Incorporating Uncertainty into a Life Cycle Assessment(LCA) Model of Short-Rotation Willow Biomass(Salix spp.) Crops

Jesse Caputo & Steven B. Balogh & Timothy A. Volk & Leonard Johnson &

Maureen Puettmann & Bruce Lippke & Elaine Oneil

# Springer Science+Business Media New York 2013

Abstract To estimate fossil fuel demand and greenhouse gasemissions associated with short-rotation willow (Salix spp.)crops in New York State, we constructed a life cycle assess-ment model capable of estimating point values and measuresof variability for a number of key processes across eightmanagement scenarios. The system used 445.0 to1,052.4 MJ of fossil energy per oven-dry tonne (odt) ofdelivered willow biomass, resulting in a net energy balanceof 18.3:1 to 43.4:1. The largest fraction of the energy demandacross all scenarios was driven by the use of diesel fuels. Thelargest proportion of diesel fuel was associated withharvesting and delivery of willow chips seven times on 3-year rotations over the life of the crop. Similar patterns werefound for greenhouse gas emissions across all scenarios, asfossil fuel use served as the biggest source of emissions in thesystem. Carbon sequestration in the belowground portion ofthe willow system provided a large carbon sink that more thancompensated for carbon emissions across all scenarios,resulting in final greenhouse gas balances of −138.4 to

−52.9 kg CO2 eq. per odt biomass. The subsequent uncertain-ty analyses revealed that variability associated with data onwillow yield, litterfall, and belowground biomass eliminatedsome of the differences between the tested scenarios. Evenwith the inclusion of uncertainty analysis, the willow systemwas still a carbon sequestration system after a single cropcycle (seven 3-year rotations) in all eight scenarios. A betterunderstanding and quantification of factors that drive thevariability in the biological portions of the system is necessaryto produce more precise estimates of the emissions and energyperformance of short-rotation woody crops.

Keywords Bioenergy . Biomass . Short-rotation woodycrops . Life cycle analysis . Uncertainty analysis

Introduction

Interest in shrub willow (Salix spp.) as a perennial energy cropfor the production of biomass has developed in Europe andNorth America over the past few decades because of themultiple environmental and rural development benefits asso-ciated with its production and use [1–3]. The US Billion-TonUpdate report [4] indicates that short-rotation woody crops,which include willow biomass, have the potential to providebetween 126 and 315 million dry tons of biomass annuallyacross the USA by 2030, based on assumptions regardingannual yield and economics ($60.00 per dry tonne). In orderto ensure, however, that energy crops live up to their promiseof being energy-efficient, low-carbon sources of energy, it isimportant to quantify the impact of these systems under differ-ent management regimes using accepted performance metrics.

Life cycle assessment (LCA) is one accepted methodolo-gy for quantifying the energy demand, materials usage, and

J. Caputo (*) : S. B. Balogh : T. A. VolkDepartment of Forest and Natural Resources Management, SUNYCollege of Environmental Science and Forestry, 1 Forestry Drive,Syracuse, NY 13210, USAe-mail: jcaputo@syr.edu

L. JohnsonLeonard Johnson and Associates, 1205 Kamiaken Street, MoscowID 83843, USA

M. PuettmannWoodlife Environmental Consultants, LLC, 8200 NW ChaparralDrive, Corvallis, OR 97330, USA

B. Lippke : E. OneilCollege of the Environment, University of Washington, Box352100, Seattle, WA 98195, USA

Bioenerg. Res.DOI 10.1007/s12155-013-9347-y

environmental impacts of processes over their entire lifecycle—from “cradle-to-grave”. LCA has been widely usedto assess biomass energy applications to determine green-house gas (GHG) balances, overall carbon balance, andenergy return on fossil fuel inputs [5–8]. Furthermore,LCA is explicitly identified in the US Energy Investmentand Security Act (P.L. 110-140, subtitle A) as the methodol-ogy to be used in determining whether advanced biofuelsmeet minimum standards for GHG reductions necessary tobe eligible under the national Renewable Fuel Standard.

Djomo et al. [7] performed a comprehensive literaturereview of energy analyses and LCAs for short-rotation woodybiomass crops (willow and poplar). The authors identified 26relevant studies that included net energy analysis or calcula-tion of system-wide GHG emissions. Cross-study compari-sons were hampered by the lack of transparency in thebioenergy models and the variability in the boundaries andmethodological assumptions among these studies. The meanharvestable yield for willow systems varied from 8 to 16.8-ton ha−1 year−1. The net energy ratio at the farm gate, the ratioof energy output divided by fossil energy inputs, was included(or could be calculated) in 20 out of the 26 studies. Net energyratios for willow ranged from 16:1 to 79:1 at the farm gate,due to wide ranges of assumed energy inputs (46.3 to 234.4-GJ ha−1) and outputs (1,759 to 5,435 GJ ha−1). The green-house gas balances for willow systems ranged from 0.7 to10.6 g CO2MJ−1 at the farm gate.

Our main objective in designing this study was to quantifythe fossil fuel inputs and greenhouse gas balance of thewillow biomass (Salix spp.) cropping system in New YorkState. We updated and expanded a previous life cycle anal-ysis by Heller et al. [9], using data that have been collected inthe region in the past decade. We also incorporated into themodel the uncertainty associated with a number of keyparameters in order to allow us to better understand howtheir variability may affect overall system performance.

Methods

Conceptual Model Structure and Boundaries of the LCA

We defined the functional unit of the analysis as 1 oven-drytonne (odt) of willow biomass. To estimate fossil fuel de-mand and greenhouse gas emissions associated with each odtof output, we built a model of the willow biomass cropproduction system using SimaPro 7 [10]. SimaPro 7 is acommercial software package capable of creating compre-hensive models of complex processes; tabulating the energydemand, materials usage, and emissions generated over theentire extent of a process (the life cycle inventory or LCI);and analyzing and comparing the impacts of different pro-cesses according to a wide range of criteria and indicators of

relevance to human or environmental well-being (the LCA).In constructing our model, we followed the guidelines set outby the Consortium for Research on Renewable IndustrialMaterials (CORRIM) [11], based on the principles of ISO-14040/14044 [12, 13].

Our model includes two modules. The base module in-cludes in-field activities related to planting, managing, andharvesting the willow crops, as well as transporting the chipsfrom the field to an end user. We included the production,harvesting, and storage of shrub willow planting stock in anursery in a separate nursery module. The base module(Fig. 1) is based on the recommendations by Abrahamsonet al. [14]. Field operations begin with site preparation thatincludes mowing existing vegetation, chemical weed control(glyphosphate), plowing, disking, cultipacking, as well asseeding with a cover crop (winter rye, Secale cereale L.) andmowing the cover crop prior to planting. Planting operationsinclude transportation of the cuttings to the field site, plantingthe site at a density of 15,300 plants ha−1, and coppicing theemerging willow crop at the end of the first growing season.Beginning in the fourth year, the field is harvested during thedormant season using a single pass cut-and-chip harvesterbased on a Case New Holland FR series forage harvesterand a Case New Holland FB-130 short-rotation coppice head-er. This customized head cuts stems near the ground, pulls thestems into the forage harvester, chips them, and blows thefinished chips into an adjacent forage wagon being pulled by atractor. Willow chips are transported from the field site to theend user in a 120 cubic yard chip van. After the crop resproutsthe following spring, nitrogen fertilizer (100 kg N/ha as am-monium sulfate) is applied. After seven 3-year rotations, thecrop is terminated by mowing, herbicide application, andgrinding the stools into the soil. The base module thus coversa single crop cycle of 22 years from initial site preparation tofinal grinding of stools. No assumptions were made regardingland use before and after this single crop cycle. Consequently,carbon dynamics before and after the crop cycle, which arestrongly dependent on land use, were not included in themodel. We also did not include conversion processes to finalenergy products (e.g., liquid fuels, heat, or electricity).

The nursery module includes site preparation (plowing,disking), planting, coppicing, chemical applications (urea,glyphosphate, and other herbicides), 24 annual harvests (usinga whip harvester), and a final removal of stumps (Dennis Rak,personal communication). The major differences between thenursery module and the main module are that in the nurserymodule the willow stems are harvested annually, instead ofevery 3 years, and shipped as 1.5–2.5-m stems instead of beingground into chips in situ. In addition, the nursery module alsoincludes the natural gas and electricity associated with infra-structure used in processing and cold storage of willow cuttings.

We set the model boundaries to contain only those process-es within the two sub-modules. Furthermore, within the model

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boundaries, we model only those outcomes that are directlyand immediately attributable to the production of a singletonne of willow chips (an “attributional” model) and not anyindirect or market-driven outcomes resulting from an overallincrease in willow production (a “consequentialist” model),such as increased greenhouse emissions due to regional landuse change [15, 16]. Consequentialist and attributional modelscan give different results but are ultimately best suited toanswering different questions. Attributional models are suit-able when there is interest in understanding the impacts of asingle unit of fuels, such as in administering a low-carbon fuelstandard. Consequentialist models, on the other hand, aresuitable for understanding the overall impacts of increasedproduction of biofuels, such as in assessing the sum impactsof a given policy incentive over an entire region.

The model does include the impacts of extracting, refin-ing, processing, and transporting the diesel, oil, lubricants,and chemical products that were directly consumed in allmodeled processes. In line with earlier work using theCORRIM protocols [17], however, we did not include im-pacts associated with the production of capital goods.

Model Parameters

We obtained parameter values (Table 1) for the model from anumber of sources. SimaPro incorporates a number of stan-dardized global LCI databases for a wide range of standardchemicals, materials, equipment, and energy sources. Wechoose to use data for standardized fuels, chemicals, andenergy from the USLCI [18] and EcoInvent 2.2 [19] data-bases. We obtained data on the steps and rates of operationpertaining specifically to the willow production system froma number of sources. Machinery operation rates and fuel and

chemical use were taken from the base case scenario input inthe EcoWillow 1.6 model—an economic cost–benefit modelof the willow system [20], as well as from Heller et al. [9].For data not available from these sources, as well as data onprocesses that have been updated since publication of thesesources, we collected data from experts in the region whohave had direct experience establishing and managing wil-low biomass crops. We included the most current willowbiomass yield estimates from the review by Volk et al. [21].We parameterized the planting stock nursery module basedon interviews with the owner and operator of a commercialwillow nursery in Western NY (Dennis Rak, personal com-munication). We assumed a 300-km haul distance (one way)from the nursery to the field based on the distance betweenthe existing nursery and Syracuse, NY.

We estimated the haul distance from field to end userbased on scenarios published as part of the New York Re-newable Fuels Roadmap [22]. These values were based onthe average haul distances from potential field sites to anetwork of either large centralized (>1,280 million litersper year) or smaller distributed (<860 million liters per year)biorefineries. We doubled these values in order to reflect around trip; this is a conservative estimate that assumes thetrucks would not be hauling any additional goods on thereturn trip.

Finally, we incorporated greenhouse gas emissions data,associated with belowground carbon sequestration and ni-trous oxide emissions from litterfall and fertilizer. Nitrousoxide emissions were calculated based on the willowlitterfall and leaf nitrogen content data by Adegbigi [23], aswell as the amount of nitrogen fertilizer applied. Emissionswere calculated using the IPCC method as reported in Helleret al. [9]. Belowground carbon sequestration was based on

Fig. 1 Process map for a lifecycle assessment (LCA) modelof short-rotation willow (Salixspp.) cropping system in NewYork, USA

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the total carbon sequestration in roots and woody stools overa 19-year chronosequence as reported by Pacaldo et al. [24].In a related study [25], the authors found that belowgroundsequestration in fine roots was approximately equal to car-bon loss via soil respiration, so we used only the sum of thecarbon sequestered in coarse roots and willow stools (bothabove- and belowground) over 19 years as the measure oftotal on-site carbon sequestration. Although the crop cycle inour model is 21 years long, it is unlikely that much additionalcarbon would be sequestered during the two additional yearssince the results of Pacaldo et al. [24] show that carbonstocks stabilize between 14 and 19 years. Studies on soilcarbon in willow biomass crops in upstate NY indicate that

there is no significant change in this parameter over time [24,25] so we assumed no net change in soil carbon in thissystem. Given that the objective of this study was to modelthe life cycle of feedstock production and delivery only, wedid not attempt to model the fate of carbon stored within theharvested feedstock once delivered to the end user, and sodid not include any stem biomass carbon in our measure ofon-site sequestration. Furthermore, as mentioned earlier, wedid not include any measure of carbon gain or loss after thecrop was terminated in the final year of the crop cycle.Changes in soil and belowground C after the termination ofthe crop would be allocated to the land use that followed thewillow crop.

Table 1 Parameters used in lifecycle assessment (LCA) modelof short-rotation willow (Salixspp.) cropping system in NewYork, USA

Standard errors associated withparameters were used in uncer-tainty analysisa Alternative values were used inscenario analysisb EcoInvent, “Rye seed IP, at re-gional storehouse/CH U”c EcoInvent, “Glyphosphate, atregional storehouse/RER U”d EcoInvent, “Ammonium sul-fate, as N, at regional store-house/RER U”e USLCI, “Diesel, combusted inindustrial equipment/US”f EcoInvent, “Urea, as N, at re-gional storehouse/RER U”g EcoInvent, “Herbicides, at re-gional storehouse/RER U”h USLCI, “Electricity, at grid, East-ern US/US”i USLCI, “Natural gas, combustedin industrial boiler/US”

Parameter Value

Base module

Yield (for seven 3-year rotations), lowa 225.054 (SE=17.833) odt ha−1

Yield (for seven 3-year rotations), higha 295.535 (SE=21.004) odt ha−1

Belowground sequestration (across seven rotations) −45.692 (SE=3.974) Mg CO2 eq ha−1

Planting rate 15,300 cuttings ha−1

Willow litterfall 3,820.300 (SE=430.866)kg ha−1 year−1

Leaf nitrogen content 20.76 g/kg (SE=2.0)

Rye seed (one application)b 54.66 kg ha−1

Glyphosphate (each application)c 2.5 kg ai ha−1

Ammonium sulfate fertilizer (each application)d 100 kg N ha−1

Diesel, forage harvester (434 kW)e 121.60 l ha−1

Diesel, tractor (54 kW) with forage wagone 13.00 l ha−1

Diesel, tractor (54 kW) with blowere 6.50 l ha−1

Diesel, stump grindere 29.48 l ha−1

Diesel, plow (60 kW tractor)e 22.78 l ha−1

Diesel, disk (60 kW tractor)e 18.76 l ha−1

Diesel, cultipack (60 kW tractor)e 9.51 l ha−1

Diesel, seeder (60 kW tractor)e 1.34 l ha−1

Diesel, planter (60 kW tractor)e 33.5 l ha−1

Diesel, mower (60 kW tractor)e 20.10 l ha−1

Diesel, herb. sprayer (60 kW tractor)e 6.70 l ha−1

Diesel, fert. sprayer (60 kW tractor)e 2.81 l ha−1

Diesel, 120CY chip vane 0.076 l km−1 odt−1 chips

Engine lubricant 1.8 % of diesel use (0.88 kg l−1 diesel)

Haul distance (farm to user), round trip, shorta 71 km

Haul distance (farm to user), round trip, longa 195 km

Nursery module (where different from or additional to base module)

Yield (all 24 harvests) 26,120,250 cuttings ha−1

Urea (initial application)f 449 kg N ha−1

Glyphosphate (initial application)c 2.5 kg ha−1

Other herbicides (initial application)g 10.11 kg ha−1

Diesel, whip harvestere 90.85 l ha−1

Electricity, operations (over 25 years)h 46,890 kWh ha−1

Natural gas, operations (over 25 years)i 4.95 m3 ha−1

Haul distance (nursery to field) 300 km

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Uncertainty Analysis

For the initial analysis, we used parameter values based on singlepoint estimates. However, biological systems such as willowcrop systems display significant variability. In order to under-stand how this biological variability influences overall perfor-mance of the willow system,we includedmeasures of variabilityassociated with three key biological parameters—willow yield,belowground carbon sequestration, and litterfall/leaf nitrogencontent.Means and associatedmeasures of variability were usedas reported in the original sources, and all distributions weretreated as normal.

Model Scenarios

We ran the life cycle model according to eight scenarios, basedon all possible permutations of three variables—yield, fertilizeruse, and delivery distance. We defined low yield as the meanfirst rotation yield of all clones (AC) planted at 11 sites in thenorthern USA and Canada [21] and high yield as the mean ofthe three best performing clones (T3) at each of the 11 sites.Since these values were derived from first rotation data only,and because yield is often lower in the first rotation than insubsequent rotations, we increased the yield of the secondrotation harvest by a factor of 19.4 % relative to the firstrotation and then maintained this yield through all subsequentrotations. This factor is equal to the mean yield increase ob-served between the first and second rotations in yield trials [21].We ran the model both with (F) and without (NF) fertilizationbetween harvests, based on recent studies that have reported noincrease in yield with fertilizer inputs across a range of sites andwillow crop ages [26, 27]. Finally we ran the model using theestimated haul distances associated with centralized (C) pro-duction facilities and with distributed (D) facilities.

For each of the eight scenarios, we used SimaPro 7 toassess both the cumulative energy demand (in megajoules,nonrenewable fossil fuels) and greenhouse gas emissions (inkilograms CO2 eq, IPCC 2007 GWP 100a) of a single drytonne of willow chips. We analyzed each scenario first usingSimaPro 7’s network analysis function, which calculates apoint estimate based on the mean value or fixed value of eachof the parameters. We then analyzed each scenario usingSimaPro 7’s uncertainty analysis function, a form of MonteCarlo analysis. For each scenario, the model was run 1,000times. In each run, fixed values were used for most of theparameters (as in network analysis). However, for the threevariables for which we includedmeasures of variability (yield,below ground sequestration, and litterfall/leaf nitrogen), themodel randomly selects a value for that parameter from anormal distribution based on the mean and standard deviation(or standard error) included in the model. At the end of 1,000runs, SimaPro 7 was able to calculate summary statistics foreach scenario as a whole.

Results and Discussion

Cumulative Energy Demand

Cumulative energy demand for the production, harvesting,and transportation of willow biomass to an end user covereda broad range of values (445.0 to 1,052.4MJ odt−1) among theeight scenarios (Fig. 2). The highest energy input was for thelow yield + fertilizer + long haul scenario (AC-F-C) while thelowest was for the high yield + no fertilizer + short hauldistance scenario (T3-NF-D). The largest fraction of energyinput across all scenarios was the use of diesel fuels, 48–77 %of which was utilized in the delivery of willow chips from thefield gate to the end user. The remainder of the energy input inthe system is associated with willow biomass crop productionand harvesting. The importance of the input of diesel fuel fortransportation is reflected in the transportation distance sce-narios. The inputs for centralized biorefinery scenario with alonger haul distance were 1.7 to 1.9 times greater than thecorresponding decentralized scenarios regardless of other in-puts. Harvesting operations accounted for the greatest propor-tion (approximately half) of energy inputs in the productionand harvesting of willow biomass crops. This is to be expectedfor two reasons. One is the size of the harvesting equipmentand the amount of power that is needed to cut and chip thewillow in a single pass. The harvester uses greater than 93 l offuel per hour, 3.6–9.4 times as much as any other piece ofequipment included in the base module. The second is thatharvesting is repeated multiple times during the life of thewillow crop. Harvesting occurs seven times while other oper-ations withmuch smaller equipment occur once (i.e., plowing)or a few times during the establishment process (i.e., herbicideapplication).

Increasing the yield of willow biomass crops reduced theenergy input per dry tonne in by 6.9 to 14.8 % depending onthe scenario. All of this change was associated with thewillow production part of the system since the energy inputrequired to move a dry tonne of biomass is not impacted bychanges in yield.

Removing fertilizer from the willow biomass productionsystem reduced energy inputs into the production system by31.5 % but had a smaller impact on the inputs into the entiresystem because this change had no effect on the energyinputs for transportation. The reduction in energy inputswas the same for both the low and high yield scenariosbecause changes in yield were not tied to fertilizer inputs inthis model. The recent research on willow biomass cropsacross multiple sites in the northeast has indicated that thereis no detectable change in yield when organic or inorganicfertilizer is added to willow biomass crops [26, 27].

Assuming energy content of 19.3 GJ per odt of willowchips [28], our results equate to an approximate net energyratio (also called energy return on investment) of between

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18.3:1 and 43.4:1 for willow biomass delivered to an end user.The net energy ratio was greatest for the decentralized scenar-ios since transportation of the biomass was the single greatestenergy input in the system. Higher yields increased the netenergy ratio by 7.4 to 17.4 % depending on the scenario.Removing fertilizer increased the net energy ratio by 10.9 to

24.3 % depending on the scenario. In both cases, larger per-centage increases were associated with the shorter haul dis-tance. The previous LCA of willow biomass crops in theregion showed that replacing manufactured inorganic N fertil-izer with organic waste streams could increase the net energyratio from 55:1 to as high as 80:1 [9] because these calculations

Fig. 2 Energy demand and greenhouse gas emissions from a life cycleassessment (LCA) model, short-rotation willow (Salix spp.) croppingsystem in New York, USA. System includes the production andharvesting of willow crops over seven 3-year rotations and delivery ofchips to end user under eight scenarios [AC = yields from all clones,

T3 = yields from top three clones, F/NF = fertilizer or no fertilizer, C =delivered to large centralized users (haul distance = 195 km, to end userand back), D = delivered to distributed facilities (haul distance = 71 km,to end user and back)]

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were done for willow chips delivered to the edge of the field. Inour model, the net energy ratio at the edge of the field rangedfrom 49.1 in the low yield (AC) scenario to 64.5 in the highyield (T3) scenario. Removing manufactured N fertilizer fromthe inputs increased these ratios to 71.7:1 and 94.1:1, respec-tively, an increase of approximately 46 %.

In their review, Djomo et al. [7] found that net energy ratioof willow crops ranged from 16:1 to 79:1 at the farm gate(before conversion). Our results indicate a higher net energyratio for delivered willow biomass in New York. Net energyratios were lower in a number of the European studiesbecause of the inclusion of fencing to limit rabbit damageto new willow crops and in some cases the inclusion ofdrying of willow chips.

Greenhouse Gas Emissions

Among the eight scenarios, baseline greenhouse gas emis-sions (Fig. 2) ranged from 94 to 124 kg CO2 eq odt

−1 (∼4.7 to6.2 g CO2 eq MJ−1). However, carbon sequestration in thebelowground portion of the willow system provided a largecarbon sink that more than compensated for emissions acrossall eight scenarios. Including this carbon storage resulted innet emissions from −138.4 to −52.9 kg CO2 eq odt

−1 (∼ −6.9to −2.7 g CO2 eq MJ−1). Patterns followed those outlined forenergy demand. However, it is important to note that emis-sions in the high yield scenarios (T3) were 31.2 to 37.1 kgCO2 eq odt−1 higher than in the low yield (AC) scenarios.This somewhat counterintuitive result is explained by thefact that our model did not increase the amount of below-ground sequestration in proportion to increased yield be-cause data on this relationship is lacking. In other words,the same amount of belowground carbon sequestration wasspread among a greater tonnage of willow chips in the finaloutput. In choosing this approach, we made the tacit assump-tion that higher yielding crops had proportionately greatershoot/root ratios than lower yielding crops—a conservativeassumption. The limited data available indicate that theshoot/root ratio of 5-, 14-, and 19-year-old willow were notsignificantly different and that the lower shoot/root ratio in12-year-old willow was probably due to differences in siteconditions [24]. It would also have been possible to act underthe assumption that higher yielding crops have a proportion-ately larger belowground component (and similar shoot/rootratio) than lower yielding crops. Such an assumption wouldhave further reduced the carbon emissions profile of thehigher yield scenarios.

Djomo et al. [7] found that greenhouse gas emissions inwillow systems ranged from 0.7 to 10.6 g CO2 eq MJ−1. Netgreenhouse gas emissions in our study were lower than therange of studies reviewed because of our inclusion of below-ground biomass data, which was not included in the majorityof the studies reviewed. Furthermore, the few studies that did

include belowground sequestration used much lower ratesthan were included in our study (−2.7 to −4.7 g CO2 eq-MJ−1). When sequestration is not included, the greenhousegas emissions values from all eight scenarios in this studyfall within the range found by Djomo et al. [7].

Contributions from the Nursery Module

The impacts of producing willow cuttings at the nurserywere quite small. In order to produce 15,300 willow cuttings(enough to plant one hectare), the nursery required 356.5 MJof energy and emitted 28.4 kg CO2 eq. The largest compo-nents within the nursery module were cold storage(266.7 MJ, 22.4 kg CO2 eq), harvesting (65.0 MJ, 4.6 kgCO2 eq), and use of urea fertilizer (17.4 MJ, 0.9 kg CO2 eq).When normalized per tonne of final product, however, im-pacts of cutting production were negligible. Only 68.0 and51.8 cuttings were needed per tonne of harvested willowchips in the low yield (15,300 cuttings ha−1/225.1 odt ha−1)and high yield (15,300 cuttings ha−1/295.5 odt ha−1) scenar-ios, respectively. These inputs added only 1.6 MJ odt−1 and0.1 kg CO2 eq odt−1 to the baseline scenario (AC-F-C),approximately 0.15 % of total impacts.

Understanding Uncertainty

Johnson et al. [15] describe the importance of explicitlyincorporating uncertainty into LCA analysis of biofuels andbioenergy systems, as a means of better understanding thefull range of possible outcomes. The authors categorizeuncertainty into three types: data uncertainty, model uncer-tainty, and scenario uncertainty. Our analysis illustrates anumber of salient examples of all three forms of uncertainty.

Data uncertainty, the first type of uncertainty, includesuncertainty associated with the underlying data used to pa-rameterize the model. It includes both uncertainty, in whichparameter values and probability distributions are notknown, and variance, in which quantified measures of vari-ability and probability distributions are known. Stochasticsimulation (such as the uncertainty analysis functionality inSimaPro 7) is recommended as a means of addressing datauncertainty. In our uncertainty analysis of the willow system,we found that incorporating the variance observed withinwillow yield, leaf decay, and belowground carbon seques-tration resulted in large variability in system-wide perfor-mance (Table 2, Fig. 3). The standard deviation of the modeloutputs ranged from 12.1 to 43.1 MJ odt−1 for energy de-mand and from 14.7 to 23.1 kg CO2 eq odt

−1 for greenhousegas emissions based on the data uncertainty captured by thevariance of just these three variables. Figure 3 shows thatenergy demand in the four scenarios using the shorter hauldistance (decentralized refinery model) all fell within twostandard deviations of each other; the same is true of the four

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scenarios using the longer haul distance (centralized refinerymodel). Net greenhouse gas emissions in all scenarios fellwithin 2 standard deviations of each other, with the excep-tion of the highest ranking (T3-F-C) and the lowest ranking(AC-NF-D) scenarios. These results show the importance ofbiological variability in driving the performance of the wil-low system. A better understanding of the influence of ge-ography, climate, soils, and other site factors on biologicalfunctioning of the system will be helpful in estimating sys-tem performance at a given location with greater accuracy.

The second type of uncertainty, model uncertainty, is de-fined as the uncertainty associated with the structure of themodel, with the stages, processes, and components that areincluded. Much model uncertainty is derived from the choiceof where to set finite model boundaries, particularly whenthere is interest in extrapolating model results to a largerspatial or temporal context. In the current investigation, forexample, we deliberately elected to place boundaries aroundthe production and delivery of the raw feedstock. This resultsin considerable uncertainty surrounding the fate of the systemafter the willow is harvested and delivered.

Land use change in its several forms is another importantsource of model uncertainty in our analysis. By choosing toconstruct an attributional model, we ignored the possibilityof emissions of greenhouse gases associated with market-driven indirect land use change outside of the spatial andtemporal boundaries of the model. This was deliberate, as theestablishment of willow crops has thus far focused on mar-ginal lands and currently covers only trivial acreages. Even

for ethanol produced from corn, the impact associated withindirect land use is highly uncertain. Early estimates ofindirect land use were in the 74 to 177 kg CO2 eq MJ−1

[29], but more recent estimates have reduced these values by70–85 % [30]. These values are based on modeling simula-tions and are subject to high levels of uncertainty, despite thefact that corn production and use is fairly well understood.As deployment of willow crops proceeds, however, it ispossible that willow will be grown on larger acreages andmore productive lands. If this occurs, it may contribute toland clearing and carbon emissions elsewhere, but the link-ages between the expansion of willow crops and an impact inother locations are not established at this point in time.Inclusion of these types of values in this assessment wouldbe beyond the boundary set for this study and would bebased on speculation.

Another source of model uncertainty associated with landuse change is the uncertainty regarding direct land usechange outside of the temporal boundaries of the model. Inour model, for example, a source of uncertainty is that arisingfrom greenhouse gas emissions associated with land clear-ing. Our model begins at the first stages of crop establish-ment, just before the mowing of existing vegetation. Weincluded the diesel fuel (and associated emissions) associat-ed with the use of the brush mower, but our use of the data ofPacaldo et al. [24] as a proxy for carbon sequestration as-sumes negligible standing biomass prior to willow establish-ment. We made this assumption for two reasons: because thevegetation at most of our research sites prior to planting wasgenerally sparse weeds and because we had no data on thesize of this carbon pool that we felt was generalizable acrosssites. We also assumed no change in soil carbon across thecrop cycle based on assessments that have been done in theregion under willow [25, 31]. However, if either of theseassumptions is false, carbon losses during establishmentyears could partially cancel out the carbon sequestered inthe belowground portion of the system over the life cycle ofthe crop. Nikiema et al. [32] found that carbon losses in theestablishment year were approximately equal to 7.4 and11.6 Mg CO2 eq ha−1, respectively, for willow and poplarplantations established in pastureland (under the assumptionthat roots contributed 50 % of soil CO2 flux). Similarly,Arevalo et al. [33] found that carbon losses after convertingcanola fields (Brassica napus L.) to willow plantationsamounted to 5.2 Mg CO2 eq ha−1 year−1 for the first 2 yearsafter establishment (nonsignificant). However, the accumu-lation of above- and belowground C in these young hybridpoplar crops in this study in northern Alberta was muchlower than what has been measured in young willow standsin our region [24]. This difference in production wouldpartially account for the losses reported by Arevalo et al.[33]. In our analysis, assuming a loss of 5 to 10 Mg CO2

eq ha−1 in the establishment year would cancel out

Table 2 Energy demand and greenhouse gas emissions from a lifecycle assessment (LCA) model of a short-rotation willow (Salix spp.)cropping system in New York, USA. Energy demand and greenhousegas emissions are associated with the production and harvesting ofwillow biomass and delivery of wood chips to end user under eightscenarios [AC = yields from all clones, T3 = yields from top threeclones, F/NF = fertilizer or no fertilizer, C = delivered to large central-ized users (haul distance = 195 km, to end user and back), D = deliveredto distributed facilities (haul distance = 71 km, to end user and back)].Uncertainty analysis based on variability associated with willow yield,belowground carbon storage, and litterfall/leaf nitrogen content and runover 1,000 runs

Cumulative energy demand(MJ odt−1)

Net greenhouse gas emissions(kg CO2 eq odt−1)

Scenario Mean Median SD Mean Median SD

AC-F-C 1,055.2 1,049.8 42.9 −86.5 −85.2 21.1

AC-F-D 634.0 630.2 43.1 −115.0 −114.0 21.5

AC-NF-C 929.5 927.6 21.7 −109.6 −108.8 23.1

AC-NF-D 510.4 507.9 21.6 −138.3 −137.6 22.5

T3-F-C 959.7 956.8 28.8 −52.7 −51.9 14.7

T3-F-D 539.1 536.2 27.9 −83.1 −83.3 14.8

T3-NF-C 864.8 864.1 12.1 −72.0 −71.9 15.6

T3-NF-D 446.5 445.7 12.3 −101.5 −101.2 16.0

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approximately 11–22 % of the belowground carbon seques-tered during the life cycle of the crop.

Similarly, we ended the model immediately after remov-ing existing stumps at the end of the 21-year crop cycle,without attempting to determine if and how rapidly thecarbon stored in belowground woody material decomposesand leaves the system. Without this information, it is not

possible to extrapolate beyond a single crop cycle to under-stand the cumulative impacts of multiple crop cycles. Initialresearch [Pacaldo et al., Soil CO2 effluxes, temporal andspatial variations, and root respiration in shrub willow bio-mass crops fields along a 19-year chronosequence as affect-ed by regrowth and removal plots (in review)] has shown thattotal soil carbon efflux does not significantly differ between

Fig. 3 Mean and variability (2 standard deviations) of energy demandand greenhouse gas emissions based on uncertainty analysis from a lifecycle assessment (LCA) model of a short-rotation willow (Salix spp.)cropping system in New York, USA. Results are associated with theproduction and harvesting of willow and delivery of chips to end user

under eight scenarios [AC = yields from all clones, T3 = yields from topthree clones, F/NF = fertilizer or no fertilizer, C = delivered to largecentralized users (haul distance = 195 km, to end user and back), D =delivered to distributed facilities (haul distance = 71 km, to end user andback)]; 1,000 model runs

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plots in which the crop is terminated and plots in whichwillow stools are allowed to regrow (over 2 years),suggesting that rapid loss of belowground carbon pools,particularly the coarse roots and stool, after crop terminationdoes not occur. Expanding the temporal boundaries of ouranalysis in both directions to include both the carbon lostduring the initial field clearing, as well as to include thedynamics of belowground carbon over multiple crop cycles,would be necessary to build a model for the willow crop thatfully incorporates the change from old field to willow pro-duction across multiple crop cycles.

Another source of model uncertainty originating from ourchoice of model boundaries and affecting our estimates ofgreenhouse gas emissions is that derived from our choice toend our analysis at the point of delivery of willow chips. Byrestricting the model to the production and delivery of bio-mass, the model excludes the carbon benefits of using thebiomass to displace fossil fuel energy sources. This was donedeliberately, as the current study was done in conjunction witha larger endeavor (spearheaded by CORRIM) to create a seriesof modular LCA models in which separate feedstock produc-tion modules could be combined with a number of differentconversion models. These other models internalize the con-version of feedstocks into final energy products, such as byprocessing the biomass to ethanol to displace gasoline or byburning the biomass in boilers to produce heat to replacenatural gas or fuel oil. For example, one analysis [17] madeuse of an early version of our LCA model to model theproduction of ethanol willow feedstocks by biochemical pro-cessing (∼40 % efficiency). This model estimated a 40 %reduction in fossil carbon emissions due to the displacementof gasoline per unit of carbon in the willow feedstock, thusproducing a substantial reduction in carbon emissions witheach harvest. Similarly, an analysis of burning wood biomassin a boiler (∼85% efficiency) to displace natural gas emissions[34] resulted in a 70 % reduction in fossil emissions. Produc-ing ethanol, a transportation fuel, requires much more pro-cessing energy than directly burning the biomass for heat,hence the lower efficiency in using wood fuels to displacefossil transportation fuels. However displacing fossil transpor-tation fuels may be the higher priority as it contributes directlyto energy independence with greater domestic economic ben-efits. Using the range of willow yields included in this study(225 to 295 odt ha−1 21-year rotation−1), we can estimate thatproducing ethanol to displace gasoline results in a sustainedreduction of 7.9 to 10.4 t CO2 eq ha

−1 year−1 (165 to 218 t CO2

eq ha−1 21-year cropping cycle−1). Using this feedstock todisplace natural gas could result in a sustained reduction of13.8 to 18.2 t CO2 eq ha

−1 year−1 (290 to 382 t CO2 eq ha−1 21-

year cropping cycle−1).A final, important source of model uncertainty comes

from our decision to exclude the contribution of capitalgoods. Due to the perennial nature of shrub willow biomass

crops, the 3-year rotation and the low inputs needed for thissystem, the burdens associated with the equipment that isused, are very small. For example, if a single hectare ofwillow is harvested seven times over its life at a rate of0.8 ha h−1 then a harvester will be on a given hectare for8.75 h over the entire life of the crop and during thoseoperations will process more than 225 odt of willow bio-mass. If the anticipated life of the harvester is 5,000 h ofoperational time, the time spent on a single hectare representsonly 0.175 % of its operational lifespan. On an odt basis, theproportion is much smaller. Consequently, only 0.0008 %(0.175 %/225 odt) of the resources necessary to manufacturethe harvester (steel, rubber, electricity, etc.) would be allo-cated to the biomass system. If we approximate the harvesterby a generic piece of agricultural machinery (EcoInvent,“Tractor, production/CH/I”) weighing approximately12,700 kg, we can estimate that it would require1,692,741.7 MJ to produce, 13.2 MJ of which could beallocated per odt of willow biomass.

Johnson et al. [15] define the third type of uncertainty,scenario uncertainty, as the uncertainty associated with thechoice of which particular scenarios are tested in the analy-sis. Testing multiple scenarios provides a better understand-ing of the range of uncertainty than testing only a single,default scenario. Most LCAs are complex models, however,with a great number of parameters, and an almost infinitenumber of possible scenarios to test. Are the scenarios thatare chosen sensible? Do they cover the full range of likelyoutcomes? Do the scenarios selected represent the parame-ters over which system actors can demonstrate control? Weselected our scenarios based on three parameters that we feltwere important that reflected realistic possibilities, and overwhich policy actions and individual choice could exert in-fluence—haul distance, crop yield, and the choice of whetheror not to fertilize. Among these eight scenarios, the increasefrom the lowest to the highest ranking scenario was 136 %for cumulative energy demand and 62 % for net greenhousegas emissions. This range estimates the uncertainty inherentin not knowing what choices individual willow producerswill make or how future willow systems will be structuredand managed.

An additional scenario that could add value to this analysiswould be the inclusion of additional herbicide treatments.Depending on site conditions and local weed pressure, theinclusion of a pre-emergent herbicide during site preparationas well as additional applications of glyphosphate or otherherbicides may be needed [14]. Based on our analysis, a singleadditional treatment of glyphosphate results in 3.8MJ odt−1 ofadditional energy and results in the emission of an additional0.2 kg CO2 eq odt

−1. Similar values can be found by substitut-ing 2.5 kg ai of a generic herbicide process within theEcoInvent database (Herbicides, at regional storehouse/RERU), 3.6 MJ odt−1 and 0.2 kg CO2 eq odt−1.

Bioenerg. Res.

One scenario that would probably affect model resultsonly slightly would be one in which different planting den-sities were assumed. Analysis of the relationship betweenyield and density [35] has shown that per hectare productiondoes not increase significantly between an initial plantingdensity of 8,749 and 17,498 plants ha−1, suggesting thatgrowing space is already being fully utilized at the currentlyrecommended planting density of 15,300 plants ha−1 and thatincreasing planting density would not increase yield. Reduc-ing density to as little as 8,749 plants ha−1, on the other hand,would likely have no effect on overall yield and wouldtherefore reduce the number of cuttings needed per unit offinal product. The relatively tiny contribution of the nurserymodule to the entire process, however, means that perfor-mance improvements would be very slight. For example,reducing initial planting density from 15,300 to 8,749 plantsha−1 (43 %) would only reduce inputs by 1.07 MJ odt−1 and0.07 kg CO2 eq odt−1 in the baseline scenario (AC-F-C).

Conclusions

We conclude from this analysis that short-rotation willowcontinues to be a promising energy source in central NewYork when considered from an energy and greenhouse gasperspective. Furthermore, our research indicates that a re-duction in diesel use, whether by more efficient harvestingactivities or through promotion of decentralized biorefineriesthat result in shorter haul distances, remains the most effi-cient means of improving system-wide performance. Interms of the crop production system, yield has a significantimpact on the energy and greenhouse gas balances. Finally,better understanding and incorporation of all forms of uncer-tainty into this and other LCA models will provide a betterunderstanding of the full range of performance possible fromthis system. The use of Monte Carlo-style analysis coupledwith measures of variance around model parameters is aninformative means of addressing data uncertainty. Deliber-ately and explicitly quantifying and including parametervariance, especially that associated with the biological por-tions of the system, is necessary to produce reliable andmeaningful estimates of the greenhouse gas emissions andenergy performance of willow crops. Our analysis includes agood deal of model uncertainty, especially pertaining togreenhouse gas emissions from the biological portions ofthe system. Refining and expanding the spatial and temporalboundaries of the model will help us to understand thegreenhouse gas ramifications of using willow biomass inplace of fossil fuels over longer periods and over a greaterspatial extent. Finally, a more extensive use of scenarioanalysis would reduce overall scenario uncertainty and im-prove the functionality of the LCA model as a tool to help usask and answer a wider range of specific questions with

specific relevance to energy and climate policy. The inclu-sion of all forms of uncertainty in LCA not only allows us tobetter understand and interpret model outputs but it will alsohelp us to identify and prioritize key portions of the systemwhere additional empirical research is warranted.

Acknowledgements Support for this project was provided by theU.S. Department of Energy Bioenergy Technology Office. Data usedto develop the model were collected and analyzed with support from theNew York State Energy Research and Development Authority(NYSERDA), United States Department of Agriculture's Agricultureand Food Research Initiative (USDA AFRI) program.

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