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DROPS

DROught-tolerant yielding PlantS

DROPS EU funded project (2010-2015)

Coordinated by Franรงois Tardieu (INRA)

Kick-off Meeting, Montpellier, 27-29 August, 2010


DROPS

- 8.7 million euros - 15 partners - 10 public organisations - 5 companies - 11 countries - 4 continents


DROPS A common ground from the very beginning 1. Drought tolerance is driven and limited by physics H2O

CO2 H2O Water for CO2

Water flux through plants

Courtesy of F. Tardieu

Water for heat

Leaf temperature (째C)

CO2 high transpiration low

35

low high transpiration

25 15 0

12 time of day

0


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 Four traits 1. Leaf growth / architecture

CO2 H2O - 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

Courtesy of F. Tardieu

- METHODS


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 Wheat genotypes with high WUE. Positive effect in very dry environments only (avoidance) Rebetzke et al. 2002

CO2

Yield gain (%)

H 2O

Rainfall (mm)

Courtesy of F. Tardieu


Field

Phenotyping platform + modelling: target more heritable traits

Genetic analysis of heritable traits

Tardieu & Tuberosa 2010, Current Opinion in Plant Biology

Experiments + simulation agronomic value of alleles in climatic scenarios?

Dissection : genetic variability?

Approach for phenotyping DROPS


Dissection DROPS

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


Dissection: DROPS genetic variability of plant architecture t

ďƒ˛ Architecture: which variables for a genetic and G x E analysis? Biomass =

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

0

Digitizing

Genetic / environmental analyses of parameters I

II

III

IV

V

QTL analysis


Dissection: DROPS genetic variability of leaf area/growth Biomass =

ďƒ˛

t Incident light * % Intercepted * Radiation Use Efficiency (RUE)

0

* - Daily increase in leaf area at plant level - (tentative) daily increase in leaf length, response to water deficit and evaporative demand

Courtesy of F. Tardieu


Dissection: DROPS genetic variability of seed abortion

Imaging hidden organs?

Yield =

ďƒ˛

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

0


Model-assisted phenotyping: "hidden variables" DROPS

Incident light,

Intercepted plant architecture light Biomass

}

Radiation use efficiency

Transpiration Biomass =

ďƒ˛

}

Stomatal conductance, water use efficiency

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

0

Courtesy of F. Tardieu


DROPS From phenotyping platforms to the field: modelling 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

Yield =

ďƒ˛

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

0

*


DROPS From phenotyping platforms to the field: modelling

Climatic data

virtual plant / genotype (with effect of QTLs) calculated feedbacks of plants on environment (e.g. soil depletion)

Yield =

ďƒ˛

effect of allelic composition on plant performance

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

0

Courtesy of F. Tardieu

*


DROPS From phenotyping platforms to the field: modelling Virtual genotypes tested in 100s of situation

Input

Model

Environment

Output (100 years x management)

Genetic information

Yield (median)

-

Terminal mild water deficit

Gene - to - phenotype model

0.2 0.1 0.0

Effect (Kg)

mm 째Cd-1 or mm 째Cd-1 MPa-1

QTL effect on max. elongation rate or sensitivity

QTL effects on leaf growth +100 0 -100

QTL1 QTL 2

0.2

Water deficit at seed set + seed filling

QTL 1

QTL 2

0.1 0.0

QTL1

QTL2

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


DROPS Coordinator: Francois Tardieu, INRA, France WP1 Leader: Xavier Draye From phenotyping platforms to dry fields: development of new methods 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


DROPS: An EU-funded project to improve crop performance under drought conditions