Field Data Analysis of Boar Semen Quality

This contribution provides an overview of approaches to correlate sow fertility data with boar semen quality characteristics. Large data sets of fertility data and ejaculate data are more suitable to analyse effects of semen quality characteristics on field fertility. Variation in fertility in sows...

Full description

Saved in:
Bibliographic Details
Published in:Reproduction in domestic animals 2011-09, Vol.46 (s2), p.59-63
Main Authors: Broekhuijse, MLWJ, Feitsma, H, Gadella, BM
Format: Article
Language:English
Subjects:
Animals
artificial insemination
boars
Body fluids
Breeding of animals
Data analysis
data collection
Fertility - physiology
Hogs
insemination
Insemination, Artificial - veterinary
Male
Males
semen
Semen - physiology
Semen Analysis - veterinary
sows
sperm motility
spermatozoa
Swine - physiology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This contribution provides an overview of approaches to correlate sow fertility data with boar semen quality characteristics. Large data sets of fertility data and ejaculate data are more suitable to analyse effects of semen quality characteristics on field fertility. Variation in fertility in sows is large. The effect of semen factors is relatively small and therefore impossible to find in smaller data sets. Large data sets allow for statistical corrections on both sow‐ and boar‐related parameters. Remaining sow fertility variation can then be assigned to semen quality parameters, which is of huge interest to AI (artificial insemination) companies. Previous studies of Varkens KI Nederland to find the contribution to field fertility of (i) the number of sperm cells in an insemination dose, (ii) the sperm motility and morphological defects and (iii) the age of semen at the moment of insemination are discussed in context of the possibility to apply such knowledge to select boars on the basis of their sperm parameters for AI purposes.
ISSN:0936-6768
1439-0531
DOI:10.1111/j.1439-0531.2011.01861.x