Successfully reported this slideshow.

The Earth Institute Coffee Report

0

Share

Upcoming SlideShare
ZNetLive- A Quick Overview
ZNetLive- A Quick Overview
Loading in …3
×
1 of 52
1 of 52

More Related Content

Related Books

Free with a 14 day trial from Scribd

See all

Related Audiobooks

Free with a 14 day trial from Scribd

See all

The Earth Institute Coffee Report

  1. 1. Coffee and Climate Trouble brewing Jeffrey Sachs, James Rising Tim Foreman, John Simmons, Manuel Brahm Global Coffee Forum, October 1, 2015
  2. 2. A global perspective Arabica producers Robusta producers Mixed producers Exports from consumers The network of global coffee trade
  3. 3. Three scales of impacts Suitability ProductionVariability Long-term trend Decadal variability Interannual variability
  4. 4. Climate change: Temperature  The coffee belt is warming: +2.1° C (1.7° – 2.5°)  Can coffee move 600 km from the poles, or 380 m up?  Extremes will get become extreme
  5. 5. Climate change: Precipitation  Small, uncertain increase in rainfall: +1.7% (-0.1 – 3.2%)  Extremes will get more extreme  Driest month losing 2 – 12% of precipitation
  6. 6. Suitability inputs V ANNEX 4: THE HWSD VIEWER V.1 Introduction The purpose of the HWSD-Viewer16 is to provide a simple geographical tool to query and visualize the Harmonized World Soil Database. The HWSD consists of a 30 arc-second (or ~1 km) raster image and n attribute database in Microsoft Access 2003 format. The raster image file is stored in binary format ESRI Band Interleaved by Line - BIL) that can directly be read or imported by most GIS and Remote Sensing software. For advanced use or data extraction of the HWSD, it is recommended to use a GIS oftware tool. V.2 System Requirements The HWSD-Viewer requires a Pentium III computer or better with a recommended minimum rocessor speed of 1 GHz. Windows version 98 or later is required as operating system. A minimum of 2 GB of free hard disk space is required for running the software. You can install the oftware on a computer with less free disk space, but you will not be able to view the data layer. The HWSD raster image is stored in compressed format but needs to be decompressed by the viewer. You an request to delete this file every time when closing the application, and in this case, the software Temperatures Precipitations Soil Properties Elevation Urban Regions Protected Areas
  7. 7. Current suitability  Arabica suitability  Robusta suitability Urban Protected Suitable (~potential yield)
  8. 8. Future Arabica Suitability Changes in suitability Statistical confidence Final suitability
  9. 9. Changes in Arabica suitability -2.5e+07 0.0e+00 2.5e+07 5.0e+07 7.5e+07 Brazil Indonesia C olom biaM exico VietnamEthiopiaIndiaPeruU ganda H onduras G uatem ala C am eroon VenezuelaKenya C ote d'Ivoire ElSalvador M adagascar D om inican R epublic Tanzania N icaragua Philippines D em ocratic R epublic ofthe C ongo Ecuador Papua N ew G uineaYem enAngolaBoliviaZam biaM alaw i M ozam bique South Africa N am ibia Suitabilityandchanges Current cultivation Baseline suitability Suitability change
  10. 10. Changes in Robusta suitability 0.0e+00 2.5e+07 5.0e+07 7.5e+07 Brazil Indonesia C olom bia M exico Vietnam EthiopiaIndiaPeru U ganda H onduras G uatem ala C am eroon VenezuelaKenya C ote d'Ivoire ElSalvador M adagascar D om inican R epublic Tanzania N icaragua Philippines D em ocratic R epublic ofthe C ongo Ecuador Papua N ew G uineaC hina Lao PD R Yem en Angola BoliviaC uba M alaysia C entralAfrican R epublic R epublic ofC ongo Zam bia Liberia M alaw i M ozam bique G abon Paraguay South Africa N am ibia M orocco Botsw ana Australia Argentina Suitabilityandchanges Current cultivation Baseline suitability Suitability change
  11. 11. El Niño is coming Precipitation changes during the 1997/98 event (American Meteorological Society, 1998).
  12. 12. Impact on prices  Historical price rise of 30% after an El Niño 5 10 15 20 −200204060 Arabica Price Response to El Nino Months from event beginning PercentChange 5 10 15 20 −20020406080 Robusta Price Response to El Nino Months from event beginning PercentChange
  13. 13. Projection for 2015-16
  14. 14. Empirical Model: Brazil -0.02 -0.01 0.00 0 10 20 30 40 Temperature (C) IncrementalLogYield Effect of single days
  15. 15. Empirical Model: Global 0 10 20 30 40 Temperature (C) Effect of single days −0.02 −0.01 0.00 0.01 IncrementalLogYield
  16. 16. Changes in yields: Brazil +2° 0 1000 2000 3000 4000 0 1 2 3 4 5 Yield (MT/Ha) Observationcount Recorded Predicted (+2 C) Yield impact from +2 C 0 5 10 15 20 0.5 0.6 0.7 0.8 Proporational change in yield Density Changes in yield from +2 C
  17. 17. Changes in 2050  Different model for each country, based on historical data under 0.28 0.28 - 0.63 0.63 - 0.76 0.76 - 0.82 0.82 - 1.03 1.03 - 1.22 1.22 - 1.36 1.36 - 1.64 over 1.64
  18. 18. Loss temperatures vs. current averages Cambodia Ethiopia Cameroon Ghana Saudi Arabia Guatemala Liberia Gabon Yemen Jamaica Kenya India Rwanda Peru Malawi Benin Cuba Togo Indonesia Angola Trinidad and Tobago Nicaragua Malaysia Mozambique UgandaBrazil Guinea Panama Costa Rica Nigeria Ecuador El Salvador Puerto Rico Thailand Haiti Belize Sierra Leone Philippines Colombia Burundi Madagascar Nepal Suriname Zambia Papua New Guinea Zimbabwe Paraguay Guyana Honduras Myanmar Mexico Congo Sri Lanka 35 40 45 50 22.5 25.0 27.5 30.0 32.5 35.0 Average maximum temperature (C) Temperatureatyieldloss(C)
  19. 19. The future of coffee 0 50 100 1995 2000 2005 2010 MillionofBags Production Consumption: Domestic Member Importers Non-Member Importers Global Consumption of Coffee
  20. 20. Variability in prices
  21. 21. Prices paid to farmers 0 100 200 300 400 500 Jam aica (O therM ilds) Philippines (Brazilian N aturals) Bolivia (O therM ilds) Thailand (Brazilian N aturals) Ecuador(O therM ilds) C olom bia (C olom bian M ilds) India (O therM ilds) G uatem ala (O therM ilds) C osta R ica (O therM ilds) D om inican R epublic (O therM ilds) Brazil(Brazilian N aturals) H onduras (O therM ilds) ElSalvador(O therM ilds) Zam bia (O therM ilds) Papua N ew G uinea (O therM ilds) Thailand (R obustas) Ethiopia (Brazilian N aturals) M alaw i(O therM ilds) India (R obustas) U ganda (O therM ilds) Burundi(O therM ilds) Brazil(R obustas) Philippines (R obustas) Ecuador(R obustas) C uba (O therM ilds) Vietnam (Brazilian N aturals) Vietnam (R obustas) C am eroon (O therM ilds) N icaragua (O therM ilds) U ganda (R obustas) C entralAfrican R epublic (R obustas) Togo (R obustas) C am eroon (R obustas) Angola (Brazilian N aturals) Angola (R obustas) C ôte d'Ivoire (R obustas) Papua N ew G uinea (R obustas) Sierra Leone (R obustas) PricetoFarmers(UScents/kg) Variety: Brazilian Naturals Colombian Milds Other Milds Robustas
  22. 22. Explaining farmer prices All B. Naturals C. Milds Other Milds Robustas 0.00 0.25 0.50 0.75 1.00 G lobalBrazil Ethiopia Indonesia Philippines C olom biaKenya TanzaniaBolivia Burundi C am eroon C ongo,D em .R ep.of C osta R icaC uba D om inican R epublic Ecuador ElSalvador G uatem alaH aiti H ondurasIndia Jam aica M adagascar M alaw i M exico N icaragua Panam a Papua N ew G uineaPeru R w anda U ganda Venezuela Zam bia Zim babw eAngolaBeninBrazil Burundi C am eroon C entralAfrican R epublic C ongo,D em .R ep.of C ongo,R ep.of C ôte d'Ivoire Ecuador G abon G hana G uineaIndia Indonesia M adagascar N igeria Papua N ew G uinea Philippines Sierra Leone SriLanka Tanzania ThailandTogo Trinidad & Tobago U ganda Vietnam VarianceExplained Explained by international prices Explained by local production
  23. 23. Retail prices 0 1000 2000 3000 U nited Kingdom M alta ItalyAustria Luxem bourg LatviaJapanTurkeyLithuania C zech R epublicD enm ark Sw itzerlandC yprusSlovakiaBelgiumH ungaryPortugal N etherlands G erm anyN orw ay U SASloveniaSw edenFinland SpainFrancePolandBulgaria Roastedcoffeeretailprices(UScents/kg) Sold as: Roasted Soluble
  24. 24. Thank you!  And thanks to Illy Coffee and Lavazza.
  25. 25. Extra slides: Climate
  26. 26. Changes in the tropics 0.0 0.2 0.4 0.6 24 25 26 Annual Mean Temperature 0.0 0.2 0.4 0.6 35 36 37 Max Temperature of Warmest Month 0.00 0.25 0.50 0.75 13 14 15 Min Temperature of Coldest Month 0.0 0.5 1.0 1.5 2.0 2.5 11.8 12.0 12.2 12.4 Mean Diurnal Range 0.000 0.005 0.010 1050 1075 1100 1125 1150 Annual Precipitation 0.00 0.01 0.02 0.03 0.04 190 200 210 220 Precipitation of Wettest Month 0.00 0.05 0.10 0.15 0.20 0.25 17 18 19 20 21 22 Precipitation of Driest Month 0.0 0.2 0.4 0.6 31 32 33 34 Temperature Seasonality
  27. 27. Temperature variability
  28. 28. Precipitation variability
  29. 29. Extra slides: Tools
  30. 30. (Grey) Literature on Coffee Producing Regions
  31. 31. A new coffee database  Arabica harvest area  Robusta harvest area
  32. 32. Extra slides: Suitability
  33. 33. Suitability schematic Current Climate Observed Character- is cs Suitability Model Current Suitability Future Suitability Current Growing Regions Future Climate ComparetovalidateIdenfysuitabilitychanges Descripvecoffeeregionchanges Bayesian analysis Predict suitability
  34. 34. Extra slides: Variability
  35. 35. Historical El Niños
  36. 36. PCA 1 NINO 3.4 NAO SOI PDO AMO -1.0 -0.5 0.0 0.5 1.0 0 -0.19 -0.07 -0.04 -0.02 0 0.02 0.04 0.07 0.19
  37. 37. PCA 2 NINO 3.4 NAO SOI PDO AMO -1.0 -0.5 0.0 0.5 1.0 0 -0.19 -0.07 -0.04 -0.02 0 0.02 0.04 0.07 0.19
  38. 38. PCA 3 NINO 3.4 NAO SOI PDO AMO -1.0 -0.5 0.0 0.5 1.0 0 -0.19 -0.07 -0.04 -0.02 0 0.02 0.04 0.07 0.19
  39. 39. Extra slides: Production
  40. 40. Brazil production density
  41. 41. Effect in elevation 0.0 0.2 0.4 0.6 0 500 1000 1500 Elevation (m) Yieldchangeperdecade(MT/Ha) Change in Yields vs. Elevation
  42. 42. Area by elevation 0e+00 1e+05 2e+05 3e+05 4e+05 5e+05 0 500 1000 1500 Elevation (m) HarvestedArea(Ha) Harvested Area by Elevation Robusta Arabica
  43. 43. Changes with elevation 0 1 2 −30 −20 −10 0 GDDCoeff.KDDCoeff. 0 500 1000 1500 Elevation (m) Coefficient Evolution in Elevation
  44. 44. GDD model by country under −1.92 −1.92 − −1.64 −1.64 − −1.55 −1.55 − −1.53 −1.53 − −1.49 −1.49 − −1.41 −1.41 − −1.27 −1.27 − −1.19 over −1.19 Killing Degree−Day Coefficient under 34.2 34.2 − 36.2 36.2 − 37.2 37.2 − 37.7 37.7 − 38.2 38.2 − 39.4 39.4 − 41.8 41.8 − 46.1 over 46.1 Temperature of Yield Loss
  45. 45. Yields are changing  Trends in yields, as changes per year  Many reasons for this
  46. 46. Extra slides: Market
  47. 47. Real 2000 USD Prices 0 5 10 15 20 1970 1980 1990 2000 2010 Constant2000Prices(US$/kg) Coffee Variety: Arabica Robusta Coffee Prices
  48. 48. Producer country exports
  49. 49. Consumer country exports
  50. 50. Explaining demand All By Country 0.00 0.25 0.50 0.75 1.00 G lobal AustriaBelgiumBulgaria C yprus C zech R epublicD enm ark Finland FranceG erm anyH ungary Italy Japan LatviaLithuania Luxem bourg M alta N etherlandsN orw ay PolandPortugalSlovakiaSlovenia SpainSw eden Sw itzerland Turkey U nited Kingdom U SA VarianceExplained Explained by previous year Explained by retail prices
  51. 51. Producer Costs and Inferred Markups 0 500 1000 Zim babw e Jam aica VenezuelaC ubaKenya Indonesia H onduras Trinidad & Tobago Panam aM exicoBolivia D om inican R epublicPeru Tanzania Thailand EcuadorG hana C osta R ica PhilippinesN igeria Sierra LeoneG uinea Burundi Angola Zam bia R w anda N icaraguaH aiti C olom bia ElSalvador C am eroonM alaw i G abonIndia C ongo,D em .R ep.ofBenin U ganda C ôte d'Ivoire M adagascar SriLanka C entralAfrican R epublicTogo Papua N ew G uinea Ethiopia C ongo,R ep.of (Processed) G uatem alaLiberiaBrazil Vietnam Prices To farmers Inferred markup
  52. 52. Consumer prices and inferred markups 0 1000 2000 3000 U nited Kingdom Japan M alta Italy Luxem bourg Sw itzerlandPortugal AustriaD enm ark Latvia C yprusLithuania TurkeyBelgium H ungaryG erm any N orw aySlovenia N etherlands Spain Sw eden U SASlovakia Poland FinlandBulgaria France Prices To farmers Inferred distribution Remaining markup

×