Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows

Antanaitis, Ramūnas; Anskienė, Lina; Palubinskas, Giedrius; Džermeikaitė, Karina; Bačėninaitė, Dovilė; Viora, Lorenzo; Rutkauskas, Arūnas

doi:10.3390/ani13071257

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Additionally, this study observed a gradual return to pre-calving activity levels within two weeks postpartum, indicating successful adaptation [ 11 ]. Interestingly, the present study’s findings slightly differ from the findings of [ 56 ], which indicated a quicker normalization of activity levels occurring nine days after calving, showing a recuperation from calving stress. Furthermore, these collective findings emphasize the importance of understanding and managing postpartum behavior to maximize cow comfort and welfare.…”

Section: Discussioncontrasting

confidence: 99%

Behavioral Adaptations in Tropical Dairy Cows: Insights into Calving Day Predictions

Raza,

Abbas,

Swangchan-Uthai

et al. 2024

Animals

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This study examined changes in the activity patterns of tropical dairy cows during the transition period to assess their potential for predicting calving days. This study used the AfiTag-II biosensor to monitor activity, rest time, rest per bout, and restlessness ratio in 298 prepartum and 347 postpartum Holstein Friesian cows across three lactation groups (1, 2, and ≥3). The data were analyzed using generalized linear mixed models in SPSS, and five machine learning models, including random forest, decision tree, gradient boosting, Naïve Bayes, and neural networks, were used to predict the calving day, with their performance evaluated via ROC curves and AUC metrics. For all lactations, activity levels peak on the calving day, followed by a gradual return to prepartum levels within two weeks. First-lactation cows displayed the shortest rest duration, with a prepartum rest time of 568.8 ± 5.4 (mean ± SE), which is significantly lower than higher-lactation animals. The random forest and gradient boosting displayed an effective performance, achieving AUCs of 85% and 83%, respectively. These results indicate that temporal changes in activity behavior have the potential to be a useful indicator for calving day prediction, particularly in tropical climates where seasonal variations can obscure traditional prepartum indicators.

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Section: Discussioncontrasting

confidence: 99%

Behavioral Adaptations in Tropical Dairy Cows: Insights into Calving Day Predictions

Raza,

Abbas,

Swangchan-Uthai

et al. 2024

Animals

View full text Add to dashboard Cite

show abstract

“…Moreover, several papers recently appeared in the literature related to our works. Studies on feeding behaviors around calving in dairy cattle [22] and predicting the feed intake of cattle based on jaw movement using a triaxial accelerometer [23] are some of them. Although the approaches are different from ours, since feeding time estimation and feed intakes are related, further analysis would be worthwhile to investigate in more detail [24,25].…”

Section: Discussionmentioning

confidence: 99%

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

2023

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With the increasing number of cows per farmer in Japan, an automatic cow monitoring system is being introduced. One important aspect of such a system is the ability to identify individual cows and estimate their feeding time. In this study, we propose a method for achieving this goal through facial region analysis. We used a YOLO detector to extract the cow head region from video images captured during feeding with the head region cropped as a face region image. The face region image was used for cow identification and transfer learning was employed for identification. In the context of cow identification, transfer learning can be used to train a pre-existing deep neural network to recognize individual cows based on their unique physical characteristics, such as their head shape, markings, or ear tags. To estimate the time of feeding, we divided the feeding area into vertical strips for each cow and established a horizontal line just above the feeding materials to determine whether a cow was feeding or not by using Hough transform techniques. We tested our method using real-life data from a large farm, and the experimental results showed promise in achieving our objectives. This approach has the potential to diagnose diseases and movement disorders in cows and could provide valuable insights for farmers.

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Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows

Cited by 2 publications

References 34 publications

Behavioral Adaptations in Tropical Dairy Cows: Insights into Calving Day Predictions

Behavioral Adaptations in Tropical Dairy Cows: Insights into Calving Day Predictions

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

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