REVIEW PAPER
A critique of the Cornell Net Carbohydrate and
Protein System with emphasis on dairy cattle.
1. The rumen model
More details
Hide details
1
The University of Reading, Department of Agriculture,
Farley Gate, Reading RG6 2AT, UK
Publication date: 2001-01-22
J. Anim. Feed Sci. 2001;10(1):1-24
KEYWORDS
ABSTRACT
The Cornell Net Carbohydrate and Protein System (CNCPS) is primarily a nutrient supply model, since it does not model the utilisation of the absorbed nutrients, other than by using the NRC
(1988) net energy and NRC (1985) available protein systems to calculate the animal's energy, protein
and amino acid (AA) requirements. The model incorporates details of the metabolism in the rumen
not found in other published models of energy and protein requirements, particularly rates of carbohydrate degradation, predicted rumen pH and rumen nitrogen and peptide balance. The model does
not predict volatile fatty acid (VFA) proportions or methane production. Microbial growth is
assumed to be dependent upon the rate of carbohydrate degradation, whereas other mathematical
models of the rumen relate microbial growth to the concentration of nutrients in the rumen, and also
require estimates of rumen volume and microbial mass. The microbial yield of structural carbohydrate (SC) bacteria is not limited or altered by the rumen ammonia supply, with the result that
stoichiometrically unsound amounts of microbial protein and negative rumen ammonia levels can be
predicted. Only estimated peptide supply modifies the growth of non structural carbohydrate (NSC)
bacteria. Solid outflow rates adopted are low at higher levels of feeding compared to ARC (1984)
and AFRC (1992), with the result that the proportion of OM digested in the rumen is predicted to be
higher (c. 0.75) than the mean value 0.65 adopted by ARC (1980). The effects of liquid outflow rate
upon the outflow of NPN, AA and soluble proteins, pectins, sugars, organic acids and VFA are
ignored. Only peptides are affected by the liquid outflow rate in the model. The degradation rates for
carbohydrate fractions A and B1 proposed are very high, exceeding the possible rate of microbial
growth so that microbial synthesis does not respond to considerable variations in these high rates.
The adopted maximum microbial yields of SC and NSC rumen bacteria are lowered by 20% to allow
for the effects of protozoal predation, which has the effect of compensating for the high predicted
microbial yields adopted, but the protozoa are not accounted as contributing to the microbial AAN output. Starch disappearance in the rumen is not corrected for protozoal ingestion and its re-appearance in the intestine, nor is there is any accounting for the protozoal contribution to fat uptake in the
rumen. Predicted TDN values of forages are therefore sensitive mainly to the rate of cell wall degradation selected. Only one dietary parameter, effective neutral detergent fibre (eNDF%) defines or
modifies maximum microbial yield, microbial maintenance, realised microbial yield, and rates of SC
and NSC degradation. The consequence is that both energy and protein supply to the cow are affected by the parameter eNDF when values fall below 24.5% in diet DM.
CITATIONS (8):
1.
Evaluation and application of the CPM Dairy Nutrition model
L. O. TEDESCHI, W. CHALUPA, E. JANCZEWSKI, D. G. FOX, C. SNIFFEN, R. MUNSON, P. J. KONONOFF, R. BOSTON
The Journal of Agricultural Science
2.
The role of dynamic modelling in understanding the microbial contribution to rumen function
Jan Dijkstra, Jonathan A. N. Mills, James France
Nutrition Research Reviews
3.
Effect of expander processing on fractional rate of maize and barley starch degradation in the rumen of dairy cows estimated using rumen evacuation and in situ techniques
R Tothi, P Lund, M.R Weisbjerg, T Hvelplund
Animal Feed Science and Technology
4.
The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion
D.G Fox, L.O Tedeschi, T.P Tylutki, J.B Russell, M.E Van Amburgh, L.E Chase, A.N Pell, T.R Overton
Animal Feed Science and Technology
5.
A Meta-Analysis Examining the Relationship Among Dietary Factors, Dry Matter Intake, and Milk and Milk Protein Yield in Dairy Cows
A.N. Hristov, W.J. Price, B. Shafii
Journal of Dairy Science
6.
A revised CNCPS feed carbohydrate fractionation scheme for formulating rations for ruminants
C. Lanzas, C.J. Sniffen, S. Seo, L.O. Tedeschi, D.G. Fox
Animal Feed Science and Technology
7.
ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2
Luis Tedeschi
Journal of Animal Science
8.
Modeling microbial growth based on time-dependent kinetic mechanisms of digestion and passage in the ruminoreticulum
R.A.M. Vieira, C.C. Cordeiro, K.R. Lima, A.M. Fernandes, L.S. Cabral, A.L.A. Neves, L.O. Tedeschi
Animal Feed Science and Technology