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Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic prediction model to test the hypothesis that the value of cows in reference populations depends on the availability of phenotypic records. To test the hypothesis, we investigated different strategies of building a reference population for a new functional trait over a 10-year period. The trait was either recorded on a large scale (30 000 cows per year) or on a small scale (2000 cows per year). For large-scale recording, we compared four scenarios where the reference population consisted of 30 sires; 30 sires and 170 test bulls; 30 sires and 2000 cows; or 30 sires, 2000 cows and 170 test bulls in the first year with measurements of the new functional trait. In addition to varying the make-up of the reference population, we also varied the heritability of the trait (h2 = 0.05 v. 0.15). The results showed that a reference population of test bulls, cows and sires results in the highest accuracy of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new functional trait. The results showed that a reference population of cows results in the highest accuracy of the DGV whether the heritability is 0.05 or 0.15, because variation is lost when phenotypic data on cows are summarized in EBV of their sires. The main conclusions from this study are: (i) the fewer phenotypic records, the larger effect of including cows in the reference population; (ii) for small-scale recording, the accuracy of the DGV will continue to increase for several years, whereas the increases in the accuracy of the DGV quickly decrease with large-scale recording; (iii) it is possible to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls.

The aim of this study was to test whether the use of X-semen in a dairy cattle population using genomic selection (GS) and multiple ovulation and embryo transfer (MOET) increases the selection intensity on cow dams and thereby the genetic gain in the entire population. Also, the dynamics of using different types of sexed semen (X, Y or conventional) in the nucleus were investigated. The stochastic simulation study partly supported the hypothesis as the genetic gain in the entire population was elevated when X-semen was used in the production population as GS exploited the higher selection intensity among heifers with great accuracy. However, when MOET was applied, the effect was considerably diminished as was the exchange of females between the nucleus and the production population, thus causing modest genetic profit from using X-sorted semen in the production population. In addition, the effect of using sexed semen on the genetic gain was very small compared with the effect of MOET and highly dependent on whether cow dams or bull dams were inseminated with sexed semen and on what type of semen that was used for the bull dams. The rate of inbreeding was seldom affected by the use of sexed semen. However, when all young bull candidates were born following MOET, the results showed that the use of Y-semen in the breeding nucleus tended to decrease the rate of inbreeding as it enabled GS to increase within-family selection. This implies that the benefit from using sexed semen in a modern dairy cattle breeding scheme applying both GS and MOET may be found in its beneficial effect on the rate of inbreeding.

The aim of this study was to characterize preferences of farmers for breeding goal traits with Danish Red (DR) or Danish Jersey (DJ) cows. A breed-specific survey was established to characterize farmers’ preferences for improvements in 10 traits, by means of pairwise rankings using the online software 1000Minds. These pairwise rankings were based on equal economic worth of trait improvements. The DR survey was filled in by 87 farmers and the DJ survey by 76 farmers. Both DR and DJ farmers gave the highest preference to improvements in mastitis, and the lowest to calving difficulty. By means of a cluster analysis, three distinct clusters of farmers were identified per breed. Comparisons of herd characteristics between clusters suggest that farmers choose to improve traits that are problematic in their herds. This study shows that heterogeneity exists in farmers’ preferences for trait improvements and that herd characteristics influence these preferences in DR and DJ.

The aim of this study was to characterize preferences of farmers with Red Danish Cattle (RDC) or Jersey herds. A breed-specific survey was established to characterize preferences of farmers for improvements in ten traits, by means of pairwise rankings using the online software 1000Minds. These pairwise rankings were based on equal economic worth of trait improvements. The RDC-survey was filled in by 87 farmers and the Jersey-survey by 76 farmers. Both RDC and Jersey herds had the highest preference for improvements in mastitis, followed by milk production. The lowest preference was given to calving difficulty. By means of a cluster analysis distinct clusters of farmers were identified and named according to the trait improvements that werethe most preferred per cluster. RDC herds were divided into three different clusters: Robustness, Production & Health, and Production & Fertility. The Jersey herds were divided into the clusters Production & Fertility, Production & Robustness, and Survival. For RDC herds we found that the cluster Production & Fertility had the highest percentage of organic farmers (50%), the lowest percentage of crossbreeding and the lowest yields and herd size. The cluster Robustness had the highest yields and the highest prevalence of udder disorders, and these farmers ranked production traits very low and mastitis very high. For Jersey herds, the percentage of dead cows was highest for the cluster Survival. This cluster also ranked mastitis the highest compared to the other clusters, and contained herds with the highest prevalence of udder disorders. These results suggest that farmers choose to improve traits that are more problematic in their herds. This study shows that heterogeneity exists in farmers’ preferences for trait improvements and that some herd characteristics can be linked to the different farmer clusters. The results from this study give a strong basis for setting up customized indexes or breeding goals for the different farmer clusters, which might increase the uptake of genetic merit.

The objectives of this study were to investigate genotype by environment interaction effects, with environments defined as calving month and geographic location, on the interval from calving to first insemination (CFI) of Holstein cows in Denmark and Sweden. The data set included 811,285 records on CFI for first-parity cows from January 2010 to January 2014 housed in 7,458 herds. The longest mean CFI was 84.7 d for cows calving in April and the shortest was 76.3 d for cows calving in September. The longest mean CFI of 87.1 d was recorded at the northernmost location (LOC-8), whereas the shortest mean CFI of 73.5 d was recorded at the southernmost location (LOC-1). The multiple trait approach, in which CFI values in different calving months and different geographic locations were treated as different traits, was used to estimate the variance components and genetic correlations for CFI by using the average information (AI)-REML procedure in a bivariate sire model. Estimates of genetic variance and heritability were highest for January calvings and 3 times smaller for June calvings. Location 2 had the highest heritability and LOC-8 the lowest, with heritability estimates decreasing from LOC-2 to LOC-8. Genetic correlations of CFI between calving months were weakest between cold months (December and January) and warm months (June, August, and September); the lowest estimate was found between January and September calvings. Genetic correlations of CFI between the different geographic locations were generally strong, and the weakest correlation was between LOC-3 and LOC-8. These results indicate a genotype by environment interaction for CFI primarily regarding seasons described by calving months. The effect of geographic location was less important, mostly producing a scaling effect of CFI in different locations. We concluded that CFI is more sensitive to seasonal effects than geographic locations in Denmark and Sweden.

Within a group of cooperating countries, all breeding animals are judged according to the same criteria if a joint breeding goal is applied in these countries. This makes it easier for dairy farmers to compare national and foreign elite bulls and may lead to more selection across borders. However, a joint breeding goal is only an advantage if the countries share the same production environment. In this study, we investigated whether the development of a joint breeding goal for each of the major dairy cattle breeds across Denmark, Finland, and Sweden would be an advantage compared with national breeding goals. For that purpose, economic values for all breeding goal traits in the 3 countries were derived, and estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were compared. The economic values within country were derived by means of an objective bio-economic model, and the basic situation in each of the 3 production environments was based on an average dairy cattle herd with regard to production system, production level, and management strategy. The common Nordic economic values for each trait were calculated as the average of that specific trait in each of the 3 production environments. Balanced breeding goals were obtained in all situations because the derived economic values for traits related to health, fertility, milk production, and longevity were sizeable. For both Nordic Red Dairy Cattle and Nordic Holstein, the estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were very high. Thus, a joint breeding goal within breed is feasible for Denmark, Finland, and Sweden.

The aim of this simulation study was to test the hypothesis that phenotype information of specific indicator traits of environmental importance recorded on asmall-scale can be implemented in breeding schemes with genomic selection to reduce the environmental impact of milk production. A stochastic simulationwas 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 requiresthat the established reference population for the indicator trait is large enough so that the accuracy of direct genomic values will be reasonably high

Summary A breeding scheme using genomic selection and an indicator trait for environmental impact (EI) was studied to find the most effective recording strategy in terms of annual monetary genetic gain and breakeven price for the recording of indicator traits. The breakeven price shows the investment space for developing a recording system for an indicator trait. The breeding goal consisted of three traits – milk production, functional trait and environmental impact – with economic values of €83, €82 and €−83, respectively. The first scenario included only breeding goal traits and no indicator traits (NoIT). The other scenarios included all three breeding goal traits and one indicator trait (IT) for EI. The indicator traits were recorded on a large scale (stayability after first lactation and stature), medium scale (live weight and greenhouse gases (GHG) measured in the breath of the cow during milking) or small scale (residual feed intake and total enteric methane measured in a respiration chamber). In the scenario with stayability, the genetic gain in EI was over 11% higher than it was in NoIT. The breakeven price of recording stayability was €8 per record. Stayability is easy to record in the national milk recording system, and its use as an indicator trait for EI would not generate any additional recording costs. Therefore, stayability would be a good indicator trait to use to mitigate EI. The highest genetic gain in EI (23% higher compared to NoIT) was achieved when the GHG measured in the breath of the cow was used as indicator trait. The breakeven price for this indicator trait was €29 per record in the reference population. Ideally the recording of a specific indicator trait for EI would take place when: (i) the genetic correlation between the IT and EI is high; and (ii) the number of phenotypic records for the indicator trait is high enough to achieve a moderately high reliability of direct genomic values.

For a number of traits the phenotype considered to be the goal trait is a combination of 2 or more traits, like methane (CH4) emission (CH4/kg of milk). Direct selection on CH4 emission defined as a ratio is problematic, because it is uncertain whether the improvement comes from an improvement in milk yield, a decrease in CH4 emission or both. The goal was to test different strategies on selecting for 2 antagonistic traits– improving milk yield while decreasing methane emissions. The hypothesis was that to maximize genetic gain for a ratio trait, the best approach is to select directly for the component traits rather than using a ratio trait or a trait where 1 trait is corrected for the other as the selection criteria. Stochastic simulation was used to mimic a dairy cattle population. Three scenarios were tested, which differed in selection criteria but all selecting for increased milk yield: 1) selection based on a multitrait approach using the correlation structure between the 2 traits, 2) the ratio of methane to milk and 3) gross methane phenotypi-cally corrected for milk. Four correlation sets were tested in all scenarios, to access robustness of the results. An average genetic gain of 66 kg of milk per yr was obtained in all scenarios, but scenario 1 had the best response for decreased methane emissions, with a genetic gain of 24.8 l/yr, while scenarios 2 and 3 had genetic gains of 27.1 and 27.3 kg/yr. The results found were persistent across correlation sets. These results confirm the hypothesis that to obtain the highest genetic gain a multitrait selection is a better approach than selecting for the ratio directly. The results are exemplified for a methane and milk scenario but can be generalized to other situations where combined traits need to be improved.

We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔGAG), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔGAG and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔGAG if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔGAG, greater contributions of the functional trait to ΔGAG and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔGAG. Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔGAG in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔGAG, and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.

Using stochastic simulation, the effect of using sexed semen to cow dams (CD) in a dairy cattle breeding scheme, with or without use of multiple ovulation and embryo transfer (MOET) to bull dams (BD), on annual genetic gain at the population level was examined. Three levels of sexed semen were combined with three levels of MOET: no sexed semen, sexed semen to the best CD and sexed semen to all heifers, combined with no MOET, MOET on all BD and MOET randomly on 20% of the BD. In total, nine scenarios were compared. The simulated population was monitored for 30 years and included 450 herds with 100 cows each. Each year 50 young bulls (YB), 10 active sires and 215 BD were selected on best linear unbiased prediction estimated breeding values by truncation selection across the simulated population, and the YB were tested within the population. Use of sexed semen alone gave a positive increase in the annual genetic gain of 2.1% when used on the best CD and 2.7% when used on all heifers, but only the latter was statistically significant. The increased annual genetic gain was caused by a larger contribution from the CD to the BD. Use of sexed semen together with MOET on BD increased the annual genetic gain by 1.8–2.5% compared with schemes without sexed semen and MOET on all BD. Performing MOET on all BD enables selection of offspring with high Mendelian deviations, which increase the annual genetic gain. Use of sexed semen decreased the genetic lag between the sires and the CD by 12–14% when used on the best CD and by 6% when used to all heifers. The decrease in the genetic lag is caused by the increased selection intensity of the cow dams

Artikel til Herefordbladet og hjemmesiden www.hereford.dk - november 2010 af Morten Kargo Sørensen og Anders Fogh

Udgivelsesdato: Juni 2010

Over the last few years, an increasing awareness has arisen in Denmark of the existence of cows with a generally lowered health and production status, referred to as "loser cows." A previous study has estimated that the overall prevalence of loser cows in Danish Holstein herds is 3.2%. The aim of this study was to estimate genetic and phenotypic parameters for the loser cow state and the underlying traits: lameness, hock lesions, other cutaneous lesions, and condition of hair coat. Records on 6,098 cows were analyzed with an animal model including fixed effects of herd, season of scoring and location of scoring, age at first calving, lactation stage, and parity in addition to additive genetic effects and permanent environmental effects. The heritability of the loser cow score was 0.08 and for the underlying traits the heritability ranged from 0.05 to 0.12. The genetic correlations between various pairs of traits included in the loser cow score ranged from 0.04 to 0.68 and the phenotypic correlations ranged from 0.09 to 0.21. The genetic and phenotypic correlations between the loser cow score and the underlying traits ranged from 0.25 to 0.89 and 0.20 to 0.85, respectively, supporting the concept of the loser cow score. The traits included in the loser cow score are easy to assess and all showed genetic variation. They are therefore suitable for inclusion in a total merit index aimed at breeding for more robust cows Udgivelsesdato: September 2010 Over the last few years, an increasing awareness has arisen in Denmark of the existence of cows with a generally lowered health and production status, referred to as "loser cows." A previous study has estimated that the overall prevalence of loser cows in Danish Holstein herds is 3.2%. The aim of this study was to estimate genetic and phenotypic parameters for the loser cow state and the underlying traits: lameness, hock lesions, other cutaneous lesions, and condition of hair coat. Records on 6,098 cows were analyzed with an animal model including fixed effects of herd, season of scoring and location of scoring, age at first calving, lactation stage, and parity in addition to additive genetic effects and permanent environmental effects. The heritability of the loser cow score was 0.08 and for the underlying traits the heritability ranged from 0.05 to 0.12. The genetic correlations between various pairs of traits included in the loser cow score ranged from 0.04 to 0.68 and the phenotypic correlations ranged from 0.09 to 0.21. The genetic and phenotypic correlations between the loser cow score and the underlying traits ranged from 0.25 to 0.89 and 0.20 to 0.85, respectively, supporting the concept of the loser cow score. The traits included in the loser cow score are easy to assess and all showed genetic variation. They are therefore suitable for inclusion in a total merit index aimed at breeding for more robust cows

Udgivelsesdato: Oktober

Udgivelsesdato: September

International audience

(2000). Udder Conformation and Mastitis Resistance in Danish First-lactation Cows: Heritabilities, Genetic and Environmental Corelations. Acta Agriculturae Scandinavica, Section A — Animal Science: Vol. 50, No. 2, pp. 72-82.

The economic benefit of crossbreeding has been well known for many years within dairy production. However, in most countries with an intensive dairy production, an extended use of systematic crossbreeding has not occurred. This may be due to the myth that heterosis is expressed mainly in low-producing herds. The aim of the study was to investigate the effect of heterosis with different management levels in Danish Jersey herds. More than 300,000 records of 305-d milk, fat, and protein yield from first-lactation Danish Jersey cows with different contributions from original Danish and US Jersey were analyzed using an animal model. The herds were distributed in 5 management groups based on production level. First, the results showed a large increase in additive genetic variance from the herds with lowest production level to the high-producing ones, and second, heterosis for all 3 production traits were lowest within the low-intensity management group and tended to be highest in the intermediate management groups. The results, therefore, support that crossbreeding is a breeding system that should be considered valuable for all management levels.

In dairy cattle breeding, breeding goals (BG) are developed and subsequently a selection index that farmers want to use. Therefore it is important to take their preferences for BG traits into account. Two production systems that are expected to influence farmer preferences for BG traits are organic and conventional systems. The aim of this study was to characterize preferences of organic and conventional Danish dairy farmers for improvements in BG traits for Holstein cattle. A survey was established to characterize preferences for improvements in ten traits, by means of pairwise rankings using the online software 1000Minds. These pairwise rankings were based on equal economic worth of trait improvements. The survey was filled in by 106 organic and 290 conventional farmers. The most preferred trait improvement for both production systems was in cow fertility, and the least preferred improvement was in calving difficulty. Organic farmers ranked milk production and feed efficiency higher than conventional farmers. The reason for this may be that the average milk yield is lower and the milk price is higher in organic herds compared to conventional herds. Organic farmers also ranked calf mortality higher. Conventional farmers ranked mastitis, leg and claw diseases, other diseases and cow mortality higher compared to organic farmers. By means of a cluster analysis distinct clusters of farmers were identified and named according to the trait improvements that were the most preferred per cluster. The cluster analysis for organic farmers identified two production-based clusters (‘Production & Fertility’ and ‘Production & Mastitis’) and one robustness-based cluster, whereas a cluster analysis for conventional farmers found one production-based cluster, one health-based cluster and one survival-based cluster. This study shows that organic and conventional farmers differ in their preferences for improvements in BG traits, and the results can be used to set up specific breeding goals for organic and conventional farming systems.

In contrast to other species, production animals in dairy cattle are mainly purebreds. However, crossbred cows are becoming more widespread due to better profitability from heterosis of economically important production and health traits. Currently, breeding goals (BGs) for purebreds used in crossbreeding systems are optimized for maximizing genetic gain in purebred cows. This might not be optimal when the aim is to increase heterosis and breed complementarity in order to maximize profit in crossbred cows. We hypothesize that differentiated BGs in purebreds that complement each other can improve the performance of crossbred production animals. To test this hypothesis, we set up a pure- and crossbreeding design using the simulation tool ADAM with two breeds, Danish Jersey (DJ) and Nordic Holstein (NH). Real marker genotypes from these two breeds were used as reference to resemble the real population structure of the two breeds. A progeny testing scheme was simulated for 20 years to build large genomic reference populations in each breed followed by a genomic testing scheme of 5 years. Finally, one generation of crossbreeding between the two purebreds was generated. The BG in the purebreds included two traits, a production trait and a health trait, with a genetic correlation between the 2 traits of -0.46 for DJ and -0.35 for NH. Three BGs with different economic values of the traits were set up to test the hypothesis: (1) a reference scenario resembling theweights in the present Nordic total merit index for DJ and NH; (2) the same BG in NH as in 1 but in DJ the BG had a higher weight on health; and (3) BGs only weighing production in NH and health in DJ. The three scenarios was evaluated in the crossbred population according to genetic level and dominance. Preliminary results show a higher degree of dominance in the crossbred animals compared to the purebred animals, but a lower genetic level, especially in scenario 3. The outcomes of this study can be used to evaluate the value of setting up BGs in purebreds specified for crossbreeding

Crossbreeding is the most common breeding system for pigs and poultry, but not for dairy cattle. When selecting purebred lines for crossbred performance, it becomes possible to take advantage of both breed complementarity and heterosis. In this paper, we present a new structure of dairy cattle breeding with the aim to improve crossbred performance in production cows. The structureof the dairy sector can be organised like a pyramid with nucleus herds, multiplier herds, and production herds. Genetic improvement of purebreds takes place in the nucleus. Surplus females from the breeding nucleus are transferred to the multiplier level, where they are used as dams of crossbred animals for production herds. In addition, we hypothesise that joint breeding planning and formulation of breeding goals for each parental breed are necessary in order to optimisecrossbred performance.