This study analysed the use of longitudinal data, i.e. repeat ed assessment
of the same individuals at different ages, in the context of early selecti
on. Autoregressive relationships, banded correlations and unstructured ('un
smoothed') matrices were used to model the additive genetic covariance matr
ix (Go) for 10 total height measurements of a Pinus radiata open-pollinated
progeny test. We examined the effects on response to selection of inferred
covariance structure, mass versus combined selection, one or multiple asse
ssments, and two breeding-delay intervals. End results are expressed as pre
dicted average gain per year. The patterns of predicted response to selecti
on vary widely between inferred covariance structures. Considering the auto
regressive model (based on logarithm of age ratios between assessments) as
an example, the effect of combining information from relatives on response
to selection is more important (16% to 41% extra gain) than using extra mea
surements (2% to 25%), when predicting individual breeding values, although
the economics of extra gain vs extra assessment costs must be carefully an
alysed. It is expected that using multiple assessments could be advisable f
or datasets with lower genetic autocorrelations. An approximate comparison
across covariance models showed the autoregressive model to exhibit the bes
t ability to produce 'correct' selections as well as the highest predicted
response to selection.