Hostname: page-component-7b7567c8c-wxvwp Total loading time: 0 Render date: 2026-11-15T18:13:47.633Z Has data issue: false hasContentIssue false
  • English
  • Français

Using visual image analysis to describe pig growth in terms of size and shape

Published online by Cambridge University Press:  18 August 2016

A. B. Doeschl-Wilson,
C. T. Whittemore,
P. W. Knap and
C. P. Schofield
A. B. Doeschl-Wilson
Affiliation:
lPIC International Group, 2 Kingston Business Park, Kingston Bagpuize, Oxfordshire OX13 5FE, UK
C. T. Whittemore
Affiliation:
School ofGeosciences, SAC Building, King's Buildings, University of Edinburgh, West Mains Road, Edinburgh EH9 JJG, UK
P. W. Knap
Affiliation:
lPIC International Group, 2 Kingston Business Park, Kingston Bagpuize, Oxfordshire OX13 5FE, UK
C. P. Schofield
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Silsoe, Bedford MK45 4HS, UK
Get access

Abstract

Random regression models were used to analyse the daily growth data for a total of 25 pigs of two commercial crossbred types between 75 and 140 days of age. A visual imaging system placed above a feeding station provided daily the plan area and length measurements of different body parts. Daily live-weight measurements of the pigs were obtained from a platform balance integrated into an electronic feeding station. Growth curves associated with different measures, pigs and types were compared. Significant differences in the age growth curves between the pig types could only be found in the ham width measurements (P < 0.05). The linear measure of ham width showed the greatest difference between the two types, and the lowest coefficient of variation among individual animals. Size measures were shown to be a more consistent indicator of pig performance during growth than live weight: pigs with a relatively large surface area or ham width at the early growth stage also have relatively large surface area or ham width at later stages and the between-animal variation in these measurements remains constant with age. Gain in live weight relative to increase in size differed significantly between the two pig types (P < 0.05). Pigs of the two types had significantly different shapes, but the change of shape during growth did not differ significantly between them. The allometric relationships between surface area and ham width1.85 and between body length and ham width0.85 indicate that the ham widths of pigs increase faster in proportion to full body measures. Variations between individual animals in size increase and shape change are significant (P < 0.05). The analysis suggests that VIA size and shape measurements provide valid descriptors of pig growth.

Keywords

growthpigsregression analysisvisual image analysis

Information

Type
Research Article
Information
Animal Science , Volume 79 , Issue 3 , December 2004, pp. 415 - 427
Copyright
Copyright © British Society of Animal Science 2004

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.)

Article purchase

Temporarily unavailable

References

Akaike, H. 1974. A new look at the statistical model identification. IEEE transactions on automatic control 19: 716–723.CrossRefGoogle Scholar
Andersen, S. and Pedersen, B. 1996. Growth and food intake curves for group-housed gilts and castrated male pigs. Animal Science 63: 457–464.CrossRefGoogle Scholar
Bridges, D. M., Turner, U. W., Smith, E. M., Stahly, T. S. and Loewer, O. J. 1986. A mathematical procedure for estimating animals growth and body composition. Transactions of the ASAE 29: 1342.CrossRefGoogle Scholar
Brody, S. 1945. Bioenergetics and growth: with special reference to the efficiency complex in domestic animals. Reinhold, New York.Google Scholar
Brown, C. 2004. Who matters? The changing market: perspective from multiple retailers. In The appliance of pig science (ed. Thompson, J. E., Gill, B. P. and Varley, M. A.), British Society of Animal Science publication no. 31, pp. 19–22. University of Nottingham.Google Scholar
Brown, J. E., Fitzhugh , H. A. Jr and Cartwright, T. C. 1976. A comparison of nonlinear models for describing weight-age relationships in cattle. Journal of Animal Science 42: 810.CrossRefGoogle Scholar
Craig, B. A. and Schinckel, A. P. 2001. Nonlinear mixed effects model for swine growth. The Professional Animal Scientist 17: 256.CrossRefGoogle Scholar
Davison, A. C. and Hinkley, D. V. 1997. Bootstrap methods and their application. Cambridge University Press.CrossRefGoogle Scholar
Doeschl, A. B., Green, D. M., Whittemore, C. T., Schofield, C. P., Fisher, A. V. and Knap, P. W. 2004. The relationship between the body shape of living pigs and their carcass morphology and composition. Animal Science 79: 73–83.CrossRefGoogle Scholar
Hammond, J. 1932. Growth and development of mutton qualities in sheep. Oliver and Boyd, London.Google Scholar
Jensen, J. 2001. Genetic evaluation of dairy cattle using test-day models. Journal of Dairy Science 84: 2803–2812.CrossRefGoogle ScholarPubMed
Knap, P. 1995. Aspects of stochasticity: variation between animals. In Modelling growth in the pig (ed. Moughan, P. J., Verstegen, M. W. A. and Visser-Reyneveld, M. I.), European Association for Animal Production publication no. 78, pp. 165–172.Google Scholar
Littell, R. C., Henry, P. R. and Ammerman, C. B. 1998. Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science 76: 1216–1231.CrossRefGoogle Scholar
Marchant, J. A., Schofield, C. P. and White, R. P. 1999. Pig growth and conformation monitoring using image analysis. Animal Science 68: 141–150.CrossRefGoogle Scholar
Pomar, C., Kyriazakis, I., Emmans, G. C. and Knap, P. W. 2003. Modeling stochasticity: dealing with populations rather than individual pigs. Journal of Animal Science 81: 178–186.Google Scholar
Schinckel, A. P. and Craig, B. A. 2002. Evaluation of alternative nonlinear mixed effects models of swine growth. The Professional Animal Scientist 18: 219–226.CrossRefGoogle Scholar
Schinckel, A. P., PAS, Li, N., Preckel, P. V., Einstein, M. E. and Miller, D. 2003. Development of a stochastic pig compositional growth model. The Professional Animal Scientist 19: 255–260.CrossRefGoogle Scholar
Schofield, C. P., Marchant, J. A., White, R. P., Brandl, N. and Wilson, M. 1998. Monitoring pig growth using a prototype imaging system. Journal of Agricultural Engineering Research 72: 205–210.CrossRefGoogle Scholar
Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6: 461–464.CrossRefGoogle Scholar
Statistical Analysis Systems Institute. 1999. SAS/STAT user's guide, version 8. SAS Institute Inc., Cary, NC.Google Scholar
Verbeke, G. and Molenbergh, G. 1997. Linear mixed models in practice. A SAS-oriented approach. In Lecture notes in statistics, volume 126. Springer Verlag, New York.Google Scholar
White, R. P., Schofield, C. P., Green, D. M., Parsons, D. J. and Whittemore, C. T. 2004. The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs. Animal Science 78: 409–418.CrossRefGoogle Scholar
Whittemore, C. T. 1998. The science and practice of pig production. Blackwell Science, Oxford.Google Scholar
Whittemore, C. T., Green, D. M., Wood, J. D., Fisher, A. V. and Schofield, C. P. 2003. Physical and chemical composition of the carcass of three different types of pigs grown from 25 to 115 kg live weight. Animal Science 77: 235–245.CrossRefGoogle Scholar
Whittemore, C. T. and Schofield, C. P. 2000. A case for size and shape scaling for understanding nutrient use in breeding sows and growing pigs. Livestock Production Science 65: 203–208.CrossRefGoogle Scholar