In Australia, and particularly in the northern part of the grain belt, wheat is grown in an extremely variable climate. The wheat crop manager in this region is faced with complex decisions on choice of planting time, varietal development pattern, and fertiliser strategy. A skilful seasonal forecast would provide an opportunity for the manager to tailor crop management decisions more appropriately to the season. Recent developments in climate research have led to the development of a number of seasonal climate forecasting systems. The objectives of this study were to determine the value of the capability in seasonal forecasting to wheat crop management, to compare the value of the existing forecast methodologies, and to consider the potential value of improved forecast quality. We examined decisions on nitrogen (N) fertiliser and cultivar maturity using simulation analyses of specific production scenarios at a representative location (Goondiwindi) using long-term daily weather data (1894-1989). The average profit and risk of making a loss were calculated for the possible range of fixed (i.e. the same every year) and tactical (i.e. varying depending on seasonal forecast) strategies. Significant increase in profit (up to 20%) and/or reduction in risk (up to 35%) were associated with tactical adjustment of crop management of N fertiliser or cultivar maturity. The forecasting system giving greatest value was the Southern Oscillation Index (SOI) phase system of Stone and Auliciems (1992), which classifies seasons into 5 phases depending on the value and rate of change in the SOI. The significant skill in this system for forecasting both seasonal rainfall and frost timing generated the value found in tactical management of N fertiliser and cultivar maturity. Possible impediments to adoption of tactical management, associated with uncertainties in forecasting individual years, are discussed. The scope for improving forecast quality and the means to achieve it are considered by comparing the value of tactical management based on SO1 phases with the outcome given perfect prior knowledge of the season. While the analyses presented considered only one decision at a time, used specific scenarios, and made a number of simplifying assumptions, they have demonstrated that the current skill in seasonal forecasting is sufficient to justify use in tactical management of crops. More comprehensive studies to examine sensitivities to location, antecedent conditions, and price structure, and to assumptions made in this analysis, are now warranted. We have examined decisions related only to management of wheat. It would be appropriate to pursue similar analyses in relation to management decisions for other crops, cropping sequences, and the whole farm enterprise mix.

The pasture growth module AgPasture was integrated into the APSIM (Agricultural Production System Simulator) simulation model, allowing pasture-based systems to be modelled in combination with other land uses at farm scale or within land use change studies. The model's predictions of pasture growth were evaluated against 32 pasture growth datasets from a diverse range of soil types and climatic zones across New Zealand. The pasture herbage accumulation simulated by the model closely matched actual measurements over varying intervals. Both predicted and measured pasture growth rate demonstrated the same seasonal pattern, including mean growth rate and inter-annual variation across measurement years. Predicted and measured annual average net herbage accumulation (NHA) on a dryland pasture was similar over 37 observation years (mean, 6.83 and 7.27 t DM/ha respectively; coefficient of variation, 29% and 27% respectively) and highly correlated (R 2 0 0.838, P B 0.0001; relative root mean squared deviation (RMSD) 0 16%). The model's prediction of annual average NHA of all simulated pastures, spanning a wide range of pasture environments, also matched the measurement data well (R 2 0 0.777, P B 0.0001; relative RMSD 0 21%). However, discrepancies between simulated and observed values occurred in some seasons and at some sites. Analysis of these discrepancies identified areas where the model could be improved by incorporating more accurate descriptions of the effects of plant development and grazing, soil temperature and the interactive effects of high temperature and soil moisture dynamics.

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