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中国农学通报

中国农学通报 ›› 2016, Vol. 32 ›› Issue (18): 165-169.doi: 10.11924/j.issn.1000-6850.casb15110023

所属专题： 水稻； 农业气象

• 植物保护 农药 • 上一篇    下一篇

广西稻飞虱发生程度的气象预测预警

孟翠丽1,何 燕2,陈中云3,龙梦玲4,谢茂昌4   

  1. (1武汉农业气象试验站,武汉 430040；2广西区气象减灾研究所/国家卫星气象中心遥感应用示范基地,南宁530022；3贵州省山地气候研究所,贵阳550022；4广西区植保总站,南宁530022）
  • 收稿日期:2015-11-03 修回日期:2015-11-24 接受日期:2015-11-25 出版日期:2016-06-28 发布日期:2016-06-28
  • 通讯作者: 何 燕
  • 基金资助:
    广西自然科学基金项目“稻飞虱发生的气象监测预警技术研究”(2011GXNSFA018098)；国家农业科技成果转化项目“水稻低温冷害监测预警及防控技术中试”(2014GB2E100281)；广西农业重点科技计划项目“基于GIS的水稻高温热害监测预警技术研究”(201305)。

Monitoring and Forecasting Warning of Meteorological Grade for Rice Planthopper in Guangxi

Meng Cuili1, He Yan2, Chen Zhongyun3, Long Mengling4, Xie Maochang4   

  1. (1Agro-meteorology Station of Wuhan, Wuhan 430040;2Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Experiment Station of National Satellite Meteorological Center, Nanning 530022;3Climate Center of Guizhou, Guiyang 550022; 4Plant Protection Station of Guangxi, Nanning 530022)
  • Received:2015-11-03 Revised:2015-11-24 Accepted:2015-11-25 Online:2016-06-28 Published:2016-06-28

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摘要： 稻飞虱是影响广西水稻最严重的害虫，其发生发展与气象条件关系十分密切，为探索气象条件与稻飞虱发生程度的关系，进而开展稻飞虱发生程度的预测预警。笔者利用1991—2008年广西64个农业病虫测报站和对应县市的气象台站资料，对早稻稻飞虱发生等级与各项气象因子单因子做相关分析，找出具有生物学意义且指示性强的主要气象因子作为建模备选因子，采用逐步回归分析方法，构建早稻稻飞虱发生程度的预测预警模型，并对模型进行历史回代及2009—2012年的独立样本预测试报检验。结果表明：通过历史回代检验，早稻稻飞虱发生气象等级的模型与实测值达到基本一致，为89%；独立样本预测试报的模型预测检验结果达到基本一致以上，为75%，模型总体效果良好，其预测预警可为相关部门及时制定稻飞虱防控对策提供科学依据。

关键词: 水旱灾害, 水旱灾害, 生态风险评价, 生态风险管理, 河南省

Abstract: Rice planthopper is one of the most serious pest affected rice in Guangxi, its occurrence and development has very close relationship with meteorological conditions. In order to explore the relationship between meteorological conditions and the occurrence degree of rice planthopper, forecasting and early warning of the rice planthopper occurrence degree must be carried out, the data in 1991-2008 from 64 monitoring stations for agricultural pests in Guangxi and meteorological stations corresponding to their counties and cities were used in the paper. Based on single-factor correlation analysis between rice planthopper’s occurrence grades and different meteorological factors, meteorological factors that were biological significance and strong indication had been found as modeling alternative factors. The method of multiple linear regression was adopted to build monitoring and warning model at meteorological grade for rice planthopper’s occurrence degree. Through history back substitution test and forecast verification for independent samples, the results by the historical test showed that: the average correctness rate of forecast was 89% by comparing meteorological grade with actual grade; the correctness rate of model values was 75% compared with actually occurred for forecast verification, and overall forecast effect was good, which could provide scientific decision-making for the rice planthopper prevention.