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The use of novel phenotyping methods for validation of equine conformation scoring results

Published online by Cambridge University Press:  13 January 2015

T. Druml ,
M. Dobretsberger  and
G. Brem
T. Druml*
Affiliation:
Institute of Animal Breeding and Genetics, Veterinary University Vienna, Veterinärplatz 1, 1220 Vienna, Austria
M. Dobretsberger
Affiliation:
Institute of Animal Breeding and Genetics, Veterinary University Vienna, Veterinärplatz 1, 1220 Vienna, Austria
G. Brem
Affiliation:
Institute of Animal Breeding and Genetics, Veterinary University Vienna, Veterinärplatz 1, 1220 Vienna, Austria
*
†E-mail: [email protected]
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Abstract

In this experiment, which is based on a cohort of 44 Lipizzan mares from the Austrian state stud farm of Piber, we present new statistical techniques for the analysis of shape and equine conformation using image data. In addition, we examined which strategies and procedures of image processing techniques led to a successful interpretation of the traits implemented in horse breeding programs. A total of 246 two-dimensional anatomical and somatometric landmarks were digitized from standardized photographs, and the variation of shape has been analyzed by the use of generalized orthogonal least-squares Procrustes (generalized Procrustes analysis (GPA)) procedures. The resulting shape variables have been regressed on the results from linear type trait classifications. In addition, the rating scores of six conformation classifiers were tested for agreement, yielding an inter-rater correlation (inter-class correlation) ranging from 0.41 to 0.68, respectively, a κ coefficient ranging from 0.16 to 0.53. From the 12 linear type traits assessed on a valuating scale, only the type-related traits (type, breed-type and harmony) revealed significant (P<0.05) results in the regression analysis of shape variables on linear type traits. The other nine traits were characterized by a lower agreement between classifiers and did not result in a significant ‘shape regression’. Finally, the ‘horse shape space’ defined by shape variables resulting from GPA procedures offered the possibility to assist in trait definition and in the evaluation of ratings, and it is an adequate biological and objective scale to human perception of conformation, which is expressed in numerical data only.

Keywords

image analysishorse conformationgeometric morphometricslinear type traitrater reliability
Type
Research Article
Information
animal , Volume 9 , Supplement 6 , June 2015 , pp. 928 - 937
Copyright
© The Animal Consortium 2015 

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References

Bookstein, F 1991. Morphometric tools for landmark data: geometry and biology. Cambridge University Press, Cambridge, UK.Google Scholar
Brem, G and Kräußlich, H 1998. Ziele der Exterieurbeurteilung. In Exterieurbeurteilung landwirtschaftlicher Nutztiere (ed. G Brem), pp. 118–120. Ulmer, Stuttgart, Germany.Google Scholar
Cervantes, I, Baumung, R, Molina, A, Druml, T, Gutiérrez, JP, Sölkner, J and Valera, M 2009. Size and shape analysis of morphofunctional traits in the Spanish Arab horse. Livestock Science 125, 43–49.CrossRefGoogle Scholar
Chen, B, Zaebst, D and Seel, L 2005. A macro to calculate Kappa statistics for categorizations by multiple raters. Proceedings of the 30th Annual SAS User Group International Conference, 10–13 April 2005, Philadelphia, Pennsylvania, USA, Paper No. 155-30.Google Scholar
Druml, T, Baumung, R and Sölkner, J 2008. Morphological analysis and effect of selection for conformation in the Noriker draught horse population. Livestock Science 115, 118–129.Google Scholar
Duensing, J, Stock, KF and Krieter, J 2014. Implementation and prospects of linear profiling in the Warmblood horse. Journal of Equine Veterinary Science 34, 360–368.Google Scholar
Fleiss, JL 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin 76, 378–382.Google Scholar
Goodall, CR 1991. Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society Series B 53, 285–339.Google Scholar
Grundler, C and Pirchner, F 1991. Wiederholbarkeit der Beurteilung von Exterieurmerkmalen und Reiteigenschaften. Züchtungskunde 63, 273–281.Google Scholar
Kant, I 1790. Critique of judgment, edition meredith 1928. Oxford University Press, Oxford.Google Scholar
Kendall, DG 1984. Shape-manifolds, Procrustes metrics and complex projective spaces. Bulletin of the London Mathematical Society 16, 81–121.CrossRefGoogle Scholar
Kendall, DG 1985. Exact distributions for shapes of random triangles in convex sets. Advances of Applied Probability 17, 308–329.CrossRefGoogle Scholar
Koenen, EPC, VanVeldhuizen, AE and Brascamp, EW 1995. Genetic parameters of linear scored conformation traits and their relation to dressage and show jumping performance in the Dutch Warmblood Riding Horse population. Livestock Production Science 43, 85–94.CrossRefGoogle Scholar
Koenen, EPC, Aldridge, LI and Philipsson, J 2004. An overview of breeding objectives for warmblood sport horses. Livestock Production Science 88, 77–84.Google Scholar
Kristensen, E, Dueholm, L, Vink, D, Andersen, JE, Jakobsen, EB, Illum-Nielsen, S, Petersen, FA and Enevoldsen, C 2006. Within- and across-person uniformity of body condition scoring in Danish Holstein cattle. Journal of Dairy Science 89, 3721–3728.Google Scholar
Kristjansson, T, Bjornsdottir, S, Sigurdsson, A, Crevier-Denoix, N, Pourcelot, P and Arnason, T 2013. Objective quantification of conformation of the Icelandic horse based on 3-D video morphometric measurements. Livestock Science 158, 12–23.CrossRefGoogle Scholar
Mitteröcker, P and Gunz, P 2009. Advances in geometric morphometrics. Evolutionary Biology 36, 235–247.Google Scholar
Molina, A, Valera, M, Dos Santos, R and Rodero, A 1999. Genetic parameters of morphofunctional traits in Andalusian horse. Livestock Production Science 60, 295–303.Google Scholar
Rohlf, FJ 2004. tpsUtil, file utility program, version 1.26. Department of Ecology and Evolution, State University of New York at Stony Brook, New York, USA.Google Scholar
Rohlf, FJ 2005. tpsDig, digitize landmarks and outlines, version 2.05. Department of Ecology and Evolution, State University of New York at Stony Brook, New York, USA.Google Scholar
Rohlf, FJ 2011. tpsRegr, shape regression, version 1.40. Department of Ecology and Evolution, State University of New York at Stony Brook, New York, USA.Google Scholar
Rohlf, FJ 2013a. tpsRelw, relative warp analysis, version 1.53. Department of Ecology and Evolution, State University of New York at Stony Brook, New York, USA.Google Scholar
Rohlf, FJ 2013b. tpsSuper, superimposition, version 2.00. Department of Ecology and Evolution, State University of New York at Stony Brook, New York, USA.Google Scholar
Rohlf, FJ and Slice, DE 1990. Extensions of the Procrustes method for the optimal superimposition of landmarks. Systematic Zoology 39, 40–59.CrossRefGoogle Scholar
Sanchez, MJ, Gomez, MD, Molina, A and Valera, M 2013. Genetic analyses for linear conformation traits in Pura Raza Español horses. Livestock Science 157, 57–64.Google Scholar
SAS Institute 2002–2003. SAS version 9.1. SAS Institute Inc., Cary, NC, USA.Google Scholar
Schäfer, K and Bookstein, F 2009. Does geometric morphometrics serve the needs of plasticity research? Journal of Biosciences 34, 589–599.CrossRefGoogle Scholar
Schäfer, K, Mitteröcker, P, Fink, B and Bookstein, F 2009. Psychomorphospace – from Biology to perception, and back: towards an integrated quantification of facial form variation. Biological Theory 4, 98–106.CrossRefGoogle Scholar
Slice, DE 1999. Morpheus et al.: software for morphometric research. Department of Ecology and Evolution, State University of New York at Stony Brook, New York, USA.Google Scholar
Slice, DE 2007. Geometric morphometrics. Annual Review of Anthropology 36, 261–281.Google Scholar
Small, CG 1996. The statistical theory of shape. Springer, New York, USA.CrossRefGoogle Scholar
Veerkamp, RF, Gerritsen, CLM, Koenen, EPC, Hamoen, A and De Jong, G 2002. Evaluation of classifiers that uses linear type traits and body condition score using common sires. Journal of Dairy Science 85, 976–983.Google Scholar
Viklund, A, Braam, A, Näsholm, A, Strandberg, E and Philipsson, J 2010. Genetic variation in competition traits at different ages and time periods and correlations with traits at field test of 4-year-old Swedish Warmblood horses. Animal 4, 682–691.CrossRefGoogle Scholar
Windhager, S, Schäfer, K and Fink, B 2011. Geometric morphometrics of male facial shape in relation to physical strength and perceived attractiveness, dominance and masculinity. American Journal of Human Biology 23, 805–814.Google Scholar
Zangwill, N 2003. Aesthetic judgment. In The Stanford encyclopedia of philosophy (Summer 2014 Edition) (ed. EN Zalta). Retrieved June 2, 2014, from http://plato.stanford.edu/archives/sum2014/entries/aesthetic-judgment/ Google Scholar
Zelditch, M, Swiderski, D and HWLF, Sheets 2004. Geometric morphometrics for biologists. A primer. Elsevier, San Diego, USA.Google Scholar
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