Category: research (Page 4 of 8)

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.

We just finished a genetic analysis, got breeding values for all our selection criteria, combined them with genetic parameters and economic weights in a selection index (I) that predicts the total genetic-economic value in dollars (H). We rank our trees from best to worst, perhaps with some constraint on relatedness, and go to the field to collect genetic material.

1. Perhaps the trial is not well labelled, so we are in the wrong block.
2. Or we are entering perpendicular to it, so the rows are the columns.
3. Or the tree labels are gone, so we are picking up the wrong tree.
4. Or we identified the right tree, but set the wrong label when collecting seeds or cuttings.
5. We grafted the cuttings and set a new, incorrect label. Or we sowed the seeds in the wrong part of the nursery.
6. Maybe the grafts ended up in the wrong part of our orchard, or with the wrong labels.
7. Did we properly label the pollen we just collected. And dried. And stored.
8. Is the pollen from the male parent going to the right female?
9. Once we collected the cones (or capsules, or fruits) are they separate in the kiln, and the machine that removes the seeds?
10. Are the seeds sown with the right labels?
11. Are the seedlings going to the right place in the trial? Go to 1.

We could genotype the trees to check for pedigree errors. But see 1, 2, 3 & 4. Is the pedigree wrong, or the sample for genotyping, or both?

The question is not if we have errors in the pedigree. One would need superhuman luck to avoid having any errors in a small breeding programme and a real miracle if you have close to a million trees with records. The real questions are:

• Can we reduce future errors?
• Do the pedigree errors we have make a significant (in the sense of important, not statistically) difference to the breeding and deployment programmes?

For example, over the years I have seen huge improvements in Proseed (the largest tree seed producer in New Zealand). Multiple validation steps, QR codes for orchard blocks, pollen, cones under processing, etc. There are similar changes going in parts of the breeding programme.

What’s the percentage of errors that remain in the system? I don’t know. Does it make a substantial difference? Maybe.

Are there other problems that could be even bigger? Yes. Maybe breeding for the wrong thing, but that’s another post.

In the previous post we were able to screen trees for wood properties at 2 years of age, separating normal and compression wood by leaning the trees. We obtained genetic parameters, breeding values, etc. However, we also discovered that planting the trees directly in the ground was still subject to too much environmental variability. So, take a guess… we planted another trial.

This time we had 90 families and 10 clones, for a total of 3,000 trees growing in 75 litre bags, leaning, with slow-release fertiliser, an irrigation system. Not only that, but we tested 3 different populations: clonal (derived from trees selected at ‘traditional’ 8 years of age for growth and stiffness), seed orchard (derived from trees selected at ‘traditional’ 8 years of age for growth and basic density), and new selections (selected for a combination of traits).

As a start, heritabilities increased substantially (sometimes doubling it), which is not surprising considering that the trees were in bags. One cool thing was that we could observe differences between populations at 2 years of age; so selecting at ‘traditional’ 1/4-1/3 of rotation does actually pick up trees on different wood properties trajectories. This was 1- confirmation of something we hinted some years ago (coming post) and 2- guiding other work I am writing up at the moment.

This “bagged trial” was published Open Access as: Apiolaza LA and Sharma M. 2023. Selection history affects very early expression of wood properties in Pinus radiata. New Forests DOI: 10.1007/s11056-023-09997-3.

In my previous post we were able to detect extremes of wood density and stiffness with leaning trees at 8 months of age, vertically splitting the sample to separate normal and compression wood. This was doable, but the size of the wood samples was a tad small to screen large numbers of trees, so we proposed screening parents of a seed orchard with a trial including 49 controlled-pollinated families at ages 2, 3, 4 and 5 (harvesting one quarter of the trees per year).

After 2 years, we processed the first quarter of the trial (492 trees) extracting a 200 mm long stem bolt from the leaning trees (see photo below) and found that:

• Wood properties were under moderate genetic control at 2 years of age, so we felt we could, at least, screen out the worst families.
• There was more environmental variation than desired (particularly soil) and lots of wind, which altered the leaning angle of the trees.
• This variability left us feeling that there was no point on continuing with the experiment in that site (sometimes you win, sometimes you lose) and that we needed to setup an alternative experiment. TO BE CONTINUED. YES, OF COURSE THERE IS ANOTHER EXPERIMENT.

More details in: Apiolaza, L.A., Chauhan, S. and Walker, J.C.F. 2011. Genetic
control of very early compression and opposite wood in Pinus radiata and its implications for selection. Tree Genetics & Genomes 7(3): 563-571. PDF available at https://luis.apiolaza.net/publications/