The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.

Knowledge of population structure and breed composition of a population can be advantageous for a number of reasons; these include designing optimal (cross)breeding strategies in order to maximise non-additive genetic effects, maintaining flockbook integrity by authenticating animals being registered and as a quality control measure in the genotyping process. The objectives of the present study were to 1) describe the population structure of 24 sheep breeds, 2) quantify the breed composition of both flockbook-recorded and crossbred animals using single nucleotide polymorphism BLUP (SNP-BLUP), and 3) quantify the accuracy of breed composition prediction from low-density genotype panels containing between 2000 and 6000 SNPs. In total, 9334 autosomal SNPs on 11 144 flockbook-recorded animals and 1172 crossbred animals were used. The population structure of all breeds was characterised by principal component analysis (PCA) as well as the pairwise breed fixation index (Fst). The total number of animals, all of which were purebred, included in the calibration population for SNP-BLUP was 2579 with the number of animals per breed ranging from 9 to 500. The remaining 9559 flockbook-recorded animals, composite breeds and crossbred animals represented the test population; three breeds were excluded from breed composition prediction. The breed composition predicted using SNP-BLUP with 9334 SNPs was considered the gold standard prediction. The pairwise breed Fst ranged from 0.040 (between the Irish Blackface and Scottish Blackface) to 0.282 (between the Border Leicester and Suffolk). Principal component analysis revealed that the Suffolk from Ireland and the Suffolk from New Zealand formed distinct, non-overlapping clusters. In contrast, the Texel from Ireland and that from New Zealand formed integrated, overlapping clusters. Composite animals such as the Belclare clustered close to its founder breeds (i.e., Finn, Galway, Lleyn and Texel). When all 9334 SNPs were used to predict breed composition, an animal that had a majority breed proportion predicted to be ≥0.90 was defined as purebred for the present study. As the panel density decreased, the predicted breed proportion threshold, used to identify animals as purebred, also decreased (≥0.85 with 6000 SNPs to ≥0.60 with 2000 SNPs). In all, results from the study suggest that breed composition for purebred and crossbred animals can be determined with SNP-BLUP using ≥5000 SNPs.

Although food from grazed animals is increasingly sought by consumers because of perceived animal welfare advantages, grazing systems provide the farmer and the animal with unique challenges. The system is dependent almost daily on the climate for feed supply, with the importation of large amounts of feed from off farm, and associated labour and mechanisation costs, sometimes reducing economic viability. Furthermore, the cow may have to walk long distances and be able to harvest feed efficiently in a highly competitive environment because of the need for high levels of pasture utilisation. She must, also, be: (1) highly fertile, with a requirement for pregnancy within ~80 days post-calving; (2) ‘easy care’, because of the need for the management of large herds with limited labour; (3) able to walk long distances; and (4) robust to changes in feed supply and quality, so that short-term nutritional insults do not unduly influence her production and reproduction cycles. These are very different and are in addition to demands placed on cows in housed systems offered pre-made mixed rations. Furthermore, additional demands in environmental sustainability and animal welfare, in conjunction with the need for greater system-level biological efficiency (i.e. ‘sustainable intensification’), will add to the ‘robustness’ requirements of cows in the future. Increasingly, there is evidence that certain genotypes of cows perform better or worse in grazing systems, indicating a genotype×environment interaction. This has led to the development of tailored breeding objectives within countries for important heritable traits to maximise the profitability and sustainability of their production system. To date, these breeding objectives have focussed on the more easily measured traits and those of highest relative economic importance. In the future, there will be greater emphasis on more difficult to measure traits that are important to the quality of life of the animal in each production system and to reduce the system’s environmental footprint.

Body condition score (BCS) is a subjective assessment of the proportion of body fat an animal possesses and is independent of frame size. There is a growing awareness of the importance of mature animal live-weight given its contribution to the overall costs of production of a sector. Because of the known relationship between BCS and live-weight, strategies to reduce live-weight could contribute to the favouring of animals with lesser body condition. The objective of the present study was to estimate the average difference in live-weight per incremental change in BCS, measured subjectively on a scale of 1 to 5. The data used consisted of 19 033 BCS and live-weight observations recorded on the same day from 7556 ewes on commercial and research flocks; the breeds represented included purebred Belclare (540 ewes), Charollais (1484 ewes), Suffolk (885 ewes), Texel (1695 ewes), Vendeen (140 ewes), as well as, crossbreds (2812 ewes). All associations were quantified using linear mixed models with the dependent variable of live-weight; ewe parity was included as a random effect. The independent variables were BCS, breed (n=6), stage of the inter-lambing interval (n=6; pregnancy, lambing, pre-weaning, at weaning, post-weaning and mating) and parity (1, 2, 3, 4 and 5+). In addition, two-way interactions were used to investigate whether the association between BCS and live-weight differed by parity, a period of the inter-lambing interval or breed. The association between BCS and live-weight differed by parity, by a period of the inter-lambing interval and by breed. Across all data, a one-unit difference in BCS was associated with 4.82 (SE=0.08) kg live-weight, but this differed by parity from 4.23 kg in parity 1 ewes to 5.82 kg in parity 5+ ewes. The correlation between BCS and live-weight across all data was 0.48 (0.47 when adjusted for nuisance factors in the statistical model), but this varied from 0.48 to 0.53 by parity, from 0.36 to 0.63 by stage of the inter-lambing interval and from 0.41 to 0.62 by breed. Results demonstrate that consideration should be taken of differences in BCS when comparing ewes on live-weight as differences in BCS contribute quite substantially to differences in live-weight; moreover, adjustments for differences in BCS should consider the population stratum, especially breed.

Understanding how critical sow live-weight and back-fat depth during gestation are in ensuring optimum sow productivity is important. The objective of this study was to quantify the association between sow parity, live-weight and back-fat depth during gestation with subsequent sow reproductive performance. Records of 1058 sows and 13 827 piglets from 10 trials on two research farms between the years 2005 and 2015 were analysed. Sows ranged from parity 1 to 6 with the number of sows per parity distributed as follows: 232, 277, 180, 131, 132 and 106, respectively. Variables that were analysed included total born (TB), born alive (BA), piglet birth weight (BtWT), pre-weaning mortality (PWM), piglet wean weight (WnWT), number of piglets weaned (Wn), wean to service interval (WSI), piglets born alive in subsequent farrowing and sow lactation feed intake. Calculated variables included the within-litter CV in birth weight (LtV), pre-weaning growth rate per litter (PWG), total litter gain (TLG), lactation efficiency and litter size reared after cross-fostering. Data were analysed using linear mixed models accounting for covariance among records. Third and fourth parity sows had more (P<0.05) TB, BA and heavier BtWT compared with gilts and parity 6 sow contemporaries. Parities 2 and 3 sows weaned more (P<0.05) piglets than older sows. These piglets had heavier (P<0.05) birth weights than those from gilt litters. LtV and PWM were greater (P<0.01) in litters born to parity 5 sows than those born to younger sows. Sow live-weight and back-fat depth at service, days 25 and 50 of gestation were not associated with TB, BA, BtWT, LtV, PWG, WnWT or lactation efficiency (P>0.05). Heavier sow live-weight throughout gestation was associated with an increase in PWM (P<0.01) and reduced Wn and lactation feed intake (P<0.05). Deeper back-fat in late gestation was associated with fewer (P<0.05) BA but heavier (P<0.05) BtWT, whereas deeper back-fat depth throughout gestation was associated with reduced (P<0.01) lactation feed intake. Sow back-fat depth was not associated with LtV, PWG, TLG, WSI or piglets born alive in subsequent farrowing (P>0.05). In conclusion, this study showed that sow parity, live-weight and back-fat depth can be used as indicators of reproductive performance. In addition, this study also provides validation for future development of a benchmarking tool to monitor and improve the productivity of modern sow herd.

Milk mineral concentration is important from both the perspective of processing milk into dairy products and its nutritive value for human consumption. Precise estimates of genetic parameters for milk mineral concentration are lacking because of the considerable resources required to collect vast phenotypes quantities. The milk concentration of calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and phosphorus (P) in the present study was quantified from mid-IR spectroscopy on 12 223 test-day records from 1717 Holstein-Friesian cows. (Co)variance components were estimated using random regressions to model both the additive genetic and within-lactation permanent environmental variances of each trait. The coefficient of genetic variation averaged across days-in-milk (DIM) was 6.93%, 3.46%, 6.55%, 5.20% and 6.68% for Ca, K, Mg, Na and P concentration, respectively; heritability estimates varied across lactation from 0.31±0.05 (5 DIM) to 0.67±0.04 (181 DIM) for Ca, from 0.18±0.03 (60 DIM) to 0.24±0.05 (305 DIM) for K, from 0.08±0.03 (15 DIM) to 0.37±0.03 (223 DIM) for Mg, from 0.16±0.03 (30 DIM) to 0.37±0.04 (305 DIM) for Na and from 0.21±0.04 (12 DIM) to 0.57±0.04 (211 DIM) for P. Genetic correlations within the same trait across different DIM were almost unity between adjacent DIM but weakened as the time interval between pairwise compared DIM lengthened; genetic correlations were weaker than 0.80 only when comparing both peripheries of the lactation. The analysis of the geometry of the additive genetic covariance matrix revealed that almost 90% of the additive genetic variation was accounted by the intercept term of the covariance functions for each trait. Milk protein concentration and mineral concentration were, in general, positively genetically correlated with each other across DIM, whereas milk fat concentration was positively genetically correlated throughout the entire lactation with Ca, K and Mg; the genetic correlation with fat concentration changed from negative to positive with Na and P at 243 DIM and 50 DIM, respectively. Genetic correlations between somatic cell score and Na ranged from 0.38±0.21 (5 DIM) to 0.79±0.18 (305 DIM). Exploitable genetic variation existed for all milk minerals, although many national breeding objectives are probably contributing to an indirect positive response to selection in milk mineral concentration.

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

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.

Accurate genomic analyses are predicated on access to a large quantity of accurately genotyped and phenotyped animals. Because the cost of genotyping is often less than the cost of phenotyping, interest is increasing in generating genotypes for phenotyped animals. In some instances this may imply the requirement to genotype older animals with greater phenotypic information content. Biological material for these older informative animals may, however, no longer exist. The objective of the present study was to quantify the ability to impute 11 129 single nucleotide polymorphism (SNP) genotypes of non-genotyped animals (in this instance sires) from the genotypes of their progeny with or without including the genotypes of the progenys’ dams (i.e. mates of the sire to be imputed). The impact on the accuracy of genotype imputation by including more progeny (and their dams’) genotypes in the imputation reference population was also quantified. When genotypes of the dams were not available, genotypes of 41 sires with at least 15 genotyped progeny were used for the imputation; when genotypes of the dams were available, genotypes of 21 sires with at least 10 genotyped progeny were used for the imputation. Imputation was undertaken exploiting family and population level information. The mean and variability in the proportion of genotypes per individual that could not be imputed reduced as the number of progeny genotypes used per individual increased. Little improvement in the proportion of genotypes that could not be imputed was achieved once genotypes of seven progeny and their dams were used or genotypes of 11 progeny without their respective dam’s genotypes were used. Mean imputation accuracy per individual (depicted by both concordance rates and correlation between true and imputed) increased with increasing progeny group size. Moreover, the range in mean imputation accuracy per individual reduced as more progeny genotypes were used in the imputation. If the genotype of the mate of the sire was also used, high accuracy of imputation (mean genotype concordance rate per individual of 0.988), with little additional benefit thereafter, was achieved with seven genotyped progeny. In the absence of genotypes on the dam, similar imputation accuracy could not be achieved even using genotypes on up to 15 progeny. Results therefore suggest, at least for the SNP density used in the present study, that it is possible to accurately impute the genotypes of a non-genotyped parent from the genotypes of its progeny and there is a benefit of also including the genotype of the sire’s mate (i.e. dam of the progeny).

A range of precision farming technologies are used commercially for variable rate applications of nitrogen (N) for cereals, yet these usually adjust N rates from a pre-set value, rather than predicting economically optimal N requirements on an absolute basis. This paper reports chessboard experiments set up to examine variation in N requirements, and to develop and test systems for its prediction, and to assess its predictability. Results showed very substantial variability in fertiliser N requirements within fields, typically >150 kg ha−1, and large variation in optimal yields, typically >2 t ha−1. Despite this, calculated increases in yield and gross margin with N requirements perfectly matched across fields were surprisingly modest (compared to the uniform average rate). Implications are discussed, including the causes of the large remaining variation in grain yield, after N limitations were removed.

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.

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 (Fst) 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.

The objective of the present study was to quantify the extent of genetic variation in three health-related traits namely dagginess, lameness and mastitis, in an Irish sheep population. Each of the health traits investigated pose substantial welfare implications as well as considerable economic costs to producers. Data were also available on four body-related traits, namely body condition score (BCS), live weight, muscle depth and fat depth. Animals were categorised as lambs (<365 days old) or ewes (⩾365 days old) and were analysed both separately and combined. After edits, 39 315 records from 264 flocks between the years 2009 and 2015 inclusive were analysed. Variance components were estimated using animal linear mixed models. Fixed effects included contemporary group, represented as a three-way interaction between flock, date of inspection and animal type (i.e. lamb, yearling ewe (i.e. females ⩾365 days but <730 days old that have not yet had a recorded lambing) or ewe), animal breed proportion, coefficients of heterosis and recombination, animal gender (lambs only), animal parity (ewes only; lambs were assigned a separate ‘parity’) and the difference in age of the animal from the median of the respective parity/age group. An additive genetic effect and residual effect were both fitted as random terms with maternal genetic and non-genetic components also considered for traits of the lambs. The direct heritability of dagginess was similar across age groups (0.14 to 0.15), whereas the direct heritability of lameness ranged from 0.06 (ewes) to 0.12 (lambs). The direct heritability of mastitis was 0.04. For dagginess, 13% of the phenotypic variation was explained by dam litter, whereas the maternal heritability of dagginess was 0.05. The genetic correlation between ewe and lamb dagginess was 0.38; the correlation between ewe and lamb lameness was close to zero but was associated with a large standard error. Direct genetic correlations were evident between dagginess and BCS in ewes and between lameness and BCS in lambs. The present study has demonstrated that ample genetic variation exists for all three health traits investigated indicating that genetic improvement is indeed possible.

Information on the genetic diversity and population structure of cattle breeds is useful when deciding the most optimal, for example, crossbreeding strategies to improve phenotypic performance by exploiting heterosis. The present study investigated the genetic diversity and population structure of the most prominent dairy and beef breeds used in Ireland. Illumina high-density genotypes (777 962 single nucleotide polymorphisms; SNPs) were available on 4623 purebred bulls from nine breeds; Angus (n=430), Belgian Blue (n=298), Charolais (n=893), Hereford (n=327), Holstein-Friesian (n=1261), Jersey (n=75), Limousin (n=943), Montbéliarde (n=33) and Simmental (n=363). Principal component analysis revealed that Angus, Hereford, and Jersey formed non-overlapping clusters, representing distinct populations. In contrast, overlapping clusters suggested geographical proximity of origin and genetic similarity between Limousin, Simmental and Montbéliarde and to a lesser extent between Holstein, Friesian and Belgian Blue. The observed SNP heterozygosity averaged across all loci was 0.379. The Belgian Blue had the greatest mean observed heterozygosity (HO=0.389) among individuals within breed while the Holstein-Friesian and Jersey populations had the lowest mean heterozygosity (HO=0.370 and 0.376, respectively). The correlation between the genomic-based and pedigree-based inbreeding coefficients was weak (r=0.171; P<0.001). Mean genomic inbreeding estimates were greatest for Jersey (0.173) and least for Hereford (0.051). The pair-wise breed fixation index (Fst) ranged from 0.049 (Limousin and Charolais) to 0.165 (Hereford and Jersey). In conclusion, substantial genetic variation exists among breeds commercially used in Ireland. Thus custom-mating strategies would be successful in maximising the exploitation of heterosis in crossbreeding strategies.

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<0.01) to use pooled colostrum supplies, while smaller herds were more likely (P<0.05) to allow the calf to suckle the dam. The majority (86%) of respondents had stored supplies of colostrum; average-sized herds had the greatest likelihood of storing colostrum (P<0.05), compared to other herd sizes; larger sized herds had a lesser likelihood (P<0.05) of storing colostrum in a freezer, compared to other herd sizes. Although freezing colostrum was the most common method used to store colostrum (54% of respondents), 17% of respondents stored colostrum at room temperature, 29% of which stored it at room temperature for greater than 4 days. The results from the present study indicate that a particular focus needs to be placed on calving and colostrum management because this study has highlighted a number of areas which are below international standards, and may have repercussions for calf health. Furthermore, management practices on larger farms could be improved and, as these represent the future of dairy farming, a focus needs to be placed on them. Expanding herds are not a particular concern as herd expansion, independent of herd size, does not seem to be associated with calving and colostrum management practices on Irish spring-calving dairy herds.

The increased demand for animal-derived protein and energy for human consumption will have to be achieved through a combination of improved animal genetic merit and better management strategies. The objective of the present study was to quantify whether differences in genetic merit among animals materialised into phenotypic differences in commercial herds. Carcass phenotypes on 156 864 animals from 7301 finishing herds were used, which included carcass weight (kg), carcass conformation score (scale 1 to 15), carcass fat score (scale 1 to 15) at slaughter as well as carcass price. The price per kilogram and the total carcass value that the producer received for the animal at slaughter was also used. A terminal index, calculated in the national genetic evaluations, was obtained for each animal. The index was based on pedigree index for calving performance, feed intake and carcass traits from the national genetic evaluations. Animals were categorised into four terminal index groups on the basis of genetic merit estimates that were derived before the expression of the phenotypic information by the validation animals. The association between terminal index and phenotypic performance at slaughter was undertaken using mixed models; whether the association differed by gender (i.e. young bulls, steers and heifers) or by early life experiences (animals born in a dairy herd or beef herd) was also investigated. The regression coefficient of phenotypic carcass weight, carcass conformation and carcass fat on their respective estimated breeding values (EBVs) was 0.92 kg, 1.08 units and 0.79 units, respectively, which is close to the expectation of one. Relative to animals in the lowest genetic merit group, animals in the highest genetic merit group had, on average, a 38.7 kg heavier carcass, with 2.21 units greater carcass conformation, and 0.82 units less fat. The superior genetic merit animals were, on average, slaughtered 6 days younger than their inferior genetic merit contemporaries. The superior carcass characteristics of the genetically elite animals materialised in carcasses worth €187 more than those of the lowest genetic merit animals. Although the phenotypic difference in carcass traits of animals divergent in terminal index differed statistically by animal gender and early life experience, the detected interactions were generally biologically small. This study clearly indicates that selection on an appropriate terminal index will produce higher performing animals and this was consistent across all production systems investigated.

The relative weighting on traits within breeding goals are generally determined by bio-economic models or profit functions. While such methods have generally delivered profitability gains to producers, and are being expanded to consider non-market values, current approaches generally do not consider the numerous and diverse stakeholders that affect, or are affected, by such tools. Based on principles of respondent anonymity, iteration, controlled feedback and statistical aggregation of feedback, a Delphi study was undertaken to gauge stakeholder opinion of the importance of detailed milk quality traits within an overall dairy breeding goal for profit, with the aim of assessing its suitability as a complementary, participatory approach to defining breeding goals. The questionnaires used over two survey rounds asked stakeholders: (a) their opinion on incorporating an explicit sub-index for milk quality into a national breeding goal; (b) the importance they would assign to a pre-determined list of milk quality traits and (c) the (relative) weighting they would give such a milk quality sub-index. Results from the survey highlighted a good degree of consensus among stakeholders on the issues raised. Similarly, revelation of the underlying assumptions and knowledge used by stakeholders to make their judgements illustrated their ability to consider a range of perspectives when evaluating traits, and to reconsider their answers based on the responses and rationales given by others, which demonstrated social learning. Finally, while the relative importance assigned by stakeholders in the Delphi survey (4% to 10%) and the results of calculations based on selection index theory of the relative emphasis that should be placed on milk quality to halt any deterioration (16%) are broadly in line, the difference indicates the benefit of considering more than one approach to determining breeding goals. This study thus illustrates the role of the Delphi technique, as a complementary approach to traditional approaches, to defining breeding goals. This has implications for how breeding goals will be defined and in determining who should be involved in the decision-making process.

The objective of this study was to establish the risk factors associated with both lambing difficulty and lamb mortality in the Irish sheep multibreed population. A total of 135 470 lambing events from 42 675 ewes in 839 Irish crossbred and purebred flocks were available. Risk factors associated with producer-scored ewe lambing difficulty score (scale of one (no difficulty) to four (severe difficulty)) were determined using linear mixed models. Risk factors associated with the logit of the probability of lamb mortality at birth (i.e. binary trait) were determined using generalised estimating equations. For each dependent variable, a series of simple regression models were developed as well as a multiple regression model. In the simple regression models, greater lambing difficulty was associated with quadruplet bearing, younger ewes, of terminal breed origin, lambing in February; for example, first parity ewes experienced greater (P<0.001) lambing difficulty (1.56±0.02) than older ewes. The association between lambing difficulty and all factors persisted in the multiple regression model, and the trend in fixed effects level solutions did not differ from the trend observed in the simple regression models. In the simple regression analyses, a greater odds of lamb mortality was associated with male lambs (1.31 times more likely of death than females), lambs of very light (2 to 3 kg) and very heavy (>7.0 kg) birth weights, quadruplet born lambs and lambs that experienced a more difficult lambing (predicted probability of death for lambs that required severe and veterinary assistance of 0.15 and 0.32, respectively); lambs from dual-purpose breeds and born to younger ewes were also at greater risk of mortality. In the multiple regression model, the association between ewe parity, age at first lambing, year of lambing and lamb mortality no longer persisted. The trend in solutions of the levels of each fixed effect that remained associated with lamb mortality in the multiple regression model, did not differ from the trends observed in the simple regression models although the differential in relative risk between the different lambing difficulty scores was greater in the multiple regression model. Results from this study show that many common flock- and animal-level factors are associated with both lambing difficulty and lamb mortality and management of different risk category groups (e.g. scanned litter sizes, ewe age groups) can be used to appropriately manage the flock at lambing to reduce their incidence.

The aim of the present study was to estimate genetic parameters for calcium (Ca), phosphorus (P) and titratable acidity (TA) in bovine milk predicted by mid-IR spectroscopy (MIRS). Data consisted of 2458 Italian Holstein−Friesian cows sampled once in 220 farms. Information per sample on protein and fat percentage, pH and somatic cell count, as well as test-day milk yield, was also available. (Co)variance components were estimated using univariate and bivariate animal linear mixed models. Fixed effects considered in the analyses were herd of sampling, parity, lactation stage and a two-way interaction between parity and lactation stage; an additive genetic and residual term were included in the models as random effects. Estimates of heritability for Ca, P and TA were 0.10, 0.12 and 0.26, respectively. Positive moderate to strong phenotypic correlations (0.33 to 0.82) existed between Ca, P and TA, whereas phenotypic weak to moderate correlations (0.00 to 0.45) existed between these traits with both milk quality and yield. Moderate to strong genetic correlations (0.28 to 0.92) existed between Ca, P and TA, and between these predicted traits with both fat and protein percentage (0.35 to 0.91). The existence of heritable genetic variation for Ca, P and TA, coupled with the potential to predict these components for routine cow milk testing, imply that genetic gain in these traits is indeed possible.