添加行为特征以选择奶牛的耐热性

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引用次数: 0

摘要

热浪期的增加导致奶牛热应激事件的增加,从而引发福利问题、生产损失和健康问题。然而,由于牛奶记录数据的频率较低,因此耐热性遗传评估仍是一项挑战。一个可能的解决方案是增加通过传感器捕获的行为数据,这些数据是永久记录的,大多每天报告一次。本研究的目的是评估添加行为特征作为耐热性遗传评估代理的潜在收益。从2019年10月到2022年7月,在比利时瓦隆大区的6个牛群中记录了453头配备了SenseHub(Allflex牲畜智能)项圈的荷斯坦奶牛的行为特征,包括活动时间(ACT)、反刍时间(RUM)和进食时间(EAT)。采用了基于独立温湿度指数(THI)阈值的多性状反应标准模型。结果表明,行为特征不仅是耐热性遗传评估的有趣特征,也是牧场热应激检测的有趣特征。事实上,传感器可以记录泌乳和非泌乳动物在所有热应激事件中的行为。行为性状的遗传率也达到了中等水平(ACT 为 0.14,RUM 为 0.19,EAT 为 0.12),一般遗传变异和耐热性附加遗传变异之间的比率也很高。此外,ACT 和 EAT 的耐热性与产奶量的耐热性(脂肪和蛋白质校正奶:分别为 0.45 和 0.28)呈正相关,ACT 的耐热性与体细胞(体细胞得分:-0.39)呈负遗传相关。通过遗传相关矩阵,我们可以根据行为数据解释两个经济性状(脂肪和蛋白质校正奶:59%,体细胞评分:31%)对热应激反应的大部分变异。基于这些结果,行为性状可用于评估非哺乳期牛的热应激反应,而对非哺乳期牛耐热性的遗传评估数量仍然有限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adding behavior traits to select for heat tolerance in dairy cattle

The increase in periods of heat waves leads to an increase in heat stress events in dairy cattle leading to welfare issues, production losses, and health issues. However, the low frequency of milk recording data makes genetic evaluation for heat tolerance still a challenge. A possible solution could be to add behavior data captured through sensors which are recorded permanently, mostly reported on a daily basis. The objective of this study was to evaluate the potential gain of adding behavior traits as proxies for genetic evaluation of heat tolerance. Behavior traits including activity time (ACT), rumination time (RUM), and eating time (EAT) were recorded for 453 Holstein cows equipped with SenseHub (Allflex Livestock Intelligence) collars from October 2019 to July 2022 in 6 herd located in the Walloon Region of Belgium. A multitrait reaction norm model based on separate temperature and humidity index (THI) thresholds was used. Results showed that behavior traits present not only interesting characteristics for genetic evaluation of heat tolerance but also for heat stress detection in farms. Indeed, sensors allow recording of behavior for all events of heat stress in lactating and nonlactating animals. Moderate heritability values were also found for the behavior traits (0.14 for ACT, 0.19 for RUM, and 0.12 for EAT), and a high ratio between the general and thermotolerance additive genetic variances was obtained. In addition, positive correlations of thermotolerance for ACT and EAT with thermotolerance for milk production (fat- and protein-corrected milk: 0.45 and 0.28, respectively) and negative genetic correlations of thermotolerance for ACT with somatic cells (somatic cell score: −0.39) were estimated. The genetic correlation matrix allows us to explain a high part of the variation for the reaction to heat stress of 2 economic traits (fat- and protein-corrected milk: 59% and somatic cell score: 31%) based on behavior data. Based on these results, behavior traits could be used to assess heat stress in nonlactating cattle for which the number of genetic evaluations for heat tolerance is still limited.

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来源期刊
JDS communications
JDS communications Animal Science and Zoology
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