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文章摘要
引用本文:屈振江,郑小华,刘璐,张勇.2017.陕西不同生态区苹果品质差异及与气象因子的关系.气象,43(7):872-878.
陕西不同生态区苹果品质差异及与气象因子的关系
Relationship Between Apple Quality and Meteorological Factors in Different Ecological Regions in Shaanxi
投稿时间:2016-10-30  最后修改时间:2017-03-08
DOI:10.7519/j.issn.1000-0526.2017.07.011
中文关键词: 苹果,品质,气象因子,相关分析
英文关键词: apple, quality, meteorological factor, relational analysis
基金项目:中国气象局气象关键技术集成与应用项目(CMAGJ2015M60)、中国气象局气候变化专项(CCSF201621)和陕西省经济作物气象服务台业务维持经费共同资助
作者单位
屈振江 陕西省经济作物气象服务台西安 710015 
郑小华 陕西省气象台西安 710015 
刘璐 陕西省经济作物气象服务台西安 710015 
张勇 陕西省经济作物气象服务台西安 710015 
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中文摘要:
      为做好苹果气候品质认证工作,基于2013—2015年陕西31个苹果种植生产基地县155片果园的果实品质调查数据和物候监测资料,应用灰色关联度分析和逐步回归方法,分延安果区、渭北东部果区、渭北西部果区和关中果区4个生态类型区,分析了苹果品质的分异特征,研究了影响苹果品质的关键物候期及主要气象因子。结果表明:各生态类型区的果形指数和可滴定酸含量较为一致,单果重、可溶性固形物含量和果实着色度差异较为明显。第一次膨大期最高气温、生长季最高气温和幼果期平均气温对苹果品质的影响最为显著。其中开花期和幼果期气温高、降水少,第一次膨大期气温低、日照多,着色期气温低、相对湿度大有利于提高陕西苹果品质。利用多元回归构建的品质预测模型拟合效果较好,可在业务中应用。
英文摘要:
      With the purpose of improving the certification of meteorological quality for apple, this paper utilizes the survey data as well as meteorological and phenological monitoring data and analyzes the quality of the apples from 31 planting and production bases and 155 orchards in Shannxi by means of grey relational analysis and stepwise regression. The differences in the apply quality among the four ecological areas, namely Yanan area, eastern Weibei area, western Weibei area and Guanzhong area, are also analyzed. In addition, the key phenological period and meteorological factors are studied too. It is found that the fruit shape index and titratable acid content in those ecological areas are at similar levels. There is an obvious difference in the single fruit weight, soluble solid content and fruit coloring degree. The maximum temperature in the first fruit swelling period, the maximum temperature during the growth season and the average temperature in the young fruit period have very significant influences on apple quality. The quality of apples in Shannxi can be enhanced by higher temperature and less rain in the bloom period and young fruit period, low temperature and abundant sunshine in the first swelling period as well as the low temperature and high relative humidity in the coloring period. The quality prediction model built through the multi variable regression can achieve the better fitting effect and thus could be applied to the actual production.
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