2015Reproduction in Domestic AnimalsOpen access

In Vitro Measures for Assessing Boar Semen Fertility

Markus Jung, K Rüdiger, Martin Schulze

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Abstract

Optimization of artificial insemination (AI) for pig production and evaluation of the fertilizing capacity of boar semen are highly related. Field studies have demonstrated significant variation in semen quality and fertility. The semen quality of boars is primarily affected by breed and season. AI centres routinely examine boar semen to predict male fertility. Overall, the evaluation of classical parameters, such as sperm morphology, sperm motility, sperm concentration and ejaculate volume, allows the identification of ejaculates corresponding to poor fertility but not high-efficiency prediction of field fertility. The development of new sperm tests for measuring certain sperm functions has attempted to solve this problem. Fluorescence staining can categorize live and dead spermatozoa in the ejaculate and identify spermatozoa with active mitochondria. Computer-assisted semen analysis (CASA) provides an objective assessment of multiple kinetic sperm parameters. However, sperm tests usually assess only single factors involved in the fertilization process. Thus, basing prediction of fertilizing capacity on a selective collection of sperm tests leads to greater accuracy than using single tests. In the present brief review, recent diagnostic laboratory methods that directly relate to AI performance as well as the development of a new boar fertility in vitro index are discussed.

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Optimization of artificial insemination (AI) for pig production and evaluation of the fertilizing capacity of boar semen are highly related. Field studies have demonstrated significant variation in semen quality and fertility. The semen quality of boars is primarily affected by breed and season. AI centres routinely examine boar semen to predict male fertility. Overall, the evaluation of classical parameters, such as sperm morphology, sperm motility, sperm concentration and ejaculate volume, allows the identification of ejaculates corresponding to poor fertility but not high-efficiency prediction of field fertility. The development of new sperm tests for measuring certain sperm functions has attempted to solve this problem. Fluorescence staining can categorize live and dead spermatozoa in the ejaculate and identify spermatozoa with active mitochondria. Computer-assisted semen analysis (CASA) provides an objective assessment of multiple kinetic sperm parameters. However, sperm tests usually assess only single factors involved in the fertilization process. Thus, basing prediction of fertilizing capacity on a selective collection of sperm tests leads to greater accuracy than using single tests. In the present brief review, recent diagnostic laboratory methods that directly relate to AI performance as well as the development of a new boar fertility in vitro index are discussed.

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Available abstract

Optimization of artificial insemination (AI) for pig production and evaluation of the fertilizing capacity of boar semen are highly related. Field studies have demonstrated significant variation in semen quality and fertility. The semen quality of boars is primarily affected by breed and season. AI centres routinely examine boar semen to predict male fertility. Overall, the evaluation of classical parameters, such as sperm morphology, sperm motility, sperm concentration and ejaculate volume, allows the identification of ejaculates corresponding to poor fertility but not high-efficiency prediction of field fertility. The development of new sperm tests for measuring certain sperm functions has attempted to solve this problem. Fluorescence staining can categorize live and dead spermatozoa in the ejaculate and identify spermatozoa with active mitochondria. Computer-assisted semen analysis (CASA) provides an objective assessment of multiple kinetic sperm parameters. However, sperm tests usually assess only single factors involved in the fertilization process. Thus, basing prediction of fertilizing capacity on a selective collection of sperm tests leads to greater accuracy than using single tests. In the present brief review, recent diagnostic laboratory methods that directly relate to AI performance as well as the development of a new boar fertility in vitro index are discussed.

Key concepts: Sperm, Semen, Fertility, BOAR, Artificial insemination, Biology, Andrology, Semen quality

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