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Browsing Theses and Dissertations by Author "Ames, David R., committee member"

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    Factors that impact probability of pregnancy when using AI boars
    (Colorado State University. Libraries, 2013) Kaysen, Brett L., author; LeValley, Steve B., advisor; Ames, David R., committee member; Dalsted, Norman L., committee member; Schwab, Clinton R., committee member; Tatum, J. Daryl, committee member; Kimberling, Cleon V., committee member
    Measurements collected during a period of 3.5 years at Tempel Genetics Inc. in Gentryville, IN were analyzed to evaluate the effects of genetic and environmental factors on pregnancy rate using data from 15,375 parity records of two breeds (Landrace and Yorkshire). Female records utilized in the current study ranged from maiden gilts to mature sows through parity 7. All matings were performed via artificial insemination by semen produced within a boar housing facility also operated by Tempel Genetics. Semen was collected, processed, and evaluated on the farm and was not frozen. Pregnancy rate (measured as probability of pregnancy at 21 days post breeding via ultra-sound) of the females was significantly affected by number of services (P<0.05), season of insemination (P<0.05) and parity category (P<0.05). Interactions of (season by number of services and parity by number of services) were also evaluated. Boar age (P<0.05) and days from collection to insemination (P<0.05) were also significant sources of variation for pregnancy rate, while breed did not significantly affect pregnancy rate. The highest pregnancy rate (94.29%) was observed in sows of the parity category 3-4 that were inseminated with three services and using semen from boars less than 5 years of age. Potential opportunities to optimize these three factors should be evaluated by producers who expect to attain maximum pregnancy rate of sows inseminated using fresh boar semen. A model was also developed in Microsoft Excel format using results from the aforementioned analysis as a tool to assist swine producers in evaluating various management options to enhance pregnancy rate. With the use of this model, smaller producers who do not have access to large amounts of internal data can evaluate the potential impact of implementing different management options evaluated within a typical commercial-based swine enterprise.