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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
- 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 (°C)
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

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 =

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 =

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 =

}
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 =

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 =

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 °Cd -1 or mm °Cd -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