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Abstract Research has shown that total bacterial count (TBC), which is the bacterial growth per ml of milk over a fixed period of time, can be decreased by good hygiene and farm management practices. The objective of the current study was to quantify the associations between herd management factors and bulk tank TBC in Irish spring calving, grass-based dairy herds. The relationship between bulk tank TBC and farm management and infrastructure was examined using data from 400 randomly selected Irish dairy farms where the basal diet was grazed grass. Herd management factors associated with bulk tank TBC were identified using linear models with herd annual total bacterial score (i.e., arithmetic mean of the natural logarithm of bulk tank TBC) included as the dependent variable. All herd management factors were individually analysed in a separate regression model, that included an adjustment for geographical location of the farm. A multiple stepwise regression model was subsequently developed. Median bulk tank TBC for the sample herds was 18,483 cells/ml ranging from 10,441 to 130,458 cells/ml. Results from the multivariate analysis indicated that the following management practices were associated with low TBC; use of heated water in the milking parlour; participation in a milk recording scheme; and tail clipping of cows at a frequency greater than once per year. Increased level of hygiene of the parlour and cubicles were also associated with lower TBC. Herd management factors associated with bulk tank TBC in Irish grazing herds were generally in agreement with most previous studies from confinement systems of milk production.

Abstract The relationship between bulk tank somatic cell count (SCC) and farm management and infrastructure was examined using data from 398 randomly selected, yet representative, Irish dairy farms where the basal diet is grazed grass. Median bulk tank SCC for the farms was 282,887 cells/ml ranging from 82,209 to 773,028 cells/ml. Two questionnaires were administered through face-to-face contact with each farmer. Herd-level factors associated with bulk tank SCC were determined using linear models with annual somatic cell score (i.e., arithmetic mean of the natural logarithm of bulk tank SCC) included as the dependent variable. All herd level factors were analysed individually in separate regression models, which included an adjustment for geographical location of the farm; a multiple regression model was subsequently developed. Management practices associated with low SCC included the use of dry cow therapy, participation in a milk recording scheme and the use of teat disinfection post-milking. There was an association between low SCC and an increased level of hygiene and frequency of cleaning of the holding yard, passageways and cubicles. Herd management factors associated with bulk tank SCC in Irish grazing herds are generally in agreement with most previous studies from confinement systems of milk production.

Background: In patients in whom attempted endoscopic stenting of malignant biliary obstruction fails, combined percutaneous-endoscopic stenting and percutaneous stenting using expandable metallic endoprostheses offer alternative approaches to biliary drainage. Despite the popularity of the percutaneous route, there is no available evidence to support its superiority over combined stenting in this patient group. The objective of this study was to present the short- and long-term results of a large series of combined percutaneous-endoscopic stenting procedures and identify factors associated with adverse outcome.Methods: Data were retrospectively collected on patients undergoing combined percutaneous-endoscopic biliary stenting for malignant biliary obstruction between January 2002 and December 2006. Short- and long-term outcomes were recorded, and pre-procedure variables correlated with adverse outcome.Results: Combined biliary stenting was technically successful in 102 (96.2%) of 106 patients. Procedure-associated mortality rate was 0%. In-hospital morbidity and mortality rates were 24.5% and 16.7%, respectively, with the majority of deaths resulting from biliary sepsis. Median survival was 100 days, with a 13.7% stent occlusion rate. On multivariable analysis, baseline American Society of Anaesthesiologists (ASA) grade, decreasing serum albumin and increasing leucocyte count were independently associated with in-hospital mortality following combined stenting.Conclusion: Combined biliary stenting is associated with short- and long-term outcomes equal to those reported in recent series of percutaneous transhepatic stenting. Randomised control trials, including cost-effectiveness analyses, are required to further compare these techniques. Outcomes following combined stenting may be further improved by early recognition and treatment of sepsis and scrupulous management of co-morbid disease. [ABSTRACT FROM AUTHOR]

Introduction: Many young women presenting with lower abdominal pain are referred to general surgeons with possible appendicitis. For some of these patients there will be a gynaecological cause for their pain. There has been a steady increase in the incidence of Chlamydia infections and pelvic inflammatory disease (PID) among the general population. Therefore, are general surgeons considering this as a diagnosis for lower abdominal pain in women?Methods: One hundred and ninety three women who had been admitted with lower abdominal pain to a single hospital between 1999 and 2001 were identified using computerised records and the notes were examined. One hundred and eighty six women were included in the audit. Investigations and treatments instigated for these patients were then carefully recorded.Results: Seventy-four patients underwent appendicectomy, of which 59 were histologically confirmed. Eighty-nine patients (47.8%) of admissions had no final diagnosis and were not screened for Chlamydia trachomatis. Sexual history was recorded in only 51% of admissions. Vaginal swabs were sent in only 7.3% of admissions.Conclusion: Current guidelines for Chlamydia trachomatis screening produced by the Chief Medical Officer (CMO) include screening in women presenting with lower abdominal pain as well as those with post-coital or intermenstrual bleeding. Most women who present with classical symptoms of PID will present to gynaecological specialities for further management. However, a significant number of women presenting atypically will be referred to surgeons to exclude gastrointestinal causes for their lower abdominal pain. These women could and probably should be screened for Chlamydia trachomatis. [ABSTRACT FROM AUTHOR]

Despite the multiple definitions currently used to express enteric methane emissions from ruminants, no consensus has been reached on the most appropriate definition. The objective of the present study was to explore alternative trait definitions reflecting animal level differences in enteric methane emissions in growing cattle. It is likely that no single methane trait definition will be best suited to all intended use cases, but at least, knowing the relationships between the different traits may help inform the selection process. The research aimed to understand the complex interrelationships between traditional and novel methane traits and their association with performance traits across multiple breeds and sexes of cattle; also of interest was the extent of variability in daily enteric methane emissions independent of performance traits like feed intake, growth and live-weight. Methane and carbon dioxide data were collected using the Greenfeed system on 939 growing crossbred cattle from a commercial feedlot. Performance traits including feed intake, feeding behavior, liveweight, live animal ultrasound, subjectively scored skeletal and muscular traits, and slaughter data were also available. A total of 13 different methane traits were generated, including (average) daily methane production, five ratio traits and seven residual methane (RMP) traits. The RMP traits were defined as methane production adjusted statistically for different combinations of the performance traits of energy intake, liveweight, average daily gain and carcass weight; terms reflecting systematic effects were also included in the fixed effects linear models. Of the performance traits investigated, liveweight and energy intake individually explained more of the variability in methane production than growth rate or fat. All definitions of RMP were strongly phenotypically correlated with each other (>0.90) as well as with methane production itself (>0.86); the RMP traits were also moderately correlated with the methane ratio traits (>0.57). The dataset included heifers, steers and bulls; bulls were either fed a total mixed ration or ad lib concentrates. When all sexes fed total mixed ration were compared, bulls, on average, emitted the most enteric methane per day of 269.53g, while heifers and steers produced 237.54g and 253.26g, respectively. Breed differences in the methane traits existed, with Limousins, on average, producing the least amount of methane of the breeds investigated. Herefords and Montbéliardes produced 124.50g and 130.77g more methane per day, respectively, than Limousins. The most efficient 10% of test-day records, as defined by daily methane independent of both energy intake and liveweight emitted, on average, 54.60 g/d less methane than animals that were average for daily methane independent of both energy intake and liveweight. This equates to 6.5kg less methane production per animal over a 120-day finishing period for the same feed intake and liveweight., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Although a significant cost, genotyping an entire population offers many benefits, many of which can reduce the workload and effort in decision-making on farm. As well as providing more accurate predictions of the genetic merit of individuals (and by extension their expected performance), national genotyping strategies enable complete traceability from the cradle to the grave as well as parentage discovery. The information available per animal aids more informed breeding and management decisions, including mating advice, and determining the optimal role and eventual fate of each animal., Competing Interests: Disclosure The authors have no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Genetic evaluations in beef cattle have evolved over the past 50 years relative to the hardware or software used, the statistical methodology underpinning them, and the traits evaluated. However, the underlying premise has remained the same; to generate predictions of genetic merit such that selection decisions can be made that materialize as phenotypic changes in commercial animals. The wide-spread availability and adoption of genomic technology has enabled more accurate genetic predictions of young animals albeit with the requirement of continual collection and reporting of phenotypic data., Competing Interests: Disclosure The authors have no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Twin birth in dairy cattle is generally unfavorably associated with reproductive performance and calf survival in dairy cows. Genetic selection to reduce twinning rate in dairy cattle may be desirable, provided there are no undesirable correlated responses in other traits. The current study was undertaken to characterize the genomic architecture of twinning rate in the Irish Holstein-Friesian population, and to quantify the genetic relationship of twinning with other reproductive traits and milk yield. Calving records from the years 1996 to 2022 were used together with pedigree information to generate breeding value estimates for twinning rate. Genome-wide association analyses of twinning rate, calving interval, cow survival and age at first calving were conducted using de-regressed breeding values estimates for 2,656 Holstein-Friesian sires. Full genome sequence data imputed from approximately 50,000 single nucleotide polymorphisms were available for all sires. The heritability of twinning rate was 0.0118 ± 0.0010. Twinning rate was very weakly genetically correlated with both milk yield (0.13) and the reproductive traits (-0.26 to 0.14). Genomic analyses detected an association with twinning rate at 31.1 Mb on BTA11 in close proximity to genes for follicle-stimulating hormone receptor and luteinizing hormone-chorionic gonadotropin receptor, supporting previous studies. The most significant SNP in this region was not associated with milk yield, indicating the potential for selection to reduce twinning rate without detrimentally affecting milk yield. Novel SNP associations with age at first calving on BTA27 and from a meta-analysis of calving interval and age at first calving on BTA29 were also identified and are candidates for future validation and study., (© 2025, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Aneuploidy, a genetic condition characterised by the deletion (monosomy) or duplication (trisomy) of a chromosome, has been extensively studied in humans, particularly in the context of trisomy on chromosome 21, also known as Down syndrome. Research on autosomal aneuploidy in live-born cattle has been limited to case reports, resulting in a lack of prevalence estimates of aneuploidy in cattle. Furthermore, the viability or lethality of aneuploidy on specific autosomes in cattle has not been well documented. The objective of this study was to estimate the prevalence of autosomal aneuploidy in a large population of new-born and juvenile beef and dairy cattle using single nucleotide polymorphism (SNP) chip genotype intensity data. Of the population of 779,138 cattle genotyped when younger than 15 months of age, 139 cattle (i.e., 0.017%) were diagnosed with one case of autosomal trisomy. Trisomy in only 10 different autosomes were detected (BTA 4, 6, 12, 15, 20, 24, 26, 27, 28 and 29) albeit the one case of trisomy detected on Bos taurus autosome (BTA) 4 was in an additional population of 341,927 cattle that were genotyped at > 15 months of age and was therefore excluded from prevalence estimates to minimise bias. The prevalence of trisomy per chromosome was generally inversely related to the length of the chromosome. Although the number of affected individuals was few, there was no evidence of differences in prevalence by breed, inbreeding level or parental age. The parental origin of the detected cases of trisomy was maternal for 92% of the cases. No cases of monosomy were detected despite the large dataset, which included calves genotyped at birth, indicating the potential lethal nature of monosomy in cattle. Cytogenetic testing was used to verify three of the animals with detected autosomal trisomy who were still alive. Eighteen of the 139 animals identified with autosomal trisomy were recorded as being stillborn, resulting in a prevalence of autosomal aneuploidy in live-born cattle of 0.015%. Of the 121 live-born cattle with autosomal trisomy, a total of 68 died on farm at, on average (standard deviation), 6.8 (8.7) months of age. All animals with autosomal trisomy on BTA 6, 12, 15, 20 or 24 were either stillborn or died on farm within 15 days of birth. This study is the first report of trisomy on BTA 4, 6, 15, 20 and 27 in live-born cattle, as well as the first to document fertile cows with trisomy on BTA 4, 27 or 28. Given that genotype intensity SNP data from SNP-chips are readily available, identifying animals affected with autosomal aneuploidy as well as quantifying and monitoring the incidence can be easily undertaken., (© 2024 The Author(s). Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

As the proportion of prime carcasses originating from dairy herds increases, the focus is shifting to the beef merit of the progeny from dairy herds. Several dairy cow total merit indexes include a negative weight on measures of cow size. However, there is a lack of knowledge on the effect of genetic selection, solely for lighter or smaller-sized dairy cows, on the beef performance of their progeny. Therefore, the objective of this study was to quantify the genetic correlations among cow size traits (i.e., cow BW, cow carcass weight [CW]), cow BCS, cow carcass conformation (CC), and cow carcass fat cover (CF), as well as the correlations between these cow traits and a series of beef performance slaughter-related traits (i.e., CW, CC, CF, and age at slaughter [AS]) in their progeny. After data editing, there were 52,950 cow BW and BCS records, along with 57,509 cow carcass traits (i.e., CW, CC, and CF); carcass records from 346,350 prime animals along with AS records from 316,073 prime animals were also used. Heritability estimates ranged from moderate to high (0.18-0.62) for all cow and prime animal traits. The same carcass trait in cows and prime animals were strongly genetically correlated with each other (0.76-0.85), implying that they are influenced by very similar genomic variants. Selecting exclusively for cows with higher BCS (i.e., fatter) will, on average, produce more conformed prime animal carcasses, owing to a moderate genetic correlation (0.30) between both traits. Genetic correlations revealed that selecting exclusively for lighter BW or CW cows will, on average, result in lighter prime animal carcasses of poor CC, while also delaying slaughter age. Nonetheless, selective breeding through total merit indexes should be successful in breeding for smaller dairy cows and desirable prime animal carcass traits concurrently, because of the nonunity genetic correlations between the cow and prime animal traits; this will help to achieve a more ethical, environmentally sustainable, and economically viable dairy-beef industry., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

This international survey provides insights into public awareness of the importance of iodine as an essential trace mineral in human health along with knowledge of iodine dietary sources. The online questionnaire included sociodemographic aspects and dietary iodine consumer awareness on 7-point Likert-type questions. A total of 4,704 questionnaires from 16 countries were considered. Answers were analyzed through a multiple regression linear model including country, gender, age, education level, and employment status as fixed effects. Respondents were moderately aware of the importance of fish (4.86) and seafood (4.90) as dietary iodine sources, but less aware of milk as a primary iodine source (3.32). Respondent awareness varied considerably across countries. Age, education level, and employment status only modified their perception when asked about fish and seafood as a source of iodine, with elderly respondents, those highly educated and of working age being more aware of their relevance as dietary iodine sources. Respondent knowledge did not vary by age, education level, employment status, or gender when asked about cereals, vegetables and fruits, meat and milk as iodine-rich food sources. Consequently, labeling milk and dairy products as an iodine-rich food source should be considered. Public authorities can consider the results from this survey in promotional campaigns to improve the awareness of different iodine sources and their beneficial effect on health., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Routine milk samples are commonly subjected to spectroscopic analysis within the mid-infrared (MIR) region of the electromagnetic spectrum to estimate macro-constituents of milk like fat, protein, lactose, and urea content. These spectra, however, can also be used to predict other traits, such as daily body condition score (BCS) change. The objective of the present study was to assess the transferability across countries of equations to predict daily body condition score change (ΔBCS) developed using milk MIR data collected in Ireland and in Canada. Body condition was scored on a scale from 1 (emaciated) to 5 (obese) in both countries. A total of 347,254 BCS records from 80,400 Canadian cows were available along with 73,193 BCS records from 6,572 Irish cows. Partial least squares regression (PLSR) and neural networks (NN) were separately used to predict daily ΔBCS. Two scenarios were studied 1) using Canadian and Irish data combined as the calibration data set to predict daily ΔBCS in Canada and in Ireland separately, and 2) Canadian and Irish data used separately to predict daily ΔBCS in each country separately. These prediction methods were applied to data with and without pretreatment (i.e., first derivative of the spectrum) as well as with and without standardizing daily ΔBCS across countries. For all the scenarios investigated, the correlation between actual and predicted daily ΔBCS when calibrated and validated (using cross-validation) in the same country ranged from 0.92 to 0.94, and from 0.85 to 0.87 for the Canadian and Irish data sets, respectively. When the data from Canada and Ireland were combined in the calibration process to predict daily ΔBCS, the correlations between actual and predicted ΔBCS were ≥ 0.90 and ≥ 0.80 for Canadian and Irish daily ΔBCS, respectively indicating no improvement in predictive ability. Predictive performance when calibrated using just Canadian data and validated using just Irish data was poor, and vice versa. Nonetheless, when developing equations for a country for which a limited database (i.e., 100 records) of gold standard and MIR data were available, predictive performance improved when the limited database was supplemented with the large data set from the other country. In general, for some of the investigated scenarios, standardizing the daily ΔBCS data within country before undertaking the calibration improved prediction accuracy. In conclusion, the benefit of merging data from different countries, at least based on the trait (i.e., daily ΔBCS) and countries (i.e., Ireland and Canada) considered in the present study were limited and, in cases, counter-productive., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Many sport horse studbooks worldwide use microsatellite markers for parentage verification. However, many have expressed a desire to introduce genomic selection using genome-wide dense single nucleotide polymorphism (SNP) genotypes to complement their current breeding programmes. Hence, it does not make sense to genotype the same animal for both microsatellite markers and SNP markers. Transitioning to SNP-based parentage verification is an obvious solution but one barrier to this transition is the lack of SNP data on parents from which to verify parentage against. Therefore, the objective of this study was to assess the ability to impute the SNP genotype of a stallion from the genotypes of its progeny, with or without the consideration of the genotype of the progeny's dam. Genotype information from 55 935 SNPs was available on 13 327 horses. A total of 98 stallions had genotype data on 10 progeny and the genotypes of these stallions were used as a test population. Genome-wide genotype imputation was undertaken by combining a family- and population-based imputation approach. Several different scenarios were assessed to quantify the ability to accurately impute the genotype of a stallion based on genotype data of incrementally more half-sibling progeny. Using genomic information from four progeny the average genotype concordance of the imputed sire genotype compared to the actual sire's genotype was 0.932. The average genotype concordance rate increased to 0.960 when the genotypes of 10 progeny were included in the imputation process. The inclusion of the genotypes of the dams of the progeny improved the concordance rate from 0.932 to 0.977 when based on the genotype of just four progeny and their dams and from 0.960 to 0.996 when based on the genotype of 10 progeny and their dams. These results suggest it is possible to impute the genotype of a non-genotyped horse from the genotypes of its progeny and that the inclusion of the genotypes of the dams of the progeny improves this imputation accuracy further., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Aneuploidy is a genetic condition characterized by the loss or gain of one or more chromosomes. Aneuploidy affecting the sex chromosomes can lead to infertility in otherwise externally phenotypically normal cattle. Early identification of cattle with sex chromosomal aneuploidy is important to minimize the costs associated with rearing infertile cattle and futile breeding attempts. As most livestock breeding programs routinely genotype their breeding populations using single nucleotide polymorphism (SNP) arrays, this study aimed to assess the feasibility of integrating an aneuploidy screening tool into the existing pipelines that handle dense SNP genotype data. A further objective was to estimate the prevalence of sex chromosome aneuploidy in a population of 146,431 juvenile cattle using available genotype intensity data. Three genotype intensity statistics were used: the LogR Ratio (LRR), R-value (the sum of X and Y SNP probe intensities), and B-allele frequency (BAF) measurements. Within the female-verified population of 124,958 individuals, the estimated prevalence rate was 0.0048% for XO, 0.0350% for XXX, and 0.0004% for XXY. The prevalence of XXY in the male-verified population was 0.0870% (i.e., 18 out of 20,670 males). Cytogenetic testing was used to verify 2 of the XXX females who were still alive. The proposed approach can be readily integrated into existing genomic pipelines, serving as an efficient, large-scale screening tool for aneuploidy. Its implementation could enable the early identification of infertile animals with sex-chromosome aneuploidy., (© 2024 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

Minimizing pollution from the dairy sector is paramount; one potential cause of such pollution is excess nitrogen. Nitrogen pollution contributes to a deterioration in water quality as well as an increase in both eutrophication and greenhouse gases. It is therefore essential to minimize the loss of nitrogen from the sector, including excretion from the cow. Breeding programs are one potential strategy to improve the efficiency with which nitrogen is used by dairy cows, but they rely on routine access to individual cow information on how efficiently each cow uses the nitrogen it ingests. A total of 3,497 test-day records for individual-cow nitrogen efficiency metrics along with milk yield and the associated milk spectra were used to investigate the ability of milk infrared spectral data to predict these nitrogen traits; both traditional partial least squares regression and neural networks were used in the prediction process. The data originated from 4 farms across 11 yr. The nitrogen traits investigated were nitrogen intake, nitrogen use efficiency, and nitrogen balance. Both nitrogen use efficiency and nitrogen balance were calculated considering nitrogen intake, nitrogen in milk, nitrogen in the conceptus, nitrogen used for the growth, nitrogen stored in body reserves, and nitrogen mobilized from body reserves. Irrespective of the nitrogen-related trait being investigated, the best predictions from 4-fold cross validation were achieved using neural networks that considered both the morning and evening milk spectra along with milk yield, parity, and DIM in the prediction process. The coefficient of determination in the cross validation was 0.61, 0.74, and 0.58 for nitrogen intake, nitrogen use efficiency, and nitrogen balance, respectively. In a separate series of validation approaches, the calibration and validation was stratified by herd (n = 4) and separately by year. For these scenarios, partial least squares regression generated more accurate predictions compared with neural networks; the coefficient of determination was always lower than 0.29 and 0.60 when validation was stratified by herd and year, respectively. Therefore, if the variability of the data being predicted in the validation datasets is similar to that in the data used to develop the predictions, then nitrogen-related traits can be predicted with reasonable accuracy. In contrast, where the variability of the data that exists in the validation dataset is poorly represented in the calibration dataset, then poor predictions will ensue., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objective of the study was to quantify the association between the birth weight of a calf and the subsequent performance of its dairy dam in the absence of any recorded calving assistance. A total of 11,592 lactation records from 4,549 spring-calving dairy cows were used. The association between a series of quantitative cow performance metrics (dependent variable) and calf birth weight (independent variable) was determined using linear mixed models; logistic regression was used where the dependent variable was binary. Nuisance factors in the models were calf sex, heterosis coefficient of both the cow and calf, dry period length immediately before the birth of the calf, cow age at calving relative to the median cow age per parity, breed proportion of the cow, cow live weight between 100 and 200 d of lactation relative to the mean cow weight per parity, and contemporary group. Calf birth weight was included in the model as either a continuous or a categorical variable. Primiparous and multiparous cows were analyzed separately. Mean (SD) calf birth weight was 36.2 (6.8) kg. In primiparous cows, calf birth weight was associated with milk yield in the first 60 d of lactation, calving to first service interval, calving body weight (BW), and both nadir BW and body condition score (BCS). In multiparous cows, calf birth weight was associated with total milk, fat, and protein yield in the first 60 and 305 d of lactation, peak milk yield, total milk solids, both calving and nadir BW, and BCS loss from calving to nadir. Relative to primiparous cows that gave birth to calves weighing 34 to 37 kg (i.e., population mean), their contemporaries who gave birth to calves that weighed 15 to 29 kg produced 9.82 kg more milk in the first 60 d of lactation, had a 2-d shorter interval to first service, and were 8.08 kg and 5.51 kg lighter at calving and nadir BW, respectively; the former was also 0.05 units lower in BCS (5-point scale, 1 = emaciated and 5 = obese) at nadir. Relative to multiparous cows that gave birth to calves that were 34 to 37 kg birth weight, multiparous cows that gave birth to calves that were 15 to 29 kg yielded 59.63 kg, 2.44 kg, and 1.76 kg less milk, fat, and protein, respectively, in the first 60 d of lactation; produced 17.69 kg less milk solids throughout the 305-d lactation; and were also 10.49 kg lighter at nadir and lost 0.01 units more BCS to nadir. In a separate series of analyses, sire breed was added to the model as a fixed effect with and without calf birth weight. When calf birth weight was not adjusted for, 60-d milk yield for multiparous cows who gave birth to calves sired by a traditional beef breed (i.e., Angus, Hereford) produced 59.63 kg more milk than multiparous cows who gave birth to calves sired by a Holstein-Friesian. Hence, calf birth weight is associated with some subsequent dam performance measures; however, where associations do exist, the effect is biologically small., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Methane (CH 4 ) is a potent gas produced by ruminants, and new measurement techniques are required to generate large datasets suitable for genetic analysis. One such technique are portable accumulation chambers (PAC), a short-term sampling method. The objectives of the current study were to explore the relationship between CH 4 and carbon dioxide (CO 2 ) output measured using both PAC and respiration chambers (RC) in growing lambs, and separately investigate the relationship among CH 4 , CO 2 and measured ad libitum DM intake (DMI). Methane, CO 2 and DMI were measured on 30 Suffolk and 30 Texel ewe lambs (age 253 ± 12 days) using the RC and PAC sequentially. The experiment was conducted over a 14-day period, with DMI measured from days 1 to 14; measurements in RC were conducted from days 10 to 12, while measurements in PAC were taken twice, the day immediately prior to the lambs entering the RC (day 9; PAC Pre-RC) and on the day lambs exited the RC (day 13; PAC Post-RC). Greater CH 4 and CO 2 output was measured in the RC than in the PAC (P < 0.01); similarly mean CH 4 yield was greater when measured in the RC (15.39 ± 0.452 g CH 4 /kg DMI) compared to PAC (8.01 ± 0.767 g CH 4 /kg DMI). A moderate correlation of 0.37 was found between CH 4 output measured in PAC Pre-RC and the RC, the corresponding regression coefficient of CH 4 output measured in the RC regressed on CH 4 output measured in PAC Pre-RC was close to unity (0.74; SE 0.224). The variance of CH 4 and CO 2 output within the measurement technique did not differ from each other (P > 0.05). Moderate to strong correlations were found between CH 4 and CO 2 per kg of live weight and CH 4 and CO 2 yield. Results from this study highlight the suitability of PAC as a ranking tool to rank animals based on their gaseous output when compared to the RC. However, repeated measurements separated by several days may be beneficial if precise rankings are required. Given the close to unity regression coefficient of CH 4 output measured in the RC regressed on CH 4 output measured in PAC Pre-RC suggests that PAC could also be potentially used to estimate absolute CH 4 output; however, further research is required to substantiate this claim. When DMI is unknown, CH 4 and CO 2 per kg of live weight are a suitable alternative to the measurement of CH 4 and CO 2 yield., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Improved nitrogen utilization of dairy production systems should improve not only the economic output of the systems but also the environmental metrics. One strategy to improve efficiency is through breeding programs. Improving a trait through breeding is conditional on the presence of exploitable genetic variability. Using a database of 1,291 deeply phenotyped grazing dairy cows, the genetic variability for 2 definitions of nitrogen utilization was studied: nitrogen use efficiency (i.e., nitrogen output in milk and meat divided by nitrogen available) and nitrogen balance (i.e., nitrogen available less nitrogen output in milk and meat). Variance components for both variables were estimated using animal repeatability linear mixed models. Genetic variability was detected for both nitrogen utilization metrics, even though their heritability estimates were low (<0.10). Validation of genetic evaluations revealed that animals divergent for nitrogen use efficiency or nitrogen balance indeed differed phenotypically, further demonstrating that breeding for improved nitrogen efficiency should result in a shift in the population mean toward better efficiency. Nitrogen use efficiency and nitrogen balance were not genetically correlated with each other (<|0.28|), and neither metric was correlated with milk urea nitrogen (<|0.12|). Nitrogen balance was unfavorably correlated with milk yield, showing the importance of including the nitrogen utilization metrics in a breeding index to improve nitrogen utilization without negatively impacting milk yield. In conclusion, improvement of nitrogen utilization through breeding is possible, even if more nitrogen utilization phenotypic data need to be collected to improve the selection accuracy considering the low heritability estimates., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Genetic selection has been identified as a promising approach for reducing enteric methane (CH4) emissions; a prerequisite for genetic evaluations; however, these are estimates of the necessary genetic parameters based on a population representative of where the genetic evaluations will be used. The objective of this study was, therefore, to derive genetic parameters for a series of definitions of CH4, carbon dioxide (CO2), and dry matter intake (DMI) as well as genetic correlations between CH4, CO2, and DMI in a bid to address the paucity of studies involving methane emissions measured in beef cattle using GreenFeed systems. Lastly, estimated breeding values (EBV) were generated for nine alternative definitions of CH4 using the derived genetic parameters; the EBV were validated against both phenotypic performance (adjusted for non-genetic effects) and the Legarra and Reverter method comparing EBV generated for a subset of the dataset compared to EBV generated from the entire dataset. Individual animal CH4 and CO2 records were available from a population of 1,508 multi-breed growing beef cattle using 10 GreenFeed Emission Monitoring systems. Nine trait definitions for CH4 and CO2 were derived: individual spot measures, the average of all spot measures within a 3-h, 6-h, 12-h, 1-d, 5-d, 10-d, and 15-d period and the average of all spot measures across the full test period (20 to 114 d on test). Heritability estimates from 1,155 animals, for CH4, increased as the length of the averaging period increased and ranged from 0.09 ± 0.03 for the individual spot measures trait to 0.43 ± 0.11 for the full test average trait; a similar trend existed for CO2 with the estimated heritability ranging from 0.17 ± 0.04 to 0.50 ± 0.11. Enteric CH4 was moderately to strongly genetically correlated with DMI with a genetic correlation of 0.72 ± 0.02 between the spot measures of CH4 and a 1-d average DMI. Correlations, adjusted for heritability, between the adjusted phenotype and (parental average) EBV ranged from 0.56 to 1.14 across CH4 definitions and the slope between the adjusted phenotype and EBV ranged from 0.92 to 1.16 (expectation = 1). Validation results from the Legarra and Reverter regression method revealed a level bias of between -0.81 and -0.45, a dispersion bias of between 0.93 and 1.17, and ratio accuracy (ratio of the partial evaluation accuracies on whole evaluation accuracies) from 0.28 to 0.38. While EBV validation results yielded no consensus, CH4 is a moderately heritable trait, and selection for reduced CH4 is achievable., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Directly measuring individual cow energy balance is not trivial. Other traits such as body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used worldwide to estimate cow body reserves, but the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec, Canada. Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from (1) MIR spectra only, (2) DIM only, or (3) MIR spectra and DIM together. The ΔBCS data in both the first 120 and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40 × 10 -3 BCS units in the 120-d dataset and of 3.63 × 10 -3 BCS units in the 305-d dataset. A 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81 × 10 -3 BCS units and 1.51 × 10 -3 BCS units, respectively, using the 120-d and 305-d dataset). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data were more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Reducing nitrogen pollution while maintaining milk production is a major challenge of dairy production. One of the keys to delivering on this challenge is to improve the efficiency of how dairy cows use nitrogen. Thus, estimating the nitrogen utilization of lactating grazing dairy cows and exploring the association between animal factors and productivity with nitrogen utilization are the first steps to understanding the nitrogen utilization complex in dairy cows. Nitrogen utilization metrics were derived from milk and body weight records from 1,291 grazing dairy cows of multiple breeds and crossbreeds; all cows had sporadic information on nitrogen intake concurrent with information on nitrogen sinks (and other nitrogen sources, such as body tissue mobilization). Several nitrogen utilization metrics were investigated, including nitrogen use efficiency (nitrogen output as products such as milk and meat divided by nitrogen intake) and nitrogen excreted (nitrogen intake less the nitrogen output as products such as milk and meat). In the present study, a primiparous Holstein-Friesian used, on average, 20.6% of the nitrogen it ate, excreting the surplus as feces and urine, representing 402 g of nitrogen per day. Intercow variability existed, with a between-cow standard deviation of 0.0094 for nitrogen use efficiency and 24 g of nitrogen per day for nitrogen excretion. As lactation progressed, nitrogen use efficiency declined and nitrogen excretion increased. Nevertheless, nitrogen use efficiency improved (i.e., decreased) from first to second parity, even though it did not improve from second to third parity or greater. Furthermore, nitrogen excretion continued to increase from first to third parity or greater. Nitrogen use efficiency and nitrogen excretion were negatively correlated (-0.56 to -0.40), signifying that dairy cows who partition more of the ingested nitrogen into products such as milk and meat, on average, also excrete less nitrogen. Milk urea nitrogen was, at best, weakly correlated with nitrogen use efficiency and nitrogen excretion; the correlations were between -0.01 and 0.06. In conclusion, several cow-level factors such as parity, stage of lactation, and breed were associated with the range of different nitrogen efficiency metrics investigated; moreover, even after accounting for such effects, 4.8% to 6.3% of the remaining variation in the nitrogen use efficiency and nitrogen balance metrics were attributable to intercow differences., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)