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Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
Approaches to predict CC impact and devise breeding based strategies
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Approaches to predict CC impact and devise breeding based strategies

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Presented by Michael Dingkuhn at the CCAFS Workshop on Developing Climate-Smart Crops for a 2030 World, ILRI, Addis Ababa, Ethiopia, 6-8 December 2011.

Presented by Michael Dingkuhn at the CCAFS Workshop on Developing Climate-Smart Crops for a 2030 World, ILRI, Addis Ababa, Ethiopia, 6-8 December 2011.

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  • 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
  • 3. Pre-molecular breeding 3
  • 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 IntelligentStrategic choices choice of CC populations Target Knowledge Ideotype environments & intuition TPE Intelligent phenotypingMethodology designs Gene/allele Modeling Function & discoveryDiscovery Biparental regulation Diversity Pops Panels Marker Marker libraries validation,Validation GxExM MolecularApplication 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?

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