Predicting the Onset Date of Cucumber Powdery Mildew Based on Growing Degree Days and Leaf Wetness Duration in Greenhouse Environment.

Abstract

Cucumber powdery mildew, caused by Podosphaera xanthii, can lead to significant yield losses in greenhouse cultivation. A calendar-based fungicide spray program is commonly employed by farmers, often leading to excessive spraying irrespective of disease conduciveness under certain weather conditions. Therefore, a disease model that can predict the onset of symptoms for determining when to start the first spray applications during a season is needed. This study developed a disease onset forecasting model, which uses growing degree days and leaf wetness duration as input variables, to aid the spray program for cucumber powdery mildew in the greenhouse environment. The model was calibrated using disease onset dates and corresponding weather data collected from two consecutive greenhouse experiments in 2022. As a result, we successfully simulated the symptom onset date with a margin of error of 5.5 days across two validation trials in 2023 and 2024. Further improvements to the model are needed to establish a model-based fungicide program in the greenhouse environment, which can be done by securing more data from additional trials for further modification and calibration of the model.