Relevant bibliographies by topics / Pig behavior

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Pig behavior'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "Pig behavior"

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Zhang, Kaifeng, Dan Li, Jiayun Huang, and Yifei Chen. "Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks." Sensors 20, no. 4 (2020): 1085. http://dx.doi.org/10.3390/s20041085.

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The detection of pig behavior helps detect abnormal conditions such as diseases and dangerous movements in a timely and effective manner, which plays an important role in ensuring the health and well-being of pigs. Monitoring pig behavior by staff is time consuming, subjective, and impractical. Therefore, there is an urgent need to implement methods for identifying pig behavior automatically. In recent years, deep learning has been gradually applied to the study of pig behavior recognition. Existing studies judge the behavior of the pig only based on the posture of the pig in a still image frame, without considering the motion information of the behavior. However, optical flow can well reflect the motion information. Thus, this study took image frames and optical flow from videos as two-stream input objects to fully extract the temporal and spatial behavioral characteristics. Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. A standard pig video behavior dataset that included 1000 videos of feeding, lying, walking, scratching and mounting from five kinds of different behavioral actions of pigs under natural conditions was created. The dataset was used to train and test the proposed models, and a series of comparative experiments were conducted. The experimental results showed that the TSN model whose feature extraction network was ResNet101 was able to recognize pig feeding, lying, walking, scratching, and mounting behaviors with a higher average of 98.99%, and the average recognition time of each video was 0.3163 s. The TSN model (ResNet101) is superior to the other models in solving the task of pig behavior recognition.
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Hao, Wangli, Kai Zhang, Li Zhang, et al. "TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network." Sensors 23, no. 11 (2023): 5092. http://dx.doi.org/10.3390/s23115092.

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Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.
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Tu, Shuqin, Jiaying Du, Yun Liang, et al. "Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach." Animals 14, no. 19 (2024): 2828. http://dx.doi.org/10.3390/ani14192828.

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Smart farming technologies to track and analyze pig behaviors in natural environments are critical for monitoring the health status and welfare of pigs. This study aimed to develop a robust multi-object tracking (MOT) approach named YOLOv8 + OC-SORT(V8-Sort) for the automatic monitoring of the different behaviors of group-housed pigs. We addressed common challenges such as variable lighting, occlusion, and clustering between pigs, which often lead to significant errors in long-term behavioral monitoring. Our approach offers a reliable solution for real-time behavior tracking, contributing to improved health and welfare management in smart farming systems. First, the YOLOv8 is employed for the real-time detection and behavior classification of pigs under variable light and occlusion scenes. Second, the OC-SORT is utilized to track each pig to reduce the impact of pigs clustering together and occlusion on tracking. And, when a target is lost during tracking, the OC-SORT can recover the lost trajectory and re-track the target. Finally, to implement the automatic long-time monitoring of behaviors for each pig, we created an automatic behavior analysis algorithm that integrates the behavioral information from detection and the tracking results from OC-SORT. On the one-minute video datasets for pig tracking, the proposed MOT method outperforms JDE, Trackformer, and TransTrack, achieving the highest HOTA, MOTA, and IDF1 scores of 82.0%, 96.3%, and 96.8%, respectively. And, it achieved scores of 69.0% for HOTA, 99.7% for MOTA, and 75.1% for IDF1 on sixty-minute video datasets. In terms of pig behavior analysis, the proposed automatic behavior analysis algorithm can record the duration of four types of behaviors for each pig in each pen based on behavior classification and ID information to represent the pigs’ health status and welfare. These results demonstrate that the proposed method exhibits excellent performance in behavior recognition and tracking, providing technical support for prompt anomaly detection and health status monitoring for pig farming managers.
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Tu, Shuqin, Qiantao Zeng, Yun Liang, et al. "Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method." Agriculture 12, no. 11 (2022): 1907. http://dx.doi.org/10.3390/agriculture12111907.

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Pig behavior recognition and tracking in group-housed livestock are effective aids for health and welfare monitoring in commercial settings. However, due to demanding farm conditions, the targets in the pig videos are heavily occluded and overlapped, and there are illumination changes, which cause error switches of pig identify (ID) in the tracking process and decrease the tracking quality. To solve these problems, this study proposed an improved DeepSORT algorithm for object tracking, which contained three processes. Firstly, two detectors, YOLOX-S and YOLO v5s, were developed to detect pig targets and classify four types of pig behaviors including lying, eating, standing, and other. Then, the improved DeepSORT was developed for pig behavior tracking and reducing error changes of pig ID by improving trajectory processing and data association. Finally, we established the public dataset annotation of group-housed pigs, with 3600 images in a total from 12 videos, which were suitable for pig tracking applications. The advantage of our method includes two aspects. One is that the trajectory processing and data association are improved by aiming at pig-specific scenarios, which are indoor scenes, and the number of pig target objects is stable. This improvement reduces the error switches of pig ID and enhances the stability of the tracking. The other is that the behavior classification information from the detectors is introduced into the tracking algorithm for behavior tracking. In the experiments of pig detection and behavior recognition, the YOLO v5s and YOLOX-S detectors achieved a high precision rate of 99.4% and 98.43%, a recall rate of 99% and 99.23, and a mean average precision (mAP) rate of 99.50% and 99.23%, respectively, with an AP.5:.95 of 89.3% and 87%. In the experiments of pig behavior tracking, the improved DeepSORT algorithm based on YOLOX-S obtained multi-object tracking accuracy (MOTA), ID switches (IDs), and IDF1 of 98.6%,15, and 95.7%, respectively. Compared with DeepSORT, it improved by 1.8% and 6.8% in MOTA and IDF1, respectively, and IDs had a significant decrease, with a decline of 80%. These experiments demonstrate that the improved DeepSORT can achieve pig behavior tracking with stable ID values under commercial conditions and provide scalable technical support for contactless automated pig monitoring.
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He, Zejun, Yunfei Jia, and Yifan Ji. "Analysis of Influencing Factors and Mechanism of Farmers’ Green Production Behaviors in China." International Journal of Environmental Research and Public Health 20, no. 2 (2023): 961. http://dx.doi.org/10.3390/ijerph20020961.

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The green production behavior of pig farmers is the basis for high-quality development in animal husbandry. In order to solve the problem of poor green production behaviors in small- and medium-sized pig farmers, it is necessary to analyze the influencing factors and how they interact with each other. The Rational Peasant Theory and Prospect Theory were used in this paper to analyze the occurrence motivation of the green production behaviors of small- and medium-sized pig farmers. The Logit model and the ISM analysis method were used to test the influencing factors and their mechanisms. This was conducted using data from a study of 747 small- and medium-sized pig farmers in Henan Province. The results show that the green production behaviors of small- and medium-sized pig farmers are motivated by internal expected return, affected by the monitoring pressure from external stakeholders and limited by their own resource capacity; the influencing factors of different green production behaviors are different, and there are more influencing factors of scientific disease control, standardized management and waste recycling than of rational feeding. The following shows how the influencing factors on pig farmers’ green production behavior interact with one another: level of education → external pressure, farming conditions and operating characteristics → cognition of return → green production behavior (i.e., cognition of return is the direct factor; external pressure, farming conditions and operating characteristics are indirect factors; and level of education is the underlying factor). Some measures should be implemented to promote green production behaviors, such as the continuation of the support for green production, the strengthening of supervision and publicity, the increasing of investment in technology and equipment, and the improving of the green production literacy of farmers. In conclusion, this paper deepens the understanding of the mechanism of green production behaviors of small- and medium-sized pig farmers, and provides the theoretical basis and concrete measures for the government and for pig farmers.
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Mluba, Hassan Seif, Othmane Atif, Jonguk Lee, Daihee Park, and Yongwha Chung. "Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring." Sensors 24, no. 7 (2024): 2185. http://dx.doi.org/10.3390/s24072185.

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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.
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Zhang, Ye, Xuesong Yang, Fang Sun, et al. "Emotional “Contagion” in Piglets after Sensory Avoidance of Rewarding and Punishing Treatment." Animals 14, no. 7 (2024): 1110. http://dx.doi.org/10.3390/ani14071110.

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In the pig farming industry, it is recommended to avoid groups when treating individuals to reduce adverse reactions in the group. However, can this eliminate the adverse effects effectively? Piglets were assigned to the Rewarding Group (RG), the Punishing Group (PG), and the Paired Control Group (PCG). There were six replicates in each group, with two paired piglets per replicate. One piglet of the RG and PG was randomly selected as the Treated pig (TP), treated with food rewards or electric shock, and the other as the Naive pig (NP). The NPs in the RG and PG were unaware of the treatment process, and piglets in the PCG were not treated. The behavior and heart rate changes of all piglets were recorded. Compared to the RG, the NPs in the PG showed longer proximity but less contact behavior, and the TPs in the PG showed more freezing behavior. The percentage change in heart rate of the NPs was synchronized with the TPs. This shows that after sensory avoidance, the untreated pigs could also feel the emotions of their peers and their emotional state was affected by their peers, and the negative emotions in the pigs lasted longer than the positive emotions. The avoidance process does not prevent the transfer of negative emotions to peers via emotional contagion from the stimulated pig.
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Wang, Lei, and Zi-Gang Yang. "Analysis on Risk Avoidance Behaviors and Influential Factors of Epidemic Disease among Pig Farmers." African and Asian Studies 21, no. 1-2 (2022): 90–111. http://dx.doi.org/10.1163/15692108-12341531.

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Abstract The initial outbreak, high mortality and wide range of influence of African swine fever in China have an impact on the stable development of China’s pig market price and industry. Choosing the correct risk aversion behavior of pig farmers is the only way to promote the development of modern pig breeding industry and protect people’s livelihood. Studying the epidemic risk aversion behavior of pig farmers and its influencing factors is conducive to enhance the ability of farmers to resist risks and improve the efficiency and production level of pig breeding. Taking pig farmers in three northeastern provinces as the survey object, this paper analyzes the willingness of pig farmers to take epidemic prevention and control measures by using multivariate ordered logistic and multiple logistic models, and discusses the main factors affecting the epidemic risk avoidance behavior of pig farmers. The study found that factors such as pig breeding scale, understanding of pig insurance and epidemic prevention and control knowledge, risk preference and so on can significantly affect the willingness of pig farmers to take epidemic prevention and control measures. Education, specialization and breeding prospect judgment can significantly affect the choice of epidemic risk aversion behavior of pig farmers.
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Andersen, Heidi Mai-Lis, Anne Grete Kongsted, and Malene Jakobsen. "Pig elimination behavior—A review." Applied Animal Behaviour Science 222 (January 2020): 104888. http://dx.doi.org/10.1016/j.applanim.2019.104888.

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Winkel, Carolin, Marie von Meyer-Höfer, and Heinke Heise. "Understanding German Pig Farmers’ Intentions to Design and Construct Pig Housing for the Improvement of Animal Welfare." Animals 10, no. 10 (2020): 1760. http://dx.doi.org/10.3390/ani10101760.

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Improving farm animal welfare requires modifications to the behavior of many stakeholders. Investments in more animal-friendly barns to improve animal welfare have already been made by some farmers. However, more farmers must be persuaded to modernize their barns. The marketing of animal-friendly products is the responsibility of retailers, and consumers have to purchase these products. Currently, little is known about what (and how) underlying psychological factors influence a farmer’s intention to construct pig housing to improve farm animal welfare. Pig farmers in Germany were questioned via an online questionnaire in May 2020 (n = 424). Based on the Theory of Planned Behavior (TPB), partial least squares path modeling was used. The constructs: attitude, subjective norm, direct and indirect experience associated with the construction of pig housing substantially influenced the farmers’ behaviors. As expected, the impact of perceived behavioral control on intention was negative but was also very low and only slightly significant. Contrary to expectations, the perceived behavioral control had no significant influence on farmers’ behaviors. Pig farmers who have already rebuilt their pigs’ housing should be motivated to share their experiences to influence their colleagues’ intentions to construct. Our results will encourage policy makers to consider the important role of the different psychological and intrinsic factors influencing pig farmers. Thus, the sustainability of pig farming can be improved by giving politicians a better understanding of farmers’ behaviors.
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Dissertations / Theses on the topic "Pig behavior"

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Alexander, Vincent Rasahd. "The Role of Corticotropin-Releasing Factor in the Behavior and Proinflammatory Activity of Separated Guinea Pig Pups." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1345847290.

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Wenner, Benjamin A. "Effect of Bio-Mos® and outdoor access housing on pig growth, feed efficiency, health, behavior and carcass ultrasound traits." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345574215.

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Buzzard, Brandi L. "Evaluation of hypobaric hypoxia as a low stress alternative to carbon dioxide euthanasia for use with nursery piglets." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14867.

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Master of Science<br>Department of Animal Sciences and Industry<br>Timothy G. Rozell<br>Malnourished piglets that suffer from periweaning failure to thrive syndrome (PFTS) may show no signs of respiratory or enteric diseases but may have decreased feed intake and become debilitated after weaning. Euthanasia is a necessary component of swine production as it is sometimes the only option to alleviate suffering of piglets that are born with congenital defects or suffer from PFTS. Fifty-eight nursery-aged piglets were utilized in two experiments to evaluate blood parameter differences between healthy and unthrifty piglets and to compare euthanasia methods. Piglets were categorized into two health groups: healthy or unthrifty. During selection, blood was collected for analysis of blood parameters. Piglets were euthanized 24-32 hours after initial blood sampling and a second sample was collected for comparison. After euthanasia, piglets were necropsied for evaluation of euthanasia on pulmonary lesions. No significant difference in number of pulmonary lesions was found between health groups (P = 0.88). Healthy piglets had higher concentrations of glucose, ionized calcium and sodium, and greater pCO₂ than unthrifty piglets (P ≤ 0.05). Unthrifty piglets showed higher concentrations of hemoglobin and hematocrit (P = 0.0002) than healthy piglets. Piglets were assigned to one of two euthanasia methods to compare electrophysiological and behavioral parameters of hypobaric hypoxia and carbon dioxide gas. Two piglets at a time were euthanized for each method. One animal in the pair was fitted with electrocardiogram and electroencephalogram monitoring devices during euthanasia. Behavioral parameters were also recorded. The average treatment times were 27.4 ± 6.7 minutes for HH and 13.8 ± 5.1 minutes for CO2. Piglets euthanized via CO2 reached an isoelectric state faster than piglets euthanized via HH (P = 0.009). Behavioral observations revealed gasping in 100% of CO2 euthanized piglets during the first five minutes of treatment and only 28.6% of HH euthanized piglets during the same period. During HH, 57.1% of piglets became ataxic in the first five minutes while 76.9% of CO2¬ piglets became ataxic during the same period. Results of this trial indicate that HH may be a lower stress alternative to CO₂ as it causes fewer incidences of aversive behaviors in early stages of treatment.
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Schreibeis, Amanda Danielle. "Cross Sensitization of Depressive-Like Behavior through Two Depression Related Paradigms: Maternal Separation and Its Effect on the Forced Swim Test In the Guinea Pig." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1484638271891606.

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Dazey, Jennifer. "Influence of Daily Electrical Stimulation of Periaqueductal Grey on Vocalization and Depressive-like Behavior during Separation in Guinea Pigs." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1355011112.

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Andresen, Niels. "The foraging pig : resource utilisation, interaction, performance and behaviour of pigs in cropping systems /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2000. http://epsilon.slu.se/avh/2000/91-576-5764-5.pdf.

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Sarubbi, Juliana. "Bem-estar dos animais e uso racional de energia eletrica em sistemas de aquecimento para leitões desmamados." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/256978.

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Orientadores: Luiz Antonio Rossi, Daniella Jorge de Moura<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola<br>Made available in DSpace on 2018-08-13T09:10:46Z (GMT). No. of bitstreams: 1 Sarubbi_Juliana_D.pdf: 16417417 bytes, checksum: 878d474575ba837c886a9dcf562c9520 (MD5) Previous issue date: 2009<br>Resumo: Para que os mecanismos que visam promover condições de bem-estar aos suínos possam ser utilizados, é necessário que se desenvolvam pesquisas para reduzir seus custos e aumentar sua praticidade, possibilitando a sustentabilidade do processo. O aquecimento de leitões em fase de creche e maternidade pode ser trabalhado no sentido de viabilizar a eficiência do uso da energia elétrica sem, no entanto, prejudicar o bem-estar dos suínos. Por meio deste trabalho, objetivou-se comparar três tecnologias de aquecimento para leitões a partir de três semanas de idade, em fase de creche, com relação ao uso de energia elétrica, condições de bem-estar e desempenho dos animais. Estudaram-se três tecnologias de aquecimento: TR - resistências elétricas suspensas, TP - piso aquecido e TA - aquecimento por convecção. O sistema de aquecimento por pisos foi melhor sob o aspecto de uso de energia elétrica. O sistema de aquecimento por resistências elétricas foi o melhor com relação ao atendimento das necessidades térmicas dos animais e o sistema de aquecimento por convecção foi o melhor considerando a resposta produtiva, comportamental e fisiológica dos animais. De maneira geral, atribuindo-se pesos aos aspectos analisados (implantação e operação, uso de energia elétrica, respostas do ambiente e respostas dos animais), o sistema de aquecimento por resistências suspensas foi o melhor. Porém, sugerem-se testes para aprimoramento das tecnologias com a utilização de sistemas híbridos.<br>Abstract: In order to the mechanisms which promote the welfare in pig production can be used, it is necessary to develop research works to lower their costs and increase their practicality, enabling the sustainability of the process. Heating of piglets in nursery and maternity phases may be improved to raise energy savings, without undermining the welfare of animals. This research work aimed to compare three of heating technologies for piglets of three weeks aged, nursery phase, in relation to electrical energy use, welfare conditions and animal performance. The studied heating technologies were: TR - suspended electrical resistors, TP - heated floor and TA - heating by convection. The heated floor system was better under the aspect of electrical energy use. The electrical resistance heating system was the best regarding to the animals environmental needs, and convection heating system was the best considering the productive, behavior and physiological animal responses. Considering installation and operation, electrical energy use, environmental and animal welfare aspects, the heating system by suspended resistors was the best. However, more tests are suggested in order to improve TA and hybrid systems use.<br>Doutorado<br>Construções Rurais e Ambiencia<br>Doutor em Engenharia Agrícola
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McIntyre, Jayne. "Tail biting in pigs." Thesis, University of Newcastle Upon Tyne, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275520.

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Durrell, Julie Louise. "Improving the welfare of group housed sows." Thesis, Queen's University Belfast, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343048.

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Appleyard, Stephen John. "The maternal behaviour of the pig in the farrowing crate." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/26132.

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Piglet-weaning mortality is an enduring problem for the pig industry but is likely to be strongly influenced by the maternal behaviour and overall rearing ability, of the sow. Farrowing crates are the most common method of housing lactating sows in this and many other countries. However, farrowing crates pose a welfare dilemma: they are aversive for sows prior to parturition but limit the number of piglet crushing mortalities. One solution to this dilemma would be to breed sows better adapted to the crate system. To this end, a longitudinal breed study was set up that observed the behaviour of individual sows over their first two parities, in farrowing crates both with and without straw. Consistent behavioural traits are identified, most notably sow-piglet interactions and posture changing, and genetic influences are inferred from breed differences. The mean number of still-births and crushing mortalities per litter are modelled for non-behavioural influences. Season and litter size feature strongly for both these models. The behavioural characteristics of crushing and savaging sows are described and discussed in relation to functional explanations and possible causal mechanisms. An argument is developed that identifies piglet-savaging behaviour with being hypersensitive to restriction during pre-farrowing nest-building, resulting in physiological changes influencing the progress of parturition. Sows possess behavioural mechanisms to reduce the risk of crushing, which are modified by the farrowing crate in both positive and negative ways.
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Books on the topic "Pig behavior"

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Katz, Andrea. A big, big pig. Macmillan/McGraw-Hill, 2002.

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Jones, Sally Lloyd. Being a pig is nice: A child's-eye view of manners. Schwartz & Wade Books, 2009.

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Scarry, Richard. Richard Scarry's Pig Will: 2-in-1 turn-around books. Random House, 1990.

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Gliori, Debi. A present for big pig. Walker, 1994.

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Gliori, Debi. A present for Big Pig. Candlewick Press, 1995.

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Malam, John. The very sleepy pig. Pippbrook, 2013.

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Malam, John. The very sleepy pig. Mustard, 1999.

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Jenkins, Steve. How to swallow a pig: Step-by-step advice from the animal kingdom. Houghton Mifflin Harcourt Publishing Company, 2015.

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Malam, John. Tan shui de zhu = The very sleepy pig. Shang ren wen hua shi ye shi ye gu fen you xian gong si, 2000.

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Canevari, Green Anne, ed. Becoming best friends with your hamster, guinea pig, or rabbit. Millbrook Press, 1997.

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Book chapters on the topic "Pig behavior"

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Singh, Subir. "Pig Behavior and Welfare." In Livestock Diseases and Management. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4665-1_6.

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Gonyou, Harold W. "Pig Behavior and Biomedical Research." In Advances in Swine in Biomedical Research. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-5885-9_5.

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Hao, Wangli, Hao Shu, Xinyuan Hu, Meng Han, and Fuzhong Li. "ST_TransNeXt: A Novel Pig Behavior Recognition Model." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2885-8_23.

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Signoret, J. P. "Sexualisation of Behavior During Development in the Pig." In The Development of Sex Differences and Similarities in Behavior. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1709-8_16.

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Guiry, Eric, Céline Dupont-Hébert, and Vaughan Grimes. "Reassessing the Abandonment of Pig Husbandry in Post-Viking Iceland: An Isotopic Approach." In Exploring Human Behavior Through Isotope Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32268-6_9.

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den Ouden, Marjan, Ruud B. M. Huirne, and Aalt A. Dijkhuizen. "The Impact of Changing Pig Welfare Preferences on the Economics of Pork Production-Marketing Chains." In Agricultural Marketing and Consumer Behavior in a Changing World. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6273-3_4.

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Łagan, Sylwia, and Aneta Liber-Kneć. "The Use of Hyperelastic Material Models for Estimation of Pig Aorta Biomechanical Behavior." In Innovations in Biomedical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52180-6_8.

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Leng, Bi-Bin, Qiao Hu, Wen-Bo Zhang, and Jia-Ling Liu. "A study on farmers’ behavior game evolution of the scale pig breeding ecological energy system." In Advances in Energy Science and Equipment Engineering II. CRC Press, 2017. http://dx.doi.org/10.1201/9781315116167-20.

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Benjamin, Madonna, and Steven Yik. "Precision livestock farming and technology in swine welfare." In Improving animal welfare: a practical approach, 3rd ed. CABI, 2021. http://dx.doi.org/10.1079/9781789245219.0376.

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Abstract This chapter describes the application of technology in pig farming, including understand how computer systems learn, the different types of sensors to monitor health and behaviour, the basic operation of precision livestock farming (PLF) and future possibilities.
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Broom, Donald M. "Spacing behaviour." In Broom and Fraser’s domestic animal behaviour and welfare, 6th ed. CABI, 2021. http://dx.doi.org/10.1079/9781789249835.0013.

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Abstract This chapter discusses the effects of space on the health and behaviour of animals highlighting the following aspects: types of spacing (home range, territory, and individual space); spatial features; association versus avoidance; spatial needs; and crowding. The spacing behaviour for dogs, cats, cattle, horses, sheep, pigs and poultry are presented.
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Conference papers on the topic "Pig behavior"

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Shi, Linyue, Hong Ying, Peng Yang, and Chen Yang. "Pig Behavior Detection Model Based on Improved RT-DETR." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696250.

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Ranjan, Pranjal, Sanjana Bharadwaj, Yingqi Pei, et al. "Improved Pig Behavior Analysis Through Strategic Data Preprocessing Framework in Machine Learning." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00141.

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Liu, Zhitao, Jiaji Liu, Yumei Zhou, and Zhongsheng Wang. "A Method for Pig Behavior Target Detection Based on the Lightweight RTMDet Model." In 2024 International Symposium on Digital Home (ISDH). IEEE, 2024. https://doi.org/10.1109/isdh64927.2024.00011.

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Zhu, Juanhua, Yuan Zhang, and Ang Wu. "Research on pig behavior detection technology." In International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), edited by Jiandong Hu. SPIE, 2022. http://dx.doi.org/10.1117/12.2657288.

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MARTIN, Dorin. "PORK MEAT MARKET BEHAVIOR SINCE 2018 UNTIL NOW IN ROMANIA." In Competitiveness of Agro-Food and Environmental Economy. Editura ASE, 2022. http://dx.doi.org/10.24818/cafee/2020/9/18.

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This period was chosen due to the occurrence during this time interval of some phenomena that disturbed a pork market, at the same time and a challenge for the pig farmers forced to adapt to the new situation. In the context of the appearance in Romania and in other EU countries of the African Swine Fever epidemic which led to the drastic reduction of the pig herds and subsequently of the COVID 19 epidemic the pork market suffered several fluctuations during this period. Due to the specific situation of Romania with a large number of pigs raised in households, the epidemic has a significant impact on the number of pigs, who drastically reduced during this period. For this purpose, we performed for the period 2018 - 2020 an analysis of pork prices, pig herds, imports and exports and pork market behaviour for this period. Main objective is to provide an overview of the Romanian economic situation in terms of livestock, in order to highlight the importance of local farms and, therefore, its major impact on the economy.
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坂口, 聖弥, 根浩 李, 光汰 岡部, and 綾一 相澤. "Understanding and Observing Pig Behavior with BLE." In 産業応用工学会全国大会2022. 産業応用工学会, 2022. http://dx.doi.org/10.12792/iiae2022.022.

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Parish, A., W. Chen, and T. Weerasooriya. "High strain-rate tensile behavior of pig bones." In DYMAT 2009 - 9th International Conferences on the Mechanical and Physical Behaviour of Materials under Dynamic Loading. EDP Sciences, 2009. http://dx.doi.org/10.1051/dymat/2009128.

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Sakaguchi, Seiya, Geunho Lee, Kouta Okabe, and Ryoichi Aizawa. "Understanding and Observing Pig Behavior with BLE Tags." In The 9th International Conference on Intelligent Systems and Image Processing 2022. The Institute of Industrial Applications Engineers, 2022. http://dx.doi.org/10.12792/icisip2022.028.

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Zhang, Zongtao, Sijie Xu, Shengan Lu, and Ling Chen. "Advancing Precision Pig Behavior Recognition through Real-Time Detection Transformer." In 2024 4th International Conference on Neural Networks, Information and Communication (NNICE). IEEE, 2024. http://dx.doi.org/10.1109/nnice61279.2024.10498479.

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Bergamini, Luca, Stefano Pini, Alessandro Simoni, et al. "Extracting Accurate Long-term Behavior Changes from a Large Pig Dataset." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010288405240533.

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Reports on the topic "Pig behavior"

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Weimar, Shawna, Anna K. Johnson, Kenneth J. Stalder, Locke A. Karriker, and Thomas Fangman. Pig Age and Approachability Behavior to a Human Observer. Iowa State University, 2015. http://dx.doi.org/10.31274/ans_air-180814-1327.

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Johnson, Anna K., Thomas Hoff, Larry J. Sadler, and Chad H. Stahl. Dietary Inclusion of Colicin E.: Effect on Pig Behavior. Iowa State University, 2008. http://dx.doi.org/10.31274/ans_air-180814-769.

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Holmes, Ashley. Pig behavior related to pen-based oral fluid sample collection. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-1125.

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Rujiravanit, Ratana. Preparation and characterization of hydrogel from chitin derivative and silk fibroin. Thailand Research Fund, 2003. https://doi.org/10.58837/chula.res.2003.80.

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Natural polymer blend films composed of chitosan and silk fibroin were prepared by solution casting technique with various ratios of chitosan to silk fibroin, using glutaraldehyde as crosslinking agent. The effects of the ratio of chitosan to silk fibroin and crosslinking agent on mechanical properties, swelling behavior and drug releasing property of the blend films were studied. For the swelling behavior, the blend films exhibited a dramatic change in the degree of swelling when immersed in acidic solutions. The blend film with 80% chitosan content had the maximum degree of swelling. It appeared that crosslinking occurred in the blend films helped the films retain their three dimensional structure. In addition, FTIR spectra of the films showed evidence of hydrogen bonding interaction between chitosan and silk fibroin. Drug release characteristics of the blend films with various blend compositions were investigated using theophylline, diclofenac sodium, amoxicillin trihydrate and salicylic acid as model drugs. It was found that the blend film with 80% chitosan content showed the maximum amount of drug release at pH 2.0 for all types of drugs. The maximum amount of salicylic acid, theophylline, diclogenac sodium and amoxicillin release from blend films with 80% chitosan content at pH 2.0 were 92.7%, 81.1%, 76.6%, and 37.2%, respectively. Drug release properties of the films with various blend compositions were also investigated using a modified Franz Diffusion cell and pig skin was used as material representing human skin.
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Johnson, Anna K., Roy A. Edler, J. Tyler Holck, Brad V. Lawrence, and Robert G. Baker. Drinking Behavior of Nursery Pigs for Oral Vaccine Administration. Iowa State University, 2007. http://dx.doi.org/10.31274/ans_air-180814-626.

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Johnson, Anna K., Jennifer Kline, Rachel Witte, et al. Differences in Nursery Pigs’ Behavior on the Day of Vaccination. Iowa State University, 2011. http://dx.doi.org/10.31274/ans_air-180814-175.

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Young, Jennifer, Weiguo Cai, Daniel S. Nettleton, and Jack C. M. Dekkers. Feeding Behavior of Yorkshire Pigs Selected for Residual Feed Intake. Iowa State University, 2009. http://dx.doi.org/10.31274/ans_air-180814-850.

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Sadler, Larry J., Jill R. Garvey, Tony J. Uhlenkamp, et al. Drinker to Nursery Pigs Ratio: Effects on Drinking Behavior and Performance. Iowa State University, 2008. http://dx.doi.org/10.31274/ans_air-180814-704.

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Bowden, Josh M., Locke A. Karriker, Kenneth J. Stalder, and Anna K. Johnson. Scan Sampling Techniques for Behavioral Validation in Nursery Pigs. Iowa State University, 2008. http://dx.doi.org/10.31274/ans_air-180814-852.

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Johnson, Anna K., Thomas J. Fangman, Roy Edler, David A. Baumert, and Paul DuBois. Willingness to Approach Behavior and Feed Disappearance of Weaned Pigs Following Vaccination with Mycoplasma hyopneumoniae. Iowa State University, 2010. http://dx.doi.org/10.31274/ans_air-180814-670.

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