Evaluation of a disease forecast model for peach leaf curl in the Prefecture of Imathia, Greece

Abstract

Disease forecasting models assist producers in estimating the likely appearance of disease in their crops and in the selection and timing of preventative applications. The first objective of this study was to evaluate the accuracy of a weather-driven model for predicting infection by Taphrina deformans in different peach-growing areas. In 12 peach-growing areas in the Prefecture of Imathia, Greece, and over two years, the timing of initial infections differed depending on differences in the microclimate (mainly in temperatures and leaf wetness). There was a difference up to 9 days in the date of disease onset predicted by differences in the microclimate between regions. The model accurately predicted the observed differences in date of first symptom appearance. There was also a good correlation between predicted risk and observed disease severity. In addition, differences were observed among areas in the level of risk and the intensity of symptoms with a trend that areas at higher risk having a higher intensity of symptoms. The second objective of this study was to investigate advantages arising from using the model for scheduling fungicide applications against peach leaf curl. In five years of trials, the use of the model reduced the number of sprays compared to conventional spray program while achieving similar level of control. The simple rule of "spray one day before first forecasted rain after bud break" also gave good results. Trials were set up in order to determine the risk threshold for spraying based on model predictions. The results indicated that spraying only when the predicted risk was between 40 and 60 (over a maximum of 100) might be the most effective rule, but further investigations should be conducted to clarify the relationship between the predicted risk, actual peach leaf curl incidence and, more importantly, yield in order to determine the time when fungicide sprays are economically justified.