Buzağılarda Dijitalleşme ve Akıllı İzleme Sistemleri
Özet
Bu bölüm, buzağı yetiştiriciliğinde dijitalleşme ve akıllı izleme sistemlerinin modern hayvancılık işletmeleri açısından önemini ele almaktadır. Geleneksel gözleme dayalı yönetim anlayışının yerini, sürekli veri toplayan, analiz eden ve karar süreçlerini destekleyen akıllı çiftlik yaklaşımları almaktadır. Bu kapsamda otomatik buzağı besleyicileri, ivmeölçerler, kulak küpesi tabanlı sensörler, kamera sistemleri, kısmi tartım platformları, görüntü işleme uygulamaları ve dijital kayıt yazılımları buzağıların büyüme, beslenme, davranış, sağlık ve refah durumlarının bireysel düzeyde izlenmesine olanak sağlamaktadır. Süt tüketimi, içme hızı, aktivite düzeyi, yatma süresi, canlı ağırlık artışı ve çevresel koşullara verilen davranışsal tepkiler, hastalıkların ve gelişim geriliklerinin erken belirlenmesinde önemli göstergeler olarak değerlendirilmektedir. Dijital kayıt ve karar destek sistemleri, elde edilen verilerin yalnızca depolanmasını değil, aynı zamanda anlamlı uyarılara ve yönetimsel kararlara dönüştürülmesini sağlamaktadır. Bu sistemler iş gücü kullanımını iyileştirmekte, refah değerlendirmesini objektifleştirmekte ve işletme performansını artırmaktadır. Sonuç olarak, buzağı yetiştiriciliğinde dijitalleşme; daha hassas, bireyselleştirilmiş, sürdürülebilir ve veri temelli bir yönetim modelinin temel bileşeni olarak öne çıkmaktadır.
This section discusses the importance of digitalization and smart monitoring systems in calf rearing within modern livestock enterprises. Traditional management practices based mainly on visual observation are increasingly being replaced by smart farm approaches that continuously collect, analyze, and integrate data into decision-making processes. In this context, automated calf feeders, accelerometers, ear-tag sensors, camera-based systems, partial weighing platforms, computer vision applications, and digital record-keeping software enable individual-level monitoring of calf growth, feeding behavior, activity, health, and welfare. Parameters such as milk intake, drinking speed, activity level, lying time, body weight gain, and behavioral responses to environmental conditions are considered important indicators for the early detection of disease, growth retardation, nutritional problems, and management failures. Digital records and decision-support systems not only store data but also transform them into meaningful alerts and practical management decisions. These technologies help optimize labor use, improve the objectivity of welfare assessment, support early intervention, and enhance overall farm performance. Moreover, the integration of different data sources allows farmers to move from delayed, treatment-oriented management toward preventive and predictive strategies. In conclusion, digitalization in calf rearing represents a key component of a more precise, individualized, sustainable, and data-driven management model.
Referanslar
Costa JHC, Cantor MC, Neave HW. Symposium review: Precision technologies for dairy calves and management applications. Journal of Dairy Science. 2021;104(1):1203-1219. doi:10.3168/jds.2019-17885.
Papadopoulos G, Papantonatou MZ, Uyar H, et al. Economic and environmental benefits of digital agricultural technological solutions in livestock farming: A review. Smart Agricultural Technology. 2025;10:100783. doi:10.1016/j.atech.2025.100783.
Bach A, Ahedo J. Record keeping and economics of dairy heifers. Veterinary Clinics of North America: Food Animal Practice. 2008;24(1):117-138. doi:10.1016/j.cvfa.2007.10.001.
Pesenti Rossi G, Dalla Costa E, Barbieri S, et al. A systematic review on the application of precision livestock farming technologies to detect lying, rest and sleep behavior in dairy calves. Frontiers in Veterinary Science. 2024;11:1477731. doi:10.3389/fvets.2024.1477731.
Cantor MC, Pertuisel CH, Costa JHC. Technical note: Estimating body weight of dairy calves with a partial-weight scale attached to an automated milk feeder. Journal of Dairy Science. 2020;103(2):1914-1919. doi:10.3168/jds.2019-16918.
Negreiro A, Bresolin T, Ferreira REP, et al. Computer vision systems for monitoring hutch-housed dairy calves. Journal of Dairy Science. 2025. doi:10.3168/jds.2025-26267.
Duthie CA, Bowen JM, Bell DJ, et al. Feeding behaviour and activity as early indicators of disease in pre-weaned dairy calves. Animal. 2021;15(3):100150. doi:10.1016/j.animal.2020.100150.
Ramezani Gardaloud N, Guse C, Lidauer L, et al. Early detection of respiratory diseases in calves by use of an ear-attached accelerometer. Animals. 2022;12(9):1093. doi:10.3390/ani12091093.
Goharshahi M, Azizzadeh M, Lidauer L, et al. Monitoring selected behaviors of calves by use of an ear-attached accelerometer for detecting early indicators of diarrhea. Journal of Dairy Science. 2021;104(5):6013-6019. doi:10.3168/jds.2020-18989.
Bushby EV, Thomas M, Vázquez-Diosdado JA, et al. Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data. Scientific Reports. 2024;14:9737. doi:10.1038/s41598-024-58206-4.
Perttu RK, Peiter M, Bresolin T, et al. Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States. Journal of Dairy Science. 2023;106(2):1272-1282. doi:10.3168/jds.2022-22043.
Cantor MC, Welk AA, Creutzinger KC, et al. The development and validation of a milk feeding behavior alert from automated feeder data to classify calves at risk for a diarrhea bout: A diagnostic accuracy study. Journal of Dairy Science. 2024;107(5):3140-3156. doi:10.3168/jds.2023-23635.
Cabrera VE, Fadul-Pacheco L. Future of dairy farming from the Dairy Brain perspective: Data integration, analytics, and applications. International Dairy Journal. 2021;121:105069. doi:10.1016/j.idairyj.2021.105069.
Barry J, Bokkers EAM, Berry DP, et al. Pre-weaning management of calves on commercial dairy farms and its influence on calf welfare and mortality. Animal. 2020;14(12):2580-2587. doi:10.1017/S1751731120001615.
Godden SM, Lombard JE, Woolums AR. Colostrum management for dairy calves. Veterinary Clinics of North America: Food Animal Practice. 2019;35(3):535-556. doi:10.1016/j.cvfa.2019.07.005.
Morrison JL, Winder CB, Medrano-Galarza C, et al. Case-control study of behavior data from automated milk feeders in healthy or diseased dairy calves. JDS Communications. 2022;3(3):201-206. doi:10.3168/jdsc.2021-0153.
Tekin K, Yurdakök Dikmen B, Kanca H, et al. Precision livestock farming technologies: Novel direction of information flow. Ankara Üniversitesi Veteriner Fakültesi Dergisi. 2021;68(2):193-212. doi:10.33988/auvfd.837485.
Sun D, Webb L, van der Tol PPJ, et al. A systematic review of automatic health monitoring in calves: Glimpsing the future from current practice. Frontiers in Veterinary Science. 2021;8:761468. doi:10.3389/fvets.2021.761468.
Silva FG, Conceição C, Pereira AMF, et al. Literature review on technological applications to monitor and evaluate calves' health and welfare. Animals. 2023;13(7):1148. doi:10.3390/ani13071148.
