中国农业气象 ›› 2026, Vol. 47 ›› Issue (10): 1670-1679.doi: 10.3969/j.issn.1000-6362.2026.10.011

• 农业生物气象栏目 • 上一篇    下一篇

基于VAR模型的冰雹天气对贵州蔬菜价格的影响

崔蕾,付芳婧,唐辟如   

  1. 贵州省生态与农业气象中心,贵阳 550002
  • 收稿日期:2025-07-08 出版日期:2026-10-20 发布日期:2026-10-19
  • 作者简介:崔蕾,E-mail:cccuuuiiillleeeiii@163.com
  • 基金资助:
    贵州省气象局省市联合科研基金项目(黔气科合SS−KF[2024]10号);中国气象局烤烟气象服务中心开放式研究基金项目(KYZX2024−09)

Impact of Hail Weather on Vegetable Prices in Guizhou Based on the VAR Model

CUI Lei, FU Fang-jing, TANG Pi-ru   

  1. Guizhou Ecological Meteorology and Agrometeorology Center, Guiyang 550002, China
  • Received:2025-07-08 Online:2026-10-20 Published:2026-10-19

摘要:

利用2017−2024年3−5月贵州94个农产品批发价格市场监测点蔬菜价格和同期降雹资料,基于向量自回归模型(VAR模型)分析降雹日数、冰雹平均直径和冰雹最大直径的冰雹强度指标对蔬菜价格的影响,以期为贵州蔬菜价格预测及防雹效益评估等提供参考。结果表明:(1)2017−2024年3−5月贵州省蔬菜周平均价格与降雹日数呈显著正相关关系,相关系数达0.94(P<0.05),且降雹日数每增加1d,蔬菜周平均价格上涨0.14元·kg−1。(2)相同降雹日数下,蔬菜周平均价格随冰雹直径增大而升高;相同冰雹直径区间下,蔬菜周平均价格随降雹日数增加而上升。(3)降雹日数、冰雹平均直径和冰雹最大直径对蔬菜周平均价格的格兰杰检验结果均未通过0.1水平的显著性检验,说明降雹日数、冰雹平均直径和冰雹最大直径对蔬菜周平均价格波动的直接预测作用不显著。(4)对蔬菜周平均价格波动幅度的影响,影响因子依次为降雹日数>冰雹平均直径>冰雹最大直径;对蔬菜周平均价格波动持续时长的影响,影响因子依次为降雹日数<冰雹平均直径≈冰雹最大直径。(5)对贵州蔬菜周平均价格冲击的整体影响而言,主要为降雹日数,其次是冰雹平均直径,最后是冰雹最大直径,三者合计对价格冲击的贡献度为4.021%。

关键词: VAR模型, 冰雹天气, 蔬菜价格, 价格冲击

Abstract:

By using the price monitoring data from 94 agricultural wholesale markets and concurrent hail observation data from March to May during the period 2017 to 2024, the impact of hail weather on vegetable prices in Guizhou province was analyzed based on the VAR model, which revealed how hail intensity indicators (including hail frequency, average hail diameter and maximum hail diameter) affect vegetable price fluctuations. The study aimed to provide a scientific basis for forecasting vegetable prices and assessing the benefits of hail suppression in Guizhou. The results showed that: (1) the weekly average price of vegetables showed a significant positive correlation with days of hail, the correlation coefficient was 0.94 (P<0.05), and the weekly average price of vegetables rose with the increasing number of days of hail in a week, each additional hail day per week increased the weekly average price of vegetables by 0.14yuan·kg−1. (2) For the same number of hail days, the weekly average price of vegetables increased with larger hail diameters. Within the same hail diameter range, the weekly average price of vegetables increased as the number of hail days increases. (3) Granger causality tests indicated that neither days of hail, average hail diameter and maximum hail diameter significantly predicted the weekly average price of vegetables fluctuations as none of them passed the significance test at the 0.1 level. (4) Regarding the impact on the weekly average vegetable price fluctuation range, the influencing factors are ranked as follows: the days of hail>the average hail diameter>the maximum hail diameter. Regarding the impact on the duration of the weekly average vegetable price fluctuations, the influencing factors are ranked as follows: the days of hail<the average hail diameter≈maximum hail diameter. (5) The contribution of hail frequency to the weekly average vegetable price fluctuations exceeded that of average hail diameter, which was greater than that of maximum hail diameter. Collectively, these three factors accounted for 4.021% contribute to the weekly average vegetable price. 

Key words: VAR model, Hail weather, Vegetable prices, Price shocks