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For the visit of Tim Shilling, the Executive Director of the Global Coffee Quality Research Initiative we put together a presentation about our capacity and experience in coffee research
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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 Adaptation P. 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 Scenarios
Global Circulation Models
• Current Climate: Worldclim database
Crop Prediction Models• CANASTA• Maxent• Ecocrop
Biophysical Data BasisDownscaling
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)
Exposure
Sensitivity
Adaptive capacity
Risk Reduction
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 Importerp
q
p
q
Producerp
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
• Identifying potential (regional)
– Geographic information systems
– Models
• Realizing the potential (site specific)
– Niche management
– Information management
– Sustainable access to market
Identifying potential: CaNaSTA
Field value
Evidence
Probability map
Empirical data)(
),()(
EP
EHPEHP
Coffee samples
Farms sample Standardazied post-harvest process GPS georeferenced fields
Lote1
Standard methodology of cupping
Environmental conditions
What are the variables influencing coffee quality?
Geographical databases:
DEM Topography
WorldClim Annual precipitation, dry months, annual average temperature, diurnal temperature range, dew point temperature, solar radiation
Topography: Elevation
Topography: Orientation
Climate: Annual average temperature
Identifying potential: CaNaSTA
Field value
Evidence
Probability map
Empirical data)(
),()(
EP
EHPEHP
Results: Probability for each quality level
Results: Probability for highest quality level
Results: Most likely quality level
Highest acidity level
Competitive to comparative advantage
Identifies places climatically and pedologically similar to a known individual location.
Concept: Depending on the degree with which climate and soils influence product quality, places with similar climates and soils can have similar qualities.
Provides means to identify places with potential for the introduction of a promesing variety / technology.
Realizing potential: site specific management
Management EI QI RI AV1
Aspect Low High Low HighVariety High Low –
mediumHigh Low –
medium mediumSlopeposition
Medium Low Medium Low
Shade management
Medium Medium Medium Medium
Fruit thinning High Low-medium
High Low – medium
Harvest time Low Medium Low HighHarvest 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 desease
management
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
Mycena citricolor attack intensity index
Sun points
Pest and desease management
high shade (15 - 65%) and low shade (0 -15 %) cover
Comparison of score predictions for Mycena citricolor attack intensity 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
Pred
icci
ón h
echa
con
sol
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
Pred
ictio
n m
ade
with
sun
mod
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 4Group 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 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”
Approach
Environmental differences 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
Growing Environments
Defining the domains
• There are spatial differences for bean characteristics
• These differences are (a) variety specific and (b) not equal for the quality descriptors
Bean Characteristics
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 Relationships
Bean 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
Uniqueness
Uniqueness
• 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 definition and quality management
Creditos
Titulo
Titulo
www.ciat.cgiar.org
Robert AndradeJune 8, 2011
Eco-Efficient Agriculture for the Poor
Coffee Impact AssessmentMethods and ongoing work
Impact Assessment
Time
Pri
mar
y R
esul
t
Impact
Counterfactual
Replicate or Build up
Random non-random
Intervention
Current conditions
Bernardo Creamer
Policy Analysis
Jeimar TapascoNatural
Resource
Robert AndradeImpact
Analysis
Carolina Gonzalez
Trend 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
Random SampleDescriptive Statistics
Random sample
Samplerandomly selected from the interest area
CounterfactualSelect treatment and control
Random sample and Counterfactual
EconometricDefine 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
Adoption0
10
20
30
40
50
60
Treatment Control
Dry coffee production in kg/yr
2009 20100
200400600800
10001200140016001800
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.
Thank you
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
Trad-Poly Com-Poly Shad-Mono Unshad-Mono-2
0
2
4
6
8
10
5,4
4,9
7,88,0
Product Carbon Footprint (PCF)
Pesticide productionGas useDiesel useElectricity useOff-farm transportCrop residue managmentWaste water productionFertiliser induced N2OFertiliser productionBiomass shade
kg C
O2-
e/kg
-1 p
arch
men
t coff
ee
RESULTS
16%
20%
16%
34%
11%
2% 1% 0% 0% 0%
Mean share of GHG emissions
Biomass shadeFertiliser productionFertiliser induced N2OWaste water productionCrop residue managmentOff-farm transportElectricity useDiesel useGas usePesticide production
CONTACTS
Henk van RikxoortStudent Tropical AgricultureConsultant – Agriculture and Climate Change
WageningenThe Netherlands
Mobile Colombia +573105325712Mobile Europe +31618187108E-mail [email protected]
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 HERRENAPANTASMA, JINOTEGA, NicaraguaCurrent situationSuitability: 78% (Very Good)
DAPA Expertise on Coffee
Short Summary of Partners and Country Experiences
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 Onlus
Italy
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
The DAPA Team
