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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 (No IT). 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 No IT. 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 No IT) 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. [ABSTRACT FROM AUTHOR]

Presents a study which aimed to estimate genetic parameters for the multiple-trait herd cluster model, the reaction norm model and three models that treat country as production environment, using data on Guernsey cows from four countries. Methodology; Results and discussion.

This study compared the abilities of virgin heifer genomically enhanced breeding values (GEBV) and parent average breeding values (PA) to predict future cow performance. To increase confidence in genomic technology among farmers, a clear demonstration of the relationship between genomic predictions and future phenotypes is needed. We analyzed 12 different traits in first parity, including production, conformation, fertility, and other functional traits. Phenotype data were obtained from national milk recording schemes and breeding values from the Nordic Cattle Genetic Evaluation. Direct genomic breeding values were calculated using genomic BLUP and combined with traditional breeding values, using bivariate blending. The data covered 14,862 Red Dairy Cattle, 17,145 Holstein, and 7,330 Jersey genotyped virgin heifers born between 2013 and 2015 in Denmark, Finland, and Sweden. Phenotypes adjusted for systematic environmental effects were used as measures of cow performance. Two methods were used to compared virgin heifer GEBV and PA regarding their ability to predict future cow performance: (1) correlations between breeding values and adjusted phenotypes, (2) ranking cows into 4 quartiles for their virgin heifer GEBV or PA, and calculating actual cow performance for each quartile. We showed that virgin heifer GEBV predicted cow performance significantly better than PA for the vast majority of analyzed traits. The correlations with adjusted phenotypes were 38 to 136% higher for GEBV than for PA in Red Dairy Cattle, 42 to 194% higher for GEBV in Holstein, and 11 to 78% higher for GEBV in Jersey. The relative change between GEBV bottom and top quartiles compared with that between PA bottom and top quartiles ranged from 9 to 261% for RDC, 42 to 138% for Holstein, and 4 to 90% for Jersey. Hence, farmers in Denmark, Finland, and Sweden can have confidence in using genomic technology on their herds. [ABSTRACT FROM AUTHOR]

Three models for genetic evaluation of milk yield of Uruguayan Holstein cattle were compared using 159 169 lactation records from 81 928 cows calving between 1989 and 1998. Model I included the effects of herd-year-season, parity by age group, additive genetic merit, permanent environment, and residual. Model II included all effects in Model I, as well as number of days open and length of the dry period. Model III included all factors in Model II, and it accommodated heterogeneity of variance within contemporary groups (CG) through a pre-adjustment of the data based on empirical Bayes estimates of the CG variance. Estimates of heritability for milk yield were 0.23, 0.24 and 0.25, and estimates of repeatability were 0.55, 0.56 and 0.57 for Models I, II and III, respectively. Models were contrasted by examining changes in sire ranking and by a cross-validation procedure, based on the ability of the models to predict first, second and later lactations. Data were divided into two subsets, and records from one subset were predicted using location parameters estimated from the other subset. A resampling procedure was used to minimise the dependency on the sample structure. Correspondence between observed and predicted values was assessed in terms of square root of empirical mean square errors of prediction, percentage squared bias and the coefficient of determination ‘R2’. Adjustment for heterogeneous CG variance had a marked effect on rankings of animals, especially elite cows, where correlations between solutions from Models I and II versus Model III ranged from 0.53 to 0.80. The percentage of animals selected in common by each pair of models decreased when selection intensity increased. Cross-validation analyses suggested that an assumption of heterogeneity of CG variance is tenable, especially in later lactations, whereas some doubts arise in first lactations, most probably due to the data structure used in the analyses. [Copyright &y& Elsevier]