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Data, Information and Knowledge in Agricultural Decision-Making

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  • Houskova Berankova, Martina
  • Houska, Milan

Abstract

It is very important to work with data, information and knowledge correctly, when a decision model is used as a support for managerial decision-making. Unfortunately, these terms are understood differently in various branches; particularly, the definitions of knowledge are very different. It causes problems in praxis; it is not clear, in which case data processing, information or knowledge/expert systems are appropriate to use. In this paper we introduce modern approaches to indentifying these terms. The objective of the paper is to identify data, information and knowledge in decision-making process, particularly in multiple-criteria decision-making model, to help users of such models to better understand it. To reach this objective, we need to provide appropriate definitions of data, information and knowledge as well as the specific algorithms of decision-making models used in the following sections. Then we go through the decision-making process and analyze the needs of data, information and knowledge in its individual phases. We demonstrate our approach on grains dryer selection problem under conditions of a specific agriculture company.

Suggested Citation

  • Houskova Berankova, Martina & Houska, Milan, 2011. "Data, Information and Knowledge in Agricultural Decision-Making," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 3(2), pages 1-9, June.
    • Handle: RePEc:ags:aolpei:109740
    DOI: 10.22004/ag.econ.109740
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    References listed on IDEAS

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    1. Gomez-Limon, Jose A. & Arriaza, Manuel & Riesgo, Laura, 2003. "An MCDM analysis of agricultural risk aversion," European Journal of Operational Research, Elsevier, vol. 151(3), pages 569-585, December.
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