Journal of Agricultural Big Data >
Monitoring Dataset of Vegetable Production and Sales in Beijing- Tianjin-Hebei Region (2021-2023)
Received date: 2025-02-13
Accepted date: 2025-04-03
Online published: 2025-06-23
Vegetables are one of the important supporting industries for agriculture and rural economy, and also an important component of the "vegetable basket" for urban and rural residents. Under the coordinated development of the Beijing-Tianjin-Hebei region, dynamic monitoring of vegetable production and sales information is of great significance for stabilizing regional vegetable supply, improving agricultural resource allocation efficiency, increasing farmers' income, and promoting regional integration development. This dataset gathers the production and sales data of 108 types of vegetables in the Beijing-Tianjin-Hebei region from 2021 to 2023, including data indicators such as planting area, planting method, sales price, sales quantity, sales destination, sales channels, etc. The data covers 83 districts and counties in the Beijing-Tianjin-Hebei region, with 415 micro production entities selected as monitoring points, including vegetable growers, family farms, cooperatives, and enterprises. This dataset can provide data support for vegetable planting planning, yield and price forecasting, market supply and demand research, etc. in the region.
Data summary:
| Items | Description |
|---|---|
| Dataset name | Monitoring Dataset of Vegetable Production and Sales in Beijing-Tianjin-Hebei Region (2021-2023) |
| Specific subject area | Agricultural Science |
| Research Topic | Vegetable production and sales |
| Time range | 2021-2023 |
| Temporal resolution | Day |
| Geographical scope | Beijing, Tianjin, Hebei |
| Spatial resolution | Monitoring point |
| Data types and technical formats | .xlsx |
| Dataset structure | This dataset comprises a single tabular file that contains vegetable production and sales data collected from 415 monitoring points in the Beijing-Tianjin-Hebei region, covering the period from 2021 to 2023. |
| Volume of dataset | 91.5 MB |
| Key index in dataset | Cultivated variety, planting area, transplanting date, quality, planting method, market availability date, sales date, sales volume, sales price, sales destination, sales channel |
| Data accessibility | CSTR:sciencedb.agriculture.00193; https://cstr.cn/17058.11.sciencedb.agriculture.00193 DOI:10.57760/sciencedb.agriculture.00193; |
| Financial support | 2024 Agricultural Product Market Information Collection and Analysis Project; Beijing Rural Revitalization Agricultural Science and Technology Project(NY2502270125) |
CHEN Li , WANG Jian , ZHAO AnPing , WANG XiaoDong , LIU Juan , WANG ShiRui , NING XiaoHan , WANG ZengFei , YANG WeiJia . Monitoring Dataset of Vegetable Production and Sales in Beijing- Tianjin-Hebei Region (2021-2023)[J]. Journal of Agricultural Big Data, 2025 , 7(2) : 276 -280 . DOI: 10.19788/j.issn.2096-6369.100054
| [1] | 国家统计局: 农业发展阔步前行现代农业谱写新篇——新中国75年经济社会发展成就系列报告之二. https://www.stats.gov.cn/sj/sjjd/202409/t20240910_1956334.html |
| [2] | CAO Y L, MOHIUDDIN M. Sustainable emerging country agro-food supply chains: fresh vegetable price formation mechanisms in rural China. Sustainability, 2019, 11(10):2814. https://doi.org/10.3390/su11102814 |
| [3] | 李优柱, 付辉, 杨鸿宇. 蔬菜产销市场价格波动溢出效应研究:基于极端气温冲击视角. 农林经济管理学报, 2023, 22(3): 311-321. |
| [4] | 沈辰, 张玉梅, 李志强. 我国不同类型蔬菜价格波动分解与贡献分析. 中国蔬菜, 2015, 1(5): 52. |
| [5] | 彭红星, 郑楷航, 黄国彬, 等. 基于BP、LSTM和ARIMA模型的蔬菜价格预测. 中国农机化学报, 2020, 41(4): 193-199. |
| [6] | LI Y Z, LI C G, ZHENG M Y. A hybrid neural network and H-P filter model for short-term vegetable price forecasting. Mathematical Problems in Engineering, 2014. DOI:10.1155/2014/135862. |
| [7] | LI B, DING J Q, YIN Z Q, et al. Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting. Expert Systems with Applications, 2021, 168. DOI: 10.1016/j.eswa.2020.114232. |
| [8] | 赵安平, 赵友森, 王晓东, 等. 北京市蔬菜供需平衡研究. 中国农业信息, 2023, 35(2): 53-62. |
/
| 〈 |
|
〉 |