Editing and modeling of milk production data for genetic evaluation of Murrah buffaloes
Abstract: The objective of this work was to assess the effect of editing and modeling of milk production data for genetic evaluation of Murrah buffaloes. Six strategies for evaluating milk production were analyzed: observed milk production (OMP); adjustment of milk production data to 305 (MP305) and 270 (MP270) days of lactation; removal of the 5 (MP5%) and 10% (MP10%) shortest lactation periods; and milk production along the lactation period as linear covariate (MPCO). Genetic parameters were estimated using the Bayesian inference, with heritability estimates of 0.19 to 0.23 and repeatability estimates of 0.35 to 0.36. Sires classified by OMP were high correlated to those classified by the other models, however, correlations to MP270, MP305 and MPCO decreased when considering only the best 20% sires. OMP showed greater differences in absolute mean deviations when compared with MPCO, MP270 and MP305. The strategies of analysis had similar heritabilities and stabilities. However, changes in the ranking of sires with better classifications, due to overestimation of genetic values, as occurred in the models MP305, MP270 and MPCO, may lead to a decrease in the genetic progress of the herd.