| description abstract | The economic value of seasonal climate forecasting is assessed using a whole-of-chain analysis. The entire system, from sea surface temperature (SST) through pasture growth and animal production to economic and resource outcomes, is examined. A novel statistical forecast method is developed using the partial least squares spatial correlation technique with near-global SST. This method permits forecasts to be tailored for particular regions and industries. The method is used to forecast plant growth days rather than rainfall. Forecast skill is measured by performing a series of retrospective forecasts (hindcasts) over the previous century. The hindcasts are cross-validated to guard against the possibility of artificial skill, so there is no skill at predicting random time series. The hindcast skill is shown to be a good estimator of the true forecast skill obtained when only data from previous years are used in developing the forecast. Forecasts of plant growth, reduced to three categories, are used in several agricultural examples in Australia. For the northeast Queensland grazing industry, the economic value of this forecast is shown to be greater than that of a Southern Oscillation index (SOI) based forecast and to match or exceed the value of a ?perfect? category rainfall forecast. Reasons for the latter surprising result are given. Resource degradation, in this case measured by soil loss, is shown to remain insignificant despite increasing production from the land. Two further examples in Queensland, one for the cotton industry and one for wheat, are illustrated in less depth. The value of a forecast is again shown to match or exceed that obtained using the SOI, although further investigation of the decision-making responses to forecasts is needed to extract the maximum benefit for these industries. | |
