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Research| Volume 104, ISSUE 9, P9981-9998, September 2021

Economic and environmental effects of revised metabolizable protein and amino acid recommendations on Canadian dairy farms

Open ArchivePublished:June 04, 2021DOI:https://doi.org/10.3168/jds.2020-19893
Economic and environmental effects of revised metabolizable protein and amino acid recommendations on Canadian dairy farms
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      ABSTRACT

      The objective of this research was to evaluate the potential economic and environmental effects of the formulation model used to balance dairy rations for metabolizable protein (MP) or 3 essential AA (EAA: His, Lys, and Met) in 3 regions of Canada with different farming systems. The Maritimes, Central Canada, and the Prairies reference dairy farms averaged 63, 71, 144 mature cows per herd and 135, 95, 255 ha of land, respectively. Using N-CyCLES, a whole-farm linear program model, dairy rations were balanced for (1) MP, based on National Research Council (NRC) requirements (MP_2001); (2) MP plus Lys and Met, based on NRC (AA_2001); (3) MP (MP_Rev); or (4) for His, Lys, and Met (AA_Rev), both based on a revised factorial approach revisiting both supply and requirements of MP and EAA. Energy was balanced to meet requirements based on NRC (2001). Assuming the requirements were met within each approach, it was considered that milk yield and composition were not affected by the type of formulation. Given the assumptions of the study, when compared with MP_2001 formulation, balancing dairy rations using the AA_Rev approach reduced calculated farm N balance by 3.8%, on average from 12.71 to 12.24 g/kg of fat- and protein-corrected milk; it also enhanced farm net income by 4.5%, from 19.00 to 19.70 $CAN/100 kg of fat- and protein-corrected milk, by reducing inclusion of protein concentrate in dairy rations. Calculated animal N efficiency was on average 4.3% higher with AA_Rev than with MP_2001 for mid-lactation cows. This gain in N efficiency would result in a reduction in N2O emission by manure, contributing to a partial decrease of total greenhouse gas emission by 1.7%, through a reduction of N excreted in manure. With the AA_2001 formulation, farm N balance was 1% higher than with MP_2001 formulation while reducing farm net income by 6.4%, due to the need to purchase rumen-protected AA, with no effect on total greenhouse gas emission. Both MP formulations lead to fairly similar outputs. The AA_Rev formulation also indicated that His might be a co-limiting AA with Met in dairy rations balanced with ingredients usually included in Canadian dairy rations. Given the assumptions of the study, balancing dairy rations for 3 EAA (His, Lys, and Met) rather than MP, has some potential positive effects on Canadian dairy farms by increasing net incomes through a reduction of crude protein supply, leading to a decreased environmental effect.

      Key words

      INTRODUCTION

      Amino acid supply is crucial to support protein synthesis. However, any excess represents an unnecessary cost of dairy rations and, additionally, has negative effects on the environment, due to its associated N content. Indeed, manure N may be volatilized in ammonia causing smog and water acidification, in nitrous oxide a strong greenhouse gas (GHG), or may run off as nitrite or nitrate contributing to water pollution (
      • NRC (National Research Council)
      Air Emissions from Animal Feeding Operations: Current Knowledge, Future Needs.
      National Academies Press, 2003
      ). Protein content, degradability, and digestibility have significant effects on N excretion, influencing types of excretion (i.e., fecal vs. urinary, and the quantity excreted;
      • Castillo A.R.
      • Kebreab E.
      • Beever D.E.
      • Barbi J.H.
      • Sutton J.D.
      • Kirby H.C.
      • France J.
      The effect of energy supplementation on nitrogen utilization in lactating dairy cows fed grass silage diets.
      J. Anim. Sci. 2001; 79 (11204706): 240-246
      https://doi.org/10.2527/2001.791240x
      ;
      • Agle M.
      • Hristov A.N.
      • Zaman S.
      • Schneider C.
      • Ndegwa P.
      • Vaddella V.K.
      The effects of ruminally degraded protein on rumen fermentation and ammonia losses from manure in dairy cows.
      J. Dairy Sci. 2010; 93 (20338440): 1625-1637
      https://doi.org/10.3168/jds.2009-2579
      ). Whole-farm N balance can be used as an indicator of on-farm N excess and nutrient efficiency: a high value indicates that N importations are higher than exportations plus N recycled within the farm, which can lead to increased volatilization losses and leaching (
      • Cela S.
      • Ketterings Q.M.
      • Czymmek K.
      • Soberon M.
      • Rasmussen C.
      Characterization of nitrogen, phosphorus, and potassium mass balances of dairy farms in New York State.
      J. Dairy Sci. 2014; 97 (25468709): 7614-7632
      https://doi.org/10.3168/jds.2014-8467
      ;
      • Pellerin D.
      • Charbonneau E.
      • Fadul-Pacheco L.
      • Soucy O.
      • Wattiaux M.A.
      Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
      J. Dairy Sci. 2017; 100 (28780106): 8614-8629
      https://doi.org/10.3168/jds.2016-11984
      ).
      Improving precision feeding through a better match between supply and requirement of MP and AA may provide an opportunity to decrease farm N balance. Updates of predictions of protein and AA supplies have been recently proposed. First, the true net supply of MP and AA should be the sum of digested RUP and microbial protein (MCP) and should not include a contribution from duodenal endogenous CP flow (
      • 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 requirements.
      in: Cornell Nutrition Conference for Feed Manufacturers. Cornell University, 2016: 205-218
      ). For MCP,
      • White R.R.
      • Roman-Garcia Y.
      • Firkins J.L.
      Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction.
      J. Dairy Sci. 2016; 99 (27448854): 7932-7944
      https://doi.org/10.3168/jds.2015-10662
      proposed estimations of rumen outflow based on feed composition, whereas
      • Sok M.
      • Ouellet D.R.
      • Firkins J.L.
      • Pellerin D.
      • Lapierre H.
      Amino acid composition of rumen bacteria and protozoa in cattle.
      J. Dairy Sci. 2017; 100 (28501407): 5241-5249
      https://doi.org/10.3168/jds.2016-12447
      proposed an AA profile from a deep literature review. Correction factors to acknowledge for incomplete recovery of AA with 24-h protein hydrolysis have also been proposed (
      • Lapierre H.
      • Binggeli S.
      • Sok M.
      • Pellerin D.
      • Ouellet D.R.
      Estimation of correction factors to determine the true amino acid concentration of protein after a 24-hour hydrolysis.
      J. Dairy Sci. 2019; 102 (30591335): 1205-1212
      https://doi.org/10.3168/jds.2018-15392
      ). Second, on the requirement aspect,
      • 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 requirements.
      in: Cornell Nutrition Conference for Feed Manufacturers. Cornell University, 2016: 205-218
      published a review on the main export proteins in dairy cows, their AA composition, and the efficiency of utilization of MP and EAA. They presented averaged combined efficiencies of utilization of MP and individual EAA that could be used as a basis for recommendations for milk protein yield, metabolic fecal protein, scurf and urinary endogenous losses. Pregnancy requirements and heifer growth requirements for MP have also been reviewed in Nutrient Requirements of Beef Cattle from National Academies of Sciences, Engineering and Medicine (
      • NASEM (National Academies of Sciences Engineering and Medicine)
      Nutrient Requirements of Beef Cattle.
      8th rev. ed. National Academies Press, 2016
      ). The accumulated knowledge on protein and AA metabolism and nutrition cited above, combined with a variable efficiency of utilization, should have progressed enough to use a factorial approach to determine EAA requirements rather than the proportional approach proposed by
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      . Indeed,
      • Fadul-Pacheco L.
      • Pellerin D.
      • Chouinard P.Y.
      • Wattiaux M.A.
      • Duplessis M.
      • Charbonneau É.
      Nitrogen efficiency of eastern Canadian dairy herds: Effect on production performance and farm profitability.
      J. Dairy Sci. 2017; 100 (28601444): 6592-6601
      https://doi.org/10.3168/jds.2016-11788
      showed that in commercial dairy farms, high N efficient herds had a negative calculated MP balance, but did not have lower milk production than herds with a lower efficiency and positive MP balance: this could indicate the limitations of a fixed efficiency of utilization of MP, currently used in most of the North American dairy ration formulations.
      The application of the updated estimations of supply and requirement mentioned above may have significant effects on dairy farm economics and nutrient balances. Indeed, an AA factorial formulation for dairy rations with a variable efficiency of utilization could reduce the amount of CP fed to the cows but still meet EAA requirements. Consequently, this would reduce both feeding costs and N excretion, i.e., pollution on farm, while maintaining cows' performance. Although milk production in Canada is under supply management, and milk prices are relatively stable, feed costs and feed availability vary widely across regions. This is due to geographical status, including climate, soil structure, and fertility, as well as overall farm management. The global production context could alter the effects of the update of formulation models.
      Therefore, the main objective of this research was to evaluate the potential economic and environmental effects of an update of MP and AA supply and recommendations on Canadian dairy farms using a modeling and simulation approach. We hypothesized that moving from balancing dairy rations for MP including or not a proportional recommendation for Lys and Met (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ) to an updated factorial approach balancing for MP or His, Lys, and Met can improve the net income and reduce the environmental effects of dairy farms; the effects of this update, however, might differ depending on the reference farm situation.

      MATERIALS AND METHODS

      Simulation Model Used

      Farm simulations were conducted using N-CyCLES (Nutrient cycling: crops, livestock, environment, and soil), as described by
      • Pellerin D.
      • Charbonneau E.
      • Fadul-Pacheco L.
      • Soucy O.
      • Wattiaux M.A.
      Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
      J. Dairy Sci. 2017; 100 (28780106): 8614-8629
      https://doi.org/10.3168/jds.2016-11984
      . The N-CyCLES model is a whole-dairy-farm linear program model based on National Research Council nutritional requirements (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ;
      • Pellerin D.
      • Wattiaux M.A.
      • Charbonneau E.
      • Soucy O.
      • Moreira V.
      • Flis S.A.
      Introduction to N-CyCLES (Nutrient Cycling: Crops, Livestock, Environment and Soil).
      https://kb.wisc.edu/dairynutrient/60349
      Date: 2017
      Date accessed: May 26, 2021
      ). It integrates TMR formulation for 5 groups, including 2 groups of lactating cows (early: ≤152 DIM, mid-late: >152 DIM), one group of dry cows, and 2 groups of growing heifers (0–11 mo, 12 mo to calving), as well as manure management, crop production, and fertilization. The optimization objective of this whole-farm model can be set either to maximize farm net income (FNI), or to minimize farm N or P balance. In the current study, FNI was maximized. The variables in the optimizer are feed ingredients of the TMR and rotations to produce crops. Simulations were done using Opensolver 2.7.1, because of its known stability and reliability during optimization (
      • Mason A.J.
      OpenSolver—An Open Source Add-in to Solve Linear and Integer Progammes in Excel.
      in: Operations Research Proceedings 2011. Springer, 2012: 401-406
      ). All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (
      • Bank of Canada
      Historical noon and closing rates.
      http://www.bankofcanada.ca/rates/exchange/legacy-noon-and-closing-rates/
      Date: 2017
      Date accessed: July 10, 2016
      ). For comparison purposes, most results are reported in relation to the production of fat- and protein-corrected milk (FPCM), according to
      • International Dairy Federation
      A common carbon footprint approach for the dairy sector: The IDF guide to standard life cycle assessment methodology.
      Bull. Int. Dairy Fed. 2015; : 63
      calculations as described below:
      FPCM = milk × (0.1226 × Fat + 0.0766 × TP + 0.2534),
      [1]


      where FPCM = fat- and protein-corrected milk, kg; milk = milk production, kg; Fat = fat content of milk, %; and TP = true protein content of milk, %.

      Updates for the Revised Recommendations

      To begin, N-CyCLES was adapted to include a life cycle process to add the effect of GHG values from imported farm input (i.e., fertilizer, feedstuff, pesticide). Then, N-CyCLES was modified to include 4 protein or AA formulation approaches. To estimate the requirements, the first approach solely fulfilled the MP recommendations of the National Research Council (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ; MP_2001). The second approach (AA_2001) added to MP_2001 a constraint to meet the Lys (6.95% of MP) and Met (2.38% of MP) recommendations from
      • Whitehouse N.L.
      • Schwab C.G.
      • Luchini D.
      • Sloan B.
      A critique of dose-response plots that relate changes in content and yield of milk protein to predicted concentrations of lysine in metabolizable protein by the NRC (2001), CPM-Dairy (v.3.0.10), and AMTS.Cattle (v.2.1.31) models.
      J. Dairy Sci. 2010; 93: 447
      . For the third approach (MP_Rev), MP requirements covering milk, metabolic fecal protein, scurf, and endogenous urinary losses were estimated according to
      • 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 requirements.
      in: Cornell Nutrition Conference for Feed Manufacturers. Cornell University, 2016: 205-218
      , assuming an efficiency of 0.67 for the 3 former protein exportations, and an efficiency of 1 for endogenous urinary losses. For growth and gestation, requirements were those of
      • NASEM (National Academies of Sciences Engineering and Medicine)
      Nutrient Requirements of Beef Cattle.
      8th rev. ed. National Academies Press, 2016
      , Nutrient Requirements of Beef Cattle. For the fourth approach (AA_Rev), first, His, Lys, and Met exportation and accretion were calculated as the product of true protein exportation or accretion as in MP_Rev and the AA profile of each type of protein (
      • 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 requirements.
      in: Cornell Nutrition Conference for Feed Manufacturers. Cornell University, 2016: 205-218
      ), which included a correction for incomplete recovery of AA from 24-h protein hydrolysis. Then, using the studies reported in Appendix Table A1, equations predicting efficiencies were developed based on the ratio of AA:energy supply, as suggested by
      • 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.
      Animal. 2020; 14 (32024565): s87-s102
      https://doi.org/10.1017/S1751731119003173
      , except that NEL was used instead of DE as the energy supply. These equations predicting efficiencies of utilization of His, Lys, and Met are reported in the Appendix. Then, for each exported EAA in each lactation group of each region, a recommended His, Lys, and Met supply was determined at the threshold supply where calculated efficiency (sum of EAA exported divided by EAA supply) was equal to the efficiency predicted by the equation. The energy supply meeting requirement (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ) was used for the prediction of the efficiencies. This approach had to be used for AA_Rev due to limitations of linear programming and the nonlinearity of efficiency. The average optimal efficiencies of utilization of the EAA were 0.83 for His, 0.73 for Lys, and 0.77 for Met.
      To estimate supplies for MP_2001 and AA_2001, predicted MP supply and EAA digestible flows from
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      were used. For the MP_Rev and AA_Rev, estimations of MCP duodenal flow for cows was adapted from
      • Roman-Garcia Y.
      • White R.R.
      • Firkins J.L.
      Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations.
      J. Dairy Sci. 2016; 99 (27448861): 7918-7931
      https://doi.org/10.3168/jds.2015-10661
      equation estimating MCP from DMI and starch intake (Equation 2).
      MCP = (−18.3 + 10.8 × DMI + 5.31 × starch – 0.0839 × starch2) × 6.25.
      [2]


      This equation needed to be linearized due to optimization restriction of linear programming; however, due to the difference in the type of rations fed to lactating versus dry cows, 2 equations were required to reach linearization¸ one for lactating (Equation 3) and one for dry cow (Equation 4), these 2 categories of cows being in different segments of the relationship.
      MCPlactating = 0.05152 × DMI + 0.01077 × starch
      [3]


      MCPdry = 0.0375 × DMI + 0.1547 × starch
      [4]


      These equations were developed using inputs and outputs of Equation 2 and deriving a first-degree multivariate regression to these data with DMI and starch content as independent variables, using a lm function from stat package in R version 3.4.0 (https://cloud.r-project.org/). The DMI and starch values used to develop the linear equations and verify their accuracy were a sequence of DMI within the minimal and maximal values from our database for DMI (between 19.5 and 25.5 kg/d and 11.7 and 14.3 kg/d for lactating and dry cows, respectively), with different starch concentrations (between 20 and 32% of DM and 15 and 22% of DM, for lactating and dry cows, respectively). Within these values, the estimates obtained with the linear equations had R2 of 0.988 (SE = 0.0009) and 0.991 (SE = 0.0017), with the original estimations from Equation 2, for the lactating and dry cows, respectively. The transfer of MCP duodenal flow to MP supply assumed 80% of true protein in MCP and 80% intestinal digestibility (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ). For growing dairy heifers, MCP duodenal flow was estimated from
      • NASEM (National Academies of Sciences Engineering and Medicine)
      Nutrient Requirements of Beef Cattle.
      8th rev. ed. National Academies Press, 2016
      , due to uncertainty of the application of
      • Roman-Garcia Y.
      • White R.R.
      • Firkins J.L.
      Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations.
      J. Dairy Sci. 2016; 99 (27448861): 7918-7931
      https://doi.org/10.3168/jds.2015-10661
      equations at this physiological stage. The MCP AA profile used was as proposed by
      • Sok M.
      • Ouellet D.R.
      • Firkins J.L.
      • Pellerin D.
      • Lapierre H.
      Amino acid composition of rumen bacteria and protozoa in cattle.
      J. Dairy Sci. 2017; 100 (28501407): 5241-5249
      https://doi.org/10.3168/jds.2016-12447
      , which includes protozoa and bacteria contribution. To be on the same basis as AA requirements, feed ingredient AA composition from Table 15 in
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      was also corrected to acknowledge for incomplete recovery of AA with 24-h hydrolysis of proteins (
      • Lapierre H.
      • Binggeli S.
      • Sok M.
      • Pellerin D.
      • Ouellet D.R.
      Estimation of correction factors to determine the true amino acid concentration of protein after a 24-hour hydrolysis.
      J. Dairy Sci. 2019; 102 (30591335): 1205-1212
      https://doi.org/10.3168/jds.2018-15392
      ). The MP_Rev supply summed the revised MCP supply and RUP estimated from
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      and did not include the MP supply from endogenous duodenal flow, as suggested by
      • 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 requirements.
      in: Cornell Nutrition Conference for Feed Manufacturers. Cornell University, 2016: 205-218
      .

      Selection of the Three Canadian Regions

      Three regions representing different Canadian agricultural systems were selected to compare the effect of the proposed recommendations. Those 3 regions correspond to the Maritimes (latitude 48°N; longitude 68°W), Central Canada (latitude 45°N; longitude 73°W), and the Prairies (latitude 52°N; longitude 113°W;
      • International Civil Aviation Organization
      World Geodetic System - 1984 (WGS-84) Manual.
      ICAO, 2002
      ). For the current simulations, 2 types of soil, referred to as land units, were used in each region: a mid-rich soil land unit (LUrich) and mid-poor soil land unit (LUpoor).

      Origin of Farm Data

      The Maritimes and Central Canada economic and productivity data originated from les Groupes Conseils Agricoles du Québec via their website (
      • GCAQ
      Agritel-Web.
      http://agritel.vialepole.com/
      Date: 2016
      Date accessed: June 20, 2020
      ), using the means of the years 2010 to 2014, inclusively. Inputs into the model for these 2 regions are presented in Table 1. The most commonly used feed ingredients in both regions from Lactanet (Dairy Production Centre of Expertise, Québec and Atlantic Provinces, Sainte-Anne-de-Bellevue, QC, Canada); registered farms were included as possible choices into the N-CyCLES model. Nutrient composition of feed ingredients and their prices were the average of the analyses for the producers of the same area registered to DHI service from Lactanet, from 2010 to 2014. The chemical compositions of these feed ingredients are shown in Appendix Table A2.
      Table 1Productivity and economic inputs for the reference farm of each region
      VariableThe MaritimesCentral CanadaThe Prairies
      Farm productivity
       BreedHolsteinHolsteinHolstein
       Mature cows, heads6371144
       Mature BW, kg656656682
       Calving interval, mo13.813.714.0
       Culling rate, %33.034.040.4
       Age at first calving, mo25.325.327.0
       Milk production, kg/cow per year8,6089,1029,198
       Milk fat, kg/hL4.134.133.89
       Milk CP, kg/hL3.353.393.29
       Milk other solids, kg/hL5.665.725.69
       FPCM,
      FPCM = fat- and protein-corrected milk, calculated from International Dairy Federation (2015).
      kg/yr      		513,696614,2961,213,288
      Farm economics input
      All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (Bank of Canada, 2017).
       Net milk price, $/100 kg of milk77.4777.9477.19
       Other incomes,
      Excluding crop costs and incomes.
      $/100 kg of milk      		7.3510.313.20
       Variable costs,
      Excluding crop costs and incomes.
      $/100 kg of milk      		9.138.627.96
       Labor, $/yr52,64554,322156,670
       Taxes and insurances, $/yr30,29130,79717,602
       Depreciation, $/yr71,52470,910111,291
       Interest, $/yr30,19340,55815,981
       Other cost, $/yr16,65721,5691,688
      1 FPCM = fat- and protein-corrected milk, calculated from
      • International Dairy Federation
      A common carbon footprint approach for the dairy sector: The IDF guide to standard life cycle assessment methodology.
      Bull. Int. Dairy Fed. 2015; : 63
      .
      2 All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (
      • Bank of Canada
      Historical noon and closing rates.
      http://www.bankofcanada.ca/rates/exchange/legacy-noon-and-closing-rates/
      Date: 2017
      Date accessed: July 10, 2016
      ).
      3 Excluding crop costs and incomes.
      Data for the Prairies originated from values published by Alberta Agriculture and Forestry Ministry (
      • Alberta Milk
      Alberta Milk.
      https://albertamilk.com/
      Date: 2016
      Date accessed: May 20, 2021
      ; Ministry of Agriculture and Forestry, 2016). Average values from years 2010 to 2014 were collected for economics and yields of cereal crops. When feed ingredient prices were not available on these sites, e.g., for minerals, they were estimated from Eastern region prices. Due to the lack of data on the chemical composition of feed ingredients for this region, values from
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      tables were used and validated by a local expert (V. S. Baron, Agriculture and Agri-food Canada, Lacombe, Canada; personal communication). The utilization of reference values instead of average regional values for the Prairies may affect absolute results, but, as all approaches within a region have the same feed ingredients, we expect that comparisons of the different approaches to be relevant to the Prairies reality.

      Crop Selection

      Crops and rotations proposed in N-CyCLES were chosen to reproduce what is done by an average producer for each region, following climate restrictions. For the Maritimes and Central Canada, crop selection and appropriate fertilization follow the recommendations from Centre de Références en Agriculture et Agroalimentaire du Québec (
      • CRAAQ
      Parent L.-E. Gagné G. Guide de référence en fertilisation. 2nd ed. CRAAQ, 2010
      ), whereas for the Prairies, crop nutrient requirements were confirmed by a local expert agronomist (V. S. Baron, personal communication). Crop yields used were 2010–2014 averages from La Finanacière Agricole du Québec (
      • FADQ (Financière Agricole de Québec)
      Rendement de références en assurance récolte.
      https://www.fadq.qc.ca/statistiques/assurance-recolte/rendements-de-reference/
      Date: 2016
      Date accessed: May 18, 2021
      ) for the Maritimes and Central Canada, whereas those of the Prairies were from the Ministry of Agriculture and Forestry databases (Ministry of Agriculture and Forestry, 2016). Cost of production of crops in the Maritimes and Central Canada were adapted from annual crop budgets published by
      • Beauregard G.
      Budget de cultures.
      https://www7-bibl-ulaval-ca.acces.bibl.ulaval.ca/doelec/AccesRestreint/budgets_agro/
      Date: 2016
      Date accessed: November 5, 2020
      . For the Prairies, they were from the Ministry of Agriculture and Forestry (
      • Ministry of Agriculture and Forestry
      Agricultural economics, competitiveness and statistics.
      Government of Alberta, 2016
      https://www.alberta.ca/agricultural-economics-competitiveness-and-statistics.aspx
      Date accessed: April 23, 2016
      ). The selected rotations and their nutrient requirements and costs are detailed in Appendix Table A3.

      Simulations

      The 4 approaches of formulation were simulated for each region. Each simulation maximized FNI and met energy requirements based on
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      and MP or AA requirements according to one of the 4 approaches, as previously described, using DMI predicted by
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      . Diets were all formulated as TMR. Although this manuscript presents only nutrition details for the lactating cows, dry cow and growing heifers' diets were optimized and considered for the reported whole-farm parameters, as they directly affect FNI and nutrient balance. Constraints applied on feed and nutrient requirement are described in Appendix Table A4. A sensitivity analysis was then performed (1) by reducing milk protein content by 5%, (2) by varying protein concentrate prices by 15%, and (3) by varying forage production costs by 15% below and above initial values. Another set of simulations was done excluding the use of blood meal in rations because of controversial usage of animal by-products in ruminant nutrition, as indicated by their banishment in European countries. Results are limited to the assumptions made on the original formulation models (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ) and revised approaches; requirements are assumed to be met within each approach and therefore, milk production and composition are assumed not to be affected by the type of formulation.

      RESULTS AND DISCUSSION

      Nutritional Outputs

      Early Lactation Groups

      For early lactation cows, for all formulation approaches and regions, NEL was either limiting or co-limiting with MP or AA (Table 2). Indeed, energy balance was always equal to zero (data not shown), whereas MP balance was also null with MP_2001. However, AA_2001, MP_Rev, and AA_Rev generated diets with positive MP balances, except in the Prairie where AA_2001 yielded a null MP balance and AA_Rev a slightly negative balance. This suggests that, at low-cost formulation, it is more difficult to fulfill energy than MP requirements at this stage of lactation. Indeed, ingredients selected to meet energy requirements also usually increased MP supply, which did not allow improvement of N efficiency with AA_2001 and MP_Rev when compared with MP_2001. Only AA_Rev formulation slightly increased N efficiency, but only by 0.2 and 0.1 percentage point in the Maritimes and in the Prairies, respectively, whereas in Central Canada, efficiency was even lower, decreasing from 28.8 to 28.5%.
      Table 2Simulated nutritional composition, MP and AA supply and balance, N efficiency, and cost for the rations of 2 lactation groups of cows by region and formulation approach
      MP_2001 = NRC 2001 MP-based formulation; AA_2001 = NRC 2001 AA-based formulation; MP_Rev = Revised MP-based formulation; AA_Rev = Revised AA-based formulation: see text for detail.
      ItemThe MaritimesCentral CanadaThe Prairies
      MP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_Rev
      Early lactation
       NEL, Mcal/kg DM1.59
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
      		1.61
      Equal to the lower requirement limits.
      		1.61
      Equal to the lower requirement limits.
      		1.61
      Equal to the lower requirement limits.
      		1.61
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
      		1.59
      Equal to the lower requirement limits.
       CP, % DM15.616.015.815.515.716.816.016.017.117.117.617.1
       MP supply,
      These values exclude duodenal endogenous MP requirement and supply values, for comparison purposes, but these were considered in the calculation for NRC (2001) formulations.
      g/cow per d      		2,1742,2362,2442,2012,2892,4512,3822,3842,2672,2672,3362,208
       MP balance,
      These values exclude duodenal endogenous MP requirement and supply values, for comparison purposes, but these were considered in the calculation for NRC (2001) formulations.
      g/cow per d      		06210765016214314400111−16
       Lys supply,
      AA supplies and requirements were corrected to account for incomplete recovery of AA with 24-h hydrolysis of proteins (Lapierre et al., 2019); AA balance for MP_2001 and MP_Rev are estimated using requirements estimated their respective AA formulation.
      g/cow per d      		142168161157150184165165165170183174
       Met supply g/cow per d495749485263535352634949
       His supply, g/cow per d464858565264535561606359
       Lys balance, g/cow per d−2042120−21111920−402422
       Met balance, g/cow per d−6244−6444−8033
       His balance, g/cow per d——44——33——33
       N efficiency,
      N efficiency = (N milk + N accretionpregnancy + N accretiongrowth)/(CP of diet × DMI/6.25).
      % of intake      		28.027.527.828.228.827.228.528.525.925.925.226.0
       Cost of ration, $/t DM268288268267276297278279250261250245
      Mid-late lactation
       NEL, Mcal/kg DM1.46
      Equal to the lower requirement limits.
      		1.501.471.46
      Equal to the lower requirement limits.
      		1.621.561.641.541.591.601.601.56
       CP, % DM16.816.317.515.516.116.316.115.016.516.417.515.9
       MP supply,
      These values exclude duodenal endogenous MP requirement and supply values, for comparison purposes, but these were considered in the calculation for NRC (2001) formulations.
      g/cow per d      		1,9351,9352,0191,7622,0322,0322,1021,9192,0162,0162,0861,797
       MP balance,
      These values exclude duodenal endogenous MP requirement and supply values, for comparison purposes, but these were considered in the calculation for NRC (2001) formulations.
      g/cow per d      		0077−1770081−1040080−215
       Lys supply,
      AA supplies and requirements were corrected to account for incomplete recovery of AA with 24-h hydrolysis of proteins (Lapierre et al., 2019); AA balance for MP_2001 and MP_Rev are estimated using requirements estimated their respective AA formulation.
      g/cow per d      		141144159138133152150143147151163140
       Met supply g/cow per d424943404852474348524543
       His supply, g/cow per d424254424554504553535643
       Lys balance, g/cow per d−302823−1803327−403828
       Met balance, g/cow/d−7032−4063−5054
       His balance, g/cow/d——42——63——84
       N efficiency,
      N efficiency = (N milk + N accretionpregnancy + N accretiongrowth)/(CP of diet × DMI/6.25).
      % of intake      		23.524.122.825.725.425.225.727.624.024.123.025.4
       Cost of ration, $/t of DM203213205189214221220208189198194178
      1 MP_2001 = NRC 2001 MP-based formulation; AA_2001 = NRC 2001 AA-based formulation; MP_Rev = Revised MP-based formulation; AA_Rev = Revised AA-based formulation: see text for detail.
      2 Equal to the lower requirement limits.
      3 These values exclude duodenal endogenous MP requirement and supply values, for comparison purposes, but these were considered in the calculation for
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      formulations.
      4 AA supplies and requirements were corrected to account for incomplete recovery of AA with 24-h hydrolysis of proteins (
      • Lapierre H.
      • Binggeli S.
      • Sok M.
      • Pellerin D.
      • Ouellet D.R.
      Estimation of correction factors to determine the true amino acid concentration of protein after a 24-hour hydrolysis.
      J. Dairy Sci. 2019; 102 (30591335): 1205-1212
      https://doi.org/10.3168/jds.2018-15392
      ); AA balance for MP_2001 and MP_Rev are estimated using requirements estimated their respective AA formulation.
      5 N efficiency = (N milk + N accretionpregnancy + N accretiongrowth)/(CP of diet × DMI/6.25).
      In all regions, diets balanced using MP_2001 did not supply enough Lys and Met to meet their respective requirements, as estimated in AA_2001. In the Maritimes and in Central Canada, cows fed according to NRC_2001 were fed 20 and 21 g/d of Lys and 6 g/d of Met below AA_2001 requirements. In the Prairies, however, only 4 g/d of Lys and 8 g/d of Met were lacking with MP_2001 compared with AA_2001 requirements. The smaller difference in Lys in this region is probably due to the lower utilization of corn-based feed ingredients, mainly corn silage and corn distiller grain, which have low Lys concentration (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      ).
      With both revised approaches, Lys was not estimated being limiting, as its balance was above 19 g/d in all regions. On the other hand, the supply of Met and His could be considered on the edge, as their balance was just slightly positive, varying by 3 to 4 g/d for both formulations. This approximately represents the 5% security margin used by the N-CyCLES model to balance rations.

      Mid-Late Lactation Groups

      Except in the Maritimes, for low-cost rations, energy was not considered limiting for the mid-late lactation cows. The higher proportion of forages, greater than 64% of the total DM in the ration (compared with less than 50% in the other regions), and their low energy content in this region, could explain this result. In Central Canada and in the Prairies, rations balanced to maximize FNI were feeding respectively 5 and 3% less energy when balanced with AA_Rev than with MP_2001, while still meeting requirements. This suggests that, in these 2 regions, to achieve MP supply, MP_Rev low-cost rations were supplying an excess of energy, which could be reduced concomitantly with MP supply using AA_Rev approach, without expected detrimental effect on performance. The lower nutrient density required in this group made it possible as opposed to the early lactation group.
      Balancing rations using MP_2001 or AA_2001 did not alter the null estimated MP balance. With rations balanced with MP_2001 for mid-lactation cows, however, Met balance would be slightly negative in all regions, whereas Lys balance would be more negative in Central Canada. This important difference in Lys supply between regions is suspected to be mainly caused by a lower utilization of corn distiller grain in the latter region. On the other hand, in the 3 regions using the MP_Rev approach, MP balance was positive (around 80 g/d), whereas Lys would be fed from 21 to 30% above requirements and Met and His would be both overfed from 7 to 13%. Because energy was not limiting in these diets, balancing only for AA using AA_Rev decreased this balance to safety margins for His and Met, while still overfeeding Lys. This might indicate that Met and His could be both co-limiting AA in low protein rations in all 3 Canadian reference farms defined in the present study. Balancing for His, Lys, and Met rather than MP in the revised approach allowed a substantial decrease of diet CP, with AA_Rev proposing the lowest CP content of diets across regions. At these low protein levels, although His, Lys, and Met requirements were met, calculated MP balances were negative (between 104 to 214 g/d/cow). However, this estimated MP deficit should not have a negative effect on milk and milk protein yields, as demonstrated by
      • 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
      when a MP-deficient diet was supplemented with His, Lys and Met. This reduction in CP and MP supply for AA_Rev formulation for mid-late cows translated into an improvement of 1.4 to 2.2 percentage points in N efficiency compared with MP_2001.

      Overall Changes in Feed Ingredients

      One major effect of AA_2001 compared with MP_2001 is the need to include rumen-protected AA in rations, necessary to meet the recommended Lys and Met proportion of MP. Also, optimization resulted in a 5% decrease in perennial crops in the diets in the Maritimes when formulating with AA_2001 and AA_Rev, compared with MP_2001 and MP_Rev (data not shown). This forage was replaced mostly by barley grain with AA_2001 formulation. With AA_Rev formulation, there was also a partial substitution of blood meal and expelled soybean meal by canola meal and barley grain. The change in perennial crops for barley is assumed to enhance MCP production because of its higher rumen digestible nutrient content.
      For simulations in Central Canada farms, formulating on an AA basis resulted in diets containing fairly similar ingredients for cows in early lactation compared with diets formulated with MP-based models. However, for mid-late groups, when formulating on an AA basis compared with MP formulation, all corn distiller grains were replaced with perennial forages, blood meal, and canola meal. The low content in Lys of corn distiller grains, although having a good MP supply potential, could explain this shift for both AA-based models. The high energy content of corn distiller grains could explain why it was still the main protein concentrate for early lactation cows, as it is relatively inexpensive for its nutrient content.
      For simulations for farms in the Prairies, MP_2001, AA_2001, and MP_Rev diets were very similar, whereas AA_Rev substituted 4% of barley silage for alfalfa silage and barley grain for early lactation cows. Those changes did not affect CP content of the diet. For mid-late groups, the main change occurred within AA_Rev, where blood meal and canola meal were replaced by legume silage and wheat distiller grain, reducing CP supply. With AA_Rev, MCP represented a higher proportion of total MP supply, compared with the other formulation approaches. The main feed component selection and changes during optimization can be partly explained by simple mathematical process work. Within each region, utilization of a multivariate maximum likelihood approach, as described by
      • St-Pierre N.R.
      • Glamocic D.
      Estimating unit costs of nutrients from market prices of feedstuffs.
      J. Dairy Sci. 2000; 83 (10877408): 1402-1411
      https://doi.org/10.3168/jds.S0022-0302(00)75009-0
      , seems to be a good predictor of the main feed used in diets by their breakeven point prices from feed components. With 4 components, RUP, RDP, energy, and NDF, feeds with the highest residuals were the main components of diets. In the present study, forages were included in the feed available for selection by considering their on-farm cost of production. However, constraints related to the formulation approach may force the utilization of more expensive feeds to meet requirements, such as rumen-protected fat to supply energy in early lactation cows, or when maximum limits were reached, as it happened with distiller grains in all regions.

      Crop and Rotation Outputs

      To understand the selections made during the optimization process, it is also important to keep in mind that harvest on the farm can have 2 purposes in the current simulations: feeding the herd and producing cash crops. The farm climate, crop yields, animal unit per land surface, and feed prices are the main factors influencing the fate of the crops, either being kept on farm and fed to the animal or sold (
      • Cela S.
      • Ketterings Q.M.
      • Czymmek K.
      • Soberon M.
      • Rasmussen C.
      Characterization of nitrogen, phosphorus, and potassium mass balances of dairy farms in New York State.
      J. Dairy Sci. 2014; 97 (25468709): 7614-7632
      https://doi.org/10.3168/jds.2014-8467
      ;
      • Pellerin D.
      • Charbonneau E.
      • Fadul-Pacheco L.
      • Soucy O.
      • Wattiaux M.A.
      Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
      J. Dairy Sci. 2017; 100 (28780106): 8614-8629
      https://doi.org/10.3168/jds.2016-11984
      ). Reasons explaining farm situation differences can be multiple. For example, in Central Canada, growing crops for sale is a common practice, and it is the only region to grow corn grain and soybeans. In the Prairies, the average farm is bigger than in Eastern regions and grows cereal grain and cereal silage more often. In the Maritimes, the colder climate makes it impossible to grow corn grain and limits corn silage yields. Therefore, in this region, which tends to be more autonomous on feed, farms are usually more centered around forages and also grow and feed barley grain, which minimizes purchased feeds.
      In the Maritimes, MP_2001 resulted in a production of 145 t DM of barley and 51 t DM of canola grown, and 347, 233, and 536 t DM of legume, mixed, and corn silage, respectively (Table 3). The AA_2001 formulation model favored production of mixed silage with a 5% enhancement in production, reducing by 1 to 4% barley, canola, and corn silage productions. Compared with MP_2001, the MP_Rev and AA_Rev formulations followed the same pattern but with stronger effects, as total legume and grass silage production increased by 12 and 9%, respectively, with a reduction in barley by 6 and 3%, canola by 10 and 6%, and corn silage by 4 and 6%, respectively. These changes in production happened through a reattribution of total land surface from one crop to another. These selections reduced the N purchased by producing and feeding more forage N sources, but also resulted in selling fewer feed ingredients (i.e., canola and barley). Rotation selection seemed to be mainly linked to the best global or possible forage margin. This appeared to favor rotation 4, producing mainly corn silage and grass silage in LUrich, and rotation 2, producing barley grain and canola with grass silage in the poor land unit (Appendix Table A3). Reducing production of barley and canola reduced the total crop sold, thus reducing crop incomes. For this region, under this scenario, all forages and most barley were used on the farm, whereas all canola produced was sold.
      Table 3Predicted total crop yields (t of DM) by region and formulation approach
      MP_2001 = NRC (2001) MP-based formulation; AA_2001 = NRC (2001) AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      ItemMP_2001AA_2001MP_RevAA_Rev
      The Maritimes
       Barley grain145143136141
       Canola51494648
       Legume silage347350358353
       Mixed silage233244260254
       Corn silage536521517506
      Central Canada
       Corn grain419381388380
       Soybean60555655
       Legume silage121131129132
       Mixed silage152165162166
       Corn silage485526518527
       Wheat12131313
      The Prairies
       Corn silage450449449461
       Legume silage268267267280
       Barley silage24242432
       Barley grain266268267250
       Pea74757569
      1 MP_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      MP-based formulation; AA_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      In Central Canada, with the MP_2001 approach, 2 commercial crops (corn grain and soybean) took an important place, with 419 and 60 t DM, respectively. Because rotation 2 had the best global margin, it was selected when forage requirements were met (Appendix Table A3). Corn and soybean land surface was reduced by 7 to 10% with AA_2001, MP_rev, and AA_Rev (Table 3). This change resulted in an increased total forage production of 7 to 9% in all 3 simulations, with 121, 152, and 485 t DM of legume, mixed, and corn silage with MP_2001 simulation. Forages were mainly grown on the rotations with the best forage production cost margins in both land units. The soil P deposition is regulated in Eastern Canada, possibly influencing Central Canada and the Maritimes' P management (
      • Gouvernement du Québec
      Règlement sur les exploitations agricoles: Loi sur la qualité de l'environnement.
      http://legisquebec.gouv.qc.ca/fr/ShowDoc/cr/Q-2
      Date: 2019
      Date accessed: January 16, 2020
      ). This could also have contributed to this crop selection due to the higher ratio of the number of cows per ha observed in Central Canada. This is supported by the absence of purchased P fertilizer with all formulations of this region. The limitation in P deposition may have resulted in selection of crops exporting large amount of P, such as corn grain and corn silage. This could also have influenced the ration formulation by selecting low phosphorus-containing feed ingredients to lower phosphorus excretion. However, these hypotheses could not be validated. Across all formulations, most corn grain was sold, and all soybean was sold.
      In the Prairies, corn silage was the main crop grown with an average of 449 t DM for MP_2001, AA_2001, and MP_Rev. The AA_Rev suggested growing 2.7% more corn silage. Legume silage followed the same pattern with 267 t DM produced for all models except for AA_Rev, which produced 5% more. In counterpart, barley and pea production would be reduced by 6 and 7%, respectively. A low variability in crop production occurred through the 4 types of formulation for this region. All barley produced was used on the farm, and pea was entirely sold in all simulations.

      Economic Outputs

      Within region, the AA_2001 formulation had the lowest calculated FNI, mainly caused by the highest purchase of feed ingredients (Table 4). More specifically, the FNI was 3 to 10% lower than MP_2001, with a 6 to 9% augmentation in purchased feedstuffs, with the largest difference in the Maritimes. This was mostly caused by the necessity to use rumen-protected AA, which are expensive feed ingredients, to reach the target proportions of Lys and Met as a percentage of MP. Those changes were reflected in ration costs (Table 2). Although our initial hypothesis assumes no change in milk protein yield with the different types of formulation, an enhancement of milk protein production might be expected when AA are balanced (
      • Patton R.A.
      Effect of rumen-protected methionine on feed intake, milk production, true milk protein concentration, and true milk protein yield, and the factors that influence these effects: A meta-analysis.
      J. Dairy Sci. 2010; 93 (20412926): 2105-2118
      https://doi.org/10.3168/jds.2009-2693
      ;
      • Robinson P.H.
      Impacts of manipulating ration metabolizable lysine and methionine levels on the performance of lactating dairy cows: A systematic review of the literature.
      Livest. Sci. 2010; 127: 115-126
      https://doi.org/10.1016/j.livsci.2009.10.003
      ;
      • Zanton G.I.
      • Bowman G.R.
      • Vázquez-Añón M.
      • Rode L.M.
      Meta-analysis of lactation performance in dairy cows receiving supplemental dietary methionine sources or postruminal infusion of methionine.
      J. Dairy Sci. 2014; 97 (25242429): 7085-7101
      https://doi.org/10.3168/jds.2014-8220
      ). We calculated that the cows in the Maritimes, Central Canada, and the Prairies would need to increase their milk protein yield by 35, 40, and 51 g/d, respectively, to compensate for economic losses associated with the purchase of rumen-protected AA with AA_2001, assuming no change in milk fat production. An enhanced milk protein production would also affect total N balance by exporting more N in milk and reducing N in manure.
      Table 4Economic output summary of simulations by region and formulation approach
      1MP_2001 = NRC (2001) MP-based formulation; AA_2001 = NRC (2001) AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      Output
      All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (Bank of Canada, 2017).
      		The MaritimesCentral CanadaThe Prairies
      MP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_Rev
      Net income,
      FPCM = fat- and protein-corrected milk, calculated according to International Dairy Federation (2015).
      $/100 kg of FPCM      		11.09.911.312.022.621.322.422.623.422.623.724.4
      Purchased feeds, $/100 kg of FPCM9.910.89.68.518.019.116.215.411.512.411.610.5
      Homegrown feeds, $/100 kg of FPCM12.612.512.712.610.310.410.410.312.112.111.911.9
      Fertilizers + manure, $/100 kg of FPCM1.82.01.71.81.61.51.51.51.01.01.01.1
      Fixed cost, $/100 kg of FPCM47.847.847.847.843.643.643.643.632.832.832.832.8
      Crop incomes, $/100 kg of FPCM3.02.82.92.512.912.610.910.32.12.22.22.0
      Milk + animal incomes, $/100 kg of FPCM80.380.380.380.383.283.283.283.278.678.678.678.6
      Global IOFC,
      IOFC = income over feed cost.
      $/cow per day      		13.2312.9213.2013.4013.9013.5813.8114.0914.6514.4314.5914.85
      1 1MP_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      MP-based formulation; AA_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      2 All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (
      • Bank of Canada
      Historical noon and closing rates.
      http://www.bankofcanada.ca/rates/exchange/legacy-noon-and-closing-rates/
      Date: 2017
      Date accessed: July 10, 2016
      ).
      3 FPCM = fat- and protein-corrected milk, calculated according to
      • International Dairy Federation
      A common carbon footprint approach for the dairy sector: The IDF guide to standard life cycle assessment methodology.
      Bull. Int. Dairy Fed. 2015; : 63
      .
      4 IOFC = income over feed cost.
      The AA_Rev showed the best results in all 3 regions, with the Maritimes benefit being the most important in terms of FNI. Compared with MP_2001, gains of FNI within a region varied between 0 to 9% (Table 4). Most of this modification in FNI was caused by a reduction in purchased feedstuffs, ranging from 9 to 14% for AA_Rev formulation in comparison to MP_2001. Compared with MP_2001 in Central Canada, MP_Rev and AA_Rev resulted in lower crop incomes of 15 and 20% respectively, but also in lower purchased feedstuff costs, showing a shift in feedstuff origin. In these 2 formulations, more of the corn grain grown on the farm was directly used in rations, which implied less corn grain was sold, reducing crop incomes. Both MP-based formulations had similar results in economic output of all 3 regions. In addition, the possibility to formulate below MP requirements in AA_Rev made it possible to buy less expensive feedstuffs, reducing global feed cost. Although in the Maritimes and Central Canada, early lactation AA_Rev cow ration costs were almost identical across all models, mid-late lactation rations costs were $13 and $6/t DM less expensive than MP_2001, respectively. In the Prairies, however, early lactation rations cost $6/t DM less and mid-late, $12/t less, compared with MP_2001 as shown in Table 2.
      Producers do not always take into account possible incomes from crops sold or how they can benefit from them. In the current optimization study, in Central Canada, crop income represented, on average, 12% of total income. From
      • GCAQ
      Agritel-Web.
      http://agritel.vialepole.com/
      Date: 2016
      Date accessed: June 20, 2020
      farm group analysis, in Central Canada global region, crop incomes averaged 25% of total income. Such large difference indicates the importance of considering crop management when maximum profit is the objective.
      The income over feed cost estimated in the Maritimes region varied between $12.92 and $13.40/cow per day for lactating cows. It varied between $13.58 to $14.09/cow per day in Central Canada and between $14.43 to $14.85/cow per day in the Prairies (Table 4). Within regions, they have the same behavior as FNI. In comparison, in the province of Québec and the Maritimes, commercial farms had an average income over feed cost of $12.16/cow per day during the 2010–2014 period (
      • Valacta
      Evolution de la production laitière.
      http://www.valacta.com/FR/Nos-publications/Pages/evolution-de-la-production-laitiere.aspx
      Date: 2017
      Date accessed: July 26, 2017
      ), indicating that our results are in plausible ranges of optimal management for Central Canada and the Maritimes.

      Environmental Outputs

      One important aspect of the N-CyCLES optimization program is to simulate the effect of different scenarios on farm nutrient balance. In the present study, the best results among simulations in all 3 regions for the N balance were usually observed with AA_ Rev (Table 5). The decreases in N balance varied between 0 and 7%, depending on the region, when compared with MP_2001 formulation originally being 11.98, 12.82, and 13.32 g of N/g of FPCM, for the Maritimes, Central Canada, and the Prairies, respectively. In contrast, N balances obtained with AA_2001 in the Maritimes and Central Canada were 1.3 and 2.0% higher, and with MP_Rev in the Prairies it increased by 11%, compared with their respective MP_2001 formulation. The main change was in the feed N purchased parameter (Table 5). Compared with MP_2001, across regions, AA_Rev reduced feed N purchased parameter by 2 to 16%. Furthermore, it also decreased cash crops sold, resulting in less feed N exported and ranging within a region from 5 to 18% compared with MP_2001. Nitrogenous fertilizers and atmospheric deposition had little effect on N balance. Overall, the farm N efficiency (N output:N input) was more influenced by the region through parameters prevailing in each reference region than by the formulation within region: it ranged from 42.7 to 44.2%, 52.6 to 54.5% ,and from 37.6 to 40.0% in the Maritimes, Central Canada, and the Prairies, respectively, depending on the nutritional approach used. The high proportion of crops sold in Central Canada explained these differences in N efficiency from the other 2 regions. These results in Central Canada are similar to those obtained in a previous optimization conducted by
      • Pellerin D.
      • Charbonneau E.
      • Fadul-Pacheco L.
      • Soucy O.
      • Wattiaux M.A.
      Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
      J. Dairy Sci. 2017; 100 (28780106): 8614-8629
      https://doi.org/10.3168/jds.2016-11984
      for the province of Québec, whereas the Maritimes and the Prairies results are comparable to those from studies conducted in New York with a median of 35% (
      • Cela S.
      • Ketterings Q.M.
      • Czymmek K.
      • Soberon M.
      • Rasmussen C.
      Characterization of nitrogen, phosphorus, and potassium mass balances of dairy farms in New York State.
      J. Dairy Sci. 2014; 97 (25468709): 7614-7632
      https://doi.org/10.3168/jds.2014-8467
      ). When excluding N atmospheric deposition, N balance varied from 0.65 to 2.21 g/kg FPCM in the Maritimes, from 5.53 to 6.47 in Central Canada and from 5.36 to 6.86 in the Prairies, with AA_Rev being the lowest in all regions.
      Table 5Simulated farm N and P balances and greenhouse gas emissions by region and formulation approach
      MP_2001 = NRC (2001) MP-based formulation; AA_2001 = NRC (2001) AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      ItemThe MaritimesCentral CanadaThe Prairies
      MP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_Rev
      N balance,
      FPCM = fat- and protein-corrected milk, calculated according to International Dairy Federation (2015).
      g/kg of FPCM      		11.9812.1412.311.1312.8213.0712.8912.2813.3213.2914.7513.32
      Feed N purchased, g/kg of FPCM8.838.729.217.4218.4118.6417.3616.1613.8914.0115.54613.63
      Fertilizer N purchased, g/kg of FPCM2.102.681.622.033.372.853.053.000.250.260.190.31
      Atmospheric N deposition, g/kg of FPCM10.339.9210.6310.496.406.606.606.757.957.897.897.90
      Milk and meat N exported, g/kg of FPCM5.885.885.885.885.905.905.905.906.096.096.096.09
      Feed N exported, g/kg of FPCM3.403.303.292.939.479.128.817.732.682.782.782.56
      Farm N efficiency, %43.743.142.744.254.553.554.552.639.740.037.639.6
      P balance, g/kg of FPCM1.701.711.681.700.410.080.320.090.840.830.890.82
      Feed P purchased, g/kg of FPCM1.201.141.211.133.002.632.692.391.811.802.001.64
      Fertilizer P purchased, g/kg of FPCM1.992.011.971.970.000.000.000.000.520.530.380.65
      Milk and meat P exported, g/kg of FPCM1.121.121.121.121.111.111.111.111.191.191.191.19
      Feed P exported, g/kg of FPCM0.370.320.370.281.481.441.251.180.290.310.310.28
      Greenhouse gases, CO2eq
      CO2eq = kg of CO2 equivalent.
      /kg of FPCM      		1.591.621.581.571.691.671.661.621.501.511.501.50
      Methane, CO2eq/kg of FPCM1.051.051.061.050.970.980.980.981.031.031.031.03
      N2O linked to manure CO2eq/kg of FPCM0.220.220.230.220.220.230.220.220.220.210.220.21
      N2O mineral fertilizer, CO2eq/kg of FPCM0.020.030.020.020.030.030.030.030.000.000.000.00
      N2O field other, CO2eq/kg of FPCM0.030.030.030.030.070.070.070.060.040.040.040.04
      CO2 fuel, CO2eq/kg of FPCM0.080.080.080.080.040.040.040.040.040.040.040.04
      CO2eq import feeds, CO2eq/kg of FPCM0.080.100.080.080.210.200.200.170.130.140.130.13
      CO2eq crop input, CO2eq/kg of FPCM0.100.120.080.080.130.110.120.120.040.040.040.04
      Milk allocation GHG, CO2eq/kg of FPCM1.271.301.261.261.211.211.231.201.161.171.161.16
      Meat allocation GHG, CO2eq/kg of FPCM0.260.270.260.260.240.240.240.240.300.300.300.30
      Crop allocation GHG, CO2eq/kg of FPCM0.060.050.050.050.230.220.190.180.040.040.040.04
      1 MP_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      MP-based formulation; AA_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      2 FPCM = fat- and protein-corrected milk, calculated according to
      • International Dairy Federation
      A common carbon footprint approach for the dairy sector: The IDF guide to standard life cycle assessment methodology.
      Bull. Int. Dairy Fed. 2015; : 63
      .
      3 CO2eq = kg of CO2 equivalent.
      For total GHG emissions, AA_Rev was from 0 to 4% lower than MP_2001 (Table 5). In the Maritimes and Central Canada, the reduction in purchased feedstuff with AA_Rev translated in a reduction in indirect emissions of 12 and 27%, respectively, when compared with MP_2001. However, in the Prairies, even if there was a reduction in purchased feedstuff, emissions were 4.5% higher, mostly because of the substitution of canola meal with wheat distiller grain, which was cheaper but emits 3.5 times more GHG during its production process (
      • Brander M.
      • Wylie C.
      The use of substitution in attributional life cycle assessment.
      Greenh. Gas Meas. Manage. 2011; 1: 161-166
      https://doi.org/10.1080/20430779.2011.637670
      ;
      • Agriculture and Agri-Food Canada
      Assessment of Net Emissions of Greenhouse Gases from Ethanol-Gasoline Blends in Southern Ontario.
      Agriculture and Agri-Food Canada, 1999
      ). Total methane emissions varied at most by 0.5% through all models. The gain in N efficiency of AA_Rev formulation translated to a 0.7 to 3.2% reduction in nitrous oxide emissions due to the lower N excretion in manure.
      In their study in Eastern Canada,
      • Mc Geough E.J.
      • Little S.M.
      • Janzen H.H.
      • McAllister T.A.
      • McGinn S.M.
      • Beauchemin K.A.
      Life-cycle assessment of greenhouse gas emissions from dairy production in Eastern Canada: A case study.
      J. Dairy Sci. 2012; 95 (22916922): 5164-5175
      https://doi.org/10.3168/jds.2011-5229
      established a global effect of milk production on GHG of 0.92 kg of CO2 Eq/kg of FPCM, which is an average of 27% lower than the value obtained in the present study. The main difference resides in the fact that a fixed enteric methane emission factor of 6.5% of gross energy supply for lactating cows was used in the present study, whereas a 5.5% of gross energy methane emission factor was selected in their study. They also considered that crops were not fertilized with nitrogenous fertilizers, whereas they were the main fertilizer sources in this study, and imported a non-negligible amount of CO2 Eq. They also considered that all feeds consumed on the farm, except minerals, were produced on farm, thus excluding all imported feed that were considered in our study. Those 2 factors combined resulted in approximately 0.15, 0.26, and 0.19 kg of CO2 Eq/kg of FPCM in the Maritimes, Central Canada, and the Prairies, respectively. A versatile or a multivariate emission factor would be strongly suggested as Tier 3 of
      • Intergovernmental Panel on Climate Change
      IPCC Guidelines for National Greenhouse Gas Inventories.
      Institute for Global Environmental Strategies, 2006
      recommended. Such emission factors should consider the effect of variation in diet composition on methane emission (
      • Moraes L.E.
      • Strathe A.B.
      • Fadel J.G.
      • Casper D.P.
      • Kebreab E.
      Prediction of enteric methane emissions from cattle.
      Glob. Chang. Biol. 2014; 20 (24259373): 2140-2148
      https://doi.org/10.1111/gcb.12471
      ). This would enable more representative interpretation of the effect of diet changes on GHG emissions.

      Sensitivity Analysis

      Sensitivity analysis was conducted to determine if altering a few main farm components would affect the general conclusions, by comparing the results of the 4 approaches within a specific alternative context, and not against their respective initial simulation values. As described previously, sensitivity analysis was conducted by removing blood meal from choices of feed, reducing milk CP content by 5%, varying protein concentrate prices or forage production cost by 15% from the initial scenario in all regions. Within region, the sensitivity analysis indicated that farm FNI showed a similar behavior as observed in the initial simulations when the studied factors varied. For example, across all different sensitivity analysis scenarios, AA_Rev have the best FNI, ranging from 100 to 111% of MP_2001 value, as observed in the initial simulations (Table 6). With AA_2001 reaching 84 to 97% of MP_2001 FNI values, results are also similar to initial simulations. The MP_Rev approach, as with the initial simulations, usually yielded slightly greater FNI than MP_2001. However, as N balance was not optimized nor constrained in our simulations, it was more susceptible to variations than FNI. Using the AA_2001 or MP_Rev approach either slightly increased or decreased the farm N balance, from 98 to 104% of their MP_2001 values, depending on the context except in the Prairies whereas MP_Rev consistently raised the N balance compared with MP_2001. However, in all scenarios of the sensitivity analysis, AA_Rev decreased N balance from 90 to 97% of MP_2001 values. As
      • Pellerin D.
      • Charbonneau E.
      • Fadul-Pacheco L.
      • Soucy O.
      • Wattiaux M.A.
      Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
      J. Dairy Sci. 2017; 100 (28780106): 8614-8629
      https://doi.org/10.3168/jds.2016-11984
      had shown, applying a constraint on N balance to a lower target would inevitably reduce FNI. However, to target the same N balance as observed for MP_2001, FNI difference may still be positive, as in the
      • Pellerin D.
      • Charbonneau E.
      • Fadul-Pacheco L.
      • Soucy O.
      • Wattiaux M.A.
      Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
      J. Dairy Sci. 2017; 100 (28780106): 8614-8629
      https://doi.org/10.3168/jds.2016-11984
      study in which a reduction of 1 g of N/kg of FPCM led to a $0.02/kg of FPCM reduction. In general, within a region, animal N efficiency variation follows a proportionally inverse change to global farm N balance variation. This followed
      • Spears R.A.
      • Kohn R.A.
      • Young A.J.
      Whole-farm nitrogen balance on western dairy farms.
      J. Dairy Sci. 2003; 86 (14740860): 4178-4186
      https://doi.org/10.3168/jds.S0022-0302(03)74033-8
      observations where, for farm growing crops, farm N balance and animal N efficiency are strongly negatively correlated. Altogether, these data indicate that when animals utilize protein more efficiently, the whole farm also becomes more efficient with N, resulting in a lower farm N balance. Across all sensitivity analysis simulations, AA_Rev showed for all regions an improvement of animal N efficiency of 3 to 8% compared with MP_2001 formulation. On the other hand, AA_2001 resulted in a lower N efficiency than MP_2001 in most of the simulations. The stability of the patterns of the responses indicates the robustness of the results from our initial simulations on FNI and N efficiency. It validates the potential use of AA_Rev formulation approach to have a beneficial effect on Canadian dairy farms, whereas AA_2001 maybe more limiting without consideration of milk or milk protein enhancement.
      Table 6Effects of a reduction of CP content of milk, a variation in protein concentrate and forages prices, and the exclusion of blood meal
      MP_2001 = NRC (2001) MP-based formulation; AA_2001 = NRC (2001) AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      Item
      All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (Bank of Canada, 2017); FPCM = fat- and protein-corrected milk, calculated from International Dairy Federation (2015); animal N efficiency = (N milk + N accretion_pregnancy + N accretion_growth)/N intake, averages for lactating cows.
      		The MaritimesCentral CanadaThe Prairies
      MP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_RevMP_2001AA_2001MP_RevAA_Rev
      Milk CP: −5%
       Net income, $/100 kg of FPCM10.49.210.711.321.820.621.822.023.622.824.024.7
       N balance, kg/FPCM11.8412.3312.4411.1712.6213.0112.6712.5213.2213.2114.5412.72
       Animal N efficiency, %24.624.424.025.826.525.426.726.724.324.323.525.6
       Protein concentrates price: +15%
       Net income, $/100 kg of FPCM10.89.511.111.922.020.621.822.223.122.223.424.6
       N balance, kg/FPCM11.8811.9311.6511.1412.8213.0612.4111.5113.2513.0413.9612.19
       Animal N efficiency, %25.926.225.926.826.926.027.728.824.924.924.826.9
      Protein concentrate prices: −15%
       Net income $/100 kg of FPCM11.410.411.812.223.322.023.123.323.923.024.224.9
       N balance, kg/FPCM12.3112.1912.6711.5313.7113.4413.3412.6313.0913.3114.2612.83
       Animal N efficiency, %24.824.924.526.325.325.426.127.525.124.924.426.2
      Forages production costs: +15%
       Net income $/100 kg FPCM9.68.59.910.721.520.22222.322.121.322.423.2
       N BALANCE, kg/FPCM12.3912.5212.5111.1612.8413.0612.8612.3713.5013.5314.5312.29
       Animal N efficiency, %25.425.225.426.826.926.027.12824.824.824.226.9
      Forages production costs: −15%
       Net income, $/100 kg of FPCM12.511.412.813.423.622.323.523.824.824.025.126.1
       N balance, kg/FPCM11.9012.1711.9711.0612.7213.0512.7412.0713.2613.2914.0612.44
       Animal N efficiency, %25.324.925.226.826.925.927.028.024.924.924.826.8
      Blood meal exclusion
       Net income, $/100 kg of FPCM10.89.111.111.722.520.722.522.723.121.923.524.3
       N Balance, kg/FPCM11.6611.5911.5211.4112.8013.3512.8212.3313.0913.0813.9712.71
       Animal N efficiency, %25.625.625.726.326.926.127.028.025.125.124.726.1
      1 MP_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      MP-based formulation; AA_2001 =
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      AA-based formulation; MP_Rev = revised MP-based formulation; AA_Rev = revised AA-based formulation; see text for details.
      2 All monetary values are in Canadian dollars; the average exchange rate used was US$0.97 for 2010 to 2014 (
      • Bank of Canada
      Historical noon and closing rates.
      http://www.bankofcanada.ca/rates/exchange/legacy-noon-and-closing-rates/
      Date: 2017
      Date accessed: July 10, 2016
      ); FPCM = fat- and protein-corrected milk, calculated from
      • International Dairy Federation
      A common carbon footprint approach for the dairy sector: The IDF guide to standard life cycle assessment methodology.
      Bull. Int. Dairy Fed. 2015; : 63
      ; animal N efficiency = (N milk + N accretion_pregnancy + N accretion_growth)/N intake, averages for lactating cows.

      Domain of Validity and Limitations

      The approach of whole-farm optimization, as opposed to optimizing only the diet, makes it possible to see the dynamic effect of changes, as a single-unit of decision. As a farm can grow a limited quantity of crops, resource management is important, even more so when animal density is elevated. With changes of requirement and supply estimations in simulations where FNI is maximized, diet optimization may involve a reallocation of some feed resources from one group of animals to another to meet nutrient requirements of all animal groups on the farm. By extension, crop production also changes. In addition, modifications to diets affect manure production and composition (
      • Arndt C.
      • Powell J.M.
      • Aguerre M.J.
      • Wattiaux M.A.
      Performance, digestion, nitrogen balance, and emission of manure ammonia, enteric methane, and carbon dioxide in lactating cows fed diets with varying alfalfa silage-to-corn silage ratios.
      J. Dairy Sci. 2015; 98 (25465537): 418-430
      https://doi.org/10.3168/jds.2014-8298
      ), which will have an effect on inputs needed for fertilization.
      As N-CyCLES is a requirement-based model, we must assume that as long as initial requirements are met, animals and crops will maintain productivity; thus, we assumed no response to diet or fertilizer changes. Assuming change in production when requirements are exceeded is an error often made in requirement-based models and should not be done (
      • St-Pierre N.R.
      • Weiss W.P.
      What we have learned about optimizing efficiency of dairy production requirements versus response-based systems.
      Adv. Dairy Technol. 2012; 24: 31-49
      ). Furthermore, it will be important to validate that proposed requirements and diets for AA_rev can sustain milk production and composition set in our simulations. As explained before, linear programming optimization may also force the selection or modification of equations to meet linearity which may then reflect less accurately the biology. Finally, the interpretation of the analysis must be limited to the scenario situations and inputs. Nevertheless, the consistent conclusions across regions and models, and the different scenarios tested with the sensitivity analysis suggest that the reported trends are robust enough to be generalizable.

      CONCLUSIONS

      The current trend toward balancing dairy rations for individual EAA seems to be justified. Simulations to maximize income while balancing for His, Lys, and Met using a factorial approach positively affect the FNI predictions and decrease the environmental effect of dairy farming across different dairy regions in Canada with different management scenarios. Assuming no change in milk protein yield, formulation for 3 EAA (His, Lys, and Met) using a variable efficiency of utilization with a factorial approach of requirement and supply without balancing for MP has the potential to reduce GHG and N farm balance, with no negative and even a slight positive effect on FNI. This could also enhance animal N efficiency in opposition of having both MP and AA constraints as in the AA_2001 approach. However, more work is still needed to refine AA formulation model to meet requirements of all EAA, and also include potential interaction with energy supply, both in terms of type of energy and quantity. As this was a modeling and simulation approach, applications on commercial dairy farms should be undertaken to validate that diet optimization performs as predicted, and also to determine if gains in FNI are really obtainable.

      ACKNOWLEDGMENTS

      This study was funded by the Dairy Farmers of Canada, Agriculture and Agri-Food Canada, Canadian Dairy Network, and Canadian Dairy Commission through Dairy Research for a Healthy World-Dairy Research Cluster. The authors thank V. Ouellet and J. V. Guillen at Université Laval (Québec, QC, Canada) for their help on data collection and V. S. Baron at Agriculture and Agri-Food Canada (Lacombe, AB, Canada) for his help on the Prairies scenario. The authors have not stated any conflicts of interest.

      APPENDIX

      Equations and studies used to develop the equations of the efficiency of utilization of His, Lys, and Met for the revised AA formulation approach:
      Hiseffi=2.001-HissupiNEL×1.051+(HissupiNEL)2×0.180-DIM×0.00167+DIM×5.78e-6,


      Lyseffi=1.983-LyssupiNEL×0.371+(LyssupiNEL)2×0.0246-DIM×0.00147+DIM×4.23e-6,and


      Meteffi=1.571-MetsupiNEL×0.715+(MetsupiNEL)2×0.1019-DIM×0.00017,


      where Hiseffi, Lyseffi, and Meteffi = His, Lys, and Met efficiency of utilization at AA supply i; Hissupi, Lyssupi, and Metsupi = AA supply level i, g/d; NEL = NEL requirements, Mcal/d.
      Table A1.Studies used to develop the equations of the efficiency of utilization of His, Lys, and Met for the revised AA formulation approach
      Study
      • Agle M.
      • Hristov A.N.
      • Zaman S.
      • Schneider C.
      • Ndegwa P.
      • Vaddella V.K.
      The effects of ruminally degraded protein on rumen fermentation and ammonia losses from manure in dairy cows.
      J. Dairy Sci. 2010; 93 (20338440): 1625-1637
      https://doi.org/10.3168/jds.2009-2579
      • Arriola Apelo S.I.
      • Bell A.L.
      • Estes K.
      • Ropelewski J.
      • de Veth M.J.
      • Hanigan M.D.
      Effects of reduced dietary protein and supplemental rumen-protected essential amino acids on the nitrogen efficiency of dairy cows.
      J. Dairy Sci. 2014; 97 (25022689): 5688-5699
      https://doi.org/10.3168/jds.2013-7833
      • Berthiaume R.
      • Thivierge M.C.
      • Patton R.A.
      • Dubreuil P.
      • Stevenson M.
      • McBride B.W.
      • Lapierre H.
      Effect of ruminally protected methionine on splanchnic metabolism of amino acids in lactating dairy cows.
      J. Dairy Sci. 2006; 89 (16606732): 1621-1634
      https://doi.org/10.3168/jds.S0022-0302(06)72229-9
      • Blouin J.P.
      • Bernier J.F.
      • Reynolds C.K.
      • Lobley G.E.
      • Dubreuil P.
      • Lapierre H.
      Effect of supply of metabolizable protein on splanchnic fluxes of nutrients and hormones in lactating dairy cows.
      J. Dairy Sci. 2002; 85 (12416816): 2618-2630
      https://doi.org/10.3168/jds.S0022-0302(02)74347-6
      • Borucki Castro S.I.
      • Phillip L.E.
      • Lapierre H.
      • Jardon P.W.
      • Berthiaume R.
      The relative merit of ruminal undegradable protein from soybean meal or soluble fiber from beet pulp to improve nitrogen utilization in dairy cows.
      J. Dairy Sci. 2008; 91 (18832219): 3947-3957
      https://doi.org/10.3168/jds.2007-0638
      • Boucher S.E.
      • Ordway R.S.
      • Whitehouse N.L.
      • Lundy F.P.
      • Kononoff P.J.
      • Schwab C.G.
      Effect of incremental urea supplementation of a conventional corn silage-based diet on ruminal ammonia concentration and synthesis of microbial protein.
      J. Dairy Sci. 2007; 90 (18024754): 5619-5633
      https://doi.org/10.3168/jds.2007-0012
      • Brito A.F.
      • Broderick G.A.
      Effect of varying dietary ratios of alfalfa silage to corn silage on production and nitrogen utilization in lactating dairy cows.
      J. Dairy Sci. 2006; 89 (16960068): 3924-3938
      https://doi.org/10.3168/jds.S0022-0302(06)72435-3
      • Broderick G.A.
      • Stevenson M.J.
      • Patton R.A.
      Effect of dietary protein concentration and degradability on response to rumen-protected methionine in lactating dairy cows.
      J. Dairy Sci. 2009; 92 (19448006): 2719-2728
      https://doi.org/10.3168/jds.2008-1277
      • Broderick G.A.
      • Faciola A.P.
      • Armentano L.E.
      Replacing dietary soybean meal with canola meal improves production and efficiency of lactating dairy cows.
      J. Dairy Sci. 2015; 98 (26074230): 5672-5687
      https://doi.org/10.3168/jds.2015-9563
      • Davidson S.
      • Hopkins B.A.
      • Diaz D.E.
      • Bolt S.M.
      • Brownie C.
      • Fellner V.
      • Whitlow L.W.
      Effects of amounts and degradability of dietary protein on lactation, nitrogen utilization, and excretion in early lactation Holstein cows.
      J. Dairy Sci. 2003; 86 (12778579): 1681-1689
      https://doi.org/10.3168/jds.S0022-0302(03)73754-0
      • Doepel L.
      • Hewage I.I.
      • Lapierre H.
      Milk protein yield and mammary metabolism are affected by phenylalanine deficiency but not by threonine or tryptophan deficiency.
      J. Dairy Sci. 2016; 99 (26851853): 3144-3156
      https://doi.org/10.3168/jds.2015-10320
      • Giallongo F.
      • Hristov A.N.
      • Oh J.
      • Frederick T.
      • Weeks H.
      • Werner J.
      • Lapierre H.
      • Patton R.A.
      • Gehman A.
      • Parys C.
      Effects of slow-release urea and rumen-protected methionine and histidine on performance of dairy cows.
      J. Dairy Sci. 2015; 98 (25726096): 3292-3308
      https://doi.org/10.3168/jds.2014-8791
      • Giallongo F.
      • Harper M.T.
      • Oh J.
      • Lopes J.C.
      • Lapierre H.
      • Patton R.A.
      • Parys C.
      • Shinzato I.
      • Hristov A.N.
      Effects of rumen-protected methionine, lysine, and histidine on lactation performance of dairy cows.
      J. Dairy Sci. 2016; 99 (27060815): 4437-4452
      https://doi.org/10.3168/jds.2015-10822
      • Giallongo F.
      • Harper M.T.
      • Oh J.
      • Parys C.
      • Shinzato I.
      • Hristov A.N.
      Histidine deficiency has a negative effect on lactational performance of dairy cows.
      J. Dairy Sci. 2017; 100 (28131569): 2784-2800
      https://doi.org/10.3168/jds.2016-11992
      • Halmemies-Beauchet-Filleau A.
      • Vanhatalo A.
      • Toivonen V.
      • Heikkilä T.
      • Lee M.R.F.
      • Shingfield K.J.
      Effect of replacing grass silage with red clover silage on nutrient digestion, nitrogen metabolism, and milk fat composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio.
      J. Dairy Sci. 2014; 97 (24679932): 3761-3776
      https://doi.org/10.3168/jds.2013-7358
      • Korhonen M.
      • Vanhatalo A.
      • Huhtanen P.
      Effect of protein source on amino acid supply, milk production, and metabolism of plasma nutrients in dairy cows fed grass silage.
      J. Dairy Sci. 2002; 85 (12512607): 3336-3351
      https://doi.org/10.3168/jds.S0022-0302(02)74422-6
      • 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
      • Lee C.
      • Giallongo F.
      • Hristov A.N.
      • Lapierre H.
      • Cassidy T.W.
      • Heyler K.S.
      • Varga G.A.
      • Parys C.
      Effect of dietary protein level and rumen-protected amino acid supplementation on amino acid utilization for milk protein in lactating dairy cows.
      J. Dairy Sci. 2015; 98 (25547302): 1885-1902
      https://doi.org/10.3168/jds.2014-8496
      • Leonardi C.
      • Stevenson M.
      • Armentano L.E.
      Effect of two levels of crude protein and methionine supplementation on performance of dairy cows.
      J. Dairy Sci. 2003; 86 (14740841): 4033-4042
      https://doi.org/10.3168/jds.S0022-0302(03)74014-4
      • Mjoun K.
      • Kalscheur K.F.
      • Hippen A.R.
      • Schingoethe D.J.
      Performance and amino acid utilization of early lactation dairy cows fed regular or reduced-fat dried distillers grains with solubles.
      J. Dairy Sci. 2010; 93 (20630235): 3176-3191
      https://doi.org/10.3168/jds.2009-2974
      • 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
      • Nursoy H.
      • Ronquillo M.G.
      • Faciola A.P.
      • Broderick G.A.
      Lactation response to soybean meal and rumen-protected methionine supplementation of corn silage-based diets.
      J. Dairy Sci. 2018; 101 (29290449): 2084-2095
      https://doi.org/10.3168/jds.2017-13227
      • Olmos Colmenero J.J.
      • Broderick G.A.
      Effect of amount and ruminal degradability of soybean meal protein on performance of lactating dairy cows.
      J. Dairy Sci. 2006; 89 (16606733): 1635-1643
      https://doi.org/10.3168/jds.S0022-0302(06)72230-5
      • Ordway R.S.
      • Boucher S.E.
      • Whitehouse N.L.
      • Schwab C.G.
      • Sloan B.K.
      Effects of providing two forms of supplemental methionine to periparturient Holstein dairy cows on feed intake and lactational performance.
      J. Dairy Sci. 2009; 92 (19762834): 5154-5166
      https://doi.org/10.3168/jds.2009-2259
      • Pereira A.B.D.
      • Whitehouse N.L.
      • Aragona K.M.
      • Schwab C.S.
      • Reis S.F.
      • Brito A.F.
      Production and nitrogen utilization in lactating dairy cows fed ground field peas with or without ruminally protected lysine and methionine.
      J. Dairy Sci. 2017; 100 (28601445): 6239-6255
      https://doi.org/10.3168/jds.2016-12140
      • Raggio G.
      • Pacheco D.
      • Berthiaume R.
      • Lobley G.E.
      • Pellerin D.
      • Allard G.
      • Dubreuil P.
      • Lapierre H.
      Effect of level of metabolizable protein on splanchnic flux of amino acids in lactating dairy cows.
      J. Dairy Sci. 2004; 87 (15377624): 3461-3472
      https://doi.org/10.3168/jds.S0022-0302(04)73481-5
      • Reynal S.M.
      • Broderick G.A.
      Effect of dietary level of rumen-degraded protein on production and nitrogen metabolism in lactating dairy cows.
      J. Dairy Sci. 2005; 88 (16230710): 4045-4064
      https://doi.org/10.3168/jds.S0022-0302(05)73090-3
      • Savari M.
      • Khorvash M.
      • Amanlou H.
      • Ghorbani G.R.
      • Ghasemi E.
      • Mirzaei M.
      Effects of rumen-degradable protein:rumen-undegradable protein ratio and corn processing on production performance, nitrogen efficiency, and feeding behavior of Holstein dairy cows.
      J. Dairy Sci. 2018; 101 (29224859): 1111-1122
      https://doi.org/10.3168/jds.2017-12776
      • Socha M.T.
      • Putnam D.E.
      • Garthwaite B.D.
      • Whitehouse N.L.
      • Kierstead N.A.
      • Schwab C.G.
      • Ducharme G.A.
      • Robert J.C.
      Improving intestinal amino acid supply of pre- and postpartum dairy cows with rumen-protected methionine and lysine.
      J. Dairy Sci. 2005; 88 (15738245): 1113-1126
      https://doi.org/10.3168/jds.S0022-0302(05)72778-8
      • Socha M.T.
      • Schwab C.G.
      • Putnam D.E.
      • Whitehouse N.L.
      • Garthwaite B.D.
      • Ducharme G.A.
      Extent of methionine limitation in peak-, early-, and mid-lactation dairy cows.
      J. Dairy Sci. 2008; 91 (18420630): 1996-2010
      https://doi.org/10.3168/jds.2007-0739
      • Vargas-Rodriguez C.F.
      • Yuan K.
      • Titgemeyer E.C.
      • Mamedova L.K.
      • Griswold K.E.
      • Bradford B.J.
      Effects of supplemental chromium propionate and rumen-protected amino acids on productivity, diet digestibility, and energy balance of peak-lactation dairy cattle.
      J. Dairy Sci. 2014; 97 (24731641): 3815-3821
      https://doi.org/10.3168/jds.2013-7767
      • Wang C.
      • Liu H.Y.
      • Wang Y.M.
      • Yang Z.Q.
      • Liu J.X.
      • Wu Y.M.
      • Yan T.
      • Ye H.W.
      Effects of dietary supplementation of methionine and lysine on milk production and nitrogen utilization in dairy cows.
      J. Dairy Sci. 2010; 93 (20655436): 3661-3670
      https://doi.org/10.3168/jds.2009-2750
      Table A2.Chemical composition of feed ingredients used in ration formulations for each region
      % of DM, unless noted otherwise.
      ItemDM, % feedEE
      Ether extract.
      		NDFADLCPNDINADINStarch
      The Maritimes
       Corn silage-35
      Number represents feed ID in the NRC (2001) tables.
      		34.833.2346.303.079.181.300.8131.37
       Alfalfa silage-8338.673.0245.687.1418.573.281.601.94
       Mix silage-7441.673.1451.526.2216.053.511.392.12
       Grass hay-5285.342.5258.684.8311.883.901.131.48
       Corn grain-2787.674.259.481.029.790.700.3068.89
       Straw-12090.601.7272.408.807.280.000.801.70
       Barley grain-0888.832.2120.802.0912.711.800.5154.44
       Wheat grain-11688.332.3016.751.8214.161.700.2262.31
       Canola meal-1990.005.4029.8010.5639.206.302.491.46
       Calcium soaps fat-4095.7683.940.000.000.000.000.000.00
       Soybean meal-10488.009.0929.700.7053.4114.991.061.65
       Soybean meal-10789.601.119.790.5652.490.700.411.65
       Corn gluten meal-2586.672.5011.641.7462.543.603.0815.30
       Corn distillers-2364.3715.7235.714.5235.508.602.664.10
      Central Canada
       Corn silage-3535.003.0742.592.747.881.320.8031.37
       Alfalfa silage-8340.172.8845.277.2220.082.901.601.94
       Mix silage-7438.833.1251.356.0016.843.531.352.12
       Grass hay-5285.342.5959.924.8412.833.901.141.48
       Corn grain-2787.004.259.511.039.610.710.3368.89
       Straw-12088.501.9071.508.949.800.000.800.00
       Wheat grain-11688.003.1125.752.8015.141.700.2062.31
       Barley grain-0891.002.2020.802.0912.161.800.5254.44
       Canola meal-1989.504.3629.9610.5640.766.302.401.46
       Calcium soaps fat-4096.4884.360.000.000.000.000.000.00
       Soybean meal-10489.608.1021.701.5046.307.750.401.65
       Soybean meal-10789.501.109.800.5053.800.700.401.65
       Corn gluten meal-2586.672.5011.641.7462.543.603.0815.30
       Corn distillers-2368.5015.1532.303.5529.657.783.584.10
      The Prairies
       Corn silage-3534.833.2346.303.079.181.300.8131.37
       Alfalfa silage-8345.272.8845.277.2220.082.901.601.94
       Barley silage-1035.503.5056.305.6012.001.600.906.30
       Pea-3487.281.8613.671.0623.914.444.3848.00
       Barley grain-0888.002.2020.801.9012.401.800.5054.44
       Straw-12088.501.9071.508.945.000.000.800.00
       Wheat grain-11688.002.309.481.029.791.700.3062.31
       Grass hay-5285.342.5258.684.8311.883.901.131.48
       Legume-haylage-8348.332.7644.826.9817.823.101.601.94
       Wheat distillers-4390.605.0034.0013.4037.5021.455.233.20
       Canola meal-1992.004.3629.9610.5640.006.302.401.46
       Calcium soaps fat-4096.4884.360.000.000.000.000.000.00
       Soybean meal-10488.009.0929.700.7053.4114.991.061.65
       Soybean meal-10789.501.109.800.5652.490.700.411.65
       Corn gluten meal-2586.672.5011.641.7462.543.603.0815.30
      MineralCaPMgNaClS
       CaCO398.0038.900.000.050.060.000.00
       CaHPO497.0022.0019.000.590.050.001.14
       MgO98.000.000.0056.200.000.000.00
       CaSO4·2H2097.0023.280.000.000.000.0018.62
       NaCl98.000.000.000.0040.0060.000.00
       MgSO4·7H2097.000.000.009.800.000.0013.23
      1 % of DM, unless noted otherwise.
      2 Ether extract.
      3 Number represents feed ID in the
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      7th ed. National Academies Press, 2001
      tables.
      Table A3.Crops
      BG = barley grain, CS = corn silage, AS = alfalfa silage, MS = mixed silage, CA = canola, GH = grass hay, CG = corn grain, SB = soybean, WG = wheat grain, BS = barley silage, PE = field pea, AS-I = alfalfa silage pure implantation.
      used in rotation (R) for all land units (LU) for each region with their yields and requirements (rqt)
      Year1234567Yield t/haN rqt kg/haP rqt, kg/haCost $/haMargin $/ha2Forage margin $/ha2
      The Maritimes
       LUrich R1BG+ASASMSMS4.318.826.9413.86389.92329.63
       LUrich R2BGBGCA2.767.930.8293.79419.3782.41
       LUrich R3CSBG+ASASMSMS5.928.827.5457.40530.55482.32
       LUrich R4CSCSBG+ASASMS7.346.828.5513.72655.73607.50
       LUrich R5BG+ASASMSMSGH4.233.327.3368.89410.73362.50
       LUpoor R1BGBG+ASASMSMS4.137.043.3385.88383.40288.43
       LUpoor R2BGCABG+ASASMSMS3.749.046.3386.94420.39208.99
       LUpoor R3BG+ASASMSMS4.330.640.6413.86389.92329.63
       LUpoor R4BG+ASASMS4.427.542.5439.20387.49307.11
       LUpoor R5BG+ASASMSMSGH4.246.539.8368.89410.73362.50
      Central Canada
       LUrich R1CSAS-IASMS8.738.429.3576.24848.18848.18
       LUrich R2CGCGSB6.497.521.6623.681,281.790.00
       LUrich R3CGCSWG+ASASMSMSGH7.766.028.0545.65920.37642.25
       LUrich R4WG+ASASMSMS5.520.626.8521.75518.79501.22
       LUrich R5CSCSWG+ASASMSMS9.857.727.9600.64968.29956.58
       LUpoor R1CSAS-IASMS8.755.048.1576.24848.18848.18
       LUpoor R2CGCGSB6.4115.038.3623.681,281.790.00
       LUpoor R3CGCSWG+ASASMSMSGH7.783.542.6545.65920.37642.25
       LUpoor R4WG+ASASMSMS5.532.540.6521.75518.79501.22
       LUpoor R5CSCSWG+ASASMSMS9.775.043.7600.64968.29956.58
      The Prairies
       LUrich R1CSCSBG+ASASASAS6.318.323.3573.85373.66413.68
       LUrich R2CSBGBG+ASASASAS5.510.022.5511.55320.14294.29
       LUrich R3BGBGPE5.126.630.0478.39422.3080.39
       LUrich R4CSCSPEBG+ASASASAS5.818.525.7569.43353.91354.58
       LUrich R5CSBGPEBG+ASASASAS5.111.425.0516.02308.03252.25
       LUpoor R1CSBSBG+ASASASAS5.7924.1747.50494.65573.29613.31
       LUpoor R2CSBGBG+ASASASAS5.524.147.5511.55320.14294.29
       LUpoor R3BGBGPE5.143.343.3478.39422.3080.39
       LUpoor R4CSBSPEBG+ASASASAS5.3323.547.8501.54525.02525.69
       LUpoor R5CSBGPEBG+ASASASAS5.123.547.8516.02308.03252.25
      1 BG = barley grain, CS = corn silage, AS = alfalfa silage, MS = mixed silage, CA = canola, GH = grass hay, CG = corn grain, SB = soybean, WG = wheat grain, BS = barley silage, PE = field pea, AS-I = alfalfa silage pure implantation.
      Table A4.Nutrient and feed minimal and maximal constraints used in the formulation of the lactating dairy rations
      ItemMinimumMaximum
      Nutrient
       NDF, % DM25—
       Forage NDF, % DM19—
       NFC, % DM—42
       RDP, % DM912
       Ether extract, % DM—6.5
      Feed
       Corn silage, % DM—40
       Corn grain, % DM—40
       Wheat grain, % DM—20
       Barley grain, % DM—20
       Grass hay, % DM—10
       Distiller grain, % DM—10
       Rumen-protected fat, % DM—7

      REFERENCES

        • Agle M.
        • Hristov A.N.
        • Zaman S.
        • Schneider C.
        • Ndegwa P.
        • Vaddella V.K.
        The effects of ruminally degraded protein on rumen fermentation and ammonia losses from manure in dairy cows.
        J. Dairy Sci. 2010; 93 (20338440): 1625-1637
        https://doi.org/10.3168/jds.2009-2579
        View in Article
        • Agriculture and Agri-Food Canada
        Assessment of Net Emissions of Greenhouse Gases from Ethanol-Gasoline Blends in Southern Ontario.
        Agriculture and Agri-Food Canada, 1999
        View in Article
        • Alberta Milk
        Alberta Milk.
        https://albertamilk.com/
        Date: 2016
        Date accessed: May 20, 2021
        View in Article
        • Arndt C.
        • Powell J.M.
        • Aguerre M.J.
        • Wattiaux M.A.
        Performance, digestion, nitrogen balance, and emission of manure ammonia, enteric methane, and carbon dioxide in lactating cows fed diets with varying alfalfa silage-to-corn silage ratios.
        J. Dairy Sci. 2015; 98 (25465537): 418-430
        https://doi.org/10.3168/jds.2014-8298
        View in Article
        • Arriola Apelo S.I.
        • Bell A.L.
        • Estes K.
        • Ropelewski J.
        • de Veth M.J.
        • Hanigan M.D.
        Effects of reduced dietary protein and supplemental rumen-protected essential amino acids on the nitrogen efficiency of dairy cows.
        J. Dairy Sci. 2014; 97 (25022689): 5688-5699
        https://doi.org/10.3168/jds.2013-7833
        View in Article
        • Bank of Canada
        Historical noon and closing rates.
        http://www.bankofcanada.ca/rates/exchange/legacy-noon-and-closing-rates/
        Date: 2017
        Date accessed: July 10, 2016
        View in Article
        • Beauregard G.
        Budget de cultures.
        https://www7-bibl-ulaval-ca.acces.bibl.ulaval.ca/doelec/AccesRestreint/budgets_agro/
        Date: 2016
        Date accessed: November 5, 2020
        View in Article
        • Berthiaume R.
        • Thivierge M.C.
        • Patton R.A.
        • Dubreuil P.
        • Stevenson M.
        • McBride B.W.
        • Lapierre H.
        Effect of ruminally protected methionine on splanchnic metabolism of amino acids in lactating dairy cows.
        J. Dairy Sci. 2006; 89 (16606732): 1621-1634
        https://doi.org/10.3168/jds.S0022-0302(06)72229-9
        View in Article
        • Blouin J.P.
        • Bernier J.F.
        • Reynolds C.K.
        • Lobley G.E.
        • Dubreuil P.
        • Lapierre H.
        Effect of supply of metabolizable protein on splanchnic fluxes of nutrients and hormones in lactating dairy cows.
        J. Dairy Sci. 2002; 85 (12416816): 2618-2630
        https://doi.org/10.3168/jds.S0022-0302(02)74347-6
        View in Article
        • Borucki Castro S.I.
        • Phillip L.E.
        • Lapierre H.
        • Jardon P.W.
        • Berthiaume R.
        The relative merit of ruminal undegradable protein from soybean meal or soluble fiber from beet pulp to improve nitrogen utilization in dairy cows.
        J. Dairy Sci. 2008; 91 (18832219): 3947-3957
        https://doi.org/10.3168/jds.2007-0638
        View in Article
        • Boucher S.E.
        • Ordway R.S.
        • Whitehouse N.L.
        • Lundy F.P.
        • Kononoff P.J.
        • Schwab C.G.
        Effect of incremental urea supplementation of a conventional corn silage-based diet on ruminal ammonia concentration and synthesis of microbial protein.
        J. Dairy Sci. 2007; 90 (18024754): 5619-5633
        https://doi.org/10.3168/jds.2007-0012
        View in Article
        • Brander M.
        • Wylie C.
        The use of substitution in attributional life cycle assessment.
        Greenh. Gas Meas. Manage. 2011; 1: 161-166
        https://doi.org/10.1080/20430779.2011.637670
        View in Article
        • Brito A.F.
        • Broderick G.A.
        Effect of varying dietary ratios of alfalfa silage to corn silage on production and nitrogen utilization in lactating dairy cows.
        J. Dairy Sci. 2006; 89 (16960068): 3924-3938
        https://doi.org/10.3168/jds.S0022-0302(06)72435-3
        View in Article
        • Broderick G.A.
        • Faciola A.P.
        • Armentano L.E.
        Replacing dietary soybean meal with canola meal improves production and efficiency of lactating dairy cows.
        J. Dairy Sci. 2015; 98 (26074230): 5672-5687
        https://doi.org/10.3168/jds.2015-9563
        View in Article
        • Broderick G.A.
        • Stevenson M.J.
        • Patton R.A.
        Effect of dietary protein concentration and degradability on response to rumen-protected methionine in lactating dairy cows.
        J. Dairy Sci. 2009; 92 (19448006): 2719-2728
        https://doi.org/10.3168/jds.2008-1277
        View in Article
        • Castillo A.R.
        • Kebreab E.
        • Beever D.E.
        • Barbi J.H.
        • Sutton J.D.
        • Kirby H.C.
        • France J.
        The effect of energy supplementation on nitrogen utilization in lactating dairy cows fed grass silage diets.
        J. Anim. Sci. 2001; 79 (11204706): 240-246
        https://doi.org/10.2527/2001.791240x
        View in Article
        • Cela S.
        • Ketterings Q.M.
        • Czymmek K.
        • Soberon M.
        • Rasmussen C.
        Characterization of nitrogen, phosphorus, and potassium mass balances of dairy farms in New York State.
        J. Dairy Sci. 2014; 97 (25468709): 7614-7632
        https://doi.org/10.3168/jds.2014-8467
        View in Article
        • CRAAQ
        Parent L.-E. Gagné G. Guide de référence en fertilisation. 2nd ed. CRAAQ, 2010
        View in Article
        • Davidson S.
        • Hopkins B.A.
        • Diaz D.E.
        • Bolt S.M.
        • Brownie C.
        • Fellner V.
        • Whitlow L.W.
        Effects of amounts and degradability of dietary protein on lactation, nitrogen utilization, and excretion in early lactation Holstein cows.
        J. Dairy Sci. 2003; 86 (12778579): 1681-1689
        https://doi.org/10.3168/jds.S0022-0302(03)73754-0
        View in Article
        • Doepel L.
        • Hewage I.I.
        • Lapierre H.
        Milk protein yield and mammary metabolism are affected by phenylalanine deficiency but not by threonine or tryptophan deficiency.
        J. Dairy Sci. 2016; 99 (26851853): 3144-3156
        https://doi.org/10.3168/jds.2015-10320
        View in Article
        • FADQ (Financière Agricole de Québec)
        Rendement de références en assurance récolte.
        https://www.fadq.qc.ca/statistiques/assurance-recolte/rendements-de-reference/
        Date: 2016
        Date accessed: May 18, 2021
        View in Article
        • Fadul-Pacheco L.
        • Pellerin D.
        • Chouinard P.Y.
        • Wattiaux M.A.
        • Duplessis M.
        • Charbonneau É.
        Nitrogen efficiency of eastern Canadian dairy herds: Effect on production performance and farm profitability.
        J. Dairy Sci. 2017; 100 (28601444): 6592-6601
        https://doi.org/10.3168/jds.2016-11788
        View in Article
        • GCAQ
        Agritel-Web.
        http://agritel.vialepole.com/
        Date: 2016
        Date accessed: June 20, 2020
        View in Article
        • Giallongo F.
        • Harper M.T.
        • Oh J.
        • Lopes J.C.
        • Lapierre H.
        • Patton R.A.
        • Parys C.
        • Shinzato I.
        • Hristov A.N.
        Effects of rumen-protected methionine, lysine, and histidine on lactation performance of dairy cows.
        J. Dairy Sci. 2016; 99 (27060815): 4437-4452
        https://doi.org/10.3168/jds.2015-10822
        View in Article
        • Giallongo F.
        • Harper M.T.
        • Oh J.
        • Parys C.
        • Shinzato I.
        • Hristov A.N.
        Histidine deficiency has a negative effect on lactational performance of dairy cows.
        J. Dairy Sci. 2017; 100 (28131569): 2784-2800
        https://doi.org/10.3168/jds.2016-11992
        View in Article
        • Giallongo F.
        • Hristov A.N.
        • Oh J.
        • Frederick T.
        • Weeks H.
        • Werner J.
        • Lapierre H.
        • Patton R.A.
        • Gehman A.
        • Parys C.
        Effects of slow-release urea and rumen-protected methionine and histidine on performance of dairy cows.
        J. Dairy Sci. 2015; 98 (25726096): 3292-3308
        https://doi.org/10.3168/jds.2014-8791
        View in Article
        • Gouvernement du Québec
        Règlement sur les exploitations agricoles: Loi sur la qualité de l'environnement.
        http://legisquebec.gouv.qc.ca/fr/ShowDoc/cr/Q-2
        Date: 2019
        Date accessed: January 16, 2020
        View in Article
        • Halmemies-Beauchet-Filleau A.
        • Vanhatalo A.
        • Toivonen V.
        • Heikkilä T.
        • Lee M.R.F.
        • Shingfield K.J.
        Effect of replacing grass silage with red clover silage on nutrient digestion, nitrogen metabolism, and milk fat composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio.
        J. Dairy Sci. 2014; 97 (24679932): 3761-3776
        https://doi.org/10.3168/jds.2013-7358
        View in Article
        • Intergovernmental Panel on Climate Change
        IPCC Guidelines for National Greenhouse Gas Inventories.
        Institute for Global Environmental Strategies, 2006
        View in Article
        • International Civil Aviation Organization
        World Geodetic System - 1984 (WGS-84) Manual.
        ICAO, 2002
        View in Article
        • International Dairy Federation
        A common carbon footprint approach for the dairy sector: The IDF guide to standard life cycle assessment methodology.
        Bull. Int. Dairy Fed. 2015; : 63
        View in Article
        • Korhonen M.
        • Vanhatalo A.
        • Huhtanen P.
        Effect of protein source on amino acid supply, milk production, and metabolism of plasma nutrients in dairy cows fed grass silage.
        J. Dairy Sci. 2002; 85 (12512607): 3336-3351
        https://doi.org/10.3168/jds.S0022-0302(02)74422-6
        View in Article
        • Lapierre H.
        • Binggeli S.
        • Sok M.
        • Pellerin D.
        • Ouellet D.R.
        Estimation of correction factors to determine the true amino acid concentration of protein after a 24-hour hydrolysis.
        J. Dairy Sci. 2019; 102 (30591335): 1205-1212
        https://doi.org/10.3168/jds.2018-15392
        View in Article
        • 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.
        Animal. 2020; 14 (32024565): s87-s102
        https://doi.org/10.1017/S1751731119003173
        View in Article
        • 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 requirements.
        in: Cornell Nutrition Conference for Feed Manufacturers. Cornell University, 2016: 205-218
        View in Article
        • Lee C.
        • Giallongo F.
        • Hristov A.N.
        • Lapierre H.
        • Cassidy T.W.
        • Heyler K.S.
        • Varga G.A.
        • Parys C.
        Effect of dietary protein level and rumen-protected amino acid supplementation on amino acid utilization for milk protein in lactating dairy cows.
        J. Dairy Sci. 2015; 98 (25547302): 1885-1902
        https://doi.org/10.3168/jds.2014-8496
        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
        • Leonardi C.
        • Stevenson M.
        • Armentano L.E.
        Effect of two levels of crude protein and methionine supplementation on performance of dairy cows.
        J. Dairy Sci. 2003; 86 (14740841): 4033-4042
        https://doi.org/10.3168/jds.S0022-0302(03)74014-4
        View in Article
        • Mason A.J.
        OpenSolver—An Open Source Add-in to Solve Linear and Integer Progammes in Excel.
        in: Operations Research Proceedings 2011. Springer, 2012: 401-406
        View in Article
        • Mc Geough E.J.
        • Little S.M.
        • Janzen H.H.
        • McAllister T.A.
        • McGinn S.M.
        • Beauchemin K.A.
        Life-cycle assessment of greenhouse gas emissions from dairy production in Eastern Canada: A case study.
        J. Dairy Sci. 2012; 95 (22916922): 5164-5175
        https://doi.org/10.3168/jds.2011-5229
        View in Article
        • Ministry of Agriculture and Forestry
        Agricultural economics, competitiveness and statistics.
        Government of Alberta, 2016
        https://www.alberta.ca/agricultural-economics-competitiveness-and-statistics.aspx
        Date accessed: April 23, 2016
        View in Article
        • Mjoun K.
        • Kalscheur K.F.
        • Hippen A.R.
        • Schingoethe D.J.
        Performance and amino acid utilization of early lactation dairy cows fed regular or reduced-fat dried distillers grains with solubles.
        J. Dairy Sci. 2010; 93 (20630235): 3176-3191
        https://doi.org/10.3168/jds.2009-2974
        View in Article
        • Moraes L.E.
        • Strathe A.B.
        • Fadel J.G.
        • Casper D.P.
        • Kebreab E.
        Prediction of enteric methane emissions from cattle.
        Glob. Chang. Biol. 2014; 20 (24259373): 2140-2148
        https://doi.org/10.1111/gcb.12471
        View in Article
        • NASEM (National Academies of Sciences Engineering and Medicine)
        Nutrient Requirements of Beef Cattle.
        8th rev. ed. National Academies Press, 2016
        View in Article
        • NRC (National Research Council)
        Nutrient Requirements of Dairy Cattle.
        7th ed. National Academies Press, 2001
        View in Article
        • NRC (National Research Council)
        Air Emissions from Animal Feeding Operations: Current Knowledge, Future Needs.
        National Academies Press, 2003
        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
        • Nursoy H.
        • Ronquillo M.G.
        • Faciola A.P.
        • Broderick G.A.
        Lactation response to soybean meal and rumen-protected methionine supplementation of corn silage-based diets.
        J. Dairy Sci. 2018; 101 (29290449): 2084-2095
        https://doi.org/10.3168/jds.2017-13227
        View in Article
        • Olmos Colmenero J.J.
        • Broderick G.A.
        Effect of amount and ruminal degradability of soybean meal protein on performance of lactating dairy cows.
        J. Dairy Sci. 2006; 89 (16606733): 1635-1643
        https://doi.org/10.3168/jds.S0022-0302(06)72230-5
        View in Article
        • Ordway R.S.
        • Boucher S.E.
        • Whitehouse N.L.
        • Schwab C.G.
        • Sloan B.K.
        Effects of providing two forms of supplemental methionine to periparturient Holstein dairy cows on feed intake and lactational performance.
        J. Dairy Sci. 2009; 92 (19762834): 5154-5166
        https://doi.org/10.3168/jds.2009-2259
        View in Article
        • Patton R.A.
        Effect of rumen-protected methionine on feed intake, milk production, true milk protein concentration, and true milk protein yield, and the factors that influence these effects: A meta-analysis.
        J. Dairy Sci. 2010; 93 (20412926): 2105-2118
        https://doi.org/10.3168/jds.2009-2693
        View in Article
        • Pellerin D.
        • Charbonneau E.
        • Fadul-Pacheco L.
        • Soucy O.
        • Wattiaux M.A.
        Economic effect of reducing nitrogen and phosphorus mass balance on Wisconsin and Québec dairy farms.
        J. Dairy Sci. 2017; 100 (28780106): 8614-8629
        https://doi.org/10.3168/jds.2016-11984
        View in Article
        • Pellerin D.
        • Wattiaux M.A.
        • Charbonneau E.
        • Soucy O.
        • Moreira V.
        • Flis S.A.
        Introduction to N-CyCLES (Nutrient Cycling: Crops, Livestock, Environment and Soil).
        https://kb.wisc.edu/dairynutrient/60349
        Date: 2017
        Date accessed: May 26, 2021
        View in Article
        • Pereira A.B.D.
        • Whitehouse N.L.
        • Aragona K.M.
        • Schwab C.S.
        • Reis S.F.
        • Brito A.F.
        Production and nitrogen utilization in lactating dairy cows fed ground field peas with or without ruminally protected lysine and methionine.
        J. Dairy Sci. 2017; 100 (28601445): 6239-6255
        https://doi.org/10.3168/jds.2016-12140
        View in Article
        • Raggio G.
        • Pacheco D.
        • Berthiaume R.
        • Lobley G.E.
        • Pellerin D.
        • Allard G.
        • Dubreuil P.
        • Lapierre H.
        Effect of level of metabolizable protein on splanchnic flux of amino acids in lactating dairy cows.
        J. Dairy Sci. 2004; 87 (15377624): 3461-3472
        https://doi.org/10.3168/jds.S0022-0302(04)73481-5
        View in Article
        • Reynal S.M.
        • Broderick G.A.
        Effect of dietary level of rumen-degraded protein on production and nitrogen metabolism in lactating dairy cows.
        J. Dairy Sci. 2005; 88 (16230710): 4045-4064
        https://doi.org/10.3168/jds.S0022-0302(05)73090-3
        View in Article
        • Robinson P.H.
        Impacts of manipulating ration metabolizable lysine and methionine levels on the performance of lactating dairy cows: A systematic review of the literature.
        Livest. Sci. 2010; 127: 115-126
        https://doi.org/10.1016/j.livsci.2009.10.003
        View in Article
        • Roman-Garcia Y.
        • White R.R.
        • Firkins J.L.
        Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations.
        J. Dairy Sci. 2016; 99 (27448861): 7918-7931
        https://doi.org/10.3168/jds.2015-10661
        View in Article
        • Savari M.
        • Khorvash M.
        • Amanlou H.
        • Ghorbani G.R.
        • Ghasemi E.
        • Mirzaei M.
        Effects of rumen-degradable protein:rumen-undegradable protein ratio and corn processing on production performance, nitrogen efficiency, and feeding behavior of Holstein dairy cows.
        J. Dairy Sci. 2018; 101 (29224859): 1111-1122
        https://doi.org/10.3168/jds.2017-12776
        View in Article
        • Socha M.T.
        • Putnam D.E.
        • Garthwaite B.D.
        • Whitehouse N.L.
        • Kierstead N.A.
        • Schwab C.G.
        • Ducharme G.A.
        • Robert J.C.
        Improving intestinal amino acid supply of pre- and postpartum dairy cows with rumen-protected methionine and lysine.
        J. Dairy Sci. 2005; 88 (15738245): 1113-1126
        https://doi.org/10.3168/jds.S0022-0302(05)72778-8
        View in Article
        • Socha M.T.
        • Schwab C.G.
        • Putnam D.E.
        • Whitehouse N.L.
        • Garthwaite B.D.
        • Ducharme G.A.
        Extent of methionine limitation in peak-, early-, and mid-lactation dairy cows.
        J. Dairy Sci. 2008; 91 (18420630): 1996-2010
        https://doi.org/10.3168/jds.2007-0739
        View in Article
        • Sok M.
        • Ouellet D.R.
        • Firkins J.L.
        • Pellerin D.
        • Lapierre H.
        Amino acid composition of rumen bacteria and protozoa in cattle.
        J. Dairy Sci. 2017; 100 (28501407): 5241-5249
        https://doi.org/10.3168/jds.2016-12447
        View in Article
        • Spears R.A.
        • Kohn R.A.
        • Young A.J.
        Whole-farm nitrogen balance on western dairy farms.
        J. Dairy Sci. 2003; 86 (14740860): 4178-4186
        https://doi.org/10.3168/jds.S0022-0302(03)74033-8
        View in Article
        • St-Pierre N.R.
        • Glamocic D.
        Estimating unit costs of nutrients from market prices of feedstuffs.
        J. Dairy Sci. 2000; 83 (10877408): 1402-1411
        https://doi.org/10.3168/jds.S0022-0302(00)75009-0
        View in Article
        • St-Pierre N.R.
        • Weiss W.P.
        What we have learned about optimizing efficiency of dairy production requirements versus response-based systems.
        Adv. Dairy Technol. 2012; 24: 31-49
        View in Article
        • Valacta
        Evolution de la production laitière.
        http://www.valacta.com/FR/Nos-publications/Pages/evolution-de-la-production-laitiere.aspx
        Date: 2017
        Date accessed: July 26, 2017
        View in Article
        • Vargas-Rodriguez C.F.
        • Yuan K.
        • Titgemeyer E.C.
        • Mamedova L.K.
        • Griswold K.E.
        • Bradford B.J.
        Effects of supplemental chromium propionate and rumen-protected amino acids on productivity, diet digestibility, and energy balance of peak-lactation dairy cattle.
        J. Dairy Sci. 2014; 97 (24731641): 3815-3821
        https://doi.org/10.3168/jds.2013-7767
        View in Article
        • Wang C.
        • Liu H.Y.
        • Wang Y.M.
        • Yang Z.Q.
        • Liu J.X.
        • Wu Y.M.
        • Yan T.
        • Ye H.W.
        Effects of dietary supplementation of methionine and lysine on milk production and nitrogen utilization in dairy cows.
        J. Dairy Sci. 2010; 93 (20655436): 3661-3670
        https://doi.org/10.3168/jds.2009-2750
        View in Article
        • White R.R.
        • Roman-Garcia Y.
        • Firkins J.L.
        Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction.
        J. Dairy Sci. 2016; 99 (27448854): 7932-7944
        https://doi.org/10.3168/jds.2015-10662
        View in Article
        • Whitehouse N.L.
        • Schwab C.G.
        • Luchini D.
        • Sloan B.
        A critique of dose-response plots that relate changes in content and yield of milk protein to predicted concentrations of lysine in metabolizable protein by the NRC (2001), CPM-Dairy (v.3.0.10), and AMTS.Cattle (v.2.1.31) models.
        J. Dairy Sci. 2010; 93: 447
        View in Article
        • Zanton G.I.
        • Bowman G.R.
        • Vázquez-Añón M.
        • Rode L.M.
        Meta-analysis of lactation performance in dairy cows receiving supplemental dietary methionine sources or postruminal infusion of methionine.
        J. Dairy Sci. 2014; 97 (25242429): 7085-7101
        https://doi.org/10.3168/jds.2014-8220
        View in Article

      Article info

      Publication history

      Published online: June 04, 2021
      Accepted: April 16, 2021
      Received: November 10, 2020

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      DOI: https://doi.org/10.3168/jds.2020-19893

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