A Temperature and Leaf Wetness Duration-Based Model for Prediction of Gray Leaf Spot of Perennial Ryegrass Turf

Abstract

ABSTRACT Gray leaf spot is a serious disease of perennial ryegrass (Lolium perenne), causing severe epidemics in golf course fairways. The effects of temperature and leaf wetness duration on the development of gray leaf spot of perennial ryegrass turf were evaluated in controlled environment chambers. Six-week-old Legacy II ryegrass plants were inoculated with an aqueous conidial suspension of Pyricularia grisea (approximately 8 x 10(4) conidia per ml of water) and subjected to four different temperatures (20, 24, 28, and 32 degrees C) and 12 leaf wetness durations (3 to 36 h at 3-h intervals). Three days after inoculation, gray leaf spot developed on plants at all temperatures and leaf wetness durations. Disease incidence (percent leaf blades symptomatic) and severity (index 0 to 10; 0 = leaf blades asymptomatic, 10 = >90% leaf area necrotic) were assessed 7 days after inoculation. There were significant effects ( alpha = 0.0001) of temperature and leaf wetness duration on disease incidence and severity, and there were significant interactions ( alpha = 0.0001) between them. Among the four temperatures tested, 28 degrees C was most favorable to gray leaf spot development. Disease incidence and severity increased with increased leaf wetness duration at all temperatures. A shorter leaf wetness duration was required for disease development under warmer temperatures. Analysis of variance with orthogonal polynomial contrasts and regression analyses were used to determine the functional relationships among temperature and leaf wetness duration and gray leaf spot incidence and severity. Significant effects were included in a regression model that described the relationship. The polynomial model included linear, quadratic, and cubic terms for temperature and leaf wetness duration effects. The adjusted coefficients of determination for the fitted model for disease incidence and severity were 0.84 and 0.87, respectively. The predictive model may be used as part of an integrated gray leaf spot forecasting system for perennial ryegrass turf.