Browse > Article
http://dx.doi.org/10.5713/ajas.2011.10142

Longitudinal Analysis of Body Weight and Feed Intake in Selection Lines for Residual Feed Intake in Pigs  

Cai, W. (Department of Animal Science, Iowa State University)
Wu, H. (Department of Statistics, Iowa State University)
Dekkers, J.C.M. (Department of Animal Science, Iowa State University)
Publication Information
Asian-Australasian Journal of Animal Sciences / v.24, no.1, 2011 , pp. 17-27 More about this Journal
Abstract
A selection experiment for reduced residual feed intake (RFI) in Yorkshire pigs consisted of a line selected for lower RFI (LRFI) and a random control line (CTRL). Longitudinal measurements of daily feed intake (DFI) and body weight (BW) from generation 5 of this experiment were used. The objectives of this study were to evaluate the use of random regression (RR) and nonlinear mixed models to predict DFI and BW for individual pigs, accounting for the substantial missing information that characterizes these data, and to evaluate the effect of selection for RFI on BW and DFI curves. Forty RR models with different-order polynomials of age as fixed and random effects, and with homogeneous or heterogeneous residual variance by month of age, were fitted for both DFI and BW. Based on predicted residual sum of squares (PRESS) and residual diagnostics, the quadratic polynomial RR model was identified to be best, but with heterogeneous residual variance for DFI and homogeneous residual variance for BW. Compared to the simple quadratic and linear regression models for individual pigs, these RR models decreased PRESS by 1% and 2% for DFI and by 42% and 36% for BW on boars and gilts, respectively. Given the same number of random effects as the polynomial RR models, i.e., two for BW and one for DFI, the non-linear Gompertz model predicted better than the polynomial RR models but not as good as higher order polynomial RR models. After five generations of selection for reduced RFI, the LRFI line had a lower population curve for DFI and BW than the CTRL line, especially towards the end of the growth period.
Keywords
Longitudinal Analysis; Pigs; Residual Feed Intake; Selection;
Citations & Related Records

Times Cited By Web Of Science : 4  (Related Records In Web of Science)
Times Cited By SCOPUS : 4
연도 인용수 순위
1 Schulze, V., R. Roehe, H. Looft and E. Kalm. 2001. Effects of continuous and periodic feeding by electronic feeders on accuracy of measuring feed intake information and their genetic association with growth performances. J. Anim. Breed. Genet. 118:403-416.   DOI   ScienceOn
2 Von Felde, A., R. Roehe, H. Looft and E. Kalm. 1996. Genetic association between feed intake and feed intake behaviour at different stages of growth of group-housed boars. Livest. Prod. Sci. 47:11-22.   DOI   ScienceOn
3 Whittemore, C. T., J. B. Tullis and G. C. Emmans. 1988. Protein growth in pigs. Anim. Prod. 46:437-445.   DOI
4 Eissen, J. J., A. G. de Haan and E. Kanis. 1999. Effect of missing data on the estimate of average daily feed intake of growing pigs. J. Anim. Sci. 77:1372-1378.
5 Eissen, J. J., E. Kanis and J. W. M. Merks. 1998. Algorithms for identifying errors in individual feed intake data of growing pigs in group-housing. Appl. Eng. Agric. 14:667-673.   DOI
6 Knap, P. W. 2000. Time trends of gompertz growth parameters in "Meat-type" pigs. Anim. Sci. 70: 39-49.
7 Kyriazakis, I. and C. T. Whittemore. 2006. Whittemore's science and practice of pig production. 3rd ed. Blackwell Publishing Ltd, Oxford, UK.
8 Lindsey, J. K. 2001. Families of nonlinear regression functions. Page 37-47 in nonlinear models in medical statistics (Ed. J. K. Lindsey). Oxford University Press, New York, USA.
9 Lorenzo Bermejo, J., R. Roehe, G. Rave and E. Kalm. 2003. Comparison of linear and nonlinear functions and covariance structures to estimate feed intake pattern in growing pigs. Livest. Prod. Sci. 82:15-26.   DOI   ScienceOn
10 Ratkowsky, D. A. 1990. Handbook of nonlinear regression models. Marcel Dekker, Inc., New York.
11 Sandland, R. L. and C. A. McGilchrist. 1979. Stochastic growth curve analysis. Biometrics 35:255-271.   DOI   ScienceOn
12 SAS Institute. 2008. Sas online documentation, version 9.1.3. SAS Institute Inc., Cary, NC.
13 Schaeffer, L. R. 2004. Application of random regression models in animal breeding. Livest. Prod. Sci. 86:35-45.   DOI   ScienceOn
14 Schaeffer, L. R. and J. C. M. Dekkers. 1994. Random regressions in animal models for test-day production in dairy cattle. In: Proc. 5th World Congr. Genet. Appl. Livest. Prod., Guelph, Ont., Canada, 18:443-446.
15 Andersen, S. and B. Pedersen. 1996. Growth and food intake curves for group-housed gilts and castrated male pigs. Anim. Sci. 63:457-464.   DOI   ScienceOn
16 Cai, W., D. S. Casey and J. C. M. Dekkers. 2008. Selection response and genetic parameters for residual feed intake in yorkshire swine. J. Anim Sci. 86:287-298.
17 Casella, G. and R. L. Berger. 2002. Statistical inference, second edition. Duxbury Press, Pacific Grove, California.
18 Casey, D. S. 2003. The use of electronic feeders in genetic improvement programs for swine, PhD Diss. Iowa State Univ., Ames.
19 Casey, D. S., H. S. Stern and J. C. M. Dekkers. 2005. Identification of errors and factors associated with errors in data from electronic swine feeders. J. Anim. Sci. 83:969-982.
20 Diggle, P. J., P. J. Heagerty, K. Y. Liang and S. L. Zeger. 2002. Analysis of longitudinal data (second ed). 2nd ed. Oxford University Press, New York.