Let’s start with the obvious: trees are plants and—unless you are breeding Ents—they do not walk around. Therefore, the first obvious statement is that tree breeding heavily relies on experimental designs to account for environmental variability.

But trees are much larger and long-lived than corn or potatoes, so we need much larger trials, in land that’s often not flat, because if it were flat you’d be growing a plant crop. So our trials often need incomplete blocks, and within-trial spatial analysis tends to make sense.

Trees are also much closer to the wild populations. Most tree breeding programmes are not beyond a 4th generation from undomesticated trees, so we have shallow pedigrees, unlike plants and animals.

Another difference is that we normally don’t test a few cultivars, but we are testing thousands of genotypes, which puts us closer to animal breeding. Animal breeding breeders often assume a set of “standard” genetic parameters for genetic evaluation; instead, like other plant-people, we estimate genetic parameters from the trials under analysis. This means we may end up trying to run a multivariate analysis with, say, 100 trials (sites) with half a million genotypes, using an tree (animal) model BLUP, which doesn’t work, unless we move to a factor analytic, reduced tree (animal) model, etc with all sort of compromises. We are still looking for the right level of complexity for genetic analyses.

Trees also have a very clear distinction between the objective traits and selection criteria. Traits have an effect on profit and normally are expressed at rotation age (say ~25 years in radiata pine), like volume per ha, wood stiffness, stem form, etc. Criteria are easier, earlier and cheaper to assess, usually at 1/4 to 1/3 of rotation, like stem diameter and height, acoustic velocity, etc.

Trees are heterogeneous, anisotropic, hard to quantify sometimes. We invest substantial efforts developing phenotyping tools to describe these gigantic organisms. Sometimes, in the wind, they look just like Ents.