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Alga 1.0, a product containing bromoform, was fed to cattle to evaluate its effects on methane (CH 4 ) and carbon dioxide (CO 2 ) emissions and diet digestibility. Twelve nonlactating, nonpregnant Jersey cows (490 ±â€…19 kg body weight) were used in four replicated 3 × 3 Latin squares with three periods, each consisting of 21 d. Cows were blocked by feed intake (averaged intakes over 4 wk prior to trial) and assigned randomly to one of three treatments. Treatments included Alga 1.0 fed at 0, 69, and 103 g/d in a 0.454 kg/d dry matter (DM) top-dress daily in a modified distillers grains plus solubles (MDGS) carrier. Diet consisted of 60% dry-rolled corn, 20% corn silage, 15% modified distillers grains, and 5% supplement (DM basis). Headbox-style indirect calorimeters were utilized to evaluate gas production from individual cows with two nonconsecutive 23-h collections in each period. Data were analyzed using the GLIMMIX procedure of SAS with cow within square as experimental unit and as a random effect, and treatment and period as fixed effects. Linear and quadratic contrasts were used to compare treatments. Feeding Alga 1.0 linearly reduced dry matter intake (DMI, P < 0.01) by 10.1% for 69 g/d inclusion and 13.3% for 103 g/d inclusion compared to the control. Nutrient intakes decreased linearly (P < 0.01) due to lower DMI, but nutrient digestibility was not impacted (P ≥ 0.28). Inclusion of Alga 1.0 did not impact gross energy or digestible energy concentration of the diets expressed as Mcal/kg DM (P ≥ 0.22) but did linearly reduce energy intake (Mcal/d; P < 0.01). Feeding Alga 1.0 linearly reduced enteric CH4 emissions measured as g/kg DMI (P < 0.01) by 39 and 64% for 69 g/d and 103 g/d inclusion, respectively. Linear reductions (P < 0.01) of 64% to 65% were also observed in enteric CH4 emissions when expressed per kilogram of DM or organic matter digested. Respired CO2 as g/d linearly decreased (P = 0.03) for cattle fed Alga 1.0 but did not differ when expressed as g/kg of DMI (P ≥ 0.23). Oxygen consumption did not differ between treatments for g/d and g/kg DMI (P ≥ 0.19). In conclusion, feeding Alga 1.0 reduced DMI up to 13.3%, did not impact digestibility, and significantly reduced CH4 emissions up to 63%.

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Physically effective neutral detergent fiber (peNDF) is the fraction of neutral detergent fiber (NDF) that stimulates chewing activity and contributes to the floating mat of large particles in the rumen. Multiplying dietary NDF by particle size has been used as an estimate of peNDF. In re-evaluating the concept of peNDF, we compared the use of peNDF as dietary NDF × particle size with the use of individual NDF and particle size descriptors (physically adjusted NDF; paNDF) when used with other physical and chemical diet descriptors to predict dry matter (DM) intake (DMI), rumination time, and ruminal pH in lactating dairy cows. The purpose is to ultimately use these equations to estimate diet adequacy to maintain ruminal conditions. Each response variable had 8 models in a 2 (peNDF, paNDF) × 2 (diet, diet and ruminal factors) × 2 (DM, as fed basis) factorial arrangement. Particle size descriptors were those determined with the Penn State Particle Separator. Treatment means (n = 241) from 60 publications were used in backward elimination multiple regression to derive models of response variables. When available, peNDF terms entered equations. Models containing peNDF terms had similar or lower unadjusted concordance correlation coefficients (an indicator of similar or lower accuracy and precision) than did models without peNDF terms. The peNDF models for rumen pH did not differ substantially from paNDF models. This suggests that peNDF can account for some variation in ruminal pH; however, overt advantages of peNDF were not apparent. Significant variables that entered the models included estimated mean particle size; as fed or DM proportions retained on 19- and 8-mm sieves of the Penn State Particle Separator; DMI; dietary concentrations of forage; forage NDF; CP; starch; NDF; rumen-degraded starch and rumen-degraded NDF; and the interaction terms of starch × mean particle size, acid detergent fiber/NDF, and rumination time/DMI. Many dietary factors beyond particle size and NDF were identified as influencing the response variables. In conclusion, these results appear to justify the development of a modeling approach to integrate individual physical and chemical factors to predict effects on factors affecting rumen conditions.

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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.

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We hypothesized that ruminal degradability of essential AA (EAA) and the intestinal digestibility of the ruminally undegraded EAA residue in feeds could be evaluated in a meta-analysis. The objective was to characterize methodological factors for ruminal incubation (time of incubation of feed in situ) and method of simulating digestion of the ruminally undegraded AA (incubation of residue in digestive enzymes in vitro or in mobile bags inserted into the duodenum). To increase numbers of observations, feeds were categorized before ANOVA. An approach is described to predict differential ruminal degradability (or undegradability) of individual EAA by normalizing them as a proportion of total AA (TAA) degradability (undegradability) and similarly to normalize the intestinal digestibility of EAA using TAA. Interaction of feed category with individual EAA justifies future studies with a broader range of feeds and more replication within feed to bolster this approach. With broader data, the approach to normalize EAA as a proportion of TAA should allow a better defined EAA library to be integrated with more robust CP databases (that can be updated with specific feed information from more routine laboratory analyses) in dairy supply-requirement models.

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The rumen microbial community in dairy cows plays a critical role in efficient milk production. However, there is a lack of data comparing the composition of the rumen bacterial community of the main dairy breeds. This study utilizes 16S rRNA gene sequencing to describe the rumen bacterial community composition in Holstein and Jersey cows fed the same diet by sampling the rumen microbiota via the rumen cannula (Holstein cows) or esophageal tubing (both Holstein and Jersey cows). After collection of the rumen sample via esophageal tubing, particles attached to the strainer were added to the sample to ensure representative sampling of both the liquid and solid fraction of the rumen contents. Alpha diversity metrics, Chao1 and observed OTUs estimates, displayed higher (P = 0.02) bacterial richness in Holstein compared to Jersey cows and no difference (P > 0.70) in bacterial community richness due to sampling method. The principal coordinate analysis displayed distinct clustering of bacterial communities by breed suggesting that Holstein and Jersey cows harbor different rumen bacterial communities. Family level classification of most abundant (>1%) differential OTUs displayed that OTUs from the bacterial families Lachnospiraceae and p-2534-18B5 to be predominant in Holstein cows compared to Jersey cows. Additionally, OTUs belonging to family Prevotellaceae were differentially abundant in the two breeds. Overall, the results from this study suggest that the bacterial community between Holstein and Jersey cows differ and that esophageal tubing with collection of feed particles associated with the strainer provides a representative rumen sample similar to a sample collected via the rumen cannula. Thus, in future studies esophageal tubing with addition of retained particles can be used to collect rumen samples reducing the cost of cannulation and increasing the number of animals used in microbiome investigations, thus increasing the statistical power of rumen microbial community evaluations.

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The objective of this paper was to develop the structure and concepts of a dynamic model to simulate dry matter intake (DMI) pattern and the fluxes of fat and protein in the body reserves of cattle associated with changes in body condition score (BCS) for application within the structure of applied nutrition models. This model was developed to add the capability of evaluating the effects of factors affecting pre- and postcalving DMI, daily energy and protein balances, and changes in BCS over a reproductive cycle. Input variables are average DMI, diet metabolizable energy, and animal information (body weight, BCS, milk production, and calf birth body weight) from each diet fed over the reproductive cycle. Because the depletion and repletion of body reserves in cattle is a complex system of coordinated metabolic processes that reflect hormonal and physiological changes caused by negative or positive energy balances, the system dynamics modeling methodology was used to develop this model. The model was used to evaluate the effect of the dynamic interactions between dietary supply and animal requirements for energy and protein on the fluxes of body fat and body protein of dairy cows over the reproductive cycle and Monte Carlo simulations were used to assess the sensitivity of the parameters. The main long-term factor affecting DMI pattern was the growth of the gravid uterus causing an increase in the volume of abdominal organs and a compression of the rumen, consequentially reducing feed intake. Changes in body reserves (fat and protein) were computed based on metabolizable energy balance, assuming different efficiency of utilization coefficients for fat and protein during repletion and mobilization. The model was evaluated with data from 37 dairy cows individually fed 3 different diets over the lactation and dry periods. The model was successful in simulating the observed pattern of DMI (mean square error was 3.59, 3.97, and 3.66 for diets A, B, and C, respectively), but it tended to underpredict DMI during late lactation [around 200 to 285 d in milk (DIM)] for all diets, suggesting changes in the model structure might be needed. The predicted BCS pattern had a trend similar to the observed values. Assuming that observed BCS represents actual body fat, the model tended to overpredict observed BCS during early lactation (0.125 BCS for 0 to 120 DIM) and underpredict it during late lactation (0.06 BCS for 180 to 270 DIM). A long-term simulation (5 lactations and 4 dry periods) with diet A indicated that the cows on this diet would have a net loss of body fat if all conditions were constant.

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