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Stochastic simulation of growth in pigs: relations between body composition and maintenance requirements as mediated through protein turn-over and thermoregulation

Published online by Cambridge University Press:  18 August 2016

P. W. Knap*
Affiliation:
PIC Group, Fyfield Wick, Kingston Bagpuize, Abingdon OX13 5NA, UK
*
Stationed at the Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, UK. Correspondence to this address.
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Abstract

A dynamic model for simulation of growth in pigs, extended to describe thermoregulatory processes, was made stochastic to simulate groups of pigs with between-animal variation in mature body protein (Pα) and lipid mass (Lα), in the potential rate at which mature mass is attained (B), and in the distribution of body protein and lipid over pools and depots. The resulting variation in body composition leads to variation in energy requirements for protein turn-over and thermoregulation, causing between-animal variation in maintenance requirements (MEmaint).

Simulated population means for Pα, Lα /Pα and B were varied in three steps each. Excluding unrealistic parameter combinations this led to 33 – 6 = 21 simulated genotypes. Simulated within-population coefficients of variation (CV) were 7, 15 and 3%. Random replicates of each genotype were simulated five times, in climatic conditions that were in turn severely cold, mildly cold (about 5 and 1ºC below lower critical temperature), thermoneutral, mildly hot and severely hot (about 1 and 5ºC above upper critical temperature), during the entire growth period of 23 to 100 kg live weight. Simulated food intake was ad libitum.

Simulated thermoneutral within-population standard deviations of body protein and lipid content were 0·21 to 0·46 kg and 0·78 to 2·14 kg at 100 kg body weight. On average, the corresponding values in cold and hot conditions were slightly higher.

MEmaint showed a protein-turn-over-related within-population CV of 1·5% at thermoneutrality. Thermoregulatory action contributed about 4% extra variance in cold and hot conditions but CV values were not affected. A genetic increase in the maximum protein deposition rate from 100 to 250 g/day would increase MEmaint as related to protein turn-over and thermoregulation by 11% at thermoneutrality, and by 6 to 11% in cold or hot conditions. Two relevant groups of genotypes could be distinguished based on the within-population regression coefficients of MEmaint on daily or cumulative protein deposition (bdailyPdep, bcumPdep). These ranged from 0·250 to 0·428 kJ/kg0·75 per day per g/day and from 2·77 to 5·45 kJ/kg0·75 per day per kg, respectively, in 12 ‘conventional’ genotypes at thermoneutrality. On average, bdailyPdep was increased by 48%, 20%, –11% and –36% in the other climatic conditions mentioned above, respectively. The corresponding increase of bcumPdep was 32%, 14%, 8% and 48%. Three fast-growing lean genotypes showed similar bdailyPdep and bcumPdep at thermoneutrality, but much more pronounced increases in cold and hot conditions: 137%, 49%, –12% and + 88% for bdailyPdep and 248%, 108%, 17% and 196% for bcumPdep.

It is concluded that differences in body composition traits between pig genotypes do not cause important between-genotype differences in thermoregulatory MEmaint, and that thermoregulatory processes contribute little body-composition-related variation to hot or cold MEmaint within most genotypes.

The inferences to be made from this with regard to experimental design are discussed. The verification of the above predictions will require a very elaborate and large-scale experiment.

Type
Breeding and genetics
Copyright
Copyright © British Society of Animal Science 2000

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References

Backus, G. B. C., Vermeer, H. M., Roelofs, P. F. M. M., Vesseur, P. C., Adams, J. H. A. N., Binnendijk, G. P., Smeets, J. J. J., Peet-Schwering, C. M. C. van der and Wilt, F. J. van der. 1997. Comparison of four housing systems for non-lactating sows. Research Institute for Pig Husbandry, Rosmalen. Report P1171.Google Scholar
Black, J. L., Bray, H. J. and Giles, L. R. 1999. The thermal and infectious environment. In A quantitative biology of the pig (ed. Kyriazakis, I.), pp. 7197. CAB International, Wallingford.Google Scholar
Brandt, H. and Götz, K. U. 1993. Progeny test for AI sires based on field results regarding growth and carcass traits using electronic identification systems in pigs. In Application of mixed linear models in the prediction of genetic merit in pigs (ed. Groeneveld, E.), pp. 7782. FAL Institute of Animal Husbandry and Animal Ethology, Mariensee.Google Scholar
Brück, K. 1986. Basic mechanisms in thermal long-term and short-term adaptation. Journal of Thermal Biology 11: 7377.Google Scholar
Bullock, K. D., Bertrand, J. K. and Benyshek, L. L. 1993. Genetic and environmental parameters for mature weight and other growth measures in Polled Hereford cattle. Journal of Animal Science 71: 17371741.Google Scholar
Bünger, L. and Schönfelder, E. 1984. Zur Lebensleistung wachstumsselektierter Labormausweibchen: Wachstumsverlauf. Probleme der Angewandten Statistik 11: 185196.Google Scholar
Cameron, N. D., Pearson, M., Richardson, B. and Brade, M. 1990. Genetic and phenotypic parameters for performance traits in pigs with ad-libitum and restricted feeding. Proceedings of the fourth world congress on genetics applied to livestock production, Edinburgh, vol. 15, pp. 473476.Google Scholar
Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. 1983. Graphical methods for data analysis. Wadsworth and Brooks, Pacific Grove, CA.Google Scholar
Close, W. H. 1978. The effects of plane of nutrition and environmental temperature on the energy metabolism of the growing pig. 3. The efficiency of energy utilization for maintenance and growth. British Journal of Nutrition 40: 433438.Google Scholar
Close, W. H. and Mount, L. E. 1978. The effects of plane of nutrition and environmental temperature on the energy metabolism of the growing pig. 1. Heat loss and critical temperature. British Journal of Nutrition 40: 413421.CrossRefGoogle ScholarPubMed
Clutter, A. C. and Brascamp, E. W. 1998. Genetics of performance traits. In The genetics of the pig (ed. Rothschild, M. F. and Ruvinsky, A.), pp. 427462. CAB International, Wallingford.Google Scholar
DeNise, K. R. S. and Brinks, J. S. 1985. Genetic and environmental aspects of the growth curve parameters in beef cows. Journal of Animal Science 61: 14311440.Google Scholar
Derno, M., Jentsch, W. and Hoffmann, L. 1995. Effect of long time exposure to different environmental temperatures on heat production of growing pigs. Livestock Production Science 43: 149152.Google Scholar
Ducos, A. 1994. Paramètres génétiques des caractères de production chez le porc. Mise au point bibliographique. Techni-Porc 17: 3567.Google Scholar
Emmans, G. C. 1988. Genetic components of potential and actual growth. In Animal breeding opportunities (ed. Land, R.B., Bulfield, G. and Hill, W. G.). British Society of Animal Production occasional publication no. 12, pp. 153181.Google Scholar
Emmans, G. C. 1997. A method to predict the food intake of domestic animals from birth to maturity as a function of time. Journal of Theoretical Biology 186: 189199.Google Scholar
Emmans, G. C. and Fisher, C. 1986. Problems in nutritional theory. In Nutrient requirements of poultry and nutritional research (ed. Fisher, C. and Boorman, K. N.), pp. 939. Butterworths, London.Google Scholar
Felde, A. von, Roehe, R., Looft, H. and Kalm, E. 1996. Genetic association between feed intake and feed intake behaviour at different stages of growth of group-housed boars. Livestock Production Science 47: 1122.Google Scholar
Fender, M., Kühnle, S. and Fewson, D. 1979. Selektionsversuch beim Schwein zur Verbesserung der Schlachtkörperzusammensetzung. Zeitschrift für Tierzüchtungslehre und Züchtungsbiologie 96: 8695.Google Scholar
Ferguson, N. S. and Gous, R. M. 1993. Evaluation of pig genotypes. 1. Theoretical aspects of measuring genetic parameters. Animal Science 56: 233243.Google Scholar
Ferguson, N. S. and Gous, R. M. 1997. The influence of heat production on voluntary food intake in growing pigs given protein-deficient diets. Animal Science 64: 365378.Google Scholar
Ferguson, N. S., Gous, R. M. and Emmans, G. C. 1994. Preferred components for the construction of a new simulation model of growth, feed intake and nutrient requirements of growing pigs. South African Journal of Animal Science 24: 1017.Google Scholar
Ferguson, N. S., Gous, R. M. and Emmans, G. C. 1997. Predicting the effects of animal variation on growth and food intake in growing pigs using simulation modelling. Animal Science 64: 513522.Google Scholar
Gibson, J. P., Aker, C. and Ball, R. 1998. Levels of genetic variation for growth, carcass and meat quality traits of purebred pigs. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 23, pp. 499502.Google Scholar
Giles, L. R. and Black, J. L. 1989. Voluntary food intake in growing pigs at ambient temperatures above the zone of thermal comfort. In Manipulating pig production, vol. 2 (ed. Barnett, J. L. and Hennessy, D. P.), pp. 162166. Australasian Pig Science Association, Werribee.Google Scholar
Grandhi, R. R. 1992. Effect of feeding supplemental fat or lysine during the postweaning period on the reproductive performance of sows with low or high lactation body weight and fat losses. Canadian Journal of Animal Science 72: 679690.Google Scholar
Groeneveld, E., Wolf, J., Wolfová, M., Jelínková, V. and Vec˘ erová, D. 1998. Schätzung genetischer Parameter für tschechische Schweinerassen mit einem Mehrmerkmals-Tiermodell. Züchtungskunde 70: 96107.Google Scholar
Hall, A. D., Hill, W. G., Bampton, P. R. and Webb, A. J. 1998. The use of feeding pattern traits in pigs as selection criteria to improve the accuracy of selection for feed conversion ratio and growth traits. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 23, pp. 547550.Google Scholar
Hancock, C. E., Bradford, G. D., Emmans, G. C. and Gous, R. M. 1995. The evaluation of the growth parameters of six strains of commercial broiler chickens. British Poultry Science 36: 247264.CrossRefGoogle ScholarPubMed
Hermesch, S., Luxford, B. G. and Graser, H.-U. 1998. Genetic relationships of growth and lean meat with meat quality and reproduction traits in Australian pigs. Proceedings of the 6th world congress on genetics applied to livestock production, Armidale, vol. 23, pp. 511514.Google Scholar
Holmes, C. W. and Close, W. H. 1977. The influence of climatic variables on energy metabolism and associated aspects of productivity in the pig. In Nutrition and the climatic environment (ed. W.Haresign, , Swan, H. and Lewis, D.), pp. 5173. Butterworths, London.Google Scholar
Jenkins, T. G., Kaps, M., Cundiff, L. V. and Ferrell, C. L. 1991. Evaluation of between- and within-breed variation in measures of weight-age relationships. Journal of Animal Science 69: 31183128.Google Scholar
Johansson, K., Andersson, K. and Danell, Ø. 1986. Estimation of breeding values for performance tested pigs with sibs at test stations. Proceedings of the third world congress on genetics applied to livestock production, Lincoln, vol. 10, pp. 174181.Google Scholar
Kachman, S. D., Baker, R. L. and Gianola, D. 1988. Phenotypic and genetic variability of estimated growth curve parameters in mice. Theoretical and Applied Genetics 76: 148156.Google Scholar
Kalm, E. 1986. Evaluation and utilisation of breed resources: as sire lines in crossbreeding. Proceedings of the third world congress on genetics applied to livestock production, Lincoln, vol. 10, pp. 3544.Google Scholar
Karras, K., Niebel, E., Karb, H., Grüninger, A. and Ramirez, M. 1993. A 7-trait multivariate genetic evaluation of growth, body composition and reproductive performance. In Application of mixed linear models in the prediction of genetic merit in pigs (ed. Groeneveld, E.), pp. 3241. FAL Institute of Animal Husbandry and Animal Ethology, Mariensee.Google Scholar
Kemp, B., Bakker, G. C. M., Hartog, L. A. den and Verstegen, M. W. A. 1991. The effect of semen collection frequency and food intake on semen production in breeding boars. Animal Production 52: 355360.Google Scholar
Kielanowski, J. 1976. The chemical composition of the live-weight gain and the performance of growing pigs. Livestock Production Science 3: 257269.Google Scholar
Knap, P. W. 1990. Selection indexes for fattening and slaughter traits. Abstracts of the 41st annual meeting of the European Association for Animal Production, p. 328.Google Scholar
Knap, P. W. 1996. Stochastic simulation of growth in pigs: protein turn-over-dependent relations between body composition and maintenance requirements. Animal Science 63: 549561.Google Scholar
Knap, P. W. 1999. Simulation of growth in pigs: evaluation of a model to relate thermoregulation to body protein and lipid content and deposition. Animal Science 68: 655679.Google Scholar
Knap, P. W. 2000. Time trends of Gompertz growth parameters in ‘meat-type’ pigs. Animal Science 70: 3949.Google Scholar
Knap, P. W. and Jørgensen, H. 2000. Animal-intrinsic variation in the partitioning of body protein and lipid in growing pigs. Animal Science 70: 2937.Google Scholar
Knap, P. W. and Schrama, J. W. 1996. Simulation of growth in pigs: approximation of protein turn-over parameters. Animal Science 63: 533547.Google Scholar
Knapp, P., Willam, A. and Sölkner, J. 1997. Genetic parameters for lean meat content and meat quality traits in different pig breeds. Livestock Production Science 52: 6973.Google Scholar
Koenen, E. P. C., Groen, A. F. and Gengler, N. 1999. Phenotypic variation in live weight and live-weight changes of lactating Holstein-Friesian cows. Animal Science 68: 109114.Google Scholar
Kownacki, M. and Keller, J. 1978. The basal metabolic rate in selected and unselected mice. Genetica Polonica 19: 340344.Google Scholar
Labroue, F., Gueblez, R. and Sellier, P. 1997. Genetic parameters of feeding behaviour and performance traits in group-housed Large White and French Landrace growing pigs. Genetics, Selection, Evolution 29: 451468.Google Scholar
Luiting, P. and Urff, E. M. 1991. Optimization of a model to estimate residual feed consumption in the laying hen. Livestock Production Science 27: 321338.Google Scholar
Meyer, K. 1995. Estimates of genetic parameters for mature weight of Australian beef cows and its relationships to early growth and skeletal measures. Livestock Production Science 44: 125137.Google Scholar
Moughan, P. J. and Smith, W. C. 1984. Prediction of dietary protein quality based on a model of the digestion and metabolism of nitrogen in the growing pig. New Zealand Journal of Agricultural Research 27: 501507.Google Scholar
Näsholm, A. and Danell, Ö. 1996. Genetic relationships of lamb weight, maternal ability and mature ewe weight in Swedish Finewool sheep. Journal of Animal Science 74: 329339.Google Scholar
Northcutt, S. L. and Wilson, D. E. 1993. Genetic parameter estimates and expected progeny differences for mature size in Angus cattle. Journal of Animal Science 71: 11481153.CrossRefGoogle ScholarPubMed
Oliveira, H. N., Lobo, R. and Pereira, C. S. 1994. Relationships among growth curve parameters, weights and reproductive traits in Guzera beef cows. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 19, pp. 189192.Google Scholar
Parratt, A. and Barker, J. S. F. 1982. Parameters of nonlinear growth models as selection criteria: an alternative approach to selection for growth and efficiency. Proceedings of the second world congress on genetics applied to livestock production, Madrid, vol. 7, pp. 405409.Google Scholar
Pohl, H. 1976. Thermal adaptation in the whole animal. In Environmental physiology of animals (ed. Bligh, J., Cloudsley-Thompson, J.L. and Mac, A. G.Donald), pp. 261286. Blackwell, Oxford.Google Scholar
Rook, A. J., Ellis, M., Whittemore, C. T. and Phillips, P. 1987. Relationships between whole-body chemical composition, physically dissected carcass parts and backfat measurements in pigs. Animal Production 44: 263273.Google Scholar
Short, T. H., Wilson, E. R. and McLaren, D. G. 1994. Relationships between growth and litter traits in pig dam lines. Proceedings of the 5th world congress on genetics applied to livestock production, Guelph, vol. 17, pp. 413416.Google Scholar
Siemens, A. L., Erickson, T. B., Lipsey, R. J., Hedrick, H. B., Seevers, D. L., Rates, R. O., Williams, F. L. and Yokley, S. W. 1989. Predictive equations for estimating lean cuts, fat standardized lean, chemical composition, bone and value of pork carcasses. Journal of Animal Science 67: 20332039.Google Scholar
LStatistical Analysis Systems Institute. 1990. SAS language: reference. Statistical Analysis Systems Institute, Cary NC.Google Scholar
Stern, S., Johansson, K., Rydhmer, L. and Andersson, K. 1994. Performance testing of pigs for lean tissue growth rate in a selection experiment with low and high protein diets. 2. Correlated responses of lean percentage and growth rate. Acta Agriculturæ Scandinavica 44: 1–7.Google Scholar
Stobart, R. H., Bassett, J. W., Cartwright, T. C. and Blackwell, R. L. 1986. An analysis of body weights and maturing patterns in western range ewes. Journal of Animal Science 63: 729740.Google Scholar
Taylor, St C. S. 1985. Use of genetic size-scaling in evaluation of animal growth. Journal of Animal Science 61: (suppl. 2) 118143.Google Scholar
Tholen, E., Kirstgen, B., Trappmann, W. and Schellander, K. 1998. Genotype ✕ environmental interactions in a German pig breeding herdbook society using crossbred progeny information. Archiv für Tierzucht 41: 5363.Google Scholar
Whittemore, C. T. and Fawcett, R. H. 1974. Model responses of the growing pig to the dietary intake of energy and protein. Animal Production 19: 221231.Google Scholar
Young, B. A., Walker, B., Dixon, A. E. and Walker, V. A. 1989. Physiological adaptation to the environment. Journal of Animal Science 67: 24262432.Google Scholar