Refining climate change impact estimates while generating climate-change-adaptive technologies E.g. CIMMYT has distributed...
International yield data –if matched with weather data- can help:   <ul><li>1) Identify factors associated with drastic re...
International yield data –if matched with weather data- can help (cont)… :   2) Pinpoint ‘analog’ sites where new technolo...
Germplasm deployment <ul><li>GxE analysis to identify favorable “outliers” for: </li></ul><ul><ul><li>Immediate deployment...
Crop management innovations <ul><li>Identify environments for which crop management interventions may be complementary or ...
Links of yield analysis to GEC community <ul><li>Simulation of climate data </li></ul><ul><li>Use of climate and socioecon...
Links of yield analysis to GEC community <ul><li>Stratification of analogue sites over time (10y, 20y, 30y) as well as spa...
 
Mining historical yield data to steer crop adaptation strategies for climate change <ul><li>Objectives </li></ul><ul><li>U...
Objectives cont <ul><li>Use climate models to pinpoint specific analogue sites: </li></ul><ul><ul><li>Based on temperature...
Objectives cont <ul><li>Map adaptation potential of resilient crops and germplasm. </li></ul><ul><li>Map adaptation gaps -...
TOOLS/RESOURCES <ul><li>Meteorological data bases </li></ul><ul><li>Weather simulation groups </li></ul><ul><li>Yield data...
CROPS <ul><li>Selected CG crops for which good historic performance data exist (on station/on farm)  </li></ul><ul><li>Tre...
SPIN-OFFS <ul><li>Use variance parameters to develop confidence parameters on network data </li></ul>
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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.

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

  1. 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. 2. International yield data –if matched with weather data- can help:   <ul><li>1) Identify factors associated with drastic reductions in productivity, e.g.: </li></ul><ul><ul><li>temperature thresholds (e.g. Lobell et al., 2011) </li></ul></ul><ul><ul><li>extreme in-season weather variation </li></ul></ul><ul><ul><li>specific geographic regions/communities </li></ul></ul><ul><ul><li>vulnerable stages of crop development </li></ul></ul>
  3. 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. 4. Germplasm deployment <ul><li>GxE analysis to identify favorable “outliers” for: </li></ul><ul><ul><li>Immediate deployment of germplasm to collaborators/ farmers in climate vulnerable regions (via NARES) </li></ul></ul><ul><ul><li>Crossing with locally-adapted material (via NARS) </li></ul></ul><ul><ul><li>Targeting genetic resource exploration (via gene banks) </li></ul></ul><ul><ul><li>Basic research addressing genetic bottlenecks (via AIs) </li></ul></ul>
  5. 5. Crop management innovations <ul><li>Identify environments for which crop management interventions may be complementary or superior to genetic strategies. </li></ul><ul><li>Through identification of susceptible growth stages, target most appropriate crop management intervention(s). </li></ul>(in partnership with environmental crop modelers, NARES, NGOs, farmers)
  6. 6. Links of yield analysis to GEC community <ul><li>Simulation of climate data </li></ul><ul><li>Use of climate and socioeconomic models to prioritize crop adaptation strategies: </li></ul><ul><ul><li>Breeding objectives </li></ul></ul><ul><ul><li>Use of genetic resources (where low genetic variance identified) </li></ul></ul><ul><ul><li>Genetic resource collection in terms of priority targets and rate of climate change (how urgent is it to collect genetic resources) </li></ul></ul><ul><ul><li>Crop management interventions where genetic solutions may not be feasible. </li></ul></ul><ul><ul><li>Poverty and vulnerability focus. </li></ul></ul>
  7. 7. Links of yield analysis to GEC community <ul><li>Stratification of analogue sites over time (10y, 20y, 30y) as well as space </li></ul><ul><li>Understand environmental basis of biological (rather than physical) analog sites (based on behavior of genotypes, GxE etc). </li></ul><ul><li>Food security modeling (e.g. Lobell, Batisti, etc) </li></ul>
  8. 9. Mining historical yield data to steer crop adaptation strategies for climate change <ul><li>Objectives </li></ul><ul><li>Use simulated climate data to identify adaptation needs of crops in a changing climate. </li></ul><ul><li>  </li></ul><ul><li>Predict potential resilience of crops and cultivars to future climates using historic yield and climate data. </li></ul><ul><li>Integrate climate and crop models into a calibration and validation “reality check”. </li></ul><ul><li>  </li></ul>
  9. 10. Objectives cont <ul><li>Use climate models to pinpoint specific analogue sites: </li></ul><ul><ul><li>Based on temperature thresholds </li></ul></ul><ul><ul><li>Extreme weather variation </li></ul></ul><ul><ul><li>Crop sensitive stages </li></ul></ul><ul><li>  </li></ul><ul><li>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) </li></ul><ul><li>Use climate models to identify regions with promising gene pools and to map genetic resource collection priorities. </li></ul>
  10. 11. Objectives cont <ul><li>Map adaptation potential of resilient crops and germplasm. </li></ul><ul><li>Map adaptation gaps -i.e. environments where zero genetic resilience is expressed related to biophysical factors- to prioritize other types of intervention. </li></ul><ul><li>  </li></ul><ul><li>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). </li></ul>
  11. 12. TOOLS/RESOURCES <ul><li>Meteorological data bases </li></ul><ul><li>Weather simulation groups </li></ul><ul><li>Yield data (National programs, GCIAR, private sector) </li></ul><ul><li>GxE analytical tools (PLS, factorial regression) </li></ul><ul><li>  </li></ul>
  12. 13. CROPS <ul><li>Selected CG crops for which good historic performance data exist (on station/on farm) </li></ul><ul><li>Trees- Provenance Trials (Agro-forestry) </li></ul>
  13. 14. SPIN-OFFS <ul><li>Use variance parameters to develop confidence parameters on network data </li></ul>

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