This document discusses approaches for plant breeders to predict climate change impacts and develop breeding strategies. It questions what information breeders currently use and will need in the future as climate changes. Breeding approaches may involve modeling target populations of environments to evaluate crops, as well as using genomic selection and ideotypes tailored to climate change. An example focuses on irrigated rice in South Asia and identifying heat tolerant and water efficient ideotypes. The document emphasizes using existing knowledge and projects to guide innovative climate-smart breeding.
Boost Fertility New Invention Ups Success Rates.pdf
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
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?