摘要
小麦蚜虫是危害许昌小麦生产的主要虫害之一,其发生面积广、危害重。为此,对许昌市小麦蚜虫的种群变化规律进行研究,并建立其发生危害的预测模型。根据许昌植保站2007年、2008年监测资料进行分析,蚜虫在小麦上迁移危害其种群数量变化遵从logistic增长曲线,可以划分为开始增长期（3月下旬-4月上旬）、加速增长期（4月中旬-5月上旬）和减速增长期（5月中旬-6月上旬）,其中4月下旬-5月上旬是蚜虫种群繁殖增长的关键期。根据1999-2008年许昌植保站监测资料和许昌国家基本气象站数据资料,通过相关法分析小麦蚜虫始见期、高峰期及高峰期蚜虫量与气象因子的关系,结果表明,热量和水分条件是影响蚜虫种群消长的关键气象因素,其中光热因子对蚜虫发生发展具有促进作用,水分因子具有抑制作用。小麦蚜虫始见期、高峰期出现日数、高峰期蚜量分别与3月份地面0 cm低温,5月份降水量,3-4月份相对湿度的相关性最高,相关系数分别为-0.728、0.615、-0.597,均达到显著水平。采用SPSS软件利用逐步回归法构建了蚜虫始见期、高峰期及危害程度的预报预测模型,预测准确率在73%~80%,预测精度较高,可为生产服务。
According to the monitoring data of 2007 and 2008 from Xuchang Plant Protection Station,aphid population changes in wheat complied with logistic growth curve.The population dynamics could be divided into growth beginning period（late March to early April）,accelerated growth period（mid-April to early May） and slow growth period（mid-May to early June）.The stage of late April to early May was the key period for aphid population growth.According to the monitoring data of 1999-2008 from Xuchang Plant Protection Station and Xuchang Weather Station,relationships of the periods of appearance and peak and the peak quantity of aphids with meteorological factors were analyzed.The results showed that heat and moisture conditions were the key meteorological factors affecting aphid population dynamics,which could promote and inhibit development of aphids,respectively.The periods of appearance and peak and the peak quantity of aphids were most ctosely related to 0cm ground temperature in March,precipitation in May,relative humidity in March and April.The correlation coefficients were-0.728,0.615 and-0.597,respectively,all reaching significant level.Using SPSS software,prediction models of the periods of appearance and peak and the peak quantity of aphids were separately constructed by stepwise regression method.The prediction models could be used in actual business due to their high prediction accuracy rate of 73% to 80%.
出处
《河南农业科学》
CSCD
北大核心
2011年第3期81-84,共4页
Journal of Henan Agricultural Sciences
关键词
逐步回归
小麦蚜虫
种群变化规律
预测
气象因子
Stepwise regression
Wheat aphid
Population variation
Prediction
Meteorological factors
