METEOROLOGICAL DATA COLLECTION, ANALYSIS AND SUGARCANE DISEASE FORECASTING FOR ORANGE RUST By T.N. STAIER 1, R.C. MAGAREY 2 and W.A. FINLAYSON 2 BSES Limited Mackay 1 Tully 2 tstaier@bses.org.au KEYWORDS: Meteorological, Disease, Forecasting, Orange Rust. Abstract AT THE TIME of the orange rust (Puccinia kuehnii) disease epidemic in 2000, there was evidence of a relationship between location and disease incidence and severity in the highly susceptible variety Q124. To understand the factors governing disease occurrence, laboratory research was undertaken. This provided information on temperature and relative humidity requirements for orange rust spore germination. Subsequently, weather stations were placed within sugarcane crops in the Mackay district of central Queensland and meteorological conditions recorded within the crop on a frequent basis. Results were analysed in accordance with the laboratory findings. This paper reviews the experience gained over several seasons and the extension strategies used to deliver information to industry. Use of this knowledge may help to improve our understanding of disease incidence in the Australian sugar industry, not only with orange rust but also with other leaf pathogens. Introduction Orange rust, caused by Puccinia kuehnii, caused large yield and financial losses to the Australian sugar industry in the 2000 2002 period (Magarey et al., 2002; Staier et al., 2003; Magarey et al., 2003). It is thought the epidemic resulted from a change in strain of the pathogen; the previous strain only caused minor yield losses in a couple of historically minor varieties. In the current epidemic, the central district, including Mackay, Proserpine and Sarina areas, was especially affected since the susceptible Q124 constituted up to 85% of the crop In order to minimise economic effects, research was directed to alternative control strategies to provide time for farmers to replant with more resistant canes. This research identified several fungicides with activity against orange rust (Magarey et al., 2002; Staier et al., 2003) and emergency registration enabled the industry to apply fungicide to minimise yield losses. In any fungicide disease control program, application of the minimum required material leading to good disease control will have several important benefits:
(i) (ii) there will be lower amounts of chemical released into the environment leading to a reduced risk of environmental problems; and the cost of the control program will be lower since less material needs to be purchased. In other higher value crops, this has led to research into the epidemiology of diseases in order to be able to predict when, and how severe, the disease will be (Magarey et al., 2001a). These disease models usually consider parameters such as relative humidity, leaf wetness and temperature as the main controlling variables in disease development (Magarey et al., 2001b). Once a link between weather and disease has been established, it is possible to monitor weather conditions, use the disease models to predict outbreaks, and apply fungicide only when it will be needed, thus minimising costs and adverse environmental impacts. In this paper, we consider the application of these techniques to orange rust of sugarcane, what was done during the orange rust epidemic of 2000 2002, and the options for similar applications with other leaf diseases. A preliminary disease development x weather parameter model was constructed which in time would be refined to predict orange rust development more accurately. In the meantime, predictions can be used to assist the cane-growing community to control the disease at minimum cost. This paper reports on model development and the results of extending the predictions to industry. Materials and methods The effect of weather parameters on orange rust There are several weather parameters that may influence disease development at various times in the development cycle. There was insufficient time or funding to completely examine all the influences of weather on orange rust development during the 2000 2002 epidemic. Magarey et al. (2004), therefore, investigated the influence of two weather parameters, relative humidity and temperature on Orange rust spore germination. Data from these studies enabled the development of a preliminary disease development model, which could be refined to predict orange rust occurrence and severity more accurately. They found that relative humidities of 97% and above were needed for significant spore germination, and that temperatures between 17 0 C and 24 0 C were optimal (Magarey et al., 2004). This enabled weather data to be analysed to determine the time periods each day/week/month in given locations that favoured spore germination. Field disease observations were made regularly at several geographical locations, as well as adjacent to the Agrilink weather station. These data were used to compare predicted disease activity with that actually occurring.
Weather data collection Bureau of Meteorology data Data were collected from two sources: from the Bureau of Meteorology, and from the commercial weather monitoring company Agrilink. Weather Bureau data were available for four locations, Cairns, Townsville, Mackay and Brisbane with records of relative humidity and temperature recorded each 15 minutes each day. The records were obtained over a four-year period. Weather Bureau data were analysed using the Excel program. Agrilink data An Agrilink weather station was stationed within a crop at BSES Mackay. This facilitated within crop data recording. The sensors for humidity and temperature were adjusted to 1 metre above ground level and the weather station situated >10 m inside the crop. Radio telemetry allowed data to be sent to a central recording facility, enabling on-line data access. Data were recorded at 15 minute intervals. Software from Agrilink was used to analyse incoming data and to provide an online display of the accumulated time periods favourable to orange rust spore germination. Outputs were the number of hours each day where conditions were suitable for orange rust spore germination. Crop disease monitoring Crop monitoring was undertaken to determine how weather predictions related to disease development. Monitoring began in the susceptible Q124 in plant or ratoon crops when they were approximately 400 mm in height. Visual assessment of leaf area affected was performed at least every 14 days, or even more frequently when disease build-up was rapid. Eight stalks were selected at five metre intervals and the area affected on the 7 th unfurled leaf (from top) was assessed. Younger leaves had insufficient time to express disease symptoms while older leaves often had already begun to senesce. Extension to industry Results were extended to industry via a range of methods including radio, newspaper and other BSES publications. Reports were presented at regular intervals and at times when decisions were to be made regarding disease control strategies. Results Weather Bureau data When weather data were obtained and analysed for the previous 5 years for Mackay, Townsville, Brisbane and Cairns, clear differences between sites were evident. The data indicated Mackay was the most favourable of the four sites for spore germination in terms of optimum temperature and relative humidity (Figure 1).
6 Year Average of Optimum Times for Orange Rust Development 80 70 average of hours at optimum conditions 60 50 40 30 20 10 Brisbane Cairns Mackay Tow nsville 0 January March May July September November Fig. 1 Monthly average hours where conditions were suitable for orange rust spore germination in Brisbane, Mackay, Townsville and Cairns. Agrilink weather monitoring The sites additional to the Agrilink weather station site provided insight into overall disease incidence, and a better guide to variation in disease and climate within sub-districts. Disease predictions were assessed qualitatively by comparing disease incidence, severity in relation to stage of crop growth, weather conditions and disease inoculum levels. These factors are critical inputs for a model. When regular infection events were predicted, field disease levels were assessed and recorded (Figure 2). The predictions based on this information typically carried forward 4 days allowing growers to organise disease control strategies (fungicide application) in accordance with the emergency use permit. As initial disease level at the time of an infection event affects how much disease will develop at any one time, a disease threshold was needed. If the disease increased above this level, and suitable environmental conditions were experienced, a disease warning was issued to farmers to consider fungicide application. The initial threshold was set at 10% leaf area affected, but this was revised down to 5% from the calculated relationship between leaf area affected and yield in fungicide control experiments. Conditional formatting was applied to the data in MS Excel and, when average values for the 7 th leaf exceeded 5%, a cell would go red indicating that disease control needed to be investigated (Figure 3).
Extension The disease predictions proved useful to growers; in several instances BSES provided a one-to-one grower service with excellent results. Other activities included publishing the inoculum levels weekly, together with suggestions concerning control strategies, in industry newspapers (e.g. Bush Telegraph). Radio reports were also issued and any major developments televised. Shed meetings provided informal detailed discussions about control options and sub-district disease developments. Discussion This paper illustrates how weather data can be used to predict leaf disease occurrence and when control strategies need to be applied. We used only data relating to spore germination and this was only a guide to predicting disease occurrence. Spore germination is only one aspect of the pathogen life cycle; the conditions required in total for disease development have not yet been fully studied. Much more research is needed to relate weather parameters to disease incidence, including the effect of temperature and relative humidity on the leaf infection process, pustule development, spore production and spore dispersal. Only when this information is to hand will there be confidence that the disease model has a high degree of accuracy. Even given this limitation, our observations suggest spore germination information did provide a reasonable degree of predictability as to when orange rust was likely to occur in central Queensland crops. Further work is needed on the following: (i) research relating other aspects of disease development to temperature and relative humidity; and (ii) refinement of the threshold leaf area diseased above which yield parameters are reduced. Both aspects are important to the generation of an accurate disease model. Magarey et al. (2001b) review disease models in other crops, relating environmental parameters to control programs. Much research is needed not only with sugarcane but also with most other crops. In Queensland, a larger set of weather monitoring data loggers spread through out the state would provide a valuable data set for determining how favourable conditions for disease vary between districts. With a long-term set of data, the probability of severe disease in any one year could be predicted and the probabilities between districts compared. This information could then be used by plant breeders to determine what level of resistance was needed in commercial varieties to minimise yield losses. Such information and outputs may not only be applied to orange rust but could equally be applied to brown rust and yellow spot diseases. Currently, the area planted to varieties susceptible to orange rust in the central district is now much reduced and at a level where district production is no longer significantly subject to this disease. Control is therefore largely accomplished through resistant varieties. Further research of epidemiology traits will provide inputs for more complex models of increasing accuracy. The industry and environment were spared ineffective fungicide application through the utilisation of the predictive models described here.
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