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A new protein requirement system for dairy cows

Open AccessPublished:December 23, 2022DOI:https://doi.org/10.3168/jds.2022-22348

      ABSTRACT

      Accurate prediction of protein requirements for maintenance and lactation is needed to develop more profitable diets and reduce N loss and its environmental impact. A new factorial approach for accounting for net protein requirement for maintenance (NPM) and metabolizable protein (MP) efficiency for lactation (EMPL) was developed from a meta-analysis of 223 N balance trials. We defined NPM as the sum of the endogenous protein fecal and urinary excretion and estimated it from the intercept of a nonlinear equation between N intake and combined total N fecal and urinary excretion. Our model had a strong goodness-of-fit to estimate NPM (6.32 ± 0.15 g protein/kg metabolic body weight; n = 807 treatment means; r = 0.91). We calculated the EMPL as a proportion of the N intake, minus N excreted in feces and urine, that was secreted in milk. A fixed-EMPL value of 0.705 ± 0.020 was proposed. In a second independent data set, nonammonia-nonmicrobial-N and microbial-N ruminal outflows were measured, and the adequacy of the MP prediction (51 studies; n = 192 means treatments) was assessed. Our system based on the fixed-EMPL model predicted the MP requirement for lactation and maintenance with higher accuracy than several North American and European dairy cattle nutrition models, including the INRA (2018) and NASEM (2021). Only the NRC (2001), CNCPS 6.5, and Feed into Milk (2004) models had similar accuracy to predict MP requirement. Our system may contribute to improve the prediction for MP requirements of maintenance and lactation. However, most refined predictive models of intestinal digestibility for rumen undegradable protein and microbial protein are still needed to reduce the evaluation biases in our model and external models for predicting the MP requirements of dairy cows.

      Key words

      INTRODUCTION

      Considerable progress has been made in the protein nutrition of dairy cows (
      • Schwab C.G.
      • Broderick G.A.
      A 100-year review: Protein and amino acid nutrition in dairy cows.
      ;
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      ). However, ongoing global demand for dairy foods and high social pressure to reduce their environmental footprint continue to motivate the development of models to predict protein requirements to optimize milk production and economic performance, increase dietary N captured in milk, and minimize N excretion into the environment.
      Maintenance and lactation are the two most important components of the protein requirement for lactating dairy cows. The net protein requirement for maintenance (NPM) has been assumed to be the sum of endogenous protein fecal and urinary excretion (EPFU) and protein scurf losses (
      • Owens F.N.
      Maintenance protein requirements.
      ;
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      ). Invasive, expensive, and labor-intensive methods (N-free intragastric nutrition, digesta exchange, and labeling by stable isotopes) have been adopted to obtain EPFU, but they have provided limited amounts of animal and diet data (
      • Marini J.C.
      • Fox D.G.
      • Murphy M.R.
      Nitrogen transactions along the gastrointestinal tract of cattle: A meta-analytical approach.
      ). An alternative approach is to estimate EPFU by extrapolation of the intercept-regression between N intake and N total fecal and urinary excretion (NFU;
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      ;
      • Owens F.N.
      Maintenance protein requirements.
      ). As balance N trials have been widely published, meta-analyses of N balance trials may provide a robust estimate of EPFU and NPM.
      Several dairy cow nutrition models (
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      ;
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ) have adopted EPFU from
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      meta-analysis of N metabolism trials of nonlactating cattle. As lactating dairy cows typically have higher N intake and excretion per body mass than nonlactating cattle, the
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      model may underestimate NPM. The NASEM (2021) committee adopted the assumptions of
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      to predict EPFU from an admittedly scarce literature review of dairy cows. The new NASEM (2021) model seems to have improved the prediction of milk protein yield compared with
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , but an evaluation of its modeled protein requirement against observed values was not reported.
      The net protein requirement for lactation (NPL) is more easily determined and represents the milk true protein secretion. However, to obtain the MP requirement for lactation (MPL), it is necessary to know the efficiency of the use of MP to NPL (EMPL). Most dairy cattle nutrition committees have adopted fixed-EMPL values to predict MPL from NPL, with EMPL ranging from 0.67 to 0.70 (
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      ; CSIRO, 2007;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ;
      • INRA
      INRA Feeding System for Ruminants.
      ; NASEM 2021). The
      • INRA
      INRA Feeding System for Ruminants.
      and NASEM (2021) models adopted variable-EMPL-based models to predict MP supply and milk protein yield, but fixed-EMPL values of 0.67 and 0.69 were used as targets to predict MPL requirements from NPL.
      To the best of our knowledge, only the Dutch protein evaluation system for ruminants (DVE/OEB2010;
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ) and
      • NorFor
      models have used variable-EMPL to predict MPL from NPL requirements, using the milk protein to energy ratio and the MP supply available for milk production to milk energy ratio as inputs, respectively. In DVE/OEB2010, EMPL decreases with milk protein yield (
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ), whereas in
      • NorFor
      neither the milk protein yield nor the feeding level affects EMPL. However, sufficient evidence exists to show that milk yield and N milk efficiency (N milk/N intake) are positively correlated (
      • Nadeau E.
      • Englund J.E.
      • Gustafsson A.H.
      Nitrogen efficiency of dairy cows as affected by diet and milk yield.
      ), but a better prediction of the MP requirement using a variable-EMPL-based model instead of fixed-EMPL remains unclear.
      A more comprehensive and accurate system to predict NP and MP requirements for lactating cows is necessary. We hypothesized that (1) a new model from a meta-analysis of N balance trials may provide a robust estimate of EPFU, NPM, and EMPL; and (2) our new system may improve MP requirement prediction for lactating dairy cows compared with external dairy cattle nutrition models.
      Our objectives were (1) to propose new values for EPFU (NPM) and EMPL for lactating dairy cows from a N balance trials meta-analysis, and (2) to compare the adequacy of our protein requirements system with external models to predict the MP requirement for lactating dairy cows. The proposed protein requirement will be used to update the protein submodel of the Nutrition System for Dairy Cattle (
      • Oliveira A.S.
      The Nutrition System for Dairy Cattle (NS Dairy Cattle): A Model of Energy and Nutrients Requirements and Diet Evaluation for Dairy Cattle. 1th edition. Mendeley Data, V4.
      ).

      MATERIALS AND METHODS

      Institutional Animal Care and Use Committee approval was not necessary, because our data set was built from a systematic review of peer-reviewed papers.

      Protein Requirement System Development

      Data Set.

      A large data set of N balance trials was built to develop our protein requirement system, based on a search of peer-reviewed papers using the terms “nitrogen” and “dairy cows” in the Web of Science and Science Direct databases. The literature search yielded 5,418 peer-reviewed papers. The inclusion criteria for the data set were (1) peer-reviewed papers; (2) studies conducted with lactating cows; (3) reporting of the treatment means for CP intake, milk protein yield, and N excretion in feces and urine; and (4) reporting of the standard error of the mean (SEM) or the standard error of the difference (SED). When SED was reported in studies evaluated as fixed models, SEM was calculated as SEM = SED/√2. A flowchart showing the process of identification, exclusion, and inclusion of studies to construct the protein requirement model is described in Supplemental Material S1 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      Based on the inclusion criteria, 212 peer-reviewed papers involving 223 N balance trials were selected for data extraction and derivation of our protein requirement system (Table 1). No procedure was adopted to estimate missing data, except for the SEM of N urinary excretion. Data that did not report on studies were considered as missing and subsequently excluded from the final model. No adjustment was made to fecal N obtained from oven-dried samples (
      • Juko C.D.
      • Bredon R.M.
      • Marshall B.
      Nutrition of Zebu cattle Part II. The techniques of digestibility trials with special reference to sampling, preservation and drying of faeces.
      ;
      • Higgs R.J.
      • Chase L.E.
      • Van Amburgh M.E.
      Development and evaluation of equations in the Cornell Net Carbohydrate and Protein System to predict nitrogen excretion in lactating dairy cows.
      ). The complete data set in the Excel file is available in an open research data repository (
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      ), and the references used to develop the models are available in Appendix 1 of Supplemental Material S2 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      Table 1Descriptive statistics of the data set used to develop the new protein requirement system for dairy cows
      ItemMeanMedianMaximumMinimumSDn
      Treatment means from 223 N balance trials in 212 peer-reviewed papers. The complete data set is available in an Excel file in Silva and Oliveira (2022a). The list of references is available in Supplemental Material S2 (https://data.mendeley.com/datasets/z6pdk5pyg9; Silva and Oliveira, 2022b). Missing data represent nonreported data in studies. No missing data of response or predictor variables were estimated, except for the SEM of N urinary.
      Animal
       BW, kg61662278835166.9856
       Milk yield, kg/d30.830.350.19.028.40784
       DIM11310332314.055.5653
       Milk protein, g/kg31.731.339.625.72.29573
       Milk fat, g/kg37.937.560.822.75.19743
       Milk lactose, g/kg47.547.754.041.01.74571
       Milk urea-N, mg/dL13.112.734.03.664.17425
      Diet
       DMI, kg/d21.021.231.89.204.16784
       Forage, g/kg DM5395331,00021891.5507
       DM, g/kg548537900317101292
       OM, g/kg DM92892799487418.5265
       NDF, g/kg DM33933453420560.7525
       CP, g/kg DM16716528194.021.5562
       Ether extract, g/kg DM38.737.289.015.012.4302
       Starch, g/kg DM21722635933.967.2241
       NFC, g/kg DM41641855010163.2249
       DM total-tract digestibility0.6750.6770.8340.4820.047545
       OM total-tract digestibility0.6970.7000.8480.5710.044466
       NEL diet, Mcal/kg DM1.601.601.781.370.07275
      N balance and efficiency
       N intake, g/d550557954183134856
       N milk, g/d14714824746.639.6856
       N urinary, g/d18418243715.368.9856
       N-urea urinary, g/d16014366341.089.2159
       N fecal, g/d18417638634.455.8856
       SEM of N urinary,
      The SEM of N urinary excretions reported in studies. The SEM of N urinary was not reported in 104 observations (missing data). On final models, these missing data were estimated using observed overall means across studies and evaluated separately by mixed and fixed models, after the SEM truncate procedure.
      g/d
      10.29.2054.81.495.67752
       N milk efficiency
      N milk (g/d)/N intake (g/d).
      0.270.270.470.110.05856
      1 Treatment means from 223 N balance trials in 212 peer-reviewed papers. The complete data set is available in an Excel file in
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      . The list of references is available in Supplemental Material S2 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ). Missing data represent nonreported data in studies. No missing data of response or predictor variables were estimated, except for the SEM of N urinary.
      2 The SEM of N urinary excretions reported in studies. The SEM of N urinary was not reported in 104 observations (missing data). On final models, these missing data were estimated using observed overall means across studies and evaluated separately by mixed and fixed models, after the SEM truncate procedure.
      3 N milk (g/d)/N intake (g/d).
      Each observation (treatment means) was classified by genetic group (Bos taurus or B. taurus × Bos indicus), feed system (TMR or pasture), DIM group (<100 DIM or ≥100), fecal output method (total collection or fecal marker), and urinary output method (total collection or spot sample using creatinine as marker) for use as potential covariates in models.

      Data Weighting.

      Each observation (treatment means) used to derive our models was weighted by the normalized inverse of the SEM (
      • St-Pierre N.R.
      Invited review: Integrating quantitative findings from multiple studies using mixed model methodology.
      ) of N urinary excretion (g/d), as follows. The normalized inverse of the SEM of N urinary excretion was calculated as W1/W2, where W1 = 1/reported SEM of N urinary excretion (g/d) of each observation/study (g/d), and W2 = overall mean of W1 across studies. To prevent overweighting of studies with extremely low SEM (
      • Elliott M.R.
      Model averaging methods for weight trimming.
      ), we truncated (i.e., trimmed) the SEM to 0.35 × overall mean SEM (
      • Roman-Garcia Y.
      • White R.R.
      • Firkins J.L.
      Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations.
      ) of N urinary excretion. This procedure was conducted separately for the studies that adopted mixed and fixed effects models because mixed models tend to have higher SEM (
      • Littell R.C.
      • Henry P.R.
      • Ammerman C.B.
      Statistical analysis of repeated measures data using SAS procedures.
      ). Missing data of SEM of N urinary (n = 104 observations not reported in studies; Table 1) were estimated using observed overall mean across studies, evaluated separately by mixed and fixed models, and after the SEM truncation procedure.

      Net Protein Requirement for Maintenance.

      We assumed that NPM is the EPFU. Scurf protein losses (skin and hair scaling) were not accounted for in NPM in our system for 2 reasons. First, data were absent from our data set. Second, scurf protein represents only 1.8% (range 1.4–2.6%) of the sum of the EPFU, calculated from
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      and assuming dairy cows with 616 kg of BW, 21 kg/d DMI, and 34% NDF in DM diet (Table 1).
      The net protein requirement for maintenance was modeled as 6.25 (factor N-protein) × intercept of the regression between N intake [g of N/kg metabolic body weight (BW0.75); predictor variable] and NFU (g of N/kg of BW0.75; response variable), using a nonlinear mixed model and adaptive Gaussian quadrature as the integration method, as follows:
      Yij=β1×exp(Nintake×β2)+trialj+eij,
      [1]


      where Yij = NFU of the treatment means i of the N balance trial j; β1 = overall intercept across all studies (fixed effects), and it represents the sum of the endogenous fecal and urinary N excretion (g of N/kg of BW0.75); β2 = overall nonlinear statistics across all trials (fixed effect), without nutritional significance; trialj = random effect of N balance trial j; and eij = random error associated with each observation, assuming a normal distribution (0, σ2). The Kleiber's 0.75 interspecific body mass exponent was adopted for N intake (g of N/kg of BW0.75) and NFU (g of N/kg of BW0.75). Although several conversion factors of N-protein have been proposed for feedstuffs (
      • Mariotti F.
      • Tomé D.D.
      • Mirand P.P.
      Converting nitrogen into protein—Beyond 6.25 and Jones' factors.
      ), we adopted the classical Jones conversion factor of 6.25 for the EPFU (
      • NASEM. (National Academies of Sciences, Engineering, and Medicine)
      Nutrient Requirements of Beef Cattle.
      ;
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      ). To calculate the MP requirement for maintenance from NPM, we assumed that maintenance has the same MP efficiency for lactation.
      We initially evaluated the interaction effect of feed system, genetic group, DIM group, fecal output method, and urinary output method with the intercept, using a linear multivariable mixed model with a variance component structure (
      • St-Pierre N.R.
      Invited review: Integrating quantitative findings from multiple studies using mixed model methodology.
      ). If these interaction effects had a P-value >0.10, an overall nonlinear mixed Equation 1 without covariates was proposed to obtain NPM.
      Observations were weighted by normalized inverse of the SEM of N urinary excretion (g/d), as previously described. Observations were removed if the studentized residual was outside the range of −2.0 to 2.0. A list of removed observations (n = 49) is available in
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      . Significance was declared at P ≤ 0.05. Analyses were conducted using the PROC MIXED and PROC NLMIXED procedures (
      • Littell R.
      • Milliken C.G.A.
      • Stroup W.W.
      • Wolfinger R.D.
      • Schabenberger O.
      SAS for Mixed Models.
      ) of SAS OnDemand for Academics (SAS Institute Inc.). As the WEIGHT statement is not available in the PROC NLMIXED procedure, the REPLICATE statement was adopted as a WEIGHT statement (SAS Institute Inc., 2015). The SAS codes and outputs are available in Supplemental Material S5 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).

      Efficiency of MP Utilization for Lactation.

      We calculated the EMPL (0–1) as a proportion of the N intake that was secreted in milk, discounting N excreted in feces and urine, as follows:
      EMPL=Nmilk(g/kgBW0.75)×TP/TPCPCPNintake(g/kgBW0.75)Nfecal(g/kgBW0.75)Nurinary(g/kgBW0.75),
      2


      where TP/CP is the ratio of true protein to CP in milk and is equal to 0.955 (
      • Moraes L.E.
      • Keabreab E.
      • Firkins J.L.
      • White R.R.
      • Martineau R.
      • Lapierre H.
      Predicting milk protein responses and the requirement of metabolizable protein by lactating dairy cows.
      ). We also initially analyzed the interaction effect of feed system, genetic group, DIM group, fecal output method, and urinary output method on EMPL, using a multivariable mixed model with a component variance structure (
      • St-Pierre N.R.
      Invited review: Integrating quantitative findings from multiple studies using mixed model methodology.
      ), considering N balance trial as a random effect and covariates as fixed effects, similar to that adopted for the NFU model.
      We proposed a fixed-EMPL model for predicting MP requirements. The fixed-EMPL value represented the least squares means obtained from the estimated EMPL (Equation 2) of each observation (treatment mean), using a mixed model with N balance trial as a random effect. Observations used to obtain the fixed EMPL were weighed by the normalized inverse of the SEM of N urinary excretion (g/d). Observations were removed if the studentized residual was outside the range of −2.0 to 2.0. A list of removed observations (n = 112) is available in
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      . Significance was declared at P ≤ 0.05. Analyses were conducted using the PROC MIXED procedures (
      • Littell R.
      • Milliken C.G.A.
      • Stroup W.W.
      • Wolfinger R.D.
      • Schabenberger O.
      SAS for Mixed Models.
      ) of SAS OnDemand for Academics.

      MP Model Evaluations

      To evaluate the prediction of the MP requirement (maintenance + lactation) of our proposed model and eight external models (Supplemental Material S3; https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ), we built an independent data set of 51 trials (from 51 peer-reviewed papers) that measured total nonammonia-N and microbial-N ruminal outflows in lactating dairy cows from duodenal or omasal digesta sampling (n = 192 treatment means; Table 2). Nonammonia nonmicrobial-N ruminal outflow (NANMN; as a proxy for RUP) was obtained from difference between total NAN and microbial-N ruminal outflows. Total nonammonia-N and microbial-N ruminal outflows were measured from duodenal (41 studies) or omasal (10 studies) digesta sampling, using purine (31 studies), 15N label (14 studies), diaminopimelic acid (5 studies), or RNA-cytosine (1 study) for microbial markers. The complete data set in the Excel file is available in an open research data repository (
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      ), and references used to evaluate the models are available in Appendix 2 of Supplemental Material S2 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      Table 2Descriptive statistics of the complete data set used to evaluate MP requirements models for lactating dairy cows
      ItemMeanMedianMaximumMinimumSDn
      Treatment means from 51 studies in 51 peer-reviewed papers. The complete data set is available in an Excel file in Silva and Oliveira (2022a). List of reference is available in Supplemental Material S3. Missing data (nonreported in studies) of DMI (n = 18) were estimated from the NRC (2001) equation because DMI is an input for the proposed Model II and several external models (Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9; Silva and Oliveira, 2022b). Missing data (nonreported in studies) of NDF (n = 10), total-tract DM digestibility (n = 103), and OM digestibility (n = 32) were estimated using observed overall mean values across studies because they also are inputs for some external models (Supplemental Material S3).
      Animal
       BW, kg60159873648059.2192
       Milk yield, kg/d28.628.348.213.07.1192
       DIM1141102473255170
       Milk protein, g/kg31.431.337.425.92.65192
       Milk fat, g/kg35.035.146.722.94.34192
       Milk urea N, mg/dL13.613.323.54.874.6029
      Diet
       DMI, kg/d20.420.731.69.113.88174
       Forage in diet, g/kg DM489487800250101123
       DM, g/kg57657578230495.893
       OM, g/kg DM91991994781115.383
       NDF, g/kg DM34334262322058.7182
       CP, g/kg DM17017121211317.0139
       Ether extract, g/kg DM42.242.555.628.19.9424
       Starch, g/kg DM26928247632.910563
       DM digestibility, g/kg66467073058234.189
       OM digestibility, g/kg67567377255741.3160
       NEL diet, Mcal/kg DM1.681.671.861.540.0768
      N intake and efficiency
       N intake, g/d553567769246112191
       N milk, g/d14113822964.934.3192
       N milk efficiency
      N milk efficiency = N milk (g/d)/N intake (g/d).
      0.260.260.360.160.04189
       N milk/DMI, g/kg DM7.087.0110.24.251.03174
      Ruminal N outflow
      NAN = nonammonia N ruminal outflow; microbial n = microbial nitrogen ruminal outflow; NANMN = nonammonia nonmicrobial N ruminal outflow.
       NAN, g/d513.5503.9858246132192
       Microbial N, g/d288.4268.857013690.0192
       NANMN, g/d225.1222.557675.088.0192
       MP,
      Observed MP was calculated according to Equation 3.
      g/d
      2,2942,2363,8801,096597192
      1 Treatment means from 51 studies in 51 peer-reviewed papers. The complete data set is available in an Excel file in
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      . List of reference is available in Supplemental Material S3. Missing data (nonreported in studies) of DMI (n = 18) were estimated from the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      equation because DMI is an input for the proposed Model II and several external models (Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ). Missing data (nonreported in studies) of NDF (n = 10), total-tract DM digestibility (n = 103), and OM digestibility (n = 32) were estimated using observed overall mean values across studies because they also are inputs for some external models (Supplemental Material S3).
      2 N milk efficiency = N milk (g/d)/N intake (g/d).
      3 NAN = nonammonia N ruminal outflow; microbial n = microbial nitrogen ruminal outflow; NANMN = nonammonia nonmicrobial N ruminal outflow.
      4 Observed MP was calculated according to Equation 3.
      The observed MP of each treatment mean was calculated as follows:
      Observed MP (g/cow/d) = NANMN (g/cow/d) × 6.25 × ID-RUP + microbial-N ruminal outflow (g/cow/d) × 6.25 × TP/CP microbial × ID-TP microbial
      3


      where 6.25 is the conversion factor N to protein; ID-RUP = overall intestinal digestibility of the RUP of 0.79, calculated from intestinal digestibility of total AA of 25 feeds reported in Supplementary Table S4 of
      • White R.R.
      • Kononoff P.J.
      • Firkins J.F.
      Technical note: Methodological and feed factors affecting prediction of ruminal degradability and intestinal digestibility of essential amino acids.
      ; TP/CP microbial = true protein to CP ratio in microbial protein of 0.82 (
      • Sok M.
      • Ouellet D.R.
      • Firkins J.L.
      • Pellerin D.
      • Lapierre H.
      Amino acid composition of rumen bacteria and protozoa in cattle.
      ); and ID-TP microbial = overall intestinal digestibility of the true protein microbial of 0.80 (NASEM, 2021). Endogenous protein duodenal flux was not calculated as observed MP, according to
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      We compared the adequacy of the MP requirement prediction of our proposed model with several external models:
      • AFRC (Agricultural and Food Research Council)
      Energy and Protein Requirements of Ruminants.
      ,
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , Feed into Milk (
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ), CSIRO (2007), DVE/OEB2010 (Van Duinkerken et al., 2010), CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ),
      • INRA
      INRA Feeding System for Ruminants.
      , and NASEM (2021). The description of the external models is available in Supplemental Material S3 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ). The
      • NorFor
      model was not evaluated because it requires inputs that were not reported in studies used in our data set (
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      ).
      Missing data (nonreported in studies) of DMI (n = 18) were estimated from the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      equation because DMI is an input for several external models (Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ). Missing data (nonreported in studies) of NDF (n = 10), total-tract DM digestibility (n = 103), and OM digestibility (n = 32) were estimated using observed overall mean value across studies because they also are inputs for some external models (Supplemental Material S3).
      The values of observed MP (Equation 4) of each treatment mean (Table 2) were each compared with its respective predicted MP requirements (maintenance + lactation) values from each model (proposed and external). The adequacy of the MP requirement predictive models was assessed for precision and accuracy by simple linear regression of the observed MP values (Y) with the predicted MP requirement (X), using the following procedures: graphical analysis of observed versus predicted values and its residuals, coefficient of determination (R2), mean square of prediction error (MSPE) and its decomposition in 3 sources of error (error caused by the bias, error caused by the deviation of regression slope from unity, and random error;
      • Theil H.
      Applied Economic Forecasting.
      ;
      • Bibby J.
      • Toutenburg D.H.
      Prediction and Improved Estimation in Linear Models.
      ), and concordance correlation coefficient (CCC) and its decomposition into precision (ρ, correlation coefficient) and accuracy (Cb, bias correction factor) indicators (
      • Lin L.I.K.
      A concordance correlation coefficient to evaluate reproducibility.
      ).
      The slope and intercept between residuals and predicted MP requirements of all models were tested to quantify the magnitude of the mean bias and linear bias centralized to their mean values, respectively (
      • St-Pierre N.R.
      Reassessment of biases in predicted nitrogen flows to the duodenum by NRC 2001.
      ). An ANOVA component was conducted to identity potential effects of animal performance (DMI, milk protein yield, and BW) and dietary (NDF and CP content, and OM total-tract digestibility) variables, and digesta sampling method (duodenal versus omasal) affecting MP residual (observed minus predicted MP) of our proposed model, using a linear multivariable mixed model with variance component structure (
      • Littell R.
      • Milliken C.G.A.
      • Stroup W.W.
      • Wolfinger R.D.
      • Schabenberger O.
      SAS for Mixed Models.
      ).
      In addition, to compare accuracy between the models, the root MSPE (RMSPE) was calculated as RMSPE = √[Σ SPE/(n − 2)], and its standard error [SE(SPE)] was calculated as SE(SPE) = √SD(SPE)/√(n), where SPE = square prediction error = (observed MP − predicted MP requirement)2, n = number of observations (treatment means used to independently evaluate the models), and SD(SPE) = standard deviation of the SPE.

      RESULTS

      Proposed Protein Requirement System

      Data Set.

      The complete data set used to develop our protein requirement system for lactating dairy cows was from 26 countries and represented a wide range of animal performance (milk yield of 9–50 kg/d; BW of 351–788 kg), diet (218–1,000 g forage/kg DM; 94–281 g CP/kg DM), and N metabolism (Table 1). The United States was the principal country of origin (38.1%), followed by Canada (16.5%), the United Kingdom (6.9%), Brazil (5%), Finland (5%), France (3.5%), Switzerland (3.5%), and 20 other countries (21.5% data set). Rotative trials were the most common experimental design used (81.7%), and B. taurus (Holstein) was the main genotype (96.3%). The predominant feeding system was TMR (91.2%), and DIM ≥ 100 d represented 56.4% of the data set. Forages were used as silage (54.8%), haylage (27.8%), hay (6.1%), pasture (8.8%), and other (2.5%). Total collection for quantifying fecal and urinary N excretion was used in 55.8% and 59.3% of studies, respectively.

      Protein Requirement for Maintenance.

      Feed system (TMR or pasture; P = 0.28), genetic group (B. taurus or B. taurus × B. indicus; P = 0.15), lactation stage (<100 DIM or ≥100 DIM; P = 0.38), fecal output method (total collection or fecal marker; P = 0.94), and urinary output method (total collection or spot; P = 0.61) did not affect endogenous NFU (intercept between N intake and sum of the total fecal and urinary excretion; Table 3). Therefore, we proposed to use one overall equation to predict endogenous NFU and NPM (Figure 1, Figure 2):
      Table 3ANOVA of the effect of feeding system, genetic group, lactation stage, fecal output method, and urinary output method on the sum of N total fecal and urinary excretion (NFU), and efficiency of MP utilization for lactation (EMPL) of dairy cows
      EffectNFUEMPL
      Solution for fixed effects
      Using mixed model with random effect of study and variance component structure, weighed by normalized inverse of SEM of the N urinary excretion (g/d). The results were from multivariable model.
       Intercept (estimated endogenous NFU), g/kg BW0.75<0.01
       Nitrogen intake (NI), g/kg BW0.75<0.01
       Feed system (TMR or pasture)0.280.17
       Genetic group (Bos taurus or Bos taurus × Bos indicus)0.150.01
       Lactation stage (≤100 DIM or >100 DIM)0.380.15
       Fecal output method (total collection or fecal marker)0.940.83
       Urinary output method (total collection or spot)0.610.46
       Feed system × NI0.40
       Genetic group × NI0.10
       Lactation stage × NI0.81
       Fecal output method × NI0.93
       Urinary output method × NI0.80
      Observations used571548
      1 Using mixed model with random effect of study and variance component structure, weighed by normalized inverse of SEM of the N urinary excretion (g/d). The results were from multivariable model.
      Figure thumbnail gr1
      Figure 1Relationship between the sum of N total fecal and urinary excretion (NFU; g of N/kg of BW0.75) and N intake (g of N/kg of BW0.75) from 807 means treatment (experimental diets) of 199 N balance trials. Endogenous NFU excretion represents the intercept of the regression between NFU and N intake (1.012 ± 0.024 g of N × kg of BW0.75). Net protein for maintenance (NPM; g of protein/kg of BW0.75) = 6.25 × endogenous NFU excretion (g of N/kg of BW0.75); therefore NPM = 6.32 ± 0.15 g of protein × kg of BW0.75. RMSE = root mean square error. In the total 856 observations available (), 49 were removed from analysis of studentized residues (outliers). The complete data set and a list of the 49 observations evaluated as outliers are available in
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      . The SAS (SAS OnDemand for Academics, SAS Institute Inc.) codes and outputs to derive the model are available in Supplemental Material S5.1 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      Figure thumbnail gr2
      Figure 2Relationship between the sum of N total fecal and urinary excretion (NFU; g of N/kg of BW0.75) and N intake (g of N/kg of BW0.75) by each N balance trial. The dotted lines are the observed values in each balance trial (n = 199 N balance trials). The model is presented in . Variance analysis of the MP residual was obtained from a mixed model with random effect of study and variance component structure.
      Endogenous NFU = 1.012 ± 0.024 g of N × kg of BW0.75,
      4


      NPM = 6.32 ± 0.15 g of protein × kg of BW0.75.
      5


      Nitrogen fecal and urinary excretion (g of N/BW0.75) had a strong correlation (r = 0.91) with N intake (g of N/BW0.75) (Figure 1, Figure 2). Study (P = 0.24) and N intake × study (P = 0.79) did not affect NFU, and variance of study (between-study heterogeneity) contributed less than 1% of the total variance of NFU (Figure 2).

      Efficiency of MP for Lactation.

      The feed system, DIM, fecal output method, and urinary output method did not affect EMPL (Table 3), but the genetic group affected (P = 0.01) EMPL (Table 3). Therefore, fixed-EMPL values were obtained for each genetic group: B. taurus EMPL = 0.705 ± 0.020 [n = 705; milk yield = 10–50 kg/d, BW = 351–788 kg, DMI = 9.2–31.8 kg/d, and N milk efficiency = 0.11–0.27 (mean 0.27 ± 0.05); Table S4.1 of Supplemental Material S4, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ]; and B. taurus × B. indicus EMPL = 0.594 ± 0.045 [n = 26; milk yield = 9–21 kg/d, BW = 389–580 kg, DMI = 11.5–20.3 kg/d, and N milk efficiency = 0.16–0.30 (mean 0.24 ± 0.03); Table S4.2 of Supplemental Material S4). However, the EMPL for B. taurus × B. indicus should be interpreted with caution owing to the low number of treatments means (n = 26) compared with B. taurus (n = 705; mainly Holstein).

      Protein Requirement System.

      We proposed a model to predict MP requirements (maintenance and lactation) for dairy cows using a fixed value of MP efficiency for maintenance and lactation (Table 4). We assumed that maintenance (combined EPFU) has the same MP efficiency as lactation (Table 4).
      Table 4Description of the proposed system of net protein (NP) and MP requirements for maintenance and lactation of dairy cows
      ItemProposed model (±SE)
      MY = milk yield (kg/d); MTP = milk true protein (g/kg milk) = milk CP × 0.955 (g/kg milk).
      Net protein requirement for maintenance (NPM), g/d(6.32 ± 0.15) × BW0.75
      Efficiency of conversion MP to NP for maintenance (EMPM)0.705 ± 0.02
      MP requirement for maintenance (MPM), g/dNPM/EMPM
      Net protein requirement for lactation (NPL), g/dMY × MTP
      Efficiency of the use MP to NP for lactation (EMPL)0.705 ± 0.02
      MP requirement for lactation (MPL), g/dNPL/EMPL
      Total MP requirement, g/dMPM + MPL
      1 MY = milk yield (kg/d); MTP = milk true protein (g/kg milk) = milk CP × 0.955 (g/kg milk).

      Model Evaluation of Protein Requirement

      Proposed Model.

      Our proposed model predicted the MP requirement (maintenance + lactation) with a nonsignificant mean bias (observed minus predicted; −4.0 ± 32.8 g of MP/d; P = 0.90; and 0.0% MSPE) and linear (slope) bias (slope = 0.19 ± 0.10; P = 0.07; 5.8% MSPE), and with high random error (not explained by the model; 94.1% MSPE; Table 5 and Figure 3). The model predicted with ρ = 0.65 (precision), Cb = 0.84 (accuracy), CCC = 0.55 (combined precision and accuracy), and RMSPE = 20.0 ± 1.7% (observed MP ± SE; Figure 5).
      Table 5Summary of statistical measures to assess the adequacy of the proposed and external
      External models are described in Supplemental Material S3 (https://data.mendeley.com/datasets/z6pdk5pyg9; Silva and Oliveira, 2022b).
      predictive models of MP requirement for lactating dairy cows using regression between observed (Y) and predicted MP requirement
      Item
      MSPE = mean squared prediction error; CCC = concordance correlation coefficient; ρ = correlation coefficient estimate (precision); Cb = bias correction factor (accuracy).
      Proposed model
      Table 4.
      AFRC (1993)NRC (2001)FIM (2004)
      FIM = Feed into Milk (Thomas, 2004).
      DVE/OEB2010
      DVE/OEB2010 = the Dutch protein evaluation system for ruminants (Van Duinkerken et al., 2011).
      CSIRO (2007)CNCPS 6.5
      Fox et al. (2004) and Van Amburgh et al. (2015).
      INRA (2018)NASEM (2021)
      Observed MP (Y), g/d2,2942,2942,2942,2942,2942,2942,2942,2942,294
      Predicted MP requirement (X), g/d2,2981,5352,0122,0111,4351,7662,0021,9271,797
      N192192192192192192192192192
      Residuals analysis
      Regression between residual (Y − X) and predicted MP centralized to its mean value (St-Pierre, 2003), adjusted for random effect of study. (Values ± SE.)
      (Y − X)
       Intercept (β0; mean bias)−4.0 ± 32.8759 ± 33.1282 ± 30.7283 ± 31.1859 ± 33.4528 ± 31.6292 ± 30.3367 ± 30.1497 ± 32.1
      P-value (H0, β0 = zero)0.90<0.01<0.01<0.01<0.01<0.01<0.01<0.01<0.01
       Slope (β1; slope bias)0.19 ± 0.100.22 ± 0.110.03 ± 0.080.01 ± 0.080.06 ± 0.090.11 ± 0.090.04 ± 0.070.10 ± 0.080.12 ± 0.09
      P-value (H0, β1 = zero)0.070.040.720.880.540.200.600.190.18
      Root MSPE, g/d459893512516980689512558670
      Partition of MSPE, %
       Error due to mean bias0.072.330.630.277.543.632.743.055.1
       Error due to slope not equal to 15.81.40.30.30.10.70.51.71.1
       Random error94.126.369.169.522.455.766.855.343.8
      CCC (0–1)0.550.230.570.550.220.380.570.520.39
       ρ (0–1)0.650.640.700.690.630.680.710.720.67
       Cb (0–1)0.840.360.810.800.350.560.800.720.58
      1 External models are described in Supplemental Material S3 (https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      2 MSPE = mean squared prediction error; CCC = concordance correlation coefficient; ρ = correlation coefficient estimate (precision); Cb = bias correction factor (accuracy).
      4 FIM = Feed into Milk
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      .
      5 DVE/OEB2010 = the Dutch protein evaluation system for ruminants (
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ).
      6
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      and
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      .
      7 Regression between residual (Y − X) and predicted MP centralized to its mean value (St-Pierre, 2003), adjusted for random effect of study. (Values ± SE.)
      Figure thumbnail gr3
      Figure 3Relationship between observed (blue +) and residual (observed − predicted; red circles) MP, with predicted MP values for lactating dairy cows using the proposed model (),
      • AFRC (Agricultural and Food Research Council)
      Energy and Protein Requirements of Ruminants.
      ,
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , Feed into Milk (FIM;
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ), and the Dutch protein evaluation system for ruminants (DVE/OEB2010;
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ) models (descriptions of the external models are available in Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      Among the evaluated variables, DMI (15.6% total variance), OM total-tract digestibility (10.7%), and CP diet (10.1%) had the greatest effect (P < 0.01) on the variance of the MP residual of the proposed model (Table 6). The increase of DMI and CP diet increased the MP residual, while the increase of the OM total-tract digestibility reduced the MP residual (Table 6). Milk protein yield and NDF diet also affected the variance of the MP residual, but at a lower magnitude (3.1 and 3.3% total variance of the MP residual; Table 6). Digesta sampling method (duodenal vs. omasal) did not affect the MP residual of the proposed model (Table 6).
      Table 6ANOVA component to identify potential animal performance and dietary variables, and digesta sampling method affecting the MP residual (observed minus predicted) of the proposed model of MP requirement for maintenance and lactation
      EffectMultivariable model (n = 192 observations)
      Y = MP residual (observed minus predicted) from the proposed model (Table 4).
      Estimate (SE)P-valueVariance (% total)
      Variance analysis of the MP residual was obtained from a mixed model with random effect of study and variance component structure.
      DMI, kg/d61.1 (12.1)<0.0115.6
      Milk protein yield, kg/d−558 (195)<0.013.1
      BW, kg−0.59 (0.47)0.210.2
      NDF diet, g/kg DM−1.48 (0.49)<0.013.3
      CP diet, g/kg DM8.22 (1.81)<0.0110.1
      OM total-tract digestibility−3.80 (0.75)<0.0110.7
      Duodenal versus omasal sampling
      Duodenal sampling = 0; omasal sampling = 1.
      43.6 (75.6)0.570.002
      Random effect of study2.6
      Random residual54.4
      1 Y = MP residual (observed minus predicted) from the proposed model (Table 4).
      2 Variance analysis of the MP residual was obtained from a mixed model with random effect of study and variance component structure.
      3 Duodenal sampling = 0; omasal sampling = 1.

      Comparison of the Proposed Model with External Models.

      The
      • AFRC (Agricultural and Food Research Council)
      Energy and Protein Requirements of Ruminants.
      , CSIRO (2007), DVE/OEB2010 (
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ),
      • INRA
      INRA Feeding System for Ruminants.
      , and NASEM (2021) models underestimated the MP requirement and had a higher mean bias (367 ± 30.1 to 859 ± 33.4 g/d; P < 0.01; 43.0–77.5% MSPE), lower CCC (0.22–0.52; Table 5), and higher RMSPE (24.3–42.7% observed MP; Figure 5) than our proposed model (Figure 3, Figure 4).
      Figure thumbnail gr4
      Figure 4Relationship between observed (+) and residual (observed − predicted; circles) MP, with predicted MP values for lactating dairy cows using CSIRO (2007), CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ),
      • INRA
      INRA Feeding System for Ruminants.
      , and NASEM (2021) models (descriptions of the external model are available in Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      Compared with our proposed model, all external evaluated models had similar precision (ρ = 0.63–0.72; Table 5), but only the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , Feed into Milk (
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ), and CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ) models predicted the MP requirement with similar visual graphic adjustment between observed and predicted values (Figure 3, Figure 4) and had similar accuracy (CCC = 0.55–0.57; RMSPE = 22.3–22.5% observed MP; Table 5; Figure 5). However, these 3 external models predicted MP with higher error owing to mean bias (30.2–32.7% MSPE) compared with the proposed model (Table 5; Figure 3, Figure 4).
      Figure thumbnail gr5
      Figure 5Values of root mean square prediction error (MSPE) and the standard error of the MP requirement (% observed MP) predictive for lactating dairy cows from our proposed model (), and
      • AFRC (Agricultural and Food Research Council)
      Energy and Protein Requirements of Ruminants.
      ,
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , Feed into Milk (FIM;
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ), the Dutch protein evaluation system for ruminants (DVE/OEB2010;
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ), CSIRO (2007), CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ),
      • INRA
      INRA Feeding System for Ruminants.
      , and NASEM (2021) models (descriptions of the external models are available in Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).

      DISCUSSION

      We developed a new factorial protein requirement system for dairy cow maintenance and lactation from a meta-analysis of a large and comprehensive database of N balance trials. A model for NPM and fixed EMPL (EMPL = 0.705 ± 0.020) was proposed and evaluated. In addition, we compared the adequacy of our system and several external models for predicting MP requirements of lactating dairy cows.
      Overall, our findings indicate that our proposed fixed-EMPL model predicts the MP requirement with better accuracy than the
      • AFRC (Agricultural and Food Research Council)
      Energy and Protein Requirements of Ruminants.
      , CSIRO (2007), DVE/OEB2010 (
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ),
      • INRA
      INRA Feeding System for Ruminants.
      , and NASEM (2021) models. Only the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , Feed into Milk (
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ), and CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ) models predicted MP requirements with similar accuracy to our model, but their predictions had a higher error than our proposed fixed-EMPL model due to mean bias.
      Our study also showed that the new NASEM (2021) model did not improve the MP requirement compared with the previous edition (
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      ) and other models, such as CNCPS 6.5 and
      • INRA
      INRA Feeding System for Ruminants.
      . We should highlight that the NASEM (2021) committee improved the prediction of milk protein yield from absorbed EAA supply, digestible energy intake, digestible NDF in diet, and BW compared with
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      ; however, an evaluation of its proposed model to predict MP requirement against observed MP seems not to have been reported in NASEM (2021).
      All external models predicted the MP requirement with higher error than our proposed model, owing to mean bias. However, prediction mean bias may be partially solved through incorporation of an empirical constant of mean bias adjustment. For example, the underestimate of the MP requirement of the NASEM (2021) model (mean bias = 497 ± 32.1 g of MP/d and 55.1% of MSPE) and
      • INRA
      INRA Feeding System for Ruminants.
      (mean bias = 367 ± 30.1 g of MP/d and 43.0% of MSPE) can be partially corrected by a further empirical incorporation of a positive constant of mean adjustment (i.e., intercept) in these models.
      Dry matter intake, OM total-tract digestibility diet, and CP diet were the evaluated variables that most affected the linear prediction bias of our proposed model. In addition, we observed that the increase of DMI and CP diet increased MP residual, while the increase of OM total-tract digestibility reduced MP residual. These findings indicate the use of energy and protein supplies as inputs may further improve the MP prediction of our model.

      Protein Requirement for Maintenance

      We assumed that NPM represents the endogenous NFU × 6.25 (N to protein conversion factor). Scurf protein losses (skin and hair scaling) were not accounted for in NPM in our system for two reasons: (1) absence of data in our data set and (2) scurf protein represents only 1.8% of the EPFU, calculated from
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      and assuming dairy cows with 616 kg of BW, 21 kg/d DMI, and 34% NDF in DM diet (Table 1). To calculate the MP requirement for maintenance from NPM, we assumed that maintenance has the same MP efficiency for lactation; a similar approach was adopted for most of the external models evaluated in this study (Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ).
      The endogenous NFU excretion was estimated from the intercept of a nonlinear meta-regression between N intake (g/kg of BW0.75; X) and total NFU (g/kg of BW0.75; Y). The model showed a strong correlation between N intake and NFU, and it supported our hypothesis that meta-analysis of N balance trials may provide a robust estimate of EPFU and NPM from regression between N intake and NFU.
      To the best of our knowledge, this study was the first to derive endogenous NFU to predict NPM from a meta-analysis of N balance trials with lactating dairy cows. Several dairy cow nutrition models [
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      ; Feed into Milk (
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ; DVE/OEB2010 (Van Duinkerken, 2011); CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      )] have adopted endogenous protein urinary (2.75 × BW0.50; g/d), fecal [0.068 × DMI × indigestible DM diet (%) × 10; g/d], or both types of excretion from
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      meta-regression trials with nonlactating cattle. In addition,
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      calculated EPFU from 16 low-protein and 70 low-N natural semisynthetic diets, respectively. Low or protein-free diets are a limited approach because feed intake is usually reduced when N is deficient, and the animals might adapt to the low-protein diets ingested; thus, the estimates obtained might not represent the fecal and urinary excretion under normal feeding conditions (
      • Marini J.C.
      • Fox D.G.
      • Murphy M.R.
      Nitrogen transactions along the gastrointestinal tract of cattle: A meta-analytical approach.
      ).
      The NASEM (2021) committee adopted the
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      assumptions, and its model is based on an admittedly scarce literature review of dairy cows to predict true protein, endogenous urinary (g/d; 0.331 × BWkg) and fecal [g/d; (8.50 + 0.10 × NDF diet%DM) × DMIkg]. Similarly, the
      • INRA
      INRA Feeding System for Ruminants.
      committee adopted the
      • Lapierre H.
      • Ouellet D.R.
      • Martineau R.
      • Spek J.W.
      Key roles of amino acids in cow performance and metabolism. Considerations for defining amino acid requirement.
      recommendation to predict net protein endogenous urinary (g/d; 0.312 × BWkg), but the
      • INRA
      INRA Feeding System for Ruminants.
      committee developed their model based on meta-regression between undigested OM and N endogenous fecal from 214 experiments.
      Our model estimated NPM of 668 to 860 g/d for cows of 500 to 700 kg of BW, while
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      estimated 539 to 880 g/d (assuming DMI of 15.9 and 26.9 kg/d), CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ) 542 to 883 g/d,
      • INRA
      INRA Feeding System for Ruminants.
      404 to 634 g/d (assuming undigestible OM in diet of 330 g/kg DM), and NASEM (2021) 379 to 537 g/d (assuming 25 and 45% NDF in DM diet; Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ). Our proposed model was developed from the analysis of N balance of lactating dairy cows, while these external nutrition models adopted equations from a limited lactating dairy cow data set. Therefore, the higher N intake and excretion per body mass of lactating dairy cows compared with nonlactating cattle might partially explain the higher estimated NPM values in our model compared with these external models. In addition, we estimated NPM from production-level diets, while
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      used low or protein-free diet data.
      The
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      and CNCPS 6.5 (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ) also adopted the
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      equation to estimate endogenous urinary protein excretion and scurf protein. However, the adoption of an adapted approach from
      • Swanson E.W.
      Factors for computing requirements of protein for maintenance of cattle.
      to estimate endogenous fecal excretion seems to have contributed to improving the MP requirement prediction of the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      and CNCPS 6.5 models. The Feed into Milk model (
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ) adopted the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      model to predict MP requirement for maintenance.

      MP Efficiency for Lactation

      Most dairy cattle nutrition committees have also adopted fixed-EMPL values to predict MPL from NPL, with EMPL ranging from 0.67 to 0.70 (
      • AFRC (Agricultural and Food Research Council)
      Energy and Protein Requirements of Ruminants.
      ;
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      ;
      • CSIRO (Commonwealth Scientific and Industrial Research Organization)
      Nutrient Requirements of Domesticated Ruminants.
      ;
      • Thomas C.
      Feed into Milk: A New Applied Feeding System for Dairy Cows: An Advisory Manual.
      ;
      • Van Amburgh M.E.
      • Collao-Saenz E.A.
      • Higgs R.J.
      • Ross D.A.
      • Recktenwald E.B.
      • Raffrenato E.
      • Chase L.E.
      • Overton T.R.
      • Mills J.K.
      • Foskolos A.
      The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.
      ;
      • INRA
      INRA Feeding System for Ruminants.
      ; NASEM 2021).
      • INRA
      INRA Feeding System for Ruminants.
      and NASEM (2021) have adopted variable-EMPL models to predict MP supply and milk protein yield, but only fixed-EMPL values of 0.67 and 0.69 were used as targets to predict the MPL requirement from NPL.
      Only the DVE/OEB2010 (
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ) and
      • NorFor
      models have used variable-EMPL to predict MPL from NPL requirements. The DVE/OEB2010 model has used the milk protein to energy ratio as input for predicting EMPL, and
      • NorFor
      used the ratio of milk production to milk energy. In DVE/OEB2010, EMPL decreases with milk protein yield (
      • Van Duinkerken G.
      • Blok M.C.
      • Bannink A.
      • Cone J.W.
      • Dijkstra J.
      • Van Vuuren A.M.
      • Tamminga S.
      Update of the Dutch protein evaluation system for ruminants: The DVE/OEB2010 system.
      ), whereas in
      • NorFor
      , neither the milk protein yield nor the feeding level affects EMPL. Although variable-EMPL-based models are biochemically more realistic, the DVE/OEB2010 model predicted MP requirement with less accuracy than fixed-EMPL-based models. These results indicate that developing accurate variable-EMPL-based models remains a challenge.
      The lower EMPL of B. taurus × B. indicus crossbred dairy cows may be associated with lower ME efficiency for lactation compared with B. taurus × B. taurus (
      • Oliveira A.S.
      Meta-analysis of feeding trials to estimate energy requirements of dairy cows under tropical condition.
      ). Differences in homeostasis and homeorhetic regulations for lactation and body reserve have been proposed as causal hypotheses for this difference in ME efficiency for lactation (
      • Oliveira A.S.
      Meta-analysis of feeding trials to estimate energy requirements of dairy cows under tropical condition.
      ). As milk energy efficiency (energy in milk/digestible energy intake) and N milk efficiency (N milk/digestible N intake) are also positively correlated (
      • Phuong H.N.
      • Friggens N.C.
      • de Boer I.J.M.
      • Schmidely P.
      Factors affecting energy and nitrogen efficiency of dairy cows: A meta-analysis.
      ), it is possible to hypothesize that the lower EMPL of B. taurus × B. indicus is associated with lower ME efficiency.
      We calculated EMPL as a proportion of N intake that was secreted in milk, discounting NFU (Equation 2).
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , CNCPS 6.5, and
      • INRA
      INRA Feeding System for Ruminants.
      have adopted an EMPL of 0.67, which was originally proposed by
      • Vérité R.
      • Journet M.
      • Jarrige R.
      A new system for the protein feeding of ruminants: The PDI system.
      from the equation: EMPL = observed milk protein yield/(estimated MP intake − estimated MP requirement for maintenance). NASEM (2021) has adopted a variable-EMPL-based model to predict MP supply and milk protein yield. However, only a fixed and combined efficiency for use of MP maintenance and lactation (EffMP) of 0.69 was proposed as a target to predict the MPL requirement from NPL, estimated from the equation of
      • Lapierre H.
      • Martineau R.
      • Hanigan M.D.
      • Van Lingen H.J.
      • Kebreab E.
      • Spek J.W.
      • Ouellet D.R.
      Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.
      : EffMP = (estimated true protein scurf secretion + estimated true protein metabolic fecal secretion + observed milk protein yield from 921 treatment means of 216 studies)/(estimated MP supply − estimated true protein endogenous urinary excretion). Although we proposed a different approach to measure the EMPL in this study, our obtained EMPL value was similar to the values from most of the external evaluated models (Supplemental Material S3, https://data.mendeley.com/datasets/z6pdk5pyg9;
      • Silva H.M.
      • Oliveira A.S.
      A new protein requirement system for dairy cows: Supplementary material of the article.
      ). Therefore, differences in the quality of the MP requirement (maintenance + lactation) prediction between the evaluated models are more associated with differences in the maintenance requirement than lactation.

      Limitations

      Our study has some limitations. First, specific phenomena that may potentially affect the estimation of maintenance requirements, such as the efficiency and use of endogenous protein and recycled urea N by rumen microbes (
      • Lapierre H.
      • Ouellet D.R.
      • Martineau R.
      • Spek J.W.
      Key roles of amino acids in cow performance and metabolism. Considerations for defining amino acid requirement.
      ), were not considered. Second, although our nonlinear equation showed strong goodness-of-fit to estimate EPFU from N intake, more factors may be involved in endogenous N fecal losses, such as NDF content and rate of fermentation of dietary carbohydrates (
      • Marini J.C.
      • Fox D.G.
      • Murphy M.R.
      Nitrogen transactions along the gastrointestinal tract of cattle: A meta-analytical approach.
      ). Third, our data set has insufficient B. taurus × B. indicus data (n = 26 treatment means) to compare with B. taurus (n = 705; mainly Holstein). Therefore, our finding that B. taurus × B. indicus dairy cows have lower EMPL than B. taurus (0.594 ± 0.045 vs. 0.705 ± 0.020) has limited robustness and needs to be confirmed in further investigations.
      Finally, to evaluate and compare the adequacy of our predictive MP requirement system with external models, we calculated the observed MP from an independent trial data set for which microbial N and NANMN (as a proxy for RUP) ruminal outflows were measured (Equation 3). However, we had to assume fixed values of intestinal digestibilities for microbial protein (NASEM, 2021) and RUP (
      • White R.R.
      • Kononoff P.J.
      • Firkins J.F.
      Technical note: Methodological and feed factors affecting prediction of ruminal degradability and intestinal digestibility of essential amino acids.
      ). Microbial protein intestinal digestibility has been found to range from 57 to 87% according to bacterial/protozoa/fungal proportion and adopted method (
      • Jouany J.P.
      Effects of rumen protozoa on nitrogen metabolism by ruminants.
      ;
      • Larsen M.
      • Madsen T.G.
      • Weisbjerg M.R.
      • Hvelplund T.
      • Madsen J.
      Small intestinal digestibility of microbial and endogenous amino acids in dairy cows.
      ;
      • Fonseca A.C.
      • Fredin S.
      • Ferraretto L.
      • Parsons C.
      • Utterback P.
      • Shaver R.
      Short communication: Intestinal digestibility of amino acids in fluid-and particle-associated rumen bacteria determined using a precision-fed cecectomized rooster bioassay.
      ;
      • Fessenden S.W.
      • Hackmann T.J.
      • Ross D.A.
      • Foskolos A.
      • Van Amburgh M.E.
      Ruminal bacteria and protozoa composition, digestibility, and amino acid profile determined by multiple hydrolysis times.
      ), while RUP-total AA intestinal digestibility has ranged from 52 to 94% according to feed, feed processing, and method (i.e., incubation of residue in digestive enzymes in vitro or in mobile bags inserted into the duodenum) (
      • White R.R.
      • Kononoff P.J.
      • Firkins J.F.
      Technical note: Methodological and feed factors affecting prediction of ruminal degradability and intestinal digestibility of essential amino acids.
      ). Therefore, more refined predictive models of intestinal digestibility for RUP and microbial protein are needed to reduce these evaluation biases in our protein requirement system and external models for lactating dairy cows.

      CONCLUSIONS

      A new factorial system for accounting NPM and MP efficiency for lactation was developed from a meta-analysis of N balance trials of lactating dairy cows. We estimated the EPFU (NPM) from the intercept of a nonlinear equation between N intake and combined N fecal and urinary excretions. The proposed model provided a robust estimation of EPFU. We proposed a fixed-EMPL calculated as a proportion of the N metabolizable (N intake minus N fecal and urinary) that was secreted in milk. Our system predicted the MP requirement for lactation and maintenance with higher accuracy than several North American and European dairy cattle nutrition models, including
      • INRA
      INRA Feeding System for Ruminants.
      and
      • NASEM (National Academies of Sciences, Engineering, and Medicine)
      Nutrient Requirements of Dairy Cattle.
      . Only the
      • National Research Council
      Nutrient Requirements of Dairy Cattle.
      , Feed into Milk, and CNCPS 6.5 models presented similar accuracy for predicting the MP requirement, but they had higher error than our fixed-EMPL-based model owing to mean bias. Our system may contribute to improve the MP requirement prediction for maintenance and lactation. However, more refined predictive models of intestinal digestibility for RUP and microbial protein are still needed to reduce the evaluation biases in our system and external models for predicting NP and MP requirements of dairy cows.

      ACKNOWLEDGMENTS

      Scholarship of master's degree in Animal Science for Henrique Melo da Silva at the Universidade Federal de Mato Grosso – Campus Sinop in 2017-2019 was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil; Number: 131086/2017-0). A scientific merit fellowship for Professor André Soares de Oliveira was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil; Number: 309450/2019-5). Open access funding was provided by Universidade Federal de Mato Grosso (Edital Apoio à Pesquisa 2021) e Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The authorship contribution statement is as follows: Henrique Melo da Silva: investigation (data extraction); André Soares de Oliveira: conceptualization, resources, methodology, software, investigation (data extraction and review), formal analysis, data curation, writing—original draft, writing—review and editing, supervision, and funding acquisition. The complete data set is publicly available on Mendeley Data (
      • Silva H.M.
      • Oliveira A.
      Complete dataset used to develop the protein requirement sub-model for dairy cows of the Nutrition System for Dairy Cows (NS Dairy Cattle).
      ). The authors have not stated any conflicts of interest.

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