Journal of Dairy Science (Dec 2024)

Enhancing evaluation of bull fertility through multivariate analysis of sperm

  • H.C. Azevedo,
  • H.D. Blackburn,
  • E.A. Lozada-Soto,
  • S.F. Spiller,
  • P.H. Purdy

Journal volume & issue
Vol. 107, no. 12
pp. 11774 – 11784

Abstract

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ABSTRACT: Computer-assisted sperm analysis (CASA) has become the predominant tool for assessing bull semen in AI programs. Despite such popularity CASA's ability to predict fertility has been limited, especially when emphasis is based on single motion characteristics. Our hypothesis is that numerical sets of CASA measures provide a more effective method to differentiate the potential fertilization capacity of bulls and that bulls can be clustered based on sets of CASA measures. Therefore, we used CASA to evaluate frozen-thawed semen samples from 307 Holstein and 152 Jersey bulls sourced from USDA Agricultural Research Service's National Animal Germplasm Program gene bank. Sperm was evaluated immediately after thawing and 30 min later. We evaluated sperm kinetic and morphometric means and variances to capture the structure of CASA data in relation to various sources of variation. These data were subjected to univariate and multivariate statistical methods to investigate animal and management factors affecting sperm characteristics measured by CASA. Clustering with K-means identified 4 clusters of bulls based upon each cluster's set of CASA parameters after thawing. There was little overlap among clusters for sets of CASA measures. At the extremes, bull cluster 1 (BC1, n = 180) and BC3 (n = 101) had different sire conception rates (SCR) −0.07 versus −1.29, respectively, and sets of CASA measures. Interestingly, bull cluster 2 (BC2) had CASA measures that could be perceived as negative, for example, cell size at 8.18 mm2 versus 6.37 mm2 for bull cluster 4 (BC4) and total motility of 29.7% versus 48.7% for BC3, but SCR for BC2 were higher (−0.79) than those for BC3 (−1.29). Despite such discrepancies for some BC2 CASA values it appears the potentially negative effects were offset by the levels of other CASA values. Our findings suggest improved approaches for using CASA could lie in evaluating multiple CASA measures as sets within specific numerical ranges rather than as independent measures.

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