化州市晚稻稻飞虱主害代发生期及发生程度气象预测模型

化州是广东省重要的水稻生产基地。稻飞虱是化州市主要的水稻害虫之一。笔者就6代稻飞虱发生期和发生程度与气象条件之间的关系进行分析研究，利用气象因子预测稻飞虱的发生与发展，以提高稻飞虱发生期和发生程度预测的准确性。应用SPSS分析软件进行逐步回归分析，对广东省化州市1996—2011年的晚稻稻飞虱主害代调查资料和气象观测资料进行分析，筛选出适合的气象预报因子，分别建立晚稻稻飞虱主害代成虫高峰期、若虫高峰期、发生程度和发生面积统计预报模型，用2012年和2013年的资料作为独立样本用于模型效果检验。结果表明，上述预测模型均通过0.01显著性统计检验。将化州市1996—2011年各年度对应的气象观测数据代入各式，模拟值与实测值的逐年变化趋势比较吻合，相对准确率分别为87.5%、93.8%、90.9%、94.2%。对建模内预报值和2012、2013年预报应用效果进行验证，模拟值与实测值基本吻合，可以为该区稻飞虱预测预报服务。可见通过逐步回归分析法对化州市晚稻稻飞虱主害代（6代）的发生期及发生程度进行预测，只要所选择的气象因子与相应的实测值有较高的相关性，就能较准确预测出发生期及发生程度范围；在稻飞虱发生期和发生程度模型建立中，选取的气象因子取了前驱值，所建立的模型更具预测性。

Weather Prediction Model of the Occurrence Period and Extent of Rice Planthopper in Huazhou

Huazhou is an important rice production base in Guangdong Province. Planthopper is one of the major rice pests. The weather condition is a key factor causing the occurrence and development of rice planthopper. The main harm generation of rice planthopper on late rice in Huazhou is the 6^th generation. In order to acquire the rules between meteorological factors and rice planthopper occurrence, the author studied the relationship between the 6^th generation rice planthopper occurrence period and extent and meteorological conditions. Therefore, the author could improve the accuracy of the prediction on rice planthopper occurrence period and extent by using meteorological factors. The author used stepwise regression with SPSS analysis software to analyze the data of the rice planthopper main harm generation’s impact on late rice and corresponding meteorological information of Huazhou in Guangdong Province from 1996 to 2011. Applicable meteorological factors were selected and prediction models were built on the late rice planthopper main harm generation’s adult peak period, nymph peak period, occurrence extent and occurrence area. The author chose 2012 and 2013 data as independent samples to do the model test. The statistical forecast models of adult peak period, nymph peak period, occurrence extent and occurrence area of the 6^th generation rice planthopper were all approved by significant testing at 1% level. Substitute the corresponding meteorological observation data from 1996 to 2011 in the formulas, and the annual variation trend between the measured and simulated values was consistent, and the relative accuracy was 87.5%, 93.8%, 90.9% and 94.2% respectively. The results showed that the measured and simulated values could be in good agreement with the actual values in the year of 2012 and 2013, therefore they could be used in rice planthopper forecast. By stepwise regression analysis of the occurrence and extent of rice planthopper main harm generation (the 6^th generation) prediction examples in Huazhou, the author could find that the prediction ability of this method was good. When there was a high correlation between the selected meteorological factors and the measured values, the author could predict the occurrence extent range. While establishing the model of the rice planthopper occurrence period and extent, the author took the precursor value, therefore the model was more predictable.