Jason Crean

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

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.

Valuing seasonal climate forecasts in a state-contingent manner

We applied state-contingent theory to climate uncertainty at a farm level to assess the value of seasonal climate forecasts in the Central West region of NSW. We find that modelling uncertainty in a state-contingent manner results in a lower estimate of forecast value than the typical expected value approach. We attribute this finding to a more conservative long-term farm plan in the discrete stochastic programming (DSP) model, which is better balanced for climate uncertainty. Hence, a climate forecast, even though it still revises probabilities held by farmers, does not call forth such large changes in farm plans and associated farm incomes. We then use the DSP model to assess how attributes of a hypothetical forecasting system, particularly its skill and timeliness, as well as attributes of the decision environment, influence its value. Lastly, we assess the value of current operational forecast systems and show that the value derived from seasonal climate forecasts is relatively limited in the case study region largely because of low skill embodied in forecasts at the time when major farm decisions are being made.

A Systems Approach to Climate Risk in Rainfed Farming Systems

Climate is a major source of risk in rainfed farming systems. Systems thinking from natural sciences is used to define and explore concepts of weather, climate and climate change before discussion of how climate data can be used in simulation models of agricultural production systems. We then use systems engineering to consider the nature of climate risk and the use of seasonal climate forecasts in managing risk in rainfed cropping decisions in case studies from Australia and the Philippines. Finally, we consider some of the human factors in managing climate risk using soft systems methodology.