Price and cost variability matters: Merino sheep enterprise economic insight using GrassGro. Paper accepted for Graham Centre beef and sheep forum combined with the Australian Society of Animal Production’s biennial Animal Production 2018 conference in Wagga Wagga. NSW, Australia

CSIRO’s GrassGro (Moore et al. 1997) is a computer program that simulates pasture and animal production in response to weather and management. Variability in production and risk in the grazing systems modelled can be assessed to assist farmers in making informed decisions. The program, however, assumes prices and costs to be constant for the duration of the analysis. Price variability (Kimura and Antón 2011) in Australian agriculture, however, is one of the biggest sources of risk to farm profitability.
To assess the impact of price variability, a 1000ha farm in Tarcutta, southern NSW was modelled. The farm consisted of three paddocks; two 400ha paddocks of phalaris and one 200ha paddock of lucerne. The enterprise simulated was a merino ewe flock, with replacement ewes purchased, at a stocking rate of 5 ewes/ha. The simulation used ewes weighing 55 kg, producing 20-micron wool. The ewes were joined on 1 February to a terminal ram to produce crossbred lambs which were born in July and sold on 15 December. The ewes were supplementary fed wheat grain when pastures were inadequate. Lambs post-weaning, were production fed as required to achieve a target 45 kg sale weight on 15 December.
Gross margins (GM) were calculated (Table 1) to compare the impact of price and cost variability. GrassGro was used to simulate production and calculate GM for the period 2012-15 with the costs and prices used being the mean (constant) value for the period. The output of the model was then extracted into an excel spreadsheet and GM calculated for each year using historical (variable) annual prices and costs (Table 1). GM calculations using variable prices and costs resulted in 150% higher coefficient of variation (CV) over the same four-year period compared with using constant prices and costs. The CV is an indicator of risk which is important in projecting economic and financial outcomes of changes in farming systems. The CV would be expected to increase when a longer time series is simulated, due to an increased likelihood of variations such as drought impacting on production, prices and costs.