New laboratory and animal sampling methods and data have been generated over the last 10 yr that had the potential to improve the predictions for energy, protein, and AA supply and requirements in the Cornell Net Carbohydrate and Protein System (CNCPS). The objectives of this study were to describe updates to the CNCPS and evaluate model performance against both literature and on-farm data. The changes to the feed library were significant and are reported in a separate manuscript. Degradation rates of protein and carbohydrate fractions were adjusted according to new fractionation schemes, and corresponding changes to equations used to calculate rumen outflows and postrumen digestion were presented. In response to the feed-library changes and an increased supply of essential AA because of updated contents of AA, a combined efficiency of use was adopted in place of separate calculations for maintenance and lactation to better represent the biology of the cow. Four different data sets were developed to evaluate Lys and Met requirements, rumen N balance, and milk yield predictions. In total 99 peer-reviewed studies with 389 treatments and 15 regional farms with 50 different diets were included. The broken-line model with plateau was used to identify the concentration of Lys and Met that maximizes milk protein yield and content. Results suggested concentrations of 7.00 and 2.60% of metabolizable protein (MP) for Lys and Met, respectively, for maximal protein yield and 6.77 and 2.85% of MP for Lys and Met, respectively, for maximal protein content. Updated AA concentrations were numerically higher for Lys and 11 to 18% higher for Met compared with CNCPS v6.0, and this is attributed to the increased content of Met and Lys in feeds that were previously incorrectly analyzed and described. The prediction of postruminal flows of N and milk yield were evaluated using the correlation coefficient from the BLUP (R(2)BLUP) procedure or model predictions (R(2)MDP) and the concordance correlation coefficient. The accuracy and precision of rumen-degradable N and undegradable N and bacterial N flows were improved with reduced bias. The CNCPS v6.5 predicted accurate and precise milk yield according to the first-limiting nutrient (MP or metabolizable energy) with a R(2)BLUP=0.97, R(2)MDP=0.78, and concordance correlation coefficient=0.83. Furthermore, MP-allowable milk was predicted with greater precision than metabolizable energy-allowable milk (R(2)MDP=0.82 and 0.76, respectively, for MP and metabolizable energy). Results suggest a significant improvement of the model, especially under conditions of MP limitation.

The Cornell Net Carbohydrate and Protein System (CNCPS) is a nutritional model that evaluates the environmental and nutritional resources available in an animal production system and enables the formulation of diets that closely match the predicted animal requirements. The model includes a library of approximately 800 different ingredients that provide the platform for describing the chemical composition of the diet to be formulated. Each feed in the feed library was evaluated against data from 2 commercial laboratories and updated when required to enable more precise predictions of dietary energy and protein supply. A multistep approach was developed to predict uncertain values using linear regression, matrix regression, and optimization. The approach provided an efficient and repeatable way of evaluating and refining the composition of a large number of different feeds against commercially generated data similar to that used by CNCPS users on a daily basis. The protein A fraction in the CNCPS, formerly classified as nonprotein nitrogen, was reclassified to ammonia for ease and availability of analysis and to provide a better prediction of the contribution of metabolizable protein from free AA and small peptides. Amino acid profiles were updated using contemporary data sets and now represent the profile of AA in the whole feed rather than the insoluble residue. Model sensitivity to variation in feed library inputs was investigated using Monte Carlo simulation. Results showed the prediction of metabolizable energy was most sensitive to variation in feed chemistry and fractionation, whereas predictions of metabolizable protein were most sensitive to variation in digestion rates. Regular laboratory analysis of samples taken on-farm remains the recommended approach to characterizing the chemical components of feeds in a ration. However, updates to the CNCPS feed library provide a database of ingredients that are consistent with current feed chemistry information and laboratory methods and can be used as a platform to formulate rations and improve the description of biology within the model.

Nitrogen utilization in grazing cows is often low due to high concentrations of rapidly soluble and degradable protein in the pasture-based diet. Broadly, opportunities to improve N utilization lie in either reducing the amount of N consumed by the animal, or incorporating more N into milk protein. The goal of this study was to compare the relative importance of dietary N intake and productive N output for improving N utilization in grazing cows fed either starch-, fiber-, or sugar-based supplements. Also, the Cornell Net Carbohydrate and Protein System (CNCPS; Cornell University, Ithaca, NY) was evaluated as a tool to assess cow performance and improve N utilization in pasture-based systems. Eighty-five cows were randomly assigned to 1 of 5 treatments at parturition (17 cows per treatment). Treatments consisted of a pasture-only control and pasture with a starch- (St and StN), fiber- (FbN), or a sugar-based supplement. The StN and FbN treatments contained additional dietary N. Diets were formulated using the CNCPS to supply similar levels of dietary metabolizable energy, but differing levels of dietary N and metabolizable protein. Nitrogen utilization ranged from 22 to 26% across the 5 groups. Cows fed the St diet had the lowest levels of milk urea N, blood urea N, and urinary N excretion and had the highest productive N output (149 g/d). Cows fed the FbN treatment had similar productive N output (137 g/d) and consumed approximately 100g/d more dietary N than the St treatment, resulting in greater urinary N excretion. Although milk protein yield was moderately greater in the St treatment, quantitatively the difference in N intake (100g/d) had the greatest effect on N utilization and suggests that controlling dietary N intake should be the first priority when attempting to improve N utilization in grazing cows. No effect was observed of supplementing pasture-fed cattle with sugar on production or N utilization under the conditions of this experiment. Predictions of metabolizable energy and protein availability for milk yield from the CNCPS were similar to actual milk yield for all treatments. Model-predicted N utilization and excretion reflected the trends observed in the measured data and suggests that the CNCPS can be a useful tool for formulating and evaluating diets to improve N utilization in pasture-based systems.

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 reported total collection N balance results. Considerable care was taken to ensure the data included in the development data set (n=104) accounted for >90% of the N intake (NI). Unaccounted N for the compiled data set was 1.47±4.60% (mean ± SD). The results showed that FN predictions could be improved by using a modification of a previously published equation: FN (g/d) = [[NI (g/kg of organic matter) × (1 - 0.842)] + 4.3 × organic matter intake (kg/d)] × 1.20, which, when evaluated against the compiled N balance data, had a squared coefficient of determination based on a mean study effect R(MP)(2) of 0.73, concurrent correlation coefficient (CCC) of 0.83 and a root mean square error (RMSE) of 10.38 g/d. Urinary N is calculated in the CNCPS as the difference between NI and other N excretion and losses. Incorporating the more accurate FN prediction into the current CNCPS framework and correcting an internal calculation error considerably improved UN predictions (RMSE=12.73 g/d, R(MP)(2)=0.86, CCC=0.90). The changes to FN and UN translated into an improved prediction of total manure N excretion (RMSE=12.42 g/d, R(MP)(2)=0.96, CCC=0.97) and allows nutritionists and farm advisors to evaluate these factors during the ration formulation process.

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