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Using classification trees to detect induced sow lameness with a transient model

Published online by Cambridge University Press:  09 April 2014

C. E. Abell
Affiliation:
Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
A. K. Johnson
Affiliation:
Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
L. A. Karriker
Affiliation:
Swine Medicine Education Center, Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Iowa 50011, USA
M. F. Rothschild
Affiliation:
Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
S. J. Hoff
Affiliation:
Department of Agriculture and Biosystems Engineering, Iowa State University, Ames, Iowa 50011, USA
G. Sun
Affiliation:
Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas 77843, USA
R. F. Fitzgerald
Affiliation:
Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA PIC North America, 100 Bluegrass Commons Blvd., Suite 2200, Hendersonville TN 37075, USA
K. J. Stalder
Affiliation:
Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
Corresponding
E-mail address:
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Abstract

Feet and legs issues are some of the main causes for sow removal in the US swine industry. More timely lameness detection among breeding herd females will allow better treatment decisions and outcomes. Producers will be able to treat lame females before the problem becomes too severe and cull females while they still have salvage value. The objective of this study was to compare the predictive abilities and accuracies of weight distribution and gait measures relative to each other and to a visual lameness detection method when detecting induced lameness among multiparous sows. Developing an objective lameness diagnosis algorithm will benefit animals, producers and scientists in timely and effective identification of lame individuals as well as aid producers in their efforts to decrease herd lameness by selecting animals that are less prone to become lame. In the early stages of lameness, weight distribution and gait are impacted. Lameness was chemically induced for a short time period in 24 multiparous sows and their weight distribution and walking gait were measured in the days following lameness induction. A linear mixed model was used to determine differences between measurements collected from day to day. Using a classification tree analysis, it was determined that the mean weight being placed on each leg was the most predictive measurement when determining whether the leg was sound or lame. The classification tree’s predictive ability decreased as the number of days post-lameness induction increased. The weight distribution measurements had a greater predictive ability compared with the gait measurements. The error rates associated with the weight distribution trees were 29.2% and 31.3% at 6 days post-lameness induction for front and rear injected feet, respectively. For the gait classification trees, the error rates were 60.9% and 29.8% at 6 days post-lameness induction for front and rear injected feet, respectively. More timely lameness detection can improve sow lifetime productivity as well as animal welfare.

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Copyright
© The Animal Consortium 2014 

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