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

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  • Crean, Jason
  • Parton, Kevin
  • Mullen, John
  • Jones, Randall

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.

Suggested Citation

  • Crean, Jason & Parton, Kevin & Mullen, John & Jones, Randall, 2013. "Representing climatic uncertainty in agricultural models – an application of state-contingent theory," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(3).
    • Handle: RePEc:ags:aareaj:245953
    DOI: 10.22004/ag.econ.245953
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    References listed on IDEAS

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    Cited by:

    1. Mallawaarachchi, Thilak & Auricht, Christopher & Loch, Adam & Adamson, David & Quiggin, John, 2020. "Water allocation in Australia’s Murray–Darling Basin: Managing change under heightened uncertainty," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 345-369.
    2. Tang, Kai, 2024. "Agricultural adaptation to the environmental and social consequences of climate change in mixed farming systems: Evidence from North Xinjiang, China," Agricultural Systems, Elsevier, vol. 217(C).
    3. Young, Michael & Young, John & Kingwell, Ross S. & Vercoe, Philip E., 2023. "Representing weather-year variation in whole-farm optimisation models: Four-stage single-sequence vs eight-stage multi-sequence," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(01), September.
    4. Crean, Jason & Parton, Kevin & Mullen, John & Hayman, Peter, 2015. "Valuing seasonal climate forecasts in a state-contingent manner," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(1), January.
    5. Young, Michael & Young, John & Kingwell, Ross S. & Vercoe, Philip E., 2023. "Evaluation of the least cost option to manage pastures in a wet winter in south-eastern Australia," AFBM Journal, Australasian Farm Business Management Network, vol. 20(3), August.
    6. Claire Settre & Jeff Connor & Sarah Ann Wheeler, 2017. "Reviewing the Treatment of Uncertainty in Hydro-economic Modeling of the Murray–Darling Basin, Australia," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 1-35, July.

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