Department of Plant Pathology, University of Wisconsin-Madison, Madison.
Saint-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC, Canada.
Plant Dis. 2018 Jan;102(1):73-84. doi: 10.1094/PDIS-04-17-0504-RE. Epub 2017 Nov 20.
Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable. Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).
由核盘菌引起的大豆菌核茎腐病（SSR）在美国每年导致高达 100 万吨的产量损失。季候期病害管理主要依赖于化学防治，但它的效率和成本效益取决于所用的化学物质以及菌核形成、萌发和在易感大豆生长阶段进一步散布子囊孢子的风险。因此，准确预测大豆开花期核盘菌菌核的风险可以使农民能够改善季候期 SSR 管理。在 2014 年至 2016 年期间，在爱荷华州（n = 156）、密歇根州（n = 1,400）和威斯康星州（n = 2,310）的三个灌溉（n = 1,505 个地块级观测值）和六个非灌溉（n = 2,361 个地块级观测值）田间试验中监测了菌核的存在或不存在，共进行了 3,866 个地块级观测值。在每个地点，还连续监测了每小时的空气温度、相对湿度、露点、风速、叶片湿度和降雨量，使用高分辨率网格化天气数据。使用逻辑回归模型，以菌核的存在作为二元响应变量，对灌溉和非灌溉条件进行了分析。对生育期变量（行宽）和与天气相关的变量（定义为菌核存在之前的 30 天移动平均值）进行了预测能力测试。在灌溉大豆田中，菌核的存在与行宽（r = -0.41，P < 0.0001）、30 天移动平均值的日最高空气温度（r = 0.27，P < 0.0001）和日最高相对湿度（r = 0.16，P < 0.05）关系最密切。在非灌溉田，菌核的存在与日最高空气温度（r = -0.30，P < 0.0001）和风速（r = -0.27，P < 0.0001）的移动平均值关系最密切。这些模型正确预测了（整体准确率为 67%至 70%）四个独立数据集（n = 1,102 个地块级观测值或 30 个日平均观测值）在大豆开花期菌核的存在。
