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Approaches to predict CC impact and
     devise breeding based strategies
             Michael Dingkuhn, CIRAD-IRRI-CCAFS

“Developing Climate-Smart Crops for a 2030 World” Workshop
       ILRI, Addis Ababa, Ethiopia, 6-8 December 2011
Questions
• What info do breeders use? What do they need?
• How will breeding be like in 10, 20, 30 years?

• What will the environment (climate) be like in 2030, 2050… ?
• What does that mean in terms of adaptation and potential?

• What will systems and markets be like in 2030, 2050… ?
• E.g.: Biofuel vs food, demography, water available for agriculture
• Good land & water becoming most valuable (costly) resources?

   Impact & adaptation strategies ???
    Colorful impact maps please donors, don’t help breeders
Pre-molecular breeding




                         3
Breeding with 3rd generation MAS: Genome-wide selection




• Phenotyping &
 • Very dense genotyping in a
 • Training population                  Selection using
(representative of breeding program)
                                        genotypic data - Genomic selection
                                                        - Genome-wide MARS
• Estimate trait value of all markers        only
(BLUP, linear model)                                                 4
Creative thinking & wild bets                     Forcing by target environment

                                       Intelligent
Strategic choices                       choice of
                                                                                  CC
                                      populations
                                                                                 Target
              Knowledge
                                                              Ideotype        environments
              & intuition                                                          TPE
                                       Intelligent
                                      phenotyping
Methodology                              designs


                                      Gene/allele                             Modeling
                                                           Function &
                                       discovery
Discovery                Biparental                        regulation
                 Diversity Pops
                  Panels
                                        Marker                   Marker
                                       libraries                validation,
Validation                                                       GxExM


                                       Molecular
Application                            breeding
Key concept: TPE
            Target population of environments


•   Target Population of Environments
•   Needed to guide breeding
•   Evaluate ‘thru eyes of the crop’: Modeling
•   Diversity in space and time (inter/intra-annual)
•   Present => future TPEs


      Global                                 Potential paths
     analysis                                to solutions

                        TPE Zoom-ins
Knowledge




                               Number of environments



 Focus boldly                               Avoid misleading quantitities
 Use existing knowledge                    (yield), they will be wrong
 Bind in existing projects                  Weed-out non-sensical
 Capture the tendon of Achilles            results (wheat in Amazonia)
 Give impulses for innovation
                                   Consultative process
Example: Tropical Irrigated Rice
• Global study must get right the following:
    – Geographic projection domain (current & potential areas)
    – Phenology & climatic yield potential, potential water use
    – Impact of thermal stresses & CO2 on the above (current HYV)
• Zoom-in Nr. 1 (of 3):
   TPE Dry-season Irrigated rice in IGP (rice-wheat)
    –   How will CC & CO2 increase affect YP and water use?
    –   What will be the heat effect on sterility? Interaction w/ CO2?
    –   What is the margin for water saving, & trade-off with heat?
    –   How effective will heat avoidance be? (transpiration cooling, time of F)
    –   How effective would optimized phenology x sowing dates be?
    Hypothetical ideotype:
    Ultra-short duration (to save water), efficient use of CO2 increase (vigor),
    Crowding tolerance (for direct seeding to save water), early-morning anthesis
    (to escape from heat), high transpiration (to cool canopy and increase vigor)

                                                 One PhD thesis per zoom-in?

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Approaches to predict CC impact and devise breeding based strategies

  • 1. Approaches to predict CC impact and devise breeding based strategies Michael Dingkuhn, CIRAD-IRRI-CCAFS “Developing Climate-Smart Crops for a 2030 World” Workshop ILRI, Addis Ababa, Ethiopia, 6-8 December 2011
  • 2. Questions • What info do breeders use? What do they need? • How will breeding be like in 10, 20, 30 years? • What will the environment (climate) be like in 2030, 2050… ? • What does that mean in terms of adaptation and potential? • What will systems and markets be like in 2030, 2050… ? • E.g.: Biofuel vs food, demography, water available for agriculture • Good land & water becoming most valuable (costly) resources? Impact & adaptation strategies ??? Colorful impact maps please donors, don’t help breeders
  • 4. Breeding with 3rd generation MAS: Genome-wide selection • Phenotyping & • Very dense genotyping in a • Training population Selection using (representative of breeding program) genotypic data - Genomic selection - Genome-wide MARS • Estimate trait value of all markers only (BLUP, linear model) 4
  • 5. Creative thinking & wild bets Forcing by target environment Intelligent Strategic choices choice of CC populations Target Knowledge Ideotype environments & intuition TPE Intelligent phenotyping Methodology designs Gene/allele Modeling Function & discovery Discovery Biparental regulation Diversity Pops Panels Marker Marker libraries validation, Validation GxExM Molecular Application breeding
  • 6. Key concept: TPE Target population of environments • Target Population of Environments • Needed to guide breeding • Evaluate ‘thru eyes of the crop’: Modeling • Diversity in space and time (inter/intra-annual) • Present => future TPEs Global Potential paths analysis to solutions TPE Zoom-ins
  • 7. Knowledge Number of environments  Focus boldly  Avoid misleading quantitities  Use existing knowledge (yield), they will be wrong  Bind in existing projects  Weed-out non-sensical  Capture the tendon of Achilles results (wheat in Amazonia)  Give impulses for innovation Consultative process
  • 8. Example: Tropical Irrigated Rice • Global study must get right the following: – Geographic projection domain (current & potential areas) – Phenology & climatic yield potential, potential water use – Impact of thermal stresses & CO2 on the above (current HYV) • Zoom-in Nr. 1 (of 3): TPE Dry-season Irrigated rice in IGP (rice-wheat) – How will CC & CO2 increase affect YP and water use? – What will be the heat effect on sterility? Interaction w/ CO2? – What is the margin for water saving, & trade-off with heat? – How effective will heat avoidance be? (transpiration cooling, time of F) – How effective would optimized phenology x sowing dates be? Hypothetical ideotype: Ultra-short duration (to save water), efficient use of CO2 increase (vigor), Crowding tolerance (for direct seeding to save water), early-morning anthesis (to escape from heat), high transpiration (to cool canopy and increase vigor) One PhD thesis per zoom-in?