Genetic comparison of breeding schemes based on semen importation and local breeding schemes: framework and application to Costa Rica

Local breeding schemes for Holstein cattle of Costa Rica were compared with the current practice based on continuous semen importation (SI) by deterministic simulation. Comparison was made on the basis of genetic response and correlation between breeding goals. A local breeding goal was defined on the basis of prevailing production circumstances and compared against a typical breeding goal for an exporting country. Differences in genetic response were <3%, and the correlation between breeding goals was 0.99. Therefore, difference between breeding objectives proved negligible. For the evaluation of genetic response, the current scheme based on SI was evaluated against a progeny testing (PT) scheme and a closed nucleus (CN) breeding scheme, both local. Selection intensities and accuracy of selection were defined according to current population size and reproduction efficiency parameters. When genotype x environment interaction (G x E) was ignored, SI was the strategy with the highest genetic response: 5.0% above the CN breeding scheme and 33.2% above PT. A correlation between breeding values in both countries lower than one was assumed to assess the effect of G x E. This resulted in permanent effects on the relative efficiencies of breeding strategies because of the reduction in the rate of genetic response when SI was used. When the genetic correlation was assumed equal to 0.75, the genetic response achieved with SI was reduced at the same level as local PT. When an initial difference in average genetic merit of the populations was assumed, this only had a temporal effect on the relative ranking of strategies, which is reverted after some years of selection because the rate of change in genetic responses remains unchanged. Given that the actual levels of genetic correlation between countries may be around 0.60, it was concluded that a local breeding scheme based on a nucleus herd could provide better results than the current strategy based on SI.     

Evaluation of closed adult nucleus multiple ovulation and embryo transfer and conventional progeny testing breeding schemes for milk production in tropical crossbred cattle

The potential benefits of closed adult nucleus multiple ovulation and embryo transfer (MOET) and conventional progeny testing (CNS) schemes, and the logistics of their integration into large-scale continuous production of crossbred cattle were studied by deterministic simulation. The latter was based on F1 (Bos taurus x Bos indicus) production using AI or natural mating and MOET, and continuous F2 production by mating of F1 animals. The gene flow and the cumulative discounted expressions (CDES) were also calculated. Both schemes had 8, 16, 32, or 64 dams with 2, 4, 8, 16, or 32 sires selected. In the MOET nucleus scheme (MNS), the test capacity was 1, 2, 8, or 16 offspring, and the number of matings per dam per year was 1, 2, or 4. A scheme of 8 sires with 64 dams and a test capacity of 4 female offspring per dam per year resulted in an annual genetic gain (in phenotypic standard deviation) of 0.324 and 0.081 for MNS and CNS, respectively. In the MNS, there was substantial genetic gain with a relatively small number of animals compared with a CNS. The F1 had the highest, and the F2 scheme the lowest CDES. However, a very large number of B. indicus females would be required in the F1 scheme. This scheme may not be practical under conditions in developing countries. The F2 scheme was logistically attractive because it produces its own replacements, and the number of B. taurus females required would be easy to attain. Accompanying technical and financial constraints of nucleus schemes should be addressed before applying them.     

Derivation of economic values for German dairy breeds by means of a bio-economic model—with special emphasis on functional traits

To assess the economic importance of breeding traits, economic values (EV) were derived for 3 German dairy cattle breeds: German Holstein (HOL), Angler (ANG), and Red and White Dual-Purpose (RDN). For that purpose, the stochastic bio-economic model SimHerd (SimHerd A/S, Viborg, Denmark) was used, which simulates the expected monetary gain in dairy herds. The EV was calculated as the alteration in average net return of the herd responding to a marginal change in the trait of interest. When deriving EV using SimHerd, economic consequences resulting from changes in the age structure of a dairy herd (i.e., structural herd effects) are considered. However, this requires the simulation of relationships between traits in the bio-economic model. To avoid double counting, the EV of a trait was corrected for effects from alterations in correlated traits using multiple regression analysis. The EV were derived for 23 traits in terms of production, conformation and workability, dairy health, calf survival, and reproduction performance. Furthermore, the relative economic importance of the breeding traits was calculated. Relative emphasis on production was between 39.9 and 44.4% in the breeds studied. Total costs per case of ketosis and metritis ranged from €167 to €196 and €173 to €182, respectively. Highest marginal EV of direct health traits were found for mastitis (€257 to €271 per case) and lameness (€270 to €310 per case). Consequently, relative emphasis on direct health traits was between 15.7 and 17.9%. The EV of reproduction performance showed largest differences among the cattle breeds. Overall relative emphasis on reproduction was 10.5% in HOL, 10.8% in ANG, and 6.5% in RDN. The relative economic importance of cow mortality ranged from 15.5 to 16.0% across the breeds. Collectively, the study showed the high economic importance of functional traits in the cattle breeds studied.     

Superiority of QTL-assisted selection in dairy cattle breeding schemes

The superiority of selection schemes employing information about a known quantitative trait locus (QTL) over conventional schemes is examined for dairy cattle breeding schemes. Stochastic simulation of a dairy cattle population with selection practices, structures, and parameters similar to the US Holstein population was implemented. Additive genetic effects were estimated by an animal model. Two schemes were compared: a QTL-assisted selection scheme in which the genotype of a known QTL was accounted for in the animal model as a fixed factor, and a QTL-free selection scheme in which the QTL was simulated but was not fit separately in the animal model. Under the QTL-assisted selection scheme, all animals in the mixed model were assumed to be genotyped for the QTL. The effect of using QTL information on the genetic response, the frequency of the favorable QTL allele, and the accuracy of evaluation were examined. Moreover, the effect was studied in four distinct paths of selection: active sires, proven young bulls, bull dams, and first-lactation cows. Average superiority values of 4.6, 7.6, 11.7, and 1.1% for genetic response were observed over 16 yr of selection for active sires, young bulls, bull dams, and first-lactation cows, respectively. Frequency of the favorable QTL allele changed faster in bull dams than males, and was the slowest in first-lactation cows. Finally, accuracy of evaluation under the QTL-assisted selection scheme was higher than under the QTL-free selection scheme. Young bulls ofthe QTL-assisted selection scheme on average had 0.049 higher accuracy, and first-lactation cows had on average 0.185 higher accuracy than corresponding animals of the QTL-free selection scheme.     

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.     

Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h² = 0.30) and a functional trait (h² = 0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.     

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.     

Effects of including a quantitative trait locus in selection under different waiting plans of young bulls

The advantage of using the genotype of a quantitative trait locus (QTL) in selection schemes of dairy cattle was quantified using stochastic simulation. Three selection plans were studied. In the first plan, young bulls waited for 3 yr until their sisters completed a lactation and then were evaluated and selected based on an animal model. In a second plan, young bulls waited for 5 yr until their daughters completed a lactation. An intermediate 4-yr waiting plan was also studied. Simulation was for 16 yr with overlapping generations. Population and model parameters were proportional to the U.S. Holstein population. The advantage of using a QTL was quantified as the percentage of superiority of QTL-assisted over QTL-free selection using cumulative genetic response. Percentage of superiority was reported for four selection pathways: active sires, young bulls, bull dams, and first lactation cows. A general trend was observed: low superiority in early years of selection that increased to a plateau in later years and then decreased. The superiority of the QTL information was greatest in the 3-yr waiting plan and least in the 4-yr waiting plan. Superiority at plateau for selection pathways ranged from 16 to 26% for the 3-yr waiting plan, from 3 to 12% for the 4-yr waiting plan, and from 5 to 13% for the 5-yr waiting plan. The contribution to selection response attributed to the QTL and the polygenes was quantified. The rate at which the favorable allele approached fixation and the accuracy of predicting breeding values on the percentage of superiority were studied.     

Economic basis for the Nordic Total Merit Index

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.     

Short communication: effect of leptin gene polymorphisms on breeding value for milk production traits

New molecular techniques focused on genome analysis open new possibilities for complex evaluation of economically important traits in farm animals. Milk production traits are typical quantitative characteristics controlled by a number of genes. Mutations in their sequences may alter animal performance as well as their breeding values. In this study, we investigated the effect of 3 restriction fragment length polymorphisms (RFLP): HphI, Kpn2I, and Sau3AI in the leptin gene, on bull breeding values for milk, fat, and protein yield, and fat and protein content. One hundred seventeen Polish Black and White AI bulls were genotyped. Pedigree analysis indicated a relatively close relationship between the bulls. Statistical analysis indicated that the HphI polymorphism has a significant effect on milk and protein yield. Animals with the TT genotype had approximately 2x higher estimated breeding values for milk and protein yields. No effect was found for the other 2 polymorphisms.     

Integration of Interbull's multiple across-country evaluation approach breeding values into the multiple-trait single-step random regression test-day genetic evaluation for yield traits of Australian Red breeds

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model.     

Cross-sectional analyses of a national database to determine if superior genetic merit translates to superior dairy cow performance

Various studies have validated that genetic divergence in dairy cattle translates to phenotypic differences; nonetheless, many studies that consider the breeding goal, or associated traits, have generally been small scale, often undertaken in controlled environments, and they lack consideration for the entire suite of traits included in the breeding goal. Therefore, the objective of the present study was to fill this void, and in doing so, provide producers with confidence that the estimated breeding values (EBV) included in the breeding goal do (or otherwise) translate to desired changes in performance among commercial cattle; an additional outcome of such an approach is the identification of potential areas for improvements. Performance data on 536,923 Irish dairy cows (and their progeny) from 13,399 commercial spring-calving herds were used. Association analyses between the cow's EBV of each trait included in the Irish total merit index for dairy cows (which was derived before her own performance data accumulated) and her subsequent performance were undertaken using linear mixed models; milk production, fertility, calving, maintenance (i.e., liveweight), beef, health, and management traits were all considered in the analyses. Results confirm that excelling in EBV for individual traits, as well as on the total merit index, generally delivers superior phenotypic performance; examples of the improved performance for genetically elite animals include a greater yield and concentration of both milk fat and milk protein, despite a lower milk volume, superior reproductive performance, better survival, improved udder and hoof health, lighter cows, and fewer calving complications; all these gains were achieved with minimal to no effect on the beef merit of the dairy cow's progeny. The associated phenotypic change in each performance trait per unit change in its respective EBV was largely in line with the direction and magnitude of expectation, the exception being for calving interval. Per unit change in calving interval EBV, the direction of phenotypic response was as anticipated but the magnitude of the response was only half of what was expected. Despite the deviation from expectation between the calving interval EBV and its associated phenotype, a superior total merit index or a superior fertility EBV was indeed associated with an improvement in all detailed fertility performance phenotypes investigated. Results substantiate that breeding is a sustainable strategy of improving phenotypic performance in commercial dairy cattle and, by extension, profit.     

Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit

Conventional prediction of dairy cattle merit involves setting up and solving linear equations with the number of unknowns being the number of animals, typically millions, multiplied by the number of traits being simultaneously assessed. The coefficient matrix has been large and sparse and iteration on data has been the method of choice, whereby the coefficient matrix is not stored but recreated as needed. In contrast, genomic prediction involves assessment of the merit of genome fragments characterized by single nucleotide polymorphism genotypes, currently some 50,000, which can then be used to predict the merit of individual animals according to the fragments they have inherited. The prediction equations for chromosome fragments typically have fewer than 100,000 unknowns, but the number of observations used to predict the fragment effects can be one-tenth the number of fragments. The coefficient matrix tends to be dense and the resulting system of equations can be ill behaved. Equivalent computing algorithms for genomic prediction were derived. The number of unknowns in the equivalent system grows with number of genotyped animals, usually bulls, rather than the number of chromosome fragment effects. In circumstances with fewer genotyped animals than single nucleotide polymorphism genotypes, these equivalent computations allow the solving of a smaller system of equations that behaves numerically better. There were 3 solving strategies compared: 1 method that formed and stored the coefficient matrix in memory and 2 methods that iterate on data. Finally, formulas for reliabilities of genomic predictions of merit were developed.     

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions

Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting cows require accurate and large-scale measurements of methane (CH₄) emissions from individual cows. Several techniques have been developed to measure CH₄ in a research setting but most are not suitable for large-scale recording on farm. Several groups have explored proxies (i.e., indicators or indirect traits) for CH₄; ideally these should be accurate, inexpensive, and amenable to being recorded individually on a large scale. This review (1) systematically describes the biological basis of current potential CH₄ proxies for dairy cattle; (2) assesses the accuracy and predictive power of single proxies and determines the added value of combining proxies; (3) provides a critical evaluation of the relative merit of the main proxies in terms of their simplicity, cost, accuracy, invasiveness, and throughput; and (4) discusses their suitability as selection traits. The proxies range from simple and low-cost measurements such as body weight and high-throughput milk mid-infrared spectroscopy (MIR) to more challenging measures such as rumen morphology, rumen metabolites, or microbiome profiling. Proxies based on rumen samples are generally poor to moderately accurate predictors of CH₄, and are costly and difficult to measure routinely on-farm. Proxies related to body weight or milk yield and composition, on the other hand, are relatively simple, inexpensive, and high throughput, and are easier to implement in practice. In particular, milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH₄ emission in dairy cows. No single proxy was found to accurately predict CH₄, and combinations of 2 or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by 15 to 35%, mainly because different proxies describe independent sources of variation in CH₄ and one proxy can correct for shortcomings in the other(s). The most important applications of CH₄ proxies are in dairy cattle management and breeding for lower environmental impact. When breeding for traits of lower environmental impact, single or multiple proxies can be used as indirect criteria for the breeding objective, but care should be taken to avoid unfavorable correlated responses. Finally, although combinations of proxies appear to provide the most accurate estimates of CH₄, the greatest limitation today is the lack of robustness in their general applicability. Future efforts should therefore be directed toward developing combinations of proxies that are robust and applicable across diverse production systems and environments.     

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.     

Efficiency of converting nutrient dry matter to milk in Holstein herds

Production of milk from feed dry matter intakes (DMI), called dairy or feed efficiency, is not commonly measured in dairy herds as is feed conversion to weight gain in swine, beef, and poultry; however, it has relevance to conversion of purchased input to salable product and proportion of dietary nutrients excreted. The purpose of this study was to identify some readily measured factors that affect dairy efficiency. Data were collected from 13 dairy herds visited 34 times over a 14-mo period. Variables measured included cool or warm season (high ambient temperature <21 degrees C or >21 degrees C, respectively), days in milk, DMI, milk yield, milk fat percent, herd size, dietary concentrations (DM basis) and kilograms of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and forage. Season, days in milk, CP % and forage % of diet DM, and kilograms of dietary CP affected dairy efficiency. When evaluated using a model containing the significant variables, dairy efficiency was lower in the warm season (1.31) than in the cool season (1.40). In terms of simple correlations, dairy efficiency was negatively correlated with days in milk (r = -0.529), DMI (r = -0.316), forage % (r = -0.430), NDF % (r = -0.308), and kilograms of forage (r = -0.516), NDF (r = -0.434), and ADF (r = -0.313), in the diet, respectively. Dairy efficiency was positively correlated with milk yield (r = 0.707). The same relative patterns of significance and correlation were noted for dairy efficiency calculated with 3.5% fat-corrected milk yield. Diets fed by the herds fell within such a small range of variation (mean +/- standard deviation) for CP % (16.3 +/- 0.696), NDF % (33.2 +/- 2.68), and forage % (46.9 +/- 5.56) that these would not be expected to be useful to evaluate the effect of excessive underfeeding or overfeeding of these dietary components. The negative relationships of dairy efficiency with increasing dietary fiber and forage may reflect the effect of decreased diet digestibility. The results of this study suggest that managing herd breeding programs to reduce average days in milk and providing a cooler environment for the cows may help to maximize dairy efficiency. The mechanisms for the effects of the dietary variables on dairy efficiency need to be understood and evaluated over a broader range of diets and conditions before more firm conclusions regarding their impact can be drawn.     

Symposium review: Challenges and opportunities for evaluating and using the genetic potential of dairy cattle in the new era of sensor data from automation

Sensor data from automation are becoming available on an increasingly large scale, and associated research is slowly starting to appear. This new era of sensor data from automation leads to many challenges but also new opportunities for assessing and maximizing the genetic potential of dairy cattle. The first challenge is data quality, because all uses of sensor data require careful data quality validation, potentially using external references. The second issue is data accessibility. Indeed, sensor data generated from automation are often designed to be available on-farm in a given system. However, to make these data useful—for genetic improvement for example—the data must also be made available off-farm. By nature, sensor data often are very complex and diverse; therefore, a data consolidation and integration layer is required. Moreover, the traits we want to select have to be defined precisely when generated from these raw data. This approach is obviously also beneficial to limit the challenge of extremely high data volumes generated by sensors. An additional challenge is that sensors will always be deployed in a context of herd management; therefore, any efforts to make them useful should focus on both breeding and management. However, this challenge also leads to opportunities to use genomic predictions based on these novel data for breeding and management. Access to relevant phenotypes is crucial for every genomic evaluation system. The automatic generation of training data, on both the phenotypic and genomic levels, is a major opportunity to access novel, precise, continuously updated, and relevant data. If the challenges of bidirectional data transfer between farms and external databases can be solved, new opportunities for continuous genomic evaluations integrating genotypes and the most current local phenotypes can be expected to appear. Novel concepts such as federated learning may help to limit exchange of raw data and, therefore, data ownership issues, which is another important element limiting access to sensor data. Accurate genome-guided decision-making and genome-guided management of dairy cattle should be the ultimate way to add value to sensor data from automation. This could also be the major driving force to improve the cost–benefit relationship for sensor-based technologies, which is currently one of the major obstacles for large-scale use of available technologies.     

Accuracy of breeding values in small genotyped populations using different sources of external information—A simulation study

Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population (P_L) with >20 generations was simulated, and a small subpopulation (P_S) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for P_S; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from P_L as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for P_L; and (S4) single nucleotide polymorphism (SNP) effects calculated based on P_L data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from P_L did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for P_S and P_L was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from P_L were used to predict GEBV for young genotyped animals from P_S, accuracy was greatest (0.56). With 6,000 genotyped animal in P_S, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population.