Collinearity potentially has a negative impact on the prediction of genetic gains in tree breeding programs. This study
investigated the reliability and impact of best linear unbiased prediction (BLUP) using various collinearity mitigation
techniques and of two computational numerical precisions on the genetic gains in breeding populations. Multiple-trait,
multiple-trial BLUP selection scenarios were run on Eucalyptus grandis (F1, F2 and F3) and Pinus patula (F1 and F2) data,
comparing predicted breeding values of parents (forward prediction) with those realised in progeny (backward prediction of
parents). Numeric precision had an impact on intergenerational correlations of BLUPs of some scenarios, indicating that it
may not always be optimal to use higher precision when there is collinearity in the data. The relative difference in genetic
gains between techniques varied by up to 0.38 standard deviation units in the less-stable pine population. This highlights
the potentially large impact that instability can have on the efficiency of a breeding programme. BLUP performed close to
expected in the relatively stable (less collinear) population (eucalypt F1), and performed poorly in the other two populations.
In the unstable pine data, some of the techniques resulted in improved intergenerational correlations coming in line with
expected performance. This study indicates that BLUP can perform as expected and also confirms the potential problem
of instability and consequences thereof. BLUP users should examine the nature of the population of predicted values and
should these be outside expectation, various mitigation techniques should be explored.