % Intercepted *

*

Radiation Use Efficiency (RUE) t

0 t

0

Courtesy of F. Tardieu

DROPS

Imaging hidden organs?

Dissection: genetic variability of seed abortion

Incident light * % intercepted * Radiation Use Efficiency (RUE) Yield = Incident light * % intercepted * Radiation Use Efficiency (RUE) * Harvest index t

0 t

0

DROPS

CO2 H2O

Heritable traits collected in

phenotyping platform (max growth,

architecture with responses to water deficit...)

Allow calculation of biomass accumulation

in field situations with diverse scenarios:

EFFECT OF ALLELIC DIVERSITY

From phenotyping platforms to the field: modelling

*

Yield = Incident light * % intercepted

*

Radiation Use Efficiency (RUE) * Harvest index t

0 t

0

DROPS

virtual plant / genotype

(with effect of QTLs)

effect of allelic

composition on

plant performance

Climatic data

calculated feedbacks of plants on

environment (e.g. soil depletion)

From phenotyping platforms to the field: modelling

*

Yield = Incident light * % intercepted

*

Radiation Use Efficiency (RUE) * Harvest index t

0 t

0

Courtesy of F. Tardieu

DROPS

Input Output

(100 years x management)

Model

Environment

Gene - to - phenotype

model

Yield (median)Genetic information

-

QTL1 QTL 2

-100

0

+100

QTL 1 QTL 2

QTL1 QTL2

0.0

0.1

0.2

Terminal mild

water deficit

Water deficit at

seed set + seed filling

Effect

(Kg)

QTL effects on leaf growth

0.0

0.1

0.2

QT

L e

ffect

on m

ax.

elo

ngation

rate

or

sensitiv

ity

mm

°C

d-1

or

mm

°C

d-1

MP

a-1

Environment

Gene - to - phenotype

model

Yield (median)Genetic information

-

QTL1 QTL 2

-100

0

+100

QTL 1 QTL 2

QTL1 QTL2

0.0

0.1

0.2

Terminal mild

water deficit

Water deficit at

seed set + seed filling

Effect

(Kg)

QTL effects on leaf growth

0.0

0.1

0.2

QT

L e

ffect

on m

ax.

elo

ngation

rate

or

sensitiv

ity

mm

°C

d-1

or

mm

°C

d-1

MP

a-1

Chenu et al. 2009, Genetics; Tardieu and Tuberosa, 2010, Current Opinion Plant Biol

Virtual genotypes tested in 100s of situation

From phenotyping platforms to the field: modelling

DROPS Phenotyping is king…

…and heritability is queen!

N28E N28

N28E N28

Vegetative to generative transition 1 (Vgt1)

Salvi et al., 2007. Proc. Nat. Acad. Sci. 104: 11376

Gaspé Flint

www.generationcp.org/drought_phenotyping

INTERDROUGHT-IV 5-9 September 2013

Burswood Entertainment Complex

Perth, Western Australia

Congress Chair: Roberto Tuberosa, Italy Program Committee Chair: Graeme Hammer, Australia Local Organizing Committee Chair: Mehmet Cakir, Australia

www.interdrought4.com