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Genetic evaluation of dairy bulls for energy balance traits using random regression

Published online by Cambridge University Press:  18 August 2016

M. P. Coffey*
Affiliation:
Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
G. C. Emmans
Affiliation:
Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
S. Brotherstone
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
*
E-mail m.coffey@ed.sac.ac.uk
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Abstract

Current selection objectives for dairy cattle breeding may be favouring cows that are genetically predisposed to mobilize body tissue. This may have consequences for fertility since cows may resume reproductive activity only once the nadir of negative energy balance (NEB) has passed. In this study, we repeatedly measured food intake, live weight, milk yield and condition score of Holstein cattle in their first lactation. They were given either a high concentrate or low concentrate diet and were either selected or control animals for genetic merit for kg milk fat plus milk protein. Orthogonal polynomials were used to model each trait over time and random regression techniques allowed curves to vary between animals at both the genetic and the permanent environmental levels. Breeding values for bulls were calculated for each trait for each day of lactation. Estimates of genetic merit for energy balance were calculated from combined breeding values for either (1) food intake and milk yield output, or (2) live weight and condition-score changes.

When estimated from daily fluxes of energy calculated from food intake and milk output, the average genetic merit of bulls for energy balance was approximately -15 MJ/day in early lactation. It became positive at about day 40 and rose to +18 MJ/day at approximately day 150. When estimated from body energy state changes the NEB in early lactation was also -15 MJ/day. It became positive at about day 80 and then rose to a peak of +10 MJ/day. The difference between the two methods may arise either because of the contribution of food wastage to intake measures or through inadequate predictions of body lipid from equations using live weight and condition score or a combination of both. Body energy mobilized in early lactation was not fully recovered until day 200 of lactation. The results suggest that energy balance may be estimated from changes in body energy state that can be calculated from body weight and condition score. Since body weight can be predicted from linear type measures, it may be possible to calculate breeding values for energy balance from national evaluations for production and type. Energy balance may be more suitable as a breeding objective than persistency.

Type
Breeding and genetics
Copyright
Copyright © British Society of Animal Science 2001

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References

Agricultural Research Council. 1993. Energy and protein requirements of ruminants. An advisory manual prepared by the AFRC Technical Committee on Responses to Nutrients. CAB International, Wallingford, UK.Google Scholar
Beam, S. W. and Butler, W. R. 1998. Energy balance, metabolic hormones and early postpartum follicular development in dairy cows fed prilled lipid. Journal of Dairy Science 81: 121131.Google Scholar
Brotherstone, S., White, I. M. S. and Meyer, K. 2000. Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves. Animal Science 70: 407415.Google Scholar
Butler, R. W. and Smith, R. D. 1989. Interrelationships between energy balance and postpartum reproductive function in dairy cattle. Journal of Dairy Science 72: 767783.Google Scholar
Dekkers, J. C. M., Ten Hag, J. H. and Weersink, A. 1998. Economic aspects of persistency of lactation in dairy cattle. Livestock Production Science 53: 237252.Google Scholar
Emmans, G. C. 1994. Effective energy: a concept of energy utilization applied across species. British Journal of Nutrition 71: 801821.Google Scholar
Hill, W. G. and Brotherstone, S. 1999. Advances in methodology for utilizing sequential records. In Metabolic stress in dairy cows (ed. Oldham, J. D., Simm, G., Groen, A. F., Nielsen, B. L., Pryce, J. E. and Lawrence, T.L. J.), British Society of Animal Science occasional publication no. 24, pp. 5561.Google Scholar
Holness, M. J., Munns, M. J. and Sugden, M. C. 1999. Current concepts concerning the role of leptin in reproductive function. Mollecular and Cellular Endoncrinology 157: 1120.CrossRefGoogle ScholarPubMed
Jamrozik, J. and Schaeffer, L. R. 1997. Estimates of genetic parameters for a test day model with random regressions for yield traits of first lactation Holsteins. Journal of Dairy Science 80: 762770.Google Scholar
Jones, H. E., White, I. M. S. and Brotherstone, S. 1999. Genetic evaluation of Holstein Friesian sires for daughter condition score changes using a random regression model. Animal Science 68: 467476.CrossRefGoogle Scholar
Koenen, E. P. C. and Groen, A. F. 1998. Genetic evaluation of body weight of lactating Holstein heifers using body measurements and conformation traits. Journal of Dairy Science 81: 17091713.Google Scholar
Langhill, . 1999. Report from the Langhill Dairy Cattle Research Centre, Roslin, UK.Google Scholar
Lowman, B. G., Scott, N. and Somerville, S. 1976. Condition scoring of cattle. East of Scotland College of Agriculture, Edinburgh, bulletin no. 6.Google Scholar
Meyer, K. 1998. ‘DxMRR’ A program to estimate covariance functions for longitudinal data by restricted maximum likelihood. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, Australia, vol. 25, pp. 517527.Google Scholar
Nielson, B. L. 1999. Perceived welfare issues in dairy cattle, with special emphasis on metabolic stress. In Metabolic stress in dairy cows (ed. Oldham, J. D., Simm, G., Groen, A. F. Nielsen, B. L., Pryce, J. E. and Lawrence, T. L. J.), British Society of Animal Science occasional publication no. 24, pp. 18.Google Scholar
Olori, V. E., Hill, W. G., McGuirk, B. J. and Brotherstone, S. 1999. Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model. Livestock Production Science 61: 5363.Google Scholar
Pond, C. M. and Newsholme, E. A. 1999. Coping with metabolic stress in wild and domesticated animals. In Metabolic stress in dairy cows (ed. Oldham, J. D., Simm, G., Groen, A. F., Nielsen, B. L., Pryce, J. E. and Lawrence, T. L. J.), British Society of Animal Science occasional publication no. 24, pp. 920.Google Scholar
Pryce, J. E., Nielson, B. L., Veerkamp, R. F. and Simm, G. 1999. Genotype and feeding system effects and interactions for health and fertility traits in dairy cattle. Livestock Production Science 57: 193201 CrossRefGoogle Scholar
Royal, M. D., Darwash, A. O. and Lamming, G. E. 1999. Trends in the fertility of dairy cows in the United Kingdom. Proceedings of the British Society of Animal Science, 1999, p. 1.Google Scholar
Schaeffer, L. R. and Dekkers, J. C. M. 1994. Random regressions in animal models for test-day production in dairy cattle. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 18, p. 443.Google Scholar
Swalve, H. H. and Gengler, N. 1999. Genetics of lactation persistency. In Metabolic stress in dairy cows (ed. J. D. Oldham, G. Simm, A. F. Groen, B. L. Nielsen, J. E. Pryce and T. Lawrence, L. J.), British Society of Animal Science occasional publication no. 24, pp. 7582.Google Scholar
Veerkamp, R. F. and Brotherstone, S. 1997. Genetic correlations between linear type traits, food intake, live weight and condition score in Holstein Friesian dairy cattle. Animal Science 64: 385392.Google Scholar
Veerkamp, R. F. and Koenen, E. P. C. 1999. Genetics of food intake, liveweight, condition score and energy balance. In Metabolic stress in dairy cows (ed. Oldham, J. D., Simm, G., Groen, A. F., Nielsen, B. L., Pryce, J. E. and Lawrence, T. L. J.), British Society of Animal Science occasional publication no. 24, pp. 6374.Google Scholar
Veerkamp, R. F., Oldenbroek, J. K., Gaast, H. J. van der and Werf, J. H. J. van der. 2000. Genetic correlation between days until start of luteal activity and milk yield, energy balance and live weights. Journal of Dairy Science 83: 577583.Google Scholar
Veerkamp, R. F., Simm, G. and Oldham, J. D. 1995. Genotype by environment interaction — experience from Langhill. In Breeding and feeding the high genetic merit dairy cow (ed. Lawrence, T. L. J., Gordon, F. J. and Carson, A.), British Society of Animal Science occasional publication. no. 19, pp. 5966.Google Scholar
Veerkamp, R. F. and Thompson, R. 1999. A covariance function for feed intake, live weight and milk yield estimated using a random regression model. Journal of Dairy Science 82: 15651573.Google Scholar
Vries, M. J. de, Beek, S. van der, Kaal-Lansbergen, L. M. T. E., Ouweltjes, W. and Wilmink, J. B. M. 1999. Modelling of energy balance in early lactation and the effect of energy deficits in early lactation on first detected estrus postpartum in dairy cows. Journal of Dairy Science 82: 19271934.CrossRefGoogle ScholarPubMed
Vries, M. J. de and Veerkamp, R. F. 2000. Energy balance of dairy cattle in relation to milk production variables and fertility. Journal of Dairy Science 83: 6269.Google Scholar
Wright, I. A. 1982. Studies on the body composition of beef cows. Ph.D. thesis, University of Edinburgh, Scotland.Google Scholar