impacts of variability in cellulosic biomass yields on energy security

Impacts of Variability in Cellulosic Biomass Yields on Energy SecurityKimberley A. Mullins,†,‡,⊥ H. Scott Matthews,†,‡ W. Michael Griffin,*,†,§ and Robert Anex∥

†Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States‡Civil and Environmental Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States§Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States∥Biological Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States

*S Supporting Information

ABSTRACT: The practice of modeling biomass yields on the basis of deterministic pointvalues aggregated over space and time obscures important risks associated with large-scalebiofuel use, particularly risks related to drought-induced yield reductions that may becomeincreasingly frequent under a changing climate. Using switchgrass as a case study, thiswork quantifies the variability in expected yields over time and space through switchgrassgrowth modeling under historical and simulated future weather. The predictedswitchgrass yields across the United States range from about 12 to 19 Mg/ha, and the80% confidence intervals range from 20 to 60% of the mean. Average yields are predictedto decrease with increased temperatures and weather variability induced by climatechange. Feedstock yield variability needs to be a central part of modeling to ensure thatpolicy makers acknowledge risks to energy supplies and develop strategies or contingencyplans that mitigate those risks.

■ INTRODUCTION

The federal Energy Independence and Security Act of 2007(EISA) addresses the United States’ undesired dependence onforeign sources of oil.1 The Renewable Fuel Standard (RFS)specified in this legislation is a liquid biofuel mandate designedin part to address energy security. Energy security can beapproached from various perspectives, including fossil energysupply diversity, demand resiliency, and the impact of oil supplyfluctuations on U.S. gross domestic product.2−6 Researchersand policy makers who advocate the use of biofuels as a way todecrease oil consumption and reduce the U.S. dependence onunstable foreign oil sources take as a given that a shift towardbiofuels will reduce the risks associated with fuel availability orhave simply chosen not to specifically define the term “energysecurity” in their context (e.g., see refs 4, 7, and 8). Discussionsin the literature do not sufficiently acknowledge that a changein the domestic energy portfolio to include biomass does notnecessarily translate to a reduced supply risk. An increase inrenewable fuels in the supply portfolio brings different supplyrisks that may or may not be preferable to the risks associatedwith fossil fuel supply (e.g., supply disruptions due to fossil fuelinfrastructure damage). Biomass availability is subject to naturaltemporal variation due to recurring periods of weather-inducedcrop water and temperature stress, and the resulting biomassyield fluctuations can create chronic bioenergy feedstockshortages. Of the two stresses, a lack of water is the moreimportant one9 and is particularly relevant as cellulosic cropsare usually assumed to be produced though dryland (rain-fed)farming.Drought is usually regional and yet can have national

significance. Understanding the potential to supply a significant

amount of biomass-derived transportation fuel requires notonly knowing the areas most likely to produce biomass andestimates of the yield under ideal conditions but also how theyield is impacted by weather-related risks, which vary across thecountry. In this study, switchgrass is used as a case study for theexpected variability in cellulosic crop production. In thisanalysis, the United States is disaggregated to the state level, apolitically relevant regional distinction, and weather data areused to estimate biofuel feedstock yields. Yield variability isassessed using historic and simulated future weather data andthe switchgrass yield model developed by Grassini et al.10

Nair and colleagues have presented a review of manydifferent crop models,11 highlighting that while model resultsare improving, there is still a need for better input data andbetter model calibration. Therefore, the overall yield trendsobserved in this article are reliable, but the specific yield valuesshould not be taken as precise predictions. It is important tokeep in mind that the annual yield time series output by thismodel vary as a result of changes in precipitation andtemperature (which drive drought conditions) but not otherfactors such as an overabundance of local water or acuteweather phenomena (e.g., hail) because the model is notequipped to deal with these situations (e.g., there is no “hail”module). The variability over time would be greater if theseother causes of crop loss were included, and this could be afruitful line of investigation for future work.

Received: February 20, 2014Revised: May 31, 2014Accepted: June 11, 2014Published: June 18, 2014

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■ DATA AND METHODSSwitchgrass Yield Model. The switchgrass growth model

built for this study was defined by Grassini et al.10 and waspublished with the intention of generating annual crop yieldvalues that could be used to inform energy policy models anddiscussions. The switchgrass growth model is composed of fiveinteracting modules and operates at a daily time step incalculating biomass production. The key model parameters aresummarized in Table 1. Inputs include daily rainfall; mean,

high, and low temperatures; solar insolation; and informationabout soil characteristics and crop growth performance. Thirtyyears of daily data are used as the historical meteorological dataset. Data from one city in an agricultural area from each stateare taken, so one location is assumed to be representative of thewhole state (as historical weather records are available for onlyone to a handful of locations in each state). The city selectedfrom each state is listed in the Supporting Information (SI).The historical data are taken from the database assembled bythe United States Department of Agriculture (USDA) for usein their weather generation model, named GEM (generation ofweather elements for multiple applications).12 These datainclude daily observations for maximum and minimumtemperature, precipitation, and solar radiation, and datacovering 1961 to 1990 are available. Mean daily temperaturesare calculated as averages of the high and low values. Soilcharacteristics for each state, specifically the available soil water-holding capacity, are calculated using data from the USDA’sSoil Survey Geographical Database (SSURGO).13 The availablewater-holding capacities for the top and bottom soil layers inthis study are calculated using the 200 and 1500 mm depthlayers in SSURGO, respectively, and averaging all of the datapoints from within each state at these depths. A complete tableof values can be found in the SI.When daily biomass accumulation values (output in g/m2)

are summed over an entire growing season, the result is anannual crop yield value. When many years are modeled,variability in the yield over time can be assessed. The Grassinimodel is not publically available as a stand-alone program or ascode, so a model was built in MATLAB (version R2012b).14

The five interacting modules mentioned previously are definedas follows:

Module 1: Crop Development Index. The crop develop-ment index is a dimensionless index that increases from 0 to 1and is affected by the daily mean temperature in relation tocultivar-specific temperature parameters, including the optimaltemperature and the temperatures above or below which nodevelopment occurs. Daily, incremental development is trackedand taken as input to various other modules.

Module 2: Leaf Area Index (LAI) Expansion. The LAI is adimensionless index that describes the fullness of the canopy. Itaffects rainfall partitioning in the soil water balance module andthe rate of conversion of solar radiation to biomass (i.e., cropgrowth). The LAI value depends on the crop developmentindex and a water stress factor.

Module 3: Soil Water Balance. The soil water balancemodel is the most complex of the five modules. There are fivesubmodules within this function. Rainfall can be intercepted bythe canopy (a function of the LAI) and not be available to thesoil layer. Heavy rainfall can result in water lost to surfacerunoff: the top layer can hold 110% of capacity right afterrainfall, and the excess becomes runoff. Water evaporates fromthe upper layer of soil in a quantity that depends ontemperature, solar radiation intensity, LAI, and previous waterconcentration in the upper soil layer. Water transpires throughthe plant during growth in a quantity determined by theexisting biomass/development stage and temperature. Waterfor transpiration can come from either the top or bottom soillayer. Finally, excess water that may be in the top soil layer atthe end of a day filters down to the bottom layer so that the toplayer is not oversaturated. Water flow through these layers isillustrated in Figures S2 and S3 in the SI.

Module 4: Crop Growth and Biomass Production. Themass of new biomass produced each day is a function of theamount of photosynthetically active radiation intercepted bythe canopy (which is a function of the LAI), the rate at whichthe plant can convert radiation energy into biomass, and waterand temperature stress factors. The growth rate parameter usedis for the Blackwell switchgrass cultivar, which shows midrangeperformance.

Module 5: Water and Temperature Stress Factors. Cropgrowth is retarded by insufficient water, or insufficiently hightemperatures. Specific water stress factors are applied to theLAI function and the biomass growth function, and a specifictemperature stress factor is applied to the biomass growthfunction.Supplemental calculation details that are necessary to

complete the model but are not explicitly documented in thestudy by Grassini et al.10 or its references are taken from apublication on crop evapotranspiration published by the Foodand Agriculture Organization of the United Nations (FAO).15

These include the necessary formulation and coefficientdefinitions for the Priestley−Taylor equation to calculate thedaily reference evapotranspiration and a formula for calculatingsaturated vapor pressure.A key parameter assumed at the beginning of the model run

is the fraction of available water-holding capacity filled at thedate of growth initiation (FAWHCAGI), as an initial abundanceof soil water can make up for some lack of rain during thegrowing season and a deficit of soil water can make thatsituation worse. This fraction varies from 0 to 1. TheFAWHCAGI is assumed to decrease as the standardprecipitation index (SPI) decreases. The SPI as a droughtindex is discussed by Guttman.16 A FAWHCAGI of 0.6corresponds to the median precipitation scenario (the SPI

Table 1. Summary of Key Input to the Switchgrass GrowthModela

variable magnitude

minimum growth temperature 13 °Cmaximum growth temperature 42 °Coptimal growth temperature 33 °Cmaximum LAI 10radiation use efficiency 4.7 g/MJsoil layer 1 (SL1) depth 150 mmsoil layer 2 (SL2) depth* 1450 mmsoil available water-holding capacity, SL1 0.10 to 0.18soil available water-holding capacity, SL2 0.10 to 0.24extinction coefficient for incoming solar radiation 0.48fraction of available water-holding capacity at day of growthinitiation, SL1*

0.6

fraction of available water-holding capacity at day of growthinitiation, SL2*

0.6

aTaken from Grassini et al.10 and modified if necessary (indicated by*) in order to better replicate empirical yield data.

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median value is 0), and this is taken as the default value for themodel runs in Grassini et al.10 It seems unlikely that even in asevere drought scenario the top two meters of soil would becompletely dry, so the model was run assuming no precipitationat all over various FAWHCAGI values. For a range ofFAWHCAGI values, the value for the lower soil layer at thetime of growth does not drop below 0.1, while the top layer isconstantly exhausted of moisture. As a result of thisinformation, the FAWHCAGI is assumed to decrease linearlyfrom 0.6 to 0.1 with a decrease in precipitation scenariolikelihood and to increase from 0.6 to 1 with an increase inprecipitation scenario likelihood.The validation of the model is presented in the SI.Weather Generation Model. To assess the impacts of a

changing climate on switchgrass yields, this study simulatedweather data using historical data for which certain parametershave been modified so that the output weather data arerepresentative of a changed, future climate. As stated previously,this exercise is intended to show changing trends in yields, notto suggest exact magnitudes for the changes in variability orexpected values for yields in any region. Weather simulationprograms have been tools for modelers for the past 30 years.Many used for applications that requite daily data, such as thiscrop yield model, are built upon the stochastic simulationmethod for temperature, precipitation, and solar radiationsuggested by C. W. Richardson, as described in refs 17 and 18and more fully defined as the WGEN model by Richardson andWright in ref 19.In most daily weather generation models, temperature and

solar radiation data are conditioned on precipitation.Precipitation is modeled in two parts: whether there is anyprecipitation (dry or wet conditions; a binary random variable)and, when there is precipitation, how much falls. In this model,the wet or dry state is simulated as a Markov process; the statusof the current day depends on the status of the previous day(s).In a first-order Markov model, precipitation status isconditioned only on the previous day; if it was dry yesterday,the likelihood that it will be dry today is P1 and the likelihoodthat it will be wet is (1 − P1), where P1 is estimated usinghistorical data. Higher-order models have been used by morerecent studies to more accurately reproduce long wet or dryspells (see the excellent discussion in ref 20). The quantity ofprecipitation on wet days is generally modeled using either anexponential function or a two-parameter gamma function, asboth heavily weigh small amounts of precipitation but skewpositively to allow for the low probability of much higherquantities of precipitation. With the precipitation time seriesgenerated, temperature and solar radiation data are thenmodeled using a first-order linear autoregressive model. Meansand standard deviations for each temperature−time series,conditioned on precipitation, are calculated over some relevanttime period (daily, weekly, monthly) across all years ofhistorical data (i.e., two means for each two-week period arecalculated: {μdry, σdry}week i and {μwet, σwet}week i). Time series ofresiduals are generated for maximum temperature, minimumtemperature, and solar radiation using the calculated standarddeviations. These residual values are modified by a laggedcorrelation matrix (modified on the basis of what temperaturesand solar radiation happened the previous day) to which asimultaneous correlation matrix of normally distributed errors(modified on the basis of what the other meteorological valuesfor the same day were) is added. Finally, the means andstandard deviations calculated from the initial, historical data

are added to these modified temperature and radiationresiduals.The simulated data for this analysis are generated using

methods for precipitation and temperature from Chen andcolleagues,21 who implemented the Richardson model asdescribed above (using a second-order Markov chain) andincluded a smoothing algorithm to reduce jaggedness in themean temperature profiles. The jaggedness arises because thetemperature means are calculated for two-week time periods, soeach year is characterized by a set of 26 max/min temperatures.Without any modifications, there tends to be a jump betweenthe temperature on the last day of one period and the first dayof the next period. This particular implementation of theRichardson model was used because Chen and colleagues havegraciously made the MATLAB code available online (the URLis listed in the SI). Means and standard deviations of solarradiation data are calculated using dry days, and a scaling factoris used to simulate radiation for wet days, following methodsdescribed by Zhang et al.22 In summary, the simulated weatherdata employed as input to the switchgrass yield model use thefollowing:

• a second-order Markov chain to generate precipitationoccurrence patterns;

• a two-parameter gamma distribution to generateprecipitation magnitude data, smoothed to reducejumps between successive two-week periods;

• a conditional relationship between the maximum andminimum temperatures;

• a scaling factor of 0.5 to scale the mean daily solarradiation on dry days for wet days.

Given this weather simulation program from Chen andcolleagues, certain parameters in the model are modified inorder to produce a climate that is changed from the historicalclimate. The following scenarios are examined, with datapresented in the form of box plots to illustrate key statistics:

1. Base-case weather, simulated using historical data(sources and locations described previously) andunmodified parameters (so that yields from simulated,modified climate data are not compared to historicaldata).

2. A higher-temperature case based on a scenario used in afuture U.S. drought study.23 In the weather generationmodel, mean values for Tmin and Tmax time series (μtemp +2) are modified as suggested in the study by Roy et al.23

3. A case with higher variability in temperature based onresults shown in a study of temperature anomalies byHansen et al.24 This is implemented though modifiedstandard deviation values for Tmin and Tmax (1.2σtemp and1.3σtemp) as shown in the Hansen study.

4. A scenario with higher dry- and wet-spell lengths. Ratherthan modification of precipitation amounts, which varyregionally (see ref 25), the impacts of increased dry- orwet-spell length are examined. This is accomplished bymodifying the transition probabilities for precipitationoccurrence in the Markov matrices, making it 10%(relative, not percentage points) more likely that therewill be longer dry spells or wet spells. This is done notspecifically following a procedure undertaken in anotherstudy or using empirical data found elsewhere but as away to compare the yield sensitivity to spell lengthalongside temperature modifications.



By using the three modifications in some combination witheach other, six scenarios (in addition to the base-casesimulation results) are compared in the following section.The results ought only to be compared among themselves inorder to get a sense of the sensitivity of crop yields to differentchanges in climate conditions and to see whether there aregeneral trends to anticipate.

■ RESULTS

Results using historical weather data for the 19 states forecast tocontribute at least 1% of U.S. switchgrass production by massare presented in Figure 1A (results for all of the continentalstates are presented the SI). The 19 states are ordered on thebasis of how much crop production area is anticipated withinthe state and colored on the basis of climate region. Mostswitchgrass is expected to be grown in the South, Southeast,and Central regions of the United States (identified in thefigure).26 The yield model results show mean yield valuesacross the states ranging from about 12 to 19 Mg/ha. There isalso substantial variability within each state; the range of yieldsrepresenting the 80% confidence intervals (the 10th to 90thpercentile range) plotted in Figure 1 cover at least 8 Mg/ha,and some span a range of more than 17 Mg/ha. For meanvalues in the range of 12 to 19 Mg/ha, this translates to avariability of 20 to 60% of the mean. A complete ordered list ofstates in Figure 1A is included in Table S1 in the SI. Themaximum yields from this model are higher than those fromanother popular switchgrass yield model, Environmental PolicyIntegrated Climate (EPIC),27 because growth in the modelfrom Grassini et al.10 is not limited by nutrient availability; iftemperature conditions are optimal and water is available,biomass is produced at optimal rates, leading to high yields.These high yields are close to those reported from switchgrasstest plots, some few of which had annual yields greater than 30Mg/ha.28

On the basis of Figure 1B,C, which presents state yielddistributions ordered by increasing variability and decreasingmean value, respectively, the Central states (which show highyields and comparatively low variability) are well-represented in

terms of crop production area, as shown in Figure 1A, whereasother high-yield, low-variability regions (such as the Southeast)are not forecast to contribute as much to the switchgrasssupply. Adding crop yield variability to the list of metrics bywhich cellulosic crop regions are evaluated might reorder thestates in Figure 1A and could lead to a more stable expectedenergy crop yield.At present, the use of cellulosic feedstocks such as

switchgrass is mandated at the national level through theRFS, so targets are met through an aggregated nationalcellulosic ethanol production volume. From this perspective,then, the national aggregate yield is of interest. Figure 2

presents the national aggregate switchgrass yield given historicweather data as well as curves showing the maximum andminimum yields from contributing states for comparison.Despite aggregation over many states, the fluctuation of thetotal yield over time is not so different from the yield variabilityin the individual states; the expected national yield over 30years is 14.6 Mg/ha, and the 80% confidence interval of annual

Figure 1. Yield model variability results for the 19 states that are forecast to contribute at least 1% of national switchgrass production. Each dot is themean yield over 30 years in one state, and error bars are the 10th and 90th percentile yields. Colors correspond to NOAA climate regions. The statesare ordered by (A) decreasing crop production area, (B) increasing variance, and (C) decreasing mean.

Figure 2. National average yields, an aggregation of state-by-stateyields using the POLYSYS distribution of acreage. See Table S1 in theSI for the acreage per state.



yield values (11.2 to 17.8 Mg/ha) represents an uncertaintyrange of about 50% of the mean.The results for the base-case weather data (i.e., simulated

weather using unmodified parameters calculated from historicaldata) are plotted in the top panel of Figure 3, with those for the

modified climate scenarios shown in the six panels below. Thecentroid on each plot is shown relative to the centroid for thebase case. Detailed figures similar to Figure 3 in which the dataare labeled with the corresponding states are included in the SI.Increasing the standard deviation of temperature by 20%(“1.2sigma”) results in the smallest decrease in the mean yield.Increasing the standard deviation of temperature by 30%(“1.3sigma”) has a greater impact, reducing some of the highestmean yield values. Modifying the precipitation patterns byincreasing the likelihood of multiday wet or dry spells by 10%(“Precip.”) tends to decrease the mean yields as well, though italso produces the highest single-state average yield value, and ittends to increase the standard deviation. Increasing the meantemperature by 2 °C (“T+2”) also tends to lower the yield.When the temperature is lower and more variable (“1.3sigma, T+2”), the points show a slightly reduced range of mean valuesand a slightly wider range of standard deviation values, thoughthe centroid is not much changed from when either of thosescenarios is run individually. Finally, modifying the precip-itation patterns and increasing the expected temperatures(“Precip., T+2”) has the greatest effect on the mean andstandard deviation statistics; the overall mean yield (centroid yvalue) is about 20% lower than for the base case, and the pointsare increasingly concentrated around the mean of the yieldstandard deviation (centroid x value).An important component of historical yields that is not

maintained in yields estimated using simulated weather data isregional correlation. The meteorological variables generated in

the weather simulator do not represent the spatial correlationthat is a common feature of many droughts.29 Correlations inyield are derived using the switchgrass model results fromhistorical weather data, and the results for selected relevantstates are presented in the SI. Some neighboring statesdemonstrate significant correlation, though not all do. All butone of the statistically significant correlation statistics arepositive. The fact that most of the correlations are positive andare between about 0.4 and 0.8 suggests that there are not a lotof obvious opportunities to diversify planting locations in orderto reduce the likelihood that reduced switchgrass yield is likelyto occur regionally. This highlights the potential for wide-spread, simultaneous yield decreases and calls into question theability of the U.S. biomass industry to consistently meet RFSbiofuel targets.

■ DISCUSSIONThe results in Figure 3 suggest that as the climate warms andbecomes increasingly variable, switchgrass yields will tend todecrease in many states. Using historical average yield data inprojections without adjusting for these climate changes will leadto overprediction of yields, and this shows cause for concernwhen past yield data are assumed to be representative of futureyields in planning models. To ensure that sufficient biomassfeedstock is available to supply biofuel conversion facilities,either large amounts of biomass must be kept in storage oradditional acreage must be planted. Because of the low bulkdensity of biomass, very large scale multiyear storage is notpractical.30 Storage does play an important role in eliminatingnegative price spikes in agricultural markets,31 and the limitedability to store biomass and the need to meet productionvolume mandates will tend to lead to high price volatility(another facet of energy security).32 Additional acreage doesnot address production variability but could be a strategy toraise total production amounts throughout the years, therebyreducing the likelihood of simply not having enough biomassfor production facilities to operate at a profitable level. Ofcourse, dealing with surplus production in high-yield years is achallenge, particularly with little storage capacity. Modelscombining stochastic yields by state (or other region) couldbe used to ensure that national feedstock yields are sufficient tomeet EISA policy goals with some appropriate degree ofconfidence following the method outlined in ref 33.Consistency in dryland crop yields is likely to decrease in the

future, as a number of studies have reported that droughtseverity and frequency is predicted to increase under the mostlikely future climate scenarios (see refs 25 and 34 for twoexamples). Tied to this are expectations of increasing andincreasingly variable temperatures.24 Crop water stress willincrease as a result of both meteorological changes andincreases in water demand due to economic growth.23 Thiscould lead to chronic multiyear biomass shortages, which wouldbe very costly.Biomass energy forecasts are developed from models based

on temporally and spatially aggregated yields.35−37 Policyrecommendations informed by modelers’ predictions based onaverage productivity do not sufficiently address how policyshould accommodate yield variation such as seen in the severedrought of the 1987−1988 crop year or how the developingbiomass industry in, for example, Tennessee might deal withthe resulting 90% reduction from the mean. For decisions thathinge on biomass yield, the models that predict yields mustinclude stochastic analysis that accounts for expected changes in

Figure 3. Comparison of means and standard deviations for yields forall states for each of the seven simulated weather scenarios. The y-axisvalues are yields (Mg/ha), and the x-axis values are standarddeviations. Solid red dots are the centroids of the data. The hollowblue dots show the centroid of the base case, which has been plottedon the other scenarios for comparison. Each gray point represents oneof the 19 states.



not only the mean yield but also the yield variability. Aprobabilistic approach is central to assessing the consequencesof yield variability on model recommendations. Without anacknowledgment of variability, there is no basis upon which theemerging biofuel industry and biofuel policy makers can beginto consider and evaluate mitigation options and contingencyplans.

■ ASSOCIATED CONTENT*S Supporting InformationFurther details of model validation and detailed statistics onstate-by-state variability. This material is available free of chargevia the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATIONCorresponding Author*Phone: (412) 268-2299; fax: (412) 268-3757; e-mail:[email protected] Address⊥K.A.M: Department of Bioproducts and Biosystems Engineer-ing, University of Minnesota, St. Paul, MN 55108.NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSThe authors thank Chris Hendrikson for his assistance in studydesign and Ryan Noe for his assistance in extracting data fromSSURGO. Funding was provided by the Center for Climateand Energy Decision Making through a cooperative agreementbetween the National Science Foundation and Carnegie MellonUniversity (SES-0949710), the USDA (NIFA Grant 2013-67009-20377), and the US DOE (EERE Grant DE-EE0004397).

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