An agent-based model evaluation of economic control strategies for paratuberculosis in a dairy herd

This paper uses an agent-based simulation model to estimate the costs associated with Mycobacterium avium ssp. paratuberculosis (MAP), or Johne's disease, in a milking herd, and to determine the net benefits of implementing various control strategies. The net present value (NPV) of a 1,000-cow milking herd is calculated over 20 yr, parametrized to a representative US commercial herd. The revenues of the herd are generated from sales of milk and culled animals. The costs include all variable and fixed costs necessary to operate a representative 1,000-cow milking herd. We estimate the NPV of the herd with no MAP infection, under an expected endemic infection distribution with no controls, and under an expected endemic infection distribution with various controls. The initial number of cows in a herd with an endemic MAP infection is distributed as 75% susceptible, 13% latent, 9% low MAP shedding, and 3% high MAP shedding. Control strategies include testing using ELISA and fecal culture tests and culling of cows that test positive, and culling based on observable milk production decrease. Results show that culling cows based on test results does not increase the herd's NPV and in most cases decreases NPV due to test costs as well as false positives and negatives with their associated costs (e.g., culling healthy cows and keeping infected cows). Culling consistently low producing cows when MAP is believed to be present in the herd produces higher NPV over the strategy of testing and culling MAP infected animals, and over the case of no MAP control.     

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments.     

一类经济数学模型及其应用

利用灰色模型分析民航旅客运量，为民航运量的预测、航空公司的经济分析提供了一种数学模型。     

Optimal insemination and replacement decisions to minimize the cost of pathogen-specific clinical mastitis in dairy cows

Mastitis is a serious production-limiting disease, with effects on milk yield, milk quality, and conception rate, and an increase in the risk of mortality and culling. The objective of this study was 2-fold: (1) to develop an economic optimization model that incorporates all the different types of pathogens that cause clinical mastitis (CM) categorized into 8 classes of culture results, and account for whether the CM was a first, second, or third case in the current lactation and whether the cow had a previous case or cases of CM in the preceding lactation; and (2) to develop this decision model to be versatile enough to add additional pathogens, diseases, or other cow characteristics as more information becomes available without significant alterations to the basic structure of the model. The model provides economically optimal decisions depending on the individual characteristics of the cow and the specific pathogen causing CM. The net returns for the basic herd scenario (with all CM included) were $507/cow per year, where the incidence of CM (cases per 100 cow-years) was 35.6, of which 91.8% of cases were recommended for treatment under an optimal replacement policy. The cost per case of CM was $216.11. The CM cases comprised (incidences, %) Staphylococcus spp. (1.6), Staphylococcus aureus (1.8), Streptococcus spp. (6.9), Escherichia coli (8.1), Klebsiella spp. (2.2), other treated cases (e.g., Pseudomonas; 1.1), other not treated cases (e.g., Trueperella pyogenes; 1.2), and negative culture cases (12.7). The average cost per case, even under optimal decisions, was greatest for Klebsiella spp. ($477), followed by E. coli ($361), other treated cases ($297), and other not treated cases ($280). This was followed by the gram-positive pathogens; among these, the greatest cost per case was due to Staph. aureus ($266), followed by Streptococcus spp. ($174) and Staphylococcus spp. ($135); negative culture had the lowest cost ($115). The model recommended treatment for most CM cases (>85%); the range was 86.2% (Klebsiella spp.) to 98.5% (Staphylococcus spp.). In general, the optimal recommended time for replacement was up to 5 mo earlier for cows with CM compared with cows without CM. Furthermore, although the parameter estimates implemented in this model are applicable to the dairy farms in this study, the parameters may be altered to be specific to other dairy farms. Cow rankings and values based on disease status, pregnancy status, and milk production can be extracted; these provide guidance when determining which cows to keep or cull.     

A stochastic dynamic model of a dairy farm to evaluate the technical and economic performance under different scenarios

Dairy farms need to improve their competitiveness through decisions that are often difficult to evaluate because they are highly dependent on many economic and technical factors. The objective of this project was to develop a stochastic and dynamic mathematical model to simulate the functioning of a dairy farm to evaluate the effect of changes in technical or economic factors on performance and profitability. Submodels were developed for reproduction, feeding, diseases, heifers, environmental factors, facilities, management, and economics. All these submodels were simulated on an animal-by-animal and day-by-day basis. Default values for all variables are provided, but the user can change them. The outcome provides a list of technical and economic indicators essential for the decision-making process. Performance of the program was verified by evaluating the effects and sensitivity analysis of different scenarios in 20 different dairy farms. As an example, a case study of a dairy farm with 300 cows producing 40 L/d and a 12% pregnancy rate (PR) was used. The effect of using a time-fixed artificial insemination (TFAI) protocol in the first insemination at 77 d in milk, with 45 and 40% conception rates for first-lactation and older cows, respectively, and a cost of €13 was explored. During the 5-yr simulation, the TFAI increased PR (12 to 17%) and milk yield per milking cow (39.8 to 41.2 L/d) and reduced days to first AI (93 to 74), days open (143 to 116), and the proportion of problem cows (24.3 to 15.9%). In the TFAI, cows were dried 30 d earlier, resulting in more dry cows, and a smaller difference in milk yield by present cows (35.5 vs 36.0 L/d for control and TFAI, respectively). A longer productive life (2.56 vs. 2.79 yr) with shorter lactations in TFIA resulted in less first-lactation cows (42 vs 36%), 32 more calvings per year, and, therefore, more cases of postpartum diseases. Total (32.5 to 29.9%) and reproductive (10.5 vs 6.8%) culling rates decreased in TFIA. Overall, the net margin was €245 and €309/cow per year in control and TFIA, respectively. The net margin of applying TFAI decreased as PR of the farm increased, with limited benefit of TFAI at a PR of 30%. The model provides a powerful web-based tool to explore the short- and medium-term consequences of technical and economic decisions on the economic sustainability of dairy farms.     

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2- and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.     

Estimation of relative economic weights and the marginal willingness to pay for breeding traits of Brown Swiss cattle using discrete choice experiments

Breeding traits are usually combined in a total merit index according to their economic weights to maximize genetic gain based on economic merit. However, this maximization may not always be the aim of the selection decisions by farm managers. A discrete choice experiment was used to evaluate the importance of traits in terms of the selection decisions of farm managers operating in different environments. Six trait complexes, the semen price, the interactions between these traits, and significant characteristics of the farms were included in a conditional logit model to estimate relative economic weights and the marginal willingness to pay for all traits. Milk value, conformation/udder, and fitness were the most important traits for the farmers, and significant interactions indicated that fitness is of greater importance on organically managed farms than on conventional farms. Farm managers with an advanced education placed more weight on the milk value trait than farm managers without advanced education. On conventional farms, managers weighted the traits milk value and conformation/udder highly. The conformation/udder and fitness trait complexes were important on organic farms. A new trait called perinatal sucking behavior of newborn calves should be included in the total merit index.     

Genetic parameters for dairy calf and replacement heifer wellness traits and their association with cow longevity and health indicators in Holstein cattle

High mortality and involuntary culling rates cause great economic losses to the worldwide dairy cattle industry. However, there is low emphasis on wellness traits in replacement animals (dairy calves and replacement heifers) during their development stages in modern dairy cattle breeding programs. Therefore, the main objectives of this study were to estimate genetic parameters of wellness traits in replacement cattle (replacement wellness traits) and obtain their genetic correlations with 12 cow health and longevity traits in the Chinese Holstein population. Seven replacement wellness traits were analyzed, including birth weight, survival from 3 to 60 d (Sur1), survival from 61 to 365 d (Sur2), survival from 366 d to the first calving (Sur3), calf diarrhea, calf pneumonia, and calf serum total protein (STP). Single and bivariate animal models were employed to estimate (co)variance components using the data from 189,980 Holstein cattle. The genetic correlations between replacement wellness traits and cow longevity, health traits were calculated by employing bivariate models, including 6 longevity traits and 6 health traits (clinical mastitis, metritis, ketosis, displaced abomasum, milk fever, and hoof health or hoof disease). The estimated heritabilities (± SE) were 0.335 (± 0.008), 0.088 (± 0.005), 0.166 (± 0.006), 0.102 (±0 .006), 0.048 (± 0.003), 0.063 (± 0.004), and 0.170 (± 0.019) for birth weight, Sur1, Sur2, Sur3, pneumonia, diarrhea, and STP, respectively. The majority of the genetic correlations among the 7 replacement wellness traits were negligible. The genetic correlations among Sur1, Sur2, and Sur3 ranged from 0.112 (Sur1 and Sur3) to 0.445 (Sur1 and Sur2) when fitting a linear model (estimates in the observed scale), and from 0.560 (Sur1 and Sur3) to 0.773 (Sur1 and Sur2) when fitting a threshold model (estimates in the liability scale). The genetic correlations between replacement wellness and cow longevity were low (absolute value lower than 0.30), but some of them were significantly different from zero. Compared with other replacement wellness traits, Sur3 and STP had relatively high genetic correlations with cow longevity. Replacement wellness traits are heritable and can be improved through direct genetic and genomic selection. The results from the current study will contribute for better balancing dairy cattle breeding goals to genetically improve dairy cattle wellness in the period from birth to first calving.     

Estimation of economic values for traits of dairy sheep: II. Model application to a production system with one lambing per year

A bioeconomic model for dairy sheep was applied to a production system with one lambing per year. The classical extensive Carpathian system with indoor winter lambing, traditional weaning, sale of surplus lambs before Easter, and a summer milking period with ewes on pasture was modeled. The economic values of 15 performance and functional traits were calculated for the Slovakian Improved Walachian multi-purpose breed. The economic values per unit increase in the traits were 0.32 €/kg of milk yield during the standardized milking period of 150 d, 0.29 €/0.1% milk fat, 0.42 €/0.1% milk protein, 0.28 €/% and 0.56 €/% for conception rates of female lambs and ewes, respectively, 0.20 €/0.01 lamb born, 0.0036 €/% and 0.0040 €/% for lamb survival at birth and until weaning, respectively, 0.65 €/kg of birth weight, 0.032 €/g per d daily gain from birth until weaning, −0.030 €/kg of mature weight, −0.38 €/0.1 and −0.21 €/0.1 conformation quality grade for weaned lambs and adult sheep, respectively, 0.42 €/kg of fleece weight and 11.10 €/year of productive lifetime for ewes.     

A large Markovian linear program to optimize replacement policies and dairy herd net income for diets and nitrogen excretion

The purpose of the study was 2-fold: 1) to propose a novel modeling framework using Markovian linear programming to optimize dairy farmer-defined goals under different decision schemes and 2) to illustrate the model with a practical application testing diets for entire lactations. A dairy herd population was represented by cow state variables defined by parity (1 to 15), month in lactation (1 to 24), and pregnancy status (0 nonpregnant and 1 to 9 mo of pregnancy). A database of 326,000 lactations of Holsteins from AgSource Dairy Herd Improvement service (http://agsource.crinet.com/page249/DHI) was used to parameterize reproduction, mortality, and involuntary culling. The problem was set up as a Markovian linear program model containing 5,580 decision variables and 8,731 constraints. The model optimized the net revenue of the steady state dairy herd population having 2 options in each state: keeping or replacing an animal. Five diets were studied to assess economic, environmental, and herd structural outcomes. Diets varied in proportions of alfalfa silage (38 to 98% of dry matter), high-moisture ear corn (0 to 42% of dry matter), and soybean meal (0 to 18% of dry matter) within and between lactations, which determined dry matter intake, milk production, and N excretion. Diet ingredient compositions ranged from one of high concentrates to alfalfa silage only. Hence, the model identified the maximum net revenue that included the value of nutrient excretion and the cost of manure disposal associated with the optimal policy. Outcomes related to optimal solutions included the herd population structure, the replacement policy, and the amount of N excreted under each diet experiment. The problem was solved using the Excel Risk Solver Platform with the Standard LP/Quadratic Engine. Consistent replacement policies were to (1) keep pregnant cows, (2) keep primiparous cows longer than multiparous cows, and (3) decrease replacement rates when milk and feed prices are favorable. The optimal policy called for the replacement of open cows between 7 and 12 mo in lactation depending on parity, diet, and market conditions. Under favorable market conditions, net revenue was greatest with the greatest concentrate diet, which was $15.24 and $52.32/mo per cow greater than the optimal net revenue realized with the intermediate and the no-concentrate (all-forage) diets, respectively. A suboptimal solution to limit the N excretion to 12 kg/mo per cow when market conditions were favorable resulted in a diet with the second-greatest amount of concentrates being the one with the greatest net revenue. Under unfavorable market conditions, the diet with the greatest concentrate content had the least net revenue compared with all the others. A suboptimal solution for a maximum N excretion of 12 kg/mo per cow with unfavorable market conditions resulted in the least-concentrate diet having the greatest net revenue ($22/mo per cow), followed by the second-greatest concentrate diet ($20/mo per cow) and the all-forage diet ($18/mo per cow). The implementation of a Markovian linear program for dairy decision making provides both robustness and versatility in operations research. The model could become a valuable tool for economic decision making for dairy farms.     

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.     

Genetic analysis of the Israeli Holstein dairy cattle population for production and nonproduction traits with a multitrait animal model

Milk, fat, and protein production, somatic cell score (SCS), and female fertility in the Israeli Holstein dairy cattle population were analyzed using a multitrait animal model (AM) with parities 1 through 5 as separate traits. Female fertility was measured as the inverse of the number of inseminations to conception in percent. Variance components were estimated using both the repeatability AM and multitrait AM. The multitrait heritabilities for individual parities were greater than the heritabilities from the repeatability AM, and heritabilities decreased with an increase in parity number. Heritabilities were higher for production traits, lower for SCS, and lowest for female fertility. The genetic correlations were higher than the environmental correlations. Genetic correlations between parities decreased with an increase in the difference in parity number, but all were greater than 0.5. The environmental correlations were higher for production traits, lower for SCS, and close to zero for female fertility. In the analysis of the complete milk recorded population, genetic trends from the repeatability and multitrait models were very similar. The genetic trend for SCS was economically unfavorable until 1993, and favorable since then. The genetic trend for female fertility was close to zero, but the annual environmental trend was -0.2%. The multitrait lactation model is an attractive compromise between repeatability lactation models, which do not account for maturing trends across parities, and test-day models, which are much more demanding computationally.     

A genome-wide association study for susceptibility and infectivity of Holstein Friesian dairy cattle to digital dermatitis

Selection and breeding can be used to fight transmission of infectious diseases in livestock. The prevalence in a population depends on the susceptibility and infectivity of the animals. Knowledge on the genetic background of those traits would facilitate efficient selection for lower disease prevalence. We investigated the genetic background of host susceptibility and infectivity for digital dermatitis (DD), an endemic infectious claw disease in dairy cattle, with a genome-wide association study (GWAS), using either a simple linear mixed model or a generalized linear mixed model based on epidemiological theory. In total, 1,513 Holstein-Friesian cows of 12 Dutch dairy farms were scored for DD infection status and class (M0 to M4.1) every 2 wk for 11 times; 1,401 of these cows were genotyped with a 75k SNP chip. We performed a GWAS with a linear mixed model on 10 host disease status traits, and with a generalized linear mixed model with a complementary log-log link function (GLMM) on the probability that a cow would get infected between 2 scorings. With the GLMM, we fitted SNP effects for host susceptibility and host infectivity, while taking the variation in exposure of the susceptible cow to infectious herd mates into account. With the linear model we detected 4 suggestive SNP (false discovery rate < 0.20), 2 for the fraction of observations a cow had an active lesion on chromosomes 1 and 14, one for the fraction of observations a cow had an M2 lesion on at least one claw on chromosome 1 (the same SNP as for the fraction of observations with an active lesion), and one for the fraction of observations a cow had an M4.1 lesion on at least one claw on chromosome 10. Heritability estimates ranged from 0.09 to 0.37. With the GLMM we did not detect significant nor suggestive SNP. The SNP effects on disease status analyzed with the linear model had a correlation coefficient of only 0.70 with SNP effects on susceptibility of the GLMM, indicating that both models capture partly different effects. Because the GLMM better accounts for the epidemiological mechanisms determining individual disease status and for the distribution of the y-variable, results of the GLMM may be more reliable, despite the absence of suggestive associations. We expect that with an extended GLMM that better accounts for the full genetic variation in infectivity via the environment, the accuracy of SNP effects may increase.     

Derivation and calculation of approximate reliabilities and daughter yield-deviations of a random regression test-day model for genetic evaluation of dairy cattle

Test-day milk, fat, protein yield, and somatic cell score (SCS) were analyzed separately using data from the first 3 lactations and a random regression model. Data used in the model were from Austria, Germany, and Luxembourg and from Holstein, Red, and Jersey dairy cattle. For reliability approximation, a multiple-trait effective daughter contribution (MTEDC) method was developed under general multiple trait models, including random regression test-day models, by extending the single-trait daughter equivalents concept. The MTEDC was applied to the very large dairy population, with about 15.5 million animals. The calculation of reliabilities required less computer memory than the corresponding iteration program and a significantly lower computing time equivalent to 24 rounds of iteration. A formula for daughter-yield deviations was derived for bulls under multiple-trait models. Reliability associated with daughter-yield deviations was approximated using the MTEDC method. Both the daughter-yield deviation formula and associated reliability method were verified in a simulation study using the random regression test-day model. Correlations of lactation daughter-yield deviations with estimated breeding values calculated from a routine genetic evaluation were 0.996 for all bulls and 0.95 for young bulls having only daughters with short lactations.     

An evaluation of Molly cow model predictions of ruminal metabolism and nutrient digestion for dairy and beef diets

Model evaluation, as a critical process of model advancement, is necessary to identify adequacy and consistency of model predictions. The objectives of this study were (1) to evaluate the accuracy of Molly cow model predictions of ruminal metabolism and nutrient digestion when simulating dairy and beef cattle diets; and (2) to identify deficiencies in representations of the biology that could be used to direct further model improvements. A total of 229 studies (n = 938 treatments) including dairy and beef cattle data, published from 1972 through 2016, were collected from the literature. Root mean squared errors (RMSE) and concordance correlation coefficients (CCC) were calculated to assess model accuracy and precision. Ruminal pH was very poorly represented in the model with a RMSE of 4.6% and a CCC of 0.0. Although volatile fatty acid concentrations had negligible mean (2.5% of mean squared error) and slope (6.8% of mean squared error) bias, the CCC was 0.28, implying that further modifications with respect to volatile fatty acid production and absorption are required to improve model precision. The RMSE was greater than 50% for ruminal ammonia and blood urea-N concentrations with high proportions of error as slope bias, indicating that mechanisms driving ruminal urea N recycling are not properly simulated in the model. Only slight mean and slope bias were exhibited for ruminal outflow of neutral detergent fiber, starch, lipid, total N, and nonammonia N, and for fecal output of protein, neutral detergent fiber, lipid, and starch, indicating the mechanisms encoded in the model relative to ruminal and total-tract nutrient digestion are properly represented. All variables related to ruminal metabolism and nutrient digestion were more precisely predicted for dairy cattle than for beef cattle. This difference in precision was mostly related to the model's inability to simulate low forage diets included in the beef studies. Overall, ruminal pH was poorly simulated and contributed to problems in ruminal nutrient degradation and volatile fatty acid production predictions. Residual analyses suggested ruminal ammonia concentrations need to be considered in the ruminal pH equation, and therefore the inaccuracies in predicting ruminal urea N recycling must also be addressed. These modifications to model structure will likely improve model performance across a wider array of dietary inputs and cattle type.     

Short communication: Genotyping of cows to speed up availability of genomic estimated breeding values for direct health traits in Austrian Fleckvieh (Simmental) cattle—Genetic and economic aspects

The aim of this study was to quantify the impact of genotyping cows with reliable phenotypes for direct health traits on annual monetary genetic gain (AMGG) and discounted profit. The calculations were based on a deterministic approach using ZPLAN software (University of Hohenheim, Stuttgart, Germany). It was assumed that increases in reliability of the total merit index (TMI) of 5, 15, and 25 percentage points were achieved through genotyping 5,000, 25,000, and 50,000 cows, respectively. Costs for phenotyping, genotyping, and genomic estimated breeding values vary between €150 and €20 per cow. The gain in genotyping cows for traits with medium to high heritability is more than for direct health traits with low heritability. The AMGG is increased by 1.5% if the reliability of TMI is 5 percentage points higher (i.e., 5,000 cows genotyped) and 6.53% higher AMGG can be expected when the reliability of TMI is increased by 25 percentage points (i.e., 50,000 cows genotyped). The discounted profit depends not only on the costs of genotyping but also on the population size. This study indicates that genotyping cows with reliable phenotypes is feasible to speed up the availability of genomic estimated breeding values for direct health traits. But, because of the huge amount of valid phenotypes and genotypes needed to establish an efficient genomic evaluation, it is likely that financial constraints will be the main limiting factor for implementation into breeding program such as Fleckvieh Austria.