Evaluation of the effectiveness of pH and rumination monitoring in cow feeding control
https://doi.org/10.32634/0869-8155-2025-399-10-174-181
Abstract
The study compares two technologies of precision animal husbandry — the SCR Heatime collar (Allflex Livestock Intelligence, Israel) and the “Agrobiotest” bolus (“Agrobiotest” LLC, Russia) — for monitoring the physiological state of dairy cows. The experiment was conducted on 10 Holstein cows at the KubSAU farm. The cicatricial bolus demonstrated high accuracy (97.8%) in measuring the pH of the scar, which is critically important for assessing the acidity and health of the gastrointestinal tract. The SCR Heatime collar (Allflex Livestock Intelligence, Israel) proved to be more effective for tracking rumination (89.4%), which allows monitoring eating behavior. The combined use of the devices increased the overall accuracy of diagnosing the metabolic status of cows to 98.5%, providing a more complete picture of the animals’ condition. A moderate correlation (r = 0.73) was found between the rumination activity and the pH level, which confirms the relationship between eating behavior and metabolic processes. The study confirms that the integration of data from different sensors (collars and boluses) makes it possible to increase the effectiveness of early diagnosis of metabolic disorders and optimize diets. The results obtained open up prospects for the creation of digital animal twins, which contributes to the development of precision animal husbandry.
About the Authors
F. E. Vladimirov
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Fedor Evgenievich Vladimirov - Candidate of Technical Sciences, Senior Researcher,
5 1st Institutsky proezd, Moscow, 109428
A. R. Khakimov
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Artyom Rustamovich Khakimov - Candidate of Technical Sciences, Senior Researcher,
5 1st Institutsky proezd, Moscow, 109428
S. S. Yurochka
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Sergey Sergeevich Yurochka - Candidate of Technical Sciences, Senior Researcher,
5 1st Institutsky proezd, Moscow, 109428
D. Yu. Pavkin
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Dmitry Yurievich Pavkin - Candidate of Technical Sciences, Senior Researcher,
5 1st Institutsky proezd, Moscow, 109428
S. O. Bazaev
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Savr Olegovich Bazaev - Candidate of Agricultural Sciences, Researcher,
5 1st Institutsky proezd, Moscow, 109428
References
1. Simitzis P., Tzanidakis C., Tzamaloukas O., Sossidou E. Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals. Dairy. 2022; 3(1): 12–28. https://doi.org/10.3390/dairy3010002
2. Norton T., Chen C., Larsen M.L.V., Berckmans D. Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer. Animal. 2019; 13(12): 3009–3017. https://doi.org/10.1017/S175173111900199X
3. Golovina Yu.I. Digitalization in the agro-industrial sector of Russia. Digital technologies in the development of modern economic systems: Proceedings of the II All-Russian research conference with international participation. Lipetsk: Lipetsk State Technical University. 2024; 29–32 (in Russian). https://elibrary.ru/wbqlab
4. Dyshekova A.A., Ivanov Z.A., Shabatukov I.A., Balkarova A.R. Development of digital technologies in the agro-industrial complex of Russia. Economy, management and finance of the agro-industrial complex: modern prospects and innovations. Proceedings of the International scientific and practical conference dedicated to the 95th anniversary of the birth of Sh.M. Mamedov. Makhachkala: Dagestan State Agrarian University named after M.M. Dzhambulatov. 2024; 230–233 (in Russian). https://elibrary.ru/jamffs
5. Bakkuev E.S., Sarbasheva E.M. The Impact of Digital Transformation on the Dynamics of Regional Agroeconomic Development. Science, Education, and Business: A New View or a Strategy for Integrated Interaction. Proceedings of the IV International Scientific and Practical Conference Dedicated to the Memory of the First President of the Kabardino-Balkarian Republic V.M. Kokov. Nalchik: Kabardino-Balkarian State Agrarian University named after V.M. Kokov. 2024; 261–263 (in Russian). https://elibrary.ru/uzugih
6. Panina O.V. Innovative approaches to improving the efficiency of agriculture in the context of the transformation of the agro-industrial complex. Agrarian science. 2025; 393(04): 180–184 (in Russian). https://doi.org/10.32634/0869-8155-2025-393-04-180-184
7. Ulybina L.V. Trends in the development of the Russian livestock sector in the context of food security. Agrarian science. 2025; 392(03): 144–149 (in Russian). https://doi.org/10.32634/0869-8155-2025-392-03-144-149
8. Lobachevsky Ya.P., Dorokhov A.S. Digital technologies and robotic devices in the agriculture. Agricultural Machinery and Technologies. 2021; 15(4): 6–10 (in Russian). https://doi.org/10.22314/2073-7599-2021-15-4-6-10
9. Tsench Yu.S. Scientific and Technological Potential as the Main Factor for Agricultural Mechanization Development. Agricultural Machinery and Technologies. 2022; 16(2): 4–13 (in Russian). https://doi.org/10.22314/2073-7599-2022-16-2-4-13
10. Avanesyan D.N. Digital transformation in the agro-industrial complex. Collection of articles based on the materials of the annual scientific and practical conference of teachers on the results of research for 2024. Collection of conference papers. Krasnodar: Kuban State Agrarian University named after I.T. Trubilin. 2025; 538– 540 (in Russian). https://elibrary.ru/cfdjgl
11. Speshilov E.A., Bogach E.V. Information technologies and artificial intelligence in agricultural production in the context of the transformation of management processes. Artificial and natural intelligence: algorithms, thinking and educational technologies: Proceedings of the XXI International congress with elements of a scientific school for young scientists. Moscow: Moscow University named after S.Yu. Witte. 2025; 447–456 (in Russian). https://elibrary.ru/igbisq
12. Galkin A.I. Application of neural networks and big data technologies in agriculture: increasing the efficiency and sustainability of agricultural production. Agrarian science. 2025; 393(04): 167–171 (in Russian). https://doi.org/10.32634/0869-8155-2025-393-04-167-171
13. Petrovski K.R., Cusack P., Malmo J., Cockcroft P. The Value of ‘Cow Signs’ in the Assessment of the Quality of Nutrition on Dairy Farms. Animals. 2022; 12(11): 1352. https://doi.org/10.3390/ani12111352
14. Lemesh E.A. Increasing the level of milk productivity of dairy cows and improving the quality indicators of milk when using different diet compositions. Modern trends in the development of agricultural science. Collection of scientific papers of the III International scientific and practical conference. Bryansk: Bryansk State Agrarian University. 2024; 206–209 (in Russian). https://elibrary.ru/kitiri
15. Krupin E.O. et.al. Qualitative indicators of milk and fermented milk products when activated zeolite and probiotics are included in the diet of cows. Agrarian science. 2024; 387(10): 72–79 (in Russian). https://doi.org/10.32634/0869-8155-2024-387-10-72-79
16. Golovin A.V. Efficiency of using calcium salts of fatty acids in optimizing energy nutrition of dairy cows. Agrarian science. 2025; 392(03): 76–82 (in Russian). https://doi.org/10.32634/0869-8155-2025-392-03-76-82
17. Pereira G.M., Sharpe K.T., Heins B.J. Evaluation of the RumiWatch system as a benchmark to monitor feeding and locomotion behaviors of grazing dairy cows. Journal of Dairy Science. 2021; 104(3): 3736–3750. https://doi.org/10.3168/jds.2020-18952
18. Rahmawati D., Ms A.U., Faradhilah N., Alfita R., Nahari R.V., Setiawan H. Design of a Real Time Cow Smart Collar Health and Position Monitoring System. 2023 IEEE 9th Information Technology International Seminar (ITIS). IEEE. 2023; 1–7. https://doi.org/10.1109/ITIS59651.2023.10420353
19. Džermeikaitė K., Bačėninaitė D., Antanaitis R. Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases. Animals. 2023; 13(5): 780. https://doi.org/10.3390/ani13050780
20. Hajnal É., Kovács L., Vakulya G. Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods. Sensors. 2022; 22(18): 6812. https://doi.org/10.3390/s22186812
21. Zhang F. et al. Research Advances and Prospect of Intelligent Monitoring Systems for the Physiological Indicators of Beef Cattle. Smart Agriculture. 2024; 6(4): 1–17 (in Chinese). https://doi.org/10.12133/j.smartag.SA202312001
22. Dorokhov A.S., Pavkin D.Yu., Yurochka S.S. Digital twin technology in agriculture: prospects for use. Agricultural Engineering (Moscow). 2023; 25(4): 14–25 (in Russian). https://doi.org/10.26897/2687-1149-2023-4-14-25
23. Ayadi S., Ben Said A., Jabbar R., Aloulou C., Chabbouh A., Achballah A.B. Dairy Cow Rumination Detection: A Deep Learning Approach. Jemili I., Mosbah M. (eds.). Distributed Computing for Emerging Smart Networks. Second International Workshop (DiCES-N 2020). Cham: Springer. 2020; 123–139. https://doi.org/10.1007/978-3-030-65810-6_7
24. Antanaitis R., Anskienė L., Palubinskas G., Rutkauskas A., Baumgartner W. The Relationship between Reticuloruminal Temperature, Reticuloruminal pH, Cow Activity, and Clinical Mastitis in Dairy Cows. Animals. 2023; 13(13): 2134. https://doi.org/10.3390/ani13132134
25. Bikker J.P. et al. Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity. Journal of Dairy Science. 2014; 97(5): 2974–2979. https://doi.org/10.3168/jds.2013-7560
26. Neubauer V., Humer E., Kröger I., Braid T., Wagner M., Zebeli Q. Differences between pH of indwelling sensors and the pH of fluid and solid phase in the rumen of dairy cows fed varying concentrate levels. Journal of Animal Physiology and Animal Nutrition. 2018; 102(1): 343–349. https://doi.org/10.1111/jpn.12675
Review
For citations:
Vladimirov F.E., Khakimov A.R., Yurochka S.S., Pavkin D.Yu., Bazaev S.O. Evaluation of the effectiveness of pH and rumination monitoring in cow feeding control. Agrarian science. 2025;(10):174-181. (In Russ.) https://doi.org/10.32634/0869-8155-2025-399-10-174-181
Views:
9
Email this article 