The role of precision pig farming technologies in sustainable and competitive animal husbandry (Review)
DOI:
https://doi.org/10.17108/ActAgrOvar.2025.66.2.167Keywords:
Precision Livestock Farming (PLF), Digitalization of Pig Production, Animal Welfare and Sustainability, , IoT and Sensor Technology, Feed OptimizationAbstract
The application of Precision Livestock Farming (PLF) technologies in Hungarian livestock production remains limited, although international trends clearly indicate that these solutions contribute to improved sustainability, animal welfare, and production efficiency. The aim of this review was to examine, based on the literature, how precision technologies enhance the economic and environmental performance of pig farming, and which factors influence their successful implementation. Analysis of the literature reveals that sensor-based data collection, automated feeding, and behaviour monitoring play a crucial role in optimizing production and improving animal welfare standards. However, the widespread adoption of these technologies is hindered by high investment costs, farmers’ limited digital skills, and uncertainties surrounding data management. Research clearly supports that the successful application of PLF requires not only technological development but also a paradigm shift, targeted training, and effective knowledge transfer. The future challenge lies in integrating data-driven decision-making into daily practice, which in the long term may lead to more competitive and environmentally sustainable production in Hungary as well.
References
Akinyemi, B. E., Akaichi, F., Siegford, J. M., & Turner, S. P. (2023). US Swine Industry Stakeholder Perceptions of Precision Livestock Farming Technology: A Q-Methodology Study. Animals, 13(18), 2930. https://doi.org/10.3390/ani13182930
Alexy, M., Pai, R. R., Ferenci, T., & Haidegger, T. (2024). The potential of RFID technology for tracking Mangalica pigs in the extensive farming system – a research from Hungary. Pastoralism: Research, Policy and Practice, 14, 12854. https://doi.org/10.3389/past.2024.12854
Arulmozhi, E., Bhujel, A., Moon, B.-E., & Kim, H.-T. (2021). The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals. Animals, 11(8), 2343. https://doi.org/10.3390/ani11082343
Balontong, A. (2023, August 1-3). TimBaboy: Swine Management System in PLF Integrating Image Processing for Weight Monitoring [Conference session]. Pacuit 2023 International and National Conference, Bulacan State University, Malolos Bulacan.
Banhazi, T., & Black, J. L. (2009). Precision Livestock Farming: A Suite of Electronic Systems to Ensure the Application of Best Practice Management on Livestock Farms. Australian Journal of Multi-Disciplinary Engineering, 7(1), 1-14. https://doi.org/10.1080/14488388.2009.11464794
Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, P., Tscharke, M., & Berckmans, D. (2012a). Precision Livestock Farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1-9. https://doi.org/10.3965/j.ijabe.20120503.001
Banhazi, T. M., Babinszky, L., Halas, V., & Tscharke, M. (2012b). Precision Livestock Farming: Precision feeding technologies and sustainable animal production. International Journal of Agricultural and Biological Engineering, 5(4), 1-8. https://doi.org/10.3965/j.ijabe.20120504.006
Benjamin, M., & Yik, S. (2019). Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals, 9(4), 133. https://doi.org/10.3390/ani9040133
Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Scientifique et Technique, 33(1), 189-196. https://doi.org/10.20506/rst.33.1.2273
Bruininx, E. M. A. M., Van Der Peet-Schwering, C. M. C., Schrama, J. W., Den Hartog, L. A., Everts, H., & Beynen, A. C. (2003). The IVOG® feeding station: a tool for monitoring the individual feed intake of group-housed weanling pigs. Journal of Animal Physiology and Animal Nutrition, 85(3-4), 81-87. https://doi.org/10.1046/j.1439-0396.2001.00305.x
Burns, R. T., & Spajić, R. (2024). Precision Livestock Farming in Swine Production. In G. Kušec & I. D. Kušec (Eds.), Tracing the Domestic Pig. IntechOpen. https://doi.org/10.5772/intechopen.114845
Da Fonseca, F. N., Abe, J. M., de Alencar Nääs, I., da Silva Cordeiro, A. F., do Amaral, F. V., & Ungaro, H. C. (2020). Automatic prediction of stress in piglets (Sus Scrofa) using infrared skin temperature. Computers and Electronics in Agriculture, 168. https://doi.org/10.1016/j.compag.2019.105148
Da Silva Cordeiro, A. F., de A. Nääs, I., Oliveira, S. R. M., Violaro, F., De Almeida, A. C. M., & Neves, D. P. (2013). Understanding Vocalization Might Help to Assess Stressful Conditions in Piglets. Animals, 3(3), 923-934. https://doi.org/10.3390/ani3030923
Da Silveira, F., da Silva, S. L. C., Machado, F. M., Barbedo, J. G. A., & Amaral, F. G. (2023). Farmers' perception of the barriers that hinder the implementation of agriculture 4.0. Agricultural Systems, 208, 103656. https://doi.org/10.1016/j.agsy.2023.103656
Faltys, G. L., Young, J. M., Odgaard, R. L., Murphy, R. B., & Lechtenberg, K. F. (2014). Technical note: Validation of electronic feeding stations as a swine research tool. Journal of Animal Science, 92(1), 272-276. https://doi.org/10.2527/jas.2013-6808
Farahnakian, F., Farahnakian, F., Björkman, S., Bloch, V., Pastell, M., & Keikkonen, J. (2024). Pose Estimation of Sow and Piglets During Free Farrowing Using Deep Learning. Journal of Agriculture and Food Research, 16, 101067. https://doi.org/10.1016/j.jafr.2024.101067
Fernández, J., Fàbrega, E., Soler, J., Tibau, J., Ruiz, J. L., Puigvert, X., & Manteca, X. (2011). Feeding strategy in group-housed growing pigs of four different breeds. Applied Animal Behaviour Science, 134(3-4), 109-120. https://doi.org/10.1016/j.applanim.2011.06.018
Gaillard, C., Brossard, L., & Dourmad, J. Y. (2020). Improvement of feed and nutrient efficiency in pig production through precision feeding. Animal Feed Science and Technology, 268, 114611. https://doi.org/10.1016/j.anifeedsci.2020.114611
Garrido-Izard, M., Correa, E.-C., Requejo, J.-M., & Diezma, B. (2019). Continuous Monitoring of Pigs in Fattening Using a Multi-Sensor System: Behavior Patterns. Animals, 10(1), 52. https://doi.org/10.3390/ani10010052
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. Frontiers in Veterinary Science, 8. https://doi.org/10.3389/fvets.2021.660565
Halas, V. (2019, May 20). Perspektívák a sertések precíziós takarmányozásában. Agrárágazat. https://agraragazat.hu/hir/perspektivak-a-sertesek-precizios-takarmanyozasaban
Hartung, J., Banhazi, T., Vranken, E., & Guarino, M. (2017). European farmers' experiences with precision livestock farming systems. Animal Frontiers, 7(1), 38-44. https://doi.org/10.2527/af.2017.0107
InraPorc. (2023). https://inraporc.inrae.fr/inraporc/index_en.html
Islam, M. M., Ahmed, S. T., Mun, H.-S., Bostami, A. B. M. R., Kim, Y.-J., & Yang, C.-J. (2015). Use of thermal imaging for the early detection of signs of disease in pigs challenged orally with Salmonella typhimurium and Escherichia coli. African Journal of Microbiology Research, 9(26), 1667-1674. https://doi.org/10.5897/AJMR2015.7580
Ko, E., Jeong, K., Oh, H., Park, Y., Choi, J., & Lee, E. (2023). A deep learning-based framework for predicting pork preference. Current Research in Food Science, 6, 100495. https://doi.org/10.1016/j.crfs.2023.100495
Jiao, S., Tiezzi, F., Huang, Y., Gray, K. A., & Maltecca, C. (2016). The use of multiple imputation for the accurate measurements of individual feed intake by electronic feeders. Journal of Animal Science, 94(2), 824-832. https://doi.org/10.2527/jas.2015-9667
Maselyne, J., Saeys, W., De Ketelaere, B., Mertens, K., Vangeyte, J., Hessel, E. F., Millet, S., & Van Nuffel, A. (2014). Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs. Computers and Electronics in Agriculture, 102, 10-18. https://doi.org/10.1016/j.compag.2013.12.015
Maselyne, J., Saeys, W., Briene, P., Mertens, K., Vangeyte, J., De Ketelaere, B., Hessel, E. F., Sonck, B., & Van Nuffel, A. (2016). Methods to construct feeding visits from RFID registrations of growing-finishing pigs at the feed trough. Computers and Electronics in Agriculture, 128, 9-19. https://doi.org/10.1016/j.compag.2016.08.010
Moi, M., de A. Nääs, I., Caldara, F. R., de L. Almeida Paz, I. C., Garcia, R. G., & Cordeiro, A. F. S. (2014). Vocalization data mining for estimating swine stress conditions. Engenharia Agrícola, 34(3). https://doi.org/10.1590/S0100-69162014000300008
Nemzeti Jogszabálytár. (2023). 32/1999. (III. 31.) FVM rendelet a mezőgazdasági haszonállatok tartásának állatvédelmi szabályairól. https://net.jogtar.hu/jogszabaly?docid=99900032.fvm
Papadopoulos, G., Papantonatou, M.-Z., Uyar, H., Kriezi, O., Mavrommatis, A., Psiroukis, V., Kasimati, A., Tsiplakou, E., & Fountas, S. (2025). Economic and environmental benefits of digital agricultural technological solutions in livestock farming: A review. Smart Agricultural Technology, 10, 100783. https://doi.org/10.1016/j.atech.2025.100783
Reiners, K., Hegger, A., Hessel, E. F., Böck, S., Wendl, G., & Van den Weghe, H. F. A. (2009). Application of RFID technology using passive HF transponders for the individual identification of weaned piglets at the feed trough. Computers and Electronics in Agriculture, 68(2), 178-187. https://doi.org/10.1016/j.compag.2009.05.010
Talero-Sarmiento, L. H., Parra-Sanchez, D. T., & Lamos-Diaz, H. (2022, November 2-7). Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review [Conference session]. X. Conference on Innovation, Documentation, Education and Teaching Technologies, INNODOCT 2022, Valencia. https://doi.org/10.4995/INN2022.2022.15746
Tóth, T., & Halas, V. (2016, August 9). Precíziós állattenyésztés – a szavakon túl. Agronapló. https://www.agronaplo.hu/agrofokusz/20160809/precizios-allattenyesztes-a-szavakon-tul-37595
Tikász, I. (2023, June 9). Smart technológiák – tapasztalatok a magyarországi sertés- és baromfitartásban. Agronapló. https://www.agronaplo.hu/agrofokusz/20230609/smart-technologiak-tapasztalatok-a-magyarorszagi-sertes-es-baromfitartasban-39917
Tullo, E., Finzi, A., & Guarino, M. (2019). Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy. Science of The Total Environment, 650(2), 2751-2760. https://doi.org/10.1016/j.scitotenv.2018.10.018
Tuyttens, F. A. M., Molento, C. F. M., & Benaissa, S. (2022). Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare. Frontiers in Veterinary Science, 9. https://doi.org/10.3389/fvets.2022.889623
Tzanidakis, C., Simitzis, P., Arvanitis, K., & Panagakis, P. (2021). An overview of the current trends in precision pig farming technologies. Livestock Science, 249, 104530. https://doi.org/10.1016/j.livsci.2021.104530
Van Klompenburg, T., & Kassahun, A. (2022). Data-driven decision making in pig farming: A review of the literature. Livestock Science, 261, 104961. https://doi.org/10.1016/j.livsci.2022.104961
Vranken, E., & Berckmans, D. (2017). Precision livestock farming for pigs. Animal Frontiers, 7(1), 32-37. https://doi.org/10.2527/af.2017.0106
Wageningen University & Research. (2023). Precision feed for pigs reduces environmental impact. Retrieved October 10, 2023, from https://www.wur.nl/en/research-results/research-institutes/livestock-research/show-wlr/precision-feed-for-pigs-reduces-environmental-impact.htm
Wang, S., Jiang, H., Qiao, Y., Jiang, S., Lin, H., & Sun, Q. (2022). The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming. Sensors, 22(17), 6451. https://doi.org/10.3390/s22176541
Zhou, L., Wang, J., & Li, H. (2023). Effect of digital economy on large-scale pig farming: Evidence from China. Cogent Food & Agriculture, 9(1), 1206452. https://doi.org/10.1080/23311932.2023.2238985
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
