emerging issues and organizational approachescips.forestry.oregonstate.edu/sites/cips/files/... ·...

1

Douglas-fir Breeding:

The Technological Cutting Edge

Emerging Issues and Organizational Approaches

Glenn HoweDirector, Pacific Northwest Tree Improvement Research Cooperative

Oregon State University

PACIFIC NORTHWEST TREE IMPROVEMENT

RESEARCH COOPERATIVE



Technological advances and applications

Wood stiffness remains a high priority for genetic improvement

Clonal forestry is receiving much attention in the SE, but not in the

PNW

New climate models are available to predict climate and weather for

specific sites (e.g., ClimateWNA)

Genomic markers are being developed and tested in breeding

programs

Genetic considerations will play an important role in assessing the

potential effects of climate change and helping forest managers adapt

Large, collaborative external grant programs increasingly important!

2





Large collaborative projects are an important foundation for future advances

Pacific Northwest Tree Improvement Research Cooperative

(PNWTIRC)

NSF Center for Advanced Forestry Systems (CAFS)

Conifer Translational Genomics Network (CTGN)

Western Conifer Climate Change Consortium (WCCCC)

These projects need stakeholder involvement to be successful!!





Trend toward shorter rotations, faster growth

More wood from the juvenile wood core

Juvenile wood:

Genetics of wood stiffness

- Lower wood density

- Higher microfibril angle

- Lower stiffness

- More shrinkage

3





Rationale

Selection for improved wood stiffness is feasible in 25

year-old Douglas-fir trees

Log-based acoustic tools work very well, but trees must be

harvested (RE = 78-93%)

Standing-tree acoustic tools can be used, but gains are lower

(RE = 57-58%)

Breeders want to select at younger ages (e.g., 6-12)

How well will these or other tools work?





Wood stiffness of 25 year-old trees

4





Wood stiffness of young trees?

Genetic selections are often

made at ages 6-12

Current tools are probably

unsuitable

More juvenile wood

Branches a problem

What are age-age

correlations?

Approach

Test alternative methods for measuring stiffness of

young trees in the field (phenotypic analyses)

Can we reliably measure stiffness of young trees?

Test best approaches in progeny tests

Understand the genetics of juvenile wood stiffness

Analyze increment cores collected from mature trees

(phenotypic analyses)

What is the potential for early selection?

Analyze age trends and age-age correlations of wood properties

Juvenile (core) wood Mature (outer) wood

5





Wood density is measured by x-ray

densitometry

Microfibril angle (MFA) is measured by

x-ray diffractometry

Microfibril angle

Wood density

Wood stiffness (indirect)

Fiber diameter

Fiber wall perimeter

Fiber wall thickness

Fiber Coarseness

Fiber specific surface area

Ring width

SilviScan

www.ffp.csiro.au/photos/SilviScanLayout-small.jpg

10

Other approaches

Method Property Cost Quality

X-ray densitometry Density Moderate High

X-ray diffraction MFA, MOE Moderate-high High

Tensile testing MOE Prohibitive High

Compression testing MOE Prohibitive High

Volumetric density Density Moderate Moderate-high

Contact ultrasonics MOE, MFA Moderate High

Bending 3- and 4-point MOE, MOR Prohibitive High

In-tree acoustics MOE, MFA Low Moderate-high

X-ray μCAT Density Moderate High

Resistograph Density Low Moderate

NIR Density, MFA, MOE Moderate Moderate-high

Modified from Gary Peter et al. 2008

6





FAKOPP tools for small trees

TreeSonic with alternative sensors (SD02)

– Standard sensors too large?

– Physical signal

Microsecond timer

Ultrasonic timer

– Electrical signal

SD02

IML tools for small trees

Acoustic velocity

IML Micro Hammer

Wood density

Resistograph

7





Center for Advanced Forestry Systems (CAFS)

http://cnr.ncsu.edu/fer/cafs/researchareas.html





Center for Advanced Forestry Systems (CAFS)

National Science Foundation

Industrial Innovation Partnership (IIP) Division

Industry / University Cooperative Research Centers

Center for Advanced Forestry SystemsNorth Carolina State University – Jose Stape

Oregon State University – Glenn Howe

Purdue University – Charles Michler

University of Florida – Eric Jokela

University of Georgia – Michael Kane

University of Idaho – Mark Coleman

University of Maine – Robert Wagner

University of Washington – David Briggs

Virginia Tech – Thomas Fox

8





Clone-specific modeling and silviculture

Developing Varietal Precision Silvicultural Regimes in Pine and

Hardwood Plantations Based on Crown Ideotype (Fox; VT)

Developing Growth and Yield Predictions for Diverse Genotypes

and Silvicultural Practices (Burkhart; VT)

Developing Growth and Yield Predictions for Enhanced Genotypes

(Borders; UGA)

Integrating Wood Quality Predictions into Growth and Yield Models

for Evaluating Advanced Genotypes and Silvicultural Responses

(Daniels; UGA)

Scaling Competitive Dynamics from the Individual to the Stand

Using Clonal and Full-Sib Family Block Trials (Jokela; UF)





Other CAFS genetics projects

Genetic Architecture of Growth, Disease Resistance and Wood

Quality Traits in Loblolly Pine (Peter; UF)

Early genetic selection for wood stiffness in Douglas-fir (Howe;

OSU)

Effects of Site and Genetics on Douglas-fir Growth, Stem Quality,

and Adaptability (Howe; OSU)

9

Economic Environment ($)

Industry

organization

Products

Rotations

Crop value

Seedlings

produced ~1000 M

100 M

20-35 yrs

45-70 yrs

Pulp/paper/OSB

more important

Pulp/paper/OSB less important

TIMOs/REITs increasing

Implications: Douglas-fir Loblolly pine

Interest in growth and wood quality High High

Investment in breeding/research Lower Higher

Interest in clonal forestry Lower Higher

Interest in GMOs ~Non-existent Some

Higher

Lower

= Douglas-fir

= Loblo lly p ine

= Douglas-fir

= Loblo lly p ine

Physical Environment & Lands


Seed zones/Breeding zones 123 / 10-13 <<123 / 7

First-gen breeding pops Larger Smaller

Breeding program More costly Less costly

Breeding objectives More on adaptability, diversity Less on adaptability, diversity

Clonal forestry/GMOs More difficult, expensive Easier, less expensive

Public land

Frosts

Drought

Environmental

variability Low

Important

High

Important

Important

Rarely a problem?

Lots

Little

= Douglas-fir

= Loblo lly p ine

= Douglas-fir

= Loblo lly p ine

10

Social Environment


Interest in clonal forestry Lower Higher

Interest in GMOs ~Non-existent Some

= Douglas-fir

= Loblo lly p ine

= Douglas-fir

= Loblo lly p ine

Environmental

activism

Concerns about

genetics/GMOs

Skepticism about

forest mgmt.

Low

High

Low

High

Low

High

Low

High

Agrarian culture

Desktop version

Web version

ClimateWNA provides easy access

to over 20,000 climate surfaces

ClimateWNA - Climate interpolation

11

Mean Annual Temperature (MAT) by PRISM (4 x 4 km)

21A mountain area near North Vancouver

Downscaled MAT by ClimateWNA (90m)

22A mountain area near North Vancouver

12

Many derived climate variables in ClimateWNA

Degree-days <0°C

Degree-days>5°C

Frost-freeperiod

Number of frost-free days

Extreme minimumtemperature

Snow fall

23Wang et al. 2006. Intl. J. Climatology 23

ClimateWNA generates climate data for the past (1901 – 2009)

2424

13

It predicts climate data for the future periods

2525

Spatial pattern of temperature for baseline period

ClimateBC output (90m) overlaid on a satellite image (1970s)26

14

GCM changes added onto the baseline data

ClimateBC output (90m) overlaid on a satellite image (CGCM2 A2_2050s)27





Site characterization: parent trees

3458 parent trees estimated to be within 80

meters of the real world location

Elevation: 6 to 954 meters

Parents are from 21 NWTIC testing programs

Analyses include 158 progeny test sites

Methods of verification and digitization

– Spatial database of parent trees within a GIS

– Cooperator maps were used with NAIP and Google

Earth (NAIP = National Agricultural Imagery

Program)

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15





Site characterization: progeny tests

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205 progeny test sites in Oregon and

Washington

Sites belong to 32 NWTIC testing programs

Elevation 15 to 1090 meters

Average of 3500 trees per site (400 to 9400)

Measured variables− Height at ages ~5, 10, 15

− DBH at ages ~5, 10 15

− Sinuosity

− Forking

− Ramicorn branching

ClimateWNA variables related to height growth

Annual height growth from sowing

to ~15 years-old

Importance of climatic and other variables

evaluated using Random Forest

Program code

Sowing year

Precip as snow

Aspect

Ave spring temp

Degree-days < 0°

Winter precip

Annual heat-moisture index

Winter ave temp

Mean annual precip

Degree-days > 5°

Elev

Fall precip

Mean coldest month temp

Min spring temp

16

Seedlot Selection Tool (SST)

Given a specific planting site …

Which seedlot is well adapted today?…

And in the future given a climate change scenario?

http://sst.forestry.oregonstate.edu/PNW/

Transfer limits for Douglas-fir

Statistic MAT MAP MCMT MWMT FFP MSP AHM

Mean 1.6 667 1.9 1.6 43 96 4.7

Min 0.4 80 0.5 0.3 12 15 0.8

Max 2.5 1915 3.5 4.0 72 221 17.5

Std 0.5 454 0.6 0.5 14 53 3.2

MAT = mean annual temperature

MAP = mean annual precipitation

MCMT = mean coldest month temperature

MWMT = mean warmest month temperature

FFP = length of frost-free period

MSP = mean summer precipitation

AHM = annual heat:moisture index

ClimateWNA variables

Mean transfer distances for Douglas-fir Oregon modified seed zones (Randall 1996)

17

Find seedlots for my planting site

2010-2039

2070-20992040-2069

1961-1990

Conifer Translational Genomics Network

Coordinated Agricultural Project

www.pinegenome.org/ctgn

http://www.pinegenome.org/ctgn

18


Single nucleotide polymorphism (SNP)

Tree 1 is heterozygous Trees 2 and 3 are homozygous

A C G T G T C G G T C T T A Maternal chrom.

A C G T G T C A G T C T T A Paternal chrom.

A C G T G T C G G T C T T A Maternal chrom.

A C G T G T C G G T C T T A Paternal chrom.

A C G T G T C A G T C T T A Maternal chrom.

A C G T G T C A G T C T T A Paternal chrom.

Tree 1

Tree 2

Tree 3

SNP


A genome from many short sequences

Next-generation

sequencing



19


Find SNP markers


Potential SNP markers in Douglas-fir

Douglas-fir variety No. of SNPsNo. of genes

with SNPs

Coastal 238,760 17,556

Interior 151,918 16,580

Both (in common) 71,376 13,759



20


The promise of genomic selection

Paradigm shift in perspective

Forget about finding individual markers associated with desirable

traits

Explain desirable traits by using many, many markers at the same

time

Now possible because we can genotype many SNP markers at

modest cost

– SNP, single nucleotide polymorphism, = changes between A, G, C, T

Why might it be useful?


BC Forest Service



21


The promise of genomic selection

GEBV = genomic estimated breeding value

Phenotypic selection

(select on BLUP BV)

Phenotype

progeny

(field tests)

Genotype

progeny

(SNP markers)

Genomic selection

(select on GEBV)

Make

crosses

Genomic

Selection

Cycle

Phenotypic

Selection

Cycle

Genotype, then use existing phenotypes

to train GEBV model

Model training step

Phenotypic selection

(select on BLUP BV)

Phenotype

progeny

(field tests)

Genotype

progeny

(SNP markers)

Genomic selection

(select on GEBV)

Make

crosses

Genomic

Selection

Cycle

Phenotypic

Selection

Cycle

Genotype, then use existing phenotypes

to train GEBV model

Model training step

Western Conifer Forest Systems:

Strategies for Climate Change

Adaptation and Mitigation

Western Conifer

Climate Change Consortium (WCCCC)

USDA Coordinated Agricultural Project


22

Large climatic transfer distances can result in maladapted

plantations

Transfer limits can be determined directly from provenance tests

Sufficiently large provenance tests are rare

Sufficiently large transfer distances are rarely tested

Lodgepole pine provenances from maritime areas

are not adapted to the winters of eastern Finland

Superior adaptability of a Douglas-fir seed

source from California growing in Spain

(Hernandez et al 1993)

Finnish Forest Research Institute Lodgepole pine provenance test in

New Zealand (Wright 1976)

Seed source adaptability is criticalSeed source adaptability is critical

Lodgepole pine provenance test in B.C.

Local = productivity increases by 7% up to 1.5ºC (2030), but then decreases.

Optimal = productivity increased by 14-36%.

-70

-50

-30

-10

10

30

50

70

0

2012

|

1

2038

|

2

2063

|

3

2088

|

4

2114

|

5

2139

6

M AT increase (°C )

Ch

an

ge

in

pro

du

cti

vit

y (

m3/h

a)

— O ptim ized sources

— Local sources

-70

-50

-30

-10

10

30

50

70

0

2012

|

1

2038

|

2

2063

|

3

2088

|

4

2114

|

5

2139

6

M AT increase (°C )

Ch

an

ge

in

pro

du

cti

vit

y (

m3/h

a)

— O ptim ized sources

— Local sources

Wang et al. (2006) Global Change Biol. 12:2404.

140 populations 60 test sites

20 years old

Effects of climate change on lodgepole pineEffects of climate change on lodgepole pine

23

Regional CAP for 2011

Regional approaches to Climate Change: CAP

Application deadline – July 16, 2011?

$4,000,000 per year ($20 million total) for up to 5 years

Anticipates making 5 to 8 awards in FY 2011?

Regional integrated CAP focusing on mitigation and

adaptation, involving research, education, and outreach in:

– Cropping systems: legume or forage production systems

– Animal systems: ruminant livestock and dairy

– Forest systems: western conifers

– Grassland, pastureland, and rangeland systems

Stakeholders are critical

“Demonstrate the adoption of approaches and practices

across the region…”

Stakeholders are seed orchard managers, nursery

managers, silviculturists, managers of forest operations,

wood products manufacturers, managers of carbon offsets

programs, policy makers, teachers, and students

Organizations are forest industry, governmental agencies,

tribes, small private landowners, NGOs, and universities

Included in project advisory groups

24

Long-term goal

Synthesize existing knowledge and develop new

knowledge on the impacts of climate change on western

forest production systems, and then design, convey,

and implement management strategies that maximize

forest health, forest productivity, and greenhouse gas

mitigation under changing climates

WCCCC planning meeting tomorrow

25





Technological advances and applications

Wood stiffness remains a high priority for genetic improvement

Clonal forestry is receiving much attention in the SE, but not in the

PNW

New climate models are available to predict climate and weather for

specific sites (e.g., ClimateWNA)

Genomic markers are being developed and tested in breeding

programs

Genetic considerations will play an important role in assessing the

potential effects of climate change and helping forest managers adapt

Large, collaborative external grant programs increasingly important!

emerging issues and organizational approachescips.forestry.oregonstate.edu/sites/cips/files/... ·...

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