Impact on prime animal beef merit from breeding solely for lighter dairy cows.

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 impact 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 body weight (BW), cow carcass weight (CW)), cow body condition score (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 to 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 to 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 animals 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 non-unity 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.

Comparison of greenhouse gas emissions from sheep measured using both respiration and portable accumulation chambers.

Methane (CH4) 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 CH4 and carbon dioxide (CO2) output measured using both PAC and respiration chambers (RC) in growing lambs, and separately investigate the relationship among CH4, CO2 and measured ad libitum DM intake (DMI). Methane, CO2 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 CH4 and CO2 output was measured in the RC than in the PAC (P < 0.01); similarly mean CH4 yield was greater when measured in the RC (15.39 ± 0.452 g CH4/kg DMI) compared to PAC (8.01 ± 0.767 g CH4/kg DMI). A moderate correlation of 0.37 was found between CH4 output measured in PAC Pre-RC and the RC, the corresponding regression coefficient of CH4 output measured in the RC regressed on CH4 output measured in PAC Pre-RC was close to unity (0.74; SE 0.224). The variance of CH4 and CO2 output within the measurement technique did not differ from each other (P > 0.05). Moderate to strong correlations were found between CH4 and CO2 per kg of live weight and CH4 and CO2 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 CH4 output measured in the RC regressed on CH4 output measured in PAC Pre-RC suggests that PAC could also be potentially used to estimate absolute CH4 output; however, further research is required to substantiate this claim. When DMI is unknown, CH4 and CO2 per kg of live weight are a suitable alternative to the measurement of CH4 and CO2 yield.

Using milk mid-infrared spectroscopy to estimate cow-level nitrogen efficiency metrics.

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 relies on routine access to individual cow information on how efficiently each cows 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 years. 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 the reserves, and nitrogen mobilized from the reserves. Irrespective of the nitrogen-related trait being investigated, the best prediction 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 days in milk 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 data sets 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 data set is poorly represented in the calibration data set, then poor predictions will ensue.

Differences in the choice of beef artificial insemination sires used by dairy producers versus beef producers.

The growing demand among dairy producers for suitable beef sires to mate to their females creates the possibility of separate breeding programs to generate beef sires for the dairy sector versus those for the beef sector. Informing such a decision is the extent of the genetic differences among beef sires used by dairy producers relative to those used by beef producers. The objective therefore of the present study was to use a large national database of artificial insemination (AI) records in dairy and beef cow herds to establish the difference in mean genetic merit of beef AI sires used by dairy producers versus those used by cow-calf beef producers. The traits explored were gestation length, calving difficulty, and perinatal mortality as well as the 3 carcass traits of carcass weight, conformation, and fat score. Carcass conformation and fat score are mechanically assessed on a scale of 1 (poor conformation and low fat cover) to 15 (excellent conformation and high fat cover). Sire genetic merit differences for feed intake and docility were also examined. Estimates of genetic merit for all 8 traits on individual AI sires available at the time of service were used. A total of 1,230,622 AI records comprised 909,719 services from dairy herds and 320,903 services from beef herds were used. Of the 1,802 beef AI sires represented in the entire dataset, over half were used by both dairy and beef herds representing ≥98% of the services in each production system. However, the usage rate of individual AI sires differed between dairy and beef herds with the Spearman rank correlation between the quantity of inseminations per sire in dairy and beef herds being just 0.38. This correlation means that beef AI sires used heavily in the beef herd were not always those heavily used in dairy herds. A clear difference in the mean genetic merit of beef AI sires selected by dairy producers relative to those selected by beef cow-calf producers was obvious with the extent of the difference being a function of whether the female served was a nulliparous heifer or a cow. Much of the differences in genetic merit of chosen beef AI sires between dairy and beef producers was actually attributable to differences in breed choice, albeit some within-breed selection was also evident. Irrespective, dairy producers, on average, chose shorter gestation length sires whose progeny were genetically less predisposed to require intervention during the birthing process; these sires had genetic merit estimates expected to result in lighter and less conformed progeny carcasses relative to the beef AI sires used by beef producers. Results point to large differences in genetic merit of the beef AI sires chosen by dairy versus beef producers, much of which actually reflected differences in breed choice among dairy and beef producers.

Comparison of calving and revenue-generating qualities in beef-sired male and female progeny from dairy cows.

Although interest in beef-on-dairy breeding strategies is intensifying, little is actually known of the performance differences between beef-sired male and female progeny of dairy cows. The objective was therefore to use a large cross-sectional database of up to 1,389,670 animals to investigate if performance differences existed between male and female progeny generated from beef-on-dairy matings; the focus was on characteristics of interest to both the dairy producer (i.e., gestation length, calving performance, perinatal mortality, and calf sale value) and the beef producer (i.e., slaughter-related traits). While statistical differences existed between both sexes, the observed differences were not always biologically large, with some favoring females (e.g., calving traits and age at slaughter) and some favoring males (i.e., carcass weight). Beef-sired male calves had, on average, a 0.8 d longer gestation than their female counterparts; the sex difference in dairy-sired calves was, on average, 1.1 d, with the advantage to females. The odds of a difficult calving was 2.2 times greater for beef-sired male calves relative to beef-sired female calves; this translated to a difference in predicted probability of dystocia between the sexes of 1.8 percentage units. Male beef-sired calves sold at auctions <42 d of age were worth, on average, €32.40 more than beef-sired female calves. Focusing just on beef-sired progeny, relative to heifer carcasses (mean weight of 280.0 kg), the carcasses of steers (mean weight of 336.9 kg) and bulls (mean weight of 335.4) were 55.4 to 56.9 kg heavier. Based on a 15-point conformation scale, the carcasses of bulls were 1 unit superior to heifers, with the carcasses of the latter being 0.06 units better than steers. Heifers were slaughtered, on average, 79.1 d younger than steers although heifers were slaughtered, on average, 93.8 d older than bulls, the latter generally being finished on a more intensive diet relative to steers and heifers in Ireland. In conclusion, many benefits exist for beef-sired heifer calves in that they had, on average, shorter gestations with less expected assistance required at calving and, although their calf value was less and their carcasses were lighter than their male counterparts, they were slaughtered several months younger than steers.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples.

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.

Genetic covariance components for measures of nitrogen utilization in grazing dairy cows.

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 association between calf birth weight and the postcalving performance of its dairy dam in the absence of dystocia.

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.

Animal board invited review: Practical applications of genomic information in livestock.

Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal's (and by extension its progeny's) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application.

Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows.

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.

Mean breed performance of the progeny from beef-on-dairy matings.

Gains through breeding can be achieved through a combination of both between-breed and within-breed selection. Two suites of traits of particular interest to dairy producers when selecting beef bulls for mating to dairy females are calving-related attributes and the expected value of the subsequent calf, the latter usually being a function of expected carcass value. Estimated breed effects can be informative, particularly in the absence of across-breed genetic evaluations. The objective of the present study was to use a large national database of the progeny from beef-on-dairy matings to estimate the mean breed effects of the used beef sires. Calving performance (i.e., gestation length, calving difficulty score, and perinatal morality) as well as calf value were investigated; a series of slaughter-related traits (i.e., carcass metrics and age at slaughter) of the prime progeny were also investigated. Phenotypic data on up to 977,037 progeny for calving performance, 79,903 for calf price and 103,175 for carcass traits (including dairy × dairy progeny for comparative purposes) were used; sire breeds represented were Holstein-Friesian, Angus, Aubrac, Belgian Blue, Charolais, Hereford, Limousin, Salers, and Simmental. Large interbreed differences existed. The mean gestation length of male calves from beef sires varied from 282.3 d (Angus) to 287.4 d (Limousin) which were all longer than the mean of 280.9 d for Holstein-Friesian sired male calves. Relative to a Holstein-Friesian sire, the odds of dystocia varied from 1.43 (Angus) to 4.77 (Belgian Blue) but, once adjusted for both the estimated maternal genetic merit of the dam and direct genetic merit of the calf for calving difficulty, the range in odds ratios shrunk. A difference of €125.4 existed in calf sale price between the progeny of the different beef breeds investigated which represented over twice the residual standard deviation in calf price within the day of sale-Angus was the cheapest while Charolais calves were, on average, the most expensive calves. Mean carcass weight of steers, not adjusted for age at slaughter or carcass fat, varied from 327.1 kg (Angus) to 363.2 kg (Belgian Blue) for the beef breeds with the mean carcass weight of Holstein-Friesian steer progeny being 322.4 kg. Belgian Blues had, on average, the best carcass conformation with the Herefords and Angus having the worst of all beef breeds. Angus and Hereford steers were slaughtered the youngest of all beef breeds but just 9 d younger than the average of all other beef breeds yet 24 d younger than Holstein-Friesian sired progeny. Clear breed differences in calving and carcass performance exist among beef breeds mated to dairy females. Those breeds excelling in calving performance were not necessarily the best for carcass merit.

Cow-level factors associated with nitrogen utilization in grazing dairy cows using a cross-sectional analysis of a large database.

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.

Genetic (co)variance components for slaughter traits in a multi-breed sheep population.

Carcass value is one of the main contributors to revenue in meat sheep enterprises, while age at slaughter is also a major component to the cost of production. Despite the contribution of such traits to overall profit, little is actually known on the extent of exploitable genetic variability in the traits that govern carcass value (i.e. carcass weight, carcass conformation, carcass fat) and age at slaughter, especially independent of each other. The objective of the present study was to estimate genetic (co)variances for and among carcass weight, carcass conformation, carcass fat, kill-out percentage and age at slaughter as well as their genetic (co)variances with traits measured earlier in life. Data consisted of slaughter records from 15 714 lambs, with 12 630 of these lambs having at least one live weight measure. The heritability (SE) of carcass weight, carcass conformation, carcass fat, kill-out percentage, and age at slaughter was 0.14 (0.02), 0.19 (0.02), 0.08 (0.01), 0.22 (0.03), and 0.16 (0.02), respectively. The maternal heritability for age at slaughter was 0.07 (0.02); no maternal genetic influence was found on any of the other slaughter traits. The coefficient of genetic variation for carcass weight and age at slaughter was 3 and 8%, respectively. The correlations between the direct genetic effects for live weight throughout life, and carcass weight were weak up to weaning but were strong (0.83) thereafter. The correlation between the direct genetic effects of birth weight and age at slaughter was zero, but varied from -0.91 to -0.56 between live weight measured later in life and age at slaughter. Results demonstrate significant exploitable genetic variability in a range of slaughter traits with the prediction of genetic merit for carcass traits and age at slaughter being possible using live weight measures taken on live animals. For example, the accuracy of selection for slaughter traits (comprising of age at slaughter, carcass conformation and carcass fat) from weaning weight records available on 100 progeny was 0.37; when slaughter data were also available for 10 progeny, the accuracy of selection increased to 0.56.

Double pulsed field gradient diffusion MRI to assess skeletal muscle microstructure.

PURPOSE: Preliminary study to determine whether double pulsed field gradient (PFG) diffusion MRI is sensitive to key features of muscle microstructure related to function. METHODS: The restricted diffusion profile of molecules in models of muscle microstructure derived from histology were systematically simulated using a numerical simulation approach. Diffusion tensor subspace imaging analysis of the diffusion signal was performed, and spherical anisotropy (SA) was calculated for each model. Linear regression was used to determine the predictive capacity of SA on the fiber area, fiber diameter, and surface area to volume ratio of the models. Additionally, a rat model of muscle hypertrophy was scanned using a single PFG and a double PFG pulse sequence, and the restricted diffusion measurements were compared with histological measurements of microstructure. RESULTS: Excellent agreement between SA and muscle fiber area (r2 = 0.71; p < 0.0001), fiber diameter (r2 = 0.83; p < 0.0001), and surface area to volume ratio (r2 = 0.97; p < 0.0001) in simulated models was found. In a scanned rat leg, the distribution of these microstructural features measured from histology was broad and demonstrated that there is a wide variance in the microstructural features observed, similar to the SA distributions. However, the distribution of fractional anisotropy measurements in the same tissue was narrow. CONCLUSIONS: This study demonstrates that SA-a scalar value from diffusion tensor subspace imaging analysis-is highly sensitive to muscle microstructural features predictive of function. Furthermore, these techniques and analysis tools can be translated to real experiments in skeletal muscle. The increased dynamic range of SA compared with fractional anisotropy in the same tissue suggests increased sensitivity to detecting changes in tissue microstructure.

Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information.

Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.

Early detection of subclinical mastitis in lactating dairy cows using cow-level features.

Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence. The data set used consisted of 1,346,207 milk-day (i.e., a day when milk was collected on both morning and evening) records spanning 9 yr from 2,389 cows producing on 7 Irish research farms. Individual cow composite milk yield and maximum milk flow were available twice daily, whereas milk composition (i.e., fat, lactose, protein) and somatic cell count (SCC) were collected once per week. Other features describing parity, calving dates, predicted transmitting ability for SCC, body weight, and history of subclinical mastitis were also available. The results of the study showed that a gradient boosting machine model trained to predict the onset of subclinical mastitis 7 d before a subclinical case occurs achieved a sensitivity and specificity of 69.45 and 95.64%, respectively. Reduced data collection frequency, where milk composition and SCC were recorded only every 15, 30, 45, and 60 d was simulated by masking data, to reflect the frequency of recording of this data on commercial dairy farms in Ireland. The sensitivity and specificity scores reduced as recording frequency reduced with respective scores of 66.93 and 80.43% when milk composition and SCC were recorded just every 60 d. Results demonstrate that models built on data that could be recorded routinely available on commercial dairy farms, can achieve useful predictive ability of subclinical mastitis even with reduced frequency of milk composition and SCC recording.

Cancer Decedents' Hospital End-of-Life Care Documentation: A Retrospective Review of Patient Records.

Objective: International standards of end-of-life care (EOLC) intend to guide the delivery of safe and high-quality EOLC. Adequately documented care is conducive to higher quality of care, but the extent to which EOLC standards are documented in hospital medical records is unknown. Assessing which EOLC standards are documented in patients' medical records can help identify areas that are performed well and areas where improvements are needed. This study assessed cancer decedents' EOLC documentation in hospital settings. Methods: Medical records of 240 cancer decedents were retrospectively evaluated. Data were collected across six Australian hospitals between 1/01/2019 and 31/12/2019. EOLC documentation related to Advance Care Planning (ACP), resuscitation planning, care of the dying person, and grief and bereavement care was reviewed. Chi-square tests assessed associations between EOLC documentation and patient characteristics, and hospital settings (specialist palliative care unit, sub-acute/rehabilitation care settings, acute care wards, and intensive care units). Results: Decedents' mean age was 75.3 years (SD 11.8), 52.0% (n = 125) were female, and 73.7% lived with other adults or carers. All patients (n = 240; 100%) had documentation for resuscitation planning, 97.6% (n = 235) for Care for the Dying Person, 40.0% for grief and bereavement care (n = 96), and 30.4% (n = 73) for ACP. Patients living with other adults or carers were less likely to have a documented ACP than those living alone or with dependents (OR 0.48; 95% CI 0.26-0.89). EOLC documentation was significantly greater in specialist palliative care settings than that in other hospital settings (P < .001). Conclusion: The process of dying is well documented among inpatients diagnosed with cancer. ACP and grief and bereavement support are not documented enough. Organizational endorsement of a clear practice framework and increased training could improve documentation of these aspects of EOLC.

Estimation of body condition score change in dairy cows in a seasonal calving pasture-based system using routinely available milk mid-infrared spectra and machine learning techniques.

Body condition score (BCS) is a subjective estimate of body reserves in cows. Body condition score and its change in early lactation have been associated with cow fertility and health. The aim of the present study was to estimate change in BCS (ΔBCS) using mid-infrared spectra of the milk, with a particular focus on estimating ΔBCS in cows losing BCS at the fastest rate (i.e., the cows most of interest to the producer). A total of 73,193 BCS records (scale 1 to 5) from 6,572 cows were recorded. Daily BCS was interpolated from cubic splines fitted through the BCS records, and subsequently used to calculate daily ΔBCS. Body condition score change records were merged with milk mid-infrared spectra recorded on the same week. Both morning (a.m.) and evening (p.m.) spectra were available. Two different statistical methods were used to estimate ΔBCS: partial least squares regression and a neural network (NN). Several combinations of variables were included as model features, such as days in milk (DIM) only, a.m. spectra only and DIM, p.m. spectra only and DIM, and a.m. and p.m. spectra as well as DIM. The data used to estimate ΔBCS were either based on the first 120 DIM or all 305 DIM. Daily ΔBCS had a standard deviation of 1.65 × 10-3 BCS units in the 305 DIM data set and of 1.98 × 10-3 BCS units in the 120 DIM data set. Each data set was divided into 4 sub-data sets, 3 of which were used for training the prediction model and the fourth to test it. This process was repeated until all the sub-data sets were considered as the test data set once. Using all 305 DIM, the lowest root mean square error of validation (RMSEV; 0.96 × 10-3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.82) was achieved with NN using a.m. and p.m. spectra and DIM. Using the 120 DIM data, the lowest RMSEV (0.98 × 10-3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.87) was achieved with NN using DIM and either a.m. spectra only or a.m. and p.m. spectra together. The RMSEV for records in the lowest 2.5% ΔBCS percentile per DIM in early lactation was reduced up to a maximum of 13% when spectra and DIM were both considered in the model compared with a model that considered just DIM. The performance of the NN using DIM and a.m. spectra only with the 120 DIM data was robust across different strata of farm, parity, year of sampling, and breed. Results from the present study demonstrate the ability of mid-infrared spectra of milk coupled with machine learning techniques to estimate ΔBCS; specifically, the inclusion of spectral data reduced the RMSEV over and above using DIM alone, particularly for cows losing BCS at the fastest rate. This approach can be used to routinely generate estimates of ΔBCS that can subsequently be used for farm decisions.

The response to genetic merit for milk production in dairy cows differs by cow body weight.

Attention is increasing on both cow size and body weight (BW) as energy sinks and thus as contributors to differences in production efficiency among cows. What is not currently clear, however, is how cow BW affects the increase in yield per cow per unit increase in genetic merit for milk production. This void in knowledge was filled in the present study using BW data from 20,470 lactations on 16,980 Holstein-Friesian dairy cows stratified into 4 groups on BW adjusted for differences in parity, days in milk, and body condition score. Using linear mixed models that adjusted for nuisance factors, cow phenotypic milk production variables were regressed on estimates of parental average genetic merit for the respective trait within each stratum of BW defined within contemporary group; estimates of genetic merit were from the national genetic evaluations. Both the intercept and linear regression coefficients on genetic merit were compared across BW strata. The intercepts representing the mean phenotypic yield at a genetic merit of zero differed among BW strata; irrespective of yield trait, the least squares means yield per BW stratum increased numerically as cows got heavier, although not every stepwise increase in BW stratum was associated with significantly greater yield compared with the previous (lighter) stratum. Nonetheless, the yield of the cows in the lightest of the 4 strata was always less than that of the heaviest 2 strata; relative to the lightest stratum, cows in the heaviest BW stratum produced only 3 to 4% more yield. Furthermore, the association between phenotypic yield and its respective measures of genetic merit differed by BW stratum; the response to selection for each of the yield traits was 15 to 23% greater for the heaviest stratum of cows compared with their contemporaries in the lightest stratum. Although BW stratum was associated with mean fat and protein concentration after adjusting for differences in genetic merit for fat and protein concentration, the association did not differ by BW stratum for either fat or protein concentration. The effect of BW on efficiency should consider the association between BW and not only mean phenotypic yield at a given genetic merit, but also how the differences in yield diverge as genetic merit increases.