A framework for modelling financial risk in Southern Australia: the intensive farming (IF) model

Australian farmers operate in a financial environment, which is many times more variable than their competitors in the developed world, yet make decisions using static systems which ignore risk. This paper examines the use of dynamic modelling, using the Intensive Farming (IF) model, which can quantify and compare the physical and financial risks associated with various management scenarios on dryland farms in south-eastern Australia. In this paper the Intensive Farming (IF) model is used to simulate the whole-farm, 10-year cashflow for a typical 1,000 ha farm in the region, with a farming system based on dryland crops and breeding Merino sheep. It shows that conventional budgets, based on annual average yields and prices at 80% equity, produce outcomes with similar median values to the more complex dynamic budgets, and predict a positive margin for average years. In contrast dynamic analysis shows that the 10-year cash margins are 38% more likely to be negative than positive. Such a farming system was therefore unlikely to be viable in the long term. Dynamic budgeting also shows that the cropping enterprise is three times more variable (CV 50%) than the sheep enterprise (CV 18%). The effect of including risk reduces the crop gross margin by 14% and the sheep gross margin by 7% compared to static budgeting, which is based on annual average rainfall and prices. Further analysis showed that whole-farm cash margins are very sensitive to debt, with the cost of a 20% increase in debt reducing the 10-year cash margin by 19%. Within-year tactical adjustments had little significant effect on the long-term margin, because such adjustments are usually made in years of low income, where the marginal dollar effect was small. This paper concludes that conventional budgets could encourage sub-optimal, and even loss-making farming practices, and should be replaced with whole-farm, long-term, dynamic budgeting systems, which explicitly account for the natural variability of key inputs