Infrared Thermography as a Non-Invasive Supportive Diagnostic Tool for Subclinical Hypocalcaemia in Frieswal Dairy Cows during the Transition Period
DOI:
https://doi.org/10.48165/ijvsbt.22.2.03Keywords:
Infrared thermography, Frieswal cows, Postpartum, Subclinical hypocalcaemia.Abstract
Subclinical hypocalcaemia (SCH) remains a pervasive and underdiagnosed metabolic disorder in high-yielding dairy cows, contributing to compromised postpartum health and productivity. This study investigates the utility of infrared thermography (IRT) as a non-invasive physiological indicator for the early detection of SCH in Frieswal cows during the transition period under subtropical climatic conditions in India. A total of 22 pregnant Frieswal cows were monitored from 7 (±1) days prepartum to 7 days postpartum. Animals were classified as normocalcaemic (≥8.0 mg/dL) or SCH (6.0-8.0 mg/dL) based on serum calcium concentrations. The highest incidence of SCH occurred on the day of calving (day 0, 68%) and the third postpartum day (64%), with 27% of cows persistently hypocalcaemic across both timepoints. Thermal imaging was conducted using a calibrated high-resolution infrared camera targeting ear surfaces, and rectal temperature (RT) was recorded. On day 0, SCH cows had significantly lower serum calcium (6.80 ± 0.10 mg/dL) and average ear surface temperature (100.60 ± 0.40 °F) compared to normocalcaemic cows. Strong negative correlations (r = –0.91 to –0.96) were observed between serum calcium and IRT parameters, (RT), and temperature-humidity index (THI) on day 0. By day 3, these relationships reversed, yielding strong positive correlations (r = 0.97 to 0.99). IRT parameters remained consistently correlated with RT and THI (r = 0.92–0.96). These findings underscore the diagnostic value of IRT as a real-time, non-invasive tool for early SCH identification. This approach offers a practical solution for integrating precision livestock farming technologies into metabolic health monitoring protocols, particularly in tropical dairy production systems where heat stress amplifies physiological vulnerabilities.Downloads
References
Armstrong, D. V. (1994). Heat stress interaction with shade and cooling. Journal of Dairy Science, 77(7), 2044–2050.
BAHS – Basic Animal Husbandry Statistics. (2024). Basic animal husbandry statistics. Department of Animal Husbandry and Dairying, Government of India, New Delhi. https://dahd.gov.in/schemes/programmes/animal-husbandry-statistics
Brezov, D., Hristov, H., Dimov, D., & Alexiev, K. (2023). Predicting the rectal temperature of dairy cows using infrared thermography and multimodal machine learning. Applied Sciences, 13(20), 11416.
Caixeta, L. S., Ospina, P. A., Capel, M. B., & Nydam, D. V. (2017). Association between subclinical hypocalcemia and reproductive performance in postpartum dairy cows. Journal of Dairy Science, 100(9), 7437–7446.
Džermeikaitė, K., Bačėninaite, D., & Antanaitis, R. (2023). Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13(5), 780.
Emam, M. H., Elmaghawry, S., & Abdelaal, A. M. (2024). Transition period in dairy cows: Monitoring, challenges, and future perspectives. Egyptian Journal of Veterinary Sciences, 56(11), 2849–2859.
Goff, J. P. (2008). The monitoring, prevention, and treatment of milk fever and subclinical hypocalcemia in dairy cows. The Veterinary Journal, 176(1), 50–57.
Grünberg, W. (2014). Treatment of phosphorus imbalance in dairy cows. Veterinary Clinics: Food Animal Practice, 30(2), 383–408.
Jonsson, N. N., & Daniel, R. C. W. (1997). Cortisol and corticosteroid binding globulin in plasma of cows with milk fever. Research in Veterinary Science, 62(1), 37–41.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
Lukas, J. M., Hawkins, D. M., & Grant, R. J. (2009). Inter- and intra-cow variation in plasma calcium concentration. Journal of Dairy Science, 92(8), 3517–3521.
Martinez, N., Risco, C. A., Lima, F. S., Bisinotto, R. S., Greco, L. F., Ribeiro, E. S., Maunsell, F., Galvão, K., & Santos, J. E. P. (2012). Evaluation of peripartal calcium status, energetic profile, and neutrophil function in dairy cows at risk of uterine disease. Journal of Dairy Science, 95(12), 7158–7172.
Mohtashamipour, F., Dirandeh, E., Ansari-Pirsaraei, Z., & Colazo, M. G. (2020). Postpartum health disorders in lactating dairy cows and their associations with reproductive responses and pregnancy status after first timed-AI. Theriogenology, 141, 98–104.
Neethirajan, S. (2020). Transforming the adaptation physiology of farm animals through sensors. Animals, 10(9), 1512.
Reinhardt, T. A., Lippolis, J. D., McCluskey, B. J., Goff, J. P., & Horst, R. L. (2011). Prevalence of subclinical hypocalcemia in dairy herds. The Veterinary Journal, 188(1), 122–124.
Reynolds, C. K., Aikman, P. C., Lupoli, B., Humphries, D. J., & Beever, D. E. (2003). Splanchnic metabolism of dairy cows during the transition from late gestation through early lactation. Journal of Dairy Science, 86(4), 1201–1217.
Seifi, H. A., Mohri, M., & Zadeh, K. J. (2004). Use of pre-partum urine pH to predict the risk of milk fever in dairy cows. The Veterinary Journal, 167(3), 281–285.
USDA. (2024). Milk production report. United States Department of Agriculture. https://esmis.nal.usda.gov
Van Leerdam, M., Hut, P. R., Liseune, A., & Slavco, E. (2024). A predictive model for hypocalcaemia in dairy cows utilizing behavioural sensor data combined with deep learning. Computers and Electronics in Agriculture, 220, 108877.
Venjakob, P. L., Borchardt, S., & Heuwieser, W. (2017). Hypocalcemia: Cow-level prevalence and preventive strategies in German dairy herds. The Veterinary Journal, 222, 1–7.
Venjakob, P. L., Borchardt, S., & Thiele, G. (2016). Evaluation of ear skin temperature as a cow-side test to predict postpartum calcium status in dairy cows. Journal of Dairy Science, 99(8), 6754–6761.
Wang, C., Mamedova, L. K., Christopherson, T. L., Goff, J. P., & Bradford, B. J. (1991). Stress hormone response and neutrophil function in dairy cows with experimentally induced subclinical hypocalcemia. American Journal of Veterinary Research, 52(9), 1432–1436.
Wang, Y., Li, Q., Chu, M., Kang, X., & Liu, G. (2023). Application of infrared thermography and machine learning techniques in cattle health assessments: A review. Theriogenology, 230, 361–387.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Indian Journal of Veterinary Sciences and Biotechnology
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
