Technical note: Validation and comparison of 2 commercially available activity loggers

Systemic inflammation in early lactation and its relation to the cows' oxidative and metabolic status, productive and reproductive performance as well as activity

A dysregulated inflammatory response contributes to the occurrence of disorders in cows during the transition period from pregnancy to lactation. However, a detailed characterization of clinically healthy cows that exhibit enhanced inflammatory response during this critical period remains incomplete. In this experiment, a total of 99 individual transition dairy cows and 109 observations (18 cows monitored in 2 consecutive lactations), submitted to similar transition management were involved to evaluate the relationship between elevated inflammatory response and metabolic, oxidative status as well as transition outcomes. Blood was taken at -7, 3, 6, 9 and 21 d in milk (DIM) and concentrations of metabolic parameters (glucose, β-hydroxybutyric acid (BHBA), nonesterified fatty acids (NEFA), insulin, insulin-like growth factor 1 (IGF-1) and fructosamine) were analyzed. Additionally, oxidative parameters (proportion of oxidized glutathione to total glutathione in red blood cells (GSSG (%)), the activity of glutathione peroxidase (GPx) and of superoxide dismutase (SOD), concentrations of malondialdehyde (MDA) and oxygen radical absorbance capacity (ORAC)) and acute phase proteins (APP) including haptoglobin (Hp), serum amyloid A (SAA) and albumin-to-globulin ratio (A:G) were determined in the blood of 21 DIM. The 3 APP parameters were used to group clinically healthy cows into 2 categories through k-medoids clustering, i.e., a group showing an acute phase response (APR, n = 39) and a group not showing such a response, i.e., non-APR (n = 50). Diseased cases (n = 20) were handled in a separate group. Lower SAA and Hp concentrations as well as higher A:G were observed in the non-APR group, although for Hp differences were observed from the APR group, not from the diseased group. Only one of the 5 oxidative parameters differed between the groups, with the non-APR group exhibiting lower GPx activity compared with the diseased group. The non-APR group showed the highest IGF-1 levels among the 3 groups, and lower NEFA concentrations compared with the diseased groups. The diseased group also showed reduced dry matter intake and milk yield compared with clinically healthy cows, regardless of their inflammatory status. Moreover, the APR group exhibited temporarily lower activity levels compared with the non-APR group. These findings highlight that cows with a lower inflammatory status after 21 DIM exhibited better metabolic health characteristics, productive performance as well as activity levels. Nevertheless, the detrimental effects of a higher inflammatory status in the absence of clinical symptoms are still relatively limited.

Validation of a leg-mounted pedometer for the measurement of steps in lactating Holstein cows

The aim of this study was to validate the pedometer AfiAct II (AfiMilk) for the measurement of steps in lactating Holstein cows housed in a freestall design by assessing its agreement with visual observation of step counts. A total of 41 primiparous (n = 12) and multiparous (n = 29) cows were enrolled in the study between August and September 2018. Steps were monitored continuously by the pedometer and visually assessed for a 24-h period using video recordings. Visually observed steps were categorized as walking and stationary steps. The total number of steps taken per cow was calculated using the sum of walking and stationary steps. Unprocessed step count data from the study day were retrieved from the AfiMilk system in time-blocks of approximately 15 min. Repeated measures correlation was used to quantify the association between the pedometer measurements and visual observation of step counts. Nonindependence among observations were accounted adjusting for interindividual (cow) variability with an analysis of covariance. Pearson correlation coefficients (r) were categorized from negligible (0.00-0.30) to very high (0.90-1.00). Bland-Altman plots were created to evaluate the bias between the pedometer and visual observations. A total of 2,261 time-blocks were used in this study with an average (mean ± standard deviation) of 55.14 ± 8.1 time-blocks per cow. A high correlation was found for the evaluation between the pedometer and observed walking steps (r = 0.74; 95% confidence interval [CI] = 0.73-0.76), stationary steps (r = 0.71; 95% CI = 0.69-0.63), and total steps (r = 0.88; 95% CI = 0.87-0.89). The results of the Bland-Altman plot suggested limited bias between the pedometer step counts and visual observation of steps, independent of the type of steps. Numerical differences and several time-block differences outside of the 95% interval of agreement suggested an overestimation of step counts by the pedometer, which increased as the number of steps increased. The pedometer measured, on average, 97.6 ± 118.5 (28%), 249.2 ± 126.2 (125%), and 297.2 ± 205.4 (196%) steps/day more than the visual observed total steps, stationary steps, and walking steps, respectively. Our findings indicate that the pedometer counts all movement in which the pedometer leg is lifted off the floor without distinguishing if there was body movement of the animal.

Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud

The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

Effect of milk flow rate switch-point settings on cow comfort and milking duration

Automatic cluster removers (ACR) operate by ceasing vacuum to the cluster and detaching the milking unit from the udder by means of a retracting cord once the milk flow has decreased to a predefined level (i.e., the milk flow rate switch-point). There is a large body of literature on this topic indicating that increasing the flow rate switch-point (e.g., from 0.2 kg/min to 0.8 kg/min at the udder level) is effective in reducing milking duration while having little effect on milk yield or milk somatic cell count (SCC). However, despite these findings many farms still use a switch-point of 0.2 kg/min because it is believed that emptying the udder completely at each milking is a prerequisite for good dairy cow management, especially in relation to maintaining a low milk SCC. However, there may be additional undocumented benefits in terms of cow comfort to increasing the milk flow rate switch-point, because the low milk flow period at the end of milking is a high-risk time for inducing teat-barrel congestion. The objective of this study was to quantify the effect of 4 milk flow rate switch-point settings on cow comfort, milking duration, and milk yield. In this study, we applied 4 treatments consisting of different milk flow rate switch-points to cows in a crossover design in a spring calving grass based dairy herd in Ireland. The treatments were (1) MFR0.2, where the cluster was removed at a milk flow rate of 0.2 kg/min; (2) MFR0.4, where the cluster was removed at 0.4 kg/min; (3) MFR0.6, where the cluster was removed at 0.6 kg/min, and (4) MFR0.8, where the cluster was removed at 0.8 kg/min. Milking parameters were recorded by the parlor software and leg movements (i.e., kicks or steps) during milking were recorded with an accelerometer. These data were used as a proxy for cow comfort during milking. The results of this study showed significant differences in cow comfort across treatments, as indicated by cow stepping during milking, for a.m. milkings, but these differences were not detected for p.m. milkings, possibly because a.m. milkings were longer than p.m. milkings due to a 16:8 h milking interval on the research farm. Differences tended to distinguish the 2 lower-flow switch-point settings with greater leg movement against the 2 higher-flow switch-point settings with less leg movement during milking. The effect of treatment (milk flow rate switch-point) on daily milking duration was significant. The milk duration for MFR0.8 was 89 s (14%) shorter than MFR0.2. There was no significant effect of treatment on SCC in this study.

Heat stress in a temperate climate leads to adapted sensor-based behavioral patterns of dairy cows

Most research on heat stress has focused on (sub)tropical climates. The effects of higher ambient temperatures on the daily behavior of dairy cows in a maritime and temperate climate are less studied. With this retrospective observational study, we address that gap by associating the daily time budgets of dairy cows in the Netherlands with daily temperature and temperature-humidity index (THI) variables. During a period of 4 years, cows on 8 commercial dairy farms in the Netherlands were equipped with neck and leg sensors to collect data from 4,345 cow lactations regarding their daily time budget. The time spent eating, ruminating, lying, standing, and walking was recorded. Individual cow data were divided into 3 data sets: (1) lactating cows from 5 farms with a conventional milking system (CMS) and pasture access, (2) lactating cows from 3 farms with an automatic milking system (AMS) without pasture access, and (3) dry cows from all 8 farms. Hourly environment temperature and relative humidity data from the nearest weather station of the Dutch National Weather Service was used for THI calculation for each farm. Based on heat stress thresholds from previous studies, daily mean temperatures were grouped into 7 categories: 0 = (<0°C), 1 = (0-12°C, reference category), 2 = (12-16°C), 3 = (16-20°C), 4 = (20-24°C), 5 = (24-28°C), and 6 = (≥28°C). Temperature-humidity index values were grouped as follows: 0 = (THI <30), 1 = (THI 30-56, reference category), 2 = (THI 56-60), 3 = (THI 60-64), 4 = (THI 64-68), 5 = (THI 68-72) and 6 = (THI ≥72). To associate daily mean temperature and THI with sensor-based behavioral parameters of dry cows and of lactating cows from AMS and CMS farms, we used generalized linear mixed models. In addition, associations between sensor data and other climate variables, such as daily maximum and minimum temperature, and THI were analyzed. On the warmest days, eating time decreased in the CMS group by 92 min/d, in the AMS group by 87 min/d, and in the dry group by 75 min/d compared with the reference category. Lying time decreased in the CMS group by 36 min/d, in the AMS group by 56 min/d, and in the dry group by 33 min/d. Adaptation to daily temperature and THI was already noticeable from a mean temperature of 12°C or a mean THI of 56 or above, when dairy cows started spending less time lying and eating and spent more time standing. Further, rumination time decreased, although only in dry cows and cows on AMS farms. With higher values for daily mean THI and temperature, walking time decreased as well. These patterns were very similar for temperature and THI variables. These results show that dairy cows in temperate climates begin to adapt their behavior at a relatively low mean environmental temperature or THI. In the temperate maritime climate of the Netherlands, our results indicate that daily mean temperature suffices to study the effects of behavioral adaptation to heat stress in dairy cows.

Tracking the Impact of Weather on Equine Activity While Pastured

Keeping horses outdoors on pasture full-time with free access to shelter holds numerous advantages over housing in stalls, promoting both better mental and physical health. One reason for these benefits is the potential for increased physical activity in horses outdoors on pasture versus those confined to stalls. However, it is not guaranteed the horse will take advantage of this opportunity for greater movement. For this reason, it is important to understand the various reasons why horse activity patterns change. The objective of this study was to investigate how various weather factors - including temperature, humidity, precipitation, and wind speed - directly affect equine movement. To achieve this, horses on two similarly-managed farms were equipped with triaxial accelerometers during five independent time periods from January to August. These devices tracked number of steps, standing time, time lying down, and number of lying bouts. The movement data were then compared to the corresponding weather conditions. No strong correlations were found between the recorded movement of the horses and any of the environmental conditions. However, differences in average number of steps and average time lying down were observed between farms and across testing periods, suggesting other influences such as ground conditions and the use of blankets. Further studies are needed to determine the best management practices to encourage pasture activity and support optimal equine physical health.

Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours

Cows from 8 commercial Dutch dairy farms were equipped with 2 sensors to study their complete time budgets of eating, rumination, lying, standing and walking times as derived from a neck and a leg sensor. Daily sensor data of 1074 cows with 3201 lactations was used from 1 month prepartum until 10 months postpartum. Farms provided data over a 5 year period. The final models (lactational time budget and 24h time budget) showed significant effects of parity, farm and calving season. When primiparous cows were introduced in the lactational herd, they showed a decrease in lying time of 215 min (95% CI: 187–242) and an increase in standing time of 159 min (95% CI: 138–179), walking time of 23 min (95% CI: 20–26) and rumination time of 69 min (95% CI: 57–82). Eating time in primiparous cows increased from 1 month prepartum until 9 months in lactation with 88 min (95% CI: 76–101) and then remained stable until the end of lactation. Parity 2 and parity 3+ cows decreased in eating time by 30 min (95% CI: 20–40) and 26 min (95% CI: 18–33), respectively, from 1 month before to 1 month after calving. Until month 6, eating time increased 11 min (95% CI: 1–22) for parity 2, and 24 min (95% CI: 16–32) for parity 3+. From 1 month before calving to 1 month after calving, they showed an increase in ruminating of 17 min (95% CI: 6–28) and 28 min (95% CI: 21–35), an increase in standing time of 117 min (95% CI: 100–135) and 133 min (95% CI: 121–146), while lying time decreased with 113 min (95% CI: 91–136) and 130 min (95% CI: 114–146), for parity 2 and 3+, respectively. After month 1 in milk to the end of lactation, lying time increased 67 min (95% CI: 49–85) for parity 2, and 77 min (95% CI: 53–100) for parity 3+. Lactational time budget patterns are comparable between all 8 farms, but cows on conventional milking system (CMS) farms with pasture access appear to show higher standing and walking time, and spent less time lying compared to cows on automatic milking system (AMS) farms without pasture access. Every behavioral parameter presented a 24h pattern. Cows eat, stand and walk during the day and lie down and ruminate during the night. Daily patterns in time budgets on all farms are comparable except for walking time. During the day, cows on CMS farms with pasture access spent more time walking than cows on AMS farms without pasture access. The average 24h pattern between parities is comparable, but primiparous cows spent more time walking during daytime compared to older cows. These results indicate a specific behavioral pattern per parameter from the last month prepartum until 10 months postpartum with different patterns between parities but comparable patterns across farms. Furthermore, cows appear to have a circadian rhythm with varying time budgets in the transition period and during lactation.

The effects of cow introductions on milk production and behaviour of the herd measured with sensors

This research paper addresses the hypothesis that cow introductions in dairy herds affect milk production and behaviour of animals already in the herd. In dairy farms, cows are commonly regrouped or moved. Negative effects of regroupings on the introduced animals are reported in other studies. However, little is known about the effects on lactating cows in the herd. In this research a herd of 53 lactating dairy cows was divided into two groups in a cross-over design study. 25 cows were selected as focal cows for which continuous sensor data were collected. The treatment period consisted of replacing non-focal cows three times a week. Many potentially influencing factors were taken into account in the analysis. Replacement of cows in the treatment period indeed affected the focal animals. During the treatment period these cows showed increased walking and reduced rumination activity and produced less milk compared to the control period. Milk production per milking decreased in the treatment period up to 0.4 kg per milking on certain weekdays. Lying and standing behaviour were similar between the control and the treatment period. The current study suggests that cow introductions affect welfare and milk production of the cows already in the herd.

Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review

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.

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

In order to base welfare assessment of dairy cattle on real-time measurement, integration of valid and reliable precision livestock farming (PLF) technologies is needed. The aim of this study was to provide a systematic overview of externally validated and commercially available PLF technologies, which could be used for sensor-based welfare assessment in dairy cattle. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was conducted to identify externally validated sensor technologies. Out of 1,111 publications initially extracted from databases, only 42 studies describing 30 tools (including prototypes) met requirements for external validation. Moreover, through market search, 129 different retailed technologies with application for animal-based welfare assessment were identified. In total, only 18 currently retailed sensors have been externally validated (14%). The highest validation rate was found for systems based on accelerometers (30% of tools available on the market have validation records), while the lower rates were obtained for cameras (10%), load cells (8%), miscellaneous milk sensors (8%), and boluses (7%). Validated traits concerned animal activity, feeding and drinking behavior, physical condition, and health of animals. The majority of tools were validated on adult cows. Non-active behavior (lying and standing) and rumination were the most often validated for the high performance. Regarding active behavior (e.g., walking), lower performance of tools was reported. Also, tools used for physical condition (e.g., body condition scoring) and health evaluation (e.g., mastitis detection) were classified in lower performance group. The precision and accuracy of feeding and drinking assessment varied depending on measured trait and used sensor. Regarding relevance for animal-based welfare assessment, several validated technologies had application for good health (e.g., milk quality sensors) and good feeding (e.g., load cells, accelerometers). Accelerometers-based systems have also practical relevance to assess good housing. However, currently available PLF technologies have low potential to assess appropriate behavior of dairy cows. To increase actors' trust toward the PLF technology and prompt sensor-based welfare assessment, validation studies, especially in commercial herds, are needed. Future research should concentrate on developing and validating PLF technologies dedicated to the assessment of appropriate behavior and tools dedicated to monitoring the health and welfare in calves and heifers.

Associations between body condition score, locomotion score, and sensor-based time budgets of dairy cattle during the dry period and early lactation

Lameness, one of the most important disorders in the dairy industry, is related to postpartum diseases and has an effect on dairy cow welfare, leading to changes in cows' daily behavioral variables. This study quantified the effect of lameness on the daily time budget of dairy cows in the transition period. In total, 784 multiparous dairy cows from 8 commercial Dutch dairy farms were visually scored on their locomotion (score of 1-5) and body condition (score of 1-5). Each cow was scored in the early and late dry period as well as in wk 4 and 8 postpartum. Cows with locomotion scores 1 and 2 were grouped together as nonlame, cows with score 3 were considered moderately lame, and cows with scores 4 and 5 were grouped together as severely lame. Cows were equipped with 2 types of sensors that measured behavioral parameters. The leg sensor provided number of steps, number of stand-ups (moving from lying to standing), lying time, number of lying bouts, and lying bout length. The neck sensor provided eating time, number of eating bouts, eating bout length, rumination time, number of rumination bouts, and rumination bout length. Sensor data for each behavioral parameter were averaged between 2 d before and 2 d after locomotion scoring. The percentage of nonlame cows decreased from 63% in the early dry period to 46% at 8 wk in lactation; this decrease was more severe for cows with higher parity. Cows that calved in autumn had the highest odds for lameness. Body condition score loss of >0.75 point in early lactation was associated with lameness in wk 4 postpartum. Moderately lame cows had a reduction of daily eating time of around 20 min, whereas severely lame cows had a reduction of almost 40 min. Similarly, moderately and severely lame dry cows showed a reduction of 200 steps/d, and severely lame cows in lactation showed a reduction of 600 steps/d. Daily lying time increased by 26 min and lying bout length increased by 8 min in severely lame cows compared with nonlame cows. These results indicate a high prevalence of lameness on Dutch dairy farms, with an increase in higher locomotion scores from the dry period into early lactation. Time budgets for multiparous dairy cows differed between the dry period and the lactating period, with a higher locomotion score (increased lameness) having an effect on cows' complete behavioral profile. Body condition score loss in early lactation was associated with poor locomotion postpartum, whereas lameness resulted in less eating time in the dry period and early lactation, creating a harmful cycle.

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.

Accelerometer systems as tools for health and welfare assessment in cattle and pigs - A review

Welfare assessment has traditionally been performed by direct observation by humans, providing information at only selected points in time. Recently, this assessment method has been questioned, as 'Precision Livestock Farming' technologies may be able to deliver more valid, reliable and feasible real-time data at the individual level and serve as early monitoring systems for animal welfare. The aim of this paper is to describe how accelerometers can be used for welfare assessment based on the principles of the Welfare Quality assessment protocol. Algorithm development is based mainly on the detection of behavioural traits. So far, high accuracies have been found for movement and resting behaviours in cows and pigs, while algorithm development for feeding and drinking behaviours in pigs lag behind progress in cows where valid algorithms are already available. Welfare studies have used accelerometer technology to address the effects on behaviour of diet, daily cycle, enrichment, housing, social mixing, oestrus, lameness and disease. Additional aspects to consider before a decision is made upon its use in research and in practical applications include battery life and sensor location. While accelerometer systems for cows are already being used by farmers, application in pigs has mainly remained at the research level.

Gain and loss of subcutaneous and abdominal adipose tissue depot mass of German Holstein dairy cows with different body conditions during the transition period

Subcutaneous adipose tissue (SCAT) and abdominal adipose tissue (AAT) depots are mobilized during the fresh cow period (FCP) and early lactation period (ELP) to counteract the negative energy balance (NEB). Earlier studies suggested that fat depots contribute differently to lipomobilization and may vary in functionality. Differences between the adipose depots might influence the development of metabolic disorders. Thus, the gain and loss of subcutaneous and abdominal adipose depot masses in Holstein cows with lower and higher body condition (mean body condition scores: 3.48 and 3.87, respectively) were compared in the period from d -42 to d 70 relative to parturition in this study. Animals of the 2 experimental groups represented adequately conditioned and overconditioned cows. Estimated depot mass (eDM) of SCAT, AAT, retroperitoneal, omental, and mesenteric adipose depots of 31 pluriparous German Holstein cows were determined via ultrasonography at d -42, 7, 28, and 70 relative to parturition. The cows were grouped according to the eDM of SCAT on d -42 [low body condition (LBC) group: n = 16, mean eDM 8.6 kg; high body condition (HBC) group: n = 15, mean eDM 15.6 kg]. Average daily change (prepartum gain and postpartum loss) in depot masses during dry period (DP; from d -42 to d 7), FCP (d 7 to d 28), and ELP (d 28 to d 70) were calculated and daily dry matter intake and lactation performance recorded. Cows of this study stored about 2 to 3 times more fat in AAT than in SCAT depots. After parturition, on average more adipose tissue mass was lost from the AAT than the SCAT depot (0.23 kg/d vs. 0.14 kg/d). Cows with high compared with low body condition had similar gains in AAT (0.33 kg/d) and SCAT (0.14 kg/d) masses during the DP but mobilized significantly more adipose tissue mass from both depots after calving (AAT, HBC vs. LBC: 0.30 vs. 0.17 kg/d; SCAT, HBC vs. LBC: 0.19 vs. 0.10 kg/d). Correlation analysis indicated a functional disparity between AAT and SCAT. In the case of AAT (R² = 0.36), the higher the gain in adipose mass during DP, the higher the loss in FCP, but this was not the case for SCAT. During FCP, a greater NEB resulted in greater loss of mass from SCAT (R² = 0.18). In turn, greater mobilization of SCAT mass led to a higher calculated feed efficiency (R² = 0.18). However, AAT showed no such correlations. On the other hand, during ELP, loss of both SCAT and AAT mass correlated positively with feed efficiency (R² = 0.35 and 0.33, respectively). The results indicate that feed efficiency may not be an adequate criterion for performance evaluation in cows during NEB. Greater knowledge of functional disparities between AAT and SCAT depots may improve our understanding of excessive lipomobilization and its consequences for metabolic health and performance of dairy cows during the transition period.

Use of a Tri-Axial Accelerometer Can Reliably Detect Play Behaviour in Newborn Calves

Traditionally, the welfare assessment of farm animals has focused on health and production outcomes. Positive welfare is, however, not merely the absence of negative welfare and is an important part of a life worth living. Play behaviour is widely considered to be an indicator of positive emotions because it is a "luxury" behaviour. Direct visual observation is considered the most accurate method of behavioural analysis, but it is time consuming and laborious. There is increasing interest in the use of remote monitoring technology to quantify behaviour. We compared the data output ("motion index" (MI)) from a commercially available tri-axial accelerometer fitted to newborn dairy calves to video footage of the same calves, with a focus on play behaviour. The motion index values over 48 h were positively correlated with both the duration of play behaviour and the number of play bouts. The motion index threshold in each sample interval with the optimal sensitivity and specificity for the identification of play behaviour was MI ≥ 2.5 at a 1 min resolution (sensitivity (Se) = 98.0%; specificity (Sp) = 92.9%) and MI ≥ 24.5 at a 15 min resolution (Se = 98.0%; Sp = 89.9%), but these values consistently overestimated the overall proportion of sample intervals in which play was observed. The MI that best reflected the results obtained from visual one-zero sampling was MI ≥ 23 for 1 min intervals and MI ≥ 62 for 15 min intervals-this may therefore be the basis of a more conservative approach to the identification of play behaviour from accelerometer-generated data. Our results indicate that accelerometer-generated data can usefully indicate the amount of play behaviour shown by newborn calves for up to 48 h, providing an efficient method for identifying this important parameter in future work.

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.