| 摘要: |
| 【目的 】 为促进农业大数据在我国农业生产中的应用，提高农业生产效率和质量。 【方法 】 文章首先通过梳理大数据相关文献，对农业大数据理论的提出和发展进行回顾，总结大数据在农业中的重要作用；其次，对我国以及美国、以色列、日本这3个主要发达国家的农业大数据发展现状进行分析，进而总结了国内外农业大数据的特点并归纳国内外农业大数据存在的问题。 【结果/结论 】 我国需借鉴国外先进的农业生产模式，从农业规范化、精准化、智能化三方面发展，这将对我国农业大数据在农业生产中的应用具有重要指导作用，从而提高生产效率、减少浪费、充分利用有限的农业资源，为我国粮食安全提供保障。 |
| 关键词: 大数据 农业大数据 精准农业 智慧农业 |
| DOI：10.12105/j.issn.1672-0423.20210301 |
| 分类号: |
| 基金项目:草地碳收支监测评估技术合作研究（2017YFE0104500）；中央级公益性科研院所基本科研业务费专项（1610132020014） |
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| Current situation and problems of agricultural big data development at domestic and overseas |
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Yuan Zijing1, Mao Kebiao1,2, Cao Mengmeng1, Wang Han1, Fang Shu1, Wang Ping1
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1.National Hulunber Grassland Ecosystem Observation and Research Station，Institute of Agricultural Resources and Regional Planning，Chinese Academy of Agricultural Sciences，Beijing 100081，China;2.School of Physics and Electronic-Electrical Engineering，Ningxia University，Yingchuan 750021，China
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| Abstract: |
| ［ Purpose ］ In order to promote the application of agricultural big data in China’s agricultural production，improve its production efficiency and quality. ［Method ］ Therefore，this research first reviews the proposal and development of agricultural big data theory by investigating the literature on big data and summarizes the important role of big data in agriculture. Second，it analyzes the current situation of agricultural big data development in China and the three major developed countries of the United States，Israel，and Japan. And then summarized the characteristics and problems of agricultural big data at domestic and foreign. ［ Result/Conclusion ］ China needs to learn from advanced foreign agricultural production models and develop them into three aspects：standardized，precise，and intelligent agriculture. This will have an important guiding role in the application of China’s agricultural big data in agricultural production，thereby improving production efficiency，reducing waste，making full use of limited agricultural resources，and providing guarantees for China’s food security. |
| Key words: big data agricultural big data precision agriculture smart agriculture |
