1. Mining historical yield data to steer crop adaptation strategies for climate change 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)

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), while looking at the potential for climate change mitigation.

12. Objectives cont ● Map cropping system mosaic based on predicted adaptive potential of specific crops in different regions in future climate scenarios (10y, 20y, 30y)

13. TOOLS/RESOURCES ● Meteorological data bases ● Weather simulation groups ● Yield data (National programs, GCIAR, private sector) ● GxE analytical tools (PLS, factorial regression)

14. CROPS ● Selected CG crops for which good historic performance data exist (on station/on farm) ● Trees- Provenance Trials (Agro-forestry)

15. SPIN-OFFS ● Use variance parameters to develop confidence parameters on network data and to develop additional ‘special focus’ networks

17. Mining historical yield data to steer crop adaptation strategies for climate change The wealth of data from decades of international crop yield trials –if collated and matched with weather data- can help: ● Identify climatic/geographic factors associated with drastic reductions in productivity. ● Identify climate-ready technologies; e.g. germplasm, crop management innovations. ● Pinpoint ‘analogue’ sites where networks can be established and new technologies can be developed/tested ● Pinpoint areas where policy interventions may help accelerate adaptation of cropping systems

18. Science content (interdisciplinary) Ag/resource management ● Genetic resources management through improved knowledge of adaptation targets Climate Change/uncertainty ● Adding a genetic and GxE dimension to climate projection based crop models Institutions/governance ● Pinpoint highly vulnerable regions as targets for institutional/governance interventions

19. Science content (interdisciplinary)… Gender Social differentiation ● Pinpoint highly vulnerable regions with extreme poverty/gender issues Spatial temporal scales ● 10, 20, 30y maps of high risk zones of present cropping systems to provide suites of options  Adaptation potential of crops to biotic/abiotic stress  Modify crop management  Design new crops/cropping system mosaic (e.g. involving South-South partnerships)  Policy interventions: irrigation, extension, etc

20. Science content relating to outcomes for: Food security (availability, access, utilization, stability) ● Analyses permit timely and ground-based recommendations for interventions Environment (hydrology, biodiversity, BGC incl GHG) ● Analyses will pinpoint changes in:  Demand for water, issues of water quality  Need for genetic resources exploration  Risk of soil degradation/desertification  Risk of extreme weather events causing crop failure  Threats and opportunities to mitigation strategies

21. Science content relating to outcomes for... Livelihoods (socioeconomic capitals) ● Recommendation to stabilize/ improve productivity ● Communication platforms developed to disseminate findings

22. Selling points Development value ● Fine tune deployment of agricultural technologies GEC science value (interdisciplinary)** ● Develop an easily accessible data base of field trials for future hypothesis testing within data or within the trial networks Ag Science value ● Meta-analysis of yield trials in context of climate change predictions How advancing CC agenda ** ● Adding an evidence base from genetic resources creating potential for greater impacts from crop-climate model outputs How helps deliver CCAFS agenda ● Significant syngergy with Theme 1 Link to stakeholders** ● Better use of genetic resources nationally and internationally ● Provides crop adaptation information to help shape policy