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林芝地区小麦条锈菌与气象因子的关系及流行动态模型的构建
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引用本文:巩文峰,帅玉婷,马占鸿.林芝地区小麦条锈菌与气象因子的关系及流行动态模型的构建.植物保护学报,2018,45(1):167-172
DOI:10.13802/j.cnki.zwbhxb.2018.2018917
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作者单位E-mail
巩文峰 西藏农牧学院植物科学学院, 林芝 860000  
帅玉婷 西藏农牧学院植物科学学院, 林芝 860000  
马占鸿 中国农业大学植物病理学系, 北京 100193 mazh@cau.edu.cn 
中文摘要:为了解西藏林芝地区气象因子对小麦条锈病的影响及其流行动态,2016年采用五点取样法对林芝地区小麦条锈病的发病情况进行监测,通过相关性分析、逐步回归Cp)统计法和线性回归等方法,分析了病情指数与气象因子的关系,并结合时间和病情指数建立了病害预测模型。结果表明,在林芝地区,温度X1、湿度X2均与小麦条锈病病情指数Y呈极显著相关,降雨量X3与病情指数Y呈显著相关;线性回归方程为:Y=-482.5991+19.7494X1+3.7974X2-0.8439X3。根据模拟情况选择的病害流行动态方程为Y=1/e(0.914t+0.385),决定系数为0.952,模型的拟合效果较好,表明该模型能够为林芝地区小麦条锈病的预测预报提供有效的参考依据。
中文关键词:小麦条锈病  流行动态  气象因子  预测模型
 
Interaction between Puccinia striiformis f. sp. tritici and meteorological factors and the model construction of temporal dynamic
Author NameAffiliationE-mail
Gong Wenfeng Plant Sciences College, Tibet Agriculture & Animal Husbandry University, Linzhi 860000, Tibet Autonomous Region, China  
Shuai Yuting Plant Sciences College, Tibet Agriculture & Animal Husbandry University, Linzhi 860000, Tibet Autonomous Region, China  
Ma Zhanhong Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China mazh@cau.edu.cn 
Abstract:To understand the meteorological factors affecting the stripe rust in wheat and its epidemiology in Linzhi, Tibet, the incidence of Puccinia striiformis f. sp. tritici was investigated with five point sampling method, and the relationship between the disease index and meteorological factors was studied with using correlation analysis, stepwise regression and linear regression analysis. The results showed that there was highly significant positive correlation among temperature, relative humidity and the disease index. Significant positive correlation between the disease index and rainfall was found. The equation of linear regression was Y=-482.5991+19.7494X1+3.7974X2-0.8439X3. According to the date and disease index, the disease prediction models were constructed, and the regression equation was Y=1/e(0.914t+0.385) with R square value was 0.952, indicating that this model could well reflect the temporal dynamic of Puccinia striiformis f. sp. tritici in Linzhi.
keywords:wheat yellow rust  temporal dynamic model  environment factors  forecasting model
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