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The objective was to compare differences in reproductive performance for dairy cows grouped based on the combination of data for predictors available during the prepartum period and before the end of the voluntary waiting period (VWP), automated estrus alerts (AEA) during the VWP, and the combination of both factors. In a cohort study, data for AEA and potential predictors of the percentage of cows that receive AI at detected estrus (AIE), pregnancies per AI (P/AI) for first service, and the percentage of cows pregnant by 150 DIM (P150) were collected from -21 to 49 DIM for lactating Holstein cows (n = 886). The association between each reproductive outcome with calving season (cool, warm), calving-related events (yes, no), genomic daughter pregnancy rate (gDPR; high, medium, low), days in the close-up pen (ideal, not ideal), health disorder events (yes, no), rumination time (high or low CV prepartum and high or low increase rate postpartum), and milk yield (MY) by 49 DIM (high, medium, low) were evaluated in univariable and multivariable logistic regression models. Individual predictors (health disorders, gDPR, and MY) associated with the 3 reproductive outcomes in all models were used to group cows based on risk factors (RF; yes, n = 535 or no, n = 351) for poor reproductive performance. Specifically, cows were included in the RF group if any of the following conditions were met: the cow was in the high MY group, had low gDPR, or had at least 1 health disorder recorded. Cows were grouped into estrus groups during the VWP based on records of AEA (estrus VWP [E-VWP], n = 476 or no estrus VWP [NE-VWP], n = 410). Finally, based on the combination of levels of AEA and RF, cows were grouped into an estrus and no RF (E-NoRF, n = 217), no estrus and RF (NE-RF, n = 276), no estrus and no RF (NE-NoRF, n = 134), and estrus and RF (E-RF, n = 259) groups. Cows received AIE up to 31 d after the end of the VWP, and if they did not receive AIE, they received timed AI after an Ovsynch plus progesterone protocol. Logistic and Cox proportional hazard regression compared differences in reproductive outcomes for different grouping strategies. The NoRF (AIE: 76.9%; P/AI: 53.1%; P150: 84.5%) and E-VWP (AIE: 86.8%; P/AI: 44.8%; P150: 82.3%) groups had more cows AIE and higher P/AI and P150 than the RF (AIE: 64.5%; P/AI: 34.9%; P150: 72.9%) and NE-VWP (AIE: 50.0%; P/AI: 38.9%; P150: 72.1%) groups, respectively. When both factors were combined, the largest and most consistent differences were between the E-NoRF (AIE: 91.3%; P/AI: 58.7%; P150: 88.5%) and NE-RF groups (AIE: 47.3%; P/AI: 35.8%; P150: 69.5%). Compared with the whole population of cows or cows grouped based on a single factor, the E-NoRF and NE-RF groups had the largest and most consistent differences with the whole cow cohort. The E-NoRF and NE-RF groups also had statistically significant differences of a large magnitude when compared with the remaining cow cohort after removal of the respective group. We conclude that combining data for AEA during the VWP with other predictors of reproductive performance could be used to identify groups of cows with larger differences in expected reproductive performance than if AEA and the predictors are used alone.

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Estrus detection in buffaloes primarily relies on behavioral and physiological signs. Especially during summer, these signs are less prominent to recognize. Thus, estrus detection is a pronounced challenge within the realm of buffalo husbandry, particularly in the summer. Therefore, a simple and accurate estrus detection method is required for buffalo farmers. The observation of fern-like salivary crystallization patterns is one such simple method to detect estrus in buffaloes, bactrian camels, beagle bitches, and cows. However, the exact mechanism for the formation of typical fern-like is not known. We hypothesized that it might be because of the estrus-specific mucins and salts. To test this hypothesis, we prepared the smears by combining different concentrations of mucin type -2 (MUC2) and -3 (MUC3) with sodium chloride (NaCl). Microscopic examination confirmed that fern-like patterns resulted from a combination of the MUC3 and NaCl produced more realistic fern patterns than that of MUC2 or BSA with salt. To predict possible mucin and salt concentration showing natural fern-like patterns at the estrus stage in buffalo saliva, we constructed a guide tree of artificially generated fern-like patterns using an image analysis online tool. This computation analysis revealed that most of the natural buffalo estrus saliva samples showing typical fern-like patterns clustered in the cluster 2 of the guide tree comprising of 13 clusters. In the cluster 2, MUC3 in combination with the salt concentrations of 100, 150, and 250 mM was commonly found in a close proximity to the natural typical fern-like patterns of saliva smear of buffaloes at estrus. Conclusively, the buffalo saliva at estrus is predicted to have a gel-forming heavily glycosylated protein such as mucin along with at least 100 mM of NaCl. RESEARCH HIGHLIGHTS: Glycoprotein and salts combination replicates fern-like pattern of buffalo saliva at estrus. MUC3 and NaCl salt combination produces more realistic fern-like patterns compared with MUC2 or BSA and salt combination. MUC3 with NaCl at 100, 150, and 250 mM consistently resembled natural estrus saliva fern-like patterns. During estrus, buffalo saliva is expected to contain heavily glycosylated mucin and at least of 100 mM NaCl.

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In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

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Studying various animal models is important for comparative biology and to better understand evolutionary development. Furthermore, when aiming to translate findings to human development, it is crucial to select an appropriate animal model that closely resembles the specific aspect of development under study. The guinea pig is highlighted as a useful platform for reproductive studies due to similarities in in utero development and general physiology with the human. This chapter outlines the methods required for guinea pig mating and collection of embryos for in vitro culture and molecular characterization. Specifically, this chapter provides detailed guidance on monitoring the estrus cycle to determine the mating time, performing a vaginal flush and smear to confirm successful mating, performing euthanasia of the guinea pig, and flushing in vivo embryos. Once collected, the embryos can be utilized for numerous downstream applications. Here we will cover embryo culturing and processing embryos for immunofluorescence.

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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.

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Visual estrus observation can only be confirmed at a rate of 50%-60%, which is lower than that obtained using a biosensor. Thus, the use of biosensors provides more opportunities for artificial insemination because it is easier to confirm estrus than by visual observation. This study determines the accuracy of estrus prediction using a ruminoreticular biosensor by analyzing ruminoreticular temperature during the estrus cycle and measuring changes in body activity. One hundred and twenty-five Hanwoo cows (64 with a ruminal biosensor in the test group and 61 without biosensors in the control group) were studied. Ruminoreticular temperatures and body activities were measured every 10 min. The first service of artificial insemination used gonadotropin-releasing hormone (GnRH)-based fixed-time artificial insemination protocol in the control and test groups. The test group received artificial insemination based on the estrus prediction made by the biosensor, and the control group received artificial insemination according to visual estrus observation. Before artificial insemination, the ruminoreticular temperature was maintained at an average of 38.95 ± 0.05°C for 13 h (-21 to -9 h), 0.73°C higher than the average temperature observed at -48 h (38.22 ± 0.06°C). The body activity, measured using an indwelling 3-axis accelerometer, averaged 1502.57 ± 27.35 for approximately 21 h from -4 to -24 h before artificial insemination, showing 203 indexes higher body activity than -48 hours (1299 ± 9.72). Therefore, using an information and communication techonology (ICT)-based biosensor is highly effective because it can reduce the reproductive cost of a farm by accurately detecting estrus and increasing the rate of estrus confirmation in cattle.

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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.

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The current estrus detection method is generally time-consuming and has low accuracy. As such, a deeper understanding of the physiological processes during the estrous cycle accelerates the development of estrus detection efficiency and accuracy. In this study, the label-free acquisition mass spectrometry was used to explore salivary proteome profiles during the estrous cycle (day -3, day 0, day 3, and day 8) in pigs, and the parallel reaction monitoring (PRM) was applied to verify the relative profiles of protein expression. A total of 1,155 proteins were identified in the label-free analysis, of which 115 were identified as differentially expressed proteins (DEPs) among different groups (p ≤ 0.05). Functional annotation revealed that the DEPs were clustered in calcium ion binding, actin cytoskeleton, and lyase activity. PRM verified the relative profiles of protein expression, in which PHB domain-containing protein, growth factor receptor-bound protein 2, elongation factor Tu, carboxypeptidase D, carbonic anhydrase, and trefoil factor 3 were confirmed to be consistent in both label-free and PRM approaches. Comparative proteomic assays on saliva would increase our knowledge of the estrous cycle in sows and provide potential methods for estrus detection.

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The objective of this study was to compare the reproductive performance of lactating dairy cows submitted to first AI after combination of estrus detection and fixed timed AI (FTAI) and FTAI only. Cows were randomly assigned to receive AI at detected estrus between 50 and 70 d in milk (DIM), if not detected in estrus, were enrolled in either Ovsynch (ED-Ov, n = 485) or PRIDsynch (ED-PR, n = 505) protocols; or received FTAI at 80 DIM after Double-Ovsynch protocol (DO, n = 501). Cows were body condition scored (BCS) at calving and at 43 DIM; and evaluated for postpartum disorders within 7 d postpartum; clinical mastitis, lameness and bovine respiratory disease were recorded until first AI. Ovarian cyclicity was monitored at 43 and 50 DIM, and at 70 and 77 DIM. Pregnancy diagnoses (PD) were performed at 32 and 63 d after AI. Overall prevalence of postpartum anovulation was 7.8%. Pregnancy per AI (P/AI) did not differ between reproductive strategies at 32 d PD (ED-Ov = 43.2%; ED-PR = 41.7%; DO= 45.3%). Primiparous cows had greater P/AI than multiparous cows (53.7% vs 36.8%). Cows on farm 1 had lower P/AI compared with their counterparts on farm 2 (42.1% vs 45.4%). Cows with BCS > 2.5 at 43 DIM had greater P/AI compared with cows with BCS ≤ 2.5 (44.5% vs 34.7%). Similar P/AI for cow's receiving AI at detected estrus and FTAI, low prevalence of disease anovulation may have contributed to the similar performance of ED-Ov, ED-PR and DO.

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Adoption of automated monitoring devices (AMD) affords the opportunity to tailor reproductive management according to the cow's needs. We hypothesized that a targeted reproductive management (TRM) would reduce the use of reproductive hormones while increasing the percentage of cows pregnant 305 d in milk (DIM). Holstein cows from 2 herds (n = 1,930) were fitted with an AMD at 251.0 ± 0.4 d of gestation. Early-postpartum estrus characteristics (EPEC; intense estrus = heat index ≥70; 0 = minimum, 100 = maximum) of multiparous cows were evaluated at 40 (herd 1) or 41 (herd 2) DIM and EPEC of primiparous cows were evaluated at 54 (herd 1) or 55 (herd 2) DIM. Control cows received the first artificial insemination at fixed time (TAI; primiparous, herd 1 = 82 and herd 2 = 83 DIM; multiparous, herd 1 = 68 and herd 2 = 69 DIM) following the Double-Ovsynch (DOV) protocol. Cows enrolled in the TRM treatment were managed as follows: (1) cows with at least one intense estrus were inseminated upon AMD detected estrus for 42 d and, if not inseminated, were enrolled in the DOV protocol; and (2) cows without an intense estrus were enrolled in the DOV protocol at the same time as cows in the control treatment. Control cows were re-inseminated based on visual or patch aided detection of estrus, whereas TRM cows were re-inseminated as described for control cows with the aid of the AMD. Cows received a GnRH injection 27 ± 3 d after insemination and, if diagnosed as nonpregnant, completed the 5-d Cosynch protocol and received TAI 35 ± 3 d after insemination. Among cows in the TRM treatment, 55.8 and 42.9% of primiparous and multiparous cows, respectively, received the first insemination in spontaneous estrus. The interaction between treatment and parity affected pregnancy 67 d after the first AI (primiparous: control = 37.6%, TRM = 27.4%; multiparous: control = 41.0%, TRM = 44.7%). The TRM treatment increased re-insemination in estrus (control = 48.3%, TRM = 70.5%). Pregnancy 67 d after re-inseminations tended to be affected by the interaction between treatment and EPEC (no intense estrus: control = 25.3%, TRM = 32.0%; intense estrus: control = 32.9%, TRM = 32.2%). The interaction between treatment and EPEC affected pregnancy by 305 DIM (no intense estrus: control = 80.8%, TRM = 88.2%; intense estrus: control = 87.1%, TRM = 86.1%). Treatment did not affect the number of reproductive hormone treatments among cows that had not had an intense estrus (control = 10.5 ± 0.3, TRM = 9.1 ± 0.2 treatments/cow), but cows in the TRM treatment that had an intense estrus received fewer reproductive hormone treatments than cows in the control treatment (2.0 ± 0.1 vs. 9.6 ± 0.2 treatments/cow). Selecting multiparous cows for first AI in estrus based on EPEC reduced the use of reproductive hormones without impairing the likelihood of pregnancy to first AI. The use of AMD for re-insemination expedited the establishment of pregnancy among cows that did not display an intense estrus early postpartum.

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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.

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Precision Livestock Farming (PLF) techniques include sensors and tools to install on livestock farms and/or animals to monitor them and support the decision making process of farmers, finally early detecting alerting conditions and improving the livestock efficiency. Direct consequences of this monitoring include enhanced animal welfare, health and productivity, improved farmer lifestyle, knowledge, and traceability of livestock products. The indirect consequences, instead, include improved Carbon Footprint and socio-economic indicators of livestock products. In this context, the aim of this paper is to develop an indicator applicable to dairy cattle farming that takes into account concurrently these indirect consequences. The indicator was developed combining the three sustainability pillars (with specific criteria): environmental (carbon footprint), social (5 freedoms of animal welfare and antimicrobial use) and economic (cost of technology and manpower use). The indicator was then tested on 3 dairy cattle farms located in Italy, where a baseline traditional scenario (BS) was compared with an alternative scenario (AS) where PLF techniques and improved management solutions were adopted. The results highlighted that the carbon footprint reduced in all AS by 6-9 %, and the socio-economic indicators entailed improvements in animals and workers welfare with some differences based on the tested technique. Investing in PLF techniques determines positive effects on all/almost all the criteria adopted for the sustainability indicator, with case-specific aspects to consider. Being a user-friendly tool that supports the testing of different scenarios, this indicator could be used by stakeholders (policy makers and farmers in particular) to identify the best direction towards investments and incentive policies.

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BACKGROUND: Good estrus detection in sows is essential to predict the best moment of insemination. Nowadays, a technological innovation is available that detects the estrus of the sow via connected sensors and cameras. The collected data are subsequently analyzed by an artificial intelligence (AI) system. This study investigated whether such an AI system could support the farmer in optimizing the moment of insemination and reproductive performance. M&M: Three Belgian sow farms (A, B and C) where the AI system was installed, participated in the study. The reproductive cycles (n = 6717) of 1.5 years before and 1.5 years after implementation of the system were included. Parameters included: (1) farrowing rate (FR), (2) percentage of repeat-breeders (RB), (3) farrowing rate after first insemination (FRFI) and (4) number of total born piglets per litter (NTBP). Also, data collected by the system were analyzed to describe the weaning-to-estrus interval (WEI), estrus duration (ED) and the number of inseminations used per estrus. This dataset included 2261 cycles, collected on farms B and C. RESULTS: In farm A, all parameters significantly improved namely FR + 4.3%, RB - 3.75%, FRFI + 6.2% and NTBP + 1.06 piglets. In farm B, the NTBP significantly decreased with 0.48 piglets, but in this farm the insemination dose was too low (0.8 × 109 spermatozoa per dose). In farm C, only the NTBP significantly increased with 0.45 piglets after the implementation of the system. The WEI as determined by the system varied between 78 and 90 h, being 10-20 h shorter in comparison with the WEI as determined by the farmer. The ED, determined by the system ranged from 48 to 60 h, and was less variable as compared to the ED as assessed by the farmer. The mean number of inseminations per estrus remained similar over time in farm B whereas it decreased over time from approximately 1.6-1.2 in farm C. CONCLUSION: The AI system can help farmers to improve the reproductive performance, assess estrus characteristics and reduce the number of inseminations per estrus. Results may vary between farms as many other variables such as farm management, genetics and insemination dose also influence reproductive performance.

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OBJECTIVE: We aimed to evaluate the efficacy of a modified Double-Ovsynch protocol vs artificial insemination following estrus detection (AIED) for the enhancement of reproductive performance in Hanwoo cattle. METHODS: Four hundred twelve Hanwoo cows were allocated to two treatment groups. The first group of cows were administered gonadotropin releasing hormone (GnRH) on Day 36 (±0.6), prostaglandin F2α (PGF2α) on Day 46 (8 to 12 days later), and GnRH on Day 49, which was followed by Ovsynch, consisting of an injection of GnRH on Day 56, PGF2α on Day 63, and GnRH 56 h and timed AI (TAI) 16 h later (modified Double-Ovsynch group, n = 203). The second group of cows underwent AIED (AIED group, n = 209) and were designated as controls. RESULTS: The pregnancy per AI 60 days after the first AI was higher in the modified Double- Ovsynch (68.5%) than in the AIED (56.5%) group, resulting in a higher probability of pregnancy per AI (odds ratio: 1.68, p<0.05). Moreover, cows in the modified Double- Ovsynch group were more likely (hazard ratio: 1.28, p<0.05) to be pregnant by 150 days after calving than cows in the AIED group, and this difference was associated with a lower mean number of AIs per conception (1.27 vs 1.39, p<0.05) and a shorter median interval between calving and pregnancy (72 vs 78 days, p<0.1). CONCLUSION: The modified Double-Ovsynch protocol, adjusted according to the herd visit schedule, can be readily used to increase the pregnancy per AI following the first AI and to shorten the interval between calving and pregnancy in beef herds.

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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.

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Lab animals, such as Guinea pigs (Cavia porcellus), are crucial for scientific development, as they play an important role in the development and quality control chain of vaccines and drugs distributed by the Brazilian public health system. Investigating their biological and physiological parameters is fundamental to raise and keep these animals, so the handling of the facilities that hold them can be updated whenever new information comes up, with the well-being of the animals and alignment with the 3 Rs in mind. In the search for understanding reproductive aspects of Guinea pigs, the present study had the main goal of studying puberty by means of estrous cycle analysis in short-haired Guinea pigs. Guinea pigs have a vaginal occlusive membrane that covers the vaginal orifice. Its rupture takes place gradually and naturally, moments before labor and during estrus. The present study followed 42 females as for the presentation of the vaginal occlusive membrane. Once the membranes ruptured spontaneously, a swab was collected to study vaginal cytology. Membrane rupture was observed in 39 females; six females showed membrane rupture with less than 21 days of age (17 to 21 days). Twenty-three females were characterized as being in estrus due to cytology showing a prevalence of anucleated superficial cells. One of these females was younger than 21 days old. The opening of the vaginal occlusive membrane took place most frequently in intervals between 17 and 18 days, and the membrane remained open between one and three consecutive days. It was possible to follow three cycles of membrane opening on six females. The present study showed the need to adapt handling guidelines for C. porcellus kept in research animal facilities. The early age of puberty imposes the need of separate the female daughters from their fathers at 16 days old.

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The dairy industry is searching for new technologies to address low (<50%) estrus detection. However, the lack of information on the potential economic benefits regarding new technology implementation has led some dairy producers to continue using conventional estrus detection methods (e.g. visual observation of standing to be mounted). The objective of this study was to compare the costs of infrared thermography (IRT), visual observation (VO) and ovulation synchronization (Ovsynch: OVS) as breeding strategies at different accuracy levels (Sensitivity [Se], Specificity [Sp]) and pregnancy rates (PR). The costs associated with Breeding, Feeding, Operation Costs, Return to Equity and Culling Risk per estrus detection rate (ER; 30-100%, conception rate for OVS; 30-100%), PR [PR per Parity group; 1-2 (50%), 3-4 (43%), and >4 (41%)], and ER accuracy determined the potential financial benefit of each breeding method for a representative farm. Breeding Cost results (Canadian dollars per cow; CAD/cow) showed a higher cost of OVS (138.99) as compared to VO (115.78) and IRT (127.69). Pregnancy Costs were affected by Breeding Cost; however, ER had a significant effect on PR expense for each method, IRT (ER; 30%: 210.38; 100%: 132.19), VO (ER; 30%: 205.93; 100%: 129.39), and OVS (ER; 30%: 247.21; 100%: 155.33). The minimum Se level with a positive Financial Effect for IRT and VO was 60% with a Sp of 100%, and for the OVS was Se 65% and Sp 100%. However, when the Se was 100% a positive Financial Effect was observed with a minimum Sp of 85% for IRT and 75% for VO. Culling Risk was reduced if ER increases differently depending on the parity group. Implementing of IRT as an estrus detection method yields a competitive breeding cost compared to VO and OVS. Further, breeding methods must accomplish at least ∼60% accuracy to have a positive net return.

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The objectives of this study were to determine the effects of GnRH at the time of artificial insemination (AI) on ovulation, progesterone 7 d post-AI, and pregnancy in cows detected in estrus using traditional methods (tail chalk removal and mount acceptance visualization) or an automated activity-monitoring (AAM) system. We hypothesized that administration of GnRH at the time of AI would increase ovulation rate, plasma progesterone post-AI, and pregnancy per AI (P/AI) in cows detected in estrus. In experiment 1, Holstein cows (n = 398) were blocked by parity and randomly assigned to receive an injection of GnRH at the time of estrus detection/AI (GnRH, n = 197) or to remain untreated (control, n = 201) on 4 farms. The GnRH was administered as 100 µg of gonadorelin acetate. Ovarian structures and plasma progesterone were assessed in a subset of cows (GnRH, n = 52; control, n = 55) in experiment 1 at the time of AI and 7 d later. In experiment 2, a group of 409 cows in an AAM farm were enrolled as described for experiment 1 (GnRH, n = 207; control, n = 202). Data were categorized for parity (primiparous vs. multiparous), season (cool vs. warm), number of services (first vs. > first), DIM (>150 DIM vs. ≤150 DIM), and for AAM cows in experiment 2 for activity level (high: 90-100 index vs. low: 35-89 index). Pregnancy diagnosis was performed between 32 and 45 d post-AI (P1) and 60 to 115 d post-AI (P2). In experiment 1, there was no difference in plasma progesterone at day of estrus detection (control = 0.09 ng/mL vs. GnRH = 0.16 ng/mL), 7 d later (control = 2.03 ng/mL vs. GnRH = 2.18 ng/mL), and ovulation rate (GnRH = 83.2% vs. control = 77.9%) between treatments. There were no effects of GnRH in experiment 1 for P/AI at P1 (control = 43.3% vs. GnRH = 38.6%), P2 (control = 38.4% vs. GnRH = 34.5%), and for pregnancy loss (control = 9.8% vs. GnRH = 8.2%). In experiment 2, there were no effects of GnRH for P/AI at P1 (control = 39.6% vs. GnRH = 40.1%), P2 (control = 35.0% vs. GnRH = 37.4%), and for pregnancy loss (control = 9.5% vs. GnRH = 6.2%). There was a tendency for a parity effect on P/AI for P1, but not P2 or for pregnancy loss. High-activity cows had greater P/AI in P1 (low activity = 27.9% vs. high activity = 44.1%), P2 (low activity = 21.8% vs. high activity = 41.2%), and lower pregnancy loss (low activity = 20.7% vs. high activity = 5.1%), but there were no interactions between treatment and activity level. The current study did not support the use of GnRH at estrus detection to improve ovulatory response, progesterone 1 wk post-AI, and P/AI. More research is needed to investigate the relationship between GnRH at the time of AI and activity level in herds using AAM systems.

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In this study, we determined the applicability of the background image subtraction technique to detect estrus in tie-stalled cows. To investigate the impact of the camera shooting direction, webcams were set up to capture the front, top, and rear views of a cow simultaneously. Video recording was performed for a total of ten estrous cycles in six cows. Standing estrus was confirmed by testing at 6 h intervals. From the end of estrus, transrectal ultrasonography was performed every 2 h to confirm ovulation time. Foreground objects (moving objects) were extracted in the videos using the background subtraction technique, and the pixels were counted at each frame of five frames-per-second sequences. After calculating the hourly averaged pixel counts, the change in values was expressed as the pixel ratio (total value during the last 24 h/total value during the last 24 to 48 h). The mean pixel ratio gradually increased at approximately 48 h before ovulation, and the highest value was observed at estrus, regardless of the camera shooting direction. When using front-view videos with an appropriate threshold, estrus was detected with 90% sensitivity and 50% precision. The present method in particular has the potential to be a non-contact estrus detection method for tie-stalled cows.

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BACKGROUND AND AIM: Estrus detection plays a crucial role in the success of animal reproduction. It was previously reported that body temperature changes during estrus. This study aimed to investigate the relationship between vaginal temperatures (VTs) measured by a data logger, ovarian activity, and hormonal cyclic changes in camels. MATERIALS AND METHODS: Six mature, healthy, non-pregnant dromedary, and 10-12-year-old camels were included in the study. The ovarian activity was monitored with ultrasonography, and estrus behavior was evaluated using an active and virile male camel. Animals were inserted with a blank controlled internal drug release device attached with an intravaginal data logger. Every hour, the ambient temperature was recorded by another data logger. Blood samples were collected, and sera were used to measure estradiol and progesterone levels. RESULTS: The whole follicular cycle lasted 25.41±1.36 days, and the maximum sizes of the dominant follicle in the first and second follicular waves were 1.63±0.27 cm and 1.94±0.42 cm, respectively. There was a significant positive correlation between the follicular diameter and estradiol-17b level (p<0.01, r=0.397). There was no correlation between the follicular diameter and progesterone level (p>0.05, r=0.038), which remained low during the whole period of the experiment. The mean daily VT was significantly correlated with the diameter of the dominant follicle (1.7-2.2 cm, p<0.01, r=0.52). CONCLUSION: Measurement of VT will improve the accuracy of estrus prediction. Further studies are recommended to validate VT in camel reproduction.