Estrus Detection System Research Articles

Ovarian function and endocrine phenotypes of lactating dairy cows during the estrous cycle are associated with genomic-enhanced predictions of fertility potential

The objectives of this prospective cohort study were to characterize associations among genomic merit for fertility with ovarian and endocrine function and the estrous behavior of dairy cows during an entire nonhormonally manipulated estrous cycle. Lactating Holstein cows entering their first (n = 82) or second (n = 37) lactation had ear-notch tissue samples collected for genotyping using a commercial genomic test. Based on genomic predicted transmitting ability values for daughter pregnancy rate (gDPR), cows were classified into high (Hi-Fert; gDPR > 0.6, n = 36), medium (Med-Fert; gDPR -1.3 to 0.6, n = 45), and low fertility (Lo-Fert; gDPR < -1.3, n = 38) groups. At 33 to 39 DIM, cohorts of cows were enrolled in the Presynch-Ovsynch protocol for synchronization of ovulation and initiation of a new estrous cycle. Thereafter, the ovarian function and endocrine dynamics were monitored daily until the next ovulation by transrectal ultrasonography and concentrations of progesterone (P4), estradiol, and FSH. Estrous behavior was monitored with an ear-attached automated estrus detection system that recorded physical activity and rumination time. Overall, we observed an association between fertility group and the ovarian and hormonal phenotype of dairy cows during the estrous cycle. Cows in the Hi-Fert group had greater circulating concentrations of P4 than cows in the Lo-Fert group from d 4 to 13 after induction of ovulation and from day -3 to -1 before the onset of luteolysis. The frequency of atypical estrous cycles was 3-fold greater for cows in the Lo-Fert than the Hi-Fert group. We also observed other modest associations between genomic merit for fertility with the follicular dynamics and estrous behavior. We found several associations between milk yield and parity with ovarian, endocrine, and estrous behavior phenotypes as cows with greater milk yield and in the second lactation were more likely to have unfavorable phenotypes. These results demonstrate that differences in reproductive performance between cows of different genomic merit for fertility classified based on gDPR may be partially associated with circulating concentrations of P4, the incidence of atypical phenotypes during the estrous cycles, and, to a lesser extent, the follicular wave dynamics. The observed physiological and endocrine phenotypes might help explain part of the differences in reproductive performance between cows of superior and inferior genomic merit for fertility.

CowXNet: An automated cow estrus detection system

Estrus detection is essential for dairy farms to take cows for artificial insemination promptly. Conventional approaches for detecting estrus cows use electronic devices attached to cows to gather data for software analysis. However, electronic devices can be costly and make a cow moody and uncomfortable while moving. In a common approach, observers detect estrus cows by observing their behaviors. However, continuous observation can easily lead to errors due to the observer’s fatigue. Therefore, we designed CowXNet, an automatic estrus detection system for cows, to assist farmers to detect estrus cows. CowXNet requires only a camera attached in a pen and a computer to analyze recorded videos. CowXNet analyzes the estrus behaviors of each cow in a pen and helps farmers to identify estrus cows. To develop and evaluate CowXNet efficiently and effectively, we collected data from Chokchai Farm, the biggest dairy farm in Asia (14.65483∘N, 101.34853∘E). CowXNet has four modules: (i) cow detection uses YOLOv4 to detect cows in recorded videos; (ii) body part detection uses a convolutional neural network to estimate locations of body parts of detected cows; (iii) estrus behavior detection uses body part coordinates to extract a set of discriminative features, and a classification algorithm to detect estrus behaviors, and (iv) behavior analysis module displays estrus behavior for analysis purposes. We evaluated CowXNet for two instances: module-independent evaluation and end-to-end framework evaluation. Overall, CowXNet was promising; it correctly detected estrus behavior interval of cows 83% of cases.

Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

Smart farming is the practice of intelligent agricultural management based on technological data gathered from farm practice for the purpose of increased levels of quality, production and environmental protection. The Internet of Things (IoT) technology is revolutionized in various aspects of agriculture around the world and its application found success in some countries. In this paper, a non-invasive and non-contact estrus detection system integrated IoT technology to improve the detection efficiency of standing-heat behaviors of cows is proposed. The outcome of this study will improve the farm’s management practices through the integration of IoT technology that can remotely monitor the activities of the cows and provide data for analysis and evaluation, will increase the likelihood of future estrus instances for every correct prediction, and will improve the fertility and milk production rates of the cows. Such benefits can contribute to the growth and development of the dairy cattle industry in the Philippines. This study also shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom (PTZ) cameras and a Python-driven Web Application. The dimensions of the barn are measured, and the Cameras' Field of Views (FOVs) is pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The result is sent to the cloud server to display on the web application for analysis. The web app allows updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. The system initially and correctly detected 4 standing-heat signs, but with 2 false predictions and identifications leading to 2 “True Positive” and 2 “False Positive” results, attaining a 50% detection efficiency. Based on the results, the program performed satisfactorily at 50% detection efficiency.

Evaluation and characterization of estrus alerts and behavioral parameters generated by an ear-attached accelerometer-based system for automated detection of estrus

Our objectives were to evaluate the performance of an ear-attached automated estrus detection (AED) system (Smartbow; Zoetis) that monitored physical activity and rumination time, and to characterize AED system estrus alert features (i.e., timing and duration). Lactating Holstein cows (n = 216) commenced a protocol for the synchronization of estrus at 50 ± 3 DIM or 18 ± 3 d after artificial insemination. For 7 d after induction of luteolysis with PGF2α (d 0), we used visual observation of estrous behavior (30 min, 2 times per day) and data from an automated mounting behavior monitoring system based on a pressure-activated tail-head sensor (HeatWatch; Cowchips LLC) as a reference test (RTE) to detect behavioral estrus. Concomitantly, estrus alerts and their features were collected from the AED system. Progesterone levels confirmed luteal regression, and transrectal ultrasonography confirmed the occurrence and timing of ovulation. Performance metrics for the AED system were estimated with PROC FREQ in SAS, using the RTE or ovulation only as a reference. Performance was also estimated after the removal of cows with a discrepancy between the RTE and ovulation. Continuous outcomes with or without repeated measurements were evaluated by ANOVA using PROC MIXED in SAS. Based on the RTE, 86.6% (n = 187) of the cows presented estrus and ovulated; 1.4% (n = 3) presented estrus and did not ovulate; 6.4% (n = 14) did not present estrus but ovulated; and 5.6% (n = 12) did not present estrus or ovulation. We found no difference in the proportion of cows detected in estrus and with ovulation for the AED system (83.4%) and the RTE (86.6%). Compared with estrus events as detected by the RTE, sensitivity for the AED was 91.6% (95% CI: 87.6-95.5) and specificity was 69.2% (95% CI: 51.5-87.0). Using ovulation as reference, sensitivity was 89.6% (95% CI: 85.3-93.8) and specificity was 86.7% (95% CI 69.5-100). For all cows with agreement between the RTE and ovulation, sensitivity was 92.5% (95% CI: 88.7-96.3) and specificity was 91.7% (95% CI: 76.0-100). The mean (±SD) interval from induction of luteolysis to estrus alerts, estrus alert duration, and the onset of estrus alerts to ovulation interval were 72.2 ± 18.1, 13.5 ± 3.8, and 23.8 ± 7.1 h, respectively. We concluded that an ear-attached AED system that monitored physical activity and rumination time was effective at detecting cows in estrus and generated few false positive alerts when accounting for ovulation, cow physiological limitations, and the limitations of the RTE.

Automated Estrus Detection for Dairy Cattle through Neural Networks and Bounding Box Corner Analysis

Thorough and precise estrus detection plays a crucial role in the fertility of dairy cows. Farmers commonly used direct visual monitoring in recognizing estrus signs which demands time and effort and causes misinterpretations. The primary sign of estrus is the standing heat, where the dairy cows stand to be mounted by other cows for a few seconds. Through the years, researchers developed various detection methods, yet most of these methods involve contact and invasive approaches that affect the estrus behaviors of cows. So, the proponents developed a non-invasive and non-contact estrus detection system using image processing to detect standing heat behaviors. Through the TensorFlow Object Detection API, the proponents trained two custom neural network models capable of visualizing bounding boxes of the predicted cow objects on image frames. The proponents also developed an object overlapping algorithm that utilizes the bounding box corners to detect estrus activities. Based on the conducted tests, an estrus event occurs when the centroids of the detected objects measure a distance of less than 360px and have two interior angles with another fixed point of less than 25° and greater than 65° for Y and X axes, respectively. If the conditions are met, the program will save the image frame and will declare an estrus activity. Otherwise, it will restart its estrus detection and counting. The system observed 17 cows, a carabao, and a bull through the cameras installed atop of a cowshed, and detects the estrus events with an efficiency of 50%.

Automated detection of health disorders in lactating dairy cattle on pasture: a preliminary study.

Since previous health monitoring systems have shown themselves to be unsuccessful in predicting health disorders in dairy cows managed on pasture, the aim of this study was to evaluate the performance of automated health monitoring integrated in an accelerometer-based oestrus detection system (ODS) for dairy cows on pasture. Mixed-breed lactating dairy cows (n=109) in a seasonal-calving herd managed at pasture were fitted with an ODS that provided automated health monitoring. The ODS performed multimetric analysis of behavioural patterns to generate health alerts. Data were collected during the artificial insemination period of 66 days. Clinical examinations and farmer's observations were used to evaluate the performance of automated health monitoring. During the insemination period, the farmer generated two health alerts, which were classified false positives (2/2; 100%). The ODS generated 31 automated health alerts. Of all automated health alerts, 3/31 (9.7%) were confirmed as true health disorders and 28/31 (90.3%) alerts were classified as false positives. The positive predictive value (PPV) of automated health monitoring was 9.7 (95% CI=2-25.8) %. The ODS was able to alert lactating dairy cows on pasture suffering from health disorders. True health disorders were alerted by the ODS before the farmer noticed them, which could provide early and successful treatment when using the system on-farm for automated health monitoring. The evaluated accuracy of automated health monitoring is opposed to a targeted use of the system for on-farm health monitoring. For further validation, testing on other farms and during the transition period would be of interest.

Evaluation of activity meters for estrus detection: A stochastic bioeconomic modeling approach

Although estrus detection makes a relevant contribution to the reproductive performance of dairy cattle, studies on the economic evaluation of automatic estrus detection systems are rare. The objective of the present study is to provide an economic evaluation of activity meters used for estrus detection. The effect of different estrus detection rates on gross margins was modeled with SimHerd (SimHerd A/S, Viborg, Denmark). The analysis considers all costs associated with the investment in activity meters. The economic evaluation was carried out through simulation of Simmental herds with yearly milk yields of 7,000 or 9,000 kg and Holstein Friesian herds with yearly milk yields of 9,000 or 11,000 kg, each with herd sizes of 70 or 210 cows. Furthermore, we distinguished between 2 investment scenarios. In scenario 1, only cows are equipped with activity meters for estrus detection, whereas scenario 2 assumes that cows and heifers are equipped with activity meters. Because existing empirical information for some variables shows significant variability (estrus detection rates, time for estrus detection), they were modeled with distributions using the Monte Carlo method in @RISK (Palisade Corporation, Ithaca, NY), allowing us to model a probability distribution of net returns (NR) of investment in activity meters for estrus detection. The simulation results show that the average NR of investment in activity meters for estrus detection over all scenarios ranges from +€7 to +€40 per cow per year for the Simmental breed, and from +€19 to +€46 per cow per year for the Holstein Friesian breed. Generally, the NR depends on the milk production level assumed. For the Simmental breed, depending on the scenario, the simulation results show a 54 to 200% larger average NR of investment in activity meters for estrus detection with a milk yield of 9,000 kg/yr compared with 7,000 kg/yr. For the Holstein Friesian breed, the effect of the modeled milk yield on the NR is much less pronounced. Average NR of investment in activity meters are greater for larger herd sizes because of cost degression effects. An additional equipping of heifers has, on average, a positive effect on the economics of activity meters for estrus detection because of the resulting reduction in the age at first calving. Considering all scenarios, the probability of a positive NR of investment in activity meters ranges between 74 and 98% for the Simmental breed and between 85 and 99% for the Holstein Friesian breed.

Pregnant!

Artificial insemination of cows based on detected estrus is still a regular practice included in most reproductive management programs in the United States and elsewhere. Traditional methods of estrus detection are time-consuming, repetitive, and require qualified labor. In recent years, there has been an explosion in the development and adoption of automated estrus detection systems for dairy farms, especially those monitoring individual changes in physical activity. This article will discuss the potential benefits of using automated estrus detection, limitations of programs relying solely on insemination at detected estrus, and ideas for integrating activity monitors into effective reproductive programs.

217 Influence of daily temperature fluctuations on estrus activity determined by an electronic estrus detection system and conception to artificial insemination in cross-bred beef heifers

Abstract Behavioral estrus occurs in response to estradiol, and the intensity of activity registered in some electronic estrus monitoring systems is greater in dairy cows that conceive to artificial insemination compared to cows that do not conceive. Ambient temperature may influence estrus activity and follicular development. Therefore, we hypothesized that differences in temperature would associate with peak activity and conception to artificial insemination in cross-bred beef heifers. Heifers (n = 346) in south central Nebraska were observed twice daily for 21 d for behavioral estrus and artificially inseminated between June 4 and June 25, 2018. Onset of estrus and peak activity were extracted from the electronic system. Ambient temperature was merged with onset of estrus, and data were analyzed using the MIXED procedure of SAS with pregnancy status to artificial insemination as a class effect. There was no difference in peak activity between heifers that conceived to artificial insemination and heifers that did not conceive to artificial insemination (P = 0.45). The ambient temperature at onset of estrus was greater for heifers that did not conceive to artificial insemination than for heifers that conceived to artificial insemination (P &lt; 0.05). Time of onset significantly influenced peak activity (P &lt; 0.01), with the greatest peak activity occurring in the early morning hours and in the early evening hours. Daily temperatures increased until 1600 h and decreased thereafter (P &lt; 0.01). Differences in peak activity do not associate with conception to artificial insemination in beef heifers as in dairy cows, but temperature at onset of estrus influenced conception to artificial insemination. Based on increased peak activity in the evening when temperatures were still greater than the early morning hours, the perceived trajectory of temperature may influence behavioral estrus more than the actual temperature. USDA is an equal opportunity provider and employer.

A Survey of Italian Dairy Farmers' Propensity for Precision Livestock Farming Tools.

Simple SummaryThe use of information and technology in precision livestock farming (PLF) to improve animal health, welfare, and farm efficiency is a developing area with great scientific and commercial interest. To gain a better understanding of the use of PLF tools by Italian dairy farmers and to analyze their perceptions of the importance of new technologies, a survey was carried out in the province of Cremona, an area of well-recognized economic importance for the Italian dairy industry. Technicians at the Provincial Breeder Association interviewed 490 farmers (who represent 82% of the associated dairy farms in this province) to collect information on the parameters currently measured by PLF tools on their dairy farms. The survey provided evidence of the interest in, and significant presence of, PLF tools for the individual monitoring of dairy cows. Most farmers reported using sensors for automated milk yield recording and estrus detection. Although the use of sensors for the detection of mastitis and estrus did not appear to be widespread, this technology was considered very important for the improvement of herd management. Propensity for PLF technology use was greater in farmers managing larger herd sizes. The benefit-to-cost ratio was evaluated as the most important factor in determining whether to use a particular tool. In addition, the farmers stated that before purchase, they considered the time required to manage the data generated by technology as an important issue.A targeted survey was designed with the aim of describing the diffusion of precision livestock farming (PLF) tools in one of the most intensive dairy farming provinces in Italy. Technicians at the Provincial Breeder Association of Cremona interviewed 490 dairy farmers and obtained data regarding the role and age of the respondents; the land owned by the farmers; their herd sizes (HS, lactating plus dry cows; small HS < 101, medium HS 101–200, large HS > 200 cows/herd); their average 305 day milk yield (low MY < 9501, medium MY 9501–10,500, high MY > 10,500 kg/head); the cow to employed worker ratio (low CW < 33, medium CW 33–47, high CW > 47 cows/worker); the use of PLF tools to monitor production, reproduction, and health; and the criteria and motivations for investing in PLF tools. The use of automated MY recording and estrus detection systems was primarily associated with HS (more present in larger farms), followed by MY (more present in more productive farms), and then CW (more present with a high cow: worker ratio). Concern about the time required to manage data was the most common subjective issue identified as negatively affecting the purchase of these tools. The future of PLF use in this region will depend upon the availability of an effective selection of tools on the market.

Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge

Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone-based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which model-based indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied.

Estrus Detection System for Improving Productivity of Korean Native Cattle based on Internet of Things

Estrus Detection System for Improving Productivity of Korean Native Cattle based on Internet of Things

Automated oestrus detection using multimetric behaviour recognition in seasonal-calving dairy cattle on pasture

AIM To evaluate the performance of a novel accelerometer-based oestrus detection system (ODS) for dairy cows on pasture, in comparison with measurement of concentrations of progesterone in milk, ultrasonographic examination of ovaries and farmer observations. METHODS Mixed-breed lactating dairy cows (n=109) in a commercial, seasonal-calving herd managed at pasture under typical farming conditions in Ireland, were fitted with oestrus detection collars 3 weeks prior to mating start date. The ODS performed multimetric analysis of eight different motion patterns to generate oestrus alerts. Data were collected during the artificial insemination period of 66 days, commencing on 16 April 2015. Transrectal ultrasonographic examinations of the reproductive tract and measurements of concentrations of progesterone in milk were used to confirm oestrus events. Visual observations by the farmer and the number of theoretically expected oestrus events were used to evaluate the number of false negative ODS alerts. The percentage of eligible cows that were detected in oestrus at least once (and were confirmed true positives) was calculated for the first 21, 42 and 63 days of the insemination period. RESULTS During the insemination period, the ODS generated 194 oestrus alerts and 140 (72.2%) were confirmed as true positives. Six confirmed oestrus events recognised by the farmer did not generate ODS alerts. The positive predictive value of the ODS was 72.2 (95% CI=65.3–78.4)%. To account for oestrus events not identified by the ODS or the farmer, four theoretical missed oestrus events were added to the false negatives. Estimated sensitivity of the automated ODS was 93.3 (95% CI=88.1–96.8)%. The proportion of eligible cows that were detected in oestrus during the first 21 days of the insemination period was 92/106 (86.8%), and during the first 42 and 63 days of the insemination period was 103/106 (97.2%) and 105/106 (99.1%), respectively. CONCLUSIONS and CLINICAL RELEVANCE The ODS under investigation was suitable for oestrus detection in dairy cows on pasture and showed a high sensitivity of oestrus detection. Multimetric analysis of behavioural data seems to be the superior approach to developing and improving ODS for dairy cows on pasture. Due to a high proportion of false positive alerts, its use as a stand-alone system for oestrus detection cannot be recommended. As it is the first time the system was investigated, testing on other farms would be necessary for further validation.

Research of Estrus Detection Models in Dairy Cows by Activity

The detection of estrus in large-scale ranch is an extremely labor-intensive task. Accurate and efficient detection can shorten the tires spacing of cow and increase economic income of ranch.The change in activity is one of the most important external features of dairy cows' estrus.In this paper, the activity of cows was collected remotely using activity collectors, wireless transmission networks and cloud servers, and the data was framed, labeled and preprocessed. The logistic regression, MLPs and SVMs model were trained by differential activity data of current and historical dates within the same hour.The experimental results show that the detection model by cow activity has a higher accuracy in predicting the estrus of cows. The SVMs in the three models have the best prediction effect. The recall and accuracy rate reach 90.12% and 93.74%, respectively.In the actual ranch test, the estrus detection system using the SVM model has more than double the exposure rate than the manual disclosure.

Automatic systems for heat detection in cows as a tool for improving herd management

There are at least several ways of detecting bovine heat, which vary in accuracy, as well as in the effort and cost associated with the implementation and operation of equipment designed for this purpose. Heat in cows can be most easily detected by thorough and systematic observation of animals in the herd. Other methods include the measurements of vaginal mucosal resistance, the fitting of balloons with paint at the base of the tail or on the back, temperature measurements in the vagina, rumen and milk, and determination of progesterone levels in milk and blood. Currently, controlled breeding programmes, including timed artificial insemination (TAI) and automated estrus detection (AED) or activity monitoring system (AMS), are routinely introduced in high-performance dairy herds. Most of the commercially available heat detection devices are based on accelerometers or pedometers. These devices are capable of detecting heat in 81.4% to 91.3% of cows. The sensitivity of heat detection was 58.9% for accelerometers (Heatime), 63.3% for pedometers (SAE Afikim, Kibbutz Afikim, Israel), 56.7% for KaMaR markers and 35.9% for heat detectors (Scratchcard). When two of the three systems were combined (different combinations), the sensitivity increased to 75.9%. Similarly, after adding visual detection, sensitivity increased to 96% and specificity to 90% (for visual observation combined with AMS). Activity monitoring systems are a proven tool for improving the management of cows. They are an alternative to reproductive management based solely on traditional methods of detecting ovulation or hormone programmes (TAI) and they respect the well-being of animals. It seems, however, that further improvement in cows’ fertility on industrial cattle farms will be achieved by combining several different methods of heat detection and synchronization, rather than by using a single modern heat detection system..

Design and Implementation of Cattle Estrus Detection System based on Wireless Communication and Internet of Things

Design and Implementation of Cattle Estrus Detection System based on Wireless Communication and Internet of Things

Evaluation of reproductive performance of dairy heifers subjected to two estrus detection systems and treated with two prostaglandin formulations

Efficient and accurate identification of estrus is critical for the success of estrous detection based reproductive management. We hypothesized that management of dairy heifers with the aid of an automated estrous detection system (AES) would improve service rate and consequently the reproductive performance of dairy heifers. In addition we hypothesized that treatment of heifers with cloprostenol (CLO) would improve pregnancy per service compared with treatment with dinoprost (DIN). The objectives were to evaluate the reproductive performance of dairy heifers managed with the aid of an AES vs a mounting detector device (MD) and synchronized with CLO vs DIN.

Farmers' preferences for automatic lameness-detection systems in dairy cattle

As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic lameness detection. A sensor attached to the cow was preferred, followed by a walkover system and a camera system. In general, visual lameness detection was preferred over automatic detection systems, but this preference changed after informing farmers about the consequences of lameness. To conclude, the system cost and performance were important features, but dairy farmers should be sensitized on the consequences of lameness and its effect on farm profitability.

Characterizing body temperature and activity changes at the onset of estrus in replacement gilts

Accurate estrus detection can improve sow conception rates and increase swine production efficiency. Unfortunately, current practices based on individual animal behavior may be inefficient due to large sow populations at commercial farms and the associated labor required. Therefore, the study objective was to characterize body temperature and activity changes in replacement gilts at the onset of estrus in order to provide initial data to develop a remote estrus detection system for commercial swine operations. Twelve replacement gilts (130.2±1.9kg BW) were administered altrenogest to synchronize estrus, and data loggers that detect vaginal temperature (TV) and quantify total activity 24h/d in 5min intervals were attached to a blank controlled internal drug release device and inserted intra-vaginally for 7 d prior to expected estrus. Activity was quantified as counts per minute (cnt/min) using an internal accelerometer. During the 7 d of monitoring, gilts were checked for estrus twice daily (0800 and 1500h) by two trained individuals. To standardize data, only the day of estrus detection (dE) and the 3 d prior (d−3, d−2, d−1, n =4 total days) were used in the final analysis since gilts came into estrus on different days. Data were analyzed using the PROC MIXED procedure in SAS 9.4. Overall, TV was reduced (P<0.01; 0.26°C) on dE compared to the previous 3 d; however, no differences were detected between d−3, d−2, and d−1. Activity was increased (P<0.01; 37.8%) on dE compared to d−3 and d−2, but no differences were detected between dE and d−1. In summary, TV was reduced and activity was increased at the onset of estrus in replacement gilts.

Automatic oestrus detection system enables monitoring of sexual behaviour in sheep

An experiment was conducted to observe the oestrous behaviour in ewes through an automated oestrus detection system. Five mature adult Merino male rams, equipped with an electronic detector (Alpha-D), were included with a total of 60 ewes from the same breed. The libido of the males was assessed as the number and frequency of mounts. The accepted mountings by ewes were recorded by an electronic identifier worn by the female. In the first synchronized oestrus (C1), the ewes were exposed to rams wearing an anti-mating apron. In the following spontaneous cycle (C2), all the rams were allowed to mate. In the third cycle (C3) and the fourth cycle (C4), the rams were assumed to detect ewes in oestrus (open ewes). After the exclusion of isolated readings, the mean interval from sponge withdrawal to onset of oestrus was 31±11h and duration of oestrus in C1–C3 was 22±18, 16±11 and 16±8h, respectively. The libido differed widely among the rams and cycles number with an average number of mounts per ram in C1–C4, that were 213, 322, 85 and 3 respectively. It is concluded that all the females were correctly detected and the assessed libido of males was repeated.