Power, B. and Cacho, O. J. (2014) Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia. Agricultural Systems, 125 . pp. 23-32.
Article Link(s): http://dx.doi.org/10.1016/j.agsy.2013.11.002
In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer.
We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.
|Business groups:||Crop and Food Science|
|Subjects:||Agriculture > Agriculture (General) > Improvement, reclamation, fertilisation, irrigation etc., of lands (Melioration)|
Agriculture > Agriculture (General) > Agricultural economics
Science > Statistics > Simulation modelling
|Deposited On:||19 Aug 2014 01:30|
|Last Modified:||08 Jun 2015 16:02|
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