Environmental Limitation Mapping Of Potential Biomass Resources Across .

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GCB Bioenergy (2018), doi: 10.1111/gcbb.12496Environmental limitation mapping of potential biomassresources across the conterminous United StatesCHRISTOPHER DALY1, M I C H A E L D . H A L B L E I B 1 , D A V I D B . H A N N A W A Y 2 and3LAURENCE M. EATON1PRISM Climate Group, Northwest Alliance for Computational Science and Engineering, 2000 Kelley Engineering Center,Oregon State University, Corvallis, OR, USA, 2Department of Crop and Soil Science, Oregon State University, 125 CropScience Building, Corvallis, OR, USA, 3Bioenergy Resource and Engineering Systems Group, Environmental Sciences Division,Oak Ridge National Laboratory, PO BOX 2008 MS6036, Oak Ridge, TN 37831-6036, USAAbstractSeveral crops have recently been identified as potential dedicated bioenergy feedstocks for the productionof power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work hasbeen undertaken to characterize the spatial distribution of their long-term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a needfor this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun GrantRegional Feedstock Partnership (RFP), an approach to mapping these potential biomass resources wasdeveloped to take advantage of the informational synergy realized when bringing together coordinated fieldtrials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. Amodeling and mapping system called PRISM-ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long-term production patterns of a givencrop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, whereproductivity is determined by the most limiting of the factors addressed in submodels that simulate waterbalance, winter low-temperature response, summer high-temperature response, and soil pH, salinity, anddrainage. Yield maps are developed through linear regressions relating soil and climate attributes toreported yield data. The model was parameterized and validated using grain yield data for winter wheatand maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass,CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served aspotential production inputs to analyses comparing the viability of biomass crops under various economicscenarios. The modeling and parameterization framework can be expanded to include other biomass crops.Keywords: biomass crop, biomass production potential, biomass resource map, biomass resources, biomass sorghum, energycane, miscanthus, PRISM-ELM, Sun Grant, switchgrassReceived 13 February 2017; revised version received 27 July 2017 and accepted 30 August 2017IntroductionIn 2005, the US Department of Energy (USDOE)released its Billion Ton Study (updated in 2011 and2016), which envisioned an expansion of domesticbioenergy production to one billion tons per year as away to increase and diversify the nation’s energyresources (USDOE, 2005, 2011, 2016). Presently, the USbioeconomy consumes roughly one million tons per dayfor the generation of power, fuels, and chemicals fromCorrespondence: Christopher Daly, tel. 1 541 737 2531,fax 1 541 737 6609, e-mail: Chris.Daly@oregonstate.eduagricultural, forestry, and waste resources (USDOE,2016). To achieve a domestic billion-ton bioeconomy, anadditional 635 million tons per year of biomass must beproduced on an annual basis from US land resources.The near-term potential can be generated from agricultural and forestry residues and waste resources equal toapproximately 345 million tons per year. Traditionalagricultural crops such as wheat and maize provideresidues that can serve as sources of biomass; thesecrops have long production histories and rich knowledge bases with regard to physiology, production, andspatial distribution. To fill the supply deficit, dedicatedbioenergy crops have become a subject of nationalfocus. 2017 The Authors. GCB Bioenergy Published by John Wiley & Sons Ltd.This is an open access article under the terms of the Creative Commons Attribution License,which permits use, distribution and reproduction in any medium, provided the original work is properly cited.1

2 C . D A L Y et al.Several crops have been identified as potential dedicated bioenergy crops for the production of power,fuels, and bioproducts. Despite many crops being identified as potential feedstocks as early as the 1980s, theystill have little commercial production history in theUnited States, and hence, relatively little is knownabout the spatial distribution of their long-term production potential across the United States (Evans et al.,2010). Such information is a starting point for plannersand economic modelers tasked with assessing landrequirements, management options, harvest and transportation methods, processing needs, and infrastructure for biomass crops. Equally important is the needfor this spatial information to be developed in a consistent manner for a variety of crops, so that their production potential can be intercompared to supportcrop selection decisions (Miguez et al., 2012; Castilloet al., 2015).Efforts to map the spatial distribution of biomassresources in the United States have focused on one ortwo biomass crops at a time, with several potential biomass crops receiving little attention. Two approaches tomapping biomass resources are empirical modeling andmechanistic plant growth modeling. Commonly usedempirical approaches have involved statistical extrapolation of plot or field-level yield data to larger regionsusing climatic envelope methods (e.g., Jager et al., 2010;Wullschleger et al., 2010; Tulbure et al., 2012). The maindrawback of empirical approaches has been a lack ofsuitable yield data (Miguez et al., 2012), and a limitedability to extrapolate beyond the range of the explanatory data (Jager et al., 2010). Relationships between yielddata and environmental conditions can be masked andeven misled by factors other than environment, such asfertilization, cutting rotation, supplemental irrigation,and other management practices and economic considerations, making it difficult to quantify what the actualenvironmental tolerances are (Jager et al., 2010). Information needed to control for these factors is not alwaysavailable in the literature, and access to researchers whoconducted the trials is often limited. In addition, yieldhistories can be as short as a single year and are thusaffected by year-to-year variability in weather conditions, making it difficult to estimate long-term yieldpotentials (Lobell et al., 2009). Finally, yield data aretypically collected from demonstration plots in areaswhere the crop is likely to succeed, and thus provide little guidance as to how environmental factors limit production near the edges of a crop’s range or across steepclimatic gradients (Miguez et al., 2012). Despite theseshortcomings, empirical approaches provide importantassessment tools for planning activities and supplyguidance for more mechanistic modeling approaches(Jager et al., 2010).Mechanistic plant growth models attempt to simulatethe important physiological processes that affectgrowth, development, and yield. Examples of simulation models that have been used to model biomasscrops include ALMANAC (Kiniry et al., 2008), EPIC(Williams et al., 1984; Brown et al., 2000; Thomson et al.,2009; Balkovic et al., 2013), MISCANFOR (Hastingset al., 2009; Miguez et al., 2012), and STICS (Brissonet al., 2008; Strullu et al., 2015). These models have thepotential to provide detailed information on crop performance and yield. However, they require significantinputs of environmental data and detailed knowledgeof crop physiology (e.g., Brown et al., 2000). In addition,calibration and validation of models require detailedplot-level data, which is often scarce or poorly distributed for many new crops (Nair et al., 2012). Parameterization of some models to specific crop varieties andlocations can make it difficult to generalize results overlarge areas (e.g., Miguez et al., 2012). As more information on bioenergy crops becomes available, mechanisticmodels will become increasingly useful in planning fora biobased economy.The resource mapping approach described here stemsfrom the need for many different biomass crops to becompared within the same modeling framework toavoid confounding model differences with biologicaldifferences (Miguez et al., 2012). It stems from the recognition that many biomass crops have insufficient yielddata from which to spatially extrapolate and estimatelong-term yields. In addition, little quantitative information is available on the tolerances of these crops to environmental conditions. Our approach, undertaken aspart of the Sun Grant RFP, was to take advantage of theinformational synergy realized when bringing togetherfield trials, close interaction with expert agronomists,and spatial modeling into a single, collaborative effort.The first component consisted of a coordinated set offield trials of several of the most promising herbaceousand woody biomass options conducted over a 3- to 7year period (Lee et al., 2017; Volk et al., 2017), plus otherrelevant trials. The spatial representativeness of thecoordinated field trials was optimized whenever possible through adherence to consistent, best-practice management protocols, thus controlling for the effects ofmanagement on the responses of crops to basic environmental limitations created by climate and soils. The second component was face-to-face interactions betweenthe modeling group and the agronomists conductingthe RFP and other field trials. During these meetings,yield data from the field trials were evaluated for theirquality and representativeness, published literature wasexamined, and qualitative information on a crop’s spatial distribution based on personal experience was provided. 2017 The Authors. GCB Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12496

MAPPING POTENTIAL BIOMASS RESOURCES 3The third component was a biogeographical modeling and mapping system called Parameter-elevationRegressions on Independent Slopes Model Environmental Limitation Model (PRISM-ELM). An early version of PRISM-ELM was first developed to estimatethe potential suitability zones of US-grown perennialgrass exports to China (Hannaway et al., 2005). PRISMis the name of the system used to generate high-quality, spatial climate datasets that drive the model (Dalyet al., 1994, 2008). PRISM-ELM was designed to answera basic question: How do climate and soil characteristics affect the spatial suitability and long-term production patterns of a given crop? It draws from bothempirical and mechanistic approaches and thereforefalls into a hybrid category that is becoming morepowerful as high-quality climate, remote sensing, landuse, and soils data become available (Song et al., 2015;Wightman et al., 2015; Richter et al., 2016). It employs asimple water balance model to simulate the correspondence, or lack thereof, between water availability(based on precipitation and soil moisture) and growingseason timing (based on a temperature responsecurve). The model uses simplified metrics to representcomplex processes. January mean minimum temperature and July mean maximum temperature are used toidentify areas that have cold or warm-season temperature extremes that may limit meaningful crop production. Soil pH, salinity, and drainage response curvesalso serve as metrics for unsuitable soil conditions. Thefocus is on a general approach to modeling climaticand soil constraints on biomass production for anycrop, rather than a detailed accounting of the particularphenology or other morpho-physiological features of agiven species or genotype. Suitability maps estimatedby PRISM-ELM are transformed into yield potentialmaps through statistical regressions between the levelof environmental suitability and biomass yield datafrom the field trials. These maps serve as potential production inputs to analyses that compare the viability ofbiomass crops under various economic scenarios(USDOE, 2016).The objective of this article was to present (1) adescription of our biomass resource mapping process,including an overview of the work flow and interaction with RFP agronomists; (2) PRISM-ELM modelunderpinnings, structure and function; (3) model validation and parameterization; (4) environmental ntal suitability to biomass yield potential.Dedicated herbaceous biomass crops included in theRFP evaluation, and in this article, were upland andlowland switchgrass (Panicum virgatum L.), Giant Miscanthus (Miscanthus 9 giganteus), energycane (Saccharum officinarum L. 9 Saccharum spontaneam L.), biomasssorghum (Sorghum bicolor), and mixed ConservationReserve Program (CRP) grasses (Lee et al., 2017).Woody biomass crops included willow (Salix spp.) andpoplar (Populus spp.) (Volk et al., 2017).Materials and methodsData and processingClimate data. Climate inputs for PRISM-ELM were grids ofdaily maximum, mean, and minimum temperature (Tmax, T,and Tmin, respectively) and precipitation (P) from the PRISMAN81d dataset (PRISM Climate Group, 2015). PRISM climatedatasets have been peer-reviewed and used in many agricultural and natural resource applications (Daly et al., 1994,2008). The daily data were summarized at a semi-monthlytime step for use in PRISM-ELM; temperature values wereaveraged and precipitation values summed twice eachmonth for the period 1981–2010, resulting in 720 grids. Eachsemi-monthly grid was then averaged across each of thethirty grids representing that semi-monthly period (e.g., thefirst half of January) to obtain 30-year averages. The resultwas 24 semi-monthly averages representing a 1981–2010 climatological ‘year’. Spatial resolution of the gridded datawas 30 arc-seconds, or approximately 800 m, across the conterminous United States.PRISM-ELM required ET0 and bare soil evaporation asinputs. Given that only temperature and precipitation wereavailable from the PRISM climate dataset at the time of access,daily ET0 was estimated using methods outlined by Hargreaves & Samani (1985), which requires Tmax, T, Tmin, and estimates of extraterrestrial radiation based on solar geometry.Daily ET0 values were summed to semi-monthly totals. Soilevaporation (Es) over each semi-monthly time step was estimated as a proportion of ET0, which varies with rainfall frequency (Allen et al., 1998). Once calculated on a semi-monthlybasis for each year, ET0 and Es were averaged over the 1981–2010 climatological period in the same manner as temperatureand precipitation.Soils data. Soil characteristics greatly influence the suitabilityof plants for a particular location and their potential production. Important factors include water holding capacity, pH,salinity, and drainage. Soils data were obtained from the USDANatural Resources Conservation Service (NRCS) in the form ofthe U.S. General Soil Map Coverage oils/survey/geo/?cid nrcs142p2053629). The data were available as shapefile polygons andrelated data tables. Using standard GIS tools to view and querythe data, the NRCS ‘representative’ data fields were selected thatcontained the variables for available water holding capacity(AWC), soil pH, salinity, and drainage class for each polygon.The linked polygon data for each variable were cast to an 800-mgrid that was coincident with the 800-m PRISM climate data. Thesmallest General Soil Map Coverage polygon is 1012 ha in size,which is an area equal to about 4 9 4 800-m grid cells. NRCSSSURGO data, while at a much higher spatial resolution, were 2017 The Authors. GCB Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12496

4 C . D A L Y et al.not used in this study, because at the time of access, the datawere not yet complete and consistent over the entire modelingdomain.Yield data. County-level grain yield data from winter wheatand maize, commonly grown cool-season and warm-seasoncrops, respectively, were used to initially calibrate and validatePRISM-ELM. These data are described in Supporting Information, Data S6.Yield data from biomass crop field trials were used in theparameterization of PRISM-ELM and the transformation ofPRISM-ELM suitability estimates into potential annual biomass production. The yield trials used are summarized inTable S1. Details on the RFP yield trials for herbaceous andwoody crops are provided in Lee et al. (2017) and Volk et al.(2017), respectively. RFP yield trials were conducted in acoordinated fashion, which provided a unique opportunity tocontrol for management practices across sites by selecting trials that were most internally consistent. Since managementpractices greatly influence yields (e.g., Wullschleger et al.,2010), controlling for these practices allowed the modelingwork to focus on how climate and soil constraints influencepotential production patterns. Management practices weredesigned to approximate those used in farm-scale production.Trials conducted outside the RFP were also evaluated in asimilar manner, although information on management practices and other details was sometimes not as readily availableas that from the RFP trials.Each of the RFP yield trials was evaluated in face-to-facemeetings with the agronomists that were directly responsiblefor the trial. This allowed insight into the data that was notobvious when examining yield values alone; examplesincluded reports of damaging single day weather events,unusual field conditions, residual pesticides, or other management issues. This additional information about the yielddata helped to determine if they met the inclusion criteriafor this study. These criteria were developed based on producer needs for maps that represent long-term productionpotential at field scale, assuming best management practices,including minimal inputs of fertilizer and pesticides. It wasunderstood that the field trials lacked a long history andconsisted of only 3–7 years, thus reducing the strength ofthe relationship to long-term average yields. To be mostuseful, the field trials were selected to identify those thatrepresented: Dryland conditions (nonirrigated). Absence of significant damaging weather events and fieldconditions. Yields from the best local cultivar available at the time ofthe yield trials. Once-per-year harvest frequency. Field-scale yields, as opposed to test plot-scale yields.Estimated mean annual volume increment (MAI) at maturity for woody perennials (defined as total incrementdivided by age).Yields of fully established crops, if perennials; establishment years not included. Best-practice fertilizer application using a combination ofpre-establishment soil test recommendations and mass balance approach to replace only what is removed by the crop. Best-practice pesticide application, typically minimal inputs.Mapping process overviewThe mapping process took advantage of the informational synergy realized when bringing together three components – fieldtrials, close interaction with expert agronomists, and spatialmodeling – into a single, collaborative effort. An overview ofthe process is shown in Figure 1. PRISM-ELM was providedwith gridded climate and soils data, and a preliminary controlfile with crop-specific parameters was developed. PRISM-ELMproduced an initial grid of the Environmental Suitability Index(ESI) from 0 to 100%, where 100 represented no climate or soilconstraints on production and zero represented a full limitation. For a given crop, yield data from field trials conducted byRFP agronomists and others were examined at face-to-facemeetings with the modeling group. During this meeting, eachyield data point was evaluated for adherence to the inclusioncriteria presented previously. The initial PRISM-ELM ESI gridwas also used to provide a framework for evaluating the yielddata. The goal of each meeting was to come to an agreementon which yield data points would be included in a nationalregression function relating PRISM-ELM ESI to field trial yield.This nationwide regression function allowed the PRISM-ELMESI grid to be transformed into a potential yield grid. The process of adjusting PRISM-ELM crop parameters and comparingthe ESI map to the observed data was done iteratively duringand subsequent to the meeting until a final solution wasreached that was consistent with expert opinion, yield data,and published literature. Attempts were made to achieve thebest agreement possible between PRISM-ELM and yield data,but within the constraints of model parameter values that wereconsistent with the type of crop being mapped (see Modelparameterization section).Model rationalePRISM-ELM is based on the well-understood biogeographicaltenant that long-term climate and soil conditions place limitson average plant production across the United States. On anannual average basis, precipitation, and hence dryland production, becomes increasingly limited as one moves from east towest across the Great Plains (Fig. 2a). The seasonality of precipitation determines the likelihood of successfully growing coolseason crops vs. warm-season crops. Over much of the easternUnited States, average precipitation is sufficient for most cropproduction during the warm season, but in the West, very littleprecipitation falls during the warm season (Fig. 2b). Long-termaverage annual temperature largely determines the north–south and elevational range of crop species and varieties, andthe timing of their production cycles (Fig. S1a). In addition,winter cold can limit the production of overwintering plants(Fig. S1b) and summer heat can limit production during thegrowing season (Fig. S1c). 2017 The Authors. GCB Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12496

MAPPING POTENTIAL BIOMASS RESOURCES 5Fig. 1 Schematic of the Parameter-elevation Regressions on Independent Slopes Model Environmental Limitation Model (PRISMELM) workflow for mapping bioenergy resources. Inputs to PRISM-ELM were gridded climate and soils data, and a preliminaryparameter file for the crop being modeled. An initial Environmental Suitability Index (ESI) grid was produced, and during a face-toface meeting with agronomists, the ESI grid was evaluated against observed yield data to help understand data outliers and adjustmodel parameters. Once an agreement was reached on model parameters and yield data to be used, a final regression function wasdeveloped and applied to the ESI grid to produce a potential biomass yield grid.In addition to climatic constraints, plant production is limitedby soil characteristics, four of which are AWC, pH, salinity, anddrainage. Shallow, sandy, or rocky soils have a low AWC, whichlimits their ability to store water, thus requiring greater precipitation inputs to maintain a water balance suitable for plantgrowth. Soil AWC is highly variable across the United States,but is greatest in parts of the Great Plains and Midwest(Fig. S2a). Very acid and alkaline soils decrease the solubility ofmany major plant nutrients and may also release toxic amountsof trace metals harmful to plant life. Soils are typically alkalinein arid areas of the West, acidic in parts of the east coast, andslightly acidic to neutral in the Midwest (Fig. S2b). Highly salinesoils reduce the osmotic potential of the soil solution and maylimit the uptake of some nutrients. High soil salinity is primarilyfound along coastlines and in arid areas of the western UnitedStates (Fig. S2c). Poorly drained soils can limit oxygen residingin soil pore spaces, necessary for healthy root activity. In contrast, water may leach rapidly through well-drained sandy soils,flushing nutrients in addition to storing little water. Soils aretypically well drained in the western United States, but much ofthe eastern United States is poorly drained, especially in theMidwest (Fig. S2d).Model organizationPRISM-ELM is composed of series of algorithms and metricsthat evaluate the major climate and soil limiting factors to production discussed above. The PRISM-ELM ESI is the lowestsuitability index resulting from the model response functionsas follows:ESI ¼ minðSw ; Sc ; Sh ; Sp ; Ss ; Sd Þ;where Sw, Sc, Sh, Sp, Ss, and Sd are the suitability indexes fromthe water balance model, and response functions to winter lowtemperature, summer high temperature, soil pH, soil salinity,and soil drainage, respectively.The water balance model contains generalized process-basedalgorithms that account for soil water availability, use and deficit, and works in concert with a temperature response curve.The other functions consist of response curves that serve asmetrics for climate and soil processes that could limit plantproduction. These include the potential for low-temperatureinjury of overwintering crops, damage or growth reductiondue to heat during the growing season, and plant responses tosoil pH, salinity, and drainage. Each of these functions is summarized briefly below; model equations and further details areprovided in Supporting Information, Data S3 and S4.Water balance model. The PRISM-ELM water balance modelis an Food and Agriculture Organization (FAO)-style function(Allen et al., 1998), operating on a semi-monthly time step,using 30-year average climate data described previously. Gridded inputs to the model are soil AWC, and semi-monthly average T, P, ET0, and Es. Crop-specific scalar inputs provided bythe user are parameters defining the optimum temperaturegrowth curve; average crop rooting depth (Droot); the crop coefficient (Kc), which encompasses canopy characteristics (e.g.,height, coverage), stomatal control, and other factors that affect 2017 The Authors. GCB Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12496

6 C . D A L Y et al.that window, the final water balance suitability Sw is calculatedby the model as the average suitability during the MaximumSuitability Window, which is typically the three consecutivemonths for which the monthly suitability is highest (the number of consecutive months can be changed by the user). Waterbalance model equations are provided in Supporting Information, Data S3, and examples of its operation in two contrastingparts of the country are given in Supporting Information,Data S4.Heat and cold temperature responses. The winter low-temperature response function is a metric for production limitations in overwintering crops that may occur because of injuryor death caused by excessively low temperatures (Levitt, 1980;Beck et al., 2004). Conversely, in some species, low winter temperatures are required for induction of the plant’s floweringresponse (vernalization) through accumulation of chillinghours (Dennis, 1984). While low temperatures may be neededto maximize flowering and grain production in crops such aswheat, diversion of energy away from vegetative productionand into flowering may reduce biomass yields (Schwartz et al.,2010). The summer high-temperature response function is ametric for production limitations that may occur because ofstress caused by high temperatures during the growing season.Excessively high temperatures can cause direct damage tocrops, and water stress in dryland crops, both of which canlead to reductions in performance. Crop-specific parameters forheat and cold injury are discussed in the Model parameterization section.Fig. 2 Conterminous US 1981–2010 (a) mean annual precipitation and (b) mean April–September precipitation. Data source:PRISM Climate Group (http://prism.oregonstate/edu).crop evapotranspiration; and the stress response factor (p),which is the fraction of soil water that a crop can extract fromthe root zone without suffering water stress. Gridded internalvariables calculated by the model are the temperature response(Tr), actual evapotranspiration (ETa), water stress coefficient(Ks), total available water in the root zone (TAW), readily available water in the root zone, and root zone water depletion (Dr).In concert with the water balance calculations, the temperatureresponse of the crop is evaluated at each semi-monthly timestep. User-defined parameters describe the mean daily temperature at which production is optimal, and the maximum andminimum temperatures at which production declines to zero.At each time step t, a water balance suitability index, St, is calculated as the product of the water stress coefficient and thetemperature response (S KsTr). The semi-monthly values ofSt are averaged to create monthly values (Sm). A PotentialSuitability Window, the period within which a crop is expectedto be in its most active production phase in an agricultural setting, is set by the user. This window is necessary becausePRISM-ELM, being an environmental suitability model, doesnot simulate the timing of the life cycle stages of a crop. WithinSoil pH response. The soil pH response function accounts forproduction limitations caused by excessively acidic (low pH)or alkaline (high pH) soils. Most plants prosper in the pHrange from 5.6 to 7.3, classified as moderately acid to neutral(NRCS, 2003). As soils become more acidic the solubility ofmost major plant nutrients as well as some micronutrients,such as molybdenum, decrease. Nutrients must be soluble inwater to be adsorbed by plant roots. Very acid soils may alsorelease toxic amounts of aluminum, iron, and manganese.Alkaline soils can also decrease plant nutrient solubility, principally phosphorus, boron, copper, iron, manganese, and zinc.Often the largest problem with alkaline soils is their high saltcontent. Soil pH can be modified by addition of liming agents;this is discussed in greate

potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops. Keywords: biomass crop, biomass production potential, biomass resource map, biomass resources, biomass sorghum, energy-

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