Woody Crops for Bioenergy Production in Florida
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Woody Crops for Bioenergy Production in Florida Gary Peter [email protected] , Alejandro Riveros -Walker, Jianxing Zhang, Patricio Munoz, Bijay Tamang, Don Rockwood, Matias Kirst, Evandro Novaes School of Forest & Resources Conservation, University of Florida, Gainesville, FL 32611 Background A recent analysis of the effect of a hypothetical renewable portfolio standard (RPS) in Florida concludes: “To sustainably achieve 1% to 3% of electricity production from wood sources, logging residues and urban wood waste have to be utilized in addition to merchant-able timber along with an enhanced reforestation program. Reforestation must at least keep pace with forest harvest removals. Beyond 3% of electricity generation from wood sources, short rotation energy crops need to make up a larger share of the fuel mix in addition to all other feedstock sources mentioned above. The study concluded that a 7% RPS (equivalent of 1% to 3% electricity production from wood sources over time) would have little impact to the existing forest products industry and Florida’s forest would remain sustainable.” “With increased reforestation, afforestation and planting of high-yielding short rotation woody crops on up to 15% of non-forested lands, a 12% and higher RPS could be achieved without depletion of the forest resources of the state, or significant impacts to the existing forest industries.” (http://www.fl-dof.com/forest_ management/fm_pdfs/Final%20Report%20Woody% 20Biomass%20Economic%20Study.pdf) Project Objectives Forest tree species have a number of favorable advantages for use as a feedstock for bioenergy production. However, commercial use is limited by the feedstock cost and efficiency of conversion to bioenergy and biofuels. We are addressing these limitations by conducting 1) fundamental research with loblolly pine and Populus to identify genes controlling biomass chemical composition and productivity that can be used ultimately to tailor crops for improved conversion efficiencies to biofuels and electricity, and 2) applied short rotation woody crop research with Eucalyptus species, to provide important agronomic practice, yield, and chemical composition information for Florida growers, producers and policy makers. Energy density wood 9 month old – citrus beds 7 month old – abandoned mine land (clay settling) Location Site Soil Age (mo) Response G1 G2 G3 South Citrus beds Poorly drained sand 9 Height (m) Survival (%) 3.3 92.3 3.3 89.4 3.5 91.3 South Agricultural land Sandy muck 8 Height (m) up to 6.1 Central Mined land (Reclaimed site) Sandy 7 Height (m) Survival (%) 0.5 100 0.5 100 0.7 100 Central Mined land (Clay settling area) Clay 7 Height (m) Survival (%) 0.9 100 1.8 100 1.3 100 Central Citrus grove windbreak Well drained sand 15 Height (m) 4.3 4.4 5.0 North Former Pine Plantation Sandy clay 5 Height (m) Survival (%) 0.5 100 0.6 100 0.5 96 Short Rotation Field Trials Established with Three UF/IFAS Eucalyptus grandis cultivars Wood Chemical Composition is Key to Yields Wood is composed principally of complex carbohydrates, cellulose and hemicelluloses, lignin and extractives, terpenoids. For combustion, gasification and pyrolysis, wood with elevated lignin and terpene contents have greater energy per unit mass. For bioconversion to liquid fuels wood with greater carbohydrates will have greater yields. Thus, understanding the genetic mechanisms controlling the ratio of carbohydrates, lignin and extractives will lead to the development of trees that have greater yields of bioenergy and biofuels. To understand the mechanisms that control wood chemical composition in loblolly pine and Populus, we are conducting mapping studies to identify genes that control cellulose, hemicellulose, liginin and extractive content. Collect Diverse Wood Samples Calibration Part Prediction Part Wood Energy Density Analysis FTIR-NIR Spectra Wood Energy Density Analysis FTIR-NIR Spectra Calibration Model Energy Density Prediction Model Validation Industrial Application Loblolly Pine: Association Genetics GENOTYPE PHENOTYPE Wood Chemistry py-MBMS SNP Discovery SNP Genotyping Association Genetics Linkage Mapping QTL Location Candidate Genes Candidate Alleles- QTN Candidate Genes Phenotype Method Measured # trees Wood Chemistry Pyrolysis Molecular Beam Mass Spectrometry Rings 3 & 4 3888 Effect (% of phenostev) Annotation Single Peak Significant 9.3 glycoside hydrolase family 28 protein 114 7.2 hypothetical protein OsJ_08791 114 8.2 hypothetical protein OsJ_36445 114 & 144 7.4 No Hits Found 114 & 144 6.1 No Hits Found - 10.6 ASC1-like protein/putative resistance protein 144 6.5 short-chain dehydrogenase, putative 144 7.4 heat shock protein 70 (HSP70)- interacting protein, putative 144 8.6 Acetylornithine aminotransferase, mitochondrial; Short=ACOAT 144 8.1 hypothetical protein OsJ_05018 - Effect (% of phenostev) Annotation 7.3 unknown 4.2 Os08g0518100 4.1 amino acid permease 3.5 serine-threonine protein kinase, plant-type, putative 3.5 No Hits Found 3.3 PREDICTED: hypothetical protein 4.8 unknown 3.7 Activating signal cointegrator, putative 4.2 GTP binding protein, putative 5.2 mitochondrial Acetylornithine aminotransferase 4.0 unnamed protein product 3.8 predicted protein 4.3 conserved hypothetical protein 4.1 PREDICTED: hypothetical protein Carbohydrate Genes Lignin Genes High Throughput Wood Chemistry Estimation 10000.0 9600 9200 8800 8400 8000 7600 7200 6800 6400 6000 5600 5200 4800 4400 4000.0 0.170 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.569 cm-1 A Absorbance 0.17 0.30 0.50 0.569 10000 8800 7600 6400 5200 4000 cm-1 4000.0 3800 3600 3400 3200 3000 2800 2600 2400 2200 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600 500 400.0 -0.022 -0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.260 cm-1 A -0.022 0.10 0.20 0.26 4000 3000 2000 1000 400 cm-1 Absorbance MIR NIR NIR Integrating Sphere MIR - ATR MIR & NIR - XY y = 0.8352x + 3.2719 R² = 0.875 10 12 14 16 18 20 22 24 26 13 18 23 28 Corrected lignin y = 0.7661x + 0.2502 R² = 0.7454 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 0.5 1 1.5 2 Cellulose Phenotypic distribution for % lignin. Lignin content varied from 24.5 to 35.5% with a mean of 29.3% Phenotypic distribution for wall carbohydrates. Peak intensity varied from 80 to 47.5 with a mean of 60.3. Populus Predictions for Lignin & Cellulose y = 0.8101x + 2.2854 R² = 0.8303 5.5 7.5 9.5 11.5 13.5 15.5 17.5 7 9 11 13 15 17 19 S-lignin Conclusions We have identified candidate genes in pine and Populus that appear to control cellulose and lignin amounts. We are following up on these genes in pine and Populus with the long-term goal of developing high lignin trees that can be converted with high efficiency to bioenergy with combustion, gasification and pyrolysis or high carbohydrate trees that can be converted efficiently to biofuels and valuable co-products via biochemical conversion. We have developed validated predictive models to estimate wood chemical composition in Populus. We have established six new field plantings of Eucalyptus grandis cultivars developed by UF. These cultivars survived well in south and central Florida as expected. They have excellent height growth on various soils and we will be measuring their growth to quantify yields. Acknowledgements: This project was funded by The Florida Energy Systems Consortium, NSF, DOE
Transcript of Woody Crops for Bioenergy Production in Florida
Woody Crops for Bioenergy Production in FloridaGary Peter [email protected], Alejandro Riveros -Walker, Jianxing Zhang, Patricio Munoz, Bijay Tamang, Don Rockwood, Matias Kirst, Evandro Novaes
School of Forest & Resources Conservation, University of Florida, Gainesville, FL 32611
BackgroundA recent analysis of the effect of a hypothetical renewable portfolio standard (RPS) in Florida concludes: “To sustainably achieve 1% to 3% of electricity production from wood sources, logging residues and urban wood waste have to be utilized in addition to merchant-able timber along with an enhanced reforestation program. Reforestation must at least keep pace with forest harvest removals. Beyond 3% of electricity generation from wood sources, short rotation energy crops need to make up a larger share of the fuel mix in addition to all other feedstock sources mentioned above. The study concluded that a 7% RPS (equivalent of 1% to 3% electricity production from wood sources over time) would have little impact to the existing forest products industry and Florida’s forest would remain sustainable.” “With increased reforestation, afforestation and planting of high-yielding short rotation woody crops on up to 15% of non-forested lands, a 12% and higher RPS could be achieved without depletion of the forest resources of the state, or significant impacts to the existing forest industries.” (http://www.fl-dof.com/forest_management/fm_pdfs/Final%20Report%20Woody%20Biomass%20Economic%20Study.pdf)
Project ObjectivesForest tree species have a number of favorable advantages for use as a feedstock for bioenergyproduction. However, commercial use is limited by the feedstock cost and efficiency of conversion to bioenergy and biofuels. We are addressing these limitations by conducting 1) fundamental research with loblolly pine and Populus to identify genes controlling biomass chemical composition and productivity that can be used ultimately to tailor crops for improved conversion efficiencies to biofuels and electricity, and 2) applied short rotation woody crop research with Eucalyptus species, to provide important agronomic practice, yield, and chemical composition information for Florida growers, producers and policy makers.
Energy density
wood
9 month old – citrus beds
7 month old – abandoned mine land (clay settling)
Location Site SoilAge
(mo)Response G1 G2 G3
SouthCitrus beds Poorly drained
sand
9 Height (m)
Survival (%)
3.3
92.3
3.3
89.4
3.5
91.3
South Agricultural land Sandy muck 8 Height (m) up to 6.1
CentralMined land
(Reclaimed site)
Sandy 7 Height (m)
Survival (%)
0.5
100
0.5
100
0.7
100
CentralMined land (Clay
settling area)
Clay 7 Height (m)
Survival (%)
0.9
100
1.8
100
1.3
100
CentralCitrus grove
windbreak
Well drained
sand
15 Height (m) 4.3 4.4 5.0
NorthFormer Pine
Plantation
Sandy clay 5 Height (m)
Survival (%)
0.5
100
0.6
100
0.5
96
Short Rotation Field Trials Established with Three UF/IFAS Eucalyptus grandis cultivarsWood Chemical Composition is Key to Yields
Wood is composed principally of complex carbohydrates, cellulose and hemicelluloses, lignin and extractives, terpenoids. For combustion, gasification and pyrolysis, wood with elevated lignin and terpene contents have greater energy per unit mass. For bioconversion to liquid fuels wood with greater carbohydrates will have greater yields. Thus, understanding the genetic mechanisms controlling the ratio of carbohydrates, lignin and extractives will lead to the development of trees that have greater yields of bioenergy and biofuels. To understand the mechanisms that control wood chemical composition in loblolly pine and Populus, we are conducting mapping studies to identify genes that control cellulose, hemicellulose, liginin and extractive content.
Collect Diverse
Wood Samples
Calibration
Part
Prediction
Part
Wood Energy
Density Analysis
FTIR-NIR Spectra
Wood Energy
Density Analysis
FTIR-NIR Spectra
Calibration
Model
Energy Density
Prediction
Model
Validation
Industrial
Application
Loblolly Pine: Association Genetics
GENOTYPEPHENOTYPE
Wood Chemistry py-MBMS
SNP Discovery
SNP Genotyping
Association GeneticsLinkage Mapping
QTL Location
CandidateGenes
CandidateAlleles-
QTN
Candidate Genes
Phenotype Method Measured # trees
Wood Chemistry Pyrolysis Molecular Beam Mass Spectrometry
Rings 3 & 4 3888
Effect (% of phenostev) Annotation
Single Peak Significant
9.3glycoside hydrolase family 28 protein 114
7.2 hypothetical protein OsJ_08791 114
8.2 hypothetical protein OsJ_36445 114 & 144
7.4 No Hits Found 114 & 144
6.1 No Hits Found -
10.6ASC1-like protein/putative resistance protein 144
6.5short-chain dehydrogenase, putative 144
7.4heat shock protein 70 (HSP70)-interacting protein, putative 144
8.6Acetylornithine aminotransferase, mitochondrial; Short=ACOAT 144
8.1 hypothetical protein OsJ_05018 -
Effect (% of phenostev) Annotation
7.3 unknown
4.2 Os08g0518100
4.1 amino acid permease
3.5 serine-threonine protein kinase, plant-type, putative
3.5 No Hits Found
3.3 PREDICTED: hypothetical protein
4.8 unknown
3.7 Activating signal cointegrator, putative
4.2 GTP binding protein, putative
5.2 mitochondrial Acetylornithine aminotransferase
4.0 unnamed protein product
3.8 predicted protein
4.3 conserved hypothetical protein
4.1 PREDICTED: hypothetical protein
Carbohydrate Genes Lignin Genes
High Throughput Wood Chemistry Estimation
10000.0 9600 9200 8800 8400 8000 7600 7200 6800 6400 6000 5600 5200 4800 4400 4000.0
0.170
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.569
cm-1
A
Abs
orba
nce
0.17
0.30
0.50
0.569
10000 8800 7600 6400 5200 4000cm-14000.0 3800 3600 3400 3200 3000 2800 2600 2400 2200 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600 500 400.0
-0.022
-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.20
0.21
0.22
0.23
0.24
0.25
0.260
cm-1
A
-0.022
0.10
0.20
0.26
4000 3000 2000 1000 400cm-1
Abs
orba
nce
MIRNIR
NIR Integrating SphereMIR - ATR
MIR & NIR - XY
y = 0.8352x + 3.2719R² = 0.875
10
12
14
16
18
20
22
24
26
13 18 23 28
Corrected lignin
y = 0.7661x + 0.2502R² = 0.7454
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
2.3
0.5 1 1.5 2
Cellulose
Phenotypic distribution for % lignin. Lignin content varied from 24.5 to 35.5% with a mean of 29.3%
Phenotypic distribution for wall carbohydrates. Peak intensity varied from 80 to 47.5 with a mean of 60.3.
Populus Predictions for Lignin & Cellulosey = 0.8101x + 2.2854
R² = 0.8303
5.5
7.5
9.5
11.5
13.5
15.5
17.5
7 9 11 13 15 17 19
S-lignin
ConclusionsWe have identified candidate genes in pine and Populus that appear to control cellulose and lignin amounts. We are following up on these genes in pine and Populus with the long-term goal of developing high lignin trees that can be converted with high efficiency to bioenergy with combustion, gasification and pyrolysis or high carbohydrate trees that can be converted efficiently to biofuels and valuable co-products via biochemical conversion.We have developed validated predictive models to estimate wood chemical composition in Populus.We have established six new field plantings of Eucalyptus grandis cultivars developed by UF. These cultivars survived well in south and central Florida as expected. They have excellent height growth on various soils and we will be measuring their growth to quantify yields.
Acknowledgements: This project was funded byThe Florida Energy Systems Consortium, NSF, DOE
