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Summary: Background: People with diabetes are at increased risk of hospitalisation, morbidity, and mortality following SARS-CoV-2 infection. Long-term outcomes for people with diabetes previously hospitalised with COVID-19 are, however, unknown. This study aimed to determine the longer-term physical and mental health effects of COVID-19 in people with and without diabetes. Methods: The PHOSP-COVID study is a multicentre, long-term follow-up study of adults discharged from hospital between 1 February 2020 and 31 March 2021 in the UK following COVID-19, involving detailed assessment at 5 and 12 months after discharge. The association between diabetes status and outcomes were explored using multivariable linear and logistic regressions. Findings: People with diabetes who survived hospital admission with COVID-19 display worse physical outcomes compared to those without diabetes at 5- and 12-month follow-up. People with diabetes displayed higher fatigue (only at 5 months), frailty, lower physical performance, and health-related quality of life and poorer cognitive function. Differences in outcomes between diabetes status groups were largely consistent from 5 to 12-months. In regression models, differences at 5 and 12 months were attenuated after adjustment for BMI and presence of other long-term conditions. Interpretation: People with diabetes reported worse physical outcomes up to 12 months after hospital discharge with COVID-19 compared to those without diabetes. These data support the need to reduce inequalities in long-term physical and mental health effects of SARS-CoV-2 infection in people with diabetes. Funding: UK Research and Innovation and National Institute for Health Research. The study was approved by the Leeds West Research Ethics Committee (20/YH/0225) and is registered on the ISRCTN Registry (ISRCTN10980107).

Background The long-term outcomes of COVID-19 hospitalisation in individuals with pre-existing airway diseases are unknown. Methods Adult participants hospitalised for confirmed or clinically suspected COVID-19 and discharged between 5 March 2020 and 31 March 2021 were recruited to the Post-hospitalisation COVID-19 (PHOSP-COVID) study. Participants attended research visits at 5 months and 1 year post discharge. Clinical characteristics, perceived recovery, burden of symptoms and health-related quality of life (HRQoL) of individuals with pre-existing airway disease (i.e., asthma, COPD or bronchiectasis) were compared to the non-airways group. Results A total of 615 out of 2697 (22.8%) participants had a history of pre-existing airway diseases (72.0% diagnosed with asthma, 22.9% COPD and 5.1% bronchiectasis). At 1 year, the airways group participants were less likely to feel fully recovered (20.4% versus 33.2%, p

ABSTRACT: The objective of this study was to evaluate lactational performance, enteric gas emissions, ruminal fermentation, nutrient use efficiency, milk fatty acid profile, and energy and inflammatory markers in blood of peak-lactation dairy cows fed diets supplemented with Capsicum oleoresin or a combination of Capsicum oleoresin and clove oil. A 10-wk randomized complete block design experiment was conducted with 18 primiparous and 30 multiparous Holstein cows. Cows were blocked based on parity, days in milk, and milk yield (MY), and randomly assigned to 1 of 3 treatments (16 cows/treatment): (1) basal diet (CON); (2) basal diet supplemented with 300 mg/cow per day of Capsicum oleoresin (CAP); and (3) basal diet supplemented with 300 mg/cow per day of a combination of Capsicum oleoresin and clove oil (CAPCO). Premixes containing ground corn (CON), CAP, or CAPCO were mixed daily with the basal diet at 0.8% of dry matter intake (DMI). Supplementation of the diet with CAP or CAPCO did not affect DMI, MY, milk components, and feed efficiency of the cows. Body weight (BW) was increased during the last 2 wk of the experiment by CAP and CAPCO, compared with CON. The botanicals improved BW gain (0.85 and 0.66 kg/d for CAP and CAPCO, respectively, compared with −0.01 kg/d for CON) and CAP enhanced the efficiency of energy utilization, compared with CON (94.5% vs. 78.4%, respectively). Daily CH4 emission was not affected by treatments, but CH4 emission yield (per kg of DMI) and intensity (per kg of MY) were decreased by up to 11% by CAPCO supplementation, compared with CON and CAP. A treatment × parity interaction indicated that the CH4 mitigation effect was pronounced in primiparous but not in multiparous cows. Ruminal molar proportion of propionate was decreased by botanicals, compared with CON. Concentrations of trans-10 C18:1 and total trans fatty acids in milk fat were decreased by CAP and tended to be decreased by CAPCO, compared with CON. Total-tract apparent digestibility of nutrients was not affected by treatments, except for a tendency for decreased starch digestibility in cows supplemented with botanicals. Blood concentrations of β-hydroxybutyrate, total fatty acids, and insulin were not affected by botanicals. Blood haptoglobin concentration was increased by CAP in multiparous but not in primiparous cows. Lactational performance of peak-lactation dairy cows was not affected by the botanicals in this study, but they appeared to improve efficiency of energy utilization and partitioned energy toward BW gain. In addition, CH4 yield and intensity were decreased in primiparous cows fed CAPCO, suggesting a potential positive environmental effect of the combination of Capsicum oleoresin and clove oil supplementation.

ABSTRACT: The present study investigated the potential consequences, positive or negative, that selection for favorable production-related traits may have on concentrations of vitamin B12 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 B12. 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 Ca (genetic correlation between traits, ra = −0.58 and −0.63, respectively) as well as the trace elements Mn, Fe, Ni, Cu, Zn, and Mo (ra = −0.32, −0.58, −0.52, −0.40, −0.34, and −0.96, respectively); and in serum, Mg, Ca, Co, Fe, and Zn (ra = −0.50, −0.36, −0.68, −0.54, and −0.90, respectively). Strong genetic correlations were noted between dry matter intake with V (ra = 0.97), Fe (ra = −0.69), Ni (ra = −0.81), and Zn (ra = −0.75), and in serum, strong negative genetic correlations were observed between dry matter intake with Ca and Se (ra = −0.95 and −0.88, respectively). Body condition score was negatively correlated with serum P, Cu, Se, and Pb (ra = −0.45, −0.35, −0.51, and −0.64, respectively) and positively correlated with Mn, Fe, and Zn (ra = 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).

ABSTRACT: Botanical extracts have a potential to modify ruminal fermentation while enhancing metabolism and immunity in dairy cows. The objective of this study was to investigate the effects of a combination of Capsicum oleoresin and clove essential oil (botanicals; BTC) on lactational performance, nutrient utilization, enteric methane (CH4) emissions, and blood parameters in dairy cows. Twenty Holstein cows (12 multiparous and 8 primiparous) averaging (±SD) 77 ± 28 d in milk in the beginning of the study were used in a replicated 4 × 4 Latin square design experiment with 4 periods of 28 d each. Cows were grouped into squares based on parity, milk yield and days in milk, and assigned to 1 of 4 treatments: control (CON), 150, 300, or 600 mg/cow per day of BTC. Cows received the same basal diet and BTC were top-dressed on the total mixed ration once daily. Dry matter intake, milk production, and milk composition were not affected by BTC supplementation, except for milk fat content that tended to be increased in BTC, compared with CON. Daily CH4 emission (measured using the GreenFeed system) was linearly decreased by up to 7.5% with increasing doses of BTC. Treatment decreased CH4 yield (kg of CH4 ÷ kg of DMI) and tended to decrease CH4 intensity (kg of CH4 ÷ kg of milk or energy-corrected milk yields) by 5% in BTC, compared with CON. Supplementation of BTC resulted in a quadratic decrease of serum β-hydroxybutyrate in all cows, and a linear decrease of serum insulin concentration in primiparous but not in multiparous cows. Nutrient utilization and other blood parameters (e.g., blood cells count) were not affected by BTC in the current study. The reduction of enteric CH4 emission demonstrates a moderate mitigation effect on carbon footprint of milk by BTC supplementation. These results must be further investigated and confirmed in longer-term experiments.

ABSTRACT: 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.

Summary: Background: The scale of COVID-19 and its well documented long-term sequelae support a need to understand long-term outcomes including frailty. Methods: This prospective cohort study recruited adults who had survived hospitalisation with clinically diagnosed COVID-19 across 35 sites in the UK (PHOSP-COVID). The burden of frailty was objectively measured using Fried's Frailty Phenotype (FFP). The primary outcome was the prevalence of each FFP group—robust (no FFP criteria), pre-frail (one or two FFP criteria) and frail (three or more FFP criteria)—at 5 months and 1 year after discharge from hospital. For inclusion in the primary analysis, participants required complete outcome data for three of the five FFP criteria. Longitudinal changes across frailty domains are reported at 5 months and 1 year post-hospitalisation, along with risk factors for frailty status. Patient-perceived recovery and health-related quality of life (HRQoL) were retrospectively rated for pre-COVID-19 and prospectively rated at the 5 month and 1 year visits. This study is registered with ISRCTN, number ISRCTN10980107. Findings: Between March 5, 2020, and March 31, 2021, 2419 participants were enrolled with FFP data. Mean age was 57.9 (SD 12.6) years, 933 (38.6%) were female, and 429 (17.7%) had received invasive mechanical ventilation. 1785 had measures at both timepoints, of which 240 (13.4%), 1138 (63.8%) and 407 (22.8%) were frail, pre-frail and robust, respectively, at 5 months compared with 123 (6.9%), 1046 (58.6%) and 616 (34.5%) at 1 year. Factors associated with pre-frailty or frailty were invasive mechanical ventilation, older age, female sex, and greater social deprivation. Frail participants had a larger reduction in HRQoL compared with before their COVID-19 illness and were less likely to describe themselves as recovered. Interpretation: Physical frailty and pre-frailty are common following hospitalisation with COVID-19. Improvement in frailty was seen between 5 and 12 months although two-thirds of the population remained pre-frail or frail. This suggests comprehensive assessment and interventions targeting pre-frailty and frailty beyond the initial illness are required. Funding: UK Research and Innovation and National Institute for Health Research.

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.

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.

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.

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.

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.

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.

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).

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 (

A better understanding of how livestock respond to weather is essential to enable farming to adapt to a changing climate. Climate change is mainly expected to impact dairy cattle through heat stress and an increase in the frequency of extreme weather events. We investigated the effects of weather on milk yield and composition (fat and protein content) in an experimental dairy herd in Scotland over 21 years. Holstein Friesian cows were either housed indoors in winter and grazed over the summer or were continuously housed. Milk yield was measured daily, resulting in 762 786 test day records from 1369 individuals, and fat and protein percentage were sampled once a week, giving 89 331 records from 1220 cows/trait. The relative influence of 11 weather elements, measured from local outdoor weather stations, and two indices of temperature and humidity (THI), indicators of heat stress, were compared using separate maximum likelihood models for each element or index. Models containing a direct measure of temperature (dry bulb, wet bulb, grass or soil temperature) or a THI provided the best fits to milk yield and fat data; wind speed and the number of hours of sunshine were most important in explaining protein content. Weather elements summarised across a week’s timescale from the test day usually explained milk yield and fat content better than shorter-scale (3 day, test day, test day −1) metrics. Then, examining a subset of key weather variables using restricted maximum likelihood, we found that THI, wind speed and the number of hours of sunshine influenced milk yield and composition. The shape and magnitude of these effects depended on whether animals were inside or outside on the test day. The milk yield of cows outdoors was lower at the extremes of THI than at average values, and the highest yields were obtained when THI, recorded at 0900 h, was 55 units. Cows indoors decreased milk yield as THI increased. Fat content was lower at higher THIs than at intermediate THIs in both environments. Protein content decreased as THI increased in animals kept indoors and outdoors, and the rate of decrease was greater when animals were outside than when they were inside. Moderate wind speeds appeared to alleviate heat stress. These results show that milk yield and composition are impacted at the upper extreme of THI under conditions currently experienced in Scotland, where animals have so far experienced little pressure to adapt to heat stress.

Excellent reproductive performance in both males and females is fundamental to profitable dairy and beef production systems. In this review we undertook a meta-analysis of genetic parameters for female reproductive performance across 55 dairy studies or populations and 12 beef studies or populations as well as across 28 different studies or populations for male reproductive performance. A plethora of reproductive phenotypes exist in dairy and beef cattle and a meta-analysis of the literature suggests that most of the female reproductive traits in dairy and beef cattle tend to be lowly heritable (0.02 to 0.04). Reproductive-related phenotypes in male animals (e.g. semen quality) tend to be more heritable than female reproductive phenotypes with mean heritability estimates of between 0.05 and 0.22 for semen-related traits with the exception of scrotal circumference (0.42) and field non-return rate (0.001). The low heritability of reproductive traits, in females in particular, does not however imply that genetic selection cannot alter phenotypic performance as evidenced by the decline until recently in dairy cow reproductive performance attributable in part to aggressive selection for increased milk production. Moreover, the antagonistic genetic correlations among reproductive traits and both milk (dairy cattle) and meat (beef cattle) yield is not unity thereby implying that simultaneous genetic selection for both increased (milk and meat) yield and reproductive performance is indeed possible. The required emphasis on reproductive traits within a breeding goal to halt deterioration will vary based on the underlying assumptions and is discussed using examples for Ireland, the United Kingdom and Australia as well as quantifying the impact on genetic gain for milk production. Advancements in genomic technologies can aid in increasing the accuracy of selection for especially reproductive traits and thus genetic gain. Elucidation of the underlying genomic mechanisms for reproduction could also aid in resolving genetic antagonisms. Past breeding programmes have contributed to the deterioration in reproductive performance of dairy and beef cattle. The tools now exist, however, to reverse the genetic trends in reproductive performance underlying the observed phenotypic trends.

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat–protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

Milk production, fertility, longevity and health records, were extracted from databases of two milk recording organisations in the United Kingdom for the first three lactations of the Holstein–Friesian breed. These included data related to health events (mastitis and lameness), voluntarily recorded on a proportion of farms. The data were analysed to calculate disease incidence levels and to estimate genetic parameters for health traits and their relationships with production and other functional traits. The resulting dataset consisted of 124 793 lactations from 75 137 animals of 1586 sires, recorded in 2434 herds. Incidence of health events increased with parity. The overall incidence of mastitis (MAS) and lameness (LAM), defined as binary traits, were 17% and 16%, respectively. Heritability estimates for MAS and LAM were 0.04 and 0.02, respectively, obtained from repeatability linear sire models. Heritability estimates of mastitis and lameness as count traits were slightly higher, 0.05 and 0.03, respectively. Genetic correlations were obtained by bivariate analyses of all pair-wise combinations between milk 305-day yield (MY), protein 305-day yield (PY), fat 305-day yield (FY), lactation average loge transformed lactation average somatic cell count (SCS), calving interval (CI), days to first service (DFS), non-return at 56 days (NR56), number of inseminations (NINS), mastitis (MAS), number of mastitis episodes (NMAS), lameness (LAM), number of lameness episodes (NLAM) and lifespan score (LS). As expected, MAS was correlated most strongly with SCS (0.69), which supports the use of SCS as an indicator trait for mastitis. Genetic correlations between MAS and yield and fertility traits were of similar magnitude ranging from 0.27 to 0.33. Genetic correlations between MAS with LAM and LS were 0.38 and −0.59, respectively. Not all genetic correlations between LAM and other traits were significant because of fewer numbers of lameness records. LAM had significant genetic correlations with MY (0.38), PY (0.28), CI (0.35), NINS (0.38) and LS (−0.53). The heritability estimates of mastitis and lameness were low; therefore, genetic gain through direct selection alone would be slow, yet still positive and cumulative. Direct selection against mastitis and lameness as additional traits should reduce incidence of both diseases, and simultaneously improve fertility and longevity. However, both health traits had antagonistic relationships with production traits, thus genetic gain in production would be slower.

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein–Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all

Many governments have signed up to greenhouse gas emission (GHGE) reduction programmes under their national climate change obligations. Recently, it has been suggested that the use of extended lactations in dairy herds could result in reduced GHGE. Dairy GHGE were modelled on a national basis and the model was used to compare emissions from lactations of three different lengths (305, 370 and 440 days), and a current ‘base’ scenario on the basis of maintaining current milk production levels. In addition to comparing GHGE from the average ‘National Herd’ under these scenarios, results were used to investigate how accounting for lactations of different lengths might alter the estimation of emissions calculated from the National Inventory methodology currently recommended by Intergovernmental Panel on Climate Change. Data for the three lactation length scenarios were derived from nationally recorded dairy performance information and used in the GHGE model. Long lactations required fewer milking cows and replacements to maintain current milk yield levels than short ones, but GHGEs were found to rise from 1214 t of CO2 equivalent (CE)/farm per year for lactations of 305 days to 1371 t CE/farm per year for 440-day lactations. This apparent anomaly can be explained by the less efficient milk production (kg milk produced per kg cow weight) found in later lactation, a more pronounced effect in longer lactations. The sensitivity of the model to changes in replacement rate, persistency and level of milk yield was investigated. Changes in the replacement rate from 25% to 20% and in persistency by −10% to +20% resulted in very small changes in GHGE. Differences in GHGE due to the level of milk yield were much more dramatic with animals in the top 10% for yield, producing about 25% less GHGE/year than the average animal. National Inventory results were investigated using a more realistic spread of lactation lengths than recommended for such calculations using emissions calculated in the first part of the study. Current UK emission calculations based on the National Inventory were 329 Gg of methane per year from the dairy herd. Using the national distribution of lactation lengths, this was found to be an underestimate by about 10%. This work showed that the current rise in lactation length or a move towards calving every 18 months would increase GHGE by 7% to 14% compared with the current scenario, assuming the same milk yield in all models. Increased milk yield would have a much greater effect on reducing GHGE than changes to lactation length, replacement rate or persistency. National Inventory methodology appears to underestimate GHGE when the distribution of lactation lengths is considered and may need revising to provide more realistic figures.

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

Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin – Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events – external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein–Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with the feed utilisation complex: fat and protein corrected milk yield (FPCM), dry matter intake (DMI), body condition score (BCS) and live-weight (LW). Phenotypic data and 37 590 single nucleotide polymorphisms (SNPs) were available on up to 1629 animals. Genetic parameters of the traits were estimated using a linear animal model with pedigree information, and univariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. The variation in the phenotypes explained by the SNPs on each chromosome was related to the size of the chromosome and was relatively consistent for each trait with the possible exceptions of BTA4 for BCS, BTA7, BTA13, BTA14, BTA18 for LW and BTA27 for DMI. For LW, BCS, DMI and FPCM, 266, 178, 206 and 254 SNPs had a Bayes factor >3, respectively. Olfactory genes and genes involved in the sensory smell process were overrepresented in a 500 kbp window around the significant SNPs. Potential candidate genes were involved with functions linked to insulin, epidermal growth factor and tryptophan.

Genetic improvement of livestock is a particularly effective technology, producing permanent and cumulative changes in performance. This paper highlights some of the options for including mitigation in livestock breeding schemes, focusing on ruminant species, and details three routes through which genetic improvement can help to reduce emissions per kg product via: (i) improving productivity and efficiency, (ii) reducing wastage in the farming system and (iii) directly selecting on emissions, if or when these are measurable. Selecting on traits that improve the efficiency of the system (e.g. residual feed intake, longevity) will have a favourable effect on the overall emissions from the system. Specific examples of how genetic selection will have a favourable effect on emissions for UK dairy systems are described. The development of breeding schemes that incorporate environmental concerns is both desirable and possible. An example of how economic valuation of public good outcomes can be incorporated into UK dairy selection indices is given. This paper focuses on genetic selection tools using, on the whole, currently available traits and tools. However, new direct and indirect measurement techniques for emissions will improve the potential to reduce emissions by genetic selection. The complexities of global forces on defining selection objectives are also highlighted.