大数据再循环的愿景为韩国智能畜牧业的智能畜牧业
Seung-Hoon Lee1,2, Kyu-Sang Lim1,3, Hakkyo Lee1,4
1The Korean Society of Animal Big Data Research, Korean Society of Animal Science and Technology, Seoul 06367, Korea.
Journal of animal science and technology
|April 23, 2025
概括
智能畜牧场产生复杂的数据,但共享和标准化问题限制了研究. 整合遗传,环境和表型数据是推动畜牧养殖和气候适应战略的关键.
科学领域:
- 农业科学 农业科学
- 动物科学动物科学
- 数据科学数据科学数据科学
背景情况:
- 智能畜牧场利用信息和通信技术 (ICT) 来优化农场管理.
- 信息和通信技术系统产生复杂和多样化的表型数据,增加了对综合数据分析的需求.
- 结合环境,表型和遗传信息的融合数据对于理解牲畜复杂特征至关重要.
研究的目的:
- 突出融合数据在智能畜牧业中的价值,以促进繁殖和环境适应.
- 解决阻碍研究进步的数据共享和标准化方面的局限性.
- 为智能畜牧业大数据传播和管理提出一个框架.
主要方法:
- 审查智能畜牧场的概念及其数据生成能力.
- 分析"表型 = 遗传价值 + 环境价值"模型.
- 讨论数据仓库和数据传播策略的重要性.
主要成果:
- 智能畜牧场生产大量复杂的表型数据.
- 数据共享限制和非标准格式阻碍了研究和开发.
- 综合数据分析为畜牧养殖和气候适应提供了巨大的潜力.
结论:
- 克服数据共享和标准化障碍对于释放智能牲畜数据的全部潜力至关重要.
- 发展适应气候的牲畜需要综合的遗传,环境和表型数据.
- 促进数据循环和建立强大的数据管理系统对于未来的进步是必要的.
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