Near-term pasture growth rate forecasts: which method works best?

Knowledge of near-term pasture growth rates helps livestock farmers with important management decisions, particularly feed budgeting. Here we contrast three approaches for generating three-month pasture growth rate forecasts using a biophysical plant model. Two methods were based on statistical growth rates simulated using either historical climate data or historical data having Southern Oscillation Indices (SOI) matching those of the current month. The third method accounted for current earth and ocean measurements using dynamic climate outlooks from the global circulation model POAMA. We used twelve months of measured pasture growth rates to calibrate the model, and then contrast each forecasting method over several threemonth periods using empirical cumulative distribution functions. In general, dynamic forecasts from POAMA had the greatest skill and reliability in forecasting the near term (30 day) pasture growth rates, indicating that the use of current climate outlooks and recent weather measurements are more reliable than using methods based on historically measured data. This work is being developed into a graphical-user interface that will allow farmers to view a near -term pasture growth rates forecast using an online tool.