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