Genetic evaluation for stillbirth and pre-weaning mortality in Australian dairy cattle.

The welfare of calves is important to both farmers and consumers. Practices that increase the proportion of calves born alive and enable them to thrive through to weaning contribute to improved sustainability. Stillbirths (SB) are calvings where the calf dies at birth or within 24 h after birth. Pre-weaning mortality (PWM) refers to calves that die after the first day of life but before weaning based on termination data. Both SB and PWM are binary traits characterized by low heritability. Data collection for these traits is incomplete, compared with traits like milk yield in cows. Despite these challenges, genetic variation can be measured and used to produce breeding tools, such as estimated breeding values (EBV), to reduce calf mortality over time. The aim of this study was to compare the performance of various linear models to predict SB and PWM traits in Holstein and Jersey cattle and evaluate their applicability for industry-wide use in the Australian dairy industry. Calving records from around 2.25 million Holstein and Jersey dams were obtained from DataGene's Central Data Repository from 2000 onwards to calculate genetic parameters. About 7% of calves were recorded as stillborn in the period 2000-2021 (n = 1.48 million calvings). The prevalence of PWM was much lower than stillbirth during the same period at 2% (n = 0.89 million calves). Genetic parameters were estimated for SB direct, SB maternal and PWM using bivariate linear models with calving ease (CE) as the second trait in the model. The heritability of these calf traits was low and varied between 1 to 5% depending on the breed, trait and model. In Holstein cattle, heritabilities were 2% for PWM and SB direct and 1% for SB maternal while in Jersey cattle heritabilities were 5% for PWM, 2% for SB direct and 1% for SB maternal. The genetic trends for both SB direct and maternal in Holstein cattle indicate improvement in both traits whereas there was no apparent increase or decrease in PWM in the past 2 decades. The coefficient of genetic variation for SB direct and PWM was between 11.7 and 23.0% in Holstein and Jersey cattle demonstrating that there was considerable genetic variation in calf survival traits as a first step to using genetic selection to increase the proportion of calves born alive and calves weaned. A focus on improved calf and calving recording practices is expected to increase the reliability of genetic predictions.

Genome-wide association study of age at puberty and its (co)variances with fertility and stature in growing and lactating Holstein-Friesian dairy cattle.

Reproductive performance is a key determinant of cow longevity in a pasture-based, seasonal dairy system. Unfortunately, direct fertility phenotypes such as intercalving interval or pregnancy rate tend to have low heritabilities and occur relatively late in an animal's life. In contrast, age at puberty (AGEP) is a moderately heritable, early-in-life trait that may be estimated using an animal's age at first measured elevation in blood plasma progesterone (AGEP4) concentrations. Understanding the genetic architecture of AGEP4 in addition to genetic relationships between AGEP4 and fertility traits in lactating cows is important, as is its relationship with body size in the growing animal. Thus, the objectives of this research were 3-fold. First, to estimate the genetic and phenotypic (co)variances between AGEP4 and subsequent fertility during first and second lactations. Second, to quantify the associations between AGEP4 and height, length, and BW measured when animals were approximately 11 mo old (standard deviation = 0.5). Third, to identify genomic regions that are likely to be associated with variation in AGEP4. We measured AGEP4, height, length, and BW in approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey crossbred yearling heifers across 54 pasture-based herds managed in seasonal calving farm systems. We also obtained calving rate (CR42, success or failure to calve within the first 42 d of the seasonal calving period), breeding rate (PB21, success or failure to be presented for breeding within the first 21 d of the seasonal breeding period) and pregnancy rate (PR42, success or failure to become pregnant within the first 42 d of the seasonal breeding period) phenotypes from their first and second lactations. The animals were genotyped using the Weatherby's Versa 50K SNP array (Illumina, San Diego, CA). The estimated heritabilities of AGEP4, height, length, and BW were 0.34 (90% credibility interval [CRI]: 0.30, 0.37), 0.28 (90% CRI: 0.25, 0.31), 0.21 (90% CRI: 0.18, 0.23), and 0.33 (90% CRI: 0.30, 0.36), respectively. In contrast, the heritabilities of CR42, PB21 and PR42 were all <0.05 in both first and second lactations. The genetic correlations between AGEP4 and these fertility traits were generally moderate, ranging from 0.11 to 0.60, whereas genetic correlations between AGEP4 and yearling body-conformation traits ranged from 0.02 to 0.28. Our GWAS highlighted a genomic window on chromosome 5 that was strongly associated with variation in AGEP4. We also identified 4 regions, located on chromosomes 14, 6, 1, and 11 (in order of decreasing importance), that exhibited suggestive associations with AGEP4. Our results show that AGEP4 is a reasonable predictor of estimated breeding values for fertility traits in lactating cows. Although the GWAS provided insights into genetic mechanisms underpinning AGEP4, further work is required to test genomic predictions of fertility that use this information.

Genome-wide association study of anogenital distance and its (co)variances with fertility in growing and lactating Holstein-Friesian dairy cattle.

Anogenital distance (AGD) is a moderately heritable trait that can be measured at a young age that may provide an opportunity to indirectly select for improved fertility in dairy cattle. In this study, we characterized AGD and its genetic and phenotypic relationships with a range of body stature and fertility traits. We measured AGD, shoulder height, body length, and body weight in a population of 5,010 Holstein-Friesian and Holstein-Friesian × Jersey crossbred heifers at approximately 11 mo of age (AGD1). These animals were born in 2018 across 54 seasonal calving, pasture-based dairy herds. A second measure of AGD was collected in a subset of herds (n = 17; 1,956 animals) when the animals averaged 29 mo of age (AGD2). Fertility measures included age at puberty (AGEP), then time of calving, breeding, and pregnancy during the first and second lactations. We constructed binary traits reflecting the animal's ability to calve during the first 42 d of their herd's seasonal calving period (CR42), be presented for breeding during the first 21 d of the seasonal breeding period (PB21) and become pregnant during the first 42 d of the seasonal breeding period (PR42). The posterior mean of sampled heritabilities for AGD1 was 0.23, with 90% of samples falling within a credibility interval (90% CRI) of 0.20 to 0.26, whereas the heritability of AGD2 was 0.29 (90% CRI 0.24 to 0.34). The relationship between AGD1 and AGD2 was highly positive, with a genetic correlation of 0.89 (90% CRI 0.82 to 0.94). Using a GWAS analysis of 2,460 genomic windows based on 50k genotype data, we detected a region on chromosome 20 that was highly associated with variation in AGD1, and a second region on chromosome 13 that was moderately associated with variation in AGD1. We did not detect any genomic regions associated with AGD2 which was measured in fewer animals. The genetic correlation between AGD1 and AGEP was 0.10 (90% CRI 0.00 to 0.19), whereas the genetic correlation between AGD2 and AGEP was 0.30 (90% CRI 0.15 to 0.44). The timing of calving, breeding, and pregnancy (CR42, PB21, and PR42) during first or second lactations exhibited moderate genetic relationships with AGD1 (0.19 to 0.52) and AGD2 (0.46 to 0.63). Genetic correlations between AGD and body stature traits were weak (≤0.16). We conclude that AGD is a moderately heritable trait, which may have value as an early-in-life genetic predictor for reproductive success during lactation.

Validating the female fertility estimated breeding value in Australian commercial dairy herds.

We conducted a retrospective cohort study to validate the efficacy of the Australian multitrait fertility estimated breeding value (EBV). We did this by determining its associations with phenotypic measures of reproductive performance (i.e., submission rate, first service conception rate, and early calving). Our secondary aim was to report the associations between these reproductive outcomes and management and climate-related factors hypothesized to affect fertility. Our study population included 38 pasture-based dairy herds from the northern Victorian irrigation region in Australia. We collected records for 86,974 cows with 219,156 lactations and 438,578 mating events from the date on which managers started herd recording until December 2016, comprising both fertility-related data such as insemination records, calving dates, and pregnancy test results, and systems-related data such as production, herd size, and calving pattern. We also collected hourly data from 2004 to 2017 from the closest available weather station to account for climate-related factors (i.e., temperature humidity index; THI). Multilevel Cox proportional hazard models were used to analyze time-to-event outcomes (days to first service, days to cow calving following the planned herd calving start date), and multilevel logistic regression models for binomial outcomes (conception to first service) in the Holstein-Friesian and Jersey breeds. A 1-unit increase in daughter fertility EBV was associated with a 5.4 and 8.2% increase in the daily hazard of calving in the Holstein-Friesian and Jersey breeds respectively. These are relative increases (i.e., a Holstein-Friesian herd with a 60% 6-wk in-calf rate would see an improvement to 63.2% with a 1-unit increase in herd fertility EBV). Similar results were obtained for submission and conception rate. Associations between 120-d milk yield and reproductive outcome were complicated by interactions with 120-d protein percentage and calving age, depending on the breed and outcome. In general, we found that the reproductive performance of high milk-yielding animals deteriorated faster with age than low milk-yielding animals, and high protein percentage exacerbated the differences between low and high milk-yielding animals. Climate-related factors were also associated with fertility, with a 1-unit increase in maximum THI decreasing first service conception rate by 1.2% for Holstein-Friesians but having no statistically significant association in the Jersey breed. However, THI had a negative association in both breeds on the daily hazard of calving. Our study validates the efficacy of the daughter fertility EBV for improving herd reproductive performance and identifies significant associations between 120-d milk and protein yields and THI on the fertility of Australian dairy cows.

Variance parameter estimation for age at puberty phenotypes under 2 levels of phenotype censorship.

Age at puberty (AGEP) is a moderately heritable trait in cattle that may be predictive of an animal's genetic merit for reproductive success later in life. In addition, under some mating strategies (for example, where mating begins before all animals have attained puberty) animals that attain puberty at a relatively young age will also likely conceive earlier than their herd mates, and thus begin their productive life earlier. Unfortunately, AGEP is challenging to measure because animals must be observed over a period of several months. Our objectives for this study were twofold. The first objective was to produce variance components for AGEP. The second objective was to investigate the implications of a simplified phenotyping strategy for AGEP, when the interval between repeated blood plasma progesterone measures was extended from weekly to monthly, increasing the extent of left, interval, and right censoring. We measured AGEP in a closely monitored population of around 500 Holstein-Friesian heifers, born in 2015 and managed under a seasonal, pasture-based dairy system. Animals were blood tested weekly from approximately 240 to 440 d of age and were deemed to have reached puberty when blood plasma progesterone elevation (>1 ng/mL) was detected in 2 of 3 consecutive blood tests (AGEP_Weekly). To simulate a simplified phenotyping strategy based on monthly herd visits (AGEP_Monthly), we selectively disregarded data from all but 3 blood test events, when animals were around 300, 330, and 360 d of age (standard deviation = 14.5 d). The posterior mean of estimated heritabilities for AGEP_Weekly was 0.54, with a 90% credibility interval (90% CRI) of 0.41 to 0.66, whereas it was 0.44 (90% CRI 0.32 to 0.57) for AGEP_Monthly. The correlation between EBVs for AGEP_Weekly and AGEP_Monthly was 0.87 (90% CRI, 0.84 to 0.89). We conclude that in this population, AGEP is a moderately heritable trait. Further, increasing phenotype censorship from weekly to monthly observations would not have altered the main conclusions of this analysis. Our results support the strategic use of censoring to reduce costs and animal ethics considerations associated with collection of puberty phenotypes.

Erratum to "Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle" (JDS Commun. 3:114-119).

[This corrects the article DOI: 10.3168/jdsc.2021-0150.].

Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle.

Although selection for increased milk production traits has led to a genetic increase in body weight (BW), the genetic gain in milk production has exceeded the gain in BW, so gross feed efficiency has improved. Nonetheless, greater gains may be possible by directly selecting for a measure of feed efficiency. Australia first introduced Feed Saved (FS) estimated breeding value (EBV) in 2015. Feed Saved combines residual feed intake (RFI) genomic EBV and maintenance requirements calculated from mature BW EBV. The FS EBV was designed to enable the selection of cows for reduced energy requirements with similar milk production. In this study, we used a reference population of 3,711 animals in a multivariate analysis including Australian heifers (AUSh), Australian cows (AUSc), and overseas cows (OVEc) to update the Australian EBV for lifetime RFI (i.e., a breeding value that incorporated RFI in growing and lactating cows) and to recalculate the FS EBV in Australian Holstein bulls (AUSb). The estimates of genomic heritabilities using univariate (only AUSc or AUSh) to trivariate (including the OVEc) analyses were similar. Genomic heritabilities for RFI were estimated as 0.18 for AUSc, 0.27 for OVEc, and 0.36 for AUSh. The genomic correlation for RFI between AUSc and AUSh was 0.47 and that between AUSc and OVEc was 0.94, but these estimates were associated with large standard errors (range: 0.18-0.28). The reliability of lifetime RFI (a component of FS) in the trivariate analysis (i.e., including OVEc) increased from 11% to 20% compared with the 2015 model and was greater, by 12%, than in a bivariate analysis in which the reference population included only AUSc and AUSh. By applying the prediction equation of the 2020 model, the average reliability of the FS EBV in 20,816 AUSb that were born between 2010 and 2020 improved from 33% to 43%. Previous selection strategies-that is, using the predecessor of the Balanced Performance Index (Australian Profit Ranking index) that did not include FS-have resulted in an unfavorable genetic trend in FS. However, this unfavorable trend has stabilized since 2015, when FS was included in the Balanced Performance Index, and is expected to move in a favorable direction with selection on Balanced Performance Index or the Health Weighted Index. Doubling the reference population, particularly by incorporating international data for feed efficiency, has improved the reliability of the FS EBV. This could lead to increased genetic gain for feed efficiency in the Australian industry.

Effectiveness of the Australian breeding value for heat tolerance at discriminating responses of lactating Holstein cows to heat stress.

Heat stress has negative consequences for milk production and reproduction of dairy cattle. These adverse effects are likely to increase because of climate change and anticipated increases in milk yield. Some of the variation among cows in ability to resist effects of heat stress is genetic. The current objective of this observational study was to assess the effectiveness of the Australian breeding value for heat tolerance (ABVHT) based on the decline in milk yield with heat stress for predicting cow differences in effects of heat stress on regulation of body temperature, milk production, and reproductive function. Genomic breeding values for heat tolerance were calculated for 12,487 cows from a single California dairy farm. Rectal temperature in the afternoon (1100-2045 h) was measured on a subset of 626 lactating cows with ABVHT ≥102 (heat tolerant) or <102 (heat sensitive). Rectal temperature was 0.12°C lower for heat-tolerant cows than heat-sensitive cows. Vaginal temperatures were measured every 15 min for 5 d in 118 cows with ABVHT ≥108 (extreme heat tolerant) or <97 (extreme heat sensitive). Vaginal temperature was 0.07°C lower for extreme heat-tolerant cows than extreme heat-sensitive cows. Lactation records for 4,703 cows with ABVHT were used to evaluate seasonal variation in first 90-d milk yield, fat percent, and protein percent for each ABVHT quartile. Overall, cows with higher ABVHT had lower milk yield, fat percentage, and protein percentage and higher first service pregnancy rate. There was no summer depression in production or reproduction or interactions between season and ABVHT quartile. We observed that ABVHT can successfully identify heat-tolerant cows that maintain lower body temperatures during heat stress. The lack of a pronounced seasonality in milk production or reproduction precluded evaluation of whether ABVHT is related to the magnitude of effect of heat stress on those traits.

Reducing greenhouse gas emissions through genetic selection in the Australian dairy industry.

This research explores possible options to reduce greenhouse gas (GHG) emissions in the Australian dairy industry by (1) including an environmental component in the national breeding program and (2) estimating the economic and environmental impacts of implementation of the subsequent indexes. A total of 12 possible selection indexes were considered. These indexes were developed to predict changes in gross per-animal methane production (using 3 scenarios depending on availability and efficacy of a direct methane trait breeding value prediction) with 4 different carbon prices, integrating them into an augmentation of the current conventional national selection index. Although some economic response is lost with inclusion of the GHG subindexes in the Balanced Performance Index, options do exist where this loss is marginal and, even in scenarios where all selection pressure is based on the environmental weighting, economic progress is still made in all cases. When including environmental traits within an index, if a relatively low percentage of economic gain or index progression is sacrificed, then approximately 40 to 50% of the maximum possible reductions in emissions may be achieved. This concurrent selection of estimated breeding values that have a correlated favorable response in emissions in addition to direct selection on a residual methane trait allows a high level of methane reduction to be achieved with a realized cost to farmers that is far lower than the economic value placed on carbon. By implementing a GHG subindex in the national breeding program, we can achieve up to a 7.9% decrease in residual methane and 9 times the reduction in gross emissions in 10 yr, compared with the current breeding program, with little to no cost to farmers. By 2050, selection based on one of the more moderate index scenarios at a carbon price of AUD$250/t (AUD$1 = US$0.71), or opportunity cost to farmers of AUD$87.22, will reduce gross emissions by 8.23% and emissions intensity by 21.25%, therefore offering a mitigation strategy that will be effective at reducing emissions with little compromise to profit.

Machine learning approaches for the prediction of lameness in dairy cows.

Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.

Use of insemination data for joint evaluation of male and female fertility in predominantly seasonal-calving dairy herds.

Conception in dairy cattle is influenced by the fertility of the cow and the bull and their interaction. Despite genetic selection for female fertility in many countries, selection for male fertility is largely not practiced. The primary objective of this study was to quantify variation in male and female fertility using insemination data from predominantly seasonal-calving herds. Nonreturn rate (NRR) was derived by coding each insemination as successful (1) or failed (0) based on a minimum of at least 25 d. The NRR was treated as a trait of the bull with semen (male fertility) and the cow that is mated (female fertility). The data (805,463 cows that mated to 5,776 bulls) were used to estimate parameters using either models that only included bulls with mating data or models that fitted the genetic and permanent environmental (PE) effects of bulls and cows simultaneously. We also evaluated whether fitting genetic and PE effects of bulls as one term is better for ranking bulls based on NRR compared with a model that ignored genetic effect. The age of cows that were mated, age of the bulls with semen data, season of mating, breed of cow that mated, inbreeding of cows and bulls, and days from calving to mating date were found to have a significant effect on NRR. Only about 3% of the total variance was explained by the random effects in the model, despite fitting the genetic and PE effects of the bull and cow. The 2 components of fertility (male and fertility) were not correlated. The heritability of male fertility was low (0.001 to 0.008), and that of female fertility was also low (~0.016). The highest heritability estimate for male fertility was obtained from the model that fitted the additive genetic relationship matrix and PE component of the bull as one term. When this model was used to calculate bull solutions, the difference between bulls with at least 100 inseminations was up to 19.2% units (-9.6 to 9.6%). Bull solutions from this model were compared with bull solutions that were predicted fitting bull effects ignoring pedigree. Bull solutions that were obtained considering pedigree had (1) the highest accuracy of prediction when early insemination was used to predict yet-to-be observed insemination data of bulls, and (2) improved model stability (i.e., a higher correlation between bull solutions from 2 randomly split herds) compared with the model which fitted bull with no pedigree. For practical purposes, the model that fitted genetic and PE effect as one term can provide more accurate semen fertility values for bulls than the model without genetic effect. To conclude, insemination data from predominantly seasonal-calving herds can be used to quantify variability between bulls for male fertility, which makes their ranking on NRR feasible. Potentially this information can be used for monitoring bulls and can supplement efforts to improve herd fertility by avoiding or minimizing the use of semen from subfertile bulls.

How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd, genomic information nucleus, and bulls.

Genomic selection has been commonly used for selection for over a decade. In this time, the rate of genetic gain has more than doubled in some countries, while inbreeding per year has also increased. Inbreeding can result in a loss of genetic diversity, decreased long-term response to selection, reduced animal performance and ultimately, decreased farm profitability. We quantified and compared changes in genetic gain and diversity resulting from genomic selection in Australian Holstein and Jersey cattle populations. To increase the accuracy of genomic selection, Australia has had a female genomic reference population since 2013, specifically designed to be representative of commercial populations and thus including both Holstein and Jersey cows. Herds that kept excellent health and fertility data were invited to join this population and most their animals were genotyped. In both breeds, the rate of genetic gain and inbreeding was greatest in bulls, and then the female genomic reference population, and finally the wider national herd. When comparing pre- and postgenomic selection, the rates of genetic gain for the national economic index has increased by ~160% in Holstein females and ~100% in Jersey females. This has been accompanied by doubling of the rates of inbreeding in female populations, and the rate of inbreeding has increased several fold in Holstein bulls since the widespread use of genomic selection. Where cow genotype data were available to perform a more accurate genomic analysis, greater rates of pedigree and genomic inbreeding were observed, indicating actual inbreeding levels could be underestimated in the national population due to gaps in pedigrees. Based on current rates of genetic gain, the female reference population is progressing ahead of the national herd and could be used to infer and track the future inbreeding and genetic trends of the national herds.

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program.

The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO2-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO2-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

Including milk production, conformation, and functional traits in multivariate models for genetic evaluation of lameness.

Lameness is a serious health and welfare issue that can negatively affect the economic performance of cows, especially on pasture-based dairy farms. However, most genetic predictions (GP) of lameness have low accuracy because lameness data are often incomplete as data are collected voluntarily by farmers in countries such as Australia. The objective of this study was to find routinely measured traits that are correlated with lameness and use them in multivariate evaluation models to improve the accuracy of GP for lameness. We used health events and treatments associated with lameness recorded by Australian farmers from 2002 to early 2019. The lameness incidence rates in Holstein and Jersey cows were 3.3% and 4.6%, respectively. We analyzed the records of 36 other traits (milk production, conformation, fertility, and survival traits) to estimate genetic correlations with lameness. The estimated heritability ± standard error (and repeatability ± standard error) for lameness in both Holstein and Jersey breeds were very low: 0.007 ± 0.002 (and 0.029 ± 0.002) and 0.005 ± 0.003 (and 0.027 ± 0.006), respectively, in univariate sire models. For the GP models, we tested including measurements of overall type to prediction models for Holsteins, stature and body length for Jersey, and milk yield and fertility traits for both breeds. The average accuracy of GP, calculated from prediction error variances, were 0.38 and 0.24 for Holstein and Jersey sires, respectively, when estimated using univariate sire models and both increased to 0.43 using multivariate sire models. In conclusion, we found that the accuracy of GP for lameness could be improved by including genetically correlated traits in a multivariate model. However, to further improve the accuracy of predictions of lameness, precise identification and recording incidences of hoof or leg disorder, or large-scale recording of locomotion and claw scores by trained personnel should be considered.

Breeding for reduced methane emission and feed-efficient Holstein cows: An international response.

Selecting for lower methane (CH4) emitting animals is one of the best approaches to reduce CH4 given that genetic progress is permanent and cumulative over generations. As genetic selection requires a large number of animals with records and few countries actively record CH4, combining data from different countries could help to expedite accurate genetic parameters for CH4 traits and build a future genomic reference population. Additionally, if we want to include CH4 in the breeding goal, it is important to know the genetic correlations of CH4 traits with other economically important traits. Therefore, the aim of this study was first to estimate genetic parameters of 7 suggested methane traits, as well as genetic correlations between methane traits and production, maintenance, and efficiency traits using a multicountry database. The second aim was to estimate genetic correlations within parities and stages of lactation for CH4. The third aim was to evaluate the expected response of economically important traits by including CH4 traits in the breeding goal. A total of 15,320 methane production (MeP, g/d) records from 2,990 cows belonging to 4 countries (Canada, Australia, Switzerland, and Denmark) were analyzed. Records on dry matter intake (DMI), body weight (BW), body condition score, and milk yield (MY) were also available. Additional traits such as methane yield (MeY; g/kg DMI), methane intensity (MeI; g/kg energy-corrected milk), a genetic standardized methane production, and 3 definitions of residual methane production (g/d), residual feed intake, metabolic BW (MBW), BW change, and energy-corrected milk were calculated. The estimated heritability of MeP was 0.21, whereas heritability estimates for MeY and MeI were 0.30 and 0.38, and for the residual methane traits heritability ranged from 0.13 to 0.16. Genetic correlations between different methane traits were moderate to high (0.41 to 0.97). Genetic correlations between MeP and economically important traits ranged from 0.29 (MY) to 0.65 (BW and MBW), being 0.41 for DMI. Selection index calculations showed that residual methane had the most potential for inclusion in the breeding goal when compared with MeP, MeY, and MeI, as residual methane allows for selection of low methane emitting animals without compromising other economically important traits. Inclusion of residual feed intake in the breeding goal could further reduce methane, as the correlation with residual methane is moderate and elicits a favorable correlated response. Adding a negative economic value for methane could facilitate a substantial reduction in methane emissions while maintaining an increase in milk production.

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia.

Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of ß-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk mid-infrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R2) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R2 values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R2 remained comparable, however, the root mean squared errors increased substantially (4-10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.

Estimating the environmental impact of dairy cattle breeding programs through emission intensity.

A recently developed methodological approach for determining the greenhouse gas emissions impact of national breeding programs was applied to measure the effects of current and future breeding goals on the emission intensity (EI) of the Canadian dairy industry. Emission intensity is the ratio of greenhouse gas outputted in comparison to the product generated. Traits under investigation affected EI by either decreasing the direct emissions yield (i.e. increasing feed performance), changing herd structure (i.e. prolonging herd life) or through the dilution effect of increased production (i.e. increasing fat yield). The intensity value (IV) of each trait, defined as the change in emissions' intensity per unit change in each trait, was calculated for each of the investigated traits. The IV trend of these traits was compared for the current and prospective selection index, as well as for a system with and without quota (the supply management policy designed to prevent overproduction). The overall EI of the average genetic merit Canadian dairy herd per breeding female was 5.07 kg CO2eq/kg protein equivalent output. The annual reduction in EI due to the improvement of production traits was -0.027, -0.018 and -0.006 for fat, protein and milk other solids, respectively. The functional traits, herd life and mastitis resistance, had more modest effects (-0.008 and -0.001, respectively). These results are consistent with international studies that identified traits related to production, survival, health and fertility as having the largest impact on the environmental footprint of dairy cattle. Overall, the dairy industry is becoming more efficient by reducing its EI through selection of environmentally favorable traits, with a 1% annual reduction of EI in Canada.

Herd manager attitudes and intentions regarding the selection of high-fertility EBV sires in Australia.

Reproductive performance in dairy cattle has declined over the last 50 years as an unintended consequence of selection for high milk yield. Since the early 2000s, dairy geneticists have released successive versions of fertility estimated breeding values (EBV) to assist in reversing this trend. At the herd level, fertility EBV can help managers accelerate improvements in reproductive performance by acting as a second selection criteria when used in tandem with a breeding index. However, use of the fertility EBV in sire selection currently varies between herd managers. The aim of this study was to better understand the reasons why herd managers choose or do not choose to select high-fertility EBV sires, using the Theory of Planned Behavior (TPB) as a social research framework. Thirty-five Victorian dairy herd managers were recruited as part of a larger study investigating the daughter fertility Australian Breeding Value and interviewed using a series of questions examining TPB constructs. The interviews were recorded and transcribed using template analysis. A wide range of herd manager types were enrolled into the study, with representation from diverse systems. Out of the 35 herd managers, 27 included fertility in their list of high-priority breeding objectives. A wide variation in results was consistent with previous studies that have demonstrated marked heterogeneity in herd manager attitudes toward bull selection. Herd manager-perceived barriers to selection of sires with high daughter fertility EBV included a lack of high daughter fertility bulls with other desirable traits, a lack of trust in the fertility EBV or in the Australian EBV system, difficulty in interpreting international proofs, information overload, semen prices, low bull reliability, and difficulty in understanding bull catalogs. Not all herd managers found the process problematic, however, particularly if a breeding consultant was employed to select all or most of the sires. Herd manager-perceived barriers for choosing to select daughter fertility as a breeding objective include a lack of awareness of the EBV, a lack of interest in genetics in general, low confidence in the impact of genetic selection for fertility, and a feeling that fertility was not important for their production system. The results of this study suggest that animal geneticists and on-farm service providers need to work together to allow the opportunities arising from appropriate use of fertility EBV to be realized more broadly across the dairy industry.

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers.

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as ß-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.

Effectiveness of contingent screening for placenta accreta spectrum disorders based on persistent low-lying placenta and previous uterine surgery.

OBJECTIVES: Maternal mortality related to placenta accreta spectrum (PAS) disorders remains substantial when diagnosed unexpectedly at delivery. The aim of this study was to evaluate the effectiveness of a routine contingent ultrasound screening program for PAS. METHODS: This was a retrospective study of data obtained between 2009 and 2019, involving two groups: a screening cohort of unselected women attending for routine mid-trimester ultrasound assessment and a diagnostic cohort consisting of women referred to the PAS diagnostic service with a suspected diagnosis of PAS. In the screening cohort, women with a low-lying placenta at the mid-trimester assessment were followed up in the third trimester, and those with a persistent low-lying placenta (i.e. placenta previa) and previous uterine surgery were referred to the PAS diagnostic service. Ultrasound assessment by the PAS diagnostic service consisted of two-dimensional grayscale and color Doppler ultrasonography, and women with a diagnosis of PAS were usually managed with conservative myometrial resection. The final diagnosis of PAS was based on a combination of intraoperative clinical findings and histopathological examination of the surgical specimen. RESULTS: In total, 57 179 women underwent routine mid-trimester fetal anatomy assessment, of whom 220 (0.4%) had a third-trimester diagnosis of placenta previa. Seventy-five of these women were referred to the PAS diagnostic service because of a history of uterine surgery, and 21 of 22 cases of PAS were diagnosed correctly (sensitivity, 95.45% (95% CI, 77.16-99.88%) and specificity, 100% (95% CI, 99.07-100%)). Univariate analysis demonstrated that parity ≥ 2 (odds ratio (OR), 35.50 (95% CI, 6.90-649.00)), two or more previous Cesarean sections (OR, 94.20 (95% CI, 22.00-656.00)) and placenta previa (OR, 20.50 (95% CI, 4.22-369.00)) were the strongest risk factors for PAS. In the diagnostic cohort, there were 173 referrals, with one false-positive and three false-negative diagnoses, resulting in a sensitivity of 96.63% (95% CI, 90.46-99.30%) and a specificity of 98.81% (95% CI, 93.54-99.97%). CONCLUSIONS: A contingent screening strategy for PAS is both feasible and effective in a routine healthcare setting. When linked to a PAS diagnostic and surgical management service, adoption of such a screening strategy has the potential to reduce the maternal morbidity and mortality associated with this condition. However, larger prospective studies are necessary before implementing this screening strategy into routine clinical practice. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Eficacia de la detección contingente para los trastornos del espectro de la placenta acreta a partir de la placenta baja persistente y en la cirugía uterina previa OBJETIVOS: La mortalidad materna relacionada con los trastornos de la gama espectral de la placenta acreta (EPA) sigue siendo considerable cuando se diagnostica de forma inesperada en el momento del parto. El objetivo de este estudio fue evaluar la efectividad de un programa rutinario de detección contingente mediante ecografía para el EPA. MÉTODOS: Este fue un estudio retrospectivo de datos obtenidos entre 2009 y 2019, en el que participaron dos grupos: una cohorte de detección de mujeres no seleccionadas que acudieron a la evaluación ecográfica rutinaria de mitad de trimestre y una cohorte de diagnóstico, integrada por mujeres remitidas al servicio de diagnóstico del EPA con un presunto diagnóstico del EPA. En la cohorte de detección, a las mujeres con una placenta baja en la evaluación de mitad de trimestre se les hizo un seguimiento en el tercer trimestre, y a aquellas con una placenta baja persistente (es decir, placenta previa) que habían tenido cirugía uterina previa se las remitió al servicio de diagnóstico del EPA. La evaluación ecográfica por el servicio de diagnóstico del EPA consistió en una ecografía Doppler bidimensional en escala de grises y en color, y a las mujeres con diagnóstico del EPA se las trató habitualmente con una resección conservadora del miometrio. El diagnóstico final del EPA se basó en una combinación de indicadores clínicos intraoperatorios y el examen histopatológico de la muestra quirúrgica. RESULTADOS: En total, 57179 mujeres se sometieron a una evaluación rutinaria de la anatomía fetal a mitad del trimestre, de las cuales a 220 (0,4%) se les diagnosticó con placenta previa en el tercer trimestre. Setenta y cinco de estas mujeres fueron remitidas al servicio de diagnóstico del EPA, debido a su historial de cirugía uterina, y 21 de los 22 casos de EPA fueron diagnosticados correctamente (sensibilidad, 95,45% (IC 95%, 77,16-99,88%) y especificidad, 100% (IC 95%, 99,07-100%)). El análisis univariante demostró que la paridad ≥2 (razón de momios (RM), 35,50 (IC 95%, 6,90-649,00)), dos o más cesáreas previas (RM, 94,20 (IC 95%, 22,00-656,00)) y la placenta previa (RM, 20,50 (IC 95%, 4,22-369,00)) fueron los factores de riesgo más fuertes para el EPA. En la cohorte de diagnóstico, se remitió a 173 mujeres, entre las cuáles hubo un diagnóstico de falso-positivo y tres diagnósticos de falsos-negativos, lo que dio como resultado una sensibilidad del 96,63% (IC 95%, 90,46-99,30%) y una especificidad del 98,81% (IC 95%, 93,54-99,97%). CONCLUSIONES: La adopción de una estrategia de detección contingente para el EPA es tanto factible como eficaz en un entorno de atención sanitaria rutinaria. Cuando se asocia a un servicio de diagnóstico y gestión quirúrgica del EPA, la adopción de esa estrategia de detección podría reducir la morbilidad y la mortalidad maternas asociadas a esta afección. Sin embargo, se necesitan estudios prospectivos más amplios antes de aplicar esta estrategia de detección en la práctica clínica habitual. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.