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Bayesian network as a modelling tool for risk management in agriculture

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Author Info

  • Svend Rasmussen

    ()
    (Department of Food and Resource Economics, University of Copenhagen)

  • Anders L. Madsen

    ()
    (HUGIN EXPERT A/S
    Aalborg University)

  • Mogens Lund

    ()
    (Department of Food and Resource Economics, University of Copenhagen)

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    Abstract

    The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network models. We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level.

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    File URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2013/IFRO_WP_2013_12.pdf
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    Bibliographic Info

    Paper provided by University of Copenhagen, Department of Food and Resource Economics in its series IFRO Working Paper with number 2013/12.

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    Length: 16 pages
    Date of creation: May 2013
    Date of revision:
    Handle: RePEc:foi:wpaper:2013_12

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    Web page: http://www.ifro.ku.dk/
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    Related research

    Keywords: Bayesian network; Risk; Conditional probabilities; Stochastic simulation; Database; Farm account;

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    1. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
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