DROPS

DROught-tolerant yielding PlantS

DROPS

EU funded project (2010-2015)

Coordinated by François Tardieu (INRA)

Kick-off Meeting, Montpellier,

27-29 August, 2010

DROPS

- 8.7 million euros

- 10 public organisations

- 11 countries

- 15 partners

- 5 companies

- 4 continents

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 can have positive, negative or no consequence

on yield. "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 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)

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

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 :

gen

etic v

ari

abili

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

plant architecture

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

Incident light, Intercepted

light

Transpiration

} Stomatal

conductance,

water use

efficiency

Model-assisted phenotyping: "hidden variables"

Biomass

} Radiation

use

efficiency

t

0 t

0

Courtesy of F. Tardieu

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

Eff

ect

(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

Eff

ect

(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

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