The National Farmer's Federation of Australia, in its publication New Horizons (White, Tupper, and Mavi, 1999), endorses the view of many farmers that improved seasonal forecasting is a high research priority to assist them in managing their properties. This has also been highlighted in several surveys (Stone and Marcussen, 1994; Elliott and Foster, 1994; Nicholls, 1985). Managers of water and other climate-sensitive sectors of the economy also claim that they would like to see significant advances in skill levels and lead times in seasonal forecasting (Albrecht and Gow, 1997). Another survey conducted by QCCA (Paul, Cliffe, and Hall, 2001) revealed that many graziers do use the forecasts to aid their stocking and stock-trading decisions, even though the reliability of forecasts remains an issue in many areas. Farmers in Queensland have certainly reacted to adverse SOI information by sending cattle to market, thereby reducing stocking rates on their properties.
Surveys of grain growers in New South Wales and Queensland have shown that farmers have used four-day weather forecasts to plan their sowing and spraying operations. They have also used frost risk information to switch crops and crop cultivars, and they have used the seasonal rainfall outlook to increase their nitrogen application rates and the area sown to crop. Meinke (2000) has cited specific examples in which farmers and several agencies in Queensland use the model-based information.
The Queensland University of Technology surveyed (Hastings and O'Sul-livan, 1998) primary producers, cattle producers with some dairy farmers, croppers, and others in agricultural production in southeast Queensland. The aim was to gauge producer opinions of the impact of seasonal climate patterns and seasonal forecasting. Two other surveys to get feedback on the needs and use of climate information were conducted in New South Wales (Albrecht and Gow, 1997; Crichton et al., 1999). Combined and generalized, the surveys revealed that producers are vitally interested in climate information and predictions of important weather parameters such as rainfall and frost. There is a large need for relevant and user-friendly information about climate in rural Australia. The surveys suggest that there is room to improve official forecasts to build more confidence and also to establish a better understanding of official forecasts.
Many investigations are available in which economic benefits from agro-climatological services are quantified (Adams et al., 2003). Additional but unquantified economic benefits of agroclimatological advisories are through checking land degradation from wind and water erosion and decreasing environmental pollution from fertilizer leaching and chemical spray drifts.
The value of the weather forecast depends on the ability of the user to effectively translate such information into economic values and profit margins at the individual-farm level. Katz and Murphy (1997) have cited studies showing savings from frost forecasts for orchards in the range from $US 667 to $1,885 per hectare. In maize production the savings range from $17 to $58 per hectare; in wheat production, a perfect forecast resulted in a savings of $196 per hectare; and in grape production, an accurate three-week forecast resulted in a net profit of $225 per hectare.
The value of a seasonal outlook depends on the skill or accuracy of the forecast and its marginal value relative to other readily available sources of information to the manager of a particular production system. Effective application of seasonal climate forecasts of reasonable accuracy leads to decisions that generate improved outcomes. To be effective, however, the decision changes must produce positive changes in value by improving the relevant aspects of targeted performances. If the information is ignored or it does not lead to changed decisions, it has no economic impact or value (Freebairn, 1996). If the forecast is inaccurate, then the information is likely to have a negative value in the current season.
In a research study, the Kondinin Group has looked at the accuracy and reviewed the usefulness of seasonal climate forecasts for on-farm decision making in southern and western Australia (Buckley, 2002). The study revealed that long-term climate forecast models can predict rainfall for three-month periods with accuracy levels that are better than a guess. Most of the models were more accurate with a lead time of zero and one month. Longer lead-time forecasts were not accurate enough to use for on-farm decision making. Furthermore, the forecast accuracy is very low during the critical time of autumn, which means climate forecasts are best used as only a small component of the farm decision-making process.
The benefits of seasonal forecasts vary between industries and across regions (Hammer, Carberry, and Stone, 2000). Soils and vegetation exposed to high climate variability in pastoral areas can benefit through destocking in advance of drought so as to avoid overgrazing, stock losses, and accelerated erosion. Crop producers can assess whether to sow or fertilize a crop if the chance of a harvest is significantly diminished. Demands for irrigation water can be better estimated.
The value of seasonal forecasts to crop producers can be significant, but it varies with management and initial conditions, as well as with cropping systems and location. The forecasts can influence decisions on what crop, when and what area to sow, and whether to irrigate and/or fertilize a crop. Hammer and colleagues (2001) have cited case studies of how the applications of climate prediction at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina have improved the profits of the farmers.
In the northern part of the Australian grain belt, significant increases in profit (up to 20 percent) and/or reduction in risk (up to 35 percent) can be achieved with wheat crops based on a seasonal forecast available at planting time (Hammer, Holzworth, and Stone, 1996). This can be achieved through tactical adjustment of nitrogen fertilizer application or cultivar maturity, with significant financial benefits (Marshall, Parton, and Hammer, 1996).
Petersen and Fraser (2001) suggest that a seasonal forecasting technology which provides a 30 percent decrease in seasonal uncertainty increases annual profits of the farmers in Western Australia by about 5 percent. In northwestern Victoria, if the seasonal forecast suggests adequate soil moisture in October, then a sunflower crop can be sown with a high probability of a good harvest (Jessop, 1977). In a similar way, seasonal forecasts can be used to determine whether a particular cereal, oilseed, or legume crop should be sown, based in particular on the probability of a favorable harvest.
The El Niño-Southern Oscillation has a dominant effect on climate in a number of the world's large-scale crop production areas. The SOI information contributes some skill to improving management decisions in Australia (Carberry et al., 2000). By changing between fallow-cotton, sorghumcotton, or cotton-cotton rotation based on SOI phase in the August to September period preceding the next two summers, the average gross margins for the two-year period increased by 14 percent over a standard fallow-cotton rotation. At the same time, soil loss from erosion was reduced by 23 percent and cash flow was improved in many years. Clewett and colleagues (1991) used a crop model to show that growing crops in seasons with a strongly negative SOI before planting were unprofitable, compared to seasons with a strongly positive SOI before planting. SOI data can therefore be used to adjust the management strategy according to the level of climatic risk.
Dudley and Hearn (1993) used a SOI model to examine irrigation options for cotton growers in the highly variable, summer rainfall environment of northern NSW. The study demonstrated that if irrigators knew the current SOI before the commencement of each cotton season, more profitable timing of investment in plant and equipment might result. These benefits might be extended to suppliers of farm inputs and to processors.
Rangelands in the eastern half of Australia are particularly sensitive to the climatic events of ENSO, with consequences for stocking rate and land degradation. A policy of reducing stocking rate on the basis of El Niño forecasts can significantly reduce environmental degradation in adverse seasons (McKeon and White, 1992; Stafford Smith et al., 1996; Clewett and Dros-dowsky, 1996).
Bowman, McKeon, and White (1995) examined the value of seasonal outlooks to wool producers in northern and western Victoria, assuming forecast accuracy for the next 12 months of 60, 80, and 100 percent. They concluded that the more accurate the seasonal forecast, the better was the long-term financial performance of the farms through reduction in livestock deaths and protection of the natural resource base.
Was this article helpful?