Behavior Of Dairy Cows Research Articles

Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal's Left Flank.

Simple SummaryThis study analyzed the possibility of automatically detecting dairy cow behavior by combining the use of a single triaxial accelerometer applied to the animal’s left flank with a machine learning technique. This combination enabled the detection of posture and the main types of behavior that are extremely useful in evaluating the animal’s welfare and health such as resting, feeding, and rumination with a high degree of accuracy. The novelty of the study was the success in reaching a high accuracy in detecting five different behaviors and the animal posture by using a single sensor and allowing farmers to save money. To the best of our knowledge, this is the first study that has successfully explored the feasibility of locating a sensor on the animal’s left flank, showing the opportunity of automatically measuring some physiological parameters, such as those ones related to respiration and rumen health, in a non-invasive way.The aim of the present study was to develop a model to identify posture and behavior from data collected by a triaxial accelerometer located on the left flank of dairy cows and evaluate its accuracy and precision. Twelve Italian Red-and-White lactating cows were equipped with an accelerometer and observed on average for 136 ± 29 min per cow by two trained operators as a reference. The acceleration data were grouped in time windows of 8 s overlapping by 33.0%, for a total of 35,133 rows. For each row, 32 different features were extracted and used by machine learning algorithms for the classification of posture and behavior. To build up a predictive model, the dataset was split in training and testing datasets, characterized by 75.0 and 25.0% of the observations, respectively. Four algorithms were tested: Random Forest, K Nearest Neighbors, Extreme Boosting Algorithm (XGB), and Support Vector Machine. The XGB model showed the best accuracy (0.99) and Cohen’s kappa (0.99) in predicting posture, whereas the Random Forest model had the highest overall accuracy in predicting behaviors (0.76), showing a balanced accuracy from 0.96 for resting to 0.77 for moving. Overall, very accurate detection of the posture and resting behavior were achieved.

Behaviour, Hygiene, and Lameness of Dairy Cows in a Compost Barn During Cold Seasons in a Subtropical Climate

AbstractThis study aimed to assess the thermal environment, behaviour, hygiene, and lameness in dairy cows with different numbers of lactations housed in a compost-bedded pack barn system in a humid subtropical climate during autumn and winter. Between April and July 2016, behavioural and microclimatic environment assessments were conducted in a compost barn system, and hygiene and lameness assessments were performed. Crossbred cows in lactation (n = 10) were divided into two groups according to the number of lactations: primiparous and multiparous. Air temperature, relative humidity, and air ventilation speed were measured at 09:00, 12:00, and 15:00. The cows’ hygiene and lameness were evaluated using subjective scores ranging from 1 to 4 and 1 to 5, respectively. The daytime behaviour assessment was performed as sampling 0/1, using the focal method. Primiparous cows showed a greater probability of walking (P<0.05) than multiparous cows between 09:20 and 11:20, and at 15:20, and increased the water intake at 14:00. Regarding the eating behaviour and the effect of time of day within each group (P<0.05), the cows ate more in the morning’s early hours. The cows’ hygiene scores were 1 or 2, and most cows presented scores of 1 or 2 for lameness. Our research has shown that microclimatic variables may influence the walking behaviour and water intake of primiparous cows during the hottest periods of the day, even during cold weather, in a humid subtropical climate. The dairy cows showed low hygiene and lameness scores, which indicates that the compost bar positively influenced cleanliness and locomotion comfort.

Validation of a Commercial Collar-Based Sensor for Monitoring Eating and Ruminating Behaviour of Dairy Cows.

Simple SummaryThis paper aims to validate a recently released commercial sensor (AFICollar®, Afimilk, Israel) to monitor dairy cows’ feeding and ruminating behavior. In order to evaluate the sensor’s performance under different feeding scenarios, the cows involved were divided into groups and fed different types of feed, including grazed pasture. Further, two version of the software used to convert raw data into behavioral information were tested and compared. Sensor data have been validated against visual observations, which served as the gold standard. Various statistical methods have been employed to assess sensor precision and accuracy. The results indicate that the sensor is adequately accurate for both feeding and ruminating time. However, the precision of the system appeared somewhat limited and should be improved.The use of sensor technologies to monitor cows’ behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar-based sensor system, the AFICollar® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm® 5.4 and the updated version AFIfarm® 5.5, were compared. The study involved twenty Holstein-Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland–Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: −19.60 min/h, 17.46 min/h; rumination: −15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively limited, and should be improved with further developments in the classification algorithm.

Effect of flooring system on maintenance behaviours of cows

To evaluate the effect of floor on maintenance behaviours of cow in loose housing system, four groups, viz. T0 (concrete in covered and brick-paved in open area), T1 (concrete in covered and sand bed in open), T2 (rubber mat in covered and sand bed in open) and T3 (rubber mat in covered and brick-paved in open) were studied. Feeding was the major activity (39–44%) of day, with significant difference between groups. In covered area, lying and standing rumination, while in open area idling, were significantly different between groups during day. During night lying-rumination was major activity (62–69%). Night idling, standing-rumination, lying and lying-rumination behaviour, were significantly different between groups. Overall most of the time in each group was devoted to lying-rumination (45–53%) and feeding (18–21%). Area wise, open area was more occupied and posture wise sitting posture dominated in all the groups. Overall feeding, idling and standing-rumination in open, lying-rumination (in open or covered area), sleeping, total standing time and total sitting time were significantly different between groups. Feeding time showed positive trend in rubber matted floor (T2, T3). More lying-rumination and sleeping time was seen in rubber matted covered area. Sand bedding in open area reduced the standing and standingrumination activity, while promoting the sitting/lying time. Rubber matting in covered area and sand bedding in open area, was found favourable for maintenance behaviour of dairy cows.

Calf-directed affiliative behaviour of dairy cows in two types of cow-calf contact systems

There is an interest in alternative rearing systems that allow for prolonged cow-calf contact (CCC). Yet, a better understanding of cows’ affiliative behaviour in those systems is needed. We evaluated the effect of type of CCC on calf-directed affiliative behaviour in dairy cows. Cows were permitted to have either: i) partial contact (PC) with their calf; calves were housed in a pen adjacent to the cow area allowing limited physical contact on initiative of the dam but no suckling (n = 18), or ii) full contact (FC) with their calf including suckling; calves were housed together with the dams in a free stall barn (n = 20). Proximity and physical contact between the cow and her own calf were recorded between 0 and 48 h postpartum in an individual maternity pen, and from 1 to 5 weeks postpartum in a free stall barn. Data were analysed with generalized linear models, except for behaviour with excess of zero-valued data where a Kruskal Wallis test was used. Principal component analysis (PCA) was carried out to identify consistency of behaviour in the maternity pen and free stall barn. After parturition, latency to onset of allogrooming did not differ among treatments (mean±SE, 8 ± 3 min, P = 0.39). Throughout the first 48 h postpartum, no treatment differences were found in percentage of observed time spent allogrooming the calf (PC: 7.7 ± 1.3%, FC: 9.5 ± 1.5%), standing in proximity (≤1 m radius) (PC: 22.9 ± 2.1%, FC: 21.2 ± 2.1%), or lying in proximity (PC: 30.5 ± 4.3%, FC: 32.5 ± 3.2%) (P > 0.10). However, in the following 5 weeks, relative to PC cows, FC cows spent more time on average in close proximity to their calf (10.9 ± 0.1% versus 3.1 ± 0.4%, P < 0.001), and on allogrooming (2.1 ± 0.2% versus 0.5 ± 0.1%, P < 0.001). PCA revealed four components (explaining 76% of the variance). Lying in close and standing in far proximity in the maternity pen loaded (positive, negative, respectively) onto component 1, whereas physical contact and standing in close proximity in the free stall barn loaded negatively onto component 2. Standing in close proximity in the maternity pen loaded onto component 3, and standing 1–2 m near the calf in the free stall barn loaded onto component 4. Our results indicate that, in comparison with FC, PC decreases the expression of calf-directed affiliative behaviours in dairy cows, except in the 48 h following parturition. The partial CCC set-up limited the calf’s accessibility, whereas calves in full CCC could initiate contact as well. Nonetheless, large inter-individual differences in calf-directed affiliative behaviour were found that lacked consistency.

Training and Validating a Machine Learning Model for the Sensor-Based Monitoring of Lying Behavior in Dairy Cows on Pasture and in the Barn.

Simple SummaryThere are various systems available for health monitoring and heat detection in dairy cows. By continuously monitoring different behavioral patterns (e.g., lying, ruminating, and feeding), these systems detect behavioral changes linked to health disorders and estrous. Most of the systems were developed for cows kept indoors, and only a few systems are available for pasture-based farms. The systems developed for the barn failed to detect the targeted behavior and thereby its changes on the pasture and vice versa. Therefore, our goal was to train and validate a machine learning model for the automated prediction of lying behavior in dairy cows kept on pastures, as well as indoors. Data collection was conducted on three dairy farms where cows were equipped with the collar-based prototype of the monitoring system and recorded with cameras in parallel. The derived dataset was used to develop the machine learning model. The model performed well in predicting lying behavior in dairy cows both on the pasture and in the barn. Therefore, the building of the model presents a successful first step towards the development of a monitoring system for dairy cows kept on pasture and in the barn.Monitoring systems assist farmers in monitoring the health of dairy cows by predicting behavioral patterns (e.g., lying) and their changes with machine learning models. However, the available systems were developed either for indoors or for pasture and fail to predict the behavior in other locations. Therefore, the goal of our study was to train and evaluate a model for the prediction of lying on a pasture and in the barn. On three farms, 7–11 dairy cows each were equipped with the prototype of the monitoring system containing an accelerometer, a magnetometer and a gyroscope. Video observations on the pasture and in the barn provided ground truth data. We used 34.5 h of datasets from pasture for training and 480.5 h from both locations for evaluating. In comparison, random forest, an orientation-independent feature set with 5 s windows without overlap, achieved the highest accuracy. Sensitivity, specificity and accuracy were 95.6%, 80.5% and 87.4%, respectively. Accuracy on the pasture (93.2%) exceeded accuracy in the barn (81.4%). Ruminating while standing was the most confused with lying. Out of individual lying bouts, 95.6 and 93.4% were identified on the pasture and in the barn, respectively. Adding a model for standing up events and lying down events could improve the prediction of lying in the barn.

Assessment of RumiWatch noseband sensors for the quantification of ingestive behaviors of dairy cows at grazing or fed in stalls

The RumiWatch System (Itin+Hoch GmbH, Liestal, Switzerland) is a sensor system that enables quantification of ingestive behaviors of cattle that may be useful, amongst other things, for estimating dry matter intake. There are currently multiple versions of the RumiWatch Converter software used to convert the recorded data into specific animal behaviors through the use of algorithms. The objective of this research was to evaluate the use of RumiWatch System noseband sensors for quantifying the number of prehension bites and mastication chews in grazing and stall-fed dairy cows using RumiWatch Converter software versions 0.7.3.36 (RWC36) and 0.7.4.13 (RWC13). Two experiments using lactating Holstein-Friesian dairy cattle were conducted. In Experiment I, ten cows grazed perennial ryegrass pasture (Lolium perenne L.) in individual plots that measured 35 m2, with a pasture mass of 2500 kg DM/ha and pasture allowance of 28 kg DM/d. In Experiment II, 14 cows were offered 16 kg DM/d of perennial ryegrass pasture silage in individual indoor feed stalls. In both experiments, ingestive behaviors were quantified with the jaw movement halters and through visual observation using video recordings. The performance of the RumiWatch Converter software was evaluated through Lin’s concordance correlation coefficients with video observation. For grazing cows, there was greater concordance between the RumiWatch System and video observation for the number of prehension bites (0.94 for RWC36 and 0.75 for RWC13) than for the number of mastication chews (0.20 for RWC36 and 0.14 for RWC13). In cows fed in stalls, there was also greater concordance for the number of prehension bites (0.54 for RWC36 and 0.64 for RWC13) than for the number of mastication chews (0.46 for RWC36 and 0.58 for RWC13). However, the RWC36 was more accurate for quantifying feeding behaviors of grazing cows than the RWC13, while for stall fed cows the reverse was true. Overall, both RumiWatch Converters were better at quantifying prehension bites in grazing cows than in stall fed cows, but better at quantifying mastication chews in stall fed cows than grazing cows. These data enable a new understanding of the how these RumiWatch sensors should best be used for measuring cows' feeding behaviors in different feeding systems.

Behavioral changes in dairy cows with metritis in seasonal calving pasture-based dairy system

The aims of this study were to determine changes in lying and rumination behavior of transition dairy cows that were healthy or affected by metritis during the autumn and spring calving season in a temperate climate and determine the effect of some weather variables (precipitation and heat stress) on these behaviors. A total of 103 multiparous cows were monitored during the autumn (February to April) and spring calving season (July to October) from 10 d before to 10 d after calving. Cows were chosen retrospectively by diagnosis of metritis [autumn season, (n = 11); spring season, (n = 13)] or as healthy [autumn season, (n = 25); spring season, (n = 25)] based on vaginal discharge characteristics evaluated during the first 10 days in milk. In all animals, electronic data loggers recorded lying (Hobo Pendant G Acceleration, Onset Computer Corp.) and rumination behavior (Hi-Tag rumination monitoring system, SCR Engineers Ltd.) during the study period. We included precipitation level (>1 mm/d = with rain, and ≤1 mm/d = without rain) and heat stress [no heat stress = temperature-humidity index (THI) < 68 vs. heat stress = THI ≥ 68] as weather factors that may have affected lying and rumination behavior during the spring and autumn season in a temperate climate, respectively. Metritis during the spring calving season was associated only with longer lying times (≥1.3 h/d) after calving. During the autumn calving season cows with metritis lay down longer the day of calving (~2.1 h/d) and had fewer lying bouts of longer duration during the prepartum period compared with healthy cows. Rumination time did not differ by health status during the spring calving season, whereas cows with metritis during autumn ruminated 30, 21, and 24 min/d less than healthy cows during the prepartum, calving, and postpartum period, respectively. Precipitation and heat stress were associated with decreased daily lying and rumination time in sick cows. Our results indicate that differences in lying and rumination behavior depended on the metritis status, and support the idea that weather factors such as rainfall or heat stress requires to be considered in analyses of transition cow behavior in seasonal calving pasture-based dairy systems.

Restricting dairy cow access time to pasture in autumn: The effects on milk production, grazing behaviour and DM intake of late lactation dairy cows

Extending the grazing season in pasture based systems of dairy production can increase farm profitability; poor weather and soil conditions can reduce the number of grazing days. The study objectives were to (i) examine the effect of restricted access to pasture in the autumn on the milk production, grazing behaviour and DM intake (DMI) of late lactation spring-calving dairy cows and (ii) establish the effect of alternating restricted and continuous access to pasture on dairy cow production, DMI and grazing behaviour. Cows were randomly assigned to one of four grazing treatments: (i) 22 h (full-time) access to pasture (22H; control); (ii) Two 5-h periods of access to pasture (2×5H); (iii) Two 3-h periods of access to pasture (2×3H); and (iv) alternating between full-time and 3-h access to pasture with no more than three continuous days on any one regime, e.g. Monday – full-time access, Tuesday − 2x3H access, Wednesday − 2x3H access; Thursday – full-time access, etc. (2×3HV). Restricted access to pasture was offered after a.m. and p.m. milking. Swards of similar quality and pregrazing herbage mass were offered. Treatment had no effect on milk yield (13.2 kg/day), milk fat (48.2 g/kg), protein (39.0 g/kg) or lactose content (42.6 g/kg) and milk solid yield (1.15 kg/day). Similarly, there was no effect of treatment on final BW (483 kg) or final BCS (2.66). There was no significant difference in DMI (15.1 kg DM/cow/day) between treatments. There was an effect on daily grazing time, 22H cows (565 min/cow/day) grazed for longest time, however, when the 2x3HV treatment had full-time access to pasture, they had a similar grazing time (543 min/cow/day) to the 22H cows and were similar to the 2x3H treatment on days with restricted access to pasture (357 min/cow/day). The 22H and 2x5H animals had similar grass DMI/min (29.2 g/min), the 2x3HV were higher (33.9 g/min) but were similar to the comparable treatment when offered 2x3H access time (41.6 g/min) and when offered 22H access time (27.7 g/min). The results from this study show how when offered a grass only diet of autumn pasture grazing behaviour can be modified by restricting pasture access time without reducing dairy cow production in late lactation at low production levels. There was also no effect of alternating access time between 22H and 2x3H on milk production and DMI in the 2x3HV treatment. Restricted access time to pasture in autumn may be a strategy which farmers can use to extend the grazing season.

Calibration and location analysis of a heterogeneous binocular stereo vision system.

In the dairy farming industry, we can obtain the temperature, color, and location information of dairy cows by patrol inspection robot so as to monitor the health status and abnormal behaviors of dairy cows. We build and calibrate a heterogeneous binocular stereo vision (HBSV) system comprising a high-definition color camera and infrared thermal camera and mount it on a patrol inspection robot. First, based on the traditional chessboard, an easy-to-make calibration board for the HBSV system is designed. Second, an accurate locating and sorting algorithm for the calibration points of the calibration board is designed. Then, the cameras are calibrated and the HBSV system is stereo-calibrated. Finally, target locating is achieved based on the above calibration results and Yolo target detection technology. In this paper, several experiments are carried out from many aspects. The target locating average error of HBSV system is 3.11%, which satisfies the needs of the dairy farming environment. The video's FPS captured by using HBSV is 7.3, which is 78% higher than that by using binocular stereo vision system and infrared thermal camera. The results show that the HBSV system has application value to a certain degree.

Dairy Cows Activity under Heat Stress: A Case Study in Spain.

Simple SummaryHeat stress is a major problem for dairy cattle welfare, and it has several implications for milk production. In this work, automatic sensors were used to monitor cows’ activity. Different behaviors were recorded for 40 animals at different heat stress conditions. The main aim of the study was to use automatic sensors to identify behavior changes caused by heat stress on dairy cows. All behaviors studied were affected by environmental conditions. Animals adapted to heat stress by modifying their behavior, and automatic sensors provided valuable information in this regard. These findings might be the early development of an automatic early warning system for heat stress based on the behavioral modifications of dairy cattle.Heat stress plays a role in livestock production in warm climates. Heat stress conditions impair animal welfare and compromise the productive and reproductive performance of dairy cattle. Under heat stress conditions, dairy cattle modify their behavior. Thus, the assessment of behavior alterations can be an indicator of environmental or physiological anomalies. Moreover, precision livestock farming allows for the individual and constant monitoring of animal behavior, arising as a tool to assess animal welfare. The purpose of this study was to evaluate the effect of heat stress on the behavior of dairy cows using activity sensors. The study was carried out in Tinajeros (Albacete, Spain) during the summer of 2020. Activity sensors were installed in 40 cows registering 6 different behaviors. Environmental conditions (temperature and humidity) were also monitored. Hourly data was calculated for both animal behavior and environmental conditions. Temperature and Heat Index (THI) was calculated for each hour. The accumulated THI during the previous 24 h period was determined for each hour, and the hours were statistically classified in quartiles according to the accumulated THI. Two groups were defined as Q4 for no stress and Q1 for heat stress. The results showed that animal behavior was altered under heat stress conditions. Increasing THI produces an increase in general activity, changes in feeding patterns and a decrease in rumination and resting behaviors, which is detrimental to animal welfare. Daily behavioral patterns were also affected. Under heat stress conditions, a reduction in resting behavior during the warmest hours and in rumination during the night was observed. In conclusion, heat stress affected all behaviors recorded as well as the daily patterns of the cows. Precision livestock farming sensors and the modelling of daily patterns were useful tools for monitoring animal behavior and detecting changes due to heat stress.

Evaluation of production parameters, feed sorting behaviour and social interactions in dairy cows: Comparison of two total mixed rations with different particle size and water content

Dairy cow performance is affected by both the nutritional composition and the physical structure of the diet. The structure of total mixed rations (TMR) largely depends on the ingredient composition and structure of forage, dry matter (DM) content, mixing time and type of mixer used. This study investigated feed intake, eating time, milk production, DM digestibility, ruminal pH, time budget, and sorting and antagonistic behaviours in lactating dairy cows fed grass/clover silage and concentrate, either in a TMR or in a compact total mixed ration (CTMR) with decreased particle size and DM content due to addition of water. Two cross-over experiments (3-week periods), each involving 40 lactating dairy cows of the Swedish Red and Holstein breeds, were run simultaneously. Experiment 1 was designed to measure production parameters, feed digestibility and ruminal pH, while Experiment 2 was designed to measure time budget, feed sorting behaviours and antagonistic behaviours at the feed bunk. Diets were fed ad libitum, with the same ingredient composition and a forage:concentrate ratio of 60:40 (DM basis). Decreased particle size in the CTMR diet was achieved by mixing the grass-clover silage in a feed mixer with a vertical auger with knives, before mixing with concentrate in a feed mixer without knives. Decreased DM content in CTMR was achieved by adding water to the mix. Mean DM content of the diet was 51% DM for TMR and 37% DM for CTMR. The results showed lower daily DM intake (26.7 kg DM) and shorter eating time (242 min) in cows fed CTMR than in cows fed TMR (28.5 kg DM and 278 min). Feed digestibility, ruminal pH, daily milk yield (35.1 kg and 35.8 kg for CTMR and TMR, respectively) and milk composition did not differ between the diets. Cows fed CTMR showed less sorting and fewer antagonistic behaviours at feeding than cows fed TMR. The daily time budget of the cows also differed, with cows fed CTMR spending more time resting (+45 min) than cows fed TMR.

Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition.

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement.

Changes in Dairy Cow Behavior with and without Assistance at Calving

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition.

Behavior, feed intake and health status in Holstein, Gyr and Girolando-F1 cows during the transition period: Behavior and health of dairy cows in the transition period

This trial aimed to evaluate the ingestive behavior, feed intake (DMI) and its relationship with health in Holstein, Gyr and Girolando-F1 (½ Holstein ½ Gyr) cows during the transition period. For that purpose, 34 cows were distributed in three groups according to their breed compositions. Behavior, DMI and health conditions were monitored daily during transition. Feed intake and ingestive behavior were distinct for Holstein, Gyr and Girolando-F1 animals. Holstein cows had higher feed intake than Gyr and Girolando-F1 cows during the whole transition period. Before calving, Girolando-F1 cows had higher intake rate, while Holstein cows ingested more feed per visit at the trough. After calving, Gyr and Girolando-F1 cows visited more often the feed trough, but remained less time and ingested less feed per visit than Holstein cows. Regarding health status, Holstein cows presented higher dystocia risk than Gyr and Girolando-F1 cows. The occurrence of subclinical ketosis and mastitis affected DMI during the transition period. The behavior of visits to the feeder, time spent at the feed trough and DMI per visit to the feed trough were modified by the occurrence of dystocia, retained placenta, subclinical hypocalcaemia, clinical and subclinical ketosis.The intake rate was lower in animals affected by subclinical ketosis four days before the disease onset. Likewise, the occurrence of subclinical hypocalcaemia reduced the total time spend at the feed trough and duration per active visit to the feed trough one day before its occurrence, as well as the intake rate four days before its occurrence. Feed intake and ingestive behavior are different for Holstein, Gyr and Girolando-F1 cows. Subclinical ketosis and mastitis affect DMI of cows during the transition period and the behaviors related to the visits to the feed trough are the most affected by the occurrence of diseases in the transition period.

Detecting Dairy Cow Behavior Using Vision Technology

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management.

Evaluation of response time in monitoring system on the accuracy of recording individual feeding behavior and feed intake in dairy cows

The monitoring system can automatically collect the quantitative data of the feeding behavior and feed intake of individual cattle, and the immediate response system always records many non-eating visits. Increasing response time of trigger helps to decrease recording non-eating visits, but may also miss some small periods of recording feed intake. This study was to evaluate the effect of response time in monitoring system on the accuracy of recording individual feeding behavior and feed intake in dairy cows. The first experiment was to investigate the distribution of 10-h feeding behaviors of 8 Holstein dairy cows after morning feeding by using infra-red camera. The second experiment was to investigate accuracy of feed intake recorded by the monitoring system with different response times (0.3, 3, 6, 12 and 24 s) in comparison with direct weighting. The third experiment was to investigate effect of excluding the data of time interval after response time on feed intake and visiting behaviors recorded by the monitoring system, when response time was set as 0.3 s. The results showed that the most of eating and non-eating visits had duration larger than 24 s (99.0 %) and less than 24 s (65.3 %), respectively. Comparing with actual feed intake obtained by direct weighting, increasing response time greatly increased the recorded feed intake, with the greatest difference occurring at 6 s of response time (P = 0.001). Although increasing time interval after response time linearly increased mean, median feed intake per visit and 10-h feed intake (P < 0.01), it had little influence on 10-h feed intake (< 0.1 %) and eating visit (< 5%). In summary, setting 0.3 s of response time in monitoring system helps to get actual feed intake, but can obtain a lot of non-eating visits. Increasing response time helps to decrease the recording of non-eating visits, but can overestimate feed intake.

Effect of dietary inclusion of winter brassica crops on milk production, feeding behavior, rumen fermentation, and plasma fatty acid profile in dairy cows

This study determined feeding behavior, dry matter (DM) intake (DMI), rumen fermentation, and milk production responses of lactating dairy cows fed with kale (Brassica oleracea) or swede (Brassica napus ssp. napobrassica). Twelve multiparous lactating dairy cows (560 ± 22 kg of body weight, 30 ± 4 kg of milk/d, and 60 ± 11 d in milk at the beginning of the experiment; mean ± standard deviation) were randomly allocated to 3 dietary treatments in a replicated 3 × 3 Latin square design. The control diet comprised 10 kg of grass silage DM/d, 4 kg of ryegrass herbage DM/d, and 8.8 kg of concentrate DM/d. Then, 25% of herbage, silage, and concentrate (DM basis) was replaced with either kale or swede. Cows offered kale had decreased total DMI compared with cows fed the control and swede diets, whereas inclusion of swede increased eating time. Milk production, composition, and energy-corrected milk:DMI ratio were not affected. Cows fed with kale had a greater rumen acetate:propionate ratio, whereas swede inclusion increased the relative percentage of butyrate. Estimated microbial N was not affected by dietary treatments, but N excretion was reduced with inclusion of kale, improving N utilization. Cows fed kale tended to have increased nonesterified fatty acids and showed presence of Heinz-Ehrlich bodies, whereas hepatic enzymes such as aspartate aminotransferase, γ-glutamyl transferase, and glutamate dehydrogenase were not affected by dietary treatments. In plasma, compared with the control, swede and kale reduced total saturated fatty acids and increased total polyunsaturated fatty acids and total n-3 fatty acids. Overall, feeding cows with winter brassicas had no negative effect on production responses. However, mechanisms to maintain milk production were different. Inclusion of swede increased the time spent eating and maintained DMI with a greater relative rumen percentage of butyrate and propionate, whereas kale reduced DMI but increased triacylglycerides mobilization, which can negatively affect reproductive performance. Thus, the inclusion of swede may be more suitable for feeding early-lactating dairy cows during winter.

Effects of prepartum dietary protein level and feed intake on postpartum lactation performance and feeding behavior of multiparous Holstein dairy cows

An experiment was conducted to determine the effects of low and high metabolizable protein (MP) diets when fed for ad libitum and controlled intake during the prepartum period on postpartum lactation performance and feeding behavior of dairy cows. Thirty-six multiparous Holstein cows were blocked by parity, expected calving date, and previous lactation milk yield at -21 d relative to expected calving and were randomly assigned to 1 of 4 close-up period dietary treatments providing low MP (LMP) or high MP (HMP) diets with controlled intake (CNI) or ad libitum intake (ALI). The concentrations of MP were 65 and 90 g/kg dry matter for LMP and HMP diets, respectively, whereas intake was controlled to supply 100 and 160% of the NRC (2001) energy requirements for CNI and ALI groups, respectively. The concentration of net energy for lactation (NEL) in the treatment diets was 1.50 Mcal/kg. All cows were fed a similar lactation diet after calving (1.50 Mcal/kg of NEL and 83.3 g/kg of MP). The HMP diet increased dry matter intake during the first 3 wk and tended to increase dry matter intake over the 9 wk of lactation. Meal size and eating rate increased in the ALI cows during the prepartum period. Meal frequency increased with the HMP diet during the postpartum period. Milk yield increased by 15.2% with the HMP diet over the 9 wk of lactation. The HMP diet increased energy-corrected milk (ECM) yield in CNI versus ALI cows, whereas the LMP diet increased ECM yield in ALI versus CNI cows over the 9 wk of lactation. The increase in ECM yield of LMP-ALI versus LMP-CNI cows was supported by greater body condition loss and serum β-hydroxybutyrate over the 9 wk of lactation. Taken together, these data indicate that prepartum controlled intake of a high protein diet can provide the benefits of both strategies.

Effects of substituting sweet potato flour for ground corn on performance, feeding behavior, and metabolism of dairy cows.

The objective of this study was to evaluate the effect of substituting sweet potato flour for ground corn in rations fed to lactating dairy on milk yield and composition, blood metabolites, and feeding behavior. Twenty lactating Holstein cows from 30 to 60days postpartum were randomly assigned to one of two groups (n = 10 each) and used in a cross-over design trial with two treatments: a standard concentrate with ground corn as an energy source or experimental concentrate with sweet potato flour (SPF) replacing all of the ground corn. Each of the 35-day periods consisted of 14days for adaptation to diet and 21days for data and sample collection. Milk yield, dry matter intake (DMI), and feeding behavior were evaluated daily throughout the trial. Milk samples were collected weekly and blood samples were collected every 3days. Milk was analyzed for fat, protein, lactose, and total solid constituents. Blood was analyzed for glucose, non-esterified fatty acid (NEFA), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), total protein (TP), albumin, and urea concentrations. Milk yield (P = 0.62) and composition (fat: P = 0.71; protein: P = 0.12; lactose: P = 0.82; total solids: P = 0.56) were not affected by dietary treatments. There were no differences between treatments in DMI or meal frequency, but total eating time (P = 0.001), feeding time (P = 0.001), and meal duration (P = 0.001) was higher for control compared with SPF. However, feeding rate (P = 0.001) and serum urea concentration (P = 0.001) were higher for SPF compared with control. No differences were observed in serum metabolites and enzymes measured among treatments. The results of this trial indicate that SPF can be substituted for ground corn without impairing the performance, feeding behavior, and metabolism in dairy cows.