Correlating fruit fly with cherry production and climate (CY13012)
What was it all about?
The presence Queensland fruit fly (Qfly) precludes cherries from entry to a number of interstate and international markets. While Tasmania is internationally recognised as a Qfly Pest Free Area, mainland cherry growing regions do not have equivalent zones. This is in spite of the fact that some regions could be considered Qfly free for cherry production due to regional management systems, terrain or climate. In particular, some areas suitable for cherry production see weather conditions during cherry growth and harvest that prevent fruit fly infestation.
In order to support applications for market access based climatic or seasonal freedom from Qfly, this project developed models that can be used to predict Qfly populations post-winter throughout Australia.
Four models were developed based on three factors:
- Qfly maturation through its various life stages
- Weather conditions up to and including harvest
- Cherry growth characteristics.
Models can be used by the cherry industry in two ways:
- To assess the economic viability of new cherry production regions, considering likely Qfly impact on production and exports
- To add scientific evidence to support submissions for area freedom status, areas of low pest prevalence, pest free production sites or pest free places of production (as described in International Standards for Phytosanitary Measures) for current and future cherry production areas.
Although based on cherries and Qfly, the models developed for this project can be applied to any crop and modified to suit any pest or disease that is dependent on weather.
In addition to the models, this research generated an improved understanding of:
- Fruit fly survival under cool to temperate conditions
- The variables that impact on the interaction between Qfly ecology, behaviour and biology with weather and cherry growth characteristics
- How climate variations throughout Australia affect Qfly numbers
- The potential for, and limitations of, using degree-day models to predict population fluctuations.
This project has been funded by Hort Innovation
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