Estrus Detection Research Articles

Targeted reproductive management for lactating Holstein cows: Economic return.

Targeted reproductive management (TRM), employing automated monitoring devices (AMD), is as an alternative to the blanket adoption of ovulation synchronization protocols (OvSP) for first postpartum artificial insemination (AI) and a means of reducing the use of OvSP for re-AI of non-pregnant cows. We hypothesized that a TRM that relies heavily on AI of cows on AMD-detected estrus increases improves reproductive performance and economic return. Early-postpartum estrus characteristics of multiparous (n = 941) cows were evaluated at 40 and 41 DIM (Herds 1and 2, respectively) and early-postpartum estrus characteristics of primiparous (n = 539) cows were evaluated at 54 and 55 DIM (Herds 1 and 2, respectively). Cows in the control treatment were enrolled in the Double-Ovsynch protocol and AI at fixed time (TAI) at 82 and 83 DIM (primiparous cows in Herds 1 and 2, respectively) and 68 and 69 DIM (multiparous in Herds 1 and 2, respectively). Cows enrolled in the TRM treatment were managed according to early-postpartum estrus characteristics as follows: 1. Cows with ≥1 intense estrus (heat index ≥70; 0 = minimum, 100 = maximum) were AI upon AMD detected estrus for 42 d and, if not AI, were enrolled in the Double-Ovsynch, 2. Cows without an intense estrus were enrolled in the Double-Ovsynch at the same time as cows in the control treatment. Control cows were re-AI based on visual or patch aided detection of estrus, whereas TRM cows were re-AI as described for control cows with the aid of the AMD. All cows received a GnRH injection 27 ± 3 d after AI and, if diagnosed as non-pregnant, completed the 5-d Cosynch protocol and received TAI 35 ± 3 d after insemination. The hazard of pregnancy was greater for cows in the TRM treatment [adjusted hazard ratio = 1.17, 95% confidence interval = 1.05, 1.32], resulting in more cows from the TRM treatment starting a new lactation (82.6 vs. 77.2%) and fewer of them sold (15.5 vs. 20.8%). Treatments did not differ regarding total milk yield (control = 12,782.1 ± 130.6 kg, TRM = 13,054.7 ± 136.1 kg). The gross profit ((milk income + sale value + subsequent lactation calf value) - (feed cost + replacement cost + fixed cost + reproductive management cost)) of cows in the TRM treatment was $108 greater than the control treatment ($3,061.6 ± 45.9 vs. $2,953.8 ± 45.2). According to a Monte Carlo stochastic simulation, the mean (±SD) difference in gross profit was $87.8 ± 12.6/cow in favor of the TRM treatment and 95% of the scenarios ranged from $67.2/cow to $108.5/cow (minimum = $30.2/cow, maximum = $141.1/cow). In the conditions of the current experiment, the TRM treatment improved the gross profit of Holstein cows because the increased hazard of pregnancy changed culling dynamics, reducing replacement cost and cow sales and increasing calf value. The findings of the current experiment emphasize the importance of efficient reproductive management and its substantial economic implications, particularly in the context of high-producing Holstein cows.

Unlocking the Secrets of Pheromones: Reproductive and Social Communication in Domestic Mammals

Pheromones play a pivotal role in the reproductive and behavioural management of livestock, influencing various physiological and behavioural responses within and across species. This review explores the application of pheromones in cattle, buffalo, sheep, goats, pigs, and equines, highlighting their significant impact on reproductive efficiency, stress reduction, and animal welfare. In cattle, pheromones are essential for estrus detection and the enhancement of maternal bonding. Similarly, in buffaloes, urinary pheromones have been identified as markers for estrus detection, aiding in overcoming the challenge of silent ovulation, while also contributing to penis erection and sperm quantity enhancement. In small ruminants such as sheep and goats, the "male effect" accelerates puberty and synchronizes estrus, which is crucial for optimizing breeding cycles. In pigs, the presence of boars accelerates puberty in gilts and reduces postpartum anestrus in sows, enhancing reproductive outcomes. Equines benefit from appeasing pheromones, which alleviate stress in foals and adult horses, particularly during transportation and other stressful events. The synthesis and application of synthetic analogs of these pheromones have demonstrated practical benefits in animal husbandry, offering promising avenues for improving reproductive performance and animal welfare. However, despite these advancements, the identification and characterization of specific pheromones across different species remain incomplete. Future research should focus on the molecular identification of these pheromones, their mechanisms of action, and the development of innovative applications to further enhance their utility in animal management.

YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera

Accurate and efficient estrus detection in sows is crucial in modern agricultural practices to ensure optimal reproductive health and successful breeding outcomes. A non-contact method using computer vision to detect a change in a sow’s vulva size holds great promise for automating and enhancing this critical process. However, achieving precise and reliable results depends heavily on maintaining a consistent camera distance during image capture. Variations in camera distance can lead to erroneous estrus estimations, potentially resulting in missed breeding opportunities or false positives. To address this challenge, we propose a robust six-step methodology, accompanied by three stages of evaluation. First, we carefully annotated masks around the vulva to ensure an accurate pixel perimeter calculation of its shape. Next, we meticulously identified keypoints on the sow’s vulva, which enabled precise tracking and analysis of its features. We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. By identifying the keypoints, we performed precise calculations of the Euclidean distances: first, between each labium (horizontal distance), and second, between the clitoris and the perineum (vertical distance). Additionally, by segmenting the vulva’s size, we gained valuable insights into its shape, which helped with performing precise perimeter measurements. Equally important was our effort to calibrate the camera using monocular depth estimation. This calibration helped establish a functional relationship between the measurements on the image (such as the distances between the labia and from the clitoris to the perineum, and the vulva perimeter) and the depth distance to the camera, which enabled accurate adjustments and calibration for our analysis. Lastly, we present a classification method for distinguishing between estrus and non-estrus states in subjects based on the pixel width, pixel length, and perimeter measurements. The method calculated the Euclidean distances between a new data point and reference points from two datasets: “estrus data” and “not estrus data”. Using custom distance functions, we computed the distances for each measurement dimension and aggregated them to determine the overall similarity. The classification process involved identifying the three nearest neighbors of the datasets and employing a majority voting mechanism to assign a label. A new data point was classified as “estrus” if the majority of the nearest neighbors were labeled as estrus; otherwise, it was classified as “non-estrus”. This method provided a robust approach for automated classification, which aided in more accurate and efficient detection of the estrus states. To validate our approach, we propose three evaluation stages. In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. Then, we provided a quantitative analysis and performance comparison, including a comparison between our previous U-Net model and our new YOLOv8 segmentation model. This comparison focused on each model’s performance in terms of accuracy and speed, which highlighted the advantages of our new approach. Lastly, we evaluated the estrus–not-estrus classification model by defining the confusion matrix. By using this comprehensive approach, we significantly enhanced the accuracy of estrus detection in sows while effectively mitigating human errors and resource wastage. The automation and optimization of this critical process hold the potential to revolutionize estrus detection in agriculture, which will contribute to improved reproductive health management and elevate breeding outcomes to new heights. Through extensive evaluation and experimentation, our research aimed to demonstrate the transformative capabilities of computer vision techniques, paving the way for more advanced and efficient practices in the agricultural domain.

Follicular persistence, oestradiol level and conception on the first and second day of oestrus and influence of thermal stress in cross-bred dairy cows manifesting prolonged oestrus

Increasing incidence of prolonged oestrus reported in cross-bred dairy cows bred through artificial insemination (AI), is attributed to various causative factors and underlying mechanisms. The present study was to assess the pattern of follicular growth, oestrogen secretion, and conception on the first and second day of oestrus and to understand the influence of thermal stress as a causative factor for oestrus prolongation. The year-round study involved 52 crossbred cows intensively managed under a hot humid climate, during their second and third months postpartum. Animals were examined for the status of ovarian follicles, oestrogen level, and conception to AI on Day 0 and Day 1 of oestrus confirmation and the findings were compared across seasons. While oestrus detection rate (ODR) and conception of AI were lowest during the summer season attributable to maximum TS, better ODR (P<0.05) and more prolonged oestrus (P<0.01) occurred during the seasons of favourable climate having least TS and better green fodder availability. Besides, the occurrence of larger follicles and almost similar oestradiol levels on Day 0 and Day 1 of oestrus, better conception rate for double AI (63.16 %) than total AI (48.48 %) indicated prolonged oestrus and involvement of delayed ovulation among the causation. Comparison of the seasonal pattern of follicular growth, oestrogen level, and conception to AI between Day 0 and Day 1 further clarifies the influence of TS and increased milk production as the potential contributors to the occurrence of prolonged oestrus in cross-bred cows under the hot humid tropical climate. Keywords: Crossbred cow, Oestrogen, Prolonged oestrus, Thermal stress

Precision livestock farming in the 21st century: Challenges and opportunities for sustainable agriculture

Abstract This study aimed to review the recent development of different technologies in precision livestock farming (PLF), along with their scopes and challenges. PLF is an innovative, contemporary, and fast-expanding approach to agriculture that aims to improve sustainable livestock farming. The growing global population has increased the demand for animal products. To meet this demand, farmers have to increase their production, so without the integration of precision technology, this cannot be achieved. PLF currently employs a variety of technologies. Some of these methods include vision-based solutions, load cells, accelerometers, microphones, thermal cameras, photoelectric sensors, and radio-frequency identification (RFID). Despite the availability of different PLF technologies, their adoption by farmers varies widely on the basis of the cost of investment, ease of operation, availability, and accessibility. These technologies are used to track different activities of livestock farming, such as feeding, drinking, physical behavior, temperature regulation, tracking and identification, estrus detection, disease detection, and milking. PLF contributes significantly to technological advancement, human-animal relationships, environmental sustainability, and increased productivity. It does, however, present a number of obstacles and eventual advantages.

Delaying induction of ovulation and timed AI in a Double-Ovsynch protocol increased expression of estrus and altered first service reproductive outcomes of lactating dairy cows

The objective of this randomized controlled experiment was to evaluate the effect of delaying induction of ovulation and timed artificial insemination (TAI) on expression of estrus before AI and first service reproductive outcomes. A secondary objective was to evaluate the effects of delaying induction of ovulation in a Double-Ovsynch protocol on ovarian function. Lactating Holstein cows (n = 4,672) from 2 commercial dairy farms fitted with sensors for automated detection of estrus were synchronized with a Double-Ovsynch protocol up to the first PGF2α (PGF-L) of the Breeding-Ovsynch portion of the protocol (Pre-Ovsynch: GnRH, 7 d later PGF2α, 3 d later GnRH, 7 d later Breeding-Ovsynch: GnRH, 7 d later PGF2α, 1 d later PGF2α). At PGF-L, cows blocked by parity (primiparous vs. multiparous) and semen used for first service (sex-sorted dairy vs. conventional beef) were randomly assigned to the G56 (n = 2,338) or G80 (n = 2,334) treatments. Cows in G56 had 56 h whereas cows in G80 had 80 h from PGF-L to induction of ovulation with the last GnRH (GnRH2) before AI. For both treatments, TAI occurred ∼16 h after GnRH2. All cows with automated estrus alerts between PGF-L and TAI were inseminated at detected estrus (AIE) without GnRH. Ovarian function and responses to synchronization were monitored based on circulating concentrations of progesterone and examination of the ovaries by ultrasonography. Data for binary outcomes were analyzed by logistic and continuous outcomes with lineal regression. More cows in G80 received AIE and had estrus before AI. Overall, pregnancies per AI (P/AI) did not differ for the G80 and G56 treatments. Cows in G80 that received TAI and had no estrus had fewer P/AI than cows with estrus that received AIE or TAI in G80, and fewer P/AI than cows AIE and cows that received TAI and had or did not have estrus in the G56 treatment. No differences were observed between treatments or for cows with and without estrus for pregnancy loss. Unlike some minor differences between treatments for concentrations of progesterone at GnRH2, the most notable differences in ovarian function were for cows in both treatments with or without estrus that received TAI. Cows with estrus, were more likely to have follicles > 16 mm, had larger follicles before ovulation, and had a greater ovulation risk after AI. Likewise, within the G80 treatment only, cows with estrus that received AIE or TAI had larger follicles, were more likely to have complete luteal regression, had greater ovulation risk, were more likely to have a functional corpus luteum, and had more circulating progesterone after AI. We concluded that delaying induction of ovulation and TAI was effective for allowing more cows to express estrus before AI which had different ovarian function outcomes and greater P/AI than cows that did not express estrus. However, the greater P/AI of cows that expressed estrus was insufficient to compensate for the reduced P/AI of cows that did not express estrus, and thus increase overall P/AI compared with the treatment without delayed induction of ovulation. Detection of estrus before AI in cows undergoing synchronization of ovulation could help identify cows with different likelihoods of pregnancy after insemination.

420 Automated identification and behavioral estrus detection in freestall-housed dairy cows using computer vision and machine learning: A two-layered algorithm approach

Abstract A critical aspect of dairy management involves maintaining accurate records of individual animals and promptly detecting estrus behavior, which is vital for successful breeding and reproductive outcomes. Recent advancements in precision livestock management, utilizing computer vision and machine learning technologies, have paved the way for automated and precise solutions in these areas. The objective of this study is to investigate the application of computer vision and machine learning for animal identification and behavioral estrus detection in dairy cows housed in freestall settings, employing a two-layered algorithmic approach. The research was conducted at the Joe Bearden Dairy Research Center, where a total of 10 lactating Holstein-Friesian cows were assessed across a 7-d trial period. For synchronization efforts, cows were provided a single injection of Prostaglandin F2α at trial initiation with expected estrus behaviors to be exhibited within the following 56 h. Continuous video was captured in 4k resolution using a five-camera closed caption media recording system strategically placed in the pen corners and over the feed bunk alleyway to capture multiple perspectives. Video data extrapolated into frames and manual annotation was utilized to generate the machine learning algorithm’s training data. Frames were randomly selected from a scattering of 56 h of video across all five camera viewpoints and down sampled to 384 x 640 p for both layers. A two-layer approach was employed utilizing an algorithm for each aspect of the study with the first model detecting the behavior, and the second model using the detected region of interest to identify specific cows. The first layer was trained on 682 frames and validated against 123 manually annotated frames. The second layer was trained on 102 frames and validated against 16 frames manually annotated for individual animals. Results demonstrated that the model used for mounting behavior identification has high precision (0.819) and recall (0.89), achieving a 0.926 mAP at an IoU of 0.5. This model achieved an F1 score of 0.86 at a confidence threshold of 0.268. The results demonstrated that the model used for individual animal identification across different classes achieved high precision (0.861) and recall (0.842), with a mAP of 0.897 at an IoU of 0.5. The F1 score for all classes was 0.89 at a confidence threshold of 0.788. In conclusion, these promising findings underscore the potential of computer vision and machine learning technologies to revolutionize dairy cow management practices. This innovative approach not only has potential to enhance operational efficiency, but also contributes significantly to improved reproductive management, thereby advancing precision and productivity in dairy operations.

Evaluation of estrus detection by cervical monitoring and pregnancy rates in ovsynch and co-synch treated Anatolian buffalo heifers

The aim of this study was to assess the relationship between the occurrence and intensity of estrous expression and the success of artificial insemination in Ovsynch and Co-synch treated Anatolian buffalo heifers, employing a novel approach utilizing smartphone-based endoscopic inspection of the cervix uteri. Twenty-seven Anatolian buffalo heifers (n=27) were utilized for the study. In the Ovsynch synchronization protocol (n=15), GnRH (buserelin acetate, 12 μg) was administered on day 0, PGF2α (cloprostenol, 500 μg) on day 7, and GnRH (buserelin acetate, 12 μg) on day 9, with artificial insemination conducted 16-20 hours after the second GnRH application. For the Co-synch protocol (n=12), GnRH (buserelin acetate, 12 μg) was administered on day 0, PGF2α (cloprostenol, 500 μg) on day 7, and GnRH (buserelin acetate, 12 μg) along with artificial insemination on day 10. At the time of artificial insemination, estrous expression was categorized as intense, moderate, weak, or negative. Pregnancies were confirmed between 40 to 70 days after artificial insemination using either pregnancy-related glycoprotein or ultrasonography examinations.Intense, moderate, weak, and negative estrus intensities were observed at rates of 20%, 20%, 26.7%, and 33.3%, respectively, in the Ovsynch group and 41.7%, 58.3%, 0%, and 0%, respectively, in the Co-synch group (P

Advancing 3Rs: The Mouse Estrus Detector (MED) as a Low-Stress, Painless, and Efficient Tool for Estrus Determination in Mice.

Determining the estrous cycle stages in mice is essential for optimizing breeding strategies, synchronizing experimental timelines, and facilitating studies in behavior, drug testing, and genetics. It is critical for reducing the production of genetically unmodified offspring in the generation and investigation of genetically modified animal models. An accurate detection of the estrus cycle is particularly relevant in the context of the 3Rs-Replacement, Reduction, and Refinement. The estrous cycle, encompassing the reproductive phases of mice, is key to refining experimental designs and addressing ethical issues related to the use of animals in research. This study presents results from two independent laboratories on the efficacy of the Mouse Estrus Detector (MED) from ELMI Ltd. (Latvia) for the accurate determination of the estrus phase. The female mice of five strains/stocks (CD1, FVB/N, C57Bl6/J, B6D2F1, and Swiss) were used. The results showed that the MEDProTM is a low-traumatic, simple, rapid, and painless method of estrus detection that supports the principles of the 3Rs. The use of the MEDProTM for estrus detection in mice caused minimal stress, enhanced mating efficiency, facilitated an increase in the number of embryos for in vitro fertilization, and allowed the production of the desired number of foster animals.

An IMU-based machine learning approach for daily behavior pattern recognition in dairy cows

Technological advancements have revolutionized livestock farming, notably in health monitoring. Traditional methods, which have been criticized for subjectivity and treatment delays, can be replaced with efficient health monitoring systems, thereby reducing costs and workload. Implementing cow behavior recognition allows for effective dairy cow health monitoring. In this research, we propose an integrated system using inertial measurement unit (IMU) devices and machine learning techniques for dairy cow behavior recognition. Six main dairy cow behaviors were studied: lying, standing, walking, drinking, feeding, and ruminating. All behavior types were manually labeled into the IMU data by reviewing the recorded footage. The labeled IMU data underwent four processing steps: selecting different window sizes, feature extraction, feature selection, and normalization. These processed data were then used to build the behavior recognition model. Various model structures, including SVM, Random Forest, and XGBoost, were tested. The top-performing model, XGBoost, with its proposed 58 features achieved an F1-score of 0.87, with specific scores of 0.93 for lying, 0.85 for walking, 0.94 for ruminating, 0.89 for feeding, 0.86 for standing, 0.93 for drinking, and 0.59 for other activities. During our online testing, we observed similar patterns for each healthy cow. The cumulative time spent on each behavior also matched the statistics from previous surveys. Additionally, our backend optimization approach resulted in a final overall percentage error of 1.55 % per day during online testing. In conclusion, our study presents an IMU-based system capable of accurately recognizing dairy cow behavior. Feature design and appropriate models are proposed herein. A functional optimization method was introduced indicating that our system has the potential with applications for estrus detection and other reproductive management practices in the dairy industry.

Infrared thermography technique for detecting estrus in Surti does

The present study evaluated the potential of the infrared thermography technique for estrus detection in Surti does. Based on the presence of estrus-specific behaviour, physiological signs as well as higher LH, FSH, estrogen and lower progesterone hormone concentration, 24 Surti does were divided into two groups, viz. estrus and non- estrus does. Recording of surface temperatures, rectal and tympanic temperatures was done using an Infrared thermal camera (FLIR T420), digital thermometer and infrared non-contact tympanic thermometer, respectively. It was concluded that temperatures of the vulva, rectal surface, rectal and tympanic temperatures are significantly higher in estrus does. Further, a strong, significant positive correlation was observed between vulvar and pudendal, rectal surface temperatures, rectal temperature as well as tympanic temperature. Thus, it may be concluded that the infrared thermography technique holds the potential for detecting estrus in Surti does.

The economic effect of cow-based reproductive management programs with a systematic use of reproductive hormones

Hormone-based reproductive management programs can be beneficial to improve dairy cow's reproductive performance. This study aimed to compare the economic impact of reproductive management programs using systematic hormonal treatments to individual cows with a specific DIM range with a reproductive management program using cow-specific hormonal treatment based on a veterinary diagnosis of ovarian dysfunction during a fertility check. An existing individual cow-based, dynamic, and stochastic bio-economic simulation model, mimicking the production dynamics of a 200 cow-herd in daily time steps, was extended with ovarian dysfunction and fertility inputs. Four hormone-based reproductive management programs were modeled. In the default reproductive management program, reflecting the current reproductive management of Dutch herds, lactating dairy cows are inseminated based on detection of estrus and non-cyclic dairy cows are treated with hormones based on a veterinary diagnosis of ovarian dysfunction during a fertility check. Hormone treatments prescribed by the veterinarian for anestrus, cystic, and sub-estrus cows were an 8-d progesterone-releasing intravaginal device (PRID)-Synch protocol (PRIDsynch), an Ovsynch protocol, and a PGF2⍺ treatment, respectively. The 3 other reproductive management programs reflected systematic hormonal treatments to cows at specific DIM and included a 1) Double-Ovsynch protocol for TAI with nonpregnant cows submitted to a resynchronization protocol (FTAI), 2) Double-Ovsynch protocol for TAI with nonpregnant cows detected in estrus or submitted to a resynchronization protocol (FTAI+ED), and 3) detection of estrus with cows not detected submitted to a PRIDsynch protocol (ED+TAI). All nonpregnant cows were submitted to a resynchronization protocol based on the absence (PRIDsynch) or presence (Ovsynch protocol) of a corpus luteum (CL). The annual mean net economic return (NER) was calculated for all reproductive management programs. Compared with the default reproductive management program, the highest NER was observed for the FTAI+ED reproductive management program with €23,764 higher net revenues, followed by the FTAI and the ED+TAI reproductive management programs with €19,550 and €14,314 higher net revenues, respectively. Overall, systematic hormone-based reproductive management programs gave higher costs due to more hormones administered and higher calving and feed costs due to more pregnant cows. Nevertheless, the additional revenues of milk and calves in the systematic hormone-based reproductive management programs outweighed the total cost. For instance, the FTAI+ED reproductive management program gave €8,953 higher total cost per year compared with the default but with €32,654 higher revenues. In summary, reproductive management programs where hormones were systematically used gave economic advantages over the current default reproductive management program in which hormones are administered to individual cows based on a veterinary diagnosis of ovarian dysfunction during a fertility check.

The Effects of Oral Progestagen Administration on the Fertility of Synchronized Goats During the Breeding Season

The objective of this study was to assess the impact of oral progesterone (altrenogest) administered after mating on fertility in goats synchronized during the breeding season. A total of 47 Hair goats were included in the study. A progesterone-impregnated intravaginal sponges were administered for 12 days. On day 10 of intravaginal sponge administration, 480 IU pregnant mare's serum gonadotropin (PMSG) and 0.075 mg cloprostenol were injected intramuscularly to the goats. Animals were exposed to bucks for 12 hours after detection of estrus by the foraging buck. The goats were randomly assigned to two distinct groups: Group 1 (G1, n=23) and Group 2 (G2, n=24). G1 goats were orally administrated 4.4 mg of altrenogest per day for 30 days after mating. G2 goats served as controls. Pregnancy examinations were performed by transrectal ultrasonography on the 30th and 42nd days after mating. Blood samples were taken from one day after mating to 30 days (3-day intervals). Statistical analysis comparing G1 and G2 revealed no significant differences between the groups regarding progesterone concentrations, conception rate, pregnancy rate, lambing rate, multiple birth rate, fecundity, and litter size (p>0.05). In conclusion, this study revealed that oral progesterone use had no impact on fertility parameters. Moreover, further research is warranted to explore the efficacy of various oral progesterone analogs.

When Implementing the Presynch-11/Ovsynch Reproductive Management Program, the Fertility of Lactating Dairy Cows Improved When They Received Timed Artificial Insemination Compared with the Inclusion of Estrus Detection.

Artificial insemination 12 h following observed standing estrus is a standard estimate of the fertility levels of cattle. The main objective of this study was to determine if controlling ovarian development with a fertility program could alter the fertility of lactating dairy cows. Lactating dairy cows (n = 1356) 60-66 days in milk (DIM) were randomly assigned to receive timed AI following Presynch-11/Ovsynch (Presynch-11) or a combination of estrus detection and timed AI using the Presynch-11/Ovsynch program (Estrus + TAI). Cows in standing estrus, following the first two cloprostenol sodium (CLO) injections, in the Estrus + TAI group were artificially inseminated using the AM/PM rule. Cows in this group that were not observed in standing estrus received Ovsynch and TAI beginning 11 d after the second CLO injection. Cows in the Presynch-11 group received two injections of 500 µg CLO 14 d apart but were not observed for estrus. The first GnRH (100 µg) of Ovsynch was administered 11 d following the second CLO injection. All cows in the Presynch-11-Ovsynch group received TAI following Ovsynch. Cows in this treatment were then assigned randomly to receive either CLO or dinoprost 7 d following the first GnRH of Ovsynch. The final GnRH of Ovsynch was administered 56 h later and TAI 16 h after the final GnRH. Pregnancies/AI (P/AI) were greater (p < 0.001) for the Presynch-11 group compared with the Estrus + TAI group (45 vs. 31%). Primiparous cows had greater fertility following observed standing estrus compared with multiparous cows. Days in milk at the first AI were greater (p < 0.01) in the Presynch-11 group vs. the Estrus + TAI group (98 vs. 80) but less variable (p < 0.01). The range of DIM at the first AI was 95 to 101 in the Presynch-11 group and 60 to 101 in the Estrus + TAI group. Within the Presynch-11 group, there were no differences in the rate of luteolysis or P/AI for the prostaglandin type at the final PGF2α of Ovsynch. Multiparous cows treated with Presynch-11 had >60% chance of pregnancy compared with multiparous cows receiving AI following standing estrus. In summary, lactating dairy cows receiving timed AI following the Presynch-11/Ovsynch program had improved fertility compared with a group of cows that received AI following standing estrus or, if not observed in estrus, timed AI. This comparison indicated that controlling ovarian development with GnRH and PGF2α positively impacted the fertility of lactating dairy cows.

Adaptive Deep Learning for Image-Based Estrus Prediction and Detection in Dairy Cows

Adaptive Deep Learning for Image-Based Estrus Prediction and Detection in Dairy Cows

The use of a recombinant equine chorionic gonadotropin (reCG) in fixed-time AI programs in beef cattle

The aim of the current study was to evaluate the effect of a novel recombinant eCG (reCG) on pregnancy rates to AI (P/AI) in suckled beef cows of different breeds that were synchronized with an estradiol/progesterone (P4)-based protocol for fixed-time AI (TAI). In experiment 1, 1244 Bos taurus suckled cows were used. On Day 0 all cows received an intravaginal P4 device (600 mg P4) and 2 mg of estradiol benzoate. On Day 7, devices were removed, and all cows received 0.150 mg of D-cloprostenol plus 1 mg of estradiol cypionate and were randomly divided to receive 140 IU or 105 IU of reCG or no reCG treatment (controls) at that time. Cows were tail painted for estrus detection and those in estrus by 48 h after P4 device removal were inseminated; whereas those not showing estrus were also inseminated and received GnRH at the same time. In experiment 2, 818 Bos taurus x Bos indicus crossbred suckled cows received the same FTAI protocol used in Experiment 1. Cows were randomly divided at the time of P4 device removal into 4 groups to receive 140 IU, 105 IU or 84 IU of reCG or no reCG treatment. In experiment 3, 345 Bos indicus suckled cows were submitted to the same FTAI protocol as those in previous experiments and were randomly divided into three groups to receive 140 IU or 105 IU of reCG, or 300 IU of serum derived eCG (PMSG). In Experiment 1, estrus rate and P/AI was greater (P < 0.05) in cows treated with reCG (79.9 and 53.5 %, 76.9 and 52.3 % for the 105 UI and 140 UI reCG groups, respectively) than those in the control group (69.9 and 44.4 %, respectively). In Experiment 2, cows treated with reCG tended (P < 0.1) to achieve a greater P/AI than control cows (38.6 %, 37.1 %, 36.2 % and 28.2 % for those receiving 84 IU, 105 IU,140 IU of reCG, and those in the control group); but when P/AI of all cows treated with reCG was contrasted to that of control cows, the difference was significant (P < 0.01). In Experiment 3, P/AI in cows treated with 84 IU of reCG (54 %) did not differ from that of cows treated with serum derived eCG (59 %) but both were greater (P < 0.05) than cows treated with 105 UI of reCG (41 %). In conclusion, treatment with reCG improved fertility in suckled Bos taurus and Bos taurus x Bos indicus beef cows. In suckled Bos indicus cows, although treatment with reCG and serum derived eCG were comparable, the higher dosage of reCG was detrimental to their P/AI.

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer-based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kg CO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3 months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems.

Estrogen to progesterone ratio is associated with conceptus attachment in dairy cows receiving artificial insemination after Double-Ovsynch but not estrus†.

Prediction of pregnancy survival in lactating dairy cows can be determined by the conceptus attachment timeframe via daily pregnancy-specific protein B (PSPB) monitoring. All factors contributing to reduced fertility in dairy cows receiving AI following estrus detection remain unclear. This study aimed to determine differences in time to conceptus attachment in lactating cows treated with the fertility program Double-Ovsynch compared to cows that were detected in estrus. Additionally, we investigated various pre- and post-conception factors potentially influencing fertility outcomes. We hypothesized that AI following a natural estrus detected with automated activity monitors would lead to an extended time to conceptus attachment and lower PSPB concentrations post-attachment compared to Double-Ovsynch. There were no differences in the average time to conceptus attachments between treatments. However, cows inseminated post-estrus that experienced pregnancy loss between conceptus attachment and 60-66days post-AI exhibited diminished PSPB concentrations on Days 2 and 3 following conceptus attachment. Steroid hormone interactions were assessed with radioimmunoassay to determine the ratios of estrogen to progesterone concentrations on the day of the luteinizing hormone (LH) surge. Notably, estrogen to progesterone ratio proved to predict conceptus attachment in cows subjected to Double-Ovsynch but not in those inseminated post-estrus detection surge. In conclusion, the estrogen to progesterone ratio measured around the time of the pre-ovulatory LH surge emerges as a potentially effective tool for estimating the fertility potential of lactating dairy cows undergoing timed AI, particularly in the context of the Double-Ovsynch program.

Cattle Artificial Insemination Service in Developing Countries: Efficiency, Major Challenges and Economic Loss: A Review

This review paper summarizes information on the efficiency, major challenges and economic loss of cattle Artificial Insemination (AI) service in developing countries. Efficiency of AI service can be measured by number of services per conception (NSC), conception rate at first insemination, and calving rate (CR). The optimum recommended NSC for profitable dairy cow ranges from 1.0-2.0. The averages of NSC of the conventional AI (1.73) and fixed time AI (1.78) in the reviewed publications were in the ranges of the recommendations of NSC. The NSC varies with animal breeds, animal factor (body condition score, age and parity), semen factor (handling procedure and quality), inseminator factor (knowledge, skill and experience), production systems, management level provided and AI breeding methods. The NSC and calving rate vary with conventional AI and fixed time AI breeding methods. Likewise, calving rate (CR) is influenced by poor semen quality, poor semen handling procedure, inadequate insemination skill, poor oestrus detection and wrong time of insemination. The average success rate (CR) of conventional AI (35.15%) in the reviewed publications is far below the success rates reported in some developing countries. Nevertheless, widespread use and longtime use of AI service on selected genotype(s) can cause cattle biodiversity loss. Dairy producers incur additional costs when cows fail to conceive at their first AI services. Moreover, challenges of AI service comprised of feed scarcity, animal diseases, climate change, poor AI infrastructure, poor livestock husbandry practices, weak livestock extension systems and incapability of Artificial Insemination Technicians (AITs). This calls designing suitable interventions to improve the efficiency of AI services. Adequate feed and high level of management should be provided to the breedable cows and heifers. AI service centers should be established as per the recommended number of breedable cows and heifers. AI inputs including AITs should be always available at each AI service center, and proper AI service recording should be practiced. Sexed semen should be used to enhance the number of replacement heifers, and capacity building should be provided to AITs and community cattle breeders. Radio, television and printed medias should be used to enhance the awareness of the community cattle breeders. Frequent extension support is essential for the success rate of the AI service.

Performance of a UHF RFID Detection System to Assess Activity Levels and Lying Behaviour in Fattening Bulls

Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually, 3D-accelerometers are the tool to detect lying duration in cattle, but the handling of bulls sometimes has special requirements because frequent manipulation in daily farming routines is often not possible. An ultrahigh-frequency (UHF) radio-frequency identification (RFID) system was installed in a beef cattle barn in Germany to measure the activity and lying time of bulls. Such UHF RFID systems are typically used for estrus detection in dairy cows via activity level, but can also be considered, for instance, as an early detection for lameness or other diseases. The aim of the study was to determine whether the estimations of activity level and lying duration can also be traced in husbandry systems for fattening bulls. Two groups of bulls (Uckermärker cattle, n = 10 and n = 13) of the same age were equipped with passive UHF RFID ear transponders. Three cameras were installed to proof the system and to observe the behaviour of the animals (standing, lying, and moving). Furthermore, accelerometers were attached to the hind legs of the bulls to validate their activity and lying durations measured by the RFID system in the recorded area. Over a period of 20 days, position (UHF RFID) and accelerometer data were recorded. Videos were recorded over a period of five days. The UHF RFID system showed an overall specificity of 95.9%, a sensitivity of 97.05%, and an accuracy of 98.45%. However, the comparison of the RFID and accelerometer data revealed residuals (ԑ) of median lying time (in minutes per day) for each group of ԑGroup1 = 51.78 min/d (p &lt; 0.001), ԑGroup2 = −120.63 min/d (p &lt; 0.001), and ԑGroup1+2 = −34.43 min/d (p &lt; 0.001). In conclusion, UHF RFID systems can provide reliable activity and lying durations in 60 min intervals, but accelerometer data are more accurate.