CAJ | 학술논문

利用冬季温度和春季寒潮可以预测烟粉虱的越冬虫源数量，利用扩散期间的风向、风力、持续时间可以预测烟粉虱的扩散方向和在田间的分布区域；利用6-9月份的温度可以预测大田烟粉虱的发生量。应用该方法，作者根据11月至翌年4月的温度、降水和日照，对2007年江苏东台市大田烟粉虱的发生进行了成功预测。
이용동계온도화춘계한조가이예측연분슬적월동충원수량，이용확산기간적풍향、풍력、지속시간가이예측연분슬적확산방향화재전간적분포구역；이용6-9월빈적온도가이예측대전연분슬적발생량。응용해방법，작자근거11월지익년4월적온도、강수화일조，대2007년강소동태시대전연분슬적발생진행료성공예측。
Bemisia tabaci is an important pest on cotton, flower and vegetable crops and brings harm yield, quality, and commercial availability to the products of these crops. The accurate prediction of the occurrence of this pest is critical for its effective management. Previous studies indicate that the north border that Bemisia tabaci can survive winter in natural environment is around 23 oN. In Jiangsu Province, Bemisia tabaci cannot survive winter in natural environment, but it can survive in protected areas such as heated greenhouses. Meteorological factors such as tempera-ture, sunlight, precipitation, and wind have distinctive effects on the growth, reproduction and diffusion of Bemisia tabaci. Low temperature in winter and early spring significantly affects the survival rate of Bemisia tabaci. Light dura-tion affects the reproduction rate and developmental rate of Bemisia tabaci. High temperature in summer affects the re-production rate and developmental rate of Bemisia tabaci. Raninfall, especially heavy rain, has significant flushing ef-fects on the adults of Bemisia tabaci and thus affect its population. Wind affects the direction and distance of the diffu-sion of Bemisia tabaci from wintering grounds to outdoor crops and thus affects the distribution areas and distribution range. Based on these results, temperature in winter and early spring can be used to predict the population size of Be-misia tabaci at the overwintering stage. The direction, strength, and duration of wind can be used to predict the direc-tion and area of the diffusion of Bemisia tabaci. The temperature from June to September can be used to predict the population size in open fields. The occurrence of Bemisia tabaci in 2007 in Dongtai was successfully predicted using the data of temperature, precipitation, and light duration from November 2006 to April 2007. Using this method, we predict that the occurrence of Bemisia tabaci in 2014 will be less serious than normal level.