在商业奶牛群中,使用耳传感器加速度计检测发情的行为变化与在线牛奶孕酮的比较

IF 2.1 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
H. J. Perez Marquez, D. Ambrose, C. Bench
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引用次数: 0

摘要

本研究的第一个目的是使用加速度计耳标(CowManager系统;传感器)比较孕酮(P4)下降期(预期发情期)和使用在线牛奶P4分析(Herd Navigator系统;HNS)确定的黄体期期间的行为和耳朵温度变化。第二个目标是评估在商业奶牛群中,与HNS相比,每个传感器指标检测发情的准确性。46头奶牛(23头幼奶牛[1至2头泌乳奶牛]和23头成熟奶牛[3至6头泌乳奶牛)在挤奶20天(DIM)时安装了传感器标签,并通过HNS测量P4图谱,直到90 DIM。分析的传感器指标包括静息、反刍、进食、活跃、高活跃和耳温(Etemp)。在HNS中,乳汁P4下降到5ng/mL阈值以下的日期被指定为d-1(LSM±SEM;3.42±0.08ng/mL),预期发情日期为d0。与黄体期(活动期:4.46±0.13 min/h;高活动期:6.40±0.22 min/h和Etemp:28.69±0.08°C)相比,活动期(5.01±0.14 min/h)和高活动期(8.70±0.25 min/h)行为反应以及Etemp(29.45±0.08℃)在第0天观察到显著增加(LSM±SEM)在所有奶牛中使用Etemp(0.24 J)、然后休息(0.20 J)和高活性(0.17 J)实现。与成熟奶牛(Etemp:0.09 J;Resting:0.12 J;High Active:0.13 J)相比,年轻奶牛(Etemp:0.44 J;Resting:0.33 J;High Active:0.25 J)的准确度更高。同样,当仅将健康奶牛(没有产后健康事件的奶牛)(Etemp:0.33 J;静息:0.31 J;高活性:0.20 J)与不健康奶牛(Etemp:0.11 J;静息:0.02 J;高活力:0.02 J)进行比较时,准确性更高。与单一指标方法相比,行为和Etemp指标的组合优化了所有奶牛(0.30 J)、年轻奶牛(0.46 J)、成熟奶牛(0.26 J),健康奶牛(0.45 J)和不健康奶牛(0.11 J)的发情检测准确性。年龄和产后健康状况影响使用传感器标签检测发情的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behavioral changes to detect estrus using ear-sensor accelerometer compared to in-line milk progesterone in a commercial dairy herd
The first objective of this study was to compare behavioral and ear temperature changes using accelerometer ear tags (CowManager system; Sensor) during the declining progesterone (P4) phase (expected estrus) and the luteal phase determined using in-line milk P4 analysis (Herd Navigator system; HNS). The second objective was to evaluate the accuracy of each Sensor metric to detect estrus compared to HNS in a commercial dairy herd. Forty-six cows (23 young [1 to 2 lactations] and 23 mature [3 to 6 lactations]) at 20 days in milk (DIM) were fitted with Sensor tags, and P4 profiles measured via HNS until 90 DIM. Sensor metrics analyzed were Resting, Ruminating, Eating, Active, High-Active, and ear temperature (Etemp). The day of milk P4 decline below the 5 ng/mL threshold in the HNS was designated as d -1 (LSM ± SEM; 3.42 ± 0.08 ng/mL) and the day of expected estrus as d 0. Significant increases (LSM ± SEM) were observed at d 0 in Active (5.01 ± 0.14 min/h) and High-Active (8.70 ± 0.25 min/h) behavior responses as well as in Etemp (29.45 ± 0.08°C) compared with the luteal phase (Active: 4.46 ± 0.13 min/h; High-Active: 6.40 ± 0.22 min/h and Etemp: 28.69 ± 0.08°C). The greatest estrus detection accuracy (Youden Index [J: performance]) single metric was achieved using Etemp (0.24 J) followed by Resting (0.20 J) and High-Active (0.17 J) in all cows. Greater accuracy was observed in Young cows (Etemp: 0.44 J; Resting: 0.33 J; and High-Active: 0.25 J) than in Mature cows (Etemp: 0.09 J; Resting: 0.12 J; and High-Active: 0.13 J). Similarly, accuracy was greater when only healthy cows (cows with no postpartum health events) were compared (Etemp: 0.33 J; Resting: 0.31 J; High-Active: 0.20 J) to unhealthy cows (Etemp: 0.11 J; Resting: 0.02 J; High-Active: 0.02 J). The combination of behavior and Etemp metrics optimized the estrus detection accuracy in all the cows (0.30 J), Young (0.46 J), Mature (0.26 J), Healthy (0.45 J), and Unhealthy (0.11 J) cows compared to a single metric approach. Age and postpartum health affected the estrus detection accuracy using Sensor tags.
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