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.

Models to predict milk fat concentration and yield of lactating dairy cows: A meta-analysis.

Few models have attempted to predict total milk fat because of its high variation among and within herds. The objective of this meta-analysis was to develop models to predict milk fat concentration and yield of lactating dairy cows. Data from 158 studies consisting of 658 treatments from 2,843 animals were used. Data from several feed databases were used to calculate dietary nutrients when dietary nutrient composition was not reported. Digested intake (DI, g/d) of each fatty acid (FA; C12:0, C14:0, C16:0, C16:1, C18:0, C18:1 cis, C18:1 trans C18:2, C18:3) and absorbed amounts (g/d) of each AA (Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, Val) were calculated and used as candidate variables in the models. A multi-model inference method was used to fit a large set of mixed models with study as the random effect, and the best models were selected based on Akaike's information criterion corrected for sample size and evaluated further. Observed milk fat concentration (MFC) ranged from 2.26 to 4.78%, and milk fat yield (MFY) ranged from 0.488 to 1.787 kg/d among studies. Dietary levels of forage, starch, and total FA (dry matter basis) averaged 50.8 ± 10.3% (mean ± standard deviation), 27.5 ± 7.0%, and 3.4 ± 1.3%, respectively. The MFC was positively correlated with dietary forage (0.294) and negatively associated with dietary starch (-0.286). The DI of C18:2 (g/d) was more negatively correlated with MFC (-0.313) than that of the other FA. The best variables for predicting MFC were days in milk, FA-free dry matter intake, forage, starch, DI of C18:2, DI of C18:3, and absorbed Met, His, and Trp. The best predictor variables for MFY were FA-free dry matter intake, days in milk, absorbed Met and Ile, and intakes of digested C16:0 and C18:3. This model had a root mean square error of 14.1% and concordance correlation coefficient of 0.81. Surprisingly, DI of C18:3 was positively related to milk fat, and this relationship was consistently observed among models. The models developed can be used as a practical tool for predicting milk fat of dairy cows, while recognizing that additional factors are likely to also affect fat yield.

Predicting ruminally undegraded and microbial protein flows from the rumen.

The objectives of the present work were (1) to identify the cause of the linear bias in predictions of rumen-undegradable protein (RUP) content of feeds, and devise methods to remove the bias from prediction equations, and (2) to further explore the impact of rumen-degradable protein (RDP) on microbial N (MiN) outflow from the rumen. The kinetic model used by NRC (2001), which is based on protein fractionation and rates of degradation (Kd) and passage (Kp), displays considerable slope bias (-0.30 kg/kg), indicating parameter or structural problems. Regressing Kp by feed class and a static adjustment factor for the in situ-derived Kd on observed RUP flows completely resolved the slope bias problem, and the model performed significantly better than models using unadjusted Kd and marker-based Kp. The Kd adjustment was 3.82%/h, which represents approximately a 50% increase in rates of degradation over the in situ values, indicating that in situ analyses severely underestimate true rates of protein degradation. The Kp for concentrate-derived protein was 5.83%/h, which was slightly less than the marker-predicted rate of 6.69%/h. However, the derived forage protein rate was 0.49%/h, which was considerably less than the marker-based rate of 5.07%/h. Compartmental analysis of data from a single study corroborated the regression analysis, indicating that a 25% reduction in the overall passage rate and an 87% increase in the rate of degradation were required to align ruminal N pool sizes and the extent of protein degradation with the observed data. Therefore, one must conclude that both the in situ-derived degradation rates and the marker-based particle passage rates are biased relative to protein passage and cannot be used directly to predict RUP outflow from the rumen. The effects of RDP supply on microbial nitrogen (MiN) flow were apparent when intakes of individual nutrients were offered but not when DM intake and individual nutrient concentrations were offered, due to collinearity problems. Microbial N flow from the rumen was found to be linearly related to ruminally degraded starch, ruminally degraded neutral detergent fiber (NDF), RDP, and forage NDF intakes; and quadratically related to residual OM intake. More complicated models containing 2- and 3-way interactions among nutrients were also supported by the data. Independent MiN responses to RDP, ruminally degraded starch, and ruminally degraded NDF aligned with the expected responses to each of those nutrients. Nonlinear representations of MiN were found to be inferior to the linear models. Despite using unbiased predictions of RUP and MiN as drivers of AA flows, predictions of Arg, His, Ile, and Lys flow exhibited linear slope bias relative to the observed data, indicating that representations of the AA composition of the proteins may be biased or the observed data are biased. This is an improvement over the NRC (2001) predictions, where bias adjustments were required for all of the essential AA. Despite the bias for 4 AA flows, the revised prediction system was a substantial improvement over the prior work.

Modeling fatty acids for dairy cattle: Models to predict total fatty acid concentration and fatty acid digestion of feedstuffs.

Development of predictive models of fatty acid (FA) use by dairy cattle still faces challenges due to high variation in FA composition among feedstuffs and fat supplements. Two meta-analytical studies were carried out to develop empirical models for estimating (1) the total FA concentration of feedstuffs, and (2) the apparent total-tract digestibility of total FA (DCFATTa) in dairy cows fed different fat types. In study 1, individual feedstuff data for total crude fat (EE) and FA were taken from commercial laboratories (total of 203 feeds, 1,170,937 samples analyzed for total FA, 1,510,750 samples analyzed for total EE), and data for FA composition were collected from the Cornell Net Carbohydrate and Protein System feed library. All feedstuffs were grouped into 7 classes based on their nutritional components. To predict total FA concentration (% of dry matter) for groups of feeds, the total EE (% of dry matter) was used as an independent variable in the model, and all models were linear. For forages, data were weighted using the inverse of the standard error (SE). Regression coefficients for predicting total FA from EE (% of dry matter) were 0.73 (SE, 0.04), 0.98 (0.02), 0.80 (0.02), 0.61 (0.04), 0.92 (0.03), and 0.93 (0.03), for animal protein, plant protein, energy sources, grain crop forage, by-product feeds, and oilseeds, respectively. The intercepts for plant protein and by-product groups were different from zero and included in the models. As expected, forages had the lowest total FA concentration (slope = 0.57, SE = 0.02). In study 2, data from 30 studies (130 treatment means) that reported DCFATTa in dairy cows were used. Data for animal description, diet composition, intakes of total FA, and DCFATTa, were collected. Dietary sources of fat were grouped into 11 categories based on their fat characteristic and FA profile. A mixed model including the random effect of study was used to regress digested FA on FA intake with studies weighted according to the inverse of their variance (SE). Dietary intake of extensively saturated triglycerides resulted in markedly lower total FA digestion (DCFATTa = 44%) compared with animals consuming unsaturated FA, such as Ca-salts of palm (DCFATTa = 76%) and oilseeds (DCFATTa = 73%). Cows fed saturated fats had lower total FA digestion among groups, but it was dependent on the FA profile of each fat source. The derived models provide additional insight into FA digestion in ruminants. Predictions of total FA supply and its digestion can be used to adjust fat supplementation programs for dairy cows.