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Two Types of Dairy Farms, One Carbon Tax: @RISK Shows Potential Financial Outcomes

  • Industry: Academic, Agriculture
  • Product(s): @RISK
  • Application: Forecasting Dairy Operating Profits


Agricultural scientist Dr. Şeyda Özkan used @RISK to examine how a carbon tax would financially impact dairy farmers in Australia that employ two different feeding systems: a traditional ryegrass pasture-based system, and a complementary forage-based system.

One of the advantages of the @RISK software is that it can iterate thousands of scenarios in just seconds, depending on the complexity of the model and the number of parameters examined.
Dr. Şeyda Özkan, Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences

Agricultural scientist Dr. Şeyda Özkan used @RISK to examine how a carbon tax would financially impact dairy farmers in Australia that employ two different feeding systems: a traditional ryegrass pasture-based system, and a complementary forage-based system. By simulating the reduction in farm operating profits in the face of the carbon tax, Dr. Özkan was able to show that the complementary forage-based system had higher operating profits than the ryegrass pasture system, but posed greater risks. The study gives valuable insight to the complex impacts a nation-wide carbon-mitigation policy would have on the Australian dairy industry.


According to the Australian Department of Climate change and Energy Efficiency, agriculture contributed to around 15% of total Australian greenhouse gas (GHG) emissions in 2010. Due to its commitment to the Kyoto Protocol, Australia is required to reduce its national GHG emissions. And according to the current policy settings, agricultural emissions in Australia are expected to voluntarily reduce by 5% below 2000 levels by 2020. A carbon tax is one of the key policy tools that has been devised as a method to incentivize GHG emissions in the private sector. The imposition of a carbon tax (the most current suggested price point from the Australian Government Treasury being $20-60/tCO2-eq) in the economy will also have indirect impacts on dairy farmers in Australia. These indirect impacts are expected to occur in the long run and include electricity, freight and aerial agricultural services. In the case that the prices of these services increase due to a carbon policy, dairy farmers will have to pay higher prices for these services.

There has been little research looking into the effects of a carbon tax on Australia dairy farm operations. Thus, Dr. Şeyda Özkan, currently a researcher with the Department of Animal and Aquacultural Sciences at Norwegian University of Life Sciences in Norway (but studying at the Department of Agriculture and Food Systems at the University of Melbourne, Australia, at the time), wanted to analyze how this new policy would affect dairy operations using two different systems for feeding their cattle: a traditional ryegrass pasture system, and a complementary forage-based system, which involves a rotational sequence of two forage crops per year. While the traditional pasture-based system utilized mainly pastures and concentrates, the complementary forage-based system, in addition to the pasture and concentrates in the traditional pasture-based system, used home-grown forages such as summer brassicas and winter cereals produced on the farm. The key principle was to maximize the voluntary intake of home-grown feeds to eventually reduce the cost of purchased feeds and increase the profitability. The distributions of the potential deficits in home-grown feed production reflect the amount of feed that would need to be purchased to fulfil the energy requirements of the dairy herd. She also wanted to determine what effects certain variables such as pasture consumption and prices of milk and feed would have on the two different operations.

Gathering Dairy Farm Inputs

Dr. Özkan used feed costs and milk prices from a previous five-year period as relevant inputs for her models. She also incorporated farm operation profits using the following calculations derived from a 2005 study by Malcom et al.

  • Total gross margin = gross income (milk, livestock trading, profit) – variable costs (herd, shed, feed).
  • Operating profit or EBIT (earnings before interest and income tax) = total gross margin – fixed costs (depreciation, operators allowance for labor and management, permanent labour, water and administration).
  • Net profit (before tax) (return on the owner’s capital, also known as net farm income, labor tax) = operating profit- interest and long term lease.

She selected key parameters–pasture consumption, as well as prices for hay, concentrates, milk, and carbon emissions–and analyzed them for uncertainty. She then defined the distributions for these variables. She used a PERT distribution for all variables except for pasture consumption and carbon prices to avoid errors that may have resulted from interpreting results from different distributions.

“It was convenient to use the PERT distribution, as limited data were available to define distributions of the full extent of the variation,” says Dr. Özkan. For the pasture consumption data, Dr. Özkan used a best fit with a Uniform distribution for the ryegrass-pasture system, and a Weibull distribution for the complementary-forage system. For carbon prices, Dr. Özkan used a Uniform distribution.

Using @RISK, Dr. Özkan then specified the correlations between related variables, such as grain and hay prices, and pasture consumption and hay prices.

Financial performance of the two systems was evaluated by calculating the annual farm operating profits including all milk receipts, variable costs and overhead costs. The specific outcome selected in @RISK was the operating profit for a single year, and the simulation was then run annually for 10,000 iterations.


Unsurprisingly, the carbon price caused losses in both feeding systems, $49,000 in the traditional pasture-based system, and $50,000 for the complementary forage-based system. Dr. Özkan then examined the cumulative distribution functions of the systems to see which one was more likely to guarantee a forecast amount of $200,000 operating profit under a carbon tax. The traditional pasture-based system had a 30.5% chance of hitting that amount, while the complementary forage-based system had a chance of 37.1%. Furthermore, while the complementary forage-based system provided higher operating profits, the standard deviation–which was the indicator of risk–was higher for this system, making it a high-risk and high-reward option.

Dr. Özkan also conducted a sensitivity analysis to see which inputs most affected the overall operating profit. Both feeding systems were most affected by milk price, and the second-most important contributor to the variance in operating profit was the price of feed—either pasture consumption, or grain for the two systems.

(a) Tornado graphs displaying the contributions of the variable inputs to the variance of operating profit—with carbon price, of (a) ryegrass pasture strategy, and (b) complementary forage strategy.

“In general, the complimentary forage-based system promised higher operating profits for the same level of risk or the same net benefit for a lower level of risk than the traditional pasture-based system,” says Dr. Özkan. “However if farmers are risk-averse, strategies with relatively low variance of income (and sometimes even at the cost of some reduction in expected outcome) may be favored over strategies with a high variance of income in general. The attitude towards risk varies among farmers, and therefore the ‘best choice’ for an individual farm is subjective… The ultimate implication of the risk analysis in this study was to justify that the uncertainties should be taken into account when making management-related decisions.”

Benefits of @RISK

“One of the advantages of the @RISK software is that it can iterate thousands of scenarios in just seconds, depending on the complexity of the model and the number of parameters examined,” says Dr. Özkan. Her favorite features of the software include BestFit, “for saving time and energy to find the best distribution,” and the probability distribution functions “for easily determining which of the two options dominates the other.”

» A stochastic analysis of the impact of input parameters on profit of Australian pasture-based dairy farms under variable carbon price scenarios

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