摘要
近年来,奶牛养殖业进入快速发展时期,而养殖高成本、高发病率和低产出是阻碍奶牛养殖业持续发展的障碍。其中,奶牛酮病的发病率有逐年升高趋势,不同的胎次、产后天数、月份、泌乳量等风险因素均影响奶牛酮病的发生。目前,基于奶牛行为、奶牛生产性能测定数据、代谢组学技术和机器学习的酮病早期预警技术已经取得巨大进展。本文综述了奶牛酮病的发病规律和酮病早期预警技术,为奶牛围产期健康管理提供理论参考,对促进奶业提质增效、提高奶牛福利具有重要意义。
In recent years,with the development of dairy industry,the high cost,high incidence rate and low profit are obstacles to the sustainable development of dairy industry.The incidence rate of ketosis in dairy cows has an increasing trend year by year,and different risk factors such as parity,postpartum time,month,and milk yield affect the occurrence of ketosis in dairy cows.Great progress has been made in the early warning technology of ketosis based on cow behavior,dairy herd improvement data,metabolomics technology and machine learning.Therefore,this paper intends to review the incidence characteristics and early warning technology of ketosis,so as to provide theoretical reference for the perinatal health management of dairy cows,which is of great significance to promote the quality and efficiency of dairy industry and improve the welfare of dairy cows.
作者
杜振隆
罗正中
周涛
曹随忠
严作廷
DU Zhenlong;LUO Zhengzhong;ZHOU Tao;CAO Suizhong;YAN Zuoting(Lanzhou Institute of Husbandry and Pharmaceutical,Chinese Academy of Agricultural Sciences,Gansu Lanzhou 730050,China;College of Veterinary Medicine,Sichuan Agricultural University,Sichuan Chengdu 611130,China)
出处
《中国畜牧杂志》
CAS
CSCD
北大核心
2024年第1期31-35,共5页
Chinese Journal of Animal Science
基金
甘肃省科技厅重点研发计划项目(20YF8NA029)
四川省自然科学基金项目(2023NSFSC0234)
重庆市自然科学基金项目(CSTB2022NSCQ-MSX1602)。
关键词
奶牛
酮病
机器学习
代谢组学
疾病预警
Cow
Ketosis
Machine learning
Metabolomics
Disease warning
