Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass

Abstract

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass.

Current understanding of the Poa annua life cycle

“…For example, anthracnose is more likely to develop during periods of water stress, which occurs more frequently in situations of high air temperature, than when water is not limiting (Roberts et al, 2011). Additionally, models predicting dollar spot and brown patch indicate disease is most likely to develop during periods of warm air temperature and high relative humidity (Fidanza et al, 1996;Smith et al, 2018). In cooler regions with a limited window This article is protected by copyright.…”

Novel action thresholds of a logistic regression model to forecast dollar spot on bentgrasses

“…Five action thresholds of the logistic regression model risk index (RI) were evaluated for accuracy in predicting dollar spot for each bentgrass cultivar ( Table 1). The first action threshold, which will be referred to as "RI 20%," used a risk index threshold of 20% for predicting dollar spot based on the findings from Smith et al (2018). Preliminary observations indicated that the negative slopes of the risk index were strongly associated with periods of inactive dollar spot and positive risk index slopes were strongly associated with increasing dollar spot.…”

“…The third action threshold combined the RI 20% and RI slope action threshold and will be referred to as "RI 20% + RI slope" hereafter. Smith et al (2018) speculated that the risk index action threshold may need to be adjusted from 20% to accurately schedule fungicide applications for host species or cultivars with varying levels of dollar spot susceptibility. Thus, the fourth action threshold evaluated adjusting the risk index-from 1 to 60% in 1% increments-to select a RI threshold associated with the maximum accuracy in predicting dollar spot for each cultivar ( Figure S1), and will be referred to as the RI max action threshold.…”

“…The RI max when RI slope was also considered is indicated by a ◊ marker along the dashed green line for each cultivar. The 20% risk index threshold previously reported by Smith et al (2018) and currently used for the RI 20% and RI 20% + RI Slope interpretations is marked by a vertical dotted blue line for reference. a Dollar spot was considered to be increasing when the change in the infection center count per day was one or more; a change of less than one infection center per day was considered inactive.…”

“…Ryan et al (2012) reported that the degree of susceptibility in creeping bentgrass was an important factor in modeling the onset of dollar spot on highly and moderately susceptible cultivars. However, the extent that host susceptibility affects dollar spot predictability by the logistic regression model (Smith et al, 2018) is not known. Therefore, a field study was initiated to assess the ability of five action thresholds of the logistic regression model to forecast dollar spot on bentgrass species and cultivars with a wide range in susceptibility.…”