Contribute to increase the competitiveness of fruit producers in Colombia by means of a “Site-specific agriculture based on farmers experiences” system for citrus, mango, avocado and plantai

1. Site-specific agriculture based on farmers experiences (SSAFE) for fruits in Colombia Secretaries of the Fruit Chains (mango, avocado , citrus, plántain)

4. Hypothesis “ Every time a farmer harvests his crop, there is a unique event , an unreplicated experiment (Cock, 2007)” If it were possible to characterize the production system in terms of management and the environmental conditions, and if we were able to collect information on the harvested product of a large number of harvesting events under varied conditions, it should be possible to deduce optimum practices for specific conditions.

7. training Information system 3 x Coordination Year 1 Year 3 Data “events” gathering Decentralized system 1 2 Get contact with partners Automated processes 3 Data Analysis Creating farmers groups 4 Establishing site-specific recommendations

11. Climate – Topography - Landscape Characterization of “events” - global” datasets on environment

12. Climate Characterization of “events” - global” datasets on environment

14. Topography

15. Significant Improvement GTOPO30 SRTM

16. Landscape Elevation Slope Aspect Landscape Class Moisture Solar Radiation Exposure Curvature

27. Citrus Antioquia produces more citrus with half of the hectares cultivated in Tolima  Opportunity MADR (average 2002- 2008) Department Area (Has) Yield (Kg/ha) Antioquia 1,163 30,035 Tolima 2,413 8,625 Cesar 1,884 11,023 Cundinamarca 1,440 9,939 Magdalena 483 18,772 Bolívar 353 7,453 Risaralda 156 10,213 Córdoba 262 18,836

30. Non-linear regressions - Artificial Neural Networks Data Analysis (to provide site-specific recommendations) Kg/plot Soil Management Extra info parameter Math. parameter Math. parameter Math. Climate Database

31. Analisis de relevancia: Interpretation of the information collected Identification of the most relevant variables in the production of Andean blackberry ( Rubus glaucus ) in Colombia (Jiménez, D., Cock, J., Satizábal, F., Barreto, M., Pérez-Uribe, A., Jarvis, A. and Van Damme, P., 2009. Computers and Electronics in Agriculture . 69 (2): 198–208

32. (a) Component plane of Andean blackberry yield, the scale bar (right) indi-cates the range value of productivity in kg/plant/week The upper side exhibits highvalues of yield, whereas the lower displays low values. (b) Kohonenmap displaying the resultant 6 clusters and their labels according to yield values. Identification of the most relevant variables in the production of Andean blackberry ( Rubus glaucus ) in Colombia Interpretation of the information collected Data Analysis

33. Components planes of the averages temperature: (a) temperature of the harvest month, (b) average temperature of the first month before harvest, (c) average temperature of the second month before harvest, and (d) average temperature of the third month before harvest. In all figures, the scale bar (right) indicates the range value in ◦C of temperature. The upper side exhibits high values, whereas the lower displays low values Identification of the most relevant variables in the production of Andean blackberry ( Rubus glaucus ) in Colombia Interpretation of the information collected

34. Component planes of the specifics geographic areas Nariño–la union–chical alto (left) and Nariño–la union–cusillo bajo (right). The highest values indicate presence and the lowest absence as they are categorical variables Identification of the most relevant variables in the production of Andean blackberry ( Rubus glaucus ) in Colombia Interpretation of the information collected

40. Thank you!