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Coffee and Climate Change in Peru


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Coffee and Climate Change in Peru

  1. 1. The impact of climate change on coffee suitability in Piura, Peru Peter Laderach (CIAT) [email_address]
  2. 2. STUDY AREA
  3. 3. METHODOLOGY WorldClim (Hijmans et al, 2005) Climate (Current)
  4. 4. METHODOLOGY WorldClim (Hijmans et al, 2005) Climate (Current)
  5. 5. METHODOLOGY Climate (Current) 45 227 257 332 Mean P. 12 218 41 65 Mean T. Piura Nicaragua Chiapas Veracruz Stations
  6. 6. METHODOLOGY <ul><li>“ Global climate models” (GCMs) based on atmospheric science, chemistry, physics, biology and some astrology </li></ul><ul><li>Runs from the past (to calibrate) and into the future </li></ul><ul><li>Uses different gas emissions scenarios </li></ul>Climate (Future)
  7. 7. METHODOLOGY Climate (Future)
  8. 8. METHODOLOGY Variables
  9. 9. METHODOLOGY <ul><li>Current Climate </li></ul><ul><li>19 bioclimatic variables (WorldClim) </li></ul><ul><li>Climate Change </li></ul><ul><li>Downscaling: Spline interpolation (same as used in WorldClim) </li></ul><ul><li>Generation of 19 bioclimatic variables </li></ul><ul><li>Future Climate </li></ul><ul><li>Current Climate + Change = Future Climate </li></ul>
  10. 10. METHODOLOGY Evidence data
  11. 11. METHODOLOGY Crop prediction models <ul><li>ECOCROP ( ) </li></ul><ul><li>BioClim (Diva-GIS) </li></ul><ul><li>Domain (Diva-GIS) </li></ul><ul><li>MAXENT (Phillips et al, 2006) </li></ul><ul><li>CaNaSTA (Obrien, 2004) </li></ul><ul><li>Neural Networks </li></ul><ul><li>… </li></ul>
  12. 12. RESULTS Cross Regional Impact
  13. 13. RESULTS Cross Regional Impact
  14. 14. RESULTS Cross Regional Impact
  15. 15. RESULTS Regional Impact Piura
  16. 16. RESULTS Regional Impact Piura
  17. 17. RESULTS Regional Impact Piura
  18. 18. RESULTS Regional Impact Piura
  19. 19. RESULTS Regional Impact Piura
  20. 20. RESULTS Regional Impact Piura
  21. 21. RESULTS Regional Impact Piura Change (Alternatives) Adapt agronomic management New opportunities
  22. 22. RESULTS Regional Impact
  23. 23. RESULTS Regional Impact
  24. 24. RESULTS Data confidence
  25. 25. CONCLUSIONS <ul><li>Magnitude of impact varies site-specifically </li></ul><ul><li>Site specific management is the answer to CC </li></ul><ul><li>There will be: </li></ul><ul><ul><li>Areas not suitable any more (other crops) </li></ul></ul><ul><ul><li>Areas still suitable (continue w. coffee) </li></ul></ul><ul><ul><li>Areas with new potential (coffee) </li></ul></ul><ul><li>Winners are the ones who are </li></ul><ul><li>prepared for change </li></ul>
  26. 26. RECOMMENDATIONS <ul><li>Use site specific approach </li></ul><ul><li>Backup results with expert knowledge </li></ul><ul><li>Define road map for specific areas </li></ul><ul><ul><li>Identify alternative crops </li></ul></ul><ul><ul><li>Adjust mgt (shade, irrigation, varieties, etc) </li></ul></ul><ul><ul><li>Identify new potentials </li></ul></ul>
  27. 27. IMPROVMENTS <ul><li>Predictions per decades </li></ul><ul><li>Evaluation of GCM’s </li></ul><ul><li>Improved sampling design </li></ul><ul><li>Better distribution of evidence data </li></ul>
  28. 28. ¡Muchas gracias! Peter Laderach (CIAT) [email_address]