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The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Previous work has highlighted that immune-associated (IA) traits measurable in blood are associated with health, productivity, and reproduction in dairy cows. The aim of the present study was to determine relationships between IA traits measured in blood serum and those simultaneously measured in milk as well as their association with disease phenotypes. All animals were Holstein-Friesian cows from the Langhill research herd (n = 546) housed at the SRUC Dairy Research Centre in Scotland. Milk and serum samples were collected on 20 separate occasions between July 2010 and March 2015 and analyzed by ELISA for haptoglobin (Hp), tumor necrosis factor-α (TNF-α), and natural antibodies binding keyhole limpet hemocyanin (NAb KLH ) and lipopolysaccharide (NAb LPS ). Data were analyzed using mixed linear models that included pedigree information. Analyses revealed positive phenotypic correlations between milk and serum NAb (0.59 ≤ r ≤ 0.77), Hp (r = 0.37), and TNF-α (r = 0.12). Milk and serum NAb were also found to have a strong genetic correlation (0.81 ≤ r ≤ 0.94) and were genetically correlated with cow lameness (0.66 and 0.79 for milk NAb KLH and serum NAb LPS , respectively). Clinical mastitis was found to be phenotypically correlated with both milk and serum Hp (0.09 ≤ r ≤ 0.23). Serum Hp was also strongly genetically correlated with other cellular IA traits such as percent NKp46 + (a natural killer cell marker; 0.35) and percent peripheral blood mononuclear cells (PBMC; -0.90). Similarly, genetic correlations were found to exist between serum TNF-α and percent NKp46 + (0.22), percent PBMC (0.41), and percent lymphocytes (0.47). Excluding serum Hp, all milk and serum IA traits were repeatable, ranging from 0.11 (milk Hp) to 0.43 (serum NAb LPS ). Between-animal variation was highest in milk and serum NAb (0.34-0.43) and significant estimates of heritability were also observed in milk and serum NAb (0.17-0.37). Our findings show that certain IA traits, such as NAb KLH and NAb LPS , found in milk and serum are strongly correlated and highlight the potential of using routinely collected milk samples as a less invasive and cost-effective source of informative data for predictive modeling of animal IA traits., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Average telomere length (TL) in blood cells has been shown to decline with age in a range of vertebrate species, and there is evidence that TL is a heritable trait associated with late-life health and mortality in humans. In non-human mammals, few studies to date have examined lifelong telomere dynamics and no study has estimated the heritability of TL, despite these being important steps towards assessing the potential of TL as a biomarker of productive lifespan and health in livestock species. Here we measured relative leukocyte TL (RLTL) in 1,328 samples from 308 Holstein Friesian dairy cows and in 284 samples from 38 female calves. We found that RLTL declines after birth but remains relatively stable in adult life. We also calculated the first heritability estimates of RLTL in a livestock species which were 0.38 (SE = 0.03) and 0.32 (SE = 0.08) for the cow and the calf dataset, respectively. RLTL measured at the ages of one and five years were positively correlated with productive lifespan (p < 0.05). We conclude that bovine RLTL is a heritable trait, and its association with productive lifespan may be used in breeding programmes aiming to enhance cow longevity.

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain β-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Visual Image analysis (VIA) of carcass traits provides the opportunity to estimate carcass primal cut yields on large numbers of slaughter animals. This allows carcases to be better differentiated and farmers to be paid based on the primal cut yields. It also creates more accurate genetic selection due to high volumes of data which enables breeders to breed cattle that better meet the abattoir specifications and market requirements. In order to implement genetic evaluations for VIA primal cut yields, genetic parameters must first be estimated and that was the aim of this study. Slaughter records from the UK prime slaughter population for VIA carcass traits was available from two processing plants. After edits, there were 17 765 VIA carcass records for six primal cut traits, carcass weight as well as the EUROP conformation and fat class grades. Heritability estimates after traits were adjusted for age ranged from 0.32 (0.03) for EUROP fat to 0.46 (0.03) for VIA Topside primal cut yield. Adjusting the VIA primal cut yields for carcass weight reduced the heritability estimates, with estimates of primal cut yields ranging from 0.23 (0.03) for Fillet to 0.29 (0.03) for Knuckle. Genetic correlations between VIA primal cut yields adjusted for carcass weight were very strong, ranging from 0.40 (0.06) between Fillet and Striploin to 0.92 (0.02) between Topside and Silverside. EUROP conformation was also positively correlated with the VIA primal cuts with genetic correlation estimates ranging from 0.59 to 0.84, whereas EUROP fat was estimated to have moderate negative correlations with primal cut yields, estimates ranged from -0.11 to -0.46. Based on these genetic parameter estimates, genetic evaluation of VIA primal cut yields can be undertaken to allow the UK beef industry to select carcases that better meet abattoir specification and market requirements.

Abattoir data are an important source of information for the genetic evaluation of carcass traits, but also for on-farm management purposes. The present study aimed to quantify the contribution of herd environment to beef carcass characteristics (weight, conformation score and fat score) with particular emphasis on generating finishing herd-specific profiles for these traits across different ages at slaughter. Abattoir records from 46,115 heifers and 78,790 steers aged between 360 and 900days, and from 22,971 young bulls aged between 360 and 720days, were analysed. Finishing herd-year and animal genetic (co)variance components for each trait were estimated using random regression models. Across slaughter age and gender, the ratio of finishing herd-year to total phenotypic variance ranged from 0.31 to 0.72 for carcass weight, 0.21 to 0.57 for carcass conformation and 0.11 to 0.44 for carcass fat score. These parameters indicate that the finishing herd environment is an important contributor to carcass trait variability and amenable to improvement with management practices., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Livestock mature at different rates depending, in part, on their genetic merit; therefore, the optimal age at slaughter for progeny of certain sires may differ. The objective of the present study was to examine sire-level genetic profiles for carcass weight, carcass conformation, and carcass fat in cattle of multiple beef and dairy breeds, including crossbreeds. Slaughter records from 126,214 heifers and 124,641 steers aged between 360 and 1,200 d and from 86,089 young bulls aged between 360 and 720 d were used in the analysis; animals were from 15,127 sires. Variance components for each trait across age at slaughter were generated using sire random regression models that included quadratic polynomials for fixed and random effects; heterogeneous residual variances were assumed across ages. Heritability estimates across genders ranged from 0.08 (±0.02) to 0.34 (±0.02) for carcass weight, from 0.24 (±0.02) to 0.42 (±0.01) for conformation, and from 0.16 (±0.03) to 0.40 (±0.02) for fat score. Genetic correlations within each trait across ages weakened as the interval between ages compared lengthened but were all >0.64, suggesting a similar genetic background for each trait across different ages. Eigenvalues and eigenfunctions of the additive genetic covariance matrix revealed genetic variability among animals in their growth profiles for carcass traits, although most of the genetic variability was associated with the height of the growth profile. At the same age, a positive genetic correlation (0.60 to 0.78; SE ranged from 0.01 to 0.04) existed between carcass weight and conformation, whereas negative genetic correlations existed between fatness and both conformation (-0.46 to 0.08; SE ranged from 0.02 to 0.09) and carcass weight (-0.48 to -0.16; SE ranged from 0.02 to 0.14) at the same age. The estimated genetic parameters in the present study indicate genetic variability in the growth trajectory in cattle, which can be exploited through breeding programs and used in decision support tools.

A new breeding value that combines the amount of feed saved through improved metabolic efficiency with predicted maintenance requirements is described. The breeding value includes a genomic component for residual feed intake (RFI) combined with maintenance requirements calculated from either a genomic or pedigree estimated breeding value (EBV) for body weight (BW) predicted using conformation traits. Residual feed intake is only available for genotyped Holsteins; however, BW is available for all breeds. The RFI component of the "feed saved" EBV has 2 parts: Australian calf RFI and Australian lactating cow RFI. Genomic breeding values for RFI were estimated from a reference population of 2,036 individuals in a multi-trait analysis including Australian calf RFI (n=843), Australian lactating cow RFI (n=234), and UK and Dutch lactating cow RFI (n=958). In all cases, the RFI phenotypes were deviations from a mean of 0, calculated by correcting dry matter intake for BW, growth, and milk yield (in the case of lactating cows). Single nucleotide polymorphism effects were calculated from the output of genomic BLUP and used to predict breeding values of 4,106 Holstein sires that were genotyped but did not have RFI phenotypes themselves. These bulls already had BW breeding values calculated from type traits, from which maintenance requirements in kilograms of feed per year were inferred. Finally, RFI and the feed required for maintenance (through BW) were used to calculate a feed saved breeding value and expressed as the predicted amount of feed saved per year. Animals that were 1 standard deviation above the mean were predicted to eat 66 kg dry matter less per year at the same level of milk production. In a data set of genotyped Holstein sires, the mean reliability of the feed saved breeding value was 0.37. For Holsteins that are not genotyped and for breeds other than Holsteins, feed saved is calculated using BW only. From April 2015, feed saved has been included as part of the Australian national selection index, the Balanced Performance Index (BPI). Selection on the BPI is expected to lead to modest gains in feed efficiency., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Genetic improvement programs around the world rely on the collection of accurate phenotypic data. These phenotypes have an inherent value that can be estimated as the contribution of an additional record to genetic gain. Here, the contribution of phenotypes to genetic gain was calculated using traditional progeny testing (PT) and 2 genomic selection (GS) strategies that, for simplicity, included either males or females in the reference population. A procedure to estimate the theoretical economic contribution of a phenotype to a breeding program is described for both GS and PT breeding programs through the increment in genetic gain per unit of increase in estimated breeding value reliability obtained when an additional phenotypic record is added. The main factors affecting the value of a phenotype were the economic value of the trait, the number of phenotypic records already available for the trait, and its heritability. Furthermore, the value of a phenotype was affected by several other factors, including the cost of establishing the breeding program and the cost of phenotyping and genotyping. The cost of achieving a reliability of 0.60 was assessed for different reference populations for GS. Genomic reference populations of more sires with small progeny group sizes (e.g., 20 equivalent daughters) had a lower cost than those reference populations with either large progeny group sizes for fewer genotyped sires, or female reference populations, unless the heritability was large and the cost of phenotyping exceeded a few hundred dollars; then, female reference populations were preferable from an economic perspective., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herd-specific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Validating genomic prediction equations in independent populations is an important part of evaluating genomic selection. Published genomic predictions from 2 studies on (1) residual feed intake and (2) dry matter intake (DMI) were validated in a cohort of 78 multiparous Holsteins from Australia. The mean realized accuracy of genomic prediction for residual feed intake was 0.27 when the reference population included phenotypes from 939 New Zealand and 843 Australian growing heifers (aged 5-8 mo) genotyped on high density (770k) single nucleotide polymorphism chips. The 90% bootstrapped confidence interval of this estimate was between 0.16 and 0.36. The mean realized accuracy was slightly lower (0.25) when the reference population comprised only Australian growing heifers. Higher realized accuracies were achieved for DMI in the same validation population and using a multicountry model that included 958 lactating cows from the Netherlands and United Kingdom in addition to 843 growing heifers from Australia. The multicountry analysis for DMI generated 3 sets of genomic predictions for validation animals, one on each country scale. The highest mean accuracy (0.72) was obtained when the genomic breeding values were expressed on the Dutch scale. Although the validation population used in this study was small (n=78), the results illustrate that genomic selection for DMI and residual feed intake is feasible. Multicountry collaboration in the area of dairy cow feed efficiency is the evident pathway to achieving reasonable genomic prediction accuracies for these valuable traits., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Detailed biological analyses (e.g. epidemiological, genetic) of animal health and fitness in the field are limited by the lack of large-scale recording of individual animals. An alternative approach is to identify immune traits that are associated with these important functions and can be subsequently used in more detailed studies. We have used an experimental dairy herd with uniquely dense phenotypic data to identify a range of potentially useful immune traits correlated with enhanced (or depressed) health and fitness. Blood samples from 248 dairy cows were collected at two-monthly intervals over a 10-month period and analysed for a number of immune traits, including levels of serum proteins associated with the innate immune response and circulating leukocyte populations. Immune measures were matched to individual cow records related to productivity, fertility and disease. Correlations between traits were calculated using bivariate analyses based on animal repeatability and random regression models with a Bonferroni correction to account for multiple testing. A number of significant correlations were found between immune traits and other recorded traits including: CD4(+):CD8(+) T lymphocyte ratio and subclinical mastitis; % CD8(+) lymphocytes and fertility; % CD335(+) natural killer cells and lameness episodes; and serum haptoglobin levels and clinical mastitis. Importantly these traits were not associated with reduced productivity and, in the case of cellular immune traits, were highly repeatable. Moreover these immune traits displayed significant between-animal variation suggesting that they may be altered by genetic selection. This study represents the largest simultaneous analysis of multiple immune traits in dairy cattle to-date and demonstrates that a number of immune traits are associated with health events. These traits represent useful selection markers for future programmes aimed at improving animal health and fitness.

As the emphasis in cattle breeding is shifting from traits that increase income toward traits that reduce costs, national breeding indices are expanding to include functional traits such as calving ease (CE). However, one issue is the lack of knowledge of genetic relationships between CE and other dairy traits. The same can be said about gestation length (GL), a potential novel selection trait with considerable heritabilities and possible genetic relationships with the calving process. This study aimed to estimate the genetic relationships between CE, GL, and other dairy traits of interest using a national data set of 31,053 primiparous cow performance records, as well as to separate direct and maternal genetic effects. Chosen dairy traits included fertility (calving interval, days to first service, nonreturn rate after 56 d, number of inseminations per conception), milk production (milk yield at d 110 in milk, accumulated 305-d milk yield, accumulated 305-d fat yield, accumulated 305-d protein yield), type (udder depth, chest width, rump width, rump angle, mammary composition, stature, body depth), and lifespan traits (functional days of productive life). To allow the separation of direct and maternal genetic effects, a random sire of the calf effect was included in the multi-trait linear trivariate sire models fitted using ASReml. Significant results showed that easily born individuals were genetically prone to high milk yield and reduced fertility in first lactation. Difficult calving primiparous cows were likely associated with being high-producing, wide and deep animals, with a reduced ability to subsequently conceive. Individuals that were born relatively early were associated with good genetic merit for milk production. Finally, individuals carrying their offspring longer were genetically associated with being wide and large animals that were themselves born relatively early. The study shows that it is feasible and valuable to separate direct and maternal effects when estimating genetic correlations between calving and other dairy traits. Furthermore, gestation length is best used as an indicator trait for lowly heritable calving traits, rather than as a novel selection trait. As estimated direct and maternal genetic correlations differ, we can conclude that genetic relationships between CE, GL, and traits of interest are present, but caution is required if these traits are implemented in national breeding indices., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Background: The focus in dairy cattle breeding is gradually shifting from production to functional traits and genetic parameters of calving traits are estimated more frequently. However, across countries, various statistical models are used to estimate these parameters. This study evaluates different models for calving ease and stillbirth in United Kingdom Holstein-Friesian cattle., Methods: Data from first and later parity records were used. Genetic parameters for calving ease, stillbirth and gestation length were estimated using the restricted maximum likelihood method, considering different models i.e. sire (-maternal grandsire), animal, univariate and bivariate models. Gestation length was fitted as a correlated indicator trait and, for all three traits, genetic correlations between first and later parities were estimated. Potential bias in estimates was avoided by acknowledging a possible environmental direct-maternal covariance. The total heritable variance was estimated for each trait to discuss its theoretical importance and practical value. Prediction error variances and accuracies were calculated to compare the models., Results and Discussion: On average, direct and maternal heritabilities for calving traits were low, except for direct gestation length. Calving ease in first parity had a significant and negative direct-maternal genetic correlation. Gestation length was maternally correlated to stillbirth in first parity and directly correlated to calving ease in later parities. Multi-trait models had a slightly greater predictive ability than univariate models, especially for the lowly heritable traits. The computation time needed for sire (-maternal grandsire) models was much smaller than for animal models with only small differences in accuracy. The sire (-maternal grandsire) model was robust when additional genetic components were estimated, while the equivalent animal model had difficulties reaching convergence., Conclusions: For the evaluation of calving traits, multi-trait models show a slight advantage over univariate models. Extended sire models (-maternal grandsire) are more practical and robust than animal models. Estimated genetic parameters for calving traits of UK Holstein cattle are consistent with literature. Calculating an aggregate estimated breeding value including direct and maternal values should encourage breeders to consider both direct and maternal effects in selection decisions.

This study set out to demonstrate the feasibility of merging data from different experimental resource dairy populations for joint genetic analyses. Data from four experimental herds located in three different countries (Scotland, Ireland and the Netherlands) were used for this purpose. Animals were first lactation Holstein cows that participated in ongoing or previously completed selection and feeding experiments. Data included a total of 60 058 weekly records from 1630 cows across the four herds; number of cows per herd ranged from 90 to 563. Weekly records were extracted from the individual herd databases and included seven traits: milk, fat and protein yield, milk somatic cell count, liveweight, dry matter intake and energy intake. Missing records were predicted with the use of random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. A total of 23 different lactation traits were derived from these records: total milk, fat and protein yield, average fat and protein percentage, average fat-to-protein ratio, total dry matter and energy intake and average dry matter intake-to-milk yield ratio in lactation weeks 1 to 44 and 1 to 15; average milk somatic cell count in lactation weeks 1 to 15 and 16 to 44; average liveweight in lactation weeks 1 to 44; and average energy balance in lactation weeks 1 to 44 and 1 to 15. Data were subsequently merged across the four herds into a single dataset, which was analysed with mixed linear models. Genetic variance and heritability estimates were greater (P < 0.05) than zero for all traits except for average milk somatic cell count in weeks 16 to 44. Proportion of total phenotypic variance due to genotype-by-environment (sire-by-herd) interaction was not different (P > 0.05) from zero. When estimable, the genetic correlation between herds ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase power of detection in a genetic analysis and augment the potential reference population in genome-wide association studies, especially of difficult-to-record traits.

The objectives of this study were to derive phenotypic and genetic prediction equations of liveweight from linear conformation traits, and estimate genetic and phenotypic parameters for these traits. Data pertained to 2,728 conformation and liveweight records of 613 cows in 1,529 lactations. Cows were raised at the Scottish Agricultural College research station and had calved between 2002 and 2010. Fifteen linear conformation traits were considered as predictors. To validate phenotypic predictions, the data set was randomly split into independent reference and validation subsets. Reference subsets were used to derive prediction equations with the use of a mixed model. Comparisons between predicted and actual liveweight in the validation subsets indicated that stature, chest width, body depth, and angularity could be used to derive phenotypic predictions of liveweight. Accuracy of these predictions was better for first-lactation than for all-lactation liveweight data. Significant genetic correlations between liveweight and the 4 predictor traits ranged from 0.49 to 0.76, and phenotypic correlations were 0.33 to 0.56. Estimated genetic (co)variances were used to develop prediction equations of animal genetic merit for liveweight from routinely calculated genetic evaluations for conformation traits., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The effect of calving ease on the fertility and production performance of both dam and calf was studied in approximately 50,000 and 10,000 UK Holstein-Friesian heifers and heifer calves, respectively. The first objective of this study was to estimate the effect of a difficult calving on the subsequent first-lactation milk production by estimating lactation curves using cubic splines. This methodology allows the estimation of daily milk, protein, and fat yields following calvings of differing degrees of difficulty. Losses in milk yield after a difficult calving have been quantified previously; however, estimates are generally restricted to the accumulated yields at specific days in lactation. By fitting cubic splines, gaps (in which the shape of the lactation curve can be merely guessed) between estimations were avoided. The second objective of this study was to estimate the effect of a difficult birth on the subsequent performance of the calf as an adult animal. Even though the calving process is known to involve cooperation between dam and calf, the effect of a difficult calving has, until now, only been estimated for the subsequent performance of the dam. Addressing the effects of a difficult birth on the adult calf strengthens the importance of calving ease as a selection trait because it suggests that the benefit of genetic improvement may currently be underestimated. The effect of calving ease on the subsequent reproductive performance of dam and calf was analyzed using linear regression and with calving ease score fitted as a fixed effect. Dams with veterinary-assisted calvings required 0.7 more services to conception and 8 more days to first service and experienced a 28-d longer calving interval in first lactation compared with dams that were not assisted at calving. Effects of calving ease on the reproductive performance of the adult calf in first lactation were not detected. Losses in milk yield of the dam were significant between d 9 to 90 in milk subsequent to a veterinary-assisted calving, creating a loss of approximately 2 kg of milk per day, compared with a nonassisted calving. Calves being born with difficulties showed a significant reduction in milk yield in first lactation, demonstrating the lifelong effect of a difficult birth. Compared with nonassisted calves, veterinary-assisted calves showed a loss of 710 kg in accumulated 305-d milk yield, which was significant from 129 to 261 d in milk. This suggests that from birth to production, physiological effects of a bad calving are not negated. Results furthermore suggest a beneficial effect of farmer assistance at calving on the milk yield of both dam and calf, when moderate difficulties occurred., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to describe results of a genome-wide map of single nucleotide polymorphisms (SNP) and assess the linkage disequilibrium (LD) level in 2 divergent selection lines of dairy cows. DNA extracted from 299 Holstein cows was used to determine genotypes in 54,001 SNP loci using the BovineSNP50 array (Illumina Inc., San Diego, CA). Animals were from 2 genetic lines (166 genetically selected for fat and protein yield vs. 133 controls) raised on an experimental farm. Data edits removed loci with a major allele frequency greater than 0.95, genotypes in fewer than 100 cows, and missing valid chromosomal assignment or position. After edits, 41,859 loci (77.5% of the original total) were kept for further analysis. Linkage disequilibrium (LD) values were calculated for all possible syntenic SNP locus pairs located within intervals of 1 million base pairs, as the squared correlation between alleles. Pairwise haplotypes were determined using parsimony. Linkage disequilibrium was calculated for all animals and then for each genetic line separately. The average LD calculated across all chromosomes was 0.069, 0.071, and 0.075 for all, control, and select line cows, respectively. Genetic line had a statistically significant effect on LD. Of all locus pairs studied, 53,487 to 95,279 (depending on the data set) were in LD >0.30, which may be considered the minimum useful for mapping purposes and genomic selection. Useful LD was mostly found between adjacent pairs located within 30,000 to 50,000 bases. A few locus pairs (844-1,070 in the 3 data sets) were found in almost perfect (>0.99) LD. The overall product-moment correlation of LD values between the control and select lines was 0.79 (significantly different from 1), ranging from 0.71 to 0.84 for different chromosomes. Looking at this correlation by SNP pair distance revealed that persistence of LD phase across the 2 lines extended chiefly for 200,000 bases. Selection is likely to have affected LD in the studied cow population. These results may be useful to gene detection and genome-wide association studies., (2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Two body condition scoring systems were compared for assessing body condition of cows at the Scottish Agricultural College's Crichton Royal Farm. The weekly body condition scores (BCS) were collected for a period of 12 weeks (5 September-21 November). Scores were obtained using the primary systems utilized within the UK and USA. The USBCS were obtained by the same evaluator each week, while the UKBCS were obtained by two different evaluators alternating between weeks. Paired scores (n=2088) between the two systems within week were moderately correlated (r=0.75, P<0.0001). Regression equations to convert scores between the two systems were created using the GLM procedure of SAS (SAS Institute Inc., Cary NC, USA). The simple GLM models to convert from UK to US scores and US to UK scores were USBCS=1.182+0.816 * UKBCS (R2=0.56) and UKBCS=0.131+0.681 (R2=0.56), respectively. These equations may be used to interpret scores within the literature obtained using these two BCS systems, although they must be used with caution.

The objective of this study was to examine whether there was genotype by environment interaction (G x E) for female fertility traits for various environmental descriptors used in the United Kingdom. Records on days to first service (DFS), nonreturn rate at 56 d (NR56), calving interval (CI), and number of inseminations (NINS) on approximately 200,000 first-lactation Holstein cows in 3,192 herds and from 1,147 sires were analyzed using both random regression reaction norm models and multiple-trait models. The environmental descriptors were herd averages of DFS, CI, and NR56, and herd production and intensity indices, the latter based on 305-d milk, fat, and protein yields, age at first calving, temperature, and rainfall. No GxE was found for NR56 and NINS. There was an indication of G x E for DFS and CI with respect to the herd average of that trait, but only from the reaction norm model. Genotype by environment interaction existed for DFS and CI with respect to both production and intensity indexes-genetic correlations between top and bottom quartiles ranged from 0.73 to 0.84, with similar results from both models, indicating reranking of bulls. Part of this G x E might be explained by low production and intensity herds also having more concentrated spring calving.

The recent decline in dairy cow fertility appears to be a feature of several countries and is often linked to increased milk production, but its causes are not always obvious. A fully recorded 200-cow dairy herd, split into 2 genetic lines maintained on 2 production systems, was used to investigate the relationship between several measures of fertility, production, and energy balance. The 2 genetic lines were composed of a selection line, derived from the highest genetic merit bulls available, and a control line, maintained at the average of UK genetic merit at the time of mating. The production systems were a high-concentrate and a high-forage system. Thrice-weekly milk progesterone samples allowed an objective measurement of luteal cycling activity, and farm observations of estrus, services, and calving provided data on various measures of fertility. Energy balance in early lactation was calculated from daily live weight and weekly BCS measurements. Control line cows commenced luteal activity (C-LA) 6 d before selection line cows, had their first heat 14 d earlier, and had longer gestation periods by 3.7 d. They also had a lower incidence of silent heats. Cows on the high-forage system commenced luteal activity 6 d before those on the high-concentrate system, had longer gestation intervals by 3.9 d, held to first service better, had longer luteal phases and shorter interluteal periods in their estrus cycles. Characteristics of energy balance were used to see if they could account for the fertility differences between both genetic lines and systems. The commencement of luteal activity and day of first heat were analyzed using a REML mixed model approach. Mean energy content and mean energy balance over the first 25 d of lactation had an effect on C-LA and accounted for the differences found between production systems but not genetic lines. Day of energy balance nadir, mean energy content in the first 25 d, and C-LA affected day of first heat, but the differences between genetic lines were still apparent. These results suggest a link between high performance and reduced dairy cow fertility; high performance originating from different feeding systems was largely due to differences in energy balance, whereas those originating from genetics remained when energy balance characteristics were taken into account. This suggests a real genetic change in fertility due to selection for high genetic merit.

Body condition scoring, an indirect measure of the level of subcutaneous fat in dairy cattle, has been widely adopted for research and field assessment or for management purposes on farms. The feasibility of utilizing digital images to determine body condition score (BCS) was assessed for lactating dairy cows at the Scottish Agricultural College Crichton Royal Farm. Two measures of BCS were obtained by using the primary systems utilized in the United Kingdom (UK-BCS) and the United States (USBCS). Means were 2.12 (+/-0.35) and 2.89 (+/-0.40), modes were 2.25 and 2.75, and ranges were 1.0 to 3.5 and 1.5 to 4.5 for the UKBCS (n = 2,346) and USBCS (n = 2,571), respectively. Up to 23 anatomical points were manually identified on images captured automatically as cows passed through a weigh station. Points around the hooks were easier to identify on images than points around pins and the tailhead. All identifiable points were used to define and formulate measures describing the cow's contour. For both BCS systems, hook angle, posterior hook angle, and tailhead depression were significant predictors of BCS. When the full data set testing only the angles around the hooks was used, 100% of predicted BCS were within 0.50 points of actual USBCS and 92.79% were within 0.25 points; and 99.87% of predicted BCS were within 0.50 points of actual UKBCS and 89.95% were within 0.25 points. In a reduced data set considering only observations in which the tailhead depression angle was available, adding the tailhead depression to models did not improve model predictions. The relationships of the calculated angles with USBCS were stronger than those with UKBCS. This research demonstrates the potential for using digital images for assessing BCS. Future efforts should explore ways to automate this process by using a larger number of animals to predict scores accurately for cows across all levels of body condition.

The impact of 9 single nucleotide polymorphisms (SNP) in the leptin (LEP), leptin receptor (LEPR), growth hormone receptor (GHR), and diacylglycerol acyltransferase (DGAT1) gene loci on daily milk production, feed intake, and feed conversion, and weekly measures of live weight, BCS, and body energy traits was evaluated using genetic and phenotypic data on 571 Holstein cows raised at the Langhill Dairy Cattle Research Center in Scotland. Six SNP were typed on the LEP gene and 1 on each of the other 3 loci. Of the 6 LEP SNP, 3 were in very high linkage disequilibrium, meaning there is little gain in typing all of them in the future. Seven LEP haplotypes were identified by parsimony-based analyses. Random-regression allele-substitution models were used to assess the impact of each SNP allele or haplotype on the traits of interest. Diacylglycerol acyltransferase had a significant effect on milk yield, whereas GHR significantly affected feed intake, feed conversion, and body energy traits. There was also evidence of dominance in allelic effects on milk yield and BCS. The LEP haplotype CCGTTT (corresponding to leptin SNP C207T, C528T, A1457G, C963T, A252T, and C305T, respectively) significantly affected milk yield and feed and dry matter intake. Animals carrying this haplotype produced 3.13 kg more milk daily and consumed 4.64 kg more feed. Furthermore, they tended to preserve more energy than average. Such results may be used to facilitate genetic selection in animal breeding programs.

The aim of this study was to simplify the description of body tissue mobilization during first lactation into 3 parameters for which breeding values can be estimated. The traits defined were 1) the rate at which a bull's daughters lost body energy early in lactation, 2) the maximum drop in body energy content, and 3) the rate at which a bull's daughters regained body energy after nadir. A theoretical framework was developed to derive economic values for the 3 traits of body tissue mobilization. The UK population parameters were used in the economic framework to derive economic weights for body tissue mobilization during lactation. Assuming the average calving pattern in the UK, the economic value for the maximum drop in body energy content is small and negative at -0.14 pence (p/MJ). The economic value for the rate of loss of body energy in early lactation is also negative (-3.1 p/MJ) and for the rate of body energy gain in later lactation is positive (19.7 p/MJ). Results showed that the economic values for the 3 traits of body tissue mobilization change dramatically depending on the date of calving due to changes in feed costs in different seasons. The framework was developed to accommodate various system parameters (e.g., the costs of differing energy sources throughout the year and calving pattern), thus allowing for farm-system-specific economic values for body tissue mobilization. The framework provides a method to select sires based on an index of body energy mobilization of their daughters.

In this study, maternal effects were described as age of dam at first and second calving, first-lactation body condition score (BCS) of the dam during gestation, and milk yield of the dam. The impact of these effects on first-lactation daughter BCS, fertility, and test-day milk yield was assessed. The effect of milk yield of dam on daughter 305-d yield in the latter's first 3 lactations was also investigated. The proportion of total phenotypic variance in daughter traits accounted for by maternal effects was calculated. Dams calving early for the first time (18 to 23 mo of age) had daughters that produced 4.5% more first-lactation daily milk, had 7% higher BCS, and had their first service 3 d earlier than cows whose dams calved late (30 to 36 mo). However, daughters of dams that calved early had difficulties conceiving as they needed 7% more inseminations and had a 7.5% higher return rate. Cows from second calvings of relatively young (36 to 41 mo) dams produced 6% more first-lactation daily milk, had 2% higher BCS, and showed a significantly better fertility profile than cows whose dams calved at a late age (47 to 55 mo). High maternal BCS during gestation had a favorable effect on daughter BCS, nonreturn rate, and number of inseminations per conception. However, it was also associated with a small decrease in daughter daily milk yield. Changes in dam BCS during gestation did not affect daughter performance significantly. Maternal effects of milk yield of the dam, expressed as her permanent environment during lactation, adversely affected daughter 305-d milk, fat, and protein yield. However, although the effect was significant, it was practically negligible (<0.3% of the mean). Finally, overall maternal effects accounted for a significant proportion of the total phenotypic variance of calving interval (1.4 +/- 0.6%) and nonreturn rate (1.1 +/- 0.5%).

Results from 4 studies were combined (representing a total of 500 lactations) to investigate the relationships between metabolic parameters and fertility in dairy cows. Information was collected on blood metabolic traits and body condition score at 1 to 2 wk prepartum and at 2, 4, and 7 wk postpartum. Fertility traits were days to commencement of luteal activity, days to first service, days to conception, and failure to conceive. Primiparous and multiparous cows were considered separately. Initial linear regression analyses were used to determine relationships among fertility, metabolic, and endocrine traits at each time point. All metabolic and endocrine traits significantly related to fertility were included in stepwise multiple regression analyses alone (model 1), including peak milk yield and interval to commencement of luteal activity (model 2), and with the further addition of dietary group (model 3). In multiparous cows, extended calving to conception intervals were associated prepartum with greater concentrations of leptin and lesser concentrations of nonesterified fatty acids and urea, and postpartum with reduced insulin-like growth factor-I at 2 wk, greater urea at 7 wk, and greater peak milk yield. In primiparous cows, extended calving to conception intervals were associated with more body condition and more urea prepartum, elevated urea postpartum, and more body condition loss by 7 wk. In conclusion, some metabolic measurements were associated with poorer fertility outcomes. Relationships between fertility and metabolic and endocrine traits varied both according to the lactation number of the cow and with the time relative to calving.

Daily breeding values for various body energy traits were estimated from nationally recorded linear type information using random regression techniques. These traits included predicted live weight, growth rate (GR), and several measures of body energy during first lactation. The relationship between the individual linear type traits, body energy traits, and other production and fitness traits was examined by estimating the approximate genetic correlations (and standard errors) from adjusted predicted transmitting ability correlations. The relationship between fitness and body traits was further examined by calculating the partial correlation between the pairs of traits at a constant milk yield. Daily sire solutions for type traits showed that there were differences in how cows changed body shape across lactation. On average, cows lost body energy in early lactation but regained it by the end of the lactation. However, there was large variation, with the daughters of some sires still in negative energy balance by the end of lactation. The estimates of genetic correlation of linear type traits with production and fitness traits agreed with previous linear point estimates. The fertility traits were correlated across lactation with all linear type traits studied and indicated that bigger animals tend to have poorer fertility. There was an unfavorable correlation between production and GR; therefore, higher producing cows were more likely to have lower GR early in first lactation. The results also showed that higher producing animals were likely to loose body energy during the peak of first lactation. This is mirrored in the relationship of early lactation energy balance with somatic cell count and longevity, indicating that peak lactation is a critical time in terms of metabolic pressures on the dairy cow.

Body weight (BW) observations on dairy cattle taken on average 35 times between birth and 1,000 d of life were used to estimate daily heritabilities and predict daily breeding values for both pregnancy-adjusted BW (PABW) and growth rate. Daily heritabilities for PABW were moderate to high, ranging from 0.41 (+/-0.027) to 0.82 (+/-0.041). Daily heritabilities for growth rate were high (>0.68 +/- 0.034). The genetic association between various health events, including mastitis and lameness, and weight and growth was investigated by regressing the incidence of health events on breeding values for weight at birth, weaning, calving, and growth rate at 56 d after calving, growth rate at 110 d after calving, and maximum growth rate. Growth at weaning was the only BW measure to significantly affect mastitis (r(g) = 0.24), indicating that cows growing faster at weaning are more prone to mastitis. Increased weight (r(g) = 0.65) and growth rate at weaning (r(g) = 0.38) and increased maximum growth rate (r(g) = 0.71) all contributed to increased feet disorders. The only significant negative genetic association was obtained between reproduction and weight at calving (r(g) = -0.61).

Weekly body condition score (BCS) and live weight records were used to calculate energy content (EC) and cumulative effective energy balance (CEEB) for 508 Holstein-Friesian cows in their first lactation. Cows were raised on an experimental farm and had calved between 1991 and 2000. Energy content was an estimate of the actual energy level of a cow at any given stage of lactation, whereas CEEB was associated with the total body energy content as defined by accumulated weekly energy balance changes since the onset of lactation. Genetic evaluations were computed for the 3 body energy traits (BCS, EC, and CEEB) for each week of first lactation. Random regression models were used to assess the association between first-lactation weekly genetic evaluations for body energy and monthly test-day log-transformed SCC, clinical mastitis, and other udder problems in the first 3 lactations. There was a significant effect of at least one body energy trait at any stage of first lactation past wk 3 on SCC in the first 3 lactations. Maximum genetic correlation estimates were -0.18 (+/-0.04) between wk-16 BCS and SCC in the first 2 lactations, -0.18 (+/-0.04) between wk-11 EC and SCC in the first 2 lactations, and -0.17 (+/-0.07) between wk-6 CEEB and SCC in the first 2 lactations. The effect of body energy traits on clinical mastitis was, in general, nonsignificant; nevertheless, moderate genetic correlations were estimated, ranging from -0.05 (+/-0.07) to -0.25 (+/-0.15). The effect of body energy traits on udder problems other than mastitis was negligible in all cases. Results suggest that, amongst the traits studied here, BCS, EC, and CEEB in the first 3 to 4 mo of lactation 1 had the greatest genetic association with SCC and mastitis in first, second, and, to a lesser extent, third lactations.

In general, genetic selection is applied after first calving to traits that manifest themselves during the animal's productive life, mostly during the early part of productive life. This selection policy has had undesirable correlated responses in other economically important traits, such as health and fertility, and may also have had an effect on the growth of animals both during productive life and before first calving. In this study, we analyzed the growth trajectory of dairy heifers that had been selected for maximum production of combined fat and protein (measured in kg; select line) or for average production (control line) in the United Kingdom. Before first calving, these divergent lines were managed as a single group. Select line heifers grew faster than did control line heifers. They were also heavier at first calving, but by the end of 3 lactations, the lines were not significantly different in live weight. Selection primarily for yield and for other traits has led to heifers that grow faster and reach higher growth rates earlier in life. A genetic analysis of birth, weaning, and calving weights yielded heritability estimates of 0.53 (birth weight), 0.45 (weaning weight), and 0.75 (calving weight). Confidence intervals for the genetic correlations between the traits indicated that these BW traits are not under the same genetic control.

Legendre polynomial and cubic spline functions were used in random regression models to model the change in body traits over the course of the first lactation for daughters of 954 sires. Both functions estimated similar genetic variances for d 50 to 250 across lactation for the majority of traits. The heritability of the traits was similar to other studies using univariate models as well as random regression models. There was little difference between the 2 functions in their predictive power for each of the body type traits, as measured by the absolute difference between the predicted and actual type traits and the proportion of the total phenotypic variance explained by the model. Overall, the Legendre polynomial appeared to model these traits slightly better. Plots of the fixed curves and daily sire solutions obtained from the random regression models showed that there were differences in how the traits and sires changed across lactation. The daily sire solutions were then used to predict differences in liveweight of sires' daughters across first lactation and showed that the daughters of some sires grew faster during first lactation than others. The spatial differences in the body traits that are displayed by this study could be an important indicator of the physical and biological changes that cows are undergoing in their first lactation. Information from these sire profiles could be harnessed to indicate production and functional traits later in life.

Weekly total body energy content (TBEC) was calculated for 444 Holstein cows in their first 3 lactations. These calculations were based on body lipid and protein changes predicted from weekly changes in body condition score and live weight of each cow. In first lactation, cows lost TBEC during the initial 8 wk, regained it by wk 22, and continued to build up their reserves until wk 37. Cows started lactations 2 and 3 with considerable reserves from the dry period that they used during the first 13 wk of lactation. Variance components for TBEC were estimated using random regression analysis allowing for heterogeneous residual variance. The genetic variance increased within each lactation, suggesting that the genetic component becomes more important as lactation progresses. The genetic correlations between very early (wk 1 to 4) and later stages of first lactation were near zero but they increased considerably between later lactation stages. Genetic correlations between TBEC on wk 5 of first lactation and the remainder of this lactation ranged from 0.64 for the more distant weeks to 0.99 for the immediately subsequent weeks. Genetic correlations with TBEC in second lactation were moderately high (0.68 to 0.70) for the early weeks (1 to 8) and decreased gradually to 0.56 for weeks at the end of lactation. For third lactation, these estimates ranged from 0.53 to 0.63. Genetic correlation estimates of TBEC in wk 12 of first lactation with subsequent first-lactation weeks varied from 0.79 to 0.99, whereas they ranged from 0.65 to 0.77 and from 0.57 to 0.68 in second and third lactations, respectively. The genetic correlation between TBEC in later weeks of first lactation and the rest of productive life increased as first lactation progressed, but the improvement diminished. Weekly genetic evaluations for first-lactation TBEC were used to predict second- and third-lactation energy content. The accuracy of these predictions increased with progressing weeks in first lactation, but about three-fourths of the improvement occurred by wk 5. Our results suggest that TBEC calculated after a month from the first calving may give useful information about the future energy content of a cow.

Daily energy balance was calculated for 111 Holstein cows in their first 3 lactations, based on combinations of smoothed preadjusted phenotypic records for milk yield, feed intake, live weight, and body condition score. Two energy balance traits were defined: one based on milk yield and feed intake (EB1) and the other on live weight and body condition score change (EB2). Bessel functions (BF), Legendre polynomials (LP), sinusoidal functions (SF), and cubic splines (CS) were used to model energy balance within and across lactations. Models with BF or LP fitted fixed regressions of order 1 to 6 and random regressions of order 1 to 10. Cubic splines were fitted at 5 to 30 equally spaced knot points. In within-lactation analyses with BF and LP models, likelihood ratio tests revealed that the fit improved significantly up to random regression order of 5 for EB1 and 4 for EB2, independently of the fixed regression order. For EB1 analyses with LP, improvement was marginal albeit significant even for higher random regression order. For CS models, optimal number of knot points was 13 and 12 for EB1 and EB2, respectively. Residual variance and comparisons between actual and predicted energy balance showed that LP of minimum order 8 and 5 modeled, respectively, EB1 and EB2 better than the other 3 functions. In across-lactation analyses with BF and LP models, likelihood ratio tests were significant as the random regression order increased, for any order of the fixed regression. For CS models, optimal number of knot points was 14 and 16 for EB1 and EB2, respectively. Residual variance and comparisons between actual and predicted energy balance showed that models fitting CS and high (>8) random order BF or LP provided the best fit to both traits. However, in an across-lactation analysis, even higher order of LP or BF will be required to provide as good a fit as within-lactation analyses.

Some dairy cattle breeders believe that dairy cows with high pin bones will have poorer fertility. The aim of this study was to examine this claim by investigating the relationships, both genetic and phenotypic, between fertility, rump angle, and other selected type traits in first-lactation Holstein-Friesians. Results showed an unfavorable genetic correlation (-0.16) between calving interval and rump angle, suggesting that animals with high pin bones would have a longer calving interval. However, no significant genetic or phenotypic correlation between days to first service and nonreturn rate and rump angle was observed. No evidence of a relationship, linear or quadratic, between any fertility trait and rump angle was found. Udder support and mammary system were unfavorably correlated to calving interval (0.25 and 0.14, respectively), suggesting that cows with more functional udders would have a longer calving interval. Legs and feet score was favorably correlated to nonreturn rate, suggesting that animals with good legs and feet would be less likely to return to service.

The effects of inbreeding, heterosis, recombination loss, and percentage Holstein on the estimation of predicted transmitting abilities for fertility traits (calving interval, number of days from calving to first insemination, nonreturn rate, number of inseminations) and correlated traits (milk yield at test nearest d 110 and body condition score) were examined in a mixed population of Holstein and Friesian cattle. An unfavorable effect of percentage Holstein on calving interval was observed, resulting in a 12-d increase for pure Holsteins compared with pure Friesians. Insemination traits were less affected by percentage Holstein, with 3% more animals returning to first service within 56 d and 0.1 more inseminations required for Holstein animals. Heterosis and recombination loss affected some of the traits. Heterosis had a favorable effect on yield, with a 0.35-kg difference between a pure and cross-bred animal for test milk. There was a reduction of 1 d to first insemination between a pure and first-crossbred animal. Inbreeding had a significant and unfavorable effect on all traits. The difference between a noninbred animal and an animal with an inbreeding coefficient of 10% was a 2.8-d increase in calving interval, a 1.7-d increase in days to first insemination, a 1% increased probability to return to estrus at first service, 0.03 more inseminations, a 0.27-unit decrease in body condition, and a 0.54-kg decrease in milk on test nearest d 110. The effect of inbreeding depression was more pronounced at higher levels of inbreeding. The rank correlations between the predicted transmitting abilities for fertility and correlated traits, with and without the additional nonadditive effects in the model, were over 0.99. Steps should be taken to control the rise in inbreeding, or the effects on fertility and correlated traits such as milk production will begin to manifest themselves.

Dairy cows make strategic use of body energy to support early lactation and replenish this lost energy later in lactation, thereby creating body energy profiles that vary both within lactation and across lactations. The interaction between genotype and diet energy content is interesting from a management viewpoint and from a cow survival viewpoint. In this study, we modeled energy balance over 3 lactations using a multivariate random regression model, for cows from the Langhill Dairy Cattle Research Centre. This herd has been selected for maximum production or to remain at the UK national average for production (control group) and has been fed 2 diets of different energy density (high or low concentrate). Cows in the various groups differed in body condition score and the way they lost and regained body lipid. Cows selected for maximum production on a low-concentrate diet lost the greatest amount of body lipid (0.54 condition score units) after 3 lactations. Cows selected for maximum production lost more than control cows on either diet, indicating that selection mainly for yield has predisposed cows to utilize body energy to support lactation on diets spanning the range used here. Cows selected for maximum production were heavier at first calving than control cows but lost more weight and regained more weight so that at the end of each lactation, cows in the 2 groups did not differ significantly in weight. Cows use body lipid to support lactation over 3 lactations and the profile of that use varies according to genotype and diet.

Important increases in the rates of inbreeding have recently been observed in dairy cattle populations, and methods have been proposed to address these increases. The aims of this study were to estimate the current level and rates of inbreeding in the UK Holstein population and to investigate the potential of applying optimized selection to manage the rates of inbreeding. Inbreeding coefficients were calculated for the entire UK Holstein population using 1940 as the base year. Rates of inbreeding were obtained for 3 time periods by regressing mean inbreeding coefficients on the year of birth of the animals. The expected average pedigree index and expected inbreeding of offspring using optimized contributions for a given set of selection candidates was compared to the expected pedigree index and inbreeding of offspring for the same set of selection candidates using observed contributions. The rate of inbreeding in the UK Holstein population has increased substantially since 1990 when compared to previous time periods. This increase is most likely due to the large influence of a few related sires on the breed in the mid- to late 1980s. The introduction of the individual animal model in the early 1990s may also have contributed to increased inbreeding. Optimized selection appears to represent a promising selection tool, not only to manage rates of inbreeding, but also to increase genetic gain at the same rate of inbreeding.

Body condition score (BCS) records of primiparous Holstein cows were analyzed both as a single measure per animal and as repeated measures per sire of cow. The former resulted in a single, average, genetic evaluation for each sire, and the latter resulted in separate genetic evaluations per day of lactation. Repeated measure analysis yielded genetic correlations of less than unity between days of lactation, suggesting that BCS may not be the same trait across lactation. Differences between daily genetic evaluations on d 10 or 30 and subsequent daily evaluations were used to assess BCS change at different stages of lactation. Genetic evaluations for BCS level or change were used to estimate genetic correlations between BCS measures and fertility traits in order to assess the capacity of BCS to predict fertility. Genetic correlation estimates with calving interval and non-return rate were consistently higher for daily BCS than single measure BCS evaluations, but results were not always statistically different. Genetic correlations between BCS change and fertility traits were not significantly different from zero. The product of the accuracy of BCS evaluations with their genetic correlation with the UK fertility index, comprising calving interval and non-return rate, was consistently higher for daily than for single BCS evaluations, by 28 to 53%. This product is associated with the conceptual correlated response in fertility from BCS selection and was highest for early (d 10 to 75) evaluations.

Poor fertility has become a major reason for involuntary culling of dairy cows in the United Kingdom. Calving interval (CI) and body condition score (BCS) are recorded, heritable, genetically correlated with each other, and could be used to extend the scope of dairy indices to include fertility traits. The use of U.K. insemination information for the evaluation of fertility has not been examined previously. Fertility and correlated traits were examined using nationally recorded milk (MILK = daily milk yield at test nearest d 110), BSC, and fertility traits (CI and the insemination traits of nonreturn rate after 56 d, NR56; days to first service, DFS; and number of inseminations per conception, INS). Genetic parameters for the traits were estimated simultaneously with a multitrait sire maternal grandsire (MGS) model and a multitrait BLUP sire MGS model was used to predict sire predicted transmitting abilities for each trait. The relationship between the fertility traits and other predicted transmitting abilities calculated in the United Kingdom was then examined. Heritabilities for the fertility traits were CI = 0.033 +/- 0.01, DFS = 0.037 +/- 0.01, NR56 = 0.018 +/- 0.001, and INS = 0.020 +/- 0.001, with a genetic correlation of 0.671 +/- 0.063 between CI and DFS and -0.939 +/- 0.031 between NR56 and INS. There was an unfavorable genetic correlation between the fertility traits and milk yield and BCS. Predicted transmitting abilities produced are similar in size and range to those produced in other studies and genetic trends are as expected. Results to date are encouraging and suggest that the planned program of work will lead to a fertility index that, when used by breeding companies, will lead to improvements in national dairy cow fertility.

The difference in body lipid between the start and end of lactation represents the body energy lost (or gained) in support of maintaining lactation including the nonproduction components of lactation. This source of energy is ignored in current genetic evaluations for production for dairy sires. The depletion and accretion of body tissue creates a pattern of body energy content over time that is, in part, under genetic control. Using random regression and field data, we modeled changes in body condition score (BCS) and liveweight, predicted from linear type traits, on first parity cows to produce daily breeding values of their sires for energy balance. These curves show that sires differ in the way their daughters lose and regain body energy throughout lactation. For all sires, the overall mean maximum daughter body energy loss was 1499 MJ (SD = 144 MJ) and occurred at d 99 (SD = 12.8 d) of lactation and the mean total daughter body energy loss at d 305 of lactation was 779 MJ (SD = 224 MJ). In this study, the profiles of body energy loss indicate that daughters of most sires lost body energy before d 150 and then recovered body energy, whereas the daughters of a few sires continued to lose body energy through to the end of lactation. Some sires with high merit for production may have daughters with body tissue mobilization profiles associated with poorer health and fertility leading to higher costs. A method of accounting for this cost could be to correct yield for body tissue mobilization. Deducting kilograms of milk from the breeding value for milk for each sire, equivalent in energy content to the body energy lost, resulted in a correlation of 0.98 between the ranking of sires for milk kilograms before and after adjustment. However, some sires changed rank bylarge amounts, the largest being +355 positions. Breeding values for energy balance can be calculated from single observations of BCS and linear type traits on daughters of a sire; data that can routinely be collected in national conformation assessment schemes.

Daily animal solutions were predicted using random regression analysis for feed intake, milk yield, live weight, and condition score recorded on 189 cows at the Langhill Dairy Cattle Research Centre. All cows had three successive lactations. Energy balance for days 1 to 305 of each of the three lactations was calculated both from daily measures of feed intake and milk output and from weekly measures of live weight and condition score. Cows returned to positive energy balance at days 72, 75, and 95 in lactations 1, 2, and 3, respectively, based on energy balance calculated from feed intake and milk output records (EB1), and at days 77, 83, and 73 based on energy balance calculated from body energy state changes (EB2). Correlations between energy balance at the same time in successive lactations ranged from 0.01 to 0.66 depending on the method of calculation and the stage of lactation. Energy balance over three lactations was modelled using sinusoidal functions which were associated with individual cows and allowed to vary between cows. The parameters of these curves are potentially useful since they have a biological interpretation. The phase relates to the period from calving to return to positive energy balance, and the amplitude relates to the degree of body energy loss (and recovery). The sinusoidal functions fitted to the curve removed a significant proportion of the variation, but accounted for only 45% and 40% of the variation in EB1 and EB2, respectively. The relationship between energy balance in the first three lactations is likely to be more complex than a simple linear function, but the profile of energy balance over the first three lactations may be a useful selection criteria in a multi-trait index. Energy balance profile over lactations one to three can be modelled with moderate accuracy using sinusoidal functions, and this warrants further research.

Body condition score (BCS) is a useful tool in assessing the energy status of dairy cattle. Previous research has shown that it is heritable and genetically correlated to reproductive performance. Currently, interest exists in developing selection indexes for fertility that include BCS information. Before such indexes are developed, it is important to assess the genetic covariance between BCS and fertility after fully accounting for the covariance of both traits with milk yield, as indices to predict selection responses require knowledge of these (co)variances. In the present study, calving interval (CI) was used as a measure of reproductive performance. The genetic correlations between BCS and CI before and after genetically adjusting for milk yield were -0.48 and -0.22, respectively. Thus, cows with low BCS have longer CI, which is exacerbated by high levels of milk production. Using selection index theory, we showed that selecting for milk yield alone will result in an increase of 768 kg of milk, an increase of 4.46 d in CI and a reduction of 0.41 BCS units for every standard deviation change in the index. Restricting BCS to no genetic change, whereas still selecting for milk yield will result in an increase of 653.1 kg of milk per standard deviation of the selection index. However, CI will still continue to increase at a rate of 3.20 d per standard deviation of the selection index. The selection indices used here are not optimum, in that they are not economically driven and do not consider all traits that contribute to profitability. However, they demonstrate that, even though restricting BCS may be seen as an attractive way of limiting reliance of body tissue mobilization to fuel milk production, this is unlikely to result in improvements in CI, although the rate of increase in CI will be reduced.

The aim of this study was to investigate the relationship between measures of body condition score collected from calving until wk 26 of lactation and reproductive measures (calving interval, days to first heat, days to first service, and conception at first service). Since 1973 sires of cows at the Langhill Dairy Cattle Research Centre have been selected for either high (selection line) or average (control line) genetic merit for fat plus protein. The data included 1211 records from 534 cows calving from 1988 to 1999. At first calving, cows were randomly assigned to one of two ad libitum diets: one that was relatively high in concentrates (approximately 3000 kg/yr) and one that was relatively low in concentrates (approximately 1500 kg/yr). Selection line cows were on average thinner and lost more condition in early lactation than control line cows. Cows that lost condition, those that were thinner than average at wk 10 of lactation and those that were thinner on average over the first 10 wk, had poorer reproductive performance. This effect was greatest in the selection line. Line x diet interaction effects were not statistically significant. Genetic correlations between body condition score and reproductive measures were unfavorable and ranged from -0.04 to -0.54. The relationship between body condition score and production was strong, but, even after adjusting for yield, an unfavorable relationship still exists between body condition score and fertility. Body condition score could be used as a management and selection tool to improve reproductive performance.

The trend to poorer fertility in dairy cattle with rising genetic merit for production over the last decade suggests that breeding goals need to be broadened to include fertility. This requires reliable estimates of genetic (co)variances for fertility and other traits of economic importance. In the United Kingdom at present, reliable information on calving dates and hence calving intervals are available for most dairy cows. Data in this study consisted of 44,672 records from first lactation heifers on condition score, linear type score, and management traits in addition to 19,042 calving interval records. Animal model REML was used to estimate (co)variance components. Genetic correlations of body condition score (BCS) and angularity with calving interval were -0.40 and 0.47, respectively, thus cows that are thinner and more angular have longer calving intervals. Genetic correlations between calving interval and milk, fat, and protein yields were between 0.56 and 0.61. Records of phenotypic calving interval were regressed on sire breeding values for BCS estimated from records taken at different months of lactation and breeding values for BCS change. Genetic correlations inferred from these regressions showed that BCS recorded 1 mo after calving had the largest genetic correlation with calving interval in first lactation cows. It may be possible to combine information on calving interval, BCS, and angularity into an index to predict genetic merit for fertility.