Keywords:
precision livestock farming
sensors
behaviour
physiological parameters
feeding behaviour
heat stress

  • Abstract

    Rising global temperatures due to climate change pose an increased risk of heat stress (HS) in livestock, which requires accurate prediction methods to improve animal welfare and mitigate heat-related economic losses. In this literature review, we aimed to critically examine the potential of digitalisation in precision livestock farming from the perspective of assessing heat stress in cattle. Particular attention was paid to the possibility of using sensors to record changes in behaviour, physiological parameters, and feed and water consumption as predictors for detecting and preventing heat stress and its consequences in animals. The use of already proven sensors, such as those that record animal behaviour, to assess physiological parameters related to heat stress may be promising, using innovative data processing technologies, which requires additional investigation in future studies. Precision livestock farming (PLF) technologies that enable real-time monitoring of herds under specific conditions, such as disease or stress, play a key role in increasing the efficiency of herd management. Sensors, cameras, microphones, GPS, accelerometers and RFID tags are commonly used to collect data on animal behaviour and physiological parameters, helping to detect stress. Machine learning algorithms applied to sensor data can distinguish between different states of declining cow welfare, helping farmers make quick decisions. In summary, the integration of sensors, artificial intelligence and precision devices can help prevent heat stress in farm animals, improving animal welfare and productivity, while mitigating the negative effects of heat stress caused by climate change.

  • References

    1. Antanaitis, R., Džermeikaitė, K., Krištolaitytė, J., Ribelytė, I., Bespalovaitė, A., Bulvičiūtė, D., Palubinskas, G., & Anskienė, L. (2024). The impacts of heat stress on rumination, drinking, and locomotory behavior, as registered by innovative technologies, and acid–base balance in fresh multiparous dairy cows. Animals, 14(8), 1169. https://doi.org/10.3390/ani14081169
    2. Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., & Pugliese, C. (2022). Review: Precision livestock farming technologies in pasture-based livestock systems. Animal, 16, 100429. https://doi.org/10.1016/j.animal.2021.100429
    3. Arai, S., Okada, H., Sawada, H., Takahashi, Y., Kimura, K., & Itoh, T. (2019). Evaluation of ruminal motility in cattle by a bolus-type wireless sensor. Journal of Veterinary Medical Science, 81(12), 1835–1841. https://doi.org/10.1292/jvms.19-0487
    4. Bonestroo, J., van der Voort, M., Fall, N., Hogeveen, H., Emanuelson, U., & Klaas, I. C. (2021). Progression of different udder inflammation indicators and their episode length after onset of inflammation using automatic milking system sensor data. Journal of Dairy Science, 104(3), 3458–3473. https://doi.org/10.3168/jds.2019-18054
    5. Burdick, N. C., Carroll, J. A., Dailey, J. W., Randel, R. D., Falkenberg, S. M., & Schmidt, T. B. (2012). Development of a self-contained, indwelling vaginal temperature probe for use in cattle research. Journal of Thermal Biology, 37(4), 339–343. https://doi.org/10.1016/j.jtherbio.2011.10.007
    6. Cartwright, S. L., Schmied, J., Karrow, N., & Mallard, B. A. (2023). Impact of heat stress on dairy cattle and selection strategies for thermotolerance: A review. Frontiers in Veterinary Science, 10. https://doi.org/10.3389/fvets.2023.1198697
    7. Chapman, N. H., Chlingaryan, A., Thomson, P. C., Lomax, S., Islam, M. A., Doughty, A. K., & Clark, C. E. F. (2023). A deep learning model to forecast cattle heat stress. Computers and Electronics in Agriculture, 211, 107932. https://doi.org/10.1016/j.compag.2023.107932
    8. Choi, E., Souza, V.C., Dillon, J.A., Kebreab, E., & Mueller, N.D. (2024). Comparative analysis of thermal indices for modelling cold and heat stress in US dairy systems. Journal of Dairy Science S0022030224007331. https://doi.org/10.3168/jds.2023-24412
    9. Cuthbertson, H., Tarr, G., & González, L. A. (2019). Methodology for data processing and analysis techniques of infrared video thermography used to measure cattle temperature in real time. Computers and Electronics in Agriculture, 167, 105019. https://doi.org/10.1016/j.compag.2019.105019
    10. Dela Rue, B., Lee, J. M., Eastwood, C. R., Macdonald, K. A., & Gregorini, P. (2020). Short communication: Evaluation of an eating time sensor for use in pasture-based dairy systems. Journal of Dairy Science, 103(10), 9488–9492. https://doi.org/10.3168/jds.2020-18173
    11. Dijkstra, J., van Gastelen, S., Dieho, K., Nichols, K., & Bannink, A. (2020). Review: Rumen sensors: data and interpretation for key rumen metabolic processes. Animal, 14, s176–s186. https://doi.org/10.1017/s1751731119003112
    12. Giannone, C., Bovo, M., Ceccarelli, M., Torreggiani, D., & Tassinari, P. (2023). Review of the Heat Stress-Induced Responses in Dairy Cattle. Animals 13, 3451. https://doi.org/10.3390/ani13223451
    13. Han, C. S., Kaur, U., Bai, H., Roqueto dos Reis, B., White, R., Nawrocki, R. A., Voyles, R. M., Kang, M. G., & Priya, S. (2022). Invited review: Sensor technologies for real-time monitoring of the rumen environment. Journal of Dairy Science, 105(8), 6379–6404. https://doi.org/10.3168/jds.2021-20576
    14. Hoffmann, G., Silpa, M. V., Mylostyvyi, R., & Sejian, V. (2021). Non-Invasive Methods to Quantify the Heat Stress Response in Dairy Cattle. Climate Change and Livestock Production: Recent Advances and Future Perspectives, 85–98. https://doi.org/10.1007/978-981-16-9836-1_8
    15. Islam, M. A., Lomax, S., Doughty, A. K., Islam, M. R., Thomson, P. C., & Clark, C. E. F. (2021). Revealing the diversity in cattle behavioural response to high environmental heat using accelerometer-based ear tag sensors. Computers and Electronics in Agriculture, 191, 106511. https://doi.org/10.1016/j.compag.2021.106511
    16. Jaddoa, M.A., Zaidan, A.A., Gonzalez, L.A., Deveci, M., Cuthbertson, H., Al-Jumaily, A., & Kadry, S. (2024). An approach-based machine learning and automated thermal images to predict the dark-cutting incidence in cattle management of healthcare supply chain. Engineering Applications of Artificial Intelligence 135, 108804. https://doi.org/10.1016/j.engappai.2024.108804
    17. Kozyr, V., Mykytiuk, V., Кalinichenko, O., Pryshedko, V., & Begma, N. (2023). Growth energy and quality of beef from bulls of Maine-Anjou, Chianina, and Santa Gertrudis breeds grown in Ukraine. Scientific Horizons, 26(4). Internet Archive. https://doi.org/10.48077/scihor4.2023.21
    18. Lardy, R., Ruin, Q., & Veissier, I. (2023). Discriminating pathological, reproductive or stress conditions in cows using machine learning on sensor-based activity data. Computers and Electronics in Agriculture, 204, 107556. https://doi.org/10.1016/j.compag.2022.107556
    19. Leso, L., Becciolini, V., Rossi, G., Camiciottoli, S., & Barbari, M. (2021). Validation of a Commercial Collar-Based Sensor for Monitoring Eating and Ruminating Behaviour of Dairy Cows. Animals, 11(10), 2852. https://doi.org/10.3390/ani11102852
    20. Levit, H., Pinto, S., Amon, T., Gershon, E., Kleinjan-Elazary, A., Bloch, V., Ben Meir, Y. A., Portnik, Y., Jacoby, S., Arnin, A., Miron, J., & Halachmi, I. (2021). Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows. Animal, 15(2), 100093. https://doi.org/10.1016/j.animal.2020.100093
    21. McNicol, L.C., Bowen, J.M., Ferguson, H.J., Bell, J., Dewhurst, R.J., & Duthie, C.-A. (2024). Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production. Front. Sustain. Food Syst. 8, 1414858. https://doi.org/10.3389/fsufs.2024.1414858
    22. Mylostyvyi, R., Skliarov, P., Izhboldina, O., Chernenko, О., Lieshchova, М., Gutyj, B., Marenkov, O., & Rahmoun, D. E. (2023). The effectiveness of an automated heat detection system in Brown Swiss heifers when using sexed semen at a large dairy unit. Veterinarska Stanica, 55(2), 157–167. https://doi.org/10.46419/vs.55.2.7
    23. Mylostуva, D., Prudnikov, V., Kolisnyk, O., Lykhach, A., Begma, N., Кalinichenko, O., Khmeleva, O., Sanzhara, R., Izhboldina, O., & Mylostyvyi, R. (2022). Biochemical changes during heat stress in productive animals with an emphasis on the antioxidant defense system. Journal of Animal Behaviour and Biometeorology, 10(1), 1–9. https://doi.org/10.31893/jabb.22009
    24. Papakonstantinou, G.I., Voulgarakis, N., Terzidou, G., Fotos, L., Giamouri, E., & Papatsiros, V.G. (2024). Precision Livestock Farming Technology: Applications and Challenges of Animal Welfare and Climate Change. Agriculture 14, 620. https://doi.org/10.3390/agriculture14040620
    25. Rahman, A., Smith, D. V., Little, B., Ingham, A. B., Greenwood, P. L., & Bishop-Hurley, G. J. (2018). Cattle behaviour classification from collar, halter, and ear tag sensors. Information Processing in Agriculture, 5(1), 124–133. https://doi.org/10.1016/j.inpa.2017.10.001
    26. Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Invited review: Sensors to support health management on dairy farms. Journal of Dairy Science, 96(4), 1928–1952. https://doi.org/10.3168/jds.2012-6107
    27. 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, 639678. https://doi.org/10.3389/fanim.2021.639678
    28. Shorten, P. R. (2023). Acoustic sensors for detecting cow behaviour. Smart Agricultural Technology, 3, 100071. https://doi.org/10.1016/j.atech.2022.100071
    29. Shu, H., Bindelle, J., &. Gu, X. (2024). Non-contact respiration rate measurement of multiple cows in a free-stall barn using computer vision methods. Computers and Electronics in Agriculture 218, 108678. https://doi.org/10.1016/j.compag.2024.108678
    30. Shu, H., Bindelle, J., Guo, L., & Gu, X. (2023). Determining the onset of heat stress in a dairy herd based on automated behaviour recognition. Biosystems Engineering, 226, 238–251. https://doi.org/10.1016/j.biosystemseng.2023.01.009
    31. Skliarov, P., Fedorenko, S., Naumenko, S., Onyshchenko, O., Bilyi, D., Mylostyvyi, R., Vakulyk, V., & Kuraksina, L. (2023). Infrared thermography as a method of diagnosing reproductive pathologies in animals. Multidisciplinary Reviews, 6(2), 2023007. https://doi.org/10.31893/multirev.2023007
    32. Sousa, R. V. de, Canata, T. F., Leme, P. R., & Martello, L. S. (2016). Development and evaluation of a fuzzy logic classifier for assessing beef cattle thermal stress using weather and physiological variables. Computers and Electronics in Agriculture, 127, 176–183. https://doi.org/10.1016/j.compag.2016.06.014
    33. Sousa, R. V. de, Rodrigues, A. V. da S., Abreu, M. G. de, Tabile, R. A., & Martello, L. S. (2018). Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables. Computers and Electronics in Agriculture, 144, 37–43. https://doi.org/10.1016/j.compag.2017.11.033
    34. Steeneveld, W., & Hogeveen, H. (2015). Characterisation of Dutch dairy farms using sensor systems for cow management. Journal of Dairy Science, 98(1), 709–717. https://doi.org/10.3168/jds.2014-8595
    35. Strutzke, S., Fiske, D., Hoffmann, G., Ammon, C., Heuwieser, W., & Amon, T. (2019). Technical note: Development of a noninvasive respiration rate sensor for cattle. Journal of Dairy Science, 102(1), 690–695. https://doi.org/10.3168/jds.2018-14999
    36. Tsai, Y.-C., Hsu, J.-T., Ding, S.-T., Rustia, D. J. A., & Lin, T.-T. (2020). Assessment of dairy cow heat stress by monitoring drinking behaviour using an embedded imaging system. Biosystems Engineering, 199, 97–108. https://doi.org/10.1016/j.biosystemseng.2020.03.013
    37. Tüfekci, H., & Sejian, V. (2023). Stress Factors and Their Effects on Productivity in Sheep. Animals, 13(17), 2769. https://doi.org/10.3390/ani13172769
    38. Turk, R., Rošić, N., Beer Ljubić, B., & Vince, S. (2024). Effects of Summer Heat on Adipose Tissue Activity in Periparturient Simmental Cows. Metabolites 14, 207. https://doi.org/10.3390/metabo14040207
    39. Tzanidakis, C., Tzamaloukas, O., Simitzis, P., & Panagakis, P. (2023). Precision Livestock Farming Applications (PLF) for Grazing Animals. Agriculture 13, 288. https://doi.org/10.3390/agriculture13020288
    40. Wijffels, G., Sullivan, M., & Gaughan, J. (2021). Methods to quantify heat stress in ruminants: Current status and future prospects. Methods, 186, 3–13. https://doi.org/10.1016/j.ymeth.2020.09.004
    41. Woodward, S.J.R., Edwards, J.P., Verhoek, K.J., & Jago, J.G. (2024). Identifying and predicting heat stress events for grazing dairy cows using rumen temperature boluses. JDS Communications S2666910224000073. https://doi.org/10.3168/jdsc.2023-0482
    42. Wrzecińska, M., Czerniawska-Piątkowska, E., Kowalewska, I., Kowalczyk, A., Mylostyvyi, R., & Stefaniak, W. (2023). Agriculture in the face of new digitisation technologies. Ukrainian Black Sea Region Agrarian Science, 27(3), 9–17. https://doi.org/10.56407/bs.agrarian/3.2023.09
Creative Commons License

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

Copyright (c) 2024 The Authors

How to cite

Mylostyvyi, R., Sejian, V., Souza-Junior, J. B. F., Wrzecińska, M., Za, T., Chernenko О., Pryshedko, V., Suslova, N., Chabanenko, D., & Hoffmann, G. (2024). Digitalisation opportunities for livestock welfare monitoring with a focus on heat stress. Multidisciplinary Reviews, 7(12), 2024300. https://doi.org/10.31893/multirev.2024300
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 523
  • PDF downloaded - 189