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农学学报 ›› 2015, Vol. 5 ›› Issue (7): 39-46.doi: 10.11923/j.issn.2095-4050.2014-xb0762

所属专题: 园艺

• 植物保护 • 上一篇    下一篇

黄板对设施蔬菜烟粉虱的诱集效应与预测模型研究

洪文英,吴燕君,王道泽,王宏,汪爱娟,朱徐燕   

  1. 杭州市植保土肥总站,杭州市植保土肥总站,杭州市植保土肥总站,杭州市农业科学研究院,余杭区农业生态与植物保护管理总站,杭州市余杭区农业技术推广中心
  • 收稿日期:2014-08-01 修回日期:2014-08-01 接受日期:2014-10-14 出版日期:2015-07-29 发布日期:2015-07-29
  • 通讯作者: 洪文英 E-mail:hongwy
  • 基金资助:
    杭州市科技计划项目“以生态控制为主的蔬菜病虫害IPM关键技术研究”(20130432B03);杭州市农业产业发展重点专项“蔬菜病虫害绿色防控关键技术集成与示范”(2012z1-3)。

Study on the Trapping Effect with Yellow Sticky Cards and Forecasting Model of Tobacco Whitefly in Protected Vegetables

  • Received:2014-08-01 Revised:2014-08-01 Accepted:2014-10-14 Online:2015-07-29 Published:2015-07-29

摘要: 为提高烟粉虱预测预报和持续控害水平,采用黄板诱集法连续4年(2009~2012年)对设施大棚内蔬菜作物上烟粉虱进行系统监测,结果表明,烟粉虱种群数量受种群内因和气候外因的协同作用上下波动,全年主要呈双峰型曲线变化,分为夏季高峰期(7~8月)与秋季高峰期(9~10月);但年度间种群数量存在较大差异:2009年和2010年为烟粉虱的重发年,2011年和2012年烟粉虱的危害较轻;不同设施蔬菜作物上烟粉虱的发生量不同,黄瓜上最大,茄子、番茄上其次,辣椒上最少,表明杭州地区烟粉虱对设施蔬菜的趋性为:黄瓜>茄子、番茄>辣椒。本研究还选择不同时期虫口基数、气象资料(温度、湿度、光照等)作为预测因子,共筛选出了29个因子(19个气象因子、10个前期虫口密度因子)进入回归模型,分析历史数据,使用逐步回归法组建了不同设施蔬菜上烟粉虱高峰发生期和发生量的预测预报模型,其中影响烟粉虱种群数量消长的关键因素为种群基数和气温。

关键词: 中棉所70, 中棉所70, 铃重, 主基因-多基因遗传, 多环境, RIL群体

Abstract: For improving forecasting and sustained control the level of harm, dynamics of Bemisia tobaci (Gennadius) on different protected vegetables was monitored by using yellow sticky cards from 2009 to 2012. The results showed that internal cause of population and external cause of climate had a synergistic effect on the pest dynamics, leading tobacco whitefly to fluctuate up and down. The pest’s population had two damage peaks, summer and autumn peak (from July to August and from September to October). There were significant differences in population size among years. Both 2009 and 2010 were the severe occurrence period, while both 2011 and 2012 were lighter occurrence period compared with the other years. There was significant difference in catches by yellow sticky cards between different protected vegetables. The catches on cucumber was the highest, followed by eggplant and tomato, and the catches on pepper was the lowest. This suggested that the preference order of the pest on the protected vegetables was cucumber> eggplant> tomato> pepper in Hangzhou Area. Based on analyzing historical data, the population cardinal number in different periods and the meteorological data (such as temperature, humidity, illumination etc.) were used in selecting forecasting factors to construct the occurrence period forecasting model and occurrence amount forecasting model in peak period. In total, twenty- nine factors including nineteen meteorological factors and ten population density factors were screened out and used in the models, and models for different protected vegetables were constructed with the methods of stepwise regression. The population cardinal number and temperature were two key factors influencing the pest’s population dynamic based on the models.