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

Abstract

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.

Determination of Cell Wall Protein from Selected Feedstuffs and its Relationship with Ruminal Protein Digestibility in Vitro

“…This value, i.e. NDICP -ADICP is called as B3 fraction in the Cornell Net Carbohydrate and Protein System (CNCPS; Higgs et al, 2012). Apparently, NDICP and ADICP are valuable parameters to determine the quality of protein of a ruminant feedstuff and thus we recommend the analyses as routine procedures, apart from proximate and Van Soest's analyses, in animal nutrition (especially ruminant nutrition) related laboratories in Indonesia.…”

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

“…The CNCPS is designed to be used in the field to predict nutrient excretion as part of a nutrient-management decision-making process. Through evaluation, the partitioning of urine and fecal N excretion was determined to be inconsistent with N balance data, thus a study was undertaken to improve this partitioning (Higgs et al, 2012). In part, this was done to help refine N feeding and excretion in relation to milk.…”

“…Because urinary urea N is the most volatile form of excreted N and also represents the true excess N, better predictions of urinary N might help nutritionists formulate to decrease this form of N excretion. The equations developed by Higgs et al (2012) were able to accurately partition between urinary and fecal N along with total manure N and have been implemented in v6.5.…”

Predicting nitrogen excretion from cattle

“…Despite the number of studies dedicated to predicting N excretion from cattle, accuracy remains low (Higgs et al, 2012). Whereas the complexity of ruminant digestion plays a role, the current accuracy of N excretion models can also be attributed to statistical methodology, a narrow pool of potential explanatory variables, and small data sets for model development and selection.…”