Reproduction in Domestic Animals

Reproductive Status Assessed by Milk Progesterone 119Proportion of cases0.160.140.120.100.080.060.040.020.0010 20 30 40 50 60 70 80 90 100110120130140150160170Days from calvingFig. 4. Distribution of pregnancy lengths for cows in which pregnancyfailed (model detected reproductive status reverted from pregnancy tooestrus cycling)The present results indicate that after 23 days the modelreliably detected pregnancy failure. This is substantiallyearlier than the timing of reliable pregnancy determinationby rectal palpation at 6 weeks post-insemination(Peters and Ball 1995).Sampling frequencyThe above results were generated from a data set inwhich average time between collection of samples was1.4 days (for the period 0–120 days from calving).Because these results are expected to be influenced bysampling frequency, we examined the effect of reducingsampling frequency. As shown in Fig. 5, the relativeproportion of ratified oestruses detected by the modeldecreased with decreasing sampling frequency. Theseresults were achieved by simulating an evenly spreadreduction in samples and thus sampling frequency.However, this contrasts with the way the model is set upProportion of oestruses detected1.00.80.60.40.20.01.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0Interval between progesterone measurementsFig. 5. The effect of reducing progesterone sampling frequency(increasing the average interval between progesterone measurements[units = days]) on the proportion of ratified oestruses detected by themodel. The proportion is expressed relative to the case with the fullprogesterone data set. The solid line indicates the effect of reducedsampling frequency using fixed sampling schedules. The solid circleindicates the proportion of oestruses detected when using the modeloutput days to next sample function that results in a variable samplingfrequencyto request samples. In a situation in which the model ispart of an automated in-line progesterone measuringsystem, it controls sampling frequency through the DNSfunction. The DNS function implements a variablesampling frequency that is low immediately afteroestrus, and then increases as a function of bothduration of luteal phase and any evidence of a decreasein progesterone (Friggens and Chagunda 2005; see alsoAppendix). Using this variable function to reduce thesampling frequency in the test data resulted in anaverage sampling frequency of 3.3 days, but the proportionalreduction in oestrus detection was only 0.73.This is notably smaller than the 0.45 reduction for theequivalent equally distributed sampling frequency(Fig. 5). Even in this conservative test of samplingfrequency effects there is a clear benefit of having avariable sampling frequency.General considerationsThis paper provides a test of one particular model forinterpreting progesterone time-series data. In this testthe model was run using limited inputs, for simplicitythe potential benefits of including additional non-progesteroneinformation such as input from other externaloestrus-detection methods (e.g. pedometers), bodyenergy status, and urea concentrations were not evaluated.Despite this, the model performed at least as wellas other oestrus-detection systems (Firk et al. 2002;Cavalieri et al. 2003; Van Eerdenburg 2006). In thiscontext, it would have been relevant to compare thismodel with other models for interpreting progesteroneprofiles. At the time of writing, we could find no otherpublished progesterone models, thus comparison islimited to specific aspects such as oestrus detectionmeasured by indicators other than progesterone (virtuallyall tests use progesterone as the reference measure).Although the present model performed substantiallybetter in terms of sensitivity than the majority ofmethods in the literature (Firk et al. 2002; Cavalieri etal. 2003; Van Eerdenburg 2006), this is not in itself adefinitive assessment of the models quality. As demonstratedby numerous authors, sensitivity and specificitycan be altered by varying the thresholds used, andgenerally have an inverse relation to each other (de Molet al. 2001; Faustini et al. 2007). In the context of timeseriesmeasurements such as progesterone profiles,sensitivity and specificity measures are particularlyunsatisfactory because they are calculated for fixedtime-points or windows (Friggens et al. 2007). Forexample, Faustini et al. (2007) reported a sensitivity forprogesterone determination of pregnancy of 98.2%,substantially greater than the 89.3% reported in thepresent study, whereas Romano et al. (2006) reported asensitivity of 74.5%. The difference was in large partbecause of the time (days post-insemination) at whichthe sensitivity was calculated. Indeed, Romano et al.(2006) reported increasing sensitivity with increasingdays post-insemination. Clearly, factors such as thechosen time-point and sampling frequency make directcomparison of methods difficult.It should also be noted that in the present study,progesterone measurements were made in a laboratoryÓ 2008 The Authors. Journal compilation Ó 2008 Blackwell Verlag