Pic by Neil Palmer (CIAT)

Assessing climate change impact in coffee systems

P Läderach, O Ovalle, A EitzingerPresented by Christian Bunn2nd Coffee & Climate Steering Committee Meeting July 2011

• Context• Methodologies + Results

i. Climate data• Precis• Downscaling• Climate Change in

Guatemala

ii. Crop suitability• Modeling Approaches• Brazilian Research• Guatemala Results

• Outlook

Outline

“The climate has become inpredictable it rains less and very irregularly, my yield has decreased and I have more pest and disease problems.”Don Pedro, Nicaragua, Madriz, January, 2010

Context Perceptions

Context Overall Approach

Global Climate Model (GCM) Outputs

Production and Quality Data

Socio Economic Information

Statistical Downscaling of

Climate Information

Crop Suitability and Niche Modeling

Vulnerability Analyses

Future Climatesat Local scale

Yield and Quality Impacts

Alternative Livelihood Strategies

DIRECT IMPACT

INDIRECT SENSITIVITYADAPTIVE CAPACITY

Inputs Process Output

ObjectivePredict the impact of climate change on coffee production and farmers

livelihoods and develop chain inclusive adaptation strategies

Beneficiaries (7000 farmers) – Mexico (GMCR)– El Salvador (GMCR)– Guatemala (GMCR)– Nicaragua (GMCR)

Method partially implemented– Peru (AdapCC, GTZ)– Kenya (AdapCC, GTZ)

Context Coffee Under Pressure (CUP) Project

The socio-economic impact of climate change on Mesoamerican coffee productionContext General livelihood impacts in Nicaragua

Physical

Natural

HumanSocial

Financial

0

2

4

SensitivityAdaptive capacity

Highly variable yieldsDependency on coffee

Postharvest managementPest and disease issues

Migration

The socio-economic impact of climate change on Mesoamerican coffee productionContext Specific vulnerability profiles of farmers in Nicaragua

Matagalpa is characterized by high exposure (coffee suitability decreases drastically) high sensitivity (high variability in yields) and low adaptive capacity (poor access to credit, poor knowledge on pest and disease management and low diversification).

The adaptation strategy focuses on diversification, capacity building, strengthening of the organizations and on the enforcement of environmental laws and development policies for the coffee sector.

Climate Change models

– Differences between regional climate scenarios– Overview of climatic change in Guatemala

Methodology Future Climate

Methodology Future Climate

Regional ClimateMethodology

Downscaling•Climatic changes only relevant at global scale•At regional scale relationships between variables are constant•Detailled and Quick and All GCMs

Regional Climate Models•Full Climate Model with detailled information•25km grid•Few GCMs and computing time intensive

Guatemala Climate ProjectionMethodology

2050

0

50

100

150

200

250

300

350

400

450

500

0

5

10

15

20

25

30

Precipitation(mm)

Temperature (ºC)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

- - -

-

- -

-

+

+

-+

-

current

2020

2050

Crop models

– Introduction to crop prediction models– Differences between models– First results for Guatemala

Crop ModelingMethodology

• Statistical Regression Models• Agro-Ecological Zoning• Mechanistic Environmental Niche Models

– Ecocrop• Correlational Environmental Niche Models

– MaxEnt– CaNaSTA

• Process Model– Caf2007

Crop ModelingMethodology

AEZ BrazilMethodology

(i) an annual water deficit of 0 to 100mm, (ii) average annual temperature between 18°C and 22°C, and a frost risk of less than 25%. Areas with annual temperature means between 22°C and 23°C and a water deficit up to 150mm are considered suboptimal.

What is the suitability of a crop to the climate?

Suitability to future climate(2050) – Current suitability = Change in suitability

Current SuitabilityFuture Suitability 2050Change in Suitability to Future Climate (2050)

Temperature

Prec

ipita

tion

Calibration with optimal points• Samples (GPS points)• Altitude range• Current Production Areas• Soil types

CalibratedTemperature and

PrecipitaciónRanges!

Ecocrop Database (FAO)(Food and Agriculture Organization of the UN)

Ranges: Temperature and precipitation

WorldClim Climate Data http://worldclim.orgMore than 47,000 stations worldwide

Crop Prediction ModelsMethodology

Ecocrop Results GuatemalaMethodology

Maxent - Points of PresenceMethodology

• Maxent– Machine

Learning Algorithm

– Principle of Maximum Entropy

– Uses monthly data

– Very accurate

Maxent Results Guatemala - IMethodology

Maxent Results Guatemala - IIMethodology

Results Guatemala - IIIMethodology

400 800 1200 1600 2000 2400 28000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0

50000

100000

150000

200000

250000

300000

350000

400000 current + Stdv av-erage 2050 - Stdv

Altitude (msnm)

Su

ita

bili

ty

Are

a (h

a)

Variable AdjustedR2

R2 due to variable

% of totalvariability

Present mean

Change by 2050s

Locations with decreasing suitability (n=89.8 % of all observations)BIO 14 – Precipitación del mes más seco 0.0817 0.0817 24.8 24.49 mm -3.27 mm

BIO 04 – Estacionalidad de temperatura 0.1776 0.0959 29.1 0.83 0.166BIO 12 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mmBIO 11 - Temperatura media del cuarto más frío 0.2633 0.0576 17.5 20.11 ºC 1.86 ºC

BIO 19 - Precipitación del cuarto más frío 0.2993 0.0155 4.7 169.13 mm -7.08 mm

BIO 05 - Temperatura máxima del mes más cálido 0.3198 0.0102 3.1 28.45 ºC 2.30 ºC

BIO 13 - Precipitación del mes más húmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mm

Otros - - 6.2

Future workOutlook

Production is affected worldwide

Can we link impact models with trade models?

Future workOutlook

Process model- Caf2007

• Daily time step data by MarkSim.

Process Crop Yield Models can be used to simulate adaptation options

• O. Ovalle is improving the implemention of CAF2007• Cost Benefit Analysis of adaptation is a key objective

• Results need to be seen within the context of their methodology

• Crop prediction modeling yields results with good confidence

• Guatemala will see drastic changes in some of their most important coffee growing regions

• Possibly this is associated with increasing lack of precipitation

• Additional research is needed

Conclusions

Pic by Neil Palmer (CIAT)

Assessing climate change impact in coffee systems

Peter Läderach (CIAT)p.laderach@cgiar.orgChristian Bunn (CIAT)Christian.Bunn@gmail.com

P Läderach, O Ovalle, A EitzingerPresented by Christian Bunn2nd Coffee & Climate Steering Committee Meeting July 2011

Thank you!