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Assessing the economic importance of traits is crucial for delivering appropriate breeding goals in dairy cattle breeding. The aim of the present study was to calculate economic values (EV) and assign the importance of health traits for three dairy cattle breeds: Lithuanian Black-and-White open population (LBW), Lithuanian Red open population (LR) and Lithuanian Red old genotype (LROG). The EV estimation was carried out using a stochastic bio-economic model SimHerd, which allows the simulation of the expected monetary gain of dairy herds. The simulation model was calibrated for LBW, LR and LROG breeds, taking into account breed-specific phenotypic and economic data. For each trait, two scenarios were simulated with a respective trait at different phenotypic levels. To obtain the EVs, the scenarios were compared with each other in terms of their economic outcomes. In order to avoid the double counting of the effects, the output results were corrected using a multiple regression analysis with mediator variables. The EVs were derived for the traits related to production ECM (energy-corrected milk), fertility, calving traits, calf survival, cow survival and direct health. To demonstrate the importance of health traits in herd management, we provided reliable EVs estimates for functional traits related to herd health. The highest EV for direct health traits, caused by an increase in of 1 percentage point, were those found for mastitis (EUR 1.73 to EUR 1.82 per cow-year) and lameness (EUR 1.07 to EUR 1.27 per cow-year). The total costs per case of ketosis, milk fever and metritis ranged from EUR 1.01 to EUR 1.30, EUR 1.14 to EUR 1.26 and EUR 0.95 to EUR 1.0, respectively. The highest economic values of dystocia were estimated for LROG (EUR −1.32), slightly lower for LBW (EUR −1.31) and LR (EUR −1.23). The results of this study show the importance of health traits to the economic features of cattle herd selection of new breeding goal and this would improve the herd health. The economic evaluation of the functional traits analyzed in this study indicated the significant economic importance of the functional traits in Lithuanian dairy cattle breeds.

In the Nordic countries of Denmark, Finland, and Sweden, a breeding value for Saved feed is targeted. Saved feed consists of two sub-traits, maintenance and metabolic efficiency, which are evaluated as residual feed intake. This study evaluated residual feed intake in relation to calculated economic values to set breeding goal. Residual feed intake typically is derived using phenotypic or genetic regression. However, when calculating economic value (EV) for residual feed intake, phenotypic and genetic residual feed intake save feed, and so are not relevant; yet, EV is important for the genetic progress of such traits. Thus, phenotypic regression coefficients were used to obtain the economic value of residual feed intake in the current simulation. The SimHerd model was used in this study to quantify the EV of RFI. The economic value of phenotypic residual feed intake ranged from 0.16 to 0.18 €/kg dry matter, corresponding to 0.16 to 0.18 €/Scandinavian feed unit (SFU – 1 SFU corresponds to 0.97 kg dry matter and 6.7 mega joule net energy) in the SimHerd simulation. Thus, the economic value of feed efficiency corresponded to the applied feed price in the simulations (0.18 €/SFU). By splitting phenotypic residual feed intake into two periods (before and after 84 days in milk), we showed that the simulation of biological changes during early lactation showed that the economic value was not dependent on (P > 0.05) residual feed intake being modelled as one or two periods. However, profit per annual cow differed significantly between simulations using one or two residual feed intake periods (P < 0.001). In conclusion, it is very important to select the appropriate the method to estimate breeding values for residual feed intake.

The aim of the study was to calculate economic values (EV) of breeding traits (production, reproduction, direct health, calving and survival traits) for the Polish dairy cattle breeds Polish Holstein (PH) and Polish Red (PR). EV are key elements in setting up economically based breeding goals for dairy cattle and they are crucial for optimizing breeding schemes. For deriving EV, a stochastic bio-economic model was used, which estimates EVs based on population-specific biological and economic characteristics. Because the bio-economic model considers correlations between different traits, a multiple regression approach was used to correct for any double counting of effects. The results showed that EVs for direct health traits and fertility differed between breeds. The biggest difference was observed for lameness (PH -1.44 €, PR -1.09 € per cow-year), ketosis (PH -0.94 €, PR -0.58 € per cow-year) and for cow conception rate (PH 1.94 €, PR 0.84 € per cow-year). The relative economic weights for production traits were estimated as 37.00 % (PH) and 34.30 % for (PR) and for fertility traits 15.10 % (PH and for 16.10 % (PR). Major economic differences between the relative weights were observed for cow mortality (22.70 % PH, 16.90 % PR). The results of our study showed that breeding the PH brings higher economic profit than breeding the PR. Total income per PR cow was equal to 1,623 € and for PH 2,970 €.

It has been a challenge to avoid double counting when economic values (EV) of traits are derived for breeding goal traits when using stochastic herd simulation models. In this study multiple regression and model building with mediator variables representing other traits in the breeding goal were evaluated to avoid double counting. EV were derived from data simulated with the SimHerd dairy herd simulation model. Scenarios were simulated to represent dairy herds with low and high levels of metritis and cow mortality. The simulated data was analyzed statistically with the economic net return per cow-year as the dependent variable and either the incidences of metritis or the incidence of cow mortality as the independent variables. In the model with metritis we corrected for mediator variables representing the direct effects of metritis on milk yield, fertility and occurrence of other diseases. The EV was estimated as the marginal change in economic net return in response to a change in the trait of interest. To avoid the multiple regression models to correct the EV for structural herd effects (changes in distribution of parities and lactation stages) we used a single animal based indicator variable for each trait of interest, such as incidence rate of cow mortality 1–100 DIM in multiparous cows. The EV value of improving the trait ‘incidence rate of metritis 1–100 DIM in multiparous cows’ by 0.01 was estimated to be €0.93. The importance of avoiding double counting was demonstrated as the EV of metritis was overestimated by 82% when no mediator variables were included in the multiple regression analysis. And by ignoring structural herd effects for the EV of metritis we demonstrated an underestimation in the order of 9%. Further pitfall of underestimation was demonstrated for EV of cow mortality. The EV of improving the trait ‘incidence rate of cow mortality 1–100 DIM in multiparous cows’ by 0.01 was estimated to be €46.4. Correcting for the independent variation in mortality between simulation replicates within the individual scenarios was found to be important. The results of this study suggests a new method for designing simulation experiments and analyzing simulated herd effects for estimation of EV of traits in a breeding goal. This deals with a number of previous and new concerns of how to correct for double counting and at the same time still include the structural herd effects.

From an economic point of view, clinical lameness is considered to be one of the major health disorders in dairy cattle. The aim of this study was to establish the costs of lameness in different herds and to improve decision-making on prevention and control of clinical lameness. Economically feasible strategies of prevention and control were defined and the potential effects were calculated by a dynamic, stochastic and mechanistic Monte Carlo model, called SimHerd. The modelled risk factors on cow level for lameness were base-risk in the herd, parity, milk yield potential, disease interrelationship, disease recurrence and season. Milk yield reduction, reduced feed intake, weight loss, reduced conception rate and mortality were modeled as effects of lameness. To study the effect of herd differences on the efficacy of the preventive methods, four different herds representing different levels of reproductive management, conception rates and milk production were designed and compared to the default situation. The loss per first case of lameness was found to be €192 in the default herd that represents the modern, average Danish dairy herd. An increase of the margin per cow-year of €22.7 was the result of halving the risk of lameness in the default herd. Implementation of footbaths and rubber flooring increased margin per cow-year by €4.2 and €17, respectively. In a sensitivity analysis, 16 model parameters were changed to their lowest and highest value found in the literature. The model seemed most sensitive to the uncertainty of the effect of lameness on milk yield.