Australian cotton production is characterised by high yielding/quality systems, earning in excess of $2.5b export revenue annually. Crops are intensively managed broadacre systems grown under an expanding scope of climatic conditions and diverse agronomic practices. Consequently there are a range of decisions affected by weather and climate affecting productivity.

 We explored the current capability of dynamical seasonal forecasting models to provide sub-season temperature predictions for early season tactical decisions. We assess the value of this source of information over current methods of utilising analysis of historical climate records as a guide. Forecasts were generated from the start of September to predict the impact of planting time on crop establishment through Oct to Nov, and to predict the time when the first flower occurs (historical ranging from Dec to Jan). Results showed that there was improvements in some locations utilising the forecast to highlight risk of poor crop establishment that could occur with various planting times, and there was improvement in identifying the first flower date compared to the average predicted time based on history. Research continues to better identify those regions where the forecasts will add value and to understand where these forecast can be utilised in other parts of the cotton season.