Genetic parameter estimates for methane emission from breath during lactation and potential inaccuracies in reliabilities assuming a repeatability versus random regression model

Abstract

Methane emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and establishing optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm) recorded in the feed bins of milking robots on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with first- to fifth-order Legendre polynomials for the additive genetic and within-lactation permanent environmental effect. The mean heritability (± SE) was 0.17 ± 0.04, and the mean within-lactation repeatability was 0.56 ± 0.03. The genetic correlations between DIM were high and ranged from 0.34 ± 0.36 to 1.00 ± <0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± <0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus the RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long of a recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and in lower genetic gains than predicted from the reliability.