Modern Management Methods in the Swine Sector – A Review

Katarzyna Olejnik-Bednarska; Anna Jankowska-Mąkosa; Ewa Popiela; Damian Knecht; Mariusz Korczyński; Sebastian Opaliński

doi:10.2478/aoas-2025-0082

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Modern Management Methods in the Swine Sector – A Review

Annals of Animal Science

Volume 26 (2026): Issue 3 (April 2026)

By: Katarzyna Olejnik-Bednarska, Anna Jankowska-Mąkosa, Ewa Popiela, Damian Knecht, Mariusz Korczyński and Sebastian Opaliński

Open Access

|Apr 2026

Abstract

Precision livestock farming (PLF) and information and communication technology (ICT) tools are revolutionizing pig production with various technologies, such as sensors, data analytics, and monitoring systems. These tools enable real-time and continuous monitoring of pig health, behavior and production parameters. ICT tools, including advanced data analysis algorithms and decision support systems, interpret the collected data and assist in making informed management decisions. By optimizing critical aspects of pig production, including feed management, environmental control, disease detection, and overall herd health management, these technologies are paving the way for a more profitable and sustainable future in precision livestock farming. The result is improved feed efficiency, reduced environmental impact, enhanced animal welfare, and increased profitability, offering a promising outlook for the industry.

References

Aarnink A.J.A., Verstegen M.W.A. (2007). Nutrition is a key factor in reducing the environmental load from pig production. Livest. Sci., 109: 194–203.
Search in Google Scholar Back to article
Ahrendt P., Gregersen T., Karstoft H. (2011). Development of a real-time computer vision system for tracking loose-housed pigs. Comput. Electron. Agric., 76: 169–174.
Search in Google Scholar Back to article
Alarcón L.V., Alberto A.A., Mateu E. (2021). Biosecurity in pig farms: a review. Porcine Health Manag., 7.
Search in Google Scholar Back to article
Andersen H.M.L., Jørgensen E., Dybkjær L., Jørgensen B. (2008). The ear skin temperature as an indicator of the thermal comfort of pigs. Appl. Anim. Behav. Sci., 113: 43–56.
Search in Google Scholar Back to article
Aquilani C., Confessore A., Bozzi R., Sirtori F., Pugliese C. (2022). Precision Livestock Farming technologies in pasture-based livestock systems. Animal, 16.
Search in Google Scholar Back to article
Atsbeha D.M., Flaten O., Olsen H.F., Kjos N.P., Kidane A., Skugor A., Prestløkken E., Øverland M. (2020). Technical and economic performance of alternative feeds in dairy and pig production. Livest. Sci., 240.
Search in Google Scholar Back to article
Bahlo C., Dahlhaus P., Thompson H., Trotter M. (2019). The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review. Comput. Electron. Agric., 156: 459–466.
Search in Google Scholar Back to article
Banhazi T.M., Black J.L. (2009). Precision livestock farming: A suite of electronic systems to ensure the application of best practice management on livestock farms. Aust. J. Multi-Discip. Eng., 7: 1–14.
Search in Google Scholar Back to article
Banhazi T.M., Seedorf J., Rutley D.L., Pitchford W.S. (2008). Identification of risk factors for sub-optimal housing conditions in Australian piggeries: Part 1. Study justification and design. J. Agric. Saf. Health, 14: 5–20.
Search in Google Scholar Back to article
Banhazi T.M., Tscharke M., Ferdous W.M., Saunders C., Lee S.H. (2011). Improved image analysis based system to reliably predict the live weight of pigs on farm: preliminary results. Aust. J. Multi-Discip. Eng., 8: 107–119.
Search in Google Scholar Back to article
Banhazi T.M., Black J.L., Schofield C.P., Tscharke M., Berckmans D. (2012). Precision livestock farming: an international review of scientific and commercial aspects. Int. J. Agric. Biol. Eng., 5: 1–9.
Search in Google Scholar Back to article
Banhazi T., Dunn M., Banhazi A. (2022). Chapter 1: Weight-DetectTM: On-farm evaluation of the precision of image analysis based weight prediction system. Practical Precision Livestock Farming, pp. 29–39.
Search in Google Scholar Back to article
Banhazi T., Banhazi A., Tikasz I.E.., Palotay S., Mallimger K., Neubauer T., Corpaci L., Marchaim U., Kopler I., Opalinski S., Olejnik K., Kokin E., Gunnarsson S., Bjerre T., Soerensen C. (2024). Facilitating PLF technology adoption in the pig and poultry industries. Stud. Agric. Econ., 126: 43–49.
Search in Google Scholar Back to article
Beluhova-Uzunova R., Dunchev D. (2019). Precision farming – concepts and perspectives. Probl. Agric. Econ., 360: 142–155.
Search in Google Scholar Back to article
Benjamin M., Yik S. (2019). Precision livestock farming in swine welfare: a review for swine practitioners. Animals, 9: 133.
Search in Google Scholar Back to article
Berckmans D. (2015). Precision livestock farming applications. In: Wageningen Academic, 25–36.
Search in Google Scholar Back to article
Berckmans D. (2017). General introduction to precision livestock farming. Anim. Front., 7: 6–11.
Search in Google Scholar Back to article
Brown D.D., Kays R., Wikelski M., Wilson R., Klimley A.P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Anim. Biotelemetry, 1.
Search in Google Scholar Back to article
Brown-Brandl T.M., Rohrer G.A., Eigenberg R.A. (2013). Analysis of feeding behavior of group housed growing-finishing pigs. Comput. Electron. Agric., 96: 246–252.
Search in Google Scholar Back to article
Chapinal N., Ruiz-de-la-Torre J.L., Cerisuelo A., Baucells M.D., Gasa J., Manteca X. (2008). Feeder use patterns in group-housed pregnant sows fed with an unprotected electronic sow feeder (Fitmix). J. Appl. Anim. Welf. Sci., 11: 319–336.
Search in Google Scholar Back to article
Chu C.M., Jong T.L. (2008). Enthalpy estimation for thermal comfort and energy saving in air conditioning system. Energy Convers. Manag., 49: 1620–1628.
Search in Google Scholar Back to article
Church J.S., Hegadoren P.R., Paetkau M.J., Miller C.C., Regev-Shoshani G., Schaefer A.L., Schwartzkopf-Genswein K.S. (2014). Influence of environmental factors on infrared eye temperature measurements in cattle. Res. Vet. Sci., 96: 220–226.
Search in Google Scholar Back to article
Cook N.J. (2012). Review: Minimally invasive sampling media and the measurement of corticosteroids as biomarkers of stress in animals. Can. J. Anim. Sci., 92: 227–259.
Search in Google Scholar Back to article
Cordeiro A.F.S., Nääs I.A., da Silva Leitão F., de Almeida A.C.M., de Moura D.J. (2018). Use of vocalisation to identify sex, age, and distress in pig production. Biosyst. Eng., 173: 57–63.
Search in Google Scholar Back to article
Cornou C., Lundbye-Christensen S. (2012). Modeling of sows diurnal activity pattern and detection of parturition using acceleration measurements. Comput. Electron. Agric., 80: 97–104.
Search in Google Scholar Back to article
Darr M., Epperson W. (2009). Embedded sensor technology for real time determination of animal lying time. Comput. Electron. Agric., 66: 106–111.
Search in Google Scholar Back to article
De Castro Júnior S.L., Silva I.J.O.d. (2022). The specific enthalpy of air as an indicator of heat stress in livestock animals. Int. J. Biometeorol., 65: 149–161.
Search in Google Scholar Back to article
Ehlers M.E., Huber R., El-Benni N., Gocht A., Sørensen C.A.G., Gussett M., Pfeifer C., Poppe K., Regan Á., Rose D.C., Wolfert S., Huber R. (2022). Scenarios for agricultural policy in the era of digitalization. Agric. Syst., 196: 103318.
Search in Google Scholar Back to article
Ellis J.L., Jacobs M., Dijkstra J., van Laar H., Cant J.P., Tulpan D., Ferguson N. (2020). Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data. Animal, 14: 223–237.
Search in Google Scholar Back to article
Escalante H.J., Rodriguez S.V., Cordero J., Kristensen A.R., Cornou C. (2013). Sow-activity classification from acceleration patterns: A machine learning approach. Comput. Electron. Agric., 93: 17–26.
Search in Google Scholar Back to article
Fournel S., Laberge B., Rousseau A.N. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosyst. Eng., 155: 96–123.
Search in Google Scholar Back to article
Gaughan J.B., Mader T.L., Gebremedhin K.G. (2012). Rethinking heat index tools for livestock. Environ. Physiol. Livest., 243–265.
Search in Google Scholar Back to article
Girard M., Bee G. (2020). Invited review: Tannins as a potential alternative to antibiotics to prevent coliform diarrhea in weaned pigs. Animal, 14: 95–107.
Search in Google Scholar Back to article
Gómez Y., Stygar A.H., Boumans I.J.M.M., Bokkers E.A.M., Pedersen L.J., Niemi J.K., Pastell M., Manteca X., Llonch P. (2021). A systematic review on validated precision livestock farming technologies for pig production and its potential to assess animal welfare. Front. Vet. Sci., 8.
Search in Google Scholar Back to article
Guesgen M.J., Bench C.J. (2017). What can kinematics tell us about the affective states of animals? Anim. Welf., 26: 383–397.
Search in Google Scholar Back to article
Hemsworth P.H., Barnett J.L. (2001). The importance of animal comfort for animal production in intensive grassland systems. Int. Grassl. Congr. Proc., São Paulo, Brazil.
Search in Google Scholar Back to article
Hintze S., Scott D., Turner S., Meddle S.L., D'Eath R.B. (2013). Mounting behaviour in finishing pigs: Stable individual differences are not due to dominance or stage of sexual development. Appl. Anim. Behav. Sci., 147: 69–80.
Search in Google Scholar Back to article
Hong M., Ahn H., Atif O., Lee J., Park D., Chung Y. (2020). Field-applicable pig anomaly detection system using vocalization for embedded board implementations. Appl. Sci., 10: 1–17.
Search in Google Scholar Back to article
Jensen P., Recien B. (1989). When to wean – Observations from free-ranging domestic pigs. Appl. Anim. Behav. Sci., 23: 49–60.
Search in Google Scholar Back to article
Jia M., Zhang H., Xu J., Yong S., Zhu W. (2021). Feeding frequency affects the growth performance, nutrient digestion and absorption of growing pigs with the same daily feed intake. Livest. Sci., 250: 104558.
Search in Google Scholar Back to article
Jin G.-Y., Lu X.-Y., Park M.-S. (2006). An indoor localization mechanism using active RFID tag. IEEE Int. Conf. Sensor Netw. Ubiquitous Trustworthy Comput. (SUTC'06), Taichung, Taiwan.
Search in Google Scholar Back to article
Jun K., Kim S.J., Ji H.W. (2018). Estimating pig weights from images without constraint on posture and illumination. Comput. Electron. Agric., 153: 169–176.
Search in Google Scholar Back to article
Kapun A., Gallmann E. (2017). Behaviour and activity monitoring of growing-finishing pigs with UHF-RFID. Proc. 8th Eur. Conf. Precis. Livest. Farm., Nantes, France, pp. 605–613.
Search in Google Scholar Back to article
Kapun A., Adrion F., Gallmann E. (2020). Case study on recording pigs' daily activity patterns with a UHF-RFID system. Agriculture, 10: 1–14.
Search in Google Scholar Back to article
Kashiha M., Bahr C., Haredasht S.A., Ott S., Moons C.P.H., Niewold T.A., Ödberg F.O., Berckmans D. (2013 a). The automatic monitoring of pigs' water use by cameras. Comput. Electron. Agric., 90: 164–169.
Search in Google Scholar Back to article
Kashiha M., Bahr C., Ott S., Moons C.P.H., Niewold T.A., Ödberg F.O., Berckmans D. (2013 b). Automatic identification of marked pigs in a pen using image pattern recognition. Comput. Electron. Agric., 93: 111–120.
Search in Google Scholar Back to article
Kashiha M.A., Bahr C., Ott S., Moons C.P.H., Niewold T.A., Tuyttens F., Berckmans D. (2014). Automatic monitoring of pig locomotion using image analysis. Livest. Sci., 159: 141–148.
Search in Google Scholar Back to article
Kim J., Suh Y., Lee J., Chae H., Ahn H., Chung Y., Park D. (2022). EmbeddedPigCount: Pig counting with video object detection and tracking on an embedded board. Sensors, 22: 2689.
Search in Google Scholar Back to article
Kollis K., Phang C.S., Banhazi T.M., Searle S.J. (2007). Weight estimation using image analysis and statistical modelling: A preliminary study. Appl. Eng. Agric., 23: 91–96.
Search in Google Scholar Back to article
Kopler I., Marchaim U., Tikász I.E., Opaliński S., Kokin E., Mallinger K., Neubauer T., Gunnarson S., Soerensen C., Phillips C.J.C., Banhazi T. (2023). Farmers' perspectives of the benefits and risks in precision livestock farming in the EU pig and poultry sectors. Animals, 13: 2868.
Search in Google Scholar Back to article
Kuczynski T., Blanes-Vidal V., Li B., Gates R.S., De Alencar Nääs I., Moura D.J., Berckmans D., Banhazi T.M. (2011). Impact of global climate change on the health, welfare and productivity of intensively housed livestock. Int. J. Agric. Biol. Eng., 4: 1–22.
Search in Google Scholar Back to article
LeCun Y., Bengio Y., Hinton G. (2015). Deep learning. Nature, 521: 436–444.
Search in Google Scholar Back to article
Lee W., Kim S.H., Ryu J., Ban T.-W. (2017). Fast detection of disease in livestock based on deep learning. J. Korea Inst. Inf. Commun. Eng., 21: 1009–1015.
Search in Google Scholar Back to article
Liakos K.G., Bisato P., Moshou D., Pearson S., Bochtis S. (2018). Machine learning in agriculture: A review. Sensors, 18: 2674.
Search in Google Scholar Back to article
Lucy M.C., Safranski T.J. (2017). Heat stress in pregnant sows: Thermal responses and subsequent performance of sows and their offspring. Mol. Reprod. Dev., 84: 946–956.
Search in Google Scholar Back to article
Macdonald R., Feldmann T., Wrigglesworth M. (2000). Comparison of heat lamp to heat pad creep heat in farrowing units. Swine Hous. Conf., American Society of Agricultural and Biological Engineers, St. Joseph, MI.
Search in Google Scholar Back to article
Madsen T.N., Kristensen A.R. (2005). A model for monitoring the condition of young pigs by their drinking behaviour. Comput. Electron. Agric., 48: 138–154.
Search in Google Scholar Back to article
Maes D.G.D., Dewulf J., Piñeiro C., Edwards S., Kyriazakis I. (2020). A critical reflection on intensive pork production with an emphasis on animal health and welfare. J. Anim. Sci., 98: 15–26.
Search in Google Scholar Back to article
Mahbub M., Hossain M.M., Gazi M.S.A. (2020). IoT-cognizant cloud-assisted energy efficient embedded system for indoor intelligent lighting, air quality monitoring, and ventilation. Internet Things, 11: 100266.
Search in Google Scholar Back to article
Mahfuz S., Mun H.S., Dilawar M.A., Ampode K.M.B., Chem V., Kim Y.H., Moon J.P., Yang C.J. (2022). Geothermal plus sun-light-based incubator for sustainable pig production. Sustainability, 14.
Search in Google Scholar Back to article
Martínez-Avilés M., Fernández-Carrión E., López García-Baones J.M., Sánchez-Vizcaíno J.M. (2017). Early detection of infection in pigs through an online monitoring system. Transbound. Emerg. Dis., 64: 364–373.
Search in Google Scholar Back to article
Matthews S.G., Miller A.L., Clapp J., Plötz T., Kyriazakis I. (2016). Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. Vet. J., 217: 43–51.
Search in Google Scholar Back to article
Matthews S.G., Miller A.L., Plötz T., Kyriazakis I. (2017). Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci. Rep., 7: 17582.
Search in Google Scholar Back to article
Meiszberg A.M., Johnson A.K., Sadler L.J., Carroll J.A., Dailey J.W., Krebs N. (2009). Drinking behavior in nursery pigs: Determining the accuracy between an automatic water meter versus human observers. J. Anim. Sci., 87: 4173–4180.
Search in Google Scholar Back to article
Mun H.S., Dilawar M.A., Jeong M.G., Rathnayake D., Won J.S., Park K.W., Lee S.R., Ryu S.B., Yang C.J. (2020). Effect of a heating system using a ground source geothermal heat pump on production performance, energy-saving and housing environment of pigs. Animals, 10: 1–17.
Search in Google Scholar Back to article
Mun H.S., Dilawar M.A., Rathnayake D., Chung I.B., Kim C.D., Ryu S.B., Park K.W., Lee S.R., Yang C.J. (2021). Effect of a geothermal heat pump in cooling mode on the housing environment and swine productivity traits. Appl. Sci., 11.
Search in Google Scholar Back to article
Nasirahmadi A., Richter U., Hensel O., Edwards S., Sturm B. (2015). Using machine vision for investigation of changes in pig group lying patterns. Comput. Electron. Agric., 119: 184–190.
Search in Google Scholar Back to article
Neethirajan S., Tuteja S.K., Huang S.T., Kelton D. (2017). Recent advancement in biosensors technology for animal and livestock health management. Biosens. Bioelectron., 98: 398–407.
Search in Google Scholar Back to article
Nilsson M., Herlin A.H., Ardö H., Guzhva O., Åström K., Bergsten C. (2015). Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal, 9: 1859–1865.
Search in Google Scholar Back to article
Norton T., Chen C., Larsen M.L.V., Berckmans D. (2019). Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer. Animal, 13: 3009–3017.
Search in Google Scholar Back to article
Oczak M., Maschat K., Berckmans D., Vranken E., Baumgartner J. (2016). Can an automated labelling method based on accelerometer data replace a human labeller? – Postural profile of farrowing sows. Comput. Electron. Agric., 127: 168–175.
Search in Google Scholar Back to article
OECD (2015). The OECD Model Survey on ICT Usage by Businesses (2nd Revision). OECD Publishing, Paris, France.
Search in Google Scholar Back to article
Olejnik K., Popiela E., Opaliński S. (2022). Emerging precision management methods in poultry sector. Agriculture, 12: 718.
Search in Google Scholar Back to article
Philippe F.X., Cabaraux J.F., Nicks B. (2011). Ammonia emissions from pig houses: Influencing factors and mitigation techniques. Agric. Ecosyst. Environ., 141: 245–260.
Search in Google Scholar Back to article
Racewicz P., Ludwiczak A., Skrzypczak E., Składanowska-Baryza J., Biesiada H., Nowak T., Nowaczewski S., Zaborowicz M., Stanisz M., Ślósarz P. (2021). Welfare, health and productivity in commercial pig herds. Animals, 11.
Search in Google Scholar Back to article
Renaudeau D., Kerdoncuff M., Anaïs C., Gourdine J.L. (2008). Effect of temperature level on thermal acclimation in Large White growing pigs. Animal, 2: 1619–1626.
Search in Google Scholar Back to article
Reza M.N., Ali M.R., Haque M.A., Jin H., Kyoung H., Choi Y.K., Kim G., Chung S.O. (2025). A review of sound-based pig monitoring for enhanced precision production. J. Anim. Sci. Technol., 67: 277.
Search in Google Scholar Back to article
Rodrigues V.C., da Silva I.J.O., Vieira F.M.C., Nascimento S.T. (2011). A correct enthalpy relationship as thermal comfort index for livestock. Int. J. Biometeorol., 55: 455–459.
Search in Google Scholar Back to article
Rosengart S., Chuppava B., Trost L.-S., Henne H., Tetens J., Traulsen I., Deermann A., Wendt M., Visscher C. (2022). Characteristics of thermal images of the mammary gland and of performance in sows differing in health status and parity. Front. Vet. Sci., 9: 920302.
Search in Google Scholar Back to article
Ross J.W., Hale B.J., Gabler N.K., Rhoads R.P., Keating A.F., Baumgard L.H. (2015). Physiological consequences of heat stress in pigs. Anim. Prod. Sci., 55: 1381–1390.
Search in Google Scholar Back to article
Schillings J., Bennett R., Rose D.C. (2021). Exploring the potential of precision livestock farming technologies to help address farm animal welfare. Front. Anim. Sci., 2.
Search in Google Scholar Back to article
Shin H., Kwak Y., Jo S.-K., Kim S.-H., Huh J.-H. (2023). Development of an optimal mechanical ventilation system control strategy based on weather forecasting data for outdoor air cooling in livestock housing. Energy, 268: 126649.
Search in Google Scholar Back to article
Tucker B.S., James S.E., Nowland T.L., Jolley J.Y.C., Terry R., Verma P.J. (2023). Validating the use of estimated weight from a 3D camera in a pig ecoshelter grower/finisher system. Anim. Sci. Proc., 14: 835–836.
Search in Google Scholar Back to article
Tzanidakis C., Simitzis P., Arvanitis K., Panagakis P. (2021). An overview of the current trends in precision pig farming technologies. Livest. Sci., 249: 104530.
Search in Google Scholar Back to article
Villanueva-García D., Mota-Rojas D., Martínez-Burnes J., Olmos-Hernández A., Mora-Medina P., Salmerón C., Gómez J., Boscato L., Gutiérrez-Pérez O., Cruz V., Reyes B., González-Lozano M. (2021). Hypothermia in newly born piglets: Mechanisms of thermoregulation and pathophysiology of death. J. Anim. Behav. Biometeorol., 9.
Search in Google Scholar Back to article
Vranken E., Berckmans D. (2017). Precision livestock farming for pigs. Anim. Front., 7: 32–37.
Search in Google Scholar Back to article
Wang Y., Yang W., Winter P., Walker L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosyst. Eng., 100: 117–125.
Search in Google Scholar Back to article
Wang Y., Dong H., Zhu Z., Gerber P.J., Xin H., Smith P., Opio C., Steinfeld H., Chadwick D. (2017). Mitigating greenhouse gas and ammonia emissions from swine manure management: A system analysis. Environ. Sci. Technol., 51: 4503–4511.
Search in Google Scholar Back to article
Yin Y., Tu D., Shen W., Bao J. (2021). Recognition of sick pig cough sounds based on convolutional neural network in field situations. Inf. Process. Agric., 8: 369–379.
Search in Google Scholar Back to article
Yoon S.U., Choi S.M., Lee J.H. (2022). A study on the development of livestock odor (ammonia) monitoring system using ICT (Information and Communication Technology). Agriculture, 12.
Search in Google Scholar Back to article
Yu J., Chen S., Zeng Z., Xing S., Chen D., Yu B., He J., Huang Z., Luo Y., Zheng P., Mao X., Luo J., Yan H. (2021). Effects of cold exposure on performance and skeletal muscle fiber in weaned piglets. Animals, 11.
Search in Google Scholar Back to article
Zheng P., Zhang J., Liu H., Bao J., Xie Q., Teng X. (2021). A wireless intelligent thermal control and management system for piglet in large-scale pig farms. Inf. Process. Agric., 8: 341–349.
Search in Google Scholar Back to article
Zhou H., Chung S., Kakar J.K., Kim S.C., Kim H. (2023). Pig movement estimation by integrating optical flow with a multi-object tracking model. Sensors, 23: 9499.
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/aoas-2025-0082 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402

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Language: English

Page range: 731 - 738

Submitted on: Jul 29, 2024

Accepted on: Jul 17, 2025

Published on: Apr 30, 2026

Published by: National Research Institute of Animal Production

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

precision livestock farming,

intensive production,

animal welfare,

modern technology,

sensors,

pig,

microclimate monitoring

Related subjects:

Zoology,

© 2026 Katarzyna Olejnik-Bednarska, Anna Jankowska-Mąkosa, Ewa Popiela, Damian Knecht, Mariusz Korczyński, Sebastian Opaliński, published by National Research Institute of Animal Production
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 26 (2026): Issue 3 (April 2026)