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Refining climate change impact estimates while generating climate-change-adaptive technologiesPresentation Transcript
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
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
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
Mining historical yield data to steer crop adaptation strategies for climate change
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”.
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.
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).
Meteorological data bases
Weather simulation groups
Yield data (National programs, GCIAR, private sector)
GxE analytical tools (PLS, factorial regression)
Selected CG crops for which good historic performance data exist (on station/on farm)
Trees- Provenance Trials (Agro-forestry)
Use variance parameters to develop confidence parameters on network data