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Reduced behavioral signs and intensive management complicates oestrus detection in dairy cows. Hence, a score card incorporating more secondary signs was prepared, comprising sheet A and B for external findings and internal changes respectively. Based on ultrasonographic verification, animals getting at least score 20 (in sheet A) were indicated for internal examination and a cumulative score of at least 50 (sheets A&B) were ready for insemination. The score card was validated for oestrus intensity assessment in 60 postpartum (PP)cows (Gr. 1), closely watched for minor signs of oestrus followed by clinico gynecological examinationand 22 cows (Gr 2) under routine management formed the control group. Against 50.00 % in Gr. 2, 71.67 %cows of Gr. 1were detected in oestrus, including 9 animals reported twice, achieving oestrus detection rate (ODR) of 86.67 %. Despite non-significant variation ofAI success (60.42 versus 55.56 %) between Gr. 1 and 2, animals conceived during the study period was 48.33% and 22.73% respectively (P<.05), producing major enhancement of reproductive performance. Thus, the new score card for oestrus detection and insemination placing more focus on secondary changes will be highly promising to enhance oestrus detection and herd fertility under intensive management. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag’s accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows.

An accurate and timely monitoring is a high demand for the oestrus behavior of dairy cows in modern dairy farming. In this research, a lightweight recognition was proposed to detect the oestrus behavior of cows using the combined YOLO v5n and channel pruning. The high precision of the network was established before that. Firstly, the model was sparsely trained, according to different sparsity rates. The best sparsity effect of the model was obtained at the sparsity rate of 0.005. Then, the channel pruning was utilized to prune the modules, including the CSPDarknet53 backbone feature extraction network, in order to compress the model structure and parameters for the high detection speed. 2 239 images of cows’ mounting behavior were also collected, including 1 473 images in the daytime, 88 images in the daytime (backlight), and 160 images in the nighttime. Four occlusion situations were considered, including 1 045 images without occlusion, as well as 397, 184, and 95 images with the slight, moderate, and heavy occlusion, respectively. Three target sizes were also considered, including 46, 1 191, and 484 images with the large, medium, and small targets, respectively. The pruned model was veried on the test set, and then to compare with the Faster R-CNN, SSD, YOLOX-Nano, and YOLOv5-Nano. The test results showed that the mean average precision (mAP) of the model after pruning was 97.70%, the Params were 0.72 M, the Floating Point operations (FLOPs) were 0.68 G, and the detection speed was 50.26 fps. Furthermore, the Params and FLOPs were reduced by 59.32%, and 49.63%, respectively, under the condition of constant mAP, and the detection speed increased by 33.71%, compared with the original model YOLOv5-Nano. Consequently, the pruning operation was effectively improved the performance of the model. The mAP of the model was close to the Faster R-CNN, SSD, YOLOX-Nano, but the Params were reduced by 135.97, 22.89, and 0.18 M, respectively, the FLOPs were reduced by 153.69, 86.73, and 0.14 G, respectively, and the detection speed increased by 36.04, 13.22, and 23.02 fps, respectively. In addition, the detection performance of the model was tested in the complex environments, such as different lighting, different occlusion, multi-scale targets, and new environments. The results showed that the mAP was 99.50% in the nighttime environment, the average mAP was 93.53% under the three occlusion conditions, the average mAP was 98.77% under the medium and small targets, the detection rate was 84.62% in the generalization test, and the false detection rate was 7.69%. To sum up, the improved model can fully meet the high requirements for the accurate and real-time monitoring of cows' oestrus behavior under complex breeding environments and all-weather conditions, due to the lightweight, high precision, robust, and high generalization. [ABSTRACT FROM AUTHOR]

Context: Most of our current knowledge regarding echidna reproductive behaviour is based on qualitative measurements; therefore, it is unclear if specific behavioural cues could be utilised in their captive reproductive management. Aims: This study aimed to identify quantitative changes in general and reproductive behaviour of echidna breeding pairs and pregnant females that might facilitate the detection of oestrus and impending oviposition and provide a summary of reproductive behaviour observed in a captive colony over a three-year observation period. Methods: Three echidna breeding pairs and two trios were were monitored daily for seven reproductive and eight general behaviours during the 2020 breeding season. After confirmed copulation, females were monitored for four egg-laying and eight general behaviours until egg incubation. General observations of reproductive behaviours during the 2018–2020 breeding seasons were recorded as part of routine husbandry. Key results: For breeding pairs, there was a significant rate of change over time before and after copulation for the behaviours 'urogenital sniffing', 'rolling' and 'copulation attempt'. For pregnant females, time engaged in 'pacing' significantly increased while 'time eating' and the 'qauntity of food eaten' significanlty decreased on the day of oviposition. We were not able to identify oestrus from specific behaviours, but our observations suggest that the female echidna's period of receptivity is less than 24 h. Conclusions: The frequency that males express 'urogenital sniffing', 'rolling' and 'copulation attempt' toward the female can be used to alert zookeepers that copulation has likely occurred. Increased pacing, reduced feeding time and quantity of food eaten can aid zookeepers to identify impending oviposition. Implications: This study demonstrates that there are quantifiable changes in specific echidna behaviours that can be incorporated into zoo husbandry practices to improve the reproductive management of this species. Important aspects of echidna reproductive behaviour are unknown, compromising captive breeding success. The frequency that males express 'urogenital sniffing', 'rolling' and 'copulation attempt' toward the female can indicate that copulation has likely occurred. When female echidnas are pregnant, an increase in pacing behaviour, reduced feeding time and a lack of eating can indicate impending egg laying. The identified behaviours can be used in the zoo industry to improve captive reproductive management of echidna colonies. [ABSTRACT FROM AUTHOR]

Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however poor success and, in some situations, high costs in Kenya, have been discouraging. Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy. A cow-side progesterone lateral flow test, P4 Rapid, was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency. Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya. Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant). Cattle with low P4 were inseminated. Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%. Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%). In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and nine of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion.

Objective. Efficiency in heat detection, determination of the duration and type of estrous cycle and progesterone concentration in sheep racial biotypes in the high tropics was evaluated. Materials and methods. In 32 adult ewes, biotype Criolla, Hampshire, Romney Marsh and Corriedale, during the postpartum period, Heat Detection Efficiency - EDC, cycle type: Short - CC, Regular - CR and Long - CL, Cycle Duration was determined. Oestrus – DCE and progesterone concentrations – CP4. CP4 were assessed by ELISA technique. Statistical analysis used a completely randomized model and repeated measures over time. Results. The average EDC was 64.88%, presenting 35.12% CC, 59.92% CR and 4.96% CL. The average of regular DCE was 16.9±0.89 days. The Criollo group presented the highest ranges of variation of CP4, showing changes in its steroidogenic activity of the corpus luteum similar to that observed with the Hampshire group. Between days 4 and 12, the Romney Marsh group presented the lowest CP4., being similar to the behavior of the Corriedale group. Conclusions. CP4 increases from days 2 to 6, remaining high and constant between days 10-12, gradually decreasing until day 18, progressively evidencing a possible functional luteolysis that leads to structural luteolysis. The CP4 may be associated with adaptation phenomena of some racial groups, which affect the reproductive efficiency that is observed to be differentiated between them. [ABSTRACT FROM AUTHOR]

Accurate and reliable monitoring of ovarian activity is challenging in many felids as current methods are either invasive or not amenable to real-time assessments. This 45-day study assessed whether accelerometry and infrared (IR) thermography can be used to address these limitations. Intact female domestic cats (n = 12) were given 0.088 mg kg−1 day−1 altrenogest (progestin) orally for 37 days to suppress follicular growth. On Day 40, cats were given 75 IU eCG im to induce follicular growth and 50 IU hCG im 80 h later to induce ovulation. Cats were ovariohysterectomised 30–31 h after the hCG treatment. Actical® accelerometers were fitted to the cats' collars and activity monitored continuously from the start of the altrenogest treatment until ovariohysterectomy. Infrared images of the perivulvar, perianal, and gluteal area were taken of each cat on Day 30 and daily from Days 36–45 of the study. Perivulvar temperature (PVT), PVT relative to gluteal temperatures (PVT-GT), and PVT relative to perianal temperature (PVT-PAT) were recorded for each image. Blood samples were collected on Days 0, 10, 30, and 40, immediately prior to the hCG treatment, and at the time of ovariohysterectomy. Serum oestradiol and progesterone concentrations indicated complete ovarian suppression by Day 30 and, together with morphological assessment of the ovaries, confirmed the induction of follicular growth and ovulation in all cats. Daily activity counts differed among cats (P < 0.001), so the daily activity counts of each cat were converted to a proportional change from the average daily activity count from Days 30–39 (defined as the 'proportional daily activity'). Proportional daily activity counts increased after the stimulation of follicular growth with eCG, with peak levels (2.03 ± 0.29-fold higher than pre-treatment levels; P = 0.006) occurring three days after the eCG treatment. The PVT-GT showed the greatest sensitivity to detect subtle changes in body temperature, increasing from early to late follicular growth (1.96 ± 0.33 °C increase from Day 41–43; P < 0.001) and decreasing after hCG-induced ovulation (1.24 ± 0.41 °C decrease from Day 43–45; P = 0.01). In conclusion, both accelerometry and IR thermography show potential as non-invasive, real-time methods for assessing ovarian activity in cats, but further research is required to determine if these methods could be used to monitor natural/non-stimulated oestrous cycles. • Accelerometry and infra-red thermography can monitor ovarian activity in cats. • Physical activity levels of cats increase after the induction of follicular growth. • Perivulvar temperatures of cats change with follicular growth and ovulation. [ABSTRACT FROM AUTHOR]

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.

Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however high costs and often poor success rates have been discouraging. Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy. A cow-side progesterone lateral flow test, P4 Rapid, was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency. Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya. Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant). Cattle with low P4 were inseminated. Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%. Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%). In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and 9 of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion.

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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. [ABSTRACT FROM AUTHOR]

This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm, applied in the field of sow production. A bionic boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus of sows after weaning. Machine vision technology was used to identify the interactive behaviour between empty sows and bionic boars and to establish deep belief network (DBN), sparse autoencoder (SAE), and support vector machine (SVM) models, and the resulting recognition accuracy rates were 96.12%, 98.25%, and 90.00%, respectively. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow’s oestrus states. In contrast with the methods of other studies, the proposed innovative design for recyclable bionic boars can be used to check emotions, and machine vision technology can be used to quickly identify oestrus behaviours. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for a sow’s conception time.

Contents: Urine samples of female goats in pro‐oestrus, oestrus and post‐oestrus phases were analysed for finding oestrus‐specific volatile compounds using gas chromatograph–mass spectrometry (GC‐MS), and proteins using sodium dodecyl sulphate‐polyacrylamide gel electrophoresis (SDS‐PAGE) and matrix‐assisted laser desorption–ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS). Fourteen urinary volatile were identified covering all three phases among which four compounds, 1‐Tetradecanol, n‐Pentadecanol, 3‐Methylene tridecane and 2‐Ethyl‐1‐dodecene, were unique to oestrus. Also, oestrus urine contained a 25 kDa protein, which was totally absent in pro‐oestrus urine, and less‐expressed in post‐oestrus urine. This protein revealed to be complement C3 fragment. This pilot study, for the first time, reveals the difference in urinary volatile compounds and proteins in the female goat during the different phases of oestrous cycle. The four unique volatile compounds and a 25 kDa protein that appeared as oestrus‐specific in this study warrant further investigation to consider them as urinary biomarkers of oestrus in goats. [ABSTRACT FROM AUTHOR]

Artificial insemination program to increase production is affected by oestrus. Breeders generally identify cows in oestrus based on their behavior. Currently, oestrus can be predicted and scheduled due to PGF2α synchronization. This study aims to determine the detection of oestrus after PGF2α synchronization, and more specifically to distinguish audio-visual behavior. The six cows used were synchronized using PGF2α. Descriptive methods include (mean, standard deviation). Test. Chi-square was used for qualitative data comparison. The observed variables were the onset of oestrus and lust behavior (looking at the audiovisual characteristics, using video and the Audio Spectrum Monitor (ASM) application. The results showed that the response to oestrus appeared 30 hours after synchronization in 5 cows. The visual behavior that appears a lot is Mounting activity, Mucus Exit, Vulva Swelling, Restlessness and Vocalization duration. ASM recordings show the frequency in cows that are in oestrus between 405 Hz -485 Hz after PGF2α injection. In conclusion, the audiovisual behavior of cows after synchronization shows that the cows are in a state of oestrus, and are ready for artificial insemination.

The aim of this study was to assess the relationship between the reproductive management practices and the performance of replacement heifers on large commercial dairy farms. The individual data of 14,763 heifers, first inseminated in 2014, were analysed from 33 Holstein-Friesian dairy herds in Hungary. The relationships between management practices and major reproductive parameters (age at first service, AFS; age at first calving, AFC; conception risk to first insemination, CR1; and pregnancy status at 20 months of age) were examined by mixed-effects models, with the herd as the random effect. The results showed that farms using oestrus detection aids experienced reduced AFS (p < 0.001) and AFC (p = 0.001). Observation of oestrus for shorter periods instead of continuously showed a tendency towards lower AFC (p = 0.057) and was associated with higher odds of pregnancy at 20 months of age (p = 0.020). Heifers on farms using sexed semen had younger AFS, but poorer CR1, compared to those using conventional semen exclusively (p < 0.05). In addition, the odds of heifers being pregnant by 20 months of age was higher on farms with more experience using sexed semen (p = 0.020). Frequent pregnancy diagnosis (i.e. more than once per week) was associated with younger AFC (p = 0.023). Our results suggest the use of certain advanced reproductive management practices for heifer reproductive management in large dairy herds (e.g. oestrus detection aids), which can improve reproductive efficiency considerably, but are currently used only to a limited extent. [ABSTRACT FROM AUTHOR]

• A neck tag equipped with low-cost dual-channel microphones and a multi-threshold-based sound extraction method were developed to effectively collect vocalisations of individual cows. • A series of statistical methods consisting of significance analysis, correlation analysis, and individual difference analysis were adopted to evaluate the availability of acoustic and statistical features of dairy cow vocalisations for heat detection. • A dual-LSTM joint discriminant strategy based on optimal parameters was proposed to achieve the identification of oestrus state and the estimation of oestrus onset. Timely identification of cows in heat is a fundamental issue in dairy farming. Although current studies have confirmed that several cow vocalisation characteristics can be used for oestrus identification, the reliability and practicability of oestrus detection methods are limited due to background noise, a lack of data support for sound feature selection, and the unsatisfactory effectiveness of vocalisation recognition algorithms. To overcome these predicaments, a dual-channel recording device and a sound event extraction method were developed in this study. The experimental results in respect of 62 Holstein dairy cows showed that the sound sample recording accuracy was 80.4%, and the noise filtering accuracy was 94.3%, indicating the effectiveness of the vocalisation extraction algorithm in a noisy farm environment. Moreover, two unique sound features with remarkable discrimination ability, i.e., the number of consecutive vocalisations and the maximum consecutive times, were used to improve the effectiveness of oestrus detection. The Friedman test, the Spearman rank order correlation coefficient, and the Kruskal-Wallis test were used to obtain the combination of features with the most optimal discrimination. Subsequently, a dual-LSTM (Long Short-Term Memory) joint discriminant strategy based on optimal combinations was proposed to promote oestrus detection performance. The results of the blind test on the vocalisation data of 20 oestrus cows and 11 non-oestrus cows demonstrated that the oestrus detection rate of the discrimination strategy reached 100%, and had a temporal advantage over the activity-index-based method in determining the onset of oestrus. The improvements of the selection and-based oestrus detection method mainly included accurately extracting individual cow vocalisations, supplementing the explicability of feature selection, and enhancing the refinement of oestrus recognition. Meanwhile, it is of positive significance for applying sound acquisition devices in oestrus detection. Furthermore, combining sound identification with other automated detection techniques appears to be a promising method for promoting the oestrus detection rate. [ABSTRACT FROM AUTHOR]

AIMS: To determine the difference in reproductive performance between cows detected in oestrus during morning or afternoon milking when artificial insemination (AI) was carried out once daily, either after the morning or afternoon milking. METHODS: The study used 20,816 records for cows in 30 spring calving dairy herds that used a camera-based system for oestrus detection during both morning and afternoon milkings. This system automatically determined whether a pressure-sensitive oestrus detection device had been activated or not, or was missing. In all study herds cows were milked twice daily and AI was performed by professional AI technicians once daily, after either morning or afternoon milking. The time of recorded oestrus was classified into AM or PM oestrus groups using 12:00 noon as the cut-off point and time of AI was classified into AM or PM AI groups using 13:00 as the cut-off point. Reproductive performance was determined using non-return to oestrus (NRR) between 2 and 24 days after AI, and was compared between oestrus and AI groups using generalised linear mixed models. RESULTS: Most AI (18,621/20,816; 89%) were carried out after morning milking, and more cows were recorded in oestrus during morning milking (12,328/20,816; 59%) than during afternoon milking (8,488/20,816; 41%). The NRR of cows inseminated after morning milking did not differ (OR=1.04; 95% CI=0.97–1.10) for cows recorded in oestrus in the morning (LSM=68.6 (95% CI=66.3–70.9)%) or afternoon (LSM=67.9 (95% CI=65.4–70.2)%); and did not differ for cows inseminated after the afternoon milking (OR=1.10; 95% CI=0.92–1.32) for cows recorded in oestrus in the morning (LSM=63.4 (95% CI=58.2–68.2)%) or afternoon (LSM=61.2 (95% CI=55.8–66.3)%). There were no differences in NRR between cows detected in oestrus during the morning or the afternoon milking after AI with either liquid (OR=1.04; 95% CI=0.97–1.12) or frozen-thawed (OR=1.02; 95% CI=0.88–1.18) semen. CONCLUSIONS AND CLINICAL RELEVANCE: Results from the present study show that, under New Zealand conditions, the difference in NRR between cows first detected in oestrus during morning milking and those first detected in oestrus during afternoon milking is likely small when AI is carried out once daily after either morning or afternoon milking, regardless of whether the semen used for AI is liquid or frozen. Because twice daily AI is more costly than once daily AI, these results do not support a change to the current once daily AI practice in New Zealand. [ABSTRACT FROM PUBLISHER]

• Sound recognition in cow heat detection remains a challenge. • Integration of hardware and filtering algorithm for extracting cow call clips. • The most suitable combination of cow sound features is determined. • Optimal match between time window and discrimination method for oestrus detection was found. • This study proves sound signals can be used as an early indicator of oestrus cows. Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag's accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows. [ABSTRACT FROM AUTHOR]

Objectives The three objectives of this study were to determine the positive predictive value (PPV) of oestrus diagnosis (heat detection accuracy) by dairy farmers, calculate the diagnostic sensitivity and specificity of the P4 Rapid milk progesterone assay for detecting a corpus luteum and evaluate the economics of using a cow-side milk progesterone assay designed to aid oestrus diagnosis. Methods Milk samples were collected from 752 cows diagnosed in oestrus by farm personnel on 14 dairy farms. Samples were tested using the P4 Rapid milk progesterone assay to estimate the PPV of oestrus diagnosis at each farm and a crude pooled mean of PPV of oestrus diagnosis across all farms. A further 156 milk samples were collected from cows with luteal tissue status determined by transrectal ultrasound and tested by the P4 Rapid assay to enable calculation of the sensitivity and specificity of the P4 Rapid assay. Results For pooled farm samples, the PPV was 97.0%, with a range between farms of 88.9-100%. Sensitivity of the P4 Rapid milk progesterone assay for detecting a corpus luteum was 90.1% and specificity was 98.7%. Misclassification of oestrus in cows previously identified as pregnant was the most common cause of false-positive oestrus diagnoses by farm personnel. Conclusion Routine testing of milk progesterone in all cows diagnosed in oestrus is not economically justified and may even slightly reduce submission rates; conversely, strategic use of cow-side milk progesterone assays can improve herd reproductive performance by facilitating decisions on whether to rebreed cows previously diagnosed as pregnant. [ABSTRACT FROM AUTHOR]

Without sensor-aided animal data measuring systems far fewer oestrus cycles are recognized because cycle length, oestrus duration and oestrus intensity have developed negatively at high animal performance rates. This development makes it eminently clear that observation of the mating season in the dairy cattle sector is even more important than assumed so far if the financial losses due to insufficient herd fertility are not to become a business problem. Electronic identification and measuring systems represent key technologies for progressive automation in animal husbandry in modern, future-oriented livestock farming. Suitable objective measuring systems are needed in animals husbandry to quickly and safely recognize animal illness, normal oestrus cycle, silent heat or suffering from stress. Pedometer and transponder from different companies play an important role for measuring from animals data and statements in animals health and oestrus monitoring. Modern sensors (sensors, bio sensors), increasingly non-invasive measuring and transfer methods make crucial improvements in the potential for measuring animal data. A new type of pedometer, called ALT pedometer, for three measurement parameters (activity, lying time, temperature), a real time watch and a change measuring time interval was developed. With this system it is possible to select different time intervals between 1 and 60 min for continuous measuring. The results for oestrus detection are excellent. The high correspondence between the measuring parameters activity and lying time allow a statement to be made early and safely on animal illnesses and the time of the oestrus cycle.

Efficient and accurate estrus detection is a key management factor determining acceptable reproductive performance in dairy herds. Previous studies have shown that dogs can differentiate between vaginal mucus samples of cows in estrus and vaginal mucus samples of cows in diestrus with an accuracy between 40.3% and 97.0%. We set out (1) to develop a specific training protocol for training dogs to identify cows in estrus from the feed alley and (2) to determine sensitivity and specificity of trained dogs to detect cows in estrus. Six dogs were trained by means of positive reinforcement to detect cows in estrus from the feed alley following the training protocol. Four of those dogs participated in the final test after an average training time of 50 h per dog. Overall, they correctly identified positive cows as being positive in 23 out of 32 cases (i.e. sensitivity of 71.9%) and falsely classified positive cows as being negative in nine cases (28.1% type II errors). Out of 128 cases 119 were correctly classified as true negative cows (i.e. specificity of 93.0%) and in nine cases negative cows were falsely identified as positive cows (7.0% type I errors). Further research is warranted to develop an optimized training protocol that allows training estrus detection dogs for practical use within an appropriate time. [ABSTRACT FROM AUTHOR]

Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however poor success and, in some situations, high costs in Kenya, have been discouraging.  Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy.  A cow-side progesterone lateral flow test, P4 Rapid , was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency.  Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya.  Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant).  Cattle with low P4 were inseminated.  Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%.  Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%).  In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and nine of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Peters AR et al.)

Abstract: Dogs can be trained for various odour detection tasks, and have been used to search for explosives, drugs or cancer biomarkers. However, there are only a few studies that have attempted to train dogs for oestrus detection using vaginal fluid, milk or urine of cows. In this study, seven dogs were trained utilizing special training equipment to discriminate oestrus against dioestrus scent in vaginal fluid, urine and milk of dairy cows. We set out (1) to determine the length of the learning phase of dogs using vaginal discharge to identify cows in oestrus, (2) to prove that dogs can generalize oestrus specific odour of cows and are not limited to an individual cow scent in oestrus, and (3) to study if dogs can identify oestrus specific odour in urine and milk, after they have been trained with vaginal fluid. Our results demonstrate that dogs can be trained for oestrus detection with vaginal swabs or urine of cows using special laboratory equipment within 52 positive reinforced contacts to the target scent. The dogs were correctly identifying the oestrus sample with a maximum accuracy of 80.3%. They could verify this accuracy in a blinded test situation testing novel samples. The dogs in this study were able to detect oestrus in urine and milk samples spontaneously, after being trained with vaginal fluid samples. [Copyright &y& Elsevier]

Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular, the ability to identify oestrus is investigated as this is of great importance to farm management. Progesterone concentration is a more accurate but more expensive method than pedometer counts, and we evaluate the added benefits of a model that includes this variable. The resulting model is biologically sensible, but validation is difficult. We utilize some auxiliary data to demonstrate the model's performance. [ABSTRACT FROM AUTHOR]

Abstract: Automated monitoring and detection of oestrus in dairy cows is attractive for reasons of economy in dairy farming. While high performance detection has been shown possible using high-priced progesterone measurements, detection results were less reliable when only low-cost sensor data were available. Aiming at improving detection scheme reliability with the use of low-cost sensor data, this study combines information from step count and leg tilt sensors. Introducing a lying balance for the individual animal, a novel change detection scheme is derived from observed distributions of the step count data and the lying balance. Detection and hypothesis testing are based on generalised likelihood ratio optimisation combined with time-wise joint probability windowing based on the duration of oestrus and oestrus intervals. It is shown to be essential that cow-specific parameters and test statistics are derived on-line from data to cope with behaviours of individuals. Performance is validated on 18 sequences of data where definite proof of prior oestrus was available in form of subsequent pregnancy. These data were extracted from data sequences from 44 dairy cows over an 8 months period. The results show sensitivity 88.9% and error rate 5.9.%, which is very satisfactory when only cheap sensor data are used. [Copyright &y& Elsevier]

Abstract: Without sensor-aided animal data measuring systems far fewer oestrus cycles are recognized because cycle length, oestrus duration and oestrus intensity have developed negatively at high-animal performance rates. This development makes it eminently clear that observation of the mating season in the dairy cattle sector is even more important than assumed so far if the financial losses due to insufficient herd fertility are not to become a business problem. Electronic identification and measuring systems represent key technologies for progressive automation in animal husbandry in modern, future-oriented livestock farming. Suitable objective measuring systems are needed in animal husbandry to quickly and safely recognize illness, normal oestrus cycle, quiet heat or stress in animals. Pedometers and transponders from various companies play an important role in collecting data from animals and assessing animal health and oestrus. Modern sensors (sensors, bio-sensors), increasingly utilize non-invasive measuring and transfer methods to make crucial improvements in the potential for collecting animal data. A new type of pedometer, called ALT pedometer, for three measurement parameters (activity, lying time, and temperature), a real-time watch and a changeable measuring time interval was developed. With this system it is possible to select different time intervals between 1 and 60min for continuous monitoring. The results for oestrus detection were excellent. The high correspondence between the measurement parameters, activity and lying time, allow a statement to be made early and safely on animal illnesses and the time of the oestrus cycle. [Copyright &y& Elsevier]

Abstract: Finland blue fox (Alopex lagopus) has great reputation in pelt industry around the world for its large size and top-ranking fur quality; however, both the herd size and the average survival rate of purebred offspring are rather low in production systems in China. Surgical transfer of blue fox embryos was investigated as a means to increase the population fox and also as a possible means to conserve endangered canine species. The animals were chosen on the basis of synchrony in natural oestrus. During the reproductive season of blue fox, 59 embryos were flushed from 6 farmed donors 9–11 days after the first insemination, and 53 embryos were transferred surgically into the uteri of the 6 paired recipients with natural synchronized oestrous. Two of the recipients littered 46–49 days after embryo transfer; one gave birth to 7 pups and the other 1 pup. This report describes the first successful embryo transfer in the farmed blue fox in China. [Copyright &y& Elsevier]

Abstract: Reproductive management, in particular timely oestrus detection, is important for profitable dairy production. The aim of this study was to develop a biological model to predict reproductive state on the basis of milk progesterone measures. A number of additional inputs were incorporated to make use of other known effectors of reproductive performance that are not reflected in progesterone levels. These are: days from calving, breed, parity, signs of behavioural oestrus, insemination dates, pregnancy determinations, energy status, body fat status, milk urea content and reproductive disorders associated with calving. A dynamic, deterministic model was developed. It is designed to run each time a new trigger input (progesterone, behavioural oestrus, inseminations, pregnancy determinations) occurs using the current and previous values and can run in the absence of the additional inputs. The milk progesterone values are smoothed using an extended Kalman filter before being processed in the biological component of the model. The model predicts the reproductive status of the cow, which can be one of three mutually exclusive states: postpartum anoestrus, oestrus cycling, and potentially pregnant. The other model outputs are all reproductive status specific with the exception of days to next sample (DNS), which is calculated in each model run regardless of reproductive status. DNS is designed to feedback to the sampling system so that the frequency of milk sampling (i.e. progesterone measurement) can be varied according to the predicted likelihood of a future reproductive event, such as onset of oestrus cycling. The other model outputs are: risk of prolonged postpartum anoestrus, risk and type of ovarian cyst, onset of oestrus, likelihood of a potential insemination succeeding, and likelihood of being pregnant (following oestrus). The model was evaluated using three simulated datasets consisting of a timeseries of progesterone values centred on each of the three reproductive statuses and including relevant additional information. Test runs were carried out on the full datasets and then on reduced data. The data reductions were made by using only those values that would have been available if the model days to next sample function was used to control sampling frequency. The sensitivity of the model to noise in the raw progesterone data was examined by adding 1, 2, or 3 residual standard deviations (1.85ng/ml) random variation to the original data and evaluating model performance. The model was found to be able to readily identify and distinguish reproductive states. A reduction in sampling frequency to 36% of original sample resulted in an average increase in days to detection of oestrus of 0.36. The addition of 1 S.D. noise did not cause additional oestruses to be detected and all oestruses were correctly identified. However, when 2 or 3 S.D. noise were added, the model found on average 1.4 and 3 extra oestruses. It was concluded that reproductive status can be predicted from milk progesterone values using a biological model and that such a model is robust to reductions in sampling frequency number and to a doubling in the random variation in the raw progesterone values. It therefore has the potential to provide the basis for a useful reproductive management tool. [Copyright &y& Elsevier]

In this study, the potential benefit of combining the traits activity and period since last oestrus for oestrus detection was investigated. The simultaneous analyses of these traits in a fuzzy logic model should reflect the consideration of the dairy farmer regarding his judging of oestrus warnings. The analyses involved 862 cows, each with one verified oestrus case. Information about previous oestrus or inseminations were available for 373 cows. For comparison only, the trait activity was analysed in a preliminary investigation by an univariate fuzzy logic model. The sensitivity was 91.7% and the error rate was 34.6%. By considering the previous information in a multivariate fuzzy logic model, the sensitivity decreased to 87.9% and the error rate improved to 12.5%. The simultaneous analysis of cows with and without previous information in the oestrus detection model resulted in an increase in error rate to 23.8%, due to the high number of cows without previous information. The obtained results indicate that the information about previous oestrus cases is suitable for multivariate oestrus detection. [Copyright &y& Elsevier]

Cow oestrus detection rates are of major constraint to good reproductive performance in dairy herds and there is a need for heat detection aid. The DEC® system is an electronic device that detects cows that stand to be mounted by a herd mate and provides a continuous monitoring of activity. Accuracy and efficiency of this system was evaluated from 30 Holstein cows. They were fitted on the fourth or fifth week post-partum with the DEC® system which was left in place until confirmation of pregnancy. During the same period, visual observation (VO) for occurence of oestrus were conducted four times daily and blood samples collected weekly for progesterone determination. A blood sample was also collected each time a cow was detected in oestrus independent of the method used. Inferred from progesterone patterns a total of 96 ovulation/oestrus periods occurred; 34 were correctly detected by the DEC® system, 66 by VO resulting in efficiencies of 35.4 and 68.8%, respectively (P<0.001). All oestruses detected by the DEC® were also detected by VO. Five cows, different for each method, were incorrectly detected in oestrus leading to accuracies of 87.2 and 93.0%, respectively, for the DEC® system and for VO (P<0.01). [Copyright &y& Elsevier]

Simple Summary: This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm; this type of monitoring is applied in the field of sow production. A boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus in sows after weaning. The models resulted in recognition accuracy rates of 96.12%, 98.25%, and 90.00%. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow's oestrus states. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for the sow's conception time. This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm, applied in the field of sow production. A bionic boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus of sows after weaning. Machine vision technology was used to identify the interactive behaviour between empty sows and bionic boars and to establish deep belief network (DBN), sparse autoencoder (SAE), and support vector machine (SVM) models, and the resulting recognition accuracy rates were 96.12%, 98.25%, and 90.00%, respectively. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow's oestrus states. In contrast with the methods of other studies, the proposed innovative design for recyclable bionic boars can be used to check emotions, and machine vision technology can be used to quickly identify oestrus behaviours. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for a sow's conception time. [ABSTRACT FROM AUTHOR]