Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.

Making dairy farming more cost-effective and reducing nitrogen environmental pollution could be reached through a reduced input of dietary protein, provided productivity is not compromised. This could be achieved through balancing dairy rations for essential amino acids (EAA) rather than their aggregate, the metabolizable protein (MP). This review revisits the estimations of the major true protein secretions in dairy cows, milk protein yield (MPY), metabolic fecal protein (MFP), endogenous urinary loss and scurf and associated AA composition. The combined efficiency with which MP (EffMP) or EAA (EffAA) is used to support protein secretions is calculated as the sum of true protein secretions (MPY + MFP + scurf) divided by the net supply (adjusted to remove the endogenous urinary excretion: MPadj and AAadj). Using the proposed protein and AA secretions, EffMP and EffAA were predicted through meta-analyses (807 treatment means) and validated using an independent database (129 treatment means). The effects of MPadj or AAadj, plus digestible energy intake (DEI), days in milk (DIM) and parity (primiparous v. multiparous), were significant in all models. Models using (MPadj, MPadj × MPadj, DEI and DEI × DEI) or (MPadj/DEI and MPadj/DEI × MPadj/DEI) had similar corrected Akaike's information criterion, but the model using MPadj/DEI performed better in the validation database. A model that also included this ratio was, therefore, used to fitting equations to predict EffAA. These equations predicted well EffAA in the validation database except for Arg which had a strong slope bias. Predictions of MPY from predicted EffMP based on MPadj/DEI, MPadj/DEI × MPadj/DEI, DIM and parity yielded a better fit than direct predictions of MPY based on MPadj, MPadj × MPadj, DEI, DIM and parity. Predictions of MPY based on each EffAA yielded fairly similar results among AA. It is proposed to ponder the mean of MPY predictions obtained from each EffAA by the lowest prediction to retain the potential limitation from AA with the shortest supply. Overall, the revisited estimations of endogenous urinary excretion and MFP, revised AA composition of protein secretions and inclusion of a variable combined EffAA (based on AAadj/DEI, AAadj/DEI × Aadj/DEI, DIM and parity) offer the potential to improve predictions of MPY, identify which AA are potentially in short supply and, therefore, improve the AA balance of dairy rations.

Multi-criteria evaluation of dairy cattle feed resources and animal characteristics for nutritive and environmental impacts.

On-farm nutrition and management interventions to reduce enteric CH4 (eCH4) emission, the most abundant greenhouse gas from cattle, may also affect volatile solids and N excretion. The objective was to jointly quantify eCH4 emissions, digestible volatile solids (dVS) excretion and N excretion from dairy cattle, based on dietary variables and animal characteristics, and to evaluate relationships between these emissions and excreta. Univariate and Bayesian multivariate mixed-effects models fitted to 520 individual North American dairy cow records indicated dry matter (DM) intake and dietary ADF and CP to be the main predictors for production of eCH4 emissions and dVS and N excreta (g/day). Yields (g/kg DM intake) of eCH4 emissions and dVS and N excreta were best predicted by dietary ADF, dietary CP, milk yield and milk fat content. Intensities (g/kg fat- and protein-corrected milk) of eCH4, dVS and N excreta were best predicted by dietary ADF, dietary CP, days in milk and BW. A K-fold cross-validation indicated that eCH4 and urinary N variables had larger root mean square prediction error (RMSPE; % of observed mean) than dVS, fecal N and total N production (on average 24.3% and 26.5% v. 16.7%, 15.5% and 16.2%, respectively), whereas intensity variables had larger RMSPE than production and yields (29.4%, 14.7% and 14.6%, respectively). Univariate and multivariate equations performed relatively similar (18.8% v. 19.3% RMSPE). Mutual correlations indicated a trade-off for eCH4 v. dVS yield. The multivariate model indicated a trade-off between eCH4 and dVS v. total N production, yield and intensity induced by dietary CP content.

Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle.

Various studies have indicated a relationship between enteric methane (CH4) production and milk fatty acid (FA) profiles of dairy cattle. However, the number of studies investigating such a relationship is limited and the direct relationships reported are mainly obtained by variation in CH4 production and milk FA concentration induced by dietary lipid supplements. The aim of this study was to perform a meta-analysis to quantify relationships between CH4 yield (per unit of feed and unit of milk) and milk FA profile in dairy cattle and to develop equations to predict CH4 yield based on milk FA profile of cows fed a wide variety of diets. Data from 8 experiments encompassing 30 different dietary treatments and 146 observations were included. Yield of CH4 measured in these experiments was 21.5 ± 2.46 g/kg of dry matter intake (DMI) and 13.9 ± 2.30 g/kg of fat- and protein-corrected milk (FPCM). Correlation coefficients were chosen as effect size of the relationship between CH4 yield and individual milk FA concentration (g/100g of FA). Average true correlation coefficients were estimated by a random-effects model. Milk FA concentrations of C6:0, C8:0, C10:0, C16:0, and C16:0-iso were significantly or tended to be positively related to CH4 yield per unit of feed. Concentrations of trans-6+7+8+9 C18:1, trans-10+11 C18:1, cis-11 C18:1, cis-12 C18:1, cis-13 C18:1, trans-16+cis-14 C18:1, and cis-9,12 C18:2 in milk fat were significantly or tended to be negatively related to CH4 yield per unit of feed. Milk FA concentrations of C10:0, C12:0, C14:0-iso, C14:0, cis-9 C14:1, C15:0, and C16:0 were significantly or tended to be positively related to CH4 yield per unit of milk. Concentrations of C4:0, C18:0, trans-10+11 C18:1, cis-9 C18:1, cis-11 C18:1, and cis-9,12 C18:2 in milk fat were significantly or tended to be negatively related to CH4 yield per unit of milk. Mixed model multiple regression and a stepwise selection procedure of milk FA based on the Bayesian information criterion to predict CH4 yield with milk FA as input (g/100g of FA) resulted in the following prediction equations: CH4 (g/kg of DMI)=23.39 + 9.74 × C16:0-iso - 1.06 × trans-10+11 C18:1 - 1.75 × cis-9,12 C18:2 (R(2) = 0.54), and CH4 (g/kg of FPCM) = 21.13 - 1.38 × C4:0 + 8.53 × C16:0-iso - 0.22 × cis-9 C18:1 - 0.59 × trans-10+11 C18:1 (R(2) = 0.47). This indicated that milk FA profile has a moderate potential for predicting CH4 yield per unit of feed and a slightly lower potential for predicting CH4 yield per unit of milk.