Comparison of behavioral patterns of dairy cows with natural estrus and induced ovulation detected by an ear-tag based accelerometer

The Influence of Selected Factors on Changes in Locomotion Activity during Estrus in Dairy Cows

The objective of this study was the evaluation of the locomotion activity of heifers and Holstein dairy cows during estrus. We have analyzed the locomotion activity using the Heatime RuminAct device on 180 cows (32 heifers and 148 dairy cows) and we evaluated a total of 633 estrus cycles during the reference period of 3 days before estrus, 3 days after estrus, and on the day ofestrus occurrence. The datawere analyzed using the DataFlowTM II program. The locomotion of cows was expressed in the units of locomotion activity in 24 h (u.24 h-1). During the reference period of 3 days before estrus, the cows showed locomotion activity of 558 u.24 h-1, with an increase in locomotion activity on the day of estrus of 836 u.24 h-1, and, during the reference period of 3 days after estrus, the level of locomotion activity decreased to 537 836 u.24 h-1, which is a similar level of locomotion activity to the reference period before estrus. Through the statistical analysis, we evaluated the impact of parity, lactation stage, milk yield, and individuality on changes in locomotion activity during estrus and throughout the reference period, and we found a significant effect of parity (F = 13.41, p < 0.001) on changes in the locomotion activity of dairy cows during estrus. Based on these results, this research offers fresh perspectives on assessing specific factors affecting the locomotion activity of dairy cows during estrus through the practical application of electronic systems for estrus detection on dairy farms.

Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning

Dairy cow behavior carries important health information. Timely and accurate detection of behaviors such as drinking, feeding, lying, and standing is meaningful for monitoring individual cows and herd management. In this study, a model called Res-DenseYOLO is proposed for accurately detecting the individual behavior of dairy cows living in cowsheds. Specifically, a dense module was integrated into the backbone network of YOLOv5 to strengthen feature extraction for actual cowshed environments. A CoordAtt attention mechanism and SioU loss function were added to enhance feature learning and training convergence. Multi-scale detection heads were designed to improve small target detection. The model was trained and tested on 5516 images collected from monitoring videos of a dairy cowshed. The experimental results showed that the performance of Res-DenseYOLO proposed in this paper is better than that of Fast-RCNN, SSD, YOLOv4, YOLOv7, and other detection models in terms of precision, recall, and mAP metrics. Specifically, Res-DenseYOLO achieved 94.7% precision, 91.2% recall, and 96.3% mAP, outperforming the baseline YOLOv5 model by 0.7%, 4.2%, and 3.7%, respectively. This research developed a useful solution for real-time and accurate detection of dairy cow behaviors with video monitoring only, providing valuable behavioral data for animal welfare and production management.

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

Characterization and analysis of transcriptomes of multiple tissues from estrus and diestrus in pigs

A comprehensive understanding of the complex regulatory mechanisms governing estrus and ovulation across multiple tissues in mammals is imperative to improve the reproductive performance of livestock and mitigate ovulation-related disorders in humans. To comprehensively elucidate the regulatory landscape, we analyzed the transcriptome of protein-coding genes and long intergenic non-coding RNAs (lincRNAs) in 58 samples (including the hypothalamus, pituitary, ovary, vagina, and vulva) derived from European Large White gilts and Chinese Mi gilts during estrus and diestrus. We constructed an intricate regulatory network encompassing 358 hub genes across the five examined tissues. Furthermore, our investigation identified 85 differentially expressed lincRNAs that are predicted to target 230 genes associated with critical functions including behavior, receptors, and apoptosis. Importantly, we found that vital components of estrus and ovulation events involve "Apoptosis" pathway in the hypothalamus, "Autophagy" in the ovary, as well as "Hypoxia" and "Angiogenesis" in the vagina and vulva. We have identified several differentially expressed transcription factors (TFs), such as SPI1 and HES2, which regulate these pathways. SPI1 may suppress transcription in the autophagy pathway, promoting apoptosis and inhibiting the proliferation of ovarian granulosa cells. Our study provides the most comprehensive transcriptional profiling information related to estrus and ovulation events.

Key performance indicators used by dairy consultants during the evaluation of reproductive performance during routine visits

Dairy farms need thorough and efficient reproduction control. Consultants specialized in reproduction use key performance indicators (KPI) to monitor the reproductive performance of farms and must be able to decipher between the approach in a first visit and routine visits. A total of 49 consultants specialized in dairy reproduction from 21 countries responded to an online survey conducted to determine the most suitable parameters during routine visits every 2 to 4 weeks. The survey was comprised of 190 questions, 178 of them rated from 0 (irrelevant) to 10 (maximum importance) points. The questions were divided into five sections: (1) consultant and farm model, (2) general data of the farm, (3) cow reproduction, (4) postpartum and metabolic disease, and (5) heifer reproduction. The median, interquartile range, minimum and maximum values, and 95% confidence interval were determined for each question. Afterward, a multivariate analysis, using between-group linkage via Ward's hierarchical clustering was conducted to generate clusters of consultants according to their response pattern. Finally, a chi-square test was conducted to assess the association between years of experience of the consultant and farm size within the clusters generated in each section of the questionnaire. The majority of the consultants considered 34 parameters to be highly important (rated 8-10) to analyze during routine visits. The consultants used several KPI (in variable quantitative range) to evaluate any of the presented sections and considered that all the five sections are critical to control. They are aware of using KPI that reflect heat detection, fertility, and farming efficiency as well as KPI that can provide information on reproductive efficiency in the near future for cows, such as postpartum and metabolic diseases. However, parameters that are relatively old and ineffective, in terms of reproductive performance control, are still highly regarded by the majority of consultants in a routine-visit scenario. Farm size and years of experience of the consultant did not influence the type or number of parameters chosen as KPI during routine visits. The parameters rated with the highest importance (rate 10) that could be considered for an easy, fast, and universal use in routine visits to assess the reproductive status were: First service CR (%), Overall pregnancy rate (%) for cows, and age at first calving (d) for heifers.

Association of activity and subsequent fertility of dairy cows after spontaneous estrus or timed artificial insemination

The objective of this observational study was to evaluate the association between increased physical activity at first artificial insemination (AI) and subsequent pregnancy per AI (P/AI) in lactating Holstein cows following spontaneous estrus or a timed AI (TAI) protocol. We also wanted to identify factors associated with the intensity of activity increase (PA) captured by automated activity monitors (AAM) and fertility. Two experiments were conducted, in which cows either were inseminated based on the alert of the AAM system (AAM cows) or received TAI following a 7-d Ovsynch protocol (TAI cows) if not inseminated within a farm-specific period after calving. Experiment 1 included 2,698 AI services from AAM cows and 1,042 AI services from TAI cows equipped with the Smarttag Neck (Nedap Livestock Management) from a dairy farm in Slovakia (farm 1). In the second experiment, 6,517 AI services from AAM cows and 1,226 AI services from TAI cows fitted with Heatime (Heatime Pro; SCR Engineers Ltd.) from 8 dairy farms in Germany (farms 2-9) were included. Pregnancy diagnosis was performed on a weekly basis by transrectal ultrasound (farms 1, 3, 7, 8) or by transrectal palpation (farms 2, 4-6, 9). Estrous intensity was represented by the peak value of the change in activity. In experiment 1, PA was categorized into low (x-factor 0-20) and high (x-factor 21-100) PA, and in experiment 2 into low (activity change = 35-89) and high (activity change = 90-100) PA. In TAI cows from both experiments, PA was additionally categorized into cows with no AAM alert. Data were analyzed separately for AAM and TAI cows using multinomial logistic regression models for PA in TAI cows and logistic regression models for PA in AAM cows and P/AI in both groups. In experiment 1, P/AI of AAM cows was greater for AI services performed with conventional frozen semen (57.6%) compared with sexed semen (47.2%), whereas type of semen only tended to be associated with P/AI in TAI cows (54.4% conventional frozen semen vs. 48.9% sexed semen). In experiment 2, P/AI was greater for fresh semen (AAM cows: 44.4% vs. TAI cows: 44.2%) compared with conventional frozen semen (AAM cows: 37.6% vs. TAI cows: 34.6%). In both experiments, pregnancy outcomes were associated with PA. In experiment 1, AAM cows with high PA (55.1%) had greater P/AI than cows with low PA (49.8%). Within TAI cows, cows with no alert (38.8%) had reduced P/AI compared with cows with low (54.2%) or high PA (61.8%). In experiment 2, AAM cows with high PA (45.8%) had greater P/AI compared with cows with low PA (36.4%). Timed AI cows with no alert (27.4%) had decreased P/AI compared with cows with low (41.1%) or high (50.8%) PA. The greatest risk factors for high PA were parity (experiment 1) and season of AI (except for TAI cows from experiment 1). We conclude that high PA at the time of AI is associated with greater odds of pregnancy for both AAM and TAI cows. In both experiments, about 2 thirds of AAM cows (experiment 1: 69.9% and experiment 2: 70.7%) reached high PA, whereas only approximately one-third or less of TAI cows (experiment 1: 37.3% and experiment 2: 23.6%) showed high PA. Although we observed similar results using 2 different AAM systems for the most part, risk factors for high PA might differ between farms and insemination type (i.e., AAM vs. TAI).

[Digitalisation in cattle practice - results of an online-survey in Austria]

OBJECTIVES

The use of digital technologies is increasing in modern livestock farming and veterinary practice. The aim of this online survey among Austrian cattle practitioners was to increase knowledge concerning the acceptance and use of digital (sensor) technologies.

MATERIAL AND METHODS

The link to the survey was sent by the Austrian animal health services (TGD) via email to the registered veterinarians. A total of 115 veterinarians participated in the survey.

RESULTS

Most of the participants were convinced that digitalisation associated with improvements in their profession in terms of economy, time-savings, collaboration with colleagues and working efficiency. The agreement ranged between 60% and 79%. On the other hand, concerns regarding data security (41%) were also raised. When asked whether they would recommend sensor systems to farmers, approximately 45% of the participants answered yes, 36% declined, 19% were undecided. From a list of specified sensors and technologies, monitoring by cameras (68%), automatic concentrate feeding systems (63%) and activity sensors (61%) were considered as beneficial for animal health. Concerning an assessment of the animals' health status the majority of respondents (58%) would trust conventional methods more than sensor systems. Data provided by farmers is mainly used to improve the understanding of patients' disease progression (67%) as well as to comply with documentation requirements (28%). In addition, we asked whether the participants could imagine running a telemedicine practice. On a scale ranging from 1 to 100, initial agreement amounted to a median of 20 which then decreased to a median of 4 in the repeated question at the end of the questionnaire.

CONCLUSIONS

The veterinarians saw advantages in using digital technologies both in their daily work and to improve animal health management. In some areas, however, clear reservations were evident . A telemedical offer does not seem to be relevant for the majority of the participants in the context of the description provided.

CLINICAL RELEVANCE

The results are intended to help identify areas in which more information is needed for veterinarians and to capture a picture of opinions that could be relevant for the changing collaboration between farmers and veterinarians.

A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network

There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model's ability to learn shallow information and improving the model's ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.

Automated Systems for Estrous and Calving Detection in Dairy Cattle

Purpose: The objective of this review is to describe the main technologies (automated activity monitors) available commercially and under research for the detection of estrus and calving alerts in dairy cattle. Sources: The data for the elaboration of the literature review were obtained from searches on the Google Scholar platform. This search was performed using the following keywords: reproduction, dairy cows, estrus detection and parturition, electronic devices. After the search, the articles found with a title related to the objective of the review were read in full. Finally, the specific articles chosen to be reported in the review were selected according to the method of identification of estrus and parturition, seeking to represent the different devices and technologies already studied for both estrus and parturition identification. Synthesis: Precision livestock farming seeks to obtain a variety of information through hardware and software that can be used to improve herd management and optimize animal yield. Visual observation for estrus detection and calving is an activity that requires labor and time, which is an increasingly difficult resource due to several others farm management activities. In this way, automated estrous and calving monitoring devices can increase animal productivity with less labor, when applied correctly. The main devices available currently are based on accelerometers, pedometers and inclinometers that are attached to animals in a wearable way. Some research efforts have been made in image analysis to obtain this information with non-wearable devices. Conclusion and applications: Efficient wearable devices to monitor cows’ behavior and detect estrous and calving are available on the market. There is demand for low cost with easy scalable technology, as the use of computer vision systems with image recording. With technology is possible to have a better reproductive management, and thus increase efficiency.

Using rumination time to manage health and reproduction in dairy cattle: a review

Early detection of disease is the key to successful management of the dairy cattle which leads to timely treatment and prevention of costs associated with prolonged treatment and reduced milk yield. Electronic systems that allow for monitoring of physiological parameters like rumination, are now commercially available. This review paper discusses different aspects of rumination time that could be used to monitor the health and reproduction of dairy cattle. This review paper explored different areas where rumination time could be utilized in monitoring dairy cattle at calving, during the estrus period, during heat stressed conditions, and to detect diseases and transition cow disorders. In conclusion, rumination time could be used as an indicator of the health status in dairy cattle.