中国水稻稻纵卷叶螟区域气象等级预测模型

为了建立水稻不同区域、不同生育期的水稻稻纵卷叶螟预测模型,利用103个植保站2000-2014年稻纵卷叶螟虫情资料与气象资料,采用SPSS软件进行相关分析与主成分分析,在逐步回归的基础上建立水稻稻纵卷叶螟不同区域、不同生育期的发生发展气象等级与迁入气象等级预测模型。结果表明：以华南早稻为例,影响早稻移栽分蘖期发生发展的关键因子包括3月下旬累积降水量、4月上旬平均气温、4月下旬平均气温、5月上旬平均气温以及5月上旬最高气温＞30℃天数；迁飞的关键因子包括4月平均相对湿度、3月下旬平均气温、3月下旬平均相对湿度以及4月上旬平均日照时数。通过2000—2012年数据回代检验发现,不同水稻种植区移栽分蘖期和抽穗开花期发生发展气象等级平均准确率能达到80%以上,迁入气象等级在85%以上。通过2013—2014年外推预报时,发生发展气象等级平均准确率在80%以上,迁入气象等级在78%以上；当预测站点样本数较少时,预报的准确率普遍下降,西南一季稻下降明显。预测模型可从气象角度对中国水稻稻纵卷叶螟发生发展和迁入进行预测。

Prediction Model of Regional Meteorological Level of Rice Leaf Roller in China

The paper aims to establish the level of regional meteorological prediction model of rice leaf roller in different plant areas of rice and in different rice growth stages. Based on the data of meteorology and monitoring on rice leaf roller during 2000?-2014 in 103 observation stations, using the principal component analysis and correlation analysis and stepwise regression method by SPSS, the occurrence and development level of meteorological prediction model and the migration level of meteorological prediction model were established in different plant areas of rice and in different rice growth stages.The results showed that, early rice in South China as an example, the key meteorological factors which affected the occurrence and development level of meteorological prediction model in transplanting to tillering stage were cumulative precipitation in the third ten days of March, average temperature in the first and third ten days of April, average temperature and the number days of highest temperature greater than 30℃. The key meteorological factors which affected the migration were average relative humidity of April, average temperature and average relative humidity in the third ten days of March, average hours of sunshine in the first ten days of April. The validation results by the meteorological dates of 2000-2012 return test showed that the forecasting accuracy of the occurrence and development level of meteorological prediction model was above 80% in transplanting to tillering stage and heading to flowering stage in different plant areas of rice; meanwhile the forecasting accuracy of the migration level of meteorological prediction model was above 85%. The meteorological dates of 2013-2014 were used to forecast which showed that the forecasting accuracy of the occurrence and development level of meteorological prediction model was still above 80%. Yet the forecasting accuracy of the migration level of meteorological prediction model was above 78%.The forecasting accuracy was general declined with less number of samples of prediction stations, especially in single season rice in Southwest. The rice prediction models could be put into the operational application in different plant areas of rice in China.