Review: Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings-8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature-humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.

Qualitative and Quantitative Detection of Typical Reproductive Hormones in Dairy Cows Based on Terahertz Spectroscopy and Metamaterial Technology

Progesterone (PROG) and estrone (E1) are typical reproductive hormones in dairy cows. Assessing the levels of these hormones in vivo can aid in estrus identification. In the present work, the feasibility of the qualitative and quantitative detection of PROG and E1 using terahertz time-domain spectroscopy (THz-TDS) and metamaterial technology was preliminarily investigated. First, the time domain spectra, frequency domain spectra, and absorption coefficients of PROG and E1 samples were collected and analyzed. A vibration analysis was conducted using density functional theory (DFT). Subsequently, a double-ring (DR) metamaterial structure was designed and simulated using the frequency domain solution algorithm in CST Studio Suite (CST) software. This aimed to ensure that the double resonance peaks of DR were similar to the absorption peaks of PROG and E1. Finally, the response of DR to different concentrations of PROG/E1 was analyzed and quantitatively modeled. The results show that a qualitative analysis can be conducted by comparing the corresponding DR resonance peak changes in PROG and E1 samples at various concentrations. The best R2 for the PROG quantitative model was 0.9872, while for E1, it was 0.9828. This indicates that terahertz spectral-metamaterial technology for the qualitative and quantitative detection of the typical reproductive hormones PROG and E1 in dairy cows is feasible and worthy of in-depth exploration. This study provides a reference for the identification of dairy cow estrus.

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.

Early prediction of oestrus for herd fertility management in cattle and buffaloes - a review

Oestrus is defined as a period when a female animal exhibits characteristic sexual behaviour in the presence of a mature male. Oestrous manifestation in dairy animals is due to the oestrogen (E2) effect on the central nervous system (CNS). It is a critical issue to be considered on a priority basis. Inefficient oestrous detection reduces the fertility status of the herd. The primary and most reliable indicator of oestrus is standing to be mounted by a bull or another female herd mate, signalling receptivity and the pre-ovulatory state in dairy cattle. Oestrous detection is primarily a management challenge requiring skill and vigilance. To improve the efficiency of oestrous detection in dairy cattle, visual observation is one of the best methods if done three times a day; however, heat detection aids, if combined, give better results. However, techniques like using teaser bulls, tail painting, chin ball markers, ultrasound (USG) examination, hormonal analysis and examination of cervicovaginal mucus (CVM) improve oestrous detection efficiency. Moreover, the changes in production systems have reduced the expression of oestrous behaviour among cows, due to higher oestrogen (E2) metabolism. Therefore, automated systems, such as pedometers, accelerometers and acoustic sensors like infrared thermography (IRT) and image processing, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules. From this review, we would conclude that oestrous detection alone contributes considerably to the reproductive status of the herd; therefore, applying different methods of oestrous detection reduces the incidence of missed oestrus and improves the fertility status of the herd.

Point-of-care impedimetric aptasensor to detect the luteinizing hormone

Luteinizing hormone (LH) is a useful biomarker for identifying ovulation events in the cows to predict the time of ovulation to achieve a high success rate of conception following artificial insemination. Although antibody-based radioimmunoassay and enzyme-linked immunosorbent assay are being used for LH measurement, these techniques are expensive, time-consuming, and require expertise and sophisticated laboratory facilities. So, there is a need for a field-applicable, affordable, easy-to-use method for LH detection. For developing such a specific, quantitative, and inexpensive system, an aptamer-based smartphone-enabled aptasensor has been investigated. The aptamer was used instead of the antibody as a biorecognition element due to its comparative stability at ambient temperature, ease of synthesis, and cost-effectiveness. Electrochemical impedance spectroscopy has been used to obtain label-free detection of LH within 20 min in ~ 20 μL sample volume. The screen-printed gold electrode is compatible with a smartphone-enabled miniaturized device (Sensit Smart; Palmsens BV, The Netherlands) and was fabricated with the aptamer to detect LH in biological fluids (limit of detection 0.80 and 0.61 ng/mL in buffer and undiluted/unprocessed serum, respectively, with the dynamic range of detection of 0.01 to 50 ng/mL). All the data were obtained in the 10 kHz to 0.10 Hz frequency range at a bias potential of 0.30 V with an alternating potential of 10 mV. The clinical relevance of the sensor was evaluated in 10 serum samples collected from dairy animals which established a high correlation with standard LH-ELISA (κ > 0.87). The aptasensor can be stored at room temperature for 30 days without any significant loss in electrochemical sensing ability.

Estrus Detection and Dairy Cow Identification with Cascade Deep Learning for Augmented Reality-Ready Livestock Farming

Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming.

Classifying Chewing and Rumination in Dairy Cows Using Sound Signals and Machine Learning

This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices.

Peri-estrus activity and mounting behavior and its application to estrus detection in Hanwoo (Korea Native Cattle)

This study was conducted to investigate the change in activity and mounting behavior in Hanwoo (Korean Native Cattle) during the peri-estrus period and its application to estrus detection. A total of 20 Hanwoo cows were fitted with a neck-collar accelerometer device, which measured the location and acceleration of cow movements and recorded the number of instances of mounting behavior by the altitude data. The data were analyzed in three periods (24-, 6-, and 2-h periods). Blood samples were collected for 5 days after the prostaglandin F2α (PGF2α) injection, and the concentrations of estradiol, progesterone, follicle-stimulating hormone, and luteinizing hormone were determined by enzyme-linked immunosorbent assays. Activity and mounting behavior recorded over 2-h periods significantly increased as estrus approached and were more efficient at detecting estrus than over 24- and 6-h periods (p < 0.05). Endocrine patterns did not differ with the variation of individual cows during the peri-estrus period (p > 0.05). Activity was selected as the best predictor through stepwise discriminant analysis. However, activity alone is not enough to detect estrus. We suggest that a combination of activity and mounting behavior may improve estrus detection efficiency in Hanwoo. Further research is necessary to validate the findings on a larger sample size.

Using behavioral observations in freestalls and at milking to improve pain detection in dairy cows after lipopolysaccharide-induced clinical mastitis

This study aimed to determine the effect of lipopolysaccharide (LPS)-induced mastitis with or without nonsteroidal anti-inflammatory drug (NSAID) on dairy cows' clinical, physiological, and behavioral responses in the milking parlor and freestalls as well as the specificity (Sp) and sensitivity (Se) of behavioral responses in detecting cows with LPS-induced mastitis. Twenty-seven cows received an intramammary infusion of 25 µg of Escherichia coli LPS in 1 healthy quarter. Following LPS infusion, 14 cows received a placebo (LPS cows), and 13 cows received 3 mg/kg of body weight of ketoprofen i.m. (LPS+NSAID cows). Cow response to the challenge was monitored at regular intervals from 24 h before to 48 h postinfusion (hpi) through direct clinical observations, markers of inflammation in milk, and via point-in-time direct behavioral observations in the barn and at milking. In LPS cows, infusion induced a significant increase of plasma cortisol levels at 3 and 8 hpi, milk cortisol levels at 8 hpi, somatic cell counts from 8 to 48 hpi, IL-6 and IL-8 at 8 hpi, milk amyloid A (mAA) and haptoglobin at 8 and 24 hpi, rectal temperature at 8 hpi, and respiratory rate at 8 hpi. Their rumen motility rate decreased at 8 and 32 hpi. Compared with before the challenge, significantly more LPS cows stopped feeding/ruminating and pressed their tail between their legs at 3 and 5 hpi, increased feeding/ruminating at 24 hpi, and had the tendency to be less responsive, dropping their head, and dropping their ears at 5 hpi. At milking, compared with before challenge, significantly more LPS cows lifted their hooves at forestripping at 8 hpi. The 2 groups showed similar patterns of response for milk cortisol, somatic cell count, respiratory rate, mAA, haptoglobin, and IL-6, IL-1β, and IL-8. Compared with LPS cows, LPS+NSAID cows had significantly lower plasma cortisol levels at 3 hpi, their rectal temperature decreased at 8 hpi, their rumen motility rate increased at 8 and 32 hpi, and their heart rate increased at 32 hpi. Compared with LPS cows, a significantly larger proportion of LPS+NSAID cows were feeding/ruminating, a lower proportion had ears down at 5 hpi, and a larger proportion lied down at 24 hpi. At milking, whatever the phase of milking, for "hoof to belly," 9 out of 14 cows did not show this behavior before infusion (Sp = 64%) and 14/14 did not kick during pre-infusion milking (Sp = 100%). Regarding sensitivity, at maximum, 5 cows out of 14 (Se = 36%) displayed "hoof to belly" after infusion. For "lifting hoof," 14/14 did not show hoof-lifting before infusion (Sp = 100%) and 6/14 displayed it after infusion (Se = 43%) at forestripping only. In the freestall barn, 9 behaviors had a Sp >75% (at minimum, 10/14 did not show the behavior) whatever the time point but Se < 60% (at maximum, 8/14 displayed the behavior). Finally, "absence of feeding and ruminating" had Sp of 86% (12/14 ate/ruminated) and Se of 71% (10/14 did not eat/ruminate) at 5 hpi. This study shows that feeding/ruminating, tail position, and reactivity at forestripping could be used as behavioral indictors for early detection of mastitis-related pain in dairy cows.

Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes

The productivity of dairy animals has significantly increased over the past few decades due to intense genetic selection. However, the enhanced yield performance of milk animals caused a proportional increase in stress and compromised reproductive efficiency. Optimal reproductive performance is mandatory for the sustainable production of dairy animals. Reproductive efficiency is marked by proper estrus detection and precise breeding to achieve maximum pregnancies. The existing conventional methods of estrus detection are somewhat labor intensive and less efficient. Similarly, the modern automated methods that rely on detecting physical activity are expensive, and their efficiency is affected by factors such as type of housing (tie stall), flooring, and environment. Infrared thermography has recently emerged as a technique that does not depend on monitoring physical activity. Furthermore, infrared thermography is a non-invasive, user-friendly, and stress-free option that aids in the detection of estrus in dairy animals. Infrared thermography has the potential to be considered a useful non-invasive tool for detecting temperature fluctuations to generate estrus alerts without physical contact in cattle and buffaloes. This manuscript highlights the potential use of infrared thermography to understand reproductive physiology and practical implementation of this technique through discussing its advantages, limitations, and possible precautions.

Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.

Do Calving-Related Injuries of the Vestibulum Vaginae and the Vagina Affect the Reproductive Performance in Primiparous Dairy Cows?

The aim of this study was to investigate the influence of calving-related injuries of the vestibulum vaginae and the vagina on fertility measures in heifers. German Holstein heifers (n = 236) were checked for vestibulum vaginae and vaginal injuries. These were scored according to localization, depth and length. The healing process was assessed until day 42 post partum. Calving ease and the occurrence of metritis and endometritis were evaluated. In 160 heifers, the following fertility measures were calculated to assess the reproductive performance of heifers: mean interval from calving to first insemination, mean days open, mean interval from first insemination to conception, mean calving interval, mean pregnancy index, percentage of animals pregnant at 200 days p.p., and first service conception rate. On the one hand, dystocia was a risk factor for injuries of the soft birth canal, and, on the other hand, those injuries were a risk factor for metritis and endometritis. In this study, calving-related injuries of the vestibulum vaginae and the vagina had no statistically significant effect on the reproductive performance of heifers. One reason for this outcome was probably the overall good healing tendencies of those injuries in heifers.

Broad-spectrum infrared thermography for detection of M2 digital dermatitis lesions on hind feet of standing dairy cattle

Low-effort, reliable diagnostics of digital dermatitis (DD) are needed, especially for lesions warranting treatment, regardless of milking system or hygienic condition of the feet. The primary aim of this study was to test the association of infrared thermography (IRT) from unwashed hind feet with painful M2 lesions under farm conditions, with lesion detection as ultimate goal. Secondary objectives were to determine the association between IRT from washed feet and M2 lesions, and between IRT from unwashed and washed feet and the presence of any DD lesion. A total of 641 hind feet were given an M-score and IRT images of the plantar pastern were captured. Multivariable logistic regression analyses were done with DD status as dependent variable and maximum infrared temperature (IRTmax), lower leg cleanliness score and locomotion score as independent variables, and farm as fixed effect. To further our understanding of IRTmax within DD status, we divided IRTmax into two groups over the median value of IRTmax in the datasets of unwashed and washed feet, respectively, and repeated the multivariable logistic regression analyses. Higher IRTmax from unwashed hind feet were associated with M2 lesions or DD lesions, in comparison with feet without an M2 lesion or without DD, adjusted odds ratio 1.6 (95% CI 1.2-2.2) and 1.1 (95% CI 1.1-1.2), respectively. Washing of the feet resulted in similar associations. Dichotomization of IRTmax substantially enlarged the 95% CI for the association with feet with M2 lesions indicating that the association becomes less reliable. This makes it unlikely that IRTmax alone can be used for automated detection of feet with an M2 lesion. However, IRTmax can have a role in identifying feet at-risk for compromised foot health that need further examination, and could therefore function as a tool aiding in the automated monitoring of foot health on dairy herds.

Revisiting the Timing of Insemination at Spontaneous Estrus in Dairy Cattle

Cows show spontaneous estrus over 8-20 h but become refractory to the bull about 10-12 h before ovulation. This indicates that ovulation occurs 10-12 h after the end of estrus behavior, yet spermatozoa from the bull ejaculate need to undergo maturation and capacitation for 6 to 8 h in the female reproductive tract before they are capable of fertilization. Traditionally, the onset of estrus has been considered the best timing for artificial insemination (AI) in cattle, that is, 6 to 24 h from the first signs of estrus. However, recent findings suggest this interval should be reduced to 16 to 6 h before ovulation, bringing it closer to the end of estrus. In this review, the end of estrus rather than its onset is proposed as the best guide for AI timing in dairy cattle, and physiological indicators of late estrus are discussed such as relaxation of the intravaginal part of the uterus, a lower cervical mucus viscosity and a softer pre-ovulatory follicular consistency as simple cues indicating a cow is ready for service.

Estrus Detection in a Dairy Herd Using an Electronic Nose by Direct Sampling on the Perineal Region

Estrus detection is very important for the profitability of dairy herds. Different automatic systems for estrus detection have been developed over the last decades. Our study aimed to assess the ability of the electronic nose (EN) MENT-EGAS prototype to detect estrus, based on odor release from the perineal headspace in dairy cattle by direct sampling. The study was performed in an Italian dairy farm using 35 multiparous Holstein-Friesian cows. The cows were divided into three groups: group I included 10 lactating 5-month pregnant cows, group II included 19 lactating cycling cows, and group III included 6 cows that were artificially inseminated 18 days before the trial. Odors from the perineal headspace were collected using the MENT-EGAS prototype. In group I, odors were collected once a day for 5 consecutive days. In group II, odors were collected twice daily from day 18 until day 1 of the reproductive cycle. In group III, odors were also collected twice daily from the presumable day 18 of gestation until day 22. Principal component analyses (PCA) of the perineal headspace samples were performed. PCA in group I revealed no significant discrimination. PCA in group II revealed clear discrimination between proestrus and estrus, and between estrus and metestrus but no significant discrimination was obtained between proestrus and metestrus. PCA in group III revealed that in four cows the results were similar to group I and in two cows the results were similar to group II. On day 40 of the presumable pregnancy, the ultrasound examination revealed that only the four cows were pregnant and the other two cows were regularly cycling. On the basis of our findings, we conclude that it is possible to accurately detect estrus in dairy cattle from directly collected odor samples using the MENT-EGAS prototype. This represents the first study of estrus detection using an EN detection by direct sampling. EN technologies, such as MENT-EGAS, could be applied in the future in dairy cattle farms as a precise, non-invasive method for estrus detection.

Can the use of digital technology improve the cow milk productivity in large dairy herds? Evidence from China's Shandong Province

Introduction

Improving milk productivity is essential for ensuring sustainable food production. However, the increasing difficulty of supervision and management, which is associated with farm size, is one of the major factors causing the inverse relationship between size and productivity. Digital technology, which has grown in popularity in recent years, can effectively substitute for manual labor and significantly improve farmers' monitoring and management capacities, potentially addressing the inverse relationship.

Methods

Based on data from a survey of farms in Shandong Province in 2020, this paper employs a two-stage least squares regression model to estimate the impact of herd size on dairy cow productivity and investigate how the adoption of digital technology has altered the impact of herd size on dairy cow productivity.

Results

According to the findings, there is a significant and negative impact of herd size on milk productivity for China's dairy farms. By accurately monitoring and identifying the time of estrus, coupled with timely insemination, digital technology can mitigate the negative impact of herd size on milk productivity per cow.

Discussion

To increase dairy cow productivity in China, the government should promote both small-scale dairy farming and focus on enhancing management capacities of farm operators, as well as large-scale dairy farms and increase the adoption of digital technologies.

Evaluation of walking activity data during pregnancy as an indicator of pregnancy loss in dairy cattle

A pregnancy loss or abortion can be assumed when a dairy cow that has been previously diagnosed pregnant shows signs of estrus. In herds using leg-based pedometers as a tool to detect cows in estrus, a sudden increase in walking activity (hereafter, activity peaks) relative to a certain threshold activity triggers an estrous alert that can be confused with a pregnancy loss. The objective of this study was to determine whether pregnant cows can show activity peaks as measured by pedometers. We used data from a dairy herd of 250 milking cows using pedometers as a means of measuring walking activity to detect cows in estrus. Two databases were used in this study, which included the walking activity of the entire herd recorded by the pedometers from January 1, 2018, to December 31, 2021 (database 1), and the calving dates, the insemination dates, the dates when a pregnancy diagnosis was declared pregnant, the dates when a pregnancy diagnosis was declared not pregnant or open, and the abortion dates (database 2). Activity peaks were identified within an experimental unit, which was defined as pregnant cows showing an insemination event followed by a confirmed pregnancy and subsequent calving. The activity peaks were identified using the peak searching algorithm that compares the step count on a given day with the step counts of its adjacent days. The candidate peaks were characterized for their magnitudes by the prominence metric. A chi-squared test was performed to test the specificity of the system. From the 4-yr database, 537 pregnancies or experimental units were identified, and 77 pregnancies showed 1 or more peaks, which means that 14.4% of the pregnancies showed activity peaks. Within the pregnancies showing peaks (n = 77), the median equaled 1 peak/pregnancy, the average equaled 1.53 peaks/pregnancy, and the maximum equaled 13 peaks/pregnancy. In conclusion, activity peaks can be observed for pregnant cows using pedometers. These peaks could generate false estrous alerts during the pregnancy period when using pedometers, and these false alerts should not be interpreted as pregnancy losses.

Postoperative Observation of Spaying with the Silicon Ring on the Ovaries in Heifers: A Retrospective Study in 28 Cases

Simple Summary

Because the estrus in cattle is thought to be energy-consuming if the animals are not planned to breed, spaying in cattle has recently been applied to improve daily weight gain and meat quality. However, postoperative changes in ligation-spaying method with the silicon ring to ovaries via transvaginal methods in heifers have not been clearly identified. This retrospective study presented that heifers spayed with this method exhibited no estrus signs at the pubertal age and the ligated ovaries disappeared within a month post-surgery due to ischemic necrosis. Whereas ovarian steroid hormone levels in spayed heifers were not changed following the puberty, luteinizing hormone level at the pubertal age was higher than unspayed controls. Although carcass weight and yield were similar between groups at the pubertal age upon slaughtering, the spayed animals presented higher marbling degree than that of controls. These results may contribute to develop new management strategies for livestock.

Abstract

Although spaying prepubertal heifers has routinely been conducted to control cattle herd and improve meat quality, understandings of the postoperative changes following new spaying methods with the silicon ring on the ovaries via colpotomy remain limited. Therefore, as a retrospective study, 28 cases of spayed heifers were reviewed for postoperative changes after employing this method, with inclusion criteria including complete medical records for clinical observation, ultrasonography, measuring reproductive hormones, and tracking slaughter records. No mortality and heat signs at the pubertal age postoperatively occurred in spayed animals. On ultrasonography during rectal examination, the ovaries were enlarged without any folliculogenesis from one week, while massive ovarian edema appeared from two weeks, and ovaries were no longer palpable at four weeks post-surgery. In hormones, whereas estrogen and progesterone levels did not change from prepubertal to pubertal age in spayed animals, luteinizing hormone levels progressively increased during this period and reached a higher level at pubertal period than unspayed controls. Although carcass weight and yield were similar between groups upon slaughter at pubertal age, the spayed animals presented higher carcass quality (marbling degree) than that of controls. These results may contribute to develop herd management strategies, including control of estrus in cattle.

Antral follicle count, productive and reproductive parameters in Bos indicus and Bos indicus-taurus prepubertal heifers with early puberty induction

ABSTRACT Prepubertal Nelore (G-N = 15) and crossbred Nelore x Aberdeen Angus heifers (G-NA = 15) were used for this study. AFC, live weight, body condition score (BCS), ovary and dominant follicle (DF) diameters were determined in each animal. Puberty induction was performed by insertion of a 4th use progesterone device (D0) which was removed on D12. Also, 1 mg estradiol benzoate was administered, and estrus intensity was classified (D12). At D21, the presence and diameter of the corpus luteum (CL) were registered. AFC was highly repeatable, regardless of hormone induction in both G-N (r=0.79) and G-NA (r=0.90). The mean AFC was greater in G-N compared to G-NA (24.2±8.5 vs. 17.7±9.0 follicles). A variation in BCS throughout the study occurred in G-NA, but not in G-N. The average weight gain (AWG) was greater in G-NA compared to G-N (0.69±0.33 vs. 0.40±0.29kg/day). The G-NA resulted in a larger diameter of DF at D12 than G-N (11.6±2.7 vs. 9.3±1.5mm). In conclusion, AFC was greater in Nelore heifers, although in both breeds this count was highly repeatable during puberty induction. Crossbred heifers had greater BCS and AWG with greater diameter of DF, indicating higher precocity when compared to Nelore heifers.

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.

Estrus Prediction Models for Dairy Gyr Heifers

Simple Summary

Intraruminal devices are already being used to predict reproductive events in cattle. For this prediction, several models and approaches can be used. The aim of this study was to evaluate changes in rumen reticulum temperature (RRT) and activity (ACT) during estrus in Dairy Gyr heifers and to evaluate different models for estrus prediction. There was an increase in both RRT and ACT in the estrus period compared to the same period on the day before and the day after estrus. Among the mathematical models, Random Forest had the best performance. The present results suggest that RRT and ACT can contribute to the identification of estrus and be of value for improving the reproductive efficiency of Zebu herds in tropical regions.

Abstract

Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (p < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers.

Daily activity measures and milk yield immediately before and after a fertile estrus and during the period of expected return to estrus after insemination in dairy cows

The objective of this study was to characterize changes in milk yield and other physical measures during a 7-d periestrual period encompassing estrus (d 0) and during a 16-d period of expected return to estrus beginning at d 17 after artificial insemination (AI) until pregnancy status was determined on d 32. Lactating dairy cows milked thrice daily were fitted with CowManager SensOor ear tags (Agis) capable of assessing real-time eating, rumination, resting, high activity (estrus), ear-surface temperature, and heat alerts. Data were uploaded to the cloud, downloaded daily into Excel (Microsoft Corp.) spreadsheets, averaged to produce daily means for each activity, and analyzed as repeated measures relative to estrus or to d 17 after AI. Daily milk was unchanged during the periestrual period but was greater in nonpregnant cows that failed to return to estrus (NP-NR) during d 21 through 26 compared with NP cows that returned to estrus (NP-R) and pregnant (PREG) cows during that same period. Daily ear-surface temperature was greater during d 1 to 3 compared with d 0 and averaged 0.6 to 1.7°C greater from d 17 through 32 in NP-NR cows compared with NP-R and PREG cows. Daily rumination and resting times reached nadirs on d 0, with decreases occurring 48 h before estrus. Both rumination and resting times increased by 25 or 81% on the day after estrus, respectively. Rumination and resting times were less in NP-R cows during d 22 through 26 compared with NP-NR and PREG cows. In contrast, daily eating time was greatest on the day of estrus compared with 3 d before and after estrus. The NP-R cows spent more time eating during d 17 through 32 compared with NP-NR and PREG cows. High activity increased by 97% during 48 h before estrus, peaked at estrus, and decreased to a constant level during d 1 through 3. The NP-R cows had greater high activity on d 22 through 26 compared with NP-NR and PREG cows. We conclude that resting and rumination activity decreased to daily nadirs, whereas eating and high activity peaked on the day of estrus. Fertile estrus was associated with 12% greater high activity, 11% less resting time, and 6% less rumination time. In addition, cows that returned to estrus after AI had greater daily eating and high activity times and less rumination and resting time during the period of expected return to estrus after AI compared with pregnant cows and cows failing to return to estrus.

The Detection of Bovine Estrus by Lactoferrin Monoclonal Antibody

Simple Summary

This study aimed to develop monoclonal antibodies with high specificity against bovine lactoferrin, which we have previously demonstrated to be overexpressed in bovine cervical mucus during estrus. Using an enzyme-linked immunosorbent assay, we observed that our monoclonal exhibited strong affinity for bovine lactoferrin protein. In addition, upon testing the new heat detection kits based on our antibody on 12 Korean native cows, we demonstrated an accurate detection of estrus during estrous synchronization. This is the first report of a non-invasive method to detect estrous using antibodies that bind to physiological material in cows. The results of this study suggest that the antibodies and a fabricated heat detection kit can be utilized to improve estrous detection in the cattle industry.

Abstract

To improve reproductive performance in cattle, the accurate detection of estrus and optimization of insemination relative to ovulation are necessary. However, poor heat detection by farm staff leads to a decreased conception rate, thus inflicting economic damage to the beef and dairy industries. This study aimed to develop monoclonal antibodies (mAb) that can specifically bind to the bovine lactoferrin (bLF) protein, which we have previously demonstrated to be overexpressed in bovine cervical mucus during estrus. Female rats were intraperitoneally immunized with bLF protein as the antigen. Anti-bLF mAbs were then purified by affinity chromatography, and their binding affinity for the bLF antigen was examined using ELISA. We found a high binding affinity between mAbs and bLF. Finally, we developed a rapid bovine heat detection kit using the anti-bLF mAbs that we generated and tested on cervical mucus from 12 cows (estrous synchronization, n = 2; natural cycling, n = 10). We found that the kits accurately detected estrus. Overall, our fabricated heat detection kit based on rat anti-bLF mAbs could pave the way for the development of potent tools for heat detection devices for dairy cattle, thereby preventing economic loss.

Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows

Simple Summary

Some studies have confirmed the variation in milk profiles when dairy cows show estrus. However, only a few milk components, such as fat, protein, and lactose, have been investigated so far, and thus any changes in the many other parts of milk’s composition due to estrus are unknown. Milk mid-infrared (MIR) spectra consist of wavenumbers, which provide insight into the chemical composition of milk. The MIR spectrum reflects the global composition of milk, but this information is currently underused. In this study, we considered MIR wavenumbers as traits, and directly studied the spectral information as a way to study the estrus of dairy cows linked to milk composition. This research provides a deeper understanding of the milk MIR spectrum and may lead to new approaches for estrus detection in dairy cows from routine milk analysis, thereby guiding an opportune insemination time.

Abstract

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm⁻¹, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

Farmers' Preferences Regarding the Design of Animal Welfare Programs: Insights from a Choice-Based Conjoint Study in Germany

Simple Summary

Numerous animal welfare schemes have been developed aiming to improve animal welfare on a voluntary basis beyond legal regulations. The success of these schemes depends decisively on whether the design of these schemes is attractive to livestock farmers and, as a result, whether they are willing to participate and thus to implement the animal welfare measures regulated in these schemes. In this study, we investigated German livestock farmers’ preferences regarding the design of animal welfare schemes with a choice experiment. Farmers were asked to select their most preferred alternative among animal welfare schemes that differed in the specifications of the following four attributes: basis for remuneration (i.e., type of animal welfare measures to be implemented), commitment period, funding agency and compensation level. The basis for remuneration and the compensation level had the greatest influence on farmers’ decisions. The commitment period also affected farmers’ decisions. Independent of the livestock species kept, farmers preferred animal health as basis for remuneration, the higher compensation level and the longer commitment period. These findings could be incorporated into the development and refinement of animal welfare programs to make them more attractive to farmers and thus increase their willingness to participate.

Abstract

As more animal welfare is required in livestock farming, several approaches have been developed to improve the well-being of farmed animals on a voluntary basis. Since farmers’ acceptance is important for the success of these approaches, their preferences should be considered when developing farm animal welfare programs. We used choice based conjoint analysis to investigate the preferences of 242 German livestock farmers (147 cattle farmers; 95 pig farmers) regarding the design of farm animal welfare programs. The conditional logit regression models show that the measures serving as basis for remuneration and the compensation level were of decisive importance for the farmers’ choices. The most preferred measure for assessing animal welfare, and thus as the basis for remuneration, was animal health. As expected, a higher compensation level led to greater acceptance of an animal welfare approach. The commitment period was only of subordinate importance with the longer commitment period being preferred. Our study outlines aspects of farm animal welfare programs that might encourage farmers to participate in these programs. Future programs could consider our findings by emphasising health parameters and by creating planning security through longer commitment periods and sufficiently high compensations for farmers’ efforts to improve animal welfare.

Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study

About 30% of producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (AI) in Canada. Days from calving to first service (CTFS) and first service to conception (FSTC) become masked phenotypes leading to biased genetic evaluations of cows for these fertility traits. The objectives of this study were to (1) demonstrate and quantify the potential amount of bias in genetic evaluations, and (2) find a procedure that could remove the bias. Simulation was used for both objectives. The proposed solution was to identify cows that have been treated by hormone protocols, make their CTFS and FSTC missing, and perform a multiple trait analysis including traits that have high genetic correlations with CTFS and FSTC, and which are not affected by the hormone protocols themselves. A total of 12 scenarios (S1-S12) were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. Cows that were given hormone protocols had CTFS of 86 d and FSTC of 0, which were used in genetic evaluation. Four criteria were used to indirectly measure the presence of bias: (1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); (2) the differences in the mean EBV of top 25, 50, and 75 sires; (3) changes in correlation between TBV and EBV rankings; and (4) the changes in mean EBV over the simulated generations. All criteria changed unfavorably and proportionally to the increased use of timed AI. The accuracy within each class of animals (cows, dams, or sires) decreased proportionally with increased use of timed AI, varying from 0.32 (S12) to 0.52 (S1) for bull EBV for CTFS. The average EBV of the top sires (best 25, 50, 75, or 100 sires) approached population average EBV values when increasing the number of treated animals. The sire rank correlation between EBV and TBV within simulated scenarios was smaller for scenarios with more synchronized animals, going from 0.38 (S12) to 0.67 (S1). The long-term use of hormonal synchronized cows clearly decreased the mean EBV over generations in the population for CTFS and FSTC. The inclusion of genetically correlated traits in a multiple trait model was effective in removing the bias due to the presence of hormonal synchronized cows. However, given the constraints within the simulation, it is important that further investigation with real data is conducted to determine the true effect of including timed AI records within genetic evaluations of fertility traits in dairy cattle.

Estimates of intra- and interclass correlation coefficients for rump touches and the number of steps during estrus in postpartum cows

Estrus traits have economic value in dairy production systems and could be incorporated into genetic selection indices. In an effort to further understand selection responses, 2 studies were performed to estimate the intra- and interclass correlation coefficients for estrus traits. Holstein-Friesian cows (n = 1,197; study 1) across 5 pasture-based grazing dairy herds were fitted with a capacitive touch sensing (CTS) device on the rump (FlashMate, Farmshed Labs Limited, Hamilton, New Zealand). The daily number of rump touches were subjected to a peak detection program to objectively identify periods of increased rump touches above baseline (indicative of estrus). The number of times touched and the sum of the touch duration were used to compare farms and estimate the intraclass correlation (repeatability). For study 2, postpartum Holstein (n = 85) and Guernsey (n = 5) cows in a confinement-style dairy were used. Cows were fitted with an IceQube accelerometer (IceRobotics Ltd., Edinburgh, United Kingdom) to measure steps taken per hour and a CTS device was applied to both rumps. The interclass correlation for the number of rump touches and number of steps taken during estrus was calculated. Data collected from 5 herds (study 1) demonstrated a 2- to 3-fold difference between herds in the number of rump touches and total touch time during estrus. The intraclass correlation (repeatability; estimates of maximum heritability) for rump touches during estrus was 0.22. For study 2, the number of steps and the number of rump touches during estrus increased in a synchronous manner. The intraclass correlation (repeatability) for number of steps during estrus was 0.26. The interclass correlation (r) for the number of rump touches and the number of steps was 0.46 (R² = 0.21). Based on the R², at least 20% of the variation in the number of steps during estrus was explained by the number of touches to the rump of the cow. Selecting cows for the number of steps taken during estrus could increase the number of rump touches (mounts, chin rests, and so on, received from other cows) if a genetic correlation exists for the phenotypic correlation that we observed.

Genomic Evaluation of Primiparous High-Producing Dairy Cows: Inbreeding Effects on Genotypic and Phenotypic Production-Reproductive Traits

Simple Summary

Improving the genomic prediction methodologies in high-producing dairy cattle is a key factor for the selection of suitable individuals to ensure better productivity. However, the most advanced prediction tools based on genotyping show ~75% reliability. Nowadays, the incorporation of new indices to genomic prediction methods, such as the Inbreeding Index (II), can significantly facilitate the selection of reliable production and reproductive traits for progeny selection. Thus, the objective of this study was to determine the impact of II (low: LI and high: HI), based on genomic analysis, and its effect on production and reproductive phenotypic traits in high-producing primiparous dairy cows. Individuals with II between ≥2.5 and ≤5.0 have shown up to a two-fold increase in negative correlations comparing LI versus HI genomic production and reproductive parameters, severely affecting important traits such as Milk Production at 305 d, Protein Production at 305 d, Fertility Index, and Daughter Pregnancy Rate. Therefore, high-producing dairy cows face an increased risk of negative II-derived effects in their selection programs, particularly at II ≥ 2.5.

Abstract

The main objective of this study was to analyze the effects of the inbreeding degree in high-producing primiparous dairy cows genotypically and phenotypically evaluated and its impacts on production and reproductive parameters. Eighty Holstein–Friesian primiparous cows (age: ~26 months; ~450 kg body weight) were previously genomically analyzed to determine the Inbreeding Index (II) and were divided into two groups: low inbreeding group (LI: <2.5; n = 40) and high inbreeding group (HI: ≥2.5 and ≤5.0; n = 40). Genomic determinations of production and reproductive parameters (14 in total), together with analyses of production (12) and reproductive (11) phenotypic parameters (23 in total) were carried out. Statistically significant differences were obtained between groups concerning the genomic parameters of Milk Production at 305 d and Protein Production at 305 d and the reproductive parameter Daughter Calving Ease, the first two being higher in cows of the HI group and the third lower in the LI group (p < 0.05). For the production phenotypic parameters, statistically significant differences were observed between both groups in the Total Fat, Total Protein, and Urea parameters, the first two being higher in the LI group (p < 0.05). Also, significant differences were observed in several reproductive phenotypic parameters, such as Number of Services per Conception, Calving to Conception Interval, Days Open Post Service, and Current Inter-Partum Period, all of which negatively influenced the HI group (p < 0.05). In addition, correlation analyses were performed between production and reproductive genomic parameters separately and in each consanguinity group. The results showed multiple positive and negative correlations between the production and reproductive parameters independently of the group analyzed, being these correlations more remarkable for the reproductive parameters in the LI group and the production parameters in the HI group (p < 0.05). In conclusion, the degree of inbreeding significantly influenced the results, affecting different genomic and phenotypic production and reproductive parameters in high-producing primiparous cows. The determination of the II in first-calf heifers is crucial to evaluate the negative effects associated with homozygosity avoiding an increase in inbreeding depression on production and reproductive traits.

Economic Viability of Adoption of Automated Oestrus Detection Technologies on Dairy Farms: A Review

Simple Summary

The accurate and timely detection of oestrus is a central element of good dairy herd management as it ultimately determines the level of milk production and is core to the economic viability of the farm business. However, the traditional method of oestrus detection, which occurs by observing the dairy cows standing immobile while being mounted, is usually time-consuming, repetitive and requires considerable skill and experience on the part of the farmer to attain a reasonable level of efficiency. Given the limitation of the traditional method of oestrus detection, a number of automated oestrus detection (AOD) technologies have been developed. However, the rate of adoption of these technologies remains low. One reason that has been proposed for farmers’ low adoption of such technologies has been their lack of knowledge around the potential economic returns from investing in AOD technologies. In this paper, we review the empirical literature on the viability of investment in AOD technologies from an economic perspective. The conclusion of this study provides evidence from which farmers can make more informed decisions in relation to investing in AOD technologies. The review and analysis is also of importance for informing policy, as it provides an examination of the incentives and levers that could improve productivity on dairy farms.

Abstract

The decision for dairy farmers to invest in automated oestrus detection (AOD) technologies involves the weighing up of the costs and benefits of implementation. In this paper, through a review of the existing literature, we examine the impacts of investment in AOD technologies in relation to the profitability and technical performance of dairy farms. Peer-reviewed articles published between 1970 and 2019 on the investment viability of AOD technologies were collated and analysed. We capture the different measures used in assessing the economic performance of investment in AOD technologies over time which include net present value (NPV), milk production, Benefit-Cost Ratio (BCR), internal rate of return (IRR) and payback period (PBP). The study concludes that investment in AOD technologies is not only worthwhile but also contributes to farm profitability.

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

Dairy farms face many challenges and changes. With increasing herd sizes and fewer farmers or employees per cow, new strategies to maintain or improve reproductive management are required. One of the major challenges is to detect cows in estrus and to estimate the perfect time for artificial insemination (AI). Several estrus and ovulation synchronization programs with timed AI as well as estrus detection aids, e.g., tail-paint, pedometer, accelerometer, and others are available. A combination of ovulation synchronization programs and technical solutions, however, has rarely been tested. This study was designed to gain insights into behavioral patterns of cows subjected to an Ovsynch program and to test if behavioral data could be used to optimize the timing of insemination within an Ovsynch program. In this study, we used an ear-tag based 3D-accelerometer system (SMARTBOW, Smartbow GmbH, Weibern, Austria) to generate data of behavioral patterns, i.e., rumination and activity. In Part 1 of this study, behavioral patterns during the peri-estrus period were compared between cows with physiological estrus and cows subjected to an Ovsynch protocol. On the day before estrus and on the day of estrus/AI, cows with natural estrus showed a clear drop in rumination and "inactivity" and an increase in "high activity", based on an algorithm of the accelerometer system, whereas, cows in the Ovsynch protocol showed only minor changes in behavioral patterns. In Part 2, we analyzed behavioral patterns between synchronized cows that became pregnant after AI and synchronized cows that remained open. As a result, no differences were detected between these two Ovsynch groups before AI. Thus, in this study we found no evidence that behavioral patterns can be used to improve conception rates within an Ovsynch protocol.

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

The aim of this study was to assess combining location, acceleration and machine learning technologies to detect estrus in dairy cows. Data were obtained from 12 cows, which were monitored continuously for 12 days. A neck mounted device collected 25,684 records for location and acceleration. Four machine-learning approaches were tested (K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), and classification and regression tree (CART)) to automatically identify cows in estrus from estrus indicators determined by principal component analysis (PCA) of twelve behavioral metrics, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, and drinking times. The study showed that the neck tag had a static and dynamic positioning accuracy of 0.25 ± 0.06 m and 0.45 ± 0.15 m, respectively. In the 0.5-h, 1-h, and 1.5-h time windows, the machine learning approaches ranged from 73.3 to 99.4% for sensitivity, from 50 to 85.7% for specificity, from 77.8 to 95.8% for precision, from 55.6 to 93.7% for negative predictive value (NPV), from 72.7 to 95.4% for accuracy, and from 78.6 to 97.5% for F1 score. We found that the BPNN algorithm with 0.5-h time window was the best predictor of estrus in dairy cows. Based on these results, the integration of location, acceleration, and machine learning methods can improve dairy cow estrus detection.

Detection of estrous biomarkers in the body exudates of Kangayam cattle (Bos indicus) from interplay of hormones and behavioral expressions

Behavioral expressions and biochemical composition of body exudates are significantly altered in concert with the endocrine status, which are all clear indicators of physiological conditions of animals. In this study, we sought to infer about the reproductive physiological status of Kangayam cattle (Bos indicus) by analyzing behaviors, endocrine pattern, and body exudates and further to discover estrous biomarkers so as to facilitate timely artificial insemination/mating and to aid in aspects of conservation of the species. Therefore, in this study, we followed Kangayam cows through pre-estrous to post-estrous phases to correlate the endocrine dependence of biochemical constituents in urine and cervical mucus and sought to identify estrous biomarkers. Behavioral estrus was confirmed in 10 cows, from which urine samples were collected and subjected to determination of LH, FSH, estrogens, progesterone, proteins, and lipids. Furthermore, urinary fatty acids and proteins were profiled using gas chromatography and SDS-PAGE, respectively. The volatile compounds in the urine and cervical mucus were identified by gas chromatography-mass spectrometry analysis. The data revealed that LH, FSH, and estrogen levels increased significantly in estrous urine compared with nonestrous urine, whereas progesterone status was vice versa (P < 0.05). The lipid content was also significantly higher in estrous urine than in pre- and post-estrous urines (P < 0.05). There were also cyclical variations of volatiles and fatty acid profiles across phases of the estrous cycle. More acidic compounds were present in estrous urine, rendering it more acidic, than in pre- and post-estrous urines. Interestingly, oleic acid, which was present as a fatty acid in estrous and post-estrous urines, appeared to be a volatile in post-estrous urine and estrous cervical mucus. In addition, octanoic and butanoic acids were specific to both estrous urine and cervical mucus, indicating their possible candidature as estrous biomarkers. SDS-PAGE analysis showed pronounced expression of a 98 kDa protein in post-estrous urine, which in matrix-assisted laser desorption ionization-time of flight mass spectrometry was identified as albumin. Our results demonstrate multiple biomarkers in estrous urine and specific volatiles in cervical mucus that offer scope to develop viable estrus detection kits for Kangayam cows.

Alterations in vaginal temperature during the estrous cycle in dairy cows detected by a new intravaginal device-a pilot study

Estrus identification is important in dairy cow production. At present, estrus identification is automated with a pedometer or accelerometer and the results remain unsatisfactory. It was previously reported that body temperature changes during estrus. In the present study, dairy cow vaginal temperature (VT) was monitored during various seasons, and an increase in VT of 0.3 °C was suggested for the onset of estrus, using an automated VT monitoring system developed in-house. Natural and synchronized estrus were measured simultaneously. The VT was determined to be in circadian rhythm and significantly higher in summer than in either autumn or winter (P < 0.05). VT difference (between estrus VT and average VT 7 days earlier) gradually increased, reached a peak of 0.56 °C ± 0.17 at 4 h before the end of estrus, and then decreased to the normal. The VT of cows in estrus and the duration of their estrus were significantly affected by seasons and estrus types (P < 0.05). VT gradually decreased in response to prostaglandin (PG) injection and was significantly lower (0.15-0.35 °C) from 9 to 33 h after the drug administration than the average VT at the same time 7 days earlier (P < 0.05). Changes in circadian and seasonal VT and in the estrous cycle can be monitored to assess the physiological status of cows and will help in developing an effective automated estrus identification technique. Results of this pilot study should be validated in further studies.

Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor

Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the 'gold standard'. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. The results are promising for the future use of vocalisation in automatic oestrus detection systems.

Genomic merit for reproductive traits. I: Estrous characteristics and fertility in Holstein heifers

Genetic selection of dairy cattle in the United States has included reproductive traits (daughter pregnancy rate, DPR; heifer conception rate, HCR), which is believed to have partly contributed to halting the decline in reproductive performance. The objectives of the current study were to evaluate the association among genomic merit for DPR (GDPR) and HCR (GHCR) with estrous characteristics measured by an automated device. Holstein heifers (n = 1,005) were genotyped at 2 mo of age and were classified into quartiles (Q1 = lowest, Q4 = highest) according to the GDPR and GHCR values of the study population. At 10 to 11 mo of age, heifers were fitted with a collar that recorded activity and rumination and determined the occurrence of estrus according to changes in activity and rumination compared with the individual's baseline values. Estrous characteristics of spontaneous estruses (SPE) and PGF_2α-synchronized estruses (PGSE) were recorded. Heifers had their estrous cycle synchronized with PGF_2α and following detection of estrus received either artificial insemination or embryo transfer according to the herd's genetic selection program. Heifers in Q2 (17.7 ± 0.3 h) of GHCR tended to have longer SPE than heifers in Q4 (16.7 ± 0.3 h). The interaction between GDPR and GHCR was associated with the likelihood of activity peak (0 = no estrus, 100 = maximum activity) ≥80 at SPE because, among heifers in Q3 and Q4 of GHCR, those in Q1 of GDPR were less likely to have an activity peak ≥80. Heifers in Q1 and Q2 of GDPR had reduced hazard of estrus within 7 d of the first PGF_2α treatment compared with heifers in Q4 of GDPR. Heifers in Q1 (16.1 ± 0.4 h) of GDPR had shorter PGSE than heifers in Q2 (17.6 ± 0.4 h) and Q4 (17.4 ± 0.4 h) and tended to have shorter PGSE than heifers in Q3 (17.4 ± 0.4 h). Rumination nadir on the day of PGSE was greater for heifers in Q1 (-30.1 ± 0.9 min/d) of GDPR compared with heifers in Q4 (-33.7 ± 0.9 min/d). Among heifers receiving only artificial insemination, those in Q1 of GHCR (adjusted hazard ratio = 0.65; 95% confidence interval = 0.48-0.88) became pregnant at a slower rate than heifers in Q4. Genomic merit for HCR was negatively associated with SPE but tended to be positively associated with hazard of pregnancy, whereas GDPR was positively associated with PGSE and hazard of estrus. Selection of dairy cattle for DPR and HCR may improve reproductive performance through different pathways, namely estrous characteristics and pregnancy establishment.

Estrous characteristics and reproductive outcomes of Holstein heifers treated with 2 prostaglandin formulations and detected in estrus by an automated estrous detection or mounting device

Dinoprost tromethamine (DIN), a molecule similar to endogenous PGF_2α, has a half-life of approximately 9 min. Cloprostenol sodium (CLO), a synthetic analog of PGF_2α, has a half-life of approximately 3 h. We hypothesized that treatment of Holstein heifers with CLO would improve estrous detection rate, estrous characteristics, service rate, and overall reproductive performance compared with DIN. Currently in the United States, heifers are largely inseminated based on signs of estrus, which is detected visually or with the aid of mounting detection devices (MD). Automated estrous detection devices (AED) are becoming more accessible to producers, but it is not clear whether they present advantages in the reproductive management of heifers. Therefore, we hypothesized that the use of an AED would improve service and pregnancy rates compared with detection of estrus with the aid of a MD. Holstein heifers (n = 1,019) were enrolled in the experiment at 10 to 11 mo of age, when they were fitted with a Heatime HR LD System (SCR Ltd., Netanya, Israel). At 12 mo of age, we paired heifers according to estrous cycle phase and randomly assigned them to treatments in a 2 × 2 design: PGF_2α formulation (CLO vs. DIN) and estrous detection treatment (AED vs. MD). Heifers in the AED treatment were detected in estrus only by the Heatime HR LD System, whereas heifers in the MD treatment were detected in estrus only by the Kamar Heatmount Detector (Kamar Products Inc., Zionsville, IN). Treatments with the same PGF_2α formulations were repeated 14 d after the first treatment if heifers had not been detected in estrus. A sub-group of heifers had blood sampled on the day of PGF_2α treatment and within 24 h of onset of estrus to determine progesterone and estradiol concentrations. Treatment with CLO reduced the progesterone concentration within 24 h of onset of estrus compared with DIN (0.04 ± 0.01 vs. 0.11 ± 0.01 ng/mL). Among heifers in mid diestrus on the day of PGF_2α treatment, CLO reduced the interval to estrus compared with DIN (72.0 ± 2.2 vs. 82.4 ± 2.4 h). Prostaglandin F_2α formulation and estrous detection treatment did not affect pregnancy to the first service. The interval between the first and second services tended to be reduced for the AED treatment compared with the MD treatment (24.4 ± 0.5 vs. 25.7 ± 0.6 d). Prostaglandin F_2α formulation and estrous detection treatment did not affect the hazard of pregnancy. Although CLO treatment may shorten the interval to estrus in heifers at mid diestrus compared with DIN, PGF_2α formulation did not affect reproductive performance. In the current experiment, no advantages in reproductive performance were observed when estrous detection was based on an AED compared with a MD.

Evaluation of an ear-attached accelerometer for detecting estrus events in indoor housed dairy cows

Precision dairy farming technologies have tremendous potential to improve and support farmers in herd management decisions, particularly in reproductive management. Nowadays, estrus detection in cows is challenging and several supporting tools are available. In this study, a 3D-accelerometer integrated into an ear-tag (SMARTBOW, Smartbow GmbH, Weibern, Austria) was used for the detection of cows in estrus. Movement pattern based on accelerometer data were analyzed and processed by algorithms and machine learning, resulting in estrus alerts. For the evaluation of the system, reproductive performance data of 579 estrus events of multiparous cows were used to retrospectively evaluate the accuracy of estrus alerts generated by the accelerometer-based system and the overall performance of the system. Estrus events were classified as 'gold standard' events, if an estrus followed by AI resulted in pregnancy, and as 'recorded estrus' events, if two estrus events with an interval of 18-25 d were in the herd records, independent of whether estrus was followed by AI or pregnancy. In total, 316 'gold standard' events were matched with estrus alerts generated by the accelerometer-based system, resulting in a sensitivity of 97%. Furthermore, 263 'recorded estrus' events were compared with correct or incorrect estrus alerts by the system. Sensitivity, specificity, positive and negative predictive values, accuracy, and error rate for 'recorded estrus' events were 97%, 98%, 96%, 94%, 96%, and 2%, respectively. In summary, the SMARTBOW system is suitable for an automated detection of estrus events of multiparous cows in indoor housed dairy cows.