Mathematical characterization of the milk progesterone profile as a leg up to individualised monitoring of reproduction status in dairy cows

Optimal reproductive performance is an important factor affecting the profitability of dairy farms. However, optimal fertility results are often confined by the time-consuming nature of classical heat detection, the fact high-producing dairy cows show estrous symptoms less clearly and long, and the occurrence of ovarian problems. Today’s commercially available solutions for this problem include activity, camera, temperature and hormone based systems such as progesterone. The latter have the advantage that besides estrus, also pregnancy and ovarian problems can be monitored. For this, an intelligent and individual interpretation of the measurements is necessary, for which today fixed thresholds are used. The use of these general thresholds is suboptimal because the variation in progesterone profiles, even between cycles within the same cow is large. An alternative solution to use progesterone for reproduction monitoring, is by taking the whole profile into account. In this way, profile characteristics can be translated into specific attentions for the farmers, based on individual rather than general guidelines. Before using the profile and cycle characteristics is possible, it is necessary that an appropriate model to describe the milk progesterone profile is developed, which we do in this paper. The proposed model comprises the basal adrenal P4 production and the growing and regressing cyclic corpus luteum. To find the most appropriate way to describe the increasing and decreasing part of each cycle, three mathematical candidate functions were evaluated on these parts separately. These functions differed in the way they described the sigmoidal shape of each profile, and were the Hill function, the logistic growth curve and the Gompertz function. From the proposed functions, the Hill-based model was selected to describe the increasing part of each cycle, and the Gompertz-based model was used to describe the decreasing parts. Combining these models, a full mathematical model to characterize the full P4 cycle was obtained. In this way, we retained the flexibility to deal with both varying baseline and luteal progesterone values, but also with prolonged or delayed cycles which occur in the presence of ovarian cysts.

ISBN:978-90-8686-283-2

Jaar van publicatie:2016

Toegankelijkheid:Closed