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

1. Dapa presentation to GCQRI June 2011 P Läderach T Oberthür M Lundy A Eitzinger Christian Bunn Expertise and Contributions With Presentations by Laure Collet, Robert Andrade, Henk van Rikxoort, Martin Wiesinger

2. DAPA Expertise on Coffee

3. Climate Change Impact and Adaptation Price and Productivity Data Climate data (worldclim, GCM) Field Survey ProductivityChange Exposition Crop niche modelling Sustainable Livelihood Market Models Exposition of Crop alternatives Cost Benefit Analysis Caf2007 Workshops Economic Scenarios Decision Support

5. Maxent

6. EcocropGlobal Circulation Models

7. Impact analysis Risk Evaluation 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

8. Vulnerability Risk Reduction Vulnerability (IPCC 2001) Exposure Participatory workshops Socio Economic Indicators on 5 Assets (DFID 1999) Vulnerability profiles more suitable no change less suitable Sensitivity Adaptive capacity

10. Carbon Footprinting

11. New Project on Emissions from Land-Use Change

12. New Project on Pest ManagementCrop Alternatives

13. Towards Integrated Policy Support 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 Market Importer Producer p p p q q 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.

14. Coffee quality management and denomination of origin Laure Collet, June 2011 l.collet@cgiar.org

16. Geographic information systems

17. Models

18. Realizing the potential (site specific)

19. Niche management

20. Information management

23. Standardazied post-harvest process

24. GPS georeferenced fields

26. Geographical databases:

27. DEM Topography

29. Topography: Orientation

30. Climate: Annualaveragetemperature

31. Probability map Empirical data Evidence Field value Identifying potential: CaNaSTA

32. Results: Probabilityforeachqualitylevel

33. Results: Probabilityforhighestqualitylevel

34. Results: Mostlikelyqualitylevel

35. Highestaciditylevel

36. Homologue Competitive to comparative advantage Identifies places climatically and pedologically similar to a known individual location. Concept: Dependingonthedegreewithwhichclimate and soilsinfluenceproductquality, places with similar climates and soils can have similar qualities. Providesmeanstoidentify places withpotentialfortheintroduction of a promesingvariety / technology.

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

38. Low Shade % High Shade % Predicted probability map of disease risk for two shade conditions Observed geo-referenced disease attack intensities under low shade and high shade conditions Disease driving environmental factors generated for the study region: rainfall; slope % and aspect, elevation Comparing score predictions with high certainty Pest and desease management

39.  Sun points Pest and desease management Mycena citricolor attack intensity index high shade (15 - 65%) and low shade (0 -15 %) cover

40. 0,8 0,7 0,6 3 0,5 2 Predicción hecha con sol 0,4 4 0,3 0,2 1 0,1 0 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Predicción hecha con sombra Comparison of score predictions for Mycenacitricolor attack intensity index with high and low shade cover 4 behaviours : 1. Low scores with high and low shade cover: environment unfavourable for disease development 2. Similar scores with high and low shade cover: no effect of shade 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

41. 0,8 0,7 3 0,6 0,5 Prediction made with sun model 4 0,4 0,3 0,2 0,1 0 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Prediction made with shade model Comparison of driving environmental factors for groups 3 and 4 3. Higher scores with low shade cover : sun exposure is favourable to disease development Interactions shade-environment for Mycena citricolor development 4. Higher scores with high shade cover : shade is favourable to disease development 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

43. Variety influence

44. Product quality differences

46. Descriptive statistics, Anova, Cluster analyses, Graphical analyses

47. Are the bean (green, roasted) characteristics different between departments?

48. Descriptive statistics, Anova, Bonferoni multivariate test, Graphical analyses

49. Are there relationships between environment and bean (green, roasted) characteristics?

50. Correlation analyses, Best Linear Unbiased Prediction

51. Are the non-random spatial distribution patterns?

52. Principal component analyses, Bayesian probability analyses, GWR, semivariograms

53. How unique are the environments globally?

55. The South of Cauca is environmentally more similar to Nariño

57. Definingthedomains

61. Uniqueness

62. Uniqueness

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

67. Impact Assessment Intervention Impact Primary Result Counterfactual Replicate or Build up Random non-random Time

69. Salomon Perez

70. Ayako Ebata

71. Marta del Río

72. Carolina Lopera

73. Diana Cordoba

74. IFPRI

75. IRRI

76. CIP

78. Random sample Random Sample Descriptive Statistics

79. Random sample and Counterfactual Sample randomly selected from the interest area Counterfactual Select treatment and control Econometric Define changes in wellbeing due to adoption

80. 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

81. Previous results Technological adoption Dry coffee production in kg/yr

82. Previous results Treatment Control

83. Thank you

84. Mark Lundy – Business Models How do we improve adoption of innovation? Template of a business model (adapted from Osterwalder, 2006)

85. Carbon Footprinting in Mesoamerican Coffee Production Cali, Colombia – June 8, 2011 Henk van Rikxoort

88. Communication with customers

89. Marketing optionsCool Farm Tool Cropster C-sar Data collection

90. RESULTS

91. RESULTS

92. CONTACTS Henk van Rikxoort Student Tropical Agriculture Consultant – Agriculture and Climate Change Wageningen The Netherlands Mobile Colombia +573105325712 Mobile Europe +31618187108 E-mail henk.vanrikxoort@wur.nl Fotos – Neil Palmer (CIAT)

93. APECAFE Maya Vinic CECOSPROCAES Yeni Navan MICHIZA OXFAM CECOSEMAC CRS ASOCAMPO Gimme Coffee! Square Mile Coffee Roasters CIAT Café Justo TCHO ACODEROL CECOCAFEN Intelligentsia Coffee FUNDESYRAM COMUS APECAFORM PRODECOOP

94. Traceability information Quality analysis data Processing information Photos, Videos

95. Rainfall Project results Climate Farms

96. ENRIQUETA HERRENA PANTASMA, JINOTEGA, Nicaragua Topographic and environmental datasets Current situationSuitability:78% (Very Good) Geo-referenced farm information (quality, management practices, etc.) Research results

97. DAPA Expertise on Coffee Short Summary of Partners and Country Experiences

98. Global Experience

99. Our Network Capacity Thomas Oberthür Director IPNI Southeast Asia Program

100. Our Network Capacity

101. Our Network Capacity

102. Our Network Capacity

103. Our experience is ample We guide technology transfer We improve impact We can do this in short time for any project region Summary

104. The DAPA Team