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

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2852
Author(s):  
Lorenzo Leso ◽  
Valentina Becciolini ◽  
Giuseppe Rossi ◽  
Stefano Camiciottoli ◽  
Matteo Barbari
Keyword(s):  
Mixed Models ◽  
Visual Observation ◽  
Feeding Time ◽  
Concordance Correlation ◽  
Sensor Performance ◽  
Cattle Feeding ◽  
Holstein Friesian ◽  
Different Types ◽  
Friesian Cows ◽  
Visual Observations

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.

Evaluation of an activity and rumination monitor in dairy cattle grazing two types of forages

2017 ◽  
Vol 57 (7) ◽  
pp. 1557 ◽  
Author(s):  
Juan Molfino ◽  
Cameron E. F. Clark ◽  
Kendra L. Kerrisk ◽  
Sergio C. García
Keyword(s):  
Monitoring System ◽  
Annual Ryegrass ◽  
Concordance Correlation ◽  
Positive Predicted Value ◽  
Holstein Friesian ◽  
Trained Observers ◽  
Sensitivity Specificity ◽  
Pearson Correlations ◽  
Friesian Cows ◽  
Visual Observations

The aim of the present study was to evaluate the accuracy of a newer version of an activity- and rumination-monitoring system by comparison against direct visual observations, for the following three different types of behaviour: grazing, resting (described as lying or standing idle) and ruminating for cows grazing either annual ryegrass or chicory-based swards. Eight non-lactating Holstein–Friesian cows were fitted with the sensor tags, and grazed on annual ryegrass pasture for a target consumption of 10 kg DM ryegrass/cow.day for 7 days. The experiment was then repeated with cattle offered a similar allowance of chicory. Observations were conducted by two trained observers in two observation periods each day, to capture the above described behaviours. In each period, electronic behavioural measurements were recorded continuously by the sensors, while visual observations were also continuous (during observation periods), and the two datasets were matched. On average, each cow was visually observed for 87.2 min/day. For each behavioural state (at 1-min intervals, n = 6963), probability of agreement, sensitivity, specificity and positive predicted value were determined for grazing as 98%, 98.3%, 97.3% and 98.9% respectively, for resting as 80%, 77.5%, 99.1% and 92.9% and for ruminating as 87%, 86.9%, 98.4% and 90.68%. Concordance correlation coefficient (CCC) and Pearson correlations (r) were used to investigate the relationships between visual observations and data generated from the tags. Different behaviours were analysed separately. Significant correlations were found for the three behaviours (grazing: CCC = 0.99, r = 0.99; resting: CCC = 0.95, r = 0.97; ruminating: CCC = 0.80, r = 0.80), with no differences detected between the two forages. We conclude that, under the conditions of the present study, the activity- and rumination-monitoring system tag measured grazing, resting and ruminating behaviours with high accuracy on the basis of comparison to visual observations.

Diurnal patterns of heat production and heart rate under thermoneutral conditions in Holstein Friesian cows differing in milk production

1990 ◽  
Vol 114 (2) ◽  
pp. 139-142 ◽  
Author(s):  
B. P. Purwanto ◽  
Y. Abo ◽  
R. Sakamoto ◽  
F. Furumoto ◽  
S. Yamamoto
Keyword(s):  
Heart Rate ◽  
Dairy Cows ◽  
Heat Production ◽  
Early Morning ◽  
Feeding Time ◽  
Milk Productivity ◽  
Diurnal Patterns ◽  
Holstein Friesian ◽  
Minimum Heat ◽  
Friesian Cows

SUMMARYTen dairy cows were allocated into three groups according to milk productivity (four high, four intermediate and two dry cows, respectively). Heat production and heart rate, but not rectal temperature, were significantly different (P < 0·05) between groups. Heat production increased during feeding in the morning and in the afternoon and reached a peak 3 h later. Minimum heat production was observed in the early morning before feeding. The diurnal pattern for heart rate reflected that of heat production. These results suggest that cooling dairy cows during hot summer days is most effective at feeding time and 3 h afterwards.

scholarly journals Choice of grazed herbage or maize silage by lactating dairy cows: influence of sward height and concentrate level

2010 ◽  
Vol 62 (5) ◽  
pp. 1158-1166 ◽  
Author(s):  
O. Hernandez-Mendo ◽  
J.D. Leaver
Keyword(s):  
Dairy Cows ◽  
Feeding Time ◽  
Maize Silage ◽  
Concentrate Supplementation ◽  
Sward Height ◽  
Lactating Dairy Cows ◽  
Herbage Intake ◽  
Holstein Friesian ◽  
Concentrate Level ◽  
Friesian Cows

The preference of lactating dairy cows for grazed herbage or maize silage (MS), simultaneously offered ad libitum in the field, was examined at two sward heights (SH 4-6 and 8-10cm) and two concentrate levels (CL 0 and 6kg day-1) in a 2x2 factorial arrangement within a completely randomised experimental design. The experiment lasted 35 days and was carried out in spring using 24 multiparous Holstein Friesian cows. On average, the cows proportionately spent more time grazing than eating MS (0.85:0.15) and even though the higher rate of intake (RI) of dry matter (DM) of MS compared with grazed herbage (76 versus 26g DM min-1), the proportion of total DM intake as herbage was higher compared to that of MS (0.56:0.44). The higher crude protein and low fibre content of grazed herbage appeared to have a higher priority of choice than RI, as the cows chose to graze for longer (grazing time 385 min, MS feeding time 67min) despite the lower RI of herbage. The low proportion MS intake indicated that RI was a secondary factor of choice. Concentrate supplementation had a greater depressing effect on herbage intake than on MS intake. These results suggest that the animals reduce the intake of feed with lower RI when the labor associated to eat is decreased. The factors influencing the choice for herbage over maize silage remain unclear.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

scholarly journals Flow Cytometric Assessment of the Viability and Functionality of Uterine Polymorphonuclear Leukocytes in Postpartum Dairy Cows

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1081
Author(s):  
Leen Lietaer ◽  
Kristel Demeyere ◽  
Stijn Heirbaut ◽  
Evelyne Meyer ◽  
Geert Opsomer ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Polymorphonuclear Leukocytes ◽  
Reproductive Tract ◽  
Inflammatory Diseases ◽  
Flow Cytometric ◽  
Holstein Friesian ◽  
Uterine Health ◽  
Intracellular Proteolysis ◽  
Friesian Cows

Postpartum dairy cows experience impaired peripheral polymorphonuclear leukocyte (PMN) functionality, which has been associated with reproductive tract inflammatory diseases. However, it has not been elucidated yet whether endometrial PMN functionality is (equally) impaired. We developed a method for endometrial PMN isolation and flow cytometric assessment of their viability and functionality. We also evaluated PMN immunolabeling, using a specific bovine granulocyte marker, CH138A. Blood and endometrial cytobrush samples were collected in duplicate from seventeen clinically healthy Holstein-Friesian cows between 9 and 37 days in milk. The proportion of viable, apoptotic, and necrotic PMN in endometrial samples roughly ranged from 10 to 80%, indicating highly dynamic endometrial PMN populations in the postpartum uteri. Endometrial PMN functionality testing revealed that PMN immunolabeling increased the accuracy, although this protocol might influence the median fluorescence intensity of the sample. Phagocytosis seemed the most stable and reliable endometrial PMN function and could be assessed satisfactorily without prior CH138A immunolabeling. However, the interpretation of oxidative burst and intracellular proteolysis tests remains challenging. The correlation between peripheral and endometrial PMN functionality was poor. Further research is warranted to unravel the role of uterine PMN viability and functionality in bovine uterine health.

The effect of space allowance and milk yield level on the performance and behaviour of dairy cattle housed in straw yards

1998 ◽  
Vol 1998 ◽  
pp. 108-108
Author(s):  
J. A. Fregonesi ◽  
J.D. Leaver
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Stocking Density ◽  
Important Variable ◽  
Early Lactation ◽  
Ways Of Coping ◽  
Yield Level ◽  
Face Width ◽  
Holstein Friesian ◽  
Friesian Cows

Space allowance could be an important variable affecting production, health, reproductive performance and behaviour of dairy cattle. Also, high and low yielding cows may have different ways of coping with insufficient space allowance. The aim of this experiment was to study the influence of space allowance and milk yield level on the performance and behaviour of strawyard housed dairy cows.The experiment was carried out using 24 Holstein Friesian cows with two groups in early lactation of high (over 30 kg/day milk yield) and two groups in late lactation of low yield (under 25 kg/day milk yield). The groups were allocated to strawyard systems with low stocking density (bed area/cow = 9 m2; pen area/cow = 13.5 m2; feed face width/cow = 1.5 m) or high stocking density (bed area/cow = 4.5 m2; pen area/cow = 6.75 m2; feed face width/cow = 0.75m) conforming to a changeover design with two periods, each of four weeks. The cows were fed a total mixed ration ad libitum and 2kg/cow/day of concentrate in the milking parlour. All animals were milked twice daily.

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