method for spatially explicit calculations of potential biomass yields and assessment of land...

Agriculture, Ecosystems and Environment 84 (2001) 207–226

Method for spatially explicit calculations of potential biomassyields and assessment of land availability for biomass energy

production in Northeastern Brazil

Laura C. Schneidera,1, Ann P. Kinzigb,∗, Eric D. Larsonc, Luis A. Solórzanod,2

a Center for Energy and Environmental Studies, Princeton University, Princeton, NJ, USAb Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA

c Center for Energy and Environmental Studies, Princeton University, Princeton, NJ 08544, USAd Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA

Received 13 August 1999; received in revised form 29 May 2000; accepted 26 July 2000

Abstract

The Intergovernmental Panel on Climate Change (IPCC) has suggested that large-scale use of carbon-neutral or low-carbonbiomass-derived energy will be essential in order to limit carbon emissions from the world’s energy sector in the future. TheIPCC envisions as much as 400 million ha being devoted to biomass energy plantations by 2050. To realize production ofbiomass energy at such levels — in a manner that would be both biogeophysically sustainable and socially beneficial — willrequire planning and policy development at sub-national levels, taking into account biogeophysical, social, cultural, economic,institutional, and other factors. This paper presents a method for spatially explicit calculations for estimating potential biomassyields over relatively large geographic regions. The calculations use geo-referenced data inputs that include rainfall, insolation,temperature, soil quality, and soil depth. The methodology is applied to the Northeast region of Brazil, which accounts for 10%of the area of South America. Northeast Brazil is an interesting site for illustrative purposes in part because it is biologically,geologically, and socio-economically diverse and in part because the main electric utility serving the region is exploring thedevelopment of biomass-based electricity generation to meet future increases in electricity demand. Results from a spatiallyexplicit, biogeophysical model like that presented here could be combined with other spatially explicit information such asroad layouts, existing land uses, population densities and growth rates, distributions of endangered species, archeologicallysignificant areas, etc. to inform planning and policy development related to biomass energy at a regional or national level.One illustration of such an analysis is included here. For on-the-ground implementation of biomass production systems,finer-resolution analysis and intimate local participation is essential. © 2001 Elsevier Science B.V. All rights reserved.

Keywords:Biomass; Energy; GIS; Brazil; Land-use; Modeling; Productivity; Spatial analysis

∗ Corresponding author. Present address: Department of Biology,Arizona State University, PO Box 871501, Tempe, AZ 85287-1501,USA. Tel.: +1-480-965-6838; fax:+1-480-965-2519.E-mail address:[email protected] (A.P. Kinzig).

1 Present address: Graduate School of Geography, Clark Univer-sity, Worcester, MA 01602, USA.

2 Present address: The Woods Hole Research Center, Woods Hole,MA 02543-0296, USA.

1. Introduction

There is growing global concern over the poten-tial negative impacts of increasing concentrations ofgreenhouse gases in the atmosphere (IPCC, 1996).Currently, over 75% of the annual anthropogenic fluxof carbon dioxide, the most important of the green-house gases, derives from fossil-fuel combustion.

0167-8809/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved.PII: S0167-8809(00)00242-5

208 L.C. Schneider et al. / Agriculture, Ecosystems and Environment 84 (2001) 207–226

Thus, any long-term strategy to address threats fromglobal climate change must focus on a reduction ofcarbon emissions from the energy sector, and willnecessitate either a shift away from fossil fuels and/orthe development of technologies for removing andsequestering fossil carbon (IPCC, 1996; Reddy et al.,1997).

The second assessment report of the Intergovern-mental Panel on Climate Change (IPCC) suggeststhat the large-scale use of renewable (carbon-neutral)biomass-derived energy will be a critical componentof any strategy to reduce carbon emissions from theenergy sector to acceptable levels while simultane-ously satisfying global energy demand. The IPCC’senergy supply scenarios for low carbon-emission fu-tures envision that as much as 400 million ha couldbe devoted to biomass plantations by 2050 (Williams,1995). This would have a substantial impact on globalland-use patterns, as significant expansion of the landscurrently devoted to biomass production would berequired under this scenario. Whether such expansionis consistent with maintaining agricultural activities,or with meeting other societal goals — involvingthe environment, human well being, and economicdevelopment — is an open question not addressedhere.

It is clear, however, that simultaneously realizingany potential benefits of biomass energy while avoid-ing excessive costs will require careful planning andpolicy development; the biogeophysical, climatic,economic, institutional, and social characteristics thatcan make biomass energy attractive or risky shouldall be weighed. Even if one considers primarily eco-logical and biogeophysical characteristics — leavingaside social or political characteristics — there is stillconsiderable local and regional variation in lands de-voted to forest-product and food production, the typeand distribution of degraded lands, and climatic con-ditions favorable or unfavorable to woody biomassproduction. Thus it would seem that even an ecolog-ical analysis of the potential for biomass-plantationestablishment would have to be conducted on aregion-by-region basis. Moreover, historical socialand cultural attitudes concerning land ownership andthe proper use of land, and the economic and polit-ical infrastructures governing land-use planning andregulation of environmental impacts of commercialactivity will be important considerations in ensuring

sustainable biomass plantation activity within thecomplex mosaic of other regional land uses; these atti-tudes and infrastructures also vary considerably fromnation to nation, state to state, and even village tovillage.

Thus, a truly meaningful assessment of the potentialfor biomass to contribute significantly to energy sup-ply on an environmentally sustainable basis ultimatelymust be conducted at a fairly local scale. Many anal-yses of the future potential for biomass energy havebeen at broad regional or even global scales (e.g., Al-camo et al., 1994; IPCC, 1996; Marrison and Larson,1996). These studies have been important in high-lighting key issues that could arise with large-scaledevelopment of biomass energy production, but im-plementation will require analyses with much higherresolution.

In this paper, a spatially explicit model is intro-duced, enabling a biogeophysically based assessmentof the range of potential yields of woody biomass in aregion at a reasonably fine-scale resolution. The modelutilizes as inputs geo-referenced data for rainfall, in-solation, temperature, soil quality, and soil depth. Thisyield assessment can be combined with other spatiallyexplicit information such as road layouts, existingland-uses, population densities and growth rates,distributions of endangered species, archeologicallysignificant areas, etc. to aid in planning and policydevelopment relating to biomass energy at a regionalor national level. Ultimately, still finer-resolutionanalysis and participation of local planners and citi-zens are needed, however, since decisions about howto value, for instance, endangered-species protec-tion versus potential improvements in human wellbeing engendered by a reliable renewable energysupply are heavily influenced by social and culturalfactors.

To illustrate the use of this model, it was appliedto Northeast Brazil, a nine-state region that accountsfor 18% of the area of Brazil and 10% of the areaof South America (Fig. 1). This spatially explicit ap-proach could, however, be applied to any area of theworld that can support woody vegetation. NortheastBrazil was selected in part because it is biologicallyand socio-economically diverse, as reflected in itscomplex geomorphology, climate, soils, and agricul-tural practices (ranging from subsistence cultivationto industrial cattle ranching) (Almeida et al., 1972;

L.C. Schneider et al. / Agriculture, Ecosystems and Environment 84 (2001) 207–226 209

Fig. 1. The nine states of Northeast Brazil, covering a total area of 1.55 million km2.

Bigarella, 1975; Hastenrath, 1985; EMBRAPA, 1993;Solórzano and Schneider, 1999). The region con-tains five of the 10 major ecosystem types definedby the Food and Agriculture Organization (FAO) ofthe United Nations for the South American continent,making this a particularly important area in terms ofconservation priorities (FAO and UNESCO, 1971;Dinerstein et al., 1995) and a challenging setting forthe design of management strategies. The region wasalso selected because the regional electric utility —Companhia Hidroelectrica do Sao Francisco (CHESF)

— is exploring the development of biomass-basedelectricity generation to meet future increases inelectricity demand, as an alternative to continuedreliance on increasingly costly new hydroelectricity.CHESF has conducted a preliminary assessment ofthe regional capacity for biomass-energy productionand concluded that up to 1/3 of the land area — or50 million ha — is potentially suitable and availablefor bioenergy plantations (Carpentieri et al., 1993).

The objective in the work presented here is to de-velop a tool to estimate spatially explicit potential


biomass yields for a region of interest and to illustratethe application of this tool to Northeast Brazil.

2. Methods

Spatially explicit data on soils and climate wereused to calculate biomass yields assuming productionwas, at any given time, either light- or water-limited.Production potential was assessed on a weekly timestep. Other factors affecting yield, such as nutrientlimitations or pests, were not considered, and onlyproduction of C3 woody vegetation was assumed.Two different calculations are made for yield of treecrops in each location: one uses highly optimistic as-sumptions (“optimistic” scenario) and one uses less-optimistic assumptions (“less-optimistic” scenario).

Yield values calculated using this approach are notintended to give exact determinations of actual fieldproductivity. Determinants of actual field productivitywill vary significantly from site to site. Similar soiltypes, for instance, may vary in fertility or degreeof erosion, or pest burdens may vary from regionto region. The resolution used for input/output vari-ables is too coarse (5.5 × 5.5 km2 grid size), andthe definition of vegetation is too physiologicallybroad (all C3 woody plants) to make detailed predic-tions. However, the “optimistic” and “less optimistic”yields are intended to serve as a guide for identifyingareas that might then receive further attention and de-tailed analysis, including on-the-ground verificationof conditions. The two yield values serve to boundaverage-yield expectations over a relatively long pe-riod of time, but this approach also allows an assess-ment of potential impact of economically damaginglow yields over shorter time periods. An initial screen-ing using this model can allow more judicious use oflimited resources in assessing promising areas identi-fied by the analysis. Since these numbers are intendedto serve as a guide to determine potentially promisingareas for plantation production, a reasonable range ofvalues were selected for those characteristics that de-termine yield — such as photosynthetic efficiencies,or canopy interception of radiation (described in moredetail below) — so as to cover the range of possibleoutcomes. As a reality check, the calculated yieldenvelope was compared with reported actual yields atexperimental or commercial forest plantation sites.

2.1. A simple model for calculating light- orwater-limited yields

The first simplifying assumption is that growth oftree crops is either light-limited or water-limited overany given time step. The data used to calculate light- orwater-limited yields — solar insolation, precipitation,temperature, soil texture and depth — are containedin a geographic information system (GIS) databasedescribed in Section 2.3.

2.1.1. Light-limited yieldsMaximum tree growth will occur when growth

is limited only by light availability. Light-limitedgrowth can be assessed by measuring maximum pho-tosynthetic efficiencies — or the percent of incidentsolar radiation that is converted to chemical energy inphotosynthetic material under ideal conditions.

Light-limited yields are modeled as

YLL = Ifε × 10−2 (1)

whereYLL is the light-limited yield in Mg (dry, above-ground) biomass ha−1 per day,I the (total) incidentsolar radiation in MJ m−2 per day,f the fraction of in-cident solar radiation intercepted by the forest canopy,andε is the photosynthetic efficiency in g (dry above-ground) biomass per MJ of intercepted radiation, andthe factor of 10−2 is for units conversion.

Values forf andε were obtained from the literature.Monteith (1977) calculated that the theoretical maxi-mum value for net photosynthesis was about 1.6 g totaldry biomass per MJ of intercepted radiation, and thatthe record dry matter production for C3 food crops inEurope was about 0.6 g total dry biomass per MJ ofintercepted radiation when averaged over the wholeyear. It was further noted that crops grown with littleor no water or nutrient limitations fixed carbohydrateat a rate of about 1.4 g total dry biomass per MJ ofintercepted radiation during the growing season. As-suming that about 30% of production is belowground,this would correspond — at the upper, or optimistic,end — to about 1.0 g aboveground (dry) biomass perMJ of intercepted radiation. This value then is used asthe “optimistic” value ofε.

Similarly, Linder (1985) determined that the photo-synthetic efficiency for young forests, when deter-mined on an annual basis, was about 1.0 g of dry,


aboveground biomass per MJ of intercepted, pho-tosynthetically active radiation (PAR); since PAR isabout 45% of incident radiation, this is equivalent toproduction of 0.45 g of dry aboveground biomass perMJ of intercepted radiation. Since short-term growthrates can exceed those measured on an annual basis(Linder et al., 1985), a value of 0.6 g of dry above-ground biomass per MJ of intercepted radiation waschosen as the “less optimistic” value ofε. (Productionis calculated in the model on a weekly basis, and it istherefore appropriate to use the short-term value forgrowth under light-limited conditions.)

Assuming an energy content of wood of 20 MJ per(dry) kg, these numbers correspond to photosyntheticefficiencies of 1.2 and 2% with respect to above-ground biomass and total intercepted radiation. Fortotal (above and belowground) biomass (assuming30% of plant biomass is belowground) and measuringefficiency with respect to PAR only, the correspondingphotosynthetic efficiencies would be 3.4–5.7%.

The amount of incident solar radiation interceptedby the canopy foliage varies for closed versus opencanopy stands, and for coniferous versus broad-leaveddeciduous tree species; numbers presented by Linderet al. (1985) suggest that the fraction of incident ra-diation intercepted by the canopy varies from a lowof about 0.56 (low interception in conifer stands)to a high of 0.99 (high interception in deciduous,broad-leaf stands). Prudent management can increasethe fraction of intercepted radiation; on the other hand,the presence of young, newly planted (and thereforelow canopy coverage) stands in biomass plantationswith short (5–7 years) rotations would decrease thelifetime-averaged intercepted radiation. Based on theabove numbers, canopy interception fractions of 0.65and 0.95 were chosen for the less optimistic andoptimistic scenarios, respectively.

2.1.2. Water-limited yieldsLight-limited yields can only be sustained if there

is an adequate supply of water. (Supply of nutri-ents must also be adequate; the present analysisassumes this condition is met. Also, temperaturesmust be high enough to support photosynthesis, andin Northeast Brazil temperatures are sufficiently highthroughout the year.) In many circumstances therewill be insufficient water available to support growthat light-limited rates. When growth is water-limited,

total plant production is strongly correlated to AE,which in turn is strongly related to temperature andsoil moisture (SM) (Aber and Melillo, 1991). Localdata for monthly average temperatures and soil char-acteristics (that allow calculation of SM) were usedto calculate potential evapotranspiration rates (PE,the amount of evapotranspiration that would occur ifsoil water supplies were not limited) using the equa-tion of Thornthwaite (1948) (given below). There areother, more accurate methods for calculating PE, butthese methods require knowledge of other climaticfactors or soil properties not currently available in thedata set, such as relative humidity. Use of the simplerThornthwaite equation may lead to an overestimate ofevapotranspiration, and thus an overestimate of yield— particularly at higher temperatures. This limitationis discussed below in the analysis of yield results.

PE in turn can be used to calculate AE, which willbe less than PE whenever soil water supplies are notadequate. AE can be calculated using the equations ofDenmead and Shaw (1962) (as reported in Eagleman(1976) and given below), provided certain soil charac-teristics — such as field capacity (FC, water contentwhen soil is saturated) and wilting point (WP, mini-mum water content of soil at which plants can obtainwater) — are known. FCs and WPs are in large part de-termined by soil texture and depth (Rawls et al., 1982).

Calculating yields under water-limited conditionsalso requires knowledge of water use efficiencies(WUEs) — the amount of aboveground dry biomassfixed per unit of water evapotranspired. WUE is de-fined here at the ecosystem level, since the interest hereis in total water used for biomass production across rel-atively large areas of intensively managed land. Withthis definition of WUE, water use includes water notdirectly used to support plant growth (e.g., evaporationfrom soil), in addition to transpiration through leaves.These calculations assume that the source of the wateris either soil storage (water “leftover” from previousmonths after being decremented for plant growth,evaporation, and runoff) or current precipitation.

Total biomass production was calculated as theproduct of AE and WUE which is given in thefollowing equation:

YWL = WUE AE × 10−2 (2)

whereYWL is the water-limited production (Mg dry(aboveground) biomass ha−1 per day), AE the actual


evapotranspiration (mm water per day), WUE theecosystem-level water use efficiency (mg dry above-ground biomass per g water) and the factor of 10−2

is for units conversions.Defensible numbers for WUEs at the ecosystem

level for closed-canopy forests range from 1.0 to3.0 mg of dry (aboveground) biomass per gram ofwater evapotranspired. The numbers vary widely, andthere is the problem of uncertainty in what is beingreported. Field et al. (1986) as reported in Aber andMelillo (1991) give values of leaf-level WUEs forshrubs between 3.33 and 8.33 mg dry biomass (CH2O)per g of water transpired from the leaf; since all pho-tosynthate is fixed in leaves, this can be modified togive the correct numerator of production (total above-ground biomass), but underestimates — for the modelpurposes — the amount of water used at the ecosys-tem level to support growth (e.g., it ignores evapora-tion of water from the soil). Kauffman (1985) reportsWUEs for “total merchantable volume production”per unit of transpiration for old (120–180-year) tem-perate pine and fir stands; numbers are reported inthe range 1.0–3.3 mg of (merchantable) biomass perg of water transpired. Since productivity is reportedas total aboveground biomass (some of which wouldnot be merchantable, such as twigs and leaves), theKauffman (1985) numerator is an underestimate ofproduction for modeling purposes, while the denomi-nator underestimates water used at the ecosystem levelto support growth. Ricklefs (1979), reports WUEs inthe range 2–4 mg of “production” per g of water tran-spired, but fails to specify whether this is total (aboveand belowground) biomass or aboveground biomass(which is easier to measure). The Ricklefs (1979)number also underestimates, for modeling purposes,the amount of water used at the ecosystem level.

Table 1Model input assumptions for deriving site-specific for the optimistic scenarios and less-optimistic scenarios for C3 woody crop species aresummarizeda

Calculation parameters Less-optimistic Optimistic

(a) Grams dry, aboveground biomass produced per MJ of intercepted radiation 0.6 1.0(b) Fraction of incident radiation intercepted by foliage 65% 95%(c) Light-limited yield: Mg of dry aboveground biomass ha−1 per day

(from (a) and (b));I is the total incident radiation in MJ m−2 per day0.0039I 0.0095I

(d) WUE: mg dry aboveground biomass per gram of H2O evapotranspired 1.0 3.0(e) Water-limited yield: Mg of dry aboveground biomass ha−1 per day) (AE in mm per day) 0.001 AE 0.003 AE(f) Harvestable fraction of aboveground production 85% 85%

a Both light-limited and water-limited cases are shown.

The light-limited and water-limited productionscalculated above are for aboveground biomass, but notall of this biomass will be harvested and recovered forenergy purposes. For trees, for example, only trunksand large branches might be utilized, with roots,twigs, and foliage left behind in the field. (The roots,twigs, and foliage are the most nutrient-rich partsof the tree. Leaving them behind recycles nutrientsback through the soil system to support future plantgrowth.) Assuming that a coppicing harvest strategyis used (where multiple rotations are obtained from asingle planting), harvestable fractions of perhaps 85%of the aboveground biomass can be achieved (Hallet al., 1993). The harvested fraction of 85% was usedfor both optimistic and less-optimistic calculations.

The numbers used for deriving site-specific opti-mistic productivities and less-optimistic productivitiesfor C3 woody crop species are summarized in Table 1.There is a two to threefold difference in less-optimisticand optimistic yields. While it might be desirable tohave a narrower and more-certain range of yields, sucha narrowing is not possible given the limits of currentknowledge of the factors governing ecosystem-levelproductivity and the inherent variability in ecologicalprocesses. Highly optimistic assumptions were usedin deriving the optimistic forecast, and this probablycorresponds roughly to maximum expected yields.On the other hand, the less-optimistic forecast foryields may not represent a lower bound—nutrientlimitations, pests, disease, soil compaction or erosion,or other factors could depress production below thatcalculated here. Judicious matching of crop species tolocal conditions (high photosynthetic efficiencies forwet regions, high WUEs for dry regions) will pushyields towards those represented by the optimisticvalue, but lower yields may be more realistic.


2.2. Procedure for calculating biomass yields

The approach outlined above can be used to calcu-late biomass yields for a specific geographic area overa specified time period. For the illustrative examplediscussed in the following two sections, a completecalculation was developed for Northeast Brazil atweekly intervals and for each cell of a GIS databaseconsisting of about 50,000 cells, each about 30 km2

in size. For each cell, data were available for soiltexture, soil depth, annual-average solar insolation,and time-resolved average ambient temperature andprecipitation. The mechanics of the biomass-yieldcalculation within each cell are as follows:

1. Select a time step for yield calculations. Aone-week time step was used in the calculationsfor Northeast Brazil.

2. For the selected time step, calculate PE using thefollowing equation of Thornthwaite (1948):

PE= 1.6

(10T

I

)a

(3)

where PE is the evapotranspiration in cm permonth,T the monthly average temperature (◦C), Ithe annual heat index which is equal to

∑i (over

previous 12 months), wherei is the monthly heatindex which is equal to(T /5)1.514, a = 6.75 ×10−7I3 − 7.71× 10−5I2 + 0.01792I + 0.49239.

3. Calculate the AE based on Denmead and Shaw(1962), as cited in Eagleman (1976):

AE = PE[a + b(MR) + c(MR)2 + d(MR)3] (4)

where AE is the actual evapotranspiration (mm perweek), PE the potential evapotranspiration (mm perweek), a = −0.050 + 5.12/(PE), b = 4.97 −0.0944(PE), c = −8.57+ 0.223(PE), d = 4.35−0.126(PE), MR is the moisture ratio, which is givenby

MR = (SMt−1 − WP)

(FC− WP)(5)

where SMt−1 is the soil moisture as calculated inthe previous (t − 1) time step (in mm) (see Eq. (7)below), WP the wilting point (mm), and FC is thefield capacity (mm).

If MR < 0 using Eq. (5), i.e., SM is less thanthe WP, MR and AE were set to zero.

For SMs less than about 0.5 mm, AE rates canactually decrease with increasing PE. This is in partdue to the development of a dry surface soil layer,which interferes with diffusion of water in the soilsystem (Eagleman, 1976). The equation presentedabove becomes problematic for MR= 0, AE con-tinues (as it should, since evaporation from thesoil would still occur even when plants are unableto access water), but AE calculated using Eq. (4)exceeds PE for PE< 0.7 mm per day (MR= 0)and becomes negative for PE> 14 mm per day(MR = 0). Under an optimistic scenario, it is as-sumed that AE= 0 when MR = 0. That is, nowater is lost from the soil system through evapora-tion when the SM has reached the WP and plantsare unable to grow. This has little impact on the re-sults, as very dry SMs occur infrequently, and littleor no growth occurs during those time periods.

4. Calculate WP and FC, which are specific to thesoil type within the cell, and do not vary overtime. WP and FC are frequently given in units ofcm3 of water per cm3 of soil (based on texture);converting these units to mm requires knowledgeof the soil depthD and is calculated as follows:

FC(or WP) in mm = FC(or WP) in

(cm3 water per cm3 of soil)D (6)

whereD is the soil depth in mm.Calculate the water-limited growth rates. These

are

YWL = 0.01 AE(less-optimistic scenario) (2a)

YWL = 0.03 AE(optimistic scenario) (2b)

whereYWL is in Mg (dry aboveground biomass)ha−1 per week, AE the actual evapotranspiration inmm per week. Note that the yield for the week iszero if the MR — and thus the AE rate — is zero.

5. Calculate the light-limited growth rates. These are

YLL = 7 × 0.0039I (less-optimistic scenario) (1a)

YLL = 7 × 0.0095I (optimistic scenario) (1b)

where YLL is in Mg (dry aboveground biomass)ha−1 per week,I is the (total) incident solar radi-ation in MJ m−2 per day.


6. Compare the light-limited and water-limited yields,and select the lower of the two as the yield for theweek.

7. Calculate the current SM as follows:

SMt = SMt−1 + Pt − AEt − Rt (7)

where SMt−1 is the soil moisture as calculated in theprevious (t − 1) time step (mm),Pt the precipitationin the current (one-week) time step (mm per week),AEt the actual-evapotranspiration as calculated above(Eq. (4)) (mm per week), andRt is the run-off(mm).

Run-off is assumed only to occur if SM is greaterthan FC, thus

R = 0 if SM < FC,

R = SM − FC if SM > FC (8)

Note that for SMt−1 there is a problem in the veryfirst time step. There is no correct a priori method forchoosing the initial SM value. The approach adoptedhere is to begin with an arbitrary SM (WP or FC willdo), follow through on the calculations in steps 1–7above for a full year, and use the resulting final SMas input to the next year’s calculations. (Since SMtends to have a “memory” of at most a few weeks,the final SM using this method is independent of thestarting SM.) Because at least the first few yield cal-culations in this first year are suspect (due to the ar-bitrary choice of initial SM), this set of calculationsis not used for calculating yields; instead the secondyear, and all subsequent years, are taken as indicativeof yields for the site in question. These calculationscan be improved by using data of finer temporal andspatial resolutions. For instance, better estimates ofPE can be obtained using the Penman–Monteith equa-tion (Eagleman, 1976). Given the type and resolutionof data available for this study, however, the currentmethod gives suitable accuracy.

2.3. Data and model inputs for biomass yieldcalculations for Northeast Brazil

The objective of the model described above is tocreate a map of potential biomass yields. To imple-ment the model in the Northeast region of Brazil, aspatially explicit database was developed including

monthly average temperature, monthly average pre-cipitation, monthly average solar radiation, soil type,soil depth, and soil texture (from which WP and FC arederived).

A grid size of 5.5 × 5.5 km2 per cell was usedbecause this is the resolution of the available soiltexture data (described below). The other data neededto calculate yields (e.g., precipitation, temperature)were not available with such a high level of reso-lution; for cells where actual field data points werelacking, interpolation algorithms were used for gen-erating cell-specific data. The interpolation schemesfor temperature, precipitation, and soil depth are alldiscussed below; solar radiation was not interpolated.(Only two values for solar radiation were used —monthly average coastal values and monthly aver-age inland values — because there is relatively littlevariation in radiation throughout the region.)

2.3.1. Temperature and precipitation dataMonthly temperature and precipitation from 1970

to 1990 were obtained from the Global HistoricalClimatology Network database “Version 2” (CDIAC,1997). There are 1300 stations for Northeast Brazilreporting precipitation data, and 30 stations reportingtemperature data. An interpolation algorithm withinthe GIS software, IDRISI, was used to generatetemperature and precipitation values for each cellin the region. The interpolation procedure generatesdistance-weighted averages, with local maxima andminima occurring where actual data are available. Asone moves away from the data points, the interpolatedvalue tends towards the local average determinedby the specified “search radius,” i.e., the number ofdata points used in determining interpolated values.A commonly applied search radius of two was used,yielding a data-point weighting equal to the recipro-cal of the square of the distance between data pointand interpolated point. For temporal interpolation ofprecipitation, the rainfall in any given month is as-sumed to fall in equal amounts for each week of thatmonth.

2.3.2. Soil texture and depthThe soil map of Northeastern Brazil was digitized

from Empresa Brasileira de Pesquisa Agropecuaria(EMBRAPA) as the soil texture database (EMBRAPA,1981). For each soil type a texture class was assigned


(e.g., Ferrasols= clay, Luvisol = loamy, etc.). Themap contains 11 textures, for each of which FC andWP were estimated based on Rawls et al. (1982).

For soil depth, data from an extensive survey doneby the Brazilian government during the 1970s and1980s were used (RADAMBRASIL, 1973–1978).This survey compiled data for different areas of Brazilconcerning vegetation, geological formations, climateregime, and soils characterization. Data from the soilsurveys were used (283 data points for the north-eastern region); each survey includes a soil profiledescription, texture analysis, and depth in cm. Thexyz file with depth information consists of latitude,longitude, and depth in cm. The data were inter-polated using the THIESSEN function in IDRISI,which divides space such that soil depth at each lo-cation takes on the value of the nearest actual datapoint.

2.3.3. Solar radiationEach point in the region was assigned an average

monthly incoming solar radiation value correspondingto the regional average for either coastal or inlandareas, as given by Jones (1992).

2.3.4. Potential and actual evapotranspirationMap layers for PE and AE were created using the

map layers for precipitation, temperature, FC, and WPaccording to the algorithms described earlier.

2.3.5. Other data setsThree additional data sets are included in this

database for Northeastern Brazil. These were notused in calculating yields, but they are used later inillustrations of how assessments of potential conflictsbetween future biomass plantations and other landuses might be evaluated at a regional level.

2.3.5.1. USGS land-use/land-cover map for North-east Brazil. From 1 km resolution global land-coverdatabase (USGS, 1997), the Northeast Brazil areawas extracted. The complete map for South Americaincludes 164 different land-cover classifications, 85of which appear in Northeastern Brazil. For mod-eling purposes the classification was aggregated to23. For example, the 22 different sub-varieties oftropical forest (tropical broadleaf, tropical evergreen,moist tropical evergreen, etc.) were aggregated into a

single class called “tropical rain forest” (Kinzig et al.,1999).

2.3.5.2. Map of agricultural activities in NortheastBrazil. The EMBRAPA developed an agro-ecologicalmap of Northeast Brazil (EMBRAPA, 1993). Thestudy covered all nine states of Northeast Brazil(Fig. 1) and part of Minas Gerais state. The map isdivided into geo-environmental units characterized byparental substrate, natural vegetation, soils distribu-tion, relief, climate, human population, and economicactivities. The demarcation of landscape units werepreserved but were categorized based on attributesthat characterize their main land-uses and type ofagricultural activity (e.g., subsistence agriculture,cattle ranching, sugar cane (Saccharumspp.), cocoa(Theoboroma cacao), etc.).

2.3.5.3. Map of susceptibility to desertification inNortheast Brazil. According to Daily (1995), ap-proximately 43% of the earth’s terrestrial vegetatedsurface has diminished capacity to supply benefits tohumanity because of recent direct impacts of landuse. Some 70% of these lands with diminished ca-pacity are arid, semi-arid, or dry lands undergoingmoderate to extreme desertification. Bezerra et al.(1994) estimate that 56% of the Northeast regionis affected by desertification from moderate to ex-treme level. A database, which includes a map ofsusceptibility to desertification in the region, basedon Ferreira et al. (1994), identifies 19 factors thatcontribute to making an area more or less susceptibleto desertification. These factors, which take a binaryvalue (present/absent), include biophysical, social,and economic parameters like the presence of min-ing activities, quality of water resources, level of soilsalinization, time of occupancy, level of agriculturalmechanization, the presence of cattle and/or goats, theuse of agriculture inputs, and an index of desertifica-tion developed by the UNEP (precipitation over PE).The susceptibility to desertification is determined foreach sub-area on the basis of an aggregated score thatincludes all the factors and the definition of three lev-els of susceptibility: moderate, grave, and very grave.A moderate level is assigned to areas were 6–10 of thefactors are present, grave to areas where 11–14 oc-cur and very grave where more than 15 factors scoreas present.


3. Model results

3.1. Potential biomass productivity across NortheastBrazil

Fig. 2 shows the 20-year average yields calculatedby the 30 km2 resolution model for all of NortheastBrazil for the optimistic (Fig. 2a) and less-optimistic(Fig. 2b) scenarios using climate data from 1970 to1990. Tables 2 and 3 show the land area in each statefor different ranges of calculated per-ha yields. Thehighest average yield of harvestable biomass for the20-year period was some 37 dry metric Mg ha−1 peryear (optimistic scenario) and the lowest was some3 Mg ha−1 per year (less-optimistic scenario).

There was considerable annual variation aroundthese 20-year averages in some regions. For example,Fig. 3 compares the 20-year average for a location inSouth Central Piaui to the calculated annual values.Production was substantially below the average from

Fig. 2. Calculated 20-year average biomass yields (dry metric tonnes aboveground biomass per ha per year) in Northeast Brazil for (a)optimistic scenario and (b) less-optimistic scenario.

1980 to 1984 and from 1986 to 1988, which weredrought periods that correspond to the El Niño eventsof 1983 and 1987. Since energy plantations will typi-cally be harvested three to five times over any 20-yearperiod, the risks to economic viability associated withsuch variations in yield would need to be carefullyassessed in any implementation program.

3.2. Comparison between predicted and actual yields

As a check on these results, the calculated 20-yearaverage yields at some specific geographic sites werecompared with data reported from four commercialplantations and 14 small-scale experimental planta-tions actually existing at those sites (Fig. 4). Data forthe commercial plantations are average yields overa minimum operating period of 10 years and overareas of 18,500, 13,900, 8600, and 1200 ha. (Theseareas correspond approximately to six, five, three, andone cell for the region.) Rotations (intervals between


Table 2Land area in Northeast Brazil by state (km2) and by average calculated yield from 1970 to 1990 (dry Mg aboveground harvestable biomassper ha per year) in the optimistic scenario

Yield (Mg ha−1 per year) Total area (km2)

7–10 11–15 16–20 21–25 26–30 31–35 36–37

Area (km2) by yield levelAlagoas 216 7820 4729 2751 6182 5471 31 27201Bahia 39348 263477 182987 33506 39008 14466 1607 574401Ceara 0 31683 99036 15362 31 0 0 146211Maranhao 0 0 21730 240851 62685 0 0 325266Paraiba 2906 20648 23677 5966 2504 742 0 56442Pernambuco 5440 57060 20432 5595 5718 3122 0 97367Piaui 185 56503 140300 49858 4760 0 0 251607Rio Grande de N. 402 28066 19535 4389 216 0 0 52609Sergipe 0 4080 5687 6831 4946 0 0 21544

Total area (km2) 48498 469337 518113 365109 126051 23801 1638 1552547

coppicings) in the commercial plantations ranged fromfour to six years. The experimental plantations were10–20 ha in size each. The yields in these cases areaverages over three to eight years. All of the actualyield data are as reported by Carpentieri et al. (1992).

Reported yield data for the commercial planta-tions all fall within the range of the optimistic andless-optimistic calculations. Reported average trialplantation yields (plus-or-minus one standard devia-tion) fall within the range of the two scenarios in 10of the 14 cases. They exceed the calculated optimisticyield in four cases, which may be due to trial planta-

Table 3Land area in Northeast Brazil by state (km2) and by average calculated yield from 1970 to 1990 (dry metric Mg aboveground biomassper ha per year) in the less-optimistic scenario

Northeast Brazil states Calculated biomass yields (Mg ha−1 per year) Total area (km2)

3–5 6–10 11–13

Area (km2) by yield levelAlagoas 7851 13446 5904 27201Bahia 323411 233092 17897 574401Ceara 25223 120889 0 146211Maranhao 0 324648 618 325266Paraiba 23059 32425 958 56442Pernambuco 62284 31250 3833 97367Piaui 58482 193126 0 251607Rio Grande de N. 25964 26644 0 52609Sergipe 4018 17495 31 21544

Total area (km2) 530292 993015 29241 1552547

tions being located in the most fertile and promisingland in a given area, with the highly favorable localclimatic and edaphic conditions not captured by thecoarse-resolution biophysical database used. Also,the relatively short periods over which the trial yieldswere obtained may have precluded inclusion ofless-favorable production years.

In any case, the fact that the calculated yield rangecaptures the actual yields at all four operating com-mercial plantations and at the majority of trial planta-tions suggests that this model gives a plausible rangeof average yields that might be obtained in practice.


Fig. 3. Comparison of calculated 20-year average biomass yield and calculated annual yields for the optimistic scenario at a location inSouth-Central Piaui state.

As noted earlier, the objective in this work is not topredict what exact yields would be at any given site,but rather to provide a tool that can be used to aid inscreening analysis bearing on biomass energy devel-opment in a region.

3.3. Calculated yields and their geographicdistribution

It is not a surprise that the highest biomass yieldswere calculated for areas with the most productiveecosystems, and the lowest yields were calculatedwhere there are less productive ones. Maximum pro-ductivity levels were calculated mostly for the easterncoast range, from the eastern part of the state ofPernambuco south along the coastline to the stateof Bahia. High yields were also calculated for thenorthwestern part of the state of Maranhao as well asfor some of its central portion (Fig. 2). These highlyproductive areas were originally covered by tropicalrain forest; rainfall is abundant, and soils are deep andwell drained. Today, most of the eastern coastal rangehas been deforested; less than 7% of the originalforested area remains.

Medium productivity levels (11–25 Mg ha−1 peryear in the optimistic scenario) were calculated formost of the western and northern part of NE Brazil inthe states of Ceara, western Piaui, southern Maranhaoand western Bahia (Fig. 2). These territories roughlycorrespond to the northern Brazilian Cerrado, wheresoils range from medium to shallow depth and pre-cipitation is markedly seasonal. There is also a nar-row inland corridor of medium productivity runningparallel to the coastline from Rio Grande do Norteto Bahia. This corridor roughly corresponds to theoriginal distribution of seasonal tropical forest andtransition forest in eastern Brazil.

The lowest productivity levels were calculatedfor the central part of NE Brazil: in central andsouthern Piaui, southern Ceara, western Rio Grandedo Norte, Pernambuco, Paraiba and north-westernBahia (Fig. 2). These lower yield areas correspondto the semi-arid and arid ecosystems locally called“Caatinga” and “Sertao”, where soils are shallow andrainfall ranges from low to very low and is seasonal.These areas are subject to recurrent drought, and theyare undergoing several types of degradation, includ-ing intense deforestation for charcoal production and


Fig. 4. Comparison of calculated 20-year average biomass yields (dry metric tonnes aboveground harvestable biomass per ha per year),including standard deviations, at specific sites in Northeast Brazil against reported yields from four actual commercial plantations and 14experimental plantations.

voracious goat grazing (Bezerra et al., 1994; Ramalhoand Bezerra, 1994).

The lack of ability to account for nutrient limita-tions was a shortcoming of the model. On the otherhand, the model reflects productivity results thatmight be expected under modern biomass plantationmanagement, which typically includes nutrient aug-mentation. Another weakness of the model was itsinability to account for the impacts of pests. Pestsmay reduce yields relative to the values presentedhere, but the ecological dynamics of pests are highlyheterogeneous, and unfortunately, not particularlyamenable to being captured by the methods usedhere.

4. Discussion

The region-wide biogeophysically based biomassproductivity model presented above can be used in“what-if” spatially explicit analyses to help informdecisions on biomass energy development. A spa-tially explicit analysis also provides an effectivemeans for individually or simultaneously consideringa large number of parameters, as is illustrated herefor Northeast Brazil.

If protection of existing cropland against biomassenergy development is a local or national planning pri-ority, then overlaying a map of agricultural activitieson the resulting map of calculated yields (Fig. 2) can


Fig. 5. Cross-tabulation of agricultural activities and yields calculated for the optimistic scenario (Fig. 2a). The total height of each barrepresents the total area used for the indicated activity. The different shadings represent the area of land that would give the indicatedbiomass yields, if the land were converted to biomass production.

help identify areas that are likely to be most attractive(from a biogeophysical perspective) for conversionto biomass energy plantations while minimizing con-flict with existing agricultural activities. High-yieldareas will have a high probability of being targetedby private-sector biomass energy developers. (Landvalues and other economic parameters could also beincluded in such an analysis, but this has not beendone here.) Fig. 5 shows results from such an overlayanalysis. Areas currently under sugar cane and peren-nial commercial crops, for instance, corresponded toareas where high biomass yields could be expected,as do areas currently under forest extraction. If it isdesired that such lands remain under their currentuses, protection policies may be needed. In contrast,policies might be structured to encourage biomassplantation development on sparsely populated land

presently used for extensive cattle ranching, whichoccupies over 40% of the land area of NortheastBrazil. Reasonable biomass yields could be expectedover much of these lands (Fig. 5).

A similar overlay analysis of potential biomassyields and present land cover (Fig. 6) gave an indi-cation of which ecosystems might be most threat-ened by unregulated establishment of biomass energyplantations. From a biogeophysical perspective, ar-eas under tropical forest and savanna-cerrado arelikely to be attractive to plantation developers. For in-stance, the western and central part of Maranhao hasbeen under human pressure, which affects not onlyforested areas but also the most northern portion ofthe Brazilian Cerrado. (The cerrado constitutes one ofthe largest savanna-forest complexes in the world andcontains a diverse mosaic of habitat types and natural


Fig. 6. Cross-tabulation of land cover types in Northeast Brazil and calculated biomass yields in the optimistic scenario (Fig. 2a). The totalheight of each bar represents the total area used for the indicated activity. The different shadings represent the area of land that wouldgive the indicated biomass yields, if the land were converted to biomass production.

communities (Dinerstein et al., 1995).) Not surpris-ingly, this model forecasted that these areas wouldprovide high biomass yields. Caatinga, on the otherhand, does not look particularly attractive, with onlymodest calculated yields even under optimistic as-sumptions. This analysis suggests that high-yieldnatural ecosystems may require more extensive pro-tection policies against biomass energy development.

A third example relates to the possibility of es-tablishing biomass energy plantations on degradedlands as a means for restoring these to productive use.This would help minimize any conflict between landused for food versus for fuel. There is a great needfor improved understanding of the physical and eco-nomic feasibility of restoring productivity to differenttypes of degraded lands, and the biomass-productivity

model developed here can provide an indication of thelong-term biomass production potential on such landsshould restoration efforts prove successful. Not sur-prisingly, the largest areas that had the best prospectsfor biomass production (highest yields) were thosethat are least susceptible to desertification (Fig. 7).However, some areas that were gravely or very gravelysusceptible also had the potential for supporting eco-nomically viable yields. Such areas might be singledout for biomass-plantation-based restoration efforts.

Finally, multiple-overlay analyses can be underta-ken. In the following example, the above three single-overlay analyses were combined in seeking to quantifybiogeophysical potential for biomass energy produc-tion in Northeast Brazil in areas with a high-priorityneed to reduce susceptibility to desertification, where


Fig. 7. Cross-tabulation of susceptibility to desertification in Northeast Brazil and calculated biomass yields in the optimistic scenario(Fig. 2a). The total height of each bar represents the total area used for the indicated activity. The different shadings represent the area ofland under each level of desertification.

minimal conflicts with existing land uses are likely,and where minimal disruption of natural ecosystemswould occur. (Using different decision criteria willobviously give different results — perhaps substan-tially different — than those given here.)

Fig. 8 shows the results of this multiple overlay anal-ysis. Areas of high-priority for the conservation of soiland requiring restoration of productivity were assignedthe levels grave or very grave susceptibility to desertifi-cation (Fig. 8a). As a criterion for minimizing land-useconflicts, only the possibility of converting land usedfor extensive-grazing of cattle was considered (Fig.8b). Cattle ranches are typically large, have a singleowner, and are situated in low-population-density ar-eas, so that there might be less conflict in convertingsuch lands to biomass plantations than convertingareas that are mosaics of smaller-scale agriculturalactivities. To minimize disturbances to natural ecosys-tems, all lands defined in the USGS land cover mapas “natural vegetation” were excluded from consi-deration for biomass plantations (Fig. 8c).

Fig. 8d shows the areas selected based on these deci-sion criteria, and Fig. 8e shows the intersection of these

three areas, shaded according to level of calculatedpotential biomass yield (optimistic scenario). Table 4shows the resulting total biomass production by state.Some 250,000 km2 (16% of the land area of North-east Brazil) met the criteria, and the calculated totalyield (optimistic scenario) over this area was an aver-age of 390 million tonnes per year of biomass. (Localanalysis would be required to determine yields thatcould actually be achieved in practice and the associ-ated economics at specific sites.) The total calculatedyield represented some 7.8 × 1018 J per year (2170TWh per year) of energy. For comparison, total pri-mary energy consumption (from all energy sources)for all of Brazil in 1995 was an estimated 7× 1018 J(EIA, 1996).

This result does not guarantee that Brazil can meetall or even a substantial portion of its energy needsin the future from biomass plantations situated atthe sites described above. Conversion of 16% of thelandholdings of any region represents substantial dis-ruption of existing ecological, social, and economicpatterns. While some development on these sites maybe warranted, such development — and the economic

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Table 4Results from overlay analysis giving area within each state for which the indicated yield level is calculated (optimistic scenario) and whichis presently gravely or very gravely susceptible to desertification, contains anthropogenically disturbed ecosystems, and is used primarilyfor extensive cattle ranching

Northeast Brazil states Calculated biomass yields (Mg ha−1 per year) Total area (km2)

8–10 11–14 16–20 21–27

Area (km2) by yield levelAlagoas 62 1638 742 0 2442Bahia 22750 67569 58513 618 149450Ceara 0 216 3678 93 3987Maranhao 0 0 278 896 1175Paraiba 124 865 340 0 1329Pernambuco 495 5657 1113 0 7264Piaui 0 16753 56658 7542 80953Rio Grande de N. 0 2844 1546 0 4389Sergipe 0 1546 1082 93 2720

Total area (km2) 24430 97088 123949 9242 253709

and employment benefits it might bring — must beweighed against the translocations that might occurin species diversity, environmental services, landownership, employment opportunities, and access toregional resources and opportunities for rich versuspoor. Not only the extent but the rate of developmentwill be crucial in determining possible positive andnegative outcomes — rapid development frequentlytaxes the institutions and mechanisms established toensure protection of environmental goods and ade-quate distributions of opportunities and resources, andthus a more measured development of biomass-energyprograms may be warranted.

Such concerns also point to the limitations ofthe methods and analyses presented here. Whilesite-specific information on biogeophysical indicatorsof biomass-plantation yields could substantially aidthe decision-making process, many other social, eco-nomic, and cultural factors that may be less amenableto such quantitatively based analysis are of profoundimportance in determining the fate of land resourcesand regional energy development. Moreover, manyof the stakeholders whose input would be most cru-cial in determining economic and energy futures forBrazil are not literate in this type of spatially explicitanalysis. Greater human capabilities for translatingresults from new technologies and approaches, whilestill valuing more traditional forms of analysis andinformation, is needed if the methods such as thosepresented here are to realize their full value to society.

5. Conclusions

A simple model is introduced for calculatingbiomass productivity at a regional scale using spa-tially resolved inputs of soil texture, soil depth, solarinsolation, ambient temperature, and precipitation.This model can provide a relatively fine resolutionassessment of the biomass energy production poten-tial of a region. In combination with other spatiallyresolved physical, economic, and demographic infor-mation, this model can be used to provide early stageinputs into regional biomass energy planning and pol-icy development. For example, to help identify areasin need of protection policies, the model might beused to anticipate areas that are likely to be targetedfor energy plantations in the absence of any man-agement/policy intervention. Alternatively, the modelmight be used to examine prospects for biomass plan-tation establishment in areas where plantations mightbe especially desirable, such as degraded areas. Fromthe perspective of implementation, our model canonly provide relatively coarse resolution information.Ultimately, finer-resolution analysis and participationof local planners and citizens are needed.

Acknowledgements

For assistance in the preparation of this paper, theauthors thank Constantin Tudan. For useful early


discussions, the authors thank Dr. Sivan Kartha. Forcomments on a draft of this paper, the authors thankthe three anonymous reviewers. For financial support,the authors thank NASA’s Office of Earth Science,the Andrew W. Mellon Foundation, and the Williamand Flora Hewlett Foundation.

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