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Monitoring estrous activity in pasture-based dairy cows

机译:监测基于牧场乳制品奶牛的原始活动

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Correctly identifying cows in estrus and inseminating them at the optimal time are critical components of reproductive management. Technologies for detecting cows in estrus have developed from tail paint in the 1970's to automated activity monitors and mount detectors in recent decades. The objectives of this study were to identify animal characteristics associated with estrous behaviour, measured using a mount detector (FlashMate; Farmshed Labs) and an accelerometer-based activity monitor (Moomonitor, Dairymaster), to examine the relationship between estrous behaviour measured by both devices, and to examine the characteristics associated with pregnancy per AI. Four hundred and sixty eight lactating dairy cows managed on three research farms were enrolled and data were available from 465 cows and 369 cows with Moomonitor and FlashMate data, respectively. Of 234 cows that provided both Moomonitor and FlashMate data, the mean (+/- SEM) onset of device activation occurred 1.1 (+/- 0.4) h earlier with the Moomonitor compared with the FlashMate. The mean (+/- SD) duration of Moomonitor activity and FlashMate activity was 17.2 +/- 6.1 h and 8.7 +/- 5.8 h, respectively. The duration of Moomonitor activity and FlashMate activity was negatively associated with total milk yield during the first five weeks of lactation. The duration of FlashMate activity, but not the duration of Moomonitor activity was positively associated with days in milk. Pregnancy per AI was positively associated with BCS, days in milk, the duration of FlashMate activity and the interval from the onset of Moomonitor and FlashMate activity to AI. Inseminating cows <= 2 h after FlashMate activation or <= 4 h after Moomonitor activation was associated with reduced odds of pregnancy compared with later timing of AI. Overall, 55% of cows received mounts for <= 8 h, highlighting the need for >= 3 periods of estrous observation daily or the use of estrous detection aids that continuously monitor cows. Finally, the study reiterated the importance of maximising body condition score and days in milk at breeding to increase behavioural expression of estrus and pregnancy per AI. (C) 2020 Elsevier Inc. All rights reserved.
机译:正确识别处于发情期的奶牛,并在最佳时间为其授精,是生殖管理的关键组成部分。检测发情期奶牛的技术已经从20世纪70年代的尾漆发展到近几十年的自动活动监测仪和安装检测器。本研究的目的是识别与发情行为相关的动物特征,使用mount检测器(FlashMate;Farmshed Labs)和基于加速度计的活动监视器(Moomonitor,Dairymaster)进行测量,以检查两种设备测量的发情行为之间的关系,并检查每种人工智能与妊娠相关的特征。在三个研究农场管理的468头泌乳奶牛被纳入研究,数据来自465头奶牛和369头奶牛,分别具有Moomonitor和FlashMate数据。在234头同时提供Moomonitor和FlashMate数据的奶牛中,Moomonitor与FlashMate相比,设备激活的平均(+/-SEM)开始时间提前了1.1(+/-0.4)小时。Moomonitor活动和FlashMate活动的平均(+/-SD)持续时间分别为17.2+/-6.1小时和8.7+/-5.8小时。Moomonitor活动和FlashMate活动的持续时间与哺乳前五周的总产奶量呈负相关。FlashMate活动的持续时间,而不是Moomonitor活动的持续时间与牛奶中的天数呈正相关。每次人工授精妊娠与BCS、哺乳天数、FlashMate活动持续时间以及从Moomonitor和FlashMate活动开始到人工授精的间隔时间呈正相关。与人工授精后期相比,在FlashMate激活后2小时或Moomonitor激活后4小时内授精的奶牛怀孕几率降低。总的来说,55%的母牛接受了小于等于8小时的发情观察,这突出表明需要每天进行大于等于3次的发情观察,或者使用发情检测辅助工具来持续监测母牛。最后,该研究重申了在繁殖时最大化身体状况评分和母乳天数的重要性,以增加每AI发情和怀孕的行为表达。(C) 2020爱思唯尔公司版权所有。

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