Date post: | 21-Aug-2015 |
Category: |
Documents |
Upload: | decision-and-policy-analysis-program |
View: | 322 times |
Download: | 2 times |
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
09.06.2011
2
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 [email protected]
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
09.06.2011
3
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
09.06.2011
4
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
09.06.2011
5
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
09.06.2011
6
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
09.06.2011
7
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
09.06.2011
8
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.
09.06.2011
9
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
09.06.2011
10
CONTACTS
Henk van Rikxoort
Student Tropical Agriculture
Consultant – 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 HERRENA
PANTASMA, JINOTEGA, Nicaragua
Current situation
Suitability: 78% (Very Good)
DAPA Expertise on Coffee
Short Summary of Partners and Country
Experiences
09.06.2011
11
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
12
The DAPA Team