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Nonreplacement dairy calves, or bobby calves, are fasted and transported to abattoirs from as young as 5 d of age in Australia. The aims of this cross-sectional observational study were (1) to assess the welfare status, as measured by blood parameters, of bobby calves in the commercial supply chain after transport and lairage, and (2) to assess whether distance and duration of transport are risk factors for poor bobby calf welfare, as measured by blood parameters. We hypothesized that bobby calves transported greater distances would be more likely to show evidence of compromised welfare, as measured by blood indicators of hydration, energy status, and muscle fatigue or damage. We also hypothesized that there would be a large amount of variability in indicators of energy status between calves from different farms. We analyzed blood samples collected at slaughter over a spring and an autumn calving period from 4,484 Australian bobby calves aged approximately 5 to 14 d old from 3 different states, after transport, fasting, and lairage. Packed cell volume (PCV), plasma glucose, and serum urea, total protein, β-hydroxybutyrate (BHB), and creatine kinase (CK) were measured. Radio frequency identification ear tag data were used to estimate the distance that the calves were transported and to identify the farm of origin. Data were analyzed using linear mixed models, except for BHB, which was analyzed using a Goodman-Kruskal gamma test due to left censoring of the data. Twelve percent of calves showed evidence of anemia (PCV less than 0.23 L/L), and 11% had urea concentrations consistent with dehydration (urea more than 7.7 mmol/L). Thirty-six percent of calves had CK activity above normal resting values, and 1% of calves had CK >2,000 U/L, indicating muscle fatigue or damage. Distance transported had significant effects on all blood variables except urea and BHB. With increasing distance transported, calves were more likely to show evidence of a negative energy balance (low plasma glucose) or dehydration (high PCV or total protein). The estimated effect of distance overall was small, but for calves transported more than 500 km, plasma glucose concentration declined more per kilometer. The calves' farm of origin accounted for a reasonable amount of the random variation between calves for plasma glucose (20%). Our results suggest that longer transport distances may increase the risk of poor calf welfare (dehydration, negative energy balance) after transport, and on-farm calf management (e.g., nutrition, timing of feeding before transport) may affect transported calves' energy status; improving this area could result in better energy availability during fasting.

To assess plasma trace mineral (TM) concentrations, the acute phase protein response, and behavior in response to a lipopolysaccharide (LPS) challenge, 96 Angus cross steers (average initial body weight [BW]: 285 ± 14.4 kg) were sorted into two groups by BW (heavy and light; n = 48/group), fitted with an ear-tag–based accelerometer (CowManager SensOor; Agis, Harmelen, Netherlands), and stagger started 14 d apart. Consecutive day BW was recorded to start the 24-d trial (days −1 and 0). Dietary treatments began on day 0: common diet with either 30 (Zn30) or 100 (Zn100) mg supplemental Zn/kg DM (ZnSO4). On day 17, steers received one of the following injection treatments intravenously to complete the 2 × 3 factorial: 1) SALINE (~2–3 mL of physiological saline), 2) LOWLPS: 0.25 µg LPS/kg BW, or 3) HIGHLPS: 0.375 µg LPS/kg BW. Blood, rectal temperature (RT), and BW were recorded on day 16 (−24 h relative to injection), and BW was used to assign injection treatment. Approximately 6, 24 (day 18), and 48 (day 19) h after treatment, BW, RT, and blood were collected, and final BW recorded on day 24. Data were analyzed in Proc Mixed of SAS with fixed effects of diet, injection, diet × injection; for BW, RT, dry matter intake (DMI), plasma TM, and haptoglobin-repeated measures analysis were used to evaluate effects over time. Area under the curve analysis determined by GraphPad Prism was used for analysis of accelerometer data. Body weight was unaffected by diet or injection (P ≥ 0.16), but there was an injection × time effect for DMI and RT (P < 0.05), where DMI decreased in both LPS treatments on day 16, but recovered by day 17, and RT was increased in LPS treatments 6 h post-injection. Steers receiving LPS spent less time highly active and eating than SALINE (P < 0.01). Steers in HIGHLPS spent lesser time ruminating, followed by LOWLPS and then SALINE (P < 0.001). An injection × time effect (P < 0.001) for plasma Zn showed decreased concentrations within 6 h of injection and remained decreased through 24 h before recovering by 48 h. A tendency for a diet × time effect (P = 0.06) on plasma Zn suggests plasma Zn repletion occurred at a greater rate in Zn100 compared to Zn30. These results suggest that increased supplemental Zn may alter the rate of recovery of Zn status from an acute inflammatory event. Additionally, ear-tag–based accelerometers used in this study were effective at detecting sickness behavior in feedlot steers, and rumination may be more sensitive than other variables.

Simple Summary The wearable wireless sensor system plays a crucial role in providing behavioral and physiological data for each individual in precision livestock farming. This article reviewed the most types of sensor systems available in the market and summarized detailed information on these systems. Additionally, through meta-analysis, the accuracy of the parameters generated by the sensor system was verified. As a result, it has been shown that there are more than 60 sensor systems of various types have been developed and sold. Most of them generate behavioral and physiological parameters of cattle with excellent performance (e.g., eating time, ruminating time, lying time, standing time, etc.), with the exception of a few parameters (e.g., drinking time and walking time). In this review, it was also investigated that the same parameters predicted by sensor systems of the same brand showed different accuracies, but it was not possible to confirm where this difference originated because the additional experimental conditions presented in the literature were not detailed. Therefore, this review suggested that guidelines for evaluation criteria for research evaluating sensor performance are needed. Abstract The review aimed to collect information about the wearable wireless sensor system (WWSS) for cattle and to conduct a systematic literature review on the accuracy of predicting the physiological parameters of these systems. The WWSS was categorized as an ear tag, halter, neck collar, rumen bolus, leg tag, tail-mounted, and vaginal mounted types. Information was collected from a web-based search on Google, then manually curated. We found about 60 WWSSs available in the market; most sensors included an accelerometer. The literature evaluating the WWSS performance was collected through a keyword search in Scopus. Among the 1875 articles identified, 46 documents that met our criteria were selected for further meta-analysis. Meta-analysis was conducted on the performance values (e.g., correlation, sensitivity, and specificity) for physiological parameters (e.g., feeding, activity, and rumen conditions). The WWSS showed high performance in most parameters, although some parameters (e.g., drinking time) need to be improved, and considerable heterogeneity of performance levels was observed under various conditions (average I2 = 76%). Nevertheless, some of the literature provided insufficient information on evaluation criteria, including experimental conditions and gold standards, to confirm the reliability of the reported performance. Therefore, guidelines for the evaluation criteria for studies evaluating WWSS performance should be drawn up.

The beef cattle rearing is an economic and social activity in La Pampa province, developed in an area of natural forages, with severe weather and edaphic limitations. The present trial was carried out on a beef farm with 320 British crossbreed cattle, at Toay Department, La Pampa. During a period of five years, a protocol as following was implemented: three-time estrous synchronization followed by fixed-time artificial insemination plus natural mating until the end of the breeding period. The protocol used for artificial insemination started 40 days post-partum and each cow was classified by a colour ear tag and by probable date of delivery. At day zero, an intra-vaginal progesterone dispositive (0.5 g, i.v., Dib, Sintex S.A., Argentina) was placed to all cows in combination with an estradiol benzoate dose, when the dispositive was removed, after 7 days, the animals received a dose of prostaglandin (Cloprostenol, 2 cc., i.m., Ciclase DL, Sintex S.A.) and estradiol cypionate (1 mg, i.m. Cipiosyn, Sintex S.A., Argentina), to finally be inseminated at day 9. Thereafter, the protocol finished with the incoming of 5% of bulls until the end of the predetermined breeding period. The pregnancy percentage of both cows and heifers increased through the study period to values of 90 and 91%, respectively. 70% of parturition was concentrated during the first month, 23% in the second and just 3% during the last month of calving season. The suggested system allows increasing the reproductive efficiency of the cattle herd, achieving both a higher percentage of early pregnancy and increasing interparturition interval allowing a better recovery of the reproductive tract.

The objective of this study was to determine whether data from an electronic drinking system that tracks individual animal attendance with ear tag transponders could be used to detect social competition between dairy cows at the drinker. Specifically, we sought to identify the interval between one cow leaving the drinker and another cow taking her place that most accurately identified competitive replacements (when physical contact initiated by one cow causes the other to entirely remove her head from the drinker and the initiator subsequently places her head in the same drinker). The optimal interval to accurately identify replacements at the drinker was determined using a receiver operating characteristic curve and by evaluating the sensitivity (Se), specificity (Sp), and confusion matrix values (true positives, true negatives, false positives, false negatives) of different thresholds identified by 3 metrics: the Youden index, the point closest to (0,1) on the curve, and the point where Se roughly equals Sp. Lactating Holstein dairy cows (n = 20) were monitored for 4 consecutive 24-h periods by video recording and with an electronic drinking system. Two periods were used to identify the optimal interval (the baseline set), and the other 2 periods were used to validate the interval (the validation set). The occurrence of a replacement identified by video was paired with the interval between drinking events of 2 cows at the same electronic drinker to identify the interval that best predicted replacement events. Based on the low prevalence of replacement events compared with nonreplacement events, the interval with the lowest number of false positives was considered optimal. Of the 3 potential metrics, the point on the receiver operating characteristic curve where Se and Sp were roughly equal yielded the fewest false positives. The optimal interval to identify replacements at the drinker was ≤29 s, with 82% Se and 83% Sp in the baseline set. This interval was found to have 85% Se and 89% Sp when applied to the validation set. These results indicate that social competition between dairy cows at the drinker can be accurately measured with an electronic drinking system.

Simple Summary In a pig farm, it is challenging for the farm caretaker to monitor the health and well-being status of all animals in a continuous manner throughout the day. Automated tools are needed to remotely monitor all the pigs on the farm and provide early alerts to the farm caretaker for situations that need immediate attention. With this goal, we developed a sensor board that can be mounted on the ears of individual pigs to generate data on the animal’s activity, vocalization, and temperature. The generated data will be used to develop machine learning models to classify the behavioral traits associated with each animal over a testing period. A number of factors influencing the technology adoption by farm caretakers are also discussed. Abstract Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status—namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics.

The objective of this study was to compare the economic performance of an ear tag automated activity monitor system (AAM) versus a timed-AI (TAI) protocol in Holstein heifers. In total, 340 heifers were enrolled onto the study at 13.5 mo of age and randomly assigned to receive either an AAM (n = 170) or TAI (n = 170) protocol before breeding eligibility (D 0). Heifers in the AAM group were fitted with an ear tag AAM and bred based on high activity alert from the system. Heifers in the TAI group received a progesterone releasing intravaginal device on D −8, followed by device removal and prostaglandin on D −3 and gonadotropin-releasing hormone with TAI on D 0. In both treatments, the majority of heifers received sex-sorted semen for the first AI and conventional semen for subsequent AIs, with three opportunities to become pregnant. All heifers were diagnosed for pregnancy approximately 25 d post AI using transrectal ultrasonography, with confirmation at 30 and 45 d. Non-pregnant heifers in the TAI group, were resynchronized using the same TAI protocol. A partial budget was used to compare the costs and benefits of switching from a TAI to an AAM protocol in heifers, including protocol, labour, and rearing costs for each treatment, as well as estimated calf and milk value. Sensitivity analyses were also conducted to determine the effect of pregnancy per AI (P/AI), outsourcing AI, AAM tag cost and herd size on the net outcome. There was no difference in overall P/AI or days to pregnancy between treatments. However, number of AI was greater in the TAI than the AAM group. For the first AI, the P/AI was less in the TAI compared to the AAM group; however, the interval to first AI was less in TAI. There was minimal difference in performance for the second and third AI. There was a net gain of $11.97 per heifer when switching from a TAI to AAM protocol, due to the increased P/AI to the first AI and reduced cost of hormones. Several variables in the sensitivity analyses affected the net outcome. Considering only the first AI, switching to an AAM collar and a larger herd size all increased the net gain. Considering a greater P/AI to the first AI in the TAI group, outsourcing AI, using more AAM ear tags, and smaller herd sizes resulted in a net loss when switching from TAI to AAM. The AAM system resulted in exceptional P/AI and may be an economically viable alternative to improve heifer reproductive efficiency in herds with suboptimal P/AI to TAI.

Neonatal piglets can experience both a decrease in body temperature and hypoxia, increasing risks for pre-weaning mortality. This research evaluated the effects of drying and providing supplemental oxygen to newborn piglets on rectal temperature (RT) over the first 24 h after birth. The study used a CRD with three Intervention Treatments (IT; applied at birth): Control (no intervention), Drying (dried using a desiccant), Oxygen [dried using a desiccant and placed in a chamber (at 40% oxygen concentration) for 20 min]. A total of 42 litters (485 piglets) were randomly allotted to treatments at the start of farrowing. At birth, each piglet was given a numbered ear tag, weighed, and the treatment was applied; RT was measured at 0, 20, 30, 45, 60, 120, and 1440 min after birth. Blood was collected from one piglet from each birth weight quartile within each litter at 24 h after birth to measure plasma immunocrit concentration. There was no effect (P > 0.05) of IT on piglet RT at 0 or 1440 min after birth. Between 20 and 60 min after birth, piglet RT was lower (P ≤ 0.05) for the Control than the Drying treatment, with the Oxygen treatment being intermediate and different (P ≤ 0.05) from the other two IT. The effect of piglet birth weight on responses to IT were evaluated by classifying piglets into Birth Weight Categories (BWC): Light (1.5 kg). There were IT by BWC interactions (P ≤ 0.05) for piglet RT at all measurement times between 20 and 120 min after birth. Relative to the Control, the effects of the Drying and Oxygen treatments on RT were greater (P ≤ 0.05) for Light than heavier piglets. Plasma immunocrit concentrations tended (P = 0.07) to be greater for piglets on the Control treatment compared to the other two IT and were lower (P ≤ 0.05) for Light than Heavy piglets, with Medium piglets being intermediate and different (P ≤ 0.05) to the other BWC. In conclusion, drying piglets at birth reduced the extent and duration of RT decline in piglets in the early postnatal period compared to undried piglets, especially for those of low birth weight. However, the combination of drying and placing piglets in an oxygen-rich environment provided no additional benefit over drying alone.

In order to improve the efficiency and accuracy of air docking operation, this paper puts forward the extension of the docking operation for auditory space of the modal auxiliary method in the form of hearing, with the needle and ring as the research object, using HoloLens2 glasses simulation reality environment, and by four groups of experiments to study the ear tag and its pitch and audio effects on docking positioning operation. The results show that the introduction of ear marker can significantly improve the efficiency and accuracy of the docking and positioning operation, and the pitch and audio frequency of ear marker can promote the perception of the opposite position.

Animal identification is necessary for records, registration, and proof of ownership. The owner of few Black Bengal Goats can identify his goats by sight but it will create a problem for a larger number of goats as they are looking almost similar. A number of identification tools have been used for Black Bengal Goats like ear tag, tattoo, branding, RFID, etc. The Tattoos are permanent identification marking but inconvenient to read after a few months or years. Most of the farmers and breeders have used ear tags, which contain a number for identification of particular goat but may be lost at the time of grazing. Some organized farmers have placed RFID chips in tags but RFID reader is necessary to read the content of chips. In this paper, an effort has been made to identify individual Black Bengal Goat using their iris image like a human. The eye images have been captured preprocessed, enhanced, and irises have been segmented. The template has been generated from each segmented iris and stored in the database. The matching has been performed among different segmented iris images from the same goat and also been performed among iris images captured from different goats. It has been observed that the average Hamming distance among iris images captured at different times from the same goat are different from the average hamming distances among iris images from other goats. Finally, the matching threshold has been decided for the identification of Black Bengal Goat.

Feeding behaviour can be used as an important indicator to support animal management. However, using feeding behaviour as a tool for dairy cow management an automatic sensor system is needed. Hence, the objective of this study was to setup, test and validate a ultra-high frequency (UHF) radio-frequency identification (RFID) system for measuring time dairy cows spent at the feed fence using two types of passive UHF ear tags. In a first experiment, the reading area of the system was evaluated in two antenna positions. Subsequently, the UHF RFID system was validated with video observations and compared to the measurements of chewing time of a noseband pressure sensor and of the time spent at the feed fence registered by a sensor system with real-time localisation. Differences in the reading area were detected between the two antenna positions and types of ear tag. The antenna position leading to less false positive registrations was chosen for the experiment with cows. The validation with video data showed a high average sensitivity (93.7 &plusmn, 5.6%, mean &plusmn, standard deviation), specificity (97.8 &plusmn, 1.1%), precision (93.8 &plusmn, 2.3%) and accuracy (96.9 &plusmn, 0.9%) of the UHF RFID system for measuring the time spent at the feed fence. The comparison with the noseband pressure sensor and the real-time localisation resulted in high correlations with a correlation coefficient of r = 0.95 and r = 0.93, respectively. However, substantial absolute differences between the three systems pointed out differences between direct and indirect measures of feeding behaviour in general and between the different sensors in particular. Thus, detailed considerations are necessary before interpreting automatically measured feeding data generally.

We investigated the effect of feeding time on behavior and stress responses in pregnant sows under isocaloric conditions. Twenty-four sows were balanced for parity and randomly assigned to 1 of 3 feeding times. Corn–soybean meal-based diet was fed once at: 0730 (Control, T1), 1130 (T2), and 1530 hours (T3). On average, sows received 7,062 kcal ME/d from 2.20 kg of diet formulated to contain SID Lys/ME of 1.71 g/Mcal. The study was conducted for 28 d (21 d acclimation to the feeding regime and 7 d data collection). Saliva samples were collected every 2 hr for 12 hr in stalls on day 52 of pregnancy. Behavior data were collected 24 hr for 7 d from day 53 of gestating by affixing a remote insights ear tag to each sow. Each sow had 120,960 data points categorized into: “Active,” “Feed,” or “Dormant”. Due to housing constraint, all sows were housed in individual stalls in the same barn presenting a potential limitation of the study. Data were analyzed using PROC MIXED and GLIMMIX procedures of SAS 9.4 for cortisol and behavior data, respectively. Sow was the experimental unit. The area under the curve (AUC) is quantitative evaluation of response as threshold varies over all possible values. A 12-hr cortisol total AUC for sows fed once daily at 1130 hours was reduced relative to sow group fed at 1530 hours (P = 0.046) but similar compared with the control sows (P = 0. 323). The control sows (0730 hours) had reduced total (P < 0.001) and feeding (P = 0.001) activity AUCs relative to sows on 1130 hours but did not differ compared with sows on 1530 hours feeding schedules (P > 0.100). Sows on 1130 hours feeding schedule had greater feed anticipatory activity, 24-hr total activity count, total (P < 0.001) and feeding (P < 0.001) activity AUC compared with sows fed daily at 1530 hours. In conclusion, feeding pregnant sows earlier in the morning (0730 hours) appears to minimize sows’ behavior but similar cortisol response. Sows on 1130 hours feeding schedule had greater activities but reduced cortisol concentration, suggesting that elevated sow activity might not necessarily indicate activation of hypothalamic–pituitary–adrenal axis.

Piglets experience a rapid decrease in body temperature immediately after birth, increasing the risk of mortality. The objective of this study was to determine the effect of drying and/or warming piglets at birth on rectal temperature over the first 24 h after birth. The study was carried out at a commercial sow facility using a completely randomized design with four treatments (applied to piglets at birth): Control (no drying or warming), Desiccant (dried using a desiccant), Warming Box (placed in a box under a heat lamp for 30 min), and Desiccant + Warming Box (both dried and warmed as above). Farrowing pens had one heat lamp, temperatures under which were similar to the warming box (35 °C). A total of 68 litters (866 piglets) were randomly allotted to a treatment at the birth of the first piglet. At birth, each piglet was identified with a numbered ear tag and weighed; rectal temperature was measured at 0, 10, 20, 30, 45, 60, 120, and 1,440 min after birth. Data were analyzed using a repeated-measures model using PROC MIXED of SAS. Litter was the experimental unit, piglet was a subsample of the litter; and the model included the fixed effects of treatment, time (the repeated measure), and the interaction. Rectal temperatures at birth and 1,440 min after birth were similar (P > 0.05) for all treatments. At all times between 10 and 120 min after birth, Control piglets had lower (P ≤ 0.05) temperatures than the other three treatments. The Desiccant and Warming Box treatments had similar (P > 0.05) temperatures at most measurement times, but the Desiccant + Warming Box treatment had the highest (P ≤ 0.05) rectal temperatures at most times between 10 and 60 min. In addition, for all treatments, light (1.5 kg) piglets at all times between 10 and 120 min. In addition, at these measurement times, the deviation in temperature between the Control and the other three treatments was greater for light than medium or heavy piglets. In conclusion, both drying and warming piglets at birth significantly increased rectal temperatures between 10 and 120 min after birth, with the combination of the two interventions having the greatest effect, especially for low birth weight piglets.

One of the efforts in increasing the productivity of dairy cows is to apply maintenance management to dairy cows in the form of recording livestock data. Lembu Alam Serambi farmer group has a recording system using livestock cards provided by the Animal Husbandry Department. Cattle cards used for data recording of dairy cows are made from a type of paper that has several disadvantages, namely, easily lost, torn, wet, burned, and dirty. Therefore, to overcome this problem, we apply Android using Near Field Communication (NFC) technology. In this community service, the NFC technology implementation system is integrated with cow's ear tag and android application. This system is used to identify dairy cows and process data recording with an Android-based application. In this cow data recording system consists of two mobile apps, namely cow data record application and NFC tap tag application. The system implementation shows that the community service activity can help facilitate the Lembu Alam Serambi farmer group in the process of recording dairy cattle data.

Heat stress causes significant economic losses by reducing the productivity and welfare of cattle whilst requiring a significant investment in resource for amelioration. Panting score (PS) is considered a robust indicator of cattle heat stress, however, individualised visual monitoring is impractical. Thermal index-based monitoring and mitigation decisions are applied at the herd level, but they have limited application for the individual animal. As such, an automated system to monitor the real-time animal response to heat stress is required for strategic mitigation. Our objectives were to validate an accelerometer-based ear tag sensor to monitor cattle panting and to determine individual variability in heat stress responses with reference to thermal indices. Two experiments were conducted: Experiment 1 validated the sensors, and Experiment 2 determined individual variability comparing sensor data against thermal indices. Ear tag sensors were fitted at feedlot entry to continuously monitor the behaviour of 100 steers of mixed breed in Experiment 1 and 200 steers and heifers of mixed breed in Experiment 2. Sensor-derived &lsquo, heavy breathing&rsquo, was validated against visually observed PS. Sensor-derived behaviour bouts were analysed as &lsquo, raw&rsquo, and single behaviour states were also converted to the preceding bout of &ge, 2 min, which was referred to as &lsquo, fill&rsquo, data for the validation study. Our results demonstrate the sensors&rsquo, ability to accurately monitor panting in feedlot cattle. Sensor-recorded &lsquo, duration per animal was highly correlated to observed panting duration for both raw (r = 0.89) and fill (r = 0.90) data, however, the concordance correlation co-efficient was lower for raw (0.45) as compared with fill (0.76). Predicted agreement for raw data were 75%, 45%, and 68% and predicted agreement for fill data were 65%, 54%, and 83% for PS0, PS1, and PS2, respectively. Sensitivity for raw data were 39%, 37%, and 45% and for fill data, they were 59%, 54% and 82% for all PS data, PS1 and PS2, respectively. Specificity and positive predictive values for both raw (77% and 79%, respectively) and fill (65% and 77%, respectively) data show the probability of reporting false positives by sensors to be low. Experiment 2 revealed that the duration of panting increased from 0800 to 1700 h alongside changes in thermal indices with significant differences between and within breed and coat colour categories of cattle, suggesting that grouping and allocating heat amelioration measures by breed and coat colour can be effective in commercial feedlots. However, there was high variability (CV &gt, 80%) in the duration of panting between individuals within the same breed and same coat colour, revealing the potential for strategic management at an individual level, and with the same data, genetic selection for heat resilience.

Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow&rsquo, s position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application&rsquo, s user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness.

One of the most important diseases in calves worldwide is neonatal calf diarrhea (NCD), which impairs calf welfare and leads to economic losses. The aim of this study was to test whether the activity patterns of calves can be used as early indicators to identify animals at risk for suffering from NCD, compared with physical examination. We monitored 310 healthy female Holstein-Friesian calves on a commercial dairy farm immediately after birth, equipped them with an ear tag–based accelerometer (Smartbow, Smartbow GmbH), and conducted daily physical examinations during the first 28 d of life. The Smartbow system captured acceleration data indicative of standing and lying periods and activity levels (active and inactive), shown as minutes per hour. We categorized calves as diarrheic if they showed fecal scores of ≥3 on a 4-point scale on at least 2 consecutive days. Incidence of diarrhea was 50.7% (n = 148). A mixed logistic regression model showed that lying [odds ratio (OR) = 1.19], inactive (OR = 1.14), and active (OR = 0.92) times, 1 d before clinical identification of diarrhea (d −1), were associated with the odds of diarrhea occurring on the subsequent day. Receiver operating characteristics curve showed that lying time at d −1 was a fair predictor for diarrhea on the subsequent day (area under curve = 0.69). Average lying time on d −1 was 64.8 min longer in diarrheic calves compared with their controls. Median lying and inactive times decreased, and active time increased with age over the study period. The 24-h pattern of behavior indices based on the output of the Smartbow system followed periods of resting and active times, and showed that between 2200 h and 0600 h, calves spent the greatest percentage of time lying and inactive. These results showed that the accelerometer system has the potential to detect early indicators associated with NCD. In future studies, additional data for the development and testing of calf- and event-specific algorithms (e.g., for detecting milk intake, playing behavior) should be collected, which might further improve the early detection of diarrhea in calves.

The timing of eating, relative to when feed is offered, is affected by the social rank of feedlot cattle due to limited feed bunk space. As cattle can select feed based on dietary preference, the timing of eating for cattle in feedlot may be associated with the ingested diet composition. Our objectives were to determine the nutritive value and timing of feed ingested by 100 feedlot cattle during transition and the association of timing of eating with feeding behaviours and average daily gain (ADG). Cattle behaviour and timing of eating were determined on 100 feedlot cattle using accelerometer-based ear tag sensors from days 3 to 6 post feedlot induction (observation period), and the ongoing impact of this period on ADG was determined for the full feed period (75 days). To determine eating patterns at the time of feed offer, cattle were grouped according to the number of days they were recorded as eating within 1 h of feed being offered across 4 observation days, G0: not present across 4 days, G1: present for 1 day, G2: 2 days, G3: 3 days and G4: present for each of the 4 days. Total mixed ration (TMR) samples were collected for nutritive value analysis from four locations along the feed bunk from the time feed was offered and at hourly intervals thereafter for 7 h each day during the observation period. The composition of feed in the bunk changed across the 7 h of measurement (P

The study focused on behavioral and cortisol responses to feeding frequency in pregnant sows under isocaloric intake. Twenty-four sows [(Landrace × Yorkshire); BW 216.70 ± 3.98 kg; parity 3.04 ± 0.53] were balanced for parity and randomly assigned to 1 of 3 feeding frequency regimes. Sows were fed corn–soybean meal-based diet 1× [0730 (Control), T1], 2× [half ration at 0730 and 1530 hours, T2], or 3× [one-third portion at 0730, 1130, and 1530 hours, T3] from days 30 to 60 of gestation. Sows received 7055 kcal ME/d during gestation from 2.21 kg of diet formulated to contain SID Lys/ME of 1.71 g/Mcal. Saliva samples were collected every 2 hr from 0630 to 1830 hours on day 52 and assayed for cortisol using ELISA procedure. Behavior data were collected for 7 d from day 53 of gestation by affixing a remote insights ear tag to each sow. Each sow had 120,960 data points categorized into: “Active”, “Feed,” or “Dormant”. Because of housing constraint, all sows were housed in individual stalls in the same room presenting a potential limitation of the study. The data were analyzed using PROC MIXED and GLIMMIX procedures of SAS 9.4 for cortisol and behavior count data, respectively. Sow was the experimental unit. The area under the curve (AUC) is quantitative evaluation of response as threshold varies over all possible values. The T2 sows had reduced 12-hr cortisol AUC compared with control sows (P = 0.024) and T3 sows (P = 0.004), respectively. The T2 sows had lower 3 hr (P = 0.039) and 5 hr (P = 0.015) postfeeding cortisol AUC compared with control sows. Feed anticipatory activity (FAA), 24-hr total activity, and feeding activities (eating and/or sham chewing) were reduced for T2 sows relative to the control and T3 sows (P < 0.01). Consequently, T2 sows had lower 24-hr total activity (P < 0.001) and feeding activities (P < 0.001) AUC compared with both the control and T3 sows, respectively. The T3 sows had greater FAA (P < 0.001) and 24-hr total activity AUC (P = 0.010) compared with control sows. Our data although inconclusive due to small sample size, twice daily feeding appears to be the threshold that reduces sows’ total activity AUC, feeding activity AUC, and activation of hypothalamic–pituitary–adrenal axis, reduced hunger, and exhibit potential to improve sow welfare in relation to once and thrice daily feeding regimes under isocaloric intake per kilogram live metabolic weight.

The objectives of this study were to evaluate the performance of the SCR eSense ear tag automated activity monitor (AAM) to detect estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to artificial insemination (AI) using sex-sorted or conventional semen. In total, 281 heifers were fitted with the AAM once eligible for breeding (13.5 m of age). For the first AI, estrus was synchronized using 500 μg of cloprostenol (PGF), given 14 d apart, and heifers were given estrus detection patches (Estrotect™) after the second PGF. Heifers were inseminated at randomly attributed times after high activity alert from the AAM system or if the estrus patch had ≥ 50% colour change. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent inseminations. Pregnancy diagnosis was performed at 30 d post AI and heifers had four opportunities to become pregnant. In a subset of heifers (n = 149), ovaries were scanned every 12 h from the time of AI until ovulation (OV). The system recorded a heat index (measure of estrus strength), maximum activity change, maximum rumination change and duration of high activity. The sensitivity was 91.0%, with a false positive and false negative rate of 8.0%, and the positive predictive value to detect true estrus events was 83.5%. Pregnancy per AI to first AI was 67.6% and 97.9% of heifers become pregnant after four inseminations. Most false positive estrus events had a heat index45 and a rumination change -20, while false negative events had a rumination change ≥ -20. Odds of pregnancy was not associated with any estrus characteristics measured by the system. However, pre-ovulatory follicle diameter had a weak correlation (r 0.25) with all estrus characteristics. The average (range) interval of onset of high activity, peak activity and end of high activity to OV was 28 h (16-46 h), 22 h (10-40 h) and 16 h (0-36 h), respectively. For conventional semen, each hour increase in interval from activity onset or peak activity to AI reduced the predicted probability of pregnancy by 3.8 and 4.2%, respectively. For sex-sorted semen, the relationship between activity onset or peak activity to AI and predicted probability of pregnancy was quadratic, but not significant. Overall, the SCR eSense ear tag AAM performed well and strategies to identify false positive and false negative estrus events, along with optimization of timing of AI, should further improve performance in Holstein heifers.

In precision dairy farming, the valid record of individual cow identification is an important factor in large herds management. In this paper, we propose a cow’s ear tag recognition system that can be used in dairy cow management. Firstly, cow’s head detection is performed by using You Only Look Once (YOLO) object detector followed by ear tag recognition. The ear tag extraction and recognition processes are carried out by image processing techniques and Convolutional Neural Network (CNN) classifier on detected cow’s head images. The experiments are conducted by using videos from dairy farm at Hokkaido prefecture, Japan. The proposed system achieved the reliable results which will support to give the informative status in smart farming.

The cow signs monitoring system is an important part of the farm farming system. It is an important indicator point for judging the signs, diseases, estrus and milk quality of dairy cows. The existing discriminating method is to manually measure the rectal temperature, which is time-consuming and not suitable for large-scale breeding. In this paper, a smart ear tag based on WIFI is designed to measure the body signs of cows with body temperature and food intake. CC3200 is used as WIFI transmitter and processor, combined with high-precision non-contact temperature sensor TMP102 to measure the temperature of the ear, and the film pressure sensor measures the food intake, and realizes the body temperature and ear temperature difference compensation algorithm in the software. The ear tag sends the collected body temperature and food intake as a topic message to the MQTT server, and the display terminal receives the information by subscribing to the related topic message. Powered by button battery, CC3200 low power management, the life of the ear tag can reach more than two years. While saving manpower, it has improved the efficiency of dairy cow monitoring and has broad market value and application prospects.

The objectives of this study were (1) to assess the effect of purulent vaginal discharge (PVD) on reproductive performance using a 4-point vaginal discharge score (VDS) and (2) to evaluate the effect of PGF2α treatment in cows with PVD on reproductive performance. Holstein cows (n = 2,473) from 1 commercial dairy farm had their vaginal discharge scored at 32 ± 3 d in milk (DIM) using a 4-point scale. Cows were also scored for body condition score and had their ovaries examined using transrectal ultrasound. A VDS-2 or greater was used to define PVD. Cows with PVD were assigned to receive 2 doses of 500 µg of PGF2α analogue (n = 277; Cloprostenol; PGF Veyx forte, Veyx Pharma Ltd., Schwarzenborn, Germany) 14 d apart (odd ear tag number) or remained untreated (n = 272; even ear tag number). All cows not detected in estrus at 90 ± 3 DIM were enrolled into a timed artificial insemination (AI) protocol. The effect of VDS and the effect of PGF2α in cows with PVD on reproductive performance were evaluated separately. First service conception risk (FSCR), time to first AI, and time to pregnancy were used as indicators for reproductive performance. Data were analyzed with Cox's regression and mixed logistic models. The prevalence of PVD was 22.2% (548/2,473). We detected an interaction of parity and VDS for FSCR and time to pregnancy. In primiparous cows, VDS had no effect on FSCR and time to pregnancy. In multiparous cows, VDS had an effect on FSCR and time to pregnancy. Multiparous cows having a VDS-2 or a VDS-3 had decreased odds of pregnancy at first AI compared with cows having a VDS-0. Multiparous cows with VDS-1 or VDS-3 had a decreased hazard of conceiving within 200 DIM. The treatment effect of PGF2α was conditional on parity. In primiparous animals with PVD without a corpus luteum present at 32 DIM, treatment with PGF2α reduced FSCR. Irrespective of the presence of a corpus luteum, treatment with PGF2α decreased the hazard of conceiving within 200 DIM in primiparous cows. Although multiparous cows with PVD treated with PGF2α had increased odds of pregnancy at first AI, no treatment effect was observed on the hazard of conceiving within 200 DIM. Our results do not support the blanket use of PGF2α as a treatment option in postpartum dairy cows having PVD.

Thirty-six newly weaned, crossbred beef steers (323 ± 12 kg; SD) from a single-source were used in a 56-d study to examine the effects of a Saccharomyces cerevisiae fermentation product (SCFP; Original XPC, Diamond V, Cedar Rapids, IA) on total tract nutrient digestibility and response to a vaccination challenge. Twelve days after arrival, steers were blocked by body weight (BW) and randomly assigned to treatments: SCFP at 0 (CON), 14 (SCFP14), or 28 (SCFP28) g·steer-1·d-1. Steers were fed via bunks that measured individual intake and received ear tags (CowManager, Select Sires, Plain City, OH) that recorded rumination and activity. BWs were collected on days 1, 0, 14, 28, 42, 55, and 56. Titanium dioxide was fed as an indigestible marker to all steers from days 12 to 27, followed by consecutive day fecal samples, for determination of total tract nutrient digestibility. On day 34, steers received a Mannheimia haemolytica vaccination (One Shot, Zoetis, Kalamazoo, MI) to induce an acute phase protein response. Blood was collected from all steers on day 34 (prior to vaccination) and 3, 6, 9, 11, and 14 d post-vaccination. Data were analyzed using Proc Mixed of SAS (experimental unit = steer; n = 12 per treatment); the model included the fixed effect of treatment and block and the random effect of steer. Blood measures, ear tag, and dry matter intake (DMI) data for the 15-d vaccination period were analyzed as repeated measures. Contrast statements (CON vs. SCFP14; SCFP14 vs. SCFP28) were used to compare treatment means. Digestibility of dry matter (DM) and organic matter was greater for SCFP14 vs. SCFP28 (P ≤ 0.03). Steers fed SCFP14 exhibited greater crude protein digestibility compared with CON (P < 0.01). Steers fed SCFP14 exhibited greater DMI for 15 d post-vaccination (P = 0.02) and greater average daily gain from days 28 to 56 (P = 0.05) vs. SCFP28-fed steers. Post-vaccination, steers fed SCFP14 spent less time ruminating per kg of DM, neutral detergent fiber (NDF), and physically effective NDF consumed than CON or SCFP28 (P ≤ 0.07). Serum IL-8 and haptoglobin concentrations tended to be lesser for steers fed SCFP14 vs. SCFP28 (P ≤ 0.08). Ceruloplasmin concentrations were lesser on day 14 post-vaccination for steers fed SCFP14 vs. CON or SCFP28 (treatment × d; P = 0.004); there were no differences on other sampling days due to treatment. Although no overall performance benefit was noted, steers fed SCFP14 responded better to a vaccination challenge vs. SCFP28-fed steers.

In this paper, we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data. Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head, on a halter positioned at the side of the head behind the mouth, or on the ear using a tag. The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours. Data were collected and animal behaviours (grazing, standing or ruminating) were observed over a common time frame. Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour. Classification accuracies were computed on separate independent test sets. The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space (representing motion patterns) between the training and test animal is similar. The paper will discuss these analyses in detail and can act as a guide for future studies.

The objective of this study was to validate an ear-tag accelerometer sensor (CowManager SensOor, Agis Automatisering BV, Harmelen, the Netherlands) using direct visual observations in a grazing dairy herd. Lactating crossbred cows (n = 24) were used for this experiment at the University of Minnesota West Central Research and Outreach Center grazing dairy (Morris, MN) during the summer of 2016. A single trained observer recorded behavior every minute for 6 h for each cow (24 cows × 6 h = 144 h of observation total). Direct visual observation was compared with sensor data during August and September 2016. The sensor detected and identified ear and head movements, and through algorithms the sensor classified each minute as one of the following behaviors: rumination, eating, not active, active, and high active. A 2-sided t-test was conducted with PROC TTEST of SAS (SAS Institute Inc., Cary, NC) to compare the percentage of time each cow's behavior was recorded by direct visual observation and sensor data. For total recorded time, the percentage of time of direct visual observation compared with sensor data was 17.9 and 19.1% for rumination, 52.8 and 51.9% for eating, 17.4 and 11.9% for not active, and 7.9 and 21.1% for active. Pearson correlations (PROC CORR of SAS) were used to evaluate associations between direct visual observations and sensor data. Furthermore, concordance correlation coefficient (CCC), bias correction factors, location shift, and scale shift (epiR package of R version 3.3.1; R Foundation for Statistical Computing, Vienna, Austria) were calculated to provide a measure of accuracy and precision. Correlations between visual observations for all 4 behaviors were highly to weakly correlated (rumination: r = 0.72, CCC = 0.71; eating: r = 0.88, CCC = 0.88; not active: r = 0.65, CCC = 0.52; and active: r = 0.20, CCC = 0.19) compared with sensor data. The results suggest that the sensor accurately monitors rumination and eating behavior of grazing dairy cattle. However, active behaviors may be more difficult for the sensor to record than others.

Acceleration sensors allow a reduction of time-consuming visual observation. The drinking behavior of bucket-fed calves has not been monitored automatically yet, although sufficient milk intake is essential for calves’ health and growth. The objectives of this pilot study were (1) to evaluate the technical and mathematical feasibility of using an acceleration sensor to detect drinking events in bucket-fed dairy calves, (2) to develop an algorithm for an acceleration sensor (SMARTBOW ear tag, Smartbow GmbH, Weibern, Austria) for monitoring drinking behavior in bucket-fed dairy calves, and (3) to validate the SMARTBOW sensor for monitoring drinking events in bucket-fed dairy calves to observations from video recordings. Three preweaned dairy calves were equipped with ear-tag accelerometers. Calves were housed in individual pens and fed milk from a teat-bucket twice a day. Acceleration data were collected and calf behavior was video-recorded for 5 d for 24 h d−1. Based on a training data set, an algorithm was developed to predict drinking events. Further 15 d of data were generated by simulation. Video recordings were used to analyze whether drinking events (n = 174) were predicted correctly for the complete data set. Sensitivity (82.9%), specificity (96.9%), and accuracy (96.2%) were good, but precision (60.4%) was not yet optimal. Cohen’s Kappa (0.68) indicated substantial agreement between sensor and video analysis. More research based on a larger number of animals with the aim to optimize the underlying algorithm and to further increase sensitivity and precision is planned.

Cattle heat stress responses elicit behavioural adaptation in the form of elevated respiration rate and panting with phase shifts (with increasing severity from closed mouth panting to closed mouth with drool, open mouth, and to open mouth with tongue out) and from “rapid-shallow” to “slow-deep” breathing with increasing temperature. Accelerometer-based sensors can accurately monitor cattle behaviour under experimental and commercial conditions, however, the accuracy of such sensors to monitor the different phases of panting is yet to be determined. Also, despite panting duration diversity between individual cattle of the same breed in the same environment, little is known as to why this occurs. Here we assess the ability of ear tag accelerometer sensors to monitor cattle panting severity and the diversity in behavioural response between heat-susceptible and heat-tolerant cattle when exposed to high heat load. A pen of 99 feedlot heifers were fitted with ear tag sensors and individual cattle panting responses were visually monitored for three consecutive heat event periods. Minute-level panting and non-panting individual cattle data as recorded by sensors were tested against visual observations. Sensitivity (Se), Specificity (Sp), and Youden index (J = Se + Sp – 1) were calculated as test diagnostics for sensor-based classification of panting considering the visual observations as gold standard. Raw minute-level sensor data classified all panting phases with Se 0.30–0.33, Sp > 0.70, and J > 0. Data filtering methods were applied which resulted in systematic improvements in the test diagnostics. Cattle growth, visual panting score (PS) and sensor-detected behaviour duration data were obtained for two selected heat event periods from the same animals. Variability of sensor-detected behaviour durations for visually detected heat-susceptible (PS ≥ 1) and heat-tolerant (PS

Monitoring the licking behaviour in grazing cattle is a potential means for quantifying block supplement intake. The current study aimed to 1) evaluate the capability of an ear-tag accelerometer to identify the licking behaviour at a block supplement in grazing cattle and 2) validate individual licking state (LS) duration predicted by an accelerometer and radio-frequency identification (RFID) system. Four out of 12 Angus steers weighing 384 ± 9.7 kg (mean ± SD) were given free access to a 900 m2 supplement yard with access to two RFID-equipped automatic supplement feeders provided daily from 15:00 to 18:00 h for 10 days. Each steer was fitted with an ear-tag containing a 3-axis accelerometer set at a frequency of 25 Hz. Accelerometer data were segmented into three window sizes (3, 5, and 10 s) and further processed using four machine learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGB), Logistic Regression (LR) and Linear Discriminant Analysis (LDA). The best performance in classifying licking behaviour was obtained from the combination of XGB and 10 s window size with an accuracy, Kappa coefficient, and F1 score of 93%, 0.88, and 0.88, respectively. Accelerometers and RFID systems consecutively under-predicted and over-predicted LS duration by 20% and 6%, with a mean absolute error (MAE) proportion of 22% and 10%, a ratio of root mean square prediction error (RSR) of 0.33 and 0.14 and a modelling efficiency (MEF) of 0.89 and 0.98. Overall, both the ear-tag accelerometer and RFID system was capable of monitoring the licking behaviour and LS duration of grazing cattle accessing block supplements.