浙江农业科学 ›› 2025, Vol. 66 ›› Issue (7): 1681-1685.DOI: 10.16178/j.issn.0528-9017.20240599

• 植保技术 • 上一篇    下一篇

嵊州市水稻稻飞虱发生情况及其预测研究

金晖1(), 陈瑜2, 张沁怡3, 应孟飞1   

  1. 1.绍兴市粮油作物技术推广中心, 浙江 绍兴 312000
    2.嵊州市农业技术推广中心, 浙江 嵊州 312400
    3.绍兴市经济作物技术推广中心, 浙江 绍兴 312000
  • 收稿日期:2024-07-23 出版日期:2025-07-11 发布日期:2025-07-28
  • 作者简介:金晖(1998—),女,浙江绍兴人,助理农艺师,硕士,从事植保技术推广工作,E-mail:jh94097277@163.com

Study on the occurrence of rice planthopper and their prediction in Shengzhou City

JIN Hui1(), CHEN Yu2, ZHANG Qinyi3, YING Mengfei1   

  1. 1. Shaoxing Grain and Oil Crop Technology Extension Center, Shaoxing 312000, Zhejiang
    2. Shengzhou Agricultural Technology Extension Center, Shengzhou 312400, Zhejiang
    3. Shaoxing City Cash Crop Technology Extension Center, Shaoxing 312000, Zhejiang
  • Received:2024-07-23 Online:2025-07-11 Published:2025-07-28

摘要:

浙东山区的粮食生产在浙江省占极其重要的地位,以嵊州市为例,嵊州市的水稻种植面积大、品种多,是产粮大县。稻飞虱在嵊州局部偏重发生,但当地的稻飞虱发生情况及其预测未见报道。本文通过整理2005—2022年历年4—10月嵊州市稻飞虱的田间发生量数据,研究其发生高峰期,同时结合灯下虫情数据和温度、降雨量等气象资料,利用多元逐步回归分析法建立发生量模型来开展预测研究。结果表明,稻飞虱田间发生高峰期在7—9月,其发生量与当地气象、前期虫源量密切相关,建立的发生量模型在2023年的预测准确率达91.25%以上。因此,明确稻飞虱发生情况,开展预测预报,能为其精准科学防治提供数据支持。

关键词: 嵊州市, 稻飞虱, 逐步回归, 发生量模型, 预测预报

Abstract:

The grain production in the eastern mountainous area of Zhejiang Province occupies an extremely important position. Taking Shengzhou City as an example, the rice planting area in Shengzhou is large and there are many varieties of rice, which is a large grain producing county. Rice planthoppers have severely occurred in some areas of Shengzhou, but there have been no reports on the occurrence and prediction of rice planthoppers in the local area. In this paper, field occurrence data of rice planthopper in Shengzhou City from April to October, from 2005 to 2022 were collected to study the peak of its occurrence. At the same time, combined with the insect situation data under the lamp and meteorological data such as temperature and rainfall, the occurrence model was established by multiple stepwise regression analysis to carry out prediction study. The results showed that the peak of rice planthopper occurrence in the field was from July to September, and the amount of rice planthopper occurrence was closely related to the local weather and the amount of insect source in the early stage. The prediction accuracy of the established model was more than 91.25% in 2023. Therefore, the identification of the occurrence of rice planthopper and the implementation of prediction and forecast could provide data support for its accurate and scientific control.

Key words: Shengzhou City, rice planthopper, stepwise regression, occurrence model, prediction and forecast

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