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.

Meta-analysis of apparent ruminal synthesis and postruminal flow of B vitamins in dairy cows

As milk production has significantly increased over the past decade(s), existing estimates of the B-vitamin needs of the modern dairy cow are currently being reconsidered, as suboptimal B-vitamin supply may affect metabolic efficiency. At the same time, however, "true" (i.e., biologically active forms, excluding nonfunctional analogs) B-vitamin supply also cannot be adequately estimated by dietary intake, as the rumen microbiota has been shown to play a significant role in synthesis and utilization of B vitamins. Given their complex impact on the metabolism of dairy cows, incorporating these key nutrients into the next generation of mathematical models could help to better predict animal production and performance. Therefore, the purpose of this study was to generate hypotheses of regulation in the absence of supplemental B vitamins by creating empirical models, through a meta-analysis, to describe true B-vitamin supply to the cow (postruminal flow, PRF) and apparent ruminal synthesis (ARS). The database used for this meta-analysis consisted of 340 individual cow observations from 15 studies with 16 experiments, where diet and postruminal digesta samples were (post hoc) analyzed for content of B vitamins (B₁, B₂, B₃, B₆, B₉, B₁₂). Equations of univariate and multivariate linear form were considered. Models describing ARS considered dry matter intake (DMI, kg/d), B-vitamin dietary concentration [mg/kg of dry matter (DM)] and rumen-level variables such as rumen digestible neutral detergent fiber (NDF) and starch (g/kg of DM), total volatile fatty acids (VFA, mM), acetate, propionate, butyrate, and valerate molar proportions (% of VFA), mean pH, and fractional rates of degradation of NDF and starch (%/h). Models describing PRF considered dietary-level driving variables such as DMI, B-vitamin dietary concentration (mg/kg of DM), starch and crude protein (g/kg of DM) and forage NDF (g/kg of DM). Equations developed were required to contain all significant slope parameters and contained no significant collinearity between driving variables. Concordance correlation coefficient was used to evaluate the models on the developmental data set due to data scarcity. Overall, modeling ARS yielded better-performing models compared with modeling PRF, and DMI was included in all prediction equations as a scalar variable. The B-vitamin dietary concentration had a negative effect on the ARS of B₁, B₂, B₃, and B₆ but increased the PRF of B₂ and B₉. The rumen digestible NDF concentration had a negative effect on the ARS of B₂, B₃, and B₆, whereas rumen digestible starch concentration had a negative effect on the ARS of B₁ and a positive effect on the ARS of B₉. In the best prediction models, the dietary starch increased PRF of B₁, B₂, and B₉ but decreased PRF of B₁₂. The equations developed may be used to better understand the effect of diet and ruminal environment on the true supply of B vitamins to the dairy cow and stimulate the development of better-defined requirements in the future.

Quantifying body water kinetics and fecal and urinary water output from lactating Holstein dairy cows

Reliable estimates of fresh manure water output from dairy cows help to improve storage design, enhance efficiency of land application, quantify the water footprint, and predict nutrient transformations during manure storage. The objective of the study was to construct a mechanistic, dynamic, and deterministic mathematical model to quantify urinary and fecal water outputs (kg/d) from individual lactating dairy cows. The model contained 4 body water pools: reticulorumen (QRR), post-reticulorumen (QPR), extracellular (QEC), and intracellular (QIC). Dry matter (DM) intake, dietary forage, DM, crude protein, acid detergent fiber and ash contents, milk yield, and milk fat and protein contents, days in milk, and body weight were input variables to the model. A set of linear equations was constructed to determine drinking, feed, and saliva water inputs to QRR and fractional water passage from QRR to QPR. Water transfer via the rumen wall was subjected to changes in QEC and total water input to QRR. Post-reticulorumen water passage was adjusted for DM intake. Metabolic water production and respiratory cutaneous water losses were estimated with functions of heat production in the model. Water loss in urine was driven by absorbed N left after being removed via milk. Model parameters were estimated simultaneously using observed fecal and urinary water output data from lactating Holstein cows (n=670). The model was evaluated with data that were not used for model development and optimization (n=377). The observations in both data sets were related to thermoneutral conditions. The model predicted drinking water intake, fecal, urinary, and total fresh manure water output with root mean square prediction errors as a percentage of average values of 18.1, 15.6, 30.6, and 14.6%, respectively. In all cases, >97% of the prediction error was due to random variability of data. The model can also be used to determine saliva production, heat and metabolic water production, respiratory cutaneous water losses, and size of major body water pools in lactating Holstein cows under thermoneutral conditions.