Performance and optimization of an ear tag automated activity monitor for estrus prediction in dairy heifers☆
Introduction
In a survey of Canadian dairy farms, 66% of respondents ranked reproduction as one of the top three challenges and 46% of respondents indicated that their targets for herd reproductive performance were not met [1]. Approximately 69% of Canadian farms are using visual observation, 9% are using automated activity monitors (AAM), 7% are using timed-AI (TAI) and 9% a combination of methods for estrus detection and insemination in heifers [2]. Each of these methods comes with their own pros and cons; visual estrus detection is low cost, but can result in low estrus detection rates [3], TAI removes the need for estrus detection, but requires strict adherence to a hormone schedule [4], and AAM can remove the labor required for visual estrus detection, but requires a large up-front investment [5]. The raising of replacement heifers is the second largest contributor to annual operating expenses, with the largest expenses being feeding cost and labour [6]. Therefore, reducing age a first calving and increasing efficiency can reduce rearing costs. As a method to reduce the labour required for estrus detection and improve estrus detection rate, the use of AAM in heifer reproduction programs may benefit dairy producers.
While numerous studies have looked at AAM in lactating cows, there is a lack of studies evaluating performance of AAM and determining the optimization of AAM performance in heifers. In addition, there are no publications evaluating a newer ear tag AAM from SCR by Allflex. The eSense ear tag AAM records activity and rumination change, providing data to producers on estrus and health alerts. Estrus detection rates (71–88%) and conception rates (31–35%) have been variable in cows [[7], [8], [9]] subjected to AAM, however, there is evidence of improved performance in primiparous compared with multiparous cows. Recently, Burnett et al. [10,11] reported increased peak activity, duration of high activity and conception rates in primiparous compared with multiparous cows. Therefore, performance between cows and heifers likely differs as well. To that end, previous studies have observed a higher activity peak and longer duration of high activity in heifers compared with cows [12,13].
When using either TAI or visual estrus detection, insemination of conventional semen within 36 h prior to ovulation (OV) in dairy heifers resulted in acceptable pregnancy rates [14]. LeRoy et al. [7] reported AI within 8 h of onset of high activity increased odds of pregnancy in primiparous cows, whereas odds of pregnancy for multiparous cows did not differ between 0 and 24 h after the onset of high activity determined by AAM. Conversely, Stevenson et al. [8] indicated that insemination of primiparous cows 13–16 h after onset of activity and insemination of multiparous cows within 12 h after onset of activity improved conception rates. For use of sex-sorted semen in TAI or visual estrus detection programs, insemination within 12 h of OV is recommended in heifers [14]. When using AAM, Bombardelli et al. [15] reported that AI from 13 to 41 h after onset of high activity and within 20 h after peak activity increased odds of pregnancy in Jersey cows. The ideal interval from onset of high activity to AI may differ in heifers compared with cows and the ideal interval to AI with conventional or sex-sorted semen may also differ.
The objectives of this study were to evaluate the performance of the SCR eSense ear tag AAM to predict estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to AI for both sex-sorted and conventional semen. We hypothesized that, due to an anticipated longer duration of high activity, delaying insemination of heifers with conventional semen after onset of high activity will improve odds of pregnancy. Additionally, delaying insemination with sex-sorted semen beyond that of conventional semen will further improve odds of pregnancy.
Section snippets
Materials and methods
This study was conducted on a commercial dairy farm located near Wetaskiwin, Alberta, Canada (53°02′54.0"N, 113°19′28.0"W) from October 2018 to August 2019. All experimental procedures used in this study were approved by the University of Alberta Animal Care Committee (AUP00002800) and conducted according to the guidelines of the Canadian Council of Animal Care [16].
Validation of the eSense ear tag monitors in heifers
In total, 281 heifers were used for the validation of the ear tag activity monitoring system. This resulted in a total of 468 estrus events and 418 AI, 257 with sex-sorted semen and 161 with conventional semen. Including all AI, the pregnancy per AI to first AI was 67.6% and the total percentage of heifers pregnant after four inseminations was 97.9%. The pregnancy per AI using conventional semen was 67.5% and the pregnancy per AI using sex-sorted semen was 63.3%. Overall, the sensitivity
Discussion
The objectives of the current study were to evaluate performance of an ear tag AAM in heifers and determine any methods to optimize performance. Although AAM have been extensively studied in lactating cows [2,7,8], there is a lack of studies evaluating performance in heifers, particularly of a new ear tag AAM from SCR. There is evidence of differences in performance of AAM when looking at primiparous and multiparous cows separately. In general, primiparous cows have increased peak activity and
Conclusion
Overall, the SCR eSense ear tag automated activity monitor performed well in heifers with a positive predictive value of 83.5%, a sensitivity of 91.0% and an overall pregnancy after four inseminations of 97.9%. There was no association between heat index (estrus intensity), activity change, rumination change or duration of high activity with pregnancy per AI. However, weak correlations were observed between all estrus characteristics and pre-ovulatory follicle diameter. One way to optimize
CRediT authorship contribution statement
K. Macmillan: Methodology, Investigation, Writing - original draft, Visualization. M. Gobikrushanth: Formal analysis, Investigation, Writing - review & editing, Visualization. G. Plastow: Writing - review & editing, Funding acquisition. M.G. Colazo: Conceptualization, Methodology, Investigation, Writing - review & editing, Supervision, Funding acquisition.
Acknowledgments
The authors would like to thank the Agriculture Funding Consortium and Alberta Agriculture and Forestry for financial support, and Breevliet Ltd. (Wetaskiwin, AB), SCR by Allflex (USA) and TeamViewer (Göppingen, Germany) for assistance and technical support during the study.
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Part of the information in this manuscript was published previously as an abstract at the Western Canadian Dairy Seminar, Red Deer, Canada, March 2020.