Bayesian network as a modelling tool for risk management in agriculture
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Bayesian network as a modelling tool for risk management in agriculture. / Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens.
Frederiksberg : Department of Food and Resource Economics, University of Copenhagen, 2013. p. 1-14.Research output: Working paper
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TY - UNPB
T1 - Bayesian network as a modelling tool for risk management in agriculture
AU - Rasmussen, Svend
AU - Madsen, Anders L.
AU - Lund, Mogens
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
M3 - Working paper
T3 - IFRO Working Paper
SP - 1
EP - 14
BT - Bayesian network as a modelling tool for risk management in agriculture
PB - Department of Food and Resource Economics, University of Copenhagen
CY - Frederiksberg
ER -
ID: 46951672