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。
Rice planthopper is one of the most serious pest affected rice in Guangxi, its occurrence anddevelopment has very close relationship with meteorological conditions. In order to explore the relationshipbetween meteorological conditions and the occurrence degree of rice planthopper, forecasting and earlywarning 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 countiesand cities were used in the paper. Based on single-factor correlation analysis between rice planthopper'soccurrence grades and different meteorological factors, meteorological factors that were biological significanceand strong indication had been found as modeling alternative factors. The method of multiple linear regressionwas adopted to build monitoring and warning model at meteorological grade for rice planthopper's occurrencedegree. Through history back substitution test and forecast verification for independent samples, the results bythe historical test showed that: the average correctness rate of forecast was 89% by comparing meteorologicalgrade with actual grade; the correctness rate of model values was 75% compared with actually occurred forforecast verification, and overall forecast effect was good, which could provide scientific decision-making for the rice planthopper prevention.
出处
《中国农学通报》
2016年第18期165-169,共5页
Chinese Agricultural Science Bulletin
基金
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)
关键词
稻飞虱发生程度
气象因子
预测模型
预测预警
grade for rice planthopper
meteorological factors
prediction model
prediction and warning