摘要
根据河北省4县2001—2010年小麦白粉病病情和逐日气象资料,采用因子膨化、秩相关分析、通径分析、Bayes准则、模糊数学(Fuzzy)和广义回归神经网络(GRNN)等方法,筛选影响小麦白粉病发生的关键期和关键因子,建立了小麦白粉病发生气象等级指标模型、基于Bayes准则的Fuzzy模型和基于Fuzzy模型的GRNN模型。结果表明:影响河北4县小麦白粉病发生气象等级的关键因子是前三候至当候的平均温度、前三候至当候的降水量、前三候至当候的降雨系数和前一候的小麦白粉病实际发生等级;3种预警模型具有层层递进的关系,预报准确率基于Fuzzy模型的GRNN模型>基于Bayes准则的Fuzzy模型>指标模型,并均超过了85%,可以用于对候尺度小麦白粉病发生等级进行中短期预报。
Based on disease index for winter wheat powdery mildew and meteorological data in Zhengding,Xinji,Guantao and Cixian in Hebei Province during 2001-2010,the key period and key factors were selected using correlation analysis,path analysis,Bayes criterion,Fuzzy math and generalized regression neural network. Bayes grade index model,Fuzzy model and GRNN model were constructed for dynamic early warning of occurrence degree of wheat powdery mildew.The results indicated that the key factors affecting occurrence degree of wheat powdery mildew were mean temperature from the previous three pentads to the current pentad,precipitation from the previous three pentads to the current pentad,rain coefficient from the previous three pentads to the current pentad,and the previous pentad occurrence degree of wheat powdery mildew. The three early warning models had progressively transformed relationships,and the prediction accuracy rates were all above 85%,which could be well applied in early warning of wheat powdery mildew at the pentad scale. Compared with Fuzzy model and Bayes grade index model,GRNN model had the highest prediction accuracy rate. The results can provide useful information that contributes to a better understanding of occurrence of wheat powdery mildew in Hebei and help make policy for disease risk management.
出处
《生态学杂志》
CAS
CSCD
北大核心
2015年第9期2489-2497,共9页
Chinese Journal of Ecology
基金
公益性行业(气象)科研专项经费项目(GYHY201006026)资助
