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BI4IPM: A Business Intelligence System for the Analysis of Olive Tree's Integrated Pest Management

Author

Listed:
  • Claudio Zaza

    (Departement of Economics, University of Foggia, Foggia, Italy)

  • Sandro Bimonte

    (IRSTEA, Aubière, France)

  • Nicola Faccilongo

    (Department of Economics, University of Foggia, Foggia, Italy)

  • Piermichele La Sala

    (Department of Economics, University of Foggia, Foggia, Italy)

  • Francesco Contò

    (Department of Economics, University of Foggia, Foggia, Italy)

  • Crescenzio Gallo

    (Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy)

Abstract

The Agri-Food sector is facing global challenges. The first challenge is feeding a world population that will reach 9.3 billion people in 2050, according to UN projections. The second challenge is the demand from consumers for high-quality products obtained through more sustainable, safe and clear agri-food chains. Integrated pest management (IPM) could be an important instrument for helping farmers face these challenges. IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. This work explores the possibility of developing a framework that combines business intelligence (BI) technologies with IPM principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with on-line analytical processing (OLAP) to verify adherence to the IPM technical specifications.

Suggested Citation

  • Claudio Zaza & Sandro Bimonte & Nicola Faccilongo & Piermichele La Sala & Francesco Contò & Crescenzio Gallo, 2018. "BI4IPM: A Business Intelligence System for the Analysis of Olive Tree's Integrated Pest Management," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 9(1), pages 16-38, January.
    • Handle: RePEc:igg:jaeis0:v:9:y:2018:i:1:p:16-38
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