Patrick J. Mitchell1, Jaclyn N. Brown1
1 CSIRO Agriculture and Food, College Rd, Sandy Bay, Tasmania, 7005, Patrick.Mitchell@csiro.au
The prediction of climate patterns and weather conditions at the farm scale represents an important innovation for managing within season and year-to-year variability in crop production. Assessing skill and potential value of long-range, seasonal climate forecasts hinges on answering the fundamental questions: “Should I use this forecast when making my decision and how ‘good’ is it?” Here, we use model output from the new seasonal forecasting system, ACCESS-S1 to compare forecast approaches for deriving relevant and credible seasonal climate information for Australia’s cropping regions. This evaluation addresses the role of two important components: categorisation of the model output and anchoring the forecast using antecedent conditions (fallow season rainfall). Overall, the model had relatively low accuracy at predicting correct forecasts across much of the forecast locations and seasons, whereas it had greater skill in the avoidance of false alarms i.e. false negative outcomes. The percentile categories used to derive the expected forecast had a large effect on the skill in terms of the rate of false alarms and the choice of categories can be matched to user requirements of both accuracy and resolution. Anchoring rainfall forecasts on antecedent conditions can reduce false alarms across the growing season and may be a useful guide when presented alongside a forecast based solely on in-season predicted rainfall. The next generation of climate data products and services for agriculture need to consider how a forecast system interacts with both on-farm biophysical drivers of yield and decision-making preferences of the user.