| 陕西杨凌麦长管蚜发生预测模型构建与验证 |
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| 引用本文： | 解 锋,王世锋,苏海燕,廖淑霞,胡想顺.陕西杨凌麦长管蚜发生预测模型构建与验证[J].陕西农业科学,2021(5):32-34. |
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| 作者姓名： | 解 锋 王世锋 苏海燕 廖淑霞 胡想顺 |
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| 作者单位： | （1.杨凌职业技术学院 ，陕西 杨凌 712100；2.陕西省西安市临潼区穆寨街办农业综合服务中心，陕西 西安 710600；3.陕西省西安市临潼区园艺工作站，陕西 西安 710600；4.杨陵区委考核督查办，陕西 杨凌 712100；5.西北农林科技大学 植保学院，陕西 杨凌 712100） |
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| 基金项目： | 国家重点研发计划（2016YFD0300705）；农业部西北荒漠绿洲作物有害生物综合治理重点实验室开放基金（KFJJ20180107）。[ZK)] |
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| 摘 要： | 为了预测麦长管蚜田间发生发展程度，及时制定综合防治策略，根据历年来杨凌区麦长管蚜的种群发生程度和气象数据，利用多元相关性性分析和多元线性回归法构建了杨凌地区3月下旬到5月下旬各旬麦长管蚜的发生程度的预测模型。从16个气象因子中发现旬均温度、旬均相对湿度和旬均气压与当旬麦长管蚜的发生相关系数排在最前3位。用三个气象因素构建的各旬线性回归模型经实践验证表明对当旬麦长管蚜发生程度预测的准确率为100%（3月下旬）、100%（4月上旬）、100%（4月中旬）、90%（4月下旬）、75%（5月上旬）、85%（5月中旬）、100%（5月下旬），总体准确率90%。由此得出结论本文构建的预测模型可用于指导实践中麦长管蚜的综合防治。
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| 关 键 词： | 小麦 麦长管蚜 预测预报 多元线性回归 主成分分析法 |
Construction of Occurrence and Predicting Model of Sitobion Avenaein Yangling of Shaanxi Province |
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| XIE Feng,WANG Shifeng,SU Haiyan,LIAO Shuxi,HU Xiangshun.Construction of Occurrence and Predicting Model of Sitobion Avenaein Yangling of Shaanxi Province[J].Shaanxi Journal of Agricultural Sciences,2021(5):32-34. |
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| Authors: | XIE Feng WANG Shifeng SU Haiyan LIAO Shuxi HU Xiangshun |
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| Abstract: | In order to predict the occurrence and development of S.avenae and to formulate IPM control strategy, a predicting model of occurrence degree on wheat from late March to late May in Yangling district was established by using multiple correlation analysis and multiple linear regression method based on the population occurrence degree and meteorological data of S. avenae in Yangling for years. It was found that the correlation coefficients of the occurrence of S. avenae with ten-day average temperature, ten-day average relative humidity and ten-day average air pressure were the top three from 16 meteorological factors. The linear regression model constructed by the three meteorological factors for each ten-day period was verified by practice. The results showed that the predicting accuracy for occurrence degree of S.avenae was 100% (late March), 100% (early April), 100% (mid April), 90% (late April), 75% (early May), 85% (mid May) and 100% (late May), and the overall accuracy was 90%. It was concluded that the predicting model established in this paper can be used to guide the comprehensive control of wheat aphid in practice. ; |
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