Context
The aim is to produce intelligent reports, comparisons and forecasts for the cooperative sector to improve its decision-making. This will be achieved by an advanced analysis of the annual data generated based on Single Agricultural Statements (DUN) using machine learning and knowledge discovery methodologies. This will entail the design, definition and validation of a Cloud platform integrating this data, these models and these reports with other public databases that complement this information in a non-relational environment (Big Data) that can be used for strategic decision-making.
Objectives
Objectives
a. Establish the main indicators for analysis of the data included in the DUN.
b. Propose a smart predictive model to support strategic decisions.
c. Define an Agricoop–BigData platform that integrates DUN data with the Spanish Agricultural Plots Geographical Information System (SIGPAC) and other public or private databases.
d. Apply Big Data methods to analyse and exploit data from the Agricoop–BigData platform
e. Design a system for anticipating and estimating future developments (forecasting) in the economic, social and environmental dimensions.
f. Analyse the various levels of confidentiality of the data and its exploitation.
g. Study the creation of new business models.
Objectives
a. Fijar los indicadores para analizar los datos incluidos en la DUN
b. Proponer un modelo inteligente que ayude en la toma de decisiones.
c. Definir una plataforma Agricoop-BigData que integre los datos de la DUN, el SIGPAC como soporte cartográfico, y otras bases de datos públicas o privadas
d. Aplicar métodos BigData para el análisis y explotación de los datos de la plataforma Agricoop-BigData
e. Diseñar un sistema de previsión y de estimación de la evolución futura de las dimensiones económica social y medioambiental de cada cooperativa.
f. Analizar los niveles de confidencialidad de los datos y su explotación
g. Explorar la creación de nuevos modelos de negocio
Activities
1. Analysis of main indicators in the DUN.
2. Predictive model for strategic decisions
3. Define an Agricoop–BigData platform
4. Application of innovative BigData methods
5. Design of scenarios and production alternatives.
Proposal for an innovative system for estimating future developments (forecasting) in production activities based on the data accumulated in the DUN and considering different production scenarios. Statistical techniques will be combined with machine learning techniques.
6. Implementation of an SaS platform.
7. Dissemination of the results
Activities
1. Análisis de indicadores principales de la DUN.
2. Modelo predictivo para decisiones estratégicas
3. Definir una plataforma Agricoop-BigData
Con la integración de datos de diferentes orígenes y diferentes periodicidades: DUN, SIGPAC, bases de datos públicas de estadísticas agrarias y bases de datos privadas FCAC.
4. Aplicación de métodos BigData innovadores
5. Diseño de escenarios y alternativas productivas.
Propuesta de un sistema innovador de estimación de la evolución futura de las actividades productivas en función de los datos acumulados en la DUN y considerando diferentes escenarios productivos.
6. Implementación de una plataforma SaS.
7. Difusión de los resultados
Project details
- Main funding source
- Rural development 2014-2020 for Operational Groups
- Rural Development Programme
- 2014ES06RDRP009 Spain - Rural Development Programme (Regional) - Cataluña
Location
- Main geographical location
- Tarragona
- Other geographical location
- Lleida, Girona
EUR 140 800.00
Total budget
Total contributions from EAFRD, national co-financing, additional national financing and other financing.
Project keyword
Contacts
Project coordinator
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CATALAN FEDERATION OF AGRICULTURAL COOPERATIVES (FCAC)
Project coordinator