Harikrishnan R, Río L E Del
Department of Plant Pathology, North Dakota State University, Fargo 58105.
Plant Dis. 2008 Jan;92(1):42-46. doi: 10.1094/PDIS-92-1-0042.
White mold, caused by Sclerotinia sclerotiorum, is the most important disease affecting dry bean production in North Dakota. This disease currently is managed mainly through fungicides applied during the flowering stage. A disease-forecasting model was developed to help growers with their decision to apply these fungicides. The model was built using weather variables collected during eight consecutive half-month periods between 1 May and 31 August 2003 to 2005 and white mold incidence data obtained from 150 fields. The model was produced using logistic regression analysis, and includes total rainfall, average minimum temperature, and number of rainy days in the first half of June, July, and August, respectively, as predictors and explained 85% of the variability. The model was validated using an independent disease data set collected from 100 fields during the same years. The model exhibited high true positive ratio (0.79) and very high accuracy (0.91) between observed and predicted probabilities of white mold incidence. Results from this study suggest that in-season macro-weather variables could be used to predict the risk of white mold, which in-turn could help growers make better-informed decisions on whether or not to apply fungicides for white mold control.
由核盘菌引起的白霉病是影响北达科他州干豆生产的最重要病害。目前，这种病害主要通过在开花期施用杀菌剂来控制。开发了一种病害预测模型，以帮助种植者决定是否施用这些杀菌剂。该模型利用2003年至2005年5月1日至8月31日期间连续八个半月收集的气象变量以及从150个田地获得的白霉病发病率数据构建而成。该模型采用逻辑回归分析得出，分别将6月、7月和8月上半月的总降雨量、平均最低温度和降雨天数作为预测因子，解释了85%的变异性。该模型使用同年从100个田地收集的独立病害数据集进行验证。该模型在白霉病发病率的观测概率和预测概率之间表现出较高的真阳性率（0.79）和非常高的准确率（0.91）。本研究结果表明，季内宏观气象变量可用于预测白霉病风险，进而帮助种植者就是否施用杀菌剂防治白霉病做出更明智的决策。
