Facility and management characteristics of large Upper Midwest dairy herds clustered by Dairy Herd Improvement records

Authors

  • R. L. Brotzman University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • N. B. Cook University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • M. R. Foy University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • J. P. Hess University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • K. V. Nordlund University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • T. B. Bennett University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • A. Gomez University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706
  • D. D. Döpfer University of Wisconsin-Madison School of Veterinary Medicine, Madison, WI 53706

DOI:

https://doi.org/10.21423/aabppro20134225

Keywords:

Principal component analysis, dairy herd improvement, data management, cluster analysis, herd management

Abstract

Principal component analysis (PCA) is used on datasets with vast numbers of numeric variables and limited observations (e.g., dairy herd improvement [DHI] data) for unbiased selection of uncorrelated variables that describe the largest amount of variance. Cluster analysis (CA) divides objects of interest (e.g., dairy herds) into groups on the basis of similarity in multiple characteristics simultaneously. The aims of this project were to develop a novel method for discovering important DHI variables by use of PCA and then grouping herds by those variables via CA, and to survey herds to determine herd management characteristics of each group.

Cover image of the 46th Conference Proceedings: An image of cattle standing in a field during th day. They are looking rather indignantly at the camera. Yellow background.

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Published

2013-09-19

Issue

2013

Section

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