Postpartum hypocalcemia is a common metabolic disorder in dairy cows, causing reduced milk production, fertility problems, secondary diseases, and decreased animal welfare. The subclinical form, in particular, often goes unnoticed in daily farm operations. Many existing preventative measures are group-based and only partially suitable for smaller farms. The aim of this project was therefore to develop a simple, cow-specific method for the early detection of at-risk cows.

In this project, the rumination and forestomach activity of cows and heifers was continuously measured around calving using a sensor bolus placed in the rumen and compared with blood calcium levels. The results showed that cows with lower rumination activity in the days leading up to calving had a significantly increased risk of post-calcemia hypocalcemia. Based on this sensor data, predictive models were developed that can reliably identify at-risk cows even before the onset of clinical symptoms. Furthermore, blood parameters describing bone metabolism can also help to identify cows with an increased risk of hypocalcemia at an early stage.

The results show that modern sensor technology offers farmers and veterinarians practical support for identifying at-risk cows early and treating them in a targeted manner. This can prevent diseases, improve animal welfare, and reduce economic losses.

Initial results from this project have already been published in an international scientific journal. You can find the article at the following link: https://www.mdpi.com/2076-2615/15/22/3306