Growth and body composition of dairy calves fed only milk replacer at 3 intakes.

Determination of energy requirements for growth depends on measuring the composition of BW gain. Previous studies have shown that the composition of gain can be altered in young dairy calves by the composition of the milk replacer diet. Here, our objective was to determine body composition and the composition of empty body gain in young calves fed increasing amounts of a milk replacer containing adequate CP. Male Holstein calves underwent an adjustment period of 14 d after birth in which they were fed whole-waste milk at 10% of BW. Calves were then stratified by BW and randomly assigned to either an initial slaughter group (n = 11) or to groups fed 1 of 3 milk replacer amounts and slaughtered after 35 d of growth. Calves in all treatments consumed the same milk replacer containing 24.8% CP (DM basis; from all milk proteins) and 18.9% fat, reconstituted to 12.5% solids. Treatments were milk replacer fed at 1.25% of BW (DM basis; n = 6), 1.75% of BW (n = 6), or 2.25% of BW (n = 8), adjusted weekly as calves grew. Calves fed at 1.25% or 1.75% of BW were fed twice daily and those fed 2.25% of BW were fed 3 times daily. No starter was offered. Postslaughter, the bodies of calves were separated into 4 fractions: carcass; total viscera minus digesta; head, hide, feet, and tail;, and blood. The sum of those 4 fractions was empty BW (EBW), which increased linearly as the amount of milk replacer increased. Final heart girth and body length, but not withers height, increased linearly as intake increased. Gain:feed increased linearly with increasing milk replacer. Feeding more milk replacer increased the amounts of lean tissue and fat in the body. The percentages of water and protein in the final body decreased linearly, whereas fat percentage and energy content increased linearly as intake increased. As gain increased, the percentage of protein in gain decreased and the percentage of fat increased, resulting in an increase of energy content of EBW gain. Efficiency of energy use (retained energy:gross energy intake) increased linearly but retained energy:ME available for growth was not different among treatments. Efficiency of protein use increased quadratically as feeding rate increased; there was no further increase at 2.25% of BW. Plasma insulin-like growth factor 1, insulin, and glucose increased linearly, whereas urea-N decreased linearly, as milk replacer intake increased. Our data document changes in body composition that affect estimates of retained energy in the bodies of calves slaughtered at a common age. These data are important for calculations of energy requirements for young calves.

Selection indices for residual feed intake derived from milk spectra.

Improving production efficiency and minimizing the environmental impact of dairy farming are 2 important goals of the dairy industry. Achieving these objectives requires improving the feed-to-milk conversion efficiency. One way to achieve this goal is through genetic selection. However, measuring feed efficiency in commercial herds is currently not feasible. As such, we conducted a study to evaluate the genetic accuracy of various selection indices derived from Fourier transform mid-infrared (FTIR)-spectra or milk composition. We use 7,793 weekly records on 537 genotyped cows (78,964 SNPs), with information on residual feed intake (RFI), and FTIR-spectra. We fitted various types of selection indexes using the complete FTIR-spectra of milk samples. The estimated heritability of RFI was 0.12 ± 0.02. The accuracy of indirect selection using the FTIR-spectra was maximized using a principal components selection index (0.16 ± 0.07), followed by a Lasso-type penalized selection index (0.14 ± 0.06). We determined that an index based on milk spectral data recorded on ~25 daughters produced a progeny average with an accuracy comparable to direct phenotypic selection for RFI. We conclude that indirect selection for RFI using FTIR-spectra data can be effective for sires with progeny; however, future studies with a larger sample size are needed to validate these results.

Assessing different cross-validation schemes for predicting novel traits using sensor data: An application to dry matter intake and residual feed intake using milk spectral data.

Feed efficiency is important for economic profitability of dairy farms; however, recording daily DMI is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (model M1); a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (model M2); and an extended model that incorporated both energy sinks and MIR data (model M3). Models M2 and M3 also included various cow-level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks versus averaged across 28 d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together, whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (model M3 vs. M2) significantly (P < 10-10) reduced average RMSE to 1.59 kg and increased average R2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes (i.e., from cow-independent to experiment-independent to herd-independent schemes), the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Symposium review: The impact of absorbed nutrients on energy partitioning throughout lactation.

Most nutrition models and some nutritionists view ration formulation as accounting transactions to match nutrient supplies with nutrient requirements. However, diet and stage of lactation interact to alter the partitioning of nutrients toward milk and body reserves, which, in turn, alters requirements. Fermentation and digestion of diet components determine feeding behavior and the temporal pattern and profile of absorbed nutrients. The pattern and profile, in turn, alter hormonal signals, tissue responsiveness to hormones, and mammary metabolism to affect milk synthesis and energy partitioning differently depending on the physiological state of the cow. In the fresh period (first 2 to 3 wk postpartum), plasma insulin concentration and insulin sensitivity of tissues are low, so absorbed nutrients and body reserves are partitioned toward milk synthesis. As lactation progresses, insulin secretion and sensitivity increase, favoring deposition instead of mobilization of body reserves. High-starch diets increase ruminal propionate production, the flow of gluconeogenic precursors to the liver, and blood insulin concentrations. During early lactation, the glucose produced will preferentially be used by the mammary gland for milk production. As lactation progresses and milk yield decreases, glucose will increasingly stimulate repletion of body reserves. Diets with less starch and more digestible fiber increase ruminal production of acetate relative to propionate and, because acetate is less insulinogenic than propionate, these diets can minimize body weight gain. High dietary starch concentration and fermentability can also induce milk fat depression by increasing the production of biohydrogenation intermediates that inhibit milk fat synthesis and thus favor energy partitioning away from the mammary gland. Supplemental fatty acids also impact energy partitioning by affecting insulin concentration and insulin sensitivity of tissues. Depending on profile, physiological state, and interactions with other nutrients, supplemental fatty acids might increase milk yield at the expense of body reserves or partition energy to body reserves at the expense of milk yield. Supplemental protein or AA also can increase milk production but there is little evidence that dietary protein directly alters whole-body partitioning. Understanding the biology of these interactions can help nutritionists better formulate diets for cows at various stages of lactation.

Transition milk stimulates intestinal development of neonatal Holstein calves.

Colostrum stimulates gastrointestinal development. Similar to colostrum, transition milk (TM; the first few milkings after colostrum) contains elevated nutrient levels and bioactive components not found in milk replacer (MR), albeit at lower levels than the first colostrum. We hypothesized that feeding neonatal calves TM, compared with MR, for 4 d following colostrum at birth would further stimulate intestinal development. Holstein bull calves were fed 2.8 L of colostrum within 20 min of birth, allocated to 1 of 11 blocks based on birth date and body weight (BW), randomly assigned to MR (n = 12) or TM (n = 11) treatments within block, and fed treatments 3 times per day. Milk from milkings 2, 3, and 4 (TM) of cows milked 2 times daily was pooled by milking number and fed at 1.89 L per feeding; milking 2 was fed at feedings 2 through 5, milking 3 at feedings 6 through 8, and milking 4 at feedings 9 through 12. TM was not pasteurized and contained 17% solids, 5% fat, 7% protein, 4% lactose, and 20 g of IgG per liter on average, whereas MR (as fed) contained 15% solids, 4% protein, 3% fat, 6% carbohydrate, and no IgG. Refusals were similar, so calves fed TM consumed 1.0 Mcal of metabolizable energy per day more than those fed MR. On the morning of d 5, calves were injected i.v. with 5 mg of bromodeoxyuridine per kg of BW and slaughtered 130 min later; then, intestinal sections were excised. Feeding TM, instead of MR, doubled villus length, villus width, villus to crypt ratio, and mucosal length in all intestinal sections, increased submucosal thickness 70% in the proximal and mid jejunum, and tended to increase submucosal thickness in duodenum and ileum. Mucosal surface area was also increased in both the ileum and mid jejunum when feeding TM by 19 and 36%, respectively. Treatment did not alter crypt depth. Bromodeoxyuridine labeling was increased 50% by TM compared with MR in the cells along the epithelium of the crypts and within the villi of all sections, indicating that TM increased cell proliferation compared with MR. Calves fed TM gained more BW than calves fed MR and had improved cough, fecal, nose, and ear scores. We conclude that feeding TM for 4 d following an initial feeding of colostrum stimulates villus, mucosal, and submucosal development in all sections of the small intestine in the first few days of life and improves health and growth.

Multiple-trait random regression modeling of feed efficiency in US Holsteins.

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05-0.07 and 0.11-0.17, respectively) and multiparous (0.03-0.13 and 0.10-0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg0.75 for primiparous and 0.12 to 0.14 kg/kg0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.

Importance of considering body weight change in response to dietary protein deficiency in lactating dairy cows.

Our objective was to quantify the contribution of body weight (BW) change to the overall response of lactating dairy cows to a shortage of dietary protein. Lactating Holstein cows (n = 166; 92 primiparous, 74 multiparous) with initial milk yield of 41 ± 10 kg/d were fed high and low-protein diets in 7 blocks. Blocks were repeated in the same crossover design with periods of 28 to 35 d. Production of 69 of the 166 cows (42 primiparous, 27 multiparous) was also measured in late lactation. Low-protein diets were 14% crude protein (CP) in peak lactation and 13% CP in late lactation and were formulated to contain adequate rumen-degradable protein to maintain rumen function but inadequate rumen undegradable protein for the average cow in this study. High-protein diets were 18% CP in peak lactation and 16% CP in late lactation and contained extra expeller soybean meal to meet metabolizable protein requirements. Body weight changes were used to predict body energy and protein changes, which were added to milk components to calculate total captured energy and protein. Fixed effects of diet, parity, treatment sequence nested in each block, treatment period nested in block, interaction of diet and parity, and the random effects of block and cow nested within block were included in the model to compare cow responses to diets within each lactation stage. In peak lactation, reducing protein from 18 to 14% resulted in estimated daily losses of 2.9 Mcal of milk energy, 2.2 Mcal of body energy, 127 g of milk protein, and 16 g of body protein. Therefore, BW loss accounted for 43% of the decrease in captured energy and 11% of the decrease in captured protein when cows were fed deficient protein. In late lactation, BW loss accounted for 51% of the decrease in captured energy and 14% of the decrease in captured protein when cows were fed deficient protein. We suggest that BW change should be considered when assessing cow responses to changes in dietary protein.

Low dietary protein resilience is an indicator of the relative protein efficiency of individual dairy cows.

Our objectives were to determine (1) the sources of variation in cow responses to dietary protein reduction, and (2) the association of low dietary protein resilience (LPR) with protein efficiency. Lactating Holstein cows in peak lactation (n = 166; 92 primiparous, 77 multiparous) with initial milk yield 41 ± 9.8 kg/d were fed high-protein (HP) or low-protein (LP) diets in 4-wk periods in a crossover design with half the cows fed LP first and half fed HP first. The study was repeated for 69 of these cows (42 primiparous, 27 multiparous) in late lactation. Low-protein diets were 14% crude protein (CP) in peak lactation and 13% CP in late lactation and were formulated to contain adequate rumen-degradable protein to maintain rumen function but inadequate rumen-undegradable protein for cows with average production in this study to maintain their production. High-protein diets were 18% CP in peak lactation and 16% CP in late lactation and contained extra expeller soybean meal to meet metabolizable protein requirements. Protein efficiency was defined as the protein captured in milk or in both milk and body tissues per unit of consumed protein. Low dietary protein resilience was calculated for each cow in peak and late lactation based on actual intake, production, and body weight measures. The ability of a cow to maintain total protein captured in milk and body gain when fed less protein varied considerably and the variation was mostly explained by a cow's captured protein per kilogram of metabolic body weight when fed HP, her parity, treatment sequence, and experiment. Protein efficiency was moderately repeatable across diets within lactation stage. Milk urea nitrogen was not associated with protein efficiency in individual cows within a diet and lactation stage. Cows with greater dietary protein resiliency (higher LPR) had similar protein efficiency on the HP diet as cows with lower LPR, but higher protein efficiency on the LP diet. In conclusion, cows generally maintained their protein efficiency rankings when switching diets between sufficient or insufficient protein; however, some high-producing cows are better able to maintain high production when fed less protein. We define this ability as LPR and suggest it might be useful for identifying cows that use protein more efficiently to enhance dairy sustainability.

Relationship of residual feed intake and protein efficiency in lactating cows fed high- or low-protein diets.

Our objectives were to determine the repeatability of residual feed intake (RFI) across dietary protein levels and to determine the association between RFI and protein efficiency in lactating cows. Holstein cows (n = 166; 92 primiparous, 74 multiparous) with initial milk yield 41.3 ± 9.8 kg/d were fed diets with high or low protein in peak lactation. Experiments were conducted as crossovers with 2 treatment periods of 28 to 35 d. Production of 69 of the 166 cows (42 primiparous, 27 multiparous) was also measured in late lactation. Low-protein diets were 14% crude protein (CP) in peak lactation and 13% CP in late lactation and were formulated to contain adequate rumen-degradable protein to maintain rumen function. High-protein diets were 18% CP in peak lactation and 16% CP in late lactation and contained extra expeller soybean meal to increase absorbed protein. Cows were milked twice daily; DMI and milk yield were recorded daily. Milk composition was measured over 4 consecutive milkings weekly, and body weight (BW) was measured 3 times weekly. Fixed effects of diet, parity, and treatment period, interaction of parity and diet, and random effects of experiment and cow nested within experiment were included in the model to compare intake and production performance between cows fed different levels of CP. The RFI value was calculated for each cow on each treatment based on the actual intake, milk energy output, metabolic BW, and body energy (calculated from BW change and body condition score over the treatment period) change. Ranking of cows for RFI was moderately repeatable across dietary protein in peak lactation (r = 0.59) but less repeatable in late lactation (r = 0.41). Negative correlation was observed between RFI and protein efficiency values (dietary protein captured in milk) for cows in both peak lactation (r = -0.42) and late lactation (r = -0.24), which suggested that cows with higher energy efficiency had greater protein efficiency. In conclusion, RFI was repeatable across dietary protein levels within lactation stage, and cows with lower RFI values utilized protein more efficiently.

Short communication: Effects of transition milk and milk replacer supplemented with colostrum replacer on growth and health of dairy calves.

Transition milk (TM, defined here as the second through fourth milkings after calving) supplies additional fat, protein, and immunoglobulins to the calf compared with milk replacer at industry-suggested feeding rates (∼14% solids). Our objective was to determine whether 9 feedings of TM on d 2 through 4 of life increase the growth rate and overall health of calves. Holstein heifer calves on a commercial farm were randomly assigned to 1 of 3 diets (n = 35/diet): milk replacer (MR; Purina Warm Front BOV MOS Medicated Milk Replacer, St. Louis, MO), TM, or a 50:50 blend of MR and colostrum replacer (MCR; Alta HiCal Colostrum Powder Replacer, the Saskatoon Colostrum Company Ltd., Saskatoon, SK, Canada). The TM was harvested from Holstein cows on the farm, pooled, and pasteurized at 71.7°C for 15 s. Nutrient composition on a dry matter basis of TM was 25.9% fat, 41.8% protein, and 14% solids; MR was 10.3% fat, 27.8% protein, and 14% solids; and MCR was 14.6% fat, 38.6% protein, and 15% solids. All calves received IgG-enriched colostrum replacer for the first 2 feedings after birth. Subsequently, calves were fed 1.9 L of MR, TM, or MCR 3 times per day for 3 d (starting on d 2). After initial diets ended, calves were fed and managed similarly. Body weights (d 1, 7, 14, 21, and 56), blood samples (d 1, 7, 14, and 21), and daily health scores (scale of 0 to 3, with 0 representing normal or healthy and 3 representing severe symptoms or ill) were collected through weaning at 56 d. All except 1 calf achieved successful transfer of passive immunity, with serum IgG values greater than 10.0 mg/mL. From birth through weaning, calves fed TM and MCR gained 3 kg more total body weight than those fed MR (34.3, 34.3, and 31.3 kg, respectively). Increased metabolizable energy (using NRC 2001 recommendations) in TM accounts for 0.68 kg of the increased gain compared with MR. Treatment did not alter health scores for ears, eyes, or feces. Haptoglobin concentrations were lower in TM and MCR than in MR calves (4.63, 3.62, and 7.54 µg/mL, respectively), whereas lipopolysaccharide binding protein concentrations were not different. In conclusion, compared with MR alone, feeding TM or MR with colostrum replacer for 3 d increased growth rate of calves throughout the preweaning period.

Effects of supplementing Holstein cows with soybean oil compared with palmitic acid-enriched triglycerides on milk production and nutrient partitioning.

Both insulin and trans-10,cis-12 C18:2 (t10c12CLA) can be increased by high-starch diets; thus, it is difficult to determine whether insulin or t10c12CLA mediates nutrient partitioning toward body tissues during milk fat depression. To minimize insulin secretion while manipulating t10c12CLA levels, diets supplemented with palmitic acid-enriched triglycerides and soybean oil were fed to cows. Thirty-two Holstein cows (93 ± 35 d in milk) were included in the crossover experiment with each treatment period being 28 d. Treatment diets contained 25% neutral detergent fiber, 32% starch, 18% crude protein, and 4.6% fatty acids (dry matter basis). Treatment diets contained either palmitic acid-enriched triglycerides (2.5% dry matter, BergaFat T-300, Berg + Schmidt America LLC, Libertyville, IL; PAT) or soybean oil (2.5% dry matter; SBO). Cows were blocked by milk yield, body weight, and parity, and then randomly assigned to 1 of 2 treatment sequences (PAT-SBO or SBO-PAT). Cows fed PAT produced milk with only 3.1% fat, indicating milk fat depression; SBO decreased fat content further to only 2.4%. No effect of treatment was observed on dry matter intake, apparent net energy intake, milk yield, body condition score, or fat thickness over the rump and rib. However, compared with PAT, SBO decreased fat-corrected milk yield, energy-corrected milk yield, milk fat yield, de novo fatty acids, and 16-carbon fatty acid yield, whereas SBO increased body weight gain. Neutral detergent fiber digestibility tended to be lower in SBO, whereas fatty acid digestibility was higher. Additionally, the concentration of plasma insulin, nonesterified fatty acids, and triglycerides, and milk metabolites (trans-10 C18:1 and t10c12CLA) were all higher in SBO. In conclusion, with similar dietary starch content, the diet containing palmitic acid-enriched triglycerides partitioned more energy toward milk synthesis, whereas the diet containing soybean oil partitioned more energy toward body tissue gain.

Genomic prediction of residual feed intake in US Holstein dairy cattle.

Genomic selection is an important tool to introduce feed efficiency into dairy cattle breeding. The goals of the current research are to estimate genomic breeding values of residual feed intake (RFI) and to assess the prediction reliability for RFI in the US Holstein population. The RFI data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States, and were pre-adjusted to remove phenotypic correlations with milk energy, metabolic body weight, body weight change, and for several environmental effects. In the current analyses, genomic predicted transmitting abilities of milk energy and of body weight composite were included into the RFI model to further remove the genetic correlations that remained between RFI and these energy sinks. In the first part of the analyses, a national genomic evaluation for RFI was conducted for all the Holsteins in the national database using a standard multi-step genomic evaluation method and 60,671 SNP list. In the second part of the study, a single-step genomic prediction method was applied to estimate genomic breeding values of RFI for all cows with phenotypes, 5,252 elite young bulls, 4,029 young heifers, as well as their ancestors in the pedigree, using a high-density genotype chip. Theoretical prediction reliabilities were calculated for all the studied animals in the single-step genomic prediction by direct inversion of the mixed model equations. In the results, breeding values were estimated for 1.6 million genotyped Holsteins and 60 million ungenotyped Holsteins, The genomic predicted transmitting ability correlations between RFI and other traits in the index (e.g., fertility) are generally low, indicating minor correlated responses on other index traits when selecting for RFI. Genomic prediction reliabilities for RFI averaged 34% for all phenotyped animals and 13% for all 1.6 million genotyped animals. Including genomic information increased the prediction reliabilities for RFI compared with using only pedigree information. All bulls had low reliabilities, and averaged to only 16% for the top 100 net merit progeny-tested bulls. Analyses using single-step genomic prediction and high-density genotypes gave similar results to those obtained from the national evaluation. The average theoretical reliability for RFI was 18% among the elite young bulls under 5 yr old, being lower in the younger generations of elite bulls compared with older bulls. To conclude, the size of the reference population and its relationship to the predicted population remain as the limiting factors in the genomic prediction for RFI. Continued collection of feed intake data is necessary so that reliabilities can be maintained due to close relationships of phenotyped animals with breeding stock. Considering the currently low prediction reliability and high cost of data collection, focusing RFI data collection on relatives of elite bulls that will have the greatest genetic contribution to the next generation will give more gains and profit.

Equation to predict feed intake response by lactating cows to factors related to the filling effect of rations.

Our objective was to predict the dry matter intake (DMI) response during ration formulation to factors related to the filling effect of rations and their interaction with milk yield (MY) by lactating cows past peak lactation. A data set was developed consisting of 134 treatment means from 34 experiments reported in 32 peer-reviewed articles published from 1990 through 2015. The data set included data for cows ranging from 60 to 309 d postpartum with mean DMI ranging from 17.6 to 30.6 kg/d and MY ranging from 20.3 to 51.1 kg/d. Ration composition among treatments ranged from 12.7 to 21.8% of dry matter (DM) for crude protein, 11.5 to 31.0% of DM for acid detergent fiber (ADF), 25.5 to 48.2% of DM for neutral detergent fiber (NDF), 9.9 to 39.3% of DM for forage NDF (FNDF), and 0.45 to 0.84 for the ratio of ADF% to NDF% (ADF/NDF). Laboratory measures of digestibility of NDF (in vitro or in situ, FNDFD) for the sole or major forage ranged from 24.1 to 72.7%. The model included the random effect of study to account for various experiment-specific effects including different methods of measurement of NDF and FNDFD among studies. The full model also included linear and quadratic effects of crude protein, ADF, NDF, FNDF, ADF/NDF, and FNDFD, as well as their linear and quadratic interactions, and mean MY for each study and its interaction with ration factors. The proposed prediction equation is DMI (kg/d) = 12.0 - 0.107 × FNDF + 8.17 × ADF/NDF + 0.0253 × FNDFD - 0.328 × (ADF/NDF - 0.602) × (FNDFD - 48.3) + 0.225 × MY + 0.00390 × (FNDFD - 48.3) × (MY - 33.1) with mean bias = 0.00 kg/d, root mean square error = 1.55 kg/d, and concordance correlation coefficient = 0.827. Dry matter intake was positively related to MY and ADF/NDF and negatively related to FNDF, and FNDFD was positively related to DMI for cows with high MY but negatively related to MY for cows with low MY. In addition, DMI was positively related to FNDFD for low ADF/NDF but negatively related to FNDFD for high ADF/NDF. The ADF/NDF was included to represent differences in forage fragility between grasses and legumes. The proposed model was compared with the equation recommended by the National Research Council (2001) that was developed using only animal factors by fitting each equation to a subset of the data set that included the required inputs for both. The National Research Council (2001) equation without diet factors had a higher root mean square error and over-predicted DMI at high DMI and under-predicted DMI at low DMI. Our proposed equation should be useful to predict DMI response to factors related to the filling effects of rations during ration formulation.

Updating predictions of dry matter intake of lactating dairy cows.

Our objective was to model dry matter intake (DMI) by Holstein dairy cows based on milk energy (MilkE), body weight (BW), change in BW (ΔBW), body condition score (BCS), height, days in milk (DIM), and parity (primiparous and multiparous). Our database included 31,631 weekly observations on 2,791 cows enrolled in 52 studies from 8 states of the United States, mostly in the Upper Midwest. The means ± standard deviations of these variables were 24 ± 5 kg of DMI, 30 ± 6 Mcal of MilkE/d, 624 ± 83 kg of BW, 0.24 ± 1.50 kg of ΔBW/d, 3.0 ± 0.5 BCS, 149 ± 6 cm height, and 102 ± 45 DIM. Data analysis was performed using a mixed-effects model containing location, study within location, diet within study, and location and cow within study as random effects, whereas the fixed effects included the linear effects of the covariates described previously and all possible 2-way interactions between parity and the other covariates. A nonlinear (NLIN) mixed model analysis was developed using a 2-step approach for computational tractability. In the first step, we used a linear (LIN) model component of the NLIN model to predict DMI using only data from mid-lactation dairy cows (76-175 DIM) without including information on DIM. In the second step, a nonlinear adjustment for DIM using all data from 0 to 368 DIM was estimated. Additionally, this NLIN model was compared with an LIN model containing a fourth-order polynomial for DIM using data throughout the entire lactation (0-368 DIM) to assess the utility of an NLIN model for the prediction of DMI. In summary, a total of 8 candidate models were evaluated as follows: 4 ways to express energy required for maintenance (BW, BW0.75, BW adjusted for a BCS of 3, and BW0.75 adjusted for a BCS of 3) × 2 modeling strategies (LIN vs. NLIN). The candidate models were compared using a 5-fold across-studies cross-validation approach repeated 20 times with the best-fitting model chosen as the proposed model. The metrics used for evaluation were the mean bias, slope bias, concordance correlation coefficient (CCC), and root mean squared error of prediction (RMSEP). The proposed prediction equation was DMI (kg/d) = [(3.7 + parity × 5.7) + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (-0.689 + parity × -1.87) × BCS] × [1 - (0.212 + parity × 0.136) × exp(-0.053 × DIM)] (mean bias = 0.021 kg, slope bias = 0.059, CCC = 0.72, and RMSEP = 2.89 kg), where parity is equal to 1 if the animal is multiparous and 0 otherwise. Finally, the proposed model was compared against the Nutrient Requirements of Dairy Cattle (2001) prediction equation for DMI using an independent data set of 9,050 weekly observations on 1,804 Holstein cows. The proposed model had smaller mean bias and RMSEP and higher CCC than the Nutrient Requirements of Dairy Cattle equation to predict DMI and has potential to improve diet formulation for lactating dairy cows.

High-density genome-wide association study for residual feed intake in Holstein dairy cattle.

Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.

Predicting nutrient digestibility in high-producing dairy cows.

Our objective was to determine the effects of dry matter intake (DMI), body weight (BW), and diet characteristics on total tract digestibilities of dry matter, neutral detergent fiber, and starch (DMD, NDFD, and StarchD, respectively) in high-producing dairy cows. Our database was composed of 1,942 observations from 662 cows in 54 studies from Michigan, Ohio, and Georgia. On average, cows ate 23 ± 4.5 kg of dry matter/d, weighed 669 ± 79 kg, and produced 38 ± 10 kg of milk/d. Diets were 31 ± 5% neutral detergent fiber, 27 ± 6% starch, 2.6 ± 1.2% fatty acids, and 17 ± 1.4% crude protein. Digestibility means were 66 ± 6, 42 ± 11, and 93 ± 5% for DMD, NDFD, and StarchD, respectively. Forage sources included corn silage, alfalfa, and grasses. Corn source was classified by its ruminal fermentability. Data were analyzed using a mixed effects model, including diet chemical composition, forage source, and corn source, all expressed as percentage of dry matter, except for DMI, which was expressed as percentage of BW (DMI%BW); location and 2-way interactions were fixed effects. Cow, block, period, treatment, and study were included as random effects. Best fitting candidate models were generated using backward and stepwise regression methods. Additionally, the simplest model was generated using only DMI and location as fixed effects and all random effects. Candidate models were cross-validated across studies, and the resulting predictive correlation coefficients across studies (PC) and root mean square error of prediction (RMSEP) were compared by t-test. For each nutrient, the digestibility model that resulted in the highest PC and lowest RMSEP was determined to be the best fitting model. We observed heterogeneous coefficients among the different locations, suggesting that specific location factors influenced digestibilities. The overall location-averaged best fitting prediction equations were: DMD = 69 - 0.83 × DMI%BW (PC = 0.22, RMSEP = 5.39); NDFD = 53 + 0.26 × grass %DM - 0.59 × starch %DM + 3.06 × DMI%BW - 0.46 × DMI%BW2 (PC = 0.53, RMSEP = 9.70); and StarchD = 96 + 0.19 × HFERM%DM - 0.12 × starch %DM - 1.13 × DMI%BW (PC = 0.34, RMSEP = 4.77); where HFERM%DM is highly-fermentable corn source as percentage of DM. Our results confirm that digestibility is reduced as DMI increases, albeit at a lower rate than that reported in National Research Council. Furthermore, dietary starch depresses NDFD. Whereas DMD can be predicted based on DMI only, the best predictions for NDFD and StarchD require diet characteristics in addition to DMI.

Short communication: Decreasing the dietary ratio of n-6 to n-3 fatty acids increases the n-3 concentration of peripheral blood mononuclear cells in weaned Holstein heifer calves.

Utilization of nutrients to improve overall heifer health is of interest because of the importance of replacement heifers to the dairy industry. The objective of our study was to compare the effect of supplementation of dietary n-3 and n-6 fatty acids (FA) on FA concentrations in peripheral blood mononuclear cells (PBMC) of Holstein calves. Twenty-seven Holstein heifer calves (107 ± 2.6 d of age; 142.6 ± 6.5 kg of body weight) from the university research and teaching herd were randomly assigned to a common TMR supplemented with 1 of 3 treatments: Ca salts of flaxseed FA (Virtus Nutrition, Corcoran, CA) containing 35% 18:3 n-3 (N3), Ca salts of soybean FA (Virtus Nutrition) containing 50% 18:2 n-6 (N6), or a 50:50 mix of N3 and N6. Treatments were supplemented with FA at 4% of dietary dry matter and fed for 30 d. Feed intake was recorded daily, and body weight, wither height, and body condition score were measured weekly throughout the study. On d 28 heifers were vaccinated with a Pasteurella vaccine and the temperature response to the vaccine was recorded. Blood was collected on d 0 and 28 for PBMC isolation. After total lipid extraction and FA methyl ester preparation, FA composition of PBMC was measured. We observed no effect of treatment on body weight gain, body condition score change, or wither height change. Heifers receiving the N3 diet had a lower temperature response to Pasteurella challenge compared with both the mix and N6 diets. Heifers consuming the N3 diet had a greater content of total n-3 FA, α-linolenic acid, and eicosapentaenoic acid in PBMC compared with heifers fed the N6 and mix diets. Heifers receiving the N3 diet also had a lower content of total n-6 FA, linoleic acid, and arachidonic acid in PBMC than heifers fed the N6 and mix diets. In conclusion, our study determined that feeding weaned female Holstein heifers a diet high in n-3 FA increased concentrations of n-3 FA in PBMC.

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency.

Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.

Relationship between residual feed intake and digestibility for lactating Holstein cows fed high and low starch diets.

We determined if differences in digestibility among cows explained variation in residual feed intake (RFI) in 4 crossover design experiments. Lactating Holstein cows (n=109; 120±30d in milk; mean ± SD) were fed diets high (HS) or low (LS) in starch. The HS diets were 30% (±1.8%) starch and 27% (±1.2%) neutral detergent fiber (NDF); LS diets were 14% (±2.2%) starch and 40% (±5.3%) NDF. Each experiment consisted of two 28-d treatment periods, with apparent total-tract digestibility measured using indigestible NDF as an internal marker during the last 5d of each period. Individual cow dry matter (DM) intake and milk yield were recorded daily, body weight was measured 3 to 5 times per week, and milk components were analyzed 2 d/wk. Individual DM intake was regressed on milk energy output, metabolic body weight, body energy gain, and fixed effects of parity, experiment, cohort (a group of cows that received treatments in the same sequence) nested within experiment, and diet nested within cohort and experiment, with the residual being RFI. High RFI cows ate more than expected and were deemed less efficient. Residual feed intake correlated negatively with digestibility of starch for both HS (r=-0.31) and LS (r=-0.23) diets, and with digestibilities of DM (r=-0.30) and NDF (r=-0.23) for LS diets but was not correlated with DM or NDF digestibility for HS diets. For each cohort within an experiment, cows were classified as high RFI (HRFI; >0.5 SD), medium RFI (MRFI; ±0.5 SD), and low RFI (LRFI; <-0.5 SD). Digestibility of DM was similar (~66%) among HRFI and LRFI for HS diets but greater for LRFI when fed LS diets (64 vs. 62%). For LS diets, digestibility of DM could account for up to 31% of the differences among HRFI and LRFI for apparent diet energy density, as determined from individual cow performance, indicating that digestibility explains some of the between-animal differences for the ability to convert gross energy into net energy. Some of the differences in digestibility between HRFI and LRFI were expected because cows with high RFI eat at a greater multiple of maintenance, and greater intake is associated with increased passage rate and digestibility depression. Based on these data, we conclude that a cow's digestive ability explains none of the variation in RFI for cows eating high starch diets but 9 to 31% of the variation in RFI when cows are fed low starch diets. Perhaps differences in other metabolic processes, such as tissue turnover, heat production, or others related to maintenance, can account for more variation in RFI than digestibility.