09.06.2011

1

Dapa presentation to GCQRI June 2011

P LäderachT Oberthür

M LundyA Eitzinger

Christian Bunn

Expertise and Contributions

With Presentations by Laure Collet, Robert Andrade, Henk van Rikxoort, Martin Wiesinger

DAPA Expertise on Coffee

Climate Change Impact and AdaptationP. LäderachA. Eitzinger

The Canasta Tool Laure Collet

Impact Assessment Robert Andrade

Business Models Mark Lundy

Carbon Footprinting Henk van Rikxoort

Traceability and Quality Martin Wiesinger

Characterization of Approaches IPCC 2007

Impact Assessment Sensitivity and Adaptive Capacity Integrated Impact Assessment

Risk Evaluation Risk Reduction Risk Management Policy Options

Global to Local Local to Regional Regional to Global

Local Sector Local/Regional Systems Cross-Sector

Climate data (worldclim, GCM) Field Survey

Crop niche modelling Sustainable Livelihood

Caf2007 Workshops

Price and Productivity Data

Market Models

Economic Scenarios

D e c i s i o n S u p p o r t

Exposition ofCrop alternatives

Exposition

Cost Benefit Analysis

Productivity Change

Climate Change Impact and Adaptation

Emission ScenariosEmission Scenarios

Global Circulation ModelsGlobal Circulation Models

•• Current Climate: Current Climate: WorldclimWorldclimdatabasedatabase

Crop Prediction ModelsCrop Prediction Models••CANASTACANASTA••MaxentMaxent••EcocropEcocrop

Biophysical Data Basis

DownscalingDownscaling

Impact analysis

• Predict future suitability and distribution of coffee sourcing areas

• Evaluate potential impacts of CC on coffee quality and quantity

• Identify alternative crops suitable under predicted climate change

• Evaluate the implications of changes in coffee quality and quantity studies on social parameters

• Accompany farmer organizations and engage supply chain actors

Risk Evaluation

Vulnerability

• Participatory workshops

• Socio Economic Indicators on 5 Assets (DFID 1999)

• Vulnerability profiles

more suitableno changeless suitable

Vulnerability

(IPCC 2001)Vulnerability

(IPCC 2001)

Exposure

SensitivitySensitivity

Adaptive

capacity

Risk Reduction

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Adaptation

Risk

Management

Identification of Breeding Needs

Crop Alternatives

• Site Specific Management

• Carbon Footprinting

• New Project on Emissions from Land-Use Change

• New Project on Pest Management

• Development of a Price Module– 80% of Coffee Production will be

negatively impacted by CC

– How does this affect markets?

– How can we integrate this into Crop Models?

• Use of a Coffee Growth Model– CAF2007

– Cooperation with CATIE

– Enables us to model adaptation options

Towards Integrated Policy Support

Market Importer

p

q

p

q

Producer

p

q

Oijen, M. V., Dauzat, J., Lawson, J.-michel H. G., Vaast, P., & Rica, C. (2010). Coffee agroforestry systems in Central America : II . Development of a simple process-based model and preliminary results.

Coffee quality management and Coffee quality management and

denomination of origindenomination of origin

Laure Collet, June 2011l.collet@cgiar.org

CoffeeCoffee qualityquality

• Identifying potential (regional)

– Geographic information systems

– Models

• Realizing the potential (site specific)

– Niche management

– Information management

– Sustainable access to market

Identifying potential: Identifying potential: CaNaSTACaNaSTA

Field value

Evidence

Probability map

Empirical data)(

),()(

EP

EHPEHP =

Coffee samplesCoffee samples

� Farms sample� Standardazied post-harvest process� GPS georeferenced fields

Lote1

����

� Standard methodology of cupping

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Environmental conditionsEnvironmental conditions

�What are the variables influencing coffee quality?�Geographical databases:

� DEM → Topography

� WorldClim → Annual precipitation, dry months, annual average temperature, diurnal temperaturerange, dew point temperature, solar radiation

Topography: ElevationTopography: Elevation

Topography: OrientationTopography: Orientation ClimateClimate: : AnnualAnnual averageaveragetemperaturetemperature

Identifying potential: Identifying potential: CaNaSTACaNaSTA

Field value

Evidence

Probability map

Empirical data)(

),()(

EP

EHPEHP =

ResultsResults: : ProbabilityProbability forfor eacheach qualityqualitylevellevel

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ResultsResults: : ProbabilityProbability forfor highesthighest qualityqualitylevellevel

ResultsResults: : MostMost likelylikely qualityquality levellevel

HighestHighest acidityacidity levellevel Competitive to comparative advantageCompetitive to comparative advantage

Identifies places climaticallyand pedologically similar to a known individual location.

Concept: Depending on thedegree with which climate and soils influence product quality, places with similar climatesand soils can have similar qualities.

Provides means to identifyplaces with potential for theintroduction of a promesingvariety / technology.

RealizingRealizing potentialpotential: : sitesite specificspecificmanagementmanagement

Management EI QI RI AV1

Aspect Low High Low High

Variety High Low –

medium

High Low –

medium mediumSlope

position

Medium Low Medium Low

Shade

management

Medium Medium Medium Medium

Fruit thinning High Low-

medium

High Low –

mediumHarvest time Low Medium Low High

Harvest by

levels

Low Medium Low Low

Evaluation of management interventions by their ease of implementation (EI), improvement of quality (QI), resource intensiveness (RI) and added value (AV)

Disease driving environmental factors generated

for the study region:

rainfall; slope % and aspect, elevation

Pest and Pest and desease desease

managementmanagement

Observed geo-referenced disease attack intensities

under low shade and high shade conditions

Predicted probability map of disease risk

for two shade conditions

Low Shade % High Shade %

Comparing score predictions with high

certainty

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Mycena citricolor attack intensity index

� Sun pointsSun pointsSun pointsSun points

Pest and desease Pest and desease managementmanagement

high shade (15 - 65%) and low shade (0 -15 %) cover

Comparison of score predictions for Comparison of score predictions for MycenaMycena citricolorcitricolor attack attack intensity index with high and low shade coverintensity index with high and low shade cover

1. Low scores with high and low shade cover: environment unfavourable for disease development

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8

Predicción hecha con sombra

Pre

dic

ció

n h

ech

a c

on

so

l

4 behaviours :

2. Similar scores with high and low shade cover: no effect of shade

1

2

3. Higher scores with low shade cover : sun exposure is favourable to disease development

3

4

4. Higher scores with high shade cover : shade is favourable to disease development

3

4

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8

Prediction made with shade model

Pre

dic

tio

n m

ad

e w

ith

su

n m

od

el

0

3. Higher scores with low shade cover : sun exposure is favourable to disease development

4. Higher scores with high shade cover : shade is favourable to disease development

Comparison of driving environmental factors for groups 3 and 4Comparison of driving environmental factors for groups 3 and 4

Group 3 Group 4

Rainfall June to August (mm)

1034 986

Rainfall August to December (mm)

1209 1154

Elevation (m) 1154 1109

Slope inclination (%) 9.4 9.5

Slope aspect (% of points with East or South orientation)

63 3

Significant differences, P < 0.05

In the study area, shade is especially favourable for Mycena development on West and North oriented slopes, and unfavourable on East and South oriented slopes

Interactions shade-environment for Mycena citricolor development

Denomination of originDenomination of origin

The objective of the study was to identify the causal but regionally-changing relationships between quality characteristics of the coffee product and the characteristics of the environment where it is grown

� Environmental differences � Variety influence� Product quality differences� Spatial structures of the differences

• Are the growing environments different between the departments? � Descriptive statistics, Anova, Cluster analyses, Graphical analyses

• Are the bean (green, roasted) characteristics different between departments?� Descriptive statistics, Anova, Bonferoni multivariate test, Graphical

analyses• Are there relationships between environment and bean (green, roasted)

characteristics?

� Correlation analyses, Best Linear Unbiased Prediction• Are the non-random spatial distribution patterns?

� Principal component analyses, Bayesian probability analyses, GWR, semivariograms

• How unique are the environments globally?� Markov Chain analyses “Homologue Screening”

ApproachApproachEnvironmentalEnvironmental differencesdifferences

� Comparing Cauca and Nariño all environmental characteristics except altitude, aspect and dew point are significantly different

� The South of Cauca is environmentally more similar to Nariño

� Within the departments coherent environmental clusters can be identified

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GrowingGrowing EnvironmentsEnvironments DefiningDefining thethe domainsdomains

• There are spatial differences for bean characteristics

• These differences are (a) variety specific and (b) not equal for the quality descriptors

BeanBean CharacteristicsCharacteristics

DOMAIN I II III IV V VI VI VIIIPhysical characteristicsScreen size 18 B1 B B B B B A AScreen size 17 A A B A B B A ABiochemical characteristicsCaffeine A BC D B BC CD E FTrigonelline A A A B A B B CDChlorogenic. acid C A AB BC AB AB D DSensory characteristicsFragrance and aroma D C C BC B BC A BCFlavor C ABC BC ABC AB ABC A BCAftertaste B A B AB AB AB A BAcidity C BC C C AB ABC A CBody C ABC BC ABC AB ABC BC AClean cup BC A BC A A AB AB COverall B A AB AB AB AB AB BUniformity D A CD AB A AB BC BDBalance B A AB AB A AB A ABSweetness B A A AB A AB A B

• There are strong relationships between bean characteristics and environmental factors

• These relationships are highly site and variety specific, i.e. clear G*E effects

Bean Environment RelationshipsBean Environment Relationships

Bean Environment RelationshipsBean Environment RelationshipsPositive influence

Factors Range ImportanceFinal score

Solar radiation (MJ m-2 d-1) 19 –20 2.09Annual average cloud frequency (%) 87–90 2.04

Negative influenceFactors Range Importance

Final scoreAnnual average cloud frequency (%) 75 –78 3.82Annual total evaporation (mm yr-1) 1321 –1470 2.59

Diurnal temperature range (°C) 9.1 –9.4 2.18

Positive influenceFactors Range Importance

Final score

Altitude (m) 1575 – 1800 2.08

Annual rainfall (mm) 1550 – 1750 2.00

Negative influenceFactors Range Importance

Final score

Average temperature (°C) 23.6 – 25.05 3.15

Altitude (m) 675 – 900 2.59

UniquenessUniqueness

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UniquenessUniqueness

• Identify the most appropriate spatial analyses domain for which the relationships between coffee quality on one side, and environmental and production system characteristics on the other side are analyzed. Such domains reduce as much as possible the environment by genotype interactions, in order to permit the generalization of a single quality profile for each identified domain.

• Understand the spatial relationships between coffee quality on one side, and environmental and production system characteristics on the other side for each identified domain.

• Identify the most important environmental factors that impact on key coffee quality characteristics.

• Provide recommendation as to how unique the identified spatial domains are if compared to other coffee growing regions.

Approach for Denomination of Origin Approach for Denomination of Origin definition and quality managementdefinition and quality management

Creditos

TituloTitulo

Titulo

www.ciat.cgiar.org

Robert AndradeJune 8, 2011

Eco-Efficient Agriculture for the Poor

Coffee Impact AssessmentCoffee Impact AssessmentMethods and ongoing work

Impact Assessment

Time

Impact

Intervention

Current conditions

Bernardo Creamer

Policy Analysis

Bernardo Creamer

Policy Analysis

Jeimar Tapasco

Natural Resource

Jeimar Tapasco

Natural Resource

Robert Andrade

Impact Analysis

Robert Andrade

Impact Analysis

Carolina Gonzalez

Trend Analysis

Carolina Gonzalez

Trend Analysis

Rafael Parra-Peña

Market and Policy Analysis

Rafael Parra-Peña

Market and Policy Analysis

• Virginia Polytechnic Institute

• University of Nebraska

• Universidad del Valle

• University of Minnesota

• Universidad de los Andes

• IFPRI

• IRRI

• CIP

• CIRAD

• 4 post-graduated students and 1 post-doc

• Salomon Perez

• Ayako Ebata

• Marta del Río

• Carolina Lopera

• Diana Cordoba

Evaluation process

• Uniform survey format with minimum information

Base Line

• Set of indicators for evaluation

Monitoring and Evaluation • Replicate

survey for impact assessment

Impact Assessment

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Random

Sample

Descriptive Statistics

Random sample

Sample

randomly selected from the interest area

Counterfactual

Select treatment

and control

Random sample and Counterfactual

Econometric

Define changes in wellbeing due to adoption

Ongoing work

• Evaluation on CAFÉ practices – Assessing the benefits for

smallholders due to fare price and associations

• Economic analysis on Boarder Coffee– Establishing base line,

monitoring and indicators and assessing impact

Previous results

Technological adoption

0

10

20

30

40

50

60

Adoption

Treatment Control

Dry coffee production in kg/yr

0

500

1000

1500

2000

2009 2010

Treatment Control

Previous results

Treatment

Income

less than 1 m.w. between 1 and 2 m.w.

between 2 and 4 m.w. more than 4 m.w.

Control

Income

less than 1 m.w. between 1 and 2 m.w.

between 2 and 4 m.w. more than 4 m.w.

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How do we improve adoption of innovation?

Mark Lundy – Business Models

Template of a business model (adapted from Osterwalder, 2006)

Carbon Footprinting in

Mesoamerican Coffee Production

Cali, Colombia – June 8, 2011

Henk van Rikxoort

METHODOLOGY

� Quantify emissions and carbon sequestration (carbon footprint) of Mesoamerican coffee production

� Four coffee production systems researched (Moguel and Toledo 1999)

DATA COLLECTION AND ANALYSIS

Cool Farm Tool Cropster C-sarData collection

�Information for

better decision

making

�Communication

with customers

�Marketing

options

RESULTS

5,4

4,9

7,88,0

-2

0

2

4

6

8

10

Trad-Poly Com-Poly Shad-Mono Unshad-Mono

kg C

O2

-e/k

g-1

par

chm

ent

coff

ee

Product Carbon Footprint (PCF)

Pesticide production

Gas use

Diesel use

Electricity use

Off-farm transport

Crop residue managment

Waste water production

Fertiliser induced N2O

Fertiliser production

Biomass shade

RESULTS

-16%

20%

16%

34%

11%

2%

1%

0% 0% 0%

Mean share of GHG emissions

Biomass shade

Fertiliser production

Fertiliser induced N2O

Waste water production

Crop residue managment

Off-farm transport

Electricity use

Diesel use

Gas use

Pesticide production

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CONTACTS

Henk van Rikxoort

Student Tropical Agriculture

Consultant – Agriculture and Climate Change

WageningenThe Netherlands

Mobile Colombia +573105325712Mobile Europe +31618187108E-mail henk.vanrikxoort@wur.nl

Fotos – Neil Palmer (CIAT)

Square Mile Coffee Roasters

OXFAM

CIAT

CRS

Intelligentsia Coffee

Gimme Coffee!

TCHO

APECAFE

COMUS

FUNDESYRAM

ACODEROL

APECAFORM

ASOCAMPO

Café Justo

Maya Vinic

Yeni Navan MICHIZA

CECOCAFEN

CECOSEMAC

CECOSPROCAES

PRODECOOP

Photos, VideosProcessing information

Qualityanalysisdata

Traceabilityinformation

Climate Rainfall Project results Farms

Topographic and environmentaldatasets

Geo-referenced farm information (quality, management practices, etc.)Research results

ENRIQUETA HERRENA

PANTASMA, JINOTEGA, Nicaragua

Current situation

Suitability: 78% (Very Good)

DAPA Expertise on Coffee

Short Summary of Partners and Country

Experiences

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Global Experience

• Thomas Oberthür Director

IPNI Southeast Asia Program

Our Network Capacity

Our Network Capacity

National Coffee Research Institutes

CENICAFE Colombia

Colombian Coffee Growers Federation Colombia

ANACAFE Guatemala

PROCAFE EL Salvador

PROMECAFE 7 central American and Caribbean Countries

IHCAFE Honduras

ICAFE Costa Rica

CONACAFE Nicaragua

Our Network Capacity

Research Insitutes and NGO‘s

CIRAD France

Rainforest Alliance USA, worldwide

4C Germany, Offices in Brazil, Uganda, Nicaragua

Catholic Relief Services USA

GIZ Germany

CATIE Costa Rica

Conservation International USA

Fontagro USA, South America

International Coffee Partners Germany

Fondazione Giuseppe e Pericle Lavazza

OnlusItaly

Our Network Capacity

Industry Partners

Mars USA

Neumann Gruppe GmbH Germany

Green Mountain Coffee USA

Illy Italy

Intelligentsia USA

Löfbergs Lila AB Sweden

Gustav Paulig Ltd Finland

Tchibo GmbH Germany

Starbucks USA

Our experience is ample

We guide technology transfer

We improve impact

We can do this in short time for any project region

Summary

09.06.2011

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The DAPA Team