A rule-based prediction system improves spray precision for the control of strawberry powdery mildew
Hannah Wileman: University of Hertfordshire
<div>Work at the University of Hertfordshire has shown that environmental conditions such as temperature and relative humidity affected strawberry powdery mildew (<em>Podosphaera aphanis)</em> conidial conidiation, germination and disease severity. A rule-based prediction system was developed into a CD software to record the accumulated number of hours (up to 144) of disease-conducive conditions (temperature 15‒25°C and RH>60%) needed for the development of the pathogen. It identifies high risk days when sporulation may occur thus allowing growers to spray at the optimal time (primary infection). Two-weekly monitoring of disease severity in field trials using this software in <span>2014 and 2015 showed that strawberry crops from the prediction spray programme had fewer fungicide applications (7 sprays in 2014, 3 in 2015) compared with the commercial programme (9 sprays in 2014, 7 in 2015), as well as less severe disease. A more user-friendly web-based system was introduced in 2017 and has shown additional results in improving the efficient use of the fungicide mode of actions and reducing fungicide applications by 5, saving £300 per hectare. The 2014-17 results showed that the use of the prediction system achieved satisfactory control of <em>P. aphanis </em>with fewer fungicide applications, enabling growers to spray with precision thus maximising fungicide effectivity for disease control. </span></div>
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