Search

The aim of this study was to quantify the effects of progesterone profile features and other cow-level factors on insemination success to provide a real-time predictor equation of probability of insemination success. Progesterone profiles from 26 dairy herds were analyzed and the effects of profile features (progesterone slope, cycle length, and cycle height) and cow traits (milk yield, parity, insemination during the previous estrus) on likelihood of artificial insemination success were estimated. The equation was fitted on a training data set containing data from 16 herds (6,246 estrous cycles from 3,404 lactations). The equation was tested on a testing data set containing data from 10 herds (8,105 estrous cycles from 3,038 lactations). Predictors were selected to be implemented in the final equation if adding them to a base model correcting for timing of insemination and parity decreased the overall likelihood distance of the model. Selected variables (cycle length, milk yield, cycle height, and insemination during the previous estrus) were used to build the final model using a stepwise approach. Predictors were added 1 by 1 in different order, and the model that had the smallest likelihood distance was selected. The final equation included the variables timing of insemination, parity, milk yield, cycle length, cycle height, and insemination during the previous estrus, respectively. The final model was applied to the testing data set and area under the curve (AUC) was calculated. On the testing data set, the final model had an AUC of 58%. When the farm effect was taken into account, the AUC increased to 63%. This equation can be implemented on farms that monitor progesterone and can support the farmer in deciding when to inseminate a cow. This can be the first step in moving the focus away from the current paradigm associated with poorer estrus detection, where each detected estrus is automatically inseminated, to near perfect estrus detection, where the question is which estrous cycle is worth inseminating?

The aim of this study was to gain a better understanding of the variability in shape and features of all progesterone profiles during estrus cycles in cows and to create templates for cycle shapes and features as a base for further research. Milk progesterone data from 1418 estrus cycles, coming from 1009 lactations, was obtained from the Danish Cattle Research Centre in Foulum, Denmark. Milk samples were analyzed daily using a Ridgeway ELISA-kit. Estrus cycles with less than 10 data points or shorter than 4 days were discarded, after which 1006 cycles remained in the analysis. A median kernel of three data points was used to smooth the progesterone time series. The time between start of progesterone rise and end of progesterone decline was identified by fitting a simple model consisting of base length and a quadratic curve to progesterone data, and this luteal-like phase (LLP) was used for further analysis. The data set of 1006 LLP's was divided into five quantiles based on length. Within quantiles, a cluster analysis was performed on the basis of shape distance. Height, upward and downward slope, and progesterone level on Day 5 were compared between quantiles. Also, the ratio of typical versus atypical shapes was described, using a reference curve on the basis of data in Q1–Q4. The main results of this article were that (1) most of the progesterone profiles showed a typical profile, including the ones that exceeded the optimum cycle length of 24 days; (2) cycles in Q2 and Q3 had steeper slopes and higher peak progesterone levels than cycles in Q1 and Q4 but, when normalized, had a similar shape. Results were used to define differences between quantiles that can be used as templates. Compared to Q1, LLP's in Q2 had a shape that is 1.068 times steeper and 1.048 times higher. Luteal-like phases in Q3 were 1.053 times steeper and 1.018 times higher. Luteal-like phases in Q4 were 0.977 times steeper and 0.973 times higher than LLP's in Q1. This article adds to our knowledge about the variability of progesterone profiles and their shape differences. The profile clustering procedure described in this article can be used as a means to classify progesterone profiles without recourse to an a priori set of rules, which arbitrarily segment the natural variability in these profiles. Using data-derived profile shapes may allow a more accurate assessment of the effects of, e.g., nutritional management or breeding system on progesterone profiles.