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Animals
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Animal Health and Welfare Assessment of Pigs
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Animal Health and Welfare Assessment of Pigs

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal Welfare".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1234

Special Issue Editor

Dr. Annalisa Scollo

E-Mail Website
Guest Editor
Department of Veterinary Sciences, University of Torino, 10095 Grugliasco, TO, Italy
Interests: pig welfare; farm management; pig health; infectious diseases; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern pig industry is driving the sector towards even more efficient animals and advanced management procedures, led by the genetic selection for more hyperprolific sows and the needs of reduced antimicrobial use, improved welfare, biosecurity in farms, and environmental sustainability. Because of these key goals of contemporary pig farming, several challenges have arisen for farmers in guaranteeing health and welfare for their animals. In this transitioning period, all physical and social stress for the pigs needs to be monitored and investigated, as they are capable of inducing either short- or long-term effects in pigs.

For this reason, quantifying and monitoring animal welfare and health across the entire life cycle (and over) is an important step towards driving the development of the pig sector. However, some areas need more research.

We are pleased to invite you to join this Special Issue, which will focus on recent research or reviews that investigate novel approaches and/or technologies used to evaluate the welfare and health of farmed pigs. Research areas may include (but are not limited to) the development of measures suitable for determining whether animals have a positive emotional state, the development of novel biomarkers, the development of new benchmarking data, and the development of versatile and informative tools to communicate findings to a broad range of stakeholders.

We look forward to receiving your contributions.

Dr. Annalisa Scollo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pigs
  • welfare
  • health
  • assessment
  • biomarkers
  • monitoring
  • modern farming

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Published Papers (3 papers)

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Research

17 pages, 274 KiB  
Article
The Economic Implications of Phasing Out Pig Tail Docking: A Pilot Study in Italy
by Francesca Menegon, Annalisa Scollo, Samuele Trestini, Rachele Urbani, Giuseppe Ru and Guido Di Martino
Animals 2025, 15(9), 1250; https://doi.org/10.3390/ani15091250 - 29 Apr 2025
Viewed by 93
Abstract
The European Commission’s ban on routine tail docking has prompted this retrospective observational study to evaluate the short-term effects of transitioning to a fully undocked system. Twenty-two farms were assessed during three subsequent phases: total tail docking (step 1), subgroups of undocked pigs [...] Read more.
The European Commission’s ban on routine tail docking has prompted this retrospective observational study to evaluate the short-term effects of transitioning to a fully undocked system. Twenty-two farms were assessed during three subsequent phases: total tail docking (step 1), subgroups of undocked pigs (step 2), and fully undocked pigs (step 3). Farmers received training in long-tail management and independently implemented it on their own farms. However, straw provision as environmental enrichment was mandatory, at least supplied during periods of pigs’ restlessness. Overall, going through step 2 appears to be successful. However, transitioning to step 3 worsened mortality (p = 0.010) and the feed conversion ratio (p = 0.015) in weaners. Compared to step 1, the cost of producing 1 kg of meat in step 3 was 33.9% greater during weaning and 7.4% during fattening. Tail lesion prevalence at slaughter was greater in step 3 (41%), followed by step 2 (10%) and step 1 (1%). The hypothetical labour required to optimize straw management compared to the adopted system, ensuring its continuous availability, was estimated as 35 min/100 piglets/weaning cycle (EUR 4.37) and 10.5 h/100 pigs/fattening cycle (EUR 109). Under the conditions of this study, transitioning to a fully undocked system was not successful. Mandating only the non-continuous use of straw has proven insufficient, and greater efforts must be systematically implemented. Full article
(This article belongs to the Special Issue Animal Health and Welfare Assessment of Pigs)
22 pages, 6453 KiB  
Article
A Lightweight Model for Small-Target Pig Eye Detection in Automated Estrus Recognition
by Min Zhao, Yongpeng Duan, Tian Gao, Xue Gao, Guangying Hu, Riliang Cao and Zhenyu Liu
Animals 2025, 15(8), 1127; https://doi.org/10.3390/ani15081127 - 13 Apr 2025
Viewed by 373
Abstract
In modern large-scale pig farming, accurately identifying sow estrus and ensuring timely breeding are crucial for maximizing economic benefits. However, the short duration of estrus and the reliance on subjective human judgment pose significant challenges for precise insemination timing. To enable non-contact, automated [...] Read more.
In modern large-scale pig farming, accurately identifying sow estrus and ensuring timely breeding are crucial for maximizing economic benefits. However, the short duration of estrus and the reliance on subjective human judgment pose significant challenges for precise insemination timing. To enable non-contact, automated estrus detection, this study proposes an improved algorithm, Enhanced Context-Attention YOLO (ECA-YOLO), based on YOLOv11. The model utilizes ocular appearance features—eye’s spirit, color, shape, and morphology—across different estrus stages as key indicators. The MSCA module enhances small-object detection efficiency, while the PPA and GAM modules improve feature extraction capabilities. Additionally, the Adaptive Threshold Focal Loss (ATFL) function increases the model’s sensitivity to hard-to-classify samples, enabling accurate estrus stage classification. The model was trained and validated on a dataset comprising 4461 images of sow eyes during estrus and was benchmarked against YOLOv5n, YOLOv7tiny, YOLOv8n, YOLOv10n, YOLOv11n, and Faster R-CNN. Experimental results demonstrate that ECA-YOLO achieves a mean average precision (mAP) of 93.2%, an F1-score of 88.0%, with 5.31M parameters, and FPS reaches 75.53 frames per second, exhibiting superior overall performance. The findings confirm the feasibility of using ocular features for estrus detection and highlight the potential of ECA-YOLO for real-time, accurate monitoring of sow estrus under complex farming conditions. This study lays the groundwork for automated estrus detection in intensive pig farming. Full article
(This article belongs to the Special Issue Animal Health and Welfare Assessment of Pigs)
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26 pages, 15621 KiB  
Article
Integrated Convolution and Attention Enhancement-You Only Look Once: A Lightweight Model for False Estrus and Estrus Detection in Sows Using Small-Target Vulva Detection
by Yongpeng Duan, Yazhi Yang, Yue Cao, Xuan Wang, Riliang Cao, Guangying Hu and Zhenyu Liu
Animals 2025, 15(4), 580; https://doi.org/10.3390/ani15040580 - 18 Feb 2025
Viewed by 564
Abstract
Accurate estrus detection and optimal insemination timing are crucial for improving sow productivity and enhancing farm profitability in intensive pig farming. However, sows’ estrus typically lasts only 48.4 ± 1.0 h, and interference from false estrus further complicates detection. This study proposes an [...] Read more.
Accurate estrus detection and optimal insemination timing are crucial for improving sow productivity and enhancing farm profitability in intensive pig farming. However, sows’ estrus typically lasts only 48.4 ± 1.0 h, and interference from false estrus further complicates detection. This study proposes an enhanced YOLOv8 model, Integrated Convolution and Attention Enhancement (ICAE), for vulvar detection to identify the estrus stages. This model innovatively divides estrus into three phases (pre-estrus, estrus, and post-estrus) and distinguishes five different estrus states, including pseudo-estrus. ICAE-YOLO integrates the Convolution and Attention Fusion Module (CAFM) and Dual Dynamic Token Mixing (DDTM) for improved feature extraction, Dilation-wise Residual (DWR) for expanding the receptive field, and Focaler-Intersection over Union (Focaler-IoU) for boosting the performance across various detection tasks. To validate the model, it was trained and tested on a dataset of 6402 sow estrus images and compared with YOLOv8n, YOLOv5n, YOLOv7tiny, YOLOv9t, YOLOv10n, YOLOv11n, and the Faster R-CNN. The results show that ICAE-YOLO achieves an mAP of 93.4%, an F1-Score of 92.0%, GFLOPs of 8.0, and a model size of 4.97 M, reaching the highest recognition accuracy among the compared models, while maintaining a good balance between model size and performance. This model enables accurate, real-time estrus monitoring in complex, all-weather farming environments, providing a foundation for automated estrus detection in intensive pig farming. Full article
(This article belongs to the Special Issue Animal Health and Welfare Assessment of Pigs)
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