Understanding income volatility and risk profile

Many farmers have an excellent ‘gut feel’ when it comes to assessing risk and considering changes to enterprise mix.

We often consider the negatives or downsides. However, it is important to remember that risk can also be positive and present an upside.

“There is no reward without risk,” Cam Nicholson, Grain & Graze 2 regional coordinator for southern Victoria, says. At recent GRDC Updates Mr Nicholson defined risk as “how often an event occurs and the impact it has when it does occur”. In other words: risk = likelihood × consequence.

Keeping this in mind, we can analyse the probability of upside or downside risk occurring.

There are two broad types of risk in farming. Business risk includes a range of production, weather, price and human risks. Financial risk includes gearing, debt-to-equity ratios and other financial elements. This article focuses on business risk and how it can be analysed, discussed and communicated.

In the Grain & Graze 2 project, risk analysis occurs using ranges and probabilities for prices, yields and some costs to calculate the range in profits for a farm. “This makes the analysis more realistic because it combines a value with likelihood,” Mr Nicholson explains. “This builds context and helps us understand exposure and risk.”

Farmers often rely on gut feel and instinct to determine which operational actions might have a greater upside or downside risk. The @RISK software, used in the Grain & Graze 2 project, is designed to support farmers, advisers and consultants in understanding risk. It separates the decisions we can control, such as area under crop or stocking rate, from the variables that cannot be predicted with certainty, such as what the yield will be when the crop is planted.

Once these assumptions are specified and set, a risk analysis of a farm business can be completed. “It is a tool that provides an opportunity to validate or qualify a farmer’s gut feel,” Mr Nicholson says.

Completing a risk analysis provides an opportunity for all partners in the business to discuss the decisions being made and the probability of upside or downside risk. It may help businesses and individuals choose the most appropriate strategy based on the information available, the personalities involved and the level of risk they are all comfortable working with.

Case study

The following example demonstrates the impact of different production or enterprise decisions on risk and operating profit.

Scenario:

  • 815-hectare farm running meat sheep, cropping and cattle;
  • crop 215ha and consider 105ha as marginal cropping country;
  • 320ha used for sheep; and
  • 280ha used for cattle.

Using average figures for livestock production, grain yield, price and costs, the average profitability for this farm is $364/ha,

excluding finance and depreciation.

The cropping enterprise makes the greatest average profit and the cattle operation the lowest average profit. However, Mr Nicholson reiterates: “It is impossible to analyse risk using averages because they only tell you about the middle, while risk is about understanding the frequency of the extremes.”

Intuitively, farmers would consider the cropping operation to be more risky than the other business enterprises in this example, but how much more risky? This is where the @RISK analysis tool provides an opportunity to look at the potential income volatility and risk profile of the enterprise.

The distribution of the whole farm’s operating profit is presented in Figure 1. While the mean profit is $364/ha, it also shows the wide profit range that occurs. The odds are that one year in 10 the farm will make at least $517/ha, and that one year in 10 it will make $216/ha or less.

If this farm needs to clear a profit of $250/ha to cover debt repayments and depreciation, pay tax and meet individual aspirations, then there is a 16.7 per cent chance (one year in six) that this profit will not be met.

“This does not mean that every six years there will be a result of $250/ha or less. There could be a run of poor years, for example,” Mr Nicholson explains. “However, it does show the probability of these poor years occurring.”

Knowing this information allows an informed discussion to take place about profit, the range in profit potential, and the type of contingency plans required to cope with the volatility. It also enables a discussion about whether the possible outcomes are going to meet the goals of both the business and individuals involved. Hopefully this results in a more comprehensive risk-management strategy. 

The profit distribution of the cropping enterprise is more variable than the profit distribution of the sheep enterprise, as shown in Figure 2. There is a greater chance of making a loss with the cropping enterprise, although the probability of achieving $250/ha is similar in both enterprises. On the other hand, the probability of making a higher profit from cropping is greater.

The analysis can also help identify the key drivers that influence the profit range or volatility, and determine which causes the downside and upside risk. In this example there are 34 pieces of information that are variable, some of which are more influential on risk outcomes than others. For example, the number of lambs weaned for ewes joined had the greatest influence on volatility, income and downside risk. Figure 3 shows the reduction in downside risk and the change in profit distribution by increasing the number of lambs weaned for ewes joined in poor years by 20 per cent. 

The second most influential driver of income and risk was grain yield on the marginal cropping country. Figure 4 shows the change in enterprise profit distribution when the marginal cropping country was replaced with sheep. This change has increased the probability of upside risk.

The adaption of the @RISK software to farm risk analysis will be a significant step forward in the discussion and presentation of risk analysis. It will add a new dimension to the farmer’s personal reaction to risk and decision-making.

Figure 1: Distribution of farm profit ($/ha)

Bell chart showing distribution of farm profit ($/ha)

Figure 2:  Distribution of enterprise profit for cropping and sheep ($/ha)

Bell chart showing distribution of enterprise profit for cropping and sheep ($/ha)

Figure 3: Distribution of enterprise profit by increasing lambs weaned for ewes joined in ‘poor’ years by 20% ($/ha)

Bell chart showing distribution of enterprise profit by increasing lambs weaned for ewes joined in ‘poor’ years by 20% ($/ha)

Figure 4: Distribution of enterprise profit by replacing ‘marginal’ cropping country with sheep ($/ha)

Bell chart showing distribution of enterprise profit by replacing ‘marginal’ cropping country with sheep ($/ha)

More information:

Cam Nicholson
Nicon Rural Services
0417 311 098
nicon@pipeline.com.au

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GRDC Project Code ORM00004, SFS00020

Region National, South