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Watertown, WI, United States

Dalton J.C.,University of Idaho | Deragon L.,Alta Genetics | Vasconcelos J.L.M.,Sao Paulo State University | Lopes C.N.,Fazenda Anita | And 2 more authors.

The objective was to determine whether the presence of fertility-associated antigen (FAA) on sperm collected from Nelore (Bos indicus) bulls can be used to assess potential fertility of sperm for use at first-service fixed-time AI (TAI). Six Nelore bulls were selected based on FAA status (FAA-negative: N = 3; FAA-positive: N = 3) and the ability to produce neat semen with ≥ 70% morphologically normal sperm and 60% estimated progressive motility before cryopreservation. In Experiment 1, suckled multiparous Nelore cows (N = 835) were evaluated for body condition score (BCS) and received an intravaginal progesterone device (CIDR) and 2.0 mg of estradiol benzoate (Day 0). On Day 9 the CIDR was removed, 12.5 mg of PGF2α and 0.5 mg of estradiol cypionate were administered, and calves were removed for 48 h. All cows received TAI on Day 11 (48 h after CIDR removal). Pregnancy per TAI (P/TAI) was not different between FAA-positive and FAA-negative bulls (41.5% vs. 39.3%, respectively). There was an effect of AI technician on P/TAI (36.0% vs. 43.9%; P < 0.05) and BCS tended to affect P/TAI (P = 0.09), as cows with BCS ≥ 2.75 were 1.4 times more likely to become pregnant compared with cows with BCS < 2.75. In Experiment 2, nulliparous Nelore heifers (N = 617) were evaluated for BCS and received a CIDR and estradiol benzoate (2.0 mg) on Day 0. On Day 7, all heifers received PGF2α (12.5 mg). On Day 9, CIDR inserts were removed and all heifers received estradiol cypionate (0.6 mg) and 200 IU eCG. All heifers received TAI on Day 11 (48 h after CIDR removal). Pregnancy/TAI was different (P = 0.04) between FAA-positive and FAA-negative bulls (33.7% vs. 40.7%, respectively). Presence of FAA on sperm was unsuccessful in assessing the potential fertility of sperm for use in TAI. © 2012 Elsevier Inc. Source

Blaschek M.,University of Wisconsin - Madison | Kaya A.,Alta Genetics | Zwald N.,Alta Genetics | Memili E..,Mississippi State University | Kirkpatrick B.W.,University of Wisconsin - Madison
Journal of Dairy Science

Increasing fertility in dairy cattle is an important goal. Male infertility represents a part of the overall infertility in dairy cattle and can be partitioned into compensatory and noncompensatory components, where compensatory refers to infertility that can be overcome by increasing sperm number and noncompensatory infertility represents the remainder, presumably due to molecular and genomic defects. Through estimation of single nucleotide polymorphism (SNP) association with noncompensatory bull fertility, it is possible to identify regions of the genome influential to this trait. Use of this information in selection can allow for an increase in cattle fertility, resulting in economic benefits. In this study, high-density SNP genotypes and noncompensatory fertility data from 795 Holstein sires were used to examine SNP associations with fertility. A Bayes B analysis was performed to develop information for genomic selection and to identify genomic regions associated with noncompensatory fertility. A cross-validation approach was used to assess the effectiveness of the models within the original set of 795 bulls. Correlations of predicted and observed fertility values were approximately 0.145 in cross-validation. © 2011 American Dairy Science Association. Source

Schefers J.M.,University of Wisconsin - Madison | Weigel K.A.,University of Wisconsin - Madison | Rawson C.L.,Alta Genetics | Zwald N.R.,Alta Genetics | Cook N.B.,University of Wisconsin - Madison
Journal of Dairy Science

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance. © 2010 American Dairy Science Association. Source

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