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Estrus traits have economic value in dairy production systems and could be incorporated into genetic selection indices. In an effort to further understand selection responses, 2 studies were performed to estimate the intra- and interclass correlation coefficients for estrus traits. Holstein-Friesian cows (n = 1,197; study 1) across 5 pasture-based grazing dairy herds were fitted with a capacitive touch sensing (CTS) device on the rump (FlashMate, Farmshed Labs Limited, Hamilton, New Zealand). The daily number of rump touches were subjected to a peak detection program to objectively identify periods of increased rump touches above baseline (indicative of estrus). The number of times touched and the sum of the touch duration were used to compare farms and estimate the intraclass correlation (repeatability). For study 2, postpartum Holstein (n = 85) and Guernsey (n = 5) cows in a confinement-style dairy were used. Cows were fitted with an IceQube accelerometer (IceRobotics Ltd., Edinburgh, United Kingdom) to measure steps taken per hour and a CTS device was applied to both rumps. The interclass correlation for the number of rump touches and number of steps taken during estrus was calculated. Data collected from 5 herds (study 1) demonstrated a 2- to 3-fold difference between herds in the number of rump touches and total touch time during estrus. The intraclass correlation (repeatability; estimates of maximum heritability) for rump touches during estrus was 0.22. For study 2, the number of steps and the number of rump touches during estrus increased in a synchronous manner. The intraclass correlation (repeatability) for number of steps during estrus was 0.26. The interclass correlation (r) for the number of rump touches and the number of steps was 0.46 (R2 = 0.21). Based on the R2, at least 20% of the variation in the number of steps during estrus was explained by the number of touches to the rump of the cow. Selecting cows for the number of steps taken during estrus could increase the number of rump touches (mounts, chin rests, and so on, received from other cows) if a genetic correlation exists for the phenotypic correlation that we observed.

Breeding cows for low CH4 emissions requires that the trait is variable and that it can be recorded with low cost from an adequate number of individuals and with high precision, but not necessarily with high accuracy if the trait is measured with high repeatability. The CH4:CO2 ratio in expired breath is a trait often used as a tracer with the production of CO2 predicted from body weight (BW), energy-corrected milk yield, and days of pregnancy. This approach assumes that efficiency of energy utilization for maintenance and production is constant. Data (307 cow-period observations) from 2 locations using the same setup for measuring CH4 and CO2 in respiration chambers were compiled, and observed production of CH4 and CO2 was compared with the equivalent predicted production using 2 different approaches. Carbon dioxide production was predicted using a previously reported model based on metabolic BW and energy-corrected milk production and a currently developed model based on energy requirements and the relationship between observed CO2 and heat production (models 1 and 2, respectively). Animals used were categorized (low, medium, and high efficiency) according to (1) residual feed intake and (2) residual milk production. Model 1 underestimated CH4 production by 15%, whereas model 2 overestimated CH4 by 1.4% for the whole database. Model 1 underestimated CO2 production by 2.8 and 0.9 kg/d for low- and high-efficiency cows, respectively, whereas model 2 underestimated CO2 production by 0.9 kg/d for low-efficient animals but overestimated it by 1.2 kg/d for high-efficiency cows. Efficient cows produce less heat, and consequently CO2, per unit of metabolic body weight and energy-corrected milk than inefficient cows, challenging the use of CO2 as a tracer gas. Because of biased estimates of CO2 production, the models overestimated CH4 production of high-efficiency cows by, on average, 17% relative to low-efficiency cows, respectively. Selecting low CH4-emitting cows using a CO2 tracer method can therefore favor inefficient cows over efficient cows.

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.

Infant formulations are constantly evolving as novel protein ingredients are added to make them more closely mimic the protein profile of human milk; however, precise analytical methods for characterizing and quantifying the major milk proteins in such formulations are currently lacking. This article describes an ultra-performance liquid chromatography-high-resolution mass spectrometry method for intact proteins that can efficiently detect, identify, and characterize the major milk proteins and their proteoforms (phosphorylation status, degree of glycation, genetic variants among others) in ingredients and final products, with an emphasis on detecting and quantifying specific genetic variants of β-casein in infant formulas. Method sensitivity allows detection of β-casein A1 in A2-based infant formulas with a limit of detection of 2% (grams of β-casein A1 per 100 g of total β-casein). Protein glycation affects signal intensity in a linear fashion, which permits proteins to be quantified from their mass spectrometry signals after correction according to their measured glycation index. The method was validated for the quantification of β-casein in infant formulas. Repeatability ranged from 2 to 3% and intermediate reproducibility from 5 to 9%. Calculated β-casein amounts ranged between 77 and 110% of the values based on formulations and published protein profiles for milk. Altogether, this method can be used for general fingerprinting as well as specific characterization and quantification of individual major milk proteins in dairy-based ingredients and products.

Ample research has described the assessment of dimensional changes for different teat traits, whereas diagnostic techniques to reliably assess blood circulation in teats of dairy cows are limited. Here, we describe the development and evaluation of a scanning technique to quantify blood flow in teats of dairy cows using power Doppler ultrasonography. In 2 consecutive trials, 384 teat scans [trial 1, n = 256 (sagittal plane, n = 128; transverse plane, n = 128); trial 2, n = 128 (transverse plane)] from 16 cows were obtained by the same 2 operators. Perfusion intensity from single images (trial 1) and video images (trial 2) were assessed using a commercially available software program. Intraclass correlation coefficients (ICC) and concordance correlation coefficients (CCC) were used to assess interoperator reproducibility (agreement between measurements performed by different operators) and intraoperator repeatability (agreement between measurements performed by the same operator). In trial 1, interoperator ICC and CCC indicated poor agreement (ICC ≤0.26, CCC ≤0.26). Intraoperator ICC and CCC demonstrated poor agreement between duplicate measurements within operators (ICC ≤0.19, CCC ≤0.19). Modifications after trial 1 included (1) a different ultrasound device, (2) analysis of video clips rather than single images, (3) restriction to 1 sectional plane (i.e., transverse), and (4) a scanning sequence such that repeated scans within operators were measured one after another. Through these modifications, intraoperator repeatability in trial 2 yielded fair to good agreement, with intraoperator ICC and CCC over both operators ranging from 0.44 to 0.70 and from 0.57 to 0.69, respectively, whereas interoperator ICC and CCC showed poor agreement (ICC = 0.35, CCC = 0.34). We conclude that repeatable measurements of blood perfusion intensity of teats in dairy cows can be attained with power Doppler ultrasonography. Power Doppler ultrasonography is a suitable tool to quantify slow flow in small vessels and may be an acceptable diagnostic technique to assess changes in blood circulation that result from machine milking in teats of dairy cows, although further research is necessary to validate this hypothesis.

The purpose of this study was to optimize the conditions of a previously proposed enzymatic method used to estimate in situ undigested neutral detergent fiber (uNDF). We used a multi-step enzymatic approach, in which samples were first solubilized in NaOH solutions as a preincubation (PreInc) phase. After rinsing, samples were incubated (24 h at 39°C) in a buffered solution (pH 6) containing hemicellulase, cellulase, and Viscozyme L enzymes (Sigma-Aldrich s.r.l., Milan, Italy), followed by incubation (24 h at 39°C) in a buffered solution (pH 5) containing xylanase. Two sets of experiments were performed: a calibration trial (that tested different PreInc conditions on 9 selected forages) and a validation trial (that verified the results by testing multiple samples of 6 different forage types and a group of fibrous by-products). In the calibration trial, samples (300 mg in Ankom F57 filter bags; Ankom Technology Corp., Fairport, NY) were preincubated at 39°C in a 0.1 M NaOH solution for 90, 180, or 240 min, or in 0.2, 0.5, 1.0, or 2.0 M NaOH solution for 90 min. The results indicated that the best PreInc method, in terms of intra-laboratory repeatability and estimation of reference in situ values, was 90 min in a 0.2 M NaOH solution. Thus, we used this PreInc condition to determine enzymatic uNDF of 257 samples in the validation trial. Although the selected method generally had good accuracy in predicting in situ uNDF, inconsistencies were noted for certain forage types. Overall, when enzymatic uNDF was used to predict the in situ uNDF of all samples, the regression was satisfactory (intercept = 7.098, slope = 0.920, R2 = 0.73). The regression models developed for alfalfa hays, corn silages, and small grain silages had also acceptable regression performances and mean square error of prediction (MSEP) values, and the main sources of MSEP variation were error due to incomplete (co)variation and random error. Even when R2 values were >0.70, the MSEP value of the regression model for grass hays was 149.55, and that for nonforage fibrous feeds was 155.16. Although enzymatic uNDF partially overestimated the in situ uNDF, particularly in grass silages, the proposed procedure seems to be promising for accurately predicting in situ uNDF, because it generally had good repeatability and provided satisfactory estimates of in situ uNDF.

Adequate milk consumption significantly contributes to meeting the human iodine recommended daily intake, which ranges from 70 µg/d for infants to 200 µg/d for lactating women. The fulfilment of iodine recommended daily intake is fundamental to prevent serious clinical diseases such as cretinism in infants and goiter in adults. In the present study iodine content was measured in raw and processed commercial cow milk, as well as in raw buffalo, goat, sheep, and donkey milk. Iodine extraction was based on 0.6% (vol/vol) ammonia, whereas iodine detection and quantification were carried out through an inductively coupled plasma mass spectrometer analyzer. Among processed commercial cow milk, partially skimmed pasteurized milk had the greatest iodine content (359.42 µg/kg) and raw milk the lowest (166.92 µg/kg). With regard to the other dairy species, the greatest iodine content was found in raw goat milk (575.42 µg/kg), followed by raw buffalo (229.82 µg/kg), sheep (192.64 µg/kg), and donkey milk (7.06 µg/kg). Repeatability of milk iodine content, calculated as relative standard deviation of 5 measurements within a day or operator, ranged from 0.96 to 1.84% and 0.72 to 1.16%, respectively. The overall reproducibility of milk iodine content, calculated as relative standard deviation of 45 measurements across 3 d of analyses and 3 operators, was 4.01%. These results underline the precision of the proposed analytical method for the determination of iodine content in milk.

Tissue biopsy metabolic activity, assessed using the oxidation-reduction indicator resazurin, may serve as a proxy to assess energy expenditure associated with maintenance in nongrowing animals or growth rate in growing animals. Herein, we evaluate the repeatability, practicality, and sensitivity of a resazurin-based assay for ranking bovine skeletal muscle biopsies based on metabolic activity. Six yearling Holstein heifers (body weight = 330 ± 11.3 kg) were fed 4 dietary treatments consisting of high or low rumen-degradable starch and fiber arranged factorially in a partially replicated Latin square design. Periods were 18 d, consisting of 3 d for diet transition, 14 d for diet adaptation, and 1 d for sample collection. Semitendinosus biopsies were collected into ice-cold Dulbecco's modified Eagle medium (Fisher Scientific, Hampton, NH) from each heifer during each period. Analysis was initiated within an hour of sample collection. To assess tissue metabolic rate, biopsies were transferred to Dulbecco's modified Eagle medium with resazurin and incubated at 37°C. Fluorescence of each sample was read at time 0 and at 15-min intervals for 2 h. Change in fluorescence was representative of skeletal muscle reducing equivalent production. Fluorescent signal strength increased with time and relative rank of treatments did not change with time; accordingly, future studies may compare fluorescence at a single time point. Change in fluorescence at 120 min was used for analysis of the fixed effects of fiber, starch, and animal when accounting for a random effect of period. Samples collected when animals were on a high-ruminally degradable starch diet were more metabolically active than samples collected from animals on low-starch diets. Significant differences in metabolic activity among individual animals were also identified. Average relative fluorescence was correlated with dry matter intake, average daily gain, and feed-to-gain ratio. The relative fluorescence tended to correlate with average daily gain (r = 0.749) and feed-to-gain ratio (r = -0.783); change in fluorescence did not correlate with dry matter intake. Although evaluated on a small sample size, this technique shows promise as a potential means of ranking animals by growth or feed efficiency. Further work on a larger experimental population is needed to confirm the usefulness of this assay as a consistent and reliable predictor of these important phenotypic parameters.

Few studies have compared individual variability of an intravenous glucose tolerance test (ivGTT) obtained daily in young cattle. The objective of this study was to evaluate the repeatability of glucose traits and insulin responses derived from ivGTT during 3 consecutive days in Holstein-Friesian cattle (bulls: n = 4, steers: n = 3, heifers: n = 3). Blood collections were performed from min 0 (basal concentrations) to min 63 (last measurement). Additional estimates included maximal concentration for glucose and insulin, glucose half-life time (GHLT), and glucose and insulin area under the curve. Glucose traits showed higher repeatability when compared with insulin responses. On average, the glucose trait and insulin response with the lowest coefficient of variation was GHLT (0.7%) and insulin area under the curve (21.3%), respectively. In addition, a general linear model with repeated measures was used to test for significant differences in glucose and insulin concentrations over time at different trial days. Both glucose and insulin concentrations were influenced by time point but not by trial day or time point × trial day. The results from this study showed that individual insulin responses were more variable than glucose traits. The high repeatability of GHLT evidenced the strict regulatory glucose disposal mechanisms occurring in young Holstein-Friesian cattle, whereas insulin responses showed high variability despite controlled management and nutritional practices.

Milk solids per kilogram of body weight (BW) is growing in popularity as a measure of dairy cow lactation efficiency. Little is known on the extent of genetic variability that exist in this trait but also the direction and strength of genetic correlations with other performance traits. Such genetic correlations are important to know if producers are to consider actively selecting cows excelling in milk solids per kilogram of BW. The objective of the present study was to use a large data set of commercial Irish dairy cows to quantify the extent of genetic variability in milk solids per kilogram of BW and related traits but also their genetic and phenotypic inter-relationships. Mid-lactation BW and body condition score (BCS), along with 305-d milk solids yield (i.e., fat plus protein yield) were available on 12,413 lactations from 11,062 cows in 85 different commercial dairy herds. (Co)variance components were estimated using repeatability animal linear mixed models. The genetic correlation between milk solids and body weight was only 0.05, which when coupled with the observed large genetic variability in both traits, indicate massive potential to select for both traits in opposite directions. The genetic correlations between both milk solids and BW with BCS; however, need to be considered in any breeding strategy. The genetic standard deviation, heritability, and repeatability of milk solids per kilogram of BW was 0.08, 0.37, and 0.57, respectively. The genetic correlation between milk solids per kilogram of BW with milk solids, BW, and BCS was 0.62, -0.75, and -0.41, respectively. Therefore, based on genetic regression, each increase of 0.10 units in genetic merit for milk solids per kilogram of BW is expected to result in, on average, an increase in 16.1 kg 305-d milk solids yield, a reduction of 25.6 kg of BW and a reduction of 0.05 BCS units (scale of 1-5 where 1 is emaciated). The genetic standard deviation (heritability) for 305-d milk solids yield adjusted phenotypically to a common BW was 27.3 kg (0.22). The genetic correlation between this adjusted milk solids trait with milk solids, BW, and BCS was 0.91, -0.12, and -0.26, respectively. Once also adjusted phenotypically to a common BCS, the genetic standard deviation (heritability) for milk solids adjusted phenotypically to a common BW was 26.8 kg (0.22) where the genetic correlation with milk solids, BW and BCS was 0.91, -0.21, and -0.07, respectively. The genetic standard deviation (heritability) of BW adjusted phenotypically for differences in milk solids was 35.3 kg (0.61), which reduced to 33.2 kg when also phenotypically adjusted for differences in BCS. Results suggest considerable opportunity exists to change milk solids yield independent of BW, and vice versa. The opportunity is reduced slightly once also corrected for differences in BCS. Inter-animal BCS differences should be considered if selection on such metrics is contemplated.

Miniaturized coagulation (MC) models have been proposed for the evaluation of curd yield (CY) in individual milk samples of different dairy species and breeds, and for the analysis of cheese microstructure and texture. It is still unclear if MC using less than 50 mL of milk is suitable to evaluate CY and chemical composition, and if preservative added to raw milk may interfere with MC process. Therefore, this study aimed at evaluating repeatability and reproducibility of CY, curd moisture, and fat and protein content on curd dry matter (DM) from MC trials using 40 g of milk. Miniaturized coagulations were performed by 3 different operators on 3 consecutive days, using raw milk (RM) and raw milk added with preservative (RMP). Repeatability of CY, calculated as relative standard deviation on 6 miniaturized curds obtained within a day by the same operator, was below 5% for MC carried out with both RM and RMP. The Horwitz ratio, which is the ratio between measured and expected reproducibility, highlighted good reproducibility for CY from RM and fair reproducibility for CY from RMP. The same ratio highlighted lower accuracies for curd moisture and fat and protein content on curd DM, especially for MC trials carried out with RMP. The z-test was performed to evaluate the similarity between curds manufactured with RM and RMP in terms of average yield and chemical composition; z-scores did not highlight significant differences between values obtained from MC carried out with RM and RMP. It can be concluded that preservative had negligible effects on MC, giving the opportunity to extend milk physical and chemical stability, to schedule laboratory trials on longer time span, and to broaden the sample size within a batch of analyses.

Ketosis is a metabolic disorder of increasing importance in high-yielding dairy cows, but accurate population-wide binary health trait recording is difficult to implement. Against this background, proper Gaussian indicator traits, which can be routinely measured in milk, are needed. Consequently, we focused on the ketone bodies acetone and β-hydroxybutyrate (BHB), measured via Fourier-transform infrared spectroscopy (FTIR) in milk. In the present study, 62,568 Holstein cows from large-scale German co-operator herds were phenotyped for clinical ketosis (KET) according to a veterinarian diagnosis key. A sub-sample of 16,861 cows additionally had first test-day observations for FTIR acetone and BHB. Associations between FTIR acetone and BHB with KET and with test-day traits were studied phenotypically and quantitative genetically. Furthermore, we estimated SNP marker effects for acetone and BHB (application of genome-wide association studies) based on 40,828 SNP markers from 4,384 genotyped cows, and studied potential candidate genes influencing body fat mobilization. Generalized linear mixed models were applied to infer the influence of binary KET on Gaussian-distributed acetone and BHB (definition of an identity link function), and vice versa, such as the influence of acetone and BHB on KET (definition of a logit link function). Additionally, linear models were applied to study associations between BHB, acetone and test-day traits (milk yield, fat percentage, protein percentage, fat-to-protein ratio and somatic cell score) from the first test-day after calving. An increasing KET incidence was statistically significant associated with increasing FTIR acetone and BHB milk concentrations. Acetone and BHB concentrations were positively associated with fat percentage, fat-to-protein ratio and somatic cell score. Bivariate linear animal models were applied to estimate genetic (co)variance components for KET, acetone, BHB and test-day traits within parities 1 to 3, and considering all parities simultaneously in repeatability models. Pedigree-based heritabilities were quite small (i.e., in the range from 0.01 in parity 3 to 0.07 in parity 1 for acetone, and from 0.03-0.04 for BHB). Heritabilites from repeatability models were 0.05 for acetone, and 0.03 for BHB. Genetic correlations between acetone and BHB were moderate to large within parities and considering all parities simultaneously (0.69-0.98). Genetic correlations between acetone and BHB with KET from different parities ranged from 0.71 to 0.99. Genetic correlations between acetone across parities, and between BHB across parities, ranged from 0.55 to 0.66. Genetic correlations between KET, acetone, and BHB with fat-to-protein ratio and with fat percentage were large and positive, but negative with milk yield. In genome-wide association studies, we identified SNP on BTA 4, 10, 11, and 29 significantly influencing acetone, and on BTA 1 and 16 significantly influencing BHB. The identified potential candidate genes NRXN3, ACOXL, BCL2L11, HIBADH, KCNJ1, and PRG4 are involved in lipid and glucose metabolism pathways.

The main objective of this study was to assess the genetic differences in metabolizable energy efficiency and efficiency in partitioning metabolizable energy in different pathways: maintenance, milk production, and growth in primiparous dairy cows. Repeatability models for residual energy intake (REI) and metabolizable energy intake (MEI) were compared and the genetic and permanent environmental variations in MEI were partitioned into its energy sinks using random regression models. We proposed 2 new feed efficiency traits: metabolizable energy efficiency (MEE), which is formed by modeling MEI fitting regressions on energy sinks [metabolic body weight (BW0.75), energy-corrected milk, body weight gain, and body weight loss] directly; and partial MEE (pMEE), where the model for MEE is extended with regressions on energy sinks nested within additive genetic and permanent environmental effects. The data used were collected from Luke's experimental farms Rehtijarvi and Minkio between 1998 and 2014. There were altogether 12,350 weekly MEI records on 495 primiparous Nordic Red dairy cows from wk 2 to 40 of lactation. Heritability estimates for REI and MEE were moderate, 0.33 and 0.26, respectively. The estimate of the residual variance was smaller for MEE than for REI, indicating that analyzing weekly MEI observations simultaneously with energy sinks is preferable. Model validation based on Akaike's information criterion showed that pMEE models fitted the data even better and also resulted in smaller residual variance estimates. However, models that included random regression on BW0.75 converged slowly. The resulting genetic standard deviation estimate from the pMEE coefficient for milk production was 0.75 MJ of MEI/kg of energy-corrected milk. The derived partial heritabilities for energy efficiency in maintenance, milk production, and growth were 0.02, 0.06, and 0.04, respectively, indicating that some genetic variation may exist in the efficiency of using metabolizable energy for different pathways in dairy cows.

The objectives were to (1) develop a standard operating procedure (SOP) for ultrasound-based measurements of teat canal dimensions and (2) determine the precision of ultrasound-based measurements of teat canal dimensions in dairy cows. Teat scans (n = 64) of the right hind and left front teats from 16 cows were obtained with a portable ultrasound device before and after machine milking. Teat dimensions measured were teat canal length (TCL), teat canal diameter at the proximal end of the teat canal (TCDPROX), teat canal diameter at the midpoint between the proximal and distal ends of the teat canal (TCDMID), teat canal diameter at the distal end of the teat canal (TCDDIS), teat end diameter at the midpoint between the proximal and distal ends of the teat canal (TMD), and teat canal cross-sectional area (TCLAREA). An SOP was developed, reviewed, and modified by 3 operators. Measurements were performed by the same 3 operators using an open source software program. To assess the effect of implemented modifications and the precision of ultrasound-based measurements, concordance correlation coefficients (CCC) were calculated to determine interoperator reproducibility and intraoperator repeatability. Through modifications in the SOP, interoperator CCC increased from 0.45 to 0.86 for TCL, from 0.14 to 0.66 for TCDPROX, from 0.24 to 0.66 for TCDMID, from 0.06 to 0.56 for TCDDIS, from 0.64 to 0.91 for TMD, and from 0.17 to 0.64 for TCLAREA. Intraoperator CCC over all operators were 0.91 for TCL, 0.73 for TCDPROX, 0.80 for TCDMID, 0.69 for TCDDIS, 0.94 for TMD, and 0.75 for TCLAREA. Our results indicate that measurements of teat canal dimensions can be conducted with satisfactory precision when following an SOP. Development and strict implementation of an SOP has the potential to decrease inadvertent variability of ultrasound-based measurements among operators for ultrasonographic assessment of teat dimensions.

The primary objective of this study was to evaluate the repeatability of measurements of β-hydroxybutyrate (BHB) concentrations in capillary blood obtained from minimal-invasive puncturing of different sites at the external vulva skin of dairy cows. A total of 240 multiparous cows sampled once within 21 d in milk were enrolled in the study. Capillary blood samples were obtained from the right and left labium of the external vulva skin and additionally from a site close to the first puncture. Samples were analyzed by using an electronic hand-held device [FreeStyle Precison Neo (FSP-Neo), Abbott GmbH & Co., Wiesbaden, Germany)]. Additionally, samples from a coccygeal vessel were analyzed as reference in a laboratory. Mean deviations (± standard deviation) between the 3 capillary sampling sites ranged from 0.01 ± 0.14 (left vs. right labium) to 0.02 ± 0.13 mmol/L (left or right labium vs. additional site). The BHB concentrations in capillary blood compared with the reference showed a mean deviation of 0.14 ± 0.18 mmol/L. The Spearman correlation coefficient between BHB concentrations in capillary blood and coccygeal blood was 85.4%. Mixed model analyses showed no significant effect of sampling site or squeezing the skin during sampling procedures on the differences toward the reference. Based on laboratory BHB concentrations of ≥1.2 and ≥1.4 mmol/L, respectively, the prevalence of hyperketonemia was 10.4 (n = 25) and 5.0% (n = 12). Thresholds to detect hyperketonemia with the FSP-Neo were optimized by receiver operating characteristic analyses. Based on a laboratory BHB concentration of ≥1.2 mmol/L thresholds were determined as 1.1 (left labium, 2nd puncture) and 1.0 mmol/L (right labium), with sensitivities between 94 and 99% and specificities between 89 and 97%. Based on a serum BHB concentration of 1.4 mmol/L, receiver operating characteristic analyses resulted in optimized cutoffs of 1.4 mmol/L for all sampling sites at the vulva, with sensitivities of 100% and specificities between 96 and 99%. In conclusion, vulvar capillary blood is suitable for accurate measuring of BHB concentrations by using the FSP-Neo. After adjusting the thresholds, the FSP-Neo provides very good test characteristics independent of the capillary sampling site or squeezing the skin and allows a practical and reliable screening tool for dairy cows.

The aim of this study was to apply Bayesian models to the Fourier-transform infrared spectroscopy spectra of individual sheep milk samples to derive calibration equations to predict traditional and modeled milk coagulation properties (MCP), and to assess the repeatability of MCP measures and their predictions. Data consisted of 1,002 individual milk samples collected from Sarda ewes reared in 22 farms in the region of Sardinia (Italy) for which MCP and modeled curd-firming parameters were available. Two milk samples were taken from 87 ewes and analyzed with the aim of estimating repeatability, whereas a single sample was taken from the other 915 ewes. Therefore, a total of 1,089 analyses were performed. For each sample, 2 spectra in the infrared region 5,011 to 925 cm −1 were available and averaged before data analysis. BayesB models were used to calibrate equations for each of the traits. Prediction accuracy was estimated for each trait and model using 20 replicates of a training-testing validation procedure. The repeatability of MCP measures and their predictions were also compared. The correlations between measured and predicted traits, in the external validation, were always higher than 0.5 (0.88 for rennet coagulation time). We confirmed that the most important element for finding the prediction accuracy is the repeatability of the gold standard analyses used for building calibration equations. Repeatability measures of the predicted traits were generally high (≥95%), even for those traits with moderate analytical repeatability. Our results show that Bayesian models applied to Fourier-transform infrared spectra are powerful tools for cheap and rapid prediction of important traits in ovine milk and, compared with other methods, could help in the interpretation of results.

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirements series is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a. literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NR,C (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils. and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 tunes into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data, and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight, into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition. Agricultural and Food Research Initiative Competitive Grant [2011-68004-30340]; USDA National Institute of Food and Agriculture (Washington, DC) [2015-03656]; Papillon (Easton, MD) In addition to the funding sources listed in the title page footnote, funding for this project was provided by Agricultural and Food Research Initiative Competitive Grant no. 2011-68004-30340 and no. 2015-03656 from the USDA National Institute of Food and Agriculture (Washington, DC) and by Papillon (Easton, MD). The authors acknowledge the contributions of the late L. F. Reutzel (Land O'Lakes/Purina Mills) to portions of the initial code used in the project and the late Gale Bateman (Akey, Lewisburg, OH) for help in assembling and collating the dietary ingredient data.

The objective of this study was to estimate the heritability of milk fat globule (MFG) size and mid-infrared (MIR) predicted MFG size in Holstein cattle. The genetic correlations between measured and predicted MFG size with milk fat and protein percentage were also investigated. Average MFG size was measured in 1,583 milk samples taken from 254 Holstein cows from 29 herds across Canada. Size was expressed as volume moment mean (D[4,3]) and surface moment mean (D[3,2]). Analyzed milk samples also had average MFG size predicted from their MIR spectral records. Fat and protein percentages were obtained for all test-day milk samples in the cow's lactation. Univariate and bivariate repeatability animal models were used to estimate heritability and genetic correlations. Moderate heritabilities of 0.364 and 0.466 were found for D[4,3] and D[3,2], respectively, and a strong genetic correlation was found between the 2 traits (0.98). The heritabilities for the MIR-predicted MFG size were lower than those estimated for the measured MFG size at 0.300 for predicted D[4,3] and 0.239 for predicted D[3,2]. The genetic correlation between measured and predicted D[4,3] was 0.685; the correlation was slightly higher between measured and predicted D[3,2] at 0.764, likely due to the better prediction accuracy of D[3,2]. Milk fat percentage had moderate genetic correlations with both D[4,3] and D[3,2] (0.538 and 0.681, respectively). The genetic correlation between predicted MFG size and fat percentage was much stronger (greater than 0.97 for both predicted D[4,3] and D[3,2]). The stronger correlation suggests a limitation for the use of the predicted values of MFG size as indicator traits for true average MFG size in milk in selection programs. Larger samples sizes are required to provide better evidence of the estimated genetic parameters. A genetic component appears to exist for the average MFG size in bovine milk, and the variation could be exploited in selection programs.

A meta-analysis based on an individual-cow data set was conducted to investigate between-cow variations in the components and measurements of feed efficiency (FE) and to explore the associations among these components. Data were taken from 31 chamber studies, consisting of a total of 841 cow/period observations. The experimental diets were based on grass or corn silages, fresh grass, or a mixture of fresh grass and straw, with cereal grains or by-products as energy supplements, and soybean or canola meal as protein supplements. The average forage-to-concentrate ratio across all diets on a dry matter basis was 56:44. Variance component and repeatability estimates of FE measurements and components were determined using diet, period, and cow within experiment as random effects in mixed procedures of SAS (SAS Institute Inc., Cary, NC). The between-cow coefficient of variation (CV) in gross energy intake (GE; CV = 0.10) and milk energy (El) output as a proportion of GE (El/GE; CV = 0.084) were the largest among all component traits. Similarly, the highest repeatability estimates (≥0.50) were observed for these 2 components. However, the between-cow CV in digestibility (DE/GE), metabolizability [metabolizable energy (ME)/GE], methane yield (CH4E/GE), proportional urinary energy output (UE/GE), and heat production (HP/GE), as well as the efficiency of ME use for lactation (kl), were rather small. The least repeatable component of FE was UE/GE. For FE measurements, the between-cow CV in residual energy-corrected milk (RECM) was larger than for residual feed intake (RFI), suggesting a greater possibility for genetic gain in RECM than in RFI. A high DE/GE was associated with increased CH4E/GE (r = 0.24), HP/GE (r = 0.12), ME/GE (r = 0. 91), energy balance as a proportion of GE (EB/GE; r = 0.35), and kl (r = 0.10). However, no correlation between DE/GE and GE intake or UE/GE was observed. Increased proportional milk energy adjusted to zero energy balance (El(0)/GE) was associated with increases in DE/GE, ME/GE, EB/GE, and kl but decreases in UE/GE, CH4E/GE, and HP/GE, with no effect on GE intake. In conclusion, several mechanisms are involved in the observed differences in FE among dairy cows, and reducing CH4E yield (CH4E/GE) may inadvertently result in reduced GE digestibility. However, the selection of dairy cows with improved energy utilization efficiencies offers an effective approach to lower enteric CH4 emissions.

The objective of this study was to estimate genetic correlations among milk fatty acid (FA) concentrations in New Zealand dairy cattle. Concentrations of each of the most common FA, expressed as a percentage of the total FA, were determined by gas chromatography on a specific cohort of animals. Using this data set, prediction equations were derived using mid-infrared (MIR) spectroscopy data collected from the same samples. These prediction equations were applied to a large data set of MIR measurements in 34,141 milk samples from 3,445 Holstein-Friesian, 2,935 Jersey, and 3,609 crossbred Holstein-Friesian × Jersey cows, sampled an average of 3.42 times during the 2007–2008 season. Data were analyzed using univariate and bivariate repeatability animal models. Heritability of predicted FA concentration in milk fat ranged from 0.21 to 0.42, indicating that genetic selection could be used to change the FA composition of milk. The de novo synthesized FA (C6:0, C8:0, C10:0, C12:0, and C14:0) showed strong positive genetic correlations with each other, ranging from 0.24 to 0.99. Saturated FA were negatively correlated with unsaturated (−0.93) and polyunsaturated (−0.84) FA. The saturated FA were positively correlated with milk fat yield and fat percentage, whereas the unsaturated FA were negatively associated with fat yield and fat percentage. Our results indicate that bovine milk FA composition can be changed through genetic selection using MIR as a phenotypic proxy.

Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH4) emitting cows. We investigated the persistence of differences in CH4 emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH4 emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH4 emission was calculated. Traits other than CH4 emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH4 production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH4 data estimated by MIR and measured with GF for individual cows was weak (R2 = 0.26). The categorization based on CH4 production also generated differences in CH4 emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH4 estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH4 production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding., Journal of Dairy Science, 102 (12), ISSN:0022-0302, ISSN:1525-3198

A respiration system consisting of 4 climate-controlled chambers and 1 set of flowmeters and analyzers was constructed and validated. Each chamber had volume of 21.10m(3) (3.68×2.56×2.24m) and was made from steel with double-glazed windows on either side enabling visual contact between animals. The chambers are independently climate-controlled and can maintain temperature and relative humidity in a range from 5 to 45°C and 30 to 80%, respectively. A flow generator and mass flowmeter continuously pull air from each chamber and a slight negative pressure inside the chamber is ensured. Air from all chambers and ambient air share a common gas analysis and data acquisition system for monitoring O2, CO2, and CH4 concentrations over the measurement period, with the cycle time set to 20min. Analyzers are regularly calibrated and the chambers have mean recoveries of 99.0 and 98.0% for CO2 and CH4, respectively. The chambers are equipped with infrared cameras and electronic feed and water bins for intake measurements, as well as sensors for monitoring animal position and heart rate. Data acquisition and analysis software is used to calculate the rate of consumption of O2 and production of CO2 and CH4. The dynamic respiration measurements are integrated with feed intake data and other sensors. The daily gas exchanges are estimated by integration to determine methane emission and heat production. We conducted a trial with 12 lactating 3/4 Holstein × 1/4 Gyr crossbred dairy cows (6 multiparous and 6 primiparous) under 2 feeding regimens (ad libitum or restricted) to validate the system. Two 22-h respiration measurements were obtained from each cow. Restricted-fed cows showed lower values for milk yield, methane emission, and heat production compared with ad libitum-fed animals. We found no difference between groups for CH4 produced per kilogram of dry matter intake. Repeatability for CH4 emission and heat production was high (0.97 and 0.92, respectively). The respiration system described herein is a useful tool for measuring the dynamic and accumulated data of heat production, methane emission, and feed intake.

Superovulation of dairy cattle is frequently used in Canada. The cost of this protocol is high, and so is the variability of the outcome. Knowing the superovulatory potential of a donor cow could influence the breeder's decision to superovulate it or not. The main objective of this study was to perform a genetic analysis for superovulatory response of Holstein cows in Canada using data recorded by Holstein Canada, and to investigate if these data could be used for genetic evaluation. Data contained the total number of embryos and the number of viable embryos from every successful flushing performed across Canada. After editing, 137,446 records of superovulation performed between 1992 and 2014 were analyzed. A univariate repeatability animal model analysis was performed for both total number of embryos and number of viable embryos. Because both data and residuals did not follow a normal distribution, records were subject to either logarithmic or Anscombe transformation. Using logarithmic transformation, heritability estimates (SE) of 0.15 (0.01) and 0.14 (0.01) were found for total number of embryos and number of viable embryos, respectively. Using Anscombe transformation, heritability estimates (SE) of 0.17 (0.01) and 0.14 (0.01) were found for total number of embryos and number of viable embryos, respectively. The genetic correlation between the 2 traits was estimated at 0.97 using logarithmic transformation and 0.95 using Anscombe transformation. Breeding values were estimated for 54,463 cows, and 3,513 sires. Only estimated breeding values of sires having a reliability higher than 40% were considered for estimated breeding values correlations with other routinely evaluated traits. The results showed that selection for a higher response to superovulation would lead to a slight decrease in milk production, but an improvement for functional traits, including all reproduction traits. In all cases, the estimated correlations are either low or modest. We conclude that genetic selection for increased superovulatory response in donors is possible; daughters of sires with high estimated breeding values for superovulatory response will tend to yield more embryos, whereas the additive effect of service sire seems not to contribute to the variability of the 2 superovulation traits and was not significantly correlated with the additive effect of the donor.

This study aimed to compare measurements of methane (CH4) and carbon dioxide (CO2) concentrations in the breath of dairy cows kept in commercial conditions using the Fourier-transform infrared spectroscopy (FTIR) and nondispersive infrared spectroscopy (NDIR) methods. The measurement systems were installed in an automated milking system. Measurements were carried out for 5 d using both systems during milkings. The measurements were averaged per milking, giving 467 observations of CH4 and CO2 concentrations of 44 Holstein Friesian cows. The Pearson correlation between observations from the 2 systems was 0.86 for CH4, 0.84 for CO2, and 0.88 for their ratio. The repeatability of FTIR (0.53 for CH4, 0.57 for CO2, and 0.28 for their ratio) was somewhat higher than that of NDIR (0.57 for CH4, 0.47 for CO2, and 0.25 for their ratio). The coefficient of individual agreement was 0.98 for CH4, 0.89 for CO2, and 0.89 for their ratio; the concordance correlation coefficient was 0.48 for both gases and 0.24 for their ratio. We showed that FTIR and NDIR give similar results in commercial farm conditions. They can therefore be used interchangeably to generate a larger data set, which could then be further used for genetic evaluation.

International audience; Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH0 PPC. had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH0 PPC and CY or coagulation traits were weak (−0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.

Cheese manufacturers indirectly determine Na in cheese by analysis of Cl using the Volhard method, assuming that all Cl came from NaCl. This method overestimates the actual Na content in cheeses when Na replacers (e.g., KCl) are used. A direct and rapid method for Na detection is needed. X-ray fluorescence spectroscopy (XRF), a mineral analysis technique used in the mining industry, was investigated as an alternative method of Na detection in cheese. An XRF method for the detection of Na in cheese was developed and compared with inductively coupled plasma optical emission spectroscopy (ICP-OES; the reference method for Na in cheese) and Cl analyzer. Sodium quantification was performed by multi-point calibration with cheese standards spiked with NaCl ranging from 0 to 4% Na (wt/wt). The Na concentration of each of the cheese standards (discs: 30 mm × 7 mm) was quantified by the 3 methods. A single laboratory method validation was performed; linearity, precision, limit of detection, and limit of quantification were determined. An additional calibration graph was created using cheese standards made from natural or process cheeses manufactured with different ratios of Na:K. Both Na and K calibration curves were linear for the cheese standards. Sodium was quantified in a variety of commercial cheese samples. The Na data obtained by XRF were in agreement with those from ICP-OES and Cl analyzer for most commercial natural cheeses. The XRF method did not accurately determine Na concentration for several process cheese samples, compared with ICP-OES, likely due to the use of unknown types of Na-based emulsifying salts (ES). When a calibration curve was created for process cheese with the specific types of ES used for this cheese, Na content was successfully predicted in the samples. For natural cheeses, the limit of detection and limit of quantification for Na that can be determined with an acceptable level of repeatability, precision, and trueness was 82 and 246 mg/100 g of cheese, respectively. Calibration graphs should be created with standards that reflect the concentration range, ratio, and salt type present in the cheeses. This XRF method can be successfully used for the rapid and direct measurement of Na content in a wide variety of natural cheeses. Commercial process cheese manufacturers use proprietary blends of ES. We did find that the XRF technique worked for process cheese when the calibration graphs were created with the specific types of ES actually used.

Body condition is an indirect estimation of the level of body reserves, and its variation reflects cumulative variation in energy balance. It interacts with reproductive and health performance, which are important to consider in dairy production but not easy to monitor. The commonly used body condition score (BCS) is time consuming, subjective, and not very sensitive. The aim was therefore to develop and validate a method assessing BCS with 3-dimensional (3D) surfaces of the cow's rear. A camera captured 3D shapes 2 m from the floor in a weigh station at the milking parlor exit. The BCS was scored by 3 experts on the same day as 3D imaging. Four anatomical landmarks had to be identified manually on each 3D surface to define a space centered on the cow's rear. A set of 57 3D surfaces from 56 Holstein dairy cows was selected to cover a large BCS range (from 0.5 to 4.75 on a 0 to 5 scale) to calibrate 3D surfaces on BCS. After performing a principal component analysis on this data set, multiple linear regression was fitted on the coordinates of these surfaces in the principal components' space to assess BCS. The validation was performed on 2 external data sets: one with cows used for calibration, but at a different lactation stage, and one with cows not used for calibration. Additionally, 6 cows were scanned once and their surfaces processed 8 times each for repeatability and then these cows were scanned 8 times each the same day for reproducibility. The selected model showed perfect calibration and a good but weaker validation (root mean square error = 0.31 for the data set with cows used for calibration; 0.32 for the data set with cows not used for calibration). Assessing BCS with 3D surfaces was 3 times more repeatable (standard error = 0.075 versus 0.210 for BCS) and 2.8 times more reproducible than manually scored BCS (standard error = 0.103 versus 0.280 for BCS). The prediction error was similar for both validation data sets, indicating that the method is not less efficient for cows not used for calibration. The major part of reproducibility error incorporates repeatability error. An automation of the anatomical landmarks identification is required, first to allow broadband measures of body condition and second to improve repeatability and consequently reproducibility. Assessing BCS using 3D imaging coupled with principal component analysis appears to be a very promising means of improving precision and feasibility of this trait measurement.

peer-reviewed Energy balance (EB) and energy intake (EI) are heritable traits of economic importance. Despite this, neither trait is explicitly included in national dairy cow breeding goals due to a lack of routinely available data from which to compute reliable breeding values. Mid-infrared (MIR) spectrometry, which is performed during routine milk recording, is an accurate predictor of both EB and EI. The objective of this study was to estimate genetic parameters of EB and EI predicted using MIR spectrometry. Measured EI and EB were available for 1,102 Irish Holstein-Friesian cows based on actual feed intake and energy sink data. A subset of these data (1,270 test-day records) was used to develop equations to predict EI, EB, and daily change in body condition score (ΔBCS) and body weight (ΔBW) using the MIR spectrum with or without milk yield also as a predictor variable. Accuracy of cross-validation of the prediction equations was 0.75, 0.73, 0.77, and 0.70 for EI, EB, ΔBCS, and ΔBW, respectively. Prediction equations were applied to additional spectral data, yielding up to 94,653 records of MIR-predicted EI, EB, ΔBCS, and ΔBW available for variance component estimation. Variance components were estimated using repeatability animal linear mixed models. Heritabilities of MIR-predicted EI, EB, ΔBCS, and ΔBW were 0.20, 0.10, 0.07, and 0.06, respectively; heritability estimates of the respective measured traits were 0.35, 0.16, 0.07, and 0.08, respectively. The genetic correlation between measured and MIR-predicted EI was 0.84 and between measured and MIR-predicted EB was 0.54, indicating that selection based on MIR-predicted EI or EB would improve true EI or EB. Genetic and phenotypic associations between EI and both the milk production and body-change traits were generally in agreement, regardless of whether measured EI or MIR-predicted EI was considered. Higher-yielding animals of higher body weight had greater EI. Predicted EB was negatively genetically correlated with milk yield (genetic correlation = −0.29) and positively genetically correlated with both milk fat and protein percent (genetic correlation = 0.17 and 0.16, respectively). Least squares means phenotypic EI of 198 animals stratified as low, average, and high estimated breeding values for MIR-predicted EI (animal phenotypes were not included in the genetic evaluation) were 154.3, 156.0, and 163.3 MJ/d, corroborating that selection on MIR-predicted EI will, on average, result in differences in phenotypic true EI.