Category: teaching (Page 2 of 14)

Monopoly money vs real money

2024-03-02 / Luis

“Tree breeding has added 2 Billion dollars to the forest industry” said the presenter during a seminar.

Two billion? With a B? How come we struggle to get funding for projects then—I asked myself.

There are two testing cultures in forestry: the inventory/modeller crowd and the breeding crowd. The inventory crowd often relies on multiple-tree plots over a given area. Plots can be rectangular, circular, defined by prism, etc. The breeding crowd tends to use single-tree plots, because they (including myself) are testing many genotypes and it is more statistically efficient to use plots defined by a single individual.

Breeders use a selection index that gives a dollar value for each individual, while competing against a mix of genotypes (remember single-tree plots?). We would like to extend those results to inventory level and multiply the values by hundreds of thousands of hectares, there is a correlation with area-based performance, but not perfect.

Don’t get me wrong, I work in breeding. However, the selection values are Monopoly money until we get realised genetic gain validated by inventory plots in Real money. The two testing cultures have to match and forest valuation go up by $2 Billion before making a claim like that.

Teaching tsunami

2024-02-29 / Luis

One day you are all happy and relaxed, with plenty of time for cooking dinner of any kind. Three course dinner? No problem! Then, one day, just around the corner comes the start of semester. It is a bit like when one is distracted at the beach, waving hello to a friend or loved one and a sneaky wave tackles us and runs us over without any consideration.

On the plus side, I am usually looking for links that could be interesting to first year forestry students. Some times they are about forestry related news, but others are nice implementations of data visualizations that tell an interesting story. That’s when I came across this interesting clustering of New York City neighbourhoods grouped by similarity of proportions of street-tree species, done by Kieran Healy.

Of course one thing leads to the next until I got to this great New York City Tree Map in which one can get information for nearly 3/4 of a million individual trees. Enough to motivate some students. Zoom in until finding a tree you like.

Neighbourhood clustering by tree species

Old dog, etc

2024-02-12 / Luis

Yesterday I attended an introduction to “Artificial Intelligence in Forestry”* workshop at the School of Forestry, University of Canterbury. There were plenty of industry people, including some of I taught “a few” years ago.

I am mostly a quantitative genetics/stats person, but I keep my eyes open for anything that could be helpful dealing with my genetics trials, and have dabbled on using spatial things, LiDAR, etc. As I have pointed out in the past: 1. I am a sucker for entertaining problems and 2. old dogs can learn, albeit more slowly than 30 years ago, new tricks.

It was a nice opportunity to dip my toes in applications of machine learning for image analysis (detection and classification), mostly using ArcGis Pro and a short exercise using R. Funnily enough, my biggest barrier in the workshop was dealing with Windows + ArcGis. I am a Mac and Linux person, generally dealing with coding statistical analyses writing commands in text files. Most of my GIS related work is done via R (which I’ve been using since 1997) and any interactive work with maps using QGIS. Finding anything with point and click took me ages, until we got to the R part, which was hard for most people but kind of easy for yours truly.

Most importantly, having a chat with people and getting ideas to try with my data were the coolest part of the day. Thanks to Vega Xu, Justing Morgenroth and Ning Ye for organising the course!

*Confession: I dislike attaching the AI label to everything these days. Perhaps a better name would have been “Machine Learning in Forestry”. Small detail.

Original Boston album cover by Roger Huyssen

More than heritability 🎶

2024-02-05 / Luis

It is easy to get obsessed with heritabilities when you start working in breeding and genetics. The idea that a fraction of that variability we are observing can be explained by pedigree (or family structure or clonal differences or whatever) is appealing. It gives us an idea of control: there is a whiff of causality in our work. If the trait I care about is heritable THEN I can successfully breed for it.

However, we need to remember that it is a fraction of the variability we care about. If there is little variability, there is little room to select farther to the right of the distribution for the trait.

A typical example in trees would be stem diameter vs basic wood density. The heritability for density is, on average, around 0.6, while for diameter is 0.2 in a lucky day. However, the within-site coefficient of variation for density is about 8% but over 20% for diameter. Much more room to move in diameter.

Nothing groundbreaking, particularly if you have been working for a while. Just a handy reminder if you’re a newbie in breeding things.

Gratuitous earworm: In my head, “more than heritability” sounds like the famous More than a feeling.

The collapse of Eucalyptus globulus or future-proofing nonsense

2024-01-27 / Luis

I cringe every time I hear the term “future proofing”. It can be read as doing something to avoid facing the future or as doing something that will survive whatever the future brings. The first meaning is non-sensical, while the second is a fool’s errand.

When working in breeding (plants, animals, trees) it is unfeasible to cover all possible environmental and market futures. What we can do, however, is to embed variability and flexibility in the breeding programme, so we can better pivot under changing conditions. There is no guarantee that the crop will always survive the new conditions.

Why was I thinking about this? I’ve been contrasting the forestry plantations stories of Australia, Chile and New Zealand and in the previous post I pointed out the changing participation of Eucalyptus species in Australia and Chile.

In the Chilean case, the idea was to use Eucalyptus globulus—a high-growth & high-quality fibre species—for the short-fibre pulp industry. The expanding estate needed another species that, although with lower pulping quality, would cope with colder environments: E. nitens. After a few years there were some efforts to create the E nitens (mother) x E globulus (father) hybrid, with the aim of getting pulping quality closer to E globulus but with cold tolerance closer to E nitens.

For a few years the E globulus estate expanded substantially, until… a climate change-induced mega drought dramatically reduced forest productivity AND defoliation by Gonipterus platensis (with 4 generations per year) took its toll. Companies are now planting either E nitens or the E nitens x E globulus hybrid (some people call it gloni, for GLObulusNItens), which far outperform E globulus under the current abiotic+biotic environmental conditions.

The predicted “future” for 20 years ago was dramatically different from reality. There was no future-proofing in any meaningful sense, but there was variability and flexibility and good work to cope with a changing environment.