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Modern Management Methods in the Swine Sector Cover

Modern Management Methods in the Swine Sector

Open Access
|Aug 2025

References

  1. 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.
  2. 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.
  3. Alarcón L.V., Alberto A.A., Mateu E. (2021). Biosecurity in pig farms: a review. Porcine Health Manag., 7.
  4. 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.
  5. Aquilani C., Confessore A., Bozzi R., Sirtori F., Pugliese C. (2022). Review: Precision Livestock Farming technologies in pasture-based livestock systems. Animal, 16.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Banhazi T., Banhazi A., Ildiko T., 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.
  13. Beluhova-Uzunova R., Dunchev D. (2019). Precision Farming – Concepts and Perspectives. Probl. Agric. Econ., 360: 142–155.
  14. Benjamin M., Yik S. (2019). Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals, 9: 133.
  15. Berckmans D. (2017). General introduction to precision livestock farming. Anim. Front., 7: 6–11.
  16. Berckmans D. (2015). Precision livestock farming applications. In: Wageningen Academic, 25-36.
  17. Brown D.D., Kays R., Wikelski M., Wilson R. and Klimley A.P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Anim. Biotelemetry, 1.
  18. Brown-Brandl T.M., Rohrer G.A. and Eigenberg R.A. (2013). Analysis of feeding behavior of group housed growing-finishing pigs. Comput. Electron. Agric., 96: 246–252.
  19. Chapinal N., Ruiz-de-la-Torre J.L., Cerisuelo A., Baucells M.D., Gasa J. and 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.
  20. Chu C.M. and Jong T.L. (2008). Enthalpy estimation for thermal comfort and energy saving in air conditioning system. Energy Convers. Manag., 49: 1620–1628.
  21. Church J.S., Hegadoren P.R., Paetkau M.J., Miller C.C., Regev-Shoshani G., Schaefer A.L. and Schwartzkopf-Genswein K.S. (2014). Influence of environmental factors on infrared eye temperature measurements in cattle. Res. Vet. Sci., 96: 220–226.
  22. 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.
  23. Cordeiro A.F.S., Nääs I.A., da Silva Leitão F., de Almeida A.C.M. and de Moura D.J. (2018). Use of vocalisation to identify sex, age, and distress in pig production. Biosyst. Eng., 173: 57–63.
  24. Cornou C. and Lundbye-Christensen S. (2012). Modeling of sows diurnal activity pattern and detection of parturition using acceleration measurements. Comput. Electron. Agric., 80: 97–104.
  25. Darr M. and Epperson W. (2009). Embedded sensor technology for real time determination of animal lying time. Comput. Electron. Agric., 66: 106-111.
  26. 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. and Huber R. (2022). Scenarios for agricultural policy in the era of digitalization. Agric. Syst., 196: 103318.
  27. Ellis J.L., Jacobs M., Dijkstra J., van Laar H., Cant J.P., Tulpan D. and 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.
  28. Escalante H.J., Rodriguez S.V., Cordero J., Kristensen A.R. and Cornou C. (2013). Sow-activity classification from acceleration patterns: A machine learning approach. Comput. Electron. Agric., 93: 17–26.
  29. Fournel S., Laberge B. and Rousseau A.N. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosyst. Eng., 155: 96–123.
  30. Gaughan J.B., Mader T.L. and Gebremedhin K.G. (2012). Rethinking heat index tools for livestock. Environ. Physiol. Livest., 243–265.
  31. Girard M. and Bee G. (2020). Invited review: Tannins as a potential alternative to antibiotics to prevent coliform diarrhea in weaned pigs. Animal, 14: 95–107.
  32. Gómez Y., Stygar A.H., Boumans I.J.M.M., Bokkers E.A.M., Pedersen L.J., Niemi J.K., Pastell M., Manteca X. and 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.
  33. Guesgen M.J. and Bench C.J. (2017). What can kinematics tell us about the affective states of animals? Anim. Welf., 26: 383–397.
  34. Hemsworth P.H. and Barnett J.L. (2001). The importance of animal comfort for animal production in intensive grassland systems. Int. Grassl. Congr. Proc., São Paulo, Brazil.
  35. Hintze S., Scott D., Turner S., Meddle S.L. and 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.
  36. Hong M., Ahn H., Atif O., Lee J., Park D. and Chung Y. (2020). Field-applicable pig anomaly detection system using vocalization for embedded board implementations. Appl. Sci., 10: 1–17.
  37. Jensen P. and Recien B. (1989). When to wean—Observations from free-ranging domestic pigs. Appl. Anim. Behav. Sci., 23: 49–60.
  38. Jia M., Zhang H., Xu J., Yong S. and 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.
  39. Jin G.-Y., Lu X.-Y. and Park M.-S. (2006). An indoor localization mechanism using active RFID tag. IEEE Int. Conf. Sensor Netw. Ubiquitous Trustworthy Comput. (SUTC’06), Taichung, Taiwan.
  40. Jun K., Kim S.J. and Ji H.W. (2018). Estimating pig weights from images without constraint on posture and illumination. Comput. Electron. Agric., 153: 169–176.
  41. Kapun A., Adrion F. and Gallmann E. (2020). Case study on recording pigs’ daily activity patterns with a UHF-RFID system. Agriculture, 10: 1–14.
  42. Kapun A. and Gallmann E. (2017). Behaviour and activity monitoring of growing-finishing pigs with UHF-RFID. Proc. 8th Eur. Conf. Precis. Livest. Farm., Nantes, France, 605–613.
  43. Kashiha M.A., Bahr C., Ott S., Moons C.P.H., Niewold T.A., Tuyttens F. and Berckmans D. (2014). Automatic monitoring of pig locomotion using image analysis. Livest. Sci., 159: 141–148.
  44. Kashiha M., Bahr C., Haredasht S.A., Ott S., Moons C.P.H., Niewold T.A., Ödberg F.O. and Berckmans D. (2013a). The automatic monitoring of pigs’ water use by cameras. Comput. Electron. Agric., 90: 164–169.
  45. Kashiha M., Bahr C., Ott S., Moons C.P.H., Niewold T.A., Ödberg F.O. and Berckmans D. (2013b). Automatic identification of marked pigs in a pen using image pattern recognition. Comput. Electron. Agric., 93: 111–120.
  46. Kim J., Suh Y., Lee J., Chae H., Ahn H., Chung Y. and Park D. (2022). EmbeddedPigCount: Pig counting with video object detection and tracking on an embedded board. Sensors, 22: 2689.
  47. Kollis K., Phang C.S., Banhazi T.M. and Searle S.J. (2007). Weight estimation using image analysis and statistical modelling: A preliminary study. Appl. Eng. Agric., 23: 91–96.
  48. Kopler I., Marchaim U., Tikász I.E., Opaliński S., Kokin E., Mallinger K., Neubauer T., Gunnarson S., Soerensen C., Phillips C.J.C. and Banhazi T. (2023). Farmers’ perspectives of the benefits and risks in precision livestock farming in the EU pig and poultry sectors. Animals, 13: 2868.
  49. Kuczynski T., Blanes-Vidal V., Li B., Gates R.S., De Alencar Nääs I., Moura D.J., Berckmans D. and 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.
  50. Lecun Y., Bengio Y. and Hinton G. (2015). Deep learning. Nature, 521: 436–444.
  51. Lee W., Seong –, Kim H., Ryu J. and Ban T.-W. (2017). Fast detection of disease in livestock based on deep learning. J. Korea Inst. Inf. Commun. Eng., 21: 1009–1015.
  52. Liakos K.G., Bisato P., Moshou D., Pearson S. and Bochtis S. (2018). Machine learning in agriculture: A review. Sensors, 18: 2674.
  53. Lucy M.C. and 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.
  54. 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.
  55. Macdonald R., Feldmann T. and Wrigglesworth M. (2001). 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.
  56. Madsen T.N. and Kristensen A.R. (2005). A model for monitoring the condition of young pigs by their drinking behaviour. Comput. Electron. Agric., 48: 138–154.
  57. Maes D.G.D., Dewulf J., Piñeiro C., Edwards S. and Kyriazakis I. (2020). A critical reflection on intensive pork production with an emphasis on animal health and welfare. J. Anim. Sci., 98: 15–26.
  58. Mahbub M., Hossain M.M. and 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.
  59. Mahfuz S., Mun H.S., Dilawar M.A., Ampode K.M.B., Chem V., Kim Y.H., Moon J.P. and Yang C.J. (2022). Geothermal plus sunlight-based incubator for sustainable pig production. Sustainability, 14.
  60. Martínez-Avilés M., Fernández-Carrión E., López García-Baones J.M. and Sánchez-Vizcaíno J.M. (2017). Early detection of infection in pigs through an online monitoring system. Transbound. Emerg. Dis., 64: 364–373.
  61. Matthews S.G., Miller A.L., Clapp J., Plötz T. and Kyriazakis I. (2016). Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. Vet. J., 217: 43–51.
  62. Matthews S.G., Miller A.L., Plötz T. and Kyriazakis I. (2017). Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci. Rep., 7: 17582.
  63. Meiszberg A.M., Johnson A.K., Sadler L.J., Carroll J.A., Dailey J.W. and 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.
  64. Mun H.S., Dilawar M.A., Jeong M.G., Rathnayake D., Won J.S., Park K.W., Lee S.R., Ryu S.B. and 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.
  65. Mun H.S., Dilawar M.A., Rathnayake D., Chung I.B., Kim C.D., Ryu S.B., Park K.W., Lee S.R. and Yang C.J. (2021). Effect of a geothermal heat pump in cooling mode on the housing environment and swine productivity traits. Appl. Sci., 11.
  66. Nasirahmadi A., Richter U., Hensel O., Edwards S. and Sturm B. (2015). Using machine vision for investigation of changes in pig group lying patterns. Comput. Electron. Agric., 119: 184–190.
  67. Neethirajan S., Tuteja S.K., Huang S.T. and Kelton D. (2017). Recent advancement in biosensors technology for animal and livestock health management. Biosens. Bioelectron., 98: 398–407.
  68. Nilsson M., Herlin A.H., Ardö H., Guzhva O., Åström K. and Bergsten C. (2015). Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal, 9: 1859–1865.
  69. Norton T., Chen C., Larsen M.L.V. and Berckmans D. (2019). Review: Precision livestock farming: Building “digital representations” to bring the animals closer to the farmer. Animal, 13: 3009–3017.
  70. Oczak M., Maschat K., Berckmans D., Vranken E. and 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.
  71. Olejnik K., Popiela E. and Opaliński S. (2022). Emerging precision management methods in poultry sector. Agriculture, 12: 718.
  72. Philippe F.X., Cabaraux J.F. and Nicks B. (2011). Ammonia emissions from pig houses: Influencing factors and mitigation techniques. Agric. Ecosyst. Environ., 141: 245–260.
  73. Racewicz P., Ludwiczak A., Skrzypczak E., Składanowska-Baryza J., Biesiada H., Nowak T., Nowaczewski S., Zaborowicz M., Stanisz M. and Ślósarz P. (2021). Welfare, health and productivity in commercial pig herds. Animals, 11.
  74. Renaudeau D., Kerdoncuff M., Anaïs C. and Gourdine J.L. (2008). Effect of temperature level on thermal acclimation in Large White growing pigs. Animal, 2: 1619–1626.
  75. Rodrigues V.C., da Silva I.J.O., Vieira F.M.C. and Nascimento S.T. (2011). A correct enthalpy relationship as thermal comfort index for livestock. Int. J. Biometeorol., 55: 455–459.
  76. Rosengart S., Chuppava B., Trost L.-S., Henne H., Tetens J., Traulsen I., Deermann A., Wendt M. and 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.
  77. Ross J.W., Hale B.J., Gabler N.K., Rhoads R.P., Keating A.F. and Baumgard L.H. (2015). Physiological consequences of heat stress in pigs. Anim. Prod. Sci., 55: 1381–1390.
  78. Schillings J., Bennett R. and Rose D.C. (2021). Exploring the potential of Precision Livestock Farming technologies to help address farm animal welfare. Front. Anim. Sci., 2.
  79. Shin H., Kwak Y., Jo S.-K., Kim S.-H. and 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.
  80. Tzanidakis C., Simitzis P., Arvanitis K. and Panagakis P. (2021). An overview of the current trends in precision pig farming technologies. Livest. Sci., 249: 104530.
  81. 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. and González-Lozano M. (2021). Hypothermia in newly born piglets: Mechanisms of thermoregulation and pathophysiology of death. J. Anim. Behav. Biometeorol., 9.
  82. Vranken E. and Berckmans D. (2017). Precision livestock farming for pigs. Anim. Front., 7: 32–37.
  83. Wang Y., Dong H., Zhu Z., Gerber P.J., Xin H., Smith P., Opio C., Steinfeld H. and Chadwick D. (2017). Mitigating greenhouse gas and ammonia emissions from swine manure management: A system analysis. Environ. Sci. Technol., 51: 4503–4511.
  84. Wang Y., Yang W., Winter P. and Walker L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosyst. Eng., 100: 117–125.
  85. Yin Y., Tu D., Shen W. and Bao J. (2021). Recognition of sick pig cough sounds based on convolutional neural network in field situations. Inf. Process. Agric., 8: 369–379.
  86. Yoon S.U., Choi S.M. and Lee J.H. (2022). A study on the development of livestock odor (ammonia) monitoring system using ICT (Information and Communication Technology). Agriculture, 12.
  87. Yu J., Chen S., Zeng Z., Xing S., Chen D., Yu B., He J., Huang Z., Luo Y., Zheng P., Mao X., Luo J. and Yan H. (2021). Effects of cold exposure on performance and skeletal muscle fiber in weaned piglets. Animals, 11.
  88. Zhou H., Chung S., Kakar J.K., Kim S.C. and Kim H. (2023). Pig movement estimation by integrating optical flow with a multi-object tracking model. Sensors, 23: 9499.
  89. Zheng P., Zhang J., Liu H., Bao J., Xie Q. and Teng X. (2021). A wireless intelligent thermal control and management system for piglet in large-scale pig farms. Inf. Process. Agric., 8: 341–349.
DOI: https://doi.org/10.2478/aoas-2025-0082 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402
Language: English
Submitted on: Jul 29, 2024
Accepted on: Jul 17, 2025
Published on: Aug 26, 2025
Published by: National Research Institute of Animal Production
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 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.

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