Better forecasting for farmers

Better forecasting for farmers

With a forecast 2015 farm gate milk price of $6.00 per kilogram of milk solids, the concern facing dairy farmers is whether their cost structures can sustain a payout at this level, or at an even lower level, as some commentators are predicting.

In May, almost 3 months before Fonterra announced its downward revision to the 2015 forecast farm gate milk price, Reserve Bank Governor, Graeme Wheeler, noted in a speech to DairyNZ in Hamilton that “dairy debt almost trebled over the past decade, and currently stands at $32 billion”; noting further that “it is concentrated among a small proportion of highly leveraged farms with around half of the dairy debt being held by only 10% of dairy farmers.”

Mike O’Connor, a Director in PwC’s Corporate Finance practice based in Christchurch says, “We can reasonably assume that a disproportionate number of the more heavily indebted dairy farmers will be located in Canterbury and Southland; provinces which have experienced rapid land use change over the past 10-15 years, as sheep and cropping units have been converted to dairying.

“Farmers will need to look to minimise the impacts to their businesses using the best techniques available. We’re considering the impact of the risks on financial outcomes using advanced quantification techniques, which can be applied to any financial modelling, but farm budgeting is particularly relevant at present given the payout uncertainties,” Mr O’Connor says.

Mr O’Connor promotes this innovative modelling approach, “we use a technique called stochastic or probabilistic modelling where we replace single number inputs – like a $6.00 payout – with a distribution of possible values for all key variables in a budget and then tie key relationships together, such as less supplementary feed and less production, or lower payout and higher fertiliser costs. Probabilities can even be attached to events like droughts and the effects on production.

“The power of this technique is enormous, allowing the probability of a farmer failing to meet his or her banking covenants or needing to increase borrowings to meet a probable cash flow shortfall to be reasonably assessed. We can go beyond the realm of ‘it might happen’ to ‘we can expect it to happen with x% probability’, which gives us more time for to implement mitigating strategies, well before any crisis point.”

The resulting outcome is a distribution of outputs, not just one single output, that can be used to assess the probability of a particular event occurring.

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