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peer-reviewed Genetic evaluations decompose an observed phenotype into its genetic and nongenetic components; the former are termed BLUP with the solutions for the systematic environmental effects in the statistical model termed best linear unbiased estimates (BLUE). Geneticists predominantly focus on the BLUP and rarely consider the BLUE. The objective of this study, however, was to define and quantify the association between 8 herd-level characteristics and BLUE for 6 traits in dairy herds, namely (1) age at first calving, (2) calving to first service interval (CFS), (3) number of services, (4) calving interval (CIV), (5) survival, and (6) milk yield. Phenotypic data along with the fixed and random effects solutions were generated from the Irish national multi-breed dairy cow fertility genetic evaluations on 3,445,557 cows; BLUE for individual contemporary groups were collapsed into mean herd-year estimates. Data from 5,707 spring-calving herds between the years 2007 and 2016 inclusive were retained; association analyses were undertaken using linear mixed multiple regression models. Pearson coefficient correlations were used to quantify the relationships among individual trait herd-year BLUE, and transition matrices were used to understand the dynamics of mean herd BLUE estimates over years. Based on the mean annual trends in raw, BLUP, and BLUE, it was estimated that BLUE were associated with at least two-thirds of the improvement in CIV and milk production over the past 10 yr. Milk recording herds calved heifers for the first time on average 15 d younger, had an almost 2 d longer CFS but 2.3 d shorter CIV than non-milk-recording herds. Larger herd sizes were associated with worse BLUE for both CFS and CIV. Expanding herds and herds that had the highest proportion of cows born on the farm itself, on average, calved heifers younger and had shorter CIV. By separating the raw performance of a selection of herds into their respective BLUE and BLUP, it was possible to identify herds with inferior management practices that were being compensated by superior genetics; similarly, herds were identified with superior BLUE, but because of their inferior genetic merit, were not reaching their full potential. This suggests that BLUE could have a pivotal role in a tailored decision support tool that would enable producers to focus on the most limiting factor hindering them from achieving their maximum performance.

peer-reviewed Bovine herpesvirus-1 (BoHV-1) is a viral pathogen of global significance that is known to instigate several diseases in cattle, the most notable of which include infectious bovine rhinotracheitis and bovine respiratory disease. The genetic variability in the humoral immune response to BoHV-1 has, to our knowledge, not ever been quantified. Therefore, the objectives of the present study were to estimate the genetic parameters for the humoral immune response to BoHV-1 in Irish female dairy cattle, as well as to investigate the genetic relationship between the humoral immune response to BoHV-1 with milk production performance, fertility performance, and animal mortality. Information on antibody response to BoHV-1 was available to the present study from 2 BoHV-1 sero-prevalence research studies conducted between the years 2010 to 2015, inclusive; after edits, BoHV-1 antibody test results were available on a total of 7,501 female cattle from 58 dairy herds. National records of milk production (i.e., 305-d milk yield, fat yield, protein yield, and somatic cell score; n = 1,211,905 milk-recorded cows), fertility performance (i.e., calving performance, pregnancy diagnosis, and insemination data; n = 2,365,657 cows) together with animal mortality data (i.e., birth, farm movement, death, slaughter, and export events; n = 12,853,257 animals) were also available. Animal linear mixed models were used to quantify variance components for BoHV-1 as well as to estimate genetic correlations among traits. The estimated genetic parameters for the humoral immune response to BoHV-1 in the present study (i.e., heritability range: 0.09 to 0.16) were similar to estimates previously reported for clinical signs of bovine respiratory disease in dairy and beef cattle (i.e., heritability range: 0.05 to 0.11). Results from the present study suggest that breeding for resistance to BoHV-1 infection could reduce the incidence of respiratory disease in cattle while having little or no effect on genetic selection for milk yield or milk constituents (i.e., genetic correlations ranged from −0.13 to 0.17). Moreover, even though standard errors were large, results also suggest that breeding for resistance to BoHV-1 infection may indirectly improve fertility performance while also reducing the incidence of mortality in older animals (i.e., animals >182 d of age). Results can be used to inform breeding programs of potential genetic gains achievable for resistance to BoHV-1 infection in cattle. Funding from the Irish Department of Agriculture, Food and the Marine STIMULUS research grant HealthyGenes (Dublin)

Linear type traits describing the skeletal, muscular, and functional characteristics of an animal are routinely scored on live animals in both the dairy and beef cattle industries. Previous studies have demonstrated that genetic parameters for certain performance traits may differ between breeds; no study, however, has attempted to determine if differences exist in genetic parameters of linear type traits among breeds or sexes. Therefore, the objective of the present study was to determine if genetic covariance components for linear type traits differed among five contrasting cattle breeds, and to also investigate if these components differed by sex. A total of 18 linear type traits scored on 3,356 Angus (AA), 31,049 Charolais (CH), 3,004 Hereford (HE), 35,159 Limousin (LM), and 8,632 Simmental (SI) were used in the analysis. Data were analyzed using animal linear mixed models which included the fixed effects of sex of the animal (except in the investigation into the presence of sexual dimorphism), age at scoring, parity of the dam, and contemporary group of herd-date of scoring. Differences (P < 0.05) in heritability estimates, between at least two breeds, existed for 13 out of 18 linear type traits. Differences (P < 0.05) also existed between the pairwise within-breed genetic correlations among the linear type traits. Overall, the linear type traits in the continental breeds (i.e., CH, LM, SI) tended to have similar heritability estimates to each other as well as similar genetic correlations among the same pairwise traits, as did the traits in the British breeds (i.e., AA, HE). The correlation between a linear function of breeding values computed conditional on covariance parameters estimated from the CH breed with a linear function of breeding values computed conditional on covariance parameters estimated from the other breeds was estimated. Replacing the genetic covariance components estimated in the CH breed with those of the LM had least effect but the impact was considerable when the genetic covariance components of the AA were used. Genetic correlations between the same linear type traits in the two sexes were all close to unity (≥0.90) suggesting little advantage in considering these as separate traits for males and females. Results for the present study indicate the potential increase in accuracy of estimated breeding value prediction from considering, at least, the British breed traits separate to continental breed traits.

Little is known of the genetic variability in semen quality traits in cattle and their inter-relationships. The objective of the present study was to estimate genetic parameters for a range of semen quality measures. The data consisted of 35,573 ejaculates from 787 artificial insemination bulls of 16 breeds. Genetic parameters were estimated using a repeatability animal linear mixed model. Large breed differences were detected with Belgian Blue bulls, on average, producing lesser semen volume than all other breeds while the Charolais bulls, on average, produced semen with fewer live sperm and reduced motility. The within-breed coefficient of genetic variation for sperm concentration, semen volume and total number of sperm per ejaculate was 0.17, 0.15 and 0.19, respectively. The genetic standard deviation for percentage live sperm pre-cryopreservation was 5.6% units while the genetic standard deviation for progressive motility pre-cryopreservation (scale 0 to 5) was 0.25 units. The heritability of all traits was between 0.13 and 0.34. The repeatability of the semen quality traits varied from 0.22 to 0.45. Sperm concentration and volume were negatively genetically correlated (-0.40) although the phenotypic correlation was near zero (-0.01). The genetic correlations between percentage live sperm and sperm motility varied from 0.68 to 0.94 irrespective of whether the traits were measured pre- or post-cryopreservation or even the change in both traits during cryopreservation. A very strong genetic correlation existed between percentage live sperm pre- and post-cryopreservation (0.96) or sperm motility pre- or post-cryopreservation (0.92). Results highlight the large genetic variability in a range of semen quality traits, many of which are actually highly heritable, and therefore useful predictors of actual phenotypic measures.

The objective of the present study was to quantify the relationship between udder skin surface temperature (USST) and somatic cell count (SCC) in lactating dairy cows. Data were recorded on the same 14 Holstein-Friesian cows, at evening (15:00 to 16:00) milking every day over a 2-mo period. Surface temperature measurements of all udders were extracted from thermal images. After imaging, milk was extracted from each quarter and analyzed for SCC. Environmental and cow-related factors (i.e., ambient temperature, humidity, rainfall, wind speed, distance walked to the parlor, number of days since the udder was shaved, parity, and stage of lactation) were recorded on each day of the experiment. A large array of descriptive temperature parameters (DTP) were extracted from every udder image including temperature-based (e.g., maximum, average and minimum USST), pixel count-based, and textural-based DTPs. Several different analytical methods were tested in an attempt to relate any given DTP to SCC; this included investigating the relationship between USST and the log transform of SCC (i.e., somatic cell score; SCS). The temperature range within each udder was also compared with the natural log of the range in SCC of the respective quarters. In a separate analysis, the temperature difference between each DTP and its respective daily baseline (i.e., average of the 5 lowest values of that DTP across the herd) was compared with SCS. Finally, the association between environmental and cow-related factors with each DTP was investigated to create prediction models for each DTP, the residuals of which were compared with SCC. Results from the present study indicate that the correlation between any DTP and SCS was weak (range of −0.16 to 0.19) and so could not be used to identify quarters with high SCC. Although some alternative measures had a significant relationship with SCS, again, the correlation was too weak for practical use on its own. Maximum and average USST could be predicted with a root mean square error of 0.23 and 0.35 °C, respectively, although the residuals from the prediction model could not be used to identify animals with high SCC. This suggests that infrared thermography alone could not be used as a real-time automated tool to detect high SCC for dairy cows in a pasture-based system.

Early detection of karyotype abnormalities, including aneuploidy, could aid producers in identifying animals which, for example, would\ud not be suitable candidate parents. Genome-wide genetic marker data in the form of single nucleotide polymorphisms (SNPs) are now\ud being routinely generated on animals. The objective of the present study was to describe the statistics that could be generated from the\ud allele intensity values from such SNP data to diagnose karyotype abnormalities; of particular interest was whether detection of\ud aneuploidy was possible with both commonly used genotyping platforms in agricultural species, namely the Applied BiosystemsTM\ud AxiomTM and the Illumina platform. The hypothesis was tested using a case study of a set of dizygotic X-chromosome monosomy 53,X\ud sheep twins. Genome-wide SNP data were available from the Illumina platform (11 082 autosomal and 191 X-chromosome SNPs) on\ud 1848 male and 8954 female sheep and available from the AxiomTM platform (11 128 autosomal and 68 X-chromosome SNPs) on\ud 383 female sheep. Genotype allele intensity values, either as their original raw values or transformed to logarithm intensity ratio (LRR),\ud were used to accurately diagnose two dizygotic (i.e. fraternal) twin 53,X sheep, both of which received their single X chromosome from\ud their sire. This is the first reported case of 53,X dizygotic twins in any species. Relative to the X-chromosome SNP genotype mean allele\ud intensity values of normal females, the mean allele intensity value of SNP genotypes on the X chromosome of the two females\ud monosomic for the X chromosome was 7.45 to 12.4 standard deviations less, and were easily detectable using either the AxiomTM or\ud Illumina genotype platform; the next lowest mean allele intensity value of a female was 4.71 or 3.3 standard deviations less than the\ud population mean depending on the platform used. Both 53,X females could also be detected based on the genotype LRR although this\ud was more easily detectable when comparing the mean LRR of the X chromosome of each female to the mean LRR of their respective\ud autosomes. On autopsy, the ovaries of the two sheep were small for their age and evidence of prior ovulation was not appreciated. In\ud both sheep, the density of primordial follicles in the ovarian cortex was lower than normally found in ovine ovaries and primary follicle\ud development was not observed. Mammary gland development was very limited. Results substantiate previous studies in other species\ud that aneuploidy can be readily detected using SNP genotype allele intensity values generally already available, and the approach\ud proposed in the present study was agnostic to genotype platform.

It is anticipated that in the future, livestock will be exposed to a greater risk of infection from parasitic diseases. Therefore, future breeding strategies for livestock, which are generally long-term strategies for change, should target animals adaptable to environments with a high parasitic load. Covariance components were estimated in the present study for a selection of dairy and beef performance traits over herd-years differing in Fasciola hepatica load using random regression sire models. Herd-year prevalence of F. hepatica was determined by using F. hepatica-damaged liver phenotypes which were recorded in abattoirs nationally. The data analyzed consisted up to 83,821 lactation records from dairy cows for a range of milk production and fertility traits, as well as 105,054 young animals with carcass-related information obtained at slaughter. Reaction norms for individual sires were derived from the random regression coefficients. The heritability and additive genetic standard deviations for all traits analyzed remained relatively constant as herd-year F. hepatica prevalence gradient increased up to a prevalence level of 0.7; although there was a large increase in heritability and additive genetic standard deviation for milk and fertility traits in the observed F. hepatica prevalence levels >0.7, only 5% of the data existed in herd-year prevalence levels >0.7. Very little rescaling, therefore, exists across differing herd-year F. hepatica prevalence levels. Within-trait genetic correlations among the performance traits across different herd-year F. hepatica prevalence levels were less than unity for all traits. Nevertheless, within-trait genetic correlations for milk production and carcass traits were all >0.8 for F. hepatica prevalence levels between 0.2 and 0.8. The lowest estimate of within-trait genetic correlations for the different fertility traits ranged from −0.03 (SE = 1.09) in age of first calving to 0.54 (SE = 0.22) for calving to first service interval. Therefore, there was reranking of sires for fertility traits across different F. hepatica prevalence levels. In conclusion, there was little or no genetic variability in sensitivity to F. hepatica prevalence levels among cattle for milk production and carcass traits. But, some genetic variability in sensitivity among dairy cows did exist for fertility traits measured across herds differing in F. hepatica prevalence.

Parasitic diseases have economic consequences in cattle production systems. Although breeding for parasite resistance can complement current control practices to reduce the prevalence globally, there is little knowledge of the implications of such a strategy on other performance traits. Records on individual animal antibody responses to Fasciola hepatica, Ostertagia ostertagi, and Neospora caninum were available from cows in 68 dairy herds (study herds); national abattoir data on F. hepatica-damaged livers were also available from dairy and beef cattle. After data edits, 9,271 dairy cows remained in the study herd dataset, whereas 19,542 dairy cows and 68,048 young dairy and beef animals had a record for the presence or absence of F. hepatica-damaged liver in the national dataset. Milk, reproductive, and carcass phenotypes were also available for a proportion of these animals as well as their contemporaries. Linear mixed models were used to estimate variance components of antibody responses to the three parasites; covariance components were estimated between the parasite phenotypes and economically important traits. Heritability of antibody responses to the different parasites, when treated as a continuous trait, ranged from 0.07 (O. ostertagi) to 0.13 (F. hepatica), whereas the coefficient of genetic variation ranged from 4% (O. ostertagi) to 20% (F. hepatica). The antibody response to N. caninum was genetically correlated with the antibody response to both F. hepatica (-0.29) and O. ostertagi (-0.67); a moderately positive genetic correlation existed between the antibody response to F. hepatica and O. ostertagi (0.66). Genetic correlations between the parasite phenotypes and the milk production traits were all close to zero (-0.14 to 0.10), as were the genetic correlations between F. hepatica-damaged livers and the carcass traits of carcass weight, conformation, and fat score evaluated in cows and young animals (0.00 to 0.16). The genetic correlation between F. hepatica-damaged livers in cows and milk somatic cell score was 0.32 (SE = 0.20). Antibody responses to F. hepatica and O. ostertagi had favorable genetic correlations with fertility traits, but conversely, antibody response to N. caninum and F. hepatica-damaged livers were unfavorably genetically correlated with fertility. This study provides the necessary information to undertake national multitrait genetic evaluations for parasite phenotypes.

Smart dairy farming has become one of the most exciting and challenging area in cloud-based data analytics. Transfer of raw data from all farms to a central cloud is currently not feasible as applications are generating more data while internet connectivity is lacking in rural farms. As a solution, Fog computing has become a key factor to process data near the farm and derive farm insights by exchanging data between on-farm applications and transferring some data to the cloud. In this context, learning in the compressed data domain, where de-compression is not necessary, is highly desirable as it minimizes the energy used for communication/computation, reduces required memory/storage, and improves application latency. Mid-infrared spectroscopy (MIRS) is used globally to predict several milk quality parameters as well as deriving many animal-level phenotypes. Therefore, compressed learning on MIRS data is beneficial both in terms of data processing in the Fog, as well as storing large data sets in the cloud. In this paper, we used principal component analysis and wavelet transform as two techniques for compressed learning to convert MIRS data into a compressed data domain. The study derives near lossless compression parameters for both techniques to transform MIRS data without impacting the prediction accuracy for a selection of milk quality traits.

peer-reviewed H.D.D., A.J.C., P.J.B. and B.J.H. would like to acknowledge the Dairy Futures Cooperative Research Centre for funding. H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr ‘Synbreed—Synergistic Plant and Animal Breeding’ (grant 0315527B). H.P., R.F., R.E. and K.-U.G. acknowledge the Arbeitsgemeinschaft Süddeutscher Rinderzüchter, the Arbeitsgemeinschaft Österreichischer Fleckviehzüchter and ZuchtData EDV Dienstleistungen for providing genotype data. A. Bagnato acknowledges the European Union (EU) Collaborative Project LowInputBreeds (grant agreement 222623) for providing Brown Swiss genotypes. Braunvieh Schweiz is acknowledged for providing Brown Swiss phenotypes. H.P. and R.F. acknowledge the German Holstein Association (DHV) and the Confederación de Asociaciones de Frisona Española (CONCAFE) for sharing genotype data. H.P. was financially supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (DFG) (grant PA 2789/1-1). D.B. and D.C.P. acknowledge funding from the Research Stimulus Fund (11/S/112) and Science Foundation Ireland (14/IA/2576). M.S. and F.S.S. acknowledge the Canadian Dairy Network (CDN) for providing the Holstein genotypes. P.S. acknowledges funding from the Genome Canada project entitled ‘Whole Genome Selection through Genome Wide Imputation in Beef Cattle’ and acknowledges WestGrid and Compute/Calcul Canada for providing computing resources. J.F.T. was supported by the National Institute of Food and Agriculture, US Department of Agriculture, under awards 2013-68004-20364 and 2015-67015-23183. A. Bagnato, F.P., M.D. and J.W. acknowledge EU Collaborative Project Quantomics (grant 516 agreement 222664) for providing Brown Swiss and Finnish Ayrshire sequences and genotypes. A.C.B. and R.F.V. acknowledge funding from the public–private partnership ‘Breed4Food’ (code BO-22.04-011- 001-ASG-LR) and EU FP7 IRSES SEQSEL (grant 317697). A.C.B. and R.F.V. acknowledge CRV (Arnhem, the Netherlands) for providing data on Dutch and New Zealand Holstein and Jersey bulls. Stature is affected by many polymorphisms of small effect in humans1. In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10−8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP–seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

International audience; As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.

peer-reviewed The objective of the present study was to investigate the phenotypic inter- and intra-relationships within and among alternative feed efficiency metrics across different stages of lactation and parities; the expected effect of genetic selection for feed efficiency on the resulting phenotypic lactation profiles was also quantified. A total of 8,199 net energy intake (NEI) test-day records from 2,505 lactations on 1,290 cows were used. Derived efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NEI minus predicted energy requirements based on lactation performance; residual energy production (REP) was defined as net energy for lactation minus predicted energy requirements based on lactation performance. Energy conversion efficiency was defined as net energy for lactation divided by NEI. Pearson phenotypic correlations among traits were computed across lactation stages and parities, and the significance of the differences was determined using the Fisher r-to-z transformation. Sources of variation in the feed efficiency metrics were investigated using linear mixed models, which included the fixed effects of contemporary group, breed, parity, stage of lactation, and the 2-way interaction of parity by stage of lactation. With the exception of REI, parity was associated with all efficiency and production traits. Stage of lactation, as well as the 2-way interaction of parity by stage of lactation, were associated with all efficiency and production traits. Phenotypic correlations among the efficiency and production traits differed not only by stage of lactation but also by parity. For example, the strong phenotypic correlation between REI and energy balance (EB; 0.89) for cows in parity 3 or greater and early lactation was weaker for parity 1 cows at the same lactation stage (0.81), suggesting primiparous cows use the ingested energy for both milk production and growth. Nonetheless, these strong phenotypic correlations between REI and EB suggested negative REI animals (i.e., more efficient) are also in more negative EB. These correlations were further supported when assessing the effect on phenotypic performance of animals genetically divergent for feed intake and efficiency based on parental average. Animals genetically selected to have lower REI resulted in cows who consumed less NEI but were also in negative EB throughout the entire lactation. Nonetheless, such repercussions of negative EB do not imply that selection for negative REI (as defined here) should not be practiced, but instead should be undertaken within the framework of a balanced breeding objective, which includes traits such as reproduction and health. Funding from the Irish Department of Agriculture, Food and Marine (Dublin, Ireland) Research Stimulus Fund project Genetics of cost production traits (GENCOST), and funding from the Horizon 2020 Marie Curie project International Research Staff Exchange Scheme Global cooperation to develop next generation whole genome sequence selection tools for novel traits (SEQSEL) are greatly appreciated.

The objective of the present study was to compare alternative growth functions fitted to pig live weight data, with particular emphasis on the function׳s ability to predict future animal live weight. The final dataset consisted of 51,893 live weight records from 10,201 pigs aged between 61 and 200 days. Fixed effects models and mixed models were applied to the three different growth functions: von Bertalanffy, Gompertz, and Richards; fixed and mixed model polynomial equations were also considered. The growth function׳s ability to predict future live weight was determined by excluding a selection of animal live weight records post 160 days of age and comparing the predicted live weight to actual live weight. Irrespective of whether a fixed or mixed model framework was used, the Gompertz function best modelled the full dataset, with an accuracy squared for the full dataset ( r w 2 ) of 0.997 and root mean square error (RMSE) of 1.60 kg for the fixed effects model and r w 2 of 0.995 and RMSE of 2.25 kg for the mixed model equation. The Gompertz mixed model also achieved the greatest accuracy squared of predicting ( r w w ^ 2 ) future live weight records, with an r w w ^ 2 of 0.846 and RMSE of 5.35 kg. A strong positive correlation was estimated between asymptotic mature weight (A; r=0.83 to 0.96) across the three growth functions for all considered equations. A strong negative correlation was estimated between parameters A and k (rate of maturation) for the Gompertz fixed effects model (−0.88) and mixed model (−0.70). Results from this study could be used to implement a decision support tool for pork producers, offering extra information when they are making important feeding, slaughter and breeding decisions.

peer-reviewed The objective of this study was to develop an index to rank dairy females on expected profit for the remainder of their lifetime, taking cognizance of both additive and nonadditive genetic merit, permanent environmental effects, and current states of the animal including the most recent calving date and cow parity. The cow own worth (COW) index is intended to be used for culling the expected least profitable females in a herd, as well as inform purchase and pricing decisions for trading of females. The framework of the COW index consisted of the profit accruing from (1) the current lactation, (2) future lactations, and (3) net replacement cost differential. The COW index was generated from estimated performance values (sum of additive genetic merit, nonadditive genetic merit, and permanent environmental effects) of traits, their respective net margin values, and transition probability matrices for month of calving, survival, and somatic cell count; the transition matrices were to account for predicted change in a cow’s state in the future. Transition matrices were generated from 3,156,109 lactation records from the Irish national database between the years 2010 and 2013. Phenotypic performance records for 162,981 cows in the year 2012 were used to validate the COW index. Genetic and permanent environmental effects (where applicable) were available for these cows from the 2011 national genetic evaluations and used to calculate the COW index and their national breeding index values (includes only additive genetic effects). Cows were stratified per quartile within herd, based on their COW index value and national breeding index value. The correlation between individual animal COW index value and national breeding index value was 0.65. Month of calving of the cow in her current lactation explained 18% of the variation in the COW index, with the parity of the cow explaining an additional 3 percentage units of the variance in the COW index. Females ranking higher on the COW index yielded more milk and milk solids and calved earlier in the calving season than their lower ranking contemporaries. The difference in phenotypic performance between the best and worst quartiles was larger for cows ranked on COW index than cows ranked on the national breeding index. The COW index is useful to rank females before culling or purchasing decisions on expected profit and is complementary to the national breeding index, which identifies the most suitable females for breeding replacements.

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.

peer-reviewed Financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697). With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in Holstein-Friesian dairy cattle, data from 10 research herds in Europe, North America, and Australasia were combined. The DMI records were available on 10,701 parity 1 to 5 records from 6,953 cows, as well as on 1,784 growing heifers. Predicted DMI at 70 d in milk was used as the phenotype for the lactating animals, and the average DMI measured during a 60- to 70-d test period at approximately 200 d of age was used as the phenotype for the growing heifers. After editing, there were 583,375 genetic markers obtained from either actual high-density single nucleotide polymorphism (SNP) genotypes or imputed from 54,001 marker SNP genotypes. Genetic correlations between the populations were estimated using genomic REML. The accuracy of genomic prediction was evaluated for the following scenarios: (1) within-country only, by fixing the correlations among populations to zero, (2) using near-unity correlations among populations and assuming the same trait in each population, and (3) a sharing data scenario using estimated genetic correlations among populations. For these 3 scenarios, the data set was divided into 10 sub-populations stratified by progeny group of sires; 9 of these sub-populations were used (in turn) for the genomic prediction and the tenth was used for calculation of the accuracy (correlation adjusted for heritability). A fourth scenario to quantify the benefit for countries that do not record DMI was investigated (i.e., having an entire country as the validation population and excluding this country in the development of the genomic predictions). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). Assuming near-unity among-country genetic correlations, the mean accuracy of prediction dropped to 0.40, and the mean within-country accuracy was 0.30. If no records were available in a country, the accuracy based on the other populations ranged from 0.23 to 0.53 for the milking cows, but were only 0.03 and 0.19 for Australian and New Zealand heifers, respectively; the overall mean prediction accuracy was 0.37. Therefore, there is a benefit in collaboration, because phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries. financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697). financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697).

Genetics is responsible for approximately half the observed change in performance internationally in well-structured cattle breeding programs. Almost all, if not all, individual characteristics, including animal health, have a genetic basis. Once genetic variation exists then breeding for improvement is possible. Although the heritability of most health traits is low to moderate, considerable exploitable genetic variation does exist. From the limited studies undertaken, and mostly from limited datasets, the direct heritability of susceptibility to BRD varied from 0.07 to 0.22 and the maternal heritability (where estimated) varied from 0.05 to 0.07. Nonetheless, considerable genetic variation clearly exists; the genetic standard deviation for the direct component (binary trait), although differing across populations, varied from 0.08 to 0.20 while the genetic standard deviation for the maternal component varied from 0.04 to 0.07. Little is known about the genetic correlation between genetic predisposition to BRD and animal performance; the estimation of these correlations should be prioritized. (Long-term) Breeding strategies to reduce the incidence of BRD in cattle should be incorporated into national BRD eradication or control strategies.

The objective of the present study was to quantify, using simulated data, the impact on estimated heritability of varying the number of panellists and their inter-correlations using meat sensory tenderness in cattle as an example. Estimated parameters from actual sensory-based tenderness scores from 9 individual panellists on 1252 beef cattle were used to parameterise the simulation. A single "tenderness score" for each of 10 panellists was simulated for 15,000 cattle. Heritability estimates were calculated for each of the 10 panellists individually as well as the mean score per animal for all n combinations of panellists. Heritability estimates improved with increasing number of panellists in line with expectations from a deterministic equation. The increase in heritability was due to a reduction in the residual variance, albeit the rate of reduction in residual variance declined with each additional panellist included in the calculated mean tenderness score. Results highlight the importance of reporting the number of panellist scores per animal as well as their inter-correlations in sensory-based studies.

The objective of the present study was to estimate genetic co-variance parameters pertaining to live weight, feed intake, and 2 efficiency traits (i.e., residual feed intake and residual daily gain) in a population of pigs over a defined growing phase using Legendre polynomial equations. The data set used consisted of 51,893 live weight records and 903,436 feed intake, residual feed intake (defined as the difference between an animal's actual feed intake and its expected feed intake), and residual daily gain (defined as the difference between an animal's actual growth rate and its expected growth rate) records from 10,201 growing pigs. Genetic co-variance parameters for all traits were estimated using random regression Legendre polynomials. Daily heritability estimates for live weight ranged from 0.25 ± 0.04 (d 73) to 0.50 ± 0.03 (d 122). Low to moderate heritability estimates were evident for feed intake, ranging from 0.07 ± 0.03 (d 66) to 0.25 ± 0.02 (d 170). The estimated heritability for residual feed intake was generally lower than those of both live weight and feed intake and ranged from 0.04 ± 0.01 (d 96) to 0.17 ± 0.02 (d 159). The heritability for feed intake and residual feed intake increased in the early stages of the test period and subsequently sharply declined, coinciding with older ages. Heritability estimates for residual daily gain ranged from 0.26 ± 0.03 (d 188) to 0.42 ± 0.03 (d 101). Genetic correlations within trait were strongest between adjacent ages but weakened as the interval between ages increased; however, the genetic correlations within all traits tended to strengthen between the extremes of the trajectory. Moderate to strong genetic correlations were evident among live weight, feed intake, and the efficiency traits, particularly in the early stage of the trial period (d 66 to 86), but weakened with age. Results from this study could be implemented into the national genetic evaluation for pigs, providing comprehensive information on the profile of growth and efficiency throughout the growing period of the animal's life, thus helping producers identify genetically superior animals.

peer-reviewed Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.

Adaption is a process that makes an individual or population more suited to their environment. Long-term adaptation is predicated on ample usable genetic variation. Evolutionary forces influencing the extent and dynamics of genetic variation in a population include random drift, mutation, recombination, selection, and migration; the relative importance of each differs by population (i.e., drift is likely to be more influential in smaller populations) and number of generations exposed to selection (i.e., mutation is expected to contribute substantially to genetic variability following many generations of selection). The infinitesimal model, which underpins most genetic and genomic evaluations, assumes that each quantitative trait is controlled by an infinitely large number of unlinked and non-epistatic loci, each with an infinitely small effect. Under the infinitesimal model, selection is not expected to noticeably alter the allele frequencies, despite a potential substantial change in the population mean; the exception is in the first few generations of selection when genetic variance is expected to decline, after which it stabilizes. Despite the common use of the heritability statistic in quantitative genetics as a descriptor of adaption or response to selection, it is arguably the coefficient of genetic variation that is more informative to gauge adaptation potential and should, therefore, always be cited in such studies; for example, the heritability of fertility traits in dairy cows is generally low, yet the coefficient of genetic variation for most traits is comparable to many other performance traits, thus supporting the observed rapid genetic gain in fertility performance in dairy populations. Empirical evidence from long-term selection studies, across a range of animal and plant species, fails to support the premise that selection will deplete genetic variability. Even after 100 yr (synonymous with 100 generations) of selection in corn for high protein or oil content, there appears to be no obvious plateauing in the response to selection. Although populations in several selection experiments did reach a selection limit after multiple generations of directional selection, this does not equate to an exhaustion of genetic variance; such a declaration is supported by the observed rapid responses to reverse selection once implemented in long-term selection studies. New technologies such as genome-wide enabled selection and genome editing, as well as having the potential to accelerate genetic gain, could also increase the genetic variation, or at least reduce the erosion of genetic variance over time. In conclusion, there is no evidence, either theoretical or empirical, to indicate that dairy cow breeding programs will be unable to adapt to evolving challenges and opportunities, at least not because of an absence of ample genetic variability.

peer-reviewed Domestication and the subsequent selection of animals for either economic or morphological features can leave a variety of imprints on the genome of a population. Genomic regions subjected to high selective pressures often show reduced genetic diversity and frequent runs of homozygosity (ROH). Therefore, the objective of the present study was to use 42,182 autosomal SNPs to identify genomic regions in 3,191 sheep from six commercial breeds subjected to selection pressure and to quantify the genetic diversity within each breed using ROH. In addition, the historical effective population size of each breed was also estimated and, in conjunction with ROH, was used to elucidate the demographic history of the six breeds. ROH were common in the autosomes of animals in the present study, but the observed breed differences in patterns of ROH length and burden suggested differences in breed effective population size and recent management. ROH provided a sufficient predictor of the pedigree inbreeding coefficient, with an estimated correlation between both measures of 0.62. Genomic regions under putative selection were identified using two complementary algorithms; the fixation index and hapFLK. The identified regions under putative selection included candidate genes associated with skin pigmentation, body size and muscle formation; such characteristics are often sought after in modern-day breeding programs. These regions of selection frequently overlapped with high ROH regions both within and across breeds. Multiple yet uncharacterised genes also resided within putative regions of selection. This further substantiates the need for a more comprehensive annotation of the sheep genome as these uncharacterised genes may contribute to traits of interest in the animal sciences. Despite this, the regions identified as under putative selection in the current study provide an insight into the mechanisms leading to breed differentiation and genetic variation in meat production. This work was supported by MultiGS Research Stimulus Fund (11/S/112) and OviGen project (14/S/849) which are funded by the Department of Agriculture, Food and Marine, Ireland.

Feed efficiency (FE) is critical in pig production for both economic and environmental reasons. As the intestinal microbiota plays an important role in energy harvest, it is likely to influence FE. Therefore, our aim was to characterize the intestinal microbiota of pigs ranked as low, medium, and high residual feed intake ([RFI] a metric for FE), where genetic, nutritional, and management effects were minimized, to explore a possible link between the intestinal microbiota and FE. Eighty-one pigs were ranked according to RFI between weaning and day 126 postweaning, and 32 were selected as the extremes in RFI (12 low, 10 medium, and 10 high). Intestinal microbiota diversity, composition, and predicted functionality were assessed by 16S rRNA gene sequencing. Although no differences in microbial diversity were found, some RFI-associated compositional differences were revealed, principally among members of Firmicutes , predominantly in feces at slaughter (albeit mainly for low-abundance taxa). In particular, microbes associated with a leaner and healthier host (e.g., Christensenellaceae , Oscillibacter , and Cellulosilyticum ) were enriched in low RFI (more feed-efficient) pigs. Differences were also observed in the ileum of low RFI pigs; most notably, Nocardiaceae ( Rhodococcus ) were less abundant. Predictive functional analysis suggested improved metabolic capabilities in these animals, especially within the ileal microbiota. Higher ileal isobutyric acid concentrations were also found in low RFI pigs. Overall, the differences observed within the intestinal microbiota of low RFI pigs compared with that of their high RFI counterparts, albeit relatively subtle, suggest a possible link between the intestinal microbiota and FE in pigs. IMPORTANCE This study is one of the first to show that differences in intestinal microbiota composition, albeit subtle, may partly explain improved feed efficiency (FE) in low residual feed intake (RFI) pigs. One of the main findings is that, although microbial diversity did not differ among animals of varying FE, specific intestinal microbes could potentially be linked with porcine FE. However, as the factors impacting FE are still not fully understood, intestinal microbiota composition may not be a major factor determining differences in FE. Nonetheless, this work has provided a potential set of microbial biomarkers for FE in pigs. Although culturability could be a limiting factor and intervention studies are required, these taxa could potentially be targeted in the future to manipulate the intestinal microbiome so as to improve FE in pigs. If successful, this has the potential to reduce both production costs and the environmental impact of pig production.

Milk processing attributes represent a group of milk quality traits that are important to the dairy industry to inform product portfolio. However, because of the resources required to routinely measure such quality traits, precise genetic parameter estimates from a large population of animals are lacking for these traits. Milk processing characteristics considered in the present study—rennet coagulation time, curd-firming time, curd firmness at 30 and 60 min after rennet addition, heat coagulation time, casein micelle size, and milk pH—were all estimated using mid-infrared spectroscopy prediction equations. Variance components for these traits were estimated using 136,807 test-day records from 5 to 305 d in milk (DIM) from 9,824 cows using random regressions to model the additive genetic and within-lactation permanent environmental variances. Heritability estimates ranged from 0.18 ± 0.01 (26 DIM) to 0.38 ± 0.02 (180 DIM) for rennet coagulation time; from 0.26 ± 0.02 (5 DIM) to 0.57 ± 0.02 (174 DIM) for curd-firming time; from 0.16 ± 0.01 (30 DIM) to 0.56 ± 0.02 (271 DIM) for curd firmness at 30 min; from 0.13 ± 0.01 (30 DIM) to 0.48 ± 0.02 (271 DIM) for curd firmness at 60 min; from 0.08 ± 0.01 (17 DIM) to 0.24 ± 0.01 (180 DIM) for heat coagulation time; from 0.23 ± 0.02 (30 DIM) to 0.43 ± 0.02 (261 DIM) for casein micelle size; and from 0.20 ± 0.01 (30 DIM) to 0.36 ± 0.02 (151 DIM) for milk pH. Within-trait genetic correlations across DIM weakened as the number of days between compared intervals increased but were mostly >0.4 except between the peripheries of the lactation. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix for all traits revealed that at least the 80% of the genetic variation among animals in lactation profiles was associated with the height of the lactation profile. Curd-firming time and curd firmness at 30 min were weakly to moderately genetically correlated with milk yield (from 0.33 ± 0.05 to 0.59 ± 0.05 for curd-firming time, and from −0.62 ± 0.03 to −0.21 ± 0.06 for curd firmness at 30 min). Milk protein concentration was strongly genetically correlated with curd firmness at 30 min (0.84 ± 0.02 to 0.94 ± 0.01) but only weakly genetically correlated with milk heat coagulation time (−0.27 ± 0.07 to 0.19 ± 0.06). Results from the present study indicate the existence of exploitable genetic variation for milk processing characteristics. Because of possible indirect deterioration in milk processing characteristics due to selection for greater milk yield, emphasis on milk processing characteristics is advised.

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

peer-reviewed Milk color is one of the sensory properties that can influence consumer choice of one product over another and it influences the quality of processed dairy products. This study aims to quantify the cow-level genetic and nongenetic factors associated with bovine milk color traits. A total of 136,807 spectra from Irish commercial and research herds (with multiple breeds and crosses) were used. Milk lightness (Lˆ*) , red-green index (aˆ*) and yellow-blue index (bˆ*) were predicted for individual milk samples using only the mid-infrared spectrum of the milk sample. Factors associated with milk color were breed, stage of lactation, parity, milking-time, udder health status, pasture grazing, and seasonal calving. (Co)variance components for Lˆ*,aˆ* , and bˆ* were estimated using random regressions on the additive genetic and within-lactation permanent environmental effects. Greater bˆ* value (i.e., more yellow color) was evident in milk from Jersey cows. Milk Lˆ* increased consistently with stage of lactation, whereas aˆ* increased until mid lactation to subsequently plateau. Milk bˆ* deteriorated until 31 to 60 DIM, but then improved thereafter until the end of lactation. Relative to multiparous cows, milk yielded by primiparae was, on average, lighter (i.e., greater Lˆ* ), more red (i.e., greater aˆ* ), and less yellow (i.e., lower bˆ* ). Milk from the morning milk session had lower Lˆ*,aˆ*, and bˆ* Heritability estimates (±SE) for milk color varied between 0.15 ± 0.02 (30 DIM) and 0.46 ± 0.02 (210 DIM) for Lˆ* , between 0.09 ± 0.01 (30 DIM) and 0.15 ± 0.02 (305 DIM) for aˆ* , and between 0.18 ± 0.02 (21 DIM) and 0.56 ± 0.03 (305 DIM) for bˆ* For all the 3 milk color features, the within-trait genetic correlations approached unity as the time intervals compared shortened and were generally

Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud based data analytics in smart farming. These issues can be resolved through data compression using unsupervisory techniques like PCA, and perform analytics in the compressed-domain i.e. without de-compressing. Compression algorithms should preserve non-linearity of MIRS data (if exists), since emerging advanced learning algorithms can improve their prediction accuracy. This study has investigated the non-linearity between the feature variables in the measurement-domain as well as in two compressed domains using standard Linear PCA and Kernel PCA. Also the non-linearity between the feature variables and the commonly used target milk quality parameters (Protein, Lactose, Fat) has been analyzed. The study evaluates the prediction accuracy using PLS and LS-SVM respectively as linear and non-linear predictive models.

Despite milk processing characteristics being important quality traits, little is known about the factors underlying their variability, due primarily to the resources required to measure these characteristics in a sufficiently large population. Cow milk coagulation properties (rennet coagulation time, curd-firming time, curd firmness 30 and 60 min after rennet addition), heat coagulation time, casein micelle size, and pH were generated from available mid-infrared spectroscopy prediction models. The prediction models were applied to 136,807 spectra collected from 9,824 Irish dairy cows from research and commercial herds. Sources of variation were investigated using linear mixed models that included the fixed effects of calendar month of test; milking time in the day; linear regressions on the proportion of Friesian, Jersey, Montbéliarde, Norwegian Red, and "other" breeds in the cow; coefficients of heterosis and of recombination loss; parity; stage of lactation; and the 2-way interaction parity × stage of lactation. Within- and across-parity cow effects, contemporary group, and a residual term were also included as random effects in the model. Supplementary analyses considered the inclusion of either test-day milk yield or milk protein concentration as fixed-effects covariates in the multiple regression models. Milk coagulation properties were most favorable (i.e., short rennet coagulation time and strong curd firmness) for cheese manufacturing in early lactation, concurrent with the lowest values of both pH and casein micelle size. Milk coagulation properties and pH deteriorated in mid lactation but improved toward the end of lactation. In direct contrast, heat coagulation time was more favorable in mid lactation and less suitable (i.e., shorter) for high temperature treatments in both early and late lactation. Relative to multiparous cows, primiparous cows, on average, yielded milk with shorter rennet coagulation time and longer heat coagulation time. Milk from the evening milking session had shorter rennet coagulation time and greater curd firmness, as well as lower heat coagulation time and lower pH compared with milk from the morning session. Jersey cows, on average, yielded milk more suitable for cheese production rather than for milk powder production. When protein concentration was included in the model, the improvement of milk coagulation properties toward the end of lactation was no longer apparent. Results from the present study may aid in decision-making for milk manufacturing, especially in countries characterized by a seasonal supply of fresh milk.

peer-reviewed Absorption of adequate IgG from colostrum is critical to provide the newborn calf with adequate immunological protection and resistance to disease. Excessive iodine supplementation of the prepartum ewe reduces IgG absorption of her offspring; it is possible that excessive iodine supplementation of the prepartum dairy cow may similarly impair the ability of the calf to acquire immunological protection. The objectives of this study were to determine whether the iodine status, health status, and ability of calves to absorb IgG from colostrum were affected by prepartum iodine supplementation strategies of their dams. Dairy cows (n = 127) received one of the following levels of iodine supplementation precalving: 15 mg of iodine/kg of dietary dry matter (DM) (HI); no additional iodine supplementation (MI); 5 mg/kg of dietary DM (SI); and 15 mg of iodine/kg of DM for the first 3.5 wk of the precalving period and no additional supplementation for the second 3.5 wk (HMI). Calves were assigned to 1 of 6 experimental treatments, based on the prepartum iodine supplementation treatment of their dam and the precalving treatment group of the cows from which the colostrum fed was obtained: (1) HI_HI: born to HI dams, fed HI colostrum (i.e., colostrum produced by cows in the HI group); (2) MI_MI: born to MI dams, fed MI colostrum; (3) SI_SI: born to SI dams, fed SI colostrum; (4) HI_MI: born to HI dams, fed MI colostrum; (5) MI_HI: born to MI dams, fed HI colostrum; and (6) HMI_HMI: born to HMI dams, fed HMI colostrum. Concentration of calf serum IgG and plasma inorganic iodine (PII) was measured at 0 and 24 h of age. Apparent efficiency of absorption for IgG was determined. Health scores were assigned to calves twice weekly and all episodes of disease were recorded. Cow experimental treatment group affected calf PII at 0 h of age; the PII of calves born to HI dams (987.2 µg/L) was greater than that of calves born to MI dams (510.1 µg/L), SI (585.2 µg/L), and HMI dams (692.9 µg/L). Calf experimental treatment group affected calf PII at 24 h of age; the PII of HI_HI (1,259.2 µg/L) and HI_MI (1,177.8 µg/L) calves was greater than MI_MI (240.7 µg/L), SI_SI (302.2 µg/L), HMI_HMI (320.7 µg/L), and MI_HI (216.3 µg/L) calves. No effect of experimental treatment was observed on the concentration of IgG measured in calf serum at 24 h of age, or on apparent efficiency of absorption. Experimental treatment had no effect on the likelihood of a calf being assigned a worse nasal, eye and ear, cough, or fecal score within the study period, nor did it affect the probability of a calf receiving treatment for a disease a greater number of times. Prepartum iodine supplementation of cows at 15mg/kg of DM increased the iodine levels in their calves at birth and 24 h of age, but did not affect their ability to absorb IgG from colostrum. Supplementation with iodine above the minimum requirements established by the National Research Council was unnecessary to ensure appropriate iodine levels in calves at birth.

peer-reviewed Previous studies have documented that ovarian antral follicle count (AFC) is positively correlated with number of healthy follicles and oocytes in ovaries (ovarian reserve), as well as ovarian function and fertility in cattle. However, environmental factors (e.g., nutrition, steroids) during pregnancy in cattle and sheep can reduce AFC in offspring. The role that genetic and environmental factors play in influencing the variability in AFC and, correspondingly, the size of the ovarian reserve, ovarian function, and fertility, are, however, poorly understood. The present study tests the hypothesis that variability in AFC in offspring is influenced not only by genetic merit but also by the dam age and lactation status (lactating cows vs. nonlactating heifers) and milk production during pregnancy. Antral follicle count was assessed by ultrasonography in 445 Irish Holstein-Friesian dairy cows and 522 US Holstein-Friesian dairy heifers. Heritability estimates for AFC (± standard error) were 0.31 ± 0.14 and 0.25 ± 0.13 in dairy cows and heifers, respectively. Association analysis between both genotypic sire data and phenotypic dam data with AFC in their daughters was performed using regression and generalized linear models. Antral follicle count was negatively associated with genetic merit for milk fat concentration. Also, AFC was greater in offspring of dams that were lactating (n = 255) compared with nonlactating dams (n = 89) during pregnancy and was positively associated with dam milk fat concentration and milk fat-to-protein ratio. In conclusion, AFC in dairy cattle is a moderately heritable genetic trait affected by age or lactation status and milk quality but not by level of dam’s milk production during pregnancy.

peer-reviewed 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.

The objective of this study was to quan- tify the phenotypic and genetic risk factors associ- ated with multiple ovulations and twin births in cattle. Prevalence and cow- and herd-level risk factors associ- ated with ovulation rate were determined using 40,617 ultrasonographic records of the reproductive tract from 27,907 dairy and beef cows from 738 commercial herds. Prevalence of twin births was estimated from the Irish national database containing 23,658,351 calving events from 8,546,695 cows from 125,251 dairy and beef herds; factors associated with twin births were deter- mined using a random subsample of 505,200 calving events from 280,638 cows in 81,329 herds. The mean prevalence of multiple ovulations was 6.83% while the prevalence of twin births was 1.74%. Occurrence of both multiple ovulations and twin births was associated with the month of scan (P < 0.0001) and month of calv- ing (P < 0.0001), respectively, and peaked in November for multiple ovulations and October for twin births. The likelihood of multiple ovulations increased with interval postpartum and peaked between 45 to 185 d postcalv- ing, after which the likelihood declined. The likelihood of both multiple ovulations (P < 0.0001) and twin births (P < 0.0001) increased with increasing cow parity. A greater proportion of Holstein, Friesian, Simmental, Hereford, and Charolais breed fractions were associated with a greater likelihood of multiple ovulations. There was no difference between breed proportion of the cow and incidence of twin births, where all breed proportions examined, litter difference existed among breeds in their association with risk of twin births. Although multiple ovulations were lowly heritable (0.028 ± 0.003), their occurrence was repeatable (0.326 ± 0.342) while twin birth rate in cattle was lowly heritable (0.017 ± 0.004) and repeatable (0.018 ± 0.011). The genetic SD of the presence of multiple ovulations and twin births was 0.04 and 0.02, respectively, indicating considerable genetic variation, especially for multiple ovulations. A moderate genetic correlation (0.66 ± 0.16) existed between mul- tiple ovulations and twin births.

The objective of the study was to determine the effect of feeding volume and frequency of feeding on the body weight (BW) of dairy heifer calves, with access to pasture, during the preweaning and postweaning periods, as well as their health status during the preweaning period. Ninety-six dairy heifer calves were offered milk at a rate of (i) 10% of birth BW, once daily from three weeks of age (10% OD) (ii) 15% of birth BW, once daily from three weeks of age (15% OD) and (iii) 15% of birth BW in two equal feeds daily (15% TD) from birth until weaning. Calf BW was recorded at 5, 7, 9, 11, 21, 25 and 30 weeks of age. The number of days taken for each calf to reach a target weaning weight was recorded, as were all episodes of disease. At 5 weeks of age, calves on the 10% OD treatment were lighter (46.7 kg) than calves on the 15% OD treatment (50.6 kg) and 15% TD treatment (50.0 kg). At 11 weeks of age, calves on the 10% OD treatment were still lighter (75.2 kg; P P =0.08) to be lighter than calves on the 15% OD treatment (80.1 kg). Calves fed 15% of BW tended ( P =0.1) to reach a target weaning weight 4 days earlier than the calves fed 10% of BW. Experimental treatment had no effect on the probability of a calf experiencing a disease a greater number of times. There was no effect of experimental treatment on BW measured postweaning.

Financial support is gratefully acknowledged from the Marie Curie Initial Training Network project Greenhouse Milk funded by the European Commission, Framework 7. Contribution of the data from the Irish Cattle Breeding Federation is also acknowledged.

peer-reviewed A survey of management practices in 309 Irish dairy herds was used to identify risk factors for the presence of antibodies to Salmonella, Neospora caninum and Leptospira interrogans serovar hardjo in extensively managed unvaccinated dairy herds. A previous study documented a herd-level seroprevalence in bulk milk of 49%, 19% and 86% for Salmonella, Neospora caninum and leptospira interrogans serovar hardjo, respectively in the unvaccinated proportion of these 309 herds in 2009. Association analyses in the present study were carried out using multiple logistic regression models. Herds where cattle were purchased or introduced had a greater likelihood of being positive to leptospira interrogans serovar hardjo (P

The objective of the present study was to identify the factors associated with both the protein composition and free amino acid (FAA) composition of bovine milk predicted using mid-infrared spectroscopy. Milk samples were available from 7 research herds and 69 commercial herds. The spectral data from the research herds comprised 94,286 separate morning and evening milk samples; the spectral data from the commercial herds comprised 40,260 milk samples representing a composite sample of both the morning and evening milkings. Mid-infrared spectroscopy prediction models developed in a previous study were applied to all spectra. Factors associated with the predicted protein and FAA composition were quantified using linear mixed models. Factors considered in the model included the fixed effects of calendar month of the test, milking time (i.e., morning, evening, or both combined), parity (1, 2, 3, 4, 5, and ≥6), stage of lactation, the interaction between parity and stage of lactation, breed proportion of the cow (Friesian, Jersey, Norwegian Red, Montbeliarde, and other), and both the general heterosis and recombination coefficients of the cow. Contemporary group as well as both within- and across-lactation permanent environmental effects were included in all models as random effects. Total proteins (i.e., total casein, CN; total whey; and total β-lactoglobulin) and protein fractions (with the exception of α-lactalbumin) decreased postcalving until 36 to 65 days in milk and increased thereafter. After adjusting the statistical model for differences in crude protein content and milk yield separately, irrespective of stage of lactation, younger animals produced more total proteins (i.e., total CN, total whey, and total β-lactoglobulin) as well as more total FAA, Glu, and Asp than their older contemporaries. The concentration of all protein fractions (except β-CN) in milk was greatest in the evening milk, even after adjusting for differences in the crude protein content of the milk. Relative to a purebred Holstein cow, Jersey cows, on average, produced a greater concentration of all CN fractions but less total FAA, Glu, Gly, Asp, and Val in milk. Relative to their respective purebred parental average, first-cross cows produced more total CN and more β-CN. Results from the present study indicate that many cow-level factors, as well as other factors, are associated with protein composition and FAA composition of bovine milk.

peer-reviewed Background Bovine tuberculosis (bTB) infection in cattle is a significant economic concern in many countries, with annual costs to the UK and Irish governments of approximately €190 million and €63 million, respectively, for bTB control. The existence of host additive and non-additive genetic components to bTB susceptibility has been established. Methods Two approaches i.e. single-SNP (single nucleotide polymorphism) regression and a Bayesian method were applied to genome-wide association studies (GWAS) using high-density SNP genotypes (n = 597,144 SNPs) from 841 dairy artificial insemination (AI) sires. Deregressed estimated breeding values for bTB susceptibility were used as the quantitative dependent variable. Network analysis was performed using the quantitative trait loci (QTL) that were identified as significant in the single-SNP regression and Bayesian analyses separately. In addition, an identity-by-descent analysis was performed on a subset of the most prolific sires in the dataset that showed contrasting prevalences of bTB infection in daughters. Results A significant QTL region was identified on BTA23 (P value >1 × 10−5, Bayes factor >10) across all analyses. Sires with the minor allele (minor allele frequency = 0.136) for this QTL on BTA23 had estimated breeding values that conferred a greater susceptibility to bTB infection than those that were homozygous for the major allele. Imputation of the regions that flank this QTL on BTA23 to full sequence indicated that the most significant associations were located within introns of the FKBP5 gene. Conclusions A genomic region on BTA23 that is strongly associated with host susceptibility to bTB infection was identified. This region contained FKBP5, a gene involved in the TNFα/NFκ-B signalling pathway, which is a major biological pathway associated with immune response. Although there is no study that validates this region in the literature, our approach represents one of the most powerful studies for the analysis of bTB susceptibility to date.

Angus and Hereford beef is marketed internationally for apparent superior meat quality attributes; DNA-based breed authenticity could be a useful instrument to ensure consumer confidence on premium meat products. The objective of this study was to develop an ultra-low-density genotype panel to accurately quantify the Angus and Hereford breed proportion in biological samples. Medium-density genotypes (13 306 single nucleotide polymorphisms (SNPs)) were available on 54 703 commercial and 4042 purebred animals. The breed proportion of the commercial animals was generated from the medium-density genotypes and this estimate was regarded as the gold-standard breed composition. Ten genotype panels (100 to 1000 SNPs) were developed from the medium-density genotypes; five methods were used to identify the most informative SNPs and these included the Delta statistic, the fixation (F st) statistic and an index of both. Breed assignment analyses were undertaken for each breed, panel density and SNP selection method separately with a programme to infer population structure using the entire 13 306 SNP panel (representing the gold-standard measure). Breed assignment was undertaken for all commercial animals (n=54 703), animals deemed to contain some proportion of Angus based on pedigree (n=5740) and animals deemed to contain some proportion of Hereford based on pedigree (n=5187). The predicted breed proportion of all animals from the lower density panels was then compared with the gold-standard breed prediction. Panel density, SNP selection method and breed all had a significant effect on the correlation of predicted and actual breed proportion. Regardless of breed, the Index method of SNP selection numerically (but not significantly) outperformed all other selection methods in accuracy (i.e. correlation and root mean square of prediction) when panel density was ⩾300 SNPs. The correlation between actual and predicted breed proportion increased as panel density increased. Using 300 SNPs (selected using the global index method), the correlation between predicted and actual breed proportion was 0.993 and 0.995 in the Angus and Hereford validation populations, respectively. When SNP panels optimised for breed prediction in one population were used to predict the breed proportion of a separate population, the correlation between predicted and actual breed proportion was 0.034 and 0.044 weaker in the Hereford and Angus populations, respectively (using the 300 SNP panel). It is necessary to include at least 300 to 400 SNPs (per breed) on genotype panels to accurately predict breed proportion from biological samples.

Genetic selection for increased milk production has resulted in cows that lose more BCS in early lactation and that, consequently, are more susceptible to health disorders after calving. Excessive BCS at calving results in greater BCS loss after calving, increasing the risk of milk fever, ketosis, and fatty liver. Recent experiments during the transition period highlight benefits from controlling (ie, high-dietary roughage) energy intake precalving, with restricted cows being less prone to ketosis, fatty liver, and milk fever. The collective data indicate that a calving BCS of 3.0 for mature cows and 3.25 for heifers with a moderate energy restriction precalving will minimize the risk of BCS-related health disorders around calving.

Increasing food production for the growing human population off a constraining land base will require greater efficiency of production. Genetic improvement of feed efficiency in cattle, which is cumulative and permanent, is one likely vehicle to achieving efficiency gains. The objective of this review is to summarize genetic parameters for feed efficiency traits in dairy and beef cattle and also to address some of the misconceptions associated with feed efficiency in these sectors, as well as discuss the potential use of feed efficiency in breeding programs. A meta-analysis of up to 39 scientific publications in growing cattle clearly showed that genetic variation in feed efficiency exists with a pooled heritability for residual feed intake (RFI) and feed conversion efficiency of 0.33 ± 0.01 (range of 0.07 to 0.62) and 0.23 ± 0.01 (range of 0.06 to 0.46), respectively. Heritability estimates for feed efficiency in cows were lower; a meta-analysis of up to 11 estimates revealed heritability estimates for gross feed efficiency and RFI of 0.06 ± 0.010 and 0.04 ± 0.008, respectively. Meta-analysis of genetic correlations between feed intake, feed efficiency and other performance traits are presented, and selection index theory is used to calculate the proportion of genetic variation in feed intake that can be explained by easy to measure, and often already collected, data. A large proportion of the genetic variation in feed intake could be explained in both growing animals and lactating animals using up to 5 predictor traits, including BW, growth rate, milk yield, body composition, and linear type traits reflecting body size and muscularity. Knowledge of genetic merit for feed intake can be used, along with estimates of genetic merit for energy sinks, to calculate genetic merit for feed efficiency. Therefore, the marginal benefit of collecting actual feed intake data, using the genetic parameters used in this study, appears to be low. There is now sufficient information available to develop a road map on how best to direct research to ensure long-term food security for a growing human population. Gaps in knowledge are identified here, and possibilities to address these gaps are discussed.

Logistic regression was used to identify factors associated with fertility outcomes in cows treated with protocols to synchronize estrus and ovulation. Lactating dairy cows (n=1,538) were enrolled in a completely randomized block design study to evaluate synchronization treatments. Within each herd (n=8), cows were divided into 3 calving groups: early [≥ 42 d in milk (DIM) at mating start date (MSD); n=1,244], mid (21 to 41 DIM at MSD; n=179), and late (0 to 20 DIM at MSD; n=115), based on DIM at MSD. Cows in the early-, mid-, and late-calving groups were synchronized to facilitate estrus or timed artificial insemination (TAI) at MSD (planned breeding 1; PB1), 21 d (PB2), and 42 d (PB3) after MSD, respectively. For each PB, cows in the relevant calving group were stratified by parity and calving date and randomly assigned to (1) d -10 GnRH (10 μg i.m. of buserelin) and CIDR [controlled internal drug release insert, 1.38 g of progesterone (P4)]; d -3 PGF(2α) (25mg i.m. of dinoprost); d -2 CIDR out and AI at observed estrus (CIDR_OBS); (2) same as CIDR_OBS, but GnRH 36 h after CIDR out and TAI 18 h later (CIDR_TAI); (3) same as CIDR_TAI, but no CIDR (i.e., Ovsynch); or (4) untreated controls (CTRL). Use of a CIDR-based ovulation synchronization protocol (i.e., CIDR_TAI) increased synchronization rates in anovular cows. Both CIDR_OBS and CIDR_TAI animals without a corpus luteum (CL) had increased likelihood of conception at first service compared with Ovsynch animals without a CL. Animals with low body condition score (BCS) treated with CIDR_OBS had an increased likelihood of conceiving at first service compared with low-BCS animals treated with CIDR_TAI, Ovsynch, or CTRL. Animals60 d in milk (DIM) treated with CIDR_OBS and CIDR_TAI had increased likelihood of conceiving at first service compared with animals treated with Ovsynch. Treatment with CIDR_TAI increased synchronization rate in cows categorized as low BCS, anovulatory, and60 DIM compared with both CIDR_OBS and Ovsynch, and increased submission rate compared with CIDR_OBS. Conception rate in cows within these categories, however, was greatest for CIDR_OBS, resulting in minimal differences in actual pregnancy rates between CIDR_OBS and CIDR_TAI treatments, both of which were superior to Ovsynch. Treatment differences in the response variables investigated were minimal in cows categorized as medium or high BCS, ovulatory, and60 DIM, indicating that CIDR-based protocols could be targeted at particular cows, and all other cows could be synchronized using Ovsynch.

Transfer of sufficient immunoglobulin G (IgG) to the neonatal calf via colostrum is vital to provide the calf with immunological protection and resistance against disease. The objective of the present study was to determine the factors associated with both colostral IgG concentration and colostral weight in Irish dairy cows. Fresh colostrum samples were collected from 704 dairy cows of varying breed and parity from four Irish research farms between January and December 2011; colostral weight was recorded and the IgG concentration was determined using an ELISA method. The mean IgG concentration in the colostrum was 112 g/l (s.d. = 51 g/l) and ranged from 13 to 256 g/l. In total, 96% of the samples in this study contained >50 g/l IgG, which is considered to be indicative of high-quality colostrum. Mean colostral weight was 6.7 kg (s.d. = 3.6 kg) with a range of 0.1 to 24 kg. Factors associated with both colostral IgG concentration and colostral weight were determined using a fixed effects multiple regression model. Parity, time interval from calving to next milking, month of calving, colostral weight and herd were all independently associated with IgG concentration. IgG concentration decreased (P < 0.01) by 1.7 (s.e. = 0.6) g/l per kg increase in the colostral weight. Older parity cows, cows that had a shorter time interval from calving to milking, and cows that calved earlier in spring or in the autumn produced colostrum with higher IgG concentration. Parity (P < 0.001), time interval from calving to milking (P < 0.01), weight of the calf at birth (P < 0.05), colostral IgG concentration (P < 0.01) and herd were all independently associated with colostral weight at the first milking. Younger parity cows, cows milked earlier post-calving, and cows with lighter calves produced less colostrum. In general, colostrum quality of cows in this study was higher than in many previous studies; possible reasons include use of a relatively low-yielding cow type that produces low weight of colostrum, short calving to colostrum collection interval and grass-based nutritional management. The results of this study indicate that colostral IgG concentration can be maximised by reducing the time interval between calving and collection of colostrum.

Animal breeding should be considered as a permanent and cumulative approach to reducing the environmental footprint of dairy cattle production systems within an overall national and global mitigation strategy. Current international dairy cattle breeding goals do not explicitly include environmental traits, but observed improvements in milk production and both fertility and longevity contribute substantially to improving the environmental footprint relative to output. Ideally, however, environmental related traits, most notably greenhouse gas emissions and nitrogen excretion, should be explicitly included in national breeding goals with their own economic weight. Access to routine phenotypic observations for the environmental traits or other information including genomic information or information on heritable correlated traits is required for inclusion in the selection index. There is, however, a considerable paucity of information on the genetic parameters for, in particular, greenhouse gas emissions in dairy cattle; these parameters include genetic variance estimates, as well as genetic and phenotypic (co)variances with other performance traits. Large studies with well phenotyped animals across a range of environments are needed to estimate such parameters and also investigate the extent, if any, of genotype-by-environment interactions across contrasting environments. Considerable genetic variation in milk urea nitrogen, as a proxy for nitrogen excretion in the urine, exist and suggest that breeding programmes to improve nitrogen use efficiency will be fruitful. However, because of the antagonistic genetic correlations between milk urea nitrogen and milk production, genetic gain in milk yield is expected to be compromised within a breeding goal that includes milk urea nitrogen.

A phenotype describes the outcome of the interacting development between the genotype of an individual and its specific environment throughout life. Animal breeding currently exploits large data sets of phenotypic and pedigree information to estimate the genetic merit of animals. Here we describe rapid, low-cost phenomic tools for dairy cattle. We give particular emphasis to infrared spectroscopy of milk because the necessary spectral data are already routinely available on milk samples from individual cows and herds, and therefore the operational cost of implementing such a phenotyping strategy is minimal. The accuracy of predicting milk quality traits from mid-infrared spectroscopy (MIR) analysis of milk, although dependent on the trait under investigation, is particularly promising for differentiating between good and poor-quality dairy products. Many fatty acid concentrations in milk, and in particular saturated fatty acid content, can be very accurately predicted from milk MIR. These results have been confirmed in many international populations. Albeit from only two studied populations investigated in the RobustMilk project, milk MIR analysis also appears to be a reasonable predictor of cow energy balance, a measure of animal robustness; high accuracy of prediction was not expected as the gold standard method of measuring energy balance in those populations was likely to contain error. Because phenotypes predicted from milk MIR are available routinely from milk testing, longitudinal data analyses could be useful to identify animals of superior genetic merit for milk quality and robustness, as well as for monitoring changes in milk quality and robustness because of management, while simultaneously accounting for the genetic merit of the animals. These sources of information can be very valuable input parameters in decision-support tools for both milk producers and processors.

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.

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 900 days, and from 22,971 young bulls aged between 360 and 720 days, 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.

Healthy calves are fundamental to any profitable dairy enterprise. Research to-date, has focused on year-round calving systems which experience many different challenges compared to spring-calving systems. The objective of the present study was to determine the on-farm dry cow, calving, and colostrum management practices of spring-calving dairy production systems, and quantify their associations with herd size and herd expansion status (i.e. expanding or not expanding). Information on these management practices was available from a survey of 262 Irish spring-calving dairy farmers, representative of the Irish national population. Herd expansion in the 2 years before, and the year that the survey was conducted was not associated with any of the management practices investigated. Fifty-three percent of respondents had an average calving season length of 10 to14 weeks with 35% of herds having a longer calving season. Previous research in cattle has documented that both colostrum source and feeding management are associated with the transmission of infectious disease from cow to calf. In the present study 60% of respondents fed calves colostrum from their own dam; however, 66% of those respondents allowed the calf to suckle the dam, 23% of survey respondents fed calves pooled colostrum. Larger herds were more likely (P

The objective of the present study was to compare serum IgG concentration, weight gain, and health characteristics in Irish spring-born dairy calves fed colostrum stored using a range of conditions. Immediately after birth, 75 dairy heifer calves were assigned to 1 of 5 experimental colostrum treatments: (1) fresh pasteurized colostrum, fed immediately after pasteurization; (2) fresh colostrum, fed immediately after collection but not pasteurized; (3) colostrum stored unpasteurized at 4°C in a temperature-controlled unit for 2d before being fed to calves; (4) colostrum stored unpasteurized at 13°C in a temperature-controlled unit for 2d before being fed to calves; and (5) colostrum stored unpasteurized at 22°C in a temperature-controlled unit for 2d before being fed to calves. All colostrum had IgG concentrations >50g/L and was fed to calves promptly after birth. Blood samples were obtained from calves via the jugular vein at 0h (before colostrum feeding) and at 24h of age to determine the rate of passive transfer of IgG; individual calf live-weights were recorded to monitor weight gain (kg/d) from birth to weaning. Colostrum stored in warmer conditions (i.e., 22°C) had >42 times more bacteria present and a pH that was 0.85 units lower and resulted in a serum IgG concentration that was almost 2 times lower compared with colostrum that was pasteurized, untreated, or stored at 4°C for 2d. Colostrum stored at 4°C for 2d had more bacteria present than pasteurized and fresh colostrum but did not result in reduced calf serum IgG concentrations. Average daily weight gain from birth to weaning did not differ among treatments. Even if colostrum has sufficient IgG (>50g/L) but cannot be fed to calves when freshly collected, storage at ≤4°C for 2d is advisable to ensure adequate passive transfer when it is consumed by the calf.

The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy in predicting milk protein and free amino acid (FAA) composition in bovine milk. Milk samples were collected from 7 Irish research herds and represented cows from a range of breeds, parities, and stages of lactation. Mid-infrared spectral data in the range of 900 to 5,000 cm(-1) were available for 730 milk samples; gold standard methods were used to quantify individual protein fractions and FAA of these samples with a view to predicting these gold standard protein fractions and FAA levels with available mid-infrared spectroscopy data. Separate prediction equations were developed for each trait using partial least squares regression; accuracy of prediction was assessed using both cross validation on a calibration data set (n = 400 to 591 samples) and external validation on an independent data set (n = 143 to 294 samples). The accuracy of prediction in external validation was the same irrespective of whether undertaken on the entire external validation data set or just within the Holstein-Friesian breed. The strongest coefficient of correlation obtained for protein fractions in external validation was 0.74, 0.69, and 0.67 for total casein, total beta-lactoglobulin, and beta-casein, respectively. Total proteins (i.e., total casein, total whey, and total lactoglobulin) were predicted with greater accuracy then their respective component traits; prediction accuracy using the infrared spectrum was superior to prediction using just milk protein concentration. Weak to moderate prediction accuracies were observed for FAA. The greatest coefficient of correlation in both cross validation and external validation was for Gly (0.75), indicating a moderate accuracy of prediction. Overall, the FAA prediction models overpredicted the gold standard values. Near-unity correlations existed between total casein and beta-casein irrespective of whether the traits were based on the gold standard (0.92) or mid-infrared spectroscopy predictions (0.95). Weaker correlations among FAA were observed than the correlations among the protein fractions. Pearson correlations between gold standard protein fractions and the milk processing characteristics of rennet coagulation time, curd firming time, curd firmness, heat coagulating time, pH, and casein micelle size were weak to moderate and ranged from -0.48 (protein and pH) to 0.50 (total casein and a(30)). Pearson correlations between gold standard FAA and these milk processing characteristics were also weak to moderate and ranged from -0.60 (Val and pH) to 0.49 (Val and K-20). Results from this study indicate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk at a population level.