Bibliographic Details
| Title: |
Methods of Observing the Signs of Approaching Calving in Cows—A Review. |
| Authors: |
Wojewodzic, Daria1 (AUTHOR), Gołębiewski, Marcin1 (AUTHOR), Grodkowski, Grzegorz1 (AUTHOR) grzegorz_grodkowski@sggw.edu.pl |
| Source: |
Animals (2076-2615). Apr2025, Vol. 15 Issue 7, p1018. 14p. |
| Subject Terms: |
*CATTLE parturition, *EFFECT of stress on animals, *ARTIFICIAL intelligence, *MACHINE learning, *BLOOD testing, *VIDEO monitors |
| Abstract: |
Simple Summary: Predicting the exact date of calving is difficult due to varying gestation lengths and environmental factors. Missing the signs of calving can lead to complications, increased calf mortality, and financial losses. Monitoring methods can be invasive, for example, temperature loggers and blood tests, or non-invasive, such as sensors and video systems. Non-invasive methods improve cow comfort, while AI and machine learning have improved prediction accuracy, helping farmers manage calving more effectively. Calving is a crucial event in dairy production, and predicting its exact date is challenging due to variable gestation lengths and environmental factors. Failure to recognize signs of impending calving can lead to dystocia, increased calf mortality, veterinary costs, and economic losses. This review discusses both invasive and non-invasive methods for monitoring calving signs. Invasive methods, such as temperature loggers, intra-vaginal GSM devices, and blood progesterone tests, provide direct physiological data but can cause stress to the animals. Non-invasive approaches, including the use of sensors on limbs, necks, or tails, as well as video monitoring systems, offer less intrusive alternatives, improving cow comfort. Advances in artificial intelligence (AI) and machine learning have enhanced the predictive accuracy of these methods, enabling better management of the calving process. [ABSTRACT FROM AUTHOR] |
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