湖南省油菜开花结荚期湿渍害指标研究

    Study on Wet Damage Index of Rape in Flowering and Pod-bearing Period in Hunan Province

    • 摘要:
      目的 研究得出湖南省油菜开花结荚期湿渍害指标,为油菜湿渍害监测预警业务提供科学依据。
      方法 根据湖南省南县、怀化市、益阳市、衡阳市4个农业气象观测站多年油菜产量实测资料,结合同年开花结荚期气象数据,分析油菜相对气象产量和减产年开花结荚期气象因子关系,得到油菜湿渍害的致灾气象因子;对油菜减产年相对气象产量和同期致灾气象因子数据进行聚类分析,得到油菜湿渍害指标,再用独立数据对该指标进行检验。
      结果 过程降水量和过程降水天数是湖南省油菜开花结荚期湿渍害的主要致灾因子。湖南省油菜开花结荚期轻度湿渍害指标是:40 mm ≤降水过程雨量<75 mm,降水过程持续天数为5 d;中度以上湿渍害指标是:降水过程雨量≥ 75 mm,降水过程持续天数≥ 6 d。
      结论 经独立数据检验,本研究提出的油菜湿渍害指标基本准确,可用于农业气象灾害监测预警业务。

       

      Abstract:
      Objective The study was carried out to obtain the wet damage index of rape during flowering and podbearing period, and provide a scientific basis for the monitoring and early warning of rape wet water damage.
      Method Based on the yield observation data of rape from 4 agrometeorological observation stations in Nanxian, Huaihua, Yiyang and Hengyang of Hunan Province, and the meteorological data of flowering and pod-bearing period in the same year, the relationship between relative meteorological yield of rape and meteorological factors during the flowering and pod-bearing period of yield reduction year were analyzed, and the disaster-causing meteorological factors were obtained. By performing cluster analysis on the relative meteorological yield of yield reduction year and the disaster-causing meteorological factors of the same year, the wet damage index of rape was determined, and then independent observation data was used to test the wet damage index.
      Result The amount of precipitation and the number of precipitation days during the precipitation process are the main factors that cause the yield reduction of rape in Hunan Province. The index of mild wet damage for rape in Hunan Province is: 40 mm ≤ rainfall during precipitation < 75 mm, 5 d ≤ precipitation duration < 6 d, and the index for moderate or above wet damage is: rainfall during precipitation ≥ 75 mm, precipitation duration ≥ 6 d.
      Conclusion After independent data inspection, the above wet damage indexes proposed in the study are basically accurate, which can be used for agrometeorological disaster monitoring and early warning services.

       

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