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郭翠荣,申存良,崔建国,公芙萍,等.冬小麦千粒重与气象因素的相关分析[J].中国农学通报,2018,34(18):1-5.,et al.Winter Wheat: Correlation Analysis of 1000-grain Weight and Meteorological Factors[J].Chinese Agricultural Science Bulletin,2018,34(18):1-5
冬小麦千粒重与气象因素的相关分析
Winter Wheat: Correlation Analysis of 1000-grain Weight and Meteorological Factors
投稿时间:2016-05-14  修订日期:2018-05-06
DOI:10.11924/j.issn.1000-6850.casb16050084
中文关键词: 冬小麦  千粒重  天气条件  相关分析
英文关键词: winter wheat  1000-grain weight  meteorological condition  correlation analysis
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作者单位E-mail
郭翠荣 山西省忻州市忻府区气象局 944028339@qq.com 
申存良 山西省临汾市气象局 616547999@qq.com 
崔建国 山西省阳泉市气象局 191929788@qq.com 
公芙萍 山西省临汾市气象局 317784327@qq.com 
中文摘要:
      为了深入了解气象因素对冬小麦千粒重的影响,为增加其产量提供科学依据。笔者利用临汾市尧都区气象局1981—2014 年气象与农业相关资料,借助数理统计、线性拟合、多元性回归等方法,对临汾市冬小麦千粒重与多种气象资料进行相关性分析。结果表明:临汾市冬小麦千粒重与5 月中旬积温、平均气温、降水、平均最低(最高)气温以及相对湿度都具有很好的相关性,除平均最低气温在0.05 水平上显著相关外,其他均在0.01水平上显著相关。温度与千粒重呈负相关,降水及相对湿度与千粒重呈正相关,建立冬小麦千粒重方程的预报回归模型,并对其进行拟合检验,预报偏差都小于20%,从而为适应气候条件、提高小麦栽培管理水平和开展冬小麦产量预报提供依据。
英文摘要:
      The paper aims to study the impact of meteorological factors on 1000-grain weight of winter wheat, and provide a basis for increasing wheat yield. Based on related meteorological and agricultural data of the Yaodu Meteorological Bureau of Linfen from 1981 to 2014, by mathematical statistics, linear fitting, multiple regression methods, and etc., we analyzed the correlation between 1000-grain weight of winter wheat in Linfen and various meteorological data. The results showed that: 1000- grain weight of winter wheat had a good correlation with the accumulated temperature, average temperature, precipitation, average lowest (highest) temperature and relative humidity of mid-May, except the correlation with the mean minimum temperature was significant at the 0.05 level, all the other correlations were significant at 0.01 level. The temperature was negatively correlated with 1000-grain weight, the precipitation and relative humidity were positively correlated with 1000- grain weight. We established a forecasting regression model of 1000- grain weight equation of winter wheat and carried out the fit test, the forecast bias was less than 20%. The study provides a basis for improving wheat cultivation management according to climate condition, and enhancing winter wheat yield forecast.
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