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.

Objective: To evaluate the effects of glucoamylase on ruminal in situ and in vitro dry matter degradability (IVDMD), in vitro starch degradability (IVSD), volatile fatty acids (VFA), gas production and methane production using cereal grains as substrates in beef cattle.Methods: Six substrates (4 mm; 0.70 g per F57 bag) including sorghum (micronized and whole), barley (whole and steam-flaked), and corn (dry-rolled and steam-flaked); were incubated with exogenous glucoamylase (from Trichoderma reesei; 0.25 mg/g substrate dry matter [DM]) and buffered rumen fluid in six replicates per run in 3 independent runs. IVDMD, IVSD, in situ DMD (ISDMD), VFA, and methane production was measured after 7, and 12 h of incubation. ISDMD was measured after 0, 1, 3, 7, and 12 h of ruminal incubation in beef cattle fed high grain diets.Results: Exogenous glucoamylase increased IVDMD and gas production for all substrates except whole sorghum and barley while ISDMD was increased for all substrates except whole barley. Glucoamylase increased IVSD for steam flaked barley and corn. Glucoamylase supplementation increased molar proportion of propionate and decreased acetate-to-propionate ratio, regardless of substrates used. Similarly, glucoamylase supplementation increased methane production with steam-flaked corn.Conclusion: Glucoamylase supplementation can potentially improve DM and starch degradability in cereal grains commonly used as ingredients in beef cattle diets.

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 pregnancy per artificial insemination (P/AI) in organic dairy cows. Quanti-fying 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 ob-jective 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 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 4,759 cows. The REI was categorized as ≤200% (6,999 inseminations), >200-400% (4,685), >400-600% (2,929), or >600% (2,625). The HRE were available for 65,684 inseminations from 13,365 cows. The HRE was categorized as mastitis (prior to 9,114 inseminations), metabolic (displaced abomasum, ketosis, milk fever; 1,941), reproductive disease (metritis, endometritis, pyometra, retained fetal membranes; 4,907), lameness (4,058), 2 different diseases (4,022), ≥3 different diseases (813), or as healthy (none of these diseases prior to insemination; 40,829). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created for 16,415 inseminations in 4,647 cows. Data were split into training and test sets. The training data were used to fit 3 logistic regression models that included either HRE, or REI, or COMBO. Each of the 3 models also included the covariates of prior 3-mo herd P/AI and days in milk (DIM), and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect ac-counted for repeated inseminations within cow. Parameter estimates, odds ratios, and the least-square 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 discrimi-nation and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 31%, 28% and 28% for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 20% (≥3 different HRE) to 30% (healthy). The estimated P/AI associated with the 4 REI categories were not different from 27% in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 18% after ≥2 HRE and >200-400% REI, to 30% when insemina-tions 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 esti-mated 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. The Brier score ranged from 0.19 to 0.21, indicating moderate performance of the prediction models. Calibration plots showed that the prediction models produced unbi-ased estimated 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 health-related events 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 mod-els produce unbiased estimated P/AI. These predictive models may inform insemination and culling decisions in organic dairy cows. A variety of goodness of fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machines learning techniques.