During the last five years there has been a surge of wood quality
data describing variation from pith to bark. This data has been generated
by low-cost repeated assessment of individuals or by SilviScan-type
analyses of increment cores. Traditionally, breeders have used multiple
univariate analyses to study the evolution of genetic control and estimate
age-age correlations.
In this paper I random regression splines to model longitudinal data. After
introducing theoretical considerations, I apply the method to two SilviScan
data sets from open-pollinated progeny trials, using microfibril angle (°) for
188 trees from an 11 year old Eucalyptus globulus trial in Australia and
basic density for 425 trees from an 28 year old Pinus radiata trial in New
Zealand.
The random regressions approach permits modeling the mean wood quality
curve and individual curves at the family and tree level. These curves show
significant variation from the mean, which can be exploited for selection
purposes, particularly when quality thresholds are important. In addition,
their use produces a smoothed additive genetic covariance matrix