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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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  • Explain Cris-schema
  • Showing basic statistics of a cupper profile. How the cupper cupped over the last month. Comparing him to the average cupping scores and also providing an overview of the scores he gave within this time span.
  • Agreement with national coffee organisationsWe have all data forcentralamericaGIZ agronomy projects in parts of africa, asiaMake a map of all project areas and network partners
  • Agreement with national coffee organisationsWe have all data forcentralamericaGIZ agronomy projects in parts of africa, asiaMake a map of all project areas and network partners
  • Agreement with national coffee organisationsWe have all data forcentralamericaGIZ agronomy projects in parts of africa, asiaMake a map of all project areas and network partners
  • DAPA Capacity GCQRI

    1. 1. Dapa presentation to GCQRI June 2011<br />P Läderach<br />T Oberthür<br />M Lundy<br />A Eitzinger<br />Christian Bunn<br />Expertise and Contributions<br />With Presentations by <br />Laure Collet, Robert Andrade, Henk van Rikxoort, Martin Wiesinger <br />
    2. 2. DAPA Expertise on Coffee<br />
    3. 3. Climate Change Impact and Adaptation<br />Price and Productivity Data<br />Climate data (worldclim, GCM)<br />Field Survey<br />ProductivityChange<br />Exposition<br />Crop niche modelling<br />Sustainable Livelihood<br />Market Models<br />Exposition of<br />Crop alternatives<br />Cost Benefit Analysis<br />Caf2007<br />Workshops<br />Economic Scenarios<br />Decision Support<br />
    4. 4. Biophysical Data Basis<br />Downscaling<br /><ul><li>Current Climate: Worldclim database</li></ul>Emission Scenarios<br />Crop Prediction Models<br /><ul><li>CANASTA
    5. 5. Maxent
    6. 6. Ecocrop</li></ul>Global Circulation Models<br />
    7. 7. Impact analysis<br />Risk Evaluation<br />Predict future suitability and distribution of coffee sourcing areas<br />Evaluate potential impacts of CC on coffee quality and quantity<br />Identify alternative crops suitable under predicted climate change<br />Evaluate the implications of changes in coffee quality and quantity studies on social parameters<br />Accompany farmer organizations and engage supply chain actors<br />
    8. 8. Vulnerability<br />Risk Reduction<br />Vulnerability<br />(IPCC 2001)<br />Exposure<br />Participatory workshops<br />Socio Economic Indicators on 5 Assets (DFID 1999)<br />Vulnerability profiles<br />more suitable<br />no change<br />less suitable<br />Sensitivity<br />Adaptive capacity<br />
    9. 9. Adaptation <br />Risk <br />Management<br />Identification of Breeding Needs<br /><ul><li> Site Specific Management
    10. 10. Carbon Footprinting
    11. 11. New Project on Emissions from Land-Use Change
    12. 12. New Project on Pest Management</li></ul>Crop Alternatives<br />
    13. 13. Towards Integrated Policy Support<br />Development of a Price Module<br />80% of Coffee Production will be negatively impacted by CC<br />How does this affect markets?<br />How can we integrate this into Crop Models?<br />Use of a Coffee Growth Model<br />CAF2007<br />Cooperation with CATIE<br />Enables us to model adaptation options <br />Market <br />Importer<br />Producer<br />p<br />p<br />p<br />q<br />q<br />q<br />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.<br />
    14. 14. Coffee quality management and denomination of origin<br />Laure Collet, June 2011<br />l.collet@cgiar.org<br />
    15. 15. Coffeequality<br /><ul><li>Identifying potential (regional)
    16. 16. Geographic information systems
    17. 17. Models
    18. 18. Realizing the potential (site specific)
    19. 19. Niche management
    20. 20. Information management
    21. 21. Sustainable access to market</li></li></ul><li>Probability map<br />Empirical data<br />Evidence<br />Field value<br />Identifying potential: CaNaSTA<br />
    22. 22. Coffee samples<br /><br />Lote1<br /><ul><li> Farms sample
    23. 23. Standardazied post-harvest process
    24. 24. GPS georeferenced fields
    25. 25. Standard methodology of cupping</li></li></ul><li>Environmental conditions<br /><ul><li>What are thevariables influencing coffee quality?
    26. 26. Geographical databases:
    27. 27. DEM Topography
    28. 28. WorldClim Annualprecipitation, drymonths, annualaveragetemperature, diurnaltemperaturerange, dewpointtemperature, solar radiation</li></li></ul><li>Topography: Elevation<br />
    29. 29. Topography: Orientation<br />
    30. 30. Climate: Annualaveragetemperature<br />
    31. 31. Probability map<br />Empirical data<br />Evidence<br />Field value<br />Identifying potential: CaNaSTA<br />
    32. 32. Results: Probabilityforeachqualitylevel<br />
    33. 33. Results: Probabilityforhighestqualitylevel<br />
    34. 34. Results: Mostlikelyqualitylevel<br />
    35. 35. Highestaciditylevel<br />
    36. 36. Homologue<br />Competitive to comparative advantage<br />Identifies places climatically and pedologically similar to a known individual location.<br />Concept: Dependingonthedegreewithwhichclimate and soilsinfluenceproductquality, places with similar climates and soils can have similar qualities.<br />Providesmeanstoidentify places withpotentialfortheintroduction of a promesingvariety / technology. <br />
    37. 37. Realizingpotential: sitespecificmanagement<br />Evaluation of management interventions by their ease of implementation (EI), improvement of quality (QI), resource intensiveness (RI) and added value (AV)<br />
    38. 38. Low Shade %<br />High Shade %<br />Predicted probability map of disease risk <br />for two shade conditions<br />Observed geo-referenced disease attack intensities under low shade and high shade conditions<br />Disease driving environmental factors generated for the study region: <br />rainfall; slope % and aspect, elevation <br />Comparing score predictions with high certainty<br />Pest and desease management<br />
    39. 39.  Sun points<br />Pest and desease management<br />Mycena citricolor attack intensity index <br />high shade (15 - 65%) and low shade (0 -15 %) cover<br />
    40. 40. 0,8<br />0,7<br />0,6<br />3<br />0,5<br />2<br />Predicción hecha con sol <br />0,4<br />4<br />0,3<br />0,2<br />1<br />0,1<br />0<br />0<br />0,1<br />0,2<br />0,3<br />0,4<br />0,5<br />0,6<br />0,7<br />0,8<br />Predicción hecha con sombra <br />Comparison of score predictions for Mycenacitricolor attack intensity index with high and low shade cover<br />4 behaviours :<br />1. Low scores with high and low shade cover: environment unfavourable for disease development<br />2. Similar scores with high and low shade cover: no effect of shade<br />3. Higher scores with low shade cover : sun exposure is favourable to disease development<br />4. Higher scores with high shade cover : shade is favourable to disease development<br />
    41. 41. 0,8<br />0,7<br />3<br />0,6<br />0,5<br />Prediction made with sun model<br />4<br />0,4<br />0,3<br />0,2<br />0,1<br />0<br />0<br />0,1<br />0,2<br />0,3<br />0,4<br />0,5<br />0,6<br />0,7<br />0,8<br />Prediction made with shade model<br />Comparison of driving environmental factors for groups 3 and 4<br />3. Higher scores with low shade cover : sun exposure is favourable to disease development<br />Interactions shade-environment for Mycena citricolor development<br />4. Higher scores with high shade cover : shade is favourable to disease development<br />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<br />
    42. 42. Denomination of origin<br />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 <br /><ul><li>Environmental differences
    43. 43. Variety influence
    44. 44. Product quality differences
    45. 45. Spatial structures of the differences</li></li></ul><li>Approach<br /><ul><li>Are the growing environments different between the departments?
    46. 46. Descriptive statistics, Anova, Cluster analyses, Graphical analyses
    47. 47. Are the bean (green, roasted) characteristics different between departments?
    48. 48. Descriptive statistics, Anova, Bonferoni multivariate test, Graphical analyses
    49. 49. Are there relationships between environment and bean (green, roasted) characteristics?
    50. 50. Correlation analyses, Best Linear Unbiased Prediction
    51. 51. Are the non-random spatial distribution patterns?
    52. 52. Principal component analyses, Bayesian probability analyses, GWR, semivariograms
    53. 53. How unique are the environments globally?
    54. 54. Markov Chain analyses “Homologue Screening”</li></li></ul><li>Environmentaldifferences<br /><ul><li>Comparing Cauca and Nariño all environmental characteristics except altitude, aspect and dew point are significantly different
    55. 55. The South of Cauca is environmentally more similar to Nariño
    56. 56. Within the departments coherent environmental clusters can be identified</li></li></ul><li>GrowingEnvironments<br />
    57. 57. Definingthedomains<br />
    58. 58. BeanCharacteristics<br /><ul><li>There are spatial differences for bean characteristics
    59. 59. These differences are (a) variety specific and (b) not equal for the quality descriptors</li></li></ul><li>Bean Environment Relationships<br /><ul><li>There are strong relationships between bean characteristics and environmental factors
    60. 60. These relationships are highly site and variety specific, i.e. clear G*E effects</li></li></ul><li>Bean Environment Relationships<br />
    61. 61. Uniqueness<br />
    62. 62. Uniqueness<br />
    63. 63. Approach for Denomination of Origin definition and quality management<br /><ul><li>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.
    64. 64. Understand the spatial relationships between coffee quality on one side, and environmental and production system characteristics on the other side for each identified domain.
    65. 65. Identify the most important environmental factors that impact on key coffee quality characteristics.
    66. 66. Provide recommendation as to how unique the identified spatial domains are if compared to other coffee growing regions.</li></li></ul><li>Titulo<br />Coffee Impact Assessment<br />Titulo<br />Methods and ongoing work <br />Creditos<br />Robert Andrade<br />June 8, 2011<br />www.ciat.cgiar.org<br />Eco-Efficient Agriculture for the Poor<br />
    67. 67. Impact Assessment<br />Intervention<br />Impact<br />Primary Result<br />Counterfactual<br />Replicate or Build up<br />Random non-random<br />Time<br />
    68. 68. Current conditions<br />Virginia Polytechnic Institute<br />University of Nebraska<br />Universidad del Valle<br />University of Minnesota<br />Universidad de los Andes<br /><ul><li>4 post-graduated students and 1 post-doc
    69. 69. Salomon Perez
    70. 70. Ayako Ebata
    71. 71. Marta del Río
    72. 72. Carolina Lopera
    73. 73. Diana Cordoba
    74. 74. IFPRI
    75. 75. IRRI
    76. 76. CIP
    77. 77. CIRAD</li></li></ul><li>Evaluation process<br />
    78. 78. Random sample<br />Random Sample<br />Descriptive Statistics<br />
    79. 79. Random sample and Counterfactual<br />Sample<br />randomly selected from the interest area<br />Counterfactual<br />Select treatment <br />and control<br />Econometric<br />Define changes in wellbeing due to adoption<br />
    80. 80. Ongoing work<br />Evaluation on CAFÉ practices <br />Assessing the benefits for smallholders due to fare price and associations<br />Economic analysis on Boarder Coffee<br />Establishing base line, monitoring and indicators and assessing impact<br />
    81. 81. Previous results<br />Technological adoption<br />Dry coffee production in kg/yr<br />
    82. 82. Previous results<br />Treatment<br />Control<br />
    83. 83. Thank you <br />
    84. 84. Mark Lundy – Business Models<br />How do we improve adoption of innovation?<br />Template of a business model (adapted from Osterwalder, 2006) <br />
    85. 85. Carbon Footprinting in <br />Mesoamerican Coffee Production<br />Cali, Colombia – June 8, 2011<br />Henk van Rikxoort<br />
    86. 86. METHODOLOGY<br /><ul><li>Quantify emissions and carbon sequestration (carbon footprint) of Mesoamerican coffee production
    87. 87. Four coffee production systems researched (Moguel and Toledo 1999)</li></li></ul><li>DATA COLLECTION AND ANALYSIS<br /><ul><li>Information for better decision making
    88. 88. Communication with customers
    89. 89. Marketing options</li></ul>Cool Farm Tool<br />Cropster C-sar<br />Data collection<br />
    90. 90. RESULTS<br />
    91. 91. RESULTS<br />
    92. 92. CONTACTS<br />Henk van Rikxoort<br />Student Tropical Agriculture<br />Consultant – Agriculture and Climate Change<br />Wageningen<br />The Netherlands<br />Mobile Colombia +573105325712<br />Mobile Europe +31618187108<br />E-mail henk.vanrikxoort@wur.nl<br />Fotos – Neil Palmer (CIAT) <br />
    93. 93. APECAFE<br />Maya Vinic<br />CECOSPROCAES<br />Yeni Navan MICHIZA<br />OXFAM<br />CECOSEMAC<br />CRS<br />ASOCAMPO<br />Gimme Coffee!<br />Square Mile Coffee Roasters<br />CIAT<br />Café Justo<br />TCHO<br />ACODEROL<br />CECOCAFEN<br />Intelligentsia Coffee<br />FUNDESYRAM<br />COMUS<br />APECAFORM<br />PRODECOOP<br />
    94. 94. Traceability<br />information<br />Quality<br />analysis<br />data<br />Processing information<br />Photos, Videos<br />
    95. 95. Rainfall<br />Project results<br />Climate<br />Farms<br />
    96. 96. ENRIQUETA HERRENA<br />PANTASMA, JINOTEGA, Nicaragua<br />Topographic and <br />environmental<br />datasets<br />Current situationSuitability:78% (Very Good)<br />Geo-referenced farm information <br />(quality, management practices, etc.)<br />Research results<br />
    97. 97. DAPA Expertise on Coffee<br />Short Summary of Partners and Country Experiences<br />
    98. 98. Global Experience<br />
    99. 99. Our Network Capacity<br />Thomas Oberthür Director <br /> IPNI Southeast Asia Program<br />
    100. 100. Our Network Capacity<br />
    101. 101. Our Network Capacity<br />
    102. 102. Our Network Capacity<br />
    103. 103. Our experience is ample<br />We guide technology transfer<br />We improve impact<br />We can do this in short time for any project region<br />Summary<br />
    104. 104. The DAPA Team <br />