Representing climatic uncertainty in agricultural models- an application of state contingent theory

Jason Crean, Kevin Parton, John Mullen, Randall Jones

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The state-contingent approach to production uncertainty presents a more general model than the conventional stochastic production approach. Here we investigate whether the state-contingent approach offers a tractable framework for representing climatic uncertainty at a farm level. We developed a discrete stochastic programming (DSP) model of a representative wheat-sheep (mixed) farm in the Central West of NSW. More explicit recognition of climatic states, and associated state-contingent responses, led to optimal farm plans that were more profitable on average and less prone to the effects of variations in climate than comparable farm plans based on the expected value framework. The solutions from the DSP model also appeared to more closely resemble farm land use than the equivalent expected value model using the same data. We conclude that there are benefits of adopting a state-contingent view of uncertainty, giving support to its more widespread application to other problems.
Original languageEnglish
Pages (from-to)359-378
Number of pages20
JournalAustralian Journal of Agricultural and Resource Economics
Volume57
Issue number3
DOIs
Publication statusPublished - 2013

Fingerprint Dive into the research topics of 'Representing climatic uncertainty in agricultural models- an application of state contingent theory'. Together they form a unique fingerprint.

Cite this