Sensitivity of two metabolic models of dairy cattle digestion and metabolism to changes in nutrient content of diets

A meta-analysis of the relationship between milk protein production and absorbed amino acids and digested energy in dairy cattle

Milk protein production is the largest draw on AA supplies for lactating dairy cattle. Prior NRC predictions of milk protein production have been absorbed protein (MP)-based and used a first-limiting nutrient concept to integrate the effects of energy and protein, which yielded poor accuracy and precision (root mean squared error [RMSE] >21%). Using a meta-data set gathered, various alternative equation forms considering MP, absorbed total EAA, absorbed individual EAA, and digested energy (DE) supplies as additive drivers of production were evaluated, and all were found to be superior in statistical performance to the first limitation approach (RMSE = 14%-15%). Inclusion of DE intake and a quadratic term for MP or absorbed EAA supplies were found to be necessary to achieve intercept estimates (nonproductive protein use) that were similar to the factorial estimates of the National Academies of Sciences, Engineering, and Medicine (2021). The partial linear slope for MP was found to be 0.409, which is consistent with the observed slope bias of -0.34 g/g when a slope of 0.67 was used for MP efficiency in a first-limiting nutrient system. Replacement of MP with the supplies of individual absorbed EAA expressed in grams per day and a common quadratic across the EAA resulted in unbiased predictions with improved statistical performance as compared with MP-based models. Based on Akaike's information criterion and biological consistency, the best equations included absorbed His, Ile, Lys, Met, Thr, the NEAA, and individual DE intakes from fatty acids, NDF, residual OM, and starch. Several also contained a term for absorbed Leu. These equations generally had RMSE of 14.3% and a concordance correlation of 0.76. Based on the common quadratic and individual linear terms, milk protein response plateaus were predicted at approximately 320 g/d of absorbed His, Ile, and Lys; 395 g/d of absorbed Thr; 550 g/d of absorbed Met; and 70 g/d of absorbed Leu. Therefore, responses to each except Leu are almost linear throughout the normal in vivo range. De-aggregation of the quadratic term and parsing to individual absorbed EAA resulted in nonbiological estimates for several EAA indicating over-parameterization. Expression of the EAA as g/100 g total absorbed EAA or as ratios of DE intake and using linear and quadratic terms for each EAA resulted in similar statistical performance, but the solutions had identifiability problems and several nonbiological parameter estimates. The use of ratios also introduced nonlinearity in the independent variables which violates linear regression assumptions. Further screening of the global model using absorbed EAA expressed as grams per day with a common quadratic using an all-models approach, and exhaustive cross-evaluation indicated the parameter estimates for BW, all 4 DE terms, His, Ile, Lys, Met, and the common quadratic term were stable, whereas estimates for Leu and Thr were known with less certainty. Use of independent and additive terms and a quadratic expression in the equation results in variable efficiencies of conversion. The additivity also provides partial substitution among the nutrients. Both of these prevent establishment of fixed nutrient requirements in support of milk protein production.

Sensitivity analysis of the INRA 2018 feeding system for ruminants by a one-at-a-time approach: Effects of dietary input variables on predictions of multiple responses of dairy cattle

In the feeding system for ruminants developed in 2018 by the French National Institute of Agricultural Research (INRA), the prediction of multiple animal responses is based on the integration of the characteristics of the animal and the available feedstuff characteristics, as well as the rationing objectives. In this framework, the characterization of feedstuffs in terms of net energy, digestible protein, and fill units requires information on their chemical composition, digestibility, and degradability. Despite the importance of these feed characteristics, a comprehensive assessment of their impact on the responses predicted by the INRA 2018 feeding system has not been carried out. Thus, our study investigated how variables predicted by the INRA feeding system (i.e., outputs) for dairy cows are affected by variation in feed characterization (i.e., inputs). We selected 5 input variables for the sensitivity analysis: CP, OM apparent digestibility (OMd), gross energy (GE), effective degradability of nitrogen assuming a passage rate of 6%/h (ED6_N), and true intestinal digestibility (dr_N) of nitrogen. A one-at-a-time sensitivity analysis was performed on predicted digestive, productive, and environmental output variables for dairy cows with 6 contrasted diets. These 6 diets were formulated to meet 95% of the potential daily milk production (37.5 kg) of a multiparous cow at wk 14 of lactation. The values of the 5 key input variables of each feedstuff were then randomly sampled around the INRA 2018 feed table values (reference point). The response of the output variable to the variation of the input variable was quantified and compared using the tangent value at the reference point and the normalized sensitivity coefficient. Among the major final output variables, CP and dr_N had the greatest impact on N excretion in urine (as a proportion of total fecal and urinary N excretion; UN/TN); OMd and GE had the greatest impact on N utilization efficiency (NUE; N in milk as proportion of intake N); and ED6_N had the greatest impact on milk protein yield (MPY). Additionally, CP, GE, and dr_N had the least effect on methane emission, OMd had the least effect on UN/TN, and ED6_N had the least effect on NUE. The responses of most output variables to ED6_N and dr_N variations were highly dependent on diet and were related to the ratio between protein truly digestible in the intestine (PDI; i.e., MP) and net energy for lactation (UFL; i.e., NEL) at the reference point of each diet. Overall, we were able to analyze the response of output variables to the variations of the input variables, using the tangent and its normalized value at the reference point. The predicted final outputs were more affected by variations in CP, GE, and OMd. The other 2 input variables, ED6_N and dr_N, had a smaller effect on the final output variables, but the responses varied between the diets according to their PDI/UFL ratio. Our present study was conducted using 6 representative diets for dairy cattle fed at their potential, but should be completed by the analysis of more diverse conditions.

Predictions of ruminal outflow of essential amino acids in dairy cattle

The objective of this work was to update and evaluate predictions of essential AA (EAA) outflows from the rumen. The model was constructed based on previously derived equations for rumen-undegradable (RUP), microbial (MiCP), and endogenous (EndCP) protein outflows from the rumen, and revised estimates of ingredient composition and EAA composition of the protein fractions. Corrections were adopted to account for incomplete recovery of EAA during 24-h acid hydrolysis. The predicted ruminal protein and EAA outflows were evaluated against a data set of observed values from the literature. Initial evaluations indicated a minor mean bias for non-ammonia, non-microbial nitrogen flow ([RUP + EndCP]/6.25) of 16 g of N per day. Root mean squared errors (RMSE) of EAA predictions ranged from 26.8 to 40.6% of observed mean values. Concordance correlation coefficients (CCC) of EAA predictions ranged from 0.34 to 0.55. Except for Leu, all ruminal EAA outflows were overpredicted by 3.0 to 32 g/d. In addition, small but significant slope biases were present for Arg [2.2% mean squared error (MSE)] and Lys (3.2% MSE). The overpredictions may suggest that the mean recovery of AA from acid hydrolysis across laboratories was less than estimates encompassed in the recovery factors. To test this hypothesis, several regression approaches were undertaken to identify potential causes of the bias. These included regressions of (1) residual errors for predicted EAA flows on each of the 3 protein-driven EA flows, (2) observed EAA flows on each protein-driven EAA flow, including an intercept, (3) observed EAA flows on the protein-driven EAA flows, excluding an intercept term, and (4) observed EAA flows on RUP and MiCP. However, these equations were deemed unsatisfactory for bias adjustment, as they generated biologically unfeasible predictions for some entities. Future work should focus on identifying the cause of the observed prediction bias.