Development and Field Validation of a Brown Patch Warning Model for Perennial Ryegrass Turf

Abstract

Microclimate in perennial ryegrass (Lolium perenne 'Caravelle') was monitored for 3 years to identify environmental conditions associated with brown patch (Rhizoctonia solani) outbreaks and to develop a weather-based disease-warning model. The micrometeorological parameters measured were ambient air temperature, relative humidity, leaf wetness duration, precipitation, soil temperature, soil moisture, and solar radiation. Brown patch outbreaks were confirmed by the visual presence of foliar R. solani mycelium. An environmental favorability index (E) was developed to relate a combination of environmental conditions with brown patch outbreaks. The initial index (E 6 ) was based on relative humidity (RH ≥ 95% for ≥8 h; mean RH ≥ 75%), leaf wetness duration (≥6 h) or precipitation (≥12 mm), and minimum air (≥16°C) and soil (≥16°C) temperatures. Further analyses, however, revealed that an equally effective E was provided by a two-variable regression model (E 2 ). The E 2 model is E = -21.5 + 0.15RH + 1.4T- 0.033T 2 , in which RH is the mean relative humidity and T is the minimum daily air temperature. For both E 6 and E 2 models, a threshold value (i.e., E ≥ 6) constituted a brown patch warning. Brown patch outbreaks were predicted by both models with 85% accuracy over a 3-year period. All major infection events were predicted. In 1993, the warning model was used to field evaluate fungicide performance in perennial ryegrass and colonial bentgrass (Agrostis tenuis 'Bardot'). There were equivalent levels of blighting between the warning model and a 14-day calendar-based spray schedule, but the warning schedule provided a 29% reduction in fungicide applications.