Precision systems in dairy farms related to monitoring and management of some reproductive and health indicators - A review

Автори

DOI :

https://doi.org/10.61308/SEJL6091

Ключови думи :

precise monitoring systems, dairy cows, health indicators, reproductive indicators

Абстракт

Precision dairy farming (PDF) systems are developed through the use of sensor technologies, sound analysis, image analysis, and processing of results through special algorithms with continuous monitoring and analysis of data in real time - 24 hours a day, 7 days a week. The aim of this study is to analyze the use of precise monitoring systems related to certain health and reproductive indicators in dairy cows. These systems have the ability to collect, process and analyze information on motor activity, detection of cows in estrus, monitoring of rumen and body temperature, condition of limbs and hooves, as well as mastitis control. Precise monitoring systems allow farmers and consultants to make management decisions based on information, both at the level of an individual animal and for the entire herd. The information from such systems cannot be subjective, nor can it be manipulated even by chance, which makes it objective. Whether the decisions that farmers and consultants make are correct depends, however, not only on the information received, but also on their knowledge and skills.

Литература (библиография)

Aalseth, E. (2005). Fresh cow management: What is important, what does it cost, and what does it return? In: Proceedings of the 7th Western Dairy Management Conference, Reno, NV, 1 - 12.

Adewuyi, A. A., Roelofs, J. B., Gruys, E., Toussaint, M. J. M. & Van Eerdenburg, F. J. C. (2006). Relationship of Plasma Non Esterified Fatty Acids and Walking Activity in Postpartum Dairy Cows. Journal of Dairy Science, 89, 2977 - 2979.

Agrawal, S., Ghosh, S., Kaushal, S., Roy, B., Nigwal, A., Lakhani, G. P., Jain, A. & Udde, V. (2023). Precision Dairy Farming: A Boon for Dairy Farm Management. International Journal of Innovative Science and Research Technology, 8(3), 509 – 518.

Alsaaod, M., Niederhauser, J. J., Beer, G., Zehner, N., Schuepbach-Regula, G. & Steiner, A. (2015). Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior, Journal of Dairy Science, 98(9), 6236 – 6242.

Aungier, S. P. M., Roche, J. F., Sheehy, M. & Crowe, M. A. (2012). Effects of management and health on the use of activity monitoring for estrus detection in dairy cows. Journal of Dairy Science, 95, 2452 – 2466.

Bewley, J. M. (2010). Precision Dairy Farming: Advanced Analysis Solutions for Future Profitability, In: First North American Conference on Precision Dairy Management, Toronto, Canada during 2–5 March.

Borchers, M. R. Y., Chang, M., Proudfoot, K. L., Wadsworth, B. A., Stone, A. E. & Bewley, J. M. (2017). Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. Journal of Dairy Science, 100(7), 1 - 11.

Crociati, M., Sylla, L., van Straten, M., Stradaioli, G. & Monaci, M. (2020). Estimating the net return of a remote calving alarm system in a dairy farm. Journal of Dairy Science, 103(10), 9646 - 9655.

M.de Vries, M., Bokkers, E. A., van Reenen, C. G., Engel, B., van Schaik, G., Dijkstra, T. & de Boer, I. J. (2014). Housing and management factors associated with indicators of dairy cattle welfare. Prev. Vet Med., 118(1), 80 - 92.

DeVries, T. J., Beauchemin, K., Dohme, F. & Schwartzkopf-Genswein, K. S. (2009). Repeated ruminal acidosis challenges in lactating dairy cows at high and low risk for developing acidosis: Feeding, ruminating and lying behavior. Journal of Dairy Science, 92, 5067 - 5078.

Eastwood, C., Chapman, D. & Paine, M. (2012). Networks of practice for construction of agricultural decision support systems: Case studies of precision dairy farms in Australia. Agricultural Systems, 108, 10 - 18.

Eastwood, C., Jago, J., Edwards, J. & Burke, J. (2015). Getting the most out of advanced farm management technologies: Roles of technology suppliers and dairy industry organizations in supporting precision dairy farmers. Animal Production Science, 56, 1752 - 1760.

Edwards, J. L. & Tozer, P. R. (2004). Using activity and milk yield as predictors of fresh cow disorders. Journal of Dairy Science, 87, 524 - 531.

Faruq, I., Syarif, I., Ahsan, A. S., Al Rasyid, M. U. & Pratama, Y. P. (2019). Health Monitoring and Early Diseases Detection on Dairy Cow Based on Internet of Things and Intelligent System. In: Proceedings of the 2019 IEEE International Electronics Symposium (IES), Surabaya, Indonesia, 27–28 September.

Friggens, N. C. & Chagunda, M. G. G. (2005). Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description. Theriogenology, 64, 155 - 190.

Hogeveen, H. (2017). The value of precision dairy farming: Going beyond labor savings. Conference on Precision Dairy Farming held at Hyatt Regency, Lexington, 112.

Holdsworth, R. J. & Markillie, N. A. R. (1982). Evaluation of pedometers for estrus detection in dairy cows. Veterinary Record, 111, 16.

Huxley, J. N. (2013). Impact of lameness and claw lesions in cows on health and production. Livestock Science, 156(1 - 3), 64 - 70.

Ipema, A., Goense, D., Hogewerf, P., Houwers, H. & Van Roest, H. (2008). Pilot study to monitor body emperature of dairy cows with a rumen bolus. Computers and Electronics in Agriculture, 64(1), 49 - 52.

Jabbar, K. A., Hansen, M. F., Smith, M. L. & Smith, L. N. (2017). Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosystems Engineering, 153, 63 - 69.

Kamilaris, A., Fonts, A. & Prenafeta-Boldú, F. X. (2019). The Rise of Blockchain Technology in Agriculture and Food Supply Chains. Trends in Food Science & Technology, 91, 640 - 652.

Krieger, S., Sattlecker, G., Kickinger, F., Auer, W., Drillich, M. & Iwersen, M. (2018). Prediction of calving in dairy cows using a tail-mounted tri-axial accelerometer: A pilot study. Biosystem Engineering, 173, 79 - 84.

LeRoy, C. N. S., Walton, J. S. & LeBlanc, S. J. (2018). Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows. Journal of Dairy Science, 101(2), 1638 – 1647.

Lovendahl, P. & Chagunda, M. G. G. (2010). On the use of physical activity monitoring for estrus detection in dairy cows. Journal of Dairy Science, 93, 249 - 259.

Maatje, K., Rossing, W. & Wiersma, F. (1987). Temperature and activity measurements for estrous and sickness detection in dairy cattle. In: Proc 3rd Symposium on Automation in dairying, Wageningen, the Netherlands, 239 - 249.

Mazrier, H., Tal, S., Aizinbud, E. & Bargai, U. (2006). A field investigation of the use of the pedometer for the early detection of lameness in cattle. Canadian Veterinary Journal, 47, 883 - 886.

Morota, G., Ventura, R., Silva, F., Koyama, M. & Fernando, S. (2018). Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4) 1540 - 1550.

O’Leary, N. W., Byrne, D. T., O’Connor, A. H. & Shalloo, L. (2020). Invited review: Cattle lameness detection with accelerometers. Journal of. Dairy Science, 103, 3895 - 3911.

Otten, M., van den Heuvel-Panhuizen, M. & Veldhuis, M. (2019). The balance model for teaching linear equations: a systematic literature review. International Journal of STEM Education, 6, 30.

Owens, F. N., Secrist, D. S., Hill, W. J. & Gill, D. R. (1998). Acidosis in cattle: A review. Journal of Animal Science, 76, 275 - 286.

Phillips, N., Mottram, T., Poppi, D., Mayer, D. & McGowan, M. R. (2010). Continuous monitoring of ruminal pH using wireless telemetry. Animal Production Science, 50, 72 - 77.

Pietersma, D., Lacroix, R., Lefebvre, D. & Wade, K. (2003). Performance analysis for machine-learning experiments using small data sets. Computers and Electronics in Agriculture, 38, 1 - 17.

Popova, Y., Laleva, S., Oblakova, M., Ivanov, N., Slavov, I. & Dimova, N. (2024). Precision livestock farming: essence and application in large and small ruminants. Review. Bulgarian Journal of Animal Husbandry, 61(2), 54 - 63 (Bg).

Redden, K. D., Kennedy, A. D., Ingalls, J. R. & Gilson, T. L. (1993). Detection of estrus by radio telemetric monitoring of vaginal and ear skin temperature and pedometer measurements of activity. Journal of Dairy Science, 76, 713 - 721.

Rutten, C. J., Kamphuis, C., Hogeveen, H., Huijps, K., Nielen, M. & Steeneveld W. (2017). Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows. Computers and Electronics in Agriculture, 132, 108 - 118.

Saint-Dizier, M. & Chastant, S. (2015). Methods and on-farm devices to predict calving time in cattle. The Veterinary Journal, 205(3), 349 - 356.

Singh, A. K. & Chopra, U. K. (2007). Geo-informatics Applications in Agriculture. New India Publishing Agency, New Delhi, 325.

Steeneveld, W., Tauer, L. W., Hogeveen, H. & Oude-Lansink, A. G. J. M. (2012). Comparing technical efficiency of farms with anvautomatic milking system and a conventional milking system. Journal of Dairy Science, 95, 7391 - 7398.

Tanejaa, M., Byabazairea, J., Jalodiaa, N., Davya, A., Olariuc, C. & Malonea, P. (2020). Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171, 105286.

Thorup, V. M., Munksgaard, L., Robert, P. E., Erhard, H. W., Thomsen, P. T. & Friggens, N. C. (2015). Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms. Animal, 9(10), 1704 - 1712.

Titler, M., Maquivar, M. G., Bas, S., Rajala-Schultz, P. J., Gordon, E., McCullough, K., Federico, P. & Schuenemann, G. M. (2015). Prediction of parturition in Holstein dairy cattle using electronic data loggers. Journal of Dairy Science, 98(8), 5304 - 5312.

Valenza, A., Giordano, J. O., Lopes, G., Vincenti, L., Amundson, M. C. & Fricke, P. M. (2012). Assessment of an accelerometer system for detection of estrus and treatment with gonadotropin releasing hormone at the time of insemination in lactating dairy cows. Journal of Dairy Science, 95, 7115 - 7127.

Van Asseldonk, M. A. P. M., Huirne, R. B. M., Dijkhuizen, A. A., Beulens, A. J. M. & Udink ten Cate, A. J. (1999). Information needs and information technology on dairy farms. Computers and Electronics in Agriculture, 22(2 - 3), 97 - 107.

Vasseur, E., Gibbons, J., Rushen, J., Pellerin, D., Pajor, E., Lefebvre, D. & de Passillé, A. M. (2015). An assessment tool to help producers improve cow comfort on their farms. Journal of Dairy Science, 98(1), 698 – 708.

von Keyserlingk, M. A. G. & Weary, D. M. (2017). A 100-Year Review: animal welfare in the journal of dairy cience - The first 100 years. Journal of Dairy Science, 100(12), 10432 - 10444.

Warner, D., Vasseur, E., Lefebvre, D. M. & Lacroix, R. (2020). A machine learning based decision aid for lameness in dairy herds using farm-based records. Comput. Electron. Agric., 169, 105193.

Wolfger, B., Jones, B., Orsel, K. & Bewley, J. (2016). Technical note: Evaluation of an ear-attached real-time location-monitoring system. Journal of dairy science, 100(3), 2219 - 2224.

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Публикуван

26.02.2026

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Раздел (Секция)

Проблеми на науката и практиката

Как да цитирате

Mitev, E., & Ivanov Duchev, Z. (2026). Precision systems in dairy farms related to monitoring and management of some reproductive and health indicators - A review . Животновъдни науки, 63(1), 54-61. https://doi.org/10.61308/SEJL6091