Our first objective was to quantify the associations between health-related events (HRE) before insemination, the relative increase in estrus intensity (REI) at insemination, and the probability of cow-level pregnancy per artificial insemination (P/AI) in organic Holstein dairy cows. Quantifying these associations may aid on-farm decision-making, such as setting the voluntary waiting period, choice of type of semen, do-not-breed and culling decisions. A second objective was to develop predictive models to estimate P/AI based on readily available data, and present common goodness-of-fit results also used in the machine learning community. All data were collected from a certified organic dairy farm in the western USA from 2019 to 2021. Health-related and reproduction data were obtained through Dairy Records Management Systems (DRMS; Raleigh, NC, USA). Activity data were collected using pedometers (IceRobotics, Stirling, UK) mounted on the rear legs. The REI, defined as walking steps per hour before insemination divided by the cow’s baseline steps per hour, was available for 17,238 inseminations from 4759 cows. The REI was categorized as ≤200%, >200–400%, >400–600%, or >600%. The HRE were available for 65,684 inseminations from 13,365 cows. The HRE were categorized as mastitis, metabolic disease (i.e., hypocalcemia, ketosis, displaced abomasum, digestive problems), reproductive disease (i.e, metritis, endometritis, pyometra, retained fetal membranes), lameness, 2 different diseases, ≥3 different diseases, or as healthy (none of these diseases prior to insemination). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created. Data were split into training and test sets. The training data were used to fit three logistic regression models that included either HRE, or REI, or COMBO. Each of the three models also included the covariates of 3-mo herd-average P/AI prior to insemination, days in milk, and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect accounted for repeated inseminations within cow. Parameter estimates, odds ratios, and the estimated marginal means of the estimated P/AI of the fixed effects were obtained from the logistic regression models. The models’ estimates were applied to the test datasets, and discrimination and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 0.31, 0.28 and 0.28 for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 0.20 (≥3 different HRE) to 0.30 (healthy). The estimated P/AI associated with four REI categories were not different from 0.27 in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 0.18 after ≥2 HRE and >200–400% REI, to 0.30 when inseminations were in healthy cows with REI >600%. Inseminations in older cows, in the spring, and outside 18–24 d after the previous insemination were also associated with lower estimated P/AI. The area underneath the Receiver Operating Characteristic curve ranged from 0.57 (COMBO) to 0.60 (HRE) for the test data, indicating fair discrimination ability of the models. Calibration plots showed that the prediction models produced unbiased predicted P/AI. In conclusion, the results showed no conclusive evidence of greater estimated P/AI related to greater REI as a measure of estrus activity. More HRE were associated with lower estimated P/AI. Combinations of low REI and more HRE were associated with notably decreased estimated P/AI. The logistic regression models produced unbiased predicted P/AI. We found no evidence that the strength of the relationship between REI and P/AI depended on the HRE category. The applications of the results are as follows. First, these predictive models may help inform insemination decisions in organic dairy cows, although further external validation is recommended, and the discriminatory performance is weak. Second, a variety of goodness-of-fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machine learning techniques.
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