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使用统计和机器学习方法来描述影响小猪断奶前死亡率的因素

Md Towfiqur Rahman¹, Tami M Brown-Brandl¹, Gary A Rohrer²

¹Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68503, USA.

Translational animal science

|November 30, 2023

概括

小猪在断奶前高死亡率 (PWM) 是一个主要问题. 这项研究确定了影响PWM的关键因素,如子大小和出生体重,使用机器学习有效预测PWM,为猪肉生产管理提供了洞察力.

科学领域:

动物科学动物科学
农业经济学农业经济学
兽医医学兽医医学兽医医学

背景情况:

小猪在断奶前的高死亡率 (PWM) 在全球范围内带来了重大的经济和动物福利挑战.
了解PWM的多因素原因对于提高猪生产效率至关重要.

研究的目的:

确定影响幼猪断奶前死亡率 (PWM) 和重叠的关键因素.
使用历史生产数据开发和评估PWM的预测模型.

主要方法:

使用通用线性模型,β回归和随机森林 (RF) 机器学习模型分析了1982个子.
数据包括从2016年到2021年的母猪,,环境和小猪的参数.
采用射频模型,根据已识别的促成因素来预测PWM和重叠.

主要成果:

射频模型对PWM表现出强大的预测性能 (r=0.89).
PWM的关键预测因素包括 litter 尺寸,平均出生体重,健康诊断,妊娠期长度和平价顺序.
对等顺序,子大小和平均出生体重是预测重叠最重要的因素.

结论:

子大小,出生体重和平价顺序是影响小猪断奶前死亡率和重叠的关键因素.
机器学习,特别是射频模型,提供了一个强大的方法来预测猪生产中的PWM.
结果为优化育种和管理策略提供了宝贵的见解,以减少小猪损失.

关键词:

机器学习是机器学习.猪仔猪小猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪断奶前死亡率断奶前死亡率随机的森林随机的森林猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪

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