Incorporation of the grazing utilization subindex and new updates to the Pasture Profit Index

Grazing efficiency has been shown to differ between perennial ryegrass varieties. Such differences affect the utilization of grass within grazing systems, influencing the profitability of grass-based ruminant production systems. The Pasture Profit Index (PPI) is an economic merit grass variety selection tool developed to identify varieties with the greatest economic potential for grass-based dairy production systems. A new grass utilization subindex was developed and incorporated into the PPI to identify varieties with superior grazing efficiency. The subindex rewards varieties with superior grazing efficiency, measured as Residual grazed height, as these varieties allow increased amounts of herbage dry matter to be used by grazing animals. The economic values of all other traits within the PPI were reviewed and updated to ensure that the index was reflective of the current economic scenarios with appropriate assumptions included in the models, thus ensuring that varieties excelling in the agronomic traits with the greatest effect on profitability were recognized. The difference between the highest and lowest performing varieties for the grass utilization trait ranged from €23 to −€24. A range of €211 to €43 was recorded between the highest and lowest ranked varieties within the updated PPI. Spearman's rank correlation between the updated and original PPI lists was 0.96. The introduction of the utilization subindex will allow farmers to make informed variety selection decisions when reseeding pasture, particularly on their grazing platforms and it will allow a demand-based communication process between the farmer and the grass merchant or breeder, ultimately affecting trait selection for future breeding strategies.     

Derivation of sustainable breeding goals for dairy cattle using selection index theory

The objective was to present 2 methods for the derivation of nonmarket values for functional traits in dairy cattle using deterministic simulation and selection index theory. A nonmarket value can be a value representing animal welfare and societal influences for animal production, which can be added to market economic values in the breeding goal to define sustainable breeding goals. The first method was restricted indices. A consequence of adding a nonmarket value to a market economic value for a given functional trait is less selection emphasis on milk yield. In the second method, the loss in selection response in milk resulting from greater emphasis on functional traits was quantified. The 2 methods were demonstrated using a breeding goal for dairy cattle with 4 traits (milk yield, mastitis resistance, conception rate, and stillbirth). Nonmarket values derived separately using restricted indices were 0.4 and 2.6 times the value of market economic values for mastitis resistance and conception rate, respectively. Nonmarket values for mastitis resistance and conception rate were both lower when derived simultaneously than when derived separately. This was due to the positive genetic correlation between mastitis resistance and conception rate, and because both traits are negatively correlated with milk yield. Using the second method and accepting a 5% loss in selection response for milk yield, nonmarket values for mastitis, conception rate, and stillbirth were 0.3, 1.4, and 2.9 times the market economic values. It was concluded that the 2 methods could be used to derive nonmarket values for functional traits in dairy cattle.     

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.     

Measuring the effect of change in selection indices

Genetic selection goals for dairy cattle, originally aimed at production traits only, have been expanded in stages over the past 30 yr to include up to 12 target traits covering production, functionality, and health and fertility. Each addition to the selection goal often involves the use of additional measured phenotypic variates. The net effect of these additions is usually described as causing change in the relative emphasis on different traits, though there are varying definitions of what this means. This paper suggests that the current definitions of this term may be inappropriate and shows that, as usually used, it tends to overstate the net effect of the changes. A new definition of the relative emphasis of each target trait is proposed. It is defined as the percentage of total economic value of genetic gain in all traits attributable to gain in that particular trait. A useful parallel statistic measures the relative contribution of each phenotypic variate recorded. The result of applying these measures is contrasted with the use of current methods using United States Holstein data.     

Incorporating heifer feed efficiency in the Australian selection index using genomic selection

The economic benefit of expanding the Australian Profit Ranking (APR) index to include residual feed intake (RFI) was evaluated using a multitrait selection index. This required the estimation of genetic parameters for RFI and genetic correlations using single nucleotide polymorphism data (genomic) correlations with other traits. Heritabilities of RFI, dry matter intake (DMI), and all the traits in the APR (milk, fat, and protein yields; somatic cell count; fertility; survival; milking speed; and temperament), and genomic correlations between these traits were estimated using a Bayesian framework, using data from 843 growing Holstein heifers with phenotypes for DMI and RFI, and bulls with records for the other traits. Heritability estimates of DMI and RFI were 0.44 and 0.33, respectively, and the genomic correlation between them was 0.03 and nonsignificant. The genomic correlations between the feed-efficiency traits and milk yield traits were also close to zero, ranging between −0.11 and 0.10. Positive genomic correlations were found for DMI with stature (0.16) and with overall type (0.14), suggesting that taller cows eat more as heifers. One issue was that the genomic correlation estimates for RFI with calving interval (ClvI) and with body condition score were both unfavorable (−0.13 and 0.71 respectively), suggesting an antagonism between feed efficiency and fertility. However, because of the relatively small numbers of animals in this study, a large 95% probability interval existed for the genomic correlation between RFI and ClvI (−0.66, 0.36). Given these parameters, and a genetic correlation between heifer and lactating cow RFI of 0.67, inclusion of RFI in the APR index would reduce RFI by 1.76 kg/cow per year. Including RFI in the APR would result in the national Australian Holstein herd consuming 1.73 × 10⁶ kg less feed, which is worth 0.55 million Australian dollars (A$) per year and is 3% greater than is currently possible to achieve. Other traits contributing to profitability, such as milk production and fertility, will also improve through selection on this index; for example, ClvI would be reduced by 0.53d/cow per year, which is 96% of the gain for this trait that is achieved without RFI in the APR.     

Claw health index for Dutch dairy cattle based on claw trimming and conformation data

The purpose of this study was to develop a model for a routine genetic evaluation of claw health traits and to develop an index including data on claw health and conformation traits. Claw health data comprised observations on 40,536 dairy cows of claw traits recorded by claw trimmers. Claw health traits scored were sole hemorrhage (SH), digital dermatitis (DD), interdigital dermatitis (ID), wall ulcer (WU), sole ulcer (SU), interdigital hyperplasia (IH), and white line disease (WL). A combined claw health trait was added as a trait to the data, combining all claw disorders. Observations on 5 feet and leg conformation traits on 41,048 animals were evaluated as predictive traits for claw health. These conformation traits were rear leg side view, rear leg rear view, foot angle, locomotion, and feet and legs. Prevalence of claw disorders ranged from 3% (WU) to 38% (SH). Overall, 69% of the animals had at least one claw disorder. Estimated heritabilities for claw health traits ranged from 0.01 (WU) to 0.13 (IH), and repeatabilities (within and across lactation) ranged from 0.15 (WU) to 0.57 (IH). Genetic correlations of claw health traits in parity 1 and parities ≥2 ranged from 0.72 to 1.00. Estimated genetic correlations among claw health traits ranged from −0.35 to 0.88 and between claw health and conformation traits ranged from −0.58 to 0.41. The breeding goal for claw health was to reduce costs due to claw disorders. The economic index for claw health, which included claw health and feet and leg conformation traits, had a reliability of 59% for an average progeny-tested bull in the Netherlands. The prevalence of claw disorders can be reduced up to 0.7% per year with selection on claw health only.     

Factors affecting the exchange of genetic material between Nordic and US Holstein populations

The possibility of profitable cooperation between dairy cattle populations depends on several factors. Among these factors is the similarity of breeding goals, for example, as measured by the correlations between selection indices. Correlations between selection indices less than unity can usually be explained by differences in economic values, trait definitions, national genetic evaluation procedures, and genotype × environment interactions. The objective of this study was to test whether uniform definitions of the female fertility traits would increase the exchange of genes across populations, and to quantify the effect on genetic gain. A second objective was to test whether a more similar relative weighting of the index traits across populations would increase the exchange of genes across populations, and to quantify the effect on genetic gain. This was done in a stochastic simulation study of the Nordic and US Holstein populations. Uniform definitions of the female fertility traits did not increase total genetic gain in the Nordic Holstein population. The standardization did not seem to affect selection across populations either. However, the results were sensitive to the assumptions made in the simulation study, especially the genetic correlations between traits. A more similar relative weighting of the index traits across populations did not change total genetic gain in the Nordic Holstein population. The possibility of exchanging genetic material with the US Holstein population led to significantly higher progress in the aggregate genotype in the Nordic Holstein population compared with a situation in which exchange was not possible. Hence, importation of US Holstein genetics for use in the Nordic Holstein population is recommended. In addition, results indicated that population size is of greater importance than differences in trait definitions and relative weighting of the index traits for the advantage of exchanging genetic material between the Nordic and the US Holstein populations. The possibility of exchanging genetic material with the Nordic Holstein population did not change progress in the aggregate genotype in the US Holstein population compared with a situation in which exchange was not possible, but it tended to result in lower genetic progress in protein yield and greater genetic progress or smaller genetic declines in the functional traits. Thus, importation of genetic material from Nordic Holsteins may slow down the deterioration of animal health and reproduction in US Holsteins.     

Major advances in determining appropriate selection goals

Substantial increases of 3,500 kg of milk, 130 kg of fat, and 100 kg of protein per cow per lactation have resulted from improvements in genetics, nutrition, and management during the past 20 yr. At the same time, the interval from calving to conception increased (unfavorable) by 24 d. Genetics has accounted for about 55% of gains in the yield traits and about one-third of the change in interval to conception. Genetic gains in the yield traits and productive life have accumulated to around 1.7 and 1.2 genetic standard deviations since 1980. Unfavorable genetic changes in conception interval since 1980 and somatic cell score since 1990 have accumulated to 1.0 and 0.12 genetic standard deviations. The most important advance in selection indexes has been the introduction of nonyield traits. Advances in selection indexes have gone hand in hand with advances in data collection and genetic evaluation. As new traits were recorded in dairy management databases and as genetic evaluations were developed for these traits, they were incorporated into selection indexes. Until 1994, when somatic cell score and productive life were introduced, selection indexes provided by USDA included only yield traits. In 2000, composite type indexes for udder, feet and legs, and body size were added. Daughter pregnancy rate and service sire- and daughter-calving ease were included in 2003. The lifetime merit indexes introduced in 2003 have, for the first time, resulted in theoretical selection responses in the desired direction for all traits. During this time, the percentage relative economic weights in selection indexes increased from 0 to 45% for the nonyield traits. Selection emphasis on nonyield traits should continue to increase as additional traits (e.g., calf survival, metabolic disease, and male fertility) are introduced in the future. Wide variation exists among countries in traits included in selection indexes and in relative economic weights. Molecular genetic studies have identified many chromosome regions with potentially important major genes for economic traits. Use of DNA markers for genetic improvement is currently limited by lack of precision in marker location. Discovery of major genes will be accelerated by the availability of the bovine genome sequence, comparative genome maps and genome sequences across species, and increased use of breed crosses in molecular studies. As major genes are identified, their effects will be incorporated into genetic evaluations and selection indexes.     

Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit

The aim of this study was to evaluate and quantify the importance of avoiding overlap between training and testing subsets of data when evaluating the effectiveness of predictions of genetic merit based on genetic markers. Genomic selection holds great potential for increasing the accuracy of selection in young bulls and is likely to lead quickly to more widespread use of these young bulls with a shorter generation interval and faster genetic improvement. Practical implementations of genomic selection in dairy cattle commonly involve results of national genetic evaluations being used as the dependent variable to evaluate the predictive ability of genetic markers. Selection index theory was used to demonstrate how ignoring correlations among errors of prediction between animals in training and testing sets could result in overestimates of accuracy of genomic predictions. Correlations among errors of prediction occur when estimates of genetic merit of training animals used in prediction are taken from the same genetic evaluation as estimates for validation of animals. Selection index theory was used to show a substantial degree of error correlation when animals used for testing genomic predictions are progeny of training animals, when heritability is low, and when the number of recorded progeny for both training and testing animals is low. Even when training involves a dependent variable that is not influenced by the progeny records of testing animals (i.e., historic proofs), error correlations can still result from records of relatives of training animals contributing to both the historic proofs and the predictions of genetic merit of testing animals. A simple simulation was used to show how an error correlation could result in spurious confirmation of predictive ability that was overestimated in the training population because of ascertainment bias. Development of a method of testing genomic selection predictions that allows unbiased testing when training and testing variables are estimated breeding values from the same genetic evaluation would simplify training and testing of genomic predictions. In the meantime, a 4-step approach for separating records used for training from those used for testing after correction of fixed effects is suggested when use of progeny averages of adjusted records (e.g., daughter yield deviations) would result in inefficient use of the information available in the data.     

Application of selection index calculations to determine selection strategies in genomic breeding programs

The availability of genomic estimated breeding values (GEBV) allows for possible modifications to existing dairy cattle breeding programs. Selection index calculations including genomic and phenotypic observations as index sources were used to determine the optimal number of offspring per genotyped sire with a focus on functional traits and the design of cooperator herds, and to evaluate the importance of a central station test for genotyped bull dams. Evaluation criteria to compare different breeding strategies were correlations between index and aggregate genotype (r_TI), and the relative selection response percentage (RSR) of an index without single nucleotide polymorphism information in relation to a single nucleotide polymorphism-based index. The number of required daughter records per sire to achieve a predefined r_TI strongly depends on the accuracy of GEBV (r_mg) and the heritability of the trait. For a desired r_TI of 0.8, h² = 0.10, and r_mg = 0.5, at least 57 additional daughters have to be included in the genetic evaluation. Daughter records of genotyped sires are not necessary for optimal scenarios where r_mg is greater than or equal to r_TI. There still is a substantial need for phenotypic daughter records, especially for low-heritability functional traits and r_mg < 0.7. Phenotypic records from genotyped potential bull dams have no relevance for increasing r_TI, even with a low value for r_mg of 0.5. Hence, genomic breeding programs should focus on recording functional traits within progeny groups, preferably in cooperator herds. For low-heritability traits and with r_mg > 0.7, the RSR of conventional breeding programs was only 10% of RSR from genomic breeding strategies. As shown in scenarios including 2 traits in the index as well as in the aggregate genotype, the availability of highly accurate GEBV for production traits and low-accuracy GEBV for functional traits increased the risk of widening the gap between selection responses in production and functionality. Counteractions are possible, such as via higher economic weights for low-heritability functional traits. Finally, an alternative selection strategy considering only 2 pathways of selection for genotyped male calves and for cow dams was evaluated. This strategy is competitive with a 4-pathway genomic breeding program if the fraction of selected male calves for the artificial insemination program is below 1% and if selection is focused on functionality, thus pointing to substantial insufficiencies caused by low reliabilities of breeding values for cows for such traits in conventional bull dam selection schemes.     

Combining somatic cell count traits for optimal selection against mastitis

Test-day records of somatic cell counts (SCC) can be used to define alternative traits to decrease genetic susceptibility to clinical mastitis (CM) and subclinical mastitis (SCM). This paper examines which combination of alternative SCC traits can be used best to reduce both CM and SCM and whether direct information on CM is useful in this respect. Genetic correlations between 10 SCC traits and CM and SCM were estimated from 3 independent data sets. The SCC traits with the strongest correlations with CM differed from those with the strongest correlations with SCM. Selection index calculations were made for a breeding goal of 50% CM and 50% SCM resistance using these correlations. They indicated that a combination of 5 SCC traits (SCC early and late in lactation, suspicion of infection based on increased SCC, extent of increased SCC, and presence of a peak pattern in SCC) gave a high accuracy, almost without loss, compared with the full set of 10 SCC traits. The estimated accuracy of this index was 0.91, assuming that the correlations had been estimated without error. To take errors in estimation into account, correlations were resampled from a normal distribution with mean and standard errors as originally estimated. The accuracy of the index calculated with the original correlations was then recalculated using the resampled correlations. The average accuracy based on 50,000 resamplings decreased to 0.81. Use of direct information on CM improved the accuracy (uncorrected for errors in correlations) only slightly, to 0.92.     

Selection of bull dams for production and functional traits in an open nucleus herd

The aim of this study was to compare different scenarios for bull dam selection in a nucleus herd. A deterministic simulation study using selection index methodology was undertaken. In the scenarios studied, differing amounts of information on functional traits were available when bull dams were selected, and the resulting genetic responses in these traits were compared. Field-recorded fertility traits used in the scenarios were available as progeny test results of artificial insemination bulls: these included pregnant at first insemination (PFI), interval between calving and first insemination (CFI), and cases of reproductive disorders (RD). Similarly, field-recorded cases of clinical mastitis (CM), lactation somatic cell score (LSCS), and protein yield (PY) were included for pedigree selection. In the scenarios, heat intensity score and progesterone levels were treated as new indicator traits of fertility recorded in the nucleus herd. Traits CFI and LSCS were assumed to be better recorded with higher heritability in the nucleus herd than in ordinary herds. Economic weights currently used in Nordic Cattle Genetic Evaluation (NAV) were adapted and used in the scenarios. The results showed that these weights, if used in multiple trait genetic evaluation, would lead to undesirable genetic changes in functional traits for the bull dam selection path in a nucleus environment. More frequent recording of additional traits failed to improve selection for functional traits, as did more frequent recording of ordinary traits. Restriction index methodology was used to derive the bull dam total weights that gave no unfavorable response (i.e., zero genetic change) in traits PFI, CFI, and CM. When summarized over lactations, the new bull dam total weights, when additional records from nucleus were used, had to be 12 to 23 times higher for fertility, and 3 times higher for mastitis, than the presently used NAV weights, if these traits were to remain unchanged through the bull dam selection path. Thus, nucleus herd selection of bull dams is questionable for low heritability traits that are already recorded in the field.     

Ranking sires using genetic selection indices based on financial investment methods versus lifetime net merit

Current USDA selection indices such as lifetime net merit (NM$) estimate lifetime profit differences, which are accurately approximated by a linear combination of 13 traits. In these indices, every animal gets credit for 2.78 lactations of the traits expressed per lactation, such as fat and protein, independent of its productive life (PL). This formulation may over- or underestimate the net revenue from traits expressed per lactation depending on PL. The objectives were to develop 2 genetic selection indices using financial investment methods to account for differences in PL and to compare them with the 2017 NM$ for marketed Holstein sires. Selection among animals with different PL is an example of investment in mutually exclusive projects that have unequal duration. Financial investment theory says that such projects are best compared with the annualized net present value (ANPV) method when replacement occurs with technologically equal assets. However, genetic progress implies that future available replacement animals are technologically improved assets. Asset replacement theory with improved assets results in an annualized value including genetic opportunity cost (AVOC) for each animal. We developed the ANPV and AVOC and compared these with the NM$ for 1,500 marketed Holstein sires from the December 2017 genetic evaluation. The lowest Pearson correlation coefficient was 0.980 between AVOC and NM$, whereas the highest was 0.999 between ANPV and NM$ among the 1,500 sires. Correlations for the top 300 sires were lower. Although we found high correlations between indices, the 95th and 5th percentiles of individual rank changes between AVOC and NM$ were +131 and ?163 positions, respectively, whereas these changes between ANPV and NM$ were +27 and ?45 positions, respectively. The relative emphasis of PL in the AVOC index was half of the relative emphasis in NM$. These results show that applying financial investment methods to value differences in genetic merit of animals changes their rankings compared with the NM$ formulation. Rank changes were meaningful enough that the new indices warrant consideration for use in practice.     

Simulating consequences of choosing a breeding goal for organic dairy production

In Denmark, Finland, and Sweden, the Nordic Total Merit index is used as the breeding selection tool for both organic and conventional dairy farmers based on common economic models for conventional dairy farming. Organic farming is based on the principles of organic agriculture (POA) defined by the International Federation of Organic Agriculture Movements. These principles are not set up with an economic point of view, and therefore it may be questionable to use a breeding goal (BG) for organic dairy production based on economic models. In addition to economics and the principles of organic agriculture, it is important to look at farmers' preferences for improving BG traits when setting up a BG for organic farming. The aim of this research was to set up, simulate, and compare long-term effects of different BG for organic and conventional dairy production systems based on economic models, farmers' preferences, and POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency. The BG based on economic models and on farmers' preferences were taken from previous studies. The other BG were desired gains indices, set up by means of a questionnaire about relatedness between the POA and BG traits. Each BG was simulated in the stochastic simulation program ADAM. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, caused favorable genetic gain in all 12 traits included in this study compared with 6 traits for the other BG. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, were very different from BG for organic and conventional production based on economic models and farmers' preferences in both simulated genetic change and correlations between BG. The BG that was created based on the principles of organic agriculture could be used as a specific index for organic dairy farming in Denmark, but this index was economically not very sustainable. Hence, an intermediate breeding goal could be developed by breeding companies to address both economics and the principles of organic agriculture.     

Evaluation of body condition score measured throughout lactation as an indicator of fertility in dairy cattle

Body condition score (BCS) records of primiparous Holstein cows were analyzed both as a single measure per animal and as repeated measures per sire of cow. The former resulted in a single, average, genetic evaluation for each sire, and the latter resulted in separate genetic evaluations per day of lactation. Repeated measure analysis yielded genetic correlations of less than unity between days of lactation, suggesting that BCS may not be the same trait across lactation. Differences between daily genetic evaluations on d 10 or 30 and subsequent daily evaluations were used to assess BCS change at different stages of lactation. Genetic evaluations for BCS level or change were used to estimate genetic correlations between BCS measures and fertility traits in order to assess the capacity of BCS to predict fertility. Genetic correlation estimates with calving interval and non-return rate were consistently higher for daily BCS than single measure BCS evaluations, but results were not always statistically different. Genetic correlations between BCS change and fertility traits were not significantly different from zero. The product of the accuracy of BCS evaluations with their genetic correlation with the UK fertility index, comprising calving interval and non-return rate, was consistently higher for daily than for single BCS evaluations, by 28 to 53%. This product is associated with the conceptual correlated response in fertility from BCS selection and was highest for early (d 10 to 75) evaluations.     

Short communication: Implementation of a breeding value for heat tolerance in Australian dairy cattle

Excessive ambient temperature and humidity can impair milk production and fertility of dairy cows. Selection for heat-tolerant animals is one possible option to mitigate the effects of heat stress. To enable selection for this trait, we describe the development of a heat tolerance breeding value for Australian dairy cattle. We estimated the direct genomic values of decline in milk, fat, and protein yield per unit increase of temperature-humidity index (THI) using 46,726 single nucleotide polymorphisms and a reference population of 2,236 sires and 11,853 cows for Holsteins and 506 sires and 4,268 cows for Jerseys. This new direct genomic value is the Australian genomic breeding value for heat tolerance (HT ABVg). The components of the HT ABVg are the decline in milk, fat, and protein per unit increase in THI when THI increases above the threshold of 60. These components are weighted by their respective economic values, assumed to be equivalent to the weights applied to milk, fat, and protein yield in the Australian selection indices. Within each breed, the HT ABVg is then standardized to have a mean of 100 and standard deviation (SD) of 5, which is consistent with the presentation of breeding values for many other traits in Australia. The HT ABVg ranged from ?4 to +3 SD in Holsteins and ?3 to +4 SD in Jerseys. The mean reliabilities of HT ABVg among validation sires, calculated from the prediction error variance and additive genetic variance, were 38% in both breeds. The range in ABVg and their reliability suggests that HT can be improved using genomic selection. There has been a deterioration in the genetic trend of HT, and to moderate the decline it is suggested that the HT ABVg should be included in a multitrait economic index with other traits that contribute to farm profit.     

Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle

Mastitis is a disease of major economic importance to the dairy cattle sector because of the high incidence of clinical mastitis and prevalence of subclinical mastitis and, consequently, the costs associated with treatment, production losses, and reduced animal welfare. Disease-recording systems compiling data from a large number of farms are still not widely implemented around the world; thus, selection for mastitis resistance is often based on genetically correlated indicator traits such as somatic cell count (SCC), udder depth, and fore udder attachment. However, in the past years, several countries have initiated collection systems of clinical mastitis, based on producers recording data in most cases. The large data sets generated have enabled researchers to assess incidence of this disease and to investigate the genetic background of clinical mastitis itself, as well as its relationships with other traits of interest to the dairy industry. The genetic correlations between clinical mastitis and its previous proxies were estimated more accurately and confirmed the strong relationship of clinical mastitis with SCC and udder depth. New traits deriving from SCC were also studied, with the most relevant findings being associated with mean somatic cell score (SCS) in early lactation, standard deviation of SCS, and excessive test-day SCC pattern. Genetic correlations between clinical mastitis and other economically important traits indicated that selection for mastitis resistance would also improve resistance against other diseases and enhance both fertility and longevity. However, milk yield remains negatively correlated with clinical mastitis, emphasizing the importance of including health traits in the breeding objectives to achieve genetic progress for all important traits. These studies enabled the establishment of new genetic and genomic evaluation models, which are more efficient for selection to mastitis resistance. Further studies that are potential keys for future improvement of mastitis resistance are deep investigation of the bacteriology of mastitis, identification of novel indicator traits and tools for selection, and development of a larger female reference population to improve reliability of genomic evaluations. These cutting-edge studies will result in a better understanding of the genetic background of mastitis resistance and enable a more accurate phenotyping and genetic selection to improve mastitis resistance, and consequently, animal welfare and industry profitability.     

Genomic selection using indicator traits to reduce the environmental impact of milk production

The aim of this simulation study was to test the hypothesis that phenotype information of specific indicator traits of environmental importance recorded on a small-scale can be implemented in breeding schemes with genomic selection to reduce the environmental impact of milk production. A stochastic simulation was undertaken to test alternative breeding strategies. The breeding goal consisted of milk production, a functional trait, and environmental impact (EI). The indicator traits (IT) for EI were categorized as large-, medium-, or small-scale, depending on how the traits were recorded. The large-scale traits were stayability and stature; the medium-scale traits were live weight and methane in the breath of the cow measured during milking; and the small-scale traits were residual feed intake and methane recorded in a respiration chamber. Simulated scenarios considered information for just one IT in addition to information for milk production and functional traits. The annual monetary genetic gain was highest in the large-scale scenario that included stayability as IT. The annual monetary gain in the scenarios with medium- or small-scale IT varied from €50.5 to 47.5. The genetic gain improvement in EI was, however, best in the scenarios where the genetic correlation between IT and EI was ≥0.30 and the accuracy of direct genomic value was ≥0.40. The genetic gain in EI was 26 to 34% higher when indicator traits such as greenhouse gases in the breath of the cow and methane recorded in respiration chamber were used compared with a scenario where no indicator trait was included. It is possible to achieve increased genetic gain in EI by using a highly correlated indicator trait, but it requires that the established reference population for the indicator trait is large enough so that the accuracy of direct genomic values will be reasonably high.     

Evaluation of classifiers that score linear type traits and body condition score using common sires

Subjective visual assessment of animals by classifiers is undertaken for several different traits in farm livestock, e.g., linear type traits, body condition score, or carcass conformation. One of the difficulties in assessment is the effect of an individual classifier. To ensure that classifiers rank animals consistently, i.e., the repeatability between classifiers and within classifier, genetic links across routinely scored observations may be used to validate scoring of individual classifiers. Eighteen classifiers of NRS scored 18 traits, and body condition for 91,589 first-lactation heifers, daughters of 601 sires. Genetic parameters were estimated in a series of bivariate analyses. In turn, observations of each individual classifier were trait 1 and all observations of all other classifiers were grouped as trait 2. Likelihoods were used to test whether additive genetic or residual variances for each classifier (trait 1) differed significantly from the grouped records (trait 2), and to test whether the genetic correlation between trait 1 and trait 2 was significantly smaller than unity. Arbitrary criteria were set to mark traits for individual classifiers when a significant deviation was found: genetic correlations of < or = 0.40, and more than 15% deviation for the standard deviation. One classifier had relatively low heritabilities, but high genetic correlations with the others. This might indicate that the repeatability within classifier should be improved. Another classifier had high genetic correlations with the others, but his sire variances were significantly higher than average for most traits. For the genetic correlations, each classifier averaged 3.3 traits marked, ranging from 0 to 9. Overall feet and legs, rump width, central ligament, and foot angle received most marks (12 to 6 classifiers), but no disagreement existed on the definition (i.e., no mark) for body condition score, stature, rump angle, teat length, overall udder, and teat placement. These simple and cheap marks can be used in training sessions to improve the quality of the scoring system.     

Selection for the direct and maternal genetic effects for dystocia in Holsteins

The effects of selection for the direct and maternal components of dystocia were estimated for first, second and later, and all parities. The effect of restricting maternal change to zero and the effect of selection for only the direct component were also examined. Gene flow procedure was used to compute economic weights as 1 and .347 for the direct and maternal effects, respectively. Genetic gain in aggregate genotype was the largest for first parity. For all parities all the gain in the aggregate genotype was accounted for by the direct effects. At the same time, a slight decrease in the genetic maternal effects was observed. For all cases, selection for both traits had almost no loss in aggregate genotype or accuracy compared with when maternal changes were restricted to zero. Total genetic gain and genetic gain for the direct effect of dystocia were greater and accuracy of selection was lower when selection was for the genetic direct effect only versus the index that included both direct and maternal effects. Selection for only the direct effects is not likely to produce any significant change in dystocia as a maternal trait.