| 卢丁伊慧1,2, 张战泓3, 张 卓2, 张德咏2, 谭新球2, 郑立敏2, 高 阳2, 史晓斌1,2*, 刘 勇2*.番茄褪绿病毒病预测预报模型的建立[J].植物保护,2021,47(3):144-149. |
| 番茄褪绿病毒病预测预报模型的建立 |
| Establishment of the prediction model for tomato chlorosis virus disease |
| 投稿时间：2020-01-10 修订日期：2020-02-29 |
| DOI：DOI:10.16688/j.zwbh.2020029 |
| 中文关键词: 番茄褪绿病毒 烟粉虱 发展规律 预测预报模型 |
| 英文关键词:Tomato chlorosis virus Bemisia tabaci occurrence and development trend prediction model |
| 基金项目:国家自然科学基金(31872932,31672003); 湖南省科技计划(2016RS2019); 国家大宗蔬菜产业技术体系(CARS-25-B-05) |
| 作者 | 单位 | E-mail | | 卢丁伊慧1,2, 张战泓3, 张 卓2, 张德咏2, 谭新球2, 郑立敏2, 高 阳2, 史晓斌1,2*, 刘 勇2* | 1. 湖南大学研究生院隆平分院, 长沙 410125
2. 湖南省农业科学院植物保护研究所, 长沙 410125
3. 湖南省农业科学院蔬菜研究所, 长沙 410125 | 史晓斌xiaobin.s@163.com; 刘勇haoasliu@163.com |
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| 中文摘要: |
| 研究烟粉虱传播番茄褪绿病毒Tomato chlorosis virus (ToCV)的发生规律,建立其预测预报模型,能够指导田间早期有效防治。本研究于2014年－2018年每年采集山东寿光蔬菜基地大棚番茄和杂草的植株叶片,并收集植株上携带的所有烟粉虱,以健康番茄、杂草叶片和室内饲养的健康烟粉虱为阴性对照;实验室ToCV侵染性克隆接种的感病叶片以及从感病叶片上获毒的烟粉虱为阳性对照,将采集的样品带回实验室进行检测和鉴定等试验。根据病情发生规律,建立了番茄褪绿病毒病的预测预报模型,模拟所得方程为:Y=2.570+0.089X4-7.548X7,其中Y为11月份番茄褪绿病毒病发生率,X4为采集的样品上平均每株所携带的烟粉虱数量,X7为采集的田间杂草的带毒率。预测模拟结果显示,烟粉虱数量以及杂草的带毒率与番茄褪绿病毒病的发生率极显著正相关,回归检测结果历史符合率为96.8%以上。明确了影响番茄褪绿病毒病发生的影响因子,基于烟粉虱数量以及杂草的带毒率,构建了病害预测预报模型。研究结果有助于及时发现番茄褪绿病毒病并采取相应预防措施。 |
| 英文摘要: |
| The occurrence and development of Tomato chlorosis virus (ToCV) transmitted by Bemisia tabaci were investigated by establishing disease prediction models to provide a reference for effective control of ToCV causing disease in the field. The plant leaves of greenhouse tomatoes and weeds were collected in the Shouguang Vegetable Base in Shandong province every year from 2014 to 2018, and all B.tabaci individuals feeding on the plants were collected. Healthy tomatoes, weed leaves and healthy B.tabaci raised indoors were used as negative controls. Leaves inoculated with ToCV in the laboratory and viruliferous B.tabaci on infected leaves were used as positive controls. The collected samples were taken back to the laboratory for ToCV testing and identification. According to the development of ToCV disease, we established the disease prediction model: Y=2.570+0.089X4-7.548X7, in which Y is the incidence of tomato chlorosis virus disease in November; X4is the average number of B.tabaci carried by each sample, and X7is the viruliferous rate of weeds collected in the field. The results revealed that the incidence of the disease showed significantly positive correlation with the population of B.tabaci and the infection rate of weeds. The compliance rate of this prediction model was above 96.8%, suggesting that it can provide an important basis for predicting diseases in time and take preventive measures. |
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