| 油菜菌核病发生流行与菌源量、气候因子关系分析及病情预测模型的建立 |
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| 引用本文： | 秦虎强,高小宁,韩青梅,黄丽丽,张吉昌,唐建祥.油菜菌核病发生流行与菌源量、气候因子关系分析及病情预测模型的建立[J].植物保护学报,2018,45(3):496-502. |
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| 作者姓名： | 秦虎强 高小宁 韩青梅 黄丽丽 张吉昌 唐建祥 |
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| 作者单位： | 西北农林科技大学植物保护学院;汉中市植保植检站;勉县植保植检站 |
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| 基金项目： | 国家公益性行业（农业）科研专项（201103016），西北农林科技大学试验示范站（基地）科技创新与成果转化专项（XNY2013-46），高等学校学科创新引智计划（No.B07049） |
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| 摘 要： | 为探究油菜菌核病田间发生流行的影响因子并建立病情预测模型,采用田间调查法测定油菜田菌核密度、子囊盘密度、花朵带菌率,并分析了2008—2014年各气候因子与病叶率、成熟期茎秆病株率、病情指数之间的关系。结果显示,田间菌核密度、子囊盘密度、花朵带菌率与病叶率及成熟期茎秆病株率均呈极显著正相关;2月下旬至5月上旬日均气温及2月下旬至4月上旬日均相对湿度、降雨量及降雨天数与各年度成熟期病株率及病情指数均呈显著正相关,而日照时数与各年度成熟期病株率及病情指数均呈极显著负相关。以油菜盛花期花朵带菌率作为指标,可预测其田间成熟期病株率;以降雨量(x_2)、降雨天数(x_3)及日照时数(x_4)与各年度成熟期病株率(y_1)及病情指数(y_2)分别建立了预测模型y_1=15.47+0.07x_2+0.42x_3-0.03x_4和y_2=10.36+0.07x_2+0.38x_3-0.03x_4,其拟合度均最高,分别为99.07%和98.43%,可作为油菜成熟期茎秆发病株率及病情指数的预测模型。表明油菜菌核病菌源量和气候因子是影响病害发生流行的关键因子。
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| 关 键 词： | 油菜菌核病 菌源量 气候因子 相关系数 流行预测 |
| 收稿时间： | 2016/9/29 0:00:00 |
Effects of inoculum density and environmental factors on the epidemics of rape Sclerotinia rot and establishment of a predicative model |
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| Qin Huqiang,Gao Xiaoning,Han Qingmei,Huang Lili,Zhang Jichang and Tang Jianxiang.Effects of inoculum density and environmental factors on the epidemics of rape Sclerotinia rot and establishment of a predicative model[J].Acta Phytophylacica Sinica,2018,45(3):496-502. |
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| Authors: | Qin Huqiang Gao Xiaoning Han Qingmei Huang Lili Zhang Jichang and Tang Jianxiang |
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| Institution: | College of Plant Protection, Northwest A & F University, Yangling 712100, Shaanxi Province, China,College of Plant Protection, Northwest A & F University, Yangling 712100, Shaanxi Province, China,College of Plant Protection, Northwest A & F University, Yangling 712100, Shaanxi Province, China,College of Plant Protection, Northwest A & F University, Yangling 712100, Shaanxi Province, China,Plant Protection and Quarantine Station of Hanzhong City, Hanzhong 723200, Shaanxi Province, China and Plant Protection and Quarantine Station of Mian County, Mian County 724200, Shaanxi Province, China |
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| Abstract: | To investigate the factors influencing the epidemics of rape Sclerotinia rot and the disease forecast, the density of sclerotia, the density of apothecia and percentage of petal infestation were studied in the field, and the relationships among several variables associated with disease development from 2008-2014 were analyzed. The variables included the density of sclerotia, the density of apothecia, the percentage of petal infestation, leaf incidence, the percentage of infected adult plants, and environmental factors. The results showed that there were significant positive correlations among the density of active apothecia, the density of sclerotia, the percentage of infected petals, the leaf incidence and the percentage of infected mature plants. The percentage of infected adult plants was significantly positively correlated with daily mean temperature from late February to early May, and both the percentage of infected mature plants and disease incidence were significantly positively correlated with the average daily air humidity, rainfall capacity, and the number of rain days from late February to early April. Meanwhile, both the percentage of infected mature plants and disease incidence were significantly and negatively correlated with sunshine duration. The percentage of petal infestation could be used to forecast disease incidence in the same year. Remarkably, three variables:rainfall capacity (x2), the number of raining days (x3) and sunshine duration (x4), fitted to the percentage of infected mature plants (y1) and disease incidence (y2) well in the equation models y1=15.47+0.07x2+0.42x3-0.03x4 and y2=10.36+0.07x2+0.38x3-0.03x4. It showed goodness-of-fit scores at 99.07% and 98.43%, respectively. This model could be used to accurately predict the percentage of infected mature plants and disease incidence in the field. The results indicated that both inoculum density and environmental factors were the key variables associated with the disease epidemic development in the field. |
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| Keywords: | rape Sclerotinia stem rot inoculum density environmental factor correlation coefficient disease forecast |
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