Comparison of NUCLEOCOUNTER, ANDROVISION with Leja chambers and the newly developed ANDROVISION eFlow for sperm concentration analysis in boars.

Exact analysis of sperm concentration in raw and diluted semen is of major importance in swine artificial insemination, as sperm concentration is one of the most important characteristics of an ejaculate determining the value of the ejaculate and the productive life of the boar. The study compares different methods for sperm concentration analysis in raw and diluted boar semen: NUCLEOCOUNTER SP-100, the ANDROVISION with Leja chambers and the new ANDROVISION eFlow system. The Concordance Correlation Coefficient (CCC) between NUCLEOCOUNTER and ANDROVISION eFlow was 0.

Relations between the time of ovulation and fecal estrogen concentration in sows.

Artificial insemination (AI) is the most important biotechnology in pig reproduction. To achieve the best possible fertility results, appropriate timing of the insemination is essential. The optimal time for AI is 12 h before to 4 h after ovulation.

In vitro effects of two different commercial freezing and thawing extenders on boar sperm quality.

This study was conducted to evaluate whether there were differences in viability of cryopreserved semen when using two different freezing (Minitube Cryoguard - F1 or Androstar® CryoPlus - F2) and thawing (Minitube Cryoguard Thawing solution - T1 or Androstar® Plus - T2) extenders. Ejaculates were collected, diluted (1:1), and cooled before shipping at 17 °C overnight. Samples were aliquoted in cryopreservation extender F1 or F2.

Development of a new antimicrobial concept for boar semen preservation based on bacteriocins.

The conventional storage temperature of 16-18 °C provides optimal conditions for the preservation of boar sperm quality, which are extremely cold sensitive cells. On the other hand, however, it requires the addition of antibiotics to inhibit bacterial growth. Rising numbers of antibiotic resistant bacteria call for alternatives to this conventional storing method.

Development of predictive models for boar semen quality.

In this study a prognosis model is developed that predicts sperm quality characteristics based on external factors such as barn climate conditions, seasonality, semen collection frequency, age and breed of artificial insemination (AI) boars. For this a k-fold cross validation framework is used to test the prediction accuracy of a wide range of regression models that are based on different functional forms (linear, log-linear) and estimation techniques (ordinary least squares, seemingly unrelated regression, two-stage least squares estimation and three-stage least squares estimation). The dataset includes 241 boars from three barns within one boar stud located in Southern Germany, consisting of 7455 ejaculates collected during one year.