Dr Roger C Stone₁, Dr Russell Muchow₂

Australian agricultural industry, especially the Australian sugar industry, operates in a particularly uncertain and highly variable climatic environment Climate forecasting based on an understanding of certain key aspects of the El Niño/Southern Oscillation phenomenon (ENSO) or of global sea-surface temperatures offers the ability to aid in risk management for the Australian sugar industry. A key reason for some of the modestly successful applications of climate forecasting in managing rural enterprises in north-east Australia has been due to the integration of statistical climate forecasting systems with simulation modeling. It is further suggested it is important to consider industry as a whole as changes in the management system due to a climate forecast at the farm scale can impact through all scales of the value chain.

Seasonal climate forecasting methods applied in seeking effective application to the Australian sugar industry have included:

• ‘SOI phases’ (or patterns) of the Southern Oscillation Index (SOI), based on a typology of the SOI, using both persistence and change, in SOI which provides probability distributions of rainfall, frost, or river flow, usually for the forthcoming three month period (Stone et al. (1996a, b),
• Sea-surface temperature (SST) ‘phases’ where patterns of sea-surface temperature anomalies in the Pacific and Indian Oceans have been clustered into groups. This method is currently under further development (Drosdowsky, 2000).

One of the main advantages in using the SOI phase method and the more recently devised SST phase-based method in applications studies is that analogue seasons or years are directly provided as output from running these procedures. This means that, based on the classification system applied to the SOI or SST phase, any given month can be placed within the context of similar months over the past 120 years for which we have a history and typology of the SOI or SST pattern. Once the required analogue information is provided as output we extract daily data for radiation, evaporation, rainfall, and temperature corresponding to that particular year or season. The ability of the SOI phase system to provide analogue information in terms of sets of similar years or seasons is one of the main reasons why the SOI phase approach has been applied so far in agricultural applications in Australia.

An example of forecast output from use of the SOI phase approach is described in Figure 1. Figure 1 provides an example of cumulative probability distributions of rainfall for Sarina, Queensland, for the key sugar harvesting period of July through September. The distributions shown are associated with each of the five SOI phases. Thus, the user is able to gain an understanding of both the probabilistic nature of the output of this type of forecast information and the relative shifts in rainfall distribution associated with each SOI phase for that location. An example of the SST phase output will provided at the workshop.

In order to achieve a more targeted approach in application of climate forecasting two main thrusts of research activity have taken place in north-east Australia over recent years.

1. Simulation Modelling.

A key aspect of developing a more targeted approach to climate forecast application activity in north-east Australia has been through the use of simulation modelling. Simulation modelling allows a more integrated approach whereby farmers and others are able to apply climate forecasting more directly to their on-farm management as the climate forecasting information has been translated, through this approach, into more useful terms of direct significance to the user. For example, it may be valuable to know the more likely forecast potential yield a crop may achieve before that crop is planted, rather than simply being provided with a rainfall forecast, in order to determine amounts of nitrogen to purchase and apply and to help in marketing that crop (including forward selling the crop).

2. Climate forecasting applications through the whole value-chain in agriculture.

A further key aspect in the successful development of an effective climate forecasting-agricultural management system is the need to clearly identify those decision points in the agricultural system where knowledge of a climate forecasting system would make a marked difference in that decision. It is then important to quantify what contribution climate forecasting would have in improving the profitability or sustainability of that industry through improving decision making. It is also particularly important to consider the industry as a whole as changes in the management system through use of climate forecasting at the farm scale may impact through all sections of the value chain. In the sugar industry we are currently investigating how climate forecasting can aid management decisions at the farm production scale, at the mill scale, and at the whole of industry marketing scale (Figure 2).

We believe some key features associated with successful development and application of climate forecasting in north-east Australia has been through the following initiatives:

Drosdowsky, W. ‘Use of SST phases and Australian rainfall ’. Aust Met. Mag (submitted).

Hammer, G.L., Holzworth, D.P., and Stone, R.C. (1996) ‘The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability’ Australian Journal of Agricultural Research, 47, 717-737.

Stone, R.C., Hammer, G.L., and Marcussen, T. (1996a) ‘Prediction of global rainfall probabilities using phases of the Southern Oscillation Index’ Nature, 384, 252-255, 21 November 1996.

Stone, R.C., Nicholls, N., and Hammer, G.L. (1996b) ‘Frost in north-east Australia: trends and influences of phases of the Southern Oscillation’ Journal of Climate (American Meteorological Society), 9, 8, 1896-1909.