Woody Feedstock Production for Bioenergy & Environmental - PowerPoint PPT Presentation

Woody Feedstock Production for Bioenergy & Environmental Sustainability in the North Central United States Ronald S. Zalesny Jr. Team Leader, Research Plant Geneticist Genetics & Energy Crop Production Unit Forest Service, United States Department of Agriculture Northern Research Station Institute for Applied Ecosystem Studies Rhinelander, WI 54501

Northern Research Station Genetics & Energy Crops Landscape Ecology Physiology Research Themes: 1) Forest Disturbance Processes 2) Providing Clean Air & Water 3) Sustaining Forests 4) Urban Natural Resource Stewardship 5) Natural Resources Inventory & Monitoring

Genetics & Energy Crop Production Unit Our objective is to use the link between energy, climate, & tree genetics to: 1) develop fast-growing tree crops as energy feedstocks; 2) develop sustainable forest biomass removal strategies; 3) understand climate change effects on natural & plantation forests; 4) fill critical knowledge gaps in 1), 2), & 3). Short rotation woody crops for fiber, energy, & phytotechnologies  Ecological sustainability of using forest residues for energy  Carbon sequestration & climate change adaptation of conifers 

Energy  Biofuels Bioenergy  Bioproducts 

Renewable Fuel Standard Energy Independence & Security Act of 2007 Annual production of 36 billion gallons of biofuels by 2022   Ethanol production from corn capped at 15 billion gal yr -1  Remaining 21 billion gallons from advanced biofuels  16 billion gallons from cellulosic biofuels Biofuels Production (billion gallons)  Seven-fold increase in current biomass 40 36 production from 190 million dry tons to 35 Total 30 1.36 billion dry tons 25  DOE / USDA goal of replacing 30% 20 16 Corn 15 15 petroleum consumption with biofuels Cellulosic 10 5 by 2030 0 2008 2010 2012 2014 2016 2018 2020 2022 Year Perlack, R.D. 2005. Biomass as feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton Source: Renewable Fuels Association. http:// annual supply. DOE-USDA. DOE/GO-102995-2135. ORNL/TM-2005/66 www.ethanolrfa.org/resource/standard

Energy Forest bioenergy & bioproducts supply chain

15-Year-Old Poplar Arlington (1995)

Hybrid Aspen ‘Crandon’ ( P. alba × P. grandidentata ) * Discovered in 1950’s * 10.3 Mg ha -1 yr -1 at 6 yrs * 24.0 Mg ha -1 hr -1 at 10 yrs 32 Hybrids * 17 to 26 Mg ha -1 yr -1 at 11 yrs * 190,000 to 300,000 sprouts ha -1 Ames Hall, R.B. 2008. Woody bioenergy systems in the United States. NRS-GTR-P-31.

Why Poplars?  Broad economic & environmental benefits Well-studied (silviculture, physiology, & genetics)  Base populations exhibit tremendous diversity  Grown on marginal lands not suitable for agriculture  Very productive  Productivity (dt ac-1) 13.5 Mg ha -1 7 6 5 4 3 2 1 0 0 2 4 6 8 10 12 Age of plantation (yrs)

Why Poplars? Realized Productivity Switchgrass 20 Mg ha -1 yr -1 Willow 18 Mg ha -1 yr -1 Poplar 16 Mg ha -1 yr -1 Potential Productivity >22 Mg ha -1 yr -1 Depends on genotype × environment interactions

Additional Advantages Energy per biomass unit:  1.9 × 10 10 to 2.0 × 10 10 J Mg -1 (16.5 to 17.2 MBtu dt -1 ) Energy returned on energy invested (EROEI)  Can be stored on the stump until harvest  Harvest throughout the year  Minimal fertilization  Cellulose 2 to 55 Extended haul distances  Willow 13 Poplar 12 Used in crop rotations to improve soil tilth  Sugar Cane 8 Elevated rates of soil carbon storage  Switchgrass 5.4 Soybean 2.5 Superior genotypes replace existing clones  Corn 1.34 Sources: 1.) http://ngm.nationalgeographic.com/2007/10/biofuels/biofuels-interactive. 2.) Schmer et al. 2008. Net energy of cellulosic ethanol from switchgrass. PNAS 105(2):464-469.

Sustainability Short rotation woody crops are one of the most sustainable sources of biomass, provided we strategically place them in the landscape & use cultural practices that… 1990 Conserve soil & water  1994 Recycle nutrients  Maintain genetic diversity  1996 *Uniformity within *Diversity among *4 ha clone -1 Hall, R.B. 2008. Woody bioenergy systems in the United States. NRS-GTR-P-31.

Long-Range Goal Develop a protocol for identifying suitable testing & deployment sites of poplar energy production systems in the Midwest, USA (& beyond…)

Objectives 1. Identify eligible lands suitable for poplar deployment based on current land use, land ownership, & local soil characteristics 2. Determine temperature-precipitation gradients important to poplar growth 3. Establish sites for field reconnaissance within the suitable lands 4. Assess the validity of the outcomes from 1) & 2) by comparing available databases with field soils data (i.e., QA/QC) 5. Apply a process-based growth model (3-PG) to predict & map poplar productivity within the identified suitable lands 6. Assess the regional sustainability of potential poplar deployment within the eligible lands (current studies) Zalesny, R.S. Jr., et al. 2012. An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. For Ecol Manage (In press)

Objectives 1. Identify eligible lands suitable for poplar deployment based on current land use, land ownership, & local soil characteristics 2. Determine temperature-precipitation gradients important to poplar growth 3. Establish sites for field reconnaissance within the suitable lands 4. Assess the validity of the outcomes from 1) & 2) by comparing available databases with field soils data (i.e., QA/QC) 5. Apply a process-based growth model (3-PG) to predict & map poplar productivity within the identified suitable lands 6. Assess the regional sustainability of potential poplar deployment within the eligible lands (current studies) Zalesny, R.S. Jr., et al. 2012. An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. For Ecol Manage (In press)

Map Development Constraints Considered Land cover class  Land ownership  Available water storage capacity  Water deficit (P – PET)  Soil texture  Precipitation / temperature  Flood frequency  Depth to bedrock  Patch size  Zalesny, R.S. Jr., et al. 2012. An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. For Ecol Manage (In press)

Map Development Final Constraints CONSTRAINTS DEFINITION OF CONSTRAINTS USED National Land Cover Grassland/Herbaceous, Pasture Hay, Cultivated Crops Dataset (NLCD 2001) GAP Stewardship 2008 Federal, Tribal, State, County (excluded) (Land Ownership) Available Water Storage ≥ 7 cm (assuming 0 to 50 cm depth, 0.15 fraction Capacity (SSURGO) available water) Precipitation – Potential PPET for the months of April and May combined Evapotranspiration (PPET) Soil Texture (SSURGO) Clay Loam, Coarse Sandy Loam, Coarse Silty, Fine Sandy Loam, Gravelly Loam, Gravelly Sandy Loam, Loam, Loamy Coarse Sand, Loamy Sand, Mixed, Sandy Clay Loam, Sandy Loam, Sandy Over Loam, Silt Loam, Silty, Silty Clay Loam, Very Fine Sandy Loam

Eligible Lands  373,630 ha MN = 249,990 ha WI = 123,641 ha  30.8% of study area  Land cover 79.1% cultivated crops 17.8% pasture/hay 3.1% grassland

Field Reconnaissance 143 sites  MN = 84 WI = 59 Most slopes 5% or less  Acceptable drainage  MN = 70% WI = 98% Acceptable erosion  MN = 81% WI = 85% Negligible stoniness  MN WI Corn 19% 49% Alfalfa 8% 17% Soybean 13% 19% Poplar 40% 8% Other 20% 7%

Poplar Productivity Within Eligible Lands Zalesny, R.S. Jr., et al. 2012. An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. For Ecol Manage (In press)

Poplar Productivity Within Eligible Lands Zalesny, R.S. Jr., et al. 2012. An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. For Ecol Manage (In press)

Poplar Productivity Within Eligible Lands Zalesny, R.S. Jr., et al. 2012. An approach for siting poplar energy production systems to increase productivity and associated ecosystem services. For Ecol Manage (In press)

Integrated Studies: Regional Sustainability Enterprise Budgets Landowner Preferences

Integrated Studies: Regional Sustainability Poplar Carbon Study Field Locations Warren Enterprise Budgets Bemidji Ulen Escanaba Landowner Preferences Milaca Belgrade Rhinelander Granite Falls Mondovi Waseca Lamberton Fairmont Key: Arlington Carbon Implications 10-yr-old plantations ( × 4) Sutherland Kanawha Lancaster Design: 10 clones × 4 trees/clone = 40 trees/site Clones: C916000, C916400, C918001, DN34 (aka Eugenei), NC13563, NC13624, NC13649, NC14018, NM2, NM6 Ames 20-yr-old plantations ( × 11) Design: 2 clones × 4 trees/clone = 8 trees/site Clones: DN34 (aka Eugenei); DN182 (aka Raverdeau) Coppice plantations ( × 2) Design: 1 clone × 4 trees/clone = 4 trees/site Clone: Crandon Soil carbon sequestration & greenhouse gas emissions  Aboveground carbon stocks  Biochemical conversion to liquid fuels 