Farming to profit - focusing on the drivers of profit in local farming systems. Do we need to concentrate on cost of production?

Author: | Date: 12 Feb 2019

Take home messages

  • Operating costs per hectare have risen over time substantially, increasing the financial risk to your business.
  • Cost of production analysis is difficult to achieve in a mixed enterprise farming business due to interactions between enterprises and ‘known unknowns’.
  • When applying inputs, in particular N, maximum expected profit is reached before maximum expected yield.
  • There is generally a very wide range of input rates over which financial returns are similar.
  • Once the precision of your decision is high enough to ensure a high probability of targeting an input rate within the payoff plateau, further precision has very limited scope to improve the payoff.
  • Managing a farm business is as much about minimising losses as maximising profits.
  • Benchmark your business - measure your operating costs as a percentage of income – this will tell you if your expenses are too high and you are placing your business at risk.
  • Time management and organisation are the key profit drivers.

Do we need to concentrate on cost of production? Is it important? What can we do about it?

Enterprise cost of production analysis within a mixed enterprise farming business is an ongoing challenge. As with any biological system different farm enterprises are interdependent on each other and removal of one or several enterprises from a business can have a dramatic effect not only on the cost of doing business but more importantly on the financial returns. Trying to sort out the value and contribution of these interdependencies between enterprises in any meaningful way is a complex task and can sometimes lead to misleading conclusions. Most importantly, analysis is impacted by what I term the ‘known unknown’; i.e. the final cost of production for an enterprise is unknown until production is known; which in farming is something that happens a long way into the future!

As a manager of a farm business is cost of production just a concept or something of practical significance that can be used in everyday decision making? What exactly are you dealing with? To address these questions let’s firstly look at costs over time.

Cost of production trends

Input costs into farming businesses have constantly risen in both nominal and real terms over the last 25 years. On a per hectare basis operating costs have risen from around $150/ha to $350/ha - a 2.3-fold increase or around $8/ha per year over that time period (Figure 1).

Figure 1. Line graph of average total operating costs per hectare from 1992 to 2017 Sourced from Grieve Client Data

Figure 1. Average total operating costs/ha 1992 -2017 (Source: Grieve Client Data).

The breakup of costs has been relatively constant across all the main categories. However, the total increases in operating costs over time reflect the increase in capital required to operate a farm business and indicates a large increase in the financial risk to the farm business over the last 25 years (Figure 2).

Figure 2. Break-up over time of operating costs such as spray, fertiliser, R and M, fuel, labour and livestock from bottom to top from 1992 to 2017. Sourced from Grieve Client Data

Figure 2. Break-up over time of operating costs (spray, fertiliser, R&M, fuel, labour, livestock from bottom to top) (1992 – 2017) (Source: Grieve Client Data).

Operating surplus trends

While costs are one thing, what are our returns? Operating surpluses in the last 25 years have ranged from around $100/ha to as high as $500/ha and have shown an increasing trend over time.

Figure 3 Average operating surplus per hectare from 1992 to 2017 Sourced from Grieve Client Data

Figure 3. Average operating surplus per hectare (1992 – 2017) (Source: Grieve Client Data).

Fortunately, in the same time period commodity prices have increased which has eased the cost price squeeze.

Costs of production (COP) calculations are useful within the farming business in determining commodity price breakeven points at varying production levels. This is useful when setting target prices within commodity marketing plans. These target prices may well vary over the production year as the underlying production level changes. The best way of calculating COP is ‘the all-in approach’ for your business – all costs need to be included including kid’s education expenses, machinery repayments, payments to non-farming family members, the new kitchen, etc. What commodity prices are required to achieve a breakeven equity position come year end? How does this price vary when the underlying production level changes? These are the key questions that ‘cost of production’ per se can answer. As an example, most of my clients’ breakeven grain prices on average yields are somewhere between $210-$230/t for barley, with wheat at $240-$270/t and canola at $520-$550/t. These ‘break even’ prices will vary on enterprise mix, production expenses and production levels.

The disadvantage of COP calculations that use total costs rather than marginal costs – is the inability to answer questions such as: what should/can be done to cut marginal costs and what will the effect be on production levels and final profit for the business? What is the business efficiency – how good is the manager at turning inputs into outputs? What level of input maximises profit? Figure 4 shows a typical wheat crop’s response to additional nitrogen (N) fertiliser.

Figure 4 Typical wheat yield response to increased rates of nitrogen fertiliser

Figure 4. Typical wheat yield response to increased rates of nitrogen fertiliser

Most farmers’ interpretation of this graph would be to apply 60-80 units of N in order to maximise yields of approximately 3.5t/ha provided they have enough moisture either stored or forecasted and the funds available to purchase the additional urea.

However, if the expected wheat price was $300/t and the urea price was $600/t the most profitable rate of applied N is around 50 units of N. Maximum profit is achieved when the additional unit of N generates the same value of wheat. For example, for 60 units of N to be the most profitable outcome, urea would have to be approximately $500t and wheat $400/t. For 70 units of N to be the most profitable, urea would have to be approximately $300/t and wheat $500/t – a combination we would all like!

In conclusion the rate of N (or any other input for that matter) that maximises expected profit is different to the rate of N that maximises yield.

When assessing the optimal level of an input that maximises expected returns, the assumption is (almost) always depicted as per the relationship illustrated in Figure 5, i.e. that there is an increasing margin from applying the additional input up to a maximum level after which the margin drops off. Returns are maximised over a very short range - in this case 50 – 60 units of N.

Figure 5 Typical theoretical wheat gross margin response to increased rates of nitrogen fertiliser

Figure 5. Typical theoretical wheat gross margin response to increased rates of nitrogen fertiliser.

In agricultural systems the economic response to increasing levels of inputs is more like the relationship illustrated in Figure 6. While the responses are similar to Figure 5 there is generally a very wide range of inputs (Pannell 2006) over which expected profits are very similar or close to the maximum. In this case the expected profit is similar with an N rate over the range of 60-90 kg/ha, i.e. the payoff function or financial return for applying increasing amounts of input is flat.

Figure 6 Typical actual wheat Gross margin response to increased rates of nitrogen fertiliser Sourced from Pannell 2006

Figure 6. Typical actual wheat Gross margin response to increased rates of nitrogen fertiliser (Source: Pannell 2006).

This means several things all of which are good news:

  • Farmers have some margin for error when applying inputs.
  • The value of information used to fine tune management decisions is often lower than what would be expected.

A good example of this is the returns from the application of lime in a study done by O’Connell et al. (1999) conducted in the low, medium and high rainfall zones in WA. In summary the study was characterised by:

  • The same rate of lime is used in all situations (very low information use/precision).
  • Generalised recommendations were made for each soil type and each rotation (low information use/precision).
  • Soil tests were completed on a paddock by paddock basis (moderate information use/precision).

Table 1 shows the incremental benefits $/ha/year of increasing the information intensity regarding the rate of lime application decision.

Table 1. The incremental benefits of increasing the intensity of information around the decision to apply lime in different soil types and rainfall zones.

Rainfall Zone

Soil Type

Change Low to Very Low Information

$/ha/year

Change Low to Moderate Information

$/ha/year

Low Rainfall

Deep Sand

$14

$4

Clay

$8

$2

Medium Rainfall

Deep Sand

$35

$3

Clay

$19

$2

High Rainfall

Deep Sand

$7

$3

 

Clay

$21

$0

(Source: O’Connell et al., 1999)

Table 1 shows that once the precision of your decision is high enough to ensure a high probability of targeting a rate within the payoff plateau, further precision has very limited scope to improve the payoff.

How do you measure how efficient you are at using your limited resources (inputs, labour and capital) and converting them into outputs? How do you measure your overall input strategy? Are you applying too much or too little? What is an acceptable level of risk?

A very simple measure is called your operating efficiency and can be calculated by dividing your operating costs by your gross income and expressing this figure as a percentage. Ideally this ratio should be around 60% - it will vary year by year, but this is the average value for operating efficiency that you should be aiming for.

In analysing the past 20 years of my client’s farm performance data, the characteristics of the ‘successful’ businesses were as follows:

  • Expenditure was low (operating efficiency of around 55%) compared to production/output.
  • Crop yields/stocking rates were good but not great.
  • Profits were not large per se but consistent and losses small (if any).
  • They were efficient users of labour.
  • Repair and fuel costs were low.
  • They had moderate investment in plant but were highly efficient in its operation.

It was interesting to find similar results from the longer-term study undertaken by Anderton (2016).

It’s interesting to note that there seems to be a never ending search for ‘something’ that characterises the ‘profit drivers’ of successful farm businesses – be it crop%, crop yield, breed of sheep, crop variety, spray expenditure, machinery investment, technology use, etc that can be analysed out and then held up as the ‘key’ to running a successful business.

However, in my opinion the factor that has the most influence on whether a business’s outcome is successful or otherwise, is management - maybe because it is so hard to analyse.

Successful farm businesses have clear direction. A successful manager is well informed and can clearly communicate this plan to family and staff. There are a series of simple systems and processes in place accessible to family and staff to ensure a free flow of information to keep everyone informed of upcoming ‘events’ so that ALL operations including the dreaded bookwork are completed on, or before time. How do your processes stack up?

Conclusion

Operating costs per hectare have risen over time substantially, increasing the financial risk to your business. Fortunately, over the last 25 years we have experienced real commodity price increases which have lessened the cost price squeeze.

Cost of production analysis is difficult to achieve in a mixed enterprise farming business due to interactions between enterprises and ‘known unknowns’.

When applying inputs, in particular N, maximum expected profit is reached before maximum expected yield. – yield is a poor measure of profit.

There is generally a very wide range of input rates over which financial returns are similar – generally apply at the lower end of the recommended range as it will lower your risk.

Once the precision of your decision is high enough to ensure a high probability of targeting an input rate within the payoff plateau, further precision has very limited scope to improve the payoff.

Managing a farm business is as much about minimising losses as maximising profits.

Benchmark your business - measure your operating costs as a percentage of income – this will tell you if your expenses are too high and you are placing your business at risk.

Time management and organisation structure are key profit drivers within businesses; understanding your system and determining the level of risk that matches the rewards you seek are imperative towards improving business.

Useful resources and references

Anderton L (2016) Financial, Productivity and Socio-Managerial Characteristics of Broadacre Farms in Western Australia: A Decadal Assessment. Masters Thesis. School of Agricultural & Resource Economics. University of Western Australia.

Ferris A and Malcolm B (1999) Sense and Nonsense in Dairy Farm management Economic Analysis, Proceedings 43rd Annual AARES Conference.

O’Connell, M., Bathgate AD & Glen NA (1999) The value of information from research to enhance testing or soil monitoring of soil acidity in Western Australia, SEA Working Paper 99/06, Agricultural and Resource Economics, University of Western Australia.

Pannell D. (2006) Flat earth economics: The far reaching consequences of flat payoff functions in economic decision making Review of Agricultural Economics (28(4), 553-566

Contact details

Rod Grieve
rod@arbitrager.com.au