Crean, J., Parton, K., Mullen, J. and Jones, R., 2013. Representing climatic uncertainty in agricultural models–an application of state‐contingent theory. Australian Journal of Agricultural and Resource Economics, 57(3), pp.359-378.

The paper seeks to determine whether the application of the contingent production theory would better account for the climatic risk than the expected value model. As stated by Crean et al. (2013, p 359) “The state-contingent approach to production uncertainty presents a more general model than the conventional stochastic production approach.” In farming, there are many uncertainties especially when it comes to climatic changes. There are many risks involved when the expected climatic conditions change and the profitability in farming is farming. Agriculture depends on the biological system, and this exerts a lot of pressure on the sector from one year to the other.

As with all other risks, climatic risks impose high costs. Farmers have to plan for all ranges of possible weather conditions, but only one ultimately occurs. If the farmers were sure of the ultimate climatic condition, then resources would be well utilized and utility maximized. The traditional methods of incorporating risks in farmers decision making limit the scope of responsiveness by farmers. In addition, these traditional methods also generate plans that do not resemble the farmers’ decisions.

The state contingent theory recognizes the fact that farmers have to make decisions based on a set of technologies, which translates to the outcomes in different seasons. This approach allows a broader set of responses to the different climatic conditions. Outcomes in agriculture can only be optimal if the expected climatic conditions ultimately occur. As Crean et al. (2013, p 375) concludes, many of the farm systems models use the expected value, stochastic function in incorporating risk. This approach is seen to produce an optimal plan only when only one state of the climate is expected. However, when there are many states expected, the contingent model comes in handy. The contingent model incorporates all the future contingencies as a practical method of fighting the uncertainties in farming.

In testing the hypothesis that the application of contingent production model through the DSP model would better account for climatic risk as compared to the expected value model, the research team came up with a discrete stochastic programming model, which was representative of a mixer of a wheat and sheep farm in central west region of NSW.

The choice of the area was because it has a typically mixed farming system. Annual rainfall in the area varies from 700 mm to 400 mm in the eastern and western parts (Crean et al., 2013 p 363). The variability of rainfall has great influence in agricultural production. Rainfall in the area is evenly distributed but the months of May and October are more effective since they coincide with the winter period. Farms in this area have adopted the mixed dry land farming system. This included farming and livestock rearing. Wheat is the most farmed cereal in the cereal and livestock rearing is typically for wool production.

In the eastern part, the Sustainable Grazing Systems and the Agricultural Production Systems Simulator were used in quantifying the interaction of production, climatic conditions and management of livestock and crops (Crean et al., 2013 p 365). Again, based on the rainfall received, the climatic conditions are classified as average, dry and wet. In the DSP model, classification of time regarded past and future (Crean et al., 2013 p 364). The choice of the area is very effective given the research question. The seasonal conditions in the area vary from season to season and thus make the area effective for study. Given that, this is a qualitative study, quantification of data has to occur, and the choice of quantifying systems is very effective.

The use of a qualitative research method occurs when the interactions in the study have a qualified relationship. The study of the interaction between production, climatic conditions and management of livestock and crops is a qualitative relationship. The data collected through qualitative is qualified regarding income generated. The relationship between production and climatic conditions is measured regarding the income generated in the specific period. The data was collected directly from the study area in a series of stages. From the recording of the planting conditions to the farm income, recording of data occurred all through. The data was analyzed and several functions generated as a measure of the relationships. The research team utilized the Simetar software in testing the simulation results (Crean et al., 2013 p 364).

Something interesting about the data used is that it is well presented in the paper in tables and graphs. The data is easy to understand, any complicated terms and software’s used in the analysis are explained. Cumulative distributions came in to show the coefficients of risk aversion (Crean et al., 2013 p 367). The results of the data collected answered the research question and proved that the null hypothesis was true.

The performance of the two approached involved measured using the consistency of farm plans and farm income. The results revealed that the DSP plan was better hedged for risk and uncertainty than the expected value approach. This was with a diversified farming practice involving mixed crops and livestock and less stocking rate. It is arguable that the paper was a success in proving that the hypothesis was correct.



Crean, J., Parton, K., Mullen, J. and Jones, R., 2013. Representing climatic uncertainty in agricultural models–an application of state‐contingent theory. Australian Journal of Agricultural and Resource Economics, 57(3), pp.359-378.


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