Methods and consequences of including reduction in greenhouse gas emission in beef cattle multiple-trait selection.

Background: Societal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitations of previous studies on GHG emissions are that they generally describe feed intake inadequately, assess the consequences of selection on particular traits only, or examine consequences for only part of the production chain.

Methods and consequences of including feed intake and efficiency in genetic selection for multiple-trait merit.

Methods are presented for including feed intake and efficiency in genetic selection for multiple-trait merit when commercial production is from any combination of pasture or concentrates. Consequences for the production system and for individual animals are illustrated with a beef cattle example. Residual feed intake at pasture (RFI-p), residual feed intake in the feedlot (RFI-f), and cow condition score are additional traits of the breeding objective.

Estimation of accuracies and expected genetic change from selection for selection indexes that use multiple-trait predictions of breeding values.

Procedures are described for estimating selection index accuracies for individual animals and expected genetic change from selection for the general case where indexes of EBVs predict an aggregate breeding objective of traits that may or may not have been measured. Index accuracies for the breeding objective are shown to take an important general form, being able to be expressed as the product of the accuracy of the index function of true breeding values and the accuracy with which that function predicts the breeding objective. When the accuracies of the individual EBVs of the index are known, prediction error variances (PEVs) and covariances (PECs) for the EBVs within animal are able to be well approximated, and index accuracies and expected genetic change from selection estimated with high accuracy.