阿萨斯-南普座谈会:动物营养中的数学建模:家禽营养建模概述
1Prestage Department of Poultry Science, North Carolina State University. Raleigh, NC 27695 United States.
Journal of animal science
|May 4, 2025
概括
数学建模通过解决实证研究的局限性来增强家禽的营养. 机器学习和大数据等先进技术可以提高可持续料配方的模型准确性.
科学领域:
- 禽类的营养 养
- 数学建模的数学建模
- 动物科学动物科学
背景情况:
- 50年来,数学建模一直是家禽营养的组成部分,构成了当前氨基酸建议的基础.
- 实证研究虽然是基础性的,但存在一些局限性,现在科学界也认识到这一点.
- 现有的模型,如EFG软件和AVINESP,为了解家禽的生物过程提供了框架.
研究的目的:
- 突出建模方法的潜力,以克服家禽营养的实证研究局限性.
- 讨论在家禽营养建模领域扩大研究的挑战和机会.
- 证明当前机械模型对可持续家禽生产的相关性和应用.
主要方法:
- 审查家禽营养现有的数学模型,包括EFG软件和AVINESP等机械模型.
- 解释一般的模型开发方法,基本方程和生物过程描述.
- 讨论知识差距和新兴技术,以提高模型的准确性和精度.
主要成果:
- 机械模型提供了适用于生长,蛋生产,组织组成和料摄入量估计的强大框架.
- 这些概念可以扩展到其他营养素和动物物种,包括家禽矿物营养的初步发展.
- 大数据,多元经济学和机器学习的整合显示出解决模型准确性和精度问题的前景.
结论:
- 数学建模在家禽营养中比实证方法具有显著的优势,提供了必要的决策工具.
- 目前的机械模型已经很成熟,可以与优化器和料配方软件集成,用于实际应用.
- 数据采集和分析技术的进步将进一步提高家禽营养建模的能力和可持续性.
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