Supplemental material for "Evaluation of ruminal amino acid outflow predictions by nutritional models in dairy cattle"

The objective was to evaluate the fit statistics for the predictions of ruminal AA outflows by NRC (2001), NASEM (2021), CNCPS (v.6.5.5) and NittanyCow (NC) models in lactating dairy cows. Sixty-seven studies published in English between 1984 and 2020 with 252 treatment means were considered for analysis. Lactational performance (DMI, milk yield, milk fat and true protein, and BW data), dietary nutrient composition (CP, NDF, ether extract, and ash), and ruminal outflow of total AA, microbial CP (MicP), RUP, and EAA determined by omasal or duodenal sampling techniques were extracted and used in the analysis. Production and dietary nutrient composition data reported in the studies were used as inputs to predict ruminal outflows of MP supply, MicP, RUP, and EAA using NRC, NASEM, CNCPS and NC models. Model fit statistics (i.e., observed – predicted means) and performances of nutritional models were assessed using root mean squared error (RMSE, g/d and % of observed mean), and Lin’s concordance correlation coefficient (CCC). Predictions were considered accurate, accurate with biological concern, acceptable with biological concern, or compromised if mean or linear biases (% of observed mean) were < 5%, 5 to 10% with P ≤ 0.10, 10 to 15% with P ≤ 0.10, or > 15%, respectively. Overall and based on mean biases, predictions of most variables were accurate or acceptable with biological concern across all nutritional models. Models performed similarly when predicting ruminal total AA outflow with CCC ranging from 65 to 71% and RMSE ranging from 19 to 20% of observed mean. Predictions of ruminal MicP and RUP outflows had CCC ranging from 33 to 57% and RMSE ranging from 24 to 42% of observed mean. Predictions of Lys, Met, and His had CCC ranging from 39 to 66% and RMSE ranging from 22 to 34% observed mean. Overall, nutritional models presented accurate or acceptable predictions with biological concern for most AA when considering both mean and linear biases. Accurate predictions based on mean bias were: Lys and Ile for NRC; Met, Arg, and Phe for NASEM and NC; His and Val for NRC and NASEM; and Leu and Thr for all nutritional models. Accurate predictions based on linear biases were: Met for NRC and NASEM; Phe for NRC, NASEM, and CNCPS; and Leu for all nutritional models. Compromised predictions were Met, His, and Arg for CNCPS based on mean bias. All models presented limitations if an acceptable threshold for the linear bias is set at < 5% observed mean to avoid Type I error. These biases should be considered by nutritionists when balancing rations for AA.

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Work Title Supplemental material for "Evaluation of ruminal amino acid outflow predictions by nutritional models in dairy cattle"
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Open Access
Creators
  1. Leoni Martins
  2. Robert Patton
  3. Alexander Hristov
License CC BY 4.0 (Attribution)
Work Type Other
Publication Date 2025
DOI doi:10.26207/aj0d-8v80
Deposited October 21, 2024

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Version 1
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  • Updated Description, Publication Date Show Changes
    Description
    • The objective was to evaluate the fit statistics for the predictions of ruminal AA outflows by NRC (2001), NASEM (2021), and NittanyCow (NC) models in dairy cattle. Sixty-seven studies published in English between 1984 and 2020 with 252 treatment means were considered for analysis. Lactational performance (DMI, milk yield, and BW data), dietary nutrient composition (CP, NDF, ADF, ether extract, and ash), and ruminal outflow of MP supply, microbial CP (MicP), RUP, and EAA determined by omasal or duodenal sampling techniques were extracted and used in the analysis. Production and dietary nutrient composition data reported in the studies were also used as inputs to predict ruminal outflows of MP supply, MicP, RUP, and EAA using NRC, NASEM, and NC models. Model fit statistics (i.e., observed – predicted means) and performances of nutritional models were assessed using root mean squared error (RMSE, g/d and % of observed mean), and Lin’s concordance correlation coefficient (CCC). Predictions were considered very accurate, accurate, acceptable, or compromised if mean or linear biases (% of observed mean) were < 5, 5 to 10, 10 to 15, or > 15%, respectively. Overall and based on mean biases, predictions of all response variables were very accurate or accurate across all nutritional models. Models performed similarly when predicting MP supply with RMSE (% of observed mean) ranging from 18 to 19% and CCC ranging from 68 to 72%. Predictions of MicP and adjusted RUP (RUP + endogenous CP) outflows had RMSE ranging from 26 to 36% and CCC ranging from 35 to 58%. Predictions of Lys, Met, and His had RMSE ranging from 23 to 30% and CCC ranging from 42 to 64%. The NASEM was superior to NRC model when predicting Arg, His, Thr, and Val outflows. Compared with observed values, NC seems to predict MicP, Arg, His, Met, Phe, and Thr outflows well. Additionally, NC and NASEM (2021) predicted MP supply, Arg, His, Ile, Leu, Lys, Phe, and Thr similarly. All models presented some limitations if an acceptable threshold for the linear bias is set at < 5% observed mean to avoid Type I error. These biases should be considered by nutritionists when balancing rations for AA.
    Publication Date
    • 2024
  • Added Creator Leoni Martins
  • Added Creator Robert Patton
  • Added Creator Alexander Hristov
  • Added AAflow_Revisions1_v2_Supplemental Material.docx
  • Added JDSC.2024-0643_MethionineDataset.xlsx
  • Added README.rtf
  • Updated License Show Changes
    License
    • https://creativecommons.org/licenses/by/4.0/
  • Published
  • Updated

Version 2
published

  • Created
  • Updated Description, Publication Date Show Changes
    Description
    • The objective was to evaluate the fit statistics for the predictions of ruminal AA outflows by NRC (2001), NASEM (2021), and NittanyCow (NC) models in dairy cattle. Sixty-seven studies published in English between 1984 and 2020 with 252 treatment means were considered for analysis. Lactational performance (DMI, milk yield, and BW data), dietary nutrient composition (CP, NDF, ADF, ether extract, and ash), and ruminal outflow of MP supply, microbial CP (MicP), RUP, and EAA determined by omasal or duodenal sampling techniques were extracted and used in the analysis. Production and dietary nutrient composition data reported in the studies were also used as inputs to predict ruminal outflows of MP supply, MicP, RUP, and EAA using NRC, NASEM, and NC models. Model fit statistics (i.e., observed – predicted means) and performances of nutritional models were assessed using root mean squared error (RMSE, g/d and % of observed mean), and Lin’s concordance correlation coefficient (CCC). Predictions were considered very accurate, accurate, acceptable, or compromised if mean or linear biases (% of observed mean) were < 5, 5 to 10, 10 to 15, or > 15%, respectively. Overall and based on mean biases, predictions of all response variables were very accurate or accurate across all nutritional models. Models performed similarly when predicting MP supply with RMSE (% of observed mean) ranging from 18 to 19% and CCC ranging from 68 to 72%. Predictions of MicP and adjusted RUP (RUP + endogenous CP) outflows had RMSE ranging from 26 to 36% and CCC ranging from 35 to 58%. Predictions of Lys, Met, and His had RMSE ranging from 23 to 30% and CCC ranging from 42 to 64%. The NASEM was superior to NRC model when predicting Arg, His, Thr, and Val outflows. Compared with observed values, NC seems to predict MicP, Arg, His, Met, Phe, and Thr outflows well. Additionally, NC and NASEM (2021) predicted MP supply, Arg, His, Ile, Leu, Lys, Phe, and Thr similarly. All models presented some limitations if an acceptable threshold for the linear bias is set at < 5% observed mean to avoid Type I error. These biases should be considered by nutritionists when balancing rations for AA.
    • The objective was to evaluate the fit statistics for the predictions of ruminal AA outflows by NRC (2001), NASEM (2021), CNCPS (v.6.5.5) and NittanyCow (NC) models in lactating dairy cows. Sixty-seven studies published in English between 1984 and 2020 with 252 treatment means were considered for analysis. Lactational performance (DMI, milk yield, milk fat and true protein, and BW data), dietary nutrient composition (CP, NDF, ether extract, and ash), and ruminal outflow of total AA, microbial CP (MicP), RUP, and EAA determined by omasal or duodenal sampling techniques were extracted and used in the analysis. Production and dietary nutrient composition data reported in the studies were used as inputs to predict ruminal outflows of MP supply, MicP, RUP, and EAA using NRC, NASEM, CNCPS and NC models. Model fit statistics (i.e., observed – predicted means) and performances of nutritional models were assessed using root mean squared error (RMSE, g/d and % of observed mean), and Lin’s concordance correlation coefficient (CCC). Predictions were considered accurate, accurate with biological concern, acceptable with biological concern, or compromised if mean or linear biases (% of observed mean) were < 5%, 5 to 10% with P ≤ 0.10, 10 to 15% with P ≤ 0.10, or > 15%, respectively. Overall and based on mean biases, predictions of most variables were accurate or acceptable with biological concern across all nutritional models. Models performed similarly when predicting ruminal total AA outflow with CCC ranging from 65 to 71% and RMSE ranging from 19 to 20% of observed mean. Predictions of ruminal MicP and RUP outflows had CCC ranging from 33 to 57% and RMSE ranging from 24 to 42% of observed mean. Predictions of Lys, Met, and His had CCC ranging from 39 to 66% and RMSE ranging from 22 to 34% observed mean. Overall, nutritional models presented accurate or acceptable predictions with biological concern for most AA when considering both mean and linear biases. Accurate predictions based on mean bias were: Lys and Ile for NRC; Met, Arg, and Phe for NASEM and NC; His and Val for NRC and NASEM; and Leu and Thr for all nutritional models. Accurate predictions based on linear biases were: Met for NRC and NASEM; Phe for NRC, NASEM, and CNCPS; and Leu for all nutritional models. Compromised predictions were Met, His, and Arg for CNCPS based on mean bias. All models presented limitations if an acceptable threshold for the linear bias is set at < 5% observed mean to avoid Type I error. These biases should be considered by nutritionists when balancing rations for AA.
    Publication Date
    • 2024
    • 2025
  • Deleted AAflow_Revisions1_v2_Supplemental Material.docx
  • Deleted JDSC.2024-0643_MethionineDataset.xlsx
  • Deleted README.rtf
  • Added AAflow_Revisions2_Supplemental_v1.docx
  • Added README.rtf
  • Published
  • Updated