Context. Feed is the largest expense on a dairy farm, therefore improving feed efficiency is important. Recording dry-matter intake (DMI) is a prerequisite for calculating feed efficiency. Genetic variation of feed intake and feed efficiency varies across lactation stages and parities. DMI is an expensive and difficult-to-measure trait. This raises the question of which time periods during lactation would be most appropriate to measure DMI. Aims. The aim was to evaluate whether sequence variants selected from genome-wide association studies (GWAS) for DMI recorded at multiple lactation time periods and parities would increase the accuracy of genomic estimated breeding values (GEBVs) for DMI and residual feed intake (RFI). Methods. Data of 2274 overseas lactating cows were used for the GWAS to select sequence variants. GWAS was performed using the average of the DMI phenotypes in a 30-day window of six different time periods across the lactation. The most significant sequence variants were selected from the GWAS at each time period for either first or later parities. GEBVs for DMI and RFI in Australian lactating cows were estimated using BayesRC with 50 k single nucleotide polymorphisms (SNPs) and selected GWAS sequence variants. Key results. There were differences in DMI genomic correlations and heritabilities between first and later parities and within parity across lactation time periods. Compared with using 50 k single-nucleotide polymorphisms (SNPs) only, the accuracy of DMI GEBVs increased by up to 11% by using the 50 k SNPs plus the selected sequence variants. Compared with DMI,the increase in accuracy for RFI was lower (by 6%) likely because the sequence variants were selected from GWAS for DMI not RFI. The accuracies for DMI and RFI GEBVs were highest by using selected sequence variants from the DMI GWAS in the mid- to late-lactation periods in later parity. Conclusions. Our results showed that DMI phenotypes in late lactation time periods could capture more genetic variation and increase genomic prediction accuracy through the use of custom genotype panels in genomic selection. Implications. Collecting DMI at the optimal time period(s) of lactation may help develop more accurate and cost-effective breeding values for feed efficiency in dairy cattle.

To predict the impact of climate change on our beef animals and systems, we need a better understanding of how beef cattle traits are affected by varying weather and frequency of extreme events. We analysed the effect of minimum and maximum temperatures and average daily precipitation on a range of important carcass traits, including age at slaughter, cold carcass weight, carcass growth rate and conformation and fat score (N = >1.6 million), as well as calf 200-day live weight and growth rate (N = >270 000), using data from abattoirs across Britain (carcass traits) and calves in Scottish suckler beef herds (live weights and growth). Animals which experienced higher daily maximum and minimum temperatures had slower carcass and calf growth rates. Increased precipitation also led to poorer cold carcass weights, conformation scores, calf 200-day weights and calf growth. We also analysed the effect of frequency of extreme weather events, including heatwaves, cold waves, and dry and wet days. The frequency of heatwaves, dry and wet days were shown to have significant negative effects on almost all traits considered, for example, predicting that an increase in the frequency of heatwaves by 1 day per 100 days of life would reduce cold carcass weights by about 200 g and increase age at slaughter by about 3 days. Results show that varying weather and frequency of extreme weather, across the lifetime of a beef animal, influences traits which affect the potential profit for a beef farmer. These effects may be due to several factors, including direct effects on the animal, as well as feed availability and management decisions made by the farmer. However, there is potential to mitigate negative effects through a range of animal management strategies.

Quantifying methane (CH4) output from ruminant animal production systems is crucial in the development of mitigation strategies. This study aimed to develop an understanding of CH4 output across life stage and the effects of key performance indicators on CH4 output in a multi breed sheep population. A total of 7,027 CH4 records from 2,404 animals collected from four flocks between 2019 and 2021 were available. Factors associated with CH4 output were determined using linear mixed models. Highest CH4 output was associated with ewes, with lambs being the lowest (P<0.001). When measuring CH4 using portable accumulation chambers the atmospheric pressure, relative humidity and time since feed removal must all be recorded (P<0.001). Singleton reared litters had lower CH4 output than multiple rearing litters when measured in both the ewe and the lamb (P<0.001). Results highlight the impact of multiple environmental and animal factors on CH4 output in sheep systems.

Breeding objectives of livestock and other agricultural species are usually profit maximising. The selection emphasis placed on specific traits to achieve a breeding objective is often informed by the financial value of a trait to a farm system. However, there are alternative, and complementary approaches to defining both the breeding objective and the selection emphasis placed on traits that are included in associated selection tools. These are based on the preferences of stakeholders, which are often heterogeneous and include broader values and motivations than profit. In this regard, stated preference methods are useful when considering traits that have either no discernible market value or whose value is not fully transferred via the market. Such approaches can guide more appropriate breeding decisions that are amenable to changing societal values, for example with reduced negative environmental externalities. However, while stated preference methods offer promising conceptualisations of value in genetic improvement programmes, there is still a substantial knowledge gap in terms of the current state of research and a catalogue of publications to date. This paper reviews publications of stated preference approaches in the field of livestock breeding (and some relevant crop breeding examples), providing a knowledge base of published applications and promoting their continued development and implementation towards the formulation of appropriate breeding objectives and selection indices. A systematic review of 84 peer-reviewed publications and an aggregate ranking of traits for the most commonly studied subject (cattle) reveals uncertainty in preference estimates which may be driven by (i) a diverse set of non-standardised methodologies, (ii) common oversights in the selection, inclusion and description of traits, and (iii) inaccurate representations of the respondent population. We discuss key considerations to help overcome these limitations, including avoiding methodological confinement to a disciplinary silo and reducing complexity so that the values of broader respondent groups may be accounted for.

Including resilience in the breeding objective of dairy cattle is gaining increasing attention, primarily as anticipated challenges to production systems, such as climate change, may make some perturbations more difficult to moderate at the farm level. Consequently, the underlying biological mechanisms by which resilience is achieved are likely to become an important part of the system itself, increasing value on the animal's ability to be unperturbed by variable production circumstances, or to quickly return to pre-perturbed levels of productivity and health. However, because the value of improving genetic traits to a system is usually based on known profit functions or bioeconomic models linked to current production conditions, it can be difficult to define longer-term value, especially under uncertain future production circumstances and where nonmonetary values may be progressively more important. We present the novel application of a discrete choice experiment, used to investigate potential antagonisms in the values of genetic improvements for 8 traits to dairy cattle system stakeholders in Europe when the production goal was either efficiency or resilience. A latent class model was used to identify heterogeneous preferences within each production goal, and postestimation was used to identify associations between these preferences and sociodemographic characteristics of respondents. Results suggested 3 distinct latent preference classes for each production goal. For the efficiency goal, yield and feed efficiency traits were generally highly valued, whereas for the resilience goal, health and robustness traits were generally highly valued. In both cases, these traits generally carried a low value in the other production scenario. Overall, in both scenarios, longevity was highly valued; however, the value of this trait in terms of resilience will depend on phenotyping across diverse environments to sufficiently capture performance under various anticipated system challenges. Additionally, results showed significant associations between membership of latent preference classes with education level and profession. In conclusion, as resilience becomes increasingly important, it is likely that a continued reliance on the short-term economic value of traits alone will lead decision makers to misrepresent the importance of some traits, including those with substantial contextual values in terms of resilience.

The present study investigated the potential consequences, positive or negative, that selection for favorable production-related traits may have on concentrations of vitamin B-12 and key chemical elements in dairy cow milk and serum and the possible impact on milk healthiness, and associated benefits, for the dairy product consumer. Milk and serum samples (950 and 755, respectively) were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period, generating 34,258 individual records, and analyzed for concentrations of key trace and quantity elements, heavy metals, and milk vitamin B-12. These data were then matched to economically important production data (milk, fat, and protein yield) and management data (dry matter intake, liveweight, and body condition score). Multivariate animal models, including full pedigree information, were used to analyze data and investigate relationships between traits of interest. Results highlighted negative genetic correlations between many quantity and trace elements in both milk and serum with production and management traits. Milk yield was strongly negatively correlated with the milk quantity elements Mg and C-a (genetic correlation between traits, r(a) = -0.58 and -0.63, respectively) as well as the trace elements Mn, Fe, Ni, Cu, Zn, and Mo (r(a) = -0.32, -0.58, -0.52, -0.40, -0.34, and -0.96, respectively); and in serum, Mg, Ca, Co, Fe, and Zn (r(a) = -0.50, -0.36, -0.68, -0.54, and -0.90, respectively). Strong genetic correlations were noted between dry matter intake with V (r(a) = 0.97), Fe (r(a) = -0.69), Ni (r(a) = -0.81), and Zn (r(a) = -0.75), and in serum, strong negative genetic correlations were observed between dry matter intake with Ca and Se (r(a) = -0.95 and -0.88, respectively). Body condition score was negatively correlated with serum P, Cu, Se, and Pb (r(a) = -0.45, -0.35, -0.51, and -0.64, respectively) and positively correlated with Mn, Fe, and Zn (r(a) = 0.40, 0.71, and 0.55, respectively). Our results suggest that breeding strategies aimed at improving economically important production-related traits would most likely result in a negative impact on levels of beneficial nutrients within milk for human consumption (such as Mg, Ca, Fe, Zn, and Se).

Breeding climate resilient cattle is an important strategy to mitigate the effects of climate change. Resilience of beef cattle is less well understood compared to dairy, especially in temperate climates. This study aims to compare resilience to climate, both average weather and extreme weather events, for carcass weight, age at slaughter and carcass growth rate. Linear models were used to analyse these traits in over 1.7 million records from UK abattoirs. Resilience was estimated using regression coefficients for interactions between breed type and weather parameters. Results show variation between breeds for resilience to climate with British breeds showing the greatest resilience. Our results suggest an increase in heat waves by 1 per 100 days of life would reduce cold carcass weights of dairy and continental animals by about 150 g but increase that of British breed animals by about 360 g.

Climate change will have direct effects on performance of livestock. Simulations that mimic livestock systems risk misrepresenting performance if these effects are not accounted for. Here, the technical, economic and environmental performance of a Scottish dairy herd under genetic selection (for 7-correlated traits) was investigated using an original simulation model. There were two scenarios: in the control scenario, the technical performance of individuals was not affected by daily weather; in the treatment scenario, technical performance was dynamic to daily weather conditions. Herds were simulated for 20-years. By the final year of the simulation, for herds in the treatment scenario relative to the control scenario, annual gains in gross margin were reduced by 2.1% and gains in greenhouse gas emissions intensity were reduced by 1.7%. Failure to account for weather effects in models that simulate herds into the future will likely cause over-estimation of technical, economic and environmental performance, even in temperate regions such as southwest Scotland.

Genetic parameters were estimated for cold carcase weight (CCW), carcase conformation (CON), carcase fat class (FAT), age at slaughter (AGE) and average daily carcase gain (ADCG) in 14 common UK breeds of cattle. These included crossbred animals but purebred datasets were also analysed for the most populous sire-breeds. Heritability estimates for beef breeds that were significant ranged from 0.24 to 0.44, 0.12 to 0.35, 0.12 to 0.36, 0.15 to 0.38 and 0.26 to 0.43 for CCW, CON, FAT, AGE and ADCG, respectively. For Holstein-Friesian, a dairy breed, heritability estimates were consistently lower than most beef breeds with estimates of 0.12, 0.13, 0.13, 0.06 and 0.15 for CCW, CON, FAT, AGE and ADCG, respectively. In all breed groups, genetic correlations were positive between CCW, CON and ADCG. In general, genetic correlations were moderate between CCW and CON (0.13 to 0.77), moderate to strong between CCW and ADCG (0.57 to 0.98) and weak or moderate between CON and ADCG (0.12 to 0.82). Genetic correlations for FAT with CCW (− 0.20 to − 0.42) and CON (− 0.16 to − 0.52) tended to be negative in the beef breed but were positive in the dairy breed, although not significant between CCW and FAT. For most beef breeds genetic correlations between AGE and carcase traits were not significant with the exceptions of AGE and CCW for Simmental (− 0.15) and Salers (− 0.24), AGE and CON for Limousin (0.15) and Simmental (0.14) and AGE and FAT from three sire-breeds (− 0.17 to − 0.35). However, the correlation between AGE and ADCG was negative and moderate to strong in magnitude (− 0.23 to − 0.67) in all beef breeds as expected since faster-growing animals reach slaughter age earlier. For Holstein-Friesian, all genetic correlations with AGE were negative and moderate to strong. Genetic correlations indicate that selection for increased carcase weight should simultaneously increase growth rate and improve conformation in all breeds and reduce carcase fatness in the majority of beef breeds. The results indicate that there is genetic variation in all five traits suitable for undertaking genetic improvement of carcase traits and age at slaughter; however, there are apparent breed differences. The use of abattoir-derived phenotypes for undertaking genetic improvement is an example where the supply chain can work together to share information to enable the cattle industry to move forward.

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

Lamb live weight is one of the key drivers of profitability on sheep farms. Previous studies in Ireland have estimated genetic parameters for live weight and carcass composition traits using a multi-breed population rather than on an individual breed basis. The objective of the present study was to undertake genetic analyses of three lamb live weight and two carcass composition traits pertaining to purebred Texel, Suffolk and Charollais lambs born in the Republic of Ireland between 2010 and 2017, inclusive. Traits (with lamb age range in parenthesis) considered in the analyses were: pre-weaning weight (20 to 65 days), weaning weight (66 to 120 days), post-weaning weight (121 to 180 days), muscle depth (121 to 180 days) and fat depth (121 to 180 days). After data edits, 137 402 records from 50 372 lambs across 416 flocks were analysed. Variance components were derived using animal linear mixed models separately for each breed. Fixed effects included for all traits were contemporary group, age at first lambing of the dam, parity of the dam, a gender by age of the lamb interaction and a birth type by rearing type of the lamb interaction. Random effects investigated in the pre-weaning and weaning weight analyses included animal direct additive genetic, dam maternal genetic, litter common environment, dam permanent environment and residual variances. The model of analysis for post-weaning, muscle and fat depth included an animal direct additive genetic and litter common environment effect only. Significant direct additive genetic variation existed in all cases. Direct heritability for pre-weaning weight ranged from 0.14 to 0.30 across the three breeds. Weaning weight had a direct heritability ranging from 0.17 to 0.27 and post-weaning weight had a direct heritability ranging from 0.15 to 0.27. Muscle and fat depth heritability estimates ranged from 0.21 to 0.31 and 0.15 to 0.20, respectively. Positive direct correlations were evident for all traits. Results revealed ample genetic variation among animals for the studied traits and significant differences between breeds to suggest that genetic evaluations could be conducted on a per-breed basis.

The high demand for resources at lambing necessitates strategies to reduce such requirements; one such strategy is breeding programs that enable selection for fewer lambing events requiring assistance as well as less perinatal mortality. The objective of the present study was to estimate the genetic (co)variances for a range of lambing traits in sheep, based on alternative trait definitions that considered differences in mating type and litter size. A total of 158,561 lambing events from both crossbred and purebred Irish sheep were available. Four lambing traits were considered in the analysis: lambing difficulty score, lambing dystocia, lamb perinatal mortality and birth weight. Variance components for each trait were estimated using animal linear mixed models either in the entire data as a whole, or within sub-populations stratified by litter size (i.e., single versus multiple) or stratified by mating type (i.e., crossbred versus purebred animals). The presence of significant re-scaling and re-ranking across population strata were investigated to determine if genotype-by-environmental interactions existed. Considerable genetic variation existed for all lambing traits investigated. Irrespective of the dataset analysed, direct heritability estimates for lambing difficulty score (0.04 +/- 0.02 to 0.12 +/- 0.03), lambing dystocia (0.04 +/- 0.01 to 0.09 +/- 0.03), and lamb mortality (0.01 +/- 0.01 to 0.09 +/- 0.02) were all low. In contrast, lamb birth weight (0.11 +/- 0.02 to 0.20 +/- 0.04) exhibited a low to moderate direct heritability. Using the entire dataset, the maternal heritability ranged from 0.01 +/- 0.005 (lamb mortality) to 0.15 +/- 0.02 (birth weight). All direct genetic effects for the lambing traits were positively correlated with each other, except between lamb birth weight and perinatal mortality (-0.15; SE= 0.16). Significantly different genetic variances in different environments and significant re-ranking between environments was detected for lamb mortality (across different mating types and litter sizes), and for lambing difficulty score (only across different litter sizes). Results clearly indicate that ample genetic variation exists for the lambing-related traits investigated.

The ability to identify ewes that can outperform their contemporaries, in terms of how long they remain productive in the flock, will help towards improving flock efficiency and profitability. The main objectives of this study were to: (1) identify the main reasons for mortality or culling within diverse sheep production systems in Ireland, Norway and UK; (2) investigate the influence of early life factors on ewe longevity within each of these systems; and (3) determine whether common approaches or recommendations could be employed to improve ewe longevity. The main reasons for mortality or culling were, in addition to old age, mastitis (Irish and Norwegian sheep) and tooth loss (UK hill sheep). In each country, there were significant differences in age at last lambing due to the year the ewe was born (but in no consistent pattern), and due to her flock of birth (P < 0.05). From the Norwegian data, there was some indication ewes from younger dams lambed for the last time at a younger age, however, this trend was not seen in the Irish or UK data. Ewes born as singletons, in the Irish data, lambed for the last time at an older age than those that had been born in larger litters, although this was not observed in the other data sets. Age at first lambing and some breed proportions (proportion of Texel and Suffolk particularly) of the animal (both not fitted in the Norwegian or UK analyses) were found to have a highly significant (P < 0.0001) effect on age at last lambing in the Irish analyses. The results suggest that longevity is influenced by a range of different factors and the early life predictors investigated could not be used to provide consistent recommendations across countries, production systems and breeds that would influence ewe longevity. One common definition or solution to select ewes for longer productive life in divergent sheep flocks may not be appropriate.

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB.

Heteropaternal superfecundation may be defined as the fertilisation of two or more ova during the same oestrus cycle as a result of more than one coital act from different males; this results in foetuses being born in the same litter of the same age but different paternity. Heteropaternal superfecundation is more likely to occur in poly-ovulatory species like sheep; moreover, female sheep are often mob-mated with several rams concurrently, thus providing an opportunity for a given female to be served by multiple males during the same oestrus cycle. The objective of the present study was to determine the frequency of heteropaternal superfecundation in six sheep flocks where most of the ewes, lambs and rams were genotyped. A total of 685 multiple-birth litters were available where the sire, dam and all lambs were genotyped. Of the 539 pairs of twins included in the analysis, 160 (i.e. 30%) were sired by two different rams. Of the 137 sets of triplets included in the analysis, 73 (i.e. 53%) were sired by more than one ram. Of the nine sets of quadruplets, eight were sired by two rams with the remaining litter being mono-paternal. The overall incidence of heteropaternal superfecundation among litters was therefore 35%. Given that the incidence of multiple births in these flocks was 65%, heteropaternal superfecundation is expected to be relatively common in sheep; this is especially true as all but two of the litter-mates were polyzygotic. Genotyping of progeny is one practical solution to identity such individuals.

More people globally depend on the water buffalo than any other domesticated species, and as the most closely related domesticated species to cattle they can provide important insights into the shared evolutionary basis of domestication. Here, we sequence the genomes of 79 water buffalo across seven breeds and compare patterns of between breed selective sweeps with those seen for 294 cattle genomes representing 13 global breeds. The genomic regions under selection between cattle breeds significantly overlap regions linked to stature in human genetic studies, with a disproportionate number of these loci also shown to be under selection between water buffalo breeds. Investigation of potential functional variants in the water buffalo genome identifies a rare example of convergent domestication down to the same mutation having independently occurred and been selected for across domesticated species. Cross-species comparisons of recent selective sweeps can consequently help identify and refine important loci linked to domestication.

Enhancing micronutrient (i.e., mineral and vitamin) concentrations within milk and serum from dairy cows is important for both the health of the cow and the nutritive value of the milk for human consumption. However, a good understanding of the genetics underlying the micronutrient content in dairy cattle is needed to facilitate such enhancements through feeding or breeding practices. In this study, milk (n = 950) and serum (n = 766) samples were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period and analyzed for concentrations of important elements. Additionally, a subset of 256 milk samples was analyzed for concentrations of vitamin B12. Cows belonged to 2 genetic lines (average and highest genetic merit for milk fat plus protein yield) and were assigned to 1 of 2 diets based on either a by-product or homegrown ration. Univariate models accounting for repeated records were used to analyze element and vitamin B12 data and investigate the effect of genotype and feeding system as well as derive estimates of variance components and genetic parameters. Bivariate models were used to study correlations both within and between milk and serum. Only concentrations of Hg in milk were seen to be affected by genotype, with higher concentrations in cows with high genetic merit. In contrast, element concentrations were influenced by feeding system such that cows fed the homegrown diet had increased milk concentrations of Ca, Cu, I, Mn, Mo, P, and K and increased serum concentrations of Cd, Cu, Fe, Mo, and V. Cows on the by-product diet had increased milk concentrations of Mg, Se, and Na and increased serum concentrations of P and Se. Heritability (h2) estimates were obtained for 6 milk and 4 serum elements, including Mg (h2milk = 0.30), K (h2serum = 0.18), Ca (h2milk = 0.20; h2serum = 0.12), Mn (h2milk = 0.14), Cu (h2serum = 0.22), Zn (h2milk = 0.24), Se (h2milk = 0.15; h2serum = 0.10), and Mo (h2milk = 0.19). Significant estimates of repeatability were observed in all milk and serum quantity elements (Na, Mg, P, K, and Ca) as well as 5 milk and 7 serum trace elements. Only K in milk and serum was found to have a significant positive genetic and phenotypic correlation (0.52 and 0.22, respectively). Significant phenotypic associations were noted between milk and serum Ca (0.17), Mo (0.19), and Na (-0.79). Additional multivariate analyses between measures within sample type (i.e., milk or serum) revealed significant positive associations, both phenotypic and genetic, between some of the elements. In milk, Se was genetically correlated with Ca (0.63), Mg (0.59), Mn (0.40), P (0.53), and Zn (0.52), whereas in serum, V showed strong genetic associations with Cd (0.71), Ca (0.53), Mn (0.63), Mo (0.57), P (0.42), K (0.45), and Hg (-0.44). These results provide evidence that element concentrations in milk and blood of dairy cows are significantly influenced by both diet and genetics and demonstrate the potential for genetic selection and dietary manipulation to alter nutrient concentration to improve both cow health and the healthfulness of milk for human consumption.

Calf mortality leads to economic losses for the farmer and is an animal welfare issue. Currently, only calf mortality within the first 24 h is accounted for in the Danish breeding goal for beef x dairy calves. However, survival throughout the rearing period is also of upmost importance. Therefore, the aim of this study was to estimate genetic parameters for young stock survival, to evaluate if it is feasible to implement such a trait. Data on 90,926 crossbred calves was extracted from the Danish Cattle Database and was provided by the Danish research center, SEGES. Two traits were defined, young stock survival from 1 to 30 d and 31–200 d after birth. The traits were analyzed with a univariate animal model using the AI-REML algorithm in the DMU package. The model contained a fixed effect of year × month of birth, herd, sex, breed combination, parity of the dam, transfer and a random effect of the calf and herd × year. The pedigree was traced back 5 generations, for both the sires and dams. Results showed low but significant heritabilities (0.045–0.075) for both survival traits. Breeding values were calculated using DMU4. Breed combinations with Belgium Blue cattle sires outperformed all other sire breeds. The lowest survival rates were found for breed combinations with Jersey dams or Blonde d’Aquitaine sires. Sufficient genetic variation between sires for young stock survival was found. The breeding values of the sires had an effect on young stock survival that ranged from −2.5 to 3.5% and −5.4 to 4.7% for survival from 1 to 30 d and 31–200 respectively. It is therefore feasible to implement young stock survival traits in a genetic evaluation for beef × dairy crossbred calves. This will increase the survival rate of the calves and hereby increase animal welfare and decrease economic loss for the farmers.