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Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 15212

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Special Issue Editor

Prof. Dr. Helen Louton

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Guest Editor

Animal Health and Animal Welfare, Faculty of Agricultural and Environmental Sciences, University of Rostock, 18059 Rostock, Germany
Interests: welfare; health; transport; slaughter; livestock; broiler; layer; pullets

Special Issue Information

Dear Colleagues,

When assessing animal welfare, health, the ability to perform species-specific behaviors, and emotional aspects are the main considerations. Monitoring the behavior, the affective states and the health of livestock is essential to ensure the welfare of animals. Parameters used to measure animals’ wellbeing should be measurable, objective, farm independent, scientifically based, meaningful, representative, and reproducible. An ability to link them to thresholds and possibly even record them automatically is also desirable.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to): methods of monitoring behavior; affective states or health of livestock; assessment of welfare at housing, transport or slaughter; the validation of measured parameters; and the influence of factors on health, affective states, and behavior of livestock.

I look forward to receiving your contributions.

Prof. Dr. Helen Louton
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

animal welfare
behavior
health
affective states
indicator
livestock
housing
transport
slaughter

Published Papers (7 papers)

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Research

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15 pages, 10305 KiB

Open AccessArticle

Effects of Social Facilitation and Introduction Methods for Cattle on Virtual Fence Adaptation

by Pernille Arent Simonsen, Niels Søborg Husted, Magnus Clausen, Amalie-Maria Spens, Rasmus Majland Dyrholm, Ida Fabricius Thaysen, Magnus Fjord Aaser, Søren Krabbe Staahltoft, Dan Bruhn, Aage Kristian Olsen Alstrup, Christian Sonne and Cino Pertoldi

Animals 2024, 14(10), 1456; https://doi.org/10.3390/ani14101456 - 14 May 2024

Viewed by 185

Abstract

Agricultural industries rely on physical fences to manage livestock. However, these present practical, financial, and ecological challenges, which may be solved using virtual fencing. This study aimed to identify how experienced cattle through social facilitation and the introduction method influence inexperienced cattle. Based on three stocks held in Fanø, Denmark, containing 12, 17 and 13 Angus (Bos taurus), we examined the virtual fence learning in three case studies using one gradual introduction with zero experienced cattle (A) and two different instant introductions with one (B) and ten (C) experienced cattle. Gradual introduction had the virtual fence moved 20 m every other day for eleven days, and in the two instant introductions, the physical fence was removed in one day. Warnings and impulses were recorded during an 11-day learning period and a 26-day post-learning period, using the impulses per warning to quantify if the cattle adapted. Case studies A and B showed a significant reduction in the warnings and impulses, but only A showed a significant reduction in the impulses per warning when comparing the learning period to the post-learning period. Due to the non-standardised experiments, it was not possible to conclude if the number of experienced cattle or the introduction method had an effect on the results. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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15 pages, 27194 KiB

Open AccessArticle

Detection of Pig Movement and Aggression Using Deep Learning Approaches

by Jiacheng Wei, Xi Tang, Jinxiu Liu and Zhiyan Zhang

Animals 2023, 13(19), 3074; https://doi.org/10.3390/ani13193074 - 30 Sep 2023

Cited by 1 | Viewed by 1484

Abstract

Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m² space; they were bred in stable social groups and a video was set up to record the whole day’s activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig’s identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R² of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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14 pages, 2856 KiB

Open AccessArticle

Dairy Cow Mastitis Detection by Thermal Infrared Images Based on CLE-UNet

by Qian Zhang, Ying Yang, Gang Liu, Yuanlin Ning and Jianquan Li

Animals 2023, 13(13), 2211; https://doi.org/10.3390/ani13132211 - 5 Jul 2023

Cited by 4 | Viewed by 1798

Abstract

Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network’s output to be closer to the position of the real label during the training process. Finally, we used a cow’s eye ellipse fitting operation based on the similarity between the shape of the cow’s eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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20 pages, 3063 KiB

Open AccessArticle

A Pilot Study on Behavioural and Physiological Indicators of Emotions in Donkeys

by Samanta Seganfreddo, Diletta Fornasiero, Marta De Santis, Franco Mutinelli, Simona Normando and Laura Contalbrigo

Animals 2023, 13(9), 1466; https://doi.org/10.3390/ani13091466 - 25 Apr 2023

Cited by 1 | Viewed by 1869

Abstract

Recognizing animal emotions is critical to their welfare and can lead to a better relationship with humans and the environment, especially in a widespread species like the donkey, which is often prone to welfare issues. This study aims to assess the emotional response of donkeys through an operant conditioning task with two presumed different emotional contents. Specifically, a within-subject design including positive and negative conditions was conducted, collecting behavioural and physiological (heart rate variability and HRV) parameters. Facial expressions, postures, and movements were analysed by principal component analysis and behavioural diversity indexes (frequencies, activity budgets, richness, Shannon and Gini-Simpson). During the positive condition, both ears were held high and sideways (left: r = −0.793, p < 0.0001; right: r = −0.585, p = 0.011), while the ears were frontally erected (left: r = 0.924, p < 0.0001; right: r = 0.946, p < 0.0001) during the negative one. The latter was also associated with an increased tendency to walk (r = 0.709, p = 0.001), walk away (r = 0.578, p = 0.012), more frequent changes in the body position (V_{Body position} = 0, p = 0.022), and greater behavioural complexity (V_{Gini-Simpson Index} = 4, p = 0.027). As for HRV analysis, the root mean square of successive beat-to-beat differences (rMSSD) was significantly lower after the negative condition. These non-invasive parameters could be considered as possible indicators of donkeys’ emotional state. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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31 pages, 1070 KiB

Open AccessArticle

Non-Invasive Methods for Assessing the Welfare of Farmed White-Leg Shrimp (Penaeus vannamei)

by Ana Silvia Pedrazzani, Nathieli Cozer, Murilo Henrique Quintiliano, Camila Prestes dos Santos Tavares, Ubiratã de Assis Teixeira da Silva and Antonio Ostrensky

Animals 2023, 13(5), 807; https://doi.org/10.3390/ani13050807 - 23 Feb 2023

Cited by 5 | Viewed by 5120

Abstract

Gradually, concern for the welfare of aquatic invertebrates produced on a commercial/industrial scale is crossing the boundaries of science and becoming a demand of other societal actors. The objective of this paper is to propose protocols for assessing the Penaeus vannamei welfare during the stages of reproduction, larval rearing, transport, and growing-out in earthen ponds and to discuss, based on a literature review, the processes and perspectives associated with the development and application of on-farm shrimp welfare protocols. Protocols were developed based on four of the five domains of animal welfare: nutrition, environment, health, and behaviour. The indicators related to the psychology domain were not considered a separate category, and the other proposed indicators indirectly assessed this domain. For each indicator, the corresponding reference values were defined based on literature and field experience, apart from the three possible scores related to animal experience on a continuum from positive (score 1) to very negative (score 3). It is very likely that non-invasive methods for measuring the farmed shrimp welfare, such as those proposed here, will become a standard tool for farms and laboratories and that it will become increasingly challenging to produce shrimp without considering their welfare throughout the production cycle. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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13 pages, 1218 KiB

Open AccessArticle

Limitations of Spatial Judgment Bias Test Application in Horses (Equus ferus caballus)

by Giovanna Marliani, Irene Vannucchi, Irini Kiumurgis and Pier Attilio Accorsi

Animals 2022, 12(21), 3014; https://doi.org/10.3390/ani12213014 - 3 Nov 2022

Cited by 1 | Viewed by 1411

Abstract

Affective states are of increasing interest in the assessment of animal welfare. This research aimed to evaluate the possible limitations in the application of a spatial judgment bias test (JBT) in horses, considering the influence of stress level, personality traits, and the possible bias due to the test structure itself. The distinction between two positions, one rewarded (Positive) and the other not (Negative), was learned by 10 horses and 4 ponies,. Then, the latency to reach three unrewarded ambiguous positions (Near Positive, Middle, Near Negative) was measured. Furthermore, the validated Equine Behavior Assessment and Research Questionnaire (E-BARQ) was employed to assess personality traits. Fecal and hair cortisol levels were measured through radioimmunoassay (RIA), and the frequency of behavioral stress indicators was recorded. Results showed that horses that had the rewarded position (Positive) on the right approached Near Negative and Middle faster than those that had Positive on the left. Certain personality traits influenced the latency to reach Middle and Near Positive, but chronic stress did not seem to affect horses’ judgment bias. This preliminary study highlighted several limitations in the employment of spatial JBT for the assessment of affective state in horses and that personality traits can partially influence the cognitive process. Further research is needed to refine the use of this test in horses, considering the peculiarities both of species and of individuals. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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Review

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31 pages, 1121 KiB

Open AccessReview

Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review

by Annika M. Voogt, Remco S. Schrijver, Mine Temürhan, Johan H. Bongers and Dick T. H. M. Sijm

Animals 2023, 13(19), 3028; https://doi.org/10.3390/ani13193028 - 26 Sep 2023

Viewed by 2321

Abstract

Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention. Full article

(This article belongs to the Special Issue Monitoring of Behavior, Affective States, and Health to Identify Welfare Concerns of Farm Animals)

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