Characterizing changes in activity and feeding behaviour of lactating dairy cows during behavioural and silent oestrus

Performance and Cost-Efficiency of Single Hormonal Treatment Protocols in Tropical Anestrous Dairy Cows

This retrospective study aimed to evaluate the performance of hormone treatment protocols, determine the factors associated with pregnancy success after hormone treatment, and compare the cost-efficiencies of two types of hormone treatment among cyclic and noncyclic anestrous dairy cows. The clinical records of 279 anestrous cows that received hormone treatment for artificial insemination (AI) from 64 herds in the western region of Thailand were obtained from Kasetsart University Veterinary Teaching Hospital from January to August 2017. The performance of the hormone treatment protocols, fixed-time AI (TAI) and estrus detection before AI (EAI), showed that the pregnancy risk for the TAI protocol was higher than that for the EAI protocol, but pregnancy per AI did not differ significantly between the two protocols in cyclic and noncyclic cows. Multivariate logistic regression analysis showed that cows receiving the TAI protocol were more likely to be pregnant compared to those treated with the EAI protocol. Cows with a 3.00 body condition score (BCS) < 3.75 after treatment and loose-housed cows were more likely to become pregnant. Treatment during winter showed higher pregnancy success than that in the summer and rainy seasons. The cost-efficiency analysis showed that the TAI protocol was the most cost-efficient option for noncyclic cows, whereas the EAI protocol was the most cost-efficient option for cyclic cows.

The Influence of Selected Factors on Changes in Locomotion Activity during Estrus in Dairy Cows

The objective of this study was the evaluation of the locomotion activity of heifers and Holstein dairy cows during estrus. We have analyzed the locomotion activity using the Heatime RuminAct device on 180 cows (32 heifers and 148 dairy cows) and we evaluated a total of 633 estrus cycles during the reference period of 3 days before estrus, 3 days after estrus, and on the day ofestrus occurrence. The datawere analyzed using the DataFlowTM II program. The locomotion of cows was expressed in the units of locomotion activity in 24 h (u.24 h-1). During the reference period of 3 days before estrus, the cows showed locomotion activity of 558 u.24 h-1, with an increase in locomotion activity on the day of estrus of 836 u.24 h-1, and, during the reference period of 3 days after estrus, the level of locomotion activity decreased to 537 836 u.24 h-1, which is a similar level of locomotion activity to the reference period before estrus. Through the statistical analysis, we evaluated the impact of parity, lactation stage, milk yield, and individuality on changes in locomotion activity during estrus and throughout the reference period, and we found a significant effect of parity (F = 13.41, p < 0.001) on changes in the locomotion activity of dairy cows during estrus. Based on these results, this research offers fresh perspectives on assessing specific factors affecting the locomotion activity of dairy cows during estrus through the practical application of electronic systems for estrus detection on dairy farms.

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

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

The effect of activity on some milking parameters in holstein cows

The study was conducted on 41-second lactation Holstein cows of German origin. The shelter type is a semi-open field type and the research period is 12 months. The activities in the first 100 days of lactation per day were 451.4 ± 133.5, and in the second 100 days, it was determined at 420.78 ± 118.0. The activities are divided into 3 parts within 24 hours (at night, during the day between two milkings, and in the evening). While there was no statistical difference between days 100 and 200 of lactation, the lowest activity was recorded at night and the highest activity during the day. The conductance, milk flow, and milking duration of the milk were within the normal range in the first 100 and 200 days and no statistical difference between them could be determined. Mean daily milk yield was 28.28 ± 3.86 kg for the first 100 days and 25.15 ± 3.61 kg for the following 100 days, and the difference was found to be significant (P

Assessing the Accuracy of Leg Mounted Sensors for Recording Dairy Cow Behavioural Activity at Pasture, in Cubicle Housing and a Straw Yard

The accuracy of CowAlert IceQube sensors (IceRobotics Ltd., Edinburgh, UK) for recording lying duration, standing and lying transitions and number of steps when dairy cows where at pasture, in cubicle housing and in a straw yard, was investigated. Holstein Friesian cows at Harper Adams University, UK were fitted with IceQube sensors; one on the back left (BL) leg and one on the front left (FL) leg. Cows at pasture (n = 48), in cubicle housing (n = 46) and in a straw yard (n = 45) were visually observed. Data were analysed in two stages: (1) an initial exploratory phase determined the correlation level between sensor measurements andvisual observations. Subsequently, (2) a mixed effects modelling framework was used to check whether sensors provide significantly different measures of cow’s activities compared to the observations. Results indicate that lying and standing times are similar between the observed and recorded times, in all three locations. In terms of sensor placement, significant differences were found between the number of steps recorded between BL and FL on straw and pasture, but all other activities were similar, in each location. The accuracy of CowAlert IceQube sensors on the BL leg gives them the potential to be used as lifelong sensors.

Use of a real-time location system to detect cows in distinct functional areas within a barn

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The RTLS achieved high accuracy in locating cows in alleys, feed bunk and cubicles.

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Location and time spent in important barn areas can be automatically determined and used as indicators of health.

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The potential of combining RTLS with other sensors technologies was discussed.

Automated sensor-based monitoring of cows has become an important tool in herd management to improve or maintain animal health and welfare. Location systems offer the ability to locate animals within the barn for, for example, artificial insemination. Furthermore, they have the potential to measure the time cows spend in important areas of the barn, which might indicate need for improvement in the management of the herd or individuals. In this study, we tested the sensor-based real-time location system (RTLS) Smartbow (SB, Smartbow GmbH) under field conditions. The objectives of this study were (1) to determine the accuracy of the system to predict the location of the cow and the agreement between visual observations and RTLS observations for the total time spent by cows in relevant areas of the barn and (2) to compare the performance of 2 different algorithms (Alg1 and Alg2) for cow location. The study was conducted on a commercial Austrian dairy farm. In total, 35 lactating cows were video recorded for 3 consecutive days. From these recordings, approximately 1 h was selected randomly each day for every cow (3 d × 35 cows). Simultaneously, location data were collected and classified by the RTLS system as dedicated to the alley, feed bunk, or cubicle on a 1-min resolution. A total of 6,030 paired observations were derived from visual observations (VO) and the RTLS and used for the final data analysis. Substantial agreement of categorical data between VO and SB was obtained by Cohen's kappa for both algorithms (Alg1 = 0.76 and Alg2 = 0.78). Similar results were achieved by both algorithms throughout the study, with a slight improvement for Alg2. The ability of the system to locate the cows in the predefined areas was assessed, and the results from Alg2 showed sensitivity, specificity, and positive predictive value of alley (74.0, 91.2, and 76.9%), feed bunk (93.5, 86.2, and 89.1%), and cubicle (90.5, 83.3, and 95.4%) and an overall accuracy of 87.6%.The correlation coefficient (r) between VO and SB for the total time cows spent (within 1 h) in the predefined areas was good to strong (r = 0.82, 0.98, and 0.92 for alley, feed bunk, and cubicle, respectively). These results show the potential of the system to automatically assess total time spent by cows in important areas of the barn for indoor settings. Future studies should focus on evaluating 24-h periods to assess time budgets and to combine technologies such as accelerometers and location systems to improve the performance of behavior prediction in dairy cows.

"HerdGPS-Preprocessor"-A Tool to Preprocess Herd Animal GPS Data; Applied to Evaluate Contact Structures in Loose-Housing Horses

Sensors delivering information on the position of farm animals have been widely used in precision livestock farming. Global Positioning System (GPS) sensors are already known from applications in military, private and commercial environments, and their application in animal science is increasing. However, as trade-offs between sensor cost, battery life and sensor weight have to be made, GPS based studies scheduling long data collection periods and including a high number of animals, have to deal with problems like high hardware costs and data disruption during recharging of sensors. Furthermore, human-animal interaction due to sensor changing at the end of battery life interferes with the animal behaviour under analysis. The present study thus proposes a setting to deal with these challenges and offers the software tool "HerdGPS-Preprocessor", because collecting position data from multiple animals nonstop for several weeks produces a high amount of raw data which needs to be sorted, preprocessed and provided in a suitable format per animal and day. The software tool "HerdGPS-Preprocessor" additionally outputs contact lists to enable a straight analysis of animal contacts. The software tool was exemplarily deployed for one month of daily and continuous GPS data of 40 horses in a loose-housing boarding facility in northern Germany. Contact lists were used to generate separate networks for every hour, which are then analysed with regard to the network parameter density, diameter and clique structure. Differences depending on the day and the day time could be observed. More dense networks with more and larger cliques were determined in the hours prior to the opening of additional pasture.

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

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

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

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

Fatty acid profile of milk for determining reproductive status in lactating Holstein Friesian cows

Large percentages of dairy cows do not express behavioural signs of oestrus. Faecal and urine fatty acid concentrations increase during oestrus. The objective of the present study was to determine the milk FA profile of dairy cows during the oestrous and dioestrous periods and the relationship with behavioural signs on the day of oestrus. The activity of 32 Holstein Friesian cows was measured continuously using GEA Rescounter ll pedometers (GEA Farm Technologies, Düsseldorf, Germany) and IceQubes (IceRobotics Ltd., Edinburgh, UK). Milk samples were collected on the day of oestrus and on day 14 of the subsequent oestrous cycle and analysed for FA concentration using gas chromatography (GC) and milk composition was also determined. All cows were artificially inseminated within 12 h of the onset of oestrus. On the day of oestrus, the concentration of acetic acid (P < 0.001), valeric acid (P = 0.016), caproic acid (P < 0.001) and myristoleic (P = 0.035) were greater in milk compared to day 14. On day 14 milk arachidonic acid concentration, however, was greater (P = 0.004) compared to the day of oestrus. Also, on day 14 arachidonic acid concentration was greater (P = 0.002) in non-pregnant compared to pregnant cows. In conclusion, the results of this study indicate there are changes in the concentrations of some milk FA during oestrus and dioestrus in lactating dairy cows.