Assessment of a UWB Real Time Location System for Dairy Cows' Monitoring.

In the field of precision livestock farming, many systems have been developed to identify the position of each cow of the herd individually in a specific environment. Challenges still exist in assessing the adequacy of the available systems to monitor individual animals in specific environments, and in the design of new systems. The main purpose of this research was to evaluate the performance of the SEWIO ultrawide-band (UWB) real time location system for the identification and localisation of cows during their activity in the barn through preliminary analyses in laboratory conditions. The objectives included the quantification of the errors performed by the system in laboratory conditions, and the assessment of the suitability of the system for real time monitoring of cows in dairy barns. The position of static and dynamic points was monitored in different experimental set-ups in the laboratory by the use of six anchors. Then, the errors related to a specific movement of the points were computed and statistical analyses were carried out. In detail, the one-way analysis of variance (ANOVA) was applied in order to assess the equality of the errors for each group of points in relation to their positions or typology, i.e., static or dynamic. In the post-hoc analysis, the errors were separated by Tukey's honestly significant difference at p > 0.05. The results of the research quantify the errors related to a specific movement (i.e., static and dynamic points) and the position of the points (i.e., central area, perimeter of the investigated area). Based on the results, specific information is provided for the installation of the SEWIO in dairy barns as well as the monitoring of the animal behaviour in the resting area and the feeding area of the breeding environment. The SEWIO system could be a valuable support for farmers in herd management and for researchers in the analysis of animal behavioural activities.

Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data.

Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning methods provide new opportunities for behavior recognition. However, the combination of the new electronics and algorithms are rarely used in PLF, and their possibilities and limitations are not well-studied. In this study, a CNN-based model for the feeding behavior classification of dairy cows was trained, and the training process was analyzed considering a training dataset and the use of transfer learning. Commercial acceleration measuring tags, which were connected by BLE, were fitted to cow collars in a research barn. Based on a dataset including 33.7 cow × days (21 cows recorded during 1-3 days) of labeled data and an additional free-access dataset with similar acceleration data, a classifier with F1 = 93.9% was developed. The optimal classification window size was 90 s. In addition, the influence of the training dataset size on the classifier accuracy was analyzed for different neural networks using the transfer learning technique. While the size of the training dataset was being increased, the rate of the accuracy improvement decreased. Beginning from a specific point, the use of additional training data can be impractical. A relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialized model weights, and a higher accuracy was achieved when transfer learning was used. These findings can be used for the estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions.

Seven steps to enhance Open Science practices in animal science.

The Open Science movement aims at ensuring accessibility, reproducibility, and transparency of research. The adoption of Open Science practices in animal science, however, is still at an early stage. To move ahead as a field, we here provide seven practical steps to embrace Open Science in animal science. We hope that this paper contributes to the shift in research practices of animal scientists towards open, reproducible, and transparent science, enabling the field to gain additional public trust and deal with future challenges to guarantee reliable research. Although the paper targets primarily animal science researchers, the steps discussed here are also applicable to other research domains.

Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition.

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry-Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction.

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare.

Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

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.

Monitoring of Cow Location in a Barn by an Open-Source, Low-Cost, Low-Energy Bluetooth Tag System.

Indoor localization of dairy cows is important for cow behavior recognition and effective farm management. In this paper, we propose a low-cost system for low-accuracy cow localization based on the reception of signals sent by an acceleration measurement system using the Bluetooth Low Energy protocol. The system consists of low-cost tags and receiving stations. The tag specifications and the localization accuracy of the system were studied experimentally. The received signal strength propagation model and dependence on the tag orientation was studied in an open-space and a barn environment. Two experiments for the evaluation of localization accuracy were conducted with 35 and 19 cows for two days. The localization reference was achieved from feeding stations, a milking robot and videos of cows decoded manually. The localization accuracy (mean ± standard deviation) was 3.27 ± 2.11 m for the entire barn (10 × 40 m2) and 1.9 ± 0.67 m for a smaller area (4 × 5 m2). The system can be used for recognizing long-distance walking, crowded areas in the barn, e.g., queues to milking robots, and cow's preferable locations. The estimated system cost was 500 + 20 × (cow number) € for one barn. The system has open-access software and detailed instructions for its installation and usage.

Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows.

In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations <0.6 mmol/L with a root mean square error of prediction of <0.143 mmol/L. However, for blood plasma with >1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping.

Rapid eye movement sleep time in dairy cows changes during the lactation cycle.

The importance of rest and sleep is well established; we know, for example, that lack of sleep impairs immune function in rats and increases pain sensitivity in humans. However, little is known about sleep in dairy cows, but a lack of rest and sleep is discussed as a possible welfare problem in cattle. A first step toward a better understanding of sleep in dairy cows is to quantify the time cows spend awake and asleep in different stages of lactation. Using electrophysiological recordings on 7 occasions in wk -2, 2, 7, 13, 22, 37, and 45 in relation to calving, we investigated changes in rapid eye movement (REM) sleep time as well as non-rapid eye movement (NREM) sleep, drowsing, awake, and rumination in 19 dairy cows of the Swedish Red breed kept in single pens with ad libitum access to feed and water. The recordings on wk -2 and 45 were conducted during the dry period, and all others during lactation. The PROC MIXED procedure in SAS (SAS Institute Inc., Cary, NC) was used to test for significant differences in REM, NREM, drowsing, awake, and rumination between the different stages of lactation cycle. Pairwise comparisons between all recording occasions showed that total REM sleep duration was shorter for cows in wk 2 relative to calving compared with wk -2, and the number of REM sleep bouts were fewer in wk 2 compared with wk -2. The REM sleep was recorded during both the day (0500-2100 h) and night (2100-0500 h), but predominantly performed at night compared with daytime, and the bout duration was longer during nighttime compared with daytime. A tendency was observed for time spent in NREM sleep to be shorter in wk 2 relative to calving compared with wk -2. The duration spent drowsing was shorter for cows in wk 2 and 13 relative to calving compared with wk -2. We found no effect of stage of lactation cycle on the duration of awake or ruminating. Our study is the first to assess sleep distribution during a lactation cycle, and our results show that stage of lactation is important to consider when moving forward with sleep investigations in dairy cows. The shortest REM sleep duration was found for cows 2 wk after calving and longest 2 wk before calving, and the difference was due a higher number of REM sleep bouts in the recording 2 wk before calving. The REM sleep and rumination predominantly occurred at night but were recorded during both day and night.

Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production.

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

First-night effect on sleep time in dairy cows.

In human sleep studies, the probability of discomfort from the electrodes and the change in environment usually results in first-night recordings being discarded. Sleep recordings from the first night in human subjects often differ in amount of REM (rapid eye movement) sleep and the overall sleep architecture. This study investigated whether recordings of sleep states in dairy cows also show a first-night effect. Non-invasive electrophysiological recordings were carried out on nine cows of the Swedish Red breed during three consecutive 24-hour periods (recording days 1-3). Overall, cows spent 12.9 ± 1.4 hours awake, 8.2 ± 1 hours ruminating, 57.2 ± 20.3 min drowsing, 44.1 ± 20.2 min in REM sleep and 64.3 ± 38.1 min in NREM (non-rapid eye movement) sleep (mean ± SD) and there were no significant differences between recording days in total duration for any of the sleep and awake states. However, the bouts of REM sleep and rumination were longer, and the awake bouts were shorter, at night time compared to daytime, regardless of recording day. The awake bouts also showed an interaction effect with longer bouts at daytime during day 1 compared to daytime on day 3. Data on sleep and awake states recorded in adult dairy cows during three consecutive 24-h periods showed great variation in sleep time between cows, but total time for each state was not significantly affected by recording day. Further and more detailed studies of how sleep architecture is affected by recording day is necessary to fully comprehend the first-night effect in dairy cows.

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.

Lameness Detection in Dairy Cows: Part 1. How to Distinguish between Non-Lame and Lame Cows Based on Differences in Locomotion or Behavior.

Due to its detrimental effect on cow welfare, health and production, lameness in dairy cows has received quite a lot of attention in the last few decades-not only in terms of prevention and treatment of lameness but also in terms of detection, as early treatment might decrease the number of severely lame cows in the herds as well as decrease the direct and indirect costs associated with lameness cases. Generally, lame cows are detected by the herdsman, hoof trimmer or veterinarian based on abnormal locomotion, abnormal behavior or the presence of hoof lesions during routine trimming. In the scientific literature, several guidelines are proposed to detect lame cows based on visual interpretation of the locomotion of individual cows (i.e., locomotion scoring systems). Researchers and the industry have focused on automating such observations to support the farmer in finding the lame cows in their herds, but until now, such automated systems have rarely been used in commercial herds. This review starts with the description of normal locomotion of cows in order to define 'abnormal' locomotion caused by lameness. Cow locomotion (gait and posture) and behavioral features that change when a cow becomes lame are described and linked to the existing visual scoring systems. In addition, the lack of information of normal cow gait and a clear description of 'abnormal' gait are discussed. Finally, the different set-ups used during locomotion scoring and their influence on the resulting locomotion scores are evaluated.

Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior.

Despite the research on opportunities to automatically measure lameness in cattle, lameness detection systems are not widely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat them properly and in a timely fashion. Many papers have focused on the automated measurement of gait or behavioral cow characteristics related to lameness. In order for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cows need to be developed and validated. Few studies have reached this latter stage of the development process. Also, comparison between the different approaches is impeded by the wide range of practical settings used to measure the gait or behavioral characteristic (e.g., measurements during normal farming routine or during experiments; cows guided or walking at their own speed) and by the different definitions of lame cows. In the majority of the publications, mildly lame cows are included in the non-lame cow group, which limits the possibility of also detecting early lameness cases. In this review, studies that used sensor technology to measure changes in gait or behavior of cows related to lameness are discussed together with practical considerations when conducting lameness research. In addition, other prerequisites for any lameness detection system on farms (e.g., need for early detection, real-time measurements) are discussed.

Visual event-related potentials of dogs: a non-invasive electroencephalography study.

Previously, social and cognitive abilities of dogs have been studied within behavioral experiments, but the neural processing underlying the cognitive events remains to be clarified. Here, we employed completely non-invasive scalp-electroencephalography in studying the neural correlates of the visual cognition of dogs. We measured visual event-related potentials (ERPs) of eight dogs while they observed images of dog and human faces presented on a computer screen. The dogs were trained to lie still with positive operant conditioning, and they were neither mechanically restrained nor sedated during the measurements. The ERPs corresponding to early visual processing of dogs were detectable at 75-100 ms from the stimulus onset in individual dogs, and the group-level data of the 8 dogs differed significantly from zero bilaterally at around 75 ms at the most posterior sensors. Additionally, we detected differences between the responses to human and dog faces in the posterior sensors at 75-100 ms and in the anterior sensors at 350-400 ms. To our knowledge, this is the first illustration of completely non-invasively measured visual brain responses both in individual dogs and within a group-level study, using ecologically valid visual stimuli. The results of the present study validate the feasibility of non-invasive ERP measurements in studies with dogs, and the study is expected to pave the way for further neurocognitive studies in dogs.

Circadian variation in ghrelin and certain stress hormones in crib-biting horses.

Crib-biting is classified as an oral stereotypy, which may be initiated by stress susceptibility, management factors, genetic factors and gastrointestinal irritation. Ghrelin has been identified in the gastric mucosa and is involved in the control of food intake and reward, but its relationship to crib-biting is not yet known. The aim of this study was to examine the concentration and circadian variation of plasma ghrelin, cortisol, adrenocorticotropic hormone (ACTH) and ß-endorphin in crib-biting horses and non-crib-biting controls. Plasma samples were collected every second hour for 24h in the daily environment of eight horses with stereotypic crib-biting and eight non-crib-biting controls. The crib-biting horses had significantly higher mean plasma ghrelin concentrations than the control horses. The circadian rhythm of cortisol was evident, indicating that the sampling protocol did not inhibit the circadian regulation in these horses. Crib-biting had no statistically significant effect on cortisol, ACTH or ß-endorphin concentrations. The inter-individual variations in ß-endorphin and ACTH were higher than the intra-individual differences, which made inter-individual comparisons difficult and complicated the interpretation of results. Further research is therefore needed to determine the relationship between crib-biting and ghrelin concentration.

CowLog: open-source software for coding behaviors from digital video.

We have developed CowLog, which is open-source software for recording behaviors from digital video and is easy to use and modify. CowLog tracks the time code from digital video files. The program is suitable for coding any digital video, but the authors have used it in animal research. The program has two main windows: a coding window, which is a graphical user interface used for choosing video files and defining output files that also has buttons for scoring behaviors, and a video window, which displays the video used for coding. The windows can be used in separate displays. The user types the key codes for the predefined behavioral categories, and CowLog transcribes their timing from the video time code to a data file. CowLog comes with an additional feature, an R package called Animal, for elementary analyses of the data files. With the analysis package, the user can calculate the frequencies, bout durations, and total durations of the coded behaviors and produce summary plots from the data.