Crop yield forecasting over large areas in Australia

Inter-annual variations in crop yield are intricately linked to fluctuations in the weather. Accurate yield forecasts prior to harvest are possible if crop-weather relationships are integrated into models that are responsive to the major yield determining factors. A network of meteorological stations was selected across the Australian wheat belt and monthly rainfall regressed with wheat yields from the surrounding shires. Autumn rains that permit an early sowing and finishing rains after July are important for higher yields. As the rainfall distribution becomes more winter dominant in nature, both crop yield variability and the usefulness of early winter rainfall decreases. Waterlogging has a large negative effect in the south-west of Western Australia, such that the rainfall distribution is more important than the amount in this region. A national sowing date survey determined that regional sowing dates have become earlier during the 1980’s and that these vary considerably, especially to the north-east. In Western Australia, earlier sowing combined with higher nitrogen inputs from fertilizers and legumes caused a significant upward trend in recent yields. Trends have been smaller in other states. Yields were also regressed with broad scale atmospheric indicators. Up to a year in advance of harvest, changes in the amplitude of the trough in the upper level westerlies (South Pacific) precede major anomalies in yields. Trends in the Southern Oscillation Index (SOI) around sowing time account for half the variance in the national yield, due to a persistence in following rainfall anomalies. Agrometeorological index models that combine the features of simulation and regression are shown to be the most appropriate models for yield forecasting. At a shire level they account for an average 55% of the yield variance in Western Australia, but 60 to 80% of the variation in eastern states yields. Satellite spectral data also resolved similar amounts of yield variance when sensor calibration bias was removed. With a mean regional index determined by station weighting, crop-weather models account for 87 to 92% of the variance of state and national yields. Tests with operational model forecasts equalled, or were more accurate than, official forecasts in 4 out of 5 years. Seasonal outlooks incorporated into model calculations brought further gains in accuracy in extreme years. Overall, the broad scale extent of yield anomalies across the Australian wheat belt is highlighted. Extreme yields, which are of most interest to the grain industry, are inseparably coupled to the ENSO phenomenon and the broad scale atmospheric circulation. Crop-weather models adjust rapidly to these anomalies in the weather and should be applied in an operational environment to provide early indications of crop prospects