Category: breeding (Page 3 of 5)

I am running analyses for a new article with my colleague Clemens Altaner (a smart cookie), reprocessing old samples to get resin data. This got me thinking on types of traits, as in there are “we always want more” traits, like stem volume in trees, or yield per ha for many agricultural crops. Assuming there were no trade-offs (like reducing quality) we always want more stem volume.

There are also traits with technical thresholds, like wood stiffness grades, or fruit quality grades, where there is a stepwise value function. There is an extra payoff when reaching a new grade, followed by a plateau, when extra expression of the trait is worth nothing until… we get to the next step/grade.

And there are traits which are highly dependent on the end-product, like resin content or heartwood content. If you want to grow a crop for resin production (like some pines in China) you want as much resin as possible. However, if you are interested in solid wood, resin and resin canals are an annoyance, as they reduce the wood grade. A similar situation occurs with heartwood. It’s great to have heartwood if I want more durability, but it may affect processing (including preservative injection) if we want the wood for other uses.

For the current analysis and end-use we want lower values of the traits, but things can change if we move countries or industries.

I would like to know if you can give me examples of this three types of traits (or if there are more types) for your species/end-product. It’s always handy to have non-forestry examples for class.

Five months ago I was saying all that glitters is not GxE interaction, putting forward the idea that, at least in forestry, some of the reported GxE interaction was not interaction at all but poor connectedness.

Today Forest Ecology and Management published the work by Duncan McLean (our PhD student, congratulations!), Jaroslav Klápště, Mark Paget and myself (Open Access article). We have eight well-replicated and connected trials, SNP-based pedigree, covering a range of environmental conditions with little signs of interaction.

For wood basic density, a typically highly heritable & low interacting trait, out of the 28 cross-site genetic correlations (Type B in forestry parlance) the lowest value was 0.86. For stem diameter at breast height, a typically low heritability & high interacting trait, 20/28 cross-site correlations were greater or equal to 0.7, often used to define interacting sites. The lowest correlations came from a single South Island site (lowest value was 0.59).

Trees live for a long time and tree breeding programmes are long-lived endeavours. Our genetic evaluations often include trees from many selection series, with many trials connected by tenuous relationships. There are publications that show we only need small connections to compare genotypes across trials: the predicted error variance of the comparison is OK. We need to remember though, that those comparisons are assuming we know the true genetic correlations across sites (as in animal breeding); instead, we are estimating them from data. Data coming from poorly connected trials, subject to biases.

Now imagine we start explaining the estimated GxE interaction, Are we explaining real interaction or just estimation noise? We still have to continue revisiting this topic, which I hope it leads to updating the genetic evaluation system.

A good understanding of the biological traits that have an effect on profit. Can we identify new, more efficient selection criteria?

A clear awareness of trade-offs between: traits, genetic evaluation options, deployment systems, etc. Trade-offs are everywhere in breeding programmes.

A database system that contains all the data.

A well-documented genetic evaluation system: we can rerun the evaluation and get exactly the same results. The system can be developed in-house or can use commercial software (like asreml, SAS, Bolt, etc) but the code must be available.

Reproductive biology: essential to sort out the best deployment.

Figured/figuring out what are the main environmental drivers affecting performance. It is easy for some species, very difficult for others.

Personnel continuity BUT the programme can survive the proverbial bus running over the breeder.

There was a time, roughly 30 years ago, when my whole career extended to an unknown, distant future. What should I do? Where should I be working? were the questions in my mind during a hot Chilean summer. At that time, New Zealand and Australia were not in the horizon and I had just applied to my first forestry job in Valdivia.

I got that job and three years later I started my PhD. I met people, travelled, gained citizenships, made friends, learned many things, forgot others. Today, thirty years after that summer, I look to the next 10 years in the future and ask myself: What should I do? Where should I be working? The same questions plus What would be the best use of my time?

Now that distant unknown is three decades closer. Should I do more administration and, strange misnomer, “service” to the profession? (as if all the other work was not of service). Should I go for a new push of research work, write up all the ideas thought but not completed and published? Maybe I should transfer all I know to other people.

All of the above, a mix of two, one only… What would be the best use of my time?