Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction

Ability of three dairy feed evaluation systems to predict postruminal outflows of amino acids in dairy cows: A meta-analysis

Adequate prediction of postruminal outflows of essential AA (EAA) is the starting point of balancing rations for EAA in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System version 6.5.5 [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict EAA outflows (Trp was not tested). The data set included a total of 354 treatment means from 70 duodenal and 24 omasal studies. To avoid Type I error, mean and linear biases were considered of concern if statistically significant and representing >5.0% of the observed mean. Analyses were conducted on raw observed values and on observations adjusted for the random effect of study. The analysis on raw data indicates the ability of the feed evaluation system to predict absolute values whereas the analysis on adjusted values indicates its ability to predict responses of EAA outflows to dietary changes. For the prediction of absolute values (based on raw data), NRC underpredicted outflows of all EAA, from 5.3% to 8.6% of the observed mean (%obs.mean) except for Leu, Lys, and Val; NASEM overpredicted Lys (10.8%obs.mean); and CNCPS overpredicted Arg, His, Lys, Met, and Val (5.2 to 26.0%obs.mean). No EAA had a linear bias of concern with NASEM, followed by NRC for His (6.8%obs.mean), and CNCPS for all EAA (5.6 to 12.2%obs.mean) except Leu, Phe, and Thr. In contrast, for the prediction of responses to dietary changes (based on adjusted data), NRC had 2 EAA presenting a linear bias of concern, followed by NASEM and CNCPS with 4 and 6 EAA, respectively. Predictions of His showed a linear bias of concern (5.3 to 9.6%obs.mean) with the 3 feed evaluation systems. Measured chemistry of crude protein and EAA were reported for 1 or more feed ingredients of the ration in 36% of the studies, and resulted in decreased linear biases in the 3 feed evaluation systems. The difference in mean biases of Met outflows was systematically positive when comparing omasal versus duodenal studies. Predictions of Met outflows with NRC had a higher concordance correlation coefficient in duodenal (used to develop NRC equations) versus omasal studies, whereas the opposite was observed with CNCPS, the latter showing the lowest mean bias for Met in omasal sampling studies. The 30% difference in Met mean biases between sampling sites appeared related to a similar difference found for observed Met versus nonammonia nitrogen outflows between duodenal and omasal studies, which is independent of predictions. In conclusion, NRC and NASEM yielded accurate predictions of EAA outflows, with a small superiority of NASEM to predict absolute values, and slight superiority of NRC to predict the responses to dietary changes. In comparison, CNCPS may present mean and linear biases of concern for many EAA. Moreover, it remains to determine which sampling site is more representative of the true supply of EAA to the cows.

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 utilized 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 essential AA (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 (non-productive protein use) that were similar to the factorial estimates of NASEM. The partial linear slope for MP was found to be 0.409, which is consistent with the observed slope bias of -0.34g/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 g/d 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 (AIC) and biological consistency, the best equations included absorbed His, Ile, Lys, Met, Thr, the non-essential AA, and individual DE intakes from fatty acids, neutral detergent fiber, residual organic matter, and starch. Several also contained a term for absorbed Leu. These equations generally had RMSE of 14.3% and a concordance correlations (CCC) 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 non-biological estimates for several EAA indicating over-parameterization. Expression of the EAA as g/100 g of 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 non-biological 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 g/d with a common quadratic using an all-models approach, and exhaustive cross-evaluation indicated the parameter estimates for body weight, all 4 DE terms, His, Ile, Lys, Met, and the common quadratic term were stable, while 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.

Optimizing dietary rumen-degradable starch to rumen-degradable protein ratio improves lactation performance and nitrogen utilization efficiency in mid-lactating Holstein dairy cows

The dietary rumen-degradable starch (RDS) to rumen-degradable protein (RDP) ratio, denoted as the RDS-to-RDP ratio (SPR), has been proven to enhance in vitro rumen fermentation. However, the effects of dietary SPR in vivo remain largely unexplored. This study was conducted to investigate the effect of dietary SPR on lactation performance, nutrient digestibility, rumen fermentation patterns, blood indicators, and nitrogen (N) partitioning in mid-lactating Holstein cows. Seventy-two Holstein dairy cows were randomly assigned to three groups (24 head/group), balanced for (mean ± standard deviation) days in milk (116 ± 21.5), parity (2.1 ± 0.8), milk production (42 ± 2.1 kg/d), and body weight (705 ± 52.5 kg). The cows were fed diets with low (2.1, control), medium (2.3), or high (2.5) SPR, formulated to be isoenergetic, isonitrogenous, and iso-starch. The study consisted of a one-week adaptation phase followed by an eight-week experimental period. The results indicated that the high SPR group had a lower dry matter intake compared to the other groups (p < 0.05). A quadratic increase in milk yield and feed efficiency was observed with increasing dietary SPR (p < 0.05), peaking in the medium SPR group. The medium SPR group exhibited a lower milk somatic cell count and a higher blood total antioxidant capacity compared to other groups (p < 0.05). With increasing dietary SPR, there was a quadratic improvement (p < 0.05) in the total tract apparent digestibility of crude protein, ether extract, starch, neutral detergent fiber, and acid detergent fiber. Although no treatment effect was observed in rumen pH, the rumen total volatile fatty acids concentration and microbial crude protein synthesis increased quadratically (p < 0.05) as dietary SPR increased. The molar proportion of propionate linearly increased (p = 0.01), while branched-chain volatile fatty acids linearly decreased (p = 0.01) with increasing dietary SPR. The low SPR group (control) exhibited higher concentration of milk urea N, rumen ammonia N, and blood urea N than other groups (p < 0.05). Despite a linear decrease (p < 0.05) in the proportion of urinary N to N intake, increasing dietary SPR led to a quadratic increase (p = 0.01) in N utilization efficiency and a quadratic decrease (p < 0.05) in the proportion of fecal N to N intake. In conclusion, optimizing dietary SPR has the potential to enhance lactation performance and N utilization efficiency. Based on our findings, a medium dietary SPR (with SPR = 2.3) is recommended for mid-lactating Holstein dairy cows. Nevertheless, further research on rumen microbial composition and metabolites is warranted to elucidate the underlying mechanisms of the observed effects.

Ability of three dairy feed evaluation systems to predict postruminal outflows of nitrogenous compounds in dairy cows: A meta-analysis

Adequate prediction of postruminal outflow of protein fractions is the starting point for the determination of metabolizable protein supply in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict outflows (g/d) of nonammonia nitrogren (NAN), microbial N (MiN), and nonammonia nonmicrobial N (NANMN). Predictions of rumen degradabilities (% of nutrient) of protein (RDP), NDF, and starch were also evaluated. The data set included 1,294 treatment means from 312 digesta flow studies. The 3 feed evaluation systems were compared using the concordance correlation coefficient (CCC), the ratio of root mean square prediction error (RMSPE) on standard deviation of observed values (RSR), and the slope between observed and predicted values. Mean and linear biases were deemed biologically relevant and are discussed if higher than a threshold of 5% of the mean of observed values. The comparisons were done on observed values adjusted or not for the study effect; the adjustment had a small effect on the mean bias but the linear bias reflected a response to a dietary change rather than absolute predictions. For the absolute predictions of NAN and MiN, CNCPS had the best-fit statistics (8% greater CCC; 6% lower RMSPE) without any bias; NRC and NASEM underpredicted NAN and MiN, and NASEM had an additional linear bias indicating that the underprediction of MiN increased at increased predictions. For NANMN, fit statistics were similar among the 3 feed evaluation systems with no mean bias; however, the linear bias with NRC and CNCPS indicated underprediction at low predictions and overprediction at elevated predictions. On average, the CCC were smaller and RSR ratios were greater for MiN versus NAN indicating increased prediction errors for MiN. For NAN responses to a dietary change, CNCPS also had the best predictions, although the mean bias with NASEM was not biologically relevant and the 3 feed evaluation systems did not present a linear bias. However, CNCPS, but not the 2 other feed evaluation systems, presented a linear bias for MiN, with responses being overpredicted at increased predictions. For NANMN, responses were overpredicted at increased predictions for the 3 feed evaluation systems, but to a lesser extent with NASEM. The site of sampling had an effect on the mean bias of MiN and NANMN in the 3 feed evaluation systems. The mean bias of MiN was higher in omasal than duodenal studies in the 3 feed evaluation systems (from 55 to 61 g/d) and this mean bias was twice as large when 15N labeling was used as a microbial marker compared with purines. Such a difference was not observed for duodenal studies. The reasons underlying these systematic differences are not clear as the type of measurements used in the current meta-analysis does not allow to delineate if one site or one microbial marker is yielding the "true" postruminal N outflows. Rumen degradabilities of protein was underpredicted with CNCPS, and RDP responses to a dietary change was underpredicted by the 3 feed evaluation systems with increased RDP predictions. Rumen degradability of NDF was underpredicted and had poor fit statistics for NASEM compared with CNCPS. Fit statistics were similar between CNCPS and NASEM for rumen degradability of starch, but with an underprediction of the response with NASEM and absolute values being overpredicted with CNCPS. Multivariate regression analyses showed that diet characteristics were correlated with prediction errors of N outflows in each feed evaluation system. Globally, compared with NAN and NANMN, residuals of MiN were correlated with several moderators in the 3 feed evaluation systems reflecting the complexity to measure and model this outflow. In addition, residuals of NANMN were correlated positively with RDP suggesting an overestimation of this parameter. In conclusion, although progress is still to be made to improve equations predicting postruminal N outflows, the current feed evaluation systems provide sufficient precision and accuracy to predict postruminal outflows of N fractions.

Supplementing branched-chain volatile fatty acids in dual-flow cultures varying in dietary forage and corn oil concentrations. I: Digestibility, microbial protein, and prokaryotic community structure

Branched-chain amino acids are deaminated by amylolytic bacteria to branched-chain volatile fatty acids (BCVFA), which are growth factors for cellulolytic bacteria. Our objective was to determine the dietary conditions that would increase the uptake of BCVFA by rumen bacteria. We hypothesized that increased forage would increase cellulolytic bacterial abundance and incorporation of BCVFA into their structure. Supplemental polyunsaturated fatty acids, supplied via corn oil (CO), should inhibit cellulolytic bacteria growth, but we hypothesized that additional BCVFA would alleviate that inhibition. Further, supplemental BCVFA should increase neutral detergent fiber degradation and efficiency of bacterial protein synthesis more with the high forage and low polyunsaturated fatty acid dietary combination. The study was an incomplete block design with 8 dual-flow continuous cultures used in 4 periods with 8 treatments (n = 4 per treatment) arranged as a 2 × 2 × 2 factorial. The factors were: high forage (HF) or low forage (LF; 67 or 33%), without or with supplemental CO (3% dry matter), and without or with 2.15 mmol/d (which included 5 mg/d of 13C each of BCVFA isovalerate, isobutyrate, and 2-methylbutyrate). The isonitrogenous diets consisted of 33:67 alfalfa:orchardgrass pellet, and was replaced with a concentrate pellet that mainly consisted of ground corn, soybean meal, and soybean hulls for the LF diet. The main effect of supplementing BCVFA increased neutral detergent fiber (NDF) degradability by 7.6%, and CO increased NDF degradability only in LF diets. Supplemental BCVFA increased bacterial N by 1.5 g/kg organic matter truly degraded (6.6%) and 0.05 g/g truly degraded N (6.5%). The relative sequence abundance decreased with LF for Fibrobacter succinogenes, Ruminococcus flavefaciens, and genus Butyrivibrio compared with HF. Recovery of the total 13C dose in bacterial pellets decreased from 144 µg/ mg with HF to 98.9 µg/ mg with LF. Although isotope recovery in bacteria was greater with HF, BCVFA supplementation increased NDF degradability and efficiency of microbial protein synthesis under all dietary conditions. Therefore, supplemental BCVFA has potential to improve feed efficiency in dairy cows even with dietary conditions that might otherwise inhibit cellulolytic bacteria.

Supplementing a Phytogenic Feed Additive Modulates the Risk of Subacute Rumen Acidosis, Rumen Fermentation and Systemic Inflammation in Cattle Fed Acidogenic Diets

Feeding with high-concentrate diets increases the risk of subacute ruminal acidosis (SARA). This experiment was conducted to evaluate whether supplementing a phytogenic feed additive based on L-menthol, thymol, eugenol, mint oil (Mentha arvensis) and cloves powder (Syzygium aromaticum) (PHY) can amend the ruminal fermentation profile, modulate the risk of SARA and reduce inflammation in cattle. The experiment was designed as a crossover design with nine non-lactating Holstein cows, and was conducted in two experimental runs. In each run, cows were fed a 100% forage diet one week (wk 0), and were then transitioned stepwise over one week (0 to 65% concentrate, wk adapt.) to a high concentrate diet that was fed for 4 weeks. Animals were fed diets either with PHY or without (CON). The PHY group had an increased ruminal pH compared to CON, reduced time to pH < 5.8 in wk 3, which tended to decrease further in wk 4, reduced the ruminal concentration of D-lactate, and tended to decrease total lactate (wk 3). In wk 2, PHY increased acetate, butyrate, isobutyrate, isovalerate, and the acetate to propionate ratio compared to CON. Phytogenic supplementation reduced inflammation compared to CON in wk 3. Overall, PHY had beneficial effects on ruminal fermentation, reduced inflammation, and modulated the risk of SARA starting from wk 3 of supplementation.

Economic and environmental effects of revised metabolizable protein and amino acid recommendations on Canadian dairy farms

The objective of this research was to evaluate the potential economic and environmental effects of the formulation model used to balance dairy rations for metabolizable protein (MP) or 3 essential AA (EAA: His, Lys, and Met) in 3 regions of Canada with different farming systems. The Maritimes, Central Canada, and the Prairies reference dairy farms averaged 63, 71, 144 mature cows per herd and 135, 95, 255 ha of land, respectively. Using N-CyCLES, a whole-farm linear program model, dairy rations were balanced for (1) MP, based on National Research Council (NRC) requirements (MP_2001); (2) MP plus Lys and Met, based on NRC (AA_2001); (3) MP (MP_Rev); or (4) for His, Lys, and Met (AA_Rev), both based on a revised factorial approach revisiting both supply and requirements of MP and EAA. Energy was balanced to meet requirements based on NRC (2001). Assuming the requirements were met within each approach, it was considered that milk yield and composition were not affected by the type of formulation. Given the assumptions of the study, when compared with MP_2001 formulation, balancing dairy rations using the AA_Rev approach reduced calculated farm N balance by 3.8%, on average from 12.71 to 12.24 g/kg of fat- and protein-corrected milk; it also enhanced farm net income by 4.5%, from 19.00 to 19.70 $CAN/100 kg of fat- and protein-corrected milk, by reducing inclusion of protein concentrate in dairy rations. Calculated animal N efficiency was on average 4.3% higher with AA_Rev than with MP_2001 for mid-lactation cows. This gain in N efficiency would result in a reduction in N₂O emission by manure, contributing to a partial decrease of total greenhouse gas emission by 1.7%, through a reduction of N excreted in manure. With the AA_2001 formulation, farm N balance was 1% higher than with MP_2001 formulation while reducing farm net income by 6.4%, due to the need to purchase rumen-protected AA, with no effect on total greenhouse gas emission. Both MP formulations lead to fairly similar outputs. The AA_Rev formulation also indicated that His might be a co-limiting AA with Met in dairy rations balanced with ingredients usually included in Canadian dairy rations. Given the assumptions of the study, balancing dairy rations for 3 EAA (His, Lys, and Met) rather than MP, has some potential positive effects on Canadian dairy farms by increasing net incomes through a reduction of crude protein supply, leading to a decreased environmental effect.

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.

A method of assessing essential amino acid availability from microbial and ruminally undegraded protein in lactating dairy cows

The objective of this study was to extend a stable isotope-based assessment of AA absorption from rumen-degradable protein (RDP) sources to include determination of essential AA (EAA) availability from microbial protein (MCP). To demonstrate the technique, a study using a 2 × 2 factorial arrangement of treatments applied in a repeated 4 × 4 Latin square design was undertaken. Factors were high and low rumen-degradable protein and high and low starch. Twelve lactating cows were blocked into 3 groups according to days in milk and randomly assigned to the 4 treatment sequences. Each period was 14 d in length with 10 d of adaption followed by 4 d of ruminal infusions of ¹⁵N-labeled ammonium sulfate. On the last day of each period, a ¹³C-labeled AA mixture was infused into the jugular vein over a 6-h period to assess total AA entry. Rumen, blood, urine, and milk samples were collected during the infusions. Ruminal bacteria and blood samples were assessed for AA enrichment. Total plasma AA absorption rates were derived for 6 EAA from plasma ¹³C AA enrichment. Absorption of 6 EAA from MCP was calculated from total AA absorption based on ¹⁵N enrichment in blood and rumen bacteria. Essential AA absorption rates from total protein, MCP, and rumen-undegradable protein were derived with standard errors of the mean of 6, 14, and 14%, respectively. An average of 45% of absorbed EAA were from MCP, which varied among 6 EAA and was interactively affected by starch and RDP in diets. Microbial AA availability measured by isotope dilution method increased with the high RDP diets and was unaffected by starch level, except for Met, which decreased with high starch. Microbial protein outflow, estimated from urinary purine derivatives, increased with RDP and was not significantly affected by starch. This was consistent with measurements from the isotope dilution method. Total AA absorption rates measured from isotope dilution were similar to estimates from CNCPS (v. 6.55), but a lower proportion of absorbed AA was derived from MCP for the former method. Compared with the isotope and CNCPS estimates, the Fleming model underestimated microbial EAA and total EAA availability. An average of 58% of the absorbed EAA was converted into milk, which varied among individual AA and was interactively affected by starch and RDP in diets. The isotope dilution approach is advantageous because it provides estimates of EAA availability for individual EAA from rumen-undegradable protein and MCP directly with fewer errors of measurement than can be achieved with intestinal disappearance methods.

Meta-analysis of the effects of on-farm management strategies on milk yields of dairy cattle on smallholder farms in the Tropics

Although East Africa is home to one of the most advanced dairy industries in Sub-Saharan Africa, regional annual milk production is insufficient to meet the demand. The challenge of increasing milk yields (MYs) among smallholder dairy cattle farmers (SDCFs) has received considerable attention and resulted in the introduction of various dairy management strategies (DMSs). Despite adoption of these DMSs, MYs remain low on-farm and there is a large discrepancy in the efficacy of DMSs across different farms. Therefore, the present study sought to: (1) identify on-farm DMSs employed by East African SDCFs to increase MYs and (2) summarize existing literature to quantify the expected MY changes associated with these identified DMSs. Data were collected through a comprehensive literature review and in-depth semi-structured interviews with 10 experts from the East African dairy sector. Meta-analysis of the literature review data was performed by deriving four multivariate regression models (i.e. models 1 to 4) that related DMSs to expected MYs. Each model differed in the weighting strategy used (e.g. number of observations and inverse of the standard errors) and the preferred model was selected based on the root estimated error variance and concordance correlation coefficient. Nine DMSs were identified, of which only adoption of improved cattle breeds and improved feeding (i.e. increasing diet quality and quantity) consistently and significantly (P < 0.05) increased daily MYs across the available studies. Improved breeds alongside adequate feeding explained ≤50% of the daily MYs observed in the metadata while improved feeding explained ≤30% of the daily MYs observed across the different models. Conversely, calf suckling significantly (P < 0.05) reduced MYs according to model 2. Other variables including days in milk, trial length and maximum ambient temperature (used as a proxy for heat stress) contributed significantly to decreasing MYs. These variables may explain some of the heterogeneity in MY responses to DMSs reported in the literature. Our results suggest that using improved cattle breeds alongside improved feeding is the most reliable strategy to increase MYs on-farm in East Africa. Nevertheless, these DMSs should not be considered as standalone solutions but as a pool of options that should be combined depending on the resources available to the farmer to achieve a balance between using dairy cattle genetics, proper husbandry and feeding to secure higher MYs.

Synchrony Degree of Dietary Energy and Nitrogen Release Influences Microbial Community, Fermentation, and Protein Synthesis in a Rumen Simulation System

Synchrony of energy and nitrogen release in rumen has been proposed to maximize ruminal microbial fermentation. However, the information regarding bacterial community composition and its metabolism under a higher or lower degree of synchronization is limited. In our study, a 0 to 6 h post-feeding infusion (first half infusion, FHI), 6 to 12 h post-feeding infusion (second half infusion, SHI), and 0 to 12 h post-feeding infusion (continuous infusion, CI) of maltodextrin were used to simulate varying degrees of synchronization of energy and nitrogen release in a rumen simulation system. In addition, the bacterial community, metabolite, enzyme activity, and microbial protein synthesis (MPS) were evaluated. Compared with the FHI and CI, the relative abundance of Fibrobacter, Ruminobacter, BF311, and CF231 decreased in the SHI, but that of Klebsiella and Succinivibrio increased in the SHI. The NH₃-N and branched-chain volatile fatty acids were significantly higher, but propionate content and activities of glutamate dehydrogenase (GDH) and alanine dehydrogenase were significantly lower in the SHI than those in the FHI and CI. The SHI had lower MPS and less efficiency of MPS than the FHI and CI, which indicated that the SHI had a lower degree of synchronization. Correlation analysis showed that MPS was positively related to GDH activity and relative abundance of Fibrobacter but negatively related to NH₃-N and relative abundance of Klebsiella. Therefore, a higher degree of synchronization of energy and nitrogen release increased MPS partly via influencing the bacterial community, metabolism, and enzyme activities of ammonia assimilation in the in vitro fermenters.

Identifying gram-negative and gram-positive clinical mastitis using daily milk component and behavioral sensor data

Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d -7 and -5, d -7 and -4, d -7 and -3, d -7 and -2, and d -7 and -1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d -10, -7, and -3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d -3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d -10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems.

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.

Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques

Nitrogen is a component of essential nutrients critical for the productivity of ruminants. If excreted in excess, N is also an important environmental pollutant contributing to acid deposition, eutrophication, human respiratory problems, and climate change. The complex microbial metabolic activity in the rumen and the effect on subsequent processes in the intestines and body tissues make the study of N metabolism in ruminants challenging compared with nonruminants. Therefore, using accurate and precise measurement techniques is imperative for obtaining reliable experimental results on N utilization by ruminants and evaluating the environmental impacts of N emission mitigation techniques. Changeover design experiments are as suitable as continuous ones for studying protein metabolism in ruminant animals, except when changes in body weight or carryover effects due to treatment are expected. Adaptation following a dietary change should be allowed for at least 2 (preferably 3) wk, and extended adaptation periods may be required if body pools can temporarily supply the nutrients studied. Dietary protein degradability in the rumen and intestines are feed characteristics determining the primary AA available to the host animal. They can be estimated using in situ, in vitro, or in vivo techniques with each having inherent advantages and disadvantages. Accurate, precise, and inexpensive laboratory assays for feed protein availability are still needed. Techniques used for direct determination of rumen microbial protein synthesis are laborious and expensive, and data variability can be unacceptably large; indirect approaches have not shown the level of accuracy required for widespread adoption. Techniques for studying postruminal digestion and absorption of nitrogenous compounds, urea recycling, and mammary AA metabolism are also laborious, expensive (especially the methods that use isotopes), and results can be variable, especially the methods based on measurements of digesta or blood flow. Volatile loss of N from feces and particularly urine can be substantial during collection, processing, and analysis of excreta, compromising the accuracy of measurements of total-tract N digestion and body N balance. In studying ruminant N metabolism, nutritionists should consider the longer term fate of manure N as well. Various techniques used to determine the effects of animal nutrition on total N, ammonia- or nitrous oxide-emitting potentials, as well as plant fertilizer value, of manure are available. Overall, methods to study ruminant N metabolism have been developed over 150 yr of animal nutrition research, but many of them are laborious and impractical for application on a large number of animals. The increasing environmental concerns associated with livestock production systems necessitate more accurate and reliable methods to determine manure N emissions in the context of feed composition and ruminant N metabolism.

Variation in animal performance explained by the rumen microbiome or by diet composition

The central aim of this meta-analysis was to determine whether the rumen microbiome can serve as an accurate predictor of performance in beef and dairy cattle compared with predictions based on diet composition. To support this comparison, a set of models was derived and compared. Models predicted milk yield (MY), average daily gain (ADG), dry matter intake (DMI), dairy feed efficiency (FE), and beef FE using different sets of independent variables: diet (D), microbial (M), and experimental (E). Diet variables included dry matter, organic matter, neutral detergent fiber, acid detergent fiber, crude protein, ether extract, nonfiber carbohydrate, starch, and forage percentages. Microbiome variables included relative abundance of 3 major rumen bacterial phyla, species richness, and species diversity. Experimental variables included publication year, breed type (dairy, beef, or Bos indicus), and rumen sampling fraction (fluid or solid). A second set of models used D and E variables as predictors for the microbiome. For both the production and microbiome model sets, predictor variable sets were used individually and in combination. Linear mixed-effects regression, weighted by 1/standard error of the mean, was used to derive models using data from 51 peer-reviewed publications. Models for the same response variable were compared on the basis of concordance correlation coefficient with study effects removed (uCCC), root-estimated variance associated with study and error, and corrected Akaike information criterion values, wherever appropriate. The MY model using D + M + E predictors outperformed all other MY models (uCCC = 0.71). ADG was most accurately predicted by D alone (uCCC = 0.92). Interestingly, M + E was more successful at predicting DMI than any model using D variables. Similarly, dairy FE was more accurately predicted by M + E than D, albeit only slightly (uCCC = 0.69 vs. 0.65). Beef FE could only be modeled using D variables. Overall, breed type proved a better predictor of relative abundances of most rumen bacterial phyla than D. Conversely, species richness and diversity indicators were unaffected by breed type, but could be predicted by D with moderate precision and accuracy (uCCC = 0.63 to 0.69). This analysis suggests that diet and the microbiome may exert independent effects on various aspects of performance. Further research is necessary to determine the reasons for these independent influences.

Do more mechanistic models increase accuracy of prediction of metabolisable protein supply in ruminants?

Ruminal microbes partially degrade dietary protein and synthesise microbial protein, which, along with undegraded true protein, contributes to metabolisable protein for the animal. Rumen models have been developed over the past several decades in an effort to better predict metabolisable protein supply for ration formulation for ruminants. These models have both empirical and mechanistic components. Separation of dietary protein into fractions that include non-protein nitrogen, true protein and unavailable protein has been a fundamental element of these models. Ruminal degradation of one or more true protein fractions is then estimated on the basis of the kinetics of digestion and passage. Some models use the same method to predict substrate supply for microbial protein production. Although mechanistic models have been extensively used in diet-formulation programs worldwide, their ability to improve accuracy of prediction of metabolisable protein over simpler empirical models is questionable. This article will address the potential of mechanistic models to better predict metabolisable protein supply in ruminants as well as their limitations.

Meta-analysis of endophyte-infected tall fescue effects on cattle growth rates

The objective of this study was to quantitatively summarize literature reporting endophyte-infected (Neotyphodium coenophialum) tall fescue (Festuca arundinacea) effects on cattle ADG. This meta-analysis evaluated endophyte infection level, climate, and forage yield using a literature dataset of 138 treatments from 20 articles. Three infection level measurements were tested: endophyte infection as a percentage of infected tillers (E%); ergovaline concentration in ppb ([E]); and total ergot alkaloid concentration ([TEA]). Three types of climate variables were used: base values (temperature, humidity, and relative humidity), climate indices (heat index and temperature-heat index [THI]), and novel climate variables accounting for duration of climate effects. Mixed effect models, weighted by 1/SEM, including a random effect of study were built for each factorial combination of measurement method and climate variable group. Because many studies were missing SEM, two datasets were used: one containing only data with SEM reported and one that also included missing-SEM data. For the complete-SEM dataset (CSD), models were weighted by 1/SEM. In the missing-SEM dataset (MSD) the mean reported 1/SEM was assigned as the weight for all missing SEM treatments. Although 18 initial models were created (2 × 3 × 3 factorial approach), the backward stepwise derivation resulted in models that included only endophyte infection level, suggesting a negative relationship between infection level and ADG. The CSD models predicted ADG to decrease 39 and 33 g/d with each increase of 100 ppb of [TEA] and [E], and by 39 g/d for each increase of 10% E%. In the MSD dataset, predicted ADG decreased by 39 and 33 g/d with each increase of 100 ppb of [TEA] and [E], and by 47 g/d for each increase of 10% E%. All relationships reported had P < 0.05. After visual inspection of the data, piecewise regression was used to identify an infection threshold (IT) of 60 ppb [E] and 11 E%, where the effect of infection level was constant on either side of the IT. The ADG was 40% and 49% greater for infection levels below the IT for [E] and E%, respectively. Across THI values in the analysis, ADG decreases ranged from 11.2% to 45.0% for cattle grazing endophyte-infected tall fescue compared to non-ergot alkaloid endophyte infected tall fescue. Pasture E%, [E], and [TEA] have a negative relationship with ADG in growing cattle, and increasing temperature decreases ADG when infection level is greater than the IT.

Physically adjusted neutral detergent fiber system for lactating dairy cow rations. II: Development of feeding recommendations

The objective of this work was to leverage equations derived in a meta-analysis into an ensemble modeling system for estimating dietary physical and chemical characteristics required to maintain desired rumen conditions in lactating dairy cattle. Given the availability of data, responsiveness of ruminal pH to animal behaviors, and the chemical composition and physical form of the diet, mean ruminal pH was chosen as the primary rumen environment indicator. Physically effective fiber (peNDF) is defined as the fraction of neutral detergent fiber (NDF) that stimulates chewing activity and contributes to the floating mat of large particles in the rumen. The peNDF of feedstuffs is typically estimated by multiplying the NDF content by a particle size measure, resulting in an estimated index of effectiveness. We hypothesized that the utility of peNDF could be expanded and improved by dissociating NDF and particle size and considering other dietary factors, all integrated into a physically adjusted fiber system that can be used to estimate minimum particle sizes of TMR and diet compositions needed to maintain ruminal pH targets. Particle size measures of TMR were limited to those found with the Penn State particle separator (PSPS). Starting with specific diet characteristics, the system employed an ensemble of models that were integrated using a variable mixture of experts approach to generate more robust recommendations for the percentage of dietary DM material that should be retained on the 8-mm sieve of a PSPS. Additional continuous variables also integrated in the physically adjusted fiber system include the proportion of material (dry matter basis) retained on the 19- and 8-mm sieves of the PSPS, estimated mean particle size, the dietary concentrations of forage, forage NDF, starch, and NDF, and ruminally degraded starch and NDF. The system was able to predict that the minimum proportion of material (dry matter basis) retained on the 8-mm sieve should increase with decreasing forage NDF or dietary NDF. Additionally, the minimum proportion of dry matter material on the 8-mm sieve should increase with increasing dietary starch. Results of this study agreed with described interrelationships between the chemical and physical form of diets fed to dairy cows and quantified the links between NDF intake, diet particle size, and ruminal pH. Feeding recommendations can be interpolated from tables and figures included in this work.