Transcript Presentation PowerPoint

Dr. Gary Peter
Professor
University of Florida
Southern Pines: The Bioenergy &
Renewable Chemicals Star of the
Southeastern US
Gary Peter
University of Florida
[email protected]
Pine Forests of the Southeastern US
• Forests occupy over 200 million acres (60% of the
land area), with a large fraction dominated by pines
– 10 species, loblolly and slash economically important
• ~85% of all forestlands are privately owned
• About half the pine forest is planted with genetically
improved seedlings
– About 10 million ha / 25 million ac each
• Contains 12 Pg of C, 36% of the sequestered forest C
in the contiguous United States
• Annually sequester 76 Tg C, equivalent to 13% of
regional greenhouse gas emissions
US South: Forestry & Forest Industry
• Largest biomass industry in world
– Produces 16% of global industrial wood supply
• More than any other country
– Supplies 60% of US & 25% of world pulp & paper markets
• 43 million tons of annual capacity
• Generates ~2/3’s of all industrial bioenergy
– Used on site
• Sustainability is a key focus for industry
– >93% of stem is utilized
Johnson & Steppleton, 2011
Southern pulp mill location & capacity
Forest Products Supply Chain
Feedstock
production
Feedstock
logistics
Biomaterials
• Scalable
– Large land area
– Large stable markets
• Sustainable
– More volume growth than
harvested
• Cost competitive for traditional
products
– Pulp, paper, wood
Distribution
& use
Operating & Proposed Wood Biomass to
Electric Power & Wood Pellet Facilities
Approx. 30 actual or
proposed plants
Approx. 40 actual or
proposed plants
TimberMart-South
Biofuel Production in the Southeast
• 2010 USDA biofuels report
estimates that ~50% of the
advanced biofuel
production capacity will be
located in the southeast US
– Most favorable growing
conditions & available land
• Advanced biofuel facilities
that can use pine feedstock
–
–
–
–
KiOR (thermochem)
Bluefire (acid hydrolysis)
Ineos (thermochem)
Bluesugar ?
Stable Cost & Large Supply
Since 1940s, planted pine productivity has tripled, primarily
due to improved genetic stock and silvicultural technology
developed and disseminated by
University / Government / Industry Research Cooperatives
50
6000
40
4000
30
20
2000
10
0
Rotation age (years)
60
Volume at Harvest
(ft3 / ac)
8000
0
1940 1950 1960 1970 1980 1990 2000 2010
Establishment Decade
Natural Stand
Weed Control
Planting
Tree Improve
Site Prep
Biotech/Clonal
Fertilization
Rotation Age
Redrawn from: Fox, T.R., E.J. Jokela and H.L. Allen. 2007. The development of pine plantation silviculture
in the southern United States. J. Forestry 105:337-347.
70
Range: 0.980 ~ 1.051
60
Mean: 1.001
50
60
Density
Density
Traditional Phenotypic Breeding with
Recurrent Selection
70
SD: 0.00519
40
Mean: -0.00026
50
SD: 0.00455
40
30
30
20
20
10
10
0
0
Phenotype: Total Height
Diagonal elements of genetic relationship matrix
(Rarw estimates)
140
120
c
120
Range: 0.983 ~ 1.043
Density
SD: 0.00434
60
Range: -0.0190 ~ 0.0214
80
Mean: 1.001
80
Mean: -0.00021
60
SD: 0.00380
40
40
20
20
0
Quantitative Trait d
100
100
Density
Off-diagonal elements of genetic relationship matrix
(Rarw estimates)
0
Supplementary Figures
Off-diagonal elements of genetic relationship matrix
(Adjusted estimates)
Diagonal elements of genetic relationship matrix
(Adjusted estimates)
100
0.690
e
80
SD: 0.00519
40
40
Variance Components
P= G + E
G= A + D + I
-Heritability (h2)
-G x E
c
-G x Age
3.9
2.9
2.4
1.9
1.4
0.9
0.4
-0.6
-1.1
-1.6
-2.1
Diagonal elements of genetic relationship matrix
(Rarw estimates)
-2.6
-3.1
-3.6
-4.1
0
3.4
0.1
Z-score
SD: 0.00455
Breeding Values
-Ranking genotypes
-Selection
Off-diagonal elements of genetic relationship matrix
(Rarw estimates)
120
d
100
Range: 0.983 ~ 1.043
80
Range: -0.0190 ~ 0.0214
Supplementary
Figure 1 Histograms
of (a) the diagonal and (b) the off-diagonal elements
of
Mean: -0.00021
Mean: 1.001
80
60
SD: 0.00434
Density
Density
Mean: -0.00026
50
0
0
b
Range: -0.0227 ~ 0.0256
10
10
Phenotype = X b + Za + e
60
20
20
BLUP
70
30
0.330
100
Density
0.450
120
80
Mean: 1.001
-0.1
Density
Density
60
140
90
Range: 0.980 ~ 1.051
0.570
0.2
100
a
60
SD: 0.00380
the raw estimates of the genetic relationship matrix, (c)
40 the diagonal and (d) the off-diagonal
40
20
20
0
0
Nature Genetics: doi:
10.1038/ng.608
Diagonal elements of genetic relationship matrix
(Adjusted estimates)
Off-diagonal elements of genetic relationship matrix
(Adjusted estimates)
CFGRP: Slash Pine Deployment Gains & Value
Genetic Gains in Harvest Yields (%)
50
40
30
20
10
0
Low Hazard
1.0 Unrogued
1.0 Rogued
1.5 Unrogued 2.0 Unrogued
2.0 Rogued
2.5 Unrogued 3.0 Unrogued
Conservative estimate
of incremental
increase in stumpage
value (6% interest) due
to increased yields
from planting
genetically improved
stock in FL estate
$ in millions
600
500
400
300
200
100
0
1
2
3
Cycle of Genetic Improvement
Source: Greg Powell, Univ Florida
Pine Breeding Cycle
Pine Breeding is a long
multi-step process
>30 years 1st to 2nd
Generation
Reduction of more
than 10 years:
White et al. 2008
-Early Selection
-Smaller populations
-Top-grafting
Can be partitioned in three stages
Breeding
Testing
Propagation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Start Breeding
Commercial
Production
Marker Assisted Selection
B. Indirect markers based on linkage disequilibrium:
I - QTL analysis
III – Genomic selection
II – Genetic association
Resolution
Low
Medium
High
Linkage Blocks
Large
Medium
Small
Genome-Wide Selection
Training
population
Phenotypes
Genotypes
Fit all SNPs in a prediction model Validation
Y =  SNP + e
Define multi-loci models
to predict phenotypes
Meuwissen et al. (2001) Genetics 157: 1819-1829
Genomic Selection “Current status in breeding”
• Genomic Selection is operational in cattle breeding and evaluated in
other animals, crops and trees
• Focus has been on development of methods (e.g. GBLUP, RR-BLUP,
Bayes A, Bayes B, LASSO, RKHS, Machine learning, etc.)
• Everybody agrees that GS application depends on the accuracy of
predicting phenotype with markers
• Theoretically accuracy depends on:
–
–
–
–
Linkage disequilibrium extent
Training population size
Heritability
Number of QTLs
• But also depends on the BV quality used to construct the GS model
GWS Accuracies in CCLONES
DBH & Height
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
DBH
HT
B.F. Grant
Cuthbert
 BV / Yr 
Nassau
Palatka
Accuracy  Intensity
Generation
 Variation
Interval
GWS Incorporated into Pine Breeding
10+ years
8+ years
8+ years
B
T
P
8+ years
B
T
4 years
8+ years
<1yr
4 years
<1yr
1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 21 22 23 24 25
P
B
T
P
TP
B
1 2 3 4 5
4 years
1yr
B
4 years
1 2 3 4 5 6 7 8 9 10 12 13 14
1yr
4 years
1yr
1 2 3 4 5 6 7 8 9 10 12 13 14
TP
B
TP
1 2 3 4 5
1 2 3 4 5
Conifer Oleoresin Canal System for
Insect and Fungal Resistance
• The wood resin canals
(vertical and horizontal) are
organized into a 3D network
for terpene synthesis and
storage
– Thin walled resin canal
epithelial cells line the canal
and synthesize and secrete
terpenes into the lumen of the
canals or duct
Resin canals
• Resin flows out of stem after
wounding, typically by boring
insects
– Constitutive resin under
positive pressure in resin canals
Why Terpenes?
Terpenes as Biofuels
• High energy density - carbon
and hydrogen rich and low in
oxygen
• Simple & efficient chemical
methods for conversion of
natural terpenes to drop-in
fuels suitable for blending or
replacement of petroleum are
available
– b pinene dimers as a jet fuel
replacement
– Bisabolene to bisabolane a D2
diesel fuel replacement
– Farensene as a diesel fuel
Terpene Biosynthesis
• Conserved biosynthetic
pathways in microbes &
plants
• Large variety of natural
terpenes with varying
chemical properties
– Mono, sesqui-, di- and
triterpenes
Biofuels & Co-products
1st GENERATION BIOFUELS
Extraction
•Sugar
– Ferment to EtOH
– Sugar
•Starch
– Amylase + ferment
to EtOH
– Oil, animal feed
•Oil
– Transesterification
to biodiesel
– Glycerin
2nd GENERATION BIOFUELS
Deconstruction
•Lignocellulose
– Sugar Platform
• Size reduction + degradation
+ fermentation
• Power, lignin
– Gas Platform
• Anaerobic digestion to biogas
• Gasification + catalytic
synthesis to liquid fuel
• Power
– Liquid Platform
• Cracking / pyrolysis +
upgrading
Conversion of Biomass to Fuel
Extraction Based
• Compound highly
concentrated in biomass
that facilitates efficient
recovery
• Starting material has high
chemical uniformity
• High efficiency conversion
with limited input costs
Deconstruction Based
• Biomass is large &
heterogeneous
• Starting material has
relatively low chemical
uniformity
• Requires substantial energy
and/or chemical inputs to
reduce
Come from domesticated
plants breed & selected for
concentration & yield of food
Non-edible parts of food
plants & undomesticated
grasses & trees
Pine Terpenes
• Naturally synthesize a
• Terpenes accumulate in
large diversity of mono-,
wood naturally to >20%
sesqui- and diterpenes
– Constitutive synthesis
as defense compounds
– Inducible synthesis
against insects & fungi
• Genetic and
environmental control
of wood terpene
content
Current Pine Terpene Industry
Pine
Biosynthesis
Extraction
Live Tree
Pulp Mill
Wood
Rosin
Crude
Products
Gum
Turpentine
& Rosin
CTO &
CST
Wood
Turpentine
& Rosin
Final
Products
Industrial
Biofuels
Specialty
Chemicals
Flavors &
Fragrances
Pine Terpenes: A $3 Billion Global Industry
• Pine Terpene collection > 1 billion tonne/yr
– Turpentine (mono- & sesquiterpene) rosin (diterpenes)
– Gum terpene (60%), crude sulfated turpentine & crude tall
oil (35%), wood naval stores (5%)
• Gum terpenes collected by tapping living trees >
850,000 tonne/yr
– China, Portugal, USSR, Brazil, Indonesia, Mexico, India
– China >500,000 tonne/yr [60% of global supply but little is
exported]
• Pulp & paper industry collects terpenes as a co-product
– Crude sulfated turpentine & Crude tall oil (CTO)
– US south 450,000 tonne/yr of CTO
Phenotypes
Intensity (arb units)
Oleoresin drymass
Resin canals
Wood terpene content
• Box-Cox transformed oleoresin • Number of resin canals per • Diterpene content in dry
drymass exuded over 24 hours
year (averaged over triplicate wood
• 1002 cloned genotypes
samples)
• 940 cloned genotypes
• 3 sites
• 543 cloned genotypes
• 2 sites
• 3 clonal replicates per site
• 3 sites
• 2 clonal replicates per
• 3 years (one site)
• 3 clonal replicates per site
site
• 2 years
• Total, mono- & diterpene
content in wet wood
• 750 cloned genotypes
• 1 site
• 4 clonal replicates per
site
14
12
10
8
6
4
2
0
93
302
204
30
80 130 180 230 280 330 380 430
m/z
Oleoresin traits are heritable
H2 resin canal number
•single site: 0.15 – 0.21
•across sites: 0.12
H2 oleoresin drymass
•single site: 0.18 – 0.34
•across sites: 0.18
Phenotypic variation in oleoresin drymass is
positively skewed
Oleoresin drymass by
site
Xylem growth
increment per year
Resin canal
number per year
Associated SNPs accurately predict additive
genetic variation in oleoresin drymass
Estimated F1 genetic gains in oleoresin
drymass under varying selection intensities
h2
Fold-increase
breed top
10%
Fold-increase
breed top 5%
Fold-increase
breed top 1%
CUT
0.14
1.62
1.74
1.98
NAS yr 6
0.31
1.86
2.05
2.41
NAS yr 7
0.24
1.80
1.98
2.33
PAL
0.12
1.54
1.61
1.77
ALL
0.12
1.61
1.72
1.92
site
h2: narrow sense heritability
TE-Pine Can Exceed PETRO Metrics
Plants Engineered To Replace Oil
Increase the mass of readily
extractable hydrocarbons to meet
technical targets at costs
competitive with crude oil
Technical Targets
Value Required
1.1-Energy density
> 26.5 MJ/L (LHV)
1.2–Melting point
< -40oC
<-63oC
1.3–Boiling point
>
35oC
>135oC
1.4-Energy
1.5-Process cost
2.1- CO2 use
2.2- H2O
requirement
> 160 GJ ha-1 y-1
< $10 GJ-1
Atmospheric CO2
> 160 GJ ha-1 y-1
< $10 GJ-1
Ambient
< 22 inch y-1
No irrigation
<201 kg ha-1 y-1 N,
<77 kg ha-1 y-1 P,
<56 kg ha-1 y-1 K
58.5 kg ha-1 y-1 N,
7.5 kg ha-1 y-1 P
2.3- Fertilizer
requirement
HT- Pine
• Scalable
– 13 million+ h planted pine exist
– Yield gains achievable
• Environmentally Sustainable
– High harvest index
– Strong positive net energy
– Strong negative CO2eq
• Economically Sustainable
– Lignocellulose & terpene coproduct synergy
– Adds value across supply chain
• Adds Flexibility
– No clear detrimental change in
current product mix
– Strengthens possibility of pine
as a dedicated biofuel crop
– Multiple routes to extraction
Discovery phase
Genetic engineering to rapidly increase
oleoresin production in pine stems
Association genetics
•Multi-site analysis of correlated
oleoresin traits in a structured
clonal population
Gene expression
• Differential expression with
chemical elicitors of resinosis
• Tissue-specific expression in
resin canals
Candidate genes
Validation phase
• RNAi mediated silencing
• Overexpression
• Wild-type v. mutant
phenotypes
Project Overview
• To increase terpene production 5 fold
Three Synergistic Strategies for
Increasing Pine Terpene Synthesis
& Storage Will Be Used
Activation
Triple Resin
Capacity
Constitutive Resinosis
Pathway
25% Greater
Flux
Upregulate Carbon Flux
to Terpenes
Enzyme
1.5X Faster
Synthesis
Optimize Composition &
Production of Terpenes
Terpenes & the Future Forest
Biorefinery
Issue
Alignment
• Land Use
• Environmental
Sustainability
• Conversion Efficiency
• Cost effective
• Net positive energy relative
to fossil fuels
• Dramatic increase in GJ/ha/y
• Increased value to landowners
sustains forest land
• Extracted as a co-product –
lignocellulose still useful for all
traditional products or energy
• Existing capital
• Flexible end product markets
• Strongly positive to fossil fuels
Acknowledgements
COLLABORATORS
• University of Florida
– John Davis, Chris Dervinis,
Matias Kirst, Patricio Munoz,
Marcio Resende, Alejandro
Riveros-Walker, Jared
Westbrook
•
ArborGen
– Will Rottmann
• NREL
– Mark Davis, Robert Sykes
• University of California,
Berkeley
– Jim Keasling, Jim Kirby, Pamela
Peralta-Yayha, Blake Simmons
FUNDING
• DOE/ARPA-E
• USDA/NIFA
• Forest Biology Research
Cooperative
– Plum Creek Timber, Rayonier,
Weyerhaeuser, RMS, F & W
Project Summary
Resinosis
Improved
enzymes
Increased
carbon flux
Discovery
Increased
Resin
canal
#/volume
Increased
terpene
synthesis
Combinatorial
engineering
Five fold
increase in
wood
terpene
20% wood terpene
Technoeconomic Modeling
Forest tree growth Value Chain Analysis & Proposition
Terpene recovery
Commercialization Partners
Germplasm providers
Fuel production
Landowners
Harvesting/transport
Wood processors
Fuel synthesis
Pulp & paper
Biofuel Producers
Wood products
Bioenergy
Oleochemical Refiners
Flavor & Fragrances
Dr. Gary Peter
Professor
University of Florida