Regression analyses and motile sperm subpopulation structure study as improving tools in boar semen quality analysis

Area of Science:

• Veterinary Science
• Animal Reproduction
• Biostatistics

Background:

• Improving pig artificial insemination (AI) success relies on accurate boar fertility assessment.
• Standard semen quality analyses offer potential predictive value for boar fertility.

Purpose of the Study:

• To develop predictive mathematical models for boar fertilizing ability using semen quality parameters.
• To evaluate the efficacy of computer-assisted semen analysis (CASA) and other tests in predicting fertility outcomes.

Main Methods:

• Logistic and linear regression analyses were applied to semen parameters.
• Key parameters included Osmotic Resistance Test (ORT), Hyperosmotic Resistance Test (HRT), and sperm viability.
• Computer-Assisted Semen Analysis (CASA) was used to characterize sperm subpopulations.

Main Results:

• Logistic regression models incorporating ORT, HRT, and viability significantly correlated with in vivo conception rates (P<0.05).
• No significant correlation was found between semen characteristics and prolificacy (litter size).
• CASA revealed distinct motile sperm subpopulations, but their structure did not clearly correlate with fertility.

Conclusions:

• Standard boar semen quality analysis, combined with logistic correlation, can reasonably predict in vivo conception rates after AI.
• Predicting prolificacy remains challenging with current semen analysis methods.
• Further research may refine predictive models for boar fertility and reproductive success.