Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-28T01:17:46.643Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  09 April 2014

C. E. Abell ,
A. K. Johnson ,
L. A. Karriker ,
M. F. Rothschild ,
S. J. Hoff ,
G. Sun ,
R. F. Fitzgerald  and
K. J. Stalder
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
*
†Email: stalder@iastate.edu
Get access

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.

Keywords

gaitlamenesssowweight distribution
Type
Full Paper
Information
animal , Volume 8 , Issue 6 , June 2014 , pp. 1000 - 1009
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anil, SS, Anil, L and Deen, J 2005. Evaluation of patterns of removal and associations among culling because of lameness and sow productivity traits in swine breeding herds. Journal of the American Veterinary Medical Association 226, 956–961.Google Scholar
CIR Systems Inc 2013. GAITRite Electronic Walkway Technical Reference (WI-02-15) Rev. L. CIR Systems Inc, Sparta, NJ.Google Scholar
Corr, SA, McCorquodale, CC, McGovern, RE, Gentle, MJ and Bennett, D 2003. Evaluation of ground reaction forces produced by chickens walking on a force plate. American Journal of Veterinary Research 64, 76–82.Google Scholar
Deen, J 2009. Lameness as a welfare and productivity concern. Proceedings of 28th Centralia Swine Research Update, 28 January 2009, Kirkton, Ontario, pp. I-11–12.Google Scholar
Evans, R, Horstman, C and Conzemius, M 2005. Accuracy and optimization of force platform gait analysis in labradors with cranial cruciate disease evaluated at a walking gait. Veterinary Surgery 34, 445–449.Google Scholar
Federation of Animal Science Society. 1999. Guide for the care and use of agricultural animals in agricultural research and teaching, 1st revised edition. Federation of Animal Science Society, Savoy, IL.Google Scholar
Fitzgerald, RF, Stalder, KJ, Karriker, LA, Sadler, LJ, Hill, HT, Kaisand, J and Johnson, AK 2012. The effect of hoof abnormalities on sow behavior and performance. Livestock Science 145, 230–238.CrossRefGoogle Scholar
Flower, FC and Weary, DM 2006. Gait assessment in dairy cattle. Animal 3, 87–95.Google Scholar
Grégoire, J, Bergeron, R, D’Allaire, S, Meunier-Salaün, MC and Devillers, N 2013. Assessment of lameness in sows using gait, footprints, postural behavior, and foot lesion analysis. Animal 7, 1163–1173.CrossRefGoogle ScholarPubMed
Kaler, J, Wassink, GJ and Green, LE 2009. The inter- and intra-observer reliability of a locomotion scoring scale for sheep. The Veterinary Journal 180, 189–194.CrossRefGoogle ScholarPubMed
Karriker, LA, Abell, CE, Pairis, MD, Holt, WA, Sun, G, Coetzee, JF, Johnson, AK, Hoff, SJ and Stalder, KJ 2013. Validation of a lameness model in sows using physiological and mechanical measurements. Journal of Animal Science 91, 130–136.Google Scholar
Keegan, KG, Dent, EV, Wilson, DA, Janicek, J, Kramer, J, Lacarrubba, A, Walsh, DM, Cassells, MW, Esther, TM, Schiltz, P, Frees, KE, Cl, Wilhite, Clark, JM, Pollitt, C, Shaw, R and Norris, T 2010. Repeatability of subjective evaluation of lameness in horses. Equine Veterinary Journal 42, 92–97.CrossRefGoogle ScholarPubMed
Knauer, MT 2006. Factors influencing sow longevity. MS Thesis, Iowa State University, Ames, Iowa, USA.Google Scholar
Liaw, A, Wiener, M, Breiman, L and Cutler, A 2013. Package ‘randomForest’. Retrieved June 4, 2013, from http://cran.r-project.org/web/packages/randomForest/randomForest.pdf Google Scholar
Main, DC, Clegg, J, Spatz, A and Green, LE 2000. Repeatability of a lameness scoring system for finishing pigs. Veterinary Record 147, 574–576.CrossRefGoogle ScholarPubMed
Manson, FJ and Leaver, JD 1988. The influence of concentrate amount on locomotion and clinical lameness in dairy cattle. Animal Production 47, 185–190.Google Scholar
Morrow, CMK, Rothschild, MF, Draper, DD and Christian, LL 1991. Analysis of gait parameters in Duroc swine genetically divergent for front-leg structure. Journal of Animal Breeding and Genetics 108, 280–289.Google Scholar
Pastell, ME and Kujala, M 2007. A probabilistic neural network model for lameness detection. Journal of Dairy Science 90, 2283–2292.Google Scholar
Pastell, ME, Hautala, M, Poikalainen, V, Praks, J, Veermae, I, Kujals, M and Ahokas, J 2008. Automatic observation of cow leg health using load sensors. Computers and Electronics in Agriculture 62, 48–53.Google Scholar
Quinn, MM, Keuler, NS, Lu, Y, Faria, MLE, Muir, P and Markel, MD 2007. Evaluation of agreement between numerical rating scales, visual analogue scoring scales, and force plate gait analysis in dogs. Veterinary Surgery 36, 360–367.Google Scholar
Rothschild, MF and Christian, LL 1988. Genetic control of front-leg weakness in Duroc swine. I. Direct response to five generations of divergent selection. Livestock Production Science 19, 459–471.Google Scholar
Smith, B 1988. Lameness in pigs associated with foot and limb disorders. In Practice 10, 113–117.CrossRefGoogle Scholar
Stalder, KJ, Lacy, RC, Cross, TL, Conaster, GE and Darroch, CS 2000. Net present value analysis of sow longevity and the economic sensitivity of new present value to changes in production, market price, feed cost, and replacement gilt costs in a farrow-to-finish operation. Professional Animal Scientist 16, 33–40.Google Scholar
Sun, G, Fitzgerald, RF, Hoff, SJ, Karriker, LA, Johnson, AK and Stalder, KJ 2011. Development of an embedded microcomputer-based force plate system for measuring sow weight distribution and detection of lameness. Applied Engineering in Agriculture 27, 475–482.Google Scholar
Therneau, T, Atkinson, B and Ripley, B 2013. Package ‘rpart’. Retrieved June 4, 2013, from http://cran.r-project.org/web/packages/rpart/rpart.pdf Google Scholar
Wells, GAH 1984. Locomotor disorders of the pig. In Practice 6, 48–53.Google Scholar
Welsh, EM, Gettinby, M and Nolan, AM 1993. Comparison of a visual analogue scale and a numerical rating scale for assessment of lameness, using sheep as a model. American Journal of Veterinary Research 54, 976–983.Google Scholar
Zinpro. 2008. Feet first: locomotion scoring [CD-ROM]. Version 1.0. Zinpro Corporation, Eden Prairie, MN.Google Scholar