Daniel Jiménez - SSAFE colombia

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

Definition of Site-specific agriculture (SSA):
The Colombian Sugarcane Research Centre (CENICAÑA) pioneer of SSA in Colombia
CENICAÑA Increased production of sugarcane from 7 t/sugar/ha/yr around 1980, to 12.6 t sugar/ha/yr in the period 2001-2005 by adopting SSA
According to (CENICAÑA):SSA is the art of managing crop according to the spatial and temporal variation in conditions of the site where they are grown with a view to optimizing production practices (Isaacs et al., 2004).

The concept
Growing conditions - highly heterogeneous
Large number of experiments or treatments required to draw ﬁrm conclusions concerning the optimum management of the crop under diverse conditions
Each farm is an experimental station
Every time a farmer plants and harvests a crop it is an experiment or event
Collective knowledge is far more valuable than individual knowledge

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.

The opportunity
Existence of “global” datasets on environment and socio-economies – these are global in extent, and local in relevance which can characterize any on-farm experiment that takes place
Capture and delivery mechanisms for information through (ICTs – mobile phones, internet )
An organizing world – revolution in rural organizations around supply chains, e.g. farmer organisations

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 plantain, The system is designed to support fruit growers in Colombia in the decision making process about the suitable conditions to grow.
Objective

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

Site-specific agriculture based on farmers experiences (SSAFE) for fruits in Colombia
Project components :
Characterization of any experiment that takes place on farm “events”
Data Analysis
Creating groups of fruit producers
Training

Characterization of any experiment that takes place on farm “events” - global” datasets on environment
Site geo-referenced using hand held GPS
Climate :
WorldClim
TRMM (Tropical Rainfall Measuring Mission satellite )
Topography
SRTM
Landscape

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

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

Topography

Significant Improvement GTOPO30 SRTM

Landscape Elevation Slope Aspect Landscape Class Moisture Solar Radiation Exposure Curvature

Soils
RASTA: Rapid Soil and Terrain Assessment

Soils : RASTA

Soils : RASTA:

Management
Previous experiences (project BIOTEC-CIAT) Diseases Pests

Recording forms (unities, type)
Parametrized devices
Real-time control
Nowadays ( ICTs – smart phones, internet)
Management

Information transferred to databases

Information system

Data Analysis
Training

HOMOLOGUE
Premise: crops will perform in a similar fashion in similar enviroments wherever they happen to be in space or time
What crops or varieties are likely to perform well where
If climate and soils have an influence on crops adaptation.Sites with similar soils and climate features will have a similar adaptability.
The objective is to identify potential sites for establishing crops and spread technologies from one site to another

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

Data Analysis (to provide site-specific recommendations)
OLS: (Ordinary least squares)
Robust regression: (Deal with outliers in data sets )
Mixed models combined with BLUP (Best Linear Unbiased Predictor: provides linear estimates of fixed effects
Linear regressions
Kg/plot = Climate (b 1 ) + Soils (b 2 ) + Management (b 3 ) + (B)

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

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

(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

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

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

Data Analysis
Training

“ Collective knowledge is far more valuable than individual knowledge ”
Example: AACREA ( Argentine Association of Regional Consortiums for Agricultural Experimentation )
http:// www.crea.org.ar / aacrea / site / PortalInstitucional - internet / index.html
AACREA : “ to promote the technological and managerial development of its members, through participative methods, to attain the all-round improvement of producers, technicians, and the rural community as a whole”.
Sharing knowledge
Discussions based on collective knowledge
Training

Get contact with fruit producers
Training
Establishing groups - workshops
National tour of a “Rolling library” namely : Frutichiva to socialize the project, promote consumption and facilitate the information exchange

Data Analysis
Training

Training
Training our partners in every component in order to improve the system and make it sustainable
Collecting information
Characterization of any experiment on farm
Methodologies to provide site-specific recommendations
Data analysis procedures
Information systems

Thank you!

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Daniel Jiménez - SSAFE colombia

by Decision and Policy Analysis Program on May 24, 2010

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