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基于气象关键因子的河南省夏玉米产量预报研究
引用本文:李树岩,刘伟昌.基于气象关键因子的河南省夏玉米产量预报研究[J].干旱地区农业研究,2014,32(5):223-227.
作者姓名:李树岩  刘伟昌
作者单位:中国气象局·河南省农业气象保障与应用技术重点实验室/河南省气象科学研究所,河南 郑州450003
基金项目:公益性行业(气象)科研专项
摘    要:以河南省13个地市1990-2006年逐旬光温水气象资料为基础,通过相关分析确定了影响河南省夏玉米产量的关键气象因子,建立了7月中旬-9月中旬的夏玉米气象产量预报模型。将全省由南至北划分为三个区域,分区回代1990-2006年资料对模型预报准确率进行回代检验,并利用2007-2010年资料对模型进行试报。检验结果表明,模型回代准确率全省为88.4%,不同区域间回代准确率差异明显,北部最高92.9%,中部次之87.4%,南部最低83.4%,分析原因主要受夏玉米产量年际波动的影响;模型预报准确率全省为94.9%,各区域差别不大,基本在95%左右。

关 键 词:产量预报  关键气象因子  夏玉米

Research on the yield prediction of summer maize in Henan Province based on meteorological key factors
LI Shu-yan,LIU Wei-chang.Research on the yield prediction of summer maize in Henan Province based on meteorological key factors[J].Agricultural Research in the Arid Areas,2014,32(5):223-227.
Authors:LI Shu-yan  LIU Wei-chang
Abstract:Base on 10-days meteorological data of average air temperature, precipitation and sunshine hours in summer maize growth period from 1990 to 2006 from 13 Cities of Henan Province, the key meteorological factors affected maize yield has been confirmed though the correlation analysis, also has established the meteorological yield prediction model for summer maize from the middle 10-days of July to the middle 10-days of September. The whole province can be divided into three regions from south to north. The forecast accuracy of the model was carried out the back substitution test by the regional return test from the data of 1990 to 2006, also the model was carried out the predictions test by the data of 2007 to 2010. The test results showed that: The accuracy rate of the model back substitution test in whole province was 88.4%. The difference of the back substitution test accurate rate was obvious between different regions, the maximum in north region was 92.9%, middle region was 87.4% belong second, and south region was lowest as 83.4%. Analyzing the causes, major affected by the yearly fluctuation of the summer maize yield. The model prediction accuracy rate was 94.9% in whole province, and it has no obvious differences for each region, the basic accuracy was about 95%.
Keywords:yield prediction  key meteorological factors  summer maize
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