Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies

Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis

The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are "Computers and Electronics in Agriculture" and "Journal of Dairy Science". It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.

Efficacy of an automated technology at detecting early postpartum estrus events: Can we detect resumption of cyclicity?

The objective of this observational study was to evaluate the efficacy of a neck-mounted automated activity monitor (AAM) at detecting early postpartum resumed ovarian cyclicity. A total of 192 lactating cows (primiparous = 73 and multiparous = 119) were enrolled in this study. Cows were continuously monitored by a neck-mounted AAM early postpartum (7 to 30 d in milk; DIM). Calving was classified as assisted (forced extraction of a calf) or unassisted (normal calving). Retained fetal membrane, metritis, hyperketonemia, clinical mastitis, and milk production were recorded. Cows were classified as healthy (i.e., no disease events) or sick (i.e., any disease event). Estrus events were alerted by the AAM using a proprietary algorithm set by the AAM company. Blood samples, from the coccygeal vein, were collected at 15, 18, 21, 24, 28, and 30 DIM for progesterone (P4) analysis. Resumption of cyclicity was considered when P4 concentration was ≥1 ng/mL on any collection day. Cows were considered anovular when P4 concentration was <1 ng/mL on all collection days. Cows were classified as true positive: P4 ≥ 1 ng/mL and at least one estrus alert; false positive: P4 < 1 ng/mL and at least one estrus alert; true negative: P4 < 1 ng/mL and no estrus alerts; and false negative: P4 ≥ 1 ng/mL and no estrus alerts. Statistical analyses were performed by frequency distribution and mixed effects logistic regression procedures on SAS (SAS Institute Inc.). The specificity, sensitivity, accuracy, and positive predictive value of the sensor to detect cows that had resumed cyclicity were 84.0%, 34.1%, 52.1%, and 79.2%, respectively. Out of the 192 cows, 35.9% (69/192) were anovulatory and 37.5% (72/192) had no estrus events between 7 to 30 DIM. Healthy cows were more likely to resume cyclicity in early lactation compared with cows that were sick (78.3 ± 1.9 vs. 32.8 ± 3.1%, respectively) independent of parity. In conclusion, the sensor had a high specificity for detecting anovular cows, but it had lower sensitivity, and thus was not effective at detecting cyclic cows, perhaps due to silent ovulation early postpartum.

Peri-estrus activity and mounting behavior and its application to estrus detection in Hanwoo (Korea Native Cattle)

This study was conducted to investigate the change in activity and mounting behavior in Hanwoo (Korean Native Cattle) during the peri-estrus period and its application to estrus detection. A total of 20 Hanwoo cows were fitted with a neck-collar accelerometer device, which measured the location and acceleration of cow movements and recorded the number of instances of mounting behavior by the altitude data. The data were analyzed in three periods (24-, 6-, and 2-h periods). Blood samples were collected for 5 days after the prostaglandin F2α (PGF2α) injection, and the concentrations of estradiol, progesterone, follicle-stimulating hormone, and luteinizing hormone were determined by enzyme-linked immunosorbent assays. Activity and mounting behavior recorded over 2-h periods significantly increased as estrus approached and were more efficient at detecting estrus than over 24- and 6-h periods (p < 0.05). Endocrine patterns did not differ with the variation of individual cows during the peri-estrus period (p > 0.05). Activity was selected as the best predictor through stepwise discriminant analysis. However, activity alone is not enough to detect estrus. We suggest that a combination of activity and mounting behavior may improve estrus detection efficiency in Hanwoo. Further research is necessary to validate the findings on a larger sample size.

Automatically Identifying Sickness Behavior in Grazing Lambs with an Acceleration Sensor

Acute disease of grazing animals can lead to alterations in behavioral patterns. Relatively recent advances in accelerometer technology have resulted in commercial products, which can be used to remotely detect changes in animals' behavior, the pattern and extent of which may provide an indicator of disease challenge and animal health status. The objective of this study was to determine if changes in behavior during use of a lipopolysaccharide (LPS) challenge model can be detected using ear-mounted accelerometers in grazing lambs. LPS infusion elevated rectal temperatures from 39.31 °C to 39.95 °C, indicating successful establishment of an acute fever response for comparison with groups (p < 0.001). For each of the five recorded behaviors, time spent eating, ruminating, not active, active, and highly active, the accelerometers were able to detect an effect of LPS challenge. Compared with the control, there were significant effects of LPS infusion by hour interaction on durations of eating (-6.71 min/h, p < 0.001), inactive behavior (+16.00 min/h, p < 0.001), active behavior (-8.39 min/h, p < 0.001), and highly active behavior (-2.90 min/h, p < 0.001) with a trend for rumination time (-1.41 min/h, p = 0.075) in lambs after a single LPS infusion. Results suggest that current sensors have the capability to correctly identify behaviors of grazing lambs, raising the possibility of detecting changes in animals' health status.

Increase of body temperature immediately after ovulation in mares

To successfully inseminate mares, precise detection of ovulation time is crucial, especially when using frozen-thawed semen. Monitoring body temperature, as has been described in women, could be a non-invasive way to detect ovulation. The objective of this study was to investigate the relationship between the time of ovulation and the variation of body temperature in mares based on automatic continuous measurements during estrus. The experimental group included 21 mares for 70 analyzed estrous cycles. When the mares showed estrous behavior, they were administered intramuscular deslorelin acetate (2.25 mg) in the evening. At the same time, monitoring of body temperature using a sensor device fixed at the left lateral thorax was started and continued for over 60 h. In 2-hour intervals, transrectal ultrasonography was performed to detect ovulation. Estimated body temperature in the 6 h following ovulation detection was on average 0.06°C +/- 0.05°C (mean +/- SD) significantly higher when compared with body temperature at the same time on the preceding day (p=0.01). In addition, a significant effect of PGF_2α administration for estrus induction on the body temperature was found, being significantly higher until 6 h before ovulation compared to that of uninduced cycles (p=0.005). In conclusion, changes in body temperature during estrus in mares were related to ovulation. The increase in body temperature immediately after ovulation might be used in the future to establish automatized and non-invasive systems to detect ovulation. However, the identified temperature rise is relatively small on average and hardly identifiable in the individual mares.

Can the use of digital technology improve the cow milk productivity in large dairy herds? Evidence from China's Shandong Province

Introduction

Improving milk productivity is essential for ensuring sustainable food production. However, the increasing difficulty of supervision and management, which is associated with farm size, is one of the major factors causing the inverse relationship between size and productivity. Digital technology, which has grown in popularity in recent years, can effectively substitute for manual labor and significantly improve farmers' monitoring and management capacities, potentially addressing the inverse relationship.

Methods

Based on data from a survey of farms in Shandong Province in 2020, this paper employs a two-stage least squares regression model to estimate the impact of herd size on dairy cow productivity and investigate how the adoption of digital technology has altered the impact of herd size on dairy cow productivity.

Results

According to the findings, there is a significant and negative impact of herd size on milk productivity for China's dairy farms. By accurately monitoring and identifying the time of estrus, coupled with timely insemination, digital technology can mitigate the negative impact of herd size on milk productivity per cow.

Discussion

To increase dairy cow productivity in China, the government should promote both small-scale dairy farming and focus on enhancing management capacities of farm operators, as well as large-scale dairy farms and increase the adoption of digital technologies.

Noninvasive monitoring of steroid hormone production and activity of zoo-housed banteng (Bos javanicus)

This study describes patterns of steroid hormone production and activity for banteng (Bos javanicus), an endangered member of the Bovidae family. Using validated assays, concentrations of fecal progestagens, androgens, and glucocorticoids were quantified for four females and one male at the Saint Louis Zoo. A commercial activity monitor was also validated for assessing movement. The devices were then used to characterize activity in relation to season, reproductive status, and fecal steroid concentrations. General linear mixed models assessed differences in activity and steroid concentrations among individuals, in regards to reproductive status and season. Ovulatory cycle patterns, changes in activity around estrus and parturition, and events correlated with increased glucocorticoid production were also documented. Cycle lengths were 24.7 ± 0.4 days, and cycle lengths varied among individuals. Females cycled year-round, but luteal progestagen concentrations, along with glucocorticoids and male androgens, increased during the summer. Activity also increased in the summer. Progestagen concentrations were greater in pregnant females, and the gestation length of one pregnancy was 254 days. Pregnant females were less active overall, but activity increased the day before parturition. Activity was also greater preceding the onset of the luteal phase. The majority of glucocorticoid concentrations were in the range of baseline concentrations. However, a small number of elevated concentrations were correlated with husbandry and veterinary events. This study is the first to validate non-invasive methods for monitoring reproduction, welfare, and activity of banteng. Our results may contribute to the improved management of captive populations.

Sıcaklık-Nem İndeks Değerlerinin Yerli Kara Erkek Sığırlarda Bazı Davranışlar Üzerine Etkileri

In this study, ear temperature and some behavioural data were determined in the four male animals of Anatolian Black cattle raised under the Institute conditions. For this purpose, a chip sensor (CowManager) was attached to the ears of the animals, and data of 51 days were obtained from each animal hourly. During the study, hourly temperature and humidity data of the barn where the animals were housed were collected, and hourly and daily Temperature - Humidity Index (THI) data were calculated from these values. According to these index values, daily THI values were classified in 3 groups, while hourly THI values were classified in 4 groups. In this study, the ear temperature of the bulls was found to be an average of 21.97 °C daily. The ear temperature values increased as the THI values increased, and the differences between the groups were found to be statistically significant (P&lt;0.001). In the study high active, active, not active, eating and rumination data were found to be 7.84, 6.86, 27.15, 26.69 and 28.31%, daily, respectively. In the analysis made according to the THI groups, the differences according to these behavioural characteristics were found to be statistically significant (P&lt;0.01). In the study, as THI values increased high active, active and not active values increased, while eating and rumination values decreased. While the activity and eating values of the animals increased during the daytime, the not active and rumination values of the animals increased during the night hours. As a result, it was concluded that there is a correlation between the THI values, ear temperature and behavioural data, and this could be an indication that the animal behaviour was affected by meteorological events.

Evaluating automated infrared thermography and vulva exposure tracking as components of an estrus detection platform in a commercial dairy herd

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P₄) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P₄ fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P₄ concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P₄; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d -7 (P₄; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d -7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d -7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE < 80%. The highest Estrus BenchMark Youden index (YJ; 0.45), diagnostic odds ratio (DOR; 9.04), and Efficiency (0.77) were achieved for AE in a ±48 h window and at ±72 h window for CQE (YJ; 0.66, DOR; 25.29, and Efficiency 0.76) relative to Herd Navigator System estrus alerts. The highest Estrus BenchMark resulted in 58% estrus detection rates for AE and 80% for cows with CQE compared to the Herd Navigator System.

Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study

Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.

Estrus Prediction Models for Dairy Gyr Heifers

Simple Summary

Intraruminal devices are already being used to predict reproductive events in cattle. For this prediction, several models and approaches can be used. The aim of this study was to evaluate changes in rumen reticulum temperature (RRT) and activity (ACT) during estrus in Dairy Gyr heifers and to evaluate different models for estrus prediction. There was an increase in both RRT and ACT in the estrus period compared to the same period on the day before and the day after estrus. Among the mathematical models, Random Forest had the best performance. The present results suggest that RRT and ACT can contribute to the identification of estrus and be of value for improving the reproductive efficiency of Zebu herds in tropical regions.

Abstract

Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (p < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers.

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow's Lactation

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows’ lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

Plasma concentrations of progesterone in the preceding estrous cycle are associated with the intensity of estrus and fertility of Holstein cows

The aim of this study was to determine the association between concentrations of progesterone (P4) during previous the estrous cycle with the intensity of spontaneous or estrogen-induced estrous expression and pregnancy per artificial insemination (P/AI). A total of 1,953 AI events from lactating Holstein cows were used, consisting of 1,289 timed AI events from experiment 1 (Exp. 1) and 664 AI events from experiment 2 (Exp. 2). In Exp. 1, cows were bred after a timed AI protocol based on estradiol and P4. In Exp. 2 animals were bred upon spontaneous estrus detection. In both experiments cows were continuously monitored by an automated activity monitor (AAM), in Exp.1 a relative increase of activity was calculated (i.e., percentage of increase activity at estrus compared to cow's baseline activity) and in Exp.2, activity data from each cow were computed into an index value that ranged from 0 to 100. In Exp.2 duration (hours) of estrus were calculated and defined as the total time above the threshold (35 index). The intensity of estrous expression was determined for each event and classified as either high or low intensity using the median of each experiment. Blood samples were collected for P4 analysis in Exp. 1 at -4 d, 0 d, and 7 d relative to timed AI, and in Exp. 2 immediately following estrus (0 d), 7 d, 14 d, and 21 d post-AI. Concentration of P4 was classified as greater or lower according to the median value in each experiment. Cows with lower concentrations of P4 at AI had greater estrous expression in Exp. 1 (363.6 ± 5.2 vs. 275.9 ± 8.0% relative increase) and Exp. 2 (76.7 ± 1.9 vs. 67.4 ± 4.7 index; and 12.5 ± 0.5 vs. 9.3 ± 1.8 hours). Cows with a greater intensity of estrous expression at timed AI had greater concentrations of P4 at -4 d than cows with lower intensity estrus or no estrous expression (4.6 ± 0.2 vs. 3.6 ± 0.2 vs. 3.7 ± 0.2 ng/mL). Cows with greater concentrations of P4 at -4 d had greater P/AI (32.8 ± 4.4 vs. 22.4 ± 4.5%), whereas cows with lesser concentrations of P4 at d0 for either timed AI (35.2 ± 3.4 vs. 19.6 ± 4.6%) or spontaneous estrus (31.8 ± 2.8 vs. 23.4 ± 3.2%) had greater P/AI. Cows with greater concentrations of P4 7 d post-timed AI had greater P/AI compared with cows that had lower concentration of P4 (39.1 ± 2.9 vs. 24.7 ± 2.6%). Similarly, cows that had lower concentrations of P4 at 7 d, 14 d and 21 d post-spontaneous estrus tended to have lower P/AI when compared with cows with greater concentrations of P4. Overall, concentrations of P4 prior to and at AI were associated with greater estrous intensity and P/AI at both spontaneous and timed AI events.

Transition cow metabolites and physical traits influence days to first postpartum ovulation in dairy cows

Physical activities are associated with the health of transition dairy cows and pregnancy outcomes are positively related to early resumption of postpartum estrous cycles. The objective was to assess key metabolites and patterns of prepartum and postpartum physical activity as they relate to the onset of first postpartum ovulation in lactating dairy cows. Close-up dry Holstein cows (n = 82) and late gestation heifers (n = 78) were enrolled beginning 3 wk before expected calving date (Day 0). Cows were fit with Cow SensOor ear tags to assess transitional changes in eating, resting, rumination, high activity, and ear-surface temperatures. Rectal temperatures were assessed and blood samples were collected on Days 0, 3, 7, and 14 to measure concentrations of glucose, free fatty acids (FFA), β-hydroxybutyrate (BHB), calcium, and haptoglobin. Body condition scores (BCS) and body weights (BW) were measured weekly, and blood samples were collected weekly from Day 21 ± 3 through 70 ± 3 to quantify changes in progesterone to detect luteal function after ovulation. Cows first ovulating before median Day 33 were classified as early (n = 76), whereas those first ovulating after Day 33 were classified as late (n = 84). Early ovulating cows first ovulated earlier (P < 0.001) than the late ovulation cows (24.3 ± 1.2 d [range: 16-32 d] vs. 48.8 ± 1.2 d [range: 33-74 d]), respectively. Mean days to first ovulation excluded seven cows that failed to ovulate before insemination. Compared with late ovulating cows, early ovulating cows had lesser (P < 0.05) concentrations of FFA, BHB, and haptoglobin on Days 0, 3, 7, and 14 in addition to having lesser (P < 0.05) rectal temperatures and ear-surface temperatures. Ear-surface temperatures began to decrease 4 d before parturition and remained less (P < 0.05) after calving than cows that subsequently ovulated late. Early ovulating cows tended (P = 0.07) to spend more time eating, and less (P = 0.02) time resting and being active during the first 3 wk after calving, and lost less (P = 0.03) BW and BCS during the first 9 wk compared with late ovulating cows. Although no differences were detected in yields of milk or energy-corrected milk during the first 9 wk after calving, early compared with late ovulating cows produced more (P < 0.01) milk protein. We concluded that metabolic measures during the first 2 wk after calving, and physical and behavioral traits are associated with the onset of postpartum ovarian activity.

Daily activity measures and milk yield immediately before and after a fertile estrus and during the period of expected return to estrus after insemination in dairy cows

The objective of this study was to characterize changes in milk yield and other physical measures during a 7-d periestrual period encompassing estrus (d 0) and during a 16-d period of expected return to estrus beginning at d 17 after artificial insemination (AI) until pregnancy status was determined on d 32. Lactating dairy cows milked thrice daily were fitted with CowManager SensOor ear tags (Agis) capable of assessing real-time eating, rumination, resting, high activity (estrus), ear-surface temperature, and heat alerts. Data were uploaded to the cloud, downloaded daily into Excel (Microsoft Corp.) spreadsheets, averaged to produce daily means for each activity, and analyzed as repeated measures relative to estrus or to d 17 after AI. Daily milk was unchanged during the periestrual period but was greater in nonpregnant cows that failed to return to estrus (NP-NR) during d 21 through 26 compared with NP cows that returned to estrus (NP-R) and pregnant (PREG) cows during that same period. Daily ear-surface temperature was greater during d 1 to 3 compared with d 0 and averaged 0.6 to 1.7°C greater from d 17 through 32 in NP-NR cows compared with NP-R and PREG cows. Daily rumination and resting times reached nadirs on d 0, with decreases occurring 48 h before estrus. Both rumination and resting times increased by 25 or 81% on the day after estrus, respectively. Rumination and resting times were less in NP-R cows during d 22 through 26 compared with NP-NR and PREG cows. In contrast, daily eating time was greatest on the day of estrus compared with 3 d before and after estrus. The NP-R cows spent more time eating during d 17 through 32 compared with NP-NR and PREG cows. High activity increased by 97% during 48 h before estrus, peaked at estrus, and decreased to a constant level during d 1 through 3. The NP-R cows had greater high activity on d 22 through 26 compared with NP-NR and PREG cows. We conclude that resting and rumination activity decreased to daily nadirs, whereas eating and high activity peaked on the day of estrus. Fertile estrus was associated with 12% greater high activity, 11% less resting time, and 6% less rumination time. In addition, cows that returned to estrus after AI had greater daily eating and high activity times and less rumination and resting time during the period of expected return to estrus after AI compared with pregnant cows and cows failing to return to estrus.

Unraveling the Relationship between Milk Yield and Quality at the Test Day with Rumination Time Recorded by a PLF Technology

Simple Summary

Precision livestock farming, by real time monitoring of dairy cows, has the potential to generate a huge amount of data to be used for farm management purposes, as well as in breeding programs. Daily rumination time (RT) recorded by commercial systems is promising in this context because it may be related to individual milk yield and composition. However, it is necessary to assess the ability of sensor data to be used in a predictive model, but also to evaluate and standardize the correct phenotypes, and how they are related to individual variability rather than from other sources. RT data and milk test day (TD) records collected from 691 cows, monitored for thirteen months, were analyzed for the already mentioned goals and to better characterize the effect of high-, medium- and low-level daily RT on milk yield and composition. Our results showed that “animal” in a farm major contributed to the RT total variability, confirming a possible use in breeding program. The higher RT class reported the best productive performance for milk and each solid yield, in spite of a small reduction in their contents, and appears to be related to a higher degree of saturation in the fatty acid profile.

Abstract

The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RT_D). Variance component analysis of RT_D, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT₁₀ variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT₁₀ ≤ 8 h/day), medium (8 h/day < MRT₁₀ ≤ 9 h/day), and high (HRT₁₀ > 9 h/day) RT. Cows with HRT₁₀ had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT₁₀ and LRT₁₀. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT₁₀, with respect to LRT₁₀ and MRT₁₀ observations.

Identification of discriminating behavioural and movement variables in lameness scores of dairy cows at pasture from accelerometer and GPS sensors using a Partial Least Squares Discriminant Analysis

The behaviour and movement of lame dairy cows at pasture have been studied little, yet they could be relevant to improve the automatic detection of lameness in cows in pasture-based systems. Our aim in this study is to identify behavioural and movement variables of dairy cows at pasture that could discriminate lameness scores. Individual cow behaviours were predicted from accelerometer data and movements measured using GPS data. Sixty-eight dairy cows from three pasture-based commercial farms were equipped with a 3-D accelerometer and a GPS sensor fixed on a neck collar for 1-5 weeks, depending on the farm, in spring and summer 2018. A lameness score was assigned to each cow by a trained observer twice a week. Behaviours were predicted every 10 s based on accelerometer data, and then combined with the GPS position. Segmentation on behavioural time series was used to delineate each behavioural bout within each outdoor period. Thirty-seven behavioural and movement variables were then calculated from the behavioural bouts for each cow. A partial least square discriminant analysis was performed to identify the variables that best discriminate lameness scores. Time spent grazing, grazing bout duration, duration before lying down in the pasture, time spent resting, number of resting bouts, distance travelled during grazing, and dispersion were the most discriminant variables in the PLS-DA (VIP > 1). Severely lame cows spent 4.5 times less time grazing and almost twice as much time resting as their sound congeners, especially in the lying position. Exploratory behaviour was also reduced for both moderately and severely lame cows, resulting in 1.2 and 1.7 times less distance travelled respectively, especially during grazing. These variables could be used as additional variables to improve the performance of existing lameness detection devices in pasture-based systems.

Do we automatically detect health- or general welfare-related issues? A framework

The early detection of health disorders is a central goal in livestock production. Thus, a great demand for technologies enabling the automated detection of such issues exists. However, despite decades of research, precision livestock farming (PLF) technologies with sufficient accuracy and ready for implementation on commercial farms are rare. A central factor impeding technological development is likely the use of non-specific indicators for various issues. On commercial farms, where animals are exposed to changing environmental conditions, where they undergo different internal states and, most importantly, where they can be challenged by more than one issue at a time, such an approach leads inevitably to errors. To improve the accuracy of PLF technologies, the presented framework proposes a categorization of the aim of detection of issues related to general welfare, disease and distress and defined disease. Each decision level provides a different degree of information and therefore requires indicators varying in specificity. Based on these considerations, it becomes apparent that while most technologies aim to detect a defined health issue, they facilitate only the identification of issues related to general welfare. To achieve detection of specific issues, new indicators such as rhythmicity patterns of behaviour or physiological processes should be examined.

Association of estrous expression detected by an automated activity monitoring system within 40 days in milk and reproductive performance of lactating Holstein cows

The objective of this observational study was to evaluate the association of estrous expression within 40 days in milk (DIM) using a neck-mounted automated activity monitor (Heatime Pro; SCR Engineers Ltd.) with reproductive performance in lactating Holstein cows. A total of 2,077 cows (614 primiparous cows and 1,463 multiparous cows) from 5 commercial dairy farms were included in the statistical analyses. Activity data from the first 7 d after calving were excluded. An estrus event was defined as an activity change index ≥35 for more than 2 h. Cows were classified according to the number of estrus events from d 7 until d 40 postpartum into 3 categories: (1) no estrus event (Estrus0); (2) one estrus event (Estrus1), and (3) 2 or more estrus events (Estrus2). Generalized linear mixed models were used to analyze continuous and categorical data. Shared frailty models were used for time to event data. Overall, 52.7% of cows had no estrus event detected by an automated activity monitor system from d 7 until d 40 postpartum. Herd level prevalence of Estrus0 ranged from 37.5 to 58.4%. Estrous expression from d 7 until d 40 postpartum affected estrous duration and estrous intensity at first artificial insemination (AI). Cows in Estrus0 had the shortest duration (13.2 ± 0.33 h) compared with cows in Estrus1 (13.8 ± 0.36 h) and Estrus2 (14.8 ± 0.41 h). Cows in Estrus2 had a longer estrous duration at first postpartum AI compared with cows in Estrus1. Among Estrus0 cows, 46.2% had an estrus event with high intensity at first postpartum AI. Among cows in Estrus1 and Estrus2, 50.8 and 53.8% had an estrus event with high intensity at first postpartum AI, respectively. There was a significant difference between Estrus2 and Estrus0 and a tendency between Estrus0 and Estrus1. There was no difference between Estrus1 and Estrus2. For Estrus0, Estrus1, and Estrus2 cows, pregnancy per AI was 29.4, 30.9, and 37.8%, respectively. There was a significant difference between Estrus0 and Estrus2 and Estrus1 and Estrus2. There was no difference between Estrus0 and Estrus1. Estrous expression from d 7 until d 40 postpartum affected time to first AI and time to pregnancy. Compared with Estrus0 cows, cows in Estrus1 [hazard risk (HR) = 1.74] and Estrus2 (HR = 1.77) had an increased hazard of being inseminated within 100 DIM. There was no difference between Estrus1 and Estrus2. Median DIM to first AI were 70, 59, and 58 for cows in Estrus0, Estrus1, and Estrus2, respectively. Compared with Estrus0 cows, cows in Estrus1 (HR = 1.28) and Estrus2 (HR = 1.33) had an increased hazard of becoming pregnant within 200 DIM. There was no difference between Estrus1 and Estrus2. Median DIM to pregnancy were 127, 112, and 103 for Estrus0 cows, Estrus1 and Estrus2, respectively. In conclusion, cows with no estrous expression from 7 to 40 DIM had reduced estrous expression at first AI and inferior reproductive performance compared with cows that displayed estrous activity.

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 PGF_2α (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.

Description of Behavioral Patterns Displayed by a Recently Weaned Cohort of Healthy Dairy Calves

Simple Summary

Modern technology has allowed researchers to track the movement patterns of cattle with increasing accuracy in order to gain a greater understanding of both overt and subtle activity trends. The aim of this study was to describe and analyze movement patterns displayed by recently weaned and healthy dairy calves. Three movement pattern clusters were identified, and calves in this study were more active in the afternoon and at night. There was a correlation between the rate of movement, linearity ratio, and the distance traveled. However, turning angles do not have any influence on the distance traveled and the rate of movement across the three cluster-type movements. The findings reported in this study could be used to further develop the interpretation of movement and behavior patterns of calves in order to establish an early detection system for poor health and welfare on dairy farms.

Abstract

Animals display movement patterns that can be used as health indicators. The movement of dairy cattle can be characterized into three distinct cluster types. These are cluster type 1 (resting), cluster type 2 (traveling), and cluster type 3 (searching). This study aimed to analyze the movement patterns of healthy calves and assess the relationship between the variables that constitute the three cluster types. Eleven Holstein calves were fitted with GPS data loggers, which recorded their movement over a two week period during spring. The GPS data loggers captured longitude and latitude coordinates, distance, time and speed. It was found that the calves were most active during the afternoon and at night. Slight inconsistencies from previous studies were found in the cluster movements. Cluster type 2 (traveling) reported the fastest rate of movement, whereas cluster type 1 (resting) reported the slowest. These diverse movement patterns could be used to enhance the assessment of dairy animal health and welfare on farms.

Predictive potential of activity and reticulo-rumen temperature variation for calving in Gyr heifers (Bos taurus indicus)

Factors related to the thermal physiology and activity of Zebu animals close to calving are still unknown. The aims of this study were 1) to describe the pattern of reticulo-rumen temperature and activity variation in nulliparous Gyr heifers close to calving, and 2) to evaluate the predictive potential of these traits for calving in Gyr heifers. Forty pregnant Gyr heifers that had calved between August and December 2017 at the Getúlio Vargas Experimental Station, Empresa de Pesquisa Agropecuária de Minas Gerais (Epamig), Brazil, were used. The animals received a rumen bolus to monitor reticulo-rumen temperature (Trr) and activity (Act) at intervals of 10 min. Mixed linear models were used. A decrease in Trr and an increase in Act were observed on the days preceding calving. Differences in Trr and Act were more evident during the final 21 and 11 h previous to calving compared to 14 days before calving, measured at the same time of day. There was a decrease of about 0.20 °C in Trr at the time of calving when compared to baseline (14 days before calving measured at the same time of day). Environmental variables, such as temperature and air humidity, as well as biological factors such as circadian rhythm, may influence the interpretation of the data. In conclusion, variations exist in the Trr and Act of Gyr heifers in the hours before calving, which is preceded by a decrease in Trr and an increase in Act. Particularities in the thermal physiology of Zebu cattle must be considered when prediction devices previously validated only for European breeds are used.

Invited review: Lying time and the welfare of dairy cows

Adequate time lying down is often considered an important aspect of dairy cow welfare. We examine what is known about cows' motivation to lie down and the consequences for health and other indicators of biological function when this behavior is thwarted. We review the environmental and animal-based factors that affect lying time in the context of animal welfare. Our objective is to review the research into the time that dairy cows spend lying down and to critically examine the evidence for the link with animal welfare. Cows can be highly motivated to lie down. They show rebound lying behavior after periods of forced standing and will sacrifice other activities, such as feeding, to lie down for an adequate amount of time. They will work, by pushing levers or weighted gates, to lie down and show possible indicators of frustration when lying behavior is thwarted. Some evidence suggests that risk of lameness is increased in environments that provide unfavorable conditions for cows to lie down and where cows are forced to stand. Lameness itself can result in longer lying times, whereas mastitis reduces it. Cow-based factors such as reproductive status, age, and milk production influence lying time, but the welfare implications of these differences are unknown. Lower lying times are reported in pasture-based systems, dry lots, and bedded packs (9 h/d) compared with tiestalls and freestalls (10 to 12 h/d) in cross-farm research. Unfavorable conditions, including too few lying stalls for the number of cows, hard or wet lying surfaces, inadequate bedding, stalls that are too small or poorly designed, heat, and rain all reduce lying time. Time constraints, such as feeding or milking, can influence lying time. However, more information is needed about the implications of mediating factors such as the effect of the standing surface (concrete, pasture, or other surfaces) and cow behavior while standing (e.g., being restrained, walking, grazing) to understand the effect of low lying times on animal welfare. Many factors contribute to the difficulty of finding a valid threshold for daily lying time to use in the assessment of animal welfare. Although higher lying times often correspond with cow comfort, and lower lying times are seen in unfavorable conditions, exceptions occur, namely when cows lie down for longer because of disease or when they spend more time standing because of estrus or parturition, or to engage in other behaviors. In conclusion, lying behavior is important to dairy cattle, but caution and a full understanding of the context and the character of the animals in question is needed before drawing firm conclusions about animal welfare from measures of lying time.

Graduate Student Literature Review: Evaluating the appropriate use of wearable accelerometers in research to monitor lying behaviors of dairy cows

Until recently, animal behavior has been studied through close and extensive observation of individual animals and has relied on subjective assessments. Wearable technologies that allow the automation of dairy cow behavior recording currently dominate the precision dairy technology market. Wearable accelerometers provide new opportunities in animal ethology using quantitative measures of dairy cow behavior. Recent research developments indicate that quantitative measures of behavior may provide new objective on-farm measures to assist producers in predicting, diagnosing, and managing disease or injury on farms and allowing producers to monitor cow comfort and estrus behavior. These recent research developments and a large increase in the availability of wearable accelerometers have led to growing interest of both researchers and producers in this technology. This review aimed to summarize the studies that have validated lying behavior derived from accelerometers and to describe the factors that should be considered when using leg-attached accelerometers and neck-worn collars to describe lying behavior (e.g., lying time and lying bouts) in dairy cows for research purposes. Specifically, we describe accelerometer technology, including the instrument properties and methods for recording motion; the raw data output from accelerometers; and methods developed for the transformation of raw data into meaningful and interpretable information. We highlight differences in validation study outcomes for researchers to consider when developing their own experimental methodology for the use of accelerometers to record lying behaviors in dairy cows. Finally, we discuss several factors that may influence the data recorded by accelerometers and highlight gaps in the literature. We conclude that researchers using accelerometers to record lying behaviors in dairy cattle should (1) select an accelerometer device that, based on device attachment and sampling rate, is appropriate to record the behavior of interest; (2) account for cow-, farm-, and management-related factors that could affect the lying behaviors recorded; (3) determine the appropriate editing criteria for the accurate interpretation of their data; (4) support their chosen method of recording, editing, and interpreting the data by referencing an appropriately designed and accurate validation study published in the literature; and (5) report, in detail, their methodology to ensure others can decipher how the data were captured and understand potential limitations of their methodology. We recommend that standardized protocols be developed for collecting, analyzing, and reporting lying behavior data recorded using wearable accelerometers for dairy cattle.

Associations between genomic merit for daughter pregnancy rate of Holstein cows and metabolites postpartum and estrus characteristics

Genetic selection of Holstein cattle in the past 2 decades has seen an increased attention to fertility traits. Our hypotheses were that genomic merit for daughter pregnancy rate (GDPR) is positively associated with metabolic responses, hazard of estrus, and estrus characteristics. Pregnant heifers (n = 821) from one herd that were genotyped within 2 mo of birth (Clarifide, Zoetis, Parsippany, NJ) were fitted with automated monitoring devices (SCR Inc., Netanya, Israel) -21 ± 14 d relative to calving. Estrus characteristics recorded from calving to 62 d postpartum were evaluated. Blood samples were collected weekly from a subsample (n = 499) of cows, from 7 to 28 d postpartum, for determination of insulin-like growth factor-1, glucose, and nonesterified fatty acids. Cows received artificial insemination or embryo transfer following detected estrus and those not detected in estrus were submitted to an ovulation synchronization protocol starting at 75 d in milk. Linear and quadratic associations between GDPR and outcomes were analyzed, but when appropriate, results are presented according to GDPR quartile (Q1 = -1.8 to 0.8; Q2 = 0.9 to 1.7; Q3 = 1.8 to 2.5; Q4 = 2.6 to 5.9) based on the parameter estimates of the multivariable models. Genomic merit for daughter pregnancy rate was positively associated with insulin-like growth factor-1 (Q1 = 24.3 ± 0.2; Q2 = 26.8 ± 0.2; Q3 = 28.2 ± 0.2; Q4 = 30.6 ± 0.3 ng/mL) and glucose (Q1 = 67.0 ± 0.1; Q2 = 69.1 ± 0.2; Q3 = 69.6 ± 0.2; Q4 = 70.8 ± 0.2 mg/dL) concentrations, but GDPR was negatively associated with nonesterified fatty acid concentration (Q1 = 281.2 ± 4.9; Q2 = 262.0 ± 5.9; Q3 = 239.3 ± 5.0; Q4 = 221.6 ± 4.7 μmol/L). A positive association was observed between GDPR and hazard of estrus [adjusted hazard ratio and 95% confidence interval = 1.16 (1.06, 1.28)] and number of estrus events (Q1 = 0.50 ± 0.03; Q2 = 0.62 ± 0.04; Q3 = 0.74 ± 0.05; Q4 = 0.86 ± 0.06) within 62 d postpartum, duration of estrus (Q1 = 14.10 ± 0.04; Q2 = 14.48 ± 0.04; Q3 = 14.67 ± 0.04; Q4 = 14.98 ± 0.04 h), probability of activity peak (0 = no estrus, 100 = maximum activity) ≥86 (Q1 = 0.80 ± 0.03; Q2 = 0.83 ± 0.02; Q3 = 0.83 ± 0.03; Q4 = 0.85 ± 0.2), and probability of heat index ≥86 (Q1 = 0.77 ± 0.04; Q2 = 0.81 ± 0.05; Q3 = 0.83 ± 0.03; Q4 = 0.86 ± 0.03). Conversely, GDPR was negatively associated with rumination nadir at estrus (Q1 = -35.5 ± 0.1; Q2 = -37.0 ± 0.1; Q3 = -38.0 ± 0.1; Q4 = -39.6 ± 0.1 min). We detected a positive association between GDPR and hazard of pregnancy (adjusted hazard ratio = 1.11, 95% confidence interval = 1.03, 1.19). Selection for GDPR may improve the hormonal and metabolic status of cows postpartum, leading to earlier resumption of cyclicity, and may improve detection of estrus in commercial herds because it was positively associated with estrus characteristics.

A Review of Welfare Indicators of Indoor-Housed Dairy Cow as a Basis for Integrated Automatic Welfare Assessment Systems

Simple Summary

Many techniques have been developed to measure single indicators of reduced welfare in farm animals, such as changes in the walking pattern to detect lameness in dairy cows. However, there is still a need to combine these single measurements to get a more complete picture of the wellbeing of an animal. Based on a literature review on dairy cow welfare, this review provides a basis for the development of an integrated automatic system to assess the welfare of dairy cows on the farm. It provides an overview of the main welfare issues for dairy cows, such as lameness, heat stress, or pain and of the most established indicators that could help to detect these welfare issues on the farm. We found that there are several indicators, such as reduced feed intake, that are common to most welfare issues and that are therefore suitable to detect reduced welfare in general, while other indicators mainly identify one welfare issue, such as increased respiratory rate, as an indicator of heat stress. Combining these different types of indicators would provide a good basis to develop an integrated automatic system that could assist farmers in the detection of reduced welfare on their farms.

Abstract

For on-farm welfare assessment many automatic methods have been developed to detect indicators of reduced welfare. However, there is still a need to integrate data from single sources to obtain a complete picture of the welfare of an animal. This review offers a basis for developing integrated automatic systems to assess dairy cow welfare by providing an overview of the main issues that challenge cow welfare (e.g., lameness) and of well-established indicators that could detect these issues on the farm. Based on a literature review of 4 reviews on cow welfare in general and 48 reviews on single welfare issues, we identified 18 different major welfare issues and 76 matching indicators that could be detected automatically on the farm. Several indicators, e.g., feed intake, showed a consistent association with welfare across many different issues. Although some of these indicators are discussed critically, this means there are many indicators that potentially could detect reduced welfare in general. Other types of indicators could detect one specific welfare issue, e.g., increased respiratory rate for heat stress. These different types of indicators combined provide a basis to develop integrated automatic systems that ultimately would help farmers to detect welfare problems at an early stage.

Estrous detection with an activity and rumination monitoring system in an organic grazing and a low-input conventional dairy herd

The objective of this study was to evaluate estrous detection using a physical activity and rumination monitoring system in a seasonal calving organic grazing (GRAZ) and a low-input conventional (ZEROGRAZ) dairy herd. The study was conducted from June 2014 to August 2017. During each breeding season, physical activity and rumination were monitored electronically using an activity and rumination monitoring system (HR-LD tags; SCR Engineers Ltd., Netanya, Israel). Signals resulting from the activity and rumination monitoring system for individual cows were used to determine consistency of the values using this system with the breeding date of cows. Breeding dates were determined using Estrotect^TM patches. The study included 1,463 breeding dates from 531 cows. Within the GRAZ herd, during the summer breeding season the monitoring system was less sensitive for estrous detection (33.8 %) than during the winter breeding season (79.8 %).The activity and rumination monitoring system had a sensitivity of 56.7 %, specificity of 99.3 % and positive predictive value of 59.8 % for the GRAZ herd, and sensitivity of 70.1 %, specificity of 99.2 % and positive predictive value of 66.3 % for the ZEROGRAZ herd. For cows that were determined to be pregnant and subsequently calved as a result of the mating, the sensitivity for estrous detection was slightly greater for the GRAZ (60.7 %) and ZEROGRAZ (72.5 %) herds. The activity and rumination monitoring system evaluated in this study has potential for estrous detection in grazing herds during the winter breeding season and in small-input dairy herds during both, winter and summer breeding seasons.

Symposium review: Precision technologies for dairy calves and management applications

There is an increasing interest in using precision dairy technologies (PDT) to monitor real-time animal behavior and physiology in livestock systems around the world. Although PDT in adult cattle is extensively reviewed, PDT use for the management of preweaned dairy calves has not been reviewed. We systematically reviewed research on the use and application of precision technologies in calves. Accelerometers have the potential to be used to monitor lying behavior, step activity, and rumination, which are useful to detect changes in behavior that may be indicative of disease, responses to painful procedures, or positive welfare behaviors such as play. Automated calf feeding systems can control delivery of nutritional plans to individualize feeding and weaning of calves; changes in feeding behaviors (such as milk intake, drinking speed, and unrewarded visits) may also be used to identify early onset of disease. The PDT devices also measure physiological and physical attributes in dairy calves. For instance, temperature monitoring devices such as infrared thermography, ruminal boluses, and implanted microchips have been assessed in calves, but no herd management-based commercial system is available. Many other PDT are in development with potential to be used in dairy calf management, such as image and acoustic-based monitoring, real-time location, and use of enrichment items for monitoring positive emotional states. We conclude that PDT have great potential for application in dairy calf management, enabling precise behavioral and physiological monitoring, targeted feeding programs, and identification of calves with poor health or behavioral impairments. We strongly encourage further development and validation of commercially available technologies for on-farm application of the monitoring of dairy calf welfare, performance, and health.

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

The aim of this study was to assess combining location, acceleration and machine learning technologies to detect estrus in dairy cows. Data were obtained from 12 cows, which were monitored continuously for 12 days. A neck mounted device collected 25,684 records for location and acceleration. Four machine-learning approaches were tested (K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), and classification and regression tree (CART)) to automatically identify cows in estrus from estrus indicators determined by principal component analysis (PCA) of twelve behavioral metrics, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, and drinking times. The study showed that the neck tag had a static and dynamic positioning accuracy of 0.25 ± 0.06 m and 0.45 ± 0.15 m, respectively. In the 0.5-h, 1-h, and 1.5-h time windows, the machine learning approaches ranged from 73.3 to 99.4% for sensitivity, from 50 to 85.7% for specificity, from 77.8 to 95.8% for precision, from 55.6 to 93.7% for negative predictive value (NPV), from 72.7 to 95.4% for accuracy, and from 78.6 to 97.5% for F1 score. We found that the BPNN algorithm with 0.5-h time window was the best predictor of estrus in dairy cows. Based on these results, the integration of location, acceleration, and machine learning methods can improve dairy cow estrus detection.

Relationships between milk fat and rumination time recorded by commercial rumination sensing systems

Low rumination in the dairy cow is often assumed to result in reduction of saliva flow, rumen buffering, and milk fat, which is a major contributor to milk value in many pricing systems. Rumination time (RT) of individual cows can be measured with commercial rumination sensing systems, but our understanding of how daily RT (minutes per day) is related to milk fat production is limited. Our hypothesis was that between cows within a herd, greater RT would be associated with lower milk fat concentration. Data from 1,823 cows on 2 commercial dairy farms in Pennsylvania over 8 DHIA tests were analyzed for a total of 8,587 cow test-days. Rumination was measured on farm A with CowManager SensoOr ear tags (Agis Automatisering BV, Harmelen, the Netherlands) and on farm B with SCR Hi-Tag neck collars (SCR Engineers, Netanya, Israel). Rumination data were collected for 7 consecutive days leading up to each DHIA test, summed within day, and averaged across days. Data were analyzed using linear mixed models with a repeated effect of test day. Daily RT reported by commercial rumination systems varied across and within cows and was strongly influenced by a cow effect. Greater RT tended to be associated with a small decrease in milk fat concentration in farm A, but was not related to milk fat in farm B. The reason for this difference is unclear, but may be related to a potentially greater prevalence of biohydrogenation-induced milk fat depression on farm A. The significant, but small, model coefficients for milk fat and RT indicate that the relationship between these variables may not be strong enough to permit identification of cows with biohydrogenation-induced milk fat depression based on RT from commercial systems alone. Research assessing changes in rumination before, during, and after onset of altered rumen fermentation is necessary to determine whether RT could be used to identify cows with altered rumen fermentation.

IoT-Based Cow Health Monitoring System

Good health and wellbeing of animals are essential to dairy cow farms and sustainable production of milk. Unfortunately, day-to-day monitoring of animals condition is difficult, especially in large farms where employees do not have enough time to observe animals and detect first symptoms of diseases. This paper presents an automated, IoT-based monitoring system designed to monitor the health of dairy cows. The system is composed of hardware devices, a cloud system, an end-user application, and innovative techniques of data measurements and analysis algorithms. The system was tested in a real-life scenario and has proved it can effectively monitor animal welfare and the estrus cycle.

Alterations in vaginal temperature during the estrous cycle in dairy cows detected by a new intravaginal device-a pilot study

Estrus identification is important in dairy cow production. At present, estrus identification is automated with a pedometer or accelerometer and the results remain unsatisfactory. It was previously reported that body temperature changes during estrus. In the present study, dairy cow vaginal temperature (VT) was monitored during various seasons, and an increase in VT of 0.3 °C was suggested for the onset of estrus, using an automated VT monitoring system developed in-house. Natural and synchronized estrus were measured simultaneously. The VT was determined to be in circadian rhythm and significantly higher in summer than in either autumn or winter (P < 0.05). VT difference (between estrus VT and average VT 7 days earlier) gradually increased, reached a peak of 0.56 °C ± 0.17 at 4 h before the end of estrus, and then decreased to the normal. The VT of cows in estrus and the duration of their estrus were significantly affected by seasons and estrus types (P < 0.05). VT gradually decreased in response to prostaglandin (PG) injection and was significantly lower (0.15-0.35 °C) from 9 to 33 h after the drug administration than the average VT at the same time 7 days earlier (P < 0.05). Changes in circadian and seasonal VT and in the estrous cycle can be monitored to assess the physiological status of cows and will help in developing an effective automated estrus identification technique. Results of this pilot study should be validated in further studies.

Effects of winter housing system on hygiene, udder health, frostbite, and rumination of dairy cows

The objective of this study was to evaluate the effects of 2 winter (December to April) housing systems on dairy cow hygiene scores, frostbite, teat condition, clinical mastitis, and activity and rumination across 3 winter seasons (2013, 2014, and 2015). Certified-organic cows (n = 268) were randomly assigned to 2 treatments (2 replicates per system): (1) outdoor straw pack (outdoor) or (2) 3-sided compost-bedded pack barn (indoor). Cows calved during 2 seasons (spring or fall) at the University of Minnesota West Central Research and Outreach Center, Morris, Minnesota, organic dairy. Organic wheat straw was used as bedding for the 2 outdoor straw packs, and bedding was maintained by farm management to keep cows dry and absorb manure throughout the winter. The compost-bedded pack barn (2 pens in the barn) was bedded with organic-approved sawdust, and the bedding material was stirred twice per day with a small chisel plow. Hygiene scores were recorded biweekly as cows exited the milking parlor. Incidence of clinical mastitis was recorded in a binary manner as treated (1) or not treated (0) at least once during a lactation. Frostbite incidence was collected monthly. Activity and rumination times (daily and 2-h periods) were monitored electronically using a neck collar sensor (HR-LD Tags, SCR Dairy, Netanya, Israel). Indoor cows had greater udder hygiene scores (1.75 vs. 1.46) and greater abdomen hygiene scores (1.79 vs. 1.43) compared with outdoor cows. Additionally, the indoor cows had greater upper and lower leg hygiene scores compared with outdoor cows. Incidence of clinical mastitis was greater for indoor cows compared with outdoor cows (27.1% vs. 15.1%, respectively). Frostbite incidence was not different between indoor (30.1%) and outdoor (17.5%) cows. Daily rumination was 509 min/d for indoor cows and 530 min/d for the outdoor cows. In summary, lactating cows housed outdoors on straw-bedded packs had cleaner udders and improved udder health compared with cows housed in a compost-bedded pack barn.

Effect of bull biostimulation on the oestrous behaviour of pubertal Sahiwal (Bos indicus) heifers

The study was conducted to determine effects of biostimulation of Sahiwal heifers through fenceline bull contact and fenceline combined with direct bull contact on oestrous behaviour when there was ovulation occurring in the absence of behavioural oestrus ("silent oestrus - SE) and overt behavioural oestrus (OBE). Prepubertal Sahiwal heifers were allotted to three treatments (T₀, T₁, T₂, n = 8 heifers/treatment). In the T₀ group, there was no bull exposure; in T₁, exposure to a bull through fenceline contact for 24 h and in T₂, exposure to a bull as in T₁ along with direct bull contact with another bull for a 6 -h period daily. The oestrous behaviours were recorded on day (d) -3, -2 and -1 (prior to oestrus), d 0 (day of oestrus) and d +3, +2 and +1 (post-estrus). With both SE and OBE, the mean frequency of sniffing, micturition, chin resting and standing to be mounted differed (P <  0.05) from d -2 to d +1 among treatment groups. The mean times devoted to eating, ruminating and lying during SE and OBE were less (P <  0.05) on d-1 and d 0 in heifers of all three groups than the respective values on reference days in both T₁ and T₂ groups. Biostimulation of Sahiwal heifers with bull exposure, therefore, resulted in greater expression of oestrous behaviour than in non-exposed heifers during the periods around when there was SE and OBE.

Comparison of different applications of automatic herd control systems on dairy farms - a review

Recent years have seen the rapid development of different devices which can be helpful in the daily work of livestock farmers. The growing size of livestock herds has led farmers to lose individual contact with their animals, while behavioral studies show that breeders can effectively and precisely monitor a herd of up to 100 cows. This was the main motivation for this study, which aims to identify and test various electronic devices which provide useful herd management data, including estrus detection, individual activity and body temperature measurement, monitoring rumen pH levels, milk quality and content as well as milk temperature and somatic cell count measurements. Some devices can detect the metabolic status of animals with a reasonable level of precision. Contemporary animal farms are offered a large number of systems for monitoring the behavior of the animals in the herd and helping to identify those that are intended for insemination or are too active or excessively apathetic. Monitoring devices support herd management and help to reduce costs through the early detection of animal diseases and nutritional problems. This review aims to compile and summarize the information currently available on the use of automatic herd control systems on dairy farms, as well as to discuss the interpretation of the results, providing a useful diagnostic tool in nutritional evaluations of dairy herds. © 2018 Society of Chemical Industry.

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.

Invited review: Current perspectives on eating and rumination activity in dairy cows

Many early studies laid the foundation for our understanding of the mechanics of chewing, the physiological role of chewing for the cow, and how chewing behavior is affected by dietary characteristics. However, the dairy cow has changed significantly over the past decades, as have the types of diets fed and the production systems used. The plethora of literature published in recent years provides new insights on eating and ruminating activity of dairy cows. Lactating dairy cows spend about 4.5 h/d eating (range: 2.4-8.5 h/d) and 7 h/d ruminating (range: 2.5-10.5 h/d), with a maximum total chewing time of 16 h/d. Chewing time is affected by many factors, most importantly whether access to feed is restricted, intake of neutral detergent fiber from forages, and mean particle size of the diet. Feed restriction and long particles (≥19 mm) have a greater effect on eating time, whereas intake of forage neutral detergent fiber and medium particles (4-19 mm) affects rumination time. It is well entrenched in the literature that promoting chewing increases salivary secretion of dairy cows, which helps reduce the risk of acidosis. However, the net effect of a change in chewing time on rumen buffing is likely rather small; therefore, acidosis prevention strategies need to be broad. Damage to plant tissues during mastication creates sites that provide access to fungi, adhesion of bacteria, and formation of biofilms that progressively degrade carbohydrates. Rumination and eating are the main ways in which feed is reduced in particle size. Contractions of the rumen increase during eating and ruminating activity and help move small particles to the escapable pool and into the omasum. Use of recently developed low-cost sensors that monitor chewing activity of dairy cows in commercial facilities can provide information that is helpful in management decisions, especially when combined with other criteria. Although accuracy and precision can be somewhat variable depending on sensor and conditions of use, relative changes in cow behavior, such as a marked decrease in rumination time of a cow or sustained low rumination time compared with a contemporary group of cows, can be used to help detect estrus, parturition, and some illnesses. This review provides a comprehensive understanding of the dietary, animal, and management factors that affect eating and ruminating behavior in dairy cows and presents an overview of the physiological importance of chewing with emphasis on recent developments and practical implications for feeding and managing the modern housed dairy cow.

Rumination time as a potential predictor of common diseases in high-productive Holstein dairy cows

We examined the hypothesis that rumination time (RT) could serve as a useful predictor of various common diseases of high producing dairy cows and hence improve herd management and animal wellbeing. We measured the changes in rumination time (RT) in the days before the recording of diseases (specifically: mastitis, reproductive system diseases, locomotor system issues, and gastroenteric diseases). We built predictive models to assess the association between RT and these diseases, using the former as the outcome variable, and to study the effects of the latter on the former. The average Pseudo-R2 of the fitted models was moderate to low, and this could be due to the fact that RT is influenced by other additional factors which have a greater effect than the predictors used here. Although remaining in a moderate-to-low range, the average Pseudo-R2 of the models regarding locomotion issues and gastroenteric diseases was higher than the others, suggesting the greater effect of these diseases on RT. The results are encouraging, but further work is needed if these models are to become useful predictors.

Potential of connected devices to optimize cattle reproduction

Estrus and calving are two major events of reproduction that benefit from connected devices because of their crucial importance in herd economics and the amount of time required for their detection. The objectives of this review are to: 1) provide an update on performances reached by sensor systems to detect estrus and calving time; 2) discuss current economic issues related to connected devices for the management of cattle reproduction; 3) propose perspectives for these devices. The main physiological parameters monitored separately or in combination by connected devices are the cow activity, body temperature and rumination or eating behavior. The combination of several indicators in one sensor may maximize the performances of estrus and calving detection. An effort remains to be made for the prediction of calvings that will require human assistance (dystocia). The main reasons to invest in connected devices are to optimize herd reproductive performances and reduce labor on farm. The economic benefit was evaluated for estrus detection and depends on the initial herd performances, herd size, labor cost and price of the equipment. Major issues associated with the use of automated sensor systems are the weight of financial investment, the lack of economic analysis and limited skills of the users to manage associated technologies. In the near future, connected devices may allow a precise phenotyping of reproductive and health traits on animals and could help to improve animal welfare and public perception of animal production.

Short communication: Automated detection of behavioral changes from respiratory disease in pre-weaned calves

Group housing of calves can pose a challenge in identifying respiratory disease; therefore, it is necessary to develop tools that can identify these disease events. In this experiment, pre-weaned calves (n = 30) were housed in groups with an automatic calf feeder and were fitted with an accelerometer. Step activity, lying behaviors, and feeding behaviors were recorded to determine the effect of respiratory disease. All calves were health scored twice daily, and calves with respiratory scores ≥5 were diagnosed with respiratory disease (n = 10). Each diseased calf was match paired with a healthy control based on the date of disease diagnosis, breed, and age. Control calves were determined to be healthy if they had respiratory scores ≤4, as well as fecal, navel, and joint scores of 0 or 1. Diseased calves were less active before, on the day of, and after respiratory disease diagnosis. Furthermore, diseased calves had reduced lying frequencies starting 2 d before diagnosis, as well as after diagnosis. Last, diseased calves consumed less milk on the day of diagnosis when compared with healthy controls. Step activity, lying bouts, and milk intake may prove to be a useful tool in identifying respiratory disease under practical farming, but this requires further research.

Daily lying time, motion index and step frequency in dairy cows change throughout lactation

Lying behaviour in dairy cows has the potential to be used for welfare assessment or problem detection, but knowledge about variation in normal lying behaviour is scarce. Accelerometer data were collected at four Danish farms from 366 Holstein dairy cows in loose-housing systems in 2008 and 2009. Daily lying time decreased steeply during early lactation to a minimum around four weeks after calving, followed by a steady increase towards the end of lactation. Motion index and step frequency during walking exhibited a similar pattern. An adapted version of Wilmink's function for lactation curves was used to model these behaviours in relation to days in milk. The results demonstrate the importance of including information about days in milk when interpreting data on lying behaviour and activity.