Breeding wood quality: looking for early bloomers Luis A. Apiolaza, - - PowerPoint PPT Presentation

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Sep 07, 2023 167 likes •368 views

Breeding wood quality: looking for early bloomers Luis A. Apiolaza, School of Forestry, University of Canterbury Plantation forestry is under strong competition Domestically from more profitable land uses; dairy, for example. Photo by pascalk,

SLIDE 1

Breeding wood quality: looking for early bloomers

Luis A. Apiolaza, School of Forestry, University of Canterbury

SLIDE 2 Domestically from more profitable land uses; dairy, for example. Photo by pascalk, Flickr.

Plantation forestry is under strong competition

SLIDE 3 Internationally from more profitable forest operations; eucalypts, for example.

Plantation forestry is under strong competition

Photo by Corey Holmes, Flickr.

SLIDE 4

NIRA

Tree breeding’s recipe What we breed for (objective) What we select for (criteria) ≠

weighted density

volume form dbh

weighted stiffness

form

early selection rotation age

branch index

age-age correlations

SLIDE 5

Implicit assumption: It works for growth...so it should for wood quality.

SLIDE 6

However, intrinsic wood quality has changed very little during the last 50 years.

SLIDE 7

We have explanations for everything, even when we claim we don't. I call explanations pacifiers*, to stop asking questions — Humberto Maturana

*[UK, AU, NZ] dummy

SLIDE 8

Features to exploit about wood quality

!" !#" !$"" !$#" !%"" !%#" !&"" !&#" !'"" !'#" % ' ( ) $" $% $' $( *+,-.,/0123+,/240567890 !0:+30;& !"#$%&'()* +, +- +. +// 012/3 012/4 012/- 012. !"#$%&'()&('*+, 0125

&'()&('*+,

5 10 15 20 25 30 0.0 0.2 0.4 0.6 0.8 1.0 Lag for age−age correlation Correlation Price and quantity Ring [number from pith] MOE [GPa] 5 10 15 20 0 5 15 25 DM001 DM002 0 5 15 25 DM003 DM004 0 5 15 25 DM005 DM006 DM007 DM008 DM009 5 10 15 20 DM010 5 10 15 20 DM011 DM012 DM013 DM014 DM015 DM016 0 5 15 25 DM017 DM018 0 5 15 25 DM020 5 10 15 20 DM021 Time trends Autoregressive radius [mm from the pith] MOE [GPa] 5 10 15 20 50 100 150 200 250 early bloomers 5 10 15 20 late bloomers Technical thresholds

SLIDE 9 !" !#" !$"" !$#" !%"" !%#" !&"" !&#" !'"" !'#" % ' ( ) $" $% $' $( *+,-.,/0123+,/240567890 !0:+30;& !"#$%&'()* +, +- +. +// 012/3 012/4 012/- 012. !"#$%&'()&('*+, 0125

&'()&('*+,

Value: not all changes are worth the same

Price in NZD for different timber grades. From Sorensson and Shelbourne 2005.

1. Real value jump
2. Lots (!) of wood here

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Assessment methods induce autocorrelation

5 10 15 20 25 30 0.0 0.2 0.4 0.6 0.8 1.0 Lag for age−age correlation Correlation Predicting rotation age performance is hopeless unless we have very high (>0.9) autocorrelation !

rlag

0.95 0.90 0.85 0.46 0.21 0.09

SLIDE 11 Ring [number from pith] MFA [degrees] 20 30 40 5 10 15 20 25 30 001 002 003 5 10 15 20 25 30 20 30 40 004 Ring [number from pith] MOE [GPa] 5 10 15 20 5 10 15 20 25 30 DM001 DM002 DM003 5 10 15 20 25 30 5 10 15 20 DM004

Time trends and technical thresholds

Threshold: 30° Threshold: 8 GPa

SLIDE 12

More than one way for changing the average

We tend to screen in ...but screening out might be easier

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Is ‘good enough’ good enough? or Would this work?

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Variability: there is plenty of room for selection

radius [mm from the pith] MOE [GPa] 5 10 15 20 50 100 150 200 250 early bloomers 5 10 15 20 late bloomers

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h2CV

What about genetic control?

0.45 14%

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In summary, we are...

going for very early selection (<4 years) ignoring ‘rotation age’ using small lags for age-age correlations screening out the worst material how early do we meet the threshold? proposing to use simple tools with moderate resolution

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In summary, the best is enemy of the good breeding for early expression

f acceptable quality

breeding for the best possible quality

SLIDE 18

Every sentence I utter must be understood not as an affirmation but as a question — Niels Bohr

Thanks J.C.F. Walker for relentless criticism RPBC and WQI for funding and data

M. Avalos for design and patience