Refining climate change impact estimates while generating climate-change-adaptive technologies
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Refining climate change impact estimates while generating climate-change-adaptive technologies

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Presentation from CCAFS Science Workshop, Bonn, 11th June 2011.

Presentation from CCAFS Science Workshop, Bonn, 11th June 2011.

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Refining climate change impact estimates while generating climate-change-adaptive technologies Presentation Transcript

  • 1. Refining climate change impact estimates while generating climate-change-adaptive technologies E.g. CIMMYT has distributed approx 1,000 new wheat genotypes p.a. in targeted environments for over 30 years
  • 2. International yield data –if matched with weather data- can help:  
    • 1) Identify factors associated with drastic reductions in productivity, e.g.:
      • temperature thresholds (e.g. Lobell et al., 2011)
      • extreme in-season weather variation
      • specific geographic regions/communities
      • vulnerable stages of crop development
  • 3. International yield data –if matched with weather data- can help (cont)… :   2) Pinpoint ‘analog’ sites where new technologies can be developed and tested 3) Integrate diverse datasets (biophysical, genetic, and socioeconomic) to help make crop and bio-economic models decision making more relevant. 4) Deploy climate-ready technologies
  • 4. Germplasm deployment
    • GxE analysis to identify favorable “outliers” for:
      • Immediate deployment of germplasm to collaborators/ farmers in climate vulnerable regions (via NARES)
      • Crossing with locally-adapted material (via NARS)
      • Targeting genetic resource exploration (via gene banks)
      • Basic research addressing genetic bottlenecks (via AIs)
  • 5. Crop management innovations
    • Identify environments for which crop management interventions may be complementary or superior to genetic strategies.
    • Through identification of susceptible growth stages, target most appropriate crop management intervention(s).
    (in partnership with environmental crop modelers, NARES, NGOs, farmers)
  • 6. Links of yield analysis to GEC community
    • Simulation of climate data
    • Use of climate and socioeconomic models to prioritize crop adaptation strategies:
      • Breeding objectives
      • Use of genetic resources (where low genetic variance identified)
      • Genetic resource collection in terms of priority targets and rate of climate change (how urgent is it to collect genetic resources)
      • Crop management interventions where genetic solutions may not be feasible.
      • Poverty and vulnerability focus.
  • 7. Links of yield analysis to GEC community
    • Stratification of analogue sites over time (10y, 20y, 30y) as well as space
    • Understand environmental basis of biological (rather than physical) analog sites (based on behavior of genotypes, GxE etc).
    • Food security modeling (e.g. Lobell, Batisti, etc)
  • 8.  
  • 9. Mining historical yield data to steer crop adaptation strategies for climate change
    • Objectives
    • Use simulated climate data to identify adaptation needs of crops in a changing climate.
    •  
    • Predict potential resilience of crops and cultivars to future climates using historic yield and climate data.
    • Integrate climate and crop models into a calibration and validation “reality check”.
    •  
  • 10. Objectives cont
    • Use climate models to pinpoint specific analogue sites:
      • Based on temperature thresholds
      • Extreme weather variation
      • Crop sensitive stages
    •  
    • Assess the full spectrum of environmental factors that determine crop adaptation (e.g. soil chemistry, salinity, water quality, pollution, soil degradation, altitude, maritime versus continental climate, etc)
    • Use climate models to identify regions with promising gene pools and to map genetic resource collection priorities.
  • 11. Objectives cont
    • Map adaptation potential of resilient crops and germplasm.
    • Map adaptation gaps -i.e. environments where zero genetic resilience is expressed related to biophysical factors- to prioritize other types of intervention.
    •  
    • Map apparent yield gaps –of on farm trials- related to agronomic (fertility, irrigation, rotation etc), socioeconomic factors (poverty, population pressures, gender), and institutional factors (subsidies, corruption, political regimes).
  • 12. TOOLS/RESOURCES
    • Meteorological data bases
    • Weather simulation groups
    • Yield data (National programs, GCIAR, private sector)
    • GxE analytical tools (PLS, factorial regression)
    •  
  • 13. CROPS
    • Selected CG crops for which good historic performance data exist (on station/on farm)
    • Trees- Provenance Trials (Agro-forestry)
  • 14. SPIN-OFFS
    • Use variance parameters to develop confidence parameters on network data