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Economic and Environmental Analysis of Tree Crops on Marginal Lands in Florida

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PAGE 1

ECONOMIC AND ENVIRONMENTA L ANALYSIS OF TREE CROPS ON MARGINAL LANDS IN FLORIDA By MATTHEW HARVEY LANGHOLTZ A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Matthew Langholtz

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iii ACKNOWLEDGMENTS I am grateful to my committee chair, Dr Donald L. Rockwood, for his academic guidance and support, as well as Drs. Janaki R.R. Alavalapati, Douglas R. Carter, Alex Green, and Dr. P.K. Ramachandran Nair, for their tenacious work ethics and lifelong dedications to forestry and sust ainable land-use systems. I particularly thank Dr. Nair for his assistance with my masters degree and assistance with acq uiring funding for my Ph.D. program. This research was made possible by direct and indirect support from Woodward and Curran on behalf of Orlando/Orange Count y, Florida, Iluka Resources, Inc., the Florida Institute for Phosphate Research, and the Alumni Fellowship from the University of Florida College of Agri cultural and Life Sciences. I thank my wife Maribel who has sacrificed for me to pursue this degree. I thank my family for their support, and particularly my sister Gabrielle for her assistance with the English language. Special thanks go to a long history of phytoremediation lab crews, Jared Mathey, Paul Proctor, Mark Torok, Richard Cardel lino, Mauricio Arias, Geoff Filshe, Chris Cosby, Erin Maehr, Brian Becker, Bijay Tamang, and Luis Achugar.

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iv TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES............................................................................................................vii LIST OF FIGURES.............................................................................................................x ABBREVIATIONS AND ACRONYMS.........................................................................xii ABSTRACT.....................................................................................................................xi ii CHAPTER 1 INTRODUCTION........................................................................................................1 Background...................................................................................................................1 Reclaimed Water...................................................................................................1 Phosphate Mined Lands........................................................................................2 Titanium Mined Lands..........................................................................................3 Objectives..................................................................................................................... 4 Literature Review.........................................................................................................4 Environmental Impacts of SRWC Production......................................................4 Carbon sequestration......................................................................................4 Phytoremediation and reclamation.................................................................6 Slash Pine Productivity on Mined Lands..............................................................7 Policy.....................................................................................................................7 Economics.............................................................................................................8 Forest Financial Analysis....................................................................................11 Procedures...................................................................................................................12 The Study Areas and Scope.................................................................................12 Methodology........................................................................................................13 Optimization of non-coppicing species........................................................13 Optimization of coppicing species...............................................................14 Valuation of the non-timber benefits...........................................................16 Optimization of non-coppicing with inclusion of the non-timber benefits......................................................................................................17 Optimization of coppicing species with inclusion of non-timber benefits......................................................................................................18

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v 2 EFFECT OF DENDROREMEDIATION INCENTIVES ON THE PROFITABILITY OF SHORTROTATION WOODY CROPPING OF Eucalyptus grandis .....................................................................................................20 Introduction.................................................................................................................20 Methodology...............................................................................................................23 Optimization of Coppicing Species.....................................................................23 Optimization of Coppicing Species with Inclusion of the Dendroremediation Service..............................................................................................................24 Model inputs........................................................................................................26 Results and Discussion........................................................................................30 Sensitivity Analysis of Dendroremedia tion Incentive and Interest Rate....................35 Conclusions.................................................................................................................38 Future Research..........................................................................................................39 3 AN ECONOMIC ANALYSIS OF Eucalyptus SPP. AS SHORT-ROTATION WOODY CROPS ON CLAY SETTLING AREAS IN POLK COUNTY, FLORIDA...................................................................................................................41 Introduction.................................................................................................................41 Methodology...............................................................................................................43 Model Inputs...............................................................................................................48 Growth Function..................................................................................................48 Carbon Values.....................................................................................................51 Market Assessment..............................................................................................52 The established market: mulch.....................................................................52 Mulchwood price..........................................................................................53 Mulchwood quantity....................................................................................55 Potential market: biomass fuels....................................................................56 Operational Costs................................................................................................59 Additional Non-Timber Benefits.........................................................................59 Below-ground C sequestration.....................................................................59 Reclamation incentives................................................................................61 Summary of Model Inputs and Assumptions......................................................62 Results and Sensitivity Analysis.................................................................................62 Conclusions.................................................................................................................69 Future Research..........................................................................................................71 4 ECONOMICS OF SLASH PINE CULTU RE ON TITANIUM MINED LANDS IN NORTH CENTRAL FLORIDA............................................................................73 Introduction.................................................................................................................73 Methodology...............................................................................................................75 Economic Model.................................................................................................75 Growth and Yield Model.....................................................................................76 Market Assessment..............................................................................................80 Silvicultural Alternatives.....................................................................................82

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vi Cost Assumptions................................................................................................93 Simulations..........................................................................................................95 Results........................................................................................................................ .98 Established Stands...............................................................................................98 Soil Amendments on Young Plantations...........................................................101 Sensitivity Analysis...........................................................................................103 Conclusions...............................................................................................................105 Future Research........................................................................................................106 5 CONCLUSIONS......................................................................................................108 Summary of Results..................................................................................................108 SRWC Production with Reclaimed Water........................................................108 SRWC Production on Cl ay Settling Areas........................................................109 Slash Pine Production on Titanium Mined Lands.............................................110 Overall Policy Implications......................................................................................110 Future Research........................................................................................................118 APPENDIX A BIOMASS CO NVERSIONS....................................................................................121 B CHAPTER 4 LEV OUTPUTS.................................................................................122 C PHOTOS...................................................................................................................127 LIST OF REFERENCES.................................................................................................130 BIOGRAPHICAL SKETCH...........................................................................................141

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vii LIST OF TABLES Table page 1-1 Farmgate (production and harvest) costs for SRWCs and herbaceous biomass crops in Florida and other regions..............................................................................9 1-2 Summary of study sites............................................................................................13 2-1 Net returns and optim um stage lengths for a Eucalyptus grandis short-rotation woody crop system irrigated with reclaimed water.................................................31 2-2 Optimum LEVs, optimum stages per cycle, and optimum stage lengths for a range of dendroremediation values fo r Eucalyptus grandis irrigated with reclaimed water in central Florida............................................................................32 2-3 Marginal increases in net returns ($ ha-1) per dollar of N dendroremediation incentive for Eucalyptus grandis in central Florida.................................................35 2-4 Changes in profit ($ ha-1) for Eucalyptus grandis in central Florida as interest rate increases from 4% to 5% and 5% to 6%...........................................................37 2-5 Estimated parameters and descriptors used in Eq. (2-9) of Eucalyptus grandis irrigated with reclaimed wa ter in central Florida (R2>0.99)....................................37 3-1 Number of observations, average DB H (cm), height (m) and inside-bark dry above-ground biomass yields of EG and EA...........................................................50 3-2 Mulch markets for Eucalyptus produced in Polk County........................................54 3-3 Estimated equivalent stumpage valu es for high and low transportation cost scenarios. All tons are green weight........................................................................55 3-4 Potential bioenergy markets for Eucalyptus produced in Polk County...................58 3-5 LEV, optimum number of stages and optimum stage length for each stage by C benefit scenario and biomass price...........................................................................63 3-6 LEV ($ ha-1), optimum stage lengths, margin al benefit, and estimated belowground benefit ($ ha-1) by C sequestration incentive ($ Mg-1).................................64 3-7 Change in LEV ($ ha-1) per 1% increase in interest rate..........................................65

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viii 3-8 Optimum harvest scheduling (stage lengths and number of stages per cycle) at interest rates of 4%, 7%, and 10%...........................................................................66 3-9 LEVs and marginal impact on LEVs by changes in site preparation, planting and weeding costs.....................................................................................................67 3-10 Estimated discounted value of below-gr ound C benefits by C price, interest rate and growth function.................................................................................................68 4-1 Merchantable standards of DBH ( ddbh) and top diameter outside bark ( dt).............79 4-2 Treatments included in the SRWC -84 and SRWC-84-2001 studies........................83 4-3 SRWC-84 age 5 and SRWC-84-2001 ag e 4 mined (SM) and unmined (UM) average heights, standard deviation and Duncan grouping......................................84 4-4 SRWC-84 age 5 and SRWC-84-2001 ag e 4 mined (SM) and unmined (UM) average survival (%) and standa rd deviation by treatment......................................85 4-5 Average of top Duncan group su rvival of SRWC-84 and SRWC-84-2001...............92 4-6 2004 Average pine plantation establis hment costs for the southeast U.S................93 4-7 Cost scenarios based on Smidt et. al (2005)............................................................94 4-8 Land type, measurement age, meas urement date, and number of 63 1/50th ha plots used in the analysis of established stands........................................................95 4-9 Number of plots, average SI, SI stan dard deviation, average LEV, and standard deviation of LEV on mined and unmined lands.......................................................99 4-10 SI (base age 25), survival, age of survival, cost of initial rotation, cost of subsequent rotations, LEV and optimum rotation..................................................101 4-11 Volume, cost, LEV and IRR of co mparative mined land simulations...................102 4-12 Minimum growth response in SI needed to meet or exceed a base scenarios LEV........................................................................................................................104 4-13 Maximum initial and subsequent rotati on establishment costs tolerated to meet or exceed a base scenarios LEV............................................................................104 5-1 Summary of LEVs ($ ha-1) of EA production on CSAs.........................................110 5-2 Delivered costs of biomass for fuel costs of electricity, and resulting divergence from costs of conventional electricity..................................................112 B-1 Titanium mined plot measurement date age, volume, SI and stand density.........123

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ix B-2 Titanium mined LEV and optimum rotation age...................................................124 B-3 Unmined plot measurement date, ag e, volume, SI and stand density....................125 B-4 Titanium mined LEV and optimum rotation age...................................................126

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x LIST OF FIGURES Figure page 2-1. Estimated high and low growth and yield functions for Eucalyptus grandis at Winter Garden, Florida............................................................................................28 2-2. Net returns ($ ha-1) as a function of dendroremediation incentive ..........................36 3-1. Inside bark yields (dry Mg ha-1) of EA and EG on a CSA near Lakeland, Florida for 5 treatments............................................................................................49 3-2. Observed and predicted inside bark stem yields of EA...........................................51 3-3. Location and potential consumption of buyers of woody biomass from Polk County......................................................................................................................56 4-1. Mean heights estimated by stem anal ysis from stands on 25 reclaimed and 25 unmined sites (Mathey, 2001)..................................................................................74 4-2. Mean diameter inside bark (DIB) esti mated by stem analysis from stands on 25 reclaimed and 25 unmined sites (Mathey, 2001).....................................................74 4-3. Representative pulp, chip-and-saw, saw timber, and total outside bark volumes (m3 ha-1)....................................................................................................................80 4-4. South-wide pine stumpage prices quarterly averages from 1995-2005 (Timber Mart South 2005).....................................................................................................81 4-5. Average heights (m) by age (year) a nd treatment, SRWC-84 mined site................85 4-6. Average survival (%) by age (year) and treatment, SRWC-84 mined site..............86 4-7. Average heights (m) by age (year) a nd treatment, SRWC-84 unmined site............86 4-8. Average survival (%) by age (year) a nd treatment, SRWC-84 unmined site..........87 4-9. Average heights (m) by age (year) a nd treatment, SRWC-84-2001 mined site......88 4-10. Average survival by age (year) a nd treatment, SRWC-84-2001 mined site............88 4-11 Average heights (m) by age (year) and treatment, SRWC-84-2001 unmined site........................................................................................................................... .89

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xi 4-12. Average survival by age (year) and treatment, SRWC-84-2001 unmined site........89 4-13. Height (m) by treatment of SRWC-8 4-2001 (age 4) and SRWC-84 (age 5), satellite mined (SM) and unmined (UM).................................................................90 4-14. Average heights (m) of subsoiled an d not subsoiled treatments on SRWC-842001 mined land.......................................................................................................91 4-15. Average survival (%) of subsoiled an d not subsoiled treatments on SRWC-842001 mined land.......................................................................................................91 4-16. Total predicted above-g round inside-bark volume (m3 ha-1) for simulations 1-9.........................................................................................................98 4-17. LEV ($ ha-1) by SI (m, base age 25) for 34 and 29 stands (Table 4-8) on mined and unmined lands, respectively............................................................................100 4-18. LEV ($ acre-1) by SI (ft, base age 25) for 34 a nd 29 stands (Table 4-8) on mined and unmined lands, respectively............................................................................100 5-1. Additional cost of elec tricity (COE) (cents kWh-1) over COE from coal, for production on CSAs and under dendroremediation (WC2)...................................113 5-2. Additional delivered cost of fuel (COF) ($ dry Mg-1) over COF coal equivalent, for production on CSAs and under dendroremediation (WC2).............................113 5-3. Estimated value of CO2 mitigation service, dendroremediation service, additional COF and COF coal equivalent ($ dry Mg-1), for production on CSAs and under dendroremediation (WC2).....................................................................115 5-4. External costs for 14 generati on technologies (Roth & Ambs, 2004b).................116 C-1. Rapid infiltration basins n ear Winter Garden, Florida...........................................127 C-2. 1.75-year-old Eucalyptus grandis irriga ted with reclaimed wastewater at the Water Conserv II facility near Winter Garden, Florida..........................................127 C-3. Cogongrass ( Imperata cylindrica ) following herbicide treatment on a clay settling area near Lakeland, Florida.......................................................................128 C-4. A three-year-old Eucalyptus grandis stand on a clay settling area near Lakeland, Florida...................................................................................................128 C-5. Dredge mining by Iluka Resources, Inc ., near Green Cove Springs, Florida........129 C-6. Eight-year-old slash pine stands on mined (left) and unmined lands....................129

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xii ABBREVIATIONS AND ACRONYMS C carbon COE cost of electricity COF cost of fuel CSA clay settling area DBH diameter at breast height DFSS dedicated feedstock supply system DIB diameter inside bark EA Eucalyptus amplifolia EG Eucalyptus grandis FASOM Forest and Agricultur al Sector Optimization Model FIPR Florida Institute of Phosphate Research FONC first order necessary condition ha hectare IPCC Intergovernmental Panel on Climate Change IRR internal rate of return kWh kilowatt hour LCOE levelized cost of electricity LEV land expectation value LHS left hand side MAI mean annual increment Mg metric ton MSY maximum sustained yield N nitrogen NTB non-timber benefit P Phosphorus REPI Renewable Energy Production Incentive RHS right hand side RIB rapid infiltration basin RPS Renewable Portfolio Standard SI site index SM satellite mined SOC soil organic carbon SRWC short-rotation woody crop TPA trees per acre TPH trees per hectare UM unmined WC2 Water Conserve II WUI wildland urban interface

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xiii Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ECONOMIC AND ENVIRONMENTA L ANALYSIS OF TREE CROPS ON MARGINAL LANDS IN FLORIDA By Matthew Langholtz December 2005 Chair: Donald L. Rockwood Major Department: Forest Resources and Conservation Tree crops can be used to remove contamin ants from reclaimed wastewater, restore ecological functions of phosphate and titaniu m mined lands and to provide renewable energy in Florida. The economic feasibility of these potential tr ee crop systems, the value of environmental services they provi de and opportunities to make up the current difference between minimum feasible and curr ent market prices are investigated. Profitability measured as land expectation value (LEV) of 128 scenarios of Eucalyptus grandis cropping irrigated with reclaimed wastewater ranged from -$2,343 to +$2,762 ha-1 and was greatly reduced by high interest rates, high irrigation costs, and low yields. Each $1 kg-1 N increment in a dendroremediation incentive increases profit by $223-$376 ha-1, depending on interest rate and site productivity. Optimum management requires harvests every 2.6 to 4.0 years and re planting after two or three harvests, though the optimum number of stages per cycle w ould increase with improved coppice growth.

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xiv LEVs of Eucalyptus amplifolia cropping on phosphate-mined lands in central Florida ranged from $762 to $6,507 ha-1 assuming interest rates of 10% and 4%, respectively, establis hment costs of $1,800 ha-1, planting costs of $1,200 ha-1, high yields, and a stumpage price of $20 dry Mg-1, excluding CO2 mitigation incentives. Incorporating CO2 mitigation incentives increased LEV, particularly when incentives recognize the CO2 emissions reduced by biofuels use. Optimum management necessitates harvests every 2.5 to 3.5 years a nd replanting after two or five harvests. Average LEVs of slash pine ( Pinus elliottii ) stands establishe d on titanium mined lands varied widely with produc tivity, but on average were pr ofitable and similar to those of unmined lands. Optimum management is comparable to that of conventional slash pine culture in northeast Florida. Early-ro tation responses to soil amendments suggest that growth and survival can be improve d by fertilizer and subsoil treatments, respectively. Plantation establishment costs including soil amendment as high as $423 to $878 ha-1 ($171 to $355 acre-1) are economically viable depe nding on growth response.

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1 CHAPTER 1 INTRODUCTION Background Landowners in Florida are in terested in profitable landuse options. Short-rotation woody crops (SRWC) in Florida have competitive growth rates and production costs compared to other states (Rahmani et al. 1997) and can provide multiple environmental benefits. Currently, th ree opportunities in Florida have the potential to contribute to forest production: 1) reclaimed water from the Water Conserv II (WC2) facility in Orlando, 2) clay settling areas (CSAs) on former phosphate mined lands, such as the Kent site in Lakeland, and 3) reclaimed titanium mine d lands such as those associated with the Iluka mining operation near Green Cove Springs. The financ ial feasibility of producing Eucalyptus spp. or slash pine ( Pinus elliottii ) on these marginalized lands and irrigated with reclaimed water requires further research. Reclaimed Water The population of Florida is expected to n early double in the ne xt 30 years, from 15.9 million permanent residents in 2000 to a projected 30.1 million in 2030 (Bureau of Economic and Business Research, 2001), which will result in an increase in both water consumption and wastewater production. As Florida faces increasing pressure on water resources, wastewater presents both a challenge of disposal and an opportunity for reuse. The WC2 facility near Winter Garden is an innovative water reuse program that has achieved international recognition for its wa ter conservation and reuse methods. Waste water from the Orlando area, following treatmen t, is pumped to ornamental nurseries and

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2 to 8,600 acres (3,480 ha) of citrus groves on sandhills of Orange and Lake Counties (State of Florida, 2003). In light of increasing competition from overseas production, particularly Brazil, future trends of citrus production in Flor ida are uncertain. The preliminary $760 million on-tree value of all Florida citrus for the 2001 season is the lowest since the 1985-86 season, while production was also down 7% from 1999-2000 (Florida Agricultural Statistics Service, 2003). SRWC production using reclaimed water could provide a crop alternative for citrus producers and other landowners on sandhill soils in Florida. Furthermore, SRWCs can be used to extr act nitrogen, phosphorous and chlorides from the reclaimed water prior to its infiltration to the aquifers. Research at WC2 between 1998 and 2001 suggests that E. grandis and cottonwood ( Populus deltoides ) can yield 13.1 and 10.9 Mg ha-1 year-1, respectively, and have great potential to extract NO3-N and NH4-N from reclaimed water (Rockwood et al. 2002a). Phosphate Mined Lands Central Florida produces 75% of the na tion's and 25% of the world's phosphate supply (IMC Phosphates, 2002). This phosphate is used primarily in agriculture, and also in a range of consumer products. In the mi ning process, the surface soil is removed and put aside as overburden a nd clays are separated from phosphates and then sent to CSAs. There are about 162,000 hectares (400,0 00 acres) of phosphate-mined lands in Florida (Segrest, 2003). The Lakeland ar ea contains over 38,000 hectares (95,000 acres) of CSA and/or overburden soil, as a result of phosphate mi ning. These CSAs, classified as clayey Haplaquents, can be a valuable resource for biomass production (Mislevy et al. 1989).

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3 Reclamation and reuse of mined landscapes are major foci of the Florida Institute of Phosphate Research (FIPR), an independe nt State research agency that has spent almost $11 million on research related to this topic. One project documented growth of cottonwood, E. grandis, and E. amplifolia on a CSA in Lakeland, FL. After 15 months cottonwood reached average heights of 4.7 and 6.1 m on double row planting (8,400 trees ha-1) and single row planting (4,200 trees ha-1) configurations, respectively; E. grandis after 9 months reached average heights of 3.8 and 2.8 m on double row planting and single row planting, respectively; E. amplifolia after 9 months reached average heights of 2.3 and 3.1 m on double row and single ro w planting, respectively (Rockwood et al. 2002b). These initial results suggest that cottonwood, E. grandis, and E. amplifolia are suitable for conditions on CSAs in central Fl orida. FIPR continues to fund research related to SRWC production on CSAs, undersco ring the demand for such research. CSAs, titanium mined lands, and other marginal lands would have a low opportunity cost, as most of these owners hips are idle. Tree crop production could provide a valuable land-use alternative to these areas. Titanium Mined Lands Near Green Cove Springs, Iluka Resources Inc. has been pr oducing titanium minerals and zircon since 1972 using dredge mining and satellite dry mining. These reclaimed mines are used extensively for slash pine plantations to produce timber products, reclaim soil productivity, and re-establish wildlife ha bitat. Following research of slash pine productivity and culture on mined lands by Mathey (2001) and Proctor (2002), an economic analysis of silvicultu ral options on reclaimed titanium mines can help landowners make sound forest management decisions.

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4 Objectives The general objective of this research is to determine the feasibility of tree crops grown on reclaimed mine lands or using reclaimed water in nort hern and central Florida. Specific objectives include the following: 1. Evaluate financial and environmenta l aspects of SRWC production on lands irrigated with reclaimed wastewater. 2. Evaluate financial and environmenta l aspects of SRWC production on CSAs. 3. Determine the financial viability of sl ash pine production on reclaimed titanium mined lands. Literature Review Environmental Impacts of SRWC Production As a production-oriented land-use option, SRWCs have the potential of providing environmental services such as reductions in CO2 emissions, carbon sequestration, and soil stabilization. Carbon sequestration Atmospheric concentrations of CO2 have increased from 280 parts per million (ppm) in the year 1850 to 370 ppm near the end of the 20th century. This increase has been attributed to the use of fossil fuels for energy and has been associated with increased global temperatures. By the year 2100, CO2 concentrations are expected to rise to between 540 and 970 ppm, and temperatur es are expected to increase by 1.4 C to 5.8 C (IPCC, 2001b). If temperatures increase, va rious environmental changes will occur, including rising sea levels, lo ss of coastline, changes in ocean currents, changes in precipitation, and a variety of associated changes in agricultur al production, habitat changes and shifts, and disease distribution (IPCC, 2001a). As with other types of forests, SRWC plantations sequester atmos pheric carbon as they grow, store the carbon

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5 on the site and in the products they produce (until the stand and products are oxidized), and provide alternatives to products that produce CO2 emissions. For these reasons, the production of SRWCs would have impli cations for reducing atmospheric CO2. Models by Heath et al. (1993) and Turner et al. (1995) show that U.S. forests will continue to sequester atmospheric car bon for the next 40 years. Barker et al. (1995) evaluate the potential of the U.S. Conserva tion Reserve Program to offset greenhouse gas emissions in the United States through car bon sequestration. Their simulations suggest that intensive afforestation on environmentally sensitive cropland could sequester about 16 Tg C equivalent. In addition to carbon se questration, they also identified the potential reduction of CO2 emissions through the production of biofuels and fuelwood, and identified this possibility as needing research. While the production of SRWCs has various potential environmental benefits, a feasibility analysis must consider potentially negative environmental implications as well. According to a review of research up to 1998, the establishment of SRWC plantations is beneficial to some wildlife species but is de trimental to others, and these impacts need to be taken into consideration in the planning of plantation establishm ent at the landscape level (Tolbert & Wright, 1998). Environmen tal impacts of the establishment of SRWCs during the first year are not unlike those of the producti on of annual crops (Joslin & Schoenholtz, 1997). SRWC plantations are ex pected to improve surface runoff and groundwater quality when compared to annua l crops following the first establishment year (Thornton et al. 1998). The land condition prior to establishment should also be considered, and SRWC establishment ma y be an improvement over post-mining conditions.

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6 In Sweden, Salix is valued for its benefits in promoting wildlife diversity and its capacity to phytoremediate cadmium contam inated soils (Perttu, 1998), as well as its minimal need for herbicides and its contri bution to soil organic matter (Ledin, 1998). The environmental benefits of SRWCs and resi stance to insect pests and weed species are reiterated by Sage (1998). Indeed, Abrahamson et al. (1998) propose that SRWC production systems in New York are ecologica lly and environmentally sustainable and that the limit to production is economic viabil ity. They conclude that environmental and ecological benefits of the system should act as an impetus for developments needed to overcome the economic constraints of the system. Phytoremediation and reclamation Biomass production systems can be associated with phytoremediation objectives, as is being done at a research site at WC2 in Orlando, and an arsenic contaminated site in Archer. Eucalyptus sp. and cottonwood are identified as SRWC candidate species with potential to accumulate nutrients and mitigat e problems associated with urban waste and stormwater runoff (Rockwood et al. 1995b; Pisano, 1998; Moffat et al. 2001). Moffat et al. conclude that approximately 100 m3 ha-1 yr-1 is an ideal applicati on rate of effluents, and their results suggest that sewage sludge should be appl ied to every rotation of the SRWC rather than annually. Co rseuil and Moreno (2001), Watson et al. (1999), Gommers et al. (2000), and Vervaeke et al. (2001) discuss research related to the use of willow in phytoremediation. Thompson et al. (1998) describe changes in poplar respiration following exposure to 2, 4,6-trinitrotoluene, and Heinonsalo et al. (2000) describe the effect of Pinus sylvestris root growth on soil hydrocarbon oxidation. Goor et al. (2001) assess the potential to use willow SR WC systems as a land-use alternative for farmland contaminated by the Chernobyl nuc lear power plant disaster. A study by

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7 Bungart and Huttl (2001) of SRWC systems in post-mine landscapes indicates that SRWC production is an ideal land-use alternative for post-mining landscapes. Slash Pine Productivity on Mined Lands Slash pine growth on mine tailings nort h of Starke was constrained by high bulk density and low organic matter content, as we ll as extremes of soil moisture (Darfus & Fisher, 1984). Slash pine growth on mine tailings can be improved by minimizing variation in topography or adding inexpensiv e manure such as waste humate or sewage sludge. Alternatively, wetter and dryer areas of the tailings could be planted with cypress ( Taxodium spp.) or longleaf pine ( P. palustris ), respectively. Math ey (2001) found site indexes and growth patterns of established slash pine plantations on mined and unmined lands to be similar. Proctor (2002) assessed growth responses to fe rtilizer and subsoiling in young plantations. The effectiveness of r eclamation practices for mined lands may be assessed by the use of growth and yield mode ls for slash pine plantations on mined and unmined sites. Pienaar and Rheney (1995) and Fang and Bailey ( 2001) developed height growth models accounting for intensive silvicul tural treatments in sl ash pine plantations. Policy Some objectives of SRWC production include mitigation of CO2 emissions, improvements in air and water quality, empl oyment generation, and other societal benefits. Policies that seek to reduce or internalize external environmental costs to society at large have great implications for SRWC production. Hohe nstein and Wright (1994), Wright and Hughes (1993), and Graham et al. (1992) describe the potential for SRWCs to offset CO2 emissions in the U.S. Tuskan (1998) identifies research needs related to SRWCs in the U.S., including longterm use of fertiliz ers and irrigation and development of improved harvesting methods. Ehrenshaft and Wri ght (1991) describe a

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8 SRWC database management system, a nd modeled projections by Fischer and Schrattenholzer (2001) suggest that bioenergy could s upply 15% of global primary energy by the year 2050. Economics The economic feasibility of SRWC production has been examined. Turhollow (1994) presented cost estimates for 1989 and 2010 for supplying biomass via five cropping strategies in five regions of the U.S. One of these strategies for the Midwest and South used SRWCs. Turhollow proposed that energy crops must sell at between $43 and $60 dry Mg-1 in 1989 and $30 and $43 dry Mg-1 in 2010 to be economically viable. Rahmani et al. (1997) described production co sts of SRWCs in Florida as consisting of a) farmgate costs (production and harvest costs), and b) transportation costs. They estimated Florida eucalyptus farmgate costs at $32.00-$39.00 dry Mg-1 and yields at 20-31 dry Mg ha-1 yr-1. These cost estimates were gene rated using levelized costs and the AGSYS Budget Generator, which calculates inputs costs such as labor, machinery, fertilizer, etc., based on a database of costs of machinery and materials. The method of cost estimation did not affect the range of estimated farmgate costs. SRWC farmgate cost estimates in Florida ranging from $16-47 dry Mg-1 compare favorably with herbaceous biomass crops in Florida and are lik ely to be lower than other regions of the U.S. (Table 1-1). These numbers show that Florida has a competitive advantage in the production of SRWCs.

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9 Table 1-1. Farmgate (production and harvest) costs for SRWCs and herbaceous biomass crops in Florida and other regions. Crop $/Dry Mg $/Dry ton Florida, SRWCs: Leucaena16-47 15-43 Eucalyptus spp.32-39 29-36 Florida, herbaceous: Sugarcane23-25 21-32 Elephantgrass24-32 22-29 Other Regions: Poplar33-132 39-120 Willow30-110 27-100 TVA Estimatesa 32-69 29-63 Source: Rahmani et al. 1997; a Adapted from Downing and Graham (1996). Transportation costs of SRWC biomass in Florida have been estimated at $3.10 dry Mg-1 (Rahmani et al. 1997), assuming an average dist ance of 32 km (20 miles) and moisture content of 15%. This transportation cost was lower than estimates for herbaceous biomass crops, which ranged from $7.85-$12.28 dry Mg-1, attributable to moisture contents ranging from 20-75%. Turhollow et al. (1996) estimated transportation costs for herbaceous biomass crops ranging from $8.37-$13.95 Mg-1 with dry matter contents of 50% and 30%, respectiv ely. Transportation estimates for Florida are competitive with th ese out-of-state costs. Projected prices and quantit ies of SRWCs are functions of the amount and quality of land, expected yields, pr oduction costs, and profit poten tial. Downing and Graham (1996) described potential SRWC-produc tion scenarios in the Tennessee Valley Authority Region, which consists of parts of Tennessee, Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi. They defined farmgate costs for SRWCs, including sweetgum ( Liquidambar styraciflua ), sycamore ( Platanus occidentalis ), and poplar ( Populus spp.), under a variety of soiland land-value categories. Under projected yields ranging from 5.4-9.7 dry Mg ha-1 year-1, farmgate costs ranged from $32-51 on

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10 former cropland, and from $48-69 dry Mg-1 on former pastureland; SRWC production costs ranged from $31.90-69.30 dry Mg-1. A farmgate price ranging from $44-55 dry Mg-1 would be needed to ensure profits sim ilar to current land uses. Increasing SRWC biomass yields 25% decreased farmgate prices 20%. On a national level, SRWCs will probably help meet growing demands for pulpwood production. Currently, SRWCs are produced on fewer than 80,000 ha (200,000 acres) in the U.S., with most of this production in the Paci fic Northwest, though a much greater area has potenti al for SRWC production. Alig et al. (2000) studied the economic potential of SRWCs on agricultural la nd in the U.S. They estimate that 0.6-1.1 million ha (1.5 to 2.8 million acres) woul d generate about 10 to 16 Tg year-1, equivalent to about 40% of current U.S. hardwood pulpw ood production. They used the Forest and Agricultural Sector Optimization Model (F ASOM), an intertemporal, price-endogenous model, linking the U.S. forest and agricultural sectors. The SRWC was assumed to be hybrid poplar, using data from the U.S. Department of Energy Oak Ridge National Laboratory. Most of the current U.S. cropland was determined to be suitable for SRWC poplar production: 0.5, 13.7, 34.0, 5.7, and 35.1 million ha (1.2, 33.9, 84.0, 14.0, and 86.8 million acres) in the Pacific Northwest, Lake States, corn belt, Southeast, and South Central regions, respectively (Walsh et al. 1998). Under the FASOM projections, SRWC plantation area is 0.9, 1.1, 0.6, and 1.1 million ha (2.1, 2.8, 1.5, and 2.6 million acres) in the first, second, third, and fifth [ sic ] decades, respectively. Even at peak pr oduction, SRWCs are projected to occupy less than 1% of cultivated U.S. cropland area. This modeling was done to estimate the potential supply of wood fiber to the pulp-an d-paper sector from SRWCs. However, the

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11 authors also mention the potential for SR WCs to produce non-pulp products such as veneer. This and fuel for bioenergy are alte rnative products that ha ve the potential for increasing the demand for SRWCs. The results from Alig et al. (2000) indicate that the contributions of hardwood biomass could be re latively high when compared to the area of land involved. Interestingly, these increased yields could reduce U.S. forest plantation area and allow more U.S. forestland to be converted into agricultural production. Forest Financial Analysis Methods for determining the optimum forest harvest cycle length, or rotation age, have improved over the past centu ry as they have progressively internalized an increasing number of factors. The most basic of forest management objectives has been to produce as much forest product as possible by ma ximizing mean annual increment (MAI). However, this method fails to account for th e establishment costs and the time value of money, i.e., discount rate. The Fisher solution for forest optimization, while an improvement from maximizing MAI, failed to capture the opportunity costs associated with occupying the land (Rideout & Hessei n, 1997). This limitation was addressed by the Faustmann model by projecting an infinite number of rotations in determining LEV that is included as a land rent cost in determining the optim um rotation age (Chang, 1984; Chang, 1998; Chang, 2001). Hartman (1976) modified the Faustmann solution to include the value of environmental services in determining LEV and optimum rotation age. While Medema and Lyon (1985) adapted the Faustmann so lution to find LEV and optimum harvesting cycles for coppicing tree species, Smart and Bu rgess (2000) incorporat ed the valuation of environmental services in determining th e LEV and optimum ro tation age of SRWC willow production systems.

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12 Many environmental services that forest s provide are non-market services or externalities for which timber producers have historically not been compensated. The internalization of these market externalitie s could provide incentiv es for landowners to manage their forests for the pr ovision of environmental servi ces. Environmental services have been categorized as intri nsic vs. instrumental (Farber et al. 2002) and as providing regulation, habita t, production, and information values (de Groot et al. 2002). The innovative forestry systems described in this research (e.g., mine reclamation and phytoremediation) are designed specifically for the instrumentally oriented environmental services such as regulation of soil and ai r quality and production of wood products or energy. The compensation for carbon sequestra tion services has been shown to lengthen the economically optimum rotation age (Pla ntinga & Birdsey, 1994; Stainback, 2002). Research regarding the impact of comp ensation for reduced carbon emissions on optimum rotation age (achieved through fossil fuel displacement, as opposed to compensation for increased carbon storage in the biomass or soil) is lacking. Procedures The Study Areas and Scope This research is relevant to Florida sandhi ll sites irrigated with reclaimed water, phosphate mined lands of central Florida, and titanium-mined lands of northeast Florida. Financial analyses were done on forestry sc enarios represented by the following three sites: 1. Water Conserv II study site: WC2 near Winter Garden receives secondary treated effluent from the City of Orlando Water Reclamation Facility and Orange County South Regional Reclamation Facility. The water contains mean NO3-N and Clconcentrations of 6.92 mg L-1 and 86 mg L-1, respectively, and is currently supplied to approximately 70 agricultu ral customers free of char ge irrigating 4,450 ha of citrus plantations. A 2.8-hectare study site at the WC2 facility is characteristic of

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13 sandhill Entisol soils where SRWCs c ould be produced using reclaimed wastewater. 2. Kent study site: a 57-hectare CSA in Lakeland is th e site for research related to reclamation of phosphate mined lands. As an anthropogenic soil (see Phosphate Mined Lands), the soil is a clayey Haplaquent (Mislevy et al. 1989), with a pH of 7.0, with little organic matter. 3. Iluka study site: Titanium mined lands are repres ented by studies at Iluka mining company at Green Cove Springs, 55 km sout h of Jacksonville. This mine has been producing titanium minerals and zircon since 1972. The operations include a dredge mine, a satellite dry mine and asso ciated mineral separation plants. Land in the area is used extensively for slash pine plantations. The Iluka site is situated on spodosol soils. Data sets utiltized include SRWC-72 fr om Winter Garden (1998-2003), SRWC-90 and plots in operational areas from Lake land (20012005), and SRWC-84 and SRWC84-2001 from Green Cove Spri ngs (1999-2005) (Table 1-2). Table 1-2. Summar y of study sites. Site Study Situation Species Dates Covered WC2, Winter Garden SRWC-72 Reclaimed Water EG, CW March 1998 to May 2003 Kent, Lakeland SRWC-90 CSA EA EG May 2001 to January 2005 Iluka, Green Cove Springs SRWC-84 SRWC-84-2000 Reclaimed Titanium Mined vs. Unmined Plots SP November 1999 to January 2005 SP=Slash pine, EG= Eucalyptus grandis EA= Eucalyptus amplifolia, CW=cottonwood. Methodology Optimization of non-coppicing species A financial feasibility analysis was applied through the use of the Faustmann solution to determine LEV and optimum ro tation ages and coppice stage lengths as described above (Forest Financial Analysis ). The basic Faustmann solution for a noncoppicing even-aged stand is defined as () 1rt rtVteC LEV e (1-1)

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14 where LEV is the land expectation value (i.e ., the land value as defined by a forestry scenario repeated in perpetuity), V(t) is the value of the stand at time t (i.e., price times volume), C is cost of stand establishment at the beginning of the rotation, r is the interest rate, and t is the optimum rotation ag e. Optimum rotation age is determined by taking the derivative of Eq. (1-1), setting it equal to zero, and solving for t The first order necessary condition (FONC) for the Faustmann solution is found by taking the derivative of Eq. (1-1) and rea rranging, resulting in *()* '()*()* 1rt rtVtC VtrVtr e (1-2) which is equivalent to '()*()* VtrVtrLEV (1-3) or Vt r VtLEV (1-4) Eq. (1-2) states that the FONC for the op timization of the Faustmann solution is the time t where the marginal benefit in growth represented by the left-hand side (LHS), just equals the opportunity cost of the forest capi tal and the land rent shown in right-hand side (RHS). This can alternativ ely be stated as the time t where the ratio of marginal benefit (growth) to opportunity costs of the forest capital and the land rent just equals the given interest rate r (Eq. (1-4)) This non-coppicing form of the Faustmann solution was used for the analysis of slash pine on titanium-mined sites. Optimization of coppicing species Determining optimum rotations of SRWC coppice systems at the Kent site and WC2 site differed from determining optimum rotations of non-coppicing systems. The optimum age of each coppice harvest must be de termined as well as the optimum time to

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15 replant (i.e., the optimum number of st ages before replanting). Following the terminology used by Smart and Burgess (2000), a coppice stage length describes the period of time between coppi ce harvests, while a coppice cycle length describes the period of time and/or number of coppice stages between replanting of the trees. Medema and Lyon (1985) modified the Faustmann formula (Eq. (1-1)) to solve for multiple coppice stage lengths given a fixed number of coppice stages (n): 1 11 1** 1 *()** 1ss jj jj n j jrtrt n ss s rtVteCe LEV e (1-5) where t0=0 n = the number of coppice stages, s, V(t) = the value as a function of tim e (as a function of growth of stage s ) r = the real interest rate (excluding inflation), t = time, the rotation age in years of stage s and Cs = costs of stage s discounted to the start of the stage. Eq. (1-5) defines LEV as the sum of the benefits of each coppice stage discounted to the present minus the sum of the costs of each coppice stage discounted to the present, for a fixed number of coppice stag es projected in perpetuity. Cs indicates a cost that may be replanting the coppice cycle, or may be a different cost associated with each coppice stage, such as weeding costs. Estimates fo r the prices and costs associated with SRWC production at the Kent site and WC2 site came from the preliminary work done by the Common Purpose Institute at the Kent site. Solving for the optimum stage lengths and cycle lengths of the Kent site and WC2 site was a two-part solution. First, n was fixed and the optimum stage lengths were determined for the fixed number of stages per cycle (i.e., n was consecutively fixed for 14). As with the non-coppicing optimization, the optimum stage length for each individual

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16 stage was the point at which the marginal be nefit of the continued biomass growth over the next unit of time is just equal to the ma rginal opportunity cost of the forgone benefit due to not harvesting, plus the marginal cost of delaying all future coppice stages and cycles. The marginal cost of delaying all future coppice stages and cycles is defined as the LEV of the subsequent coppice stages multiplied by interest rate r Next, the optimum number of stages wa s found by determining at what value of n an additional coppice stage to the coppice cycl e (i.e., LEV of n+1 minus LEV of n) has a marginal benefit less than zero. Stated diffe rently, the optimum number of stages is that which provides the highest LEV. As the number of stages n varies, the optimum stage lengths can also vary. Valuation of the non-timber benefits Two non-timber benefits (NTBs) included in the determination of LEV and optimum rotation ages were: 1. Phytoremediation of wastewater The Southern Regional Water Reclamation Facility of Orange County records costs associated w ith wastewater treatment, and the Florida Department of Environm ental Protection regulates standards for wastewater treatment used to irrigate non-edible crops. Sewage water treatment costs were used to estimate the value of the phytoremediation services at WC2. 2. C sequestration, and offset of CO2 emissions CO2 is a greenhouse gas covered in the trading policy of the Chicago Clim ate Exchange, Inc. and the International Carbon Bank and Exchange. These sources and others were used to provide ranges of potential values of C sequestration and CO2 emission reductions. As described above, several variables influence calculation of LEV and optimum rotation ages of tree crops at titanium mined lands, phosphate mined lands, and lands irrigated with reclaimed wast ewater, including production co sts, yields, product prices, interest rates and values of environmental services. The se nsitivity of LEV and optimum rotation ages to variation of each of these factors was tested.

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17 Optimization of non-coppicing with inclusion of the non-timber benefits To identify the divergence between privat e and social value maximization, the methodology described by Hartma n (1976) was used to incl ude the values of mine reclamation and carbon sequestration in the an alysis of slash pine on titanium mined sites. Hartman (1976) used an integration to account for social amenities associated with standing forest: 0()t rnNTBtNTBnedn (1-6) where NTB(t) was the present value of a stream of NTBs of one rotation quantified by the integration of the discounted value of these benefits according to stand age n The NTB defined by Eq. (1-6) was an additional benefit to be added to the numerator of Eq. (1-1): ** 0 *()*()* 1t rtrt rtNTBnednVteC LEV e (1-7) Deriving the F.O.C. for optimality of the basic Hartman model (Eq. (1-7)) was the same as the derivation of the F.O.C. of the Faustmann model: **** *** 0 2 *()*'()****1 ()*()*** 0 1rtrtrtrt t rnrtrt rtNTBteVteVtree NTBnednVteCre e (1-8) ** *** 0 *()*()* ()*'()**()*** 1t rnrt rtrtrt rt rtNTBnednVteC NTBteVterVtere e (1-9) ()'()*()* NTBtVtrVtrLEV (1-10)

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18 ()'() () NTBtVt r VtLEV (1-11) The NTB remains on the LHS of Eq. (1-10) as an additional marginal benefit, and an additional reason to delay the harvest of the stand. Similarly, the NTB in the numerator of the LHS of Eq. (1-11) serves to delay the time t at which the ratio of benefits to costs equals the interest rate r Optimization of coppicing species wi th inclusion of non-timber benefits To internalize the values of mine land r eclamation, wastewater phytoremediation, carbon sequestration, and reduction in CO2 emissions associated with Eucalyptus spp. culture at the WC2 and Kent sites, the methodology described by Smart and Burgess (2000) was used to include the social amen ity in the analysis. The NTB of a given coppice stage can be defined as 0*t rt d ss dtNTBNTBtedt (1-12) where the NTB of stage s was the definite integral of the flow of the benefits for the duration of the stage, discounted from the time of the harvest of the previous stage. This NTB can be added to Eq. (1-5), the e quation for the LEV of coppicing species: 1 11 1 1** 1 *()*** 1ss jj jj s n j jrtrt n rt sss s rtVteNTBeCe LEV e (1-13) Eq. (1-13) accounts for the value of a NT B derived from keeping the trees in the field, i.e., delaying harvest Conversely, one potential NT B of the SRWC, the reduction in CO2 emissions due to displacement of fo ssil fuels, was associated with the harvest of the trees. This NTB, which takes place at the time of the harvest of the trees, was treated

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19 as an addition to the V(t)s term in Eq. (1-13). This harvest-associated NTB served to decrease optimum stage length, counteracting the increase of optimum stage length due to NTBs derived from standing trees. This interaction was assessed in Chapter 3.

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20 CHAPTER 2 EFFECT OF DENDROREMEDIATION INCE NTIVES ON THE PROFITABILITY OF SHORT-ROTATION WOOD Y CROPPING OF Eucalyptus grandis Introduction Water resources, traditional forest produc ts, fire management, recreation, and wildlife are the five main elements of partic ular concern in the wildland-urban interface (WUI) (Macie & Hermansen, 2003). They note: "Municipal waste faci lities in rapidly developing areas face difficulties with handling and treating increased waste loads...allocating high-quality, abundant flow s of water and managing forest ecosystems at large watershed scales remain key chal lenges." Trees within and surrounding urban centers can provide a variety of envir onmental services including sequestering atmospheric carbon dioxide, enhancing biodive rsity, providing aesthe tics and recreation, and remediating urban wastewater. Nutrients from urban wastewater and other sources cause eutrophication and degradation of aquatic ecosystems. Incr easing concentrations of nitrogen (N) and phosphorus (P) are compromising water quality in Florida. The population of Florida is expected to nearly double in the next 30 y ears, from 15.9 million permanent residents in the year 2000 to a projected 30.1 million in 2030 (Bureau of Economic and Business Research, 2001), which will result in an increase in both water consumption and wastewater production. As Florida faces increasing pr essure on water resources, wastewater presents both a chal lenge of disposal and an opport unity for reuse. Trees can mitigate nutrient loading by extracting N from reclaimed wastewater, thus improving

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21 both water quality and tree growth, and reducing fertilizer inputs. This chapter assesses economic impacts of incentives to use fast-g rowing trees to remove N from reclaimed wastewater discharged from an urban center. Florida Administrative Code Chapter 62610 mandates primary treatment (removal of biosolids), secondary treatment (removal of dissolved elements), and basic disinfection at sewage treatment plants (State of Florida, 2004b). Reclaimed water leaving the Southern Regional Water Reclamation Facility of Orange County, Fl orida contains 7 ppm nitrate N (P. Duel, Orlando Wastewater Treat ment Plant Manager, pers. comm., February 2004). Following treatment, the reclaimed water can be used for irrigation. For example, 132,500 m3 (35 million gallons) day-1 of reclaimed water is pumped 35 km from sewage treatment plants in Orlando and surrounding ar eas to the Water Conserv II, a reclaimed water distribution facility, where 60% of this reclaimed water is applied to 1,700 ha of citrus groves, ornamental nurseries, and golf courses. The remaining 53,000 m3 (14 million gallons) day-1 of reclaimed water is pumped into open sand pits called rapid infiltration basins (RIBs), where the water perc olates into the Florida aquifer (State of Florida, 2003; Rabbani & Munch, 2000). The use of trees to extract contaminants from soil or water is defined as dendroremediation (Rockwood et al. 2004). An example is using tree plantations as a tertiary or finishing treatment to remove N from reclaimed water (Aronsson & Perttu, 2001; Labrecque et al. 1997; Perttu, 1998; Rosenqvist et al. 1997; Pisano, 1998; e.g. Licht & Isebrands, 2005). Dendroremediation is used to address urban waste problems in Sweden and Finland (Ettala, 1987), the United Kingdom (Alker et al. 2002), Canada (Gordon et al. 1989) and Hong Kong (Wong & Lueng, 1989). As an alternative to

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22 releasing reclaimed water in RI Bs, it could be dendroremediat ed by SRWCs. Research at Water Conserv II between 1998 and 2001 suggests that E. grandis irrigated with reclaimed water can yield about 13 dry Mg ha-1 year-1, and extract over 300 kg nitrate nitrogen (N) ha-1 year-1 (Rockwood et al. 2001). As the citrus industry in Orange County is projected to decline, biomass crops present an alternative that can produce wood for rough sawtimber, landscape mulch, or biomass for renewable energy. In addition to dendroremediation of reclaimed water, SRWC production can generate employment (Borsboom et al. 2002) and sequester carbon in aboveand below-ground bioma ss, and soil organic carbon (Eriksson et al. 2002). If the Florida Department of E nvironmental Protectio n mandates renewable portfolio standards, SRWC biomass may be used to displace fossil fuels in electricity generation, providing additional benefits including reduction of CO2, NOx, and SOx emissions and diversification of domestic energy resources (Segrest et al. 1998; Stricker et al. 2000; Roth & Ambs, 2004a). Environmental economists suggest incorpor ating environmental benefits and costs as an effective strategy using market forces work to correct extern alities (Van Kooten & Bulte, 2000). In the face of increased wast ewater treatment sta ndards, producers of wastewater search for cost effective mitigation strategies. On the other hand, tree growers who could use wastewater as an input in their production process may be willing to provide a service by utilizing wastewater The optimum use of wastewater (on a voluntary basis) by a tree grower depends on the marginal cost and marginal productivity of wastewater use. In the face of incentives for using wastewater, however, it is likely

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23 that tree growers could use wastewater at a le vel higher than that of voluntary use. This approach can be a win-win situation fo r wastewater producer s and tree growers. Dendroremediation of municipal wastewater by willow SRWCs in Sweden is economically feasible, despite a growi ng season of six m onths (Rosenqvist et al. 1997), much shorter than that in Flor ida. This chapter, the firs t known study of the impact of dendroremediation incentives on management and profitability of a SRWC system, considers eucalyptus tree crops as a reme diation strategy. An economic optimization model of a SRWC biomass production system that includes an incentive for dendroremediation of N in reclaimed water investigates how this incentive would influence land expectation value (LEV), an attribute of profitability, and optimal management of the associated SRWC production system. Methodology Optimization of Coppicing Species The basic Faustmann formula for a non-c oppicing even-aged stand is defined by Eq. (1-1). The optimum rotation age t* is determined by taking the derivative of Eq. (1-1), setting it equal to 0, and solving for t (Chang, 1984). Eq. (1-5) defines the Faustmann formula as modified by Mede ma and Lyon (1985) for coppicing forest systems used to determine both the optimum duration of each stage as well as the optimum number of stages per cycle. Estimat es for the prices and costs associated with SRWC production at Water Conserv II come from the preliminary work done by Rockwood et al. (2002a). An example of the genera lized Eq. (1-5) fixed for two stages ( n =2) is shown in Eq. (2-1):

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24 1 12 1 12* 1 * 2 *()* ()** 1 1rt pw rtt rt w a r r rttVteCC VteCe C LEVC e e (2-1) where t1 and t2 are the duration of stage one and stage two, respectively, Cp is the cost of planting at the beginning of the cycle, Cw is the cost of weedi ng at the beginning of the stage, Ca is the annual maintenance cost, and Cr is the cost of irrigation establishment at the beginning of the operation. Optimization of Coppicing Species with In clusion of the Dendroremediation Service Hartman (1976) internalized non-timber benefits (NTBs) into the Faustmann formula. The methodology described by Smart an d Burgess (2000) is used to internalize the dendroremediation service associated w ith cultivating coppicing species such as Eucalyptus spp. irrigated with reclaime d water. There are two ways to account for NTBs. If a NTB is achieved at harvest, it is cons idered a stock benefit, while a continuous amenity is calculated as a flow benefit. A dendroremediation service might be pa yable following removal of N from the site with harvest of the biomass. In this scenario, the NTB would be treated as a stock benefit and accounted for much like a timbe r benefit as defined in Eq. (2-2). S ssNTBNTBt (2-2) This NTB value can then be added to Eq. (1-5), the equation for net returns of coppicing species, as shown in Eq. (2-3). An example of Eq. (2-3) fixed for two stages is shown in Eq. (2-4).

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25 1 111 1*** 1 *()*** 1sss jjj jjj n j jrtrtrt n S sss s rtVteNTBeCe LEV e (2-3) 11 1212 1 12** 11 ** 22 *()** ()*** 1 1rtrt S pw rttrtt rt S w a r r rttVteNTBeCC VteNTBeCe C LEVC e e (2-4) Alternatively, if the dendroremediation serv ice were deemed beneficial as the N is continuously accumulated in the growing trees, th e NTB is treated as a flow benefit. The NTB of a given stage calculated as a flow can be defined as 0*t rt F d ss dtNTBNTBtedt (2-5) where the NTB of stage s is the definite integral of the flow of the benefits discounted to the beginning of the stage, for the duration of the stage. This NTB value can then be added to Eq. (1-5) as shown in Eq. (2-6). An example of Eq. (2-6) fixed for two stages is shown in Eq. (2-7). 1 11 1 1** 1 *()*** 1ss jj jj s n j jrtrt n rt F sss s rtVteNTBeCe LEV e (2-6) 1 12 11 12* 11 ** 22 *()* ()*** 1 1rt F pw rtt rtrt F w a r r rttVteNTBCC VteNTBeCe C LEVC e e (2-7) The NTB to be included in the determination of profit and optimum coppice management is the dendroremediation treatmen t of the reclaimed water. The Southern

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26 Regional Water Reclamation Facili ty records costs associated w ith wastewater treatment. These local sewage water treatment costs ar e used to estimate the shadow price of removing additional N from reclaimed water. The exact marginal cost of N removal is unknown. In an economic assessment of dendroremediation of municipal wastew ater by willow in Sweden, Rosenqvist et al. (1997) determine that the costs for removing N and P of conventional treatment are $10$27 kg-1 N (in 1994 USD). At the Southern Re gional Water Reclamation Facility, pretreatment waste contains 25 ppm ammonia N, and post-treatment reclaimed water contains 7 ppm nitrate N, resulting in a decrease of 18 milligrams liter-1 N; the total cost of sewage water treatment is $0.88 per 1,000 gallons (P. Duel, Orlando Wastewater Treatment Plant Manager, pers. comm., Februa ry 2004). Based on these values, the total cost of wastewater treatment is $12.92 kg-1 N, or $1.29 kg-1 N if 10% of the total cost is assumed associated with N removal. This co st estimate could be higher, since removal of additional N becomes increasingly costly, or it could be assumed that the value of removal of additional N should be lower, sin ce the willingness to pay for the removal of additional N has not been substantiated. This analysis assumes a range of values from $0-$3.50 kg-1 N removed. Model inputs E. grandis (EG) was identified as producing more biomass and accumulating more N than Populus deltoides when irrigated with reclaimed water (Rockwood et al. 2002a). While P. deltoides is dormant during the winter mont hs of central Florida (November through February), EG grows year-round, offering continuous dendroremediation services not possible with deciduous species (Rockwood et al. 2001). Though not a native species, EG is non-invasive in Flor ida and has been produced commercially in

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27 south central Florida since the 1970s without spreading (R ockwood, 1996). While other species could be considered in the future, th e scope of this chapter is limited to EG, because to date it has demonstrated the greatest potential for dendroremediation of reclaimed water in central Fl orida. The methodology descri bed here can be applied to other candidate species for which irrigate d growth and yield data are available. Height and DBH data were taken between 0 and 26 months for EG in central Florida at a density of 3,500 trees ha-1 irrigated with 17 mm reclaimed water day-1. Due to a lack of growth and yiel d data for irrigated EG in cen tral Florida beyond 26 months, the above observations are extended using unpublished data for EG from Belle Glade, Florida to estimate high and low growth a nd yield functions (Carter and Rockwood, personal comm., 2004). The trees in Belle Glad e were effectively irrigated because soil moisture was made adequate by controlling wate r in irrigation canals, and the growth rate of the two sites were similar through the first 26 months. This extended data set is used as a baseline for estimating a range of possible yield functions in the sensitivity analysis described below. Nonlinear regression is used to fit the data to an Arrhenius functional form: *c a B abe (2-8) where B (a) is dry Mg stemwood and bark biomass ha-1 as a function of stand age a in years for the first stage, and b and c are the estimated parameters 118.9 and 2.73 for the low growth function and 154.0 and 2.92 for the high growth function, respectively (Figure 2-1). These functions, yielding 16 and 19 dry Mg ha-1 year-1 stem biomass or 27

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28 and 32 dry Mg ha-1 year-1 total above ground biomass1 for the low and high growth functions assuming a rotation age of 3.6 year s, are at the high range of estimated unirrigated Eucayptus spp. production of 20-31 dry Mg ha-1 year-1 described by Rahmani and Hodges et al. (1997). 051015 0 50 100 150 High Growth Estimate Low Growth Estimatestand age (years)yield (dry Mg/ha) Figure 2-1. Estimated high and low growth and yield functions for Eucalyptus grandis at Winter Garden, Florida, irrigated with 17 mm day-1 reclaimed water. Yields of subsequent stages (i.e., yields of the coppice stages following the initial growth) are uncertain. In central Florida, the season in which the trees are harvested influences EG coppice productivity (Webley et al. 1986). Though coppice yields eventually decline, research of P. deltoides suggests that the yiel d of the second stage (i.e., first coppice regrowth) is generally highe r than that of the initial growth stage (Hansen et al. 1983). While growth of the second st age of EG might be higher than that of the first stage due to the benefits derived from the previously established root system, coppice mortality associated with wind throw or weed competition might also increase, reducing yields. Due to a lack of data of c oppice stages for EG irrigated with reclaimed 1 Assumes a factor of 1.7 to convert stem inside bark to total aboveground biomass (Mg ha-1) (Segrest, 2002).

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29 water, in this analysis yields of 80%, 65%, a nd 30% of the growth of the initial stage are assumed for the second, third, and fourth stag es, respectively. These estimates are based on casual observation and are consistent w ith the methodology described by Medema and Lyon (1985). To incorporate a dendroremediation bene fit, the amount of N assimilated in biomass growth is estimated. Analysis of biomass samples of EG irrigated with reclaimed water at Water Conserv II indicates that leaves, stem bark, branches, and stem wood contain 1.39%, 0.28%, 0.27% and 0. 09% nitrate N, respectively (Rockwood et al. 2001). Accounting for different rates of accu mulation of these four components of tree biomass, N accumulation functions are shown in Eq. (2-8), where parameters b and c are 54.8 and .24 for the high growth estimat e and 51.4 and .22 for the low growth estimate, respectively. While N accumulati on rates of coppice stages might decrease with reduced biomass productivity or increase wi th higher leaf/stem ratios, actual rates of N accumulation by coppice stages are unknown. This model assumes the same N accumulation function for coppice stages as for the original growth stage. Based on previous work relating to SRWC production in Florida (Rockwood et al. 2002a; Segrest et al. 1998; Rahmani et al. 1997), the following model inputs are assumed: planting cost, $500.00 ha-1; weed control following a coppice harvest, $50.00 ha-1; annual maintenance fee, $50.00 ha-1; and the price of woody biomass (for mulch), $20.00 dry Mg-1. This model assumes simulated real interest rates of 4% and 6% and two different costs for irrigation (microemitter) installation, $2,471 ha-1 and $3,707 ha-1. The high price for the irrigation system needed to distribute the reclaimed water, which is

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30 correlated with the pri ce of gasoline, is a cost not in curred by conventional forestry systems in Florida. Results and Discussion Table 2-1 illustrates net returns assu ming no dendroremediation incentive, a dendroremediation incentive treated as a stoc k benefit, and a dendroremediation incentive treated as a flow benefit2, for high and low growth estimates, at a dendroremediation value of $1.50 kg-1 N, an interest rate of 4%, price of wood of $20 dry Mg-1, and an irrigation installation cost of $2,471 ha-1, for 1, 2, 3, and 4 coppice stages. By identifying the number of stages that yields the highest profit, the optimum numb er of stages is two and three for the high and low growth models, respectively. This process was repeated for each scenario of irrigation cost, growth and yield function, and interest rate combinations to determine optimum profit, optimum number of stages per cycle, and optimum stage durations (Table 2-2). Resulting LEVs range from -$2,343 to +$2,726 ha-1, less than LEVs of a SRWC system in the United Kingdom reported by Smart and Burgess (2000) of $3,931, $6,168 and $14,814 ha-1 for market only, low NTB and high NTB model scenarios, respectivel y (stumpage price of $31 dry Mg-1, establishment cost of $1,538 ha-1 and an exchange rate of $1.54/ in November 2000). If the cost of the irrigation system were assumed sunk, LEVs reported here would range from $1,364 to $5,233 ha-1, comparable to those of Smart and Burgess (2000). To compare these findings with Florida pr oduction costs calculated by a previous study, the model was used to find minimum st umpage prices needed to achieve LEVs of 2 If treated as a stock benefit, the dendroremediation se rvice is provided when the nitrogen is taken from the site (removed with harvested biomass); if treated as a flow be nefit, the service is continuously provided as the tree grows and accumulates nitrogen.

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31 $1,235 ha-1 and $2,470 ha-1, representing LEVs of conven tional forestry (Borders & Bailey, 2001) and Florida agri cultural land (Reynolds, 2005), respectively. Stumpage prices of $26 and $30 dry Mg-1 are required to match LEVs of $1,235 ha-1 and $2,470 ha-1, respectively, assuming irrigation establishment costs of $3,707 ha-1, the high growth model and an interest rate of 5%. Rahmani et al. (1997) report Eucalyptus spp. farm gate production costs for Florida of $32-$39 dry Mg-1, less than the $48-$52 dry Mg-1 farm gate costs estimated here assu ming a harvest cost of $22 dry Mg-1 (Rahmani et al. 1998). A higher cost of production is expected give n the cost of irrigation establishment. Excluding irrigation costs Ci from the model yields stumpage prices of $15 and $19 dry Mg-1 and farmgate prices of $37 and $42 dry Mg-1 needed to match LEVs of $1,235 ha-1 and $2,470 ha-1 respectively, closer to the estimates by Rahmani and Hodges et al. (1997). Table 2-1. Net returns and optimum stage le ngths assuming 1, 2, 3, and 4 stages for a Eucalyptus grandis short-rotation woody crop system irrigated with reclaimed water in central Florida. High Growth: No N benefit N Benefit as Stock N Benefit as Flow # Stages per cycle LEV ($ ha-1) Optimum Stage Lengths LEV ($ ha-1) Optimum Stage Lengths LEV ($ ha-1) Optimum Stage Lengths 1 $653 4.4 $1,068 4.2 $1,134 4.2 2* $1,405 4.0, 3.6 $1,888 3.8, 3.6 $1,952 3.8, 3.4 3 $1,316 4.0, 3.6, 3.1 $1,824 3.8, 3.4, 2.9 $1,887 3.8, 3.4, 2.9 4 $1,120 4.1, 3.7, 3.3, 0.1 $1,610 3.9, 3.5, 3.1, 0.1 $1,674 3.9, 3.5, 3.1, 0.1 Low Growth: No N benefit N Benefit as Stock N Benefit as Flow # Stages per cycle LEV ($ ha-1) Optimum Stage Lengths LEV ($ ha-1) Optimum Stage Lengths LEV ($ ha-1) Optimum Stage Lengths 1 -$932 4.7 -$563 4.4 -$499 4.4 2 -$85 4.1, 3.7 $364 3.8, 3.4 $425 3.8, 3.4 3* -$63 4.0, 3.7, 3.2 $414 3.8, 3.4, 2.9 $475 3.8, 3.4, 2.9 4 -$258 4.2, 3.8, 3.4, 0.1 $198 3.9, 3.5, 3.1, 0.1 $259 3.9, 3.5, 3.1, 0.1 Note: These calculations include an interest rate of 4%, price of wood of $20 dry Mg-1, irrigation installation cost of $2,471 ha-1, and a dendroremediation value of $1.50 kg-1 N (where applicable). An * indicates the opt imum number of stages per cycle as show by the highest net returns. The NTB is cal culated as both a stoc k and flow benefit.

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32Table 2-2. Optimum LEVs, optimum stages per cycle, and optim um stage lengths for a range of dendroremediation values for Eucalyptus grandis irrigated with reclaimed water in central Florida. Dendroremediation Benefit as a Stock Dendroremediation Benefit as a Flow LEV ($ ha-1) Optimum Stage Lengths (years) LEV ($ ha-1) Optimum Stage Lengths (years) $ kg-1 N High Growth Low Growth High Growth Low Growth High Growth Low Growth High Growth Low Growth $0.00 $1,405 -$63 4.0, 3.6 4.0, 3.7, 3.2 $1,405 -$63 4.0, 3.6 4.0, 3.7, 3.2 $0.50 $1,563 $92 3.9, 3.5 4.0, 3.6, 3.1 $1,584 $112 3.9, 3.5 4.0, 3.6, 3.1 $1.00 $1,724 $251 3.9, 3.4 3.9, 3.5, 3.0 $1,767 $291 3.9, 3.4 3.9, 3.5, 3.0 $1.50 $1,888 $414 3.8, 3.4 3.8, 3.4, 2.9 $1,952 $475 3.8, 3.4 3.8, 3.4, 2.9 $2.00 $2,056 $581 3.7, 3.3 3.7, 3.3, 2.8 $2,141 $662 3.7, 3.3 3.7, 3.3, 2.8 $2.50 $2,227 $753 3.7, 3.2 3.6, 3.2, 2.8 $2,333 $854 3.7, 3.2 3.6, 3.2, 2.8 $3.00 $2,401 $930 3.6, 3.1 3.5, 3.1, 2.7 $2,528 $1,050 3.6, 3.2 3.5, 3.1, 2.7 A $3.50 $2,579 $1,111 3.5, 3.1 3.4, 3.0, 2.6 $2,726 $1,250 3.5, 3.1 3.4, 3.1, 2.6 $0.00 $169 -$1,299 4.0, 3.6 4.0, 3.7, 3.2 $169 -$1,299 4.0, 3.6 4.0, 3.7, 3.2 $0.50 $327 -$1,144 3.9, 3.5 4.0, 3.6, 3.1 $349 -$1,123 3.9, 3.5 4.0, 3.6, 3.1 $1.00 $488 -$985 3.9, 3.4 3.9, 3.5, 3.0 $531 -$944 3.9, 3.4 3.9, 3.5, 3.0 $1.50 $653 -$822 3.8, 3.4 3.8, 3.4, 2.9 $717 -$761 3.8, 3.4 3.8, 3.4, 2.9 $2.00 $820 -$654 3.7, 3.3 3.7, 3.3, 2.8 $906 -$574 3.7, 3.3 3.7, 3.3, 2.8 $2.50 $991 -$482 3.7, 3.2 3.6, 3.2, 2.8 $1,098 -$382 3.7, 3.2 3.6, 3.2, 2.8 $3.00 $1,166 -$306 3.6, 3.2 3.5, 3.1, 2. 7 $1,293 -$186 3.6, 3.2 3.5, 3.1, 2.7 B $3.50 $1,343 -$124 3.5, 3.1 3.4, 3.0, 2.6 $1,491 $15 3.5, 3.1 3.4, 3.1, 2.6

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33Table 2-2. Continued Dendroremediation Benefit as a Stock Dendroremediation Benefit as a Flow LEV ($ha-1) Optimum Stage Lengths (years) LEV ($ha-1) Optimum Stage Lengths (years) $ kg-1 N High Growth Low Growth High Growth Low Growth High Growth Low Growth High Growth Low Growth $0.00 -$195 -$1,108 3.9, 3.5 3.9, 3.6, 3.2 -$195 -$1,108 3.9, 3.5 3.9, 3.6, 3.2 $0.50 -$91 -$1,006 3.8, 3.5 3.8, 3.5, 3. 1 -$69 -$986 3.8, 3.5 3.7, 3.5, 3.1 $1.00 $15 -$902 3.7, 3.4 3.7, 3.4, 3.0 $58 -$862 3.7, 3.4 3.7, 3.4, 3.0 $1.50 $124 -$795 3.7, 3.3 3.6, 3.3, 2.9 $187 -$734 3.7, 3.3 3.6, 3.3, 2.9 $2.00 $236 -$685 3.6, 3.2, 2.8 3.5, 3.2, 2. 8 $320 -$605 3.6, 3.3, 2.8 3.5, 3.2, 2.9 $2.50 $355 -$571 3.5, 3.2, 2.7 3.5, 3.2, 2. 8 $459 -$472 3.5, 3.2, 2.7 3.5, 3.2, 2.8 $3.00 $476 -$455 3.5, 3.1, 2.7 3.4, 3.1, 2. 7 $601 -$336 3.5, 3.1, 2.7 3.4, 3.1, 2.7 C $3.50 $601 -$336 3.4, 3.0, 2.6 3.3, 3.0, 2. 6 $746 -$198 3.4, 3.1, 2.6 3.3, 3.0, 2.6 $0.00 -$1,430 -$2,343 3.9, 3.5 3.9, 3.6, 3.2 -$1,430 -$2,343 3.9, 3.5 3.9, 3.6, 3.2 $0.50 -$1,326 -$2,242 3.8, 3.5 3.8, 3.5, 3.1 -$1,305 -$2,221 3.8, 3.5 3.8, 3.5, 3.1 $1.00 -$1,220 -$2,137 3.7, 3.4 3.7, 3.4, 3.0 -$1,112 -$2,097 3.7, 3.3 3.7, 3.4, 3.0 $1.50 -$1,113 -$2,030 3.7, 3.3 3.6, 3.3, 2. 9 -$1,048 -$1,970 3.7, 3.3 3.6, 3.3, 2.9 $2.00 -$1,000 -$1,920 3.6, 3.2, 2.8 3.5, 3.2, 2.8 -$916 -$1,840 3.6, 3.3, 2.8 3.5, 3.2, 2.9 $2.50 -$881 -$1,807 3.5, 3.2, 2.7 3.5, 3.2, 2. 8 -$777 -$1,708 3.5, 3.2, 2.7 3.5, 3.2, 2.8 $3.00 -$759 -$1,691 3.5, 3.1, 2.7 3.4, 3.1, 2. 7 -$635 -$1,572 3.5, 3.1, 2.7 3.4, 3.1, 2.7 D $3.50 -$635 -$1,571 3.4, 3.0, 2.6 3.3, 3.0, 2. 6 -$490 -$1,433 3.4, 3.1, 2.6 3.3, 3.0, 2.6 Note: LEVs are shown for both stock and a flow benefits under high and low growth models assuming A) an interest rate of 4% an d an irrigation installation cost of $2,471 ha-1; B) an interest rate of 4% and an irrigation installation cost of $3,707 ha-1; C) an interest rate of 6% and an irrigati on installation cost of $2,471 ha-1; and D) an interest rate of 6% and an irrigation insta llation cost of $3,707 ha-1.

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34 The optimum economic rotation lengths for cycles with single stages shown in Table 2-1 are longer than maxi mum sustained yield (MSY) ages of 2.7 and 2.9 years for the low and high growth mode ls, respectively. Economic optimum rotation ages of conventional forest plantations are typically shorter than the age of MSY (Samuelson, 1976). However, low stumpage prices rela tive to high regeneration costs can extend optimal economic rotation past the rotation of MSY, especially with SRWC species as demonstrated by Binkley (1987). Though inclusi on of an environmental service provided by a standing forest extends optimal econom ic rotation (Hartman, 1976), incentives for dendroremediation, best achieved by rapidl y growing stands, favors shorter rotations (Table 2-2). While increasing interest rates tends to shorten optimum coppice stage lengths as the opportunity cost of standing biomass incr eases, it also favors increasing the number of coppice stages per cycle, thus minimizing rege neration costs. At a 4% interest rate, the optimum number of stages per cycle is two and three for simulations using the high and low growth functions, respectively. At a 6% interest rate, the optimum number of stages per cycle is two for high growth rates with a dendroremediation incentive less than $2 kg-1 N and three for the remaining scenarios. Optimum stage length duration ranges from 2.6-4.0 years. Increasing the interest rate from 4% to 6% or increasing the dendroremediation incentive by $1 kg-1 N decreases optimum stage lengths by about 0.1 years (Table 2-2). These results are consis tent with those of Smart and Burgess (2000) who observe that decreasing yields or incr easing the discount rate decreases LEV and thus the opportunity cost of the land, exte nding the coppice cycle to delay regeneration costs, while having a negligible effect on optimum stage lengths.

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35 Sensitivity Analysis of Dendroremed iation Incentive and Interest Rate This model was used to assess the sensitiv ity of profitability to changes in the dendroremediation incentive and the interest rate. Under all scenarios, the dendroremediation incentive had a positive nearly-linear relationship with profitability. Average marginal increases in profitability per dollar of N dendroremediation incentive according to growth function (high or low), bene fit (stock or flow) and interest rate (4% or 6%) are shown in Table 2-3. Assu ming an interest rate of 4%, a $1 kg-1 N increment in the dendroremediation incentive caused a ma rginal increase in pr ofitability of about $376 and $335 assuming flow and stock dendror emediation benefits, respectively, with little or no influence from the cost of irri gation or the growth model. Assuming an interest rate of 6%, a $1 kg-1 N increase in dendroremediation incentive caused a marginal increase in profitability of about $264 and $223 assuming flow and stock dendroremediation benefits, respectively. Profit sensitivity to dendroremediation incentive is shown in Figure 2-2. Table 2-3. Average marginal increases in net returns ($ ha-1) per dollar of N dendroremediation incentive according to growth function (high or low), benefit (stock or flow) and in terest rate (4% or 6%) for Eucalyptus grandis in central Florida. Marginal benefits are insensitive to changes in irrigation cost. Scenario: 4% 6% High growth, flow benefit $377 $268 High growth, stock benefit $336 $227 Low growth, flow benefit $375 $261 Low growth, stock benefit $334 $220

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36 -$1,500 -$1,000 -$500 $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000$0.00$0.50$1.00$1.50$2.00$2.50$3.00$3.50Dendroremediation Incentive ($/kg N)LEV ($/Ha) I=4%, G=H, B=F I=4%, G=H, B=S I=4%, G=L, B=F I=4%, G=L, B=S I=6%, G=H, B=F I=6%, G=H, B=S I=6%, G=L, B=F I=6%, G=L, B=S Figure 2-2. Net returns ($ ha-1) as a function of dendroremediation incentive ($ kg-1 N) (I=interest rate, H=high gr owth function, L=low growth function, F=flow benefit model, S=stock benefit model) assuming an irrigation cost of $2,471 per hectare. Changes in profitability ($ ha-1) as interest rate increases from 4% to 5% and from 5% to 6% for the high and low growth func tions at dendroremediation incentives of $0, $2, and $4 kg-1 N are shown in Table 2-4. The changes are the same assuming either flow or stock benefit models. Profitability is highly sensitive to changes in the interest rate, especially assuming the high growth function shown in Figure 2-1. Assuming a dendroremediation incentive of $2 kg-1 N, an increase in the interest rate from 4% to 5% causes a marginal decrease in net returns by $1,028 and $717 for the high and low growth assumptions, respectively, while an increase in interest rate from 5% to 6% causes a marginal decrease in profit by $791 and $550 for the high and low growth assumptions, respectively. Sensitivity to interest rate is not influenced by the price of irrigation or the type of benefit (stock or flow) assumed.

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37 Table 2-4. Changes in profit ($ ha-1) for Eucalyptus grandis in central Florida as interest rate increases from 4% to 5% and 5% to 6% for high and low growth functions shown in Figure 2-1, at dendr oremediation incentives of $0, $2, and $4 kg-1 N. Changes in profit are the sa me assuming either flow or stock benefit dendroremediation functions. Growth Function Dendroremediation Incentive ($ kg-1 N) Interest rate increase from 4% to 5% Interest rate increase from 5% to 6% $0.00 -$907 -$693 $2.00 -$1,028 -$791 High growth function $4.00 -$1,149 -$888 $0.00 -$593 -$446 $2.00 -$717 -$550 Low growth function $4.00 -$842 -$654 Using Data Fit 8.0, model outputs under the range of assumptions were condensed into LEV prediction Eq. (2-9), where I is the real interest rate, g is -1 g 1, where and 1 represent the low and high growth, resp ectively, as represented in Figure 2-1, Y is the price of irrigati on establishment ($ ha-1), v is dummy variable 0 or 1 for calculation as stock and flow benefits, respectively, and N is the value of the dendroremediation benefit ($ kg-1) (R2>0.99). This prediction equation could be used to predict LEV in the absence of modeling software. Estimated parameters and statistical descriptors are shown in Table 2-5. 3 1 58* 02 ** 467(,,,,)*** *****I I IILEVIgYvNegeY evgeN (2-9) Table 2-5. Estimated parameters an d descriptors used in Eq. (2-9) of Eucalyptus grandis irrigated with reclaimed water in central Florida (R2>0.99). Constants Value Standard Error t-ratio 0 9339.94 31.82 293.50 1 27.34 0.07 367.88 2 1903.38 20.34 93.59 3 23.76 0.26 93.15 4 759.74 10.73 70.83 5 20.45 0.30 67.73 6 41.18 0.86 47.73 7 0.02 0.00 0.00 8 84.32 5.85 14.41

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38 Conclusions In Florida, environmental and/or treat ment costs associated with municipal wastewater disposal will in tensify as urban populations gr ow. Using SRWC plantations to dendroremediate wastewater can provide various environm ental services and societal benefits in the WUI and should be considered as wastewater reme diation option. Our results suggest that financial compensati on for dendroremediation services would be required to make the system economically feas ible for private landowners. Calculations of net returns for 128 SRWC dendroremedia tion scenarios ranged from -$2,343 to +$2,762 ha-1 and are greatly reduced by high interest rates, high irrigation costs, and low growth functions. Each $1 kg-1 N increase in the dendroremediation incentive increases profit by $223-$376 ha-1, depending on interest rate and site productivity. $1 kg-1 N is probably less than the price to achieve the same service at a wastewater treatment plant. A 1% increase in interest rate can reduce profit by $446-$1,149 depending on the scenario. Increasing the interest rate from 4% to 6% or increasing the dendroremediation incentive by $1 kg-1 N decreases optimum stage lengths by about 0.1 year, which may not be operationally significant. However, the d ecision of whether to select two or three stages per cycle is influenced by the growth and yield function, whic h could be increased through improvements in weed control. Ceteris paribus, higher growth of the first stage decreases stage length and nu mber of stages per cycle, though improving growth of the second and third stages, which may be possi ble through weed and vine control, would favor longer stage duration, more stages pe r cycle, and could increase profitability. High costs of irrigation establishment greatly reduce net returns. Microemitter irrigation establishment cost about $2,471 ha-1 in 2000 and has gone up to about $3,707

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39 ha-1 in 2004 due to increasing fuel prices. However, costly microemitter irrigation systems are designed to conserve water, wh ich might not be necessary in the case of distributing reclaimed water, possibly providing an opportuni ty to apply less expensive irrigation systems. Additionally, citrus growers who want to take advantage of previously existing irrigation systems would not incur the cost of ir rigation, effectively increasing profitability by $2,471-$3,707 ha-1. Compensation to landowners for the dendroremediation service could be considered payable either when the trees and N are harvested (stock benefit), or periodically as the trees grow and accumulate N (flow bene fit). Because of the short optimum cycle lengths, differences between th e stock and flow bene fit net returns are smaller than they would be in conventiona l forest rotation ages. Accounting for the dendroremediation service as a flow benefit ra ther than a stock benefit increases profits by $61-$64 ha-1 and $138-$148 ha-1 assuming dendroremediation incentives of $1.50 and $3.50 kg-1 N, respectively. Accounting for th e dendroremediation benefit as a stock would probably be easier to administer Municipalities that use SRWCs to dendroremediate wastewater co uld also use this model to account for the value of the dendroremediation service they might achieve. Future Research Dendroremediation will be feasible for muni cipal waste facilities in the WUI if either tree farmers are paid enough for thei r dendroremediation service to make the system economically viable, or if the net cost for municipalities to dendroremediate with tree crops is cheaper than the alternative cost of treatment. Because case-specific costs, yields, and prices may vary from the assumpti ons made in this chapter, broad conclusions about the feasibility of dendroremediation of reclaimed water cannot be derived from

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40 these results. With more information, part icularly with regards to coppice growth and valuation of the dendroremediation service, the model described here can be used to make localized feasibility assessments. A study to assess the willingness to pay for reductions of N contamination from reclaimed water would elucidate the value of the dendroremediation service. An assessment of the willingness to accept exotic species in central Florida should be considered in a f easibility analysis of dendroremediation using EG. This model should be extended to in clude the most immediate environmental benefits, such as dendroremediation of P in the reclaimed water, C sequestration, and, under scenarios where the biomass is used fo r bioenergy, mitigation of atmospheric CO2 due to displacement of fossil fuels. The im proved biomass production attributable to the N, P, and K in the reclaimed water, and the sa vings associated with reduced fertilizer use should also be internaliz ed in this model.

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41 CHAPTER 3 AN ECONOMIC ANALYSIS OF Eucalyptus SPP. AS SHORTROTATION WOODY CROPS ON CLAY SETTLING AREAS IN POLK COUNTY, FLORIDA Introduction Central Florida produces 75% of the na tion's and 25% of the world's phosphate supply, primarily used for fertilizer (IM C Phosphates, 2002). There are about 162,000 hectares (400,000 acres) of phosphate-mined la nds in Florida (Segrest, 2003). In the mining process, clays are washed from phosphate ore and pumped into clay settling areas (CSAs). Polk County, Florida contains over 38,000 hectares (94,000 acres) of CSAs and/or overburden soil, as a result of phosphate mining. These CSAs, classified as clayey Haplaquents (Mislevy et al. 1989), are characterized by high bulk density, poor drainage, high levels of P, K, and micronutrients, pH of 7.0-8.3, and are commonly dominated by cogongrass ( Imperata cylindrica ), an invasive exotic species in Florida. CSAs are largely left idle because of operational difficultie s but may be a valuable resource base for biomass production. Ongoing research and ope rational trials on a 50-hectare CSA near Lakeland, FL, suggests that CSAs can be used for the production of SRWCs. This chapter assesses the economic vi ability of this practice. One environmental service that would be provided by the production of SRWCs on CSAs is atmospheric CO2 mitigation. Global carbon trading has increased from 13 Tg CO2 in 2001 to 70 MMg CO2 in 2003 (Ecosystem Marketplace, 2004), a trend that is likely to continue following the internati onal ratification of the Kyoto Protocol on February 16, 2005. The establishment of tr ee plantations on non-forested CSAs has the

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42 potential to sequester car bon by increasing the amount of C per area of land (Booth, 2003). Chaturvedi (2004) emphasizes the im portance of considering the C density of land prior to the carbon sequestering land use prac tice. He states The most clear benefit in carbon sequestration terms would be if the plantation were somehow established in a desert with an existing standing stock of vi rtually zero Mg C/ha. An advantage of SRWC production on CSAs is the near-zero C density of the land prior to plantation establishment, as the land is bare of vege tation with little accumulation of soil organic carbon (SOC) following mining. Even on 20-40 year-old CSAs, C density is likely to remain low if forest cover is not establishe d. Research suggests that SRWCs sequester and maintain SOC (Joslin & Schoenholtz, 1997 ). On a 60-year-old CSA in central Florida SOC of a 2.5-year-old E. grandis (EG) plantation was 214% and 304% greater at depths of 0-30 and 30-60 cm, respectively, than SOC quantities f ound in adjacent areas dominated by cogongrass (Wullschleger et al. 2004). In addition to C sequestration in situ if used as a dedicated feedstock supply system (DFSS), SRWC plantati ons can mitigate atmospheric CO2 by displacement of CO2 emissions associated with the combusti on of fossil fuels (Sims, 2002; Marland, 2000; Schlamadinger & Marland, 1996). The displacement of fossil fuels by biomass fuels can be an effective way to mitigate atmospheric CO2 because 1) CO2 emissions can be continuously reduced, rather than reachi ng an eventual plateau of C accumulation in standing biomass, 2) the long-term cost per Mg of CO2 is cheaper with displacement rather than sequestration, as land remains ava ilable for continued production in the future, and 3) reductions of net CO2 emissions are not as risk prone as C sequestered in situ

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43 which is susceptible to future events su ch as fire or land-use change (Eriksson et al. 2002). This chapter investigates the impact of CO2 mitigation incentives on management and profitability of SRWC DFSSs on CSAs in Polk County, Florida. An economic optimization model of a SRWC biomass produc tion system that includes an incentive for atmospheric CO2 mitigation is built and used to i nvestigate how this incentive would influence land expectation value (LEV) and optimal management of the SRWC production system. Methodology As described in Chapter 1, Eq. (1-1) defines LEV, net returns of a non-coppicing forestry system projected in perpetuity. This equation is modified in Eq. (1-5) to allow for coppicing forestry systems, which includes n number of growth st ages (initial growth stage and subsequent coppice stages). Eq. (1 -5) is used to calculate LEVs under a range of model input assumptions, ex clusive of environmental exte rnalities. To assess the divergence between private and societal benefi ts derived from the system, LEVs are then compared to those calculated by Eq. (1-13) which incorporates a non-timber benefit (NTB) for each growth stage s. Quantification and incorpora tion of the NTB requires a functional form which reflects the nature of th e benefit provided by th e forestry system. In this scenario, the externality to be incorporated is atmospheric CO2 mitigation. Trees sequester atmospheric CO2 in woody biomass as they grow. The value of standing aboveground C at time t for coppice stage s, assuming stage growth function g(t), carbon content of 47% by weight (Peter et al. 1996), and multiplying by 1.7 to convert stem inside bark to total aboveground biomass (Mg ha-1) (based on Segrest, 2002; Patzek & Pimentel, 2005) a can be estimated as

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44 0.8A SpCtgtC (3-1) where g(t) is the growth function for growth stage s as a function of time and Cp is the price of carbon. Once carbon is sequestered there is no furthe r benefit from it, so the derivative of Eq. (3-1) is used to calculat e the marginal benefit of the C sequestration service, yielding: 0*t rt AA d Ss dtCBCtedt (3-2) where the aboveground C sequest ration benefit of stage s is the definite integral of the flow of the carbon benefit discounted to the begi nning of the stage, for the duration of the stage. Central to the concept of car bon sequestration is the life span of the sequestered carbon, either in the ecosystem, or in products derived from harvests from the ecosystem (Murray, 2003). As wood products burn or de cay, sequestered carbon is re-emitted to the atmosphere in the form of CO2, countering the benefit achieved by the sequestered C. This societal cost of the decay or oxidati on of the sequestered carbon must be calculated and subtracted from Eq. (3-2). The rate of re-emission depends on the end use of the wood products. The two most likely products identified by a SRWC market survey in Polk County (below) are mulch and biofuel. The decay of C sequestered in these two products is accounted for differently. Eq. (3-3) represents the societal cost of CO2 emissions from the decay of mulch harvested from stage s at age t, where y is the life of the biomass in years assuming linear decay, discounted first to the end of the growth stage at discount rate r For example, for y =5, 1/5th of the harvested mulch would decay during each of five years. Subtracting the right hand side of Eq. (3-3) from the right hand side of Eq. (3-2) assuming that mulch

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45 decays in five years (Duryea et al. 1999; Duryea, 1999) yields Eq. (3-4), the integration of the marginal value of above-ground C seque stration discounted to the beginning of the growth stage, minus the societal cost of CO2 emissions associated with mulch decay discounted first to the end of the growth stage and then discounted to the beginning of the growth stage. Though actual mulch decay ma y be non-linear and may take longer than five years, the decay function in Eq. (3-3 ) was chosen to simplify the analysis and provide a conservative estimate of th e net C sequestration benefit. *1A ry rt S P SCt e Cte yr (3-3) *5 ** 01 5sA t r rtrt S M d sb dtCt e NTBCtedte r (3-4) This NTB calculated in Eq. (3-4) is then included in the optimization model for each growth stage of the mulch scenario a nd discounted to the beginning of the coppice cycle. Eq. (3-4) is incorporated in Eq. (1 -5), the equation for ne t returns of a coppice cycle having n number of growth stages, as shown in Eq. (3-5), where V(t) is the growth function for stage s times biomass price. To elucidat e the discounting of each benefit and cost in the model, an example of Eq. (3-5) fixed for two stages is shown in Eq. (3-6), including annual maintenance cost Ca and a one-time establishment cost Ci. 1 1 1 1 11 1** 0 1 *5 ** *()*** 1 ** 5 1s s j j j j s ss jj jj n j jt rtrt rt d sb dt n s A r rtrt S s mulch rtVteCtedte Ct e eCe r LEVt e (3-5)

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46 1 1 12 1 12 1** 1 0 *5 * ** 2 0 *5 *()** 1 5 ()*** 1 ** 5s st rtrt d b dt A r rt S pw t rtt rtrt d b dt A r rtt rt S w mulchVteCtedt Ct e eCC r VteCtedte Ct e eCe r LEVt 12*1 1a i r rttC C e e (3-6) Calculation of the societal costs associated with biofuels emissions must be handled differently than Eq. (3-3). SRWCs harvested as DFFSs for gasification or co-firing with coal are likely to be oxidized an d returned to the atmosphere as CO2 within zero to six months of harvest. However, as described above, CO2 emissions from sustainably produced (i.e., closed-loop) bi ofuels are re-sequestered in the subsequent rotation, resulting in no net emissions from bioma ss combustion, and displacing the use of fossil fuels with closed-loop biofuel reduces net CO2 emissions. Thus, bioenergy from DFSSs produces no net CO2 emissions, eliminating the need to calculate the costs of post-harvest biomass C decay. However, recognizing th at there are fossil fuel inputs to the cultivation, harvest, and processing of SRWC DFSSs consuming up to 10% of the energy produced by the bioenergy syst em (Forsberg, 2000; Heller et al. 2004; Klass, 1998), 10% of the carbon sequestration benefit achieved at stage age t is discounted to the beginning of the stage and subtracted from the carbon benefit calculated by Eq. (3-2), yielding Eq. (3-7): ** 0*0.1*st rtrt BFA d sbS dtNTBCtedtCBte (3-7)

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47 The net NTB calculated for each growth stag e for the biofuel scenario in Eq. (3-7) is then added to Eq. (1-5), resulting in Eq. (3-8). Eq. (3-9) is an example of Eq. (3-8) fixed for two growth stages. 1 1 1 1 11 1** 0 1 ** *()*** 0.1*** 1s s j j j j s ss jj jj s n j jt rtrt rt d sb dt n s rtrt bs biofuel rtVteCtedte CteCe LEVt e (3-8) 1 1 12 1 12 1 12** 1 0 * ** 2 0 * *()** 0.1** ()*** 0.1*** 1 1s s s st rtrt d b dt rt bpw t rtt rtrt d b dt rtt rt bw a biofuel r rttVteCtedt CteCC VteCtedte CteCe C LEVt e e iC (3-9) Thus, Equations (3-5) and (3-8) are used fo r incorporating C externalities in mulch and biofuel production scenario s, respectively. These m odels, with Eq. (1-5) for optimization without incorporation of extern alities, are used to calculate LEV and optimum age of each of n number of growth stages. The process is repeated iteratively adding an additional growth stage for each s cenario until the margin al benefit of the additional stage is negative, identifying th e optimum number of growth stages per coppice cycle and associated LEVs. Finally, the sensitivity of these LEVs to variation in the below model inputs is assessed.

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48 Model Inputs Growth Function Lacking published growth and yield f unctions of SRWCs produced on CSAs needed for inclusion in this model, measur ements were taken from a trial of EG and E. amplifolia (EA) on a CSA near Lakeland, Florida (Rockwood et al. 2005). Established between May and July of 2001, SRWC-90 was planted at densities of 4,200 (single row) and 8,400 (double row) trees per hectare, unfer tilized and fertilized on May 20, 2002 with 150 kg ammonium nitrate ha-1. Height and DBH measurem ents were taken August 20, 2002, July 16, 2003, December 23, 2003, August 27, 2004, and January 11, 2005. Number of surviving trees, average height and average DBH per plot by progeny after 3.5 years on January 11, 2005, are shown in Table 3-1. A modified volume prediction equati on developed by Max and Burkhart (Bredenkamp, 2000) was a good predictor of vol umes of 66 destruc tively sampled trees (R2>0.99) and used to convert height and DBH measurements to per-hectare yields assuming specific gravity of 0.40 (Rockwood et al. 1995a). Per-hectare inside-bark aboveground yields (dry Mg ha-1) of EG and EA under five treatments are shown in Table 3-1 and Figure 3-1. Decreasing rates of productivity were observed on January 11, 2005 at 3.5 years of age, suggesting an optimi zable function could be fit to the data for use in the model.

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49 EA 1 EA 2 EA 3 EA 4 EA 5 EG 1 EG 2 EG 3 EG 4 EG 5 0 10 20 30 40 50 60 7001234Age (years)Yield (dry Mg ha-1) EA 1 EA 2 EA 3 EA 4 EA 5 EG 1 EG 2 EG 3 EG 4 EG 5 Figure 3-1. Inside ba rk yields (dry Mg ha-1) of EA and EG on a CSA near Lakeland, Florida for 5 treatments: 1) 4,200 trees per hectare, unfertilized, 2) 8,400 trees per hectare, unfertilized, 3) 4,200 trees per hectare, fertilized with 150 kg ha-1 ammonium nitrate on May 20 2002 at 11 m onths, 4) 8,400 trees per hectare, fertilized as treatment 3, and 5) same as treatment 2. EA was identified as a likely candidate sp ecies due to a) greater frost resistance than EG, which allows flexibi lity to plant in late summer during increased rainfall with minimum frost damage to small trees the subseq uent winter and b) hi gher yields than EG despite being planted two months later. An air photo from 1995 revealed that Treatments 1 and 2 had been established on areas of the CSA where cogongrass was more densly established than the other treatments, probabl y explaining their lower yields. Treatments 3 and 4 were identified as be ing representative of moderate ly low and moderately high yields when compared to SRWC yields fr om other areas of the CSA. Nonlinear regression was used to fit the yi eld data to the functional form: ln bcadaBae (3-10)

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50Table 3-1. Number of observations, av erage DBH (cm), height (m) and inside-b ark dry above-ground biomass yields (Mg ha-1) by progeny of EG and EA planted at densiti es of 4,200 (single) and 8,400 (double) tr ees per hectare, unfertilized (0) and fertilized (1) on May 20, 2002, with 150 kg ammonium nitrate ha-1, and measured January 11, 2005, at 3.5 years. a and b indicate lowest and highest yielding progenies within treatments, respectively. Single 0 Double 0 Single 1 Double 1 Double 0 (2) Progeny N DBH H Mg ha-1 NDBHH Mg ha-1NDBHH Mg ha-1 N DBHH Mg ha-1NDBHH Mg ha-1 EG 3242 11 5.3a 7.5a 19.3a 184 7 10.8 a 104.5 a6.9 a 13.7 a 15 6.0 a9.1 a 29.4 a 125.0 a8.2 a 19.3 a 3469 12 6.6 8.5 27.2 205.5b 8.6 23.8 98.3 9.7 36.1 15 9.4 12.677.3 217.0 b10 65.9 b 4064 9 7.7b 8.9 30.4b 213.9 6.9 14.6 106.5 9.2 25.5 14 9.4 b12.6 b78.2 b 175.4 8.9 28.9 4200 10 7.6 9.4b 27 205.4 8.6b27.2 b 98.5 b10.3 b36.5 b 12 7.9 11.938.4 196.9 10.2 b57.8 4223 9 7 8.9 20.2 203.7 a6.8 a12.5 116.2 8.4 19.5 15 8 11.355.9 166.5 9.7 54.5 EA 4904 12 4.7 5.5 11 183.8 5.6 9.7 106.5 a7.5 a 16.0 a 20 6.1 a8.3 a 46.1 a 205.7 a7.9 a 31.6 a 4907 11 4.2 5.2 5.6 222.6 a4.0 a3.2 a 127.7 9 31.8 19 7.6 10.454.8 205.9 8.4 32.7 5025 10 3.7 a 4.6 a 5.4a 233.8 5.6 11.2 b 97.4 8.7 23.2 22 7.7 10.457.2 197.5 10 55.5 5033 12 5.9 6.5 12.1 233.8 5.6 10.2 108.7 b10.0 b32.7 23 6.9 9.7 46.6 247.4 9.9 68.2 5091 11 5.7 6.5 10.4 234.1 b5.9 b11.1 128.3 9.5 35.3 24 8.8 b11.2 b94.2 b 228.1 b10.6 b68.9 b 5108 11 7.3 b 7.8 b 22.3b 213.6 5.3 8.2 128.2 9.5 38.8 b 22 8.1 10.773.7 237.1 9.5 58.6

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51 where B(a) is dry stemwood biomass (Mg ha-1) as a function of stand age a in years for the first stage, and b, c and d are the estimated parameters 2.57, 4.00 and 1.20 for EA 3 and 2.76, 3.67 and 0.92 for EA 4, respectively (Figure 3-2). Using a factor of 1.7 for total above-ground biomass, maximum sust ained yields are 17 and 32 dry Mg ha-1 year-1, comparable to 20-31 dry Mg ha-1 year estimated for Eucalyptus in Florida (Rahmani et al., 1997) but higher than the estimated 9-17 dry Mg ha-1 yr-1 estimated by Klass (1998), who observes that yields coul d be improved with SRWC development in the sub-tropical south. EA 3 Average EA 4 Average EA 3; 4904 EA 3; 5108 EA 4; 4904 EA 4; 50910 20 40 60 80 100012345Age (years)Yield (dry Mg ha-1) EA 3 Average EA 4 Average Predicted EA3 Predicted EA4 Figure 3-2. Observed and predicted inside ba rk stem yields of EA treatments 3 and 4, 4,200 and 8,400 trees per hectare, fertil ized, and low and high progeny yields for each treatment. Carbon Values The Kyoto Protocol was ratified by 140 nations on February 16, 2005, strengthening ongoing efforts to reduce greenhou se gas emissions. While estimates for world carbon prices range from $4 to $27 Mg-1 C, $10 Mg-1 C is identified as a likely

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52 value (Vogt et al., 2005; Best & Wayburn, 2001). C pri ces assumed in this model range from $0 to $35 Mg-1 C. Market Assessment To identify products and prices to be us ed in this analysis, a SRWC market assessment was made in July 2004 in and ar ound Polk County, Florida. On-site and phone interviews were done w ith individuals from the Fl orida Division of Forestry, mulch industries, nurseries, electricity ge neration facilities, and potential biomass producers. Though not all companies inte rviewed were willing to share market information, a range of price values a nd demand quantity were derived from the interviews. Potential products from woody biomass gr own on CSAs in Polk County include mulch, energy, timber, pallets, and fiberboard The most likely products are 1) mulch, having an existing multi-million of dollar market in Polk County annually, and 2) feedstock for electricity generation, a pr ospective market with much potential for expansion. Following is a summary of thes e two most relevant woody biomass markets. The established market: mulch Mulch production is a major industry in cen tral Florida, involving companies such as Seaboard Supply in Ft. Green, Greenleaf Pr oducts, Inc. in Haines City, Florida Fence Post Co. in Ona, Forest Resources Inc. in Tampa and Aaction Mulch in Fort Myers. These companies produce mulch from various sources, including sawmill waste of cypress and pine, sand pine harv ests and forest thinnings (Gar ry Zipper, pers. com., July 15, 2004), eucalyptus plantations in south cen tral Florida, and me laleuca eradication harvests in southern Florida.

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53 Mulch consumers look for a product that will resist decay, has a desirable appearance, and is reasonably priced. Co ttonwood, lacking in decay resistance, is undesirable as a mulch product. EG is a de sirable material due to its red heartwood, attractive scent, and resistan ce to rot and termites (Mike Milliken, pers. com., August 2nd, 2004). Some mulch users express con cern about over-harvesting of cypress and want an alternative to cypress mulch products (Bobby Robins, pers. com., July 15, 2004). While demand for cypress mulch could serve as an incentive for sustainable cypress management and the establishment of cypress plantations, eucalyptus mulch marketed as cypress-free is likely to a ppeal to consumers who are c oncerned about loss of cypress trees. Mulchwood price Mike Milliken (pers. co m., August 3, 2004) of Green leaf Products suggested $14 green ton-1 stumpage price (up from $10 green ton-1 in 2002), assuming availability of minimum supply to produce 144,000 bags, requi ring 2,600 green tons (Appendix Eq.1). Dwight Knight of Seaboard Supply stated he would be willing to pay $33 green Mg-1 ($30 green ton-1), delivered (the mill is 48 km [30 miles] south of Lakeland), unprocessed (pers. com., August 5, 2004). As of Augus t 2004, transportation costs are $1.30 loaded km ($2.10 mile-1), with each load carrying 21 Mg (25 tons) (Eric Hoyer, pers. com., July 12, 2004), which equals $0.06 green Mg-1 km-1 ($0.08 green ton-1 mile-1). Assuming transportation of 48 km ( 30 miles) at $0.06 green Mg-1 km-1 ($0.08 green ton-1 mile-1), a harvesting cost of $17.64 Mg-1 ($16.00 ton-1), and a delivered price of $33 green Mg-1 ($30 ton-1), this scenario suggests an equivale nt stumpage value of about $12 green Mg-1 ($12 green ton-1) ($33-$17.64-(48*$0.06)=~$12.48 Mg-1), depending on the

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54 transportation distance. H ypothetical high and low trans portation cost scenarios and associated stumpage values are shown in Table 3-3. Table 3-2. Mulch markets for Eucal yptus produced in Polk County. Stumpage price (green Mg-1 [ton-1 ]: Volume (green Mg [tons]): Note: Location: Ha [acreage] needed Greenleaf (a) $9 [$8] 435 [500 ] per purchase Minimum purchase, to be mixed with other products. Haines City, FL n/a Greenleaf (b) $15 [$14] 2,357 [2,600] Minimum amount needed for a run of bags. Approximate amount per week, equivalent to about 122,469 green Mg (135,000 tons) year-1. Haines City, FL 3,500 [8,600] Greenleaf (c) $15-18 [$14-$16] >9,072 [10,000] Minimum amount needed for Lowes or Home Depot to list a new line item, and to set up an onsite operation. Could purchase up to 235,900 green Mg [260,000 tons] year-1. Haines City, FL 7,100 [17,500] Seaboard Supply $13 [$12] up to 22,680 [25,000] year-1 Based on $33 green Mg-1 ($30 green ton-1) delivered price assuming a shipping cost of $0.08 ton-1 mile-1, 30 miles shipping, and harvesting cost of $16 ton-1. Ft Green, FL 650 [1,600] Approximate acreage needed for sustained prod uction over a year, assuming growth of 34 green Mg ha-1 (15 tons acre-1) year-1 (i.e., annual demand divided by annual production).

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55 Table 3-3. Estimated equivalent stumpage values for high and low transportation cost scenarios. All tons are green weight. High Cost Scenario Low Cost Scenario Transportation 64 km @ $0.06 Mg-1 km-1 = -$3.84 Mg-1 (40 miles @ $0.08 ton-1 mile-1 = -$3.36 ton-1) 32 km @ $0.06 Mg-1 km-1 = -$1.92 Mg-1 (20 miles @ $0.08 ton-1 mile-1 = -$1.68 ton-1) Harvest Cost (conventional equipment) -$18 Mg-1 (-$16 ton-1) -$9 Mg-1 (-$8 ton-1) Price (delivered) +$28 Mg-1 (+$25 ton-1) +$33 Mg-1 (+$30 ton-1) Equivalent Stumpage Value +$6.16 Mg-1 ($5.64 ton-1) +$22.40Mg-1 ($20.32 ton-1) Mulchwood quantity Knight may purchase up to 22,700 green Mg (25,000 tons) year-1. Assuming yields of 34 green Mg ha-1 (15 tons acre-1) year-1, 647 ha (1,600 acres) of CSAs might be cultivated to meet the demand fo r this particular mulch mill. While significant, acreage needed to supply this particular plant w ould occupy a relatively small portion of the estimated (8,094 ha) 20,000 acres of CSAs in Polk County. Milliken (pers. com., August 2, 2004) affi rmed that Greenleaf Products is capable of purchasing 50 semi loads per day (about 24 green Mg [26 tons] of eucalyptus load-1) for 200 days per year totaling about 240, 000 green Mg (260,000 tons) per year. Producing this amount, assuming 34 green Mg ha-1 (15 tons acre-1) year-1, could occupy about 7,100 of 8,100 ha (17,500 of 20, 000 acres) of CSAs in Polk County. Mulch is currently produced in part fr om byproducts from sawmills and smalldiameter trees from forest th innings (Linda Kiella, Garry Zipper, pers. com., July 15, 2004). Because of the demand for mulch, sawmills convert waste into a product, and forest managers in some cases can reduce cost s associated with forest management, forest fuel load control, and eradication of melaleuca (Melaleuca quinquenervia). It is uncertain how much of the cu rrent biomass market (waste wood, thinnings, melaleuca

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56 control, etc.) might be displaced if add itional biomass is grown on CSAs. However, according to Milliken (pers. com., August 2nd, 2004), the market is constrained by supply of desirable material, not demand. Potential market: biomass fuels The biomass market for energy generation, while speculative, is potentially very large. Power generation plants that are using or could use bi oenergy include Ridge Generating Station in Auburnda le; Lakeland Electric in Lakeland; Big Bend Power Plant near Apollo Beach; and Tampa Electric Polk Po wer station near Mulb erry (Figure 3-3). Figure 3-3. Location and poten tial consumption of buyers of woody biomass from Polk County. Ridge Energy currently charges a tipping fee to receive biomass, ranging from $9 green Mg-1 ($8 ton-1) for low-ash biomass that is pre-chipped up to $38 green Mg-1 ($35 ton-1) for high-ash unprocessed biomass. Ridge Energy might be able to accept 635 Mg 1-22,700 22,701-45,400 45,401-127,000 127,001-235,900 Green Mg year-1

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57 (700 tons) day-1 of DFSSs for free (i.e., no tipping fee) if it contains less than 6% ash (no roots and a minimum of soiling) and if it is processed (i.e., chipped to smaller than 3) (Phil Tuohy, pers. com., July 27, 2004). If the biomass is processed and delivered for free, additional economic incentives would need to be applied to make biomass production economically viable. Lakeland Electric of Lakeland produces 2.8 million MW hours of electricity. As Lakeland Electric has had to raise rates duri ng 2004, further rate increases associated with using renewable energy w ould be difficult to impose. However, bioenergy will be an attractive option if the Florida DEP ma ndates renewable energy production. If Lakeland Electric were to have a renewable portfolio standard (RPS) mandate for 4% renewables, they would need to generate 12.5 MW of renewable energy, the equivalent of 8-14 Mg (9-15 tons) of biomass hour-1 (20% moisture content), meaning about 54,00088,900 air-dry (20% MC on green weight basis) Mg (59,000-98,000 tons) or 85,000143,000 green Mg (94,000-158,000 tons) year-1. Matt McArdle, a biofuels industry specialist contracted by Lakeland Electric, calculates a potentia l biofuel demand of 63,500 green Mg (70,000 tons) year-1 and possibly using up to 127,000 green Mg (140,000 tons) year-1, and suggests a likely price of $11 green Mg-1 ($10 ton-1) delivered (pers. com., August 27, 2004). The Big Bend Po wer Plant near Apollo Beach is another possible biomass buyer if a RPS is mandate d and could consume up to 45,000 green Mg (50,000 tons) year-1. The Tampa Electric Polk Power station near Mulberry, while a possible candidate, is more like ly to use herbaceous biomass crops due to blocking of one of the flurry feed systems of the gasifier in a trial with woody biomass in 2001 (Jeff Curry and McCardle, pers. com., July 27 and August 27, 2004) (Table 3-4).

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58 Because of relatively cheap conventional pow er generation fuels, utilities in central Florida currently pay from $-39 to $11 green Mg-1 ($-35 to +$10 ton-1) delivered for biomass. However, existing government in centives for renewable energy that could improve the profit margin of biomass for energy include the Renewable Energy Production Incentive (REPI)1 and the Section 45 Tax Credit fo r utilities that pay federal income taxes. REPI, authorized under Sect ion 1212 of the Energy Policy Act of 1992, is designed to promote increases in the genera tion and utilization of electricity from renewable energy sources (U.S.Department of Energy, 2005). REPI offers 1.76 kWh-1, and the Section 45 Tax Credit offers a reduction in taxes of 2.76 kWh-1. Assuming a heat rate of 11,500 BTUs kWh-1 and 9,343 BTUs kg-1 (4,238 BTUs lb-1) woody biomass at 50% MC on a green wei ght basis, REPI would be worth $14.29 green Mg-1 ($12.97 ton-1) or $28.59 dry Mg-1 ($25.94 ton-1) delivered, and similarly the Section 45 Tax Table 3-4. Potential bioener gy markets for Eucalyptus produced in Polk County. Prices are delivered. Estimated Price (green Mg-1 [ton-1]): Quantity (green Mg [tons]): Note: Location: Ha [acres] neededa Lakeland Electric $11 [$10] 63,500127,000 [70,000140,000] Delivered Price Lakeland, FL 2,000-4,000 [5,000-10,000] Big Bend $11 [$10] 45,400 [50,000] Delivered Price Apollo Beach, FL 1,400 [3,500] Ridge Energy $0 [$0] 91,000181,000 [100,000200,000] Economic incentives could be applied. Auburndale, FL 2,400-5,300 [6,00013,000] Tampa Electric $11 [$10] n/a Likely to favor herbaceous biomass. Mulberry, FL n/a a Approximate acreage needed for sustained prod uction over a year, assuming growth of 15 green tons acre-1 year-1 (i.e., annual demand divided by 15). 1 http://www.eere.ener gy.gov/wip/program/r epi.html, 02-15-2005

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59 Credit would be worth $44.85 dry Mg-1 ($40.69 ton-1) delivered. Based on this survey, stumpage prices for eucalyptus would range from $11-$44 dry Mg-1 ($5-$20 green ton-1, or $10-$40 dry ton-1 assuming 50% moisture content on a green weight basis). Three biomass prices assumed in this analysis are $10, $20 and $30 dry Mg-1. Operational Costs Operational costs on CSAs are higher th an those of conventional forestry, as working conditions on sites with heavy clays and/or cogongrass infestation are problematic. A commercial trial of SRWC production on a CSA near Lakeland, Florida incurred costs of $1,800 ha-1 for site preparation and $1,200 ha-1 planting cost (Ci and Cp in Equations (3-6) and (3-9)). To assess the sensitivity of LEV to changes in operational costs, values of $900 and $1,800 ha-1 for site preparation and $600-$1,200 ha-1 for planting were used, assuming decreasing co sts with increased commercialization and economies of scale. In light of apparent grow th response to weed c ontrol, a weeding cost (Cw in Equations (3-6) and (3-9)) of $0 and $200 ha-1 with the beginning of each growth stage was tested. The model was run assuming interest rates of 4%, 7% and 10%. Additional Non-Timber Benefits Below-ground C sequestration Because the response of below-ground (S OC) accumulation to harvest scheduling is not known, below-ground C seque stration is not incorporated in this model. However, below-ground C sequestration ca n be estimated and added to calculated LEV as an additional NTB. Root systems of EG grown in a clay settling ar ea in central Florida were 40% of the total biomass (Segrest, 2002), or equivalent to the above-ground inside-bark growth function. Under sustained yield SR WC management, it could be assumed that

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60 biomass in root systems peak during the c oppice stage that produces the greatest aboveground biomass, and remains steady in subs equent coppice stages and cycles, where decay of dead root systems is replaced by re -growth. Anecdotal evidence suggests that greatest yields at the Kent site occur dur ing the first coppice stage and decline in subsequent coppice stages, as described in Chapter 2. Therefore, the value of C sequestration in root systems for the first coppice stage (s=1) at time t can be defined as Eq. (3-11) and remain constant for the life of the plantation. 1()*0.47*R CCtgtP (3-11) The derivative of Eq. (3-11) is the va lue of the carbon sequestered in roots discounted to plantation age 0: 11 0*t rt RR d dtCBCtedt (3-12) Information about SOC accumulation on CSAs in Florida is limited. Wullschleger et al. (2004) found that on a 25-year-old CSA, SOC under 2.5-year-old plantation of EG at a planting density of 9,800 trees ha-1 accumulated 151 and 96 Mg ha-1 more than SOC under cogongrass in soil depths of 0-30 cm and 30-60 cm, respectively. Their model of soil carbon dynamics estimated that a SRWC EG plantation contribute s to the storage of an additional 274 Mg C ha-1 after 25 years, reaching an additional 354 Mg C ha-1 after 50 years. A polynomial function fitted to th e data simulation takes the following form: 20.1668*15.084* SOCttt (3-13) where SOC (Mg ha-1) is expressed as a function of time t (years) after SRWC plantation establishment on a CSA. Eq. (3-13) is then used in the calculation of the NPV of the carbon benefit (Eq. (3-14)). The actual SOC sequestration pr ocess is likely to be more

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61 complicated than Eq. (3-13) suggests. Howe ver, lacking better data, Equations (3-12) and (3-14) can be used to estimate the addi tional benefit of belo w-ground (root + SOC) C sequestration benefits. 0*t rt SOC d dtCBSOCtedt (3-14) Reclamation incentives As a result of high bulk density, high pH and the invasion of cogongrass, CSAs are slow to naturally revegetate and are difficult to put into agricultural or forestry production. Tree plantations can contribute to ecosystem restoration of degraded lands by facilitating natural regeneration (Haggar et al., 1997; Lamb, 1998; Lugo, 1997; Powers et al., 1997; Parrota, 1992; Parrota et al., 1997) especially in areas dominated by cogongrass (Otsamo, 2000; Kuusipalo et al., 1995). The establishment of SRWCs on CSAs can reduce soil bulk density, exclude cogongrass, and facilitate the establishment of natural regeneration of na tive tree species and ecosystem functions. Chapter 378 of the 2004 State of Florida Statutes include s provisions for reimbursement of CSA reclamation costs, ranging from $4,942 -$9,884 ha-1 ($2,000-$4,000 acre-1), funded from taxes on the phosphate mining industry (State of Florida, 2004a). Because it is not known if SRWC establishment would be recogn ized as a form of CSA reclamation, and because payment would not be a function of stand growth, mined land reclamation incentives are not included in this mode l. However, providing this reclamation compensation to SRWC systems would cont ribute to the LEV of SRWC production on CSAs.

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62 Summary of Model Inputs and Assumptions The model was run for the three scenar ios (no NTB, C sequestration in mulch production, and C sequestration/CO2 displacement in biomass production), under all combinations of interest rates (4% and 7% ), site preparation costs ($900 and $1,800 ha-1), planting costs ($600 and $1,200 ha-1), weed control costs ($0 and $200 ha-1), growth functions (low and high) and biomass st umpage prices ($10, $20 and $30 dry Mg-1 assuming whole-tree above-ground harvesting) fo r a fixed C sequestration incentive of $5 Mg-1, totaling 288 runs, allowing as many growth stages as needed until LEV begins to decline, assuming growth stages decline by 20% per stage. Additionally, se nsitivity of LEV and harvest scheduling to C prices of $15, $25 and $35 was tested at a base scenario, as was increasing the cost of cap ital to 10%. LEVs exclude below-ground C sequestration benefits, the values of wh ich are estimated independently below. Results and Sensitivity Analysis LEVs increase with growth rate and biomass stumpage price. Under all combinations of assumptions under a fixed C price of $5 Mg-1 C, LEVs range from $-2,789 to $4,616 ha-1 and $-224 to $18,121 ha-1 assuming stumpage prices of $10 and $30 Mg-1, respectively, comparable to LEVs of a SRWC system in the United Kingdom reported by Smart and Burgess (2000) of $3,931, $6,168 and $14,814 ha-1 for market only, low NTB and high NTB model scenarios, respectively (stumpage price of $31 dry Mg-1, establishment cost of $1,538 ha-1 and an exchange rate of $1.54 per in November 2000). Table 3-5 shows LEVs, optimum number of stages per cycle, and optimum stage lengths by C benefit scenario and stumpage pric e assuming a base scenario of 4% interest rate, $1,800 ha-1 site preparation cost, $1,200 ha-1 planting cost and a carbon price of $5 Mg-1 C. Under these assumptions, marginal increases in LEV per dollar increment in

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63 stumpage price range from $264-$293 a nd $588-$629 under the low growth and high growth functions, respectively. Marginal benefits of increasi ng stumpage price are greater with the high growth function, as be nefits of increased yi eld are magnified over multiple rotations. The shortest optimal initial growth stag e is 2.6 years under conditions of highest stumpage price and interest rate and lowest operational costs, a nd the longest optimal initial growth stage with a positive LEV is 3.5 years under conditions of high operational costs, low interest rate and low stumpage prices. Ceteris paribus, increasing stumpage price decreases optimum stage lengths and op timum stages per cycle, as the opportunity cost of the value of the stand increases. Incorporating the C incentive in the mulch product scenario increases op timum stage lengths, while applying the incentive in the biofuel scenario decreases op timum stage lengths due to reduced post-harvest emissions penalties, though differences in stage lengths are less than 1/10th of a year (Table 3-6). Table 3-5. LEV, optimum number of stages and optimum stage length for each stage by C benefit scenario and biomass price assu ming a base scenario of 4% interest rate, $1,800 ha-1 site preparation cost, $1,200 ha-1 planting cost, no postestablishment weeding cost, and a carbon price of $5 Mg-1 C. $10 dry Mg-1 $20 dry Mg-1 $30 dry Mg-1 NTB Growth LEV ($/ha) Optimum harvest age (years) LEV ($/ha) Optimum harvest age (years) LEV ($/ha) Optimum harvest age (years) None Low -1,967 3.1, 3.1, 3.2, 3.3, 3.4 674 2.9, 2.9, 2.8, 2.6 3,722 2.8, 2.8, 2.6 C(M) Low -1,883 3.1, 3.1, 3.2, 3.2, 3.3 771 2.9, 2.9, 2.8, 2.6 3,828 2.8, 2.8, 2.6 C(B) Low -1,424 3.0, 3.1, 3.1, 3.1, 2.9 1,320 2.9, 2.9, 2.8, 2.6 4,448 2.8, 2.8, 2.6 None High 619 3.4, 3.4, 3.3, 3.0 6,507 3.2, 3.1, 2.9 12,960 3.2, 3.0 C(M) High 810 3.4, 3.4, 3.3, 3.0 6,715 3.2, 3.1, 2.9 13,140 3.2, 3.0 C(B) High 1,832 3.4, 3.4, 3.3, 2.9 7,869 3.2, 3.1, 2.9 14,419 3.1, 3.0

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64 Raising incentives for C sequestration in creases LEV (Table 3-6). Under a base scenario of $20 dry Mg-1 stumpage price, interest ra te 4%, site preparation $1,800 ha-1, planting cost $1,200 ha-1, high growth function and no post-establishment weeding, increasing the price of C from $0 to $35 ha-1 increased LEVs from $6,507 to $7,965 and $6,507 to $16,422 ha-1 for the mulch and biofuel scenario s, respectively. The marginal increase in LEV per dollar increment in C pri ce is a constant $42 in the mulch scenario. Conversely, the marginal bene fit in the biofuel scenario was both higher and more responsive to increases in C price, ranging from a marginal increase of $272 to $292 at $5 and $35 Mg-1 C, respectively. This reflects that the biofuel model is less penalized by post-harvest decay of sequestered C, thus increasing incentives for biofuel production rather than in situ sequestration. Table 3-6. LEV ($ ha-1), optimum stage lengths, marginal benefit, and estimated belowground benefit ($ ha-1) by C sequestration incentive ($ Mg-1) under a base scenario of $20 dry Mg-1 stumpage, interest rate 4%, site preparation $1,800 ha-1, and planting cost $1,200 ha-1. $ Mg-1 C LEV ($ ha-1 ) Optimum Stage Lengths (years) Marginal Benefit ( LEV per $1 C Incentive) Below-ground ($ ha-1) Mulch scenario 0 $6,507 3.2, 3.1, 2.9 n/a n/a 5 $6,715 3.2, 3.1, 2.9 $42 $1,163 15 $7,131 3.3, 3.1, 2.9 $42 $3,492 25 $7,548 3.3, 3.2, 2.9 $42 $5,819 35 $7,965 3.3, 3.2, 2.9 $42 $8,097 Biofuel scenario 0 $6,507 3.2, 3.1, 2.9 n/a n/a 5 $7,869 3.2, 3.1, 2.9 $272 $1,163 15 $10,598 3.2, 3.1, 2.8 $273 $3,492 25 $13,505 3.1, 3.0 $291 $5,819 35 $16,422 3.1, 3.0 $292 $8,097 The marginal reduction of LEV per percent in crease in the cost of capital between 4% and 7%, assuming a C price of $5 Mg-1 C, is -$23 under the least profitable scenario and -$2,928 under optimum assumptions. For a base scenario of $1,800 ha-1 site

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65 preparation cost, $1,200 ha-1 planting cost, ca rbon price of $5 Mg-1 C, high growth function and no weeding costs, the marginal im pact of increasing interest rates between 4% and 10% ranged from -$192 to -$2,581 (Tab le 3-7). More profitable scenarios are penalized more by higher interest rates. In creasing interest rates had little effect on optimum stage lengths (Table 3-8). Increases in interest rates from 4% to 7% and from 7% to 10% decreased optimum stage lengths by 1/10th of a year or less. At increases from 7% to 10% the model selected for optim ization with an additi onal growth stage. This effect is consistent w ith results from Smart and Burg ess (2000), who observe that in SRWC biomass systems the opportunity cost of the standing biomass is low relative to Table 3-7. Change in LEV ($ ha-1) per 1% increase in inte rest rate assuming $1,800 ha-1 site preparation cost, $1,200 ha-1 planting cost, carbon price of $5 Mg-1 C, high growth function and no weedi ng costs, without C sequestration incentives, C sequestration for the mulch production scenario, and C sequestration for the biofuel production scenario. $10 dry Mg-1 $20 dry Mg-1 $30 dry Mg-1 % Interest Rate LEV ($ ha-1) LEV/+1% Interest LEV ($ ha-1) LEV/+1% Interest LEV ($ ha-1) LEV/+ 1% Interest 4% $619 $6,507 $12,960 7% -$798 -$472 $2,413 -$1,365 $5,864 -$2,365 No NTB 10% -$1,375 -$192 $762 -$550 $3,057 -$936 4% $810 $6,715 $13,140 7% -$616 -$475 $2,608 -$1,369 $6,029 -$2,370 Mulch Scenario 10% -$1,213 -$199 $946 -$554 $3,239 -$930 4% $1,832 $7,869 $14,419 7% -$88 -$640 $3,197 -$1,557 $6,677 -$2,581 Biofuel Scenario 10% -$880 -$264 $1,315 -$627 $3,611 -$1,022 the opportunity cost of the la nd, and thus increasing inte rest rate does not shorten rotations as it would with a conventional system but rather LEVs are reduced, lowering the opportunity cost of the land relative to the marginal bene fit of the stand growth, and stage lengths remain relatively unaffected, wh ile the coppice cycle is extended to delay the cost of replanting.

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66 Table 3-8. Optimum harvest scheduling (stage le ngths and number of stages per cycle) at interest rates of 4%, 7% and 10% assuming $1,800 ha-1 site preparation cost, $1,200 ha-1 planting cost, carbon price of $5 Mg-1 C, high growth function and no weeding costs, without C sequestrati on incentives, C sequestration for the mulch production scenario, and C se questration for the biofuel production scenario. $10 dry Mg-1 $20 dry Mg-1 $30 dry Mg-1 % Interest Rate Optimum number of stages per cycle Optimum stage lengths (years) Optimum number of stages per cycle Optimum stage lengths (years) Optimum number of stages per cycle Optimum stage lengths (years) 4% 4 3.4, 3.4, 3.3, 3.0 3 3.2, 3.1, 2.9 2 3.2, 3.0 7% 4 3.3, 3.3, 3.3, 3.1 3 3.2, 3.1, 2.9 2 3.1, 3.0 No NTB 10% 5 3.2, 3.2, 3.3, 3.2, 2.9 3 3.1, 3.1, 2.9 3 3.0, 3.0, 2.8 4% 4 3.4, 3.4, 3.3, 3.0 3 3.2, 3.1, 2.9 2 3.2, 3.0 7% 4 3.3, 3.3, 3.3, 3.1 3 3.2, 3.1, 2.9 2 3.1, 3.0 Mulch Scenario 10% 5 3.3, 3.3, 3.3, 3.2, 3.8 3 3.1, 3.1, 2.9 3 3.1, 3.0, 2.8 4% 4 3.4, 3.4, 3.3, 2.9 3 3.2, 3.1, 2.9 2 3.1, 3.0 7% 4 3.3, 3.3, 3.2, 3.0 3 3.2, 3.1, 2.9 2 3.1, 3.0 Biofuel Scenario 10% 5 3.2, 3.3, 3.2, 3.1, 2.5 3 3.1, 3.1, 2.9 3 3.1, 3.0, 2.7 Increases in operational costs decrease LEV (Table 3-9). Increases in site preparation, which are one-time up-front cost s, have a dollar-for-dollar reduction in LEV. LEVs decrease $3 per dollar increase in planting costs, with slightly higher marginal impacts at higher stumpage prices, reflec ting shorter coppice cycles and increased planting frequency. Weed control may be n eeded to insure high yields, though the exact impact of weed control on gr owth is not known. LEV is reduced $8 for every dollar increase in weed control cost applied at th e beginning of each growth stage. Marginal impacts shown in Table 3-9 are the same unde r the three NTB scenarios, except for the

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67 marginal impact of weeding increases from $8 to -$9 in the biofuels scenario assuming $30 Mg-1, reflecting the shorter optimal stage lengths and more frequent weeding. Table 3-9. LEVs and marginal impact on LEV s by changes in site preparation, planting and weeding costs, assuming a C price of $5 Mg-1, 4% interest rate, high growth function and no NTB. $10 dry Mg-1 $20 dry Mg-1 $30 dry Mg-1 Input Values ($ ha-1) LEV ($ ha-1) LEV/ Input LEV ($ ha-1) LEV/ Input LEV ($ ha-1) LEV/ Input Site preparation (low) $900 $1,519 $7,407 $13,860 Site preparation (high) $1,800 $619 -$1 $6,507 -$1 $12,960 -$1 Planting (low) $600 $2,354 $8,963 $15,754 Planting (high) $1,200 $619 -$3 $6,507 -$4 $12,960 -$5 Weeding (low) $0 $619 $6,507 $12,960 Weeding (high) $200 -$937 -$8 $4,831 -$8 $11,261 -$8 Base scenario assumptions. The value of below-ground C sequestrati on, exogenous in this model, was estimated separately (Table 3-10). The estimated value of SOC sequestration, comprising the majority of the below-ground carbon benefit, is influenced only by C price and interest rate. The value of C sequestration in root s is additionally influenced by the growth and yield function. The SOC model by Wullschleger et al. (2004) yields 341 Mg ha-1 from 0-60 cm depth at 45 years, at a rate of 7.5 Mg SOC ha-1 year-1,which is greater than 136.3 Mg SOC ha-1, the average for longleaf-slash pine stands to 1 meter depth reported by Heath et al. (2003). The rate of accumulation is an order of magnitude more than sequestration rates reported from tree plantations on agricultural lands (Garten, 2002) but is closer to the 1-3 Mg SOC ha-1 year-1 sequestration rate re ported in the top 30 cm of reclaimed minesoils over 25 years in Ohio (Lal & Akala, 2001), and might be influenced by the longer growing season and deeper measurement depth. Estimating carbon sequestered in roots as eq uivalent to 40% of the total biomass or 67% of the above

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68 ground biomass (based on Segrest, 2002) yields 15 and 31 Mg C ha-1 after three years for the EA 3 and EA 4 growth curves, respectively. This is more than the 6.6 and 7.4 Mg ha1 of below-ground organic matter after thr ee years with the cultivation of sycamore (Plantanus occidentalus) in Tennessee and Mississippi, re spectively, reported by Tobert and Thornton et al. (2000) though higher rates of sequest ration are to be expected with a longer growing season and faster growing Eucalyptus spp.. Assuming a C price of $5 Mg-1, total estimated below-ground C benefits range from $650 ha-1 (low growth function and 10% interest rate) to $1,172 ha-1 (high growth function and 4% interest rate). Raising the C price to $15 and $25 Mg-1 approximately increases the below ground C benefit by 3 and 5 times, respectively. Table 3-10. Estimated discounted value of below-ground C benefits by C price, interest rate and growth function. Roots Estimated Total Below-ground C Benefit C price ($ Mg-1) Interest Rate Growth Function Minimum Maximum SOC Minimum Maximum Low $67 $74 $1,014 $1,081 $1,088 4% High $123 $158 $1,014 $1,137 $1,172 Low $64 $71 $751 $815 $822 7% High $117 $149 $751 $868 $900 Low $61 $67 $589 $650 $656 $5 10% High $111 $140 $589 $700 $729 Low $202 $223 $3,042 $3,244 $3,265 4% High $368 $473 $3,042 $3,410 $3,515 Low $192 $212 $2,254 $2,446 $2,466 7% High $350 $446 $2,254 $2,604 $2,700 Low $183 $202 $1,768 $1,951 $1,970 $15 10% High $333 $421 $1,768 $2,101 $2,189 Low $336 $372 $5,069 $5,405 $5,441 4% High $614 $789 $5,069 $5,683 $5,858 Low $320 $353 $3,757 $4,077 $4,110 7% High $584 $744 $3,757 $4,341 $4,501 Low $305 $336 $2,946 $3,251 $3,282 $25 10% High $555 $702 $2,946 $3,501 $3,648

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69 To compare these findings with producti on costs calculated by a previous study, this model was used to find minimum stumpage prices needed to achieve LEVs of $1,235 ha-1 and $2,470 ha-1, representing LEVs of conventiona l forestry (Borders & Bailey, 2001) and Florida agricultural land (Reynolds, 2005), respective ly. Stumpage prices of $17 and $21 dry Mg-1 are required to ma tch LEVs of $1,235 ha-1 and $2,470 ha-1, respectively, assuming site preparation costs of $1,800 ha-1, planting costs of $1,200 ha-1 and averaging the EA 3 and EA 4 growth functions, equivalent to ~25 dry Mg ha-1 year-1 and an interest rate of 5%. Rahmani et al. (1997) report Eucalyptus spp. farm gate production costs for Florida of $32-$39 dry Mg-1, slightly less than the $39-$43 dry Mg-1 farm gate costs estimated here a ssuming a harvest cost of $22 dry Mg-1 (Rahmani et al., 1998). A higher cost of production is expected given the cost of site preparation on CSAs. Conclusions Under these assumptions, even assuming high establishment and planting costs ($1,800 and $1,200 ha-1, respectively), a reasonable stumpage price ($20 dry Mg-1) and excluding C sequestration incen tives, production of EA on CS As in central Florida is profitable, with LEVs ranging from $762 to $6,507 ha-1 assuming interest rates of 10% and 4%, respectively. With the incorpor ation of a C sequestration benefit of $5 Mg-1 LEVs increase to $946 and $6,715 ha-1, while recognizing the CO2 mitigation benefits associated with the biofuel scenar io increases LEVs to $1,315 and $7,869 ha-1 assuming interest rates of 10% and 4%, respectively. In addition, the soci etal value of belowground C sequestration (roots + SOC at $5 Mg-1 C) is likely to be $1,081-$1,172 ha-1 or $815-$900 ha-1 assuming discount rates of 4% and 7%, respectively.

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70 The influence of stumpage pric e, C sequestration benefit (CO2 mitigation scenario or C price) or interest rate (from 4% to 10%) on optimum stage lengths is less than one year, and is probably oper ationally unimportant. Increasing incentives for CO2 mitigation can increase or decrease optimum stag e lengths by about 0.1 year in the mulch and biofuels scenarios, respectively. Ha rvesting on CSAs would likely be scheduled during the months of December-February wh en sites are more accessible and coppice response to harvest is best, a nd practical application of th is model is more likely in evaluating the economic viability of the syst em rather than projecting optimum harvest scheduling to sub-year accuracy. However, this model could be used to suggest the optimum number of stages per cycle and optimal harvest schedu ling by identifying the winter closest to the optimum harvest age. Because of the short gr owth stages, penalties for post-harvest CO2 emissions from product decay are discounted much less than those of conventional rotations of 20 or more years, countering benefits of in situ C sequestration, and underscoring the importance of recognizing the CO2 mitigation benefit of displacing fossil fuels in the biofuel scenario. These results emphasize both the potential for DFSSs on CSAs to mitigate atmospheric CO2 and for CO2 mitigation incentives to contri bute to the profitability of SRWC production. Increases in LEV from CO2 displacement benefits are on par with increases gained from SOC sequest ration, and to a lesser degree, in situ sequestration in aboveand below-ground biomass. It woul d probably be impractical to provide incentives and penalties for the sequestrati on and decay of C for SRWC systems on a perharvest basis, given the frequent harvest rate vis a vis conventional forestry systems. However, this model might be used to assess the present value of CO2 mitigation benefits

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71 over the life of the stand, providing the opportunity to offer incentives without monitoring of each biomass harvest. T hough payment of C sequestration benefits independent of harvest monitoring could cause a divergence of pr ivate and socially optimum harvesting, these results suggest th ere is little difference in optimum harvest scheduling of private versus socially op timal SRWC production, and in fact both optimum stage lengths and stages per coppi ce cycle decrease in the biofuel production scenario, indicating that harvest monitori ng might not be needed for a successful CO2 mitigation program. In the biofuel production scenario, probably the easiest way to incorporate CO2 mitigation benefits would be for utilities to pass on CO2 emissions reductions incentives to producers by increasing stumpage price. In light of uncertainty associated with SR WCs, potential financiers might expect a high rate of return on their investment. These results suggest that SRWCs can be profitable at interest rates of 10%, assuming some combination of adequate yields, stumpage prices, NTB incentives and/or operational costs are achieved. Future Research Research is needed to verify the assump tions made in this analysis. The most immediate need is for a better understanding of growth response to treatment options such as weeding and fertilization. With more information, particularly with regards to below-ground C sequestration, growth functi ons and coppice growth, this model can be used to make case-specific evaluations. A better understanding of long-term impacts of SRWC production on CSAs and eligibility for mined-land reclamation incentives would be beneficial, as would assessments of economic multiplier effects on communities in Polk County. Upcoming work of SFRC student s regarding the use of SRWCs to control cogongrass and facilitate natural re generation could contribute to this analysis. In light of

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72 the 2004 hurricane season, a feasibility analysis incorporating risk assessment could be useful in assessing potential advantages of SR WCs to reduce the probability of hurricane damage.

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73 CHAPTER 4 ECONOMICS OF SLASH PINE CULTURE ON TITANIUM MINED LANDS IN NORTH CENTRAL FLORIDA Introduction Comparable to the phosphate mining industr y in central Florid a, titanium and zircon mining by Iluka Resources Inc and Dupont is prevalent in northeast Florida, with 1,600 ha (4,000 acres) mined in Clay Count y since the early 1970s. In the mining process, forest cover is removed, topsoil is retained, and through either dredge or dry mining, soil is processed, minerals are remove d, and the homogenized soil is replaced. In response to concerns of environmental imp acts of titanium mining, the Surface Mining Control and Reclamation Act of 1977 requires th at mining operations re-apply topsoil on mined sites to restore wildlife habitat and hydrologic functions. Another significant contributor to the ec onomy of northeast Florida is the forest products industry. In Clay County, Iluka a nd DuPont establish slas h pine plantations on reclaimed mines to produce timber products a nd restore ecosystem functions. Unlike the experimental production of SRWCs on mined la nds assessed in Chapter 3, slash pine culture on titanium mined lands in northeast Florida is well-established. Darfus and Fisher (1984) found young slash pine plantations established on mined lands in the mid1970s had poor survival and growth as a resu lt of unleveled contour s and disrupted soil moisture regimes. However, Mathey (2001) in a study of slash pine plantations established between 1978 and 1996 on la nds mined by Iluka found no significant difference between site indices of reclaime d and unmined lands, though averages varied

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74 slightly (21.2 m and 22.0 m respectively, base age 25 years), and stem analysis showed similar height and diameter at breast height (dbh) growth pa tterns on 8-, 10-, and 16-yearold stands on mined and unmined lands under identical management regimes (Figures 4-1 and 4-2). 0 2 4 6 8 10 12 14 16 18 12345678910111213141516 Age of Tree (years)Height (meters) Reclaimed Unmined Figure 4-1. Mean heights estimated by stem analysis from stands on 25 reclaimed and 25 unmined sites (Mathey, 2001). 0 2 4 6 8 10 12 14 16 18 20 22 24 12345678910111213141516 Age of Tree (years)DIB (centimeters) Reclaimed Unmined Figure 4-2. Mean diameter inside bark (DIB ) estimated by stem analysis from stands on 25 reclaimed and 25 unmined sites (Mathey, 2001). Iluka has a vested interest in the pr oductivity of post-mining landscapes, as do private landowners who lease mi ning rights to Iluka. An assessment of the impacts of

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75 titanium mining on the economics of forestry would contribute to land management decisions in northeast Florida. Silvicultural practices such as fer tilizing, bedding, and subsoiling may improve tree growth and forest profitabi lity on mined lands while facilitating mined land restoration (Proctor et al., 2003). This chapter assesses the profitability of slash pine production on mined and unmine d lands in northeast Florida and the economic viability of silvicultural treatments that might be used to improve production on mined lands. Methodology Economic Model As described in Chapter 1, Eq.(1-1) defi nes LEV, net returns of a forestry practice projected in perpetuity, where V(t) is the value function of the stand at age t, r is interest rate, and C is the sum of stand establishment co sts discounted to the beginning of the rotation. This model is used to compare bare-land values of mi ned and unmined lands under slash pine production. In this case of a non-coppicing species, the Faustmann model remains fixed for one growth stage per cycle (i.e., one rotation). Calculating LEV on mined land vis--vis unmined land requires that stand establishment cost C be accounted for differently. Topsoil replacement and contouring costs are considered sunk, as these treat ments are required by law regardless of the subsequent land use, and land clearing costs of the first rotation are excluded from the mined land simulations to accurately represen t actual establishment costs on un-vegetated mined land. In these cases, LEV and optimum rotation age are calcu lated using Eq.(4-1), a variation of Eq.(1-1), in which initial costs Ci are accounted for at the beginning of the projection and standard establishment costs C are assumed at the beginning of all

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76 subsequent rotations. Alternativ ely, cost savings at year zero can be added to Eq. (1-1), yielding the same result. () 1rt i rtVtCe LEVC e (4-1) To determine the financial viability of slash pine production on reclaimed titanium mined lands, Equations (1-1) and (4-1) were used to a) compare LEVs of established slash pine stands on 34 mined and 29 unmined sites, b) assess the economic viability of fertilizer and subsoil treatments on mine d lands, c) estimate minimum economically feasible growth response under estimated tr eatment costs, and d) estimate maximum economically feasible treatment costs under pr edicted fertilizer responses. Use of Equations (1-1) and (4-1) re quires that stumpage value V(t) be defined as product price times the yield function for each product. Growth and Yield Model The Plantation Management Research Cooperati ve at the University of Georgia, in conjunction with the forest industry, monitored slash pi ne growth and response to silvicultural treatments. Using data from this study, Pienaar and Rheney (1995) developed growth and yield models for slas h pine plantations. The growth and yield functions require determination of average dominant height and basal area at time t. Average height H(t) is predicted by Eq. (4-2)1: 1.804 0.07345* 0.0691* 123131.36791 *0.305 0.678*0.5461.395*0.412t tSIe Ht zzzzzte (4-2) 1 Units in this chapter are shown in both metric and imperial units to facilitate interpretation within the context of the Florida forest industry. Squared brackets in equations indicate conversion from imperial to metric.

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77 where SI is site index (SI) in m at base age 25, and z1 = 1 if fertilized, 0 otherwise; z2 = 1 if bedded, 0 otherwise; and z3 = 1 if herbicide is applied, zero otherwise. Tree survival (S) is estimated by 1.3451.345 21*ttStNe (4-3) where S (stems ha-1) at year t2 is a function of the number of surviving trees N (stems ha-1) at year t1 (alternatively, both S and N can be expressed as stems acre-1). Using Equations (4-2) and (4-3), basal area (B, m2 ha-1) at time t is predicted by Eq. (4-4): 35.668 6.2053.155 3.394 1.3660.366 0.09* 41312** *0.23 0.557*0.436*2.134*0.354****t tt teHtSt Bt zzzzzte (4-4) Using the above three equati ons, predicted total volume (m3 ha-1) at time t is estimated by Eq. (4-5): 0.320.501 0.820.0171.016***0.07tt pTtHtStBt (4-5) Equation (4-5), subsequent yield functions and eventually Eq. (1-1) are largely a function of SI, which could be used to compar e LEVs of mined lands to unmined lands. Mathey (2001) measured height and dbh in 116 1/50-hectare plots in 1, 2-, 3-, 810-, and 16-year-old plantations on mined and unm ined lands managed by Iluka. However, Mathey found no significant difference between SI s of reclaimed and unmined sites. As variation in SI does not corre spond to similar variation in LEV, and to apply a SI equation using parameters for slash pine, SI s were recalculated for 1/50-hectare plots on ten 8-, eight 10-, four 13-, tw o 15-, five 16and five 21-ye ar-old stands on mined lands, to determine LEVs under a range of SIs on mine d land. SIs were calculated with Eq. (4-6):

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78 2.0669 0.10035*0.9186 1ASIH e (4-6) where H is average height (m) of dominant and co-dominant trees at sample age A and SI is site index (m) at base age 25 (Bailey, 1982). Results of a Students t-Test again showed no significant differences of SI betw een mined and unmined sites, averaging 19.7 m and 21.0 m respectively. To better refl ect observed plot-specific volumes on mined and unmined lands Eq. (4-5) was adjusted following Davis and Johnson (1987) by Eq. (4-7): o o ap o pTt TtTt Tt (4-7) where the total volume prediction equation T(t)p was multiplied by the observed volume T(to)o divided by total predicted volume also at time of observation to. T(to)o (m3) was calculated by Eq. (4-8) 2.05780.74680.00616*0.394**3.28**0.0283obh oTtdH (4-8) where dbh is dbh in cm and H is height in m (Brister et al. 1980)2. Volumes where then summed for each plot multiplied by 50 for volume per ha. Based on the adjusted total volume function derived from Eq. (4-7), produc t-specific volumes were then derived. Eq. (4-9) yields the volume of sawtimber V(t)saw (m3 ha-1) : 3.845.72 0.12 0.52*0.69** *2.54*2.54**0.07dd tdbh St QtQtsawaVtTte (4-9) 2 Alternatively, T(to)o can be calculated in ft3 by eliminating the numbers in brackets, with dbh and H as inches and feet, respectively.

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79 where dt is top merchantable diameter outside bark and and Q(t) is the quadratic mean of dbh, as expressed by Eq. (4-10): 0.005454*0.0144 Bt St Qt (4-10) Chip-and-saw and pulpwood product classes we re then estimated by Eqs. (4-11) and (4-12), respectively: 3.845.72 0.12 0.52*0.69** *2.54*2.54**0.07dd tdbh St QtQtsaw cnsaVtTteVt (4-11) 3.845.72 0.12 0.52*0.69** *2.54*2.54**0.07dd tdbh St QtQtsawcns pwaVtTteVtVt (4-12) An example of pulp, chip-and-saw, saw timber, and total outside bark volumes predicted using Eqs. (4-2)-(4-6) and adjusted to replicate observed volumes using Eq. (4-7)-(4-12) is shown in Figur e 4-3. Northeast Florida merc hantable standards used in this analysis are s hown in Table 4-1. Table 4-1. Merchantable standards of dbh and top diameter outside bark ( dt). dbh dt cm in cm in Sawtimber 24.4 9.6 21.8 7.6 Chip-and-Saw 16.8 6.6 9.1 3.6 Pulpwood 9.1 3.6 9.1 3.6 The value of the stand at time t was then be expressed as sawcnspw s awcnspwVtpVtpVtpVt (4-13)

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80 051015202530 0 50 100 150 200 250 300 Pulp Adjusted Pulp Chip-and-saw Adjusted Chip-and-saw Sawtimber Adjusted Sawtimber Total Predicted Volume Adjusted Total Predicted VolumeTime (years)Volume (cubic meters/hectare) Figure 4-3. Representative pulp, chip-andsaw, sawtimber, and total outside bark volumes (m3 ha-1), predicted using Eqs (4-2)-(4-6) (solid lines) and adjusted to replicate observed volumes (dotted lines) using Eq. (4-7). where psaw, pcns, and ppw are the price per volume of sawtimber, chip-and-saw, and pulpwood products, respectively. Product prices were defined to incorporate Eq. (4-13) into Eq. (4-1) to calculate LEV and optimum rotation age as described in Chapter 2. Market Assessment The conventional softwood forest products market in northeast Florida is much larger and more established than that of Eucalyptus spp. in south Florida. The forest industry has the highest economic impact in Florida of any agricultural crop and contributes over $16.6 b illion annually (Hodges et al. 2004), with most of the states pine inventory in northeast Florida (Carte r & Langholtz, 2005). Assuming constant South-wide softwood demand, removals in Fl orida are projected to increase over the projection period due to relativ e abundance of supply as comp ared to other states. In northeast Florida from 2000-2020, removals are pr ojected to increase sl ightly from 5.9 to 7.0 million m3 (210 to 250 million ft3), and inventory is projected to fluctuate between 59

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81 and 67 million m3 (2.1 and 2.4 billion ft3). Even assuming South-wide removals decrease 1 percent annually, removals in northeast Florida are projected to remain fairly constant until 2020, fluctuating from 6.1 to 5.9 million m3 (216 to 209 million ft3) (Carter & Langholtz, 2005). The Iluka mining operation lies within 32 km (19 miles) of a GeorgiaPacific multi-product sawmill near Palatka a nd is expected to have access to timber markets for the foreseeable future. Mathey (2001) reported results of an ec onomic assessment of slash pine production on Ilukas mined lands. Stumpage values used in said analysis of $93-$407 m-3 ($89$391 green ton-1 assuming 1.04 green tons m-3) are inconsistent with the range of values reported from Timber Mart South over the past 10 years (Figure 4-4). Analysis in this chapter assumes values of $8.10, $26.62, and $41.27 m-3 ($20.19, $66.34 and $102.83 cord-1) for ppw, pcns, and psaw, respectively (Timber Mart-South 1st Quarter 2005 average stumpage prices for Florida)3, typical of prices since 1995 (Figure 4-4). Figure 4-4. South-wide pi ne stumpage prices quarte rly averages from 1995-2005 (Timber Mart South 2005). 3 Assumes about 2.5m3 cord-1 (Appendix A Eq. 3).

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82 Silvicultural Alternatives Fertilizer amendments, weed control, a nd mechanical soil preparation can improve tree growth and contribute to pine plantation productivity (Dickens et al. 2002). However, the benefit of these treatments on mined lands is not well known. On mined lands in the Appalachian region, inorganic N fertilizer ame ndments increased herbaceous biomass production during the first growing seas on but did not affect hybrid loblolly pine ( P. taeda ) growth at 2 and 3 years of age. Increased seedling growth with organic amendments was more a function of moisture retention than soil nutrient availability (Schoenholtz et al. 1992). In a reforestation experiment testing the growth of three pine species on surface-mined sites in coalfields of southwest Virginia, fertilization had little effect on growth and was not as beneficial for tree establishment as an herbicide treatment (Torbert et al. 2000). Mathey (2001) and Proctor (2002) established field tria ls at Iluka testing the influence of fertilizer, herbicide, and subso il treatments on slash pine and loblolly pine growth and survival on mined and unmined la nds. SRWC-84, established December 9, 1999, tested 10 combinations of fertilizer/her bicide treatments (Table 4-2), and heights were measured at 1, 2, 3 and 5 years of age. SRWC-84-2001, established January 9, 2001, included treatments of 13 fertilizer/herbi cide combinations (Table 4-2), as well as mycorrhization, humate incorporation, and subsoiling on the mined site, and was measured at 1, 2, and 4 years of age. In the SRWC-84 study, height and survival re sponses at 1, 2, 3, and 5 years of age were significantly different for both land type (satellite mined and unmined) and treatment (p< .0001). At age 5, trees averag ed 4.2 m (14 ft) and 5.5 m (18 ft) tall on mined and unmined land, respectively, but survival was better on the mined land,

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83 averaging 85% and 50% on mined and unmined la nd, respectively. This trend of reduced growth but increased survival on mined land co mpared to unmined land is consistent with measurements of young stands by Mathey (2001). On the mined land, treatment G2 had the highest average height at 2, 3 and 5 y ears of age (Figure 4-5). A Duncan grouping analysis was used to identify treatments of similar growth and survival (Table 4-3). On the mined land at ages 2 and 3, treatments G2 and B2 were grouped with highest growth; at age 5 heights of treatments G2>B2>D2 >M0 were grouped highest, averaging 4.4 m tall, suggesting better response to post-establishmen t fertilizer application. On unmined land height, responses to treatments showed less variation (Figur e 4-7) ranging from Table 4-2. Treatments included in the SRWC-84 and SRWC-84-2001 studies (Proctor, 2002). All fertilizer applications were a pplied at a rate of 40.3 kg N/ha (36lbs N/ac). Treatment Descriptio n SRWC-84 SRWC-842001 C Bedding only, no amendment X X G2 Granulite 5-3-0, broadcast in year 2 X X D2 DAP 18-46-0, broadcast in year 2 X X B2 16-4-8 with balanced micronutrients, broadcast in year 2 X X G0R Granulite 5-3-0, br oadcast at planting, herbicide treatment X X D0R DAP 18-46-0, broadcast at planting, herbicide treatment X X B0R 16-4-8 with balanced micronutrients, broadcast at planting, herbicide treatment X X G0RL Loblolly, Granulite 5-3-0, broadcast at planting, herbicide treatment X X H0 Dry aluminum humate broadcast at planting at .35% by weight X X M0 Mycorrhizal treatment at the time of planting, bedding only X X G0H Granulite 5-3-0, broa dcast at planting, and humate material at .35% X D0H DAP 18-46-0, broad cast at planting, and humate material at .35% X B0H 16-4-8 with balanced micronutrients, and humate material at .35% X

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84 4.8-5.7 m with all nine treatments sharing the same Duncan grouping by age 5 (Table 43). Average heights by treatment of SR WC-84 and SRWC-84-2001 on January 15, 2005 are shown in Figure 4-13. The survival res ponse to treatment on the mined land varied little, ranging from 78-97%, with two Duncan groups, both including the control (Figure 4-6), while survival on the unmined land vari ed by treatment from 8%-86% (Figure 4-8) resulting in five Duncan groups. Table 4-3. SRWC-84 age 5 and SRWC-842001 age 4 mined (SM) and unmined (UM) average heights, standard deviation a nd Duncan grouping ranked from tallest to shortest by treatment. Ranking SRWC-84 SM Age 5 SRWC-84 UM Age 5 2001 SM Age 4 2001 UM Age 4 1 G2, 4.6, 0.8, a G2, 5.7, 0.8, a B0R, 2.9, 23, 0.5, a B0R, 4.0, 0.4, a 2 B2, 4.5, 0.9, a G0RL, 5.7, 1.3, a D0H, 2.8, 15, 0.7, ab D2, 3.7, 0.5, ab 3 D2, 4.2, 0.9, ab D2, 5.5, 1, a D0R, 2.8, 25, 0.6, ab D0R, 3.7, 0.7, ab 4 M0, 4.2, 1.1, ab C, 5.4, 0.9, a B2, 2.6, 49, 0.7, abc G2, 3.7, 0.5, ab 5 G0R, 4.1, 0.9, bc B0R, 5.4, 0.4, a M0, 2.5, 40, 0.5, abcd B2, 3.6, 0.7, bc 6 H0, 4.1, 0.8, bc D0R, 5.3, 0.6, a G2, 2.5, 60, 0.8, abcd G0R, 3.3, 0.4, c 7 B0R, 4, 1, bc B2, 5.2, 1, a D2, 2.5, 44, 0.7, bcde G0RL, 2.9, 0.3, d 8 C, 3.9, 0.9, bc M0, 5, 0.8, a C, 2.2, 61, 0.5, cdef C, 2.8, 0.7, d 9 D0R, 3.7, 1.2, d G0R, 4.8, 1, a G0H, 2.2, 17, 0.5, defg 10 G0RL, 3.3, 0.6, d G0RL, 2.2, 30, 0.7, fg 11 H0, 2.1, 30, 0.6, fg 12 B0H, 2.1, 16, 0.8, fg 13 G0R, 1.8, 26, 0.6, g

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85 Table 4-4. SRWC-84 age 5 and SRWC-842001 age 4 mined (SM) and unmined (UM) average survival (%) and standard devi ation by treatment, ranked from highest to lowest. Letter a indicates shared highest Duncan group. Ranking SRWC-84 SM Age 5 SRWC-84 UM Age 5 2001 SM Age 4 2001 UM Age 4 1 G0RL, 97, 5.9, a G0RL, 86, 7.7, a G0H, 94, 17, 0.5, a G0RL, 81, 0.3, a 2 D0R, 92, 1.2, 5.9, ab G2, 65, 4.5, b G0RL, 92, 30, 0.7, a G2, 73, 0.5, a 3 B0R, 92, 5.9, ab D2, 59, 4.5, b B0H, 89, 16, 0.8, a C, 69, 0.7, a 4 G2, 92, 3.4, ab B2, 58, 7.7, b G2, 88, 60, 0.8, a G0R, 66, 0.4, ab 5 H0, 89, 5.9, ab C, 48, 4.5, bc C, 88, 61, 0.5, a D0R, 61, 0.7, ab 6 B2, 83, 3.4, ab G0R, 31, 7.7, cd H0, 86, 30, 0.6, ab D2, 52, 0.5, b 7 C, 82, 3.4, 0.9, ab D0R, 31, 7.7, cd D0H, 83, 15, 0.7, ab B2, 31, 0.7, c 8 G0R, 81, 5.9, 0.9, b M0, 17, 7.7, de G0R, 81, 26, 0.6, ab B0R, 25, 0.4, c 9 M0, 81, 5.9, b B0R, 8, 7.7, e M0, 74, 40, 0.5, ab 10 D2, 78, 3.4, b B2, 69, 49, 0.7, ab 11 D0R, 69, 25, 0.6, ab 12 D2, 64, 44, 0.7, b 13 B0R, 64, 23, 0.5, b 0 1 2 3 4 5 6 012345Age (years)Height (m) B0R B2 C D0R D2 G0R G0RL G2 H0 M0 Figure 4-5. Average heights (m) by age (y ear) and treatment, SRWC-84 mined site.

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86 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0246 Age (years)Survival B0R B2 C D0R D2 G0R G0RL G2 H0 M0 Figure 4-6. Average survival (%) by age (year) and treatment, SRWC-84 mined site. 0 1 2 3 4 5 6 012345Age (years)Height (m) B0R B2 C D0R D2 G0R G0RL G2 M0 Figure 4-7. Average heights (m) by age (yea r) and treatment, SRWC-84 unmined site.

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87 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0123456 Age (years)Survival B0R B2 C D0R D2 G0R G0RL G2 M0 Figure 4-8. Average survival (%) by age (year) and treatment, SRWC-84 unmined site. As with SRWC-84, the mined land in SRWC-84-2001 produced lower heights and higher survival than the unmined land (Fi gure 4-9-Figure 4-12). At age four, trees averaged 2.4 m (8 ft) and 3.4 m (11 ft) tall on mined and unmined land, respectively, with survival averaging 78% and 61% on mined and unmined land, respectively. Height and survival responses at 1, 2, and 4 years of age were significantly different for mined and unmined land and treatment (p< .0001). At age four, six treatments on the mined site were grouped higher than the control, includi ng three of the four top-grouped treatments from the SRWC-84 study (Table 4-3). The av erage of the top Duncan height group at age four was 2.6 m (9 ft). No treatment survival was grouped higher than that of the control on mined land. Survival varied on the unmined land with treatment (Figure 412).

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88 0 1 2 3 4 01234Age (years)Height (m) B0R B2 C D0R D2 G0R G0RL G2 H0 M0 B0H CHP D0H G0H Figure 4-9. Average heights (m) by age (yea r) and treatment, SRWC-84-2001 mined site. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%01234Age (years)Survival B0H B0R B2 C CHP D0H D0R D2 G0H G0R G0RL G2 H0 M0 Figure 4-10. Average survival by age (yea r) and treatment, SRWC-84-2001 mined site.

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89 0 1 2 3 4 01234Age (years)Height (m) B0R B2 C D0R D2 G0R G0RL G2 M0 Figure 4-11. Average heights (m) by age (y ear) and treatment, SRWC-84-2001 unmined site. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%01234Age (years)Survival B0R B2 C D0R D2 G0R G2 M0 G0RL Figure 4-12. Average survival by age (year ) and treatment, SRWC-84-2001 unmined site.

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90 0 1 2 3 4 5 6 72001 SM H42001 UM H4SRWC84 SM H5SRWC84UM H5 Study/Type/Treatment/AgeHeight (m) C G0R G2 G0H G0RL D0R D2 D0H B0R B2 B0H M0 H0 Figure 4-13. Height (m) by treatment of SR WC-84-2001 (age 4) a nd SRWC-84 (age 5), satellite mined (SM) and unmined (UM) Error bars indicate standard deviation. Tilling with three-in-one plow and other subsoiling me thods replaced shear-rakepile-bed as the most frequent site prepar ation technique in the South between 2002 and 2004 (Smidt et al. 2005). Subsoiling was identified as being an accessible and potentially beneficial treatm ent on mined lands (Proctor et al. 2003), as soil compaction on mined lands was negatively correlated wi th slash pine growth (Mathey, 2001). Average age-four heights of subsoil treat ments in SRWC-84-2001 were not higher than non-subsoiled treatments (Figure 4-14) though subsoiling was associated with sustained survival of 84% compared to 72% at age 4 (Figure 4-15).

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91 0 0.5 1 1.5 2 2.5 3 012345 Age (years)Height (m) Subsoiled Not Subsoiled Figure 4-14. Average heights (m) of subso iled and not subsoiled treatments on SRWC84-2001 mined land. Error bars indicate standard deviation. 0 20 40 60 80 100 120 140 012345 Age (years)Survival (%) Subsoiled Not Subsoiled Figure 4-15. Average survival (%) of subso iled and not subsoiled treatments on SRWC84-2001 mined land. Bars show standard deviation. Based on results from SRWC-84-2001 and SRWC-84, height can be improved about 0.4 and 0.5 m (1.2 and 1.6 ft) over the c ontrol at 4 and 5 years of age, respectively, through fertilizer application. No fertilizer treatment was clearly associated with high survival, though results from SRWC-84-2001 su ggest that survival of young stands can be improved about 12% at age four through subs oiling (Table 4-5). Herbicide application

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92 was not in the top four treatments of SRWC -84 at age 5, suggesting that on recently reclaimed mined lands, c ontrol of competing vegetation is not needed. Table 4-5 Average of top Duncan group surv ival (by subsoiling treatment for SRWC-842001 and by fertilizer treatment for SRWC84), height (by fertilizer treatment) and SI (base age 25, from Eq. (4 -6)) of SRWC-84 and SRWC-84-2001. SRWC-84-2001 SRWC-84 Age 4 Age 5 Survival m ft SI (m) SI (ft) Survivalm ft SI (m) SI (ft) Top Duncan group average 84% 2.68.521.6 71 90% 4.4 14.4 25.3 83 Control average 72% 2.27.418.3 60 82% 3.9 12.8 22.3 73 Difference 12% 0.41.23.4 11 7% 0.5 1.6 3.0 10 Options to translate height and surviv al improvements of young plantations to improved long-term volume yields in Eq. (4-5) and profitability in Eq (4-1) are limited. In this chapter the impact of improved grow th and survival on LEV was assessed in two ways. First, Eq. (4-6) was used to calcula te SI of young stands assuming average heights of top Duncan groups identified above. Re sulting SIs were 21.6m and 25.3m (71ft and 83ft), higher than observed SI average of 821year-old stands (19.7m/65ft) (Table 4-5). These SIs were then used with estimates of improved survival associated with subsoiling in the volume prediction Eq. (4-5) to calculate LEV. Results of SI calculations on trees younger than 10 years must be used with cau tion and, if possible, compared with alternative methods (below). The second way to assess economic impacts of soil amendments was to calculate LEVs assuming the average SI of 821-year -old stands on mined lands and assuming a fertilizer treatment was applied by z1 in Eq. (4-2). Costs associated with fertilizer and subsoil amendments were defined for use in Eq. (4-1).

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93 Cost Assumptions Results from SRWC-84 and SRWC-84-2001 suggest treatments G2 and B2 (Table 4-2) contribute to improved heights on mine d lands. Average southeast U.S. costs of treatment options reported by Smidt (2005) (s hown in Table 4-6) generally declined between 2002 and 2004, countering cost trends since the 1980s. This cost decrease is also counter-intuitive with regards to the 65% increase in the Produ cer Price Increase of #2 diesel fuel during the same years. Smidt et al. (2005) speculate that declining stumpage prices reduced investment in planta tion forestry and thus demand for plantation establishment and treatment services. Table 4-6. 2004 Average pine plantation es tablishment costs for the southeast U.S. (Smidt et al. 2005). Values for the Southern Coastal Plain were used where available. Treatment Cost ($ ha-1) Cost ($ acre-1) Bed $249.94 $101.19 Shear, rake and pile $461.77 $185.93 Shear, rake, pile and bed $493.26 $199.70 Three-in-one plow (subsoiling, di sking and bedding) $297.56 $120.47 Machine planting, cutover land $144.27 $58.41 Fertilizer, young plantation $86.45 $35.00 Fertilizer, established plantation $134.54 $54.47 Bulk fertilizer prices in northeast Florida as of June 2005 were $220 and $110 Mg-1 ($200 and $100 ton-1) for 16-8-4 with micros and Granul ite (5-3-0) respectively, but were subject to change within 30 days (H elena Chemical Company, Alachua, and AgroDistribution Company, Waldo, pers. comm., June 22nd, 2005). At rates of 252 and 1,343 kg ha-1 (225 and 1200 lbs acre-1), prices after application (assuming $25 ha-1 [$10 acre-1]) were $80 and $173 ha-1 ($33 and $70 acre-1). Average total fertilizer treatment costs for the Southern Coastal Plain in 2004 were $135 and $86 ha-1 ($54 and $35 acre-1) for post-establishment aerial and pre-estab lishment non-aerial app lication respectively, (Smidt et al. 2005). Due to the volatile nature of fe rtilizer prices and variation of local

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94 application costs, the 2004 Southern Coastal Plain aver age pre-establishment price identified by Smidt et al. (2005), comparable to the June 2005 price for 16-8-4 with micros, was used in this chapter. Cost scenarios assumed in this analysis ar e described in Table 4-7. Costs assessed on the plots in the established stands included two scenarios: standard reflecting current practices in northeast Florida and mined simp le excluding land clearing costs not needed on the first rotation of reclaimed mined lands Cost scenarios used to estimate the economic benefit of soil amendments on young plantations included mined simple with fertilizer assuming improved height, mined improved assuming improved survival, and mined improved with fertilizer assuming in creased height and survival. The mined improved scenario assumes a three-in-one plow treatment (subsoiling, disking and bedding). Additionally, standard fertilized twic e and mined simple fertilized twice cost scenarios were used to in conjunction with the fertilized predicti on equation developed by Pienaar and Rheney (1995), desc ribed below. All scenarios assumed a 7% real interest rate. Table 4-7. Cost scenarios based on Smidt et. al (2005). Scenario Treatments Cost ($ ha-1) Cost ($ acre-1) Standard Shear, rake, pile, bed, machine planting $638 $258 Mined simple Shear, rake and pile excluded on first rotation, bed, machine planting $395 1st rotation, $638 all other rotations $160 1st rotation, $258 all other rotations Mined Improved a Shear, rake and pile excluded on first rotation, three-in-one plow, machine planting $442 1st rotation, $674 all other rotations $179 1st rotation, $273 all other rotations Standard with fertilizer Shear, rake, pile, bed, machine planting, and fertilizer at age 2 $714 $289 Mined simple with fertilizer Bed, machine planting, fertilizer at age 2 (discounted to age 1) $469 1st rotation, $714 all other rotations $190 1st rotation, $289 all other rotations a Estimated combined costs.

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95 Table 4-7 continued. Scenario Treatments Cost ($ ha-1) Cost ($ acre-1) Mined improved with fertilizer Three-in-one plow, machine planting, fertilizer at age 2 (discounted to age 1) $516 1st rotation, $748a all other rotations $209 1st rotation, $303a all other rotations Standard fertilized twice Shear, rake, pile, bed, machine planting, fertilized at year 1 and 12 $776 $314 Mined simple fertilized twice Shear, rake and pile excluded on first rotation, bed, machine planting, fertilized at year 1 and 12 $534 1st rotation, $776 all other rotations $216 1st rotation, $314 all other rotations Simulations LEVs of the established stands were estimated assuming volume prediction equations adjusted based on observed volumes of 63 sample plots in slash pine stands (34 mined and 29 unmined) (Table 4-8). Average SIs of 19.7 and 21.0 m (65 and 69 ft) were used on mined and unmined land, respectively. LEVs of unmined land were estimated under the standard cost scenario, while mine d lands were estimated under standard and mined simple cost scenarios. Table 4-8. Land type, measurement age, measurement date, and number of 63 1/50th ha plots used in the analysis of established stands. Type Age at measurement (years) Measurement Date Number of plots Mined 8 November 2004 4 Mined 8 November 1999 6 Mined 10 November 1999 8 Mined 13 November 2004 4 Mined 15 November 2004 2 Mined 16 November 1999 5 Mined 21 November 2004 5 Unmined 8 November 2004 3 Unmined 9 November 1999 6 Unmined 10 November 1999 7 Unmined 14 November 2004 4 Unmined 15 November 2004 3 Unmined 16 November 1999 6

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96 To elucidate the economic impact of silvicultural treatments on LEV, nine simulations were assessed: 1. Mined improved survival : SI=19.7m (65 ft), survival=1,643 trees ha-1 (665 acre-1)4 at age 4, mined improved cost scenario representing unimproved growth with improved survival that might be achieved with subsoiling. 2. Mined simple fertilized low : SI=21.6m (70 ft), survival=1,408 trees ha-1 (570 acre-1)5 at age 4, mined simple with fertiliz er cost scenario, representing observed survival and improved SI attributable to fertilizer treatments of SRWC-84-2001 at age 4. 3. Mined simple fertilized high : SI=25.3m (83 ft), survival=1,408 trees ha-1 (570 acre-1) at age 4, mined simple with fertiliz er cost scenario, representing observed survival and improved SI attributable to fertilizer treatments of SRWC-84 at age 5. 4. Mined improved fertilized low : SI=21.6m (70 ft), survival=1,643 trees ha-1 (665 acre-1) at age 4, mined improved with fer tilizer cost scenario, representing improved survival attributable to su bsoiling in SRWC-84-2001 and improved SI attributable to fertilizer treat ments of SRWC-84-2001 at age 4. 5. Mined improved fertilized high : SI=25.3m (83 ft), survival=1,643 trees ha-1 (665 acre-1) at age 4, mined improved with fer tilizer cost scenario, representing improved survival attributable to su bsoiling in SRWC-84-2001 and improved SI attributable to fertilizer tr eatments of SRWC-84 at age 5. Results of 1-5 above were compared with the following simulations on observed SIs in established stands and predicted yiel ds assuming fertilizer responses modeled by Pienaar and Rheney (1995): 6. Average unmined : SI=21.0m (69 ft), survival=1,235 trees ha-1 (500 trees acre-1) 6 at age 10, standard cost scenario, refl ecting observed volume and survival of 10year-old unmined stands. 7. Average mined : SI=19.7m (65ft), survival=1,235 trees ha-1 (500 trees acre-1) at age 10, mined simple cost scenario, reflec ting observed volume and survival of 10year-old mined stands. 8. Average unmined fertilized twice (Pienaar and Rheney 1995) : SI=21.0m (69 ft), survival=1,235 trees ha-1 (500 acre-1) at age 10, standard fertilized twice cost scenario, reflecting observed volume and survival of 10-year-old mined stands, 4 Representing 84% survival of 1,955 trees ha-1 (792 trees acre-1) at age 4 achieved through subsoiling in SRWC-84-2001; equals 1,541 TPH (624 TPA) at age 10 by Eq. (4-3). 5 Representing 72% survival of 1,955 trees ha-1 (792 trees acre-1) at age 4 representing the control in SRWC-84-2001; equals 1,321 TPH (535 TPA) at age 10 by Eq. (4-3). 6 Equals 1,317 TPH (533 TPA) at age 4 by Eq. (4-3).

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97 with fertilizer response predicted by Equations (4-2) and (4-4) by Pienaar and Rheney (1995)7. 9. Average mined fertilized twice (Pienaar and Rheney 1995) : SI=19.7m (65 ft), survival=1,144 trees ha-1 (500 trees acre-1) at age 10, mined simple fertilized twice cost scenario, reflecting observed volume a nd survival of 10-year-old mined stands, with fertilizer response predicted by Equations (4-2) and (4-4) by Pienaar and Rheney (1995)3. Total above-ground outside-bark growth cu rves of the above nine simulations derived from Equations (4-2) though (4-5) ar e shown in Figure 4-16. Yield curves of simulations #2 and #4, using SIs of 21.6 m calcu lated from the average of the top Duncan fertilizer response group of SRWC-84-2001, ar e comparable to curves using the average SI of unmined lands with fertilizer treatme nt simulated by Pienaar and Rheney (1995). Curves of simulations #3 and #5, using SI of 25.3 m calculated from the top Duncan fertilizer response group of SRWC -84 at age five are inconsiste nt with other predictions. Notably, the SI of the control treatment on SR WC-84 is higher than the average SI of the top Duncan fertilizer response group of SRWC-84-2001, sugges ting site-specific conditions influenced growth on the experimental plots, as it has th e established stands. Observed SIs in mature stands range from 12-28m, with 25.3 m in the top 93%, suggesting the high yield curves generated from fertilizer response used in simulations #3 and #5 are exceptional. 7 The fertilizer treatment simulated by Pienaar and Rheney (1995) assumes 280 kg ha-1 (250 lbs acre-1) DAP after the first growing season followed by 168, 56 and 112 kg ha-1 (150, 50, and 100 lbs acre-1) of N, P and K broadcast after the 12th growing season, costing a total discounted to year zero of $138 ha-1 ($56 acre-1) based on Smidt et al. (2005) and assuming a discount rate of 7%.

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98 051015202530 0 100 200 300 400 500 600 1. Mined improved survival. 2. Mined simple fertilized low. 3. Mined simple fertilized high. 4. Mined improved fertilized low. 5. Mined improved fertilized high. 6. Average unmined. 7. Average mined. 8. Average unmined fertilized twice (Pienaar and Rheney 1995). 9. Average mined fertilized twice (Pienaar and Rheney 1995).Time (years)Volume (cubic m/hectare) Figure 4-16. Total predicted a bove-ground inside-bark volume (m3 ha-1) for simulations 1-9. Yields assumed in the nine simulations were used in conjunction with case-specific costs to calculate LEVs, internal rate of return (IRR) and optimum rotation ages using Equations (1-1) and (4-1). To solve for IRR, the interest rate i of Equations (1-1) and (4-1) were modified iteratively until LEV equaled zero. Results Established Stands Assuming a standard cost scenario LEVs averaged $1,115 and $1,076 ha-1 ($451 and $435 acre-1) on mined and unmined land, respectively (Table 4-9). LEVs of mined

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99 stands varied from -$549 to $5,137 ha-1 (-$222 to $2,080 acre-1), reflecting the wide range of SIs on mined lands (F igure 4-17). Observation of the relationship between SI and LEV on mined and unmine d lands reveals a Simpsons Paradox: average SI on mined lands is 6% lower than unmined lands, though average LEV is almost 4% higher, as there was an exponential trend between SI and LEV (Figure 4-17). Assuming a simplified cost scenario on mined lands (excluding land cleari ng costs of the first rotation) increased LEVs $242 ha-1 ($98 acre-1) to an average $1,357 ha-1 ($549 acre-1), $281 ha-1 ($114 acre-1) higher than that of the unmined lands (T able 4-9, Figure 417, and Figure 4-18). Table 4-9. Number of plot s, average SI, SI standard deviation, average LEV, and standard deviation of LEV based on obs erved volume and survival on mined and unmined lands. Mined (standard costs) Unmined (standard costs) Mined (reduced costs) Number of plots 34 29 34 Average SI (m) 19.7 21.0 19.7 Average SI (ft) 65 69 65 SI Standard Deviation (m) 3.6 2.3 3.6 SI Coefficient of variation 18% 11% 18% Average LEV ($ ha-1) $1,115 $1,076 $1,357 Average LEV ($ acre-1) $451 $435 $549 LEV St Dev ($ ha-1) $1,111 $744 $1,111 LEV Coefficient of variation99% 64% 81%

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100 $1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 051015202530 SI (m)LEV ($/hectare) Mined standard Unmined standard Mined simple Figure 4-17. LEV ($ ha-1) by SI (m, base age 25) for 34 and 29 stands (Table 4-8) on mined and unmined lands, respectively. The standard and mined simple cost scenarios are shown for the 34 stands on mined land, while the standard cost scenario only is shown for the 29 stands on unmined land. Figure 4-18. LEV ($ acre-1) by SI (ft, base age 25) for 34 and 29 stands (Table 4-8) on mined and unmined lands, respectively. The standard and mined simple cost scenarios are shown for the 34 stands on mined land, while the standard cost scenario only is shown for the 29 stands on unmined land.

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101 Soil Amendments on Young Plantations Details of each simulation including SI, a ssumed survival, age of assumed survival, initial rotation establishment cost, subsequent rotation establishment cost, LEV and optimum rotation age are summarized in Tabl e 4-10. Excluding simulations #3 and #5, LEVs range from $881 to $1,408 ha-1 ($356 to $570 acre-1), which are within the range of LEVs typical for the southeast U.S. ranging from $410 to $2,300 ha-1 ($166 to $933 acre-1) also assuming a real discount rate of 7% (Borders & Bailey, 2001). Table 4-11 compares volumes at age 20, first rotati on costs, LEV, and IRR of the mined land simulations, excluding simulation #3 and #5. Simulated silvicultural investments increased yields but did not always increase LEV or IRR under the assumed costs scenarios. The subsoiling treatment without fertilization associated with simulation #1, while it improved volume yields over the av erage mined scenario, did not increase LEV or IRR. Simulation #9 incorporating the fertilizer response modeled by Pienaar and Rheney (1995) increased yield, LEV and I RR over the average mined simulation #7, though not as much as simulations #2 and #4. Table 4-10. SI (base age 25), survival, age of survival, cost of initial rotation, cost of subsequent rotations, LEV and optim um rotation age of 9 comparative simulations described in the text. Simulation SI (m; ft) Survival (TPH; TPA) Age of observed survival (year) Cost, initial rotation ($/ha; $/acre) Cost, subsequent rotations ($/ha; $/acre) LEV ($/ha; $/acre) Optimum rotation age (year) 1. Mined improved survival 19.7; 65 1643; 665 4 $442; $179 $674; $273 $881; $356 22 2. Mined simple fertilized low 21.6; 71 1408; 570 4 $469; $190 $714; $289 $1,416; $573 21 3. Mined simple fertilized high 25.3; 83 1408; 570 4 $469; $190 $714; $289 $2,990; $1210 20

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102 Table 4-10 Continued. Simulation SI (m; ft) Survival (TPH; TPA) Age of observed survival (year) Cost, initial rotation ($/ha; $/acre) Cost, subsequent rotations ($/ha; $/acre) LEV ($/ha; $/acre) Optimum rotation age (year) 4. Mined improved fertilized low 21.6; 71 1643; 665 4 $516; $209 $748; $303 $1,408; $570 21 5. Mined improved fertilized high 25.3; 83 1643; 665 4 $516; $209 $748; $303 $3,049; $1234 20 6. Average unmined 21.0; 69 1235; 500 10 $638; $258 $638; $258 $1,045; $423 21 7. Average mined 19.7; 65 1144; 500 10 $395; $160 $638; $258 $896; $363 22 8. Average unmined fertilized (Pienaar and Rheney 1995) 21.0; 69 1235; 500 10 $776; $314 $776; $314 $1,280; $518 20 9. Average mined fertilized (Pienaar and Rheney 1995) 19.7; 65 1144; 500 10 $534; $216 $776; $314 $1,058; $428 20 Table 4-11. Volume, cost, LEV and IRR of comparative mined land simulations. Simulation Volume at Age 20 (m3 ha-1) Volume Rank at Age 20 Cost, initial rotation ($/ha; $/a) LEV ($/ha; $/a) LEV rank IRR IRR rank 4. Mined improved fertilized low 285 1 $516; $209 $1,408; $570 2 12.4% 2 2. Mined simple fertilized low 273 2 $469; $190 $1,416; $573 1 12.7% 1 9. Average mined fertilized twice (Pienaar and Rheney 1995) 243 3 $534; $216 $1,058; $428 3 11.5% 4 1. Mined improved survival 235 4 $442; $179 $881; $356 5 11.3% 5 7. Average mined SI 210 5 $395; $160 $896; $363 4 11.7% 3

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103 Sensitivity Analysis Recognizing the uncertainty of growth response to fertilizer treatment, a sensitivity analysis was performed to estim ate minimum growth response (expressed as SI) needed for various cost scenarios to match the LEV of a base scenario ($897 ha-1 [$363 acre-1] assuming average SI (19.7 m, [65 ft]) of the 34 established stands on mined lands, average age ten mined land survival of 1,144 TPH (500 TPA), the mined simple cost scenario and a discount rate of 7%). An intensive cost scenario (mined improved fertilized twice, including three-in-one pl ow and fertilizing at both earlyand midrotation, and excluding land cleari ng costs the first rotation) was added. Results (Table 4-12) indicate SI increases of up to 0.9 m (3 ft) equali ng volume gains of up to 25 m3 ha-1 (342 ft3 acre-1) at age 20 are needed to justify first rotation site preparation costs increases of up to $186 ha-1 ($75 acre-1). Site preparation and stand treatment cost s are subject to change, and Iluka could perform some treatments such as subsoiling at reduced costs us ing proprietary equipment. A second sensitivity analysis was performed to assess the maximum tolerable treatment costs possible to meet or exceed the base scenarios LEV ($898 ha-1 [$363 acre-1] assuming the mined average simulation #7, expressed as a percentage increase of the mined simple cost scenario (Table 4-7) assuming the growth responses used in simulations #1, #2, #4, and #9. Results (Tab le 4-13) show that to achieve the growth response estimated by Pienaar and Rheney (1995 ), establishment costs could be increased up to 58% of those of the mined simple cost scenario while still being economically viable, while even higher costs assuming th e growth responses in the mined simple fertilized low and mined improved fertili zed low scenarios could be tolerated.

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104 Table 4-12. Minimum growth response in SI ne eded to meet or exceed a base scenarios LEV ($898 ha-1 [$363 acre-1] assuming the mined average simulation #7), optimum rotation age of 5 cost scenar ios. All scenarios assume average mined land age 10 survival of 1,144 TP H (500 TPA), and a discount rate of 7%. Cost Scenario Minimum SI (m; ft) Cost, initial rotation ($/ha; $/acre) Cost, subsequent rotations ($/ha; $/acre) Optimum rotation age (year) Volume at Age 20 (m3 ha-1; ft3 acre-1) Mined simple with fertilizer 20.1; 66 $469; $190 $714; $289 22 220; 3,147 Mined Improved a 20.0; 66 $442; $179 $674; $273 22 218; 3,110 Mined improved with fertilizer 20.3; 67 $516; $209 $748; $303 22 226; 3,224 Mined simple fertilized twice 20.4; 67 $534; $216 $776; $314 22 228; 3,262 Mined improved fertilized twice 20.6; 68 $581; $235 $812; $329 22 234; 3,340 a Estimated combined costs. Table 4-13. Maximum initial and subsequent rotation establishment costs tolerated to meet or exceed a base scenarios LEV ($898 ha-1 [$363 acre-1] assuming the mined average simulation #7), % increase over simple mined cost scenario, optimum rotation age and volume at age 20 for simulations #1, #2, #4 and #9. Simulation Maximum cost, initial rotation ($/ha; $/acre) Maximum cost, subsequent rotations ($/ha; $/acre) % cost increase over mined simple Optimum rotation age (year) Volume at Age 20 (m3 ha-1) 1. Mined improved survival $423; $171 $682; $276 7% 22 235 2. Mined simple fertilized low $818; $331 $1,320; $534 107% 22 273 4. Mined improved fertilized low $878; $345 $1,273; $556 116% 22 285 9. Average mined fertilized (Pienaar and Rheney 1995) $623; $252 $1,005; $407 58% 21 243

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105 Conclusions Average LEVs of stands established on the mined lands studied here varied widely with the range of observed SIs, but on aver age are profitable and similar to those of unmined lands, and were increased when land clearing costs of the first rotation establishment are eliminated to reflect pos t-mining site conditions. As growth and silviculture on mined and unmined lands are similar, sensitivity of LEV and rotation age to management costs on mined lands are likely to be comparable to conventional slash pine culture in northeast Florida. Predicted volumes, LEVs and IRRs of fe rtilized slash pine plantations on mined lands (based on SI calculations of 4and 5-year-old height responses to fertilizer) are greater than those predicted using a model by Pien aar and Rheney (1995) (assuming observed SI and survival of stands establ ished on mined lands). Results from SRWC-842001 failed to demonstrate improved growth response to subsoiling but show survival improved by 17% at age 4. Under the cost estimates here, increased survival achieved by subsoiling did not increase LEV. However, a sensitivity analysis finds that that if site preparation with subsoiling can be achieved cheaper than $423 ha-1 ($171 acre-1) in the first rotation and $682 ha-1 ($276 acre-1) in subsequent rotations, subsoiling would be profitable. It is not known if mid-rotation fertili zation would be needed to sustain SI improvements and volume responses modele d by SRWC-84 and SRWC-84-2001, nor is it known if mined lands respond to fertilizer appl ications differently than unmined lands. More research is needed to quantify this response to assess the economic viability of silvicultural treatments on mined lands, t hough the model by Pienaar and Rheney (1995) seems to conservatively quantify fertilizer response, and is probably the best model

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106 available to date. Even these modest yiel d responses to fertilizer are shown to be economically viable assuming 2004 average stand treatment costs. A sensitivity analysis suggests that minimum SI responses needed to justify even the most expensive treatment scenario is 20.6 m (68 ft), producing 234 m3 hectare (3,340 ft-3 acre-1) at age 20, achievable by even the modest response of 235 m3 hectare (3,360 ft-3 acre-1) projected by the subsoiling treatment or 243 m3 hectare (3,474 ft-3 acre-1) projected by Pienaar and Rheney (1995). A sensitivity analysis also s hows that current cost estimates are less than maximum tolerable fertilizer co sts to achieve modeled growth responses. A discount rate less than 7% or stumpage prices higher than those used here would allow these required minimum growth responses and maximum treat ment costs to decrease and increase, respectively. Future Research While growth of slash pines on mined la nd is comparable to that on unmined land, more information is needed regard ing slash pine growth response to soil amendments on mined land. Continued m easurements of the SRWC-84 and SRWC-842001 field studies would improve understand ing of growth responses and economic implications. It is not known if observed fertilizer responses at 4 and 5 years old results in increased yields over the duration of th e rotation. Applica tion of a mid-rotation fertilization treatment may be needed to sustain growth improvements and should be incorporated in continuation of the studies. Higher rates of select fertilizers on larger blocks might reduce border effects and verify responses on mined lands. Future research could also assess if the productivity on the la nds mined by Iluka near Green Cove Springs is characteristic of ot her titanium mined lands in North east Florida, and if reclamation practices have improved.

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107 Results to date fail to demonstrate im proved height growth associated with subsoiling. However, as Darfus and Fi sher (1984) found heterogeneous reclamation conditions detrimental to slash pine growth and Mathey (2001) found highly variable productivity on mined lands nega tively correlated with bulk density, it is possible that subsoiling could be used to spot-treat particul arly compacted sites; more subsoiling trials are inexpensive and recommended. Monitoring of growth and soil conditions of second-rotation plantations on mined lands could help determine if decomposing r oot systems contribute to second-rotation productivity. Research is needed to assess th e potential of cover cr ops to stabilize soil on mined lands and facilitate slash pine esta blishment, possibly while contributing to wildlife management objectives. Efforts could be made to capitalize on the lowvegetation post-mining conditions through the es tablishment of agroforestry systems or through pine straw mulch production achieva ble though high-density plantings, though nutrient removals by pine straw harvests w ould require increased fertilizer inputs to maintain long-term sustainability. LEVs calculated here are based on stated cost assumptions and are subject to change; re-appl ying the model to case-specific conditions would be relatively easy and would provide results useful to land managers.

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108 CHAPTER 5 CONCLUSIONS Market-based instruments have achieved environmental benefits such as the reduction of leaded gasoline use, ozonedepleting chlorofluorocarbons, and acid rain from SO2 emissions and will continue to address environmental problems (Stavins, 1998). Forest production systems provide enviro nmental services such as maintenance of hydrologic functions, soil cons ervation, and atmospheric CO2 mitigation1. This research investigated the economic viability of tree production systems using marginal resources in Florida (mined lands and reclaimed water), assessed the value of environmental services of research-phase SRWC systems, and incorporated them in the economic analysis. One of the systems investigated is currently profitable, while the other two are profitable only under ideal circumstances and would be more economically feasible with compensation for environmenta l services they provide. Summary of Results SRWC Production with Reclaimed Water SRWC production with reclaimed wastew ater can mitigate nutrient loading by extracting N, though under current operational costs and market prices, production of SRWCs irrigated with reclaimed water ha s a positive LEV only under scenarios of high but feasible yields and moderate discount rates2. LEVs of 128 SRWC dendroremediation 1 Forests can mitigate atmospheric CO2 by both C sequestration and reduction of CO2 emissions by displacing fossil fuels with renewable biomass fuels. 2 However, if the irrigation system is pre-established, as might be the case with former citrus plantations, LEVs range from $1,364-$5,232 ha-1 ($552-$2,181 acre-1).

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109 scenarios ranged from -$2,343 to +$2,762 ha-1 (-$949 to $1,118 acre-1). Each $1 kg-1 N increment in a potential dendroremediation incentive increases pr ofit by an additional $223 to $376 ha-1, depending on interest rate and sta nd productivity. Th e sensitivity of optimum harvest scheduling and replanting to ch anges in productivity or discount rate are generally operationally insi gnificant, though high discount rates or low productivity can extend the optimum number of growth stages per coppice cycle. Optimum management calls for growth stages of 2.6 to 4.0 years and two or three growth stages per cycle, though the optimum number of stages per cy cle would increase with improved coppice growth, possibly achievable with weed control. SRWC Production on Clay Settling Areas SRWC production on CSAs can contribu te to mined land restoration and atmospheric CO2 mitigation and is profitable under ideal conditions. LEVs of EA production on CSAs in central Florida3 range from $762 to $6,507 ha-1 assuming interest rates of 10% and 4%, respectively, excludi ng C sequestration incentives (Table 5-1). Incorporating CO2 mitigation incentives increases LEV, particularly when incentives recognize the CO2 emissions reduced by biofuels use. A C sequestration incentive of $5 Mg-1 increases LEVs to $946 and $6,715 ha-1, while recognizing the CO2 mitigation benefits associated with the biofuel scenario increases LEVs to $1,315 and $7,869 ha-1 assuming interest rates of 10% and 4%, respec tively. Additionally, the value of belowground C sequestration (roots + SOC at $5 Mg-1 C) is likely to be about $1,167 ha-1 or $720 ha-1 assuming discount rates of 4% and 10%, respectively. Optimum stage lengths vary little from 2.5 to 3.5 years and optimum stages per cycle range from two to five 3 Assuming establishment and planting costs of $1,800 and $1,200 ha-1, respectively, the high growth function described in Chapter 3, and a stumpage price of $20 dry Mg-1.

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110 depending on the scenario, with more profita ble systems resulting in fewer stages per cycle, consistent with the results from Chap ter 2 and those of Smart and Burgess (2000). Increasing incentives for CO2 mitigation can increase or decrease optimum stage lengths by about 0.1 year in the mulch and bi ofuels scenarios, respectively. Table 5-1. Summary of LEVs ($ ha-1) of EA production on CSAs assuming establishment and planting costs of $1,800 and $1,200 ha-1, respectively, the high growth function described in Chapte r 3, and a stumpage price of $20 dry Mg-1. 10% interest rate 4% interest rate LEV $762 $6,507 LEV + Ca sequestration NTB $946 $6,715 LEV+C Sequestration+CO2 emissions reductionb $1,315 $7,869 Estimated additional below-ground C benefit $720 $1,167 a$5 Mg-1 C, above-ground sequestration only. b$5 Mg-1 C, above-ground sequestration + emissions re duction though coal displacement with cofiring. Slash Pine Production on Titanium Mined Lands Slash pine production on lands mined for titanium by Iluka is profitable, with growth and optimum management comparable to that of adjacent unmined lands. Earlyrotation responses to soil amendments suggest that growth and survival can be improved by fertilizer and subsoil treatments, resp ectively. Plantation establishment costs including soil amendment as high as $423 to $878 ha-1 ($171 to $355 acre-1) are economically viable depending on growth response. Overall Policy Implications The production of slash pine on titanium mine d lands is a profitable forestry system even without payment for environmental services, while the SRWC DFSSs investigated here are economically viable under ideal c onditions of productivity operational costs, and stumpage prices. Compensation for enviro nmental services would contribute to the profitability of these systems.

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111 It would be impractical to provide incentives and pena lties corresponding to SRWC stand growth and removal, give n the frequent harvest rate vis--vis conventional forestry systems. The primary environmental serv ices assessed here, dendroremediation and CO2 mitigation, are achieved by increased biom ass production rather than extending the rotation, allowing for incentives on a per Mg basis, facilitating policy application and minimizing the need for on-site monitoring. Because of short rotations, in situ C sequestration is not heavily influenced by variation in harvest age as conventional forestry systems, and C sequestration incenti ves could be applied on a per-hectare basis, for example, as equal annual equivalent of the net present value of the average on-site sequestration benefit, independent of harvest scheduling. For these reasons, and because industry biomass pricing is by weight, conclu ding policy implicati ons are assessed per Mg rather than per hectare. Important questi ons answerable by this re search are: a) what stumpage prices are needed for these SRWC systems to be economically viable? b) what is the divergence between these stumpage pri ces and prices that c ould be offered on the extensive margin?, and c) with the inclusion of the value of environmental services, can buyers offer economically viable stumpage prices to tree farmers? In the dendroremediation SRWC system desc ribed in Chapter 2, stumpage prices of $26 and $30 dry Mg-1 are required to match LEVs of $1,235 ha-1 and $2,470 ha-1, respectively4, while in the SRWC system on CSAs described in Chapter 3, stumpage prices of $17 and $21 dry Mg-1 are required to match LEVs of $1,235 ha-1($500 acre-1) 4 Assuming irrigation establishment costs of $3,707 ha-1, a high growth model yielding 32 dry Mg ha-1 year-1 total above ground biomass and an interest rate of 5%.

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112 and $2,470 ha-1 ($1,000 acre-1), respectively5. Though these prices might be offered by mulch producers, the immediate market to support extensive depl oyment of SRWCs is for the development of DFSSs. An analyti cal cost estimation method (Green, 2005) was used to convert these minimum stumpage prices to equivalent costs of fuel and costs of electricity, after harvesting and shipping6. Resulting electricity pr ices are estimated at 1.3 to 2.0 kWh-1 higher than those generated from coal (Table 5-2 and Figure 5-1). Table 5-2. Delivered costs of biomass fo r fuel, costs of electricity, and resulting divergence from costs of conventional electricity by minimu m stumpage price needed to match LEVs for biofuel pr oduction on CSAs and under irrigated production such as at WC2. Scenario LEV ($ha-1; $ acre-1) Stumpage ($ dry Mg-1; $ green ton-1) Delivereda ($ dry Mg-1; $ green ton-1) COFb ($ MMBTU-1) COEc ( kWh-1) Divergenced ( kWh-1) 1,235; 500 17; 8 58; 26 3.09 5.1 1.3 CSA 2,470; 1000 21; 10 62; 28 3.31 5.3 1.5 1,235; 500 26; 12 67; 30 3.57 5.6 1.8 WC2 2,470; 1000 30; 14 71; 32 3.79 5.8 2.0 a Delivered cost assuming $35 dry Mg-1 ($16 green ton-1) harvesting cost and $5.30 dry Mg-1 ($2.50 green ton-1) transportation cost (48 km (30 miles) at $0.06 green Mg-1 km-1 ($0.08 green ton-1 mile-1)). b Cost of fuel assuming higher heating value of 4,238 BTUs lb-1 EG, 50% MC c Cost of electricity assuming 34% pl ant efficiency due to co-firing. d Estimated divergence from cost of conventional electricity assuming COF $1.80 MMBTU-1 and NPHR of 10,000 Btus kWh, equaling 3.8 kWh-1. Figure 5-2 illustrates the dive rgence of the DFSS cost of fuel from the equivalent cost of fuel from coal, ranging from $24 to $37 dry Mg-1 ($11 to $17 green ton-1). Each additional dollar incentive Mg C-1 for CO2 mitigation adds $0.42 dry Mg-1 to the stumpage price7. Closing the price divergence with CO2 mitigation incentives alone 5 Assuming site preparation costs of $1,800 ha-1, planting costs of $1,200 ha-1 and averaging the EA 3 and EA 4 growth functions, equivalent to ~25 dry Mg ha-1 year-1 and an interest rate of 5%. 6 Assumes $35 dry Mg-1 ($16 green ton-1) harvesting cost and $5.30 dry Mg-1 ($2.50 green ton-1) transportation cost, operation an d maintenance of 2 cents kWh-1 and a net plant heat rate of 13,600 for biomass and 10,000 fo r coal, respectively. 7 Assuming 47% C content in dry biomass an d 90% C efficiency in fuel production.

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113 3.83.83.83.8 1.3 1.5 1.8 2.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 CSA lowCSA highWCII lowWCII high ScenarioCost of Electricity (cents/kWh) Additional COE from Biomass COE from Coal Figure 5-1. Additional cost of electricity (COE) (cents kWh-1) over COE from coal, for production on CSAs and under dendroremediation (WC2), assuming stumpage prices needed to match low ($1,235 ha-1 [$500 acre-1]) and high ($2,470 ha-1 [$1,000 acre-1]) LEVs. $34$34$34$34 $24 $28 $33 $37 $0 $10 $20 $30 $40 $50 $60 $70 $80 CSA lowCSA highWCII lowWCII high ScenarioDelivered COF ($/dry Mg equivalent) Additional Biomass COF COF coal equivalent Figure 5-2. Additional delivered cost of fuel (COF) ($ dry Mg-1) over COF coal equivalent, for production on CSAs and under dendroremediation (WC2), assuming stumpage prices needed to match low ($1,235 ha-1 [$500 acre-1]) and high ($2,470 ha-1 [$1,000 acre-1]) LEVs.

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114 would require C prices of $58 to $88 Mg-1 C. Though C prices in Norway and Sweden range from $48 to $282 Mg-1 C8 (Fischer & Newell, 2004), curre nt available prices of $5 to $10 Mg-1 C (Chicago Climate Exchange, 2005) woul d pay for 6 to 18% of this cost difference. In the dendroremediation scenar io, each dollar in the price of N adds $2.62 dry Mg-1(9). Dendroremediation incentive values of $1.50 and $3.00 kg-1 N increases stumpage values by $4 and $8 dry Mg-1, respectively. Dendroremediation as described in Chapter 2 is not practical on CSAs, though alternative negative-cost wastes such as storm debris and treated sewage sl udge can be applied to contri bute to CSA reclamation with SRWCs. In the near term, dendroremediation and CO2 mitigation incentives combined could contribute $6 to $12 dry Mg-1 to the biomass price, reducing the gap between production and market costs by 16% to 50% (F igure 5-3), though not making the systems profitable without additional incentives. Opportunities exist to compensate tree growers for the remaining $21 to $41 dry Mg-1. Implementation of Chapter 378 of the 2004 State of Florida Statutes, providing for reimbursement of CSA reclamation co sts ranging from $4,942 to $9,884 ha-1 ($2,000 to $4,000 acre-1) (State of Florida, 2004a), pa yment for below-ground C sequestration benefits ($650 to $1,172 ha-1 [$263 to $474 acre-1]10) or USDA incentives for cogongrass control would facilitate th e establishment of SRWC production on CSAs. Though not sustainable in the long-term, the Renewabl e Energy Production Incentive would increase stumpage prices by $28.59 dry Mg-1. The use of an energy conversion process for which 8 Prices range from $12-$70 ton CO2, and CO2 is 27% C by weight. 9 Based on the accumulation of 220 kg N in 84 dry Mg biomass (2.62 kg N Mg-1, %0.26 N) at 3.6 years. 10 Assuming $5 Mg-1 C, low and high growth estimates and interest rates of 4%-10%.

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115 biomass has a greater competitive advantage would make the above DFSSs more profitable. Through a biomass integrated ga sification combined cycle (BIGCC) system retrofit of a natural gas combined cycle plant, biomass could be gasified to compete with natural gas, which has a current fu el cost ranging from $5 to $10 MMBtu-1 (Green, 2005). Increasing dependence on imported natural ga s and higher gas pri ces could drive the development of gasification conversion, pot entially increasing th e price of biomass feedstocks. An application of a combinat ion of these incentive policies on a limited commercial scale would simultaneously achieve environmental benefits and research and development opportunities. $34$34$34$34 $12 $22 $21 $31 $8 $8 $4 $4 $2 $4 $2 $4 $0 $10 $20 $30 $40 $50 $60 $70 $80 CSA lowCSA highWCII lowWCII high ScenarioDelivered COF ($/dry Mg equivalent) Value of CO2 mitigation Value of dendroremediation Additional Biomass COF COF coal equivalent Figure 5-3. Estimated value of CO2 mitigation service, dendroremediation service, additional COF and COF coal equivalent ($ dry Mg-1), for production on CSAs and under dendroremediation (WC2 ), assuming low (stumpage prices needed to match $1,235 ha-1 [$500 acre-1] LEV and high valuation of environmental services) and high (stu mpage prices needed to match $2,470 ha-1 [$1,000 acre-1] LEVs and low valuation of environmental services) scenarios, respectively. Rather than incorporating incentives for CO2 mitigation through the use of biofuels, an alternative policy is to incr ease the price of conventional en ergy to reflect true costs to

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116 society. Roth and Ambs (2004b) assess the va lues of air pollution, global warming, and fuel security externalities associated with electricity generation technologies, and incorporate them in a full cost approach to determine the levelized cost of electricity (LCOE). They conclude that the LCOE is increased by 1.5 to 12.0 kWh-1, depending on the conversion technology (Figure 5-4). Poli cies to increase el ectricity prices to incorporate externalities would make DFSSs more competitive with conventional energy sources. Figure 5-4. External costs for 14 gene ration technologies (Roth & Ambs, 2004b). Forestry in general and bioenergy specifi cally, especially using non-native species, are politically charged issues in Florid a, and public preference at times seems contradictory. Local chapters of the Sie rra Club, despite the e nvironmental services achievable through bioenergy as an alternative to fossil fuels, maintain a strong position against it, even when it is de rived from wood refuse. While a survey of Florida residents by Adams (2003) found that over half of the respondents were willing to pay a $5 to $20

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117 rate increase for technology that would pr oduce cleaner energy, GRUgreen, a voluntary program in which customers can pay as little as $5 per month for renewable energy, has only 449 participants of 74,456 residential customers as of May 2005 (Gainesville Regional Utilities, 2005). Public preferences for electricity from renewable sources to achieve energy independence, progressive economic development, or environmental benefits could prompt electricity producer s to seek a least-cost renewable energy alternative, for which DFSSs would be a lik ely option, though this preference remains to be demonstrated. While these systems will be economically feasible assuming adequate stumpage prices and/or incentives, que stions remain regarding the long-term economic and environmental sustainability of DFSSs in Fl orida. Especially relevant to SRWC production in the wildland-urban interface (WUI) is the cost of competing land values. Reynolds (2005) reports that non-agricultural land specula tion increased citrus land values in central Florida 9% in 2003, from $12,076 to $13,190 ha-1 ($4,899 to $5,340 acre-1). This trend of increasi ng land values and conversion to urban land uses would be a constraint on production of biomass using reclaimed water in the WUI and underscores the value of SRWC production for reclamation services on mined lands. Alternatively, because of the short rotations and flexibil ity in harvest scheduling, SRWC production could be an ideal short-term forestry investment on lands in the WUI. Patzek and Pimental (2005) scrutinize the l ong-term sustainability and en ergy efficiency of DFSSs. While it can be argued that whole-tree harves ting should be avoided on CSAs in favor of maintaining nutrients contai ned in leaves and bark in situ so as to minimize nutrient mining, the value of both mitigating eutrophi cation from nutrient loading and minimizing

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118 energy production inputs through dendroremedia tion is clear, and the importance of deploying DFSSs that achieve multiple bene fits is emphasized. While the impending peak of oil production will cause irrigation, ha rvesting, transportati on and fertilizer costs to rise (Heinberg, 2003), the cost of conventi onal wastewater treatment value of nutrients in wastewater as an alterna tive to fertilizer, and demand for fuel alternatives will also increase. Long-term energy security concerns calls for research and development of a range of renewable energy options, including DFSSs. Future Research While the utility of forestry syst ems to provide land reclamation and dendroremediation services is apparent, obsta cles to deployment remain. Following are research needs: As of this writing, the feasibility of SRWC production on a CSA near Lakeland, Florida, remains unclear, due largely to operational cons traints on clay following the unseasonably wet winter of 2004-2005. Farmers use climate forecasting to plan agricultural operations in Florida (Fraisse et al. 2004). Research is needed to investigate the potential to predict dry wi nters when planting and harvesting will be possible and wet summers when seedling survival will be optimal. DFSSs on sandhill sites in the dendroremediation scenarios would be harvestable even in wet years and would complement feedstock supplies fr om CSAs vulnerable to flooding. Producers could use GIS a nd linear programming to coordinate production and harvest scheduling from CSA and dendroremediation DFSSs and spatially and temporally integrate these yiel ds with other biomass resources such as fuel load control efforts in the WUI, hard wood control for longleaf pine restoration, and seasonally available storm debris. The CSA pilot project site near Lakela nd was struck by three hurricanes in the summer of 2004. The influence of hurrica ne risk on DFSS feasibility and optimum harvest scheduling vis--vis conventional forestry and agricultural alternatives should be assessed. Rahmami et al. (1997) identify obstacles of st ockpiling and drying biofuels in Floridas climate. An assessment of th e feasibility, economic viability and energy efficiency of methods to shed rain and allow air flow would contribute to the operational viability of DFSSs in Florida.

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119 Politics and public opinion constrain re search and development of SRWCs. Contingent valuation could be used to assess willingness to pay for environmental services provided by SRWC DFSSs, though an information gap contributes to negative preconceptions of tree harvesting and a misunderstanding of the apparent contradiction of burning trees to reduce gr eenhouse gasses. Co llaborative research between the School of Forest Resources a nd Conservation and the Southern Center for Wildland-Urban Interface Research and Information initiated in the spring of 2005 will assess public opinion and identify extension needs and opportunities for biomass. The net energy ratio of these DFSSs should be independently assessed to verify the efficacy of these systems vis--vis other renewable energy alternatives to the public. While Chapters 2 and 3 focused on Eucalyptus spp. as a best candidate for achieving dendroremediation and reclama tion objectives, even environmental professionals confuse invasive Melaleuca quinquenervia with non-invasive E. amplifolia and E. grandis Leucaena leucocephala has not been included in this research, as it is identified as an invasive exotic speci es in Florida, though sterile hybrids of this nitrogen fixing species c ould contribute to mined land restoration objectives with a minimum of fertilizer inputs. The development of sterile Eucalyptus clones could address some concerns of invasiveness, though recent history suggests that any proposed landuse option involving nonnative trees will be met with resistance. While slash pine production on titanium mi ned lands is profitable, reclamation practices result in heterogeneous soil conditions and stand growth (Darfus & Fisher, 1984; Mathey, 2001). Future research should identify better restoration practices, if possible, and investigate the potential to improve productivity and restoration through site-specific applicati ons of subsoiling, fertilizer, and tree species selection. Future production costs, yields and market prices will vary from the assumptions made in each chapter. While the sensitivity an alysis reported is useful in estimating the influence of these changes on profitability and optimal management, these models could be run for site-specific scenarios and updated as prices and costs change. Due to their esoteric nature, the models presented here are unlikely to be pursued by more than a limited number of individuals. As such, potenti al users are urged to contact the author on an as-needed basis for implementation of the mo dels with site-specific parameters and/or updated assumptions. The net marginal benef it to society of re-applying these models with new information will be much higher than that of the development and application

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120 of the models in this document. The m odels are available upon request in Mathcad worksheet format, or alternatively this auth or can be contacted personally to run the models with updated information.

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121 APPENDIX A BIOMASS CONVERSIONS 33 33214831 144,0002,567 272,000 ftydlbseucalyputston bagstons bagftydlbs (Appendix Eq.1) Appendix Eq. 1 defines the conversion of mulch from retail product to mass. 3 3 3 30.90718 2.75* 1 1.0 88 1.0 1.0** 1.0 35.32 Mg ton ton M g m ft m cord cord ft (Appendix Eq.2) Eq. 2 estimates slash pine specific gravity given values reported by Timber MartSouth (2005). 3 33335.3 $20.19$8.10 1 ** 88 ft cord cord f tmm (Appendix Eq.3) Eq. 3 converts prices in units reported by Timber Mart-South (2005) to metric.

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APPENDIX B CHAPTER 4 LEV OUTPUTS

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123 Table B-1. Titanium mined plot measurement date, age, volume, SI and stand density. Plot Meas. date Age Volume m3 ha-1(ft3 acre-1) SI m(ft) TPH(TPA) 3M-1 Nov-04 8 11 (151) 12 (38) 1600 (648) 3M-4 Nov-04 8 21 (305) 16 (54) 1150 (466) 3M-6 Nov-04 8 51 (722) 22 (72) 1350 (547) 3M-8 Nov-04 8 11 (160) 13 (43) 1500 (607) 8M-1 Nov-99 8 22 (310) 17 (57) 1250 (506) 8M-2 Nov-99 8 29 (419) 19 (63) 1050 (425) 8M-3 Nov-04 13 102 (1454) 19 (62) 1200 (486) 8M-4 Nov-99 8 51 (722) 23 (76) 1400 (567) 8M-5 Nov-04 13 230 (3282) 25 (81) 1350 (547) 8M-6 Nov-99 8 93 (1328) 28 (91) 1400 (567) 8M-7 Nov-04 13 148 (2121) 21 (68) 1450 (587) 8M-8 Nov-99 8 32 (457) 19 (63) 1450 (587) 8M-9 Nov-04 13 42 (599) 12 (40) 1350 (547) 8M-10 Nov-99 8 31 (448) 19 (62) 1150 (466) 9M-1 Nov-99 9 29 (411) 17 (55) 1250 (506) 9M-2 Nov-99 9 45 (640) 20 (65) 1050 (425) 9M-3 Nov-99 9 45 (647) 20 (65) 1200 (486) 9M-4 Nov-99 9 69 (986) 23 (74) 1400 (567) 9M-5 Nov-99 9 110 (1569) 28 (91) 1350 (547) 9M-6 Nov-99 9 126 (1793) 27 (89) 1400 (567) 9M-7 Nov-99 9 64 (915) 21 (69) 1500 (607) 9M-8 Nov-99 9 49 (703) 20 (65) 1450 (587) 9M-9 Nov-99 9 18 (262) 13 (42) 1400 (567) 9M-10 Nov-99 9 42 (602) 19 (62) 1100 (445) 10M-1 Nov-99 10 60 (850) 22 (71) 1050 (425) 10M-2 Nov-99 10 94 (1347) 23 (75) 1300 (526) 10M-3 Nov-99 10 76 (1092) 22 (73) 1350 (547) 10M-4 Nov-99 10 38 (537) 17 (57) 1050 (425) 10M-5 Nov-99 10 24 (338) 14 (45) 850 (344) 10M-6 Nov-99 10 51 (734) 17 (54) 1300 (526) 10M-7 Nov-04 15 141 (2020) 19 (63) 1100 (445) 10M-8 Nov-99 10 74 (1061) 23 (75) 1150 (466) 10M-9 Nov-99 10 66 (948) 19 (62) 1100 (445) 10M-10 Nov-04 15 93 (1328) 16 (51) 950 (385) 16M-1 Nov-99 16 234 (3338) 21 (69) 1700 (688) 16M-2 Nov-99 16 265 (3791) 23 (74) 1350 (547) 16M-3 Nov-99 16 249 (3561) 21 (70) 1600 (648) 16M-4 Nov-99 16 257 (3665) 22 (74) 1400 (567) 16M-5 Nov-99 16 225 (3221) 22 (72) 1150 (466) 16M-6 Nov-04 21 202 (2893) 19 (64) 800 (324) 16M-7 Nov-04 21 247 (3524) 21 (70) 700 (283) 16M-8 Nov-04 21 251 (3580) 22 (71) 650 (263) 16M-9 Nov-04 21 298 (4256) 21 (69) 1100 (445) 16M-10 Nov-04 21 361 (5157) 22 (73) 1150 (466)

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124 Table B-2. Titanium mined plot LEV and optimum rotation age. Plot LEV $ ha-1 ($ acre-1) Optimum rotation age (years) 3M-1 -548 (-222) 33.3 3M-4 183 (74) 23.3 3M-6 1894 (767) 20.6 3M-8 -469 (-190) 29.6 8M-1 195 (79) 22.9 8M-2 830 (336) 21.8 8M-3 551 (223) 21.9 8M-4 1944 (787) 20.4 8M-5 3026 (1225) 19.9 8M-6 4935 (1998) 19.1 8M-7 1205 (488) 21.0 8M-8 662 (268) 21.7 8M-9 -501 (-203) 31.0 8M-10 830 (336) 21.8 9M-1 67 (27) 23.5 9M-2 936 (379) 21.6 9M-3 832 (337) 21.5 9M-4 1761 (713) 20.5 9M-5 4076 (1650) 19.2 9M-6 4602 (1863) 19.3 9M-7 1361 (551) 20.8 9M-8 798 (323) 21.4 9M-9 -437 (-177) 29.3 9M-10 731 (296) 21.8 10M-1 1023 (414) 21.2 10M-2 1944 (787) 20.5 10M-3 1326 (537) 20.7 10M-4 148 (60) 23.0 10M-5 -264 (-107) 25.9 10M-6 279 (113) 23.4 10M-7 682 (276) 21.8 10M-8 1484 (601) 20.7 10M-9 968 (392) 21.7 10M-10 20 (8) 24.0 16M-1 1235 (500) 20.8 16M-2 1934 (783) 20.4 16M-3 1465 (593) 20.6 16M-4 1806 (731) 20.4 16M-5 1586 (642) 20.7 16M-6 701 (284) 22.0 16M-7 1181 (478) 21.4 16M-8 1287 (521) 21.3 16M-9 1245 (504) 21.0 16M-10 1811 (733) 20.5

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125 Table B-3.. Unmined plot measurement da te, age, volume, SI and stand density. Plot Measurement date Age Volume m3 ha-1(ft3 acre-1) SI m(ft) TPH(TPA) 3U-3 Nov-99 8 32 (453) 19 (62)1250 (506) 3U-4 Nov-99 8 36 (512) 20 (65)1450 (587) 3U-8 Nov-99 8 31 (437) 22 (71)850 (344) 9U-1 Nov-04 14 139 (1984) 21 (70)1400 (567) 9U-2 Nov-99 9 56 (802) 24 (78)1000 (405) 9U-3 Nov-99 9 35 (503) 21 (69)1000 (405) 9U-4 Nov-04 14 117 (1677) 19 (64)1450 (587) 9U-5 Nov-04 14 88 (1255) 19 (62)1150 (466) 9U-6 Nov-04 14 116 (1652) 20 (65)1500 (607) 9U-7 Nov-99 9 51 (731) 21 (69)1500 (607) 9U-8 Nov-99 9 60 (853) 24 (79)1250 (506) 9U-9 Nov-99 9 40 (575) 19 (64)1300 (526) 9U-10 Nov-99 9 57 (812) 22 (71)1400 (567) 10U-1 Nov-04 15 220 (3137) 22 (73)1100 (445) 10U-2 Nov-04 15 178 (2549) 21 (70)1150 (466) 10U-3 Nov-99 10 46 (659) 20 (65)1250 (506) 10U-4 Nov-99 10 40 (575) 18 (60)1050 (425) 10U-5 Nov-99 10 46 (662) 18 (59)1350 (547) 10U-6 Nov-99 10 53 (762) 21 (70)950 (385) 10U-7 Nov-99 10 111 (1581) 27 (87)1250 (506) 10U-8 Nov-99 10 95 (1353) 24 (77)1350 (547) 10U-9 Nov-04 15 180 (2569) 23 (76)1050 (425) 10U-10 Nov-99 10 111 (1583) 27 (87)1450 (587) 16U-1 Nov-99 16 158 (2262) 18 (60)1700 (688) 16U-2 Nov-99 16 198 (2830) 21 (67)1600 (648) 16U-3 Nov-99 16 135 (1932) 18 (59)1350 (547) 16U-4 Nov-99 16 161 (2294) 19 (61)1600 (648) 16U-5 Nov-99 16 223 (3191) 20 (66)2050 (830) 16U-6 Nov-99 16 219 (3134) 22 (72)1350 (547)

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126 Table B-4. Unmined plot LEV and optimum rotation age. Plot LEV $ ha-1 ($ acre-1) Optimum rotation age (years) 3U-3 766 (310) 21.8 3U-4 884 (358) 21.4 3U-8 1292 (523) 21.2 9U-1 1200 (486) 20.6 9U-2 1771 (717) 20.5 9U-3 664 (269) 21.6 9U-4 783 (317) 21.3 9U-5 524 (212) 21.7 9U-6 768 (311) 21.2 9U-7 919 (372) 21.0 9U-8 1670 (676) 20.4 9U-9 568 (230) 21.7 9U-10 1233 (499) 20.8 10U-1 2211 (895) 20.4 10U-2 1573 (637) 20.6 10U-3 395 (160) 21.9 10U-4 262 (106) 22.5 10U-5 252 (102) 22.6 10U-6 894 (362) 21.4 10U-7 2900 (1174) 19.6 10U-8 1966 (796) 20.3 10U-9 1848 (748) 20.3 10U-10 2665 (1079) 19.6 16U-1 343 (139) 22.8 16U-2 904 (366) 21.2 16U-3 269 (109) 22.7 16U-4 425 (172) 22.4 16U-5 889 (360) 21.6 16U-6 1388 (562) 20.6

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127 APPENDIX C PHOTOS Figure C-1. Rapid infiltration basi ns near Winter Garden, Florida. Figure C-2. 1.75-year-old Eucal yptus grandis irrigated with reclaimed wastewater at the Water Conserv II facility near Winter Garden, Florida.

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128 Figure C-3. Cogongrass ( Imperata cylindrica ) following herbicide treatment on a clay settling area near Lakeland, Florida. Figure C-4. A three-year-old Eucalyptus grandis stand on a clay settling area near Lakeland, Florida.

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129 Figure C-5. Dredge mining by Il uka Resources, Inc., near Gr een Cove Springs, Florida. Figure C-6. Eight-year-old slash pine st ands on mined (left) and unmined lands.

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130 LIST OF REFERENCES Abrahamson, L. P., Robison, D. J., Volk, T. A., White, E. H., Neuhauser, E. F., Benjamin, W. H., and Peterson, J. M., 1998. Sustainability and environmental issues associated with willow bioener gy development in New York (U.S.A.). Biomass and Bioenergy. 15, 1, pp. 17-22. Adams,M. 2003. Assessing awareness of Florid a homeowners about the use of biomass for electricity production. M.S. Th esis, University of Florida. Alig, R. J., Adams, D. M., McCarl, B. A ., and Ince, P. J., 1-20-2000. Economic potential of short rotation woody crops on agricultur al land for pulp fiber production in the United States. Forest Products Journal. 50, 5, pp. 67-74. Alker,G., Godley,A., and Hallett,J.. 2002. Landfill leachate management using short rotation coppice final tec hnical report. CO 5126. Aronsson, P. and Perttu, K., 2001. Willow vege tation filters for wastewater treatment and soil remediation combined with biomass production. For.Chron. 77, 2, pp. 293299. Bailey,R.L. 1982. Stand structure and yield of site prepared slash pine plantations. 291. 83. Georgia Agricultural Experime nt Station Research Bulletin. Barker, J. R., Baumgardner, G. A., Turner, D. P., and Lee, J. J., 1995. Potential carbon benefits of the Conservation Reserve Pr ogram in the United States. Journal of Biogeography. 22, 4-5, pp. 743-751. Best,C., and Wayburn,L.A., 2001. America's priv ate forests. Status and stewardship. Island Press, Washington D.C. Binkley, C. S., 1987. When is the optimal ec onomic rotation longer than the rotation of maximum sustained yield? J.Env.Econ.and Manag. 14, 2, pp. 152-158. Booth,T., 2003. Carbon accounting in fo rests. 1-93. Canberra, Australia. Borders, Bruce and Bailey, R. L., 2001. Loblolly pine-pushing the limits of growth. South.J.Appl.For. 25, 2, pp. 66-74.

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131 Borsboom,N., Hektor,B., McCallum,B., and Reme dio,E., 2002. Social implications of forest energy production. In: Richards on,J., Bjorheden,R., Hakkila,P., Lowe,A., Smith,C. (Eds.), Bioenergy from Sustainable Forestry: Guiding Principles and Practice Kluwer Academic Publishers, Dord recht, The Netherlands, pp. 265-295. Bredenkamp,B., 2000. Volume and mass of l ogs and standing trees. In: Owen,D.L., Vermeulen,W.J. (Eds.), South African Forestry Handbook South African Forestry Institute, Pretoria, pp. 167-174. Brister, G. H., Clutter, J. L., and Sk inner, T. M., 1980. Forest Mensuration. South.J.Appl.For. 4, 3, pp. 139-142. Bungart, R. and Huttl, R. F., 2001. Production of biomass for energy in post-mining landscapes and nutrient dynamics. Bioma ss and Bioenergy. 20, 3, pp. 181-187. Bureau of Economic and Business Research 2001. Florida population: census summary 2000. Gainesville, Florida. Carter,D., and Langholtz,M.. 2005. Florida's so ftwood growing stock forest inventory structure with projections to 2020. Chang, S. J., 1984. Determination of the opt imal rotation age-a theoretical analysis. For.Ecol.Manage. 8, 2, pp. 137-147. Chang, S. J., 1998. A generalized Faustmann model for the determination of optimal harvest age. Can.Jour.For.Res. 28, 5, pp. 652-659. Chang, S. J., 2001. One formula, myriad conclusions, 150 years of practicing the Faustmann Formula in Central Europe and the USA. Forest Policy and Economics. 2, 2, pp. 97-99. Chaturvedi,P., 2004. Biomass-the fuel of th e rural poor in deve loping countries. In: Sims,R. (Ed.)., Bioenergy Options for a Cleaner Environment Elsevier, Palmerston North, New Zealand, pp. 161-181. Chicago Climate Exchange, 2005. Chicago Climate Exchange. Retrieved 5-15-2005 from http://www.chicagoclimatex.com/. Corseuil, H. X. and Moreno, F. N., 2001. P hytoremediation potential of willow trees for aquifers contaminated with ethanol-blende d gasoline. Water Research. 35, 12, pp. 3013-3017. Darfus, G. H. and Fisher, R. F., 1984. Site re lations of slash pine on dredge mine spoils. Journal of Environmental Quality. 13, 3, pp. 487-493. Davis,L.S., Johnson,K.N., and Kenneth,P., 1987. Forest management. McGraw-Hill, New York.

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132 de Groot, R. S., Wilson, M. A., and Boumans, R. M. J., 2002. A typology for the classification, description and valuat ion of ecosystem functions, goods and services. Ecological Economics. 41, 3, pp. 393-408. Dickens,E., Dangerfield,C., and Moorhead,D., 2002. Short rotation management options for slash and loblolly pine plantations including fertilization, thinning, and pinestraw in South Georgia, USA. 1-5. Jeckyll Island, GA, Slash Pine: Still Growing and Growing and Growing. Downing, M. and Graham, R. L., 1996. The potential supply and cost of biomass from energy crops in the Tennessee Valley Au thority region. Biomass and Bioenergy. 11, 4, pp. 283-303. Duryea,M.L. 1999. Landscape mulches: how quickly do they settle? FOR 69. University of Florida, IFAS Extension. Gainesville, FL. Duryea, M. L., Jeffery, E., and Hermansen, A., 1999. A comparison of landscape mulches: chemical, allelopathic, and decomposition properties. Journal of Arboriculture. 24, 2, pp. 88-97. Ecosystem Marketplace, 2004. Carbon Markets. Retrieved 8-3-2005 from http://www.ecosystemmarketplace.com/. Ehrenshaft, A. R. and Wright L. L., 1991. The short rotation woody crops program data base. Bioresource Technol. 36, pp. 241-246. Eriksson,H., Hall,J., and Helynen,S., 2002. Ra tional for forest energy production. In: Richardson,J., Bjorheden,R., Hakk ila,P., Lowe,A., Smith,C. (Eds.), Bioenergy from Sustainable Forestry: Guid ing Principles and Practice Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 1-17. Ettala, M. O., 1987. Influence of irriga tion with leachate on biomass production and evapotranspiration on a sanitary landfill. Aqua Fennica. 17, 1, pp. 69-86. Fang, Z. X. and Bailey, R. L., 2001. Nonlin ear mixed effects modeling for slash pine dominant height growth following intens ive silvicultural treatments. Forest Science. 47, 3, pp. 287-300. Farber, S. C., Costanza, R., and Wilson, M. A., 2002. Economic and ecological concepts for valuing ecosystem services. Ecological Economics. 41, 3, pp. 375-392. Fischer,C., and Newell,R.. 2004. Environmen tal and technology policies for climate change and renewable energy. 04-05. 1-47. Resources for the Future. Washington, D.C. Fischer, Gunther and Schrattenholzer, Leo ., 2001. Global bioenergy potentials through 2050. Biomass and Bioenergy. 20, 3, pp. 151-159.

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133 Florida Agricultural Statisti cs Service, 2003. Citrus 2000-01 summary production, price, and value production by counties and per tree. Gainesville, Florida. Forsberg, Goran., 2000. Biomass energy transport. Analysis of bioenergy transport chains using life cycle inventory method. Biom ass and Bioenergy. 19, 1, pp. 17-30. Fraisse,C., Zierden,D., Breuer,N., Jackson,J ., and Brown,C.. 2004. Climate forecast and decision making in agriculture. ABE352. Univerisity of Florida. Gainesville Regional Utilities, 2005. GRUgreen energy news letter. Retrieved 8-1-2005 from http://www.gru.com/OurEnvironment/G reenEnergy/Newsletters/May_2005.pdf. Garten, J., 2002. Soil carbon storage beneath recently established tree plantations in Tennessee and South Carolina, USA. Biomass and Bioenergy. 23, 2, pp. 93-102. Gommers, A., Vandenhove, H., Smolders, E., and Merckx, R., 2000. Screening willow clones for radiocesium uptake at varying potassium supply in solution culture. International journal of phytor emediation. 2, 3, pp. 243-253. Goor, F., Davydchuk, V., and Ledent, J.-F. 2001. Assessment of the potential of willow SRC plants for energy production in areas contaminated by radionuclide deposits: methodology and perspectives. Biomass and Bioenergy. 21, 4, pp. 225-235. Gordon, A., McBride, R., and Fisken, A., 1989. The effects of landfill leachate spraying on foliar nutrient concentrati ons and leaf transpirati on in a northern hardwood forest. Canada Forestry. 6, 1, pp. 19-28. Graham, R. L., Wright, L. L., and Turhollow, A. F., 1992. The potential for short-rotation woody crops to reduce US carbon dioxide em issions. Climatic Change. 1992, 22, pp. 223-238. Green,A., 2005. Assessment of technologies fo r biomass conversion to electricity at the wildland-urban interface. Reno, Neva da, Proceedings of ASME Turbo Expo 2005: Power for Land, Sea and Air. pp. 1-12. Haggar, Jeremy, Wightman, Kevyn, and Fi sher, Richard., 1997. The potential of plantations to foster woody regeneration w ithin a deforested landscape in lowland Costa Rica. For.Ecol.Manage. 99, 1-2, pp. 55-64. Hansen,E., Moore,L., Netzer,D., Ostry,M ., Phillips,H., and Zavitkovski,J.. 1983. Establishing intensively cu ltured hybrid poplar plantations for fuel and fiber. NC78. USDA Forest Service. Hartman, R., 1976. The Harvesting decision when a standing forest has value. Economic Inquiry. 14, March, pp. 52-58.

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141 BIOGRAPHICAL SKETCH Matthew Langholtz was born in New Rochelle, New York, in 1972, and lived in Kentucky, California, Alaska, Oklahoma, and Virginia. After earning a B.S. from the School of Forestry at Oklahoma State University in 1994, Matthew Langholtz worked as an agroforestry extension agent and coordi nator of the Environm ent Sector for Peace Corps, Paraguay, until January, 1998. He cr uised timber in northern California before earning a Master of Forest Re sources and Conservation (MFRC) in the School of Forest Resources and Conservation (SFRC) at th e University of Florida (UF) and doing fieldwork at the Phytorem ediation Lab at SFRC, UF.


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ECONOMIC AND ENVIRONMENTAL ANALYSIS OF TREE CROPS ON
MARGINAL LANDS IN FLORIDA















By

MATTHEW HARVEY LANGHOLTZ


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

Matthew Langholtz















ACKNOWLEDGMENTS

I am grateful to my committee chair, Dr. Donald L. Rockwood, for his academic

guidance and support, as well as Drs. Janaki R.R. Alavalapati, Douglas R. Carter, Alex

Green, and Dr. P.K. Ramachandran Nair, for their tenacious work ethics and lifelong

dedications to forestry and sustainable land-use systems. I particularly thank Dr. Nair for

his assistance with my master's degree and assistance with acquiring funding for my

Ph.D. program.

This research was made possible by direct and indirect support from Woodward

and Curran on behalf of Orlando/Orange County, Florida, Iluka Resources, Inc., the

Florida Institute for Phosphate Research, and the Alumni Fellowship from the University

of Florida College of Agricultural and Life Sciences.

I thank my wife Maribel who has sacrificed for me to pursue this degree. I thank

my family for their support, and particularly my sister Gabrielle for her assistance with

the English language.

Special thanks go to a long history of phytoremediation lab crews, Jared Mathey,

Paul Proctor, Mark Torok, Richard Cardellino, Mauricio Arias, Geoff Filshe, Chris

Cosby, Erin Maehr, Brian Becker, Bijay Tamang, and Luis Achugar.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iii

LIST OF TABLES ....................................................... ............ .............. .. vii

LIST OF FIGURES ............................... ... ...... ... ................. .x

ABBREVIATIONS AND ACRONYMS ..............................................................xii

A B S T R A C T .............................................. ..........................................x iii

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

B ack g rou n d ...................................... .............................. .... ......... ...... .
R claim ed W after .............................................. ...... .... .............. ..
P hosphate M ined L ands ............................................................. .....................2
T itanium M ined L ands ........................................ .......................................3
O bj ectiv e s ................................................................... ................................. . .4
Literature Review ............................................................................. ...... 4
Environmental Impacts of SRWC Production ............................................... 4
C arbon sequestration ........................................ .. .............. ...... .... .......... ..
Phytoremediation and reclamation....................... ....................... 6
Slash Pine Productivity on M ined Lands ........................................ ...................7
P o licy ............... ............................................................. 7
E conom ics ............................................................. 8
Forest Financial Analysis ............. .............. .................... 11
Procedures ............... .............................. .........12
The Study A reas and Scope........................................... .......................... 12
M methodology ............. ..... .. .......... ................................. 13
Optimization of non-coppicing species......................... .. ............... 13
Optimization of coppicing species ................................. ....................... 14
Valuation of the non-timber benefits ........................................................ 16
Optimization of non-coppicing with inclusion of the non-timber
benefits ............... .............. ........................ ........................ 17
Optimization of coppicing species with inclusion of non-timber
benefits ............. .... ................... .......... ........... 18









2 EFFECT OF DENDROREMEDIATION INCENTIVES ON THE
PROFITABILITY OF SHORT-ROTATION WOODY CROPPING OF
Eucalyptus grandis ........................ .................... ... ........... ......... 20

In tro d u ctio n ......................................... .. ............................................2 0
M methodology ........................................................... .... .... ......... 23
Optim ization of Coppicing Species............... ............ ... ............... .. 23
Optimization of Coppicing Species with Inclusion of the Dendroremediation
S erv ic e ............................................................................................ .... 2 4
M odel inputs ................................................................... ................ 2 6
R results and D discussion ......................................................... ... ...... ........... ...30
Sensitivity Analysis of Dendroremediation Incentive and Interest Rate..................35
C o n c lu sio n s..................................................... ................ 3 8
F future R research ....................................................................... 39

3 AN ECONOMIC ANALYSIS OF Eucalyptus SPP. AS SHORT-ROTATION
WOODY CROPS ON CLAY SETTLING AREAS IN POLK COUNTY,
F L O R ID A ............................................................................................................. 4 1

In tro d u ctio n .......................................................................................4 1
M e th o d o lo g y ....................................................................................................4 3
M odel Inputs ......................................................................................................48
G row th Function................................................... 48
C a rb o n V a lu e s ............................................................................................... 5 1
Market Assessment...............................................52
The established market: mulch ..............................................52
M ulchw ood price..................................................... 53
M ulchwood quantity ................. ...............................55
Potential m market: biom ass fuels............................................. 56
O operational C osts ............................................................59
Additional Non-Timber Benefits ........................ ...................................... 59
Below-ground C sequestration .........................................59
R eclam action incentives ............................................ .............. 61
Summary of Model Inputs and Assumptions .....................................................62
R results and Sensitivity A nalysis........... ...... .............................. .... .... ..............62
Conclusions................ ......... ... .. .... .. .... ................ 69
F future R research .......................................................................7 1

4 ECONOMICS OF SLASH PINE CULTURE ON TITANIUM MINED LANDS
IN NORTH CENTRAL FLORIDA ............................................................... 73

Intro du action ............................................................................................. 7 3
M eth odology ................................ ........................................................... 7 5
E conom ic M odel .................. ................ ........................................... 75
G row th and Y field M odel ................................................. ................... ...... 76
M market A ssessm ent ............................................................ ............... 80
Silvicultural A lternatives....................................... ............... ............... 82


v









C o st A ssu m ption s .............................................................. ......................93
Sim ulations ............................................................................................... .......95
R e su lts .................. ........................................................................9 8
E established Stands ............................... .. .. ......... .... ................ .. 98
Soil Amendments on Young Plantations.................................. ...............101
S en sitiv ity A n aly sis ...... .. ............................................ .......... .... ................ 103
C o n c lu sio n s.......................................................................................................... 1 0 5
F future R research ............................................................ ................... .... 106

5 C O N C L U SIO N S ................................ ........................ ................ ..... .......... 108

Sum m ary of Results....................... .... ................... .............. 108
SRWC Production with Reclaimed Water ...................................................... 108
SRWC Production on Clay Settling Areas ...................................................... 109
Slash Pine Production on Titanium Mined Lands............................................110
O overall Policy Im plications .......................................................... .............. 110
Future Research .................................... ............................ ... ......... 118

APPENDIX

A BIOMASS CONVERSIONS.......................................................... ............. 121

B CHAPTER 4 LEV OUTPUTS ...........................................................................122

C P H O T O S ...................................................................... 12 7

L IST O F R E FE R E N C E S ......................................................................... ................... 130

BIOGRAPH ICAL SKETCH .............................................................. ............... 141















LIST OF TABLES


Table p

1-1 Farmgate (production and harvest) costs for SRWCs and herbaceous biomass
crops in Florida and other regions ........................................ ........................ 9

1-2 Sum m ary of study sites. ................................................ ............................... 13

2-1 Net returns and optimum stage lengths for a Eucalyptus grandis short-rotation
woody crop system irrigated with reclaimed water. ..............................................31

2-2 Optimum LEVs, optimum stages per cycle, and optimum stage lengths for a
range of dendroremediation values for Eucalyptus grandis irrigated with
reclaim ed w ater in central Florida................................... ............................. ......... 32

2-3 Marginal increases in net returns ($ ha-1) per dollar ofN dendroremediation
incentive for Eucalyptus grandis in central Florida. .........................................35

2-4 Changes in profit ($ ha-1) for Eucalyptus grandis in central Florida as interest
rate increases from 4% to 5% and 5% to 6% ........................... ..................37

2-5 Estimated parameters and descriptors used in Eq. (2-9) of Eucalyptus grandis
irrigated with reclaimed water in central Florida (R2>0.99). ..................................37

3-1 Number of observations, average DBH (cm), height (m) and inside-bark dry
above-ground biomass yields of EG and EA ..................................................50

3-2 Mulch markets for Eucalyptus produced in Polk County. .....................................54

3-3 Estimated equivalent stumpage values for high and low transportation cost
scenarios. All tons are green weight............... ............................... 55

3-4 Potential bioenergy markets for Eucalyptus produced in Polk County ...................58

3-5 LEV, optimum number of stages and optimum stage length for each stage by C
benefit scenario and biomass price...................... ......................... 63

3-6 LEV ($ ha-1), optimum stage lengths, marginal benefit, and estimated below-
ground benefit ($ ha-) by C sequestration incentive ($ Mg-1)..............................64

3-7 Change in LEV ($ ha-)) per 1% increase in interest rate.......................................65









3-8 Optimum harvest scheduling (stage lengths and number of stages per cycle) at
interest rates of 4%, 7%, and 10%.. .......................................................66

3-9 LEVs and marginal impact on LEVs by changes in site preparation, planting
and weeding costs.................... ............................... .. 67

3-10 Estimated discounted value of below-ground C benefits by C price, interest rate
and grow th function. ........................... ........... ...... ...... ...... ...... 68

4-1 Merchantable standards of DBH (ddbh) and top diameter outside bark (di). ............79

4-2 Treatments included in the SRWC-84 and SRWC-84-2001 studies......................83

4-3 SRWC-84 age 5 and SRWC-84-2001 age 4 mined (SM) and unmined (UM)
average heights, standard deviation and Duncan grouping ....................................84

4-4 SRWC-84 age 5 and SRWC-84-2001 age 4 mined (SM) and unmined (UM)
average survival (%) and standard deviation by treatment.................................... 85

4-5 Average of top Duncan group survival of SRWC-84 and SRWC-84-2001 ..............92

4-6 2004 Average pine plantation establishment costs for the southeast U.S................93

4-7 Cost scenarios based on Smidt et. al (2005). ................................... ..................... 94

4-8 Land type, measurement age, measurement date, and number of 63 1/50th ha
plots used in the analysis of established stands .............. .....................................95

4-9 Number of plots, average SI, SI standard deviation, average LEV, and standard
deviation of LEV on mined and unmined lands................... ..................................99

4-10 SI (base age 25), survival, age of survival, cost of initial rotation, cost of
subsequent rotations, LEV and optimum rotation........................................101

4-11 Volume, cost, LEV and IRR of comparative mined land simulations................... 102

4-12 Minimum growth response in SI needed to meet or exceed a base scenario's
L E V ...............................................................................104

4-13 Maximum initial and subsequent rotation establishment costs tolerated to meet
or exceed a base scenario's LEV ..................................... ......................... 104

5-1 Summary of LEVs ($ ha-) of EA production on CSAs............... ................... 110

5-2 Delivered costs of biomass for fuel, costs of electricity, and resulting
divergence from costs of conventional electricity ............................................... 112

B-l Titanium mined plot measurement date, age, volume, SI and stand density.........123









B-2 Titanium mined LEV and optimum rotation age. .............................................124

B-3 Unmined plot measurement date, age, volume, SI and stand density.................. 125

B-4 Titanium mined LEV and optimum rotation age. .............................................126















LIST OF FIGURES


Figure pge

2-1. Estimated high and low growth and yield functions for Eucalyptus grandis at
W inter G arden, Florida.. .............................. ... ........................................28

2-2. Net returns ($ ha-1) as a function of dendroremediation incentive ........................36

3-1. Inside bark yields (dry Mg ha-) of EA and EG on a CSA near Lakeland,
Florida for 5 treatm ents........................................................................ 49

3-2. Observed and predicted inside bark stem yields of EA. ................ .................. 51

3-3. Location and potential consumption of buyers of woody biomass from Polk
C o u n ty ........................................................... ................ 5 6

4-1. Mean heights estimated by stem analysis from stands on 25 reclaimed and 25
unm ined sites (M they, 2001) ............ ........................................... ............... 74

4-2. Mean diameter inside bark (DIB) estimated by stem analysis from stands on 25
reclaimed and 25 unmined sites (M they, 2001). ............. ..................................... 74

4-3. Representative pulp, chip-and-saw, sawtimber, and total outside bark volumes
(m 3 h a ) .......................................................................................8 0

4-4. South-wide pine stumpage prices quarterly averages from 1995-2005 (Timber
M art South 2005). ........................ ........................ .. .... ...... ...............8 1

4-5. Average heights (m) by age (year) and treatment, SRWC-84 mined site ..............85

4-6. Average survival (%) by age (year) and treatment, SRWC-84 mined site .............86

4-7. Average heights (m) by age (year) and treatment, SRWC-84 unmined site............86

4-8. Average survival (%) by age (year) and treatment, SRWC-84 unmined site..........87

4-9. Average heights (m) by age (year) and treatment, SRWC-84-2001 mined site. .....88

4-10. Average survival by age (year) and treatment, SRWC-84-2001 mined site............88

4-11 Average heights (m) by age (year) and treatment, SRWC-84-2001 unmined
site .............................................................. ................ 8 9









4-12. Average survival by age (year) and treatment, SRWC-84-2001 unmined site........89

4-13. Height (m) by treatment of SRWC-84-2001 (age 4) and SRWC-84 (age 5),
satellite mined (SM ) and unmined (UM ).. ....................................... ...............90

4-14. Average heights (m) of subsoiled and not subsoiled treatments on SRWC-84-
200 1 m ined land.. ...................................................................... 9 1

4-15. Average survival (%) of subsoiled and not subsoiled treatments on SRWC-84-
200 1 m ined land.. ...................................................................... 9 1

4-16. Total predicted above-ground inside-bark volume (m3 ha-1) for
sim ulations 1-9. .......................................................................98

4-17. LEV ($ ha-1) by SI (m, base age 25) for 34 and 29 stands (Table 4-8) on mined
and unm ined lands, respectively.. ....................... .............................................100

4-18. LEV ($ acre-1) by SI (ft, base age 25) for 34 and 29 stands (Table 4-8) on mined
and unm ined lands, respectively.. ....................... .............................................100

5-1. Additional cost of electricity (COE) (cents kWh-1) over COE from coal, for
production on CSAs and under dendroremediation (WC2). ............. ...............113

5-2. Additional delivered cost of fuel (COF) ($ dry Mg-1) over COF coal equivalent,
for production on CSAs and under dendroremediation (WC2) ..........................113

5-3. Estimated value of CO2 mitigation service, dendroremediation service,
additional COF and COF coal equivalent ($ dry Mg-1), for production on CSAs
and under dendrorem edition (W C2).................................................................. 115

5-4. External costs for 14 generation technologies (Roth & Ambs, 2004b). ..............116

C-1. Rapid infiltration basins near Winter Garden, Florida ................ .....................127

C-2. 1.75-year-old Eucalyptus grandis irrigated with reclaimed wastewater at the
Water Conserv II facility near Winter Garden, Florida...................................127

C-3. Cogongrass (Imperata cylindrica) following herbicide treatment on a clay
settling area near Lakeland, Florida. ........................................... ............... 128

C-4. A three-year-old Eucalyptus grandis stand on a clay settling area near
Lakeland, Florida. ................................................................. 128

C-5. Dredge mining by Iluka Resources, Inc., near Green Cove Springs, Florida........129

C-6. Eight-year-old slash pine stands on mined (left) and unmined lands. .................129















ABBREVIATIONS AND ACRONYMS


C carbon
COE cost of electricity
COF cost of fuel
CSA clay settling area
DBH diameter at breast height
DFSS dedicated feedstock supply system
DIB diameter inside bark
EA Eucalyptus amplifolia
EG Eucalyptus grandis
FASOM Forest and Agricultural Sector Optimization Model
FIPR Florida Institute of Phosphate Research
FONC first order necessary condition
ha hectare
IPCC Intergovernmental Panel on Climate Change
IRR internal rate of return
kWh kilowatt hour
LCOE levelized cost of electricity
LEV land expectation value
LHS left hand side
MAI mean annual increment
Mg metric ton
MSY maximum sustained yield
N nitrogen
NTB non-timber benefit
P Phosphorus
REPI Renewable Energy Production Incentive
RHS right hand side
RIB rapid infiltration basin
RPS Renewable Portfolio Standard
SI site index
SM satellite mined
SOC soil organic carbon
SRWC short-rotation woody crop
TPA trees per acre
TPH trees per hectare
UM unmined
WC2 Water Conserve II
WUI wildland urban interface















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ECONOMIC AND ENVIRONMENTAL ANALYSIS OF TREE CROPS ON
MARGINAL LANDS IN FLORIDA

By

Matthew Langholtz

December 2005

Chair: Donald L. Rockwood
Major Department: Forest Resources and Conservation

Tree crops can be used to remove contaminants from reclaimed wastewater, restore

ecological functions of phosphate and titanium mined lands and to provide renewable

energy in Florida. The economic feasibility of these potential tree crop systems, the

value of environmental services they provide and opportunities to make up the current

difference between minimum feasible and current market prices are investigated.

Profitability measured as land expectation value (LEV) of 128 scenarios of

Eucalyptus grandis cropping irrigated with reclaimed wastewater ranged from -$2,343 to

+$2,762 ha-1 and was greatly reduced by high interest rates, high irrigation costs, and low

yields. Each $1 kg-1 N increment in a dendroremediation incentive increases profit by

$223-$376 ha-1, depending on interest rate and site productivity. Optimum management

requires harvests every 2.6 to 4.0 years and replanting after two or three harvests, though

the optimum number of stages per cycle would increase with improved coppice growth.









LEVs of Eucalyptus amplifolia cropping on phosphate-mined lands in central

Florida ranged from $762 to $6,507 ha-1 assuming interest rates of 10% and 4%,

respectively, establishment costs of $1,800 ha-1, planting costs of $1,200 ha-l, high yields,

and a stumpage price of $20 dry Mg-1, excluding CO2 mitigation incentives.

Incorporating CO2 mitigation incentives increased LEV, particularly when incentives

recognize the CO2 emissions reduced by biofuels use. Optimum management

necessitates harvests every 2.5 to 3.5 years and replanting after two or five harvests.

Average LEVs of slash pine (Pinus elliottii) stands established on titanium mined

lands varied widely with productivity, but on average were profitable and similar to those

of unmined lands. Optimum management is comparable to that of conventional slash

pine culture in northeast Florida. Early-rotation responses to soil amendments suggest

that growth and survival can be improved by fertilizer and subsoil treatments,

respectively. Plantation establishment costs including soil amendment as high as $423 to

$878 ha-1 ($171 to $355 acre-1) are economically viable depending on growth response.














CHAPTER 1
INTRODUCTION

Background

Landowners in Florida are interested in profitable land-use options. Short-rotation

woody crops (SRWC) in Florida have competitive growth rates and production costs

compared to other states (Rahmani et al., 1997) and can provide multiple environmental

benefits. Currently, three opportunities in Florida have the potential to contribute to

forest production: 1) reclaimed water from the Water Conserv II (WC2) facility in

Orlando, 2) clay settling areas (CSAs) on former phosphate mined lands, such as the Kent

site in Lakeland, and 3) reclaimed titanium mined lands such as those associated with the

Iluka mining operation near Green Cove Springs. The financial feasibility of producing

Eucalyptus spp. or slash pine (Pinus elliottii) on these marginalized lands and irrigated

with reclaimed water requires further research.

Reclaimed Water

The population of Florida is expected to nearly double in the next 30 years, from

15.9 million permanent residents in 2000 to a projected 30.1 million in 2030 (Bureau of

Economic and Business Research, 2001), which will result in an increase in both water

consumption and wastewater production. As Florida faces increasing pressure on water

resources, wastewater presents both a challenge of disposal and an opportunity for reuse.

The WC2 facility near Winter Garden is an innovative water reuse program that has

achieved international recognition for its water conservation and reuse methods. Waste

water from the Orlando area, following treatment, is pumped to ornamental nurseries and









to 8,600 acres (3,480 ha) of citrus groves on sandhills of Orange and Lake Counties

(State of Florida, 2003).

In light of increasing competition from overseas production, particularly Brazil,

future trends of citrus production in Florida are uncertain. The preliminary $760 million

on-tree value of all Florida citrus for the 2001 season is the lowest since the 1985-86

season, while production was also down 7% from 1999-2000 (Florida Agricultural

Statistics Service, 2003). SRWC production using reclaimed water could provide a crop

alternative for citrus producers and other landowners on sandhill soils in Florida.

Furthermore, SRWCs can be used to extract nitrogen, phosphorous and chlorides from

the reclaimed water prior to its infiltration to the aquifers.

Research at WC2 between 1998 and 2001 suggests that E. grandis and cottonwood

(Populus deltoides) can yield 13.1 and 10.9 Mg ha-1 year1, respectively, and have great

potential to extract NO3-N and NH4-N from reclaimed water (Rockwood et al., 2002a).

Phosphate Mined Lands

Central Florida produces 75% of the nation's and 25% of the world's phosphate

supply (IMC Phosphates, 2002). This phosphate is used primarily in agriculture, and also

in a range of consumer products. In the mining process, the surface soil is removed and

put aside as "overburden" and clays are separated from phosphates and then sent to

CSAs. There are about 162,000 hectares (400,000 acres) of phosphate-mined lands in

Florida (Segrest, 2003). The Lakeland area contains over 38,000 hectares (95,000 acres)

of CSA and/or overburden soil, as a result of phosphate mining. These CSAs, classified

as clayey Haplaquents, can be a valuable resource for biomass production (Mislevy et al.,

1989).









Reclamation and reuse of mined landscapes are major foci of the Florida Institute

of Phosphate Research (FIPR), an independent State research agency that has spent

almost $11 million on research related to this topic. One project documented growth of

cottonwood, E. grandis, and E. amplifolia on a CSA in Lakeland, FL. After 15 months

cottonwood reached average heights of 4.7 and 6.1 m on double row planting (8,400 trees

ha-1) and single row planting (4,200 trees ha-1) configurations, respectively; E. grandis

after 9 months reached average heights of 3.8 and 2.8 m on double row planting and

single row planting, respectively; E. amplifolia after 9 months reached average heights of

2.3 and 3.1 m on double row and single row planting, respectively (Rockwood et al.,

2002b). These initial results suggest that cottonwood, E. grandis, and E. amplifolia are

suitable for conditions on CSAs in central Florida. FIPR continues to fund research

related to SRWC production on CSAs, underscoring the demand for such research.

CSAs, titanium mined lands, and other marginal lands would have a low

opportunity cost, as most of these ownerships are idle. Tree crop production could

provide a valuable land-use alternative to these areas.

Titanium Mined Lands

Near Green Cove Springs, Iluka Resources Inc. has been producing titanium

minerals and zircon since 1972 using dredge mining and satellite dry mining. These

reclaimed mines are used extensively for slash pine plantations to produce timber

products, reclaim soil productivity, and re-establish wildlife habitat. Following research

of slash pine productivity and culture on mined lands by Mathey (2001) and Proctor

(2002), an economic analysis of silvicultural options on reclaimed titanium mines can

help landowners make sound forest management decisions.









Objectives

The general objective of this research is to determine the feasibility of tree crops

grown on reclaimed mine lands or using reclaimed water in northern and central Florida.

Specific objectives include the following:

1. Evaluate financial and environmental aspects of SRWC production on lands
irrigated with reclaimed wastewater.

2. Evaluate financial and environmental aspects of SRWC production on CSAs.

3. Determine the financial viability of slash pine production on reclaimed titanium
mined lands.

Literature Review

Environmental Impacts of SRWC Production

As a production-oriented land-use option, SRWCs have the potential of providing

environmental services such as reductions in C02 emissions, carbon sequestration, and

soil stabilization.

Carbon sequestration

Atmospheric concentrations of CO2 have increased from 280 parts per million

(ppm) in the year 1850 to 370 ppm near the end of the 20th century. This increase has

been attributed to the use of fossil fuels for energy and has been associated with increased

global temperatures. By the year 2100, CO2 concentrations are expected to rise to

between 540 and 970 ppm, and temperatures are expected to increase by 1.4 OC to 5.80C

(IPCC, 2001b). If temperatures increase, various environmental changes will occur,

including rising sea levels, loss of coastline, changes in ocean currents, changes in

precipitation, and a variety of associated changes in agricultural production, habitat

changes and shifts, and disease distribution (IPCC, 2001a). As with other types of

forests, SRWC plantations sequester atmospheric carbon as they grow, store the carbon









on the site and in the products they produce (until the stand and products are oxidized),

and provide alternatives to products that produce CO2 emissions. For these reasons, the

production of SRWCs would have implications for reducing atmospheric CO2.

Models by Heath et al. (1993) and Turner et al. (1995) show that U.S. forests will

continue to sequester atmospheric carbon for the next 40 years. Barker et al. (1995)

evaluate the potential of the U.S. Conservation Reserve Program to offset greenhouse gas

emissions in the United States through carbon sequestration. Their simulations suggest

that intensive afforestation on environmentally sensitive cropland could sequester about

16 Tg C equivalent. In addition to carbon sequestration, they also identified the potential

reduction of CO2 emissions through the production of biofuels and fuelwood, and

identified this possibility as needing research.

While the production of SRWCs has various potential environmental benefits, a

feasibility analysis must consider potentially negative environmental implications as well.

According to a review of research up to 1998, the establishment of SRWC plantations is

beneficial to some wildlife species but is detrimental to others, and these impacts need to

be taken into consideration in the planning of plantation establishment at the landscape

level (Tolbert & Wright, 1998). Environmental impacts of the establishment of SRWCs

during the first year are not unlike those of the production of annual crops (Joslin &

Schoenholtz, 1997). SRWC plantations are expected to improve surface runoff and

groundwater quality when compared to annual crops following the first establishment

year (Thornton et al., 1998). The land condition prior to establishment should also be

considered, and SRWC establishment may be an improvement over post-mining

conditions.









In Sweden, Salix is valued for its benefits in promoting wildlife diversity and its

capacity to phytoremediate cadmium contaminated soils (Perttu, 1998), as well as its

minimal need for herbicides and its contribution to soil organic matter (Ledin, 1998).

The environmental benefits of SRWCs and resistance to insect pests and weed species are

reiterated by Sage (1998). Indeed, Abrahamson et al. (1998) propose that SRWC

production systems in New York are ecologically and environmentally sustainable and

that the limit to production is economic viability. They conclude that environmental and

ecological benefits of the system should act as an impetus for developments needed to

overcome the economic constraints of the system.

Phytoremediation and reclamation

Biomass production systems can be associated with phytoremediation objectives,

as is being done at a research site at WC2 in Orlando, and an arsenic contaminated site in

Archer. Eucalyptus sp. and cottonwood are identified as SRWC candidate species with

potential to accumulate nutrients and mitigate problems associated with urban waste and

stormwater runoff (Rockwood et al., 1995b; Pisano, 1998; Moffat et al., 2001). Moffat et

al. conclude that approximately 100 m3 ha-1 yr-1 is an ideal application rate of effluents,

and their results suggest that sewage sludge should be applied to every rotation of the

SRWC rather than annually. Corseuil and Moreno (2001), Watson et al. (1999),

Gommers et al. (2000), and Vervaeke et al. (2001) discuss research related to the use of

willow in phytoremediation. Thompson et al. (1998) describe changes in poplar

respiration following exposure to 2,4,6-trinitrotoluene, and Heinonsalo et al. (2000)

describe the effect ofPinus sylvestris root growth on soil hydrocarbon oxidation. Goor et

al. (2001) assess the potential to use willow SRWC systems as a land-use alternative for

farmland contaminated by the Chemobyl nuclear power plant disaster. A study by









Bungart and Huttl (2001) of SRWC systems in post-mine landscapes indicates that

SRWC production is an ideal land-use alternative for post-mining landscapes.

Slash Pine Productivity on Mined Lands

Slash pine growth on mine tailings north of Starke was constrained by high bulk

density and low organic matter content, as well as extremes of soil moisture (Darfus &

Fisher, 1984). Slash pine growth on mine tailings can be improved by minimizing

variation in topography or adding inexpensive manure such as waste humate or sewage

sludge. Alternatively, wetter and dryer areas of the tailings could be planted with cypress

(Taxodium spp.) or longleaf pine (P. palustris), respectively. Mathey (2001) found site

indexes and growth patterns of established slash pine plantations on mined and unmined

lands to be similar. Proctor (2002) assessed growth responses to fertilizer and subsoiling

in young plantations. The effectiveness of reclamation practices for mined lands may be

assessed by the use of growth and yield models for slash pine plantations on mined and

unmined sites. Pienaar and Rheney (1995) and Fang and Bailey (2001) developed height

growth models accounting for intensive silvicultural treatments in slash pine plantations.

Policy

Some objectives of SRWC production include mitigation of CO2 emissions,

improvements in air and water quality, employment generation, and other societal

benefits. Policies that seek to reduce or internalize external environmental costs to

society at large have great implications for SRWC production. Hohenstein and Wright

(1994), Wright and Hughes (1993), and Graham et al. (1992) describe the potential for

SRWCs to offset CO2 emissions in the U.S. Tuskan (1998) identifies research needs

related to SRWCs in the U.S., including long-term use of fertilizers and irrigation and

development of improved harvesting methods. Ehrenshaft and Wright (1991) describe a









SRWC database management system, and modeled projections by Fischer and

Schrattenholzer (2001) suggest that bioenergy could supply 15% of global primary

energy by the year 2050.

Economics

The economic feasibility of SRWC production has been examined. Turhollow

(1994) presented cost estimates for 1989 and 2010 for supplying biomass via five

cropping strategies in five regions of the U.S. One of these strategies for the Midwest

and South used SRWCs. Turhollow proposed that energy crops must sell at between $43

and $60 dry Mg-1 in 1989 and $30 and $43 dry Mg-1 in 2010 to be economically viable.

Rahmani et al. (1997) described production costs of SRWCs in Florida as

consisting of a) farmgate costs (production and harvest costs), and b) transportation costs.

They estimated Florida eucalyptus farmgate costs at $32.00-$39.00 dry Mg-1 and yields at

20-31 dry Mg ha-1 yr-1. These cost estimates were generated using levelized costs and the

AGSYS Budget Generator, which calculates inputs costs such as labor, machinery,

fertilizer, etc., based on a database of costs of machinery and materials. The method of

cost estimation did not affect the range of estimated farmgate costs. SRWC farmgate

cost estimates in Florida ranging from $16-47 dry Mg-1 compare favorably with

herbaceous biomass crops in Florida and are likely to be lower than other regions of the

U.S. (Table 1-1). These numbers show that Florida has a competitive advantage in the

production of SRWCs.









Table 1-1. Farmgate (production and harvest) costs for SRWCs and herbaceous biomass
crops in Florida and other regions.
Crop $/Dry Mg $/Dry ton
Florida, SRWCs:
Leucaena 16-47 15-43
Eucalyptus spp. 32-39 29-36
Florida, herbaceous:
Sugarcane 23-25 21-32
Elephantgrass 24-32 22-29
Other Regions:
Poplar 33-132 39-120
Willow 30-110 27-100
TVA Estimatesa 32-69 29-63
Source: Rahmani et al. 1997; a Adapted from Downing and Graham (1996).

Transportation costs of SRWC biomass in Florida have been estimated at $3.10 dry

Mg-1 (Rahmani et al., 1997), assuming an average distance of 32 km (20 miles) and

moisture content of 15%. This transportation cost was lower than estimates for

herbaceous biomass crops, which ranged from $7.85-$12.28 dry Mg-1, attributable to

moisture contents ranging from 20-75%. Turhollow et al. (1996) estimated

transportation costs for herbaceous biomass crops ranging from $8.37-$13.95 Mg-1 with

dry matter contents of 50% and 30%, respectively. Transportation estimates for Florida

are competitive with these out-of-state costs.

Projected prices and quantities of SRWCs are functions of the amount and quality

of land, expected yields, production costs, and profit potential. Downing and Graham

(1996) described potential SRWC-production scenarios in the Tennessee Valley

Authority Region, which consists of parts of Tennessee, Kentucky, Virginia, North

Carolina, Georgia, Alabama, and Mississippi. They defined farmgate costs for SRWCs,

including sweetgum (Liquidambar styraciflua), sycamore (Platanus occidentalis), and

poplar (Populus spp.), under a variety of soil- and land-value categories. Under projected

yields ranging from 5.4-9.7 dry Mg ha-1 year-', farmgate costs ranged from $32-51 on









former cropland, and from $48-69 dry Mg-1 on former pastureland; SRWC production

costs ranged from $31.90-69.30 dry Mg-1. A farmgate price ranging from $44-55 dry

Mg-1 would be needed to ensure profits similar to current land uses. Increasing SRWC

biomass yields 25% decreased farmgate prices 20%.

On a national level, SRWCs will probably help meet growing demands for

pulpwood production. Currently, SRWCs are produced on fewer than 80,000 ha

(200,000 acres) in the U.S., with most of this production in the Pacific Northwest, though

a much greater area has potential for SRWC production. Alig et al. (2000) studied the

economic potential of SRWCs on agricultural land in the U.S. They estimate that 0.6-1.1

million ha (1.5 to 2.8 million acres) would generate about 10 to 16 Tg year-', equivalent

to about 40% of current U.S. hardwood pulpwood production. They used the Forest and

Agricultural Sector Optimization Model (FASOM), an intertemporal, price-endogenous

model, linking the U.S. forest and agricultural sectors. The SRWC was assumed to be

hybrid poplar, using data from the U.S. Department of Energy Oak Ridge National

Laboratory. Most of the current U.S. cropland was determined to be suitable for SRWC

poplar production: 0.5, 13.7, 34.0, 5.7, and 35.1 million ha (1.2, 33.9, 84.0, 14.0, and

86.8 million acres) in the Pacific Northwest, Lake States, corn belt, Southeast, and South

Central regions, respectively (Walsh et al., 1998).

Under the FASOM projections, SRWC plantation area is 0.9, 1.1, 0.6, and 1.1

million ha (2.1, 2.8, 1.5, and 2.6 million acres) in the first, second, third, and fifth [sic]

decades, respectively. Even at peak production, SRWCs are projected to occupy less

than 1% of cultivated U.S. cropland area. This modeling was done to estimate the

potential supply of wood fiber to the pulp-and-paper sector from SRWCs. However, the









authors also mention the potential for SRWCs to produce non-pulp products such as

veneer. This and fuel for bioenergy are alternative products that have the potential for

increasing the demand for SRWCs. The results from Alig et al. (2000) indicate that the

contributions of hardwood biomass could be relatively high when compared to the area of

land involved. Interestingly, these increased yields could reduce U.S. forest plantation

area and allow more U.S. forestland to be converted into agricultural production.

Forest Financial Analysis

Methods for determining the optimum forest harvest cycle length, or "rotation age,"

have improved over the past century as they have progressively internalized an increasing

number of factors. The most basic of forest management objectives has been to produce

as much forest product as possible by maximizing mean annual increment (MAI).

However, this method fails to account for the establishment costs and the time value of

money, i.e., "discount rate." The Fisher solution for forest optimization, while an

improvement from maximizing MAI, failed to capture the opportunity costs associated

with occupying the land (Rideout & Hessein, 1997). This limitation was addressed by

the Faustmann model by projecting an infinite number of rotations in determining LEV

that is included as a "land rent" cost in determining the optimum rotation age (Chang,

1984; Chang, 1998; Chang, 2001).

Hartman (1976) modified the Faustmann solution to include the value of

environmental services in determining LEV and optimum rotation age. While Medema

and Lyon (1985) adapted the Faustmann solution to find LEV and optimum harvesting

cycles for coppicing tree species, Smart and Burgess (2000) incorporated the valuation of

environmental services in determining the LEV and optimum rotation age of SRWC

willow production systems.









Many environmental services that forests provide are non-market services or

"externalities" for which timber producers have historically not been compensated. The

internalization of these market externalities could provide incentives for landowners to

manage their forests for the provision of environmental services. Environmental services

have been categorized as "intrinsic" vs. "instrumental" (Farber et al., 2002) and as

providing regulation, habitat, production, and information values (de Groot et al., 2002).

The innovative forestry systems described in this research (e.g., mine reclamation and

phytoremediation) are designed specifically for the instrumentally oriented environmental

services such as regulation of soil and air quality and production of wood products or

energy. The compensation for carbon sequestration services has been shown to lengthen

the economically optimum rotation age (Plantinga & Birdsey, 1994; Stainback, 2002).

Research regarding the impact of compensation for reduced carbon emissions on

optimum rotation age (achieved through fossil fuel displacement, as opposed to

compensation for increased carbon storage in the biomass or soil) is lacking.

Procedures

The Study Areas and Scope

This research is relevant to Florida sandhill sites irrigated with reclaimed water,

phosphate mined lands of central Florida, and titanium-mined lands of northeast Florida.

Financial analyses were done on forestry scenarios represented by the following three

sites:

1. Water Conserv II study site: WC2 near Winter Garden receives secondary treated
effluent from the City of Orlando Water Reclamation Facility and Orange County
South Regional Reclamation Facility. The water contains mean NO3-N and Cl-
concentrations of 6.92 mg L-1 and 86 mg L1, respectively, and is currently supplied
to approximately 70 agricultural customers free of charge irrigating 4,450 ha of
citrus plantations. A 2.8-hectare study site at the WC2 facility is characteristic of









sandhill Entisol soils where SRWCs could be produced using reclaimed
wastewater.

2. Kent study site: a 57-hectare CSA in Lakeland is the site for research related to
reclamation of phosphate mined lands. As an anthropogenic soil (see Phosphate
Mined Lands), the soil is a clayey Haplaquent (Mislevy et al., 1989), with a pH of
7.0, with little organic matter.

3. Iluka study site: Titanium mined lands are represented by studies at Iluka mining
company at Green Cove Springs, 55 km south of Jacksonville. This mine has been
producing titanium minerals and zircon since 1972. The operations include a
dredge mine, a satellite dry mine and associated mineral separation plants. Land in
the area is used extensively for slash pine plantations. The Iluka site is situated on
spodosol soils.

Data sets utiltized include SRWC-72 from Winter Garden (1998-2003), SRWC-90

and plots in operational areas from Lakeland (2001- 2005), and SRWC-84 and SRWC-

84-2001 from Green Cove Springs (1999-2005) (Table 1-2).

Table 1-2. Summary of study sites.
Site Study Situation Species Dates Covered
WC2, Winter Garden SRWC-72 Reclaimed Water EG, March 1998 to May
CW 2003
Kent, Lakeland SRWC-90 CSA EA, EG May 2001 to January
2005
Iluka, Green Cove SRWC-84 Reclaimed Titanium SP November 1999 to
Springs SRWC-84-2000 Mined vs. Unmined Plots January 2005
SP=Slash pine, EG=Eucalyptus grandis, EA=Eucalyptus amplifolia, CW=cottonwood.


Methodology

Optimization of non-coppicing species

A financial feasibility analysis was applied through the use of the Faustmann

solution to determine LEV and optimum rotation ages and coppice stage lengths as

described above (Forest Financial Analysis). The basic Faustmann solution for a non-

coppicing even-aged stand is defined as


LEV= V(t) e C


1-1)


-er*t


\









where LEV is the land expectation value (i.e., the land value as defined by a forestry

scenario repeated in perpetuity), V(t) is the value of the stand at time t (i.e., price times

volume), C is cost of stand establishment at the beginning of the rotation, r is the interest

rate, and t is the optimum rotation age. Optimum rotation age is determined by taking the

derivative ofEq. (1-1), setting it equal to zero, and solving for t. The first order

necessary condition (FONC) for the Faustmann solution is found by taking the derivative

of Eq. (1-1) and rearranging, resulting in

V(t)**'1- (1-2)-
V'(t)= r*V(t)+r* _V( (1-2)

which is equivalent to

V'(t) = r*V(t)+r *LEV (1-3)
or

V'(t)
=() r (1-4)
V(t) +LEV

Eq. (1-2) states that the FONC for the optimization of the Faustmann solution is the

time t where the marginal benefit in growth represented by the left-hand side (LHS), just

equals the opportunity cost of the forest capital and the land rent shown in right-hand side

(RHS). This can alternatively be stated as the time t where the ratio of marginal benefit

(growth) to opportunity costs of the forest capital and the land rent just equals the given

interest rate r (Eq. (1-4)). This non-coppicing form of the Faustmann solution was used

for the analysis of slash pine on titanium-mined sites.

Optimization of coppicing species

Determining optimum rotations of SRWC coppice systems at the Kent site and

WC2 site differed from determining optimum rotations of non-coppicing systems. The

optimum age of each coppice harvest must be determined as well as the optimum time to









replant (i.e., the optimum number of stages before replanting). Following the

terminology used by Smart and Burgess (2000), a coppice stage length describes the

period of time between coppice harvests, while a coppice cycle length describes the

period of time and/or number of coppice stages between replanting of the trees. Medema

and Lyon (1985) modified the Faustmann formula (Eq. (1-1)) to solve for multiple

coppice stage lengths given a fixed number of coppice stages (n):



y n1 -e-* .rj=I) J
LEV=- =_*y-- (1-5)
1-eJ-^
where
to=0
n = the number of coppice stages, s,
V(t) = the value as a function of time (as a function of growth of stage s),
r = the real interest rate (excluding inflation),
t = time, the rotation age in years of stage s, and
Cs = costs of stage s discounted to the start of the stage.

Eq. (1-5) defines LEV as the sum of the benefits of each coppice stage discounted

to the present minus the sum of the costs of each coppice stage discounted to the present,

for a fixed number of coppice stages projected in perpetuity. Cs indicates a cost that may

be replanting the coppice cycle, or may be a different cost associated with each coppice

stage, such as weeding costs. Estimates for the prices and costs associated with SRWC

production at the Kent site and WC2 site came from the preliminary work done by the

Common Purpose Institute at the Kent site.

Solving for the optimum stage lengths and cycle lengths of the Kent site and WC2

site was a two-part solution. First, n was fixed and the optimum stage lengths were

determined for the fixed number of stages per cycle (i.e., n was consecutively fixed for 1-

4). As with the non-coppicing optimization, the optimum stage length for each individual









stage was the point at which the marginal benefit of the continued biomass growth over

the next unit of time is just equal to the marginal opportunity cost of the forgone benefit

due to not harvesting, plus the marginal cost of delaying all future coppice stages and

cycles. The marginal cost of delaying all future coppice stages and cycles is defined as

the LEV of the subsequent coppice stages multiplied by interest rate r.

Next, the optimum number of stages was found by determining at what value of n

an additional coppice stage to the coppice cycle (i.e., LEV of n+1 minus LEV of n) has a

marginal benefit less than zero. Stated differently, the optimum number of stages is that

which provides the highest LEV. As the number of stages n varies, the optimum stage

lengths can also vary.

Valuation of the non-timber benefits

Two non-timber benefits (NTBs) included in the determination of LEV and optimum

rotation ages were:

1. Phytoremediation ofwastewater. The Southern Regional Water Reclamation
Facility of Orange County records costs associated with wastewater treatment,
and the Florida Department of Environmental Protection regulates standards for
wastewater treatment used to irrigate non-edible crops. Sewage water treatment
costs were used to estimate the value of the phytoremediation services at WC2.

2. C sequestration, and offset of CO2 emissions. CO2 is a greenhouse gas covered
in the trading policy of the Chicago Climate Exchange, Inc. and the International
Carbon Bank and Exchange. These sources and others were used to provide
ranges of potential values of C sequestration and CO2 emission reductions.

As described above, several variables influence calculation of LEV and optimum

rotation ages of tree crops at titanium mined lands, phosphate mined lands, and lands

irrigated with reclaimed wastewater, including production costs, yields, product prices,

interest rates and values of environmental services. The sensitivity of LEV and optimum

rotation ages to variation of each of these factors was tested.









Optimization of non-coppicing with inclusion of the non-timber benefits

To identify the divergence between private and social value maximization, the

methodology described by Hartman (1976) was used to include the values of mine

reclamation and carbon sequestration in the analysis of slash pine on titanium mined

sites. Hartman (1976) used an integration to account for social amenities associated with

standing forest:

t
NTB(t) = NTB (n) e r*dn (1-6)
0
where NTB(t) was the present value of a stream of NTBs of one rotation quantified by the

integration of the discounted value of these benefits according to stand age n. The NTB

defined by Eq. (1-6) was an additional benefit to be added to the numerator of Eq. (1-1):


jNTB(n) e *dn + V(t) e *t C
LEV = o (1-7)
1 -e-*t

Deriving the F.O.C. for optimality of the basic Hartman model (Eq. (1-7)) was the

same as the derivation of the F.O.C. of the Faustmann model:

(NTB(t)*e' + V'(t)* e' V (t) r*e re)* ( e' )

KNTB(n)*e-r'dn+V(t)*e-rt C *(r*e-ret
(1= (1-8)



NTB(t) e + V '(t) et = r *V(t)e +r N -(n) e Fd -r(t* e -*


(1-9)

NTB(t)+V'(t)= r *V(t)+r*LEV (1-10)









NTB(t) + V'(t)
=r (1-11)
V(t) +LEV

The NTB remains on the LHS of Eq. (1-10) as an additional marginal benefit, and

an additional reason to delay the harvest of the stand. Similarly, the NTB in the

numerator of the LHS of Eq. (1-11) serves to delay the time t at which the ratio of

benefits to costs equals the interest rate r.

Optimization of coppicing species with inclusion of non-timber benefits

To internalize the values of mine land reclamation, wastewater phytoremediation,

carbon sequestration, and reduction in CO2 emissions associated with Eucalyptus spp.

culture at the WC2 and Kent sites, the methodology described by Smart and Burgess

(2000) was used to include the social amenity in the analysis. The NTB of a given

coppice stage can be defined as


NTB, = (iNTB (t))e *e-))dt (1-12)


where the NTB of stage s was the definite integral of the flow of the benefits for the

duration of the stage, discounted from the time of the harvest of the previous stage. This

NTB can be added to Eq. (1-5), the equation for the LEV of coppicing species:

I nI r* s ty l 1j) ()-r*y l 1 )
LEV V(ts)*e e- J_ +NTB, *e -C, eg J1
LEV = (1-13)
1- e' rM'

Eq. (1-13) accounts for the value of a NTB derived from keeping the trees in the

field, i.e., delaying harvest. Conversely, one potential NTB of the SRWC, the reduction

in CO2 emissions due to displacement of fossil fuels, was associated with the harvest of

the trees. This NTB, which takes place at the time of the harvest of the trees, was treated






19


as an addition to the V(t), term in Eq. (1-13). This harvest-associated NTB served to

decrease optimum stage length, counteracting the increase of optimum stage length due

to NTBs derived from standing trees. This interaction was assessed in Chapter 3.














CHAPTER 2
EFFECT OF DENDROREMEDIATION INCENTIVES ON THE PROFITABILITY OF
SHORT-ROTATION WOODY CROPPING OF Eucalyptus grandis

Introduction

Water resources, traditional forest products, fire management, recreation, and

wildlife are the five main elements of particular concern in the wildland-urban interface

(WUI) (Macie & Hermansen, 2003). They note: "Municipal waste facilities in rapidly

developing areas face difficulties with handling and treating increased waste

loads...allocating high-quality, abundant flows of water and managing forest ecosystems

at large watershed scales remain key challenges." Trees within and surrounding urban

centers can provide a variety of environmental services including sequestering

atmospheric carbon dioxide, enhancing biodiversity, providing aesthetics and recreation,

and remediating urban wastewater.

Nutrients from urban wastewater and other sources cause eutrophication and

degradation of aquatic ecosystems. Increasing concentrations of nitrogen (N) and

phosphorus (P) are compromising water quality in Florida. The population of Florida is

expected to nearly double in the next 30 years, from 15.9 million permanent residents in

the year 2000 to a projected 30.1 million in 2030 (Bureau of Economic and Business

Research, 2001), which will result in an increase in both water consumption and

wastewater production. As Florida faces increasing pressure on water resources,

wastewater presents both a challenge of disposal and an opportunity for reuse. Trees can

mitigate nutrient loading by extracting N from reclaimed wastewater, thus improving









both water quality and tree growth, and reducing fertilizer inputs. This chapter assesses

economic impacts of incentives to use fast-growing trees to remove N from reclaimed

wastewater discharged from an urban center.

Florida Administrative Code Chapter 62-610 mandates primary treatment (removal

of biosolids), secondary treatment (removal of dissolved elements), and basic disinfection

at sewage treatment plants (State of Florida, 2004b). Reclaimed water leaving the

Southern Regional Water Reclamation Facility of Orange County, Florida contains 7 ppm

nitrate N (P. Duel, Orlando Wastewater Treatment Plant Manager, pers. comm., February

2004). Following treatment, the reclaimed water can be used for irrigation. For example,

132,500 m3 (35 million gallons) day1 of reclaimed water is pumped 35 km from sewage

treatment plants in Orlando and surrounding areas to the Water Conserv II, a reclaimed

water distribution facility, where 60% of this reclaimed water is applied to 1,700 ha of

citrus groves, ornamental nurseries, and golf courses. The remaining 53,000 m3 (14

million gallons) day'1 of reclaimed water is pumped into open sand pits called rapid

infiltration basins (RIBs), where the water percolates into the Florida aquifer (State of

Florida, 2003; Rabbani & Munch, 2000).

The use of trees to extract contaminants from soil or water is defined as

dendroremediation (Rockwood et al., 2004). An example is using tree plantations as a

tertiary or "finishing" treatment to remove N from reclaimed water (Aronsson & Perttu,

2001; Labrecque et al., 1997; Perttu, 1998; Rosenqvist et al., 1997; Pisano, 1998; e.g.

Licht & Isebrands, 2005). Dendroremediation is used to address urban waste problems in

Sweden and Finland (Ettala, 1987), the United Kingdom (Alker et al., 2002), Canada

(Gordon et al., 1989) and Hong Kong (Wong & Lueng, 1989). As an alternative to









releasing reclaimed water in RIBs, it could be dendroremediated by SRWCs. Research at

Water Conserv II between 1998 and 2001 suggests that E. grandis irrigated with

reclaimed water can yield about 13 dry Mg ha-1 year-, and extract over 300 kg nitrate

nitrogen (N) ha-1 year- (Rockwood et al., 2001).

As the citrus industry in Orange County is projected to decline, biomass crops

present an alternative that can produce wood for rough sawtimber, landscape mulch, or

biomass for renewable energy. In addition to dendroremediation of reclaimed water,

SRWC production can generate employment (Borsboom et al., 2002) and sequester

carbon in above- and below-ground biomass, and soil organic carbon (Eriksson et al.,

2002). If the Florida Department of Environmental Protection mandates renewable

portfolio standards, SRWC biomass may be used to displace fossil fuels in electricity

generation, providing additional benefits including reduction ofCO2, NOx, and SOx

emissions and diversification of domestic energy resources (Segrest et al., 1998; Stricker

et al., 2000; Roth & Ambs, 2004a).

Environmental economists suggest incorporating environmental benefits and costs

as an effective strategy using market forces work to correct externalities (Van Kooten &

Bulte, 2000). In the face of increased wastewater treatment standards, producers of

wastewater search for cost effective mitigation strategies. On the other hand, tree growers

who could use wastewater as an input in their production process may be willing to

provide a service by utilizing wastewater. The optimum use of wastewater (on a

voluntary basis) by a tree grower depends on the marginal cost and marginal productivity

of wastewater use. In the face of incentives for using wastewater, however, it is likely









that tree growers could use wastewater at a level higher than that of voluntary use. This

approach can be a win-win situation for wastewater producers and tree growers.

Dendroremediation of municipal wastewater by willow SRWCs in Sweden is

economically feasible, despite a growing season of six months (Rosenqvist et al., 1997),

much shorter than that in Florida. This chapter, the first known study of the impact of

dendroremediation incentives on management and profitability of a SRWC system,

considers eucalyptus tree crops as a remediation strategy. An economic optimization

model of a SRWC biomass production system that includes an incentive for

dendroremediation of N in reclaimed water investigates how this incentive would

influence land expectation value (LEV), an attribute of profitability, and optimal

management of the associated SRWC production system.

Methodology

Optimization of Coppicing Species

The basic Faustmann formula for a non-coppicing even-aged stand is defined by

Eq. (1-1). The optimum rotation age t* is determined by taking the derivative of Eq.

(1-1), setting it equal to 0, and solving for t (Chang, 1984). Eq. (1-5) defines the

Faustmann formula as modified by Medema and Lyon (1985) for coppicing forest

systems used to determine both the optimum duration of each stage as well as the

optimum number of stages per cycle. Estimates for the prices and costs associated with

SRWC production at Water Conserv II come from the preliminary work done by

Rockwood et al. (2002a). An example of the generalized Eq. (1-5) fixed for two stages

(n=2) is shown in Eq. (2-1):









(V(t)*e (C,+C,))+


LEV= (V(t Ce(- )C (2-1)
le(-r (t,+t2)) -



where tl and t2 are the duration of stage one and stage two, respectively, Cp is the cost of

planting at the beginning of the cycle, Cw is the cost of weeding at the beginning of the

stage, C, is the annual maintenance cost, and Cr is the cost of irrigation establishment at

the beginning of the operation.

Optimization of Coppicing Species with Inclusion of the Dendroremediation Service

Hartman (1976) internalized non-timber benefits (NTBs) into the Faustmann

formula. The methodology described by Smart and Burgess (2000) is used to internalize

the dendroremediation service associated with cultivating coppicing species such as

Eucalyptus spp. irrigated with reclaimed water. There are two ways to account for NTBs.

If a NTB is achieved at harvest, it is considered a stock benefit, while a continuous

amenity is calculated as a flow benefit.

A dendroremediation service might be payable following removal of N from the

site with harvest of the biomass. In this scenario, the NTB would be treated as a stock

benefit and accounted for much like a timber benefit as defined in Eq. (2-2).

NTBs = NTB (t) (2-2)

This NTB value can then be added to Eq. (1-5), the equation for net returns of

coppicing species, as shown in Eq. (2-3). An example of Eq. (2-3) fixed for two stages is

shown in Eq. (2-4).










S IV((ts)e +NTBs e( -Cs eC)


1-e' *y )
(V(t)*e(l) + NTB*e r (CC +C

+ V(t)*e r*(t+t2)) NTB2s e(r*(tl+t2))- ))
1 -e(-r*(tl+t2))


(2-4)


l-eC Cr


Alternatively, if the dendroremediation service were deemed beneficial as the N is

continuously accumulated in the growing trees, the NTB is treated as a flow benefit. The

NTB of a given stage calculated as a flow can be defined as


NTB d((NTB t) e(-rt)dt
0


(2-5)


where the NTB of stage s is the definite integral of the flow of the benefits discounted to

the beginning of the stage, for the duration of the stage. This NTB value can then be

added to Eq. (1-5) as shown in Eq. (2-6). An example of Eq. (2-6) fixed for two stages is

shown in Eq. (2-7).


SZ IV(t e)*e + NTBF e r (t- 1) r Cs


1-e J-
(V(t)* (-rl) NTBI -(C, +C,))

+ V(ti2 -r*(t+t2)) NTBF *e( r) e(
1- e(- r(t+t2))


The NTB to be included in the determination of profit and optimum coppice

management is the dendroremediation treatment of the reclaimed water. The Southern


LEV


TLEV


LEV


r*tl))


(2-6)




(2-7)


C C


L.EV


(2-3)


~









Regional Water Reclamation Facility records costs associated with wastewater treatment.

These local sewage water treatment costs are used to estimate the shadow price of

removing additional N from reclaimed water.

The exact marginal cost of N removal is unknown. In an economic assessment of

dendroremediation of municipal wastewater by willow in Sweden, Rosenqvist et al.

(1997) determine that the costs for removing N and P of conventional treatment are $10-

$27 kg-1 N (in 1994 USD). At the Southern Regional Water Reclamation Facility, pre-

treatment waste contains 25 ppm ammonia N, and post-treatment reclaimed water

contains 7 ppm nitrate N, resulting in a decrease of 18 milligrams liter- N; the total cost

of sewage water treatment is $0.88 per 1,000 gallons (P. Duel, Orlando Wastewater

Treatment Plant Manager, pers. comm., February 2004). Based on these values, the total

cost of wastewater treatment is $12.92 kg-1 N, or $1.29 kg-1 N if 10% of the total cost is

assumed associated with N removal. This cost estimate could be higher, since removal of

additional N becomes increasingly costly, or it could be assumed that the value of

removal of additional N should be lower, since the willingness to pay for the removal of

additional N has not been substantiated. This analysis assumes a range of values from

$0-$3.50 kg- N removed.

Model inputs

E. grandis (EG) was identified as producing more biomass and accumulating more

N than Populus deltoides when irrigated with reclaimed water (Rockwood et al., 2002a).

While P. deltoides is dormant during the winter months of central Florida (November

through February), EG grows year-round, offering continuous dendroremediation

services not possible with deciduous species (Rockwood et al., 2001). Though not a

native species, EG is non-invasive in Florida and has been produced commercially in









south central Florida since the 1970s without spreading (Rockwood, 1996). While other

species could be considered in the future, the scope of this chapter is limited to EG,

because to date it has demonstrated the greatest potential for dendroremediation of

reclaimed water in central Florida. The methodology described here can be applied to

other candidate species for which irrigated growth and yield data are available.

Height and DBH data were taken between 0 and 26 months for EG in central

Florida at a density of 3,500 trees ha-1 irrigated with 17 mm reclaimed water day-. Due

to a lack of growth and yield data for irrigated EG in central Florida beyond 26 months,

the above observations are extended using unpublished data for EG from Belle Glade,

Florida to estimate high and low growth and yield functions (Carter and Rockwood,

personal comm., 2004). The trees in Belle Glade were effectively irrigated because soil

moisture was made adequate by controlling water in irrigation canals, and the growth rate

of the two sites were similar through the first 26 months. This extended data set is used

as a baseline for estimating a range of possible yield functions in the sensitivity analysis

described below. Nonlinear regression is used to fit the data to an Arrhenius functional

form:


B(a) = b e (2-8)

where B(a) is dry Mg stemwood and bark biomass ha-1 as a function of stand age a in

years for the first stage, and b and c are the estimated parameters 118.9 and 2.73 for the

low growth function and 154.0 and 2.92 for the high growth function, respectively

(Figure 2-1). These functions, yielding 16 and 19 dry Mg ha-1 year1 stem biomass or 27










and 32 dry Mg ha-1 year1 total above ground biomass1 for the low and high growth

functions assuming a rotation age of 3.6 years, are at the high range of estimated

unirrigated Eucayptus spp. production of 20-31 dry Mg ha- year' described by Rahmani

and Hodges et al. (1997).


150



4 100






0 I
0 5 10 15
stand age (years)
High Growth Estimate
Low Growth Estimate

Figure 2-1. Estimated high and low growth and yield functions for Eucalyptus grandis at
Winter Garden, Florida, irrigated with 17 mm day- reclaimed water.

Yields of subsequent stages (i.e., yields of the coppice stages following the initial

growth) are uncertain. In central Florida, the season in which the trees are harvested

influences EG coppice productivity (Webley et al., 1986). Though coppice yields

eventually decline, research of P. deltoides suggests that the yield of the second stage

(i.e., first coppice regrowth) is generally higher than that of the initial growth stage

(Hansen et al., 1983). While growth of the second stage of EG might be higher than that

of the first stage due to the benefits derived from the previously established root system,

coppice mortality associated with wind throw or weed competition might also increase,

reducing yields. Due to a lack of data of coppice stages for EG irrigated with reclaimed

1 Assumes a factor of 1.7 to convert stem inside bark to total aboveground biomass (Mg ha-1) (Segrest,
2002).









water, in this analysis yields of 80%, 65%, and 30% of the growth of the initial stage are

assumed for the second, third, and fourth stages, respectively. These estimates are based

on casual observation and are consistent with the methodology described by Medema and

Lyon (1985).

To incorporate a dendroremediation benefit, the amount ofN assimilated in

biomass growth is estimated. Analysis ofbiomass samples of EG irrigated with

reclaimed water at Water Conserv II indicates that leaves, stem bark, branches, and stem

wood contain 1.39%, 0.28%, 0.27% and 0.09% nitrate N, respectively (Rockwood et al.,

2001). Accounting for different rates of accumulation of these four components of tree

biomass, N accumulation functions are shown in Eq. (2-8), where parameters b and c are

54.8 and -0.24 for the high growth estimate and 51.4 and -0.22 for the low growth

estimate, respectively. While N accumulation rates of coppice stages might decrease

with reduced biomass productivity or increase with higher leaf/stem ratios, actual rates of

N accumulation by coppice stages are unknown. This model assumes the same N

accumulation function for coppice stages as for the original growth stage.

Based on previous work relating to SRWC production in Florida (Rockwood et al.,

2002a; Segrest et al., 1998; Rahmani et al., 1997), the following model inputs are

assumed: planting cost, $500.00 ha-1; weed control following a coppice harvest, $50.00

ha-1; annual maintenance fee, $50.00 ha-1; and the price of woody biomass (for mulch),

$20.00 dry Mg-1. This model assumes simulated real interest rates of 4% and 6% and two

different costs for irrigation (microemitter) installation, $2,471 ha-1 and $3,707 ha-l. The

high price for the irrigation system needed to distribute the reclaimed water, which is









correlated with the price of gasoline, is a cost not incurred by conventional forestry

systems in Florida.

Results and Discussion

Table 2-1 illustrates net returns assuming no dendroremediation incentive, a

dendroremediation incentive treated as a stock benefit, and a dendroremediation incentive

treated as a flow benefit2, for high and low growth estimates, at a dendroremediation

value of $1.50 kg-1 N, an interest rate of 4%, price of wood of $20 dry Mg-1, and an

irrigation installation cost of $2,471 ha-1, for 1, 2, 3, and 4 coppice stages. By identifying

the number of stages that yields the highest profit, the optimum number of stages is two

and three for the high and low growth models, respectively. This process was repeated

for each scenario of irrigation cost, growth and yield function, and interest rate

combinations to determine optimum profit, optimum number of stages per cycle, and

optimum stage durations (Table 2-2). Resulting LEVs range from -$2,343 to +$2,726

ha-1, less than LEVs of a SRWC system in the United Kingdom reported by Smart and

Burgess (2000) of $3,931, $6,168 and $14,814 ha-1 for market only, low NTB and high

NTB model scenarios, respectively (stumpage price of $31 dry Mg-1, establishment cost

of $1,538 ha-1 and an exchange rate of $1.54/ in November 2000). If the cost of the

irrigation system were assumed sunk, LEVs reported here would range from $1,364 to

$5,233 ha-1, comparable to those of Smart and Burgess (2000).

To compare these findings with Florida production costs calculated by a previous

study, the model was used to find minimum stumpage prices needed to achieve LEVs of


2 If treated as a stock benefit, the dendroremediation service is provided when the nitrogen is taken from the
site (removed with harvested biomass); if treated as a flow benefit, the service is continuously provided as
the tree grows and accumulates nitrogen.









$1,235 ha-1 and $2,470 ha-1, representing LEVs of conventional forestry (Borders &

Bailey, 2001) and Florida agricultural land (Reynolds, 2005), respectively. Stumpage

prices of $26 and $30 dry Mg-1 are required to match LEVs of $1,235 ha1 and $2,470

ha-1, respectively, assuming irrigation establishment costs of $3,707 ha-1, the high growth

model and an interest rate of 5%. Rahmani et al. (1997) report Eucalyptus spp. farm gate

production costs for Florida of $32-$39 dry Mg-1, less than the $48-$52 dry Mg-1 farm

gate costs estimated here assuming a harvest cost of $22 dry Mg-1 (Rahmani et al., 1998).

A higher cost of production is expected given the cost of irrigation establishment.

Excluding irrigation costs C, from the model yields stumpage prices of $15 and $19 dry

Mg-1 and farmgate prices of $37 and $42 dry Mg-1 needed to match LEVs of $1,235 ha-1

and $2,470 ha-1 respectively, closer to the estimates by Rahmani and Hodges et al.

(1997).

Table 2-1. Net returns and optimum stage lengths assuming 1, 2, 3, and 4 stages for a
Eucalyptus grandis short-rotation woody crop system irrigated with reclaimed
water in central Florida.
High Growth:
No N benefit N Benefit as Stock N Benefit as Flow
# Stages LEV Optimum Stage LEV Optimum Stage LEV Optimum Stage
per cycle ($ ha1)Lengths ($ ha-1) Lengths ($ ha-1) Lengths
1 $653 4.4 $1,068 4.2 $1,134 4.2
2* $1,4054.0, 3.6 $1,888 3.8, 3.6 $1,952 3.8,3.4
3 $1,3164.0, 3.6, 3.1 $1,824 3.8, 3.4, 2.9 $1,887 3.8, 3.4, 2.9
4 $1,1204.1,3.7, 3.3, 0.1 $1,610 3.9, 3.5, 3.1, 0.1 $1,674 3.9, 3.5, 3.1, 0.1
Low Growth:
No N benefit N Benefit as Stock N Benefit as Flow
# Stages LEV Optimum Stage LEV Optimum Stage LEV Optimum Stage
per cycle ($ ha )Lengths ($ ha ) Lengths ($ ha ) Lengths
1 -$932 4.7 -$563 4.4 -$499 4.4
2 -$85 4.1, 3.7 $364 3.8, 3.4 $425 3.8, 3.4
3* -$63 4.0, 3.7, 3.2 $414 3.8, 3.4, 2.9 $475 3.8, 3.4, 2.9
4 -$258 4.2,3.8,3.4,0.1 $198 3.9, 3.5, 3.1, 0.1 $259 3.9, 3.5, 3.1, 0.1
Note: These calculations include an interest rate of 4%, price of wood of $20 dry Mg1,
irrigation installation cost of $2,471 ha-l, and a dendroremediation value of $1.50 kg-1 N
(where applicable). An "*" indicates the optimum number of stages per cycle as show by
the highest net returns. The NTB is calculated as both a stock and flow benefit.













Table 2-2. Optimum LEVs, optimum stages per cycle, and optimum stage lengths for a range of dendroremediation values for
Eucalyptus grandis irrigated with reclaimed water in central Florida.


Dendroremediation Benefit as a Stock


Dendroremediation Benefit as a Flow


LEV ($ ha')


Optimum Stage Lengths
(years)


LEV ($ ha')


Optimum Stage
Lengths (years)


Low Growth

4.0, 3.7, 3.2
4.0, 3.6, 3.1
3.9, 3.5, 3.0
3.8, 3.4, 2.9
3.7, 3.3, 2.8
3.6, 3.2, 2.8
3.5, 3.1, 2.7
3.4, 3.0, 2.6
4.0, 3.7, 3.2
4.0, 3.6, 3.1
3.9, 3.5, 3.0
3.8, 3.4, 2.9
3.7, 3.3, 2.8
3.6, 3.2, 2.8
3.5, 3.1, 2.7
3.4, 3.0, 2.6


High Low High Low
Growth Growth Growth Growth


$1,405
$1,584
$1,767
$1,952
$2,141
$2,333
$2,528
$2,726
$169
$349
$531
$717
$906
$1,098
$1,293
$1,491


-$63
$112
$291
$475
$662
$854
$1,050
$1,250
-$1,299
-$1,123
-$944
-$761
-$574
-$382
-$186
$15


4.0,3.6
3.9,3.5
3.9, 3.4
3.8, 3.4
3.7,3.3
3.7, 3.2
3.6, 3.2
3.5, 3.1
4.0, 3.6
3.9, 3.5
3.9, 3.4
3.8, 3.4
3.7, 3.3
3.7, 3.2
3.6, 3.2
3.5, 3.1


4.0, 3.7, 3.2
4.0, 3.6, 3.1
3.9, 3.5, 3.0
3.8, 3.4, 2.9
3.7, 3.3, 2.8
3.6, 3.2, 2.8
3.5, 3.1, 2.7
3.4, 3.1, 2.6
4.0, 3.7, 3.2
4.0, 3.6, 3.1
3.9, 3.5, 3.0
3.8, 3.4, 2.9
3.7, 3.3, 2.8
3.6, 3.2, 2.8
3.5, 3.1, 2.7
3.4, 3.1, 2.6


$ kg-'
N
A $0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50
B $0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50


High
Growth
$1,405
$1,563
$1,724
$1,888
$2,056
$2,227
$2,401
$2,579
$169
$327
$488
$653
$820
$991
$1,166
$1,343


Low
Growth
-$63
$92
$251
$414
$581
$753
$930
$1,111
-$1,299
-$1,144
-$985
-$822
-$654
-$482
-$306
-$124


High
Growth
4.0, 3.6
3.9, 3.5
3.9, 3.4
3.8, 3.4
3.7, 3.3
3.7, 3.2
3.6, 3.1
3.5, 3.1
4.0, 3.6
3.9, 3.5
3.9, 3.4
3.8, 3.4
3.7, 3.3
3.7, 3.2
3.6, 3.2
3.5, 3.1













Table 2-2. Continued
Dendroremediation Benefit as a Stock
LEV ($ha-1) Optimum Stage
Lengths (years)


$ kg-'
N
C $0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50
D $0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50


High
Growth
-$195
-$91
$15
$124
$236
$355
$476
$601
-$1,430
-$1,326
-$1,220
-$1,113
-$1,000
-$881
-$759
-$635


Low
Growth
-$1,108
-$1,006
-$902
-$795
-$685
-$571
-$455
-$336
-$2,343
-$2,242
-$2,137
-$2,030
-$1,920
-$1,807
-$1,691
-$1,571


High
Growth
3.9, 3.5
3.8, 3.5
3.7, 3.4
3.7,3.3
3.6, 3.2, 2.8
3.5, 3.2, 2.7
3.5, 3.1, 2.7
3.4, 3.0, 2.6
3.9, 3.5
3.8, 3.5
3.7, 3.4
3.7, 3.3
3.6, 3.2, 2.8
3.5, 3.2, 2.7
3.5, 3.1, 2.7
3.4, 3.0, 2.6


Dendroremediation Benefit as a Flow
LEV ($ha1) Optimum Stage
Lengths (years)


Low
Growth
3.9, 3.6, 3.2
3.8, 3.5, 3.1
3.7, 3.4, 3.0
3.6, 3.3, 2.9
3.5, 3.2, 2.8
3.5, 3.2, 2.8
3.4, 3.1, 2.7
3.3, 3.0, 2.6
3.9, 3.6, 3.2
3.8, 3.5, 3.1
3.7, 3.4, 3.0
3.6, 3.3, 2.9
3.5, 3.2, 2.8
3.5, 3.2, 2.8
3.4, 3.1, 2.7
3.3, 3.0, 2.6


High
Growth
-$195
-$69
$58
$187
$320
$459
$601
$746
-$1,430
-$1,305
-$1,112
-$1,048
-$916
-$777
-$635
-$490


Low Growth

-$1,108
-$986
-$862
-$734
-$605
-$472
-$336
-$198
-$2,343
-$2,221
-$2,097
-$1,970
-$1,840
-$1,708
-$1,572
-$1,433


High
Growth
3.9, 3.5
3.8,3.5
3.7, 3.4
3.7, 3.3
3.6, 3.3, 2.8
3.5, 3.2, 2.7
3.5, 3.1, 2.7
3.4, 3.1, 2.6
3.9, 3.5
3.8, 3.5
3.7, 3.3
3.7, 3.3
3.6, 3.3, 2.8
3.5, 3.2, 2.7
3.5, 3.1, 2.7
3.4, 3.1, 2.6


Low
Growth
3.9, 3.6, 3.2
3.7, 3.5, 3.1
3.7, 3.4, 3.0
3.6, 3.3, 2.9
3.5, 3.2, 2.9
3.5, 3.2, 2.8
3.4, 3.1, 2.7
3.3, 3.0, 2.6
3.9, 3.6, 3.2
3.8, 3.5, 3.1
3.7, 3.4, 3.0
3.6, 3.3, 2.9
3.5, 3.2, 2.9
3.5, 3.2, 2.8
3.4, 3.1, 2.7
3.3, 3.0, 2.6


Note: LEVs are shown for both stock and a flow benefits under high and low growth models assuming A) an interest rate of 4% and
an irrigation installation cost of $2,471 ha-1; B) an interest rate of 4% and an irrigation installation cost of $3,707 ha-1; C) an interest
rate of 6% and an irrigation installation cost of $2,471 ha-1; and D) an interest rate of 6% and an irrigation installation cost of $3,707
ha-.









The optimum economic rotation lengths for cycles with single stages shown in

Table 2-1 are longer than maximum sustained yield (MSY) ages of 2.7 and 2.9 years for

the low and high growth models, respectively. Economic optimum rotation ages of

conventional forest plantations are typically shorter than the age of MSY (Samuelson,

1976). However, low stumpage prices relative to high regeneration costs can extend

optimal economic rotation past the rotation of MSY, especially with SRWC species as

demonstrated by Binkley (1987). Though inclusion of an environmental service provided

by a standing forest extends optimal economic rotation (Hartman, 1976), incentives for

dendroremediation, best achieved by rapidly growing stands, favors shorter rotations

(Table 2-2).

While increasing interest rates tends to shorten optimum coppice stage lengths as

the opportunity cost of standing biomass increases, it also favors increasing the number

of coppice stages per cycle, thus minimizing regeneration costs. At a 4% interest rate, the

optimum number of stages per cycle is two and three for simulations using the high and

low growth functions, respectively. At a 6% interest rate, the optimum number of stages

per cycle is two for high growth rates with a dendroremediation incentive less than $2

kg-1 N and three for the remaining scenarios. Optimum stage length duration ranges from

2.6-4.0 years. Increasing the interest rate from 4% to 6% or increasing the

dendroremediation incentive by $1 kg-1 N decreases optimum stage lengths by about 0.1

years (Table 2-2). These results are consistent with those of Smart and Burgess (2000)

who observe that decreasing yields or increasing the discount rate decreases LEV and

thus the opportunity cost of the land, extending the coppice cycle to delay regeneration

costs, while having a negligible effect on optimum stage lengths.









Sensitivity Analysis of Dendroremediation Incentive and Interest Rate

This model was used to assess the sensitivity of profitability to changes in the

dendroremediation incentive and the interest rate. Under all scenarios, the

dendroremediation incentive had a positive nearly-linear relationship with profitability.

Average marginal increases in profitability per dollar ofN dendroremediation incentive

according to growth function (high or low), benefit (stock or flow) and interest rate (4%

or 6%) are shown in Table 2-3. Assuming an interest rate of 4%, a $1 kg-1 N increment

in the dendroremediation incentive caused a marginal increase in profitability of about

$376 and $335 assuming flow and stock dendroremediation benefits, respectively, with

little or no influence from the cost of irrigation or the growth model. Assuming an

interest rate of 6%, a $1 kg-1 N increase in dendroremediation incentive caused a

marginal increase in profitability of about $264 and $223 assuming flow and stock

dendroremediation benefits, respectively. Profit sensitivity to dendroremediation

incentive is shown in Figure 2-2.

Table 2-3. Average marginal increases in net returns ($ ha-1) per dollar of N
dendroremediation incentive according to growth function (high or low),
benefit (stock or flow) and interest rate (4% or 6%) for Eucalyptus grandis in
central Florida. Marginal benefits are insensitive to changes in irrigation cost.
Scenario: 4% 6%
High growth, flow benefit $377 $268

High growth, stock benefit $336 $227
Low growth, flow benefit $375 $261
Low growth, stock benefit $334 $220










$3,000
$2,500
$2,000
$1,500
S $1,000 "
> $500

$0
-$500
-$1,000
-$1,500
$000 $050 $1 00 $1 50 $200 $250 $300 $350
Dendroremediation Incentive ($/kg N)
A -I=4%, G=H, B=F 1 =4%, G=H, B=S
S- -I=4%, G=L, B=F 1=4%, G=L, B=S
-I=6%, G=H, B=F =6%, G=H, B=S
-- ) I=6%, G=L, B=F 1=6%, G=L, B=S

Figure 2-2. Net returns ($ ha-1) as a function of dendroremediation incentive ($ kg-1 N)
(I=interest rate, H=high growth function, L=low growth function, F=flow
benefit model, S=stock benefit model), assuming an irrigation cost of $2,471
per hectare.

Changes in profitability ($ ha-) as interest rate increases from 4% to 5% and from

5% to 6% for the high and low growth functions at dendroremediation incentives of $0,

$2, and $4 kg-1 N are shown in Table 2-4. The changes are the same assuming either

flow or stock benefit models. Profitability is highly sensitive to changes in the interest

rate, especially assuming the high growth function shown in Figure 2-1. Assuming a

dendroremediation incentive of $2 kg- N, an increase in the interest rate from 4% to 5%

causes a marginal decrease in net returns by $1,028 and $717 for the high and low growth

assumptions, respectively, while an increase in interest rate from 5% to 6% causes a

marginal decrease in profit by $791 and $550 for the high and low growth assumptions,

respectively. Sensitivity to interest rate is not influenced by the price of irrigation or the

type of benefit (stock or flow) assumed.









Table 2-4. Changes in profit ($ ha-) for Eucalyptus grandis in central Florida as interest
rate increases from 4% to 5% and 5% to 6% for high and low growth
functions shown in Figure 2-1, at dendroremediation incentives of $0, $2, and
$4 kg-1 N. Changes in profit are the same assuming either flow or stock
benefit dendroremediation functions.
Interest rate Interest rate
Dendroremediation increase from 4% increase from 5%
Growth Function Incentive ($ kg-' N) to 5% to 6%
$0.00 -$907 -$693
High growth function $2.00 -$1,028 -$791
$4.00 -$1,149 -$888
$0.00 -$593 -$446
Low growth function $2.00 -$717 -$550
$4.00 -$842 -$654

Using Data Fit 8.0, model outputs under the range of assumptions were condensed

into LEV prediction Eq. (2-9), where I is the real interest rate, g is -1 < g < 1, where -1

and 1 represent the low and high growth, respectively, as represented in Figure 2-1, Yis

the price of irrigation establishment ($ ha-1), v is dummy variable 0 or 1 for calculation as

stock and flow benefits, respectively, and Nis the value of the dendroremediation benefit

($ kg-1) (R2>0.99). This prediction equation could be used to predict LEV in the absence

of modeling software. Estimated parameters and statistical descriptors are shown in

Table 2-5.

LEV(I, g,Y,v,N)= (/ *eA + g*/* 2 *e Y) +
(2-9)
(/4 e -,* +6 v + g *.7 e#) *.N+E
Table 2-5. Estimated parameters and descriptors used in Eq. (2-9) of Eucalyptus grandis
irrigated with reclaimed water in central Florida (R2>0.99).
Constants Value Standard t-ratio
Error
Po 9339.94 31.82 293.50
/P 27.34 0.07 367.88
f2 1903.38 20.34 93.59
f3 23.76 0.26 93.15
f4 759.74 10.73 70.83
Ps 20.45 0.30 67.73
P6 41.18 0.86 47.73
Pf7 0.02 0.00 0.00
/8s 84.32 5.85 14.41









Conclusions

In Florida, environmental and/or treatment costs associated with municipal

wastewater disposal will intensify as urban populations grow. Using SRWC plantations

to dendroremediate wastewater can provide various environmental services and societal

benefits in the WUI and should be considered as wastewater remediation option. Our

results suggest that financial compensation for dendroremediation services would be

required to make the system economically feasible for private landowners. Calculations

of net returns for 128 SRWC dendroremediation scenarios ranged from -$2,343 to

+$2,762 ha-1 and are greatly reduced by high interest rates, high irrigation costs, and low

growth functions. Each $1 kg-1 N increase in the dendroremediation incentive increases

profit by $223-$376 ha-1, depending on interest rate and site productivity. $1 kg-1 N is

probably less than the price to achieve the same service at a wastewater treatment plant.

A 1% increase in interest rate can reduce profit by $446-$1,149 depending on the

scenario.

Increasing the interest rate from 4% to 6% or increasing the dendroremediation

incentive by $1 kg-1 N decreases optimum stage lengths by about 0.1 year, which may not

be operationally significant. However, the decision of whether to select two or three

stages per cycle is influenced by the growth and yield function, which could be increased

through improvements in weed control. Ceterisparibus, higher growth of the first stage

decreases stage length and number of stages per cycle, though improving growth of the

second and third stages, which may be possible through weed and vine control, would

favor longer stage duration, more stages per cycle, and could increase profitability.

High costs of irrigation establishment greatly reduce net returns. Microemitter

irrigation establishment cost about $2,471 ha-1 in 2000 and has gone up to about $3,707









ha-1 in 2004 due to increasing fuel prices. However, costly microemitter irrigation

systems are designed to conserve water, which might not be necessary in the case of

distributing reclaimed water, possibly providing an opportunity to apply less expensive

irrigation systems. Additionally, citrus growers who want to take advantage of

previously existing irrigation systems would not incur the cost of irrigation, effectively

increasing profitability by $2,471-$3,707 ha-1.

Compensation to landowners for the dendroremediation service could be

considered payable either when the trees and N are harvested (stock benefit), or

periodically as the trees grow and accumulate N (flow benefit). Because of the short

optimum cycle lengths, differences between the stock and flow benefit net returns are

smaller than they would be in conventional forest rotation ages. Accounting for the

dendroremediation service as a flow benefit rather than a stock benefit increases profits

by $61-$64 ha-1 and $138-$148 ha-1 assuming dendroremediation incentives of $1.50 and

$3.50 kg1- N, respectively. Accounting for the dendroremediation benefit as a stock

would probably be easier to administer. Municipalities that use SRWCs to

dendroremediate wastewater could also use this model to account for the value of the

dendroremediation service they might achieve.

Future Research

Dendroremediation will be feasible for municipal waste facilities in the WUI if

either tree farmers are paid enough for their dendroremediation service to make the

system economically viable, or if the net cost for municipalities to dendroremediate with

tree crops is cheaper than the alternative cost of treatment. Because case-specific costs,

yields, and prices may vary from the assumptions made in this chapter, broad conclusions

about the feasibility of dendroremediation of reclaimed water cannot be derived from









these results. With more information, particularly with regards to coppice growth and

valuation of the dendroremediation service, the model described here can be used to

make localized feasibility assessments. A study to assess the willingness to pay for

reductions of N contamination from reclaimed water would elucidate the value of the

dendroremediation service. An assessment of the willingness to accept exotic species in

central Florida should be considered in a feasibility analysis of dendroremediation using

EG. This model should be extended to include the most immediate environmental

benefits, such as dendroremediation ofP in the reclaimed water, C sequestration, and,

under scenarios where the biomass is used for bioenergy, mitigation of atmospheric CO2

due to displacement of fossil fuels. The improved biomass production attributable to the

N, P, and K in the reclaimed water, and the savings associated with reduced fertilizer use

should also be internalized in this model.














CHAPTER 3
AN ECONOMIC ANALYSIS OF Eucalyptus SPP. AS SHORT-ROTATION WOODY
CROPS ON CLAY SETTLING AREAS IN POLK COUNTY, FLORIDA

Introduction

Central Florida produces 75% of the nation's and 25% of the world's phosphate

supply, primarily used for fertilizer (IMC Phosphates, 2002). There are about 162,000

hectares (400,000 acres) of phosphate-mined lands in Florida (Segrest, 2003). In the

mining process, clays are washed from phosphate ore and pumped into clay settling areas

(CSAs). Polk County, Florida contains over 38,000 hectares (94,000 acres) of CSAs

and/or overburden soil, as a result of phosphate mining. These CSAs, classified as clayey

Haplaquents (Mislevy et al., 1989), are characterized by high bulk density, poor drainage,

high levels of P, K, and micronutrients, pH of 7.0-8.3, and are commonly dominated by

cogongrass (Imperata cylindrica), an invasive exotic species in Florida. CSAs are largely

left idle because of operational difficulties but may be a valuable resource base for

biomass production. Ongoing research and operational trials on a 50-hectare CSA near

Lakeland, FL, suggests that CSAs can be used for the production of SRWCs. This

chapter assesses the economic viability of this practice.

One environmental service that would be provided by the production of SRWCs on

CSAs is atmospheric CO2 mitigation. Global carbon trading has increased from 13 Tg

CO2 in 2001 to 70 MMg CO2 in 2003 (Ecosystem Marketplace, 2004), a trend that is

likely to continue following the international ratification of the Kyoto Protocol on

February 16, 2005. The establishment of tree plantations on non-forested CSAs has the









potential to sequester carbon by increasing the amount of C per area of land (Booth,

2003). Chaturvedi (2004) emphasizes the importance of considering the C density of

land prior to the carbon sequestering land use practice. He states "The most clear benefit

in carbon sequestration terms would be if the plantation were somehow established in a

desert with an existing standing stock of virtually zero Mg C/ha." An advantage of

SRWC production on CSAs is the near-zero C density of the land prior to plantation

establishment, as the land is bare of vegetation with little accumulation of soil organic

carbon (SOC) following mining. Even on 20-40 year-old CSAs, C density is likely to

remain low if forest cover is not established. Research suggests that SRWCs sequester

and maintain SOC (Joslin & Schoenholtz, 1997). On a 60-year-old CSA in central

Florida SOC of a 2.5-year-old E. grandis (EG) plantation was 214% and 304% greater at

depths of 0-30 and 30-60 cm, respectively, than SOC quantities found in adjacent areas

dominated by cogongrass (Wullschleger et al., 2004).

In addition to C sequestration in situ, if used as a dedicated feedstock supply

system (DFSS), SRWC plantations can mitigate atmospheric CO2 by displacement of

CO2 emissions associated with the combustion of fossil fuels (Sims, 2002; Marland,

2000; Schlamadinger & Marland, 1996). The displacement of fossil fuels by biomass

fuels can be an effective way to mitigate atmospheric CO2 because 1) CO2 emissions can

be continuously reduced, rather than reaching an eventual plateau of C accumulation in

standing biomass, 2) the long-term cost per Mg of CO2 is cheaper with displacement

rather than sequestration, as land remains available for continued production in the future,

and 3) reductions of net CO2 emissions are not as risk prone as C sequestered in situ,









which is susceptible to future events such as fire or land-use change (Eriksson et al.,

2002).

This chapter investigates the impact of CO2 mitigation incentives on management

and profitability of SRWC DFSSs on CSAs in Polk County, Florida. An economic

optimization model of a SRWC biomass production system that includes an incentive for

atmospheric CO2 mitigation is built and used to investigate how this incentive would

influence land expectation value (LEV) and optimal management of the SRWC

production system.

Methodology

As described in Chapter 1, Eq. (1-1) defines LEV, net returns of a non-coppicing

forestry system projected in perpetuity. This equation is modified in Eq. (1-5) to allow

for coppicing forestry systems, which includes n number of growth stages (initial growth

stage and subsequent coppice stages). Eq. (1-5) is used to calculate LEVs under a range

of model input assumptions, exclusive of environmental externalities. To assess the

divergence between private and societal benefits derived from the system, LEVs are then

compared to those calculated by Eq. (1-13), which incorporates a non-timber benefit

(NTB) for each growth stage s. Quantification and incorporation of the NTB requires a

functional form which reflects the nature of the benefit provided by the forestry system.

In this scenario, the externality to be incorporated is atmospheric CO2 mitigation.

Trees sequester atmospheric CO2 in woody biomass as they grow. The value of

standing aboveground C at time t for coppice stage s, assuming stage growth function

g(t), carbon content of 47% by weight (Peter et al., 1996), and multiplying by 1.7 to

convert stem inside bark to total aboveground biomass (Mg ha-) (based on Segrest, 2002;

Patzek & Pimentel, 2005) a can be estimated as









Cs (t)=g(t)*C *0.8 (3-1)
where g(t) is the growth function for growth stage s as a function of time and Cp is the

price of carbon. Once carbon is sequestered there is no further benefit from it, so the

derivative of Eq. (3-1) is used to calculate the marginal benefit of the C sequestration

service, yielding:


CBsA f((CAt))*) )dt (3-2)

where the aboveground C sequestration benefit of stage s is the definite integral of the

flow of the carbon benefit discounted to the beginning of the stage, for the duration of the

stage.

Central to the concept of carbon sequestration is the life span of the sequestered

carbon, either in the ecosystem, or in products derived from harvests from the ecosystem

(Murray, 2003). As wood products burn or decay, sequestered carbon is re-emitted to the

atmosphere in the form of C02, countering the benefit achieved by the sequestered C.

This societal cost of the decay or oxidation of the sequestered carbon must be calculated

and subtracted from Eq. (3-2). The rate of re-emission depends on the end use of the

wood products. The two most likely products identified by a SRWC market survey in

Polk County (below) are mulch and biofuel. The decay of C sequestered in these two

products is accounted for differently.

Eq. (3-3) represents the societal cost of CO2 emissions from the decay of mulch

harvested from stage s at age t, where y is the life of the biomass in years assuming linear

decay, discounted first to the end of the growth stage at discount rate r. For example, for

y=5, 1/5th of the harvested mulch would decay during each of five years. Subtracting the

right hand side of Eq. (3-3) from the right hand side of Eq. (3-2) assuming that mulch








decays in five years (Duryea et al., 1999; Duryea, 1999) yields Eq. (3-4), the integration

of the marginal value of above-ground C sequestration discounted to the beginning of the

growth stage, minus the societal cost of CO2 emissions associated with mulch decay

discounted first to the end of the growth stage and then discounted to the beginning of the

growth stage. Though actual mulch decay may be non-linear and may take longer than

five years, the decay function in Eq. (3-3) was chosen to simplify the analysis and

provide a conservative estimate of the net C sequestration benefit.


CS t)= C-r*) (- ,r*t) (3-3)
y r

NTB [ C (t())*e(t)t [1-es) *e(-t) (3-4)

This NTB calculated in Eq. (3-4) is then included in the optimization model for

each growth stage of the mulch scenario and discounted to the beginning of the coppice

cycle. Eq. (3-4) is incorporated in Eq. (1-5), the equation for net returns of a coppice

cycle having n number of growth stages, as shown in Eq. (3-5), where V(t) is the growth

function for stage s times biomass price. To elucidate the discounting of each benefit and

cost in the model, an example of Eq. (3-5) fixed for two stages is shown in Eq. (3-6),

including annual maintenance cost Ca and a one-time establishment cost C,.


V(()* e +, + C (t)) *e(-rt) dt *e(r JltJ1
V ^t)*P ^ ^Ye^[+ dt I,

s-I CSA (-r*5) -r*y t J (-r* ltJ-1)

LEVimch (t)= *e( CJi (- 3-5)
1 -r*, J
1-e









01


1 A A (I (-r*5) e (3-6)
E,- (t) l- *(xy)-



A e, e- e (-r*5)e r- s in the sseqent rtt
5 r
LEI V,, C, (3-6)









resulting in no net emissions from biomass combustion, and displacing the use of fossil
fuels with closed-loop biofuel reduces net CO2 emissions. Thus, bioenergy from DFSSs
produces no net CO2 emissions, eliminating the need to calculate the costs of post-harvest
biomass C decay. However, recognizing that there are fossil fuel inputs to the
cultivation, harvest, and processing of SRWC DFSSs consuming up to 10% of the energy
produced by the bioenergy system (Forsberg, 2000; Heller et al., 2004; Klass, 1998),
10% of the carbon sequestration benefit achieved at stage age t is discounted to the
beginning of the stage and subtracted from the carbon benefit calculated by Eq. (3-2),
yielding Eq. (3-7):

NTBF (b (t))*e(t) dt -[(0.1*C ())]*e (3-7)
0








The net NTB calculated for each growth stage for the biofuel scenario in Eq. (3-7)

is then added to Eq. (1-5), resulting in Eq. (3-8). Eq. (3-9) is an example of Eq. (3-8)

fixed for two growth stages.


V(t)*e jre J d(Cbs (t))*e(-r *t))dt *e( j t-1)
s=1
0.1* Cb, (t))*e( C e(r J

1 e J_ r j )lJ


LEVboftel/ (t)


(3-8)


V(t)*e, rt ))+ !( (cb (*t))*e dt rt)



+ o
[(0.1*Cbs (t)] *e( -1 t 2 -1
LEV (t)= -e-*(tl)) l-e C,

(3-9)
Thus, Equations (3-5) and (3-8) are used for incorporating C externalities in mulch

and biofuel production scenarios, respectively. These models, with Eq. (1-5) for

optimization without incorporation of externalities, are used to calculate LEV and

optimum age of each of n number of growth stages. The process is repeated iteratively

adding an additional growth stage for each scenario until the marginal benefit of the

additional stage is negative, identifying the optimum number of growth stages per

coppice cycle and associated LEVs. Finally, the sensitivity of these LEVs to variation in

the below model inputs is assessed.









Model Inputs

Growth Function

Lacking published growth and yield functions of SRWCs produced on CSAs

needed for inclusion in this model, measurements were taken from a trial of EG and E.

amplifolia (EA) on a CSA near Lakeland, Florida (Rockwood et al., 2005). Established

between May and July of 2001, SRWC-90 was planted at densities of 4,200 (single row)

and 8,400 (double row) trees per hectare, unfertilized and fertilized on May 20, 2002 with

150 kg ammonium nitrate ha-1. Height and DBH measurements were taken August 20,

2002, July 16, 2003, December 23, 2003, August 27, 2004, and January 11, 2005.

Number of surviving trees, average height, and average DBH per plot by progeny after

3.5 years on January 11, 2005, are shown in Table 3-1.

A modified volume prediction equation developed by Max and Burkhart

(Bredenkamp, 2000) was a good predictor of volumes of 66 destructively sampled trees

(R2>0.99) and used to convert height and DBH measurements to per-hectare yields

assuming specific gravity of 0.40 (Rockwood et al., 1995a). Per-hectare inside-bark

aboveground yields (dry Mg ha-1) of EG and EA under five treatments are shown in

Table 3-1 and Figure 3-1. Decreasing rates of productivity were observed on January 11,

2005 at 3.5 years of age, suggesting an optimizable function could be fit to the data for

use in the model.










70 EA 1

60 EA4 EA2
EG 4
S50 EA3
S4 EG5 EA4

EA 5
240 -0

EG 3 EG 12

10 -E/

0
E A 2 EG 3

+EG 4
0 1 2 3 4EG
Age (years) -- EG 5

Figure 3-1. Inside bark yields (dry Mg ha-1) of EA and EG on a CSA near Lakeland,
Florida for 5 treatments: 1) 4,200 trees per hectare, unfertilized, 2) 8,400 trees
per hectare, unfertilized, 3) 4,200 trees per hectare, fertilized with 150 kg ha-1
ammonium nitrate on May 20 2002 at 11 months, 4) 8,400 trees per hectare,
fertilized as treatment 3, and 5) same as treatment 2.

EA was identified as a likely candidate species due to a) greater frost resistance

than EG, which allows flexibility to plant in late summer during increased rainfall with

minimum frost damage to small trees the subsequent winter and b) higher yields than EG

despite being planted two months later. An air photo from 1995 revealed that Treatments

1 and 2 had been established on areas of the CSA where cogongrass was more densly

established than the other treatments, probably explaining their lower yields. Treatments

3 and 4 were identified as being representative of moderately low and moderately high

yields when compared to SRWC yields from other areas of the CSA. Nonlinear

regression was used to fit the yield data to the functional form:

B(a) = e b+c*n(a)-d(a) (3-10)













Table 3-1. Number of observations, average DBH (cm), height (m) and inside-bark dry above-ground biomass yields (Mg ha-') by
progeny of EG and EA planted at densities of 4,200 (single) and 8,400 (double) trees per hectare, unfertilized (0) and
fertilized (1) on May 20, 2002, with 150 kg ammonium nitrate ha-1, and measured January 11, 2005, at 3.5 years. "a" and
"b" indicate lowest and highest yielding progenies within treatments, respectively.
Single 0 Double 0 Single 1 Double 1 Double 0 (2)
Progeny N DBH H Ms ha- N DBH H Ms ha' N DBH H Ms ha' N DBH H Ms ha' N DBH H Ms ha-


11 5.3a 7.5a
12 6.6 8.5
9 7.7b 8.9
10 7.6 9.4b
9 7 8.9


12 4.7 5.5
11 4.2 5.2
10 3.7a 4.6a
12 5.9 6.5
11 5.7 6.5
11 7.3b 7.8b


19.3a
27.2
30.4b
27
20.2


11
5.6
5.4a
12.1
10.4
22.3b


18 4 7
20 5.5b 8.6
21 3.9 6.9
20 5.4 8.6b
20 3.7 6.8


18 3.8 5.6
22 2.6a 4.0a
23 3.8 5.6
23 3.8 5.6
23 4.1b 5.9b
21 3.6 5.3


10.8a
23.8
14.6
27.2 b
12.5


9.7
3.2
11.2b
10.2
11.1
8.2


10 4.5a 6.9a
9 8.3 9.7
10 6.5 9.2
9 8.5 10.3b
11 6.2 8.4
EA
10 6.5 a 7.5a
12 7.7 9
9 7.4 8.7
10 8.7 10.0b
12 8.3 9.5
12 8.2 9.5


13.7 15 6.0a 9.1 a
36.1 15 9.4 12.6
25.5 14 9.4b 12.6b
36.5b 12 7.9 11.9
19.5 15 8 11.3


16.0a 20 6.1a 8.3a
31.8 19 7.6 10.4
23.2 22 7.7 10.4
32.7 23 6.9 9.7
35.3 24 8.8b 11.2b
38.8b 22 8.1 10.7


29.4a 12 5.0o 8.2a 19.3a
77.3 21 7.0b 10 65.9b
78.2b 17 5.4 8.9 28.9
38.4 19 6.9 10.2b 57.8
55.9 16 6.5 9.7 54.5


46.1a 20 5.7a 7.9a 31.6a
54.8 20 5.9 8.4 32.7
57.2 19 7.5 10 55.5
46.6 24 7.4 9.9 68.2
94.2b 22 8.1b 10.6b 68.9b
73.7 23 7.1 9.5 58.6


3242
3469
4064
4200
4223


4904
4907
5025
5033
5091
5108






51


where B(a) is dry stemwood biomass (Mg ha-') as a function of stand age a in years for

the first stage, and b, c and dare the estimated parameters 2.57, 4.00 and 1.20 for EA 3

and 2.76, 3.67 and 0.92 for EA 4, respectively (Figure 3-2). Using a factor of 1.7 for

total above-ground biomass, maximum sustained yields are 17 and 32 dry Mg ha-1 year-1,

comparable to 20-31 dry Mg ha-1 year estimated for Eucalyptus in Florida (Rahmani et

al., 1997) but higher than the estimated 9-17 dry Mg ha-1 yr- estimated by Klass (1998),

who observes that yields could be improved with SRWC development in the sub-tropical

south.


-EA3
100 Average
E -J i'5-DI1

S80 EA 4
EA 4 Average
c- Average
i 60

E-1 -4 4J0 x Predicted
40- m-*EE Cio EA3
> ..- EA 3 Average
20-
.0 ..-- -- Predicted
0 ..EA4
0 1 2 3 4 5
Age (years)


Figure 3-2. Observed and predicted inside bark stem yields of EA treatments 3 and 4,
4,200 and 8,400 trees per hectare, fertilized, and low and high progeny yields
for each treatment.

Carbon Values

The Kyoto Protocol was ratified by 140 nations on February 16, 2005,

strengthening ongoing efforts to reduce greenhouse gas emissions. While estimates for

world carbon prices range from $4 to $27 Mg-1 C, $10 Mg-1 C is identified as a likely









value (Vogt et al., 2005; Best & Wayburn, 2001). C prices assumed in this model range

from $0 to $35 Mg1 C.

Market Assessment

To identify products and prices to be used in this analysis, a SRWC market

assessment was made in July 2004 in and around Polk County, Florida. On-site and

phone interviews were done with individuals from the Florida Division of Forestry,

mulch industries, nurseries, electricity generation facilities, and potential biomass

producers. Though not all companies interviewed were willing to share market

information, a range of price values and demand quantity were derived from the

interviews.

Potential products from woody biomass grown on CSAs in Polk County include

mulch, energy, timber, pallets, and fiberboard. The most likely products are 1) mulch,

having an existing multi-million of dollar market in Polk County annually, and 2)

feedstock for electricity generation, a prospective market with much potential for

expansion. Following is a summary of these two most relevant woody biomass markets.

The established market: mulch

Mulch production is a major industry in central Florida, involving companies such

as Seaboard Supply in Ft. Green, Greenleaf Products, Inc. in Haines City, Florida Fence

Post Co. in Ona, Forest Resources Inc. in Tampa and Aaction Mulch in Fort Myers.

These companies produce mulch from various sources, including sawmill waste of

cypress and pine, sand pine harvests and forest thinnings (Garry Zipper, pers. com., July

15, 2004), eucalyptus plantations in south central Florida, and melaleuca eradication

harvests in southern Florida.









Mulch consumers look for a product that will resist decay, has a desirable

appearance, and is reasonably priced. Cottonwood, lacking in decay resistance, is

undesirable as a mulch product. EG is a desirable material due to its red heartwood,

attractive scent, and resistance to rot and termites (Mike Milliken, pers. com., August

2nd, 2004). Some mulch users express concern about over-harvesting of cypress and

want an alternative to cypress mulch products (Bobby Robins, pers. com., July 15, 2004).

While demand for cypress mulch could serve as an incentive for sustainable cypress

management and the establishment of cypress plantations, eucalyptus mulch marketed as

"cypress-free" is likely to appeal to consumers who are concerned about loss of cypress

trees.

Mulchwood price

Mike Milliken (pers. com., August 3, 2004) of Greenleaf Products suggested $14

green ton-' stumpage price (up from $10 green ton1 in 2002), assuming availability of

minimum supply to produce 144,000 bags, requiring 2,600 green tons (Appendix Eq. 1).

Dwight Knight of Seaboard Supply stated he would be willing to pay $33 green Mg-1

($30 green ton-1), delivered (the mill is 48 km [30 miles] south of Lakeland), unprocessed

(pers. com., August 5, 2004). As of August 2004, transportation costs are $1.30 loaded

km ($2.10 mile-1), with each load carrying 21 Mg (25 tons) (Eric Hoyer, pers. com., July

12, 2004), which equals $0.06 green Mg-1 km1 ($0.08 green ton'1 mile-'). Assuming

transportation of 48 km (30 miles) at $0.06 green Mg-1 km-1 ($0.08 green ton1 mile-1), a

harvesting cost of $17.64 Mg-1 ($16.00 ton-'), and a delivered price of $33 green Mg-1

($30 ton-'), this scenario suggests an equivalent stumpage value of about $12 green Mg-1

($12 green ton-) ($33-$17.64-(48*$0.06)=-$12.48 Mg-1), depending on the










transportation distance. Hypothetical high and low transportation cost scenarios and

associated stumpage values are shown in Table 3-3.


Table 3-2. Mulch markets for Eucalyptus produced in Polk County.
Stumpage Volume
price (green (green Mg Ha [acreage]
Mg-1 [ton-1 ]: [tons]): Note: Location: neededT
Greenleaf (a) $9 [$8] 435 [500 ] Minimum purchase, to be Haines n/a
per mixed with other City, FL
purchase products.
Greenleaf(b) $15 [$14] 2,357 Minimum amount needed Haines 3,500
[2,600] for a run of bags. City, FL [8,600]


Greenleaf (c) $15-18
[$14-$16]


>9,072
[10,000]


$13 [$12] up to
22,680
[25,000]
year'


Approximate amount per
week, equivalent to about
122,469 green Mg
(135,000 tons) year'.
Minimum amount needed
for Lowes or Home
Depot to list a new line
item, and to set up an on-
site operation. Could
purchase up to 235,900
green Mg [260,000 tons]
year'
Based on $33 green Mg-'
($30 green ton ')
delivered price assuming
a shipping cost of $0.08
ton-1 mile-', 30 miles
shipping, and harvesting
cost of $16 ton1.


Haines
City, FL






Ft Green,
FL


7,100
[17,500]






650
[1,600]


Seaboard
Supply


tApproximate acreage needed for sustained production over a year, assuming growth of 34 green
Mg ha-1 (15 tons acre ') year' (i.e., annual demand divided by annual production).









Table 3-3. Estimated equivalent stumpage values for high and low transportation cost
scenarios. All tons are green weight.
High Cost Scenario Low Cost Scenario
64 km @ $0.06 Mg- km1 = 32 km @ $0.06 Mg1 km =
-$3.84 Mg'- -$1.92 Mg-1
Transportation (40 miles @ $0.08 ton mile' (20 miles $0.08 ton mile
-$3.36 ton-) -$1.68 ton')
Harvest Cost
(conventional -$18 Mg-1 (-$16 ton ') -$9 Mg-1 (-$8 ton')
equipment)
Price (delivered) +$28 Mg-1 (+$25 ton-) +$33 Mg-1 (+$30 ton')
Equivalent Stumpage +$6.16 Mg-~ ($5.64 ton-) +$22.40Mg-' ($20.32 ton-)
Value

Mulchwood quantity

Knight may purchase up to 22,700 green Mg (25,000 tons) yearf. Assuming

yields of 34 green Mg ha-1 (15 tons acre-) year-', 647 ha (1,600 acres) of CSAs might be

cultivated to meet the demand for this particular mulch mill. While significant, acreage

needed to supply this particular plant would occupy a relatively small portion of the

estimated (8,094 ha) 20,000 acres of CSAs in Polk County.

Milliken (pers. com., August 2, 2004) affirmed that Greenleaf Products is capable

of purchasing 50 semi loads per day (about 24 green Mg [26 tons] of eucalyptus load-1)

for 200 days per year totaling about 240,000 green Mg (260,000 tons) per year.

Producing this amount, assuming 34 green Mg ha-1 (15 tons acre-) yearf, could occupy

about 7,100 of 8,100 ha (17,500 of 20,000 acres) of CSAs in Polk County.

Mulch is currently produced in part from byproducts from sawmills and small-

diameter trees from forest thinnings (Linda Kiella, Garry Zipper, pers. com., July 15,

2004). Because of the demand for mulch, sawmills convert waste into a product, and

forest managers in some cases can reduce costs associated with forest management, forest

fuel load control, and eradication of melaleuca (Melaleuca quinquenervia). It is

uncertain how much of the current biomass market (wastewood, thinnings, melaleuca









control, etc.) might be displaced if additional biomass is grown on CSAs. However,

according to Milliken (pers. com., August 2nd, 2004), the market is constrained by supply

of desirable material, not demand.

Potential market: biomass fuels

The biomass market for energy generation, while speculative, is potentially very

large. Power generation plants that are using or could use bioenergy include Ridge

Generating Station in Auburndale; Lakeland Electric in Lakeland; Big Bend Power Plant

near Apollo Beach; and Tampa Electric Polk Power station near Mulberry (Figure 3-3).


- -U 50X 4


Figure 3-3. Location and potential consumption of buyers of woody biomass from Polk
County.

Ridge Energy currently charges a tipping fee to receive biomass, ranging from $9

green Mg-1 ($8 ton-) for low-ash biomass that is pre-chipped up to $38 green Mg-1 ($35

ton-1) for high-ash unprocessed biomass. Ridge Energy might be able to accept 635 Mg









(700 tons) day-1 of DFSSs for free (i.e., no tipping fee) if it contains less than 6% ash (no

roots and a minimum of soiling) and if it is processed (i.e., chipped to smaller than 3")

(Phil Tuohy, pers. com., July 27, 2004). If the biomass is processed and delivered for

free, additional economic incentives would need to be applied to make biomass

production economically viable.

Lakeland Electric of Lakeland produces 2.8 million MW hours of electricity. As

Lakeland Electric has had to raise rates during 2004, further rate increases associated

with using renewable energy would be difficult to impose. However, bioenergy will be

an attractive option if the Florida DEP mandates renewable energy production. If

Lakeland Electric were to have a renewable portfolio standard (RPS) mandate for 4%

renewables, they would need to generate 12.5 MW of renewable energy, the equivalent of

8-14 Mg (9-15 tons) of biomass hour- (20% moisture content), meaning about 54,000-

88,900 air-dry (20% MC on green weight basis) Mg (59,000-98,000 tons) or 85,000-

143,000 green Mg (94,000-158,000 tons) year-'. Matt McArdle, a biofuels industry

specialist contracted by Lakeland Electric, calculates a potential biofuel demand of

63,500 green Mg (70,000 tons) year- and possibly using up to 127,000 green Mg

(140,000 tons) year-', and suggests a likely price of $11 green Mg-1 ($10 ton-) delivered

(pers. com., August 27, 2004). The Big Bend Power Plant near Apollo Beach is another

possible biomass buyer if a RPS is mandated and could consume up to 45,000 green Mg

(50,000 tons) year-'. The Tampa Electric Polk Power station near Mulberry, while a

possible candidate, is more likely to use herbaceous biomass crops due to blocking of one

of the flurry feed systems of the gasifier in a trial with woody biomass in 2001 (Jeff

Curry and McCardle, pers. com., July 27 and August 27, 2004) (Table 3-4).









Because of relatively cheap conventional power generation fuels, utilities in central

Florida currently pay from $-39 to $11 green Mg-1 ($-35 to +$10 ton-) delivered for

biomass. However, existing government incentives for renewable energy that could

improve the profit margin of biomass for energy include the Renewable Energy

Production Incentive (REPI)1 and the Section 45 Tax Credit for utilities that pay federal

income taxes. REPI, authorized under Section 1212 of the Energy Policy Act of 1992, is

designed to promote increases in the generation and utilization of electricity from

renewable energy sources (U.S.Department of Energy, 2005). REPI offers 1.760 kWh-1,

and the Section 45 Tax Credit offers a reduction in taxes of 2.760 kWh-1. Assuming a

heat rate of 11,500 BTUs kWh-1 and 9,343 BTUs kg-1 (4,238 BTUs lb-1) woody biomass

at 50% MC on a green weight basis, REPI would be worth $14.29 green Mg-1 ($12.97

ton-) or $28.59 dry Mg-1 ($25.94 ton-1) delivered, and similarly the Section 45 Tax

Table 3-4. Potential bioenergy markets for Eucalyptus produced in Polk County. Prices
are delivered.
Estimated Price Quantity
(green Mg-1 (green Mg Ha [acres]
[ton-']): [tons]): Note: Location: needed
63,500-
Lakeland 127,000 2,000-4,000
Lakeland $11 [$10] 127,000 Delivered Price Lakeland, FL 2,000-,000
Electric [70,000- [5,000-10,000]
140,000]
45,400 1,400
Big Bend $11 [$10] 5 0 Delivered Price Apollo Beach, FL [3500
[50,000] [3,500]
91,000- Economic
2,400-5,300
Ridge 181,000 incentives 24053
Ridge $0 [$0]incentives Aubumdale, FL [6,000-
Energy [100,000- could be 13,000
200,000] applied. 1
Likely to favor
Tampa $11 [$10] n/a herbaceous Mulberry, FL n/a
Electric b.
biomass.
a Approximate acreage needed for sustained production over a year, assuming growth of 15 green
tons acre-' year' (i.e., annual demand divided by 15).


1 Imp \ "\ \ .eere.energy.gov/wip/program/repi.html, 02-15-2005









Credit would be worth $44.85 dry Mg-1 ($40.69 ton-) delivered.

Based on this survey, stumpage prices for eucalyptus would range from $11-$44

dry Mg1 ($5-$20 green ton', or $10-$40 dry ton' assuming 50% moisture content on a

green weight basis). Three biomass prices assumed in this analysis are $10, $20 and $30

dry Mg1.

Operational Costs

Operational costs on CSAs are higher than those of conventional forestry, as

working conditions on sites with heavy clays and/or cogongrass infestation are

problematic. A commercial trial of SRWC production on a CSA near Lakeland, Florida

incurred costs of $1,800 ha-1 for site preparation and $1,200 ha-1 planting cost (C, and Cp

in Equations (3-6) and (3-9)). To assess the sensitivity of LEV to changes in operational

costs, values of $900 and $1,800 ha1 for site preparation and $600-$1,200 ha-1 for

planting were used, assuming decreasing costs with increased commercialization and

economies of scale. In light of apparent growth response to weed control, a weeding cost

(Cw in Equations (3-6) and (3-9)) of $0 and $200 ha-1 with the beginning of each growth

stage was tested. The model was run assuming interest rates of 4%, 7% and 10%.

Additional Non-Timber Benefits

Below-ground C sequestration

Because the response of below-ground (SOC) accumulation to harvest scheduling

is not known, below-ground C sequestration is not incorporated in this model. However,

below-ground C sequestration can be estimated and added to calculated LEV as an

additional NTB. Root systems of EG grown in a clay settling area in central Florida were

40% of the total biomass (Segrest, 2002), or equivalent to the above-ground inside-bark

growth function. Under sustained yield SRWC management, it could be assumed that









biomass in root systems peak during the coppice stage that produces the greatest above-

ground biomass, and remains steady in subsequent coppice stages and cycles, where

decay of dead root systems is replaced by re-growth. Anecdotal evidence suggests that

greatest yields at the Kent site occur during the first coppice stage and decline in

subsequent coppice stages, as described in Chapter 2. Therefore, the value of C

sequestration in root systems for the first coppice stage (s=1) at time t can be defined as

Eq. (3-11) and remain constant for the life of the plantation.

CR1 (t)= g(t)* 0.47 Pc (3-11)

The derivative of Eq. (3-11) is the value of the carbon sequestered in roots

discounted to plantation age 0:


CBI =[ CR (t) e(r) dt (3-12)
d_0

Information about SOC accumulation on CSAs in Florida is limited. Wullschleger

et al. (2004) found that on a 25-year-old CSA, SOC under 2.5-year-old plantation of EG

at a planting density of 9,800 trees ha-1 accumulated 151 and 96 Mg ha-1 more than SOC

under cogongrass in soil depths of 0-30 cm and 30-60 cm, respectively. Their model of

soil carbon dynamics estimated that a SRWC EG plantation contributes to the storage of

an additional 274 Mg C ha-1 after 25 years, reaching an additional 354 Mg C ha-1 after 50

years. A polynomial function fitted to the data simulation takes the following form:

SOC(t)= -0.1668*t2 +15.084*t (3-13)

where SOC (Mg ha-1) is expressed as a function of time t (years) after SRWC plantation

establishment on a CSA. Eq. (3-13) is then used in the calculation of the NPV of the

carbon benefit (Eq. (3-14)). The actual SOC sequestration process is likely to be more









complicated than Eq. (3-13) suggests. However, lacking better data, Equations (3-12)

and (3-14) can be used to estimate the additional benefit of below-ground (root + SOC) C

sequestration benefits.


CBsoc = (SOC(t *e dt (3-14)


Reclamation incentives

As a result of high bulk density, high pH, and the invasion of cogongrass, CSAs are

slow to naturally revegetate and are difficult to put into agricultural or forestry

production. Tree plantations can contribute to ecosystem restoration of degraded lands

by facilitating natural regeneration (Haggar et al., 1997; Lamb, 1998; Lugo, 1997;

Powers et al., 1997; Parrota, 1992; Parrota et al., 1997) especially in areas dominated by

cogongrass (Otsamo, 2000; Kuusipalo et al., 1995). The establishment of SRWCs on

CSAs can reduce soil bulk density, exclude cogongrass, and facilitate the establishment

of natural regeneration of native tree species and ecosystem functions. Chapter 378 of

the 2004 State of Florida Statutes includes provisions for reimbursement of CSA

reclamation costs, ranging from $4,942 -$9,884 ha-1 ($2,000-$4,000 acre-'), funded from

taxes on the phosphate mining industry (State of Florida, 2004a). Because it is not

known if SRWC establishment would be recognized as a form of CSA reclamation, and

because payment would not be a function of stand growth, mined land reclamation

incentives are not included in this model. However, providing this reclamation

compensation to SRWC systems would contribute to the LEV of SRWC production on

CSAs.









Summary of Model Inputs and Assumptions

The model was run for the three scenarios (no NTB, C sequestration in mulch

production, and C sequestration/CO2 displacement in biomass production), under all

combinations of interest rates (4% and 7%), site preparation costs ($900 and $1,800 ha-1),

planting costs ($600 and $1,200 ha-1), weed control costs ($0 and $200 ha-1), growth

functions (low and high) and biomass stumpage prices ($10, $20 and $30 dry Mg-1

assuming whole-tree above-ground harvesting) for a fixed C sequestration incentive of $5

Mg- totaling 288 runs, allowing as many growth stages as needed until LEV begins to

decline, assuming growth stages decline by 20% per stage. Additionally, sensitivity of

LEV and harvest scheduling to C prices of $15, $25 and $35 was tested at a base

scenario, as was increasing the cost of capital to 10%. LEVs exclude below-ground C

sequestration benefits, the values of which are estimated independently below.

Results and Sensitivity Analysis

LEVs increase with growth rate and biomass stumpage price. Under all

combinations of assumptions under a fixed C price of $5 Mg-1 C, LEVs range from

$-2,789 to $4,616 ha-1 and $-224 to $18,121 ha-1 assuming stumpage prices of $10 and

$30 Mg-1, respectively, comparable to LEVs of a SRWC system in the United Kingdom

reported by Smart and Burgess (2000) of $3,931, $6,168 and $14,814 ha-1 for market

only, low NTB and high NTB model scenarios, respectively (stumpage price of $31 dry

Mg-1, establishment cost of $1,538 ha-1 and an exchange rate of $1.54 per in November

2000). Table 3-5 shows LEVs, optimum number of stages per cycle, and optimum stage

lengths by C benefit scenario and stumpage price assuming a base scenario of 4% interest

rate, $1,800 ha-1 site preparation cost, $1,200 ha-1 planting cost and a carbon price of $5

Mg-1 C. Under these assumptions, marginal increases in LEV per dollar increment in









stumpage price range from $264-$293 and $588-$629 under the low growth and high

growth functions, respectively. Marginal benefits of increasing stumpage price are

greater with the high growth function, as benefits of increased yield are magnified over

multiple rotations.

The shortest optimal initial growth stage is 2.6 years under conditions of highest

stumpage price and interest rate and lowest operational costs, and the longest optimal

initial growth stage with a positive LEV is 3.5 years under conditions of high operational

costs, low interest rate and low stumpage prices. Ceterisparibus, increasing stumpage

price decreases optimum stage lengths and optimum stages per cycle, as the opportunity

cost of the value of the stand increases. Incorporating the C incentive in the mulch

product scenario increases optimum stage lengths, while applying the incentive in the

biofuel scenario decreases optimum stage lengths due to reduced post-harvest emissions

penalties, though differences in stage lengths are less than 1/10th of a year (Table 3-6).

Table 3-5. LEV, optimum number of stages and optimum stage length for each stage by
C benefit scenario and biomass price assuming a base scenario of 4% interest
rate, $1,800 ha1 site preparation cost, $1,200 ha-1 planting cost, no post-
establishment weeding cost, and a carbon price of $5 Mg- C.
$10 dry Mg-1 $20 dry Mg-1 $30 dry Mg-1
NTB Growth LEV Optimum LEV Optimum LEV Optimum
($/ha) harvest age ($/ha) harvest age ($/ha) harvest age
(years) (years) (years)
None Low -1,967 3.1, 3.1, 3.2, 674 2.9, 2.9, 2.8, 3,722 2.8, 2.8,
3.3,3.4 2.6 2.6
C(M) Low -1,883 3.1, 3.1, 3.2, 771 2.9, 2.9, 2.8, 3,828 2.8, 2.8,
3.2,3.3 2.6 2.6
C(B) Low -1,424 3.0, 3.1, 3.1, 1,320 2.9, 2.9, 2.8, 4,448 2.8,2.8,
3.1, 2.9 2.6 2.6
None High 619 3.4, 3.4, 3.3, 6,507 3.2, 3.1, 2.9 12,960 3.2,3.0
3.0
C(M) High 810 3.4, 3.4, 3.3, 6,715 3.2, 3.1, 2.9 13,140 3.2,3.0
3.0
C(B) High 1,832 3.4, 3.4, 3.3, 7,869 3.2, 3.1, 2.9 14,419 3.1,3.0
2.9









Raising incentives for C sequestration increases LEV (Table 3-6). Under a base

scenario of $20 dry Mg-1 stumpage price, interest rate 4%, site preparation $1,800 ha-1,

planting cost $1,200 ha-1, high growth function and no post-establishment weeding,

increasing the price of C from $0 to $35 ha-1 increased LEVs from $6,507 to $7,965 and

$6,507 to $16,422 ha-1 for the mulch and biofuel scenarios, respectively. The marginal

increase in LEV per dollar increment in C price is a constant $42 in the mulch scenario.

Conversely, the marginal benefit in the biofuel scenario was both higher and more

responsive to increases in C price, ranging from a marginal increase of $272 to $292 at

$5 and $35 Mg-1 C, respectively. This reflects that the biofuel model is less penalized by

post-harvest decay of sequestered C, thus increasing incentives for biofuel production

rather than in situ sequestration.

Table 3-6. LEV ($ ha-1), optimum stage lengths, marginal benefit, and estimated below-
ground benefit ($ ha-1) by C sequestration incentive ($ Mg-1) under a base
scenario of $20 dry Mg-1 stumpage, interest rate 4%, site preparation $1,800
ha-l, and planting cost $1,200 ha-1.
Optimum Stage Marginal Benefit (ALEV Below-ground
$ Mg-1 C LEV ($ ha-1 ) Lengths (years) per $1 C Incentive) ($ ha1)
Mulch scenario
0 $6,507 3.2,3.1, 2.9 n/a n/a
5 $6,715 3.2, 3.1, 2.9 $42 $1,163
15 $7,131 3.3, 3.1, 2.9 $42 $3,492
25 $7,548 3.3, 3.2, 2.9 $42 $5,819
35 $7,965 3.3, 3.2, 2.9 $42 $8,097
Biofuel scenario
0 $6,507 3.2,3.1, 2.9 n/a n/a
5 $7,869 3.2, 3.1, 2.9 $272 $1,163
15 $10,598 3.2, 3.1, 2.8 $273 $3,492
25 $13,505 3.1,3.0 $291 $5,819
35 $16,422 3.1,3.0 $292 $8,097

The marginal reduction of LEV per percent increase in the cost of capital between

4% and 7%, assuming a C price of $5 Mg-1 C, is -$23 under the least profitable scenario

and -$2,928 under optimum assumptions. For a base scenario of $1,800 ha-1 site









preparation cost, $1,200 ha-1 planting cost, carbon price of $5 Mg-1 C, high growth

function and no weeding costs, the marginal impact of increasing interest rates between

4% and 10% ranged from -$192 to -$2,581 (Table 3-7). More profitable scenarios are

penalized more by higher interest rates. Increasing interest rates had little effect on

optimum stage lengths (Table 3-8). Increases in interest rates from 4% to 7% and from

7% to 10% decreased optimum stage lengths by 1/10th of a year or less. At increases

from 7% to 10% the model selected for optimization with an additional growth stage.

This effect is consistent with results from Smart and Burgess (2000), who observe that in

SRWC biomass systems the opportunity cost of the standing biomass is low relative to

Table 3-7. Change in LEV ($ ha-1) per 1% increase in interest rate assuming $1,800 ha-1
site preparation cost, $1,200 ha-1 planting cost, carbon price of $5 Mg-1 C,
high growth function and no weeding costs, without C sequestration
incentives, C sequestration for the mulch production scenario, and C
sequestration for the biofuel production scenario.
$10 dry Mg-' $20 dry Mg-' $30 dry Mg'
% LEV ($ ALEV/+1% LEV ($ ALEV/+1% LEV ($ ALEV/+
Interest ha1) Interest ha-') Interest ha-') 1%
Rate Interest
No NTB 4% $619 $6,507 $12,960
7% -$798 -$472 $2,413 -$1,365 $5,864 -$2,365
10% -$1,375 -$192 $762 -$550 $3,057 -$936
Mulch 4% $810 $6,715 $13,140
Scenario 7% -$616 -$475 $2,608 -$1,369 $6,029 -$2,370
10% -$1,213 -$199 $946 -$554 $3,239 -$930
Biofuel 4% $1,832 $7,869 $14,419
Scenario 7% -$88 -$640 $3,197 -$1,557 $6,677 -$2,581
10% -$880 -$264 $1,315 -$627 $3,611 -$1,022
the opportunity cost of the land, and thus increasing interest rate does not shorten

rotations as it would with a conventional system, but rather LEVs are reduced, lowering

the opportunity cost of the land relative to the marginal benefit of the stand growth, and

stage lengths remain relatively unaffected, while the coppice cycle is extended to delay

the cost of replanting.










Table 3-8. Optimum harvest scheduling (stage lengths and number of stages per cycle) at
interest rates of 4%, 7%, and 10% assuming $1,800 ha-1 site preparation cost,
$1,200 ha-' planting cost, carbon price of $5 Mg-1 C, high growth function and
no weeding costs, without C sequestration incentives, C sequestration for the
mulch production scenario, and C sequestration for the biofuel production
scenario.
$10 dry Mg-1 $20 dry Mg-1 $30 dry Mg-1


% Optimum Optimum Optimum Optimum Optimum Optimu
Interest number of stage number of stage number of stage
Rate stages per lengths stages per lengths stages per lengths
cycle (years) cycle (years) cycle (years)
No NTB 4% 4 3.4,3.4, 3 3.2,3.1, 2 3.2,3.0
3.3,3.0 2.9
7% 4 3.3,3.3, 3 3.2,3.1, 2 3.1,3.0
3.3, 3.1 2.9
10% 5 3.2,3.2, 3 3.1,3.1, 3 3.0,3.0
3.3, 3.2, 2.9 2.8
2.9
Mulch 4% 4 3.4,3.4, 3 3.2,3.1, 2 3.2,3.0
Scenario 3.3,3.0 2.9
7% 4 3.3,3.3, 3 3.2,3.1, 2 3.1,3.0
3.3,3.1 2.9
10% 5 3.3, 3.3, 3 3.1,3.1, 3 3.1, 3.0
3.3, 3.2, 2.9 2.8
3.8
Biofuel 4% 4 3.4,3.4, 3 3.2,3.1, 2 3.1,3.0
Scenario 3.3,2.9 2.9
7% 4 3.3,3.3, 3 3.2,3.1, 2 3.1,3.0
3.2,3.0 2.9
10% 5 3.2, 3.3, 3 3.1,3.1, 3 3.1, 3.0
3.2,3.1, 2.9 2.7
2.5

Increases in operational costs decrease LEV (Table 3-9). Increases in site

preparation, which are one-time up-front costs, have a dollar-for-dollar reduction in LEV.

LEVs decrease $3 per dollar increase in planting costs, with slightly higher marginal

impacts at higher stumpage prices, reflecting shorter coppice cycles and increased

planting frequency. Weed control may be needed to insure high yields, though the exact

impact of weed control on growth is not known. LEV is reduced $8 for every dollar

increase in weed control cost applied at the beginning of each growth stage. Marginal

impacts shown in Table 3-9 are the same under the three NTB scenarios, except for the


im








,














),









marginal impact of weeding increases from -$8 to -$9 in the biofuels scenario assuming

$30 Mg-1, reflecting the shorter optimal stage lengths and more frequent weeding.

Table 3-9. LEVs and marginal impact on LEVs by changes in site preparation, planting
and weeding costs, assuming a C price of $5 Mg-1, 4% interest rate, high
growth function and no NTB.
$10 dry Mg-' $20 dry Mg-' $30 dry Mg-'
Input LEV ALEV/ LEV ALEV/ LEV ALEV/
Values ($ ha') AInput ($ ha') AInput ($ ha-1) AInput
($ ha')
Site preparation $900 $1,519 $7,407 $13,860
(low)
Site preparation $1,8001 $619 -$1 $6,507 -$1 $12,960 -$1
(high)
Planting (low) $600 $2,354 $8,963 $15,754
Planting (high) $1,2001 $619 -$3 $6,507 -$4 $12,960 -$5
Weeding (low) $0o $619 $6,507 $12,960
Weeding (high) $200 -$937 -$8 $4,831 -$8 $11,261 -$8
Base scenario assumptions.

The value of below-ground C sequestration, exogenous in this model, was

estimated separately (Table 3-10). The estimated value of SOC sequestration,

comprising the majority of the below-ground carbon benefit, is influenced only by C

price and interest rate. The value of C sequestration in roots is additionally influenced by

the growth and yield function. The SOC model by Wullschleger et al. (2004) yields 341

Mg ha-1 from 0-60 cm depth at 45 years, at a rate of 7.5 Mg SOC ha-1 year-,whichis

greater than 136.3 Mg SOC ha-1, the average for longleaf-slash pine stands to 1 meter

depth reported by Heath et al. (2003). The rate of accumulation is an order of magnitude

more than sequestration rates reported from tree plantations on agricultural lands (Garten,

2002) but is closer to the 1-3 Mg SOC ha-1 year- sequestration rate reported in the top 30

cm of reclaimed minesoils over 25 years in Ohio (Lal & Akala, 2001), and might be

influenced by the longer growing season and deeper measurement depth. Estimating

carbon sequestered in roots as equivalent to 40% of the total biomass or 67% of the above









ground biomass (based on Segrest, 2002) yields 15 and 31 Mg C ha-1 after three years for

the EA 3 and EA 4 growth curves, respectively. This is more than the 6.6 and 7.4 Mg ha-

1 of below-ground organic matter after three years with the cultivation of sycamore

(Plantanus occidentalus) in Tennessee and Mississippi, respectively, reported by Tobert

and Thornton et al. (2000) though higher rates of sequestration are to be expected with a

longer growing season and faster growing Eucalyptus spp.. Assuming a C price of $5

Mg-1, total estimated below-ground C benefits range from $650 ha-1 (low growth function

and 10% interest rate) to $1,172 ha-1 (high growth function and 4% interest rate). Raising

the C price to $15 and $25 Mg-1 approximately increases the below ground C benefit by 3

and 5 times, respectively.

Table 3-10. Estimated discounted value of below-ground C benefits by C price, interest
rate and growth function.
Roots Estimated Total
Below-ground C
Benefit
C price Interest Growth Minimum Maximum SOC Minimum Maximum
($ Mg1) Rate Function
$5 4% Low $67 $74 $1,014 $1,081 $1,088
High $123 $158 $1,014 $1,137 $1,172
7% Low $64 $71 $751 $815 $822
High $117 $149 $751 $868 $900
10% Low $61 $67 $589 $650 $656
High $111 $140 $589 $700 $729
$15 4% Low $202 $223 $3,042 $3,244 $3,265
High $368 $473 $3,042 $3,410 $3,515
7% Low $192 $212 $2,254 $2,446 $2,466
High $350 $446 $2,254 $2,604 $2,700
10% Low $183 $202 $1,768 $1,951 $1,970
High $333 $421 $1,768 $2,101 $2,189
$25 4% Low $336 $372 $5,069 $5,405 $5,441
High $614 $789 $5,069 $5,683 $5,858
7% Low $320 $353 $3,757 $4,077 $4,110
High $584 $744 $3,757 $4,341 $4,501
10% Low $305 $336 $2,946 $3,251 $3,282
High $555 $702 $2,946 $3,501 $3,648









To compare these findings with production costs calculated by a previous study,

this model was used to find minimum stumpage prices needed to achieve LEVs of $1,235

ha-1 and $2,470 ha-1, representing LEVs of conventional forestry (Borders & Bailey,

2001) and Florida agricultural land (Reynolds, 2005), respectively. Stumpage prices of

$17 and $21 dry Mg-1 are required to match LEVs of $1,235 ha-1 and $2,470 ha-1,

respectively, assuming site preparation costs of $1,800 ha-1, planting costs of $1,200 ha-1

and averaging the EA 3 and EA 4 growth functions, equivalent to -25 dry Mg ha-1 year1

and an interest rate of 5%. Rahmani et al. (1997) report Eucalyptus spp. farm gate

production costs for Florida of $32-$39 dry Mg-1, slightly less than the $39-$43 dry Mg-1

farm gate costs estimated here assuming a harvest cost of $22 dry Mg-1 (Rahmani et al.,

1998). A higher cost of production is expected given the cost of site preparation on

CSAs.

Conclusions

Under these assumptions, even assuming high establishment and planting costs

($1,800 and $1,200 ha-1, respectively), a reasonable stumpage price ($20 dry Mg-1) and

excluding C sequestration incentives, production of EA on CSAs in central Florida is

profitable, with LEVs ranging from $762 to $6,507 ha-1 assuming interest rates of 10%

and 4%, respectively. With the incorporation of a C sequestration benefit of $5 Mg-1

LEVs increase to $946 and $6,715 ha-1, while recognizing the CO2 mitigation benefits

associated with the biofuel scenario increases LEVs to $1,315 and $7,869 ha-1 assuming

interest rates of 10% and 4%, respectively. In addition, the societal value of below-

ground C sequestration (roots + SOC at $5 Mg1 C) is likely to be $1,081-$1,172 ha-1 or

$815-$900 ha-1 assuming discount rates of 4% and 7%, respectively.









The influence of stumpage price, C sequestration benefit (CO2 mitigation scenario

or C price) or interest rate (from 4% to 10%) on optimum stage lengths is less than one

year, and is probably operationally unimportant. Increasing incentives for CO2

mitigation can increase or decrease optimum stage lengths by about 0.1 year in the mulch

and biofuels scenarios, respectively. Harvesting on CSAs would likely be scheduled

during the months of December-February when sites are more accessible and coppice

response to harvest is best, and practical application of this model is more likely in

evaluating the economic viability of the system rather than projecting optimum harvest

scheduling to sub-year accuracy. However, this model could be used to suggest the

optimum number of stages per cycle and optimal harvest scheduling by identifying the

winter closest to the optimum harvest age. Because of the short growth stages, penalties

for post-harvest CO2 emissions from product decay are discounted much less than those

of conventional rotations of 20 or more years, countering benefits of in situ C

sequestration, and underscoring the importance of recognizing the CO2 mitigation benefit

of displacing fossil fuels in the biofuel scenario.

These results emphasize both the potential for DFSSs on CSAs to mitigate

atmospheric CO2 and for CO2 mitigation incentives to contribute to the profitability of

SRWC production. Increases in LEV from CO2 displacement benefits are on par with

increases gained from SOC sequestration, and to a lesser degree, in situ sequestration in

above- and below-ground biomass. It would probably be impractical to provide

incentives and penalties for the sequestration and decay of C for SRWC systems on a per-

harvest basis, given the frequent harvest rate vis a vis conventional forestry systems.

However, this model might be used to assess the present value of CO2 mitigation benefits









over the life of the stand, providing the opportunity to offer incentives without

monitoring of each biomass harvest. Though payment of C sequestration benefits

independent of harvest monitoring could cause a divergence of private and socially

optimum harvesting, these results suggest there is little difference in optimum harvest

scheduling of private versus socially optimal SRWC production, and in fact both

optimum stage lengths and stages per coppice cycle decrease in the biofuel production

scenario, indicating that harvest monitoring might not be needed for a successful CO2

mitigation program. In the biofuel production scenario, probably the easiest way to

incorporate CO2 mitigation benefits would be for utilities to pass on CO2 emissions

reductions incentives to producers by increasing stumpage price.

In light of uncertainty associated with SRWCs, potential financiers might expect a

high rate of return on their investment. These results suggest that SRWCs can be

profitable at interest rates of 10%, assuming some combination of adequate yields,

stumpage prices, NTB incentives and/or operational costs are achieved.

Future Research

Research is needed to verify the assumptions made in this analysis. The most

immediate need is for a better understanding of growth response to treatment options

such as weeding and fertilization. With more information, particularly with regards to

below-ground C sequestration, growth functions and coppice growth, this model can be

used to make case-specific evaluations. A better understanding of long-term impacts of

SRWC production on CSAs and eligibility for mined-land reclamation incentives would

be beneficial, as would assessments of economic multiplier effects on communities in

Polk County. Upcoming work of SFRC students regarding the use of SRWCs to control

cogongrass and facilitate natural regeneration could contribute to this analysis. In light of






72


the 2004 hurricane season, a feasibility analysis incorporating risk assessment could be

useful in assessing potential advantages of SRWCs to reduce the probability of hurricane

damage.














CHAPTER 4
ECONOMICS OF SLASH PINE CULTURE ON TITANIUM MINED LANDS IN
NORTH CENTRAL FLORIDA

Introduction

Comparable to the phosphate mining industry in central Florida, titanium and

zircon mining by Iluka Resources Inc and Dupont is prevalent in northeast Florida, with

1,600 ha (4,000 acres) mined in Clay County since the early 1970s. In the mining

process, forest cover is removed, topsoil is retained, and through either dredge or dry

mining, soil is processed, minerals are removed, and the homogenized soil is replaced. In

response to concerns of environmental impacts of titanium mining, the Surface Mining

Control and Reclamation Act of 1977 requires that mining operations re-apply topsoil on

mined sites to restore wildlife habitat and hydrologic functions.

Another significant contributor to the economy of northeast Florida is the forest

products industry. In Clay County, Iluka and DuPont establish slash pine plantations on

reclaimed mines to produce timber products and restore ecosystem functions. Unlike the

experimental production of SRWCs on mined lands assessed in Chapter 3, slash pine

culture on titanium mined lands in northeast Florida is well-established. Darfus and

Fisher (1984) found young slash pine plantations established on mined lands in the mid-

1970s had poor survival and growth as a result of unleveled contours and disrupted soil

moisture regimes. However, Mathey (2001) in a study of slash pine plantations

established between 1978 and 1996 on lands mined by Iluka found no significant

difference between site indices of reclaimed and unmined lands, though averages varied








74



slightly (21.2 m and 22.0 m respectively, base age 25 years), and stem analysis showed


similar height and diameter at breast height (dbh) growth patterns on 8-, 10-, and 16-year-


old stands on mined and unmined lands under identical management regimes (Figures 4-1


and 4-2).


18
--- Reclaimed
16
14 -- Unmined


12





0
6 -






1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Age of Tree (years)

Figure 4-1. Mean heights estimated by stem analysis from stands on 25 reclaimed and 25
unmined sites (Mathey, 2001).



24
-- Reclaimed
22 -
-- Unmined


16
14
S12
m 10


6 --




1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Age of Tree (years)

Figure 4-2. Mean diameter inside bark (DIB) estimated by stem analysis from stands on
25 reclaimed and 25 unmined sites (Mathey, 2001).


Iluka has a vested interest in the productivity of post-mining landscapes, as do


private landowners who lease mining rights to Iluka. An assessment of the impacts of









titanium mining on the economics of forestry would contribute to land management

decisions in northeast Florida. Silvicultural practices such as fertilizing, bedding, and

subsoiling may improve tree growth and forest profitability on mined lands while

facilitating mined land restoration (Proctor et al., 2003). This chapter assesses the

profitability of slash pine production on mined and unmined lands in northeast Florida

and the economic viability of silvicultural treatments that might be used to improve

production on mined lands.

Methodology

Economic Model

As described in Chapter 1, Eq.(1-1) defines LEV, net returns of a forestry practice

projected in perpetuity, where V(t) is the value function of the stand at age t, r is interest

rate, and C is the sum of stand establishment costs discounted to the beginning of the

rotation. This model is used to compare bare-land values of mined and unmined lands

under slash pine production. In this case of a non-coppicing species, the Faustmann

model remains fixed for one growth stage per cycle (i.e., one rotation).

Calculating LEV on mined land vis-a-vis unmined land requires that stand

establishment cost C be accounted for differently. Topsoil replacement and contouring

costs are considered sunk, as these treatments are required by law regardless of the

subsequent land use, and land clearing costs of the first rotation are excluded from the

mined land simulations to accurately represent actual establishment costs on un-vegetated

mined land. In these cases, LEV and optimum rotation age are calculated using Eq.(4-1),

a variation of Eq.(1-1), in which initial costs C, are accounted for at the beginning of the

projection and standard establishment costs C are assumed at the beginning of all









subsequent rotations. Alternatively, cost savings at year zero can be added to Eq. (1-1),

yielding the same result.

LEV (V(t)- C)*e r*t
LE1 V =t C (4-1)
-e -rt

To determine the financial viability of slash pine production on reclaimed titanium

mined lands, Equations (1-1) and (4-1) were used to a) compare LEVs of established

slash pine stands on 34 mined and 29 unmined sites, b) assess the economic viability of

fertilizer and subsoil treatments on mined lands, c) estimate minimum economically

feasible growth response under estimated treatment costs, and d) estimate maximum

economically feasible treatment costs under predicted fertilizer responses. Use of

Equations (1-1) and (4-1) requires that stumpage value V(t) be defined as product price

times the yield function for each product.

Growth and Yield Model

The Plantation Management Research Cooperative at the University of Georgia, in

conjunction with the forest industry, monitored slash pine growth and response to

silvicultural treatments. Using data from this study, Pienaar and Rheney (1995)

developed growth and yield models for slash pine plantations. The growth and yield

functions require determination of average dominant height and basal area at time t.

Average height H(t) is predicted by Eq. (4-2)1:

SI*1.3679* 1-e(-007345*t)
H (t)-= /36 [0.305] (4-2)
(0.678 z + 0.546 z +1.395 *3 + 0.412* z I* 3)* te( 0691*t


1 Units in this chapter are shown in both metric and imperial units to facilitate interpretation within the
context of the Florida forest industry. Squared brackets in equations indicate conversion from imperial to
metric.









where Slis site index (SI) in m at base age 25, and zl 1 if fertilized, 0 otherwise; z2= 1 if

bedded, 0 otherwise; and z3 = 1 if herbicide is applied, zero otherwise. Tree survival (S)

is estimated by

S(t) =N *e(t21345 11345) (4-3)
where S (stems ha-1) at year t2 is a function of the number of surviving trees N (stems

ha-1) at year tl (alternatively, both S and N can be expressed as stems acre-1). Using

Equations (4-2) and (4-3), basal area (B, m2 ha-) at time t is predicted by Eq. (4-4):


B(t) = 3 394-3566) (t)(1 366+6 20) *S(t)(0 366+3 15[0.23] (4-4)

,(0.557* z4 +0.436* zl +2.134* z3 -0.354* zl* z2)*t* e(-009*t)

Using the above three equations, predicted total volume (m3 ha-) at time t is

estimated by Eq. (4-5):

T(t) = H(t)82 *S(t)( 0017 03) *B(t)(1016 +00) *[0.07] (4-5)

Equation (4-5), subsequent yield functions, and eventually Eq. (1-1) are largely a

function of SI, which could be used to compare LEVs of mined lands to unmined lands.

Mathey (2001) measured height and dbh in 116 1/50-hectare plots in 1-, 2-, 3-, 8- 10-,

and 16-year-old plantations on mined and unmined lands managed by Iluka. However,

Mathey found no significant difference between SIs of reclaimed and unmined sites. As

variation in SI does not correspond to similar variation in LEV, and to apply a SI

equation using parameters for slash pine, SIs were recalculated for 1/50-hectare plots on

ten 8-, eight 10-, four 13-, two 15-, five 16- and five 21-year-old stands on mined lands,

to determine LEVs under a range of SIs on mined land. SIs were calculated with Eq.

(4-6):









( 2 0669
SI H* 0.91861e 0 10035A (4-6)
where H is average height (m) of dominant and co-dominant trees at sample age A and SI

is site index (m) at base age 25 (Bailey, 1982). Results of a Student's t-Test again

showed no significant differences of SI between mined and unmined sites, averaging 19.7

m and 21.0 m respectively. To better reflect observed plot-specific volumes on mined

and unmined lands Eq. (4-5) was adjusted following Davis and Johnson (1987) by Eq.

(4-7):


Tt) = Tt (t t) (4-7)


where the total volume prediction equation T(t)p was multiplied by the observed volume

T(to)o divided by total predicted volume also at time of observation to. T(to)o (m3) was

calculated by Eq. (4-8)

T(to) = 0.00616 *([0.394]* dbh)20578 *([3.28]* H)07468 *[0.0283](4-8)

where dbh is dbh in cm and H is height in m (Brister et al., 1980)2. Volumes where then

summed for each plot multiplied by 50 for volume per ha. Based on the adjusted total

volume function derived from Eq. (4-7), product-specific volumes were then derived.

Eq. (4-9) yields the volume of sawtimber V(t)saw (m3 ha-) :
0 'i/ 43 f~)06 l 01 / }, 43 8
0- 5* Q 8()*[ 541 384 69*S(t)-1 12 54dd )* 5 2
V(t) T(t) *e *[0.07] (4-9)







2 Alternatively, T(t)o can be calculated in ft3 by eliminating the numbers in brackets, with dbh and H as
inches and feet, respectively.








where dt is top merchantable diameter outside bark and and Q(t) is the quadratic mean of

dbh, as expressed by Eq. (4-10):

B (t)
Q(t) =S (4-10)
0.005454*[0.0144]

Chip-and-saw and pulpwood product classes were then estimated by Eqs. (4-11)

and (4-12), respectively:

S= T (t)- 52* (t)*[254] 84-0 69*S(t-) 12d (t)*[2 54].07] (4-11)
V(t



V (t)P = T(t)a *e 052 384 (t) V7 (t)W [0.07] (4-12)


An example of pulp, chip-and-saw, sawtimber, and total outside bark volumes

predicted using Eqs. (4-2)-(4-6) and adjusted to replicate observed volumes using Eq.

(4-7)-(4-12) is shown in Figure 4-3. Northeast Florida merchantable standards used in

this analysis are shown in Table 4-1.


Table 4-1. Merchantable standards of dbh and top diameter outside bark (dr).
dbh dt
cm in cm in
Sawtimber 24.4 9.6 21.8 7.6
Chip-and-Saw 16.8 6.6 9.1 3.6
Pulpwood 9.1 3.6 9.1 3.6

The value of the stand at time t was then be expressed as

V (t) = ps,w V (t)sw+ Pns V (t) + Pp V (t)pW (4-13)















250 -


200 -








50 -- -
0 ---------- -- ------------ ----------- ----------------------------
0 5 10 15 20 25 30
Time (years)
Pulp
SAdjusted Pulp
Chip-and-saw
dj ed Chip-and-saw
Sawtmber
Adjusted Sawtimber
Total Predcted Volume
---- Adjusted Total Predcted Volume

Figure 4-3. Representative pulp, chip-and-saw, sawtimber, and total outside bark
volumes (m3 ha-1), predicted using Eqs (4-2)-(4-6) (solid lines) and adjusted to
replicate observed volumes (dotted lines) using Eq. (4-7).


where psaw, Pcns, and ppw are the price per volume of sawtimber, chip-and-saw, and


pulpwood products, respectively. Product prices were defined to incorporate Eq. (4-13)


into Eq. (4-1) to calculate LEV and optimum rotation age as described in Chapter 2.


Market Assessment


The conventional softwood forest products market in northeast Florida is much


larger and more established than that of Eucalyptus spp. in south Florida. The forest


industry has the highest economic impact in Florida of any agricultural crop and


contributes over $16.6 billion annually (Hodges et al., 2004), with most of the state's


pine inventory in northeast Florida (Carter & Langholtz, 2005). Assuming constant


South-wide softwood demand, removals in Florida are projected to increase over the


projection period due to relative abundance of supply as compared to other states. In


northeast Florida from 2000-2020, removals are projected to increase slightly from 5.9 to


7.0 million m3 (210 to 250 million ft3), and inventory is projected to fluctuate between 59









and 67 million m3 (2.1 and 2.4 billion ft3). Even assuming South-wide removals decrease

1 percent annually, removals in northeast Florida are projected to remain fairly constant

until 2020, fluctuating from 6.1 to 5.9 million m3 (216 to 209 million ft3) (Carter &

Langholtz, 2005). The Iluka mining operation lies within 32 km (19 miles) of a Georgia-

Pacific multi-product sawmill near Palatka and is expected to have access to timber

markets for the foreseeable future.

Mathey (2001) reported results of an economic assessment of slash pine production

on Iluka's mined lands. Stumpage values used in said analysis of $93-$407 m-3 ($89-

$391 green ton'1 assuming 1.04 green tons m-3) are inconsistent with the range of values

reported from Timber Mart South over the past 10 years (Figure 4-4). Analysis in this

chapter assumes values of $8.10, $26.62, and $41.27 m-3 ($20.19, $66.34 and $102.83

cord-1) forpp, pens, andppa, respectively (Timber Mart-South 1st Quarter 2005 average

stumpage prices for Florida)3, typical of prices since 1995 (Figure 4-4).

$50

$40 -----So-------- -----im w-



$20 ------------
Pu Cip-n-aw
$20





1 95 1Q 97 1Q 99 1Q01 Q 03 10 05
Figure 4-4. South-wide pine stumpage prices quarterly averages from 1995-2005
(Timber Mart South 2005).


3 Assumes about 2.5m3 cord-' (Appendix A Eq. 3).









Silvicultural Alternatives

Fertilizer amendments, weed control, and mechanical soil preparation can improve

tree growth and contribute to pine plantation productivity (Dickens et al., 2002).

However, the benefit of these treatments on mined lands is not well known. On mined

lands in the Appalachian region, inorganic N fertilizer amendments increased herbaceous

biomass production during the first growing season but did not affect hybrid loblolly pine

(P. taeda) growth at 2 and 3 years of age. Increased seedling growth with organic

amendments was more a function of moisture retention than soil nutrient availability

(Schoenholtz et al., 1992). In a reforestation experiment testing the growth of three pine

species on surface-mined sites in coalfields of southwest Virginia, fertilization had little

effect on growth and was not as beneficial for tree establishment as an herbicide

treatment (Torbert et al., 2000).

Mathey (2001) and Proctor (2002) established field trials at Iluka testing the

influence of fertilizer, herbicide, and subsoil treatments on slash pine and loblolly pine

growth and survival on mined and unmined lands. SRWC-84, established December 9,

1999, tested 10 combinations of fertilizer/herbicide treatments (Table 4-2), and heights

were measured at 1, 2, 3 and 5 years of age. SRWC-84-2001, established January 9,

2001, included treatments of 13 fertilizer/herbicide combinations (Table 4-2), as well as

mycorrhization, humate incorporation, and subsoiling on the mined site, and was

measured at 1, 2, and 4 years of age.

In the SRWC-84 study, height and survival responses at 1, 2, 3, and 5 years of age

were significantly different for both land type (satellite mined and unmined) and

treatment (p< .0001). At age 5, trees averaged 4.2 m (14 ft) and 5.5 m (18 ft) tall on

mined and unmined land, respectively, but survival was better on the mined land,









averaging 85% and 50% on mined and unmined land, respectively. This trend of reduced

growth but increased survival on mined land compared to unmined land is consistent with

measurements of young stands by Mathey (2001). On the mined land, treatment G2 had

the highest average height at 2, 3 and 5 years of age (Figure 4-5). A Duncan grouping

analysis was used to identify treatments of similar growth and survival (Table 4-3). On

the mined land at ages 2 and 3, treatments G2 and B2 were grouped with highest growth;

at age 5 heights of treatments G2>B2>D2>MO were grouped highest, averaging 4.4 m

tall, suggesting better response to post-establishment fertilizer application. On unmined

land height, responses to treatments showed less variation (Figure 4-7) ranging from

Table 4-2. Treatments included in the SRWC-84 and SRWC-84-2001 studies (Proctor,
2002). All fertilizer applications were applied at a rate of 40.3 kg N/ha (361bs
N/ac).
Treatment Description SRWC-84 SRWC-84-
2001
C Bedding only, no amendment X X
G2 Granulite 5-3-0, broadcast in year 2 X X
D2 DAP 18-46-0, broadcast in year 2 X X
B2 16-4-8 with balanced micronutrients, X X
broadcast in year 2
GOR Granulite 5-3-0, broadcast at planting, X X
herbicide treatment
DOR DAP 18-46-0, broadcast at planting, herbicide X X
treatment
BOR 16-4-8 with balanced micronutrients, X X
broadcast at planting, herbicide treatment
GORL Loblolly, Granulite 5-3-0, broadcast at X X
planting, herbicide treatment
HO Dry aluminum humate broadcast at planting at X X
.35% by weight
MO Mycorrhizal treatment at the time of planting, X X
bedding only
GOH Granulite 5-3-0, broadcast at planting, and X
humate material at .35%
DOH DAP 18-46-0, broadcast at planting, and X
humate material at .35%
BOH 16-4-8 with balanced micronutrients, and X
humate material at .35%










4.8-5.7 m with all nine treatments sharing the same Duncan grouping by age 5 (Table 4-

3). Average heights by treatment of SRWC-84 and SRWC-84-2001 on January 15, 2005

are shown in Figure 4-13. The survival response to treatment on the mined land varied

little, ranging from 78-97%, with two Duncan groups, both including the control (Figure

4-6), while survival on the unmined land varied by treatment from 8%-86% (Figure 4-8)

resulting in five Duncan groups.

Table 4-3. SRWC-84 age 5 and SRWC-84-2001 age 4 mined (SM) and unmined (UM)
average heights, standard deviation and Duncan grouping ranked from tallest
to shortest by treatment.


SRWC-84
SM
Age 5
G2, 4.6, 0.8,
a
B2, 4.5, 0.9,
a
D2, 4.2, 0.9,
ab
MO, 4.2, 1.1,
ab
GOR, 4.1,
0.9, bc
HO, 4.1, 0.8,
bc
BOR, 4, 1, bc

C, 3.9, 0.9,
bc
DOR, 3.7,
1.2, d
GORL, 3.3,
0.6, d


SRWC-84 UM
Age 5

G2, 5.7, 0.8, a

GORL, 5.7, 1.3,
a
D2, 5.5, 1, a

C, 5.4, 0.9, a

BOR, 5.4, 0.4,
a
DOR, 5.3, 0.6,
a
B2, 5.2, 1, a

MO, 5, 0.8, a

GOR, 4.8, 1, a


2001 SM
Age 4

BOR, 2.9, 23, 0.5, a

DOH, 2.8, 15, 0.7,
ab
DOR, 2.8, 25, 0.6,
ab
B2, 2.6, 49, 0.7,
abc
MO, 2.5, 40, 0.5,
abcd
G2, 2.5, 60, 0.8,
abcd
D2, 2.5, 44, 0.7,
bcde
C, 2.2, 61, 0.5, cdef

GOH, 2.2, 17, 0.5,
defg
GORL, 2.2, 30, 0.7,
fg
HO, 2.1, 30, 0.6, fg
BOH, 2.1, 16, 0.8,
fg
GOR, 1.8, 26, 0.6,


2001 UM
Age 4

BOR, 4.0, 0.4,
a
D2, 3.7, 0.5, ab

DOR, 3.7, 0.7,
ab
G2, 3.7, 0.5, ab

B2, 3.6, 0.7, bc

GOR, 3.3, 0.4, c

GORL, 2.9, 0.3,
d
C, 2.8, 0.7, d


Ranking










Table 4-4. SRWC-84 age 5 and SRWC-84-2001 age 4 mined (SM) and unmined (UM)
average survival (%) and standard deviation by treatment, ranked from highest
to lowest. Letter "a" indicates shared highest Duncan group.


Ranking SRWC-84 SM
Age 5
1 GORL, 97, 5.9, a

2 DOR, 92, 1.2, 5.9,
ab
3 BOR, 92, 5.9, ab
4 G2,92,3.4,ab

5 HO, 89, 5.9, ab

6 B2, 83, 3.4, ab
7 C, 82, 3.4, 0.9, ab
8 GOR, 81, 5.9, 0.9,
b
9 MO, 81, 5.9, b
10 D2,78,3.4,b


E

I3

2
-


SRWC-84 UM
Age 5
GORL, 86, 7.7, a

G2, 65, 4.5, b

D2, 59, 4.5, b
B2, 58, 7.7, b

C, 48, 4.5, bc

GOR, 31, 7.7, cd
DOR, 31, 7.7, cd
MO, 17, 7.7, de

BOR, 8, 7.7, e


2001 SM
Age 4
GOH, 94, 17, 0.5, a

GORL, 92, 30, 0.7, a

BOH, 89, 16, 0.8, a
G2, 88, 60, 0.8, a

C, 88, 61, 0.5, a

HO, 86, 30, 0.6, ab
DOH, 83, 15, 0.7, ab
GOR, 81, 26, 0.6, ab

MO, 74, 40, 0.5, ab
B2, 69, 49, 0.7, ab
DOR, 69, 25, 0.6, ab
D2, 64, 44, 0.7, b
BOR, 64, 23, 0.5, b


2001 UM
Age 4
GORL, 81, 0.3,
a
G2, 73, 0.5, a

C, 69, 0.7, a
GOR, 66, 0.4,
ab
DOR, 61, 0.7,
ab
D2, 52, 0.5, b
B2, 31, 0.7, c
BOR, 25, 0.4, c


- BOR
B2
C
DOR
+KD2
--GOR
--GORL
G2
HO
MO


1 2 3 4 5


Age (years)


Figure 4-5. Average heights (m) by age (year) and treatment, SRWC-84 mined site.







86



100% --
90% --- -- BOR
80% B2
70% C
S60% DOR
> --D2
50% 50%
40%-
SGORL
30% G2
20% HO
10% MO
0% -
0 2 4 6
Age (years)

Figure 4-6. Average survival (%) by age (year) and treatment, SRWC-84 mined site.




6

5 -- BOR
-- B2
4 c
E DOR
-a 3 -- -- D2
-*- GOR
2 GORL
-G2
1 MO

0 -
0 1 2 3 4 5

Age (years)



Figure 4-7. Average heights (m) by age (year) and treatment, SRWC-84 unmined site.