Cai-Xia Wang, and
Zhen-Fang Zhang, College of Crop Protection and Agronomy, Qingdao Agricultural University; Key Lab of Integrated Crop Pests Management of Shandong Province, Qingdao, Shandong 266109, P. R. China
Glomerella leaf spot (GLS) caused by Glomerella cingulata is a newly emergent disease that results in severe defoliation and fruit spots. Currently, GLS is not effectively controlled in China due to a lack of understanding of its epidemiology. Therefore, the effects of temperature, wetness duration, and moisture on conidial germination, infection, and the disease incubation period of GLS were examined by inoculating cv. Gala apple leaves with a conidial suspension and performing in vitro germination assays. Conidia could germinate and form appressoria at temperatures ranging from 5 to 35°C, with an optimum temperature of 27.6°C. The germination of conidia required free water or a nearly saturated relative humidity, with only a few conidia germinating and forming appressoria when the RH was less than 99%. The conidial germination dynamics at 10, 25, and 30°C were well represented by three logistic models. The infection of cv. Gala apple leaves by conidia occurred at temperatures ranging from 15 to 35°C. The minimum wetness duration required for infection by conidia at different temperatures was described using a polynomial equation, and the lowest minimum wetness duration was 2.76 h, which occurred at 27.6°C according to the polynomial. Successful infection by conidia was represented by the number of lesions per leaf, which increased with extended wetness durations at the conidial infection stage for six tested temperatures, with the exception of 10°C, when the minimum wetness durations were satisfied. The associations of successfully infected conidia with wetness duration at temperatures of 15, 20, 25, and 30°C were described by four logistic models. Conidia infections developed into visible lesions at temperatures ranging from 15 to 30°C, and the shortest incubation period of 2 days was observed at 25°C. These data and models can be used to construct forecasting models and develop effective control systems for Glomerella leaf spot.