Australian Breeding Values Research Articles

Using Australian genomics to predict dairy cattle performance in New Zealand

Context The national breeding objective in New Zealand (NZ) was designed with the intention to breed dairy cows that efficiently convert feed into profit. The breeding worth index (BW) is used to rank bulls and cows according to their ability to meet this objective. The Australian equivalent to BW is the balanced performance index (BPI). These selection indexes represent national economic weights for important traits in dairy industry in each country. The introduction of Australian genomics has allowed the selection and ranking of young sires from around the world on BPI. Aims This study aims to demonstrate the relationship between different traits and selection indexes in Australia (AU) and NZ by comparing sires with daughter proofs in both countries and the validity of predicting BW for NZ sires using Australian genomic predictions and regression equations. Methods Data files for sires with daughters in both AU and NZ were merged to identify common bulls used in both countries. An analysis was conducted to determine whether Australian breeding values (ABVs) for sires could be used to predict the performance of the sires that have no progeny in NZ. ABVs for nine traits in BW were converted to their equivalent NZ breeding values (NBVs) and used to calculate an index equivalent to BW. Key results On the basis of a regression equation, a new index called genomic New Zealand index (gNZI) for selecting sires for NZ dairy herds was developed. The correlation coefficients between gNZI and BW in Holstein Friesian (HF), Jersey, and Red breeds were 0.90, 0.91, and 0.88 respectively. Conclusions The regression equation from genomic ABVs to produce gNZI was a reliable genomic predictor of future BW for sires with insufficient ancestry information in NZ and to enhance the ancestry proofs and increase the reliability of selecting young NZ-born bulls. Implications The high correlations between traits in AU and NZ and the simple proposed conversion method can build industry confidence when selecting young bulls using gNZI, as genomic prediction of BW through New Zealand Animal Evaluation (NZAEL) is not currently available.

Temporal trends in estimated breeding values for daughter fertility in Holstein and Jersey cows in the Australian dairy industry

Context Milk yield and fertility are negatively genetically correlated in dairy cows, so selection for milk yield would be expected to result in genetic deterioration in fertility unless there is concurrent selection for fertility. Aim The main study aim was to describe temporal trends in estimated breeding values (Australian Breeding Values, ABVs) for daughter fertility for Holstein and Jersey cows in Australian dairy herds. Methods A retrospective descriptive study was conducted using milk recording and ABV data from cows in commercial dairy herds. Calvings and lactations from 1980 to 2022 were used, along with ABVs for cows that calved over that period and for sires of those cows. Herd-year mean estimated breeding values were calculated for each breed as the means for cows of that breed that calved in the herd that year. Key results Daughter fertility ABVs declined (i.e. deteriorated) markedly in Holsteins in the Australian dairy industry, commencing before 1980. Rates of decline were most rapid in sires, less rapid in cows and least rapid at herd level. Selection pressure for cows with higher daughter fertility ABVs due to cow culling decisions was probably occurring within Australian herds but any such effect was minimal. The decline in cow daughter fertility ABVs is being reversed, but recovery of herd means in the study population has been slow. The rate of decline in daughter fertility ABVs in Jersey cows was less than for Holsteins. Conclusions Over a period of genetic deterioration, then recovery for a trait, genetic trends in sires are not closely related to genetic trends at herd level. The rapid decline in daughter fertility ABVs for sires probably resulted in substantial reductions in herd reproductive performance and cow longevity in Holstein herds in the Australian dairy industry. Under current trends, it will be a considerable time before the adverse effects of the decline in daughter fertility ABVs are fully reversed. Implications An important ongoing task for animal breeding is to identify and monitor traits that may become important for farmers and community in future but may be declining genetically under current selection decisions.

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.

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.

Milk protein concentration, estimated breeding value for fertility, and reproductive performance in lactating dairy cows

Milk protein concentration in dairy cows has been positively associated with a range of measures of reproductive performance, and genetic factors affecting both milk protein concentration and reproductive performance may contribute to the observed phenotypic associations. It was of interest to assess whether these beneficial phenotypic associations are accounted for or interact with the effects of estimated breeding values for fertility. The effects of a multitrait estimated breeding value for fertility [the Australian breeding value for daughter fertility (ABV fertility)] on reproductive performance were also of interest. Interactions of milk protein concentration and ABV fertility with the interval from calving date to the start of the herd's seasonally concentrated breeding period were also assessed. A retrospective single cohort study was conducted using data collected from 74 Australian seasonally and split calving dairy herds. Associations between milk protein concentration, ABV fertility, and reproductive performance in Holstein cows were assessed using random effects logistic regression. Between 52,438 and 61,939 lactations were used for analyses of 4 reproductive performance measures. Milk protein concentration was strongly and positively associated with reproductive performance in dairy cows, and this effect was not accounted for by the effects of ABV fertility. Increases in ABV fertility had important additional beneficial effects on the probability of pregnancy by wk 6 and 21 of the herd's breeding period. For cows calved before the start of the breeding period, the effects of increases in both milk protein concentration and ABV fertility were beneficial regardless of their interval from calving to the start of the breeding period. These findings demonstrate the potential for increasing reproductive performance through identifying the causes of the association between milk protein concentration and reproductive performance and then devising management strategies to capitalize on them. Research should be conducted to understand the component of the relationship not captured by ABV fertility.

New breeding objectives and selection indices for the Australian dairy industry

This study comprises an update of the economic values for dairy traits for the Australian industry and the formulation of updated selection indices. An economic model, which calculates partial economic values for each trait individually, was developed to determine the economic implications of selective dairy breeding, based on the effect of trait changes on the profit of commercial dairy farms in Australia. Selection indices were developed from economic values, which were transformed into base economic weights by including the discounted genetic expressions coefficients. Economic weights (in Australian dollars) were 1.79, 6.92, -0.10, -5.44, 8.84, 7.68, 1.07, 4.86, 1.91, 3.51, 4.90, 0.31, 2.03, 2.00, and 0.59, for milk fat (kg), milk protein (kg), milk volume (L), body weight (kg), survival (%), residual survival (%), somatic cell count (cells/mL), fertility (%), mammary system [Australian Breeding Value (ABV) unit], temperament (ABV unit), milking speed (ABV unit), udder depth (%), overall type (%), fore udder attachment (%), and pin set (%), respectively. The updated economic weights presented in this study constituted the basis of the definition for 3 new indices. These indices were developed from combination of bioeconomic principles, patterns of farmer preferences for trait improvements, and desired gains approaches. The 3 indices, Balanced Performance Index, Health Weighted Index, and Type Weighted Index, have been released to the industry.

Genomic selection for tolerance to heat stress in Australian dairy cattle

Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this study was to develop genomic estimated breeding values (GEBV) for heat tolerance in dairy cattle. Heat tolerance was defined as the rate of decline in production under heat stress. We combined herd test-day recording data from 366,835 Holstein and 76,852 Jersey cows with daily temperature and humidity measurements from weather stations closest to the tested herds for test days between 2003 and 2013. We used daily mean values of temperature-humidity index averaged for the day of test and the 4 previous days as the measure of heat stress. Tolerance to heat stress was estimated for each cow using a random regression model with a common threshold of temperature-humidity index=60 for all cows. The slope solutions for cows from this model were used to define the daughter trait deviations of their sires. Genomic best linear unbiased prediction was used to calculate GEBV for heat tolerance for milk, fat, and protein yield. Two reference populations were used, the first consisted of genotyped sires only (2,300 Holstein and 575 Jersey sires), and the other included genotyped sires and cows (2,189 Holstein and 1,188 Jersey cows). The remainder of the genotyped sires were used as a validation set. All animals had genotypes for 632,003 single nucleotide polymorphisms. When using only genotyped sires in the reference set and only the first parity data, the accuracy of GEBV for heat tolerance in relation to changes in milk, fat, and protein yield were 0.48, 0.50, and 0.49 in the Holstein validation sires and 0.44, 0.61, and 0.53 in the Jersey validation sires, respectively. Some slight improvement in the accuracy of prediction was achieved when cows were included in the reference population for Holsteins. No clear improvements in the accuracy of genomic prediction were observed when data from the second and third parities were included. Correlations of GEBV for heat tolerance with Australian Breeding Values for other traits suggested heat tolerance had a favorable genetic correlation with fertility (0.29–0.39 in Holsteins and 0.15–0.27 in Jerseys), but unfavorable correlations for some production traits. Options to improve heat tolerance with genomic selection in Australian dairy cattle are discussed.

Calculation of lifetime net income per year (LTNI/year) of Australian Holstein cows to validate the Australian profit ranking of their sires. 2. Validation of the Australian profit ranking of sires and test for genotype by environment interaction based on LTNI/year

In Australia, the Australian Dairy Herd Improvement Scheme publishes the Australian profit ranking (APR), which is an economic index combining all economic traits. The APR is used to select sires that will breed the most profitable daughters. The objectives of this study were to test the predictive power of the APR by looking at the relationship between the APR of sires and lifetime net income per year (LTNI/year) of their future daughters and to test if this prediction was accurate in different types of herds. The importance of re-ranking was also tested by considering LTNI/year of cows in different herds as different traits and estimating the genetic correlation between LTNI/year in different herd types (herds differing in calving systems, region or production level). An additional objective was to test the value of individual trait Australian breeding values (ABV) of sires to predict LTNI/year of their future daughters. The results show that overall the relationship between the APR of bulls and the LTNI/year of their daughters was as expected. That is, the regression of daughter’s LTNI/year on sire’s APR equals 0.5 as expected because cows inherit half their genes from their sire. When LTNI/year of cows was regressed on sire’s APR separately for different herd types, the regression coefficients varied from 0.36 (low production herds) to 0.62 (high production herds). The genetic correlation for LTNI/year between herd types was always high indicating little re-ranking of sires. The lowest was 0.89 between LTNI/year of cows in seasonal and year-round calving systems as well as between Regions 3 (Gippsland and Tasmania) and Region 1 (New South Wales, Queensland, South Australia and Western Australia) herds. Thus, an APR calculated across all herd types will give a good ranking of bulls in any herd type. When LTNI/year of cows was regressed on individual sire ABV the regression coefficients were approximately half the economic weight for each trait as expected. Within seasonal calving and low production herds the regression on sire’s fertility ABV was higher than expected and the regression in year-round and high production herds was less than expected. In conclusion, over all herds the sire’s APR predicts LTNI/year of future daughters as expected. Owners of low production, seasonal calving herds will get slightly less benefit from 1 unit increase in APR than owners of high production, year-round calving herds and could perhaps benefit from placing additional emphasis on the fertility ABV when selecting sires.

Holstein-Friesian Dairy Cows Under a Predominantly Grazing System: Interaction Between Genotype and Environment

A 5 yr whole-system study, beginning in June 1994, compared the productivity of high [HGM; Australian Breeding Value (ABV) of 49.1kg of fat plus protein] and low [LGM; ABV of 2.3kg of fat plus protein] genetic merit cows. Cows from both groups were fed at 3 levels of concentrate (C): 0.34 (low C), 0.84 (medium C), and 1.71 (high C) t of DM/cow per lactation. Thus, there were 6 treatments (farmlets) composed of 18 cows each. The 30 blocks of pasture on each farmlet were matched between farmlets for pasture growth before the study (and soil characteristics and aspect). Cows were culled, and pasture and feed use were managed so as not to bias any one treatment. Genetic merit, level of feeding, and their interaction were significant effects for protein content, protein/cow, and milk and protein/ha. For fat and milk yield/cow, genetic merit and level of feeding were significant, whereas there was no significant effect of genetic merit on fat content. The difference of 46.8kg of fat plus protein yield between the ABV of HGM and LGM cows and the actual difference in production between the 2 groups was not significantly different except for low C (27kg) cows. This was due to a 3-fold lower protein yield difference (6kg/cow) compared with an ABV difference for protein yield of 17.9kg/cow. The dramatic effect of treatment on protein is in line with differences in the mean protein content (2.89% for the HGM-low C cows compared with a mean of 3.02% for the remaining groups) and mean body condition score [4.3 for HGM-low C cows compared with 4.8 for the mean of the remaining groups (scale 1 to 8)], both indicators reflecting a higher negative energy balance in the HGM-low C cows. When individual cow production was plotted against ABV for production of milk or protein yield all relationships were quadratic, but the slope was relatively flat (low response to ABV) for the low C cows, steeper for the medium C cows and steepest (but not linear) for the high C cows. The relationship between ABV for fat yield and actual fat yield was linear for all levels of concentrate. The mean milk yield/ha from pasture for the 6 farmlets over the 5 yr was 11,868L, 11,417L, or 7,761L for the HGM cows fed at low C, medium C, or high C, respectively, and 10,579L, 9,800L, or 5,812L for LGM cows, fed at low C, medium C, or high C, respectively. The response to concentrates fed was very high for the HGM-medium C cows at 0.115kg fat plus protein or 1.75L milk/kg of concentrate fed, with comparable figures of 0.083kg and 1.0L, 0.86kg and 1.47L and 0.066 and 0.92 L/kg of concentrate fed for the HGM-high C, LGM-medium C, and LGM-high C, respectively. The results show a significant genetic merit by environment (level of feeding) interaction for reproduction and most production parameters when considered in terms of the individual cow and the whole farm system.

Reproductive performance in Holstein-Friesian cows in relation to genetic merit and level of feeding when grazing pasture

AbstractOne hundred and eight Holstein-Friesian cows in six herds were run on six separate farmlets over a 5-year period from 1995 to 1999 at Wollongbar Agricultural Institute, on the subtropical north coast of New South Wales, Australia. Three of the herds comprised high genetic merit (HGM) cows — Australian breeding value (ABV) of +49·1 kg for milk fat (F) plus protein (Pr) and three herds comprised low genetic merit (LGM) cows-ABV of 2·3 kg. Within genetic merit groupings, one herd was given 0·34 t (l), one herd was given 0·84 t (m) and one herd 1·71 t (h), of concentrate per cow per lactation. Within each genetic merit group, cows were matched for milk yield and live weight, and over all groups for time of calving and age at the commencement of the study. The 30 paddocks within each farmlet were matched between farmlets for pasture type and pasture growth rate and soil fertility. Half the cows within each herd calved over a 3-month period in spring and the other half in autumn. Strict management criteria ensured that there was no bias towards particular treatment groups.HGM cows were ‘open’ (days from calving to conception) for 8 days longer than the LGM cows (99 v. 91 days). The lHGM cows took 11 days longer to commence luteal phase activity and 21 days longer to first observed oestrus post calving than hLGM cows (P &lt; 0·001), with the other groups being intermediate.After 24 days of mating, 22% of lHGM cows were pregnant, and this was less than half of the rate of the best herd-mLGM. After 9 weeks of mating, the chances of an LGM cow being pregnant was 87% greater than an HGM cow. After 12 weeks of mating, 70% of lHGM cows were pregnant compared with a mean pregnancy rate of 87% for the LGM cows.The number of cows treated for abnormal ovarian activity (anoestrus, cystic) was highest (P &lt; 0·001) in the HGM herds given ‘l’ and ‘m’ levels of concentrate compared with the remaining herds (0·24 v. 0·12 treatments per cow mated, respectively).There was a significant positive relationship between live-weight change from 4 weeks before, to the start of, the mating period and the chances of a cow being pregnant at 24 days (P &lt; 0·05) and at 6 and 9 weeks after the commencement of mating.There was a significant negative relationship (P &lt; 0·001) between the change in daily F plus Pr yield, from the start to 4 weeks after mating began, and pregnancy rate at 9 weeks. The change in F plus Pr yield was +63 g/day for cows pregnant at nine weeks as opposed to +154 g/day for cows not pregnant.The results of the present study indicate that the reproductive performance of HGM cows, with a mean of 61% North American (NA) genes, is lower than LGM cows (22% NA genes) under a predominantly pasture-based system of farming. The influence on reproduction was possibly due to genes favouring partitioning of energy to milk yield rather than body-condition maintenance in the HGM cows and when food intake was inadequate, then being more willing to use body reserves.These reproductive problems may be reduced by more intensive reproductive management. However, such practices are costly and time consuming. Another approach may be to ensure that live-weight loss over the mating period is minimized by strategic supplementary feeding.

Effect of Genetic Merit and Concentrate Feeding on Reproduction of Grazing Dairy Cows in a Subtropical Environment

This study was conducted to determine the effects of genetic merit for milk production and concentrate feeding on the intervals to the onset of postpartum cyclic ovarian activity, first insemination, and establishment of pregnancy. Other factors considered included biochemical measures of energy balance, liveweight, and measures of production. A farmlet study was conducted with two lines of cattle that differed in Australian Breeding Value by 735 L milk and 47kg of fat plus protein that were fed 300 or 2000kg of concentrate per cow per year in a subtropical grazing system based on ryegrass, clover, and kikuyu pasture. Milk yield was higher in cows fed 2000kg of concentrate per cow per year (23.6 L/d) than those fed 300kg/cow per year (18.2 L/d), and cows of medium genetic merit (19.6 L/d) produced less milk than those of high genetic merit (22.2 L/d). Plasma glucose concentration was lower in the cows of high genetic merit (3.11 mmol/L), than in the cows of medium genetic merit (3.39 mmol/L). The cumulative probabilities of ovulation, insemination, and establishment of pregnancy by any day postpartum were determined by a Weibull distribution. Medium genetic merit cows in winter were more likely to ovulate by any day postpartum than in summer and more likely than high genetic merit cows in either summer or winter. The cumulative probability of insemination was lower in primipara than multipara and in summer than in winter. Primiparous cattle had a lower cumulative probability of pregnancy by any day postpartum.

Comparison of purebred and crossbred dairy cattle for Victoria: estimation of genetic effects for yield

The yield performances of pure and crossbred dairy cattle (bred by artificial insemination) were compared using a sample of 253 422 herd-recorded Victorian dairy cattle. Pure breed groups included Holstein-Friesian (HF), Jersey (Jer), Ayrshire (Ayr), Guernsey (Gue) and Australian Illawarra Shorthorn (AIS), although numbers of Ayr, Gue and AIS were very small. The model analysed included breed additive and maternal effects, as well as heterosis and epistatic effects. Sire's Australian Breeding Value and number of generations of A1 breeding were also included. Purebred HF was found to outproduce all other purebred groups for kilograms of fat and litres of milk. Jer produced 14 kg fat (8%) and 850 L milk (22%) per lactation less than HF. Maternal breed had an effect with Jer and Gue dams being superior to HF (and other) dams. Heterosis for yield was of the order of 3% (or 5 kg fat and 110 L milk). There was no evidence of recombination loss in the crossbred groups. After combining estimates of direct, maternal and heterosis effects, HF x Jer, AIS x HF and Ayr x HF (where the first breed is breed of sire) were all found to compare equally or favourably with purebred HF for yield.

Evaluating dairy sires for conformation of their daughters: use of first classification records

Type classification records for 18 132 Australian Holstein-Friesian heifers were analysed. These consisted of 27 traits scored in three or six categories, from three rounds of classification between 1981 and 1983. Only first lactation, first classification records were considered. The model of analysis included herd-round-classifier subclasses as fixed and sires as random effects, fitting age at classification as a linear and quadratic covariable within subclasses. Herd-round-classifier effects explained between 18% and 37% of total sums of squares. Age accounted for 5.8-2.4% for traits related to body size and for 1.6% or less for the other traits. Heritability estimates obtained using a Restricted Maximum Likelihood procedure ranged from 0.44 for total score, 0.42 for stature and 0.40 for dairy character, to 0.10 or less for feet and legs, rear heel, rear legs set, bone quality and rear teat placement. On average, values were higher than corresponding 'all-lactation' estimates. Covariance components between all traits were obtained. The resulting genetic variance/covariance matrix was then forced to be positive semi-definite before calculating genetic and phenotypic correlations. Breeding value estimates for all sires and traits were determined using a univariate Best Linear Unbiased Prediction procedure for the above model. In addition, the relationship matrix between males was incorporated. There were 2 597 sires with an average of 23.2 effective daughters, and 474 sires without daughter records included as male ancestors. The association between breeding value estimates for different traits was examined by multiple regression. Sire-son regressions were determined and compared with their expectations. Australian Breeding Values for production were obtained for a subset of sires and contrasted to the type proofs. There seemed to be little correlation between genetic merit for type and milk production.