Modelling of Catastrophic Farm Risks Using Sparse Data

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

This paper compares alternative ways of conducting a farm risk analysis using sparse data with a special reference to catastrophe events. For this purpose kernel and multivariate normal smoothing procedures are proposed and applied to generate (simulate) the joint distributions of crop yields and prices. The analysis showed that the functional forms chosen to generate the joint distribution substantially impacted the density in the tail of the distribution, although they were parameterised with the same data. The differences in the optimal farm plan (i.e. activity levels) resulting from the optimisation of net farm income, obtained from a utility-efficient programming model, were less profound.
Original languageEnglish
Title of host publicationHandbook of Operations Research in Agriculture and the Agri-Food Industry
EditorsLluis M. Plà-Aragone
Place of PublicationNew York
PublisherSpringer New York LLC
Pages259-275
Volume224
ISBN (Print)9781493924837
DOIs
Publication statusPublished - 2015

Publication series

NameInternational Series in Operations Research & Management Science
Number224
ISSN (Print)0884-8289

Fingerprint

Joint distribution
Farm
Modeling
Programming
Functional form
Kernel
Catastrophe
Farm income
Crop yield
Risk analysis
Smoothing

Cite this

Ogurtsov, V., van Asseldonk, M. A. P. M., & Huirne, R. B. M. (2015). Modelling of Catastrophic Farm Risks Using Sparse Data. In L. M. Plà-Aragone (Ed.), Handbook of Operations Research in Agriculture and the Agri-Food Industry (Vol. 224, pp. 259-275). (International Series in Operations Research & Management Science; No. 224). New York: Springer New York LLC. https://doi.org/10.1007/978-1-4939-2483-7_12
Ogurtsov, V. ; van Asseldonk, M.A.P.M. ; Huirne, R.B.M. / Modelling of Catastrophic Farm Risks Using Sparse Data. Handbook of Operations Research in Agriculture and the Agri-Food Industry. editor / Lluis M. Plà-Aragone. Vol. 224 New York : Springer New York LLC, 2015. pp. 259-275 (International Series in Operations Research & Management Science; 224).
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Ogurtsov, V, van Asseldonk, MAPM & Huirne, RBM 2015, Modelling of Catastrophic Farm Risks Using Sparse Data. in LM Plà-Aragone (ed.), Handbook of Operations Research in Agriculture and the Agri-Food Industry. vol. 224, International Series in Operations Research & Management Science, no. 224, Springer New York LLC, New York, pp. 259-275. https://doi.org/10.1007/978-1-4939-2483-7_12

Modelling of Catastrophic Farm Risks Using Sparse Data. / Ogurtsov, V.; van Asseldonk, M.A.P.M.; Huirne, R.B.M.

Handbook of Operations Research in Agriculture and the Agri-Food Industry. ed. / Lluis M. Plà-Aragone. Vol. 224 New York : Springer New York LLC, 2015. p. 259-275 (International Series in Operations Research & Management Science; No. 224).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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AB - This paper compares alternative ways of conducting a farm risk analysis using sparse data with a special reference to catastrophe events. For this purpose kernel and multivariate normal smoothing procedures are proposed and applied to generate (simulate) the joint distributions of crop yields and prices. The analysis showed that the functional forms chosen to generate the joint distribution substantially impacted the density in the tail of the distribution, although they were parameterised with the same data. The differences in the optimal farm plan (i.e. activity levels) resulting from the optimisation of net farm income, obtained from a utility-efficient programming model, were less profound.

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Ogurtsov V, van Asseldonk MAPM, Huirne RBM. Modelling of Catastrophic Farm Risks Using Sparse Data. In Plà-Aragone LM, editor, Handbook of Operations Research in Agriculture and the Agri-Food Industry. Vol. 224. New York: Springer New York LLC. 2015. p. 259-275. (International Series in Operations Research & Management Science; 224). https://doi.org/10.1007/978-1-4939-2483-7_12