Interannual variation in pasture growth rate in Australian and New Zealand dairy regions and its consequences for system management

The profitability of dairy farms in Australia and New Zealand is closely related to the amount of pasture dry matter consumed per hectare per year. There is variability in the pasture growth curve within years (seasonal variation) and between years (interannual variation) in all dairy regions in both countries. Therefore, the biological efficiency of production systems depends on the accuracy and timeliness of the many strategic and tactical decisions that influence the balance between feed supply and demand over an annual cycle. In the case of interannual variation, decisions are made with only limited quantitative information on the range of possible pasture growth outcomes. To address this limitation, we used the biophysical simulation model 'DairyMod' to estimate mean monthly herbage accumulation rates of annual or perennial ryegrass-based pastures in 100 years (1907-2006) for five Australian sites (Kyabram in northern Victoria, Terang in southwest Victoria, Ellinbank in Gippsland, Elliott in north-west Tasmania and Vasse in south-west Western Australia) and in 35 years (1972-2006) for three sites in New Zealand (Hamilton in the Waikato, Palmerston North in the Manawatu and Winchmore in Canterbury). The aim was to evaluate whether or not a probabilistic approach to the analysis of pasture growth could provide useful information to support decision making. For the one site where annual ryegrass was simulated, Vasse, the difference between the 25th and 75th percentile years was 20 kg DM/ha. day or less in all months when pasture growth occurred. Irrigation at Kyabram and Winchmore also resulted in a narrow range of growth rates in most months. For non-irrigated sites, the 25th-75th percentile range was narrow (10-15 kg DM/ha. day) from May or June through to September or October, because plant available soil water was adequate to support perennial ryegrass growth, and the main source of interannual variability was variation in temperature. Outside of these months, however, variability in growth was large. There was a positive relationship between total annual herbage accumulation rate and mean stocking for four southern Australian regions (northern Victoria, south-west Victoria, Gippsland and Tasmania), but there was evidence of a negative relationship between the co-efficient of variation in pasture growth and stocking rate. The latter suggests that farmers do account for risk in pasture supply in their stocking rate decisions. However, for the one New Zealand region included in this analysis, Waikato, stocking rate was much higher than would be expected based on the variability in pasture growth, indicating that farmers in this region have well defined decision rules for coping with feed deficits or surpluses. Model predictions such as those presented here are one source of information that can support farm management decision making, but should always be coupled with published data, direct experience, and other relevant information to analyse risk for individual farm businesses.