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

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.

币斑病（dollar spot）是高尔夫球场草坪最常见的病害之一，生产中往往需要多次施用杀菌剂方可达到理想的防治效果。基于气象条件的病害预警系统已被开发用于更精准地确定杀菌剂施用时机，但这类系统往往效果不佳，目前尚未得到广泛应用。本研究的核心目标是开发一套全新的基于气象条件的病害预警系统，以在更广泛的地理与气候范围内，更精准地指导杀菌剂施用，从而防控币斑病的发生。

这套全新的币斑病预警系统基于2008年在美国威斯康星州麦迪逊市与俄克拉荷马州斯蒂尔沃特市的田间试验数据开发而成；2011年至2016年间，研究人员分别在威斯康星州麦迪逊市、俄克拉荷马州斯蒂尔沃特市、田纳西州诺克斯维尔市、宾夕法尼亚州州学院、密西西比州斯塔克维尔市以及康涅狄格州斯托尔斯市设立了预警系统验证站点。

研究人员对所有试验点年度数据进行了荟萃分析（meta-analysis），结果显示，用于预测币斑病发生的最优预警系统基于相对湿度与日平均气温的五日滑动平均值构建。通过该模型测算，在众多试验点与试验年份中，可实现与按日历规划的施药方案相当的防治效果的最高有效概率阈值为20%。进一步分析表明，采用20%的施药阈值可实现与按日历规划的施药方案相当的防治效果，同时可将杀菌剂用量减少最多30%；不过，当实际应用场景超出本次试验覆盖的环境范围时，仍可能需要对该系统进行进一步优化。

本研究提出的基于气象条件的币斑病预警系统，有望成为未来草坪管理者实施精准病害管理的重要工具，尤其在当前金融与监管压力不断提升、高尔夫球场草坪农药减量需求日益迫切的背景下，其应用价值将愈发凸显。