ORCID Profile
0000-0002-1488-7672
Current Organisation
University of Wisconsin Madison
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Publisher: American Dairy Science Association
Date: 11-2013
Abstract: The objective was to determine the effect of exogenous progesterone (P4) in a timed artificial insemination (TAI) protocol initiated at 2 different times post-AI on pregnancies per AI (P/AI) in lactating dairy cows. Cows (n=1,982) in 5 dairy herds were assigned randomly at a nonpregnancy diagnosis 32 ± 3 d post-AI to 1 of 4 resynchronization (RES) treatments arranged in a 2 × 2 factorial design using the Ovsynch-56 (GnRH, 7d later PGF2α, 56 h later GnRH, 16 h later TAI) protocol. Treatments were as follows: cows initiating RES 32 ± 3 d after AI with no supplemental P4 (d 32 RES-CON n=516) same as d 32 RES-CON plus a controlled internal drug release (CIDR) insert containing P4 at the onset of Ovsynch-56 (d 32 RES-CIDR n=503) cows initiating RES 39 ± 3 d after AI (d 39 RES-CON n=494) and same as d 39 RES-CON plus a CIDR (d 39 RES-CIDR n=491). Cows were inseminated if observed in estrus before TAI. The P/AI was determined 32 and 60 d after TAI. In a subgroup of cows (n=1,152), blood s les were collected and ovarian structures examined by ultrasonography on the days of the first GnRH (G1) and PGF2α of Ovsynch-56. Percentage of cows with a corpus luteum (CL) at G1 was unaffected by timing of treatments, but percentage of cows with a CL at PGF2α was greater for d 32 than for d 39 cows (87.9 vs. 79.4%). In addition, percentage of cows with P4 ≥ 1 ng/mL at G1 was unaffected by timing of treatments, but was increased for d 32 compared with d 39 RES cows on the day of the PGF2α of the RES protocols (86.5 vs. 74.3%). Treatment did not affect ovulation to G1 or P/AI 32 d after RES TAI (d 32 RES-CON=30.1%, d 32 RES-CIDR=28.8%, d 39 RES-CON=27.5%, d 39 RES-CIDR=30.5%). A greater percentage of d 39 RES cows underwent premature luteolysis during the RES protocol compared with d 32 RES cows. An interaction was detected between day of RES initiation and CIDR treatment, in which the CIDR increased P/AI 60 d after TAI for d 39 (CON=23.7% vs. CIDR=28.0%), but not for d 32 (CON=26.9% and CIDR=24.2%) cows. Pregnancy loss was unaffected by treatment. In addition, cows had improved P/AI 60 d after TAI when they received a CIDR and did not have a CL (CON-CL=28.2%, CON-No CL=19.2%, CIDR-CL=27.0%, and CIDR-No CL=26.5%) or had P4 <1 ng/mL (CON-High P4=27.8%, CON-Low P4=15.0%, CIDR-High P4=25.0%, and CIDR-Low P4=29.4%) at G1, but not if a CL was present or P4 was ≥ 1 ng/mL at G1. In conclusion, addition of a CIDR insert to supplement P4 during the RES protocol increased P/AI for cows initiating RES 39 ± 3 d after AI but not 32 ± 3 d after AI.
Publisher: American Dairy Science Association
Date: 02-2014
Abstract: When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of in idual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.
Publisher: American Dairy Science Association
Date: 03-2010
Abstract: Our objective was to determine the effect of exogenous progesterone (P4) during a timed artificial insemination (TAI) protocol on pregnancies per AI (P/AI) in dairy cows not previously detected in estrus. Lactating cows (n=3,248) from 7 commercial dairy herds were submitted to a presynchronization protocol (2 injections of PGF(2alpha) 14 d apart Presynch), and cows in estrus after the second PGF(2alpha) received AI (EDAI n=1,583). Cows not inseminated by 12 to 14 d after the second PGF(2alpha) injection were submitted to a TAI protocol (GnRH on d 0, PGF(2alpha) on d 7, and GnRH+TAI 72h after PGF(2alpha)). At onset of the TAI protocol, cows were balanced by parity and days in milk and assigned randomly to receive no exogenous P4 (control, n=803) or a controlled internal drug release (CIDR) insert containing 1.38g of P4 from d 0 to 7 (CIDR, n=862). Blood s les were collected at the second PGF(2alpha) injection of the Presynch and on the day of the first GnRH injection of the TAI protocol for P4 determination. When P4 in both s les was or=1 ng/mL were classified as cyclic. Concentration of P4 at 11 to 14 d after AI was determined in a subgroup of cows (n=453) from 2 herds. Pregnancy was diagnosed at 40+/-5 and 65+/-5 d after AI. Proportion of cows inseminated on estrus after the second PGF(2alpha) injection of the Presynch protocol differed among herds (range=26.7 to 59.8%). Overall P/AI for EDAI cows at 40+/-5 and 65+/-5 d were 36.2 and 33.7%, respectively, and pregnancy loss was 8.8%. Proportion of cyclic cows at the onset of the TAI protocol differed among herds (range from 66.5 to 86.3%), but did not differ between treatments (control=72.4%, CIDR=74.1%). Treatment affected P/AI at 40+/-5 (control=33.3%, CIDR=38.1%) and 65+/-5 (control=30.0%, CIDR=35.1%) d after AI but did not affect pregnancy loss (8.6%). Cyclic cows had greater P/AI at 40+/-5 (38.2 vs. 29.3%) and 65+/-5 d (35.1 vs. 26.1%) after AI, but cyclic status had no effect on pregnancy loss. Treatment affected P4 concentration after AI, with more CIDR cows having P4 >or=1 ng/mL (94.4 vs. 86.9%) and P4 >or=3.2 ng/mL (81.8 vs. 68.0%) at 11 to 14 d after AI compared with control cows. Treatment of cows not previously detected in estrus with a CIDR insert during a TAI protocol increased proportion of cows with functional CL after AI and P/AI.
Publisher: American Dairy Science Association
Date: 06-2015
Abstract: The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.g., with conventional artificial insemination semen or expensive sex-enhanced semen), as well as subsets of low-fertility cows that are unlikely to conceive and should therefore be passed over at that point in time. Although such a strategy might be economically viable, the management, environmental, and financial conditions on one farm might differ widely from conditions on the next, and hence the reproductive management recommendations derived from such a tool may be suboptimal for specific farms. When coupled with cost-sensitive evaluation of misclassified and correctly classified insemination events, the strategy can be a potentially powerful tool for optimizing the reproductive management of in idual farms. In the present study, lift chart analysis and cost-sensitive evaluation were applied to a data set consisting of 54,806 insemination events of primiparous Holstein cows on 26 Wisconsin farms, as well as a data set with 17,197 insemination events of primiparous Holstein cows on 3 Wisconsin farms, where the latter had more detailed information regarding health events of in idual cows. In the first data set, the gains in profit associated with limiting inseminations to subsets of 79 to 97% of the most fertile eligible cows ranged from $0.44 to $2.18 per eligible cow in a monthly breeding period, depending on days in milk at breeding and milk yield relative to contemporaries. In the second data set, the strategy of inseminating only a subset consisting of 59% of the most fertile cows conferred a gain in profit of $5.21 per eligible cow in a monthly breeding period. These results suggest that, when used with a powerful classification algorithm, lift chart analysis and cost-sensitive evaluation of correctly classified and misclassified insemination events can enhance the performance and profitability of reproductive management programs on commercial dairy farms.
Publisher: American Dairy Science Association
Date: 11-2009
Abstract: The objectives were to estimate heritabilities and genetic variances for anovulation at ~50 d in milk and pregnancy loss occurring between first and second pregnancy diagnoses after artificial insemination. Data were originally collected for trials on reproductive management. Anovulation data consisted of 5,818 records from 13 studies in 8 herds with an overall prevalence of 23.3%. A Bayesian approach using Markov Chain Monte Carlo methods was used in mixed threshold models for both traits. The statistical model for anovulation included fixed effects [parity, herd-study-treatment, and body condition score (BCS)], covariates (inbreeding and milk yield), and random effects (sire and residual). A second statistical model included all terms in the first model except BCS. In addition, 2 bivariate, mixed sire models were used to analyze anovulation with BCS and anovulation with milk yield. The posterior mean heritability estimate for anovulation was 0.171 [posterior standard deviation (PSD) = 0.052]. Correlations of anovulation with milk yield were as follows: genetic = 0.168, PSD = 0.187 residual = -0.046, PSD = 0.022 and phenotypic = -0.036. Bivariate analysis of BCS with anovulation showed a genetic correlation (-0.301, PSD = 0.177) and phenotypic correlation (-0.192, PSD = 0.019). Pregnancy-loss data consisted of 3,775 records from 14 studies in 8 herds with an overall prevalence of 14.4%. Analysis of pregnancy loss used a sire-maternal grandsire threshold model with embryo survival as the subject of analysis. Independent variables consisted of fixed effects (parity and herd-study), covariates (embryo and maternal inbreeding), and random effects (sire of embryo, maternal grandsire of embryo, and residual). In addition, separate sire models were analyzed using embryo as the subject and cow as the subject of analysis. The sire-maternal grandsire model yielded a heritability for direct effect of 0.489 (PSD = 0.221) and for maternal effects of 0.166 (PSD = 0.113). In this study, the breeding value variance for embryo effects was 3 times the breeding value variance for maternal effects, indicating that, at the level of breeding values, the embryo's ability to survive has a greater effect on pregnancy loss than does the cow's ability to maintain the pregnancy. These results suggest that genetic improvement of reproductive performance could be enhanced by selection for fundamental measures such as abnormally long periods of postpartum anovulation and pregnancy loss. Larger studies of these traits are needed to obtain parameter estimates with greater precision.
No related grants have been discovered for Paul Fricke.