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.

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.

Exploring underlying drivers of genotype by environment interactions in feed efficiency traits for dairy cattle with a mechanistic model involving energy acquisition and allocation.

Feed efficiency (FE) is a complex phenotype made up of multiple traits for which there is potential for substantial genotype by environment interaction (G × E). The objective of this study is to evaluate the extent of G × E for FE traits with a simulation approach. We used a mechanistic model of the dairy cow that simulates trajectories of phenotypes throughout lifetime, depending on trajectories of resource acquisition and allocation, driven by 4 genetic scaling parameters, and depending on the nutritional environment (quantity and quality of feed resources). The cow model, calibrated for a grass-based farming system and seasonal calving, was combined with a genetic module. This simulated genetic variation in the 4 genetic scaling parameters related to resource acquisition and allocation, based on a simple balanced pedigree structure (200 paternal half-sib groups each of 100 daughters). The population of 20,000 cows generated was simulated in 4 nutritional environment scenarios, representing a gradient of feeding constraints. In each scenario, 6 traits derived from the model outputs were analyzed to obtain population genetic parameters. Genetic correlations between second-lactation production and FE were positive and high in all scenarios and increased as the nutritional environment became more constraining. A measure of lifetime FE was positively correlated with second-lactation production under a less constrained environment, but these correlations decreased as the environment became more constraining. The genetic correlation between body reserves at second calving, and lifetime FE was positive and low in the least constraining scenario and increased as the environment became more constraining. In addition to genetic parameters, we looked at the distributions of acquisition and allocation parameters among the best performing cows for lactation and life FE, in the 2 most contrasted scenarios. The 4 subpopulations of best cows had acquisition and allocation strategies different from the whole population. In conclusion, this simulation study identifies the potential underlying biological basis for important G × E in FE traits. This highlights the importance of having a balanced breeding goal when undertaking selection that should also be based on phenotypes relevant to the target performance environment.

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.

Heifers with positive genetic merit for fertility traits reach puberty earlier and have a greater pregnancy rate than heifers with negative genetic merit for fertility traits.

This study investigated the hypothesis that dairy heifers divergent in genetic merit for fertility traits differ in the age of puberty and reproductive performance. New Zealand's fertility breeding value (FertBV) is the proportion of a sire's daughters expected to calve in the first 42 d of the seasonal calving period. We used the New Zealand national dairy database to identify and select Holstein-Friesian dams with either positive (POS, +5 FertBV, n = 1,334) or negative FertBV (NEG, -5% FertBV, n = 1,662) for insemination with semen from POS or NEG FertBV sires, respectively. The resulting POS and NEG heifers were predicted to have a difference in average FertBV of 10 percentage points. We enrolled 640 heifer calves (POS, n = 324; NEG, n = 316) at 9 d ± 5.4 d (± standard deviation; SD) for the POS calves and 8 d ± 4.4 d old for the NEG calves. Of these, 275 POS and 248 NEG heifers were DNA parent verified and retained for further study. The average FertBV was +5.0% (SD = 0.74) and -5.1% (SD = 1.36) for POS and NEG groups, respectively. Heifers were reared at 2 successive facilities as follows: (1) calf rearing (enrollment to ∼13 wk of age) and (2) grazier, after 13 wk until 22 mo of age. All heifers wore a collar with an activity sensor to monitor estrus events starting at 8 mo of age, and we collected weekly blood samples when individual heifers reached 190 kg of body weight (BW) to measure plasma progesterone concentrations. Puberty was characterized by plasma progesterone concentrations >1 ng/mL in at least 2 of 3 successive weeks. Date of puberty was defined when the first of these samples was >1 ng/mL. Heifers were seasonally bred for 98 d starting at ∼14 mo of age. Transrectal ultrasound was used to confirm pregnancy and combined with activity data to estimate breeding and pregnancy dates. We measured BW every 2 wk, and body condition and stature at 6, 9, 12, and 15 mo of age. The significant FertBV by day interaction for BW was such that the NEG heifers had increasingly greater BW with age. This difference was mirrored with the significant FertBV by month interaction for average daily gain, with the NEG heifers having a greater average daily gain between 9 and 18 mo of age. There was no difference in heifer stature between the POS and NEG heifers. The POS heifers were younger and lighter at puberty, and were at a lesser mature BW, compared with the NEG heifers. As a result, 94 ± 1.6% of the POS and 82 ± 3.2% of the NEG heifers had reached puberty at the start of breeding. The POS heifers were 20% and 11% more likely to be pregnant after 21 d and 42 d of breeding than NEG heifers (relative risk = 1.20, 95% confidence interval of 1.03-1.34; relative risk = 1.11, 95% confidence interval of 1.01-1.16). Results from this experiment support an association between extremes in genetic merit for fertility base on cow traits and heifer reproduction. Our results indicate that heifer puberty and pregnancy rates are affected by genetic merit for fertility traits, and these may be useful phenotypes for genetic selection.

Prediction of effects of dairy selection indexes on methane emissions.

Global warming caused by greenhouse gas emissions is a threat to the survival of humans and other organisms living on Earth. The greenhouse gases released from the dairy sector of New Zealand accounted for 18.2 Mt of carbon dioxide equivalent (CO2-eq) in 2016, mainly from methane generated by enteric fermentation in the rumen of milking cows and their replacement stock. A productivity commission established by the New Zealand government in 2018 estimated that methane emissions from livestock needed to be reduced from 2016 levels by 10 to 22% by 2050 (i.e., 2.8 to 6.1 million t lower), so as to restrict future increases in global temperature to less than 2°C. In this study, we evaluated genetic effects of 8 traits included in the New Zealand national dairy breeding objective, on 3 types of methane emissions metrics: gross methane emissions per dairy cow per year (E), methane emissions per hectare (EH), and methane emissions intensity per milk protein equivalents (EI), as carbon dioxide equivalents. These effects were then aligned with recent genetic changes in these traits brought about by breeding schemes, so that the overall genetic trend for each metric into the future was estimated. The results showed that EH and EI are currently being reduced at rates of -2.31 kg of CO2-eq per hectare per cow per year (current average is 6,915 kg of CO2-eq/ha per cow per year) and -0.04 kg of CO2-eq per kg of milk protein equivalents per cow per year, respectively (current average is 9.7 kg of CO2-eq/milk protein-eq per cow per year). These improvements directly reflect increased production efficiency through selection for farm profitability. If the pastureland area in New Zealand remains the same, at the same productivity and with no increase in supplementation rates from external land sources, in 20 years gross emissions would be reduced by only 0.6%, or 89 Mt. Increased production efficiency will likely result in corresponding changes to the stocking rate, to fully utilize the pasture resource available, and might further encourage a greater rate of intensification via supplementary feeding. Both consequences of current genetic selection could negate any benefits for the national greenhouse gas inventory. New selection criteria for reduced methane production are needed to help achieve New Zealand's national methane reduction targets.

A breeding index to rank beef bulls for use on dairy females to maximize profit.

The desire to increase profit on dairy farms necessitates consideration of the revenue attainable from the sale of surplus calves for meat production. However, the generation of calves that are expected to excel in efficiency of growth and carcass merit must not be achieved to the detriment of the dairy female and her ability to calve and re-establish pregnancy early postcalving without any compromise in milk production. Given the relatively high heritability of many traits associated with calving performance and carcass merit, and the tendency for many of these traits to be moderately to strongly antagonistic, a breeding index that encompasses both calving performance and meat production could be a useful tool to fill the void in supporting decisions on bull selection. The objective of the present study was to derive a dairy-beef index (DBI) framework to rank beef bulls for use on dairy females with the aim of striking a balance between the efficiency of valuable meat growth in the calf and the subsequent performance of the dam. Traits considered for inclusion in this DBI were (1) direct calving difficulty; (2) direct gestation length; (3) calf mortality; (4) feed intake; (5) carcass merit reflected by carcass weight, conformation, and fat and the ability to achieve minimum standards for each; (6) docility; and (7) whether the calf was polled. Each trait was weighted by its respective economic weight, most of which were derived from the analyses of available phenotypic data, supplemented with some assumptions on costs and prices. The genetic merit for a range of performance metrics of 3,835 artificial insemination beef bulls from 14 breeds ranked on this proposed DBI was compared with an index comprising only direct calving difficulty and gestation length (the 2 generally most important characteristics of dairy farmers when selecting beef bulls). Within the Angus breed (i.e., the beef breed most commonly used on dairy females), the correlation between the DBI and the index of genetic merit for direct calving difficulty plus gestation length was 0.74; the mean of the within-breed correlations across all other breeds was 0.87. The ranking of breeds changed considerably when ranked based on the top 20 artificial insemination bulls excelling in the DBI versus excelling in the index of calving difficulty and gestation length. Dairy breeds ranked highest on the index of calving difficulty and gestation length, whereas the Holstein and Friesian breeds were intermediate on the DBI; the Jersey breed was one of the poorest breeds on DBI, superior only to the Charolais breed. The results clearly demonstrate that superior carcass and growth performance can be achieved with the appropriate selection of beef bulls for use on dairy females with only a very modest increase in collateral effect on cow performance (i.e., 2-3% greater dystocia expected and a 6-d-longer gestation length).

Genotype by environment interactions in fertility traits in New Zealand dairy cows.

New Zealand's seasonal dairy farming system entails a condensed calving pattern with cows required to conceive within approximately 12 wk of the planned start of calving. This has resulted in strong selection for fertility through culling of nonpregnant cows and relatively strong emphasis on fertility in Breeding Worth, the national breeding objective that drives sire selection. Despite this, average herd-level fertility is highly variable across New Zealand dairy farms. We studied genotype by environment interaction in fertility-related traits, with the goal of improving selection decisions in different fertility environments. We used data from the New Zealand national dairy database, which contains records on 3,743,862 animals. Herds were classified into high-, mid-, or low-fertility categories or environments based on herd average fertility performance, and data were analyzed in 2 different ways. First, we estimated genetic parameters when the fertility trait was defined specifically for each fertility environment to determine the extent to which genetic correlations between high- and low-fertility environments differed from 1 and the extent of changes in genetic variance across environments. Second, we used simple regression to evaluate the impact of ancestral genetic merit for fertility on cow fertility phenotypes to compare the effect of changes in genetic merit on phenotypic performance between fertility environments. The genetic standard deviations of fertility-related traits were 1.5 to 3.6 times higher in low-fertility herds than in high-fertility herds, and the genetic correlations between the same fertility-related traits between the high- and low-fertility environments were moderate to high, albeit with high standard errors. The high standard errors of the correlations reflected the low heritabilities of the traits and potential problems of culling bias, particularly for traits expressed in later parities. Regression analysis revealed that the bottom 30% of herds (in terms of fertility) could achieve more than twice the benefit from selection for fertility than the top 30% of herds. Although our analyses do not support separate genetic evaluations of fertility in the different environments, they indicate that low-fertility herds could benefit more from targeted selection of sires with higher fertility estimated breeding values than from selection based solely on the multitrait national index. Conversely, high-fertility herds could focus their sire selection on traits other than fertility, provided they avoid very low fertility sires.

Economic costs of recorded reasons for cow mortality and culling in a pasture-based dairy industry.

The objective of this study was to determine the economic costs associated with different reasons for cow culling or on-farm mortality in a pasture-based seasonal system. A bioeconomic model was developed to quantify costs associated with the different farmer-recorded reasons and timing of cow wastage. The model accounted for the parity and stage of lactation at which the cows were removed as well as the consequent effect on the replacement rate and average age structure of the herd. The costs and benefits associated with the change were quantified, including animal replacement cost, cull salvage value, milk production loss, and the profitability of altered genetic merit based on industry genetic trends for each parity. The total cost of cow wastage was estimated to be NZ$23,628/100 cows per year (NZ$1 = US$0.69) in a pasture-based system. Of this total cost, NZ$14,300/100 cows worth of removals were for nonpregnancy and unknown reasons, and another NZ$3,631/100 cows was attributed to low milk production, mastitis, and udder problems. The total cost for cow removals due to farmer-recorded biological reasons (excluding unknown, production, and management-related causes) was estimated to be NZ$13,632/100 cows per year. Of this cost, an estimated NZ$10,286/100 cows was attributed to nonpregnancy, mastitis, udder problems, calving trouble, and injury or accident. There is a strong economic case for the pasture-based dairy industries to invest in genetic, herd health, and production management research focused on reducing animal wastage due to reproductive failure, mastitis, udder problems, injuries or accidents, and calving difficulties.

Combining genetic and physiological data to identify predictors of lifetime reproductive success and the effect of selection on these predictors on underlying fertility traits.

Fertility of the dairy cow relies on complex interactions between genetics, physiology, and management. Mathematical modeling can combine a range of information sources to facilitate informed predictions of cow fertility in scenarios that are difficult to evaluate empirically. We have developed a stochastic model that incorporates genetic and physiological data from more than 70 published reports on a wide range of fertility-related traits in dairy cattle. The model simulates pedigree, random mating, genetically correlated traits (in the form of breeding values for traits such as hours in estrus, estrous cycle length, age at puberty, milk yield, and so on), and interacting environmental variables. This model was used to generate a large simulated data set (200,000 cows replicated 100 times) of herd records within a seasonal dairy production system (based on an average New Zealand system). Using these simulated data, we investigated the genetic component of lifetime reproductive success (LRS), which, in reality, would be impractical to assess empirically. We defined LRS as the total number of times, during her lifetime, a cow calved within the first 42 d of the calving season. Sire estimated breeding values for LRS and other traits were calculated using simulated daughter records. Daughter pregnancy rate in the first lactation (PD_1) was the strongest single predictor of a sire's genetic merit for LRS (R2 = 0.81). A simple predictive model containing PD_1, calving date for the second season and calving rate in the first season provided a good estimate of sire LRS (R2 = 0.97). Daughters from sires with extremely high (n = 99,995 daughters, sire LRS = +0.70) or low (n = 99,635 daughters, sire LRS = -0.73) LRS estimated breeding values were compared over a single generation. Of the 14 underlying component traits of fertility, 12 were divergent between the 2 lines. This suggests that genetic variation in female fertility has a complex and multifactorial genetic basis. When simulated phenotypes were compared, daughters of the high LRS sires (HiFERT) reached puberty 44.5 d younger and calved ∼14 d younger at each parity than daughters from low LRS sires (LoFERT). Despite having a much lower genetic potential for milk production (-400 L/lactation) than LoFERT cows, HiFERT cows produced 33% more milk over their lifetime due to additional lactations before culling. In summary, this simulation model suggests that LRS contributes substantially to cow productivity, and novel selection criteria would facilitate a more accurate prediction at a younger age.

Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations.

With the new opportunities from DNA technology, multitier breeding schemes have the potential to become more effective and more integrated. Integrated breeding schemes can also be better adapted to account for potential genotype by environment interactions (G × E) between tiers. In this case, phenotypic and genotype information from lower tiers becomes more valuable as it involves measurement of traits that directly represent the breeding objective. The objective of this study was to compare scenarios that represented different selection strategies and their economic effectiveness in fine-wool commercial sheep operations that exploit multitier breeding structures. Genomic selection (GS) applied in the multiplier and the commercial tier presented the largest additional revenue among all scenarios, as it resulted in the largest amount of genetic progress. The largest benefits from GS were outweighed by the genotyping costs, which made DNA parentage the most feasible strategy for the multiplier tier, resulting in the highest cumulative net present value (CNPV). The benefits of phenotypes and genotype information from the commercial environment were larger in the presence of G × E between the nucleus and the commercial tier. The CNPV was larger with a 50% reduction in genotyping costs, which increased the returns of GS scenarios by 2.7-fold on average. Higher selection intensity when selecting multiplier rams also resulted in larger benefits. In this case, returns for the breeding scheme were 3.5-fold higher when 33% of multiplier males were selected based on commercial information, compared to scenarios selecting 50% of the available multiplier rams. The benefits of collecting commercial phenotypes and genotypes were long term, which means that return on investment often took more than 10 years to be achieved, and were largely dependent on two-stage selection to reduce cost while maintaining selection efficiency and on the cost of a genotype test.

Short communication: Estimates of genetic parameters for dairy fertility in New Zealand.

Reproductive performance of dairy cows in a seasonal calving system is especially important as cows are required to achieve a 365-d calving interval. Prior research with a small data set has identified that the genetic evaluation model for fertility could be enhanced by replacing the binary calving rate trait (CR42), which gives the probability of a cow calving within the first 42d since the planned start of calving at second, third, and fourth calving, with a continuous version, calving season day (CSD), including a heifer calving season day trait expressed at first calving, removing milk yield, retaining a probability of mating trait (PM21) which gives the probability of a cow being mated within the first 21d from the planned start of mating, and first lactation body condition score (BCS), and including gestation length (GL). The aim of this study was to estimate genetic parameters for the proposed new model using a larger data set and compare these with parameters used in the current system. Heritability estimates for CSD and PM21 ranged from 0.013 to 0.019 and from 0.031 to 0.058, respectively. For the 2 traits that correspond with the ones used in the current genetic evaluation system (mating trait, PM21 and BCS) genetic correlations were lower in this study compared with previous estimates. Genetic correlations between CSD and PM21 across different parities were also lower than the correlations between CR42 and PM21 reported previously. The genetic correlation between heifer CSD and CSD in first parity was 0.66. Estimates of genetic correlations of BCS with CSD were higher than those with PM21. For GL, direct heritability was estimated to be 0.67, maternal heritability was 0.11, and maternal repeatability was 0.22. Direct GL had moderate to high and favorable genetic correlations with evaluated fertility traits, whereas corresponding residual correlations remain low, which makes GL a useful candidate predictor trait for fertility in a multiple trait evaluation. The superiority of direct GL genetic component over the maternal GL component for predicting fertility was demonstrated. Future work planned in this area includes the implementation and testing of this new model on national fertility data.

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.

An evaluation of alternative selection indexes for a non-linear profit trait approaching its economic optimum.

This study used simulation to evaluate the performance of alternative selection index configurations in the context of a breeding programme where a trait with a non-linear economic value is approaching an economic optimum. The simulation used a simple population structure that approximately mimics selection in dual purpose sheep flocks in New Zealand (NZ). In the NZ dual purpose sheep population, number of lambs born is a genetic trait that is approaching an economic optimum, while genetically correlated growth traits have linear economic values and are not approaching any optimum. The predominant view among theoretical livestock geneticists is that the optimal approach to select for nonlinear profit traits is to use a linear selection index and to update it regularly. However, there are some nonlinear index approaches that have not been evaluated. This study assessed the efficiency of the following four alternative selection index approaches in terms of genetic progress relative to each other: (i) a linear index, (ii) a linear index updated regularly, (iii) a nonlinear (quadratic) index, and (iv) a NLF index (nonlinear index below the optimum and then flat). The NLF approach does not reward or penalize animals for additional genetic merit beyond the trait optimum. It was found to be at least comparable in efficiency to the approach of regularly updating the linear index with short (15 year) and long (30 year) time frames. The relative efficiency of this approach was slightly reduced when the current average value of the nonlinear trait was close to the optimum. Finally, practical issues of industry application of indexes are considered and some potential practical benefits of efficient deployment of a NLF index in highly heterogeneous industries (breeds, flocks and production environments) such as in the NZ dual purpose sheep population are discussed.

Genetic selection for increased mean and reduced variance of twinning rate in Belclare ewes.

It is sometimes possible to breed for more uniform individuals by selecting animals with a greater tendency to be less variable, that is, those with a smaller environmental variance. This approach has been applied to reproduction traits in various animal species. We have evaluated fecundity in the Irish Belclare sheep breed by analyses of flocks with differing average litter size (number of lambs per ewe per year, NLB) and have estimated the genetic variance in environmental variance of lambing traits using double hierarchical generalized linear models (DHGLM). The data set comprised of 9470 litter size records from 4407 ewes collected in 56 flocks. The percentage of pedigreed lambing ewes with singles, twins and triplets was 30, 54 and 14%, respectively, in 2013 and has been relatively constant for the last 15 years. The variance of NLB increases with the mean in this data; the correlation of mean and standard deviation across sires is 0.50. The breeding goal is to increase the mean NLB without unduly increasing the incidence of triplets and higher litter sizes. The heritability estimates for lambing traits were NLB, 0.09; triplet occurrence (TRI) 0.07; and twin occurrence (TWN), 0.02. The highest and lowest twinning flocks differed by 23% (75% versus 52%) in the proportion of ewes lambing twins. Fitting bivariate sire models to NLB and the residual from the NLB model using a double hierarchical generalized linear model (DHGLM) model found a strong genetic correlation (0.88 ± 0.07) between the sire effect for the magnitude of the residual (VE ) and sire effects for NLB, confirming the general observation that increased average litter size is associated with increased variability in litter size. We propose a threshold model that may help breeders with low litter size increase the percentage of twin bearers without unduly increasing the percentage of ewes bearing triplets in Belclare sheep.

Novel approaches to genetic analysis of fertility traits in New Zealand dairy cattle.

The fertility of dairy cattle in New Zealand is well below industry targets, and the current New Zealand fertility breeding value (BV) could potentially be improved using additional information and traits. Data from 169 herds were analyzed to determine the benefits of using alternative phenotypic measures in the calculation of the fertility BV. The heritability of calving season day (CSD; calving season day as an integer day of the year) and the probability of an animal calving within 42 d of the planned start of calving (CR42) increased modestly (from 0.0206 to 0.0213 and 0.0087 to 0.0092, respectively) after accounting for the use of intravaginal progesterone-releasing devices for treatment of anestrous cows (anestrum treatment) and induced calvings. Incidence of either anestrum treatment or calving induction as a single binomial trait (AT/IND) had a heritability of 0.0223 and showed moderate genetic correlation with the probability of an animal being mated within 21 d of the planned start of mating (PM21; -0.4473), but much higher with CSD (0.8445). The use of pregnancy diagnosis data allowed fertility information that would otherwise be discarded to be included in analyses; when used to assign a prolonged CSD and a value of 0 for CR42 to animals that failed to calve, it increased the heritabilities of both of these traits (to 0.0278 and 0.0114, respectively). Because CSD was found to be more than twice as heritable as its binary counterpart, it shows potential to replace CR42 as the calving trait used in the fertility BV. Postpartum anestrous interval (PPAI), derived using incomplete premating estrous recording in some herds, had a heritability of 0.0813 and hence has potential as a trait to be included in genetic improvement programs but would require more rigorous recording of estrous during the premating period to be an effective trait. Based on selection index theory, the modifications made to current selection criteria using novel fertility traits increased the accuracy of prediction of fertility merit by more than 12%. Because of the increasing economic importance of fertility traits, and low heritabilities requiring large numbers of recorded daughters to get accurate fertility BV predictions on sires, data recorded on farm will become increasingly important in the genetic improvement of fertility.

Analyzing the heterogeneity of farmers' preferences for improvements in dairy cow traits using farmer typologies.

Giving consideration to farmers' preferences for improvements in animal traits when designing genetic selection tools such as selection indexes might increase the uptake of these tools. The increase in use of genetic selection tools will, in turn, assist in the realization of genetic gain in breeding programs. However, the determination of farmers' preferences is not trivial because of its large heterogeneity. The aim of this study was to quantify Australian dairy farmers' preferences for cow trait improvements to inform and ultimately direct the choice of traits and selection indexes in the 2014 review of the National Breeding Objective. A specific aim was to analyze the heterogeneity of preferences for cow trait improvements by determining whether there are farmer types that can be identified with specific patterns of preferences. We analyzed whether farmer types differed in farming system, socioeconomic profile, and attitudes toward breeding and genetic evaluation tools. An online survey was developed to explore farmers' preferences for improvement in 13 cow traits. The pairwise comparisons method was used to derive a ranking of the traits for each respondent. A total of 551 farmers fully completed the survey. A principal component analysis followed by a Ward hierarchical cluster analysis was used to group farmers according to their preferences. Three types of farmers were determined: (1) production-focused farmers, who gave the highest preference of all for improvements in protein yield, lactation persistency, feed efficiency, cow live weight, and milking speed; (2) functionality-focused farmers with the highest preferences of all for improvements in mastitis, lameness, and calving difficulty; and (3) type-focused farmers with the highest preferences of all for mammary system and type. Farmer types differed in their age, their attitudes toward genetic selection, and in the selection criteria they use. Surprisingly, farmer types did not differ for herd size, calving, feeding system, or breed. These results support the idea that preferences for cow trait improvements are intrinsic to farmers and not to production systems or breeds. As a result of this study, and some bioeconomic modeling (not included in this study), the Australian dairy industry has implemented a main index and 2 alternative indexes targeting the different farmer types described here.

Development of an index to rank dairy females on expected lifetime profit.

The objective of this study was to develop an index to rank dairy females on expected profit for the remainder of their lifetime, taking cognizance of both additive and nonadditive genetic merit, permanent environmental effects, and current states of the animal including the most recent calving date and cow parity. The cow own worth (COW) index is intended to be used for culling the expected least profitable females in a herd, as well as inform purchase and pricing decisions for trading of females. The framework of the COW index consisted of the profit accruing from (1) the current lactation, (2) future lactations, and (3) net replacement cost differential. The COW index was generated from estimated performance values (sum of additive genetic merit, nonadditive genetic merit, and permanent environmental effects) of traits, their respective net margin values, and transition probability matrices for month of calving, survival, and somatic cell count; the transition matrices were to account for predicted change in a cow's state in the future. Transition matrices were generated from 3,156,109 lactation records from the Irish national database between the years 2010 and 2013. Phenotypic performance records for 162,981 cows in the year 2012 were used to validate the COW index. Genetic and permanent environmental effects (where applicable) were available for these cows from the 2011 national genetic evaluations and used to calculate the COW index and their national breeding index values (includes only additive genetic effects). Cows were stratified per quartile within herd, based on their COW index value and national breeding index value. The correlation between individual animal COW index value and national breeding index value was 0.65. Month of calving of the cow in her current lactation explained 18% of the variation in the COW index, with the parity of the cow explaining an additional 3 percentage units of the variance in the COW index. Females ranking higher on the COW index yielded more milk and milk solids and calved earlier in the calving season than their lower ranking contemporaries. The difference in phenotypic performance between the best and worst quartiles was larger for cows ranked on COW index than cows ranked on the national breeding index. The COW index is useful to rank females before culling or purchasing decisions on expected profit and is complementary to the national breeding index, which identifies the most suitable females for breeding replacements.

Comparison of breeding objectives across countries with application to sheep indexes in New Zealand and Ireland.

Breeding objectives and selection indexes underpin the direction, the extent and the economic implications of selection in livestock populations under specific production systems. The objective of this study was to describe the methodology to calculate correlations between national selection indexes and gain a deeper understanding of the factors influencing responses in economically important traits in both the New Zealand and Irish sheep industries. Moderate to strong correlations were calculated among indexes within and between countries, with the strongest correlation (0.86) between the New Zealand and Irish maternal indexes. In both countries, responses to selection in the maternal indexes are largely driven by growth traits; each index, however, has a different balance of traits. Ewe mature weight also accounts for an important proportion of overall response and has significant emphasis in both maternal indexes. The majority of emphasis in terminal indexes of both countries is on growth and meat traits. Results from this study indicate that differences between national breeding objectives are unlikely to be a barrier to exchange of gene stocks among countries. Future research should investigate the extent to which genotype-by-environment (G × E) interactions exist at the level of individual traits. The methodology presented in this study is robust and represents an opportunity to inform the potential merits of international exchange of germplasm.