R.J. Higgs

The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5

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.

Updating the Cornell Net Carbohydrate and Protein System feed library and analyzing model sensitivity to feed inputs

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.

Development and evaluation of equations in the Cornell Net Carbohydrate and Protein System to predict nitrogen excretion in lactating dairy cows.

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 Systems (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.

The effect of starch-, fiber-, or sugar-based supplements on nitrogen utilization in grazing dairy cows

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.

CASE STUDY: Application and evaluation of the Cornell Net Carbohydrate and Protein System as a tool to improve nitrogen utilization in commercial dairy herds

ABSTRACT Nitrogen utilization is becoming a central component in ration balancing as dairy producers try to maximize milk protein yields, decrease feed costs, and conform to current environmental standards. Feeding excess CP can result in unnecessary feeding expenses with no return in milk income in addition to having important negative environmental consequences. Recent changes to the Cornell Net Carbohydrate and Protein System (CNCPS) have resulted in increased model sensitivity to N intake and requirements allowing rations to be developed with reduced environmental impact. Farm level studies are required to evaluate the applicability of the updated model on commercial farms. In this study, the CNCPS (v6.1) was used to adjust the diets of 2 commercial herds in western New York to improve N utilization and reduce feed costs while maintaining high levels of milk production (part 1). In addition, 13 herds that were producing high levels of milk (39.3 ± 5.1 kg/d per cow; mean ± SD) on low CP diets (14.3 to 16.5% DM) were characterized as examples of attainable N utilization targets (part 2). In part 1, CP was reduced by approximately 1% DM, milk urea N was decreased by approximately 2 mg/dL, and income over feed cost was improved on both farms. Part 2 indicated that high milk and milk protein yields can be achieved on diets supplying less than 16% CP and that N use efficiency can be as high as 38%. This study confirms that the updated CNCPS can be successfully used to develop diets that enhance N use efficiency under the constraints of a modern commercial dairy farm.