Noise level in a cow milking parlor

Dimo  Dimov; Toncho  Penev; Ivaylo  Marinov

doi:10.59400/sv3745

Noise level in a cow milking parlor

Dimo Dimov
Department of Ecology and Animal Hygiene, Faculty of Agriculture, Trakia University, 6000 Stara Zagora, Bulgaria
Toncho Penev
Department of Ecology and Animal Hygiene, Faculty of Agriculture, Trakia University, 6000 Stara Zagora, Bulgaria
Ivaylo Marinov
Department of Animal Husbandry—Ruminant Animals and Animal Products Technologies, Faculty of Agriculture, Trakia University, 6000 Stara Zagora, Bulgaria

Article ID: 3745

DOI: https://doi.org/10.59400/sv3745

Keywords: noise level; dairy cows; milking parlor

Abstract

The study took place in a dairy cattle farm with 500 Holstein-Friesian cows. Animals were reared under conditions of a freestall housing system and milked in a 2 × 8 "Herringbone" type milking parlor. Noise level reporting was performed three times during each milking (at start, in the middle, and at the end of milking) during the morning, midday, and evening milkings, every month within one year. The noise level in the working environment was measured by means of a Lutron SL-4023SD sound meter. The highest average noise values were recorded during the winter season, especially during the midday and evening milking, 75–76 dB, with deviations reaching over 80 dB. The next season, in terms of noise level, was the summer season, with average values of 72–74 dB. A study on noise levels in a “fishbone” type milking parlor found average values corresponding to moderately high noise levels, exceeding 65–70 dB. Such levels may negatively affect the welfare of dairy cows, as maximum values above the permissible limits were also recorded, particularly during the winter season. Therefore, it is recommended to optimize technological processes in order to reduce noise levels during milking as much as possible, which is essential both for the operators (milkers) and for the comfort of the animals.

Published

2025-10-13

How to Cite

Dimov, D., Penev, T., & Marinov, I. (2025). Noise level in a cow milking parlor. Sound & Vibration, 59(5). https://doi.org/10.59400/sv3745

Download Citation

Issue

Vol. 59 No. 5 (2025)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Hahad O, Kuntic M, Al-Kindi S, et al. Noise and mental health: evidence, mechanisms, and consequences. Journal of Exposure Science & Environmental Epidemiology. 2025; 35(1): 16–23. doi: 10.1038/s41370-024-00642-5

[2]Merchant ND. Underwater noise abatement: Economic factors and policy options. Environmental Science & Policy. 2019; 92: 116–123. doi: 10.1016/j.envsci.2018.11.014

[3]Ramos Niño JN, Sousa FCD, Oliveira CEA, et al. Systematic review of acoustic monitoring in livestock farming: Vocalization patterns and sound source analysis. Applied Sciences. 2025; 15(18): 9910. doi: 10.3390/app15189910

[4]Banks JL, Cohen Hubal EA. Noise: a public health problem. Journal of Exposure Science & Environmental Epidemiology. 2025; 35(1): 1–2. doi: 10.1038/s41370-025-00748-4

[5]Carrascal V. JC, Pastrana Camacho ADP, López Ayala V, et al. Animal welfare evaluation at slaughterhouses for pigs at the “Eje Cafetero” region in Colombia. Meat Science. 2021; 172: 108337. doi: 10.1016/j.meatsci.2020.108337

[6]Broucek J. Effect of noise on performance, stress, and behaviour of animals. Slovak Journal of Animal Science. 2015; 47(2): 111–123. Available online: https://office.sjas-journal.org/index.php/sjas/article/view/224

[7]Dimov D, Penev T, Marinov I. Workers Risk Levels of Noise in the Dairy Cow Milking Parlor. Basrah Journal of Agricultural Sciences. 2022; 35(2): 232–239. doi: 10.37077/25200860.2022.35.2.16

[8]Yang X, Zhao Y, Qi H, et al. Characterizing Sounds of Different Sources in a Commercial Broiler House. Animals. 2021; 11(3): 916. doi: 10.3390/ani11030916

[9]Žitňák M, Lendelová J, Bureš Ľ. Working environment of dairymen in summer time. In Rural Buildings in European Regions, Architecture–Constructions–Technology–Safety. Slovak University of Agriculture in Nitra; 2011. pp. 165–172.

[10]Huang J, Zhang T, Cuan K, et al. An intelligent method for detecting poultry eating behaviour based on vocalization signals. Computers and Electronics in Agriculture. 2021; 180: 105884. doi: 10.1016/j.compag.2020.105884

[11]Olczak K, Penar W, Nowicki J, et al. The Role of Sound in Livestock Farming—Selected Aspects. Animals. 2023; 13(14): 2307. doi: 10.3390/ani13142307

[12]Kuzmichev A, Khimenko A, Tikhomirov D, et al. Study of Potential Application Air Curtains in Livestock Premises at Cattle Management Farms. Agriculture. 2023; 13(6): 1259. doi: 10.3390/agriculture13061259

[13]Weeks CA, Brown SN, Warriss PD, et al. Noise levels in lairages for cattle, sheep and pigs in abattoirs in England and Wales. Veterinary Record. 2009; 165(11): 308–314. doi: 10.1136/vr.165.11.308

[14]Zhao L, Deng K, Wang T, et al. Anuran communities increase aggregations of conspecific calls in response to aircraft noise. Current Zoology. 2025; 71(2): 223–232. doi: 10.1093/cz/zoae042

[15]Röttgen V, Schön PC, Becker F, et al. Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor. Animal. 2020; 14(1): 198–205. doi: 10.1017/S1751731119001733

[16]Castelhano-Carlos MJ, Baumans V. The impact of light, noise, cage cleaning and in-house transport on welfare and stress of laboratory rats. Laboratory Animals. 2009; 43(4): 311–327. doi: 10.1258/la.2009.0080098

[17]Manteuffel G, Puppe B, Schön PC. Vocalization of farm animals as a measure of welfare. Applied Animal Behaviour Science. 2004; 88(1–2): 163–182. doi: 10.1016/j.applanim.2004.02.012

[18]Brumm H, Schmidt R, Schrader L. Noise-dependent vocal plasticity in domestic fowl. Animal Behaviour. 2009; 78(3): 741–746. doi: 10.1016/j.anbehav.2009.07.004

[19]Kovalčik K, Šottník J. Influence of noise on the behaviour of cows. Poľnohospodársvo; 1972; 4: 336–344. (in Slovak)

[20]Psenka M, Sistkova M, Bartos P, et al. Analysis of the Noise Exposure of milking parlour operators during working shift at different technological solutions. Mendelnet. 2016; 264–268. Available online: https://mendelnet.cz/pdfs/mnt/2016/01/47.pdf

[21]Kauke M, Savary P. Effect of noise and vibration in milking parlour on dairy cow. Agrarforschung Schweiz, 2010; 1(3): 96–101. (in German)

[22]Waynert DF, Stookey JM, Schwartzkopf-Genswein KS, et al. The response of beef cattle to noise during handling. Applied Animal Behaviour Science. 1999; 62(1): 27–42. doi: 10.1016/S0168-1591(98)00211-1

[23]Nosal D, Bilgery E. Noise and vibrations in milking installations. AgrarForschung. 2002; 9 (1): 4–7. (in German)

[24]Dimov D, Penev T, Marinov I. Importance of Noise Hygiene in Dairy Cattle Farming—A Review. Acoustics. 2023; 5(4): 1036–1045. doi: 10.3390/acoustics5040059

[25]Mills R. Applied Comparative Anatomy of the Avian Middle Ear. Journal of the Royal Society of Medicine. 1994; 87(3): 155–156. doi: 10.1177/014107689408700314

[26]Algers B, Jensen P. Teat stimulation and milk production during early lactation in sows: Effects of continuous noise. Canadian Journal of Animal Science. 1991; 71(1): 51–60. doi: 10.4141/cjas91-006

[27]Phillips CJC. Housing, handling and the environment for cattle. In: Phillips CJC (editor). Principles of Cattle Production. CABI; 2009. pp. 95–128. doi: 10.1079/9781845933975.0095

[28]Venglovský J, Sasáková N, Vargová M, et al. Noise in the animal housing environment. In: Proceedings of the XIIIth International Congress in Animal Hygiene (ISAH-2007); 17–21 June 2007; Tartu, Estonia. pp. 995–999. Available online: https://www.isah-soc.org/userfiles/downloads/proceedings/Proc_ISAH_2007_Volume_II/184_Venglovsky.pdf

[29]Tanida H, Miura A, Tanaka T, et al. Behavioral response to humans in individually handled weanling pigs. Applied Animal Behaviour Science. 1995; 42(4): 249–259. doi: 10.1016/0168-1591(94)00545-P

[30]Campo JL, Gil MG, Dávila SG. Effects of specific noise and music stimuli on stress and fear levels of laying hens of several breeds. Applied Animal Behaviour Science. 2005; 91(1–2): 75–84. doi: 10.1016/j.applanim.2004.08.028

[31]Lanier JL, Grandin T, Green RD, et al. The relationship between reaction to sudden, intermittent movements and sounds and temperament. Journal of Animal Science. 2000; 78(6): 1467. doi: 10.2527/2000.7861467x

[32]Weeks C. A review of welfare in cattle, sheep and pig lairages, with emphasis on stocking rates, ventilation and noise. Animal Welfare. 2008; 17(3): 275–284. doi: 10.1017/S096272860003219X

[33]Algers B, Ekesbo I, Stromberg S. The impact of continuous noise on animal health. Acta Veterinaria Scandinavica, Supplement. 1978; 67: 1–26.

[34]Šístková M, Peterka A, Peterka B. Light and noise conditions of buildings for breeding dairy cows. Research in Agricultural Engineering. 2010; 56(3): 92–98. doi: 10.17221/43/2009-RAE

[35]John Deere. Worksite Journal 2009. John Deere; 2009.

[36]Šístková M, Pšenka M, Celjak I, et al. Noise Emissions in Milking Parlours with Various Construction Solutions. Acta Technologica Agriculturae. 2016; 19(2): 49–51. doi: 10.1515/ata-2016-0011

[37]Dimov D. Noise level in buildings for free-range milking cows. Science and technologies. 2017; 7(6): 45–51.

[38]Behrend S. Every third milking session is too loud! Top Agrar. 2003; 9: R8–R11. (in German)

[39]Regulation No. 44 of 20 April 2006 on the veterinary requirements for livestock farms. Available online: https://www.fao.org/faolex/results/details/en/c/LEX-FAOC181462 (accessed on 15 September 2025).

[40]Liagka DV, Fthenakis GC, Kalonaki SN, et al. Machine-Milking practices, animal welfare-related reactions and quality of milk produced in dairy sheep farms. Animals. 2025; 15(21): 3078. doi: 10.3390/ani15213078

[41]Gygax L, Nosal D. Short Communication: Contribution of Vibration and noise during milking to the somatic cell count of milk. Journal of Dairy Science. 2006; 89(7): 2499–2502. doi: 10.3168/jds.S0022-0302(06)72324-4

[42]Johns J, Patt A, Hillmann E. Do Bells affect behaviour and heart rate variability in grazing dairy cows? PLOS ONE. 2015; 10(6): e0131632. doi: 10.1371/journal.pone.0131632

[43]Effects of noise on cattle performance. Available online: https://www.dairyglobal.net/health-and-nutrition/health/effects-of-noise-on-cattle-performance/ (accessed on 12 September 2025).

[44]Titterington FM, Knox R, Buijs S, et al. Human–Animal interactions with Bos taurus cattle and their impacts on on-farm Safety: A Systematic Review. Animals. 2022; 12(6): 776. doi: 10.3390/ani12060776

[45]Pajor EA, Rushen J, De Passillé AMB. Aversion learning techniques to evaluate dairy cattle handling practices. Applied Animal Behaviour Science. 2000; 69(2): 89–102. doi: 10.1016/S0168-1591(00)00119-2

[46]Angrecka S, Solecka U, Vieira FMC, et al. Noise as a factor of environmental stress for cattle – A review. Annals of Animal Science. 2023; 23(3): 717–723. doi: 10.2478/aoas-2023-0046

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.