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Related Experiment Video

Updated: Jan 23, 2026

Collection of Post-mating Semen from the Female Reproductive Tract and Measurement of Semen Liquefaction in Mice

Collection of Post-mating Semen from the Female Reproductive Tract and Measurement of Semen Liquefaction in Mice

Published on: November 18, 2017

Development of predictive models for boar semen quality.

D A Kuhlgatz¹, C Kuhlgatz², M Aepli¹

¹SUISAG, Allmend 8, CH-6204, Sempach, Switzerland.

|June 7, 2019

Summary

This summary is machine-generated.

A new model predicts boar sperm quality, accurately forecasting motility but struggling with sperm output and abnormal morphology. Barn climate had no significant impact, unlike factors like collection intervals and boar age.

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Published on: February 5, 2018

Area of Science:

Animal Reproduction
Veterinary Medicine
Agricultural Science

Background:

Sperm quality in artificial insemination (AI) boars is crucial for efficient swine production.
Predicting sperm quality based on external factors can optimize breeding programs.
Limited research exists on the impact of barn climate and other variables on comprehensive sperm quality metrics.

Purpose of the Study:

To develop and validate a predictive model for key sperm quality characteristics in AI boars.
To assess the influence of barn climate conditions, seasonality, collection frequency, age, and breed on sperm quality.
To identify the most significant predictors of sperm motility, output, and morphology.

Main Methods:

A k-fold cross-validation framework was employed to evaluate various regression models.
Models utilized different functional forms (linear, log-linear) and estimation techniques (OLS, SUR, 2SLS, 3SLS).
Data comprised 7455 ejaculates from 241 boars over one year in Southern Germany.

Main Results:

The best model achieved high accuracy for sperm motility prediction (MAPE: 4.35%).
Prediction accuracy was considerably lower for sperm output (MAPE: 23.92%) and abnormal spermatozoa (MAPE: 44.67%).
Barn climate variables showed no measurable effect on sperm quality after controlling for confounders.

Conclusions:

Predictive models can effectively forecast boar sperm motility but are less reliable for output and morphology.
Sperm quality is significantly influenced by factors such as morphology-motility linkages, sperm concentration, collection intervals, boar age, and breed.
Barn climate conditions are not a primary driver of sperm quality in this study population.