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Bertelsen M, Steinerova K, Jensen MB. The effects of part-time dam-contact and stepwise weaning and separation on the voluntary human approach behaviour of dairy calves. Appl Anim Behav Sci 2023. [DOI: 10.1016/j.applanim.2023.105871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Massenet M, Anikin A, Pisanski K, Reynaud K, Mathevon N, Reby D. Nonlinear vocal phenomena affect human perceptions of distress, size and dominance in puppy whines. Proc Biol Sci 2022; 289:20220429. [PMID: 35473375 PMCID: PMC9043735 DOI: 10.1098/rspb.2022.0429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
While nonlinear phenomena (NLP) are widely reported in animal vocalizations, often causing perceptual harshness and roughness, their communicative function remains debated. Several hypotheses have been put forward: attention-grabbing, communication of distress, exaggeration of body size and dominance. Here, we use state-of-the-art sound synthesis to investigate how NLP affect the perception of puppy whines by human listeners. Listeners assessed the distress, size or dominance conveyed by synthetic puppy whines with manipulated NLP, including frequency jumps and varying proportions of subharmonics, sidebands and deterministic chaos. We found that the presence of chaos increased the puppy's perceived level of distress and that this effect held across a range of representative fundamental frequency (fo) levels. Adding sidebands and subharmonics also increased perceived distress among listeners who have extensive caregiving experience with pre-weaned puppies (e.g. breeders, veterinarians). Finally, we found that whines with added chaos, subharmonics or sidebands were associated with larger and more dominant puppies, although these biases were attenuated in experienced caregivers. Together, our results show that nonlinear phenomena in puppy whines can convey rich information to human listeners and therefore may be crucial for offspring survival during breeding of a domesticated species.
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Affiliation(s)
- Mathilde Massenet
- Equipe de Neuro-Ethologie Sensorielle, ENES/CRNL, University of Saint-Etienne, CNRS, Inserm, Saint-Etienne, France
| | - Andrey Anikin
- Equipe de Neuro-Ethologie Sensorielle, ENES/CRNL, University of Saint-Etienne, CNRS, Inserm, Saint-Etienne, France.,Division of Cognitive Science, University of Lund, 22100 Lund, Sweden
| | - Katarzyna Pisanski
- Equipe de Neuro-Ethologie Sensorielle, ENES/CRNL, University of Saint-Etienne, CNRS, Inserm, Saint-Etienne, France.,CNRS, French National Centre for Scientific Research, Laboratoire de Dynamique du Langage, University of Lyon 2, 69007 Lyon, France
| | - Karine Reynaud
- École Nationale Vétérinaire d'Alfort, EnvA, 94700 Maisons-Alfort, France.,Physiologie de la Reproduction et des Comportements, CNRS, IFCE, INRAE, University of Tours, PRC, Nouzilly, France
| | - Nicolas Mathevon
- Equipe de Neuro-Ethologie Sensorielle, ENES/CRNL, University of Saint-Etienne, CNRS, Inserm, Saint-Etienne, France.,Institut universitaire de France, Paris, France
| | - David Reby
- Equipe de Neuro-Ethologie Sensorielle, ENES/CRNL, University of Saint-Etienne, CNRS, Inserm, Saint-Etienne, France.,Institut universitaire de France, Paris, France
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Schnaider MA, Heidemann MS, Silva AHP, Taconeli CA, Molento CFM. Vocalization and other behaviors as indicators of emotional valence: The case of cow-calf separation and reunion in beef cattle. J Vet Behav 2022. [DOI: 10.1016/j.jveb.2021.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Pandeya YR, Bhattarai B, Afzaal U, Kim JB, Lee J. A monophonic cow sound annotation tool using a semi-automatic method on audio/video data. Livest Sci 2022. [DOI: 10.1016/j.livsci.2021.104811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Abstract
After approximately 9 months, cows give birth to one calf. They usually try to separate from the herd if kept outdoors, but this depends on the possibilities to hide. When the calf is born, the cow immediately stands up and starts licking the calf, and this is most intense for the first hour. During this time, the cow is very protective of her calf and may attack anyone trying to come close to it, especially if the cow is not so used to human handling. The calf tries to stand up and falls in the beginning, but it usually stands about half an hour later. Once standing, the calf searches for the udder, and after approximately 2 h, the calf is suckling the colostrum from the cow's teats. The calf is born without an immune defense, and it, therefore, needs to suckle enough colostrum from the cow to get protection against infections. If the calf does not succeed in suckling within 4-6 h after birth, the farmer must milk the cow and give the calf at least 2-4 l of colostrum in a teat bottle. If the farmer has frozen colostrum of good quality in the freezer, and it is either difficult to milk the cow or her colostrum is of too low quality, the frozen colostrum can be warmed up and given to the calf. Beef calves are usually allowed to stay with their mother until they are 6-8 months old, when they are separated from the cow and then weaned. Dairy calves are usually separated from their mother immediately or within a few days after birth and raised by artificial milk feeding. In the beginning or during the whole milk period (6-12 weeks), they may be kept individually indoors in crates or outdoors in hutches. After weaning, they are usually group housed.
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Affiliation(s)
- Lena Lidfors
- Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Skara, Sweden.
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Jung DH, Kim NY, Moon SH, Jhin C, Kim HJ, Yang JS, Kim HS, Lee TS, Lee JY, Park SH. Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering. Animals (Basel) 2021; 11:ani11020357. [PMID: 33535390 PMCID: PMC7911430 DOI: 10.3390/ani11020357] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 01/24/2021] [Accepted: 01/27/2021] [Indexed: 11/16/2022] Open
Abstract
The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.
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Affiliation(s)
- Dae-Hyun Jung
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
| | - Na Yeon Kim
- Department of Bio-Convergence Science, College of Biomedical and Health Science, Konkuk University, Chungju 27478, Korea; (N.Y.K.); (S.H.M.)
- Asia Pacific Ruminant Institute, Icheon 17385, Korea
| | - Sang Ho Moon
- Department of Bio-Convergence Science, College of Biomedical and Health Science, Konkuk University, Chungju 27478, Korea; (N.Y.K.); (S.H.M.)
| | - Changho Jhin
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
- Department of Smartfarm Research, 1778 Living Tech, Sejong 30033, Korea
| | - Hak-Jin Kim
- Department of Biosystems and Biomaterial Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea;
| | - Jung-Seok Yang
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
| | - Hyoung Seok Kim
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
| | - Taek Sung Lee
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
| | - Ju Young Lee
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
| | - Soo Hyun Park
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (D.-H.J.); (C.J.); (J.-S.Y.); (H.S.K.); (T.S.L.); (J.Y.L.)
- Correspondence: ; Tel.: +82-33-650-3661
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