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The Earth Institute Coffee Report
- 1. Coffee and Climate Troublebrewing Jeffrey Sachs, James Rising Tim Foreman, John Simmons, Manuel Brahm Global Coffee Forum, October 1, 2015
- 2. A global perspective Arabicaproducers Robusta producers Mixed producers Exports from consumers The network of global coffee trade
- 3. Three scales ofimpacts Suitability ProductionVariability Long-term trend Decadal variability Interannual variability
- 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. 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. Suitability inputs V ANNEX4: 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. Current suitability Arabicasuitability Robusta suitability Urban Protected Suitable (~potential yield)
- 8. Future Arabica Suitability Changesin suitability Statistical confidence Final suitability
- 9. Changes in Arabicasuitability -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. Changes in Robustasuitability 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. El Niño iscoming Precipitation changes during the 1997/98 event (American Meteorological Society, 1998).
- 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
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- 14. Empirical Model: Brazil -0.02 -0.01 0.00 010 20 30 40 Temperature (C) IncrementalLogYield Effect of single days
- 15. Empirical Model: Global 010 20 30 40 Temperature (C) Effect of single days −0.02 −0.01 0.00 0.01 IncrementalLogYield
- 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. 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. 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. The future ofcoffee 0 50 100 1995 2000 2005 2010 MillionofBags Production Consumption: Domestic Member Importers Non-Member Importers Global Consumption of Coffee
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- 21. Prices paid tofarmers 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. Explaining farmer prices AllB. 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
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- 24. Thank you! Andthanks to Illy Coffee and Lavazza.
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- 26. Changes in thetropics 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
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- 30. (Grey) Literature onCoffee Producing Regions
- 31. A new coffeedatabase Arabica harvest area Robusta harvest area
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- 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. 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. 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
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- 41. Effect in elevation 0.0 0.2 0.4 0.6 0500 1000 1500 Elevation (m) Yieldchangeperdecade(MT/Ha) Change in Yields vs. Elevation
- 42. Area by elevation 0e+00 1e+05 2e+05 3e+05 4e+05 5e+05 0500 1000 1500 Elevation (m) HarvestedArea(Ha) Harvested Area by Elevation Robusta Arabica
- 43. Changes with elevation 0 1 2 −30 −20 −10 0 GDDCoeff.KDDCoeff. 0500 1000 1500 Elevation (m) Coefficient Evolution in Elevation
- 44. GDD model bycountry 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. Yields are changing Trends in yields, as changes per year Many reasons for this
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- 47. Real 2000 USDPrices 0 5 10 15 20 1970 1980 1990 2000 2010 Constant2000Prices(US$/kg) Coffee Variety: Arabica Robusta Coffee Prices
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- 50. Explaining demand All ByCountry 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. Producer Costs and InferredMarkups 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. Consumer prices and inferredmarkups 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