| 注册
首页|期刊导航|畜牧兽医学报|非侵入性技术在猪胴体性状活体评估中的应用

非侵入性技术在猪胴体性状活体评估中的应用

李龙娇 何航 徐茂森 向邦全 陈脊宇 李文娟 周乾兰 杨延辉 张传师

畜牧兽医学报2026,Vol.57Issue(5):2397-2405,9.
畜牧兽医学报2026,Vol.57Issue(5):2397-2405,9.DOI:10.11843/j.issn.0366-6964.2026.05.002

非侵入性技术在猪胴体性状活体评估中的应用

Application of Non-Invasive Techniques for in Vivo Assessment of Pig Carcass Traits

李龙娇 1何航 1徐茂森 1向邦全 1陈脊宇 1李文娟 1周乾兰 1杨延辉 1张传师1

作者信息

  • 1. 重庆三峡职业学院动物科技学院,重庆 404155||重庆市功能性动物源食品技术创新中心,重庆 404155
  • 折叠

摘要

Abstract

With the development of the pig industry,carcass traits have become not only crucial for pig breeding but also of increasing concern to consumers.However,traditional phenotypic data collection for carcass traits requires slaughtering pigs before measurement,making it impossible to directly select elite breeding individuals.Instead,selection must rely on progeny or sibling testing,leading to issues such as low accuracy of estimated breeding values,high breeding costs,and poor selection efficiency.Therefore,how to assess pig carcass traits using non-destructive measurement methods has remained a key research focus.Non-invasive techniques,such as computed tomography(CT),magnetic resonance imaging(MRI),dual-energy X-ray absorptiometry(DXA),and the most commonly used ultrasound technology,can provide critical information for in vivo assessment of pig carcass traits.This,in turn,offers valuable references for pig breeding,quality control,pricing,and processing.Furthermore,with the continuous advancement of artificial intelligence,integrating modern breeding techniques,non-invasive internal detection technologies,and the Internet of Things(IoT)will optimize genetic parameter estimation for breeding selection,thereby providing core support for precision pig farming.

关键词

非侵入性技术//胴体/育种

Key words

non-invasive techniques/swine/carcass/breeding

分类

农业科技

引用本文复制引用

李龙娇,何航,徐茂森,向邦全,陈脊宇,李文娟,周乾兰,杨延辉,张传师..非侵入性技术在猪胴体性状活体评估中的应用[J].畜牧兽医学报,2026,57(5):2397-2405,9.

基金项目

重庆市教委科研项目(KJQN202503523 ()

KJQN202403529) ()

重庆市生猪产业技术体系创新团队(CQMAITS2025[12]号) (CQMAITS2025[12]号)

畜牧兽医学报

0366-6964

访问量0
|
下载量0
段落导航相关论文