Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-28T17:14:08.543Z Has data issue: false hasContentIssue false
  • English
  • Français

Long-term growth of body, body parts and composition of gain of dairy goat wethers

Published online by Cambridge University Press:  08 July 2015

R. P. ARAUJO ,
R. A. M. VIEIRA ,
N. S. ROCHA ,
M. L. C. ABREU ,
L. S. GLÓRIA ,
N. M. ROHEM JÚNIOR  and
A. M. FERNANDES
R. P. ARAUJO
Affiliation:
Graduate Program in Animal Science, Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Campos dos Goytacazes, RJ, Brazil
R. A. M. VIEIRA*
Affiliation:
Laboratório de Zootecnia (LZO), UENF, Av. Alberto Lamego, 2000, Campos dos Goytacazes, RJ, CEP 28013-602, Brazil
N. S. ROCHA
Affiliation:
Departamento de Zootecnia, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Rodovia MG 367, km 583, n° 5000, Alto da Jacuba, Diamantina, MG, CEP 39100-000, Brazil
M. L. C. ABREU
Affiliation:
Graduate Program in Animal Science, Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Campos dos Goytacazes, RJ, Brazil
L. S. GLÓRIA
Affiliation:
Graduate Program in Animal Science, Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Campos dos Goytacazes, RJ, Brazil
N. M. ROHEM JÚNIOR
Affiliation:
Graduate Program in Animal Science, Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Campos dos Goytacazes, RJ, Brazil
A. M. FERNANDES
Affiliation:
Laboratório de Zootecnia (LZO), UENF, Av. Alberto Lamego, 2000, Campos dos Goytacazes, RJ, CEP 28013-602, Brazil
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The goal of the present study was to characterize the growth of body parts and composition of the growing empty body to infer how these aspects relate to the long-term growth of goat wethers from dairy breeds. Animals were slaughtered at several ages from birth to maturity (≅900 days old). All body parts were weighed and sampled to determine chemical constituent dry matter, crude protein, crude fat, ash and specific energy. The monomolecular (Brody), Gompertz, and Richards models, a biphasic model formed by the combined Brody and Gompertz functions, and a simple linear model were fitted to the growth profiles with different variance functions and were all evaluated using likelihood-information criteria. The effect of breed (genotype) was accounted for in all models but the resulting models were not more likely than the models without the breed effect. Remarkable differences were observed regarding inflection points, growth rates and trends for all body parts and chemical constituents of the body. The biphasic model did not supplant the monomolecular, Gompertz, Richards or the linear model in terms of likelihood-information criteria. Therefore, body parts and chemical constituents of the empty body presented monomolecular, sigmoid and linear time-trends. The growth profiles of fat, protein and energy of the empty body did not scale isometrically with the empty body proper. In addition, the variance was heteroscedastic along the time scale and was better represented by both an exponential variance over time or by a power function of the mean.

Type
Animal Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 7 , September 2015 , pp. 1321 - 1340
Check for updates
Copyright
Copyright © Cambridge University Press 2015 

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.)

References

REFERENCES

AFRC (1993). Energy and Protein Requirements of Ruminants. Cambridge, UK: CAB International.Google Scholar
AFRC (1997). The nutrition of goats. Nutrition Abstracts and Reviews (Series B) 67, 765–830.Google Scholar
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.CrossRefGoogle Scholar
AOAC (1998). Official Methods of Analysis. 16th Ed., 4th Revision. Gaithersburg, MD, USA: AOAC International.Google Scholar
Bard, Y. (1974). Nonlinear Parameter Estimation. New York: Academic Press, Inc.Google Scholar
Blaxter, K. L., Fowler, V. R. & Gill, J. C. (1982). A study of the growth of sheep to maturity. Journal of Agricultural Science, Cambridge 98, 405–420.CrossRefGoogle Scholar
Box, G. E. P. & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological) 26, 211–252.CrossRefGoogle Scholar
Brody, S. (1945). Bioenergetics and Growth. With Special Reference to the Efficiency Complex in Domestic Animals. New York: Reinhold Publishing Co.Google Scholar
Brunel, T., Ernande, B., Mollet, F. M. & Rijnsdorp, A. D. (2013). Estimating age at maturation and energy-based life-history traits from individual growth trajectories with nonlinear mixed-effects models. Oecologia 172, 631–643.CrossRefGoogle ScholarPubMed
Burnham, K. P. & Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Research 33, 261–304.CrossRefGoogle Scholar
Carmichael, A. K., Kouakou, B., Gelaye, S., Kannan, G., Lee, J. H. & Terrill, T. H. (2012). Organ mass and composition in growing dairy goat wethers fed different levels of poultry fat and protein. Small Ruminant Research 104, 104–113.CrossRefGoogle Scholar
Doumit, M. E. & Merkel, R. A. (2005). Growth and development: postnatal. In Encyclopedia of Animal Science (Eds Pond, W. G. & Bell, A. W.), pp. 513–516. New York: Marcel Dekker, Inc.Google Scholar
France, J., Dijkstra, J. & Dhanoa, M. S. (1996). Growth functions and their application in animal science. Annales de Zootechnie 45 (Supplement 1), 165–174.CrossRefGoogle Scholar
Gökdal, O. (2013). Growth, slaughter and carcass characteristics of Alpine × Hair goat, Saanen × Hair goat and Hair goat male kids fed with concentrate in addition to grazing on rangeland. Small Ruminant Research 109, 69–75.CrossRefGoogle Scholar
Hossner, K. L. (2005). Hormonal Regulation of Farm Animal Growth. Wallingford, UK: CABI.CrossRefGoogle Scholar
Jardim, J. G., Vieira, R. A. M., Fernandes, A. M., Araujo, R. P., Glória, L. S., Rohem Júnior, N. M., Rocha, N. S. & Abreu, M. L. C. (2013). Application of a nonlinear optimization tool to balance diets with constant metabolizability. Livestock Science 158, 106–117.CrossRefGoogle Scholar
Jardim, J. G., Vieira, R. A. M., Fernandes, A. M., Araujo, R. P., Glória, L. S., Rohem Júnior, N. M., Rocha, N. S. & Abreu, M. L. C. (2015). Corrigendum to “Application of a nonlinear optimization tool to balance diets with constant metabolizability”. Livestock Science 173, 119–120.CrossRefGoogle Scholar
Koops, W. J. (1986). Multiphasic growth curve analysis. Growth 50, 169–177.Google ScholarPubMed
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 15, 259–263.CrossRefGoogle Scholar
Lawrence, T. L. J. & Fowler, V. R. (2002). Growth of Farm Animals. Wallingford, UK: CABI.CrossRefGoogle Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D. & Schabenberger, O. (2006). SAS® for Mixed Models. Cary, USA: SAS Institute Inc.Google Scholar
López, S., France, J., Gerrits, W. J. J., Dhanoa, M. S., Humphries, D. J. & Dijkstra, J. (2000). A generalized Michaelis–Menten equation for the analysis of growth. Journal of Animal Science 78, 1816–1828.CrossRefGoogle ScholarPubMed
Luo, J., Goetsch, A. L., Nsahlai, I. V., Sahlu, T., Ferrell, C. L., Owens, F. N., Galyean, M. L., Moore, J. E. & Johnson, Z. B. (2004 a). Metabolizable protein requirements for maintenance and gain of growing goats. Small Ruminant Research 53, 309–326.CrossRefGoogle Scholar
Luo, J., Goetsch, A. L., Sahlu, T., Nsahlai, I. V., Johnson, Z. B., Moore, J. E., Galyean, M. L., Owens, F. N. & Ferrell, C. L. (2004 b). Prediction of metabolizable energy requirements for maintenance and gain of preweaning, growing and mature goats. Small Ruminant Research 53, 231–252.CrossRefGoogle Scholar
Magistrelli, D., Aufy, A. A., Pinotti, L. & Rosi, F. (2013). Analysis of weaning-induced stress in Saanen goat kids. Journal of Animal Physiology and Animal Nutrition 97, 732–739.CrossRefGoogle ScholarPubMed
Mahgoub, O., Kadim, I. T., Al-Saqry, N. M. & Al-Busaidi, R. M. (2004). Effects of body weight and sex on carcass tissue distribution in goats. Meat Science 67, 577–585.CrossRefGoogle ScholarPubMed
Marinho, K. N. D., Freitas, A. R., Falcão, A. J. S. & Dias, F. E. F. (2013). Nonlinear models for fitting growth curves of Nellore cows reared in the Amazon Biome. Revista Brasileira de Zootecnia 42, 645–650.CrossRefGoogle Scholar
Matis, J. H. & Hartley, H. O. (1971). Stochastic compartmental analysis: model and least squares estimation from time series data. Biometrics 27, 77–102.CrossRefGoogle Scholar
Maynard, L. A., Loosli, J. K., Hintz, H. F. & Warner, R. G. (1979). Animal Nutrition. New York: McGraw-Hill, Inc.Google Scholar
McCulloch, C. E. & Searle, S. R. (2001). Generalized, Linear, and Mixed Models. New York: John Wiley & Sons.Google Scholar
Mendes, P. N., Muniz, J. A., Silva, F. F. E. & Mazzini, A. R. A. (2008). Modelo logístico difásico no estudo do crescimento de fêmeas da raça Hereford. Ciência Rural 38, 1984–1990.CrossRefGoogle Scholar
Mood, A. M., Graybill, F. A. & Boes, D. C. (1974). Introduction to the Theory of Statistics. Tokyo: McGraw-Hill Kogakusha, LTD.Google Scholar
Morand-Fehr, P. (1981). Growth. In Goat Production (Ed. Gall, C.), pp. 253–283. London: Academic Press Inc.Google Scholar
Morand-fehr, P. & Sauvant, D. (1988). Alimentation des caprins. In Alimentation des Bovins, Ovins & Caprins (Ed. Jarrige, R.), pp. 281–304. Paris: INRA.Google Scholar
Nešetřilová, H. (2005). Multiphasic growth models for cattle. Czech Journal of Animal Science 50, 347–354.CrossRefGoogle Scholar
NRC (1996). Nutrient Requirements of Beef Cattle. 7th revised edition. Washington, DC: National Academy Press.Google Scholar
NRC (2007). Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids. Washington, DC: The National Academy Press.Google Scholar
Owens, F. N., Dubeski, P. & Hanson, C. F. (1993). Factors that alter the growth and development of ruminants. Journal of Animal Science 71, 3138–3150.CrossRefGoogle ScholarPubMed
Peltier, M. R., Wilcox, C. J. & Sharp, D. C. (1998). Application of the Box-Cox data transformation to animal science experiments. Journal of Animal Science 76, 847–849.CrossRefGoogle ScholarPubMed
Pinheiro, J. C. & Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer-Verlag Inc.CrossRefGoogle Scholar
Ratkowsky, D. A. (1990). Handbook of Nonlinear Regression Models. New York: Marcel Dekker, Inc.Google Scholar
Regadas Filho, J. G. L., Tedeschi, L. O., Rodrigues, M. T., Brito, L. F. & Oliveira, T. S. (2014). Comparison of growth curves of two genotypes of dairy goats using nonlinear mixed models. Journal of Agricultural Science, Cambridge 152, 829–842.CrossRefGoogle Scholar
Richards, F. J. (1959). A flexible growth function for empirical use. Journal of Experimental Botany 10, 290–301.CrossRefGoogle Scholar
Rocha, N. S., Vieira, R. A. M., Abreu, M. L. C., Araujo, R. P., Glória, L. S., Tamy, W. P., Camisa Nova, C. H. P. & Fernandes, A. M. (2015). Traditional and biphasic nonlinear models to describe the growth of goat kids of specialized dairy breeds. Small Ruminant Research 123, 35–46.CrossRefGoogle Scholar
Santos Júnior, E., Vieira, R. A. M., Henrique, D. S. & Fernandes, A. M. (2008). Characteristics of the dairy goat primary sector at the Rio de Janeiro State, Brazil. Revista Brasileira de Zootecnia 37, 773–781.CrossRefGoogle Scholar
Souza, L. A., Carneiro, P. L. S., Malhado, C. H. M., Silva, F. F. & Silveira, F. G. (2013). Traditional and alternative nonlinear models for estimating the growth of Morada Nova sheep. Revista Brasileira de Zootecnia 42, 651–655.CrossRefGoogle Scholar
Spiess, A. N. & Neumeyer, N. (2010). An evaluation of R 2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach. BMC Pharmacology 10, article 6, 1–11. DOI: 10·1186/1471-2210-10-6.CrossRefGoogle ScholarPubMed
Stang, A., Poole, C. & Kuss, O. (2010). The ongoing tyranny of statistical significance testing in biomedical research. European Journal of Epidemiology 25, 225–230.CrossRefGoogle ScholarPubMed
Strathe, A. B., Danfaer, A., Sørensen, H. & Kebreab, E. (2010). A multilevel nonlinear mixed-effects approach to model growth in pigs. Journal of Animal Science 88, 638–649.CrossRefGoogle ScholarPubMed
Sugiura, N. (1978). Further analysis of the data by Akaike's Information Criterion and the finite corrections. Communications in Statistics, Theory and Methods A7, 13–26.CrossRefGoogle Scholar
Tedeschi, L. O., Cannas, A. & Fox, D. G. (2010). A nutrition mathematical model to account for dietary supply and requirements of energy and other nutrients for domesticated small ruminants: the development and evaluation of the Small Ruminant Nutrition System. Small Ruminant Research 89, 174–184.CrossRefGoogle Scholar
Thiex, N. J., Anderson, S. & Gildemeister, B. (2003). Crude fat, hexanes extraction, in feed, cereal grain, and forage (randall/soxtec/submersion method): collaborative study. Journal of AOAC International 86, 899–908.CrossRefGoogle ScholarPubMed
Thiex, N. J., Manson, H., Andersson, S. & Persson, J.-Á. (2002). Determination of crude protein in animal feed, forage, grain, and oilseeds by using block digestion with a copper catalyst and steam distillation into boric acid: collaborative study. Journal of AOAC International 85, 309–317.CrossRefGoogle ScholarPubMed
Vieira, R. A. M., Cabral, A. J., Souza, P. M., Fernandes, A. M., Henrique, D. S. & Corte-Real, G. S. C. P. (2009). Dairy goat husbandry amongst the household agriculture: herd and economic indexes from a case study in Rio de Janeiro, Brazil. Revista Brasileira de Zootecnia 38, 203–213.CrossRefGoogle Scholar
von Bertalanffy, L. (1957). Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32, 217–231.CrossRefGoogle ScholarPubMed
Vonesh, E. F. (2012). Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications using SAS® . Cary, NC, USA: SAS Institute Inc.Google Scholar