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Comparison of methods for prediction of rumen fermentation patterns from diet composition

Published online by Cambridge University Press:  20 November 2017

V.E. Brown
Affiliation:
Agricultural Research Institute of Northern Ireland, Large Park, Hillsborough, Co. Down, UK. BT26 6DR
R.E. Agnew
Affiliation:
Agricultural Research Institute of Northern Ireland, Large Park, Hillsborough, Co. Down, UK. BT26 6DR Department of Agriculture and Rural Development, Newforge Lane, Belfast, UK. BT9 5PX
D.J. Kilpatrick
Affiliation:
Department of Agriculture and Rural Development, Newforge Lane, Belfast, UK. BT9 5PX
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Extract

Previous attempts (Offer & Percival, 1998) have been made to develop a prediction system for rumen fermentation patterns from stepwise multiple linear regressions of the chemical constituents of the diet. These authors have also made comparisons between equations derived from diet wet chemistry and those developed from near infrared reflectance spectroscopy (NIRS). However, the potential of NIRS to predict the dynamics of rumen fermentation has not fully been explored using a wide range of forage treatments. Therefore the objective of this experiment was to develop equations from the chemical composition of the diet to predict rumen fermentation patterns and compare these with equations developed from undried and dried NIRS scans of the diets.

Type
Threatre Presentations
Copyright
Copyright © The British Society of Animal Science 2001

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References

Brown, V.E., Agnew, R.E. & Kilpatrick, D.J. 2000. The relationship between dietary chemical characteristics and the rumen fermentation patterns observed in vivo for a range of forages fed to beef steers. Animal Science (submitted).Google Scholar
Offer, N.W. & Percival, D.S. 1998. The prediction of rumen fermentation characteristics in sheep given grass silage diets. Animal Science 66: 163.CrossRefGoogle Scholar
Shenk, J.S. & Westerhaus, M.O. 1991. Population structuring of near-infrared spectra and modified partial least squares regression. Crop Science 31: 1548.Google Scholar