The integrated modeling approach developed for this study demonstrates the gains that can be made by the routine incorporation of seasonal forecasting into water management decision.
To test the likeliness of farmers to use the seasonal forecasting tools, we conducted a series of ten semistructured, scoping interviews. The interviewees were irrigators on a non-regulated tributary of the Namoi River (in the Upper MurrayDarling basin) and were therefore highly dependent on the river flows. With the uncertainty of annual water supplies to irrigate their crops, it was thought that this group might be more positive about seasonal forecasting than those on regulated rivers.
Given the small sample number, we compared the data collected with that from a similar study conducted by others in the southern Murray-Darling basin (URS Australia, 2001). That study consisted of 29 interviews followed by a workshop with six participants. The findings of both studies were similar, thus providing confidence in the outcomes of these limited interviews.
Knowledge and understanding of the term and the forms of seasonal forecasting were highly variable, ranging from a good understanding to little or misguided awareness. Participants seemed to misunderstand the difference between types of forecasting and sources of forecasting. However, considerable support exists for natural signals rather than the use of technology—for example:
Some of the best indicators in times of drought have been the ants' nests around the house—if there is a lot of movement by the ants, it's generally going to rain soon.
There are many natural signs that are more useful than the scientific information we are given.
The degree to which people understood probabilities, or thought they could be useful to them, was also variable. Many were skeptical of the probabilities given their derivation by the extrapolation of past data to the present. Usefulness was also questioned in view of past experiences:
At the last meeting we were told, "There will be a 50/50 chance that we will get above-average rainfall and 50/50 chance we will get below-average rainfall." This told us nothing.
The degree to which these farmers incorporated seasonal forecasting information into their decision making was also variable. Although no one used it as a regular aid, some said they sometimes used it, others considered it but rarely used it, and some said they did not use it at all.
Those who said they did use it indicated that it could affect changes in planning for the timing of seeding, spraying times, planting rates, the type of crops planted, and the number of stock purchased. However, they stressed that the seasonal forecasting was only one piece of information they used, combining it with natural indicators and sources of information used in the past. Decisions were still very conservative.
If they say it is going to be a dry year I won't buy more cattle. If it is going to be a wet year, I may decide to buy more cattle.
Those who did not use seasonal forecasting in their decision making were reluctant to do so because they had bad experiences in the past, or had heard of someone who had. Other reasons included a lack of understanding or restricted access to the information. They seemed to pay more attention to short-term forecasting than to seasonal. The consequences of poor short-term decisions were not seen to be as dire as the consequences of seasonal mistakes.
Really if your gut feeling tells you it is going to be dry then it probably will be. If it tells you it will be wet, it probably will be.
I'm an old-time farmer and I feel that you take what you get.
I don't have much confidence in the information. It is usually only 50% accurate which is the same as tossing a coin.
Rainfall probabilities were considered to be the most useful information seasonal forecasting could provide. However, decision making would still be highly conservative.
I would pay attention if they told me there was a 75% chance that we will go into a drought. However, if they told me that there was a 25% chance of below-average rainfall, with a 75% chance of above-average rainfall, I would pay more attention to the prediction of below-average rainfall.
The farmers were asked if they would be more willing to use a tool that predicted only extreme events with better than usual reliability, rather than more frequent rainfall predictions with lesser certainty. Generally it was agreed that this would be preferable, but there was considerable cynicism that sufficient reliability could be obtained for their purposes.
They did acknowledge, however, the difficulty associated with forecasting, especially given the limited recorded weather history in Australia. Then again, it seemed there was little likelihood that any latitude would be given to the scientists if the forecasts were mistaken. Reliability was very important, and until this could be achieved to help farmers in decision making, uptake of the technology would be limited. And memories can be very long.
Indigo Jones was a long-range forecaster a while back, and he was considered to be very good. In 1974 he predicted it would be wet and we had some of the biggest floods in history. However, in 1975 he predicted it would be wetter still, and we had one of the worst droughts on record. After that I lost faith in long-range forecasters.
It is therefore apparent that the potential market for seasonal forecasting tools in the farming community will be limited in the short term. One must understand the likely users of the technology and exactly what decisions they believe it can assist them with.
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