Calf Management: Individual or Paired Housing Affects Dairy Calf Health and Welfare.

Animals (Basel)

Division of Farm Animal, Veterinary Public Health and Epidemiology, Royal Dick School of Veterinary Studies and the Roslin Institute, Easter Bush Campus, Midlothian EH25 9RG, UK.

Published: May 2024

AI Article Synopsis

  • Previous research suggests that dairy calves raised in pairs show improved performance and welfare compared to those raised alone.
  • In this study, 130 Holstein calves were either housed in pairs or individually, with no significant differences found in weight gain, mortality, or disease treatments between the two groups.
  • However, paired calves were quicker to approach food and novel objects, and heavier calves within pairs displayed better growth and less fearfulness compared to lighter calves.*

Article Abstract

Previous research has indicated that preweaned dairy calves reared in pairs compared with individually have improved performance and indicators of animal welfare. One hundred and thirty Holstein female calves completed the trial, with eighty-five being allocated to paired housing and forty-five calves being allocated to individual housing. Daily live weight gain (DLWG), treatments and mortality were recorded throughout the preweaning period. Salivary cortisol, latency to feed and latency to approach a novel object were assessed at batching. There were no significant differences in DLWG, mortality and disease treatments between the average of the pair and the individually housed calves, although the pair-reared calves were quicker to approach the milk feed after batching and interacted more quickly with a novel object. The heaviest born calves within the pair had the highest DLWG from birth to weaning, with a higher percentage of calves approaching the novel object, compared with the lightest born calf within the pair. This study shows that calves within a pair may have significantly different performance and welfare during the preweaning period, with the heavier calf outperforming and displaying less fear and more exploratory behaviour than the lighter calf within a pair.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11171300PMC
http://dx.doi.org/10.3390/ani14111540DOI Listing

Publication Analysis

Top Keywords

novel object
12
paired housing
8
calves
8
preweaning period
8
calves pair
8
calf pair
8
calf
5
pair
5
calf management
4
management individual
4

Similar Publications

Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.

View Article and Find Full Text PDF

Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches.

View Article and Find Full Text PDF

Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.

View Article and Find Full Text PDF

Gripping Success Metric for Robotic Fruit Harvesting.

Sensors (Basel)

December 2024

Department of Computer Science & Artificial Intelligence, Jeonbuk National University, Jeonju-si 54896, Republic of Korea.

Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the model selection process for object detection, the average precision (AP) score typically provides the de facto standard.

View Article and Find Full Text PDF

In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!