CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.

1. Regional impact assessment modelling September 2011

4. Representation: aggregation of modelled response Area Production Value County/Province State Country Agro-ecological zone E.g. yield predictions extrapolated from specific (sentinel) sites (a, b, c) a b a

5. - Run model based on the distributions of regional data and sensitivity tests - Multiple factors - Identify data that are important for given agricultural system/region - Many simulations - Aggregate sentinel site yields into regional production using agro-ecological zones and remotely-sensed information cultivar % Soil % Temperature % Aggregation of modelled response

6. Aggregation of modelled response: complete pixel coverage Area Production Value County/Province State Country AEZ Generate yield predictions for every pixel / land unit

10. Soils data: one option, FAO soils map of the world For each mapping unit: Fo 50% - Grade 2 Af 20% - Grade 2 Ao 20% - Grade 2 I 10% - Non Agric. Multiple “representative” DSSAT soil profiles for each of the ~83 FAO soil types (WISE databases)

12. http://labs.harvestchoice.org/2010/08/hc27-genericprototypical-soil-profiles/

15. Average LGP (current conditions), days per year

16. Average start of the primary growing season, day of year

17. Estimating planting dates regionally

18. Total crop area from national statistics (2000) compared with land cover products for Africa “… ideally … a hybrid product that combines the best of the … products, depending upon the region and country”  IIASA leading an effort to try to create such a hybrid Fritz et al. (2010)

19. Spatial crop information http://mapspam.info

21. A software system to run the simulations and analyse the results? Many options (including within DSSAT v4.5) Customised software Do it yourself, or … Issues of speed, cost, ease-of-use, …

22. An example: Agriculture and food systems in sub-Saharan Africa in a four-plus degree world To try to answer the question, “what will a +5°C agriculture look like in sub-Saharan Africa?” Specifically, what may happen to indicator crop yields in SSA as a result of such warming?

24. Ensemble mean of LGP change estimates to the 2090s Substantial losses away from equator, some small gains in parts of E Africa

25. Ensemble CV (%) of LGP change estimates to the 2090s Three zones – background small variation (<20), then higher in cropland (dark blue), then green and brown in arid-semiarid rangelands

27. Simulated yields (30 reps) in SSA under current conditions and in the 2090s

28. Simulated yields (30 reps) in SSA under current conditions and in the 2090s High CVs of yield changes elsewhere: results depend on choice of GCM & emissions scenario

29. Simulated yields (30 reps) in SSA under current conditions and in the 2090s Low CVs of yield changes in E Africa: quite a robust result

31. CCAFS-commissioned reports coming soon on AR4 GCM evaluation on the three target regions (IGP, Wsat Africa, East Africa) ccafs.cgiar.org

34. What will a +5 °C agriculture look like in SSA ?

35. A linear approach to “cascading uncertainty” Challinor (2009)

36. Alternatively: a decision-centred approach to support good decision-making, where climate change risk is recognised as only one driver Willows and Connell (2003)

38. What is currently happening on the ground, for translating into data layers for input to models Enormous system characterisation uncertainties