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The paper asks, “What comparisons of farm financial risk characterise the key Victorian dairy regions over the past 13 years?” The dairy industry is the 3rd largest rural industry in Australia. The dairy farmers, though, are facing a continuous cost price squeeze amid a challenging global environment that suppresses milk prices and raises feed costs in the face of drier and hotter weather conditions. For this analysis, we focused on three key Victorian dairy regions, namely North (N), Gippsland (G) and South West (SW) which have a combined share of 67% in Australia's milk production. We analysed the financial risks of these regions based on a representative farm from each region. Balance sheet, profit and loss budget and cash flow were integrated to create a probabilistic model using Palisade’s @RISK version 7.6. The variability in historical inputs of dairy prices, quantities and costs for thirteen years from 2006-07 to 2018-19 in Victorian dairy regions was captured using a multivariate copula in @RISK. We, thus, generated decadal (10-year) distributions of profit and loss budgets, balance sheets and cash flows to simulate risks with one hundred thousand iterations under Monte Carlo method. The simulation showed that the net farm income was positive 55, 70 and 80 percent of decades respectively for farms in North, Gippsland and South West regions. Sensitivity analysis of the variable components of income, production and cost and their contributions to variance in net farm income showed that the price of milk was the largest source of variation in net farm income for all regions. Variations in feed costs were greatest in the North region compared to the other two. At the close of a decade (year 10), South West region outperformed on all accounts, including debt repayments, building/retaining equity, return of capital (ROC) and return on investment (ROE) and, thus, appeared the most viable for dairying. The business and financial risk for dairy farms based on variability in production, prices and costs was captured in this analysis by using historical data. We extended its usefulness through @RISK by illuminating their recent probabilistic risk profiles. The method allowed us to summarise the long-term portfolios of farm net profits, debt management and key performance indicators of ROC and ROE. There is a need to extend this analysis to capture the shortages and rising costs of water, particularly in the Northern region.


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