Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-27T06:37:42.314Z Has data issue: false hasContentIssue false
  • English
  • Français

Simplified estimation of forage degradability in the rumen assuming zero-order degradation kinetics

Published online by Cambridge University Press:  08 December 2008

M. S. DHANOA ,
S. LÓPEZ ,
R. SANDERSON  and
J. FRANCE
M. S. DHANOA
Affiliation:
Institute of Grassland and Environmental Research (IGER)†, Plas Gogerddan, Aberystwyth, Ceredigion, SY23 3EB, UK
S. LÓPEZ*
Affiliation:
Instituto de Ganadería de Montaña (IGM), Universidad de León – Consejo Superior de Investigaciones Científicas (CSIC), Departamento de Producción Animal, Universidad de León, E-24071 León, Spain
R. SANDERSON
Affiliation:
Institute of Grassland and Environmental Research (IGER)†, Plas Gogerddan, Aberystwyth, Ceredigion, SY23 3EB, UK
J. FRANCE
Affiliation:
Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, ON, N1G 2W1, Canada
*
*To whom all correspondence should be addressed. Email: s.lopez@unileon.es
Get access Rights & Permissions [Opens in a new window]

Summary

In the present paper, a simplified procedure using few in situ data points is derived and then evaluated (using a large database) against reference values estimated with the standard nylon bag first-order kinetics model. The procedure proposed involved a two-stage mathematical process, with a statistical prediction of some degradation parameters (such as lag time) and then a kinetic model derived by assuming degradation follows zero-order kinetics to determine effective degradability in the rumen (E). In addition to the estimation of washout fraction and discrete lag, which is common to both procedures, the simplified procedure requires measurement of dry matter losses at one incubation time point only. Thus, interference of the animal rumen will be much reduced, which will lead to increased capacity for feed evaluation. Calibration of the zero-order model against the first-order model showed that suitable estimates of E can be obtained with disappearance at 24, 48 or 72 h as the single incubation end time point. The strength of the calibration is such that an end incubation time point as low as 24 h may be sufficient, which may reduce substantially the total incubation time required and thus the impact on the experimental animal. Relevant regression equations to predict reference values of parameters such as lag time or E are also developed and validated.

Type
Modelling Animal Systems Paper
Information
The Journal of Agricultural Science , Volume 147 , Issue 3 , June 2009 , pp. 225 - 240
Copyright
Copyright © 2008 Cambridge University Press

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.)

Footnotes

†

Merged into The Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University.

References

REFERENCES

Bartlett, M. S. (1949). Fitting a straight line when both variables are subject to error. Biometrics 5, 207–212.CrossRefGoogle Scholar
Beever, D. E., Dhanoa, M. S., Losada, H. R., Evans, R. T., Cammell, S. B. & France, J. (1986). The effect of forage species and stage of harvest on the processes of digestion occurring in the rumen of cattle. British Journal of Nutrition 56, 439–454.CrossRefGoogle ScholarPubMed
Bibby, J. & Toutenberg, H. (1977). Prediction and Improved Estimation in Linear Models. London: John Wiley and Sons.Google Scholar
Broderick, G. A. (1994). Quantifying forage protein quality. In Forage Quality, Evaluation, and Utilization (Ed. Fahey, G. C. Jr), pp. 200–228. Madison, WI: American Society of Agronomy, Crop Science Society of America and Soil Science Society of America.Google Scholar
Calsamiglia, S., Yoon, I. K. & Stern, M. D. (1994). Effect of various incubation times on in situ estimation of ruminal crude protein degradation. Journal of Animal Science 72 (Suppl. 1), 171.Google Scholar
Dhanoa, M. S. (1988). On the analysis of dacron bag data for low degradability feeds. Grass and Forage Science 43, 441–444.Google Scholar
Dhanoa, M. S., France, J., Siddons, R. C., López, S. & Buchanan-Smith, J. G. (1995). A nonlinear compartmental model to describe forage degradation kinetics during incubation in polyester bags in the rumen. British Journal of Nutrition 73, 3–15.Google Scholar
Dhanoa, M. S., France, J., López, S., Dijkstra, J., Lister, S. J., Davies, D. R. & Bannik, A. (1999 a). Correcting the calculation of extent of degradation to account for particulate matter loss at zero time when applying the polyester bag method. Journal of Animal Science 77, 3385–3391.CrossRefGoogle ScholarPubMed
Dhanoa, M. S., Lister, S. J., France, J. & Barnes, R. J. (1999 b). Use of mean square prediction error analysis and reproducibility measures to study near infrared calibration equation performance. Journal of Near Infrared Spectroscopy 7, 133–143.Google Scholar
Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.CrossRefGoogle Scholar
Fathi Nasri, M. H., Danesh Mesgaran, M., France, J., Cant, J. P. & Kebreab, E. (2006). Evaluation of models to describe ruminal degradation kinetics from in situ ruminal incubation of whole soybeans. Journal of Dairy Science 89, 3087–3095.CrossRefGoogle Scholar
France, J., Thornley, J. H. M., López, S., Siddons, R. C., Dhanoa, M. S., Van Soest, P. J. & Gill, M. (1990). On the two compartment model for estimating the rate and extent of feed degradation in the rumen. Journal of Theoretical Biology 146, 269–287.CrossRefGoogle ScholarPubMed
France, J., Dijkstra, J., Dhanoa, M. S., López, S. & Bannink, A. (2000). Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations. British Journal of Nutrition 83, 143–150.Google Scholar
Leroy, A. & Rousseeuw, P. (1984). PROGRESS: A Program for Robust Regression. Technical Report 201. Brussels, Belgium: Center for Statistics and Operations Research, University of Brussels.Google Scholar
Lin, L. I. K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.Google Scholar
López, S. (2005). In vitro and in situ techniques for estimating digestibility. In Quantitative Aspects of Ruminant Digestion and Metabolism, 2nd edn (Eds Dijkstra, J., Forbes, J. M. & France, J.), pp. 87–121. Wallingford, UK: CAB International.CrossRefGoogle Scholar
López, S., Carro, M. D., González, J. S. & Ovejero, F. J. (1991 a). Rumen degradation of the main forage species harvested from permanent mountain meadows in North-western Spain. Journal of Agricultural Science, Cambridge 117, 363–369.CrossRefGoogle Scholar
López, S., Carro, M. D., González, J. S. & Ovejero, F. J. (1991 b). The effect of method of forage conservation and harvest season on the degradation of forages harvested from permanent mountain meadows. Animal Production 53, 177–182.Google Scholar
López, S., France, J., Dhanoa, M. S., Mould, F. & Dijkstra, J. (1999). Comparison of mathematical models to describe disappearance curves obtained using the polyester bag technique for incubating feeds in the rumen. Journal of Animal Science 77, 1875–1888.Google Scholar
McDonald, I. (1981). A revised model for the estimation of protein degradability in the rumen. Journal of Agricultural Science, Cambridge 96, 251–252.CrossRefGoogle Scholar
Mertens, D. R. (2005). Rate and extent of digestion. In Quantitative Aspects of Ruminant Digestion and Metabolism, 2nd edn (Eds Dijkstra, J., Forbes, J. M. & France, J.), pp. 13–47. Wallingford, UK: CAB International.CrossRefGoogle Scholar
Morgan, W. A. (1939). A test for the significance of the difference between the two variances in a sample from a normal bivariate population. Biometrika 31, 13–19.Google Scholar
Nivyobizi, A., Deswysen, G., Moreau, B., Dehareng, D., Larondelle, Y. & Peeters, A. (2007). The choice of a fitting model for in sacco degradation curves of some temperate and tropical grasses. Grass and Forage Science 62, 198–207.CrossRefGoogle Scholar
Olaisen, V., Mejdell, T., Volden, H. & Nesse, N. (2003). Simplified in situ method for estimating ruminal dry matter and protein degradability of concentrates. Journal of Animal Science 81, 520–528.Google Scholar
Ørskov, E. R. & McDonald, I. (1979). The estimation of protein degradability in the rumen from incubation measurements weighted according to rates of passage. Journal of Agricultural Science, Cambridge 92, 449–503.CrossRefGoogle Scholar
Pitman, E. J. G. (1939). A note on normal correlation. Biometrika 31, 9–12.Google Scholar
Rousseeuw, P. J. (1984). Least median squares regression. Journal of the American Statistical Association 79, 871–880.Google Scholar
Rousseeuw, P. J. & Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: Wiley.CrossRefGoogle Scholar
Sokal, R. R. & Rohlf, F. J. (1995). Biometry, 3rd edn. New York: W.H. Freeman.Google Scholar
Vanzant, E. S., Cochran, R. S., Titgemeyer, E. C., Stafford, S. D., Olson, K. C., Johnson, D. E. & St Jean, G. (1996). In vivo and in situ measurements of forage protein degradation in beef cattle. Journal of Animal Science 74, 2773–2784.CrossRefGoogle ScholarPubMed
Wilkerson, V. A., Klopfenstein, T. J. & Stroup, W. W. (1995). A collaborative study of in situ forage protein degradation. Journal of Animal Science 73, 583–588.CrossRefGoogle ScholarPubMed