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基于经验法则的小麦白粉病气候年型分析

姚树然1.2,霍治国3**,司丽丽4   

  1. (1河北省气象科学研究所, 石家庄 050021; 2河北省气象与生态环境重点实验室, 石家庄 050021; 3中国气象科学研究院, 北京 100081; 4保定市气象局, 河北保定 071000)
  • 出版日期:2013-04-10 发布日期:2013-04-10

Climate year type of wheat powdery mildew epidemics in Hebei Province of North China based on rule of thumb.

YAO Shu-ran1,2, HUO Zhi-guo3**, SI Li-li4   

  1. (1Meteorological Institute of Hebei Province, Shijiazhuang 050021, China; 2Hebei Provincial Key Laboratory for Meteorological and EcoEnvironment, Shijiazhuang 050021, China; 3Chinese Academy of Meteorological Sciences, Beijing 100081, China; 4Baoding Meteorological Bureau, Baoding 071000, Hebei, China)
  • Online:2013-04-10 Published:2013-04-10

摘要:

温湿度条件是影响小麦白粉病发生流行的关键气象要素。应用河北省主麦区1987—2010年小麦白粉病资料和相应气象资料,利用合成分析和秩相关分析,筛选出影响小麦白粉病流行的关键气象因子;根据经验法则,利用小麦白粉病流行阶段的温度距平和湿度距平判别白粉病流行程度,并确定了白粉病流行的气候年型与指标。经历史回代,判别小麦白粉病流行程度准确率为84%,并进行了2011年、2012年的外延指标判别,准确率达100%,综合判断准确率在85%以上。该研究结果可为小麦白粉病影响评估与长期预报提供科学依据。
 

关键词: 主成分分析, 植被覆盖度, 沟壑丘陵区, 遥感, 生态环境质量

Abstract: Temperature and humidity are the most important meteorological elements affecting the epidemics of wheat powdery mildew. Based on the wheat powdery mildew epidemics data in the main wheat production areas in Hebei Province in 1987-2010 and the related meteorological data, and by the methods of composite analysis and rank correlation analysis, the key meteorological factors affecting the wheat powdery mildew epidemics were set up. According to the rule of thumb, the epidemic degree of the powdery mildew was distinguished by the anomaly of temperature and the anomaly of humidity climate at different epidemic stages of the powdery mildew, and the year types and controlling factors of the powdery mildew epidemics were determined. From the validation of historical data, the general accordance ratio was 84%. By using the extrapolated controlling factors values of the powdery mildew epidemics in 2011 and 2012, the forecast accuracy was 100%. When integrated with the climate year types, the forecast accuracy was above 85%. Our results could provide scientific references for the assessment and long-term forecast of wheat powdery mildew epidemics.

Key words: remote sensing, ecological environment quality, principal component analysis, vegetation fraction, hilly and gully region.