Derivation of the maintenance energy requirements in Jersey cows differing in body condition score.

Research in a variety of species including cattle has suggested energy required for maintenance may be affected by body condition. The objective of this study was to use indirect calorimetry and total fecal and urine collections to estimate maintenance energy and fasting heat production (FHP) of cows differing in BCS. Twelve multiparous nonpregnant and nonlactating Jersey cows were randomly assigned to one of 2 treatment groups. To construct these groups, cows were fed 2 different TMR in a preexperimental period for 84 d resulting in a BCS of >5 (HiBCS) and a BCS <3 (LoBCS), and no difference was observed in initial BW or BCS between treatments, averaging 509 ± 26.0 kg and 4.1 ± 0.23. To begin the experiment all animals were fed at maintenance [NELmaint, (Mcal/d) = 0.10 × metabolic BW (BW0.75)] for 24 d followed by 4 d of data collection for energy balance, cows were then fasted of 96 h with data collection for energy balance once again taken over the final 24 h. During the maintenance collection period, differences in BW and BCS existed (439 and 566 ± 19.0 kg BW, and 3.0 and 5.0 ± 0.13 BCS) for LoBCS and HiBCS, respectively. Heat production increased with increasing BCS (13.1 to 16.2 ± 0.55 Mcal/d), but when expressed per unit of BW0.75 no difference was observed (0.14 ± 0.002 Mcal/d per BW0.75). When fasted, BW loss did not differ averaging 28.9 ± 0.181 kg. The FHP did not differ, averaging 0.10 ± 0.004 Mcal/d per BW0.75 and resulted in the following representation of maintenance: [NELmaint, (Mcal/d) = 0.10 ± 0.004 × BW0.75]. During fasting the nitrogen free respiratory quotient tended to differ (0.69 and 0.73 ± 0.014) and O2 consumption and CO2 production for protein oxidation differed for LoBCS and HiBCS (5.44 and 2.35 ± 0.988 O2 and 4.52 and 1.95 ± 0.821 CO2 L/BW0.75). Overall, FHP increased with increasing BCS, but FHP per unit of BW0.75 did not differ. Although BW change was similar during fasting, differences O2 consumption and CO2 production per unit of BW0.75 used for protein oxidation may indicate differences in the nature of body tissue utilization in cows differing in BCS.

Partitioning among-animal variance of energy utilization in lactating Jersey cows.

Animals vary in the way in which they use energy due to diet, genetics, and management. Energy consumed by the animal supports milk production, but considerable variation among animals in energy utilization is thought to exist. The study objective was to estimate the among-animal variance in energy utilization in data collected from Jersey cows using indirect calorimetry. Individual animal-period data from 15 studies (n = 560) were used. The dataset included 115 animals from 44 to 410 DIM producing 11.5 to 39.1 kg/d of milk. On average, the 63 treatments in the dataset ranged 14.8% to 19.5% CP, 21.4% to 43.0% NDF, 16.2% to 33.3% starch, and 2.21% to 6.44% crude fat. Data were analyzed with the GLIMMIX procedure of SAS (9.4) with random effects of cow, treatment nested within period, square, and experiment. The percentage of among-animal, dietary treatment, and experimental variance was calculated as the variance associated with each fraction divided by the sum of variance from animal, dietary treatment, experiment, and residual, which was considered the total variance. The percentage of among-animal variance was characterized as high or low when the value was greater than or less than the mean value of 29.2%. Among-animal variance explained approximately 29.3% to 42.5% of the total variance in DMI, gross energy (GE), digestible energy (DE), ME, and NEL in megacalories per day. When energetic components of feces, urine, heat, and lactation in megacalories per day were expressed per unit of DMI, the among-animal variance decreased by 20.4, 4.82, 9.55, and 3.80 percentage units, respectively. However, among-animal variance explained 4.80 and 5.02 percentage units more of the total variation for methane energy and tissue energy in megacalories per day when expressed per unit of DMI. Variance in energetic efficiencies of DE/GE, ME/GE, and ME/DE were explained to a lesser extent by among-animal variance (averaging 17.8 ± 1.95%). The among-animal contribution to total variance in milk energy was 28.8%. Milk energy was a large proportion of the energy efficiency calculation, which included milk energy plus corrected tissue energy over net energy intake, which likely contributed to the 22.2% of total among-animal variance in energy efficiency. Results indicate that among-animal variance explains a large proportion of the total variation in DMI. This contributes to the variance observed for energy fractions as well as energy components when expressed in megacalories per day. Variation in energetic loss associated with methane was primarily explained by differences among animals and was increased when expressed per unit of DMI, highlighting the role of inherent animal differences in these losses.

Influence of empirically derived filtering parameters, ASV, and OTU pipelines on assessing rumen microbial diversity.

Microbes play an important role in human and animal health as well as animal productivity. The host microbial interactions within ruminants play a critical role in animal health and productivity and provide up to 70% of the animal's energy need in the form of fermentation products. As such, many studies have investigated microbial community composition to understand microbial community changes and factors that affect microbial colonization and persistence. The advances in next generation sequencing (NGS) technologies and low cost of sequencing have gravitated many studies to utilize 16S rDNA-based analysis tools for interrogation of microbiomes at a much finer scale than traditional culturing. However, such methods that rely on single base pair differences for bacterial taxa clustering may inflate or underestimate diversity leading to inaccurate identification of bacterial diversity. Therefore, in this study, we sequenced mock communities of known membership and abundance to establish filtration parameters to reduce inflation of microbial diversity due to PCR and sequencing errors. Additionally, we evaluated the effect of the resulting filtering parameters proposed using established bioinformatic pipelines on a study consisting of Holstein and Jersey cattle to identify bread and treatment effects on the bacterial community composition and the impact of the filtering on global microbial community structure analysis and results. Filtration resulted in a sharp reduction in bacterial taxa identified, yet retain most sequencing data (retaining > 79% of sequencing reads) when analyzed using 3 different microbial analysis pipelines (DADA2, Mothur, USEARCH). After filtration, conclusions from α and ß-diversity tests show very similar results across all analysis methods. The mock community-based filtering parameters proposed in this study help provide a more realistic estimation of bacterial diversity. Additionally, the filtration reduced the variation between microbiome analysis methods and help identify microbial community differences that could have been missed due to large animal to animal variation observed in the unfiltered data. As such, we believe, the new filtering parameters described in this study will help obtain diversity estimates closer to realistic values and will improve the ability of detecting microbial community differences and help better understand microbial community changes in 16S rDNA-based studies.

Effect of acid detergent lignin concentration for diets formulated to be similar in neutral detergent fiber content on energy utilization in lactating Jersey cows.

Lignin is a polyphenolic polymer that is an important factor in limiting fiber digestibility by ruminants. The objective of the current study was to evaluate lignin's effects on whole animal energy utilization in diets similar in NDF content. A low-lignin (LoLig) diet was formulated to contain 32.5% NDF (DM basis) and 9.59% lignin (NDF basis) and the high-lignin (HiLig) diet was formulated to contain 31.0% NDF (DM basis) and 13.3% lignin (NDF basis). These diets were randomly assigned and fed to 12 late-lactation (mean ± SD; 214 ± 14.9 DIM) multiparous Jersey cows (mean ± SD; 435 ± 13.9 kg) in a 2-period crossover design. Cows fed the LoLig treatment consumed more DM than cows on the HiLig diet (mean ± SD; 19.9 vs. 18.7 ± 0.645 kg/d) and the LoLig diet was concurrently of a greater gross energy concentration (mean ± SEM; 4.27 vs. 4.23 ± 0.03 Mcal/kg). As expected, increasing the concentration of lignin resulted in a reduction in total-tract NDF digestibility (45.5% vs. 40.4% ± 0.742%). Increasing lignin also resulted in a reduction in the digestibility of starch (97.7 vs. 96.3 ± 0.420) and CP (65.0 vs. 60.0 ± 0.829). Lignin also decreased the concentration of digestible energy (2.83 vs. 2.63 ± 0.04 Mcal/kg) and ME (2.52 vs. 2.36 ± 0.05 Mcal/kg), but the concentration of NEL was similar (1.81 vs. 1.75 ± 0.06 Mcal/kg). Increasing the concentration of lignin also reduced yields of ECM (33.7 vs. 30.0 ± 0.838 kg/d), milk protein (1.00 vs. 0.843 ± 0.027 kg/d), and milk fat (1.30 vs. 1.19 ± 0.058 kg/d). Decreasing the dietary lignin concentration did not affect daily methane emissions, averaging 391 ± 29.6 L/d. Results of this study indicate that feeding a diet greater in lignin decreases the digestibility of nutrients and provides less energy for production responses and that energy supplied from digestible NDF may be less than predicted by some nutrition models.

The effect of replacing conventional alfalfa hay with lower-lignin alfalfa hay on feed intake, nutrient digestibility, and energy utilization in lactating Jersey cows.

Lower-lignin (LoL) varieties of alfalfa (Medicago sativa L.) have been developed in recent years, and have the potential to positively impact animal performance. The objective of this study was to evaluate the effects of increasing the proportion of LoL alfalfa hay in diets fed to lactating dairy cows. Research plots were planted with a conventional variety (CON; Dairyland Hybriforce 3400), and 2 LoL varieties (LLG; 54HVX42 and LLB; Aflorex HiGest 460). After harvest, the LoL varieties were blended in equal proportions for feeding. Twelve multiparous Jersey cows (100 ± 4 d in milk) were used in a 3 × 3 Latin square with 3 periods of 28 d. Cows were assigned to 3 diets containing 0 (CNTRL), 16.1 (MdLL), and 32.2% (HiLL) of the diet DM as LoL alfalfa hay, which replaced CON. The CON alfalfa had average CP, NDF, and lignin contents (DM basis) of 20.5 ± 1.15, 42.1 ± 1.37, and 6.81 ± 0.57%, respectively, while the LoL alfalfa averaged 19.8 ± 0.75, 39.9 ± 1.56, and 6.07 ± 0.28%, respectively. No difference was observed in DMI (20.4 ± 0.61 kg/d). No difference in milk yield was observed, averaging 31.0 ± 1.02 kg/d across treatments. Similarly, no difference was observed in ECM yield (averaging 36.2 ± 1.43 kg/d). Feed conversion (ECM/DMI) tended to increase linearly with LoL alfalfa inclusion (1.74 to 1.80 ± 0.03). No difference was observed for milk fat yield and content (1.39 ± 0.075 kg/d and 4.51 ± 0.219%) or milk protein yield and content (1.06 ± 0.041 kg/d and 3.43 ± 0.096%). Total methane production quadratically decreased from CNTRL to MdLL then increased to HiLL (441, 389, 412 ± 18.2 L/d, respectively). No differences were observed on total-tract digestibility of DM (averaging 67.2 ± 0.55%) and NDF (averaging 50.9 ± 1.56%). No difference was observed in the concentration of DE, ME or NEL was observed averaging 2.82 ± 0.021, 2.51 ± 0.027, and 1.72 ± 0.030 Mcal/kg respectively. Our results suggest that replacing CON alfalfa with LoL alfalfa has no effects on milk production, milk composition, or nutrient digestibility but may improve feed efficiency.

Energy utilization in lactating Jersey cows consuming a mixture of DDGS and straw replacing alfalfa hay.

Some forages require significant amounts of water to grow, causing the dairy industry to be dependent on a limited resource. Feeding crop residues and feed coproducts in dairy rations may represent opportunities when alfalfa is not readily available, and to reduce the industry's use of water. A study using indirect calorimetry and 12 multiparous lactating Jersey cows (BW = 447.5 ± 43.7 kg; DIM = 71 ± 11 d, mean ± SD) was conducted to determine the effect of feeding dried distillers grains and solubles (DDGS) and straw in replacement of alfalfa hay on milk production and energy utilization. A triplicated 4 × 4 Latin square design was used to evaluate the replacement of alfalfa hay with a coproduct mixture (COP) of wheat straw and DDGS. Animals were blocked by milk yield and randomly assigned to 1 of 4 experimental treatments including (proportions on a DM basis): a control diet (CON) containing 18.2% of alfalfa hay, a low-coproduct diet (LCOP) that contained 8.1% of COP, a medium-coproduct diet (MCOP) that contained 16.3% of COP, and a high-coproduct diet (HCOP) that contained 24.3% of COP. No differences were observed for daily dry matter intake or milk yield (mean ± SEM) 19.5 kg ± 0.60, 29.6 kg ± 0.91, respectively. A quadratic tendency was observed where increasing inclusion of COP up to 16.3% maintained ECM and milk fat yield but decreased when animals were fed 24.3% COP. Total methane production decreased linearly from 429.4 to 345.0 ± 22.8 L/d from CON to HCOP diets, respectively. The digestibility of CP increased linearly from 64.0 to 70.4 ± 0.95% and N balance increased linearly from 43.3 to 90.7 ± 15.0 g/d in animals consuming CON to HCOP diets. Total time spent ruminating was lowest in animals consuming the HCOP diet. A linear increasing tendency in digestible and metabolizable energy of 2.92 to 3.02 ± 0.041 Mcal/kg and 2.58 to 2.70 ± 0.047 Mcal/kg was observed in animals consuming CON to HCOP. The proportion ME from DE (ME/DE) tended to linearly increase from 88.3 to 89.4 ± 0.454 when COP was added to the diet. Results of this study indicate that alfalfa hay with a mixture of straw and DDGS can maintain milk production and DMI, but the partial or full replacement of alfalfa with the COP mixture may result in differences in energy utilization in part driven by effects on CH4 reduction.

Energy and nitrogen utilization of lactating dairy cattle fed increasing inclusion of a high-protein processed corn coproduct.

Advancing technologies of the corn dry-milling ethanol production process includes the mechanical separation of fiber-containing particles from a portion of plant- and yeast-based nitrogenous particles. The resulting high-protein processed corn coproduct (HPCoP) contains approximately 52% crude protein (CP), 36% neutral detergent fiber (NDF), 6.4% total fatty acids (TFA). The objective of this experiment was to examine the effects of replacing nonenzymatically browned soybean meal with the HPCoP on dry matter intake (DMI), energy and N utilization, and milk production of lactating Jersey cows. Twelve multiparous Jersey cows were used in a triplicated 4 × 4 Latin square design consisting of four 28-d periods. Cows were blocked by milk yield and assigned randomly to 1 of 4 treatment diets that contained HPCoP (dry matter [DM] basis) at (1) 0%; (2) 2.6%; (3) 5.4%; and (4) 8.0%. Diets were formulated to be isonitrogenous and thus replace nonenzymatically browned soybean meal with HPCoP in the concentrate mix, while forage inclusion remained the same across diets. Increasing the concentration of HPCoP had no effect on DMI (mean ± SE; 19.9 ± 0.62 kg/d), but tended to linearly increase milk yield (27.8, 28.5, 29.8, and 29.0 ± 1.00 kg/d). Although no difference was observed in the concentration of milk protein with increasing inclusion of HPCoP (3.40% ± 0.057%), the concentration of fat linearly increased with the inclusion of HPCoP (5.05%, 5.19%, 5.15%, 5.47% ± 0.18%). No differences were observed in the digestibility of DM, NDF, CP, TFA, and gross energy averaging 66.6% ± 0.68%, 49.0% ± 1.03%, 66.1% ± 0.82%, 73.6% ± 1.73%, 66.3% ± 0.72%, respectively, with increasing HPCoP inclusion. The concentration of dietary gross energy linearly increased with increasing concentrations of HPCoP (4.25, 4.26, 4.28, and 4.31 ± 0.01 Mcal/kg), but no difference was observed in digestible energy and metabolizable energy (ME) across treatments averaging 2.83 ± 0.033 and 2.53 ± 0.043 Mcal/kg, respectively. Concentration of dietary net energy for lactation (NEL) tended to increase with increasing HPCoP (1.61, 1.72, 1.74, 1.72 ± 0.054 Mcal/kg) with the ratio of NEL:ME increasing linearly with increasing HPCoP inclusion (0.648, 0.676, 0.687, 0.677 ± 0.0124). Results of this study suggest that inclusion of the HPCoP can replace nonenzymatically browned soybean meal and support normal milk production.

Estimates of daily oxygen consumption, carbon dioxide and methane emissions, and heat production for beef and dairy cattle using spot gas sampling.

A simulation study was conducted to examine accuracy of estimating daily O2 consumption, CO2 and CH4 emissions, and heat production (HP) using a spot sampling technique and to determine optimal spot sampling frequency (FQ). Data were obtained from 3 experiments where daily O2 consumption, emissions of CO2 and CH4, and HP were measured using indirect calorimetry (respiration chamber or headbox system). Experiment 1 used 8 beef heifers (ad libitum feeding; gaseous exchanges measured every 30 min over 3 d in respiration chambers); Experiment 2 used 56 lactating Holstein-Friesian cows (restricted feeding; gaseous exchanges measured every 12 min over 3 d in respiration chambers); Experiment 3 used 12 lactating Jersey cows (ad libitum feeding; gaseous exchanges measured every hour for 1 d using headbox style chambers). Within experiment, averages of all measurements (FQALL) and averages of measurements selected at time points with 12, 8, 6, or 4 spot sampling FQ (i.e., sampling every 2, 3, 4, and 6 h in a 24-h cycle, respectively; FQ12, FQ8, FQ6, and FQ4, respectively) were compared. Within study a mixed model was used to compare gaseous exchanges and HP among FQALL, FQ12, FQ8, FQ6, and FQ4, and an interaction of dietary treatment by FQ was examined. A regression model was used to evaluate accuracy of spot sampling within study [i.e., FQALL (observed) vs. FQ12, FQ8, FQ6, or FQ4 (estimated)]. No interaction of diet by FQ was observed for any variables except for CH4 production in experiment 1. No FQ effect was observed for gaseous exchanges and HP except in experiment 2 where CO2 production was less (5,411 vs. 5,563 L/d) for FQ4 compared with FQALL, FQ12, and FQ8. A regression analysis between FQALL and each FQ within study showed that slopes and intercepts became farther from 1 and 0, respectively, for almost all variables as FQ decreased. Most variables for FQ12 and FQ8 had root mean square prediction error (RMSPE) less than 10% of the mean and concordance correlation coefficient (CCC) greater than 0.80, and RMSPE increased and CCC decreased as FQ decreased. When a regression analysis was conducted with combined data from the 3 experiments (mixed model with study as a random effect), results agreed with those from the analysis for the individual studies. Prediction errors increased and CCC decreased as FQ decreased. Generally, all the estimates from FQ12, FQ8, FQ6, and FQ4 had RMSPE less than 10% of the means and CCC greater than 0.90 except for FQ6 and FQ4 for O2 consumption and CH4 production. In conclusion, the spot sampling simulation with 3 indirect calorimetry experiments indicated that FQ of at least 8 samples (every 3 h in a 24-h cycle) was required to estimate daily O2 consumption, CO2 and CH4 production, and HP and to detect changes in those in response to dietary treatments. This sampling FQ may be considered when using techniques that measure spot gas exchanges such as the GreenFeed and face mask systems.

Derivation of the maintenance energy requirements and efficiency of metabolizable energy utilization for dry and lactating Jersey cows.

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.

Dietary fatty acid and starch content and supplemental lysine supply affect energy and nitrogen utilization in lactating Jersey 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.

Effect of feeding hydrolyzed feather meal and rumen-protected lysine on milk protein and energy utilization in late-lactation Jersey cows.

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.

Relationship between urinary energy and urinary nitrogen or carbon excretion in lactating Jersey cows.

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.

The effects of pelleted dried distillers grains and solubles fed with different forage concentrations on rumen fermentation, feeding behavior, and milk production of lactating dairy cows.

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.

Predicting ruminally undegraded and microbial protein flows from the rumen.

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

Factors that affect heat production in lactating Jersey cows.

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.

Effects of rumen-protected lysine and histidine on milk production and energy and nitrogen utilization in diets containing hydrolyzed feather meal fed to lactating Jersey cows.

Hydrolyzed feather meal (HFM) is high in crude protein, most of which bypasses rumen degradation when fed to lactating dairy cows, allowing direct supply of AA to the small intestine. Compared with other feeds that are high in bypass protein, such as blood meal or heat-treated soybean meal, HFM is low in His and Lys. The objectives of this study were to determine the effects of supplementing rumen-protected (RP) Lys and His individually or in combination in a diet containing 5% HFM on milk production and composition as well as energy and N partitioning. Twelve multiparous Jersey cows (mean ± SD: 91 ± 18 d in milk) were used in a triplicated 4 × 4 Latin square with 4 periods of 28 d (24-d adaptation and 4-d collection). Throughout the experiment, all cows were fed the same TMR, with HFM included at 5% of diet DM. Cows were grouped by dry matter intake and milk yield, and cows within a group were randomly assigned to 1 of 4 treatments: no RP Lys or RP His; RP Lys only [70 g/d of Ajipro-L (24 g/d of digestible Lys), Ajinomoto Co. Inc., Tokyo, Japan]; RP His only [32 g/d of experimental product (7 g/d of digestible His), Balchem Corp., New Hampton, NY]; or both RP Lys and His. Plasma Lys concentration increased when RP Lys was supplemented without RP His (77.7 vs. 66.0 ± 4.69 µM) but decreased when RP Lys was supplemented with RP His (71.4 vs. 75.0 ± 4.69 µM). Plasma concentration of 3-methylhistidine decreased with RP Lys (3.19 vs. 3.40 ± 0.31 µM). With RP His, plasma concentration of His increased (21.8 vs. 18.7 ± 2.95 µM). For milk production and milk composition, no effects of Lys were observed. Supplementing RP His increased milk yield (22.5 vs. 21.6 ± 2.04 kg/d) and tended to increase milk protein yield (0.801 vs. 0.772 ± 0.051 kg/d). Across treatments, dry matter intake (18.5 ± 0.83 kg/d) and energy supply (32.2 ± 2.24 Mcal of net energy for lactation) were not different. Supplementing RP His did not affect N utilization; however, supplementing RP Lys increased N balance (25 vs. 16 ± 9 g/d). The lack of production responses to RP Lys suggests that Lys was not limiting or that the increase in Lys supply was not large enough to cause an increase in milk protein yield. However, increased N balance and decreased 3-methylhistidine with RP Lys suggest that increased Lys supply increased protein accretion and decreased protein mobilization. Furthermore, His may be a limiting AA in diets containing HFM.

Use of indirect calorimetry to evaluate utilization of energy in lactating Jersey dairy cattle consuming diets with increasing inclusion of hydrolyzed feather meal.

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.

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

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

Effects of high-starch or high-fat diets formulated to be isoenergetic on energy and nitrogen partitioning and utilization in lactating Jersey cows.

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.

Development of feed composition tables using a statistical screening procedure.

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.