Ability of commercially available dairy ration programs to predict duodenal flows of protein and essential amino acids in dairy cows (original) (raw)
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Journal of dairy science, 2017
This work evaluated the National Research Council (NRC) dairy model (2001) predictions of rumen undegradable (RUP) and degradable (RDP) protein compared with measured postruminal non-ammonia, nonmicrobial (NANMN) and microbial N flows. Models were evaluated using the root mean squared prediction error (RMSPE) as a percent of the observed mean, mean and slope biases as percentages of mean squared prediction error (MSPE), and concordance correlation coefficient (CCC). The NRC (2001) over-estimated NANMN by 18% and under-estimated microbial N by 14%. Both responses had large mean biases (19% and 20% of MSPE, respectively), and NANMN had a slope bias (22% of MSPE). The NRC NANMN estimate had high RMSPE (46% of observed mean) and low CCC (0.37); updating feed library A, B, and C protein fractions and degradation rate (Kd) estimates with newer literature only marginally improved fit. The re-fit NRC models for NANMN and microbial N had CCC of 0.89 and 0.94, respectively. When compared with...
Evaluation of models for balancing the protein requirements of dairy cows
Journal of dairy science, 1998
Most diets for dairy cattle in the US are formulated using the mathematical model developed by the National Research Council (NRC). This model is simpler than more mechanistic models and is largely empirical. Based on the research reviewed in this paper, the simpler empirical approach is recommended for routine diet formulation at the present time. Under typical conditions using feed tables for most feed descriptions, the NRC model was more accurate than the Cornell Net Carbohydrate and Protein System, and, in its present form, the model developed by Baldwin et al. (4) is too difficult to run routinely in the field. However, the more mechanistic approaches are recommended to investigate diet and animal interactions under nonstandard environmental conditions, animals, or feeds. Because the NRC model does not address many of the potential limitations of some diets or management conditions, more complex models are needed to identify why some herds appear to underperform. Mechanistic mo...
Journal of dairy science, 1998
This study evaluated the Cornell Net Carbohydrate and Protein System for dairy cows consuming diets based on pasture, assessed the sensitivity of the model to critical inputs, and demonstrated application opportunities. Data were obtained from four grazing experiments and four indoor pasture feeding experiments (25 dietary treatments) involving dairy cows in New Zealand and the US. The model provided a reasonably good estimate of changes in body condition score (r2 = 0.78; slope not significantly different from 1), estimated energy balance (r2 = 0.76; slope not significantly different from 1), blood urea N (r2 = 0.94; underprediction bias of 0.5%), microbial N flow (r2 = 0.88; slope not significantly different from 1), and milk production. The model underpredicted dry matter intake (r2 = 0.80; 13% bias) and overpredicted ruminal pH (r2 = 0.47; 1.7% bias). Predicted milk production was especially sensitive to changes in pasture lignin content, effective fiber, rate of fiber digestion...
Comparison of actual and predicted amino acid contents in the duodenal digesta of dairy cows
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2010
In this experiment on three dairy cows with the ruminal and duodenal T-cannulas, the actual and predicted amino acid (AA) profiles of the protein fraction flowed to the small intestine were compared. The prediction was calculated by two methods: with the use of mean published AA profile of microbial protein and of experimentally determined one. The actual AA profile of digesta protein was corrected for glycine (Gly) of bile origin. In comparison to the published AA profile of microbial protein the use of the actual one did not contribute to an improvement of prediction accuracy (mean prediction error: 7.36 vs. 7.54 %, respectively). Inaccurate determination of AA composition of undegraded feed protein and the insufficient correction for Gly of bile origin were the probable sources of the residual variability.
A Meta-analysis of Ruminal Outflow of Nitrogen Fractions in Dairy Cows
Advances in Dairy Research, 2015
Data from the scientific literature and statistical methods were used to estimate the magnitude and significance of the effects of the amount and source of dietary crude protein (CP) supplements on the supply of N fractions passing to the small intestine of lactating dairy cows. The passage of total N and nonammonia N from the rumen was influenced by the source of CP in the control diet and only marginally by the source of ruminally un-degradable protein (RUP) in the dietary treatment. Even though NH 3 N did not appear to limit growth of the microbes, overall flow of microbial N from the rumen was depressed when RUP partially replaced other sources of CP in the diet. This response tended to be affected by the source of RUP in the dietary treatment. The passage of nonammonia, nonmicrobial N to the duodenum increased when cows consumed diets that contained RUP supplements. The magnitude of this response, however, was distinctly altered by the source of CP in the control diet with which the RUP treatment was compared. In addition, the proportion and source of total CP supplied by RUP in the treatment diet tended to modulate the response. Feeding RUP resulted in a significant increase in the ruminal outflow of total and essential amino acids but the magnitude of this response depended on the source of RUP. Feeding some RUP sources quantitatively improved the delivery of methionine and lysine to the small intestine. Therefore, variability exists in the ruminal outflow of N fractions when different sources of RUP are fed to cows. A portion of this variation is explained by the source of CP in the control diet, the source of RUP in the dietary treatment, the amino acid composition of the dietary CP, and the CP percentage of the diet.
SUMMARY Predicting milk production is complex due to interactions between the requirement for nutrients by the cow, the characteristics of different feeds and the contributions from body reserves towards the animal's requirements. Grazing introduces more complexity because of the variation in the characteristics of the forage, variation in nutrient intake between cows, diurnal patterns of eating, and further energy demands due to the effort of grazing. Ruminal pH is a critical determinant of metabolisable energy supply because of effects on the growth of different groups of microorganisms in the rumen. Our objective was to use the Cornell Net Carbohydrate and Protein System model to determine whether it was possible to predict ruminal pH for a range of pasture-based diets, based on inputs of nutrient intake, milk composition, liveweight and body condition. There was no (P>0.05) relationship between observed and predicted ruminal pH. The average daily ruminal pH for observed ...
Evaluation of the passage rate equations in the 2001 Dairy NRC model
Journal of dairy science, 2006
Dairy ration formulation to meet protein and amino acid requirements with the National Research Council Nutrient Requirements of Dairy Cattle (NRC, 2001) model depends on accuracy of predicting feed passage rates out of the rumen. The NRC (2001) passage rate (Kp) equations were evaluated for validity and sensitivity to input variables in predicting supplies of rumen degraded protein, rumen undegraded protein, and metabolizable protein. The database used in the development of the 3 Kp equations (for dry forage, wet forage, and concentrate) was used to independently derive the 3 equations using a meta-analysis technique. To extract quantitative relationships between statistically significant input variables and rate of passage, a random coefficients model that used each study effect as a random variable was used. The database was comprised of studies that only used rare earth markers. Outliers were identified by acceptance criteria defined a priori or the difference in fit statistic (...
Journal of dairy science, 2012
Nitrogen excretion is of particular concern on dairy farms, not only because of its effects on water quality, but also because of the subsequent release of gases such as ammonia to the atmosphere. To manage N excretion, accurate estimates of urinary N (UN) and fecal N (FN) are needed. On commercial farms, directly measuring UN and FN is impractical, meaning that quantification must be based on predictions rather than measured data. The purpose of this study was to use a statistical approach to develop equations and evaluate the Cornell Net Carbohydrate and Protein System's (CNCPS) ability to predict N excretion in lactating dairy cows, and to compare CNCPS predictions to other equations in the literature. Urinary N was over-predicted by the CNCPS due to inconsistencies in N accounting within the model that partitioned more N to feces than urine, although predicted total N excretion was reasonable. Data to refine model predictions were compiled from published studies (n=32) that ...
Journal of Animal Science
The Cornell Net Carbohydrate and Protein System was modified to include an amino acid submodel for predicting the adequacy of absorbed essential amino acids in cattle diets. Equations for predicting the supply of and requirements for absorbed essential amino acids are described and presented. The model was evaluated for its ability to predict observed duodenal flows of nitrogen, nonammonia nitrogen, bacterial nitrogen, dietary nonammonia nitrogen, and individual essential amino acids. Model-predicted nitrogen, nonammonia nitrogen, bacterial nitrogen, and dietary nonammonia nitrogen explained 93.2, 94.6, 76.4, and 79.3% of the observed duodenal flows, respectively, based on R2 values from predicted vs observed regression analysis. Based on slopes of regression lines, model-predicted duodenal nitrogen and nonammonia nitrogen were different from observed duodenal flows ( P < .05), whereas model-predicted bacterial nitrogen and dietary nonammonia nitrogen were not different from observed duodenal flows ( P < .05). Model-predicted duodenal flows of individual essential amino acids explained 81 to 90% of variation in observed duodenal amino acid flows. Based on slopes of regression lines, modelpredicted duodenal threonine, leucine, and arginine were the only amino acids different from observed duodenal flows ( P < .05). Ideas for further model improvements and research in amino acid metabolism were also presented.