Esker P D, Harri J, Dixon P M, Nutter F W
Department of Plant Pathology, Iowa State University, Ames 50011.
Iowa Department of Agriculture and Land Stewardship, Ankeny 50023.
Plant Dis. 2006 Oct;90(10):1353-1357. doi: 10.1094/PD-90-1353.
Three forecasting models for Stewart's disease (Pantoea stewartii subsp. stewartii) of corn (Zea mays) were examined for their ability to accurately predict the prevalence of Stewart's disease in Iowa at the county level. The Stevens Model, which is used as a predictor of the early wilt phase of Stewart's disease, the Stevens-Boewe Model, which predicts the late leaf blight phase of Stewart's disease, and the Iowa State Model that is used to predict the prevalence of Stewart's disease, all use mean air temperatures for December, January, and February for a preplant prediction of Stewart's disease risk in a subsequent season. Models were fitted using weighted binary logistic regression with Stewart's disease prevalence data and air temperature data for 1972 to 2003. For each model, the years 1972 to 1999 (n = 786 county-years) were used for model development to obtain parameter coefficients. All three models indicated an increased likelihood for Stewart's disease occurring in growing seasons preceded by warmer winters. Using internal bootstrap validation, the Stevens Model had a maximum error between predicted and calibrated probabilities of 10%, whereas the Stevens-Boewe and Iowa State models had maximum errors of 1% or less. External validation for each model, using air temperature and seed corn inspection data between 2000 and 2003 (n = 154 county-years), indicated that overall accuracy to predict Stewart's disease at the county level was between 62 and 66%. However, both the Stevens and Stevens-Boewe models were overly optimistic in predicting that Stewart's disease would not occur within specific counties, as the sensitivity for these two models was quite low (18 and 43%, respectively). The Iowa State Model was substantially more sensitive (67%). The results of this study suggest that the Iowa State Model has increased predictive ability beyond statewide predictions for estimating the risk of Stewart's disease at the county level in Iowa.
对三种用于预测玉米（ Zea mays ）斯图尔特氏病（ Pantoea stewartii subsp. stewartii ）的预测模型进行了检验，以评估它们在县级层面准确预测爱荷华州斯图尔特氏病流行情况的能力。史蒂文斯模型用于预测斯图尔特氏病早期枯萎阶段，史蒂文斯 - 博韦模型用于预测斯图尔特氏病后期叶枯病阶段，爱荷华州立模型用于预测斯图尔特氏病的流行情况，这三个模型均使用12月、1月和2月的平均气温对后续季节斯图尔特氏病风险进行种植前预测。利用1972年至2003年斯图尔特氏病流行数据和气温数据，通过加权二元逻辑回归对模型进行拟合。对于每个模型，使用1972年至1999年（n = 786个县年）的数据进行模型开发以获得参数系数。所有三个模型均表明，在冬季较温暖之后的生长季节，发生斯图尔特氏病的可能性增加。通过内部自助验证，史蒂文斯模型预测概率与校准概率之间的最大误差为10%，而史蒂文斯 - 博韦模型和爱荷华州立模型的最大误差为1%或更小。利用2000年至2003年（n = 154个县年）的气温和种子玉米检验数据对每个模型进行外部验证表明，在县级层面预测斯图尔特氏病的总体准确率在62%至66%之间。然而，史蒂文斯模型和史蒂文斯 - 博韦模型在预测某些特定县不会发生斯图尔特氏病时都过于乐观，因为这两个模型的敏感性相当低（分别为18%和43%）。爱荷华州立模型的敏感性则高得多（67%）。本研究结果表明，爱荷华州立模型在估计爱荷华州县级斯图尔特氏病风险方面，其预测能力超出了全州范围的预测。
