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农学学报 ›› 2022, Vol. 12 ›› Issue (1): 80-83.doi: 10.11923/j.issn.2095-4050.cjas2020-0159

所属专题: 生物技术 油料作物

• 农业信息/农业气象 • 上一篇    下一篇

宣城市油菜普花期特征分析及预测

胡安霞(), 田青, 汪大林   

  1. 宣城市气象局,安徽宣城 242000
  • 收稿日期:2020-08-03 修回日期:2020-10-16 出版日期:2022-01-20 发布日期:2022-02-24
  • 作者简介:胡安霞,女,1972年出生,安徽宣城人,高级工程师,本科,研究方向:应用气象。通信地址:242000 安徽省宣城市气象局,Tel:0563-2531010,E-mail: 745276903@qq.com
  • 基金资助:
    2020年度宣城市气象局“宣城市油菜花期预报模式初探”(KY202006)

Blooming Period of Rape in Xuancheng: Analysis and Prediction

HU Anxia(), TIAN Qing, WANG Dalin   

  1. Xuancheng Meteorological Bureau, Xuancheng 242000, Anhui, China
  • Received:2020-08-03 Revised:2020-10-16 Online:2022-01-20 Published:2022-02-24

摘要:

为准确预测油菜普花期,更有效地开展气象服务,笔者选取安徽省宣城市1990—2019年油菜观测资料和温度、降水、日照等气象资料,基于Mann­Kendall法,利用SPSS和Excel软件对油菜普花期特征、普花期与气象因子之间的关系进行了分析研究。结果发现:宣城1990—2019年油菜普花期平均是3月20日,年际间变化较大,最早和最迟日期相差一个多月,总体上呈显著提前趋势,速率为2.7 d/10 a,2013年前后为突变开始时间。宣城油菜普花期与1、2月和整个冬季平均气温、≥3℃活动积温均呈负显著相关,与降水和日照的相关性不大。基于此,利用逐步回归建立的油菜普花期预报模型,经回代检验,预测值最小误差为0天,最大误差为11天,效果总体较好。

关键词: 油菜, 普花期, 特征分析, 预测, 宣城

Abstract:

To accurately predict the blooming period of rape and carry out the meteorological service more effectively, the authors selected the temperature, precipitation, sunshine and other observation data of rape from 1990 to 2019 in Xuancheng of Anhui Province, and used SPSS and Excel software based on the Mann-Kendall method, to analyze the relationship between common flowering characteristics and period with meteorological factors. The results showed that the average blooming period of rape in the past 30 years was March 20 in Xuancheng, the interannual variation changed significantly, the difference between the earliest and the latest date was more than one month, and the overall trend was ahead of time with a rate of 2.7 d/10 a. The start of the mutation was around 2013. The blooming period of rape in Xuancheng was significantly and negatively correlated with the average temperature in January, February and the whole winter, as well as the active accumulated temperature ≥3℃, but it had little correlation with precipitation and sunshine. Based on the study, the prediction model of rape blooming period was established by stepwise regression. After a retrospective test, the minimum error of prediction value was 0 d, and the maximum error was 11 d, so the effect was generally good.

Key words: Rape, Blooming Period, Feature Analysis, Prediction, Xuancheng

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