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Oliver, Amanda, and Grandmother Pig
When Grandmother Pig comes for a visit, Oliver and Amanda learn just how much fun it is to have a grandmother in the house.
Book
Invited Review — Current status, challenges and prospects for pig production in Asia
Asia is not only the primary region for global pig production but also the largest consumer of pork worldwide. Although the pig production in Asia has made great progress in the past, it still is confronted with numerous challenges. These challenges include: inadequate land and feed resources, a substantial number of small-scale pig farms, escalating pressure to ensure environmental conservation, control of devastating infectious diseases, as well as coping with high temperatures and high humidity. To solve these problems, important investments of human and financial capital are required to promote large-scale production systems, exploit alternative feed resources, implement precision feeding, and focus on preventive medicine and vaccines as alternatives to antibiotics, improve pig breeding, and increase manure recycling. Implementation of these techniques and management practices will facilitate development of more environmentally-friendly and economically sustainable pig production systems in Asia, ultimately providing consumers with healthy pork products around the world.
Journal Article
The parameters of the porcine eyeball
Background
The eye of the domestic pig (
Sus scrofa domestica
) is an ex vivo animal model often used in vision sciences research (retina studies, glaucoma, cataracts, etc.). However, only a few papers have compiled pig eye anatomical descriptions. The purpose of this paper is to describe pig and human eye anatomical parameters to help investigators in their choice of animal model depending on their study objective.
Methods
A wide search of current medical literature was performed (English language) using PubMed. Anteroposterior axial length and corneal radius, astigmatism, vertical and horizontal diameter, and pachymetry (slit-scan and ultrasound) were measured in five enucleated pig eyes of animals 6 to 8 months old.
Results
Horizontal corneal diameter was 14.31 ± 0.25 mm (CI 95% 14.03 mm–14.59 mm), vertical diameter was 12.00 ± 0 mm, anteroposterior length was 23.9 ± 0.08 mm (CI 95% 23.01 mm–29.99 mm), central corneal ultrasound pachymetry was 877.6 ± 13.58 μm (CI 95% 865.70 μm–889.50 μm) and slit-scan pachymetry was 906.2 ± 15.30 μm (CI 95% 892.78 μm–919.61 μm). Automatic keratometry (main meridians) was 41.19 ± 1.76D and 38.83 ± 2.89D (CI 95% 40.53D–41.81D and 37.76D–39.89D respectively) with an astigmatism of 2.36 ± 1.70D (CI 95% 1.72D–3.00D), and manual keratometry was 41.05 ± 0.54D and 39.30 ± 1.15D (CI 95% 40.57D–41.52D and 38.29D–40.31D respectively) with an astigmatism of 1.75 ± 1.31D (CI 95% 0.60D–2.90D).
Conclusion
This paper describes the anatomy of the pig eyeball for easy use and interpretation by researchers who are considering their choice of animal model in vision sciences research.
Journal Article
Animal Welfare and Production Challenges Associated with Pasture Pig Systems: A Review
A review of published literature was conducted to identify pasture pig production system features that pose risks to animal welfare, and to develop recommendations aimed at improving the wellbeing of the animals managed in those systems. Pasture pig production systems present specific challenges to animal welfare that are inherent to the nature of these systems where producers have little room to make improvements. However, these systems present other challenges that could be reduced with a carefully designed system, by adopting appropriate management strategies and by avoiding management practices that are likely to negatively affect animal wellbeing. In pasture pig production systems, exposure to extreme temperatures, potential contact with wildlife and pathogens (especially parasites), vulnerability to predators, risk of malnutrition, pre-weaning piglet mortality, complexity of processes for monitoring and treating sick animals, and for cleaning and disinfection of facilities and equipment are among the main threats to animal welfare.
Journal Article
Welfare of pigs on farm
This scientific opinion focuses on the welfare of pigs on farm, and is based on literature and expert opinion. All pig categories were assessed: gilts and dry sows, farrowing and lactating sows, suckling piglets, weaners, rearing pigs and boars. The most relevant husbandry systems used in Europe are described. For each system, highly relevant welfare consequences were identified, as well as related animal‐based measures (ABMs), and hazards leading to the welfare consequences. Moreover, measures to prevent or correct the hazards and/or mitigate the welfare consequences are recommended. Recommendations are also provided on quantitative or qualitative criteria to answer specific questions on the welfare of pigs related to tail biting and related to the European Citizen's Initiative ‘End the Cage Age’. For example, the AHAW Panel recommends how to mitigate group stress when dry sows and gilts are grouped immediately after weaning or in early pregnancy. Results of a comparative qualitative assessment suggested that long‐stemmed or long‐cut straw, hay or haylage is the most suitable material for nest‐building. A period of time will be needed for staff and animals to adapt to housing lactating sows and their piglets in farrowing pens (as opposed to crates) before achieving stable welfare outcomes. The panel recommends a minimum available space to the lactating sow to ensure piglet welfare (measured by live‐born piglet mortality). Among the main risk factors for tail biting are space allowance, types of flooring, air quality, health status and diet composition, while weaning age was not associated directly with tail biting in later life. The relationship between the availability of space and growth rate, lying behaviour and tail biting in rearing pigs is quantified and presented. Finally, the panel suggests a set of ABMs to use at slaughter for monitoring on‐farm welfare of cull sows and rearing pigs.
Journal Article
Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor
Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods.
Journal Article
Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring
The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs’ health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs’ health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs’ health and welfare.
Journal Article
Robust individual pig tracking
The locations of pigs in the group housing enable activity monitoring and improve animal welfare. Vision-based methods for tracking individual pigs are noninvasive but have low tracking accuracy owing to long-term pig occlusion. In this study, we developed a vision-based method that accurately tracked individual pigs in group housing. We prepared and labeled datasets taken from an actual pig farm, trained a faster region-based convolutional neural network to recognize pigs’ bodies and heads, and tracked individual pigs across video frames. To quantify the tracking performance, we compared the proposed method with the global optimization (GO) method with the cost function and the simple online and real-time tracking (SORT) method on four additional test datasets that we prepared, labeled, and made publicly available. The predictive model detects pigs’ bodies accurately, with F1-scores of 0.75 to 1.00, on the four test datasets. The proposed method achieves the largest multi-object tracking accuracy (MOTA) values at 0.75, 0.98, and 1.00 for three test datasets. In the remaining dataset, the proposed method has the second-highest MOTA of 0.73. The proposed tracking method is robust to long-term occlusion, outperforms the competitive baselines in most datasets, and has practical utility in helping to track individual pigs accurately.
Journal Article
The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming
Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.
Journal Article
Human Microbiota-Associated Pig Models for Translational Microbiome Research: A Scoping Review
The human microbiota-associated (HMA) pig model provides a physiologically relevant platform that bridges preclinical and translational research. However, its use remains limited, with existing studies showing considerable variation in establishment methods. This scoping review systematically evaluates methodological frameworks, engraftment outcomes, and research applications of HMA pig models. Additionally, it highlights their strengths, limitations, and implications for future studies. We conducted a comprehensive literature search in PubMed, Web of Science, Scopus, and Directory of Open Access Journals, following PRISMA guidelines for Scoping Reviews. The review examines the methodological foundations of HMA pig model generation and proposes a minimal reporting framework to promote standardization. It synthesizes studies on human microbiota engraftment in pigs, identifying factors that influence colonization efficiency. Finally, it summarizes current applications, discusses persistent limitations and translational challenges, and outlines opportunities for future research. Overall, these integrated insights aim to foster standardized, reproducible protocols for HMA pig model preparation and guide advancements in the field.
Journal Article