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中国农学通报 ›› 2016, Vol. 32 ›› Issue (30): 188-192.doi: 10.11924/j.issn.1000-6850.casb16010047

所属专题: 园艺

• 三农研究 • 上一篇    下一篇

都市绿叶蔬菜价格预测预警研究的思考

马 佳,马 莹,赵京音   

  1. (上海市农业科学院农业科技信息研究所,上海 201403)
  • 收稿日期:2016-01-11 修回日期:2016-06-27 接受日期:2016-07-15 出版日期:2016-10-31 发布日期:2016-10-31
  • 通讯作者: 马 莹
  • 基金资助:
    上海市市级农口系统青年人才成长计划项目“沪郊家庭农场适度规模测度研究”[沪农青字[2014]第1-12号];上海市农业委员会“上海绿叶蔬菜价格预警模型及辅助决策系统开发”[沪农科攻字(2015)第2-6号]。

Price Forecasting and Warning of Urban Green Leafy Vegetables

Ma Jia, Ma Ying, Zhao Jingyin   

  1. (Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403)
  • Received:2016-01-11 Revised:2016-06-27 Accepted:2016-07-15 Online:2016-10-31 Published:2016-10-31

摘要: 在明确都市绿叶蔬菜价格预测预警研究的重要性与必要性的基础上,系统梳理了国内外农产品价格预测预警相关研究涉及的农产品品种、价格类型和空间尺度,农产品价格预测预警的方法与模型,主要分析方法与模型的特点及适用条件,并对今后都市绿叶蔬菜价格预测预警研究的发展方向进行了展望。研究认为:国内外农产品价格预测预警研究取得了丰富的研究成果,但对绿叶菜价格预测预警的系统研究并不多见。应建立完善科学、规范、统一、符合预测预警需要的绿叶菜价格基础数据库,以提供高效的数据支撑。在现有技术条件下,结合预测精度、数据要求等,可以尝试选用两种以上方法建立组合模型,特别是结合季节预测模型和时间序列模型来预测绿叶菜价格,以便得出更符合实际的预测结果。应根据绿叶蔬菜特点,将其品种和所用数据的时间频率进行分类,选择相适应的预测预警模型。

关键词: Y58S, Y58S, 产量, 品质, 病虫抗性, 综合表现

Abstract: Based on the importance and necessary of price forecasting and warning of urban green leafy vegetables, agricultural product varieties, price types and spatial scale in the domestic and foreign researches were systematically reviewed. The main methods, model characteristics and applicable conditions of agricultural product price forecasting and warning were also studied. And the development direction of the researches on price forecasting and warning of urban green leafy vegetables was discussed. There were rich researches on price forecasting and warning of agricultural products at home and abroad, but the researches on green leafy vegetables’ price were rare. So a scientific, standardized, unified and appropriate price database of green leafy vegetables should be set up in order to provide effective data support for future research. Based on the existing technologies, considering forecasting accuracy and requirement of data, combination model composed of two or more methods, especially the combination of seasonal forecasting model and time sequence model, should be used to forecast the price of green leafy vegetables so as to effectively get more realistic result. Besides, to choose the appropriate model, the variety of vegetables and the temporal frequency of data should be classified according to the characteristics of different green leafy vegetables.

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