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Maintenance energy is the energy required to conserve the state of an animal when no work is completed. Dietary energy must be supplied to meet maintenance requirements before milk can be produced. The objectives of the current experiment were to quantify the maintenance energy requirement of Jersey cows when lactating or dry. Energetic measures were collected on 8 Jersey cows and evaluated across 3 physiological phases and nutritional planes: lactation, dry cows fed at maintenance, and fasted dry cows. Through total collection of feces and urine as well as using headbox-style indirect calorimeters, energy balance and heat production data were measured across all phases. Lactation data were collected across four 28-d periods. Data for cows fed at maintenance were collected after 14 d and fasting heat production was measured during the last 24 h of a 96-h fast. Net energy for maintenance (NEM) requirements, and the efficiency of converting metabolizable energy (ME) into net energy were compared between lactating and dry (maintenance or fasting phase) cows. Heat production of dry cows fed at maintenance, which represents ME for maintenance, was 0.146 ± 0.0087 Mcal per unit of metabolic body weight (BW0.75, MBW). Fasting heat production, which represents NEM, was 0.102 ± 0.0071 Mcal/MBW. Energy balance was calculated as tissue energy plus milk energy. When estimated via regressing energy balance on ME intake, NEM was not different between dry and lactating cows (0.120 ± 0.32 vs. 0.103 ± 0.0052 Mcal/MBW). However, the slope of the regression of energy balance on ME intake was greater for dry compared with lactating cows (0.714 ± 0.046 vs. 0.685 ± 0.010) when evaluated with a fixed intercept. This suggests that dry cows were more efficient at converting ME into net energy and that the efficiency of utilizing ME for maintenance may be greater than for lactation. Our measurements of NEM and the slope of ME on energy balance were greater than the value used by the National Research Council (2001), which are 0.080 Mcal/MBW for NEM and approximately 0.64 for the slope. Results of this study suggest that NEM and the efficiency of converting ME into NEM of modern lactating Jersey cows are similar to recent measurements on modern Holstein cows and greater than previous measurements.

Measurement of urinary energy (UE) excretion is essential to determine metabolizable energy (ME) supply. Our objectives were to evaluate the accuracy of using urinary N (UN) or C (UC) to estimate UE and ultimately improve the accuracy of estimating ME. Individual animal data (n = 433) were used from 11 studies with Jersey cows at the University of Nebraska–Lincoln, where samples were analyzed after drying (n = 299) or on an as-is basis (n = 134). Dried samples resulted in greater estimated error variance compared with as-is samples, and thus only as-is samples were used for final models. The as-is data set included a range (min to max) in dry matter intake (11.6–24.6 kg/d), N intake (282–642 g/d), UE excretion (1,390–3,160 kcal/d), UN excretion (85–220 g/d or 20.6–59.5% of N intake), and UC excretion (130–273 g/d). As indicated by a bias in residuals between observed and predicted ME as dietary crude protein (CP; range of 14.9–19.1%) increased, the National Research Council dairy model did not accurately predict ME of diets, as dietary CP varied. The relationship between UE (kcal/d) and UN (g/d) excretion was linear and had an intercept of 880 ± 140 kcal. Because an intercept of 880 is biologically unlikely, the intercept was forced through 0, resulting in linear and quadratic relationships. The regressions of UE (kcal/d) on UN (g/d) excretion were UE = 14.6 ± 0.32 × UN, and UE = 20.9 ± 1.0 × UN − 0.0357 ± 0.0056 × UN2. In the quadratic regression, UE increased, but at a diminishing rate as UN excretion increased. As UC increased, UE linearly and quadratically increased. However, error variance was greater for regression with UC compared with UN as explanatory variables (8.42 vs. 7.42% of mean UE). The use of the quadratic regression between UN and UE excretion to predict ME resulted in a slope bias in ME predictions as dietary CP increased. The linear regression between UE and UN excretion removed slope bias between predicted ME and CP, and thus may be more appropriate for predicting UE across a wider range of dietary CP. Using equations to predict UE from UN should improve our ability to predict diet ME in Jersey cows compared with calculating ME directly from digestible energy.

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 (DCFA(TTa)) 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 DCFA(TTa) in dairy cows were used. Data for animal description, diet composition, intakes of total FA, and DCFA(TTa), 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 (DCFA(TTa) = 44%) compared with animals consuming unsaturated FA, such as Ca-salts of palm (DCFA(TTa) = 76%) and oilseeds (DCFA(TTa) = 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. National Animal Nutrition Program, a National Research Support Project [NRSP-9] Published version This study is part of the activities of the National Animal Nutrition Program (https://animalnutrition.org), a National Research Support Project (NRSP-9). The authors thank Tran et al. (2020) for the develop-ment of the feed library used in study 1. The authors have not stated any conflicts of interest.

A study using indirect calorimetry and 12 lactating multiparous Jersey cows (53 ± 23 d in milk at the beginning of the experiment; mean ± standard deviation) was conducted to evaluate the utilization of energy in cattle consuming diets containing increasing hydrolyzed feather meal (HFM). A triplicated 4 × 4 Latin square design with 35-d periods (28-d adaption and 4-d collections) was used to compare 4 different dietary treatments. Treatments contained (DM basis) HFM at 0% (0HFM), 3.3% (3.3HFM), 6.7% (6.7MFM), and 10.0% (10HFM). Diets were formulated such that HFM replaced blood meal and nonenzymatically browned soybean meal. With increasing HFM, linear increases were observed for dietary NEL content (1.61, 1.64, 1.69, and 1.70 ± 0.042 Mcal/kg of DM for 0HFM, 3.3HFM, 6.7MFM, and 10HFM, respectively), and the efficiency of converting ME to NEL (0.708, 0.711, 0.717, and 0.719). Apparent total-tract digestibility of CP linearly decreased with increasing HFM (63.4, 61.1, 59.9, and 58.6 ± 1.46% for 0HFM, 3.3HFM, 6.7MFM, and 10HFM, respectively), whereas long-chain fatty acid digestibility increased with increasing HFM (77.2, 77.7, 78.5, and 80.6 ± 1.30%). With increased inclusion of HFM, fecal N excretion increased (199, 230, 239, 237 ± 12.1 g/d for 0HFM, 3.3HFM, 6.7MFM, and 10HFM, respectively), whereas urinary N excretion decreased (166, 151, 155, and 119 ± 14.8 g/d). Increasing the concentration of HFM resulted in a quadratic effect on DMI (19.6, 20.2, 20.3, and 19.1 ± 0.79 kg/d for 0HFM, 3.3HFM, 6.7MFM, and 10HFM, respectively) and milk yield (31.7, 32.0, 31.9, and 29.7 ± 1.32 kg/d). Increasing HFM linearly decreased the milk protein concentration (3.34, 3.29, 3.23, and 3.23 ± 0.158 for 0HFM, 3.3HFM, 6.7MFM, and 10HFM, respectively) and yield (1.05, 1.05, 1.02, and 0.96 ± 0.040 kg). The inclusion of HFM did not affect energy-correct milk yield (average of 39.3 ± 1.54). Results of this study suggest that HFM can increase dietary NEL content compared with blood meal and nonenzymatically browned soybean meal and maintained energy-corrected milk yield; however, feeding HFM at greater than 6.7% of diet DM decreased DMI, and protein availability may have been reduced with increased HFM, leading to a linear decrease in milk protein concentration and yield.

Although the inclusion of fat has reduced methane production in ruminants, relatively little research has been conducted comparing the effects of source and profile of fatty acids on methane production in lactating dairy cows. A study using 8 multiparous (325 ± 17 DIM; mean ± SD) lactating Jersey cows was conducted to determine effects of feeding canola meal and lard versus extruded byproduct containing flaxseed as a high-C18:3 fat source on methane production and diet digestibility in late-lactation dairy cows. A crossover design with 32-d periods (28-d adaptation and 4-d collections) was used to compare 2 different fat sources. Diets contained approximately 50% forage mixture of corn silage, alfalfa hay, and brome hay; the concentrate mixture changed between diets to include either (1) a conventional diet of corn, soybean meal, and canola meal with lard (control) or (2) a conventional diet of corn and soybean meal with an extruded byproduct containing flaxseed (EXF) as the fat source. Diets were balanced to decrease corn, lard, and canola meal and replace them with soybean mean and EXF to increase the concentration of C18:3 (0.14 vs. 1.20% of DM). Methane production was measured using headbox-style indirect calorimeters. Cattle were restricted to 95% ad libitum feed intake during collections. Milk production (17.4 ± 1.04 kg/d) and dry matter intake (15.4 ± 0.71 kg/d) were similar among treatments. Milk fat (5.88 ± 0.25%) and protein (4.08 ± 0.14%) were not affected by treatment. For methane production, no difference was observed for total production (352.0 vs. 349.8 ± 16.43 L/d for control vs. EXF, respectively). Methane production per unit of dry matter intake was not affected and averaged 23.1 ± 0.57 L/kg. Similarly, methane production per unit of energy-corrected milk was not affected by fat source and averaged 15.5 ± 0.68 L/kg. Heat production was similar, averaging 21.1 ± 1.02 Mcal/d. Digestibility of organic matter, neutral detergent fiber, and crude protein was not affected by diet and averaged 69.9, 53.6, and 73.3%, respectively. Results indicated that increasing C18:3 may not affect methane production or digestibility of the diet in lactating dairy cows.

The addition of fat and calcium sulfate to diets fed to ruminants has resulted in a reduction in methane production, but the effects on energy balance have not been studied. A study using indirect calorimetry and 16 multiparous (8 Holstein and 8 Jersey; 78 ± 15 d in milk; mean ± standard deviation) lactating dairy cows was conducted to determine how mitigating methane production by adding corn oil or calcium sulfate to diets containing reduced-fat distillers grains affects energy and nitrogen balance. A replicated 4 × 4 Latin square design with 35-d periods (28 d of adaption and 4 d of collections) was used to compare 4 different dietary treatments. Treatments were composed of a control (CON) diet, which did not contain reduced-fat distillers grain and solubles (DDGS), and treatment diets containing 20% (dry matter basis) DDGS (DG), 20% DDGS with 1.38% (dry matter basis) added corn oil (CO), and 20% DDGS with 0.93% (dry matter basis) added calcium sulfate (CaS). Compared with CON, dry matter intake was not affected by treatment, averaging 29.6 ± 0.67 kg/d. Milk production was increased for diets containing DDGS compared with CON (26.3 vs. 27.8 ± 0.47 kg/d for CON vs. DDGS, respectively), likely supported by increased energy intake. Compared with CON, energy-corrected milk was greater in DG and CO (30.1 vs. 31.4, 31.7, and 31.0 ± 0.67 kg/d for CON, DG, CO, and CaS, respectively). Compared with CON, the addition of calcium sulfate and corn oil to diets containing DDGS reduced methane production per kg of dry matter intake (22.3, 19.9, and 19.6 ± 0.75 L/kg per d for CON, CO, and CaS, respectively). Similarly, methane production per kilogram of energy-corrected milk was reduced with the addition of calcium sulfate and corn oil to diets containing DDGS (14.2, 12.5, and 12.4 ± 0.50 L/kg per d for CON, CO, and CaS, respectively). Compared with CON and CaS, the intake of digestible energy was greater for DG and CO treatments (57.7, 62.1, 62.0, and 59.0 ± 1.38 Mcal/d for CON, DG, CO, and CaS, respectively). Intake of metabolizable energy was greater in all treatments containing DDGS compared with CON (50.5 vs. 54.0 ± 1.08 Mcal/d for CON vs. DDGS, respectively). Net balance (milk plus tissue energy) per unit of dry matter was greater in CO (containing DDGS and oil) than CON (1.55 vs. 1.35 ± 0.06 Mcal/kg for CO vs. CON, respectively). Tissue energy was greater in DG and CO compared with CON (6.08, 7.04, and 3.16 ± 0.99 Mcal/d for DG, CO, and CON, respectively. Results of this study suggest that the addition of oil and calcium sulfate to diets containing DDGS may be a viable option to reduce methane production and in the case of oil also improve net energy balance in lactating dairy cows.

The effects of dietary fatty acid (FA) and starch content as well as supplemental digestible Lys (sdLys) on production, energy utilization, and N utilization were evaluated. Each factor was fed at 5 different amounts, and factor limits were as follows: 3.0 to 6.2% of dry matter (DM) for FA; 20.2 to 31.3% of DM for starch, and 0 to 17.8 g/d of sdLys. Dietary FA and starch were increased by replacing soyhulls with supplemental fat and corn grain, respectively, and sdLys increased with rumen-protected Lys. Fifteen unique treatments were fed to 25 Jersey cows (mean ± SD; 80 ± 14 d in milk) across 3 blocks in a partially balanced incomplete block design. Each block consisted of 4 periods of 28 d, where the final 4 d were used to determine milk production and composition, feed intake, energy utilization (via total collection and headbox-style indirect calorimetry), and N utilization (via total collection). Response surface models were used to evaluate treatment responses. Increasing dietary FA decreased DM intake and milk protein yield. When dietary starch was less than 24%, milk protein concentration increased with increasing sdLys, but when dietary starch was greater than 26% milk protein concentration decreased with increasing sdLys. Digestibility of FA increased when dietary FA increased from 3.0 to 4.2% and decreased as FA increased beyond 4.2%. Although neutral detergent fiber digestibility decreased as dietary starch increased, energy digestibility increased. As dietary FA increased, metabolizable energy (ME) content quadratically increased. Supply of ME increased as dietary FA increased from 3.0 to 4.2% and decreased as FA increased beyond 4.2%. Increasing dietary FA and starch decreased CH4 production and urinary energy. Increasing dietary starch increased the efficiency of utilizing dietary N for milk N. Increasing sdLys quadratically decreased N balance as sdLys increased from 0 to 8 g/d and increased N balance as sdLys increased from 8 to 18 g/d. Increasing dietary FA can increase ME content, however, at high dietary FA, decreased DM intake and FA digestibility resulted in a plateau in ME content and a decrease in ME supply. Our results demonstrate that sdLys supply is important for milk protein when dietary starch is low, and some Lys may be preferentially used for muscle protein synthesis at the expense of milk protein when sdLys is high.

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.

Hydrolyzed feather meal (HFM) is a feed that is high in rumen undegradable protein; however, it is low in Lys compared with other high rumen undegradable protein sources. Additionally, processing methods differ by facility, which affects AA composition and protein digestibility. The objective of this study was to use lactating dairy cows to determine the effects of feeding 2 sources of HFM that differed by the amount of blood they contained and also to study the effects of supplementing rumen-protected (RP) Lys when these sources of HFM are fed. In this study, 12 multiparous Jersey cows were enrolled in a triplicated 4 × 4 Latin square with 4 periods 28 d in length. Cows were fed 2 total mixed rations that differed by source of HFM. The HFM was included at 4.5% of the diet dry matter, and one source was produced with the addition of poultry blood. Cows were randomly assigned to 1 of 4 treatment sequences. Treatments were as follows: HFM without added blood and no RP-Lys, HFM with added blood and no RP-Lys, HFM without blood and with RP-Lys (22 g of digestible Lys), and HFM with added blood and RP-Lys. The source of HFM containing blood tended to increase dry matter intake (18.3 vs. 17.3 ± 0.72 kg/d), and increased milk yield (20.5 vs. 18.4 ± 1.31 kg/d) and protein yield (0.788 vs. 0.694 ± 0.040 kg/d). The inclusion of RP-Lys did not affect milk or protein yield. In cows fed HFM containing blood, plasma concentration of Lys (82.1 vs. 70.8 ± 4.06 μM) and His (27.8 vs. 17.9 ± 3.15 μM) was higher. The addition of RP-Lys had no effect on the concentration of either plasma Lys or His. Gross energy intake tended to increase for HFM containing more blood (81.4 vs. 77.3 ± 3.29 Mcal/d); however, no difference was observed for intake of digestible energy (52.0 ± 2.20 Mcal/d) or metabolizable energy (46.4 ± 2.02 Mcal/d). Similar to dry matter intake, N intake increased with the inclusion of HFM containing blood, but crude protein digestibility decreased (61.6 vs. 66.0%). Results of this study highlight that source of HFM can be a factor that affects milk production and that this in part is due to differences in the profile of AA. Additionally, the observation that plasma His and milk protein increased with the consumption of HFM containing more blood suggests that His may have played a role in increasing milk and milk protein yield.

The physical form of feeds can influence dairy cow chewing behavior, rumen characteristics, and ruminal passage rate. Changing particle size of feeds is usually done through grinding or chopping forages, but pelleting feed ingredients also changes particle size. Our objective was to determine if pelleted dried distillers grains and solubles (DDGS) affected the feeding value for lactating dairy cattle. Seven lactating Jersey cows that were each fitted with a ruminal cannula averaging (± standard deviation) 56 ± 10.3 d in milk and 462 ± 75.3 kg were used in a crossover design. The treatments contained 15% DDGS in either meal or pelleted form with 45% or 55% forage on a dry matter basis. The forages were alfalfa hay, corn silage, and wheat straw. The factorial treatment arrangement was meal DDGS and low forage (mDDGS-LF), pelleted DDGS and low forage (pDDGS-LF), meal DDGS and high forage (mDDGS-HF), and pelleted DDGS and high forage (pDDGS-HF). Dry matter intake and energy-corrected milk were both unaffected by treatment averaging 19.8 ± 2.10 kg/d and 33.9 ± 1.02 kg/d, respectively. Fat yield was unaffected averaging 1.7 ± 0.13 kg/d, but protein yield was affected by the interaction of forage and DDGS. Protein yield was similar for both low forage treatments but was increased by when pDDGS was fed in the high forage treatment (1.05 vs. 0.99 ± 0.035 kg/d). When forage concentration was increased, starch digestibility increased by 1.9 percentage units, crude protein digestibility tended to increase 1.1 percentage units, and residual organic matter digestibility decreased 3.4 percentage units. Pelleting DDGS increased digestibility of neutral detergent fiber (NDF) digestibility (49.2 vs. 47.5 ± 1.85%) and gross energy (68.2 vs. 67.1 ± 1.18%). Increasing forage increased ruminal pH (5.85 to 5.94 ± 0.052). Passage rate slowed from 2.84 to 2.65 ± 0.205 %/h when feeding HF compared with LF. Rumination time increased from 417 to 454 ± 49.4 min with increasing forage concentration but was unaffected by the form of DDGS or the interaction of forage and DDGS. Eating time increased with pDDGS (235 vs. 209 ± 19.8 min), which may be a result of increased feed sorting behavior. Pelleting DDGS increased preference for particles retained on the 8-mm sieve and decreased preference for particles on the 1.18-mm sieve and in the pan (1.18 mm). Results confirm that increasing forage concentration increases ruminal pH, rumination time, and slows passage rate, but contrary to our hypothesis increasing forage concentration did not increase NDF digestibility. Results also suggest that pelleted DDGS do not appear to affect milk production, ruminal characteristics, or passage rate, but pelleted DDGS may increase sorting behavior of lactating Jersey cows and increase NDF and gross energy digestibility.

Heat production (HP) represents a major energy cost in lactating dairy cows. Better understanding of factors that affect HP will improve our understanding of energy metabolism. Our objective was to derive models to explain variation in HP of lactating Jersey cows. Individual animal-period data from 9 studies (n = 293) were used. The data set included cows with a wide range (min to max) in days in milk (44–410) and milk yield (7.8–43.0 kg/d). Diets included corn silage as the predominate forage source, but diets varied (min to max on DM basis) in crude protein (CP; 15.2–19.5%), neutral detergent fiber (NDF; 35.5–43.0%), starch (16.2–31.1%), and crude fat (2.2 to 6.4%) contents. Average HP was (mean ± standard deviation) 22.1 ± 2.86 Mcal/d, or 28.1 ± 3.70% of gross energy intake. Eight models were fit to explain variation in HP: (1) dry matter intake (DMI; INT); (2) milk fat, protein, and lactose yield (MILKCOMP); (3) INT and milk yield (INT+MY); (4) INT and MILKCOMP/DMI (INT+MILKCOMP); (5) mass of digested NDF, CP, and starch (DIG); (6) INT and digested energy (INT+DE); (7) INT and NDF, CP, and starch digestibility (INT+DIG); or (8) INT+MILKCOMP model plus urinary N excretion (INT+MILKCOMP+UN). For all HP models, metabolic body weight was included. All models were derived via a backward elimination approach and included the random effects of study, cow, and period within block within study. The INT models adequately explained variation in HP with a nonrandom effect–adjusted concordance correlation coefficient of 0.84. Similar adjusted concordance correlation coefficients (0.79–0.85) were observed for other HP models. The HP associated with milk protein yield and supply of digestible protein was greater than other milk production and nutrient digestibility variables. The HP associated with urinary N excretion was 5.32. Overall, HP can be adequately predicted from metabolic body weight and DMI. Milk component yield, nutrient digestibility, or urinary N excretion explained similar variation as DMI. Coefficients for milk protein and protein digestion suggest that digestion and metabolism of protein and synthesis of milk protein contribute substantially to HP of a dairy cow.

Feeding fat to lactating dairy cows may reduce methane production. Relative to cellulose, fermentation of hemicellulose is believed to result in less methane; however, these factors have not been studied simultaneously. Eight multiparous, lactating Jersey cows averaging (±SD) 98 ± 30.8 d in milk and body weight of 439.3 ± 56.7 kg were used in a twice-replicated 4 × 4 Latin square to determine the effects of fat and hemicellulose on energy utilization and methane production using a headbox-type indirect calorimetry method. To manipulate the concentration of fat, porcine tallow was included at either 0 or 2% of the diet dry matter. The concentration of hemicellulose was adjusted by manipulating the inclusion rate of corn silage, alfalfa hay, and soybean hulls resulting in either 11.3 or 12.7% hemicellulose (dry matter basis). The resulting factorial arrangement of treatments were low fat low hemicellulose (LFLH), low fat high hemicellulose (LFHH), high fat low hemicellulose (HFLH), and high fat high hemicellulose (HFHH). Neither fat nor hemicellulose affected dry matter intake, averaging 16.2 ± 1.18 kg/d across treatments. Likewise, treatments did not affect milk production, averaging 23.0 ± 1.72 kg/d, or energy-corrected milk, averaging 30.1 ± 2.41 kg/d. The inclusion of fat tended to reduce methane produced per kilogram of dry matter intake from 24.9 to 23.1 ± 1.59 L/kg, whereas hemicellulose had no effect. Increasing hemicellulose increased neutral detergent fiber (NDF) digestibility from 43.0 to 51.1 ± 2.35%. Similarly, increasing hemicellulose concentration increased total intake of digestible NDF from 6.62 to 8.42 ± 0.89 kg/d, whereas fat had no effect. Methane per unit of digested NDF tended to decrease from 64.8 to 49.2 ± 9.60 L/kg with increasing hemicellulose, whereas fat had no effect. An interaction between hemicellulose and fat content on net energy balance (milk plus tissue energy) was observed. Specifically, increasing hemicellulose in low-fat diets tended to increase net energy balance, but this was not observed in high-fat diets. These results confirm that methane production may be reduced with the inclusion of fat, whereas energy utilization of lactating dairy cows is improved by increasing hemicellulose in low-fat diets.

This work evaluated the National Research Council (NRC) dairy model (2001) predictions of rumen undegradable (RUP) and degradable (RDP) protein compared with measured postruminal non-ammonia, nonmicrobial (NANMN) and microbial N flows. Models were evaluated using the root mean squared prediction error (RMSPE) as a percent of the observed mean; mean and slope biases as percentages of mean squared prediction error (MSPE), and concordance correlation coefficient, (CCC). The NRC (2001) over-estimated NANMN by 18% and under-estimated microbial N by 14%. Both responses had large mean biases (19% and 20% of MSPE, respectively); and NANMN had a slope Dias (22% of MSPE). The NRC NANMN estimate had high RMSPE (46% of observed mean) and low CCC (0.37); updating feed library A, B, and C protein fractions and degradation rate (K-d) estimates with newer literature only marginally improved fit. The re-fit NRC models for NANMN and microbial N had CCC of 0.89 and 0.94, respectively. When compared with a prediction of NANMN as a static mean fraction of N intake; the re-derived NRC approach did not have improved fit. A protein system of intermediate complexity was derived in an attempt to estimate NANMN with improved fit compared with the static mean NANMN model. In this system, postruminal appearance of A, B, and C protein fractions were predicted in a feed-type specific mariner rather than from estimated passage and degradation rates. In a comparison to independent data. achieved through cross-validation; the new protein system improved RMSPE (34 vs. 36% of observed mean) and CCC (0.42 vs. 0.30) compared with the static mean NANMN model. When the NRC microbial N equation was re-derived, the RDP term dropped from the model. Consequently, 2 new microbial protein equations were formulated, both used a saturating (increasing at a decreasing rate) form: one saturated with respect to TDN and the other saturated over increasing intakes of rumen degraded starch and NDF. Both equations expressed maximal microbial N production as a linear function of RDP intake. The function relating microbial N to intake of rumen degradable carbohydrate improved RMSPE (24 vs. 28% of the observed mean) and CCC (0.63 vs 0.30) compared with the re-derived NRC model. The newly derived equations showed modest improvements in model fit and improved capacity to account for known biological effects; however, substantial variability in NANMN and microbial N estimates remained unexplained. Agricultural and Food Research Initiative Competitive Grant [2011-68004-30340]; USDA National Institute of Food and Agriculture (Washington, DC) [2015-03656]; Papillon (Easton, MD) In addition to the funding sources listed in the title page footnote, funding for this project was provided by Agricultural and Food Research Initiative Competitive Grant no. 2011-68004-30340 and no. 2015-03656 from the USDA National Institute of Food and Agriculture (Washington, DC) and by Papillon (Easton, MD). The authors acknowledge the contributions of the late L. F. Reutzel (Laud O'Lakes/Purina Mills) to portions of the initial code used in the project and the late Gale Bateman (Akey, Lewisburg, OH) for help in assembling and collating the dietary ingredient data.

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.

A total of 2,948 steers (mean initial BW = 568.9 ± 49.4 kg) were used to evaluate the effect of the LEP R25C SNP genotype on feed intake, growth performance, and carcass characteristics over time. Steers were grouped into 5 blocks, each consisting of 10 pens initially, and then at approximately 24 d prior to the assigned slaughter date, cattle in each pen were randomly selected either to remain in the pen they were in (group A) or to be assigned to a new pen (group B). Steers were allocated to 5 blocks and 6 harvest weeks (-3, -2, 0, 2, 3, and 4 wk) relative to the projected end point. Steers were weighed and ultrasound scanned at 60 and 1 d prior to harvest. Leptin genotype affected ( ≤ 0.011) 12th-rib fat and i.m. fat percentage (IMF) for each slaughter group at both 60 and 1 d prior to slaughter, although rib eye area (REA) was not affected ( = 0.773) by leptin genotype 60 d prior to slaughter in any group. Time affected ( < 0.001) live BW as well as 12th-rib fat, REA, and IMF measured 60 and 1 d prior to each slaughter time. Dry matter intake was also higher ( = 0.003) for cattle of the animals homozygous for the T allele (TT) genotype compared to those with the animals homozygous for the C allele (CC) genotype (9.59 vs. 9.29 ± 0.075 kg). The LEP R25C genotype affected key traits related to carcass fatness; specifically, compared to cattle of the CC genotype, cattle of the TT genotype had a higher ( = 0.016) calculated empty body fat (29.1 vs. 28.8 ± 0.133%) and higher ( = 0.020 calculated yield grade (2.62 vs. 2.52 ± 0.035). Additionally, like for live measures, TT cattle tended ( = 0.093) to have a higher 12th-rib fat (13.2 vs. 12.8 ± 0.26 mm). However, the LEP R25C genotype did not affect KPH ( = 0.854) or marbling score ( = 0.240), nor did it affect any USDA quality measure ( ≥ 0.350). The leptin genotype also affected ( = 0.048) HCW, which was highest for steers of the TT genotype (400.9 vs. 403.5 ± 3.41kg). Results indicate that the leptin R25C genotype and time impacted most traits associated with fatness.

The rumen microbial community is responsible for producing a majority of the energetic needs for the animal, yet our understanding of the rumen microbiome is in its infancy. To better understand the effect of corn-ethanol coproducts on rumen microbial communities, a replicated 4 × 4 Latin square design study utilizing 12 cows in three squares was conducted to evaluate the replacement of alfalfa hay with a mixture (CoP) containing straw and dried distillers grains plus solubles (DDGS) in lactating Jersey cows. The experimental treatments were (proportions on a dry matter basis): a control diet (CON) containing 18.2% of alfalfa hay with no straw or DDGS. A low coproduct diet (LCoP) containing 12.1% alfalfa, 2.1% straw, and 6.0% DDGS. A medium coproduct diet (MCoP) containing 6.1% alfalfa, 4.2% straw, and 12.1% DDGS. A high coproduct diet (HCoP) containing 6.2% straw, 18.1% DDGS with no alfalfa. Rumen digesta samples were collected via an esophageal tube. No differences were observed for milk production and dry matter intake (P ≥ 0.307) (mean ± SEM) 19.5 kg ± 0.60, 29.6 kg ± 0.91, across treatments, while a decrease in methane was observed (P < 0.01) for the HCoP treatment. The bacterial community was assessed by sequencing the V4 region of the 16S rRNA gene. Additionally, the archaeal community was assessed by sequencing the V4-V5 region of the 16S rRNA gene on the Illumina MiSeq platform. Amplicon Sequence Variants were identified using the DADA2 pipeline. No significant differences were observed for the bacterial (P = 0.334) and archaeal (P = 0.593) communities. Although global effects in microbial community dynamics were not observed, differential taxa were observed with Lachnospiraceae being the major differentially abundant Family. The archaeal community composition demonstrated that Methanobacteriales to be the differentially abundant Order across treatments, and may contribute to methane production.

Four multiparous, lactating Holstein cows (average DIM 169.5 ± 20.5 d), fitted with ruminal and duodenal cannulas, were used in a 4 × 4 Latin square with a 2 × 2 factorial arrangement of treatments to investigate the effects of 2-hydroxy-4-methylthio-butanoic acid (HMTBA) when fed with diets differing in metabolizable protein (MP) supply and equal levels of crude protein on milk production and composition, rumen microbial activity, duodenal protein flow, and rumen bacterial community composition in vivo and in vitro. Experimental periods were 28 d in length. Cows were housed in individual tie stalls and were randomly assigned to 4 dietary treatments: low MP or high MP, supplemented with or without 25 g of HMTBA, which was top-dressed once daily at 0930 h. No interactions were observed between HMTBA and level of dietary MP, with the exception of ruminal acetate-to-propionate ratio. Milk yield was not affected by treatment and averaged 23.8 ± 2.06 kg/d. There was a tendency for increased milk protein percent in cows receiving low MP diets, averaging 3.30 ± 0.09% and 3.21 ± 0.09% for low MP and high MP, respectively. The total-tract apparent digestibility of organic matter, neutral detergent fiber, and nitrogen were greater in cows consuming the low MP diet. Rumen pH was lower in cows consuming high MP diets as well as in those consuming HMTBA. Rumen ammonia concentrations tended to be greater in cows consuming HMTBA, and volatile fatty acid concentrations were greater in cows consuming HMTBA. Duodenal dry matter flow, nitrogen flow, and microbial nitrogen flow did not differ between treatments. The bacterial community structure of cows receiving HMTBA was not affected at the phylum level. The relative abundance of bacterial phyla in vivo differed when compared with in vitro conditions for Firmicutes, Bacteroidetes, Proteobacteria, TM7, Tenericutes, Spirochaetes, SR1, and Verrucomicrobia.

The objective of this study was to determine the effects of high-starch or high-fat diets formulated to be isoenergetic on energy and N partitioning and utilization of energy. Twelve multiparous Jersey cows (mean ± standard deviation; 192 ± 11 d in milk; 467 ± 47 kg) in a crossover design with 28-d periods (24-d adaptation and 4-d collection) were used to compare 2 treatment diets. Treatments were high starch (HS; 30.8% starch, 31.8% neutral detergent fiber, and 1.9% fatty acids) or high fat (HF; 16.8% starch, 41.7% neutral detergent fiber, and 4.1% fatty acids). Diets were formulated to have net energy for lactation (NEL) content of 1.55 Mcal/kg of dry matter according to the National Research Council (2001) dairy model. Nutrient composition was varied primarily by replacing corn grain in HS with a rumen-inert fat source and cottonseed hulls in HF. Gross energy content was lower for HS (4.43 vs. 4.54 ± 0.01 Mcal/kg of dry matter), whereas digestible (2.93 vs. 2.74 ± 0.035 Mcal/kg of dry matter) and metabolizable energy (2.60 vs. 2.41 ± 0.030 Mcal/kg of dry matter), and NEL (1.83 vs. 1.67 ± 0.036 Mcal/kg of dry matter) content were all greater than for HF. Tissue energy deposited as body fat tended to be greater for HS (4.70 vs. 2.14 ± 1.01 Mcal/d). For N partitioning, HS increased milk N secretion (141 vs. 131 ± 10.5 g/d) and decreased urinary N excretion (123 vs. 150 ± 6.4 g/d). Compared with HF, HS increased apparent total-tract digestibility of dry matter (66.7 vs. 61.7 ± 1.06%), organic matter (68.5 vs. 63.2 ± 0.98%), energy (66.0 vs. 60.4 ± 0.92%), and 18-carbon fatty acids (67.9 vs. 61.2 ± 1.60%). However, apparent total-tract digestibility of starch decreased for HS from 97.0 to 94.5 ± 0.48%. Compared with HF, HS tended to increase milk yield (19.7 vs. 18.9 ± 1.38 kg/d), milk protein content (4.03 vs. 3.93 ± 0.10%), milk protein yield (0.791 vs. 0.740 ± 0.050 kg/d), and milk lactose yield (0.897 vs. 0.864 ± 0.067 kg/d). In addition, HS decreased milk fat content (5.93 vs. 6.37 ± 0.15%) but did not affect milk fat yield (average of 1.19 ± 0.09 kg/d) or energy-corrected milk yield (average of 27.2 ± 1.99 kg/d). Results of the current study suggest that the HS diet had a greater metabolizable energy and NEL content, increased partitioning of N toward milk secretion and away from urinary excretion, and may have increased partitioning of energy toward tissue energy deposited as fat.

Millions of feed composition records generated annually by testing laboratories are valuable assets that can be used to benefit the animal nutrition community. However, it is challenging to manage, handle, and process feed composition data that originate from multiple sources, lack standardized feed names, and contain outliers. Efficient methods that consolidate and screen such data are needed to develop feed composition databases with accurate means and standard deviations (SD). Considering the interest of the animal science community in data management and the importance of feed composition tables for the animal industry, the objective was to develop a set of procedures to construct accurate feed composition tables from large data sets. A published statistical procedure, designed to screen feed composition data, was employed, modified, and programmed to operate using Python and SAS. The 2.76 million data received from 4 commercial feed testing laboratories were used to develop procedures and to construct tables summarizing feed composition. Briefly, feed names and nutrients across laboratories were standardized, and erroneous and duplicated records were removed. Histogram, univariate, and principal component analyses were used to identify and remove outliers having key nutrients outside of the mean ± 3.5 SD. Clustering procedures identified subgroups of feeds within a large data set. Aside from the clustering step that was programmed in Python to automatically execute in SAS, all steps were programmed and automatically conducted using Python followed by a manual evaluation of the resulting mean Pearson correlation matrices of clusters. The input data set contained 42, 94, 162, and 270 feeds from 4 laboratories and comprised 25 to 30 nutrients. The final database included 174 feeds and 1.48 million records. The developed procedures effectively classified by-products (e.g., distillers grains and solubles as low or high fat), forages (e.g., legume or grass-legume mixture by maturity), and oilseeds versus meal (e.g., soybeans as whole raw seeds vs. soybean meal expellers or solvent extracted) into distinct sub-populations. Results from these analyses suggest that the procedure can provide a robust tool to construct and update large feed data sets. This approach can also be used by commercial laboratories, feed manufacturers, animal producers, and other professionals to process feed composition data sets and update feed libraries.

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 EA.A 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 EA.A 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 IMP 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. USDA NIFA grant (Washington, USA) [20176701526539]; Virginia Agricultural Experiment Station (Richmond, VA); Hatch Program of the National Institute of Food and Agriculture (Washington D.C., USA), U.S. Department of Agriculture; Agriculture and Agri-Food Canada (Sherbrooke, QC, Canada)Agriculture & Agri Food Canada; College of Agriculture and Life Sciences Pratt Endowment at Virginia Tech (Blacksburg, VA); Dairy Farmers of Canada (Ottawa, ON, Canada); Canadian Dairy Network (Ottawa, ON, Canada); Canadian Dairy Commission under the Agri-Science Clusters Initiative (Ottawa, ON, Canada); National Animal Nutrition Program (NANP); Hatch funds [NRSP-9] This research was supported by funding provided, in part, by a USDA NIFA grant (Award No: 20176701526539; Washington, USA); the Virginia Agricultural Experiment Station (Richmond, VA) and the Hatch Program of the National Institute of Food and Agriculture (Washington D.C., USA), U.S. Department of Agriculture; Agriculture and Agri-Food Canada (Sherbrooke, QC, Canada), the College of Agriculture and Life Sciences Pratt Endowment at Virginia Tech (Blacksburg, VA); and Dairy Farmers of Canada, the Canadian Dairy Network, and the Canadian Dairy Commission under the Agri-Science Clusters Initiative (Ottawa, ON, Canada). A portion of this work was carried out and supported as an activity of the National Animal Nutrition Program (NANP) to provide enabling technologies, support, and shared resources to the research community. The NANP, a National Research Support Project (NRSP-9) of State Agricultural Experiment Stations, is funded from Hatch funds administered by the National Institute of Food and Agriculture, US Department of Agriculture, Washington, DC. The NANP Coordinating Committee at the initiation of this work was composed of Gary Cromwell (University of Kentucky), Phillip Miller (University of Nebraska), Jack Odle (North Carolina State University), Mark Hanigan (Virginia Tech), William Weiss (The Ohio State University), Mary Beth Hall (USDA-ARS), Mike Galyean (Texas Tech), Todd Applegate (Purdue University), and Donald Beitz (Iowa State University). The NANP Modeling Subcommittee comprised Mark Hanigan (Virginia Tech), Brian Kerr (USDA/ARS), Ermias Kebreab (University of California-Davis), John McNamara (Washington State University), Luis Tedeschi (Texas A&M University), Mike VandeHaar (Michigan State University), Nathalie Trottier (Michigan State University), and Roselina Angel (University of Maryland). Public domain – authored by a U.S. government employee

Development of indexes based on milk yield of lactating dairy cows fed corn silage-based diets combined with silage quality parameters can provide clear guidance to assess the overall quality of corn silage produced. This study was done to determine the most important minimum number of silage quality parameters for corn silage quality evaluation among commonly used parameters for nutritional and fermentation quality and to develop an integrated corn silage quality index (CSQI) using standard scoring functions and weighting assignment. Principal component analysis (PCA) and multiple regression analysis (MRA) were used to select the most important silage quality parameters and to assign parameter weights, whereas standard scoring functions were used to normalize silage quality parameters. A variety of corn silage samples (n = 390) representing spatial and seasonal heterogeneity, were collected from 195 intensive dairy farms in China and analyzed for 16 frequently used chemical and fermentation parameters. Concurrent with silage sampling, average daily milk yield respective to each silage was collected from 50 mid-lactating dairy cows fed corn silage-based diets (i.e., 39–48 % DM corn silage in TMR) and used as the dependent variable in MRA. The silage quality parameters used in developing the index were; digestible NDF after 30-h in vitro incubation (g/kg NDF), and concentrations (DM basis) of starch, crude protein, ether extract, ammonia, and lactic acid. The CSQI was developed by summing normalized and weighted quality parameters. The CSQI was subsequently converted to corn silage quality scores (CSQS, 0–100). Based on the CSQS, silages were grouped into five quality grades; poor, fair, average, good and excellent having grade mean index score of 51, 62, 69, 78 and 89. The new indexes were evaluated against observed daily milk yield measurements and Milk2006 milk yield estimates. The CSQS were found to be positively correlated with observed daily milk yield measurements and Milk2006 index. The linear relationship between MRA based CSQS and observed daily milk yield measurement was higher than that of the relationship between PCA based CSQS and Milk2006 index (R2 = 0.72 vs. 0.60). Further, accuracy and precision of predicting milk yield by MRA based CSQI were higher than those of the PCA based CSQI against both observed daily milk yield measurements and Milk2006 index based on corn silage quality. Thus, among the two multivariate approaches, the MRA based CSQI method was a more accurate method for predicting performance between corn silage of different qualities.

© 2016 American Dairy Science Association Mathematical models that predict water intake by drinking, also known as free water intake (FWI), are useful in understanding water supply needed by animals on dairy farms. The majority of extant mathematical models for predicting FWI of dairy cows have been developed with data sets representing similar experimental conditions, not evaluated with modern cows, and often require dry matter intake (DMI) data, which may not be routinely available. The objectives of the study were to (1) develop a set of new empirical models for predicting FWI of lactating and dry cows with and without DMI using literature data, and (2) evaluate the new and the extant models using an independent set of FWI measurements made on modern cows. Random effect meta-regression analyses were conducted using 72 and 188 FWI treatment means with and without dietary electrolyte and daily mean ambient temperature (TMP) records, respectively, for lactating cows, and 19 FWI treatment means for dry cows. Milk yield, DMI, body weight, days in milk, dietary macro-nutrient contents, an aggregate milliequivalent concentration of dietary sodium and potassium (NaK), and TMP were used as potential covariates to the models. A model having positive relationships of DMI, dietary dry matter (DM%), and CP (CP%) contents, NaK, and TMP explained 76% of variability in FWI treatment means of lactating cows. When challenged on an independent data set (n = 261), the model more accurately predicted FWI [root mean square prediction error as a percentage of average observed value (RMSPE%) = 14.4%] compared with a model developed without NaK and TMP (RMSPE% = 17.3%), and all extant models (RMSPE% ≥ 15.7%). A model without DMI included positive relationships of milk yield, DM%, NaK, TMP, and days in milk, and explained 63% of variability in the FWI treatment means and performed well (RMSPE% = 17.9%), when challenged on the independent data. New models for dry cows included positive relationships of DM% and TMP along with DMI or body weight. The new models with and without DMI explained 75 and 54% of the variability in FWI treatment means of dry cows and had RMSPE% of 12.8 and 15.2%, respectively, when evaluated with the literature data. The study offers a set of empirical models that can assist in determining drinking water needs of dairy farms.

Twenty Holstein cows, 12 primiparous and 8 multiparous, with (mean ± SD) 91 ± 19 d in milk and 595 ± 81 kg were used in replicated 4 × 4 Latin squares to compare the effects of feeding conventional dried distillers grains with solubles (DDGS) and reduced-fat DDGS (RFDDGS) in combination with rumen-inert fat (RIF) on milk production and rumen fermentation; one square contained rumen cannulated animals for rumen measurements. In each 21-d period, cows were randomly assigned to 1 of 4 dietary treatments (values on a dry matter basis): (1) control (CON) that contained 0% DDGS; (2) DG contained 30% DDGS; (3) RFDG contained 30% RFDDGS in substitution of DDGS; and (4) RFDG+RIF was similar to RFDG with the addition of 1.9% RIF. Unlike most practical diets in the dairy field, our diets had22% forage neutral detergent fiber and18.0% crude protein. Dry matter intake was similar across treatments with any form of DDGS averaging 26.0 ± 0.6 kg/d, whereas the CON diet resulted in less dry matter intake, 21.6 ± 0.6 kg/d. Milk yield tended to be 1.7 kg/d greater for diets with either type of DDGS. Concentration of milk protein was greatest for the DG and RFDG diets, intermediate for the RFDG+RIF diet, and least for the CON diet, namely 3.22, 3.21, 3.12, and 3.07 ± 0.05%. Reduced milk fat percentage and yield were observed when cows consumed the DG diet, 3.27 ± 0.10% and 1.11 ± 0.04 kg/d, respectively, whereas these responses were similar among CON, RFDG, and RFDG+RIF, which averaged 3.68 ± 0.10% and 1.22 ± 0.04 kg/d. The presence of trans-10,cis-12 conjugated linoleic acid was only detected in milk from cows consuming the DG diet; similarly, concentration and yield of trans-10 18:1 were greater for cows consuming this diet. Rumen ammonia was similar across treatments averaging 27.0 ± 2.1mg/dL. The CON and RFDG+RIF diets had similar mean pH, 6.1 ± 0.11, whereas DG and RFDG resulted in lower pH averaging 5.79 ± 0.11. No effect on total concentration of volatile fatty acids was observed; the overall mean was 121 ± 4.11 mM; molar proportion of acetate was affected by treatment resulting in 67.3, 63.2, 61.4, and 60.9 ± 0.93 mol/100 mol for CON, RFDG+RIF, RFDG, and DG, respectively. Results from DNA sequencing showed that rumen bacterial community structure was relatively stable with minor changes at the family and genus levels; these changes may be associated with low starch diets, and hence reduced amylolytic bacteria populations. Feeding high proportions of RFDDGS resulted in greater dry matter intake with low risk for milk fat depression while supporting ruminal fermentation.

Last year, over 110 million metric tons of corn silage and 2.7 million metric tons of alfalfa were harvested (USDA, 2018). Despite these amounts and because forage is costly to transport, individual dairy producers who experience troubling seeding, growing, and/or harvest may be faced with the challenge of maintaining normal milk production by feeding less forage. Fortunately, non-forage fiber sources (NFFS) may be used to supply nutrients that would otherwise come from forages. Furthermore, many of these feeds, such are corn gluten feed, beet pulp, brewers grains, canola meal, distillers grains and solubles, and soyhulls are plentifully in the Midwestern United States. Although each NFFS is unique and the chemical composition may vary due to source, when making ration balancing decisions to include them, nutritionists may be faced with similar challenges. These challenges include that these feeds are low in effective fiber, many contain less fiber than forages, are higher in rumen undegradable protein, may vary in chemical composition, and may include toxins or antinutritional factors. Although the modern dairy cow is adaptable and can utilize use NFFS as sources of nutrients to produce milk, there are still limitations to how they can be included in diets.

The use of coproducts as an alternative feed source is a common practice when formulating dairy rations. A study using 12 multiparous (79 ± 16 d in milk; mean ± standard deviation) lactating Jersey cows was conducted over 5 mo to evaluate the effects of dried distillers grains with solubles (DDGS) or canola meal on milk and gas production. A replicated 4 × 4 Latin square design was used to compare 4 dietary treatments. Treatments comprised a control (CON) containing no coproducts, a treatment diet containing 10% (dry matter basis) low-fat DDGS (LFDG), a treatment diet containing 10% high-fat DDGS (HFDG), and a 10% canola meal (CM) treatment. The crude fat content of the LFDG, HFDG, and CM treatments was 6.05 ± 0.379, 10.0 ± 0.134, and 3.46 ± 0.085%, respectively. Coproducts were included in partial replacement for corn and soybean meal. Indirect headbox-style calorimeters were used to estimate heat production. Dry matter intake and milk yield were similar between all treatments, averaging 17.4 ± 0.56 kg/d and 24.0 ± 0.80 kg, respectively. Milk urea N was affected by treatment and was highest in CON (20.6 mg/dL; 18.0, 19.9, and 18.1 ± 0.62 mg/dL in LFDG, CM, and HFDG, respectively). Heat production per unit of metabolic body weight tended to be affected by treatment and was lowest for CON, and diets containing coproducts were not different (192, 200, 215, and 204 ± 5.91 kcal/kg of metabolic body weight for CON, LFDG, CM, and HFDG, respectively). The concentration of metabolizable energy was affected by dietary treatment; specifically, HFDG did not differ from CON but was greater than LFDG and CM (2.58, 2.46, 2.29, and 2.27 ± 0.09 Mcal/kg for HFDG, CON, LFDG, and CM, respectively). The concentration of net energy balance (milk plus tissue) tended to be affected by dietary treatment; HFDG did not differ from either CON or LFDG, but it was higher than CM (1.38, 1.36, 1.14, and 1.06 ± 0.11 Mcal/kg for HFDG, CON, LFDG, and CM, respectively). Results of this study indicate that milk production and dry matter intake were not affected by feeding common coproducts and that differences may result in whole-animal energy use; fat content of DDGS is a major factor affecting this.

When formulating dairy cow rations, characterization of protein in feeds requires estimation of protein degradation in the rumen and digestion in the intestine. The objective of this work was to evaluate experimental and feed-related factors that affect characterization using in situ, in vitro, or mobile bag techniques, of 0-h washout (A), potentially degradable (B), and undegradable (C) protein fractions, protein degradation rate (Kd), and digestibility of rumen undegradable protein (dRUP). Data sets of 136 studies on A, B, C, and Kd and 113 studies on dRUP were amassed from the literature. Mixed-effect linear models were used to relate these variables to methodological and feed factors while accounting for random differences among studies. Predictions of A, B, and C protein fractions were significantly influenced by crude protein and neutral detergent fiber interactions with bag pore size, incubation time, bag area, and sample-to-bag area ratio. For example, a 20.0% decrease in crude protein of a theoretical legume silage sample would increase A fraction prediction by 20.1%, but 34.7% with bag incubation time -1 standard deviation below the mean. Similarly, reported Kd values were significantly influenced by crude protein interactions with bag area and sample-to-bag area ratio and by neutral detergent fiber interaction with pore size. Feed variables and measurement variables influencing protein digestibility measures suggest that these analytical factors are likely associated with variance among differing methodologies and within unique samples of the same feed. When predicting dRUP, the use of mobile bag method produced significantly different estimates compared with the in vitro 3-step method. The use of mobile bag resulted in an 8.9% (±3.8%) higher estimate of dRUP compared with the in situ technique. In 618 and 977 samples, sample variation to sample mean ratio for acid detergent fiber and pepsin-acid incubation time was 63.0 and 58.0%, respectively. Variation in feedstuff content and lack of standardization of methods used to measure protein disappearance led to a lack of robustness in the measurements commonly employed.

Eight Holstein and 8 Jersey multiparous, lactating cows were used to complete 56 energy balances to determine the energy content of reduced-fat dried distillers grains with solubles (RFDDGS). A repeated switchback design was used to compare treatments with and without RFDDGS. Diets consisted of 24.2% corn silage, 18.4% alfalfa hay, 6.94% brome hay with either 22.9% rolled corn or 14.8% soybean meal (control), or 8.95% rolled corn, 28.8% RFDDGS, and 0% soybean meal [Co-P; dry-matter (DM) basis]. The inclusion of RFDDGS did not affect DM intake, averaging 21.4 ± 0.53 kg of DM for all cows, but milk production tended to increase from 29.8 to 30.9 ± 1.46 kg/d for control and Co-P treatments, respectively. Milk fat percentage and energy-corrected milk did not differ between treatments, averaging 4.33 ± 0.14% and 34.1 kg/d, respectively. Milk protein was significantly decreased by the Co-P treatment (3.56 and 3.41 ± 0.08% for control and Co-P treatments), but protein yield was not affected. Milk energies were 1.40 Mcal/d greater with Co-P. Energy lost as methane was reduced by 0.31 Mcal/d with the addition of RFDDGS to the diet. Heat loss averaged 29.9 ± 0.55 Mcal/d and was not different between diets. Average energy retained as tissue energy was −2.99 ± 0.93 Mcal/d and did not differ between treatments. Intake of digestible and metabolizable energy were not different between the control and Co-P treatments, averaging 2.68 and 2.31 Mcal/kg of DM, respectively. The net energy of lactation values of control and Co-P diets were calculated to be 1.43 and 1.47 Mcal/kg of DM, respectively. These energy estimates suggest greater energy content of diets containing RFDDGS than diets containing a mixture of corn and soybean meal in lactating dairy cows.

This study investigated the effects of continuous feeding of high inclusion of reduced-fat corn distillers grains with solubles with and without monensin on dry matter intake (DMI), production, milk fatty acid profile, and plasma AA profile in lactating cows. The experiment was conducted for 12 wk (1-wk covariate, 2-wk diet adaptation, and 9-wk experimental period of data collection) with 36 Holstein cows in a randomized complete block design. Cows were blocked by parity, days in milk, and milk yield and assigned to the following diets: (1) control (CON), (2) CON with reduced-fat corn distillers grains with solubles included at 28.8% (dry matter basis) replacing soybean meal, soyhulls, and supplemental fat (DG), and (3) DG with monensin (Rumensin; Elanco Animal Health, Greenfield, IN) supplemented at a rate of 20 mg/kg of DM offered (DGMon). Orthogonal contrasts were used to compare CON versus DG and DGMon and to compare DG versus DGMon. Milk yield was not affected (40.3 vs. 40.8 kg/d) by DG and DGMon compared with CON. However, for DG and DGMon compared with CON, decreased DMI (24.9 vs. 26.4 kg/d), milk fat yield (1.12 vs. 1.55 kg/d), milk protein yield (1.24 vs. 1.32 kg/d), and energy-corrected milk yield (37.7 vs. 43.5 kg/d) were observed. Feeding DGMon compared with DG did not affect DMI (24.4 vs. 25.4 kg/d) and milk yield (39.2 vs. 41.3 kg/d) but decreased milk fat yield (1.08 vs. 1.23 kg/d), milk protein yield (1.20 vs. 1.28 kg/d), and energy-corrected milk yield (36.0 vs. 39.4 kg/d). Interactions between treatment and week for DMI, milk fat yield, and energy-corrected milk indicate that production responses to DG and DGMon versus CON were decreased over the experimental period. Cows fed DG and DGMon had increased milk fat concentration of trans-10,cis-12 18:2, trans-10 18:1, and long-chain (>16C) and polyunsaturated fatty acids and decreased short-chain (

Aims The effect of feeding dried distillers grains with solubles (DDGS) or reduced-fat DDGS (RFDG) on ruminal methanogenesis and the rumen bacterial community of dairy cattle was evaluated. Methods and Results Treatments were CONT, a diet with no distillers grains; DG, inclusion of 20% DDGS; rfDG, inclusion of 20% RFDG; and MIX, inclusion of 10% DDGS and 10% RFDG. Methane emission was measured; rumen bacterial community was evaluated by sequencing the V4 region of the 16S rRNA gene. Total methane production remained unaffected. However, feeding distillers grains tended to reduce methanogenesis per unit of feed intake, decreased the abundance of the phylum Bacteroidetes and tended to increase Firmicutes. The abundance of Prevotellaceae positively correlated with feed intake; methane emission was positively correlated with the abundance of Prevotellaceae and was negatively correlated with the abundance of Succinivibrionaceae. Conclusions DDGS or RFDG may reduce methanogenesis per unit of feed intake; shifts in the abundance of predominant ruminal bacterial families may influence methane formation, likely because of their role on hydrogen liberation and utilization pathways. Significance and impact of the study Replacing corn and soybean meal with DDGS or RFDG in dairy rations may reduce the proportion of dietary energy wasted as methane, without detrimental effects on the overall bacterial population. This article is protected by copyright. All rights reserved.

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.

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 x 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) x 2 (diet, diet and ruminal factors) x 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 x 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. Agriculture and Food Research Initiative Competitive from the USDA National Institute of Food and Agriculture (Washington, DC) [2015-03656]; USDA Agricultural Research Service (Washington, DC) The authors thank Keri Rogan and Jared Judy for their efforts related to data entry. Funding for this project was provided by Agriculture and Food Research Initiative Competitive grant no. 2015-03656 from the USDA National Institute of Food and Agriculture (Washington, DC). Research was supported by state and federal funds appropriated to the University of Nebraska-Lincoln, Virginia Tech (Blacksburg), and The Ohio State University (Columbus) with funding from USDA Agricultural Research Service (Washington, DC). Public domain – authored by a U.S. government employee

A study was conducted to determine the rumen degradation and intestinal digestibility of crude protein (CP) and AA, and AA composition of the rumen-undegradable protein (RUP) from 3 sources of blood meal (BM1, BM2, and BM3), canola meal (CM), low-fat distillers dried grains with solubles (LFDG), soybean meal (SBM), and expeller soybean meal (ESBM). Two Holstein cows fitted with ruminal and proximal duodenal cannulas were used for in situ incubation of 16h and for the mobile bag technique. To correct for bacterial contamination of the RUP, 2 methods were used: purines and DNA as bacterial markers. Ruminal degradations of CP were 85.3, 29.8, 40.7, 75.7, 76.9, 68.8, and 37.0 ± 3.93% for BM1, BM2, BM3, CM, LFDG, SBM, and ESBM, respectively. Ruminal degradation of both total essential AA and nonessential AA followed a similar pattern to that of CP across feedstuffs. Based on the ratio of AA concentration in the RUP to AA concentration in the original feedstuff, ruminal incubation decreased (ratio1) the concentrations of His, Lys, and Trp, and increased (ratio1) the concentrations of Ile and Met across feedstuffs. Compared with purines, the use of DNA as bacterial marker resulted in a higher estimate of bacterial CP contamination for CM and lower estimates for LFDG and ESBM. Intestinal digestibility of RUP could not be estimated for BM1, BM3, and SBM due to insufficient recovery of residue. For the remaining feedstuffs, intestinal digestibility of RUP was highest for ESBM, followed by BM2 and LFDG, and lowest for CM: 98.8, 87.9, 89.7, and 72.4 ± 1.40%, respectively. Intestinal absorbable dietary protein was higher for BM2 compared with CM and LFDG, at 61.7, 17.9, and 20.7 ± 2.73% CP, respectively. As prices fluctuate, intestinal absorbable protein or AA may be used as a tool to aid in the selection among feedstuffs with different protein quality.

Castillo-Lopez, E., Klopfenstein, T. J., Fernando, S. C. and Kononoff, P. J. 2014. Effect of dried distillers’ grains and solubles when replacing corn or soybean meal on rumen microbial growth in vitro as measured using DNA as a microbial marker. Can. J. Anim. Sci. 94: 349–356. The objectives were to evaluate the use of rDNA markers to measure the effects of dried distillers’ grains with solubles (DDGS) and the potential treatment×time interaction on microbial crude protein (MCP) synthesis in vitro and secondly to measure the contribution of yeast based protein originating from DDGS. Treatments were: (1) CONT, control with no DDGS, but with alfalfa hay, corn silage, ground corn (GC) and soybean meal (SBM) included at 25% (DM basis); (2) LOWCORN, 20% DDGS (DM basis) replacing GC; (3) LOWSBM, 20% DDGS (DM basis) replacing SBM; and (4) LOWCORNSBM, 20% DDGS (DM basis) replacing 10% GC and 10% SBM. Treatments (0.5 g) were incubated in 50 mL of inoculum in duplicate. At 0, 4, 16, 32, 48 and 96 h of fermentation total DNA was extracted from each treatment and MCP was measured using rDNA markers. The sum of bacterial crude protein (BCP) and protozoal crude protein (PCP) was considered as MCP. Data were analyzed as a completely randomized design. The treatment×time interaction was tested and the SLICE option was included to evaluate the effect of treatment at each fermentation time point. There was a tendency to a treatment×time interaction (P=0.07) for MCP. Specifically, at 16 h, LOWCORNSBM yielded greater (P–1DM, for LOWCORNSBM, CONT and LOWCORN, respectively. At 48 h, however, LOWCORN yielded greater MCP (P–1DM, for LOWCORN and LOWSBM, respectively. Yeast crude protein (YCP) was not affected (P=0.21) and averaged 0.04±0.02 mg g–1of substrate (DM basis). Overall, rDNA markers were effective for quantifying MCP, but further research on the methodology is needed. With DDGS inclusion, MCP was maintained; however, yeast cells were extensively degraded during fermentation.

Two ruminally and duodenally cannulated steers were used in a 2 × 2 crossover design to evaluate the effect of supplementation of free Lys or rumen-protected Lys on duodenal flows of essential AA (EAA). Dietary treatments were achieved by the supplementation of either free Lys or rumen-protected Lys to a basal diet composed of 50% alfalfa hay and 50% wet corn gluten feed. Chromium oxide (Cr 2 O 3 ) was used as a digesta flow marker. Duodenal flow of DM was estimated by dividing the daily amount of (Cr 2 O 3 ) dosed by the (Cr 2 O 3 ) concentration in the duodenal samples. Duodenal flows of EAA were determined by multiplying the duodenal flow of DM by the concentration of the respective EAA in the duodenal sample. Duodenal flows of OM, ash, and N were determined in a similar fashion. Intakes of DM (P = 0.90), OM (P = 0.91), ash (P = 0.90), N (P = 0.89), and EAA (P ≥ 0.87) were similar for both treatments. Consequently, duodenal flows of DM (P = 0.17), OM (P = 0.14), ash (P = 0.20), N (P = 0.17), and EAA (P ≥ 0.22) did not differ. Supplementation of both Lys products resulted in similar duodenal flows of Lys.

Sixteen multiparous lactating Holstein cows were used in 2 experiments to evaluate the effects of reduced-fat dried distillers grains with solubles (RFDG) on milk production, rumen fermentation, intestinal microbial N flow, and total-tract nutrient digestibility. In experiment 1, RFDG was fed at 0, 10, 20, or 30% of diet dry matter (DM) to 12 noncannulated Holstein cows (mean ± standard deviation: 89 ± 11 d in milk and 674 ± 68.2 kg of body weight) to determine effects on milk production. In experiment 2, the same diets were fed to 4 ruminally and duodenally cannulated Holstein cows (mean ± standard deviation: 112 ± 41 d in milk; 590 ± 61.14 kg of body weight) to evaluate the effects on rumen fermentation, intestinal flow of microbial N, and total-tract nutrient digestibility. In both experiments, cows were randomly assigned to 4 × 4 Latin squares over 21-d periods. Treatments (DM basis) were (1) control (0% RFDG), (2) 10% RFDG, (3) 20% RFDG, and (4) 30% RFDG. Feed intake and milk yield were recorded daily. In both experiments, milk samples were collected on d 19 to 21 of each period for analysis of milk components. In experiment 2, ruminal pH was measured; samples of rumen fluid, duodenal digesta, and feces were collected on d 18 to 21. Microbial N was estimated by using purines and DNA as microbial markers. Milk yield was not affected by treatment and averaged 34.0 ± 1.29 kg/d and 31.4 ± 2.81 kg/d in experiments 1 and 2, respectively. Percentage of milk protein tended to increase in experiment 1; estimates were 3.08, 3.18, 3.15, and 3.19 ± 0.06% when RFDG increased from 0 to 30% in the diets. However, milk protein concentration was not affected in experiment 2 and averaged 3.02 ± 0.07%. Percentage of milk fat was not affected and averaged 3.66 ± 0.05% and 3.25 ± 0.14% in experiments 1 and 2, respectively. Total ruminal volatile fatty acids and ammonia concentrations were not affected by treatment and averaged 135.18 ± 6.45 mM and 18.66 ± 2.32 mg/dL, respectively. Intestinal microbial N flow was not affected by treatment; however, purines yielded higher estimates of flow compared with DNA markers. When averaged across treatments, intestinal flow of microbial N was 303 and 218 ± 18 g of N/d, using purines and DNA as the markers. Dry matter, organic matter, neutral detergent fiber, and nonfiber carbohydrate digestibility tended to increase with increasing inclusion of RFDG. Results from these experiments indicate that dairy rations can be formulated to include up to 30% RFDG while maintaining lactation performance, volatile fatty acids concentration, and intestinal supply of microbial N.

Twenty multiparous Holstein cows were used in four 5 × 5 Latin squares to determine the effects of feeding increasing amounts of distillers dried grains with solubles (DDGS) in diets with or without the supplementation (60 g/d) of a rumen-protected Lys (RPL) product (AminoShure-L, 38% l-Lys; Balchem Encapsulates, New Hampton, NY) on milk yield and composition and plasma concentration of AA. Dietary treatments were (1) control (CON; no DDGS), (2) 10% DDGS (10DG), (3) 20% DDGS (20DG), (4) 10% DDGS plus RPL (10DGRPL), and (5) 20% DDGS plus RPL (20DGRPL). Diets were formulated using the Cornell-Penn-Miner Dairy model (CPM v3.0; http://cahpwww.vet.upenn.edu/node/77) to provide a predicted decreasing supply of Lys (117, 99, and 91% of requirements) for the CON, 10DG, and 20DG diets, respectively. Addition of RPL to the 10DG and 20DG diets (unsupplemented diets) resulted in 2 additional treatments, 10DGRPL and 20DGRPL diets, respectively. The 10DGRPL and 20DGRPL diets met 110 and 100% of the Lys requirements, respectively. Periods lasted 21d, with the last 3d for data collection. Compared with cows fed the CON diet, cows fed diets with DDGS had a similar dry matter intake (DMI; 25.4 ± 0.88 kg/d), milk yield (30.7 ± 1.67 kg/d), and composition, except for protein percentage, which was higher (3.15 vs. 3.21 ± 0.05%) and resulted in higher (0.94 vs. 1.00 ± 0.05 kg/d) protein yield by cows fed diets containing 20% DDGS. Unexpectedly, despite diets being formulated based on predicted DMI of 23.3 kg/d and milk yield of 38.5 kg/d, cows had a greater DMI and lower milk yield across all treatments, which resulted in diets that were predicted by CPM Dairy to supply sufficient amounts of Lys (140, 118, and 104% of requirement for the CON, 10 DG, and 20 DG diet, respectively) and consequently, supplementation with RPL did not have an effect on milk production or composition. Plasma concentration of Lys decreased (11.8%) as DDGS inclusion increased. For other essential AA, plasma concentrations of cows fed diets with DDGS were lower for Arg, His, and Val and greater for Leu and Met compared with cows fed the CON diet. Supplementation with RPL failed to decrease the plasma concentration of other essential AA, which provides support that Lys was not limiting.

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.

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 deferentially 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.