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Coffee and Climate
Trouble brewing
Jeffrey Sachs, James Rising
Tim Foreman, John Simmons, Manuel Brahm
Global Coffee Forum, October 1, 2015
A global perspective
Arabica producers
Robusta producers
Mixed producers
Exports from consumers
The network of global coffee trade
Three scales of impacts
Suitability
ProductionVariability
Long-term trend
Decadal variability
Interannual variability
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
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
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
Current suitability
 Arabica suitability
 Robusta suitability
Urban Protected Suitable (~potential yield)
Future Arabica Suitability
Changes in suitability
Statistical confidence
Final suitability
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
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
El Niño is coming
Precipitation changes during the 1997/98 event (American Meteorological Society, 1998).
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
Projection for 2015-16
Empirical Model: Brazil
-0.02
-0.01
0.00
0 10 20 30 40
Temperature (C)
IncrementalLogYield
Effect of single days
Empirical Model: Global
0 10 20 30 40
Temperature (C)
Effect of single days
−0.02
−0.01
0.00
0.01
IncrementalLogYield
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
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
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)
The future of coffee
0
50
100
1995 2000 2005 2010
MillionofBags
Production
Consumption: Domestic Member Importers Non-Member Importers
Global Consumption of Coffee
Variability in prices
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
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
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
Thank you!
 And thanks to Illy Coffee and Lavazza.
Extra slides: Climate
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
Temperature variability
Precipitation variability
Extra slides: Tools
(Grey) Literature on Coffee
Producing Regions
A new coffee database
 Arabica harvest area
 Robusta harvest area
Extra slides: Suitability
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
Extra slides: Variability
Historical El Niños
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
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
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
Extra slides: Production
Brazil production density
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
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
Changes with elevation
0
1
2
−30
−20
−10
0
GDDCoeff.KDDCoeff.
0 500 1000 1500
Elevation (m)
Coefficient
Evolution in Elevation
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
Yields are changing
 Trends in yields, as changes per year
 Many reasons for this
Extra slides: Market
Real 2000 USD Prices
0
5
10
15
20
1970 1980 1990 2000 2010
Constant2000Prices(US$/kg)
Coffee Variety: Arabica Robusta
Coffee Prices
Producer country
exports
Consumer country exports
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
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
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

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The Earth Institute Coffee Report

  • 1. Coffee and Climate Trouble brewing Jeffrey Sachs, James Rising Tim Foreman, John Simmons, Manuel Brahm Global Coffee Forum, October 1, 2015
  • 2. A global perspective Arabica producers Robusta producers Mixed producers Exports from consumers The network of global coffee trade
  • 3. Three scales of impacts 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 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. Current suitability  Arabica suitability  Robusta suitability Urban Protected Suitable (~potential yield)
  • 8. Future Arabica Suitability Changes in suitability Statistical confidence Final suitability
  • 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. 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. El Niño is coming 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
  • 14. Empirical Model: Brazil -0.02 -0.01 0.00 0 10 20 30 40 Temperature (C) IncrementalLogYield Effect of single days
  • 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. 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 of coffee 0 50 100 1995 2000 2005 2010 MillionofBags Production Consumption: Domestic Member Importers Non-Member Importers Global Consumption of Coffee
  • 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. 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
  • 24. Thank you!  And thanks to Illy Coffee and Lavazza.
  • 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
  • 30. (Grey) Literature on Coffee Producing Regions
  • 31. A new coffee database  Arabica harvest area  Robusta harvest area
  • 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
  • 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. 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. Changes with elevation 0 1 2 −30 −20 −10 0 GDDCoeff.KDDCoeff. 0 500 1000 1500 Elevation (m) Coefficient Evolution in Elevation
  • 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. Yields are changing  Trends in yields, as changes per year  Many reasons for this
  • 47. Real 2000 USD Prices 0 5 10 15 20 1970 1980 1990 2000 2010 Constant2000Prices(US$/kg) Coffee Variety: Arabica Robusta Coffee Prices
  • 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. 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. 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