DROPS DROughttolerant yielding Plant S DROPS EU funded

DROPS DROught-tolerant yielding Plant. S 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 H 2 O CO 2 H 2 O Water for CO 2 Water flux through plants Courtesy of F. Tardieu Water for heat Leaf temperature (°C) CO 2 low high transpiration 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 100 s 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 andfor traits Consequence thegenetically project: related to yield 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 CO 2 H 2 O - 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 environ only (avoidance) Rebetzke et al. 2002 CO 2 Yield gain (%) H 2 O Rainfall (mm) Courtesy of F. Tardieu

Dissection : genetic variability? 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? 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 ò Architecture: which variables for a genetic and G x E analysis? Biomass = 0 Incident light * % intercepted * Radiation Use Efficiency (RUE) Digitizing Genetic / environmental analyses of parameters I II IV V QTL analysis

Dissection: DROPS genetic variability of leaf area/growth Biomass = ò t 0 Incident light * % Intercepted * Radiation Use Efficiency (RUE) * - 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 = ò t 0 Incident light * % intercepted * Radiation Use Efficiency (RUE) * Harvest index

Model-assisted phenotyping: "hidden variables" DROPS Incident light, Intercepted plant architecture light Biomass } Radiation use efficiency Transpiration Biomass = ò } 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 CO 2 H 2 O 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 = ò t 0 Incident light * % intercepted Radiation Use Efficiency (RUE) * Harvest index *

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 = ò } effect of allelic composition on plant performance t 0 Incident light * % intercepted Radiation Use Efficiency (RUE) * Harvest index Courtesy of F. Tardieu *

DROPS From phenotyping platforms to the field: modelling Virtual genotypes tested in 100 s of situation Input Model Output (100 years x management) Chenu et al. 2009 Genetics, Tardieu and Tuberosa 2010 Current Opinion Plant Biol

DROPS Coordinator: Francois Tardieu, INRA, France WP 1 Leader: Xavier Draye From phenotyping platforms to dry fields: development of new methods WP 2 Leader: Alain Charcosset Identification of genes and QTLs for drought tolerance WP 3 Leader: Graeme Hammer Comparative advantages of alleles and traits on crop performance WP 4 Leader: Bjorn Usadel Data collection, database, statistic and bioinformatic tool WP 5 Leader: Roberto Tuberosa Dissemination and technology transfer WP 6 Leader: Olga Mackre Project management