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中国农学通报 ›› 2020, Vol. 36 ›› Issue (34): 100-103.doi: 10.11924/j.issn.1000-6850.casb20191100823

所属专题: 油料作物 农业气象

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

安徽省花生产量与气象因素的关联度分析及预测模型研究

杨小兵1(), 杨峻2, 杨晨1, 任重1, 汪大林3()   

  1. 1泾县气象局,安徽泾县 242500
    2东南大学自动化学院,南京 210000
    3绩溪县气象局,安徽绩溪 245300
  • 收稿日期:2019-11-13 修回日期:2020-05-09 出版日期:2020-12-05 发布日期:2020-12-15
  • 通讯作者: 汪大林
  • 作者简介:杨小兵,男,1990年出生,江苏兴化人,工程师,硕士,研究方向:应用气象、数据分析与建模。通信地址:242500 安徽省宣城市泾县气象路135号 泾县气象局,Tel:0563-5022182,E-mail: yangxb1990@163.com
  • 基金资助:
    安徽省气象局硕博士工作启动经费项目“CIMISS结合Spark在业务中的应用研究”(RC201620)

Peanut Yield in Anhui: Correlation with Meteorological Factors and Forecast Model

Yang Xiaobing1(), Yang Jun2, Yang Chen1, Ren Zhong1, Wang Dalin3()   

  1. 1Jingxian Meteorological Bureau, Jingxian Anhui 242500
    2School of Automation, Southeast University, Nanjing 210000
    3Jixi Meteorological Bureau, Jixi Anhui 245300
  • Received:2019-11-13 Revised:2020-05-09 Online:2020-12-05 Published:2020-12-15
  • Contact: Wang Dalin

摘要:

为分析气象因子对安徽花生产量的影响,构建适用于本地的花生单位产量预测模型,以期为探讨花生经济效益、应对气象灾害风险管理提供参考。对安徽各市2000—2017年花生气象产量和气象因子进行灰色关联度分析,筛选出关联度较大的气象因子,并采用逐步回归法建立产量预测模型。结果表明:安徽花生产量与生育期气象因子关联度5月份平均气温>7月份光照时数>5月份光照时数>6月份光照时数>7月份平均气温>8月份光照时数>8月份平均气温>6月份平均气温>8月份降水量>7月份降水量>5月份降水量>6月份降水量,应用建立的花生单位产量预测模型对历年产量进行回测,结果显示预测值与实际值均方根误差为815 kg/hm2、拟合指数为0.81,总体较好,具有一定的应用价值。

关键词: 花生, 产量, 气象, 灰度关联度, 逐步回归

Abstract:

To construct a local peanut yield forecasting model based on the influence of meteorological factors in Anhui, and provide reference for exploring the economic benefits of peanut and coping with the risk of meteorological disasters, grey correlation analysis on peanut meteorological output and meteorological factors from 2000 to 2017 of all cities in Anhui was conducted. The meteorological factors with greater correlation were screened out and a yield prediction model based on stepwise regression was established. The results showed that the correlation between Anhui peanut production and meteorological factors during growth period was followed the order of average temperature in May > light hours in July > light hours in May > light hours in June > the average temperature in July > light hours in August > the average temperature in August > the average temperature in June > precipitation in August > precipitation in July > precipitation in May > precipitation in June. Furthermore, peanut production over the years was tested based on the peanut per unit yield prediction model, revealing that the root mean square error between the predicted value and actual value was 815 kg/hm2 and the fitting index was 0.81. The prediction model is proved to have certain application value.

Key words: peanut, yield, meteorology, gray correlation, stepwise regression

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