Advertisement
Research| Volume 106, ISSUE 3, P1826-1836, March 2023

Balancing dairy cattle diets for rumen nitrogen and methionine or all essential amino acids relative to metabolizable energy

Open AccessPublished:January 27, 2023DOI:https://doi.org/10.3168/jds.2022-22019
Balancing dairy cattle diets for rumen nitrogen and methionine or all essential amino acids relative to metabolizable energy
Previous ArticleEffects of direct-fed Bacillus subtilis and Bacillus licheniformis on production performance and milk fatty acid profile in dairy cows
Next ArticleThe effects of spring feeding strategy on pasture productivity, sward quality, and animal performance within intensive pasture-based dairy systems

      ABSTRACT

      Improving the ability of diet formulation models to more accurately predict AA supply while appropriately describing requirements for lactating dairy cattle provides an opportunity to improve animal productivity, reduce feed costs, and reduce N intake. The goal of this study was to evaluate the sensitivity of a new version of the Cornell Net Carbohydrate and Protein System (CNCPS) to formulate diets for rumen N, Met, and all essential AA (EAA). Sixty-four high-producing dairy cattle were randomly assigned to 1 of the 4 following diets in a 14-wk longitudinal study: (1) limited metabolizable protein (MP), Met, and rumen N (Base), (2) adequate Met but limited MP and rumen N (Base + M), (3) adequate Met and rumen N, but limited MP (Base + MU), and (4) adequate MP, rumen N, and balanced for all EAA (Positive). All diets were balanced to exceed requirements for ME relative to maintenance and production, assuming a nonpregnant, 650-kg animal producing 40 kg of milk at 3.05% true protein and 4.0% fat. Dietary MP was 97.2, 97.5, 102.3, and 114.1 g/kg of dry matter intake for the Base, Base + M, Base + MU, and Positive diets, respectively. Differences were observed for dry matter intake and milk yield (24.1 to 24.7 and 39.4 to 41.1 kg/d, among treatments). Energy corrected milk, fat, and true protein yield were greater (2.9, 0.13, and 0.08 kg/d, respectively) in cows fed the Positive compared with the Base diet. Using the updated CNCPS, cattle fed the Base, Base + M, and Base + MU diets were predicted to have a negative MP balance (−231, −310, and −142 g/d, respectively), whereas cattle fed the Positive diet consumed 33 g of MP/d excess to ME supply. Bacterial growth was predicted to be depressed by 16 and 17% relative to adequate N supply for the Base and Base + M diets, respectively, which corresponded with the measured lower apparent total-tract NDF degradation. The study demonstrates that improvements in lactation performances can be achieved when rumen N and Met are properly supplied and further improved when EAA supply are balanced relative to requirements. Formulation using the revised CNCPS provided predictions for these diets, which were sensitive to changes in rumen N, Met, all EAA, and by extension MP supply.

      Key words

      INTRODUCTION

      Diet formulation models continue to evolve as new information becomes available and the understanding of biological systems improves. The accurate prediction of AA requirement and supply in dairy cattle has been of particular interest to improve animal performance, reduce feed costs, and increase N utilization (
      • Lapierre H.
      • Pacheco D.
      • Berthiaume R.
      • Ouellet D.R.
      • Schwab C.G.
      • Dubreuil P.
      • Holtrop G.
      • Lobley G.E.
      What is the true supply of amino acids for a dairy cow?.
      J. Dairy Sci. 2006; 89 (16527873): E1-E14
      https://doi.org/10.3168/jds.S0022-0302(06)72359-1
      ). Recommendations for dietary Lys and Met supply are well established (
      • Rulquin H.
      • Pisulewski P.M.
      • Vérité R.
      • Guinard J.
      Milk production and composition as a function of postruminal lysine and methionine supply: A nutrient-response approach.
      Livest. Prod. Sci. 1993; 37: 69-90
      https://doi.org/10.1016/0301-6226(93)90065-P
      ;
      • Schwab C.G.
      Rumen-protected amino acids for dairy cattle: Progress towards determining lysine and methionine requirements.
      Anim. Feed Sci. Technol. 1996; 59: 87-101
      https://doi.org/10.1016/0377-8401(95)00890-X
      ;
      • NRC
      Nutrient Requirements of Dairy Cattle.
      The National Academies Press, 2001
      https://doi.org/10.17226/9825
      ) and numerous studies have demonstrated improvements in animal productivity when the balance of Lys and Met is improved (
      • Armentano L.E.
      • Bertics S.J.
      • Ducharme G.A.
      Response of lactating cows to methionine or methionine plus lysine added to high protein diets based on alfalfa and heated soybeans.
      J. Dairy Sci. 1997; 80 (9201591): 1194-1199
      https://doi.org/10.3168/jds.S0022-0302(97)76047-8
      ;
      • Noftsger S.
      • St-Pierre N.R.
      Supplementation of methionine and selection of highly digestible rumen undegradable protein to improve nitrogen efficiency for milk production.
      J. Dairy Sci. 2003; 86 (12703633): 958-969
      https://doi.org/10.3168/jds.S0022-0302(03)73679-0
      ;
      • Chen Z.H.
      • Broderick G.A.
      • Luchini N.D.
      • Sloan B.K.
      • Devillard E.
      Effect of feeding different sources of rumen-protected methionine on milk production and N-utilization in lactating dairy cows.
      J. Dairy Sci. 2011; 94 (21426989): 1978-1988
      https://doi.org/10.3168/jds.2010-3578
      ). In addition to Lys and Met, the potential for other EAA to limit milk production has been investigated including the branched chain AA, Arg, and His (
      • Appuhamy J.A.
      • Knapp J.R.
      • Becvar O.
      • Escobar J.
      • Hanigan M.D.
      Effects of jugular-infused lysine, methionine, and branched-chain amino acids on milk protein synthesis in high-producing dairy cows.
      J. Dairy Sci. 2011; 94 (21426986): 1952-1960
      https://doi.org/10.3168/jds.2010-3442
      ;
      • Haque M.N.
      • Rulquin H.
      • Andrade A.
      • Faverdin P.
      • Peyraud J.L.
      • Lemosquet S.
      Milk protein synthesis in response to the provision of an “ideal” amino acid profile at 2 levels of metabolizable protein supply in dairy cows.
      J. Dairy Sci. 2012; 95 (22884342): 5876-5887
      https://doi.org/10.3168/jds.2011-5230
      ,
      • Haque M.N.
      • Rulquin H.
      • Lemosquet S.
      Milk protein responses in dairy cows to changes in postruminal supplies of arginine, isoleucine, and valine.
      J. Dairy Sci. 2013; 96 (23164225): 420-430
      https://doi.org/10.3168/jds.2012-5610
      ;
      • Lee C.
      • Hristov A.N.
      • Cassidy T.W.
      • Heyler K.S.
      • Lapierre H.
      • Varga G.A.
      • de Veth M.J.
      • Patton R.A.
      • Parys C.
      Rumen-protected lysine, methionine, and histidine increase milk protein yield in dairy cows fed a metabolizable protein-deficient diet.
      J. Dairy Sci. 2012; 95 (22863104): 6042-6056
      https://doi.org/10.3168/jds.2012-5581
      ,
      • Lee C.
      • Hristov A.N.
      • Heyler K.S.
      • Cassidy T.W.
      • Lapierre H.
      • Varga G.A.
      • Parys C.
      Effects of metabolizable protein supply and amino acid supplementation on nitrogen utilization, milk production, and ammonia emissions from manure in dairy cows.
      J. Dairy Sci. 2012; 95 (22916930): 5253-5268
      https://doi.org/10.3168/jds.2012-5366
      ). Increases in milk, milk protein, and DMI have been observed when His was added to diets predicted to be His-deficient (
      • Lee C.
      • Hristov A.N.
      • Cassidy T.W.
      • Heyler K.S.
      • Lapierre H.
      • Varga G.A.
      • de Veth M.J.
      • Patton R.A.
      • Parys C.
      Rumen-protected lysine, methionine, and histidine increase milk protein yield in dairy cows fed a metabolizable protein-deficient diet.
      J. Dairy Sci. 2012; 95 (22863104): 6042-6056
      https://doi.org/10.3168/jds.2012-5581
      ,
      • Lee C.
      • Hristov A.N.
      • Heyler K.S.
      • Cassidy T.W.
      • Lapierre H.
      • Varga G.A.
      • Parys C.
      Effects of metabolizable protein supply and amino acid supplementation on nitrogen utilization, milk production, and ammonia emissions from manure in dairy cows.
      J. Dairy Sci. 2012; 95 (22916930): 5253-5268
      https://doi.org/10.3168/jds.2012-5366
      ), but mixed results have been observed when adding branched chain AA and Arg (
      • Appuhamy J.A.
      • Knapp J.R.
      • Becvar O.
      • Escobar J.
      • Hanigan M.D.
      Effects of jugular-infused lysine, methionine, and branched-chain amino acids on milk protein synthesis in high-producing dairy cows.
      J. Dairy Sci. 2011; 94 (21426986): 1952-1960
      https://doi.org/10.3168/jds.2010-3442
      ;
      • Haque M.N.
      • Rulquin H.
      • Andrade A.
      • Faverdin P.
      • Peyraud J.L.
      • Lemosquet S.
      Milk protein synthesis in response to the provision of an “ideal” amino acid profile at 2 levels of metabolizable protein supply in dairy cows.
      J. Dairy Sci. 2012; 95 (22884342): 5876-5887
      https://doi.org/10.3168/jds.2011-5230
      ,
      • Haque M.N.
      • Rulquin H.
      • Lemosquet S.
      Milk protein responses in dairy cows to changes in postruminal supplies of arginine, isoleucine, and valine.
      J. Dairy Sci. 2013; 96 (23164225): 420-430
      https://doi.org/10.3168/jds.2012-5610
      ). The interactions and interconversion between protein and energy could affect expected responses from additional AA supply, particularly branched chain AA, which are extensively oxidized and act as precursors for the synthesis of other required metabolites (
      • Lobley G.E.
      Protein-energy interactions: Horizontal aspects.
      in: Proc. Energy and Protein Metabolism and Nutrition. Wageningen Academic Publishers, 2007: 445-462
      ;
      • Lemosquet S.
      • Guinard-Flament J.
      • Raggio G.
      • Hurtaud C.
      • Van Milgen J.
      • Lapierre H.
      How does increasing protein supply or glucogenic nutrients modify mammary metabolism in lactating dairy cows?.
      in: Proc. Energy and Protein Metabolism and Nutrition. Wageningen Academic Publishers, 2010: 175-186
      ). Given the interactions between protein and energy, it has been suggested they be considered together in diet formulation models, rather than as separate entities (
      • Lobley G.E.
      Protein-energy interactions: Horizontal aspects.
      in: Proc. Energy and Protein Metabolism and Nutrition. Wageningen Academic Publishers, 2007: 445-462
      ).
      The repeatability of a response from AA balancing might also be influenced by the ability of diet formulation models to accurately estimate true AA deficiencies.
      • Pacheco D.
      • Patton R.A.
      • Parys C.
      • Lapierre H.
      Ability of commercially available dairy ration programs to predict duodenal flows of protein and essential amino acids in dairy cows.
      J. Dairy Sci. 2012; 95 (22281359): 937-963
      https://doi.org/10.3168/jds.2011-4171
      conducted an evaluation of 4 commercially available diet-balancing programs to predict EAA supply and concluded that, whereas predictions were generally accurate, all programs, including the Cornell Net Carbohydrate and Protein System (CNCPS), had areas where significant improvements could be achieved. Emphasis was placed on the ability of the currently available model (v.6.5.5) to accurately predict microbial and RUP. A new, dynamic version of the CNCPS, designated hereafter as CNCPS v.7, has been constructed that includes rumen protozoa and endogenous N secretions along the entire gastrointestinal tract, which have not been directly included in previous versions of the CNCPS (
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      ;
      • 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.
      J. Dairy Sci. 2015; 98 (26142847): 6361-6380
      https://doi.org/10.3168/jds.2015-9378
      ;
      • Higgs R.J.
      • Van Amburgh M.E.
      Evolution of the CNCPS-Development of V7.
      in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
      https://hdl.handle.net/1813/44752
      ). The model also includes a new approach for estimating post-ruminal N digestion for non-forage and low amylase- and sodium sulfite-treated NDF corrected for ash residue (aNDFom) feeds based on an in vitro estimate of unavailable nitrogen as detailed in
      • Gutierrez-Botero M.
      • Ross D.A.
      • Van Amburgh M.E.
      Formulating diets for intestinal unavailable nitrogen using blood meal in high-producing dairy cattle.
      J. Dairy Sci. 2022; 105 (35570044): 5738-5746
      https://doi.org/10.3168/jds.2021-21730
      . Modeling and research efforts have been focused on improving the capability of the CNCPS to precisely estimate N and AA availability to the animal to allow for the formulation of diets that more closely match animal requirements. An evaluation of the model showed predictions were close to measured data for microbial, feed, and total non-ammonia N flows at the omasum (
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      ;
      • Higgs R.J.
      • Van Amburgh M.E.
      Evolution of the CNCPS-Development of V7.
      in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
      https://hdl.handle.net/1813/44752
      ). Also, new optimum requirements for each EAA relative to ME supply have also been established and appear to explain more variation in EAA utilization than current recommendations expressed relative to MP supply (
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      ;
      • Higgs R.J.
      • Van Amburgh M.E.
      Evolution of the CNCPS-Development of V7.
      in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
      https://hdl.handle.net/1813/44752
      ). Although the predictions of this new model prove accurate relative to the observed data used to evaluate it, the CNCPS v.7 has yet to be used to feed lactating cattle, presenting a unique opportunity to test the ability to formulate diets with lower protein content that are prospectively balanced to meet the requirements of cattle.
      The objectives of this study were (1) to use the updated CNCPS v.7 to formulate the diets of lactating dairy cattle for rumen N and Met or all EAA using the requirements established in
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      and
      • Higgs R.J.
      • Van Amburgh M.E.
      Evolution of the CNCPS-Development of V7.
      in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
      https://hdl.handle.net/1813/44752
      , and (2) to test the ability of the model to predict both deficient and adequate rumen N supply. Under the context of this experiment, adequate ME and rumen N is defined as meeting their supply relative to the predicted requirements as described by the CNCPS v.7 model. Our hypothesis was that ECM yield, the primary measurement outcome, will be increased by supplying adequate rumen N and a balanced supply of all EAA relative to ME supply.

      MATERIALS AND METHODS

      Animals and Diets

      The experiment was conducted at the Cornell Teaching and Research Facility (Harford, NY) from May to August 2013. All procedures conducted in the study were approved by the Cornell University Institutional Animal Care and Use Committee. Sixty-four lactating Holstein dairy cattle [16 primiparous and 48 multiparous; 100 ± 31 DIM at the beginning of the study; 624 ± 68 kg of BW; 3.0 ± 0.2 BCS (1–5 scale)] were randomly assigned to one of 4 dietary treatments (Table 1 and Supplemental Table S1, https://ecommons.cornell.edu/handle/1813/112734). Diet assignment was balanced for parity, ECM yield, and DIM. Cattle were housed in individual tiestalls and fed a TMR once daily at approximately 0900 h with a 10% target refusal rate. All cows were treated with rBST (Posilac, Elanco Animal Health) on a 14-d cycle according to label. The experiment was longitudinal in design and included a 7-d adaptation period, 14-d covariate period, and 100-d period where cattle were assigned 1 of 4 diets. Dietary treatments were (1) balanced for ME (assuming 45 kg ECM), but limited in MP, rumen N, and MP Met (Base), (2) balanced for ME and MP Met but limited in MP and rumen N (Base + M), (3) balanced for ME and MP Met with adequate rumen N, but limited in MP (Base + MU), or (4) balanced for ME, MP, all EAA, and adequate in rumen N (Positive). Chemical composition of forages and major concentrate ingredients is in Table 2.
      Table 1Ingredients and chemical composition of diets balancing methionine or all EAA
      ItemDiet
      Base = balanced for ME requirements (assuming 45 kg of ECM), but limited in MP and rumen N; Base + M = balanced for ME and MP Met but limited in MP and rumen N; Base + MU = balanced for ME, MP Met, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      BaseBase + MBase + MUPositive
      Ingredient, % DM
       Corn silage47.046.546.846.1
       Grass hay8.538.538.428.46
       Corn grain, ground fine15.715.815.715.1
       Corn gluten feed8.698.758.667.07
       Soybean meal6.216.256.187.89
       Soyhulls2.072.082.062.10
       SoyPlus
      SoyPlus (West Central Cooperative) rumen-protected soybean meal.
      		2.072.082.064.11
       Dried molasses2.072.082.061.20
       NutraCor
      NutraCor (Energy Feeds International) rumen-protected fat.
      		1.901.921.901.64
       Urea0.080.080.520.12
       AjiPro-L
      AjiPro-L (Ajinomoto Heartland Inc.) rumen-protected Lys (l-Lys 40% DM).
      		0.100.100.09—
       Smartamine M
      Smartamine M (Adisseo USA Inc.) rumen-protected Met (60% MP Met).
      		—0.080.080.09
       Blood meal
      Blood meal (Perdue AgriBusiness).
      		1.661.671.652.18
       Minerals and vitamins
      Contained on a DM basis: 19.2% sodium bicarbonate, 2.4% magnesium oxide, 38.3% ground limestone, 7.2% sodium chloride, 1.4% vitamin E, 12.0% potassium sulfate, 16.8% potassium carbonate, and 2.7% mineral and vitamin premix (calcium 0.75%, magnesium 9.54%, sulfur 19.25%, iodine 330 mg/kg, cobalt 501 mg/kg, iron 0.1 mg/kg, zinc 25,709 mg/kg, manganese 22,306 mg/kg, selenium 214 mg/kg, vitamin A 3,702 kIU/kg, vitamin D 923 kIU/kg, vitamin E 12,490 IU/kg; Mercer Milling Company).
      		3.924.053.913.88
      Chemical component,
      ADICP = CP insoluble in acid detergent; NDICP = CP insoluble in neutral detergent; uNDF240 = undegraded NDF after 240 h in vitro. Nutrients are expressed as % DM unless stated otherwise.
      % DM
       DM, %47.348.149.148.8
      CP13.513.614.615.6
      Soluble protein, % CP38.838.638.837.8
      Ammonia, % SP7.57.57.97.4
      ADICP, % CP8.68.68.58.3
      NDICP, % CP12.112.111.912.0
       Acetic acid1.11.11.11.1
       Propionic acid0.10.00.00.0
       Lactic acid2.52.52.52.5
       Water-soluble carbohydrate4.74.74.64.4
       Starch31.931.931.530.9
       Soluble fiber4.54.54.44.5
       ADF16.616.516.416.5
       NDF29.729.629.329.3
      Lignin, % NDF10.210.210.110.3
      uNDF240, % NDF21.521.421.221.5
       Ash7.37.47.37.3
       Ether extract4.74.74.64.4
      1 Base = balanced for ME requirements (assuming 45 kg of ECM), but limited in MP and rumen N; Base + M = balanced for ME and MP Met but limited in MP and rumen N; Base + MU = balanced for ME, MP Met, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      2 SoyPlus (West Central Cooperative) rumen-protected soybean meal.
      3 NutraCor (Energy Feeds International) rumen-protected fat.
      4 AjiPro-L (Ajinomoto Heartland Inc.) rumen-protected Lys (l-Lys 40% DM).
      5 Smartamine M (Adisseo USA Inc.) rumen-protected Met (60% MP Met).
      6 Blood meal (Perdue AgriBusiness).
      7 Contained on a DM basis: 19.2% sodium bicarbonate, 2.4% magnesium oxide, 38.3% ground limestone, 7.2% sodium chloride, 1.4% vitamin E, 12.0% potassium sulfate, 16.8% potassium carbonate, and 2.7% mineral and vitamin premix (calcium 0.75%, magnesium 9.54%, sulfur 19.25%, iodine 330 mg/kg, cobalt 501 mg/kg, iron 0.1 mg/kg, zinc 25,709 mg/kg, manganese 22,306 mg/kg, selenium 214 mg/kg, vitamin A 3,702 kIU/kg, vitamin D 923 kIU/kg, vitamin E 12,490 IU/kg; Mercer Milling Company).
      8 ADICP = CP insoluble in acid detergent; NDICP = CP insoluble in neutral detergent; uNDF240 = undegraded NDF after 240 h in vitro. Nutrients are expressed as % DM unless stated otherwise.
      Table 2Chemical composition of forages and major concentrate ingredients in diets balancing methionine or all EAA
      Chemical component,
      ADICP = CP insoluble in acid detergent; NDICP = CP insoluble in neutral detergent; uNDF240 = undegraded NDF after 240 h in vitro.
      % DM      		Corn silageDry grass hayCorn grainSoyhullsCorn gluten feedSoyPlus
      SoyPlus (West Central Cooperative) rumen-protected soybean meal.
      		Soybean mealBlood meal
      Blood meal (Perdue AgriBusiness).
      DM, %33.086.585.886.390.688.688.193.2
      CP7.27.98.611.219.147.652.898.3
       Soluble protein, % CP60.422.210.216.134.07.115.762.7
       Ammonia, % SP15.80.00.00.00.00.00.00.0
       ADICP, % CP9.715.96.311.814.53.20.90.7
       NDICP, % CP10.731.59.334.719.418.51.30.8
       Undegraded N, % TN
      Measured according to Gutierrez-Botero et al. (2022); TN = total nitrogen, N/D = not determined.
      		N/DN/D20.022.826.09.17.21.7
      Water-soluble carbohydrate0.97.52.82.76.012.813.10.2
      Starch38.62.074.71.717.72.63.20.0
      Soluble fiber3.38.40.74.812.01.313.80.0
      ADF21.241.74.349.711.08.94.80.0
      NDF36.167.68.672.437.122.58.00.0
       Lignin, % NDF7.710.722.33.89.77.211.90.0
       uNDF240, % NDF25.535.219.76.716.414.724.10.0
      Ash2.74.91.35.45.86.97.71.3
      Ether extract3.31.83.31.92.46.41.60.1
      1 ADICP = CP insoluble in acid detergent; NDICP = CP insoluble in neutral detergent; uNDF240 = undegraded NDF after 240 h in vitro.
      2 SoyPlus (West Central Cooperative) rumen-protected soybean meal.
      3 Blood meal (Perdue AgriBusiness).
      4 Measured according to
      • Gutierrez-Botero M.
      • Ross D.A.
      • Van Amburgh M.E.
      Formulating diets for intestinal unavailable nitrogen using blood meal in high-producing dairy cattle.
      J. Dairy Sci. 2022; 105 (35570044): 5738-5746
      https://doi.org/10.3168/jds.2021-21730
      ; TN = total nitrogen, N/D = not determined.

      Sample Collection and Analysis

      Body weight and BCS (1–5 scale;
      • Wildman E.E.
      • Jones G.M.
      • Wagner P.E.
      • Boman R.L.
      • Troutt Jr., H.F.
      • Lesch T.N.
      A dairy cow body condition scoring system and its relationship to selected production characteristics.
      J. Dairy Sci. 1982; 65: 495-501
      https://doi.org/10.3168/jds.S0022-0302(82)82223-6
      ) were measured weekly. Cows were milked 2 times per day at 0900 and 2000 h and milk weights were recorded at each milking. Milk samples were collected on 2 d each week (4 consecutive milkings). Samples were placed in tubes containing 2-bromo-2-nitropropane-1,3-diol and analyzed for fat, true protein, lactose, and MUN (Dairy One) using Fourier transform infrared spectroscopy (Milkoscan 6000; Foss Electric). Milk component yield was calculated using the milk weight and composition of each individual milking during sampling and summed to produce a daily yield for each of the 2 d within a given week that milk samples were collected. These daily yields were further averaged to produce a weekly average milk component yield for each cow. Energy corrected milk was calculated for all days where milk samples were collected and averaged within a given week. Feed efficiency, ECM divided by DMI, were also calculated on days when milk samples were collected and averaged within a given week.
      Dry matter intake was measured daily for each animal. Samples of TMR and orts for each diet were sampled twice each week, composited, and analyzed using near infrared reflectance spectroscopy for the chemical components presented in Table 1 (Cumberland Valley Analytical Services). The DM content of each TMR was measured weekly by drying at 100°C in a forced-air oven. Forage samples were taken weekly and analyzed by wet chemistry for the chemical components presented in (Table 1; Cumberland Valley Analytical Services, Maugansville, MD). Corn silage DM was measured 5 d/wk using a Koster Moisture Tester (Koster Tester). Individual ingredients in the grain mix were sourced from the feed mill (CNY Feed Inc., Jordan, NY) 3 times during the experiment and analyzed by wet chemistry for the same components as forages. Subsamples of all ingredients were taken and dried at 60°C, ground to 2 mm using a Wiley Mill, and analyzed for AA concentration and undegraded N. For the analysis of AA, sample aliquots (2 mg of N) were hydrolyzed at 110°C for 21 h in a block heater (
      • Gehrke C.W.
      • Wall Sr., L.
      • Absheer J.
      • Kaiser F.
      • Zumwalt R.
      Sample preparation for chromatography of amino acids: Acid hydrolysis of proteins.
      J. Assoc. Off. Anal. Chem. 1985; 68: 811-821
      https://doi.org/10.1093/jaoac/68.5.811
      ) with 5 mL of 6 M HCl after flushing with N2 gas. Norleucine (50 µL; 125 mM) was used as an internal standard. Hydrolysates were filtered on Whatman 541 filters and diluted to 50 mL with water. Aliquots (0.5 mL) were evaporated, redissolved in 1 mL of water, evaporated again, which was repeated 2 more times to remove the acid, and dissolved in a 2-mL sample buffer for analysis. Additional aliquots (2 mg of N) were pre-oxidized with 1 mL of performic acid (4.5 mL of 88% formic acid, 0.5 mL of 30% hydrogen peroxide, 25 mg of phenol) for 16 h on ice before acid hydrolysis for analysis of Met and Cys. Amino acids were separated on a lithium cation exchange column using a 3-buffer step gradient and column temperature gradient. Detection was at 560 nm following ninhydrin post column derivation on an HPLC System Gold with 32 Karat software (Beckman-Coulter Inc.). Standards (250 nM/mL) for Asp, Thr, Ser, Glu, Gly, Ala, Val, Met, Ile, Leu, Tyr, Phe, NH3, Lys, His, Arg, and Cys (125 nM/mL) were prepared by diluting a purchased stock (Amino acid standard H, #20088; Pierce Chemical) with the sample buffer. Internal standards (250 nM/mL) norleucine for nonaromatic AA and 5-methyl-Trp for tryptophan were prepared in sample buffer and combined with the other standards. The volume of samples and standards loaded on the column was 50 mL. Tryptophan was measured in a separate analysis using fluorescence detection (excitation = 285 nm; emission = 345 nm) according to the procedure of
      • Landry J.
      • Delhaye S.
      Simplified procedure for the determination of tryptophan of foods and feedstuffs from barytic hydrolysis.
      J. Agric. Food Chem. 1992; 40: 776-779
      https://doi.org/10.1021/jf00017a014
      . Briefly, samples (2 mg of N) were hydrolyzed using 1.2 g of Ba(OH)2 at 110°C for 16 h on a block heater and subsequently cooled on ice to precipitate barium ions. An aliquot of the hydrolysate (3 µL) was added to 1 mL of acetate buffer (0.07 M sodium acetate; pH 4.5) and analyzed by HPLC (Table 3). Concentrate feeds were also analyzed for unavailable N using the in vitro procedure described in
      • Gutierrez-Botero M.
      • Ross D.A.
      • Van Amburgh M.E.
      Formulating diets for intestinal unavailable nitrogen using blood meal in high-producing dairy cattle.
      J. Dairy Sci. 2022; 105 (35570044): 5738-5746
      https://doi.org/10.3168/jds.2021-21730
      .
      Table 3The analyzed AA composition of dietary ingredients used in balancing methionine or all EAA
      AA, g/100 g of AACorn silageDry grass hayCorn grainSoyhullsCorn gluten feedSoyPlus
      SoyPlus (West Central Cooperative) rumen-protected soybean meal.
      		Soybean mealBlood meal
      Blood meal (Perdue AgriBusiness).
      EAA
       Arg1.85.14.34.93.97.07.23.7
       His1.41.42.12.02.62.22.35.9
       Ile4.24.03.03.83.03.73.60.5
       Leu10.97.911.96.59.37.87.512.7
       Lys3.04.62.66.32.14.75.57.6
       Met6.98.37.35.15.64.34.53.7
       Phe3.94.64.33.53.54.94.87.6
       Thr4.85.24.14.04.54.44.34.8
       Trp0.61.30.91.50.71.51.52.0
       Val5.65.34.34.64.44.13.86.5
      NEAA
       Ala10.46.87.54.97.44.64.49.0
       Asp6.19.34.77.94.89.08.96.9
       Cys5.97.16.17.98.33.24.12.1
       Glu15.210.717.811.417.619.719.210.6
       Gly4.45.73.59.24.64.34.24.4
       Pro7.65.17.85.29.55.25.13.5
       Ser4.85.05.06.85.05.65.55.7
       Tyr2.32.42.84.43.23.63.52.7
      EAA, % AA43.247.744.842.239.744.745.055.0
      NEAA, % AA56.852.355.257.860.355.355.045.0
      AA N, % Total N57.160.074.071.862.374.077.175.4
      1 SoyPlus (West Central Cooperative) rumen-protected soybean meal.
      2 Blood meal (Perdue AgriBusiness).
      Blood samples (10 mL) were collected from every cow, once each week (1100 h), by venipuncture of the coccygeal vessels into heparinized Vacutainers (Becton Dickinson), immediately placed on ice and centrifuged (1,500 × g for 15 min at 4°C) to obtain plasma, and frozen at −20°C before analysis. Samples were analyzed for plasma urea N (PUN) using an enzymatic colorimetric assay based on a commercial kit (no. 640; Sigma Chemical Co.). Three times during the study (wk 2 of the covariate period; wk 5 and 10 of the experimental period), an additional blood sample was taken and analyzed for plasma AA. Equal volumes (0.65 mL) of plasma and ice-cold sulfosalicylic acid (10%) containing the internal standard norleucine (250 nM) were mixed, vortexed, and refrigerated on ice for 12 h with vortexing every 4 h. Samples were then centrifuged (15,800 × g for 30 min at 4°C) and 1 mL of supernatant was lyophilized, reconstituted in 0.5 mL of 3 N LiOH, filtered through a 0.2-µm filter, and frozen at −20°C until analysis. Analysis was conducted by an automated ion-exchange chromatography system as described above.
      Sampling of feces was conducted by taking spot fecal samples (∼500 g per cow) 8 times over a 3-d period (d 1 = 1100, 1700, and 2300 h; d 2 = 0500, 1400, and 2000 h; d 3 = 0200 and 0800 h), 3 times during the experiment (wk 2 of the covariate period; wk 5 and 10 of the experimental period), and were frozen (−20°C). Samples were subsequently thawed, composited by cow (8 samples per cow), and blended to ensure uniformity. A 1,000-g subsample was dried at 60°C in a forced-air oven for 96 h and ground to 1 mm in a Wiley mill. Samples of TMR and orts were also collected for 2 d, beginning the day before the first fecal sampling. The TMR samples were taken at the time of feed delivery, composited within diet, and 3 aliquots per diet were frozen at −20°C. Individual orts samples for each cow, each day (2 d), were collected and stored frozen at −20°C. Samples were subsequently thawed and dried at 60°C in a forced-air oven and ground to 1 mm in a Wiley mill. The DM content of both the TMR and orts were measured and used to estimate DMI for each cow during the collection period. The ground fecal, TMR, and orts samples were analyzed by wet chemistry for aNDFom and undegraded NDF after 240 h in vitro (Cumberland Valley Analytical Services, Maugansville, MD) and were used to estimate total-tract NDF degradation (NDFD) as described by
      • Huhtanen P.
      • Kaustell K.
      • Jaakkola S.
      The use of internal markers to predict total digestibility and duodenal flow of nutrients in cattle given six different diets.
      Anim. Feed Sci. Technol. 1994; 48: 211-227
      https://doi.org/10.1016/0377-8401(94)90173-2
      .
      To generate model outputs from this study, all cattle inputs and outputs were compiled by week and iteratively evaluated through the CNCPS v7. Further, all feed chemistry was entered and the total-tract aNDFom degradability was used for each cow to adjust the ME predictions. Finally, to generate the nutrient requirements, information for each individual cow was used each week of the study, resulting in 15 weekly predictions per cow.

      Statistical Analysis

      Sample size calculations for this study used milk yield as the parameter of interest. Assuming cows produce on average 40 kg of milk per day and the standard deviation of the group was 3.2 kg per day, 16 cows per dietary treatment would be required for a 2.8-kg difference to be significant using an α of 0.05 and a power of 80%. Therefore, 64 cows would be adequate for this experiment. All cattle were enrolled simultaneously but were blocked using the parameters described above when balancing cows among diets. Data were analyzed using a restricted maximum likelihood model in SAS v.9.4. The statistical model used to analyze responses to dietary treatments are as follows:
      Yijkl = µ + Ti + Rj + TRij + Wk + Cl:i + Xl + εijkl,


      where Yijkl is the dependent variable, µ is the overall mean, Ti is the fixed dietary effect of the ith diet, Rj is the fixed effect of the jth measurement in time, TRij is the interaction between the ith dietary treatment and jth measurement in time, Wk is the fixed effect of the kth block, Cl:i is the random effect of lth cow nested within the ith dietary treatment, Xl is the mean covariate measure for the lth cow, and εijkl is the residual error. The fixed effect of measurement in time was considered a repeated measurement with the subject expressed as cow nested within treatment and had a first order autoregressive covariance structure applied. Multiple treatment comparisons were made among dietary treatments and probabilities were corrected using a Tukey adjustment. The means presented for data other than CNCPS model outputs are expressed as least squares means. Significant differences among means are expressed as P < 0.05 with tendencies expressed as 0.05 ≤ P < 0.10. Values presented for CNCPS outputs are arithmetic means.

      RESULTS

      Animal Performance

      Cattle fed the Base + MU tended to have greater DMI than those fed the Base and Base + M diets (P ≤ 0.07; Table 4). Milk yield and ECM yield were greater (P ≤ 0.05) in cattle fed the Positive diet compared with other diets. Further, true protein yield was statistically higher for cattle fed the Positive diet (1.18 kg) over the Base diet (1.10 kg; P = 0.01), with Base + M and Base + MU cattle producing similar yields between the Base and Positive diets. Similarly, milk fat yield was higher in the Positive diet (1.39 kg) compared with the Base diet (1.26 kg; P = 0.04) and tended to be higher than the Base + M (1.28 kg; P = 0.10) diet. True protein concentration in milk was greater in cattle fed the Positive (2.99%; P = 0.01) and Base + MU (2.97%; P = 0.03) diets than cows fed the Base diet (2.90%), although the Base + M diet was not different than the Base + MU and Positive diets (P ≥ 0.10). Body weight, BW change, and BCS were similar among diets (P ≥ 0.10).
      Table 4Effects of balancing methionine or all EAA on milk production, intake, BW, and BCS
      ItemDiet
      Base = balanced for ME (assuming 45 kg of ECM), but limited in MP, methionine and rumen N; Base + M = balanced for ME and methionine but limited in MP and rumen N; Base + MU = balanced for ME, methionine, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      		SEMProbability
      The main effect of time was significant (P < 0.01) for all items and is omitted from the table.
      BaseBase + MBase + MUPositiveDietDiet × time
      Intake and milk production, kg/d
       DMI24.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		24.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		24.7
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		24.6
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.20.03<0.01
       N intake, g/d522
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		532
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		582
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		615
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		13.2<0.010.56
       Milk yield39.4
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		39.7
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		39.9
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		41.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.40.010.06
       ECM yield
      Estimated according to Tyrrell and Reid (1965).
      		38.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		38.7
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		39.4
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		41.0
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.50.010.54
       True protein yield1.10
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		1.14
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		1.14
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		1.18
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.020.020.03
       Fat yield1.26
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		1.28
      Within a row, values with different superscripts differ significantly (P < 0.05).
      , 
      Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      		1.31
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		1.39
      Within a row, values with different superscripts differ significantly (P < 0.05).
      , 
      Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      		0.030.040.34
       Lactose yield1.891.921.931.990.040.220.07
      Milk composition, %
       True protein2.90
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.95
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.97
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.99
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.080.010.42
       Fat3.253.253.313.480.100.280.24
       Lactose4.84
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		4.83
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		4.84
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		4.85
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.010.060.98
       MUN, mg/dL6.6
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		7.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		8.6
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		10.0
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.16<0.01<0.01
      BW and condition
       BW, kg6336366386333.70.70<0.01
       BW change, kg/wk0.280.290.340.290.070.94<0.01
       BCS, 1–5 scale3.063.073.093.090.020.18—
       PUN,
      PUN = plasma urea N.
      mg/dL      		5.9
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		5.7
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		8.5
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		8.7
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.54<0.010.36
      a,b Within a row, values with different superscripts differ significantly (P < 0.05).
      x,y Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      1 Base = balanced for ME (assuming 45 kg of ECM), but limited in MP, methionine and rumen N; Base + M = balanced for ME and methionine but limited in MP and rumen N; Base + MU = balanced for ME, methionine, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      2 The main effect of time was significant (P < 0.01) for all items and is omitted from the table.
      3 Estimated according to
      • Tyrrell H.F.
      • Reid J.T.
      Prediction of the energy value of cow’s milk.
      J. Dairy Sci. 1965; 48: 1215-1223
      https://doi.org/10.3168/jds.S0022-0302(65)88430-2
      .
      4 PUN = plasma urea N.

      Nitrogen Utilization

      Nitrogen intake was similar among cows fed the Base and Base + M diets but was greater for cows fed the Base + MU and Positive diets (∼60 g/d and ∼90 g/d, respectively), which corresponded with higher levels of dietary CP (Table 1). Milk urea N and PUN in cows fed the Base and Base + M diets were similar and lower (P ≤ 0.01) than the Base + MU and Positive diets (Table 4). Milk urea N was slightly higher than PUN but both measures were in a similar range. Productive N was greater in cows fed the Positive diet due to the greater milk protein yield (Supplemental Table S2, https://ecommons.cornell.edu/handle/1813/112734).
      Predicted fecal and urinary N increased as dietary N intake increased. Predicted urinary N was ∼60 g higher in cows fed the Positive diet compared with the Base diet, and fecal N was ∼20 g higher in the Positive diet which corresponded with lower N use efficiency (Supplemental Table S2). Cows fed the Base and Base + M diets had the highest N use efficiency (0.37 and 0.38, respectively) and based on predicted N excretion, partitioned 1.65 and 1.70 more N to productive uses than urine. Total NDF and potentially degradable (pd) NDF intake were not different among diets although indigestible fiber intake was greater for cows fed the Base diet compared with Base + MU and Positive diets (Table 5; P ≤ 0.05). Apparent total-tract NDFD tended to be greater in the Positive diet (42.5% of NDF) compared with the Base + M diet (40.1% of NDF; P = 0.10) and was numerically higher than the Base diet (40.3% of NDF; P = 0.24). Similarly, apparent total-tract potentially degradable NDF degradation was significantly greater in cows fed the Positive diets (58.6% of pdNDF) compared with the Base + M diet (54.6% of pdNDF; P = 0.05) and numerically higher than the Base diet (56.0% of pdNDF; P = 0.35). The apparent total-tract NDFD and potentially degradable NDF for the Base + MU diet was 42.4% of NDF and 58.2% of pdNDF, respectively, and was numerically higher than both the Base and Base + MU diets (P ≥ 0.10). Taken together, these results suggest the increased urea N intake in both Base + MU and Positive diets improved rumen N balance, which also improved ruminal aNDFom degradation.
      Table 5Effects of balancing methionine or all EAA on fiber intake and apparent total-tract degradation
      Item
      pdNDF = potentially degradable NDF; uNDF240 = undegraded NDF after a 240-h in vitro; NDFD = NDF degraded.
      		Diet
      Base = balanced for ME (assuming 45 kg ECM), but limited in MP, methionine and rumen N; Base + M = balanced for ME and methionine but limited in MP and rumen N; Base + MU = balanced for ME, methionine, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      		SEMProbability
      Probability values are described for dietary treatment.
      BaseBase + MBase + MUPositiveDietDiet × time
      Intake, kg/d
       NDF8.127.947.667.630.180.180.48
       pdNDF5.855.825.585.540.140.240.42
       uNDF2402.27
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.12
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.08
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.09
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.050.030.27
      Digestibility
       NDFD, % NDF40.3
      Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      		40.1
      Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      		42.4
      Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      		42.5
      Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      		0.80.060.68
       NDFD, % pdNDF56.0
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		54.6
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		58.2
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		58.6
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		1.10.050.59
      a,b Within a row, values with different superscripts differ significantly (P < 0.05).
      x,y Within a row, values with different superscripts tend to differ significantly (0.05 ≤ P < 0.10).
      1 pdNDF = potentially degradable NDF; uNDF240 = undegraded NDF after a 240-h in vitro; NDFD = NDF degraded.
      2 Base = balanced for ME (assuming 45 kg ECM), but limited in MP, methionine and rumen N; Base + M = balanced for ME and methionine but limited in MP and rumen N; Base + MU = balanced for ME, methionine, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      3 Probability values are described for dietary treatment.

      AA Balance

      Predicted AA supply expressed relative to ME for each diet is in Supplemental Table S1. Compared with the ideal supply, the Base diet was low in Arg, Ile, Lys, Met, and Val. The Base + M diet was similar to the Base diet but with adequate Met (1.13 g Met/Mcal ME). All AA were adequate in cattle fed the Positive diet other than Ile, which was 0.16 g/Mcal of ME lower than the ideal supply.
      Diet differences (P ≤ 0.05) in plasma AA concentrations were observed in Gln, Gly, Ser, Arg, and Met (Table 6). Plasma Met concentration was lower in the Base diet compared with the other diets and was attributed to the dietary supplementation of Met (Table 1). Arginine increased as N supply increased and reflected the Arg supply relative to ME. Essential AA in the plasma were higher in the Positive diet but similar among the other diets, including cows fed the Base + MU diet, despite the higher predicted AA supply. Nonessential AA were not affected by diet, however, 3-methylhistidine was lower (P ≤ 0.05) in cows fed the Positive diet.
      Table 6Effects of balancing methionine or all EAA on plasma AA concentration
      AA (g/100 g of AA)Diet
      Base = balanced for ME (assuming 45 kg of ECM), but limited in MP, methionine and rumen N; Base + M = balanced for ME and methionine but limited in MP and rumen N; Base + MU = balanced for ME, methionine, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      		SEMProbability
      BaseBase + MBase + MUPositiveDietDiet × time
      NEAA
       Ala8.928.559.167.890.410.100.30
       Asn4.083.993.943.370.550.750.31
       Asp0.910.920.720.840.110.430.25
       Cit6.326.967.317.320.350.100.61
       Cys1.801.941.921.850.080.520.47
       Gln6.33
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		6.29
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		8.04
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		7.64
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.480.010.14
       Glu6.546.456.626.290.300.840.70
       Gly9.24
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		11.19
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		8.87
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		9.10
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.610.020.50
       Orn1.661.881.691.950.100.060.04
       Pro4.103.633.984.110.270.510.60
       Ser3.71
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		3.65
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		3.10
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		3.08
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.140.010.20
       Tyr3.763.603.563.390.140.240.35
      EAA
       Arg4.25
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		4.37
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		4.74
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		5.09
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.210.010.14
       His3.323.473.123.370.170.440.17
       Ile4.454.074.194.220.160.370.77
       Leu5.865.295.195.670.220.070.52
       Lys4.434.244.194.620.170.200.15
       Met1.54
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.19
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.24
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		2.14
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.09<0.010.41
       Phe2.902.612.822.700.140.390.20
       Thr4.894.634.484.570.290.750.12
       Trp1.941.751.721.930.100.270.66
       Val9.058.308.398.880.340.280.88
       3-Methylhistidine0.46
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.38
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.42
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.31
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		0.040.050.59
      NEAA
      Expressed as micrograms per milliliter.
      		104.8103.1105.6102.53.50.890.25
      EAA
      Expressed as micrograms per milliliter.
      		87.0
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		86.9
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		85.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		99.8
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		3.40.010.32
      Total AA
      Expressed as micrograms per milliliter.
      		204.1
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		211.6
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		207.0
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		230.7
      Within a row, values with different superscripts differ significantly (P < 0.05).
      		6.10.010.27
      a,b Within a row, values with different superscripts differ significantly (P < 0.05).
      1 Base = balanced for ME (assuming 45 kg of ECM), but limited in MP, methionine and rumen N; Base + M = balanced for ME and methionine but limited in MP and rumen N; Base + MU = balanced for ME, methionine, with adequate rumen N, but limited in MP; Positive = balanced for ME, MP, all EAA, and adequate rumen N.
      2 Expressed as micrograms per milliliter.

      Model Predictions

      The model predictions in Supplemental Table S2 are raw means. The cattle consumed approximately 63 Mcal of ME/d for each of the diets, which provided enough energy to support 42.1 to 46.1 kg of milk/d, close to the target of 45 kg of ECM/d. Predicted MP supply ranged from 2,323 to 2,784 g/d for cows fed the Base and Positive diets, respectively. Cows fed the Base, Base + M, and Base + MU diets were predicted to have a negative MP balance, whereas cows fed the Positive diet consumed 33 g MP/d excess to requirements. Model-predicted rumen NH3 concentration (mg/dL) ranged from 5.1 in the Base and Base + M diets to 7.8 and 7.5 in the Base + MU and Positive diets (Supplemental Table S2). Under the dietary conditions described in the rumen submodel of the CNCPS, bacterial growth was depressed 16 and 17% due to inadequate ruminal N supply for the Base and Base + M diets, respectively. When considering predicted Lys and Met balance in g/d, Lys was predicted to be negative for all diets, whereas Met was negative for the Base and Base + M diets (−15.6 g and −6.9 g, respectively), but close to requirement for the Base + MU and Positive diets. The apparent efficiency of MP use varied (72 to 83%), but was close to the optimum efficiency (73%) as calculated in
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      and
      • Higgs R.J.
      • Van Amburgh M.E.
      Evolution of the CNCPS-Development of V7.
      in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
      https://hdl.handle.net/1813/44752
      in cows fed the Positive diet.

      DISCUSSION

      The goal of this study was to apply a newly developed approach to formulate diets for dairy cows to test the concept of balancing essential AA to an ideal profile relative to ME supply and evaluate milk yield and composition (
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      ;
      • Higgs R.J.
      • Van Amburgh M.E.
      Evolution of the CNCPS-Development of V7.
      in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
      https://hdl.handle.net/1813/44752
      ).
      When AA balance has been altered in research and field settings, a variety of responses have been observed. In the study of
      • Chen Z.H.
      • Broderick G.A.
      • Luchini N.D.
      • Sloan B.K.
      • Devillard E.
      Effect of feeding different sources of rumen-protected methionine on milk production and N-utilization in lactating dairy cows.
      J. Dairy Sci. 2011; 94 (21426989): 1978-1988
      https://doi.org/10.3168/jds.2010-3578
      , an increase in ECM was observed when supplemental Met was provided, but no difference in milk yield was detected. In contrast, (
      • Lee C.
      • Hristov A.N.
      • Cassidy T.W.
      • Heyler K.S.
      • Lapierre H.
      • Varga G.A.
      • de Veth M.J.
      • Patton R.A.
      • Parys C.
      Rumen-protected lysine, methionine, and histidine increase milk protein yield in dairy cows fed a metabolizable protein-deficient diet.
      J. Dairy Sci. 2012; 95 (22863104): 6042-6056
      https://doi.org/10.3168/jds.2012-5581
      ) observed increased milk yield when cows were supplemented with Met and Lys, or Met, Lys, and His, but milk components were similar among diets. Other studies have reported changes in both components and yield (
      • Noftsger S.
      • St-Pierre N.R.
      Supplementation of methionine and selection of highly digestible rumen undegradable protein to improve nitrogen efficiency for milk production.
      J. Dairy Sci. 2003; 86 (12703633): 958-969
      https://doi.org/10.3168/jds.S0022-0302(03)73679-0
      ;
      • Appuhamy J.A.
      • Knapp J.R.
      • Becvar O.
      • Escobar J.
      • Hanigan M.D.
      Effects of jugular-infused lysine, methionine, and branched-chain amino acids on milk protein synthesis in high-producing dairy cows.
      J. Dairy Sci. 2011; 94 (21426986): 1952-1960
      https://doi.org/10.3168/jds.2010-3442
      ;
      • Haque M.N.
      • Rulquin H.
      • Andrade A.
      • Faverdin P.
      • Peyraud J.L.
      • Lemosquet S.
      Milk protein synthesis in response to the provision of an “ideal” amino acid profile at 2 levels of metabolizable protein supply in dairy cows.
      J. Dairy Sci. 2012; 95 (22884342): 5876-5887
      https://doi.org/10.3168/jds.2011-5230
      ).
      • Mepham T.B.
      Amino-acid utilization by lactating mammary-gland.
      J. Dairy Sci. 1982; 65 (7042785): 287-298
      https://doi.org/10.3168/jds.S0022-0302(82)82191-7
      classified EAA in the following 2 groups based on different patterns of mammary utilization: for group 1 AA (Met, Phe, Tyr, and Trp), there was apparent stoichiometric transfer to milk protein, whereas for group 2 AA (Ile, Lys, Leu, and Val), there was an excess picked up for milk protein secretion. Different types of responses (yield or components) have been observed among group 1 and 2 AA which can, in-part, be explained by the different ways in which the AA are metabolized (
      • Lapierre H.
      • Lobley G.E.
      • Doepel L.
      • Raggio G.
      • Rulquin H.
      • Lemosquet S.
      Triennial lactation symposium: Mammary metabolism of amino acids in dairy cows.
      J. Anim. Sci. 2012; 90 (22573843): 1708-1721
      https://doi.org/10.2527/jas.2011-4645
      ). The group 2 AA, taken up in excess, can elicit a milk yield response with the excess carbon used to generate ATP, NEAA, and lactose (
      • Maxin G.
      • Ouellet D.
      • Lapierre H.
      Contribution of amino acids to glucose and lactose synthesis in lactating dairy cows.
      in: Energy and Protein Metabolism and Nutrition in Sustainable Animal Production. Springer, 2013: 443-444
      ), whereas the uptake of group 1 AA reflects the output in milk protein and additional uptake is directly linked to an increase in milk protein yield, which can occur independently to an increase in the uptake of group 2 AA (
      • Lemosquet S.
      • Guinard-Flament J.
      • Raggio G.
      • Hurtaud C.
      • Van Milgen J.
      • Lapierre H.
      How does increasing protein supply or glucogenic nutrients modify mammary metabolism in lactating dairy cows?.
      in: Proc. Energy and Protein Metabolism and Nutrition. Wageningen Academic Publishers, 2010: 175-186
      ). Cows in the current study produced similar milk yield among diets but milk components increased when the dietary AA were closer to the ideal balance (Table 4), resulting in higher ECM in cows fed the Positive diet (P < 0.01). Supplemental Met was the only difference in AA supply between the Base and Base + M diets, which, according to the plasma Met concentration, had been delivered (Table 6). Cows did not respond to the increased Met supply as observed in other studies (
      • Noftsger S.
      • St-Pierre N.R.
      Supplementation of methionine and selection of highly digestible rumen undegradable protein to improve nitrogen efficiency for milk production.
      J. Dairy Sci. 2003; 86 (12703633): 958-969
      https://doi.org/10.3168/jds.S0022-0302(03)73679-0
      ;
      • Chen Z.H.
      • Broderick G.A.
      • Luchini N.D.
      • Sloan B.K.
      • Devillard E.
      Effect of feeding different sources of rumen-protected methionine on milk production and N-utilization in lactating dairy cows.
      J. Dairy Sci. 2011; 94 (21426989): 1978-1988
      https://doi.org/10.3168/jds.2010-3578
      ), which might have been due to a limitation of other EAA (Table 6). The concentration of EAA and total AA in plasma were not changed with the addition of urea (Base + MU), despite a predicted increase in microbial protein supply, although differences in some AA were observed (Gln, Ser, Arg). The concentration of arterial EAA has been shown to decrease when urea is given to cows fed diets adequate in rumen N, possibly due to increased hepatic catabolism to provide an N atom for the synthesis of urea (
      • Lapierre H.
      • Ouellet D.R.
      • Berthiaume R.
      • Girard C.
      • Dubreuil P.
      • Babkine M.
      • Lobley G.E.
      Effect of urea supplementation on urea kinetics and splanchnic flux of amino acids in dairy cows.
      J. Anim. Feed Sci. 2004; 13: 319-322
      https://doi.org/10.22358/jafs/73927/2004
      ). It is possible that AA supply in the Base + MU diet was offset by an increase in hepatic removal to provide N for the urea cycle resulting in no true increase in AA supply (
      • Reynolds C.K.
      Metabolism of nitrogenous compounds by ruminant liver.
      J. Nutr. 1992; 122 (1542057): 850-854
      https://doi.org/10.1093/jn/122.suppl_3.850
      ). Cows fed the Positive diet had increased concentrations of EAA and total AA in plasma (P < 0.01), which corresponded with an increase in predicted supply of both group 1 and 2 AA (Table 6).
      The low dietary CP concentration in the Base and Base + M diets (∼13.5% CP) resulted in low PUN (5.7–5.9 mg/dL) and, given the capacity for urea N recycling in lactating dairy cattle, would possibly result in lower rumen ammonia (
      • Recktenwald E.B.
      • Ross D.A.
      • Fessenden S.W.
      • Wall C.J.
      • Van Amburgh M.E.
      Urea-N recycling in lactating dairy cows fed diets with 2 different levels of dietary crude protein and starch with or without monensin.
      J. Dairy Sci. 2014; 97 (24377801): 1611-1622
      https://doi.org/10.3168/jds.2013-7162
      ), which could explain the reduction in apparent total-tract NDFD (Table 5). Similar effects have been observed in other studies that fed comparable levels of CP (
      • Colmenero J.J.O.
      • Broderick G.A.
      Effect of dietary crude protein concentration on ruminal nitrogen metabolism in lactating dairy cows.
      J. Dairy Sci. 2006; 89 (16606740): 1694-1703
      https://doi.org/10.3168/jds.S0022-0302(06)72237-8
      ;
      • Lee C.
      • Hristov A.N.
      • Heyler K.S.
      • Cassidy T.W.
      • Lapierre H.
      • Varga G.A.
      • Parys C.
      Effects of metabolizable protein supply and amino acid supplementation on nitrogen utilization, milk production, and ammonia emissions from manure in dairy cows.
      J. Dairy Sci. 2012; 95 (22916930): 5253-5268
      https://doi.org/10.3168/jds.2012-5366
      ). One of the goals of this study was to use new strategies to more precisely predict AA supply, which included using a new assay to estimate the unavailable N fraction of feeds
      • Gutierrez-Botero M.
      • Ross D.A.
      • Van Amburgh M.E.
      Formulating diets for intestinal unavailable nitrogen using blood meal in high-producing dairy cattle.
      J. Dairy Sci. 2022; 105 (35570044): 5738-5746
      https://doi.org/10.3168/jds.2021-21730
      . In addition to the supplemental Met and Lys, feed sources of AA were selected according to the assay described in
      • Gutierrez-Botero M.
      • Ross D.A.
      • Van Amburgh M.E.
      Formulating diets for intestinal unavailable nitrogen using blood meal in high-producing dairy cattle.
      J. Dairy Sci. 2022; 105 (35570044): 5738-5746
      https://doi.org/10.3168/jds.2021-21730
      that had low levels of undegraded N and high model-predicted rumen N escape (Table 2).
      • Lee C.
      • Hristov A.N.
      • Heyler K.S.
      • Cassidy T.W.
      • Lapierre H.
      • Varga G.A.
      • Parys C.
      Effects of metabolizable protein supply and amino acid supplementation on nitrogen utilization, milk production, and ammonia emissions from manure in dairy cows.
      J. Dairy Sci. 2012; 95 (22916930): 5253-5268
      https://doi.org/10.3168/jds.2012-5366
      suggested the depression in DMI they observed was due to a limitation in AA supply, not rumen N. Despite being low in CP, the Base and Base + M diets were not predicted to be severely limited in AA supply (Table 6) but were limited in rumen N, which led to an intake response when additional rumen N was provided. Nitrogen utilization of cows fed the Base + M diet was 38%, which is higher than typically observed, particularly in mid-lactation cows at high production (
      • Huhtanen P.
      • Hristov A.N.
      A meta-analysis of the effects of dietary protein concentration and degradability on milk protein yield and milk N efficiency in dairy cows.
      J. Dairy Sci. 2009; 92 (19528599): 3222-3232
      https://doi.org/10.3168/jds.2008-1352
      ).
      Predicted bacterial growth depression due to low rumen N in the Base and Base + M diets corresponded with the reduction in observed total-tract NDFD (Table 5). The model also predicted rumen N supply in the Base + MU and Positive diets was adequate, and no further response would be expected if additional dietary N was supplied.
      • Colmenero J.J.O.
      • Broderick G.A.
      Effect of dietary crude protein concentration on ruminal nitrogen metabolism in lactating dairy cows.
      J. Dairy Sci. 2006; 89 (16606740): 1694-1703
      https://doi.org/10.3168/jds.S0022-0302(06)72237-8
      measured an increase in NDFD when dietary CP was increased from 13.5 to 15% DM, similar to the current study.
      The overall milk yield and ECM were less than the predicted ME allowable yield (Supplemental Table S2) and this was expected as the diets were formulated to be AA and N limited except for the Positive diet; thus, the objectives of the formulation study could not have been met if ME allowable milk was not in excess of MP or AA allowable milk. Cows were able to yield more milk than the model-predicted MP supply would support when fed the Base, Base + M, and Base + MU diets. The efficiency factor used to estimate total MP requirements differs among models and depends on the requirements for MP accounted for by the model. Previous versions of the CNCPS have used 0.67 (
      • 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.
      Anim. Feed Sci. Technol. 2004; 112: 29-78
      https://doi.org/10.1016/j.anifeedsci.2003.10.006
      ), which is the same as the
      • NRC
      Nutrient Requirements of Dairy Cattle.
      The National Academies Press, 2001
      https://doi.org/10.17226/9825
      . The current model uses a factor 0.73, which was calculated in
      • Higgs R.J.
      Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
      Department of Animal Science, Cornell University, Ithaca, NY2014
      and is higher than previous version of the CNCPS due to an increase in the maintenance requirements accounted for by the model through the inclusion of endogenous losses along the entire gastrointestinal tract. Despite this, the apparent efficiency of MP use by cows in the current study ranged from 0.72 to 0.82 for the Positive and Base + M diets, respectively. This might be due to inaccurate predictions of MP supply, although the efficiency of MP use varies depending on the MP supply relative to other nutrients (
      • Metcalf J.A.
      • Mansbridge R.J.
      • Blake J.S.
      • Oldham J.D.
      • Newbold J.R.
      The efficiency of conversion of metabolisable protein into milk true protein over a range of metabolisable protein intakes.
      Animal. 2008; 2 (22443732): 1193-1202
      https://doi.org/10.1017/S1751731108002140
      ).
      • Metcalf J.A.
      • Mansbridge R.J.
      • Blake J.S.
      • Oldham J.D.
      • Newbold J.R.
      The efficiency of conversion of metabolisable protein into milk true protein over a range of metabolisable protein intakes.
      Animal. 2008; 2 (22443732): 1193-1202
      https://doi.org/10.1017/S1751731108002140
      pointed out that diet formulation models are not typically designed to be response models, rather they are designed to predict nutrient requirements at an optimum level. Therefore, although cows were able to use MP with a predicted efficiency of 0.82 when fed the Base + M diet, it is possible that performance would have improved if they were closer to the model-predicted requirement using an efficiency factor of 0.73 consistent with the Positive diet.

      CONCLUSIONS

      With consideration to the current study outcomes, improvements in milk and milk component yields, ECM, and fiber degradation are observed when diets are formulated and fed to meet rumen N requirements and further improved when balancing EAA supply as a function of ME. Additionally, this study demonstrated that the application of the updated version of the CNCPS is sensitive in predicting changes in rumen N, MP, and EAA supply, providing a way for users to improve EAA balancing in their diets and reduce excessive levels of N compared with previous versions of the model.

      ACKNOWLEDGMENTS

      This research was funded in partnership by Adisseo (Commentry, France) and Perdue Agribusiness (Salisbury, MD). Thanks to Dennis Stucker (Liverpool, NY) for their support of this work. The help of Bruce Berggren-Thomas (Cortland, NY), Andreas Foskolos (Larisa, Greece), Debbie Ross (Aurora, NY), the staff at the Cornell Teaching and Research Facility (Ithaca, NY), and the many other people that assisted in this experiment is gratefully acknowledged. The authors have not stated any conflicts of interest.

      REFERENCES

        • Appuhamy J.A.
        • Knapp J.R.
        • Becvar O.
        • Escobar J.
        • Hanigan M.D.
        Effects of jugular-infused lysine, methionine, and branched-chain amino acids on milk protein synthesis in high-producing dairy cows.
        J. Dairy Sci. 2011; 94 (21426986): 1952-1960
        https://doi.org/10.3168/jds.2010-3442
        View in Article
        • Armentano L.E.
        • Bertics S.J.
        • Ducharme G.A.
        Response of lactating cows to methionine or methionine plus lysine added to high protein diets based on alfalfa and heated soybeans.
        J. Dairy Sci. 1997; 80 (9201591): 1194-1199
        https://doi.org/10.3168/jds.S0022-0302(97)76047-8
        View in Article
        • Chen Z.H.
        • Broderick G.A.
        • Luchini N.D.
        • Sloan B.K.
        • Devillard E.
        Effect of feeding different sources of rumen-protected methionine on milk production and N-utilization in lactating dairy cows.
        J. Dairy Sci. 2011; 94 (21426989): 1978-1988
        https://doi.org/10.3168/jds.2010-3578
        View in Article
        • Colmenero J.J.O.
        • Broderick G.A.
        Effect of dietary crude protein concentration on ruminal nitrogen metabolism in lactating dairy cows.
        J. Dairy Sci. 2006; 89 (16606740): 1694-1703
        https://doi.org/10.3168/jds.S0022-0302(06)72237-8
        View in Article
        • 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.
        Anim. Feed Sci. Technol. 2004; 112: 29-78
        https://doi.org/10.1016/j.anifeedsci.2003.10.006
        View in Article
        • Gehrke C.W.
        • Wall Sr., L.
        • Absheer J.
        • Kaiser F.
        • Zumwalt R.
        Sample preparation for chromatography of amino acids: Acid hydrolysis of proteins.
        J. Assoc. Off. Anal. Chem. 1985; 68: 811-821
        https://doi.org/10.1093/jaoac/68.5.811
        View in Article
        • Gutierrez-Botero M.
        • Ross D.A.
        • Van Amburgh M.E.
        Formulating diets for intestinal unavailable nitrogen using blood meal in high-producing dairy cattle.
        J. Dairy Sci. 2022; 105 (35570044): 5738-5746
        https://doi.org/10.3168/jds.2021-21730
        View in Article
        • Haque M.N.
        • Rulquin H.
        • Lemosquet S.
        Milk protein responses in dairy cows to changes in postruminal supplies of arginine, isoleucine, and valine.
        J. Dairy Sci. 2013; 96 (23164225): 420-430
        https://doi.org/10.3168/jds.2012-5610
        View in Article
        • Haque M.N.
        • Rulquin H.
        • Andrade A.
        • Faverdin P.
        • Peyraud J.L.
        • Lemosquet S.
        Milk protein synthesis in response to the provision of an “ideal” amino acid profile at 2 levels of metabolizable protein supply in dairy cows.
        J. Dairy Sci. 2012; 95 (22884342): 5876-5887
        https://doi.org/10.3168/jds.2011-5230
        View in Article
        • Higgs R.J.
        Development of a dynamic rumen and gastro-intestinal model in the Cornell Net Carbohydrate and Protein System to predict the nutrient supply and requirements of dairy cattle.
        (PhD Diss.) Department of Animal Science, Cornell University, Ithaca, NY2014
        View in Article
        • Higgs R.J.
        • Van Amburgh M.E.
        Evolution of the CNCPS-Development of V7.
        in: Proc. Cornell Nutrition Conference. Cornell University, Syracuse, NY2016: 125-146
        https://hdl.handle.net/1813/44752
        View in Article
        • Huhtanen P.
        • Hristov A.N.
        A meta-analysis of the effects of dietary protein concentration and degradability on milk protein yield and milk N efficiency in dairy cows.
        J. Dairy Sci. 2009; 92 (19528599): 3222-3232
        https://doi.org/10.3168/jds.2008-1352
        View in Article
        • Huhtanen P.
        • Kaustell K.
        • Jaakkola S.
        The use of internal markers to predict total digestibility and duodenal flow of nutrients in cattle given six different diets.
        Anim. Feed Sci. Technol. 1994; 48: 211-227
        https://doi.org/10.1016/0377-8401(94)90173-2
        View in Article
        • Landry J.
        • Delhaye S.
        Simplified procedure for the determination of tryptophan of foods and feedstuffs from barytic hydrolysis.
        J. Agric. Food Chem. 1992; 40: 776-779
        https://doi.org/10.1021/jf00017a014
        View in Article
        • Lapierre H.
        • Lobley G.E.
        • Doepel L.
        • Raggio G.
        • Rulquin H.
        • Lemosquet S.
        Triennial lactation symposium: Mammary metabolism of amino acids in dairy cows.
        J. Anim. Sci. 2012; 90 (22573843): 1708-1721
        https://doi.org/10.2527/jas.2011-4645
        View in Article
        • Lapierre H.
        • Ouellet D.R.
        • Berthiaume R.
        • Girard C.
        • Dubreuil P.
        • Babkine M.
        • Lobley G.E.
        Effect of urea supplementation on urea kinetics and splanchnic flux of amino acids in dairy cows.
        J. Anim. Feed Sci. 2004; 13: 319-322
        https://doi.org/10.22358/jafs/73927/2004
        View in Article
        • Lapierre H.
        • Pacheco D.
        • Berthiaume R.
        • Ouellet D.R.
        • Schwab C.G.
        • Dubreuil P.
        • Holtrop G.
        • Lobley G.E.
        What is the true supply of amino acids for a dairy cow?.
        J. Dairy Sci. 2006; 89 (16527873): E1-E14
        https://doi.org/10.3168/jds.S0022-0302(06)72359-1
        View in Article
        • Lee C.
        • Hristov A.N.
        • Cassidy T.W.
        • Heyler K.S.
        • Lapierre H.
        • Varga G.A.
        • de Veth M.J.
        • Patton R.A.
        • Parys C.
        Rumen-protected lysine, methionine, and histidine increase milk protein yield in dairy cows fed a metabolizable protein-deficient diet.
        J. Dairy Sci. 2012; 95 (22863104): 6042-6056
        https://doi.org/10.3168/jds.2012-5581
        View in Article
        • Lee C.
        • Hristov A.N.
        • Heyler K.S.
        • Cassidy T.W.
        • Lapierre H.
        • Varga G.A.
        • Parys C.
        Effects of metabolizable protein supply and amino acid supplementation on nitrogen utilization, milk production, and ammonia emissions from manure in dairy cows.
        J. Dairy Sci. 2012; 95 (22916930): 5253-5268
        https://doi.org/10.3168/jds.2012-5366
        View in Article
        • Lemosquet S.
        • Guinard-Flament J.
        • Raggio G.
        • Hurtaud C.
        • Van Milgen J.
        • Lapierre H.
        How does increasing protein supply or glucogenic nutrients modify mammary metabolism in lactating dairy cows?.
        in: Proc. Energy and Protein Metabolism and Nutrition. Wageningen Academic Publishers, 2010: 175-186
        View in Article
        • Lobley G.E.
        Protein-energy interactions: Horizontal aspects.
        in: Proc. Energy and Protein Metabolism and Nutrition. Wageningen Academic Publishers, 2007: 445-462
        View in Article
        • Maxin G.
        • Ouellet D.
        • Lapierre H.
        Contribution of amino acids to glucose and lactose synthesis in lactating dairy cows.
        in: Energy and Protein Metabolism and Nutrition in Sustainable Animal Production. Springer, 2013: 443-444
        View in Article
        • Mepham T.B.
        Amino-acid utilization by lactating mammary-gland.
        J. Dairy Sci. 1982; 65 (7042785): 287-298
        https://doi.org/10.3168/jds.S0022-0302(82)82191-7
        View in Article
        • Metcalf J.A.
        • Mansbridge R.J.
        • Blake J.S.
        • Oldham J.D.
        • Newbold J.R.
        The efficiency of conversion of metabolisable protein into milk true protein over a range of metabolisable protein intakes.
        Animal. 2008; 2 (22443732): 1193-1202
        https://doi.org/10.1017/S1751731108002140
        View in Article
        • Noftsger S.
        • St-Pierre N.R.
        Supplementation of methionine and selection of highly digestible rumen undegradable protein to improve nitrogen efficiency for milk production.
        J. Dairy Sci. 2003; 86 (12703633): 958-969
        https://doi.org/10.3168/jds.S0022-0302(03)73679-0
        View in Article
        • NRC
        Nutrient Requirements of Dairy Cattle.
        The National Academies Press, 2001
        https://doi.org/10.17226/9825
        View in Article
        • Pacheco D.
        • Patton R.A.
        • Parys C.
        • Lapierre H.
        Ability of commercially available dairy ration programs to predict duodenal flows of protein and essential amino acids in dairy cows.
        J. Dairy Sci. 2012; 95 (22281359): 937-963
        https://doi.org/10.3168/jds.2011-4171
        View in Article
        • Recktenwald E.B.
        • Ross D.A.
        • Fessenden S.W.
        • Wall C.J.
        • Van Amburgh M.E.
        Urea-N recycling in lactating dairy cows fed diets with 2 different levels of dietary crude protein and starch with or without monensin.
        J. Dairy Sci. 2014; 97 (24377801): 1611-1622
        https://doi.org/10.3168/jds.2013-7162
        View in Article
        • Reynolds C.K.
        Metabolism of nitrogenous compounds by ruminant liver.
        J. Nutr. 1992; 122 (1542057): 850-854
        https://doi.org/10.1093/jn/122.suppl_3.850
        View in Article
        • Rulquin H.
        • Pisulewski P.M.
        • Vérité R.
        • Guinard J.
        Milk production and composition as a function of postruminal lysine and methionine supply: A nutrient-response approach.
        Livest. Prod. Sci. 1993; 37: 69-90
        https://doi.org/10.1016/0301-6226(93)90065-P
        View in Article
        • Schwab C.G.
        Rumen-protected amino acids for dairy cattle: Progress towards determining lysine and methionine requirements.
        Anim. Feed Sci. Technol. 1996; 59: 87-101
        https://doi.org/10.1016/0377-8401(95)00890-X
        View in Article
        • Tyrrell H.F.
        • Reid J.T.
        Prediction of the energy value of cow’s milk.
        J. Dairy Sci. 1965; 48: 1215-1223
        https://doi.org/10.3168/jds.S0022-0302(65)88430-2
        View in Article
        • 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.
        J. Dairy Sci. 2015; 98 (26142847): 6361-6380
        https://doi.org/10.3168/jds.2015-9378
        View in Article
        • Wildman E.E.
        • Jones G.M.
        • Wagner P.E.
        • Boman R.L.
        • Troutt Jr., H.F.
        • Lesch T.N.
        A dairy cow body condition scoring system and its relationship to selected production characteristics.
        J. Dairy Sci. 1982; 65: 495-501
        https://doi.org/10.3168/jds.S0022-0302(82)82223-6
        View in Article

      Article info

      Publication history

      Published online: January 27, 2023
      Accepted: October 17, 2022
      Received: February 26, 2022

      Identification

      DOI: https://doi.org/10.3168/jds.2022-22019

      Copyright

      © 2023 The Author(s).

      User license

      Creative Commons Attribution (CC BY 4.0) |
      How you can reuse Information Icon
      Creative Commons Attribution (CC BY 4.0)

      Permitted

      • Read, print & download
      • Redistribute or republish the final article
      • Text & data mine
      • Translate the article
      • Reuse portions or extracts from the article in other works
      • Sell or re-use for commercial purposes

      Elsevier's open access license policy

      ScienceDirect

      Access this article on ScienceDirect
      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties. The content on this site is intended for healthcare professionals.


      RELX