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.

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle.

In response to the increased concern over agriculture's contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH4) per cow per year after accounting for replacement costs, ranging 8.9-323 kg CH4/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.

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.

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 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.

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.

Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle.

Feed represents a substantial proportion of production costs in the dairy industry and is a useful target for improving overall system efficiency and sustainability. The objective of this study was to develop methodology to estimate the economic value for a feed efficiency trait and the associated methane production relevant to Canada. The approach quantifies the level of economic savings achieved by selecting animals that convert consumed feed into product while minimizing the feed energy used for inefficient metabolism, maintenance and digestion. We define a selection criterion trait called Feed Performance (FP) as a 1 kg increase in more efficiently used feed in a first parity lactating cow. The impact of a change in this trait on the total lifetime value of more efficiently used feed via correlated selection responses in other life stages is then quantified. The resulting improved conversion of feed was also applied to determine the resulting reduction in output of emissions (and their relative value based on a national emissions value) under an assumption of constant methane yield, where methane yield is defined as kg methane/kg dry matter intake (DMI). Overall, increasing the FP estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow's first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane with the economic values of CAD $0.82 and CAD $0.07, respectively. Therefore, the estimated total economic value for FP is CAD $0.89/unit. The proposed model is robust and could also be applied to determine the economic value for feed efficiency traits within a selection index in other production systems and countries.

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.

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.

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.

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.

A methodology framework for weighting genetic traits that impact greenhouse gas emission intensities in selection indexes.

A methodological framework was presented for deriving weightings to be applied in selection indexes to account for the impact genetic change in traits will have on greenhouse gas emissions intensities (EIs). Although the emission component of the breeding goal was defined as the ratio of total emissions relative to a weighted combination of farm outputs, the resulting trait-weighting factors can be applied as linear weightings in a way that augments any existing breeding objective before consideration of EI. Calculus was used to define the parameters and assumptions required to link each trait change to the expected changes in EI for an animal production system. Four key components were identified. The potential impact of the trait on relative numbers of emitting animals per breeding female first has a direct effect on emission output but, second, also has a dilution effect from the extra output associated with the extra animals. Third, each genetic trait can potentially change the amount of emissions generated per animal and, finally, the potential impact of the trait on product output is accounted for. Emission intensity weightings derived from this equation require further modifications to integrate them into an existing breeding objective. These include accounting for different timing and frequency of trait expressions as well as a weighting factor to determine the degree of selection emphasis that is diverted away from improving farm profitability in order to achieve gains in EI. The methodology was demonstrated using a simple application to dairy cattle breeding in Ireland to quantify gains in EI reduction from existing genetic trends in milk production as well as in fertility and survival traits. Most gains were identified as coming through the dilution effect of genetic increases in milk protein per cow, although gains from genetic improvements in survival by reducing emissions from herd replacements were also significant. Emission intensities in the Irish dairy industry were estimated to be reduced by ~5% in the last 10 years because of genetic trends in production, fertility and survival traits, and a further 15% reduction was projected over the next 15 years because of an observed acceleration of genetic trends.

Prediction of effects of beef selection indexes on greenhouse gas emissions.

Genetic improvement in production efficiency traits can also drive reduction in greenhouse gas emissions. This study used international 'best-practice' methodology to quantify the improvements in system-wide CO2 equivalent emissions per unit of genetic progress in the Irish Maternal Replacement (MR) and Terminal (T) beef cattle indexes. Effects of each index trait on system gross emissions (GE) and system emissions intensity (EI) were modelled by estimating effects of trait changes on per-animal feed consumption and associated methane production, per-animal meat production and numbers of animals in the system. Trait responses to index selection were predicted from linear regression of individual bull estimated breeding values for each index trait on their MR or T index value, and the resulting regression coefficients were used to calculate trait-wise responses in GE and EI from index selection. Summed over all trait responses, the MR index was predicted to reduce system GE by 0.810 kg CO2e/breeding cow per year per € index and system EI by 0.009 kg CO2e/kg meat per breeding cow per year per € index. These reductions were mainly driven by improvements in cow survival, reduced mature cow maintenance feed requirements, shorter calving interval and reduced offspring mortality. The T index was predicted to reduce system EI by 0.021 kg CO2e/kg meat per breeding cow per year per € index, driven by increased meat production from improvements in carcass weight, conformation and fat. Implications for incorporating an EI reduction index to the current production indexes and long-term projections for national breeding programs are discussed.

Farmer views on calving difficulty consequences on dairy and beef farms.

Calving difficulty (CD) is a key functional trait with significant influence on herd profitability and animal welfare. Breeding plays an important role in managing CD both at farm and industry level. An alternative to the economic value approach to determine the CD penalty is to complement the economic models with the analysis of farmer perceived on-farm impacts of CD. The aim of this study was to explore dairy and beef farmer views and perceptions on the economic and non-economic on-farm consequences of CD, to ultimately inform future genetic selection tools for the beef and dairy industries in Ireland. A standardised quantitative online survey was released to all farmers with e-mail addresses on the Irish Cattle Breeding Federation database. In total, 271 farmers completed the survey (173 beef farmers and 98 dairy farmers). Both dairy and beef farmers considered CD a very important issue with economic and non-economic components. However, CD was seen as more problematic by dairy farmers, who mostly preferred to slightly reduce its incidence, than by beef farmers, who tended to support increases in calf value even though it would imply a slight increase in CD incidence. Farm size was found to be related to dairy farmer views of CD with farmers from larger farms considering CD as more problematic than farmers from smaller farms. CD breeding value was reported to be critical for selecting beef sires to mate with either beef or dairy cows, whereas when selecting dairy sires, CD had lower importance than breeding values for other traits. There was considerable variability in the importance farmers give to CD breeding values that could not be explained by the farm type or the type of sire used, which might be related to the farmer non-economic motives. Farmer perceived economic value associated with incremental increases in CD increases substantially as the CD level considered increases. This non-linear relationship cannot be reflected in a standard linear index weighting. The results of this paper provide key underpinning support to the development of non-linear index weightings for CD in Irish national indexes.

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.

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.