Modeling carbon sequestration in afforestation, agroforestry and forest management projects

23 Pages·2003·0.62 MB·English

EcologicalModelling164(2003)177–199
Modeling carbon sequestration in afforestation, agroforestry and
forest management projects: the CO2FIX V.2 approach
Omar R. Maseraa,∗, J.F. Garza-Caligarisa, M. Kanninenb,1, T. Karjalaineng,
J. Liskic,2, G.J. Nabuursd, A. Pussinenc, B.H.J. de Jonge, G.M.J. Mohrenf
aInstitutodeEcolog´ıa,UniversidadNacionalAutónomadeMéxico(UNAM),A.P.27-3(Xangari),58089Morelia,Michoacán,Mexico
bCentroAgronómicoTropicaldeInvestigaciónyEnseñanza(CATIE),7170Turrialba,CostaRica
cEuropeanForestInstitute,Torikatu34,FIN-80100Joensuu,Finland
dALTERRA,P.O.Box47,6700AAWageningen,TheNetherlands
eElColegiodelaFronteraSur,Apdo.Postal1042,C.P.86100,Admin.deCorreos2,ColAtasta,Villahermosa,Tab,Mexico
fForestEcologyandForestManagementGroup,DepartmentofEnvironmentalSciences,WageningenUniversity,
P.O.Box342,6700AHWageningen,TheNetherlands
gFinnishForestResearchInstitute,P.O.Box68,FIN80101Joensuu,Finland
Received22January2002;receivedinrevisedform5November2002;accepted2December2002
Abstract
The paper describes the Version 2 of the CO2FIX (CO2FIX V.2) model, a user-friendly tool for dynamically estimating
thecarbonsequestrationpotentialofforestmanagement,agroforestyandafforestationprojects.CO2FIXV.2isamulti-cohort
ecosystem-levelmodelbasedoncarbonaccountingofforeststands,includingforestbiomass,soilsandproducts.Carbonstored
inlivingbiomassisestimatedwithaforestcohortmodelthatallowsforcompetition,naturalmortality,logging,andmortality
duetologgingdamage.Soilcarbonismodeledusingfivestockpools,threeforlitterandtwoforhumus.Thedynamicsofcarbon
storedinwoodproductsissimulatedwithasetofpoolsforshort-,medium-andlong-livedproducts,andincludesprocessing
efficiency,re-useofby-products,recycling,anddisposalforms.TheCO2FIXV.2modelestimatestotalcarbonbalanceofal-
ternativemanagementregimesinbothevenanduneven-agedforests,andthushasawideapplicabilityforbothtemperateand
tropicalconditions.Resultsforthemodeltestingandvalidationinselectedtemperateandtropicalforestmanagementsystems
arepresentedanddiscussed.
©2003ElsevierScienceB.V.Allrightsreserved.
Keywords:Model;Forests;Carbonsequestration;Forestmanagement;Afforestation;Agroforestry;KyotoProtocol
1. Introduction:forestsandclimatechange
Forestsplayanimportantroleintheglobalcarbon
∗ Correspondingauthor.Tel.:[phone]; cycle.Theirtemporalcarbondynamicsarecharacter-
fax:[phone]. izedbylongperiodsofgradualbuild-upofbiomass(a
E-mailaddress:[email protected](O.R.Masera). sink),alternatedwithshortperiodsofmassivebiomass
1 Present address: Center for International Forestry Research
loss (source). Forests thus switch between being a
(CIFOR),Bogor,Indonesia.
2 Present address: Department of Forest Ecology, P.O. Box 27, source or a sink of carbon, depending on the succes-
UniversityofHelsinki,Finland. sionstage,specificdisturbanceormanagementregime
0304-3800/03/$–seefrontmatter©2003ElsevierScienceB.V.Allrightsreserved.
doi:10.1016/S0304-3800(02)00419-2
178 O.R.Maseraetal./EcologicalModelling164(2003)177–199
and activities. Tropical forests as a whole, for exam- ical, descriptive models of carbon budgets within
ple, are usually seen as a net carbon source because forest stands (see, e.g. Mohren, 1987; Dewar, 1991;
ofthedeforestationthatistakingplacelocally.How- Mery and Kanninen, 1999; Kirschbaum et al., 1998;
ever, recent evidence suggests that the vast areas of SchlamadingerandMarland,1996;Whiteetal.,2000;
remaining tropical forests are not in equilibrium, but Karjalainen, 1996 for an overview). None of these
mayfunctionasanetcarbonsink(Graceetal.,1995; models have been widely disseminated, and neither
Phillipsetal.,1998).Theglobalterrestrialbiosphereis of them has been accepted as a possible standard for
thoughttohavebeenactingasasinkofapproximately carboncreditingfromprojects.
2.3GtC per year in the 1990s. This sink was partly In this paper we examine the structure of the
causedbylarge-scalevegetationreboundintheNorth- CO2FIX V.2 model and illustrate its application to
ernHemisphericforestsincombinationwithpossibly five case studies describing common management
aCO fertilizationeffect(Watsonetal.,2000). systems of temperate forests, tropical forests and
2
Available estimates suggest that forests may miti- agroforestry systems. This version of the CO2FIX
gate additionally from 1 to 2GtC per year between program aims at a widespread applicability to han-
1995 and 2050 (Brown et al., 1996; Kauppi et al., dle uneven-aged, mixed-species forest-management
2001). However, achieving the full carbon mitigation regimesormulti-cohortsystems(suchasagroforestry
potential will require, among other things, accurate or selective logging of tropical forests). The work
methods to assess the dynamics of carbon fluxes and builds on the experience of the previous model that
storage under alternative management regimes. Ac- hasbeenextensivelyusedandtestedinmanytypesof
curate estimates of the potential dynamics of carbon forest ecosystems throughout the world (Mohren and
fluxes in forest ecosystems and afforestation projects Goldewijk, 1990; Mohren et al., 1999). We begin the
are also needed for the adequate implementation of paper with a discussion of the project approach; we
the Kyoto Protocol, which currently allows for the then describe the main characteristics of the model.
so-calledARDactivities(afforestation–reforestation– In a third section we show the results and reliability
deforestation),aswellasre-vegetationandforestman- of the application of the model in five case studies.
agement projects. Models will be particularly critical Weconcludethepaperwithasetofrecommendations
for the examination of alternative carbon crediting forfuturework.
schemesofJointImplementation(JI)-andCleanDe-
velopmentMechanism(CDM)-relatedprojects.
Furthermore, quantifying the likely results of the 2. TheCASFORproject
above-mentioned options is difficult because carbon
sequestrationinforestsconsistsofstocksandfluxesin The CO2FIX model was developed as part of the
variouscompartmentsoftheforeststands—including “Carbonsequestrationinafforestationandsustainable
soil and dead wood—as well as in the manufactured forest management” (CASFOR) project, which was
woodproducts.Managementthatfocusesonenhance- funded through the European Union INCO-DC pro-
ment of carbon in, e.g. forest biomass therefore has gram. The CASFOR project is a multi-institutional
an impact on soils and wood products as well. Also, effort being carried out by ALTERRA in The
study results are difficult to compare because of dif- Netherlands, the Instituto de Ecologia from the Na-
ferences in the forest types, site types, management tional University of Mexico in Mexico, the Centro
systems,monitoringmethodologyused,becauseonly Agronómico Tropical de Investigación y Enseñanza
parts of the carbon cycle of a forest ecosystem-wood (CATIE) in Costa Rica, and by the European Forest
productschainareregarded,orbecausedifferenttime InstituteinFinland.
scalesareused. The research team already developed the CO2FIX
Up to date, several models have been developed V.1 model (Mohren et al., 1999), a forest-stand level,
that analyze and simulate carbon budgets and fluxes user-friendly model for quantification of the poten-
at the level of the forest stands. These models range tial role of forests in the global carbon cycle, in car-
from very detailed ecophysiological models used in bon sequestration, and in carbon emissions offsets as
climate impact assessment, to very general empir- part of the policy evaluation of the role of forests
O.R.Maseraetal./EcologicalModelling164(2003)177–199 179
in mitigating the greenhouse effect. This version has can be created. The carbon in vegetation—above-
been distributed as a free download through Inter- and belowground, soils as well as in wood products
net since June 1999.3 At the moment, there are 800 derived from the management activities conducted
registered users in 72 countries. Some of the results within the stand are included. The approach of the
of previous versions of CO2FIX have been used for model is comparable to stand-level carbon account-
quantifying C stocks and fluxes in a wide variety of ing models, such as GORCAM (Schlamadinger and
forestsystemsrangingfromeven-agedsingle-species Marland, 1996) and CAMFor (Richards and Evans,
temperate forest stands to agroforestry system in the 2000), and cohort-type forest growth models, such
tropics (Mohren and Goldewijk, 1990; Nabuurs and as CAFOGROM (Alder, 1995). The GORCAM and
Mohren,1993;NabuursandSchelhaas,2002;DeJong CAMFor models are limited to one-species manage-
etal.,1998;OrdoñezandMasera,2001;Olgu´ın,2001; ment regimes, while CAFOGROM only takes into
SchelhaasandNabuurs,2001).Furthermore,someof accounttreevolumeandbasalareaofvariousspecies
the CO2FIX outcomes have been used in the IPCC cohorts.
1995climatechangeassessment(Brownetal.,1996). The CO2FIX V.2 model has been programmed in
TheCO2FIXV.1simulatesthecarbondynamicsin C++usinganobject-orientedprogrammingenviron-
a single species (monoculture) stands, e.g. in forest ment. The model is divided in three main modules:
plantations, which has been limiting its applicabil- biomass, soil organic matter and products, and runs
ity. Therefore, the research team decided to develop withtime-stepsof1year(Fig.1).Themodelproduces
a new version of the model, which would be able outputintabularandgraphicforms.Itallowsestimat-
to simulate the carbon dynamics associated to more ing the time evolution of total carbon sequestered at
complex systems, such as selective logging of trop- thestandlevel.
ical forests, where multi-species (functional groups) The total carbon stored in the forest stand at any
and a multi-layer structure is common. The same is time(CTt)isconsideredtobe
true for multi-tiered agroforestry systems and for the
management of mixed-species, uneven-aged native CTt =Cbt +Cst +Cpt (tCha−1) (1)
forests. Furthermore, a new soil module and an im-
proved wood product module were added to the new where Cbt is the total carbon stored in living (above
plus belowground) biomass at any time t, in metric
versionofthemodel.
tonnes per hectare (tCha−1); Cst, the carbon stored
insoilorganicmatter(tCha−1),andCpt isthecarbon
storedinwoodproducts(tCha−1).
3. CO2FIXV.2modelstructure
The CO2FIX V.2 is a model that quantifies the C 3.1. Carbonstoredinlivingbiomass
stocksandfluxesinaforeststand,usingtheso-called
full carbon accounting approach, i.e. calculating The carbon stocks and flows in the forests’ living
changesincarbonstocksinallcarbonpoolsovertime biomass (above- and belowground) are estimated us-
(Noble et al., 2000).4 A stand is here defined as an ing a “cohort model” approach (Reed, 1980). Each
areathatcanbeconsideredasrelativelyhomogeneous cohort is defined as a group of individual trees or
in terms of vegetation structure, growth dynamics, specieswithinthestand,whichareassumedtoexhibit
and species composition, and contains a number similar growth, and which may be treated as single
of trees for which a common set of characteristics entitieswithinthemodel(Vanclay,1989;Alder,1995;
Alder and Silva, 2000). These cohorts may be, for
example: (a) successional groups in a natural forest
3 A more detailed description of the CASFOR project, and the (e.g. pioneers, intermediate, and climax), (b) species
different versions of the model can be found at http://www.efi.fi/ in a mixed forests (e.g. mixed pine–oak forests); and
projects/casfor.
(c) strata in a multi-strata agroforestry system (e.g.
4 Herewepresentaconceptualdescriptionofthemodel.Refer
understory, middle layer, upper layer). The carbon
to Nabuurs et al. (2002) for more details about the CO2FIX V.2
userinterfaceandsimulationoptions. stored in living biomass (Cbt) of the whole forest
180 O.R.Maseraetal./EcologicalModelling164(2003)177–199
Fig.1.Carbonfluxes/processes(arrows)andcarbonstocks(boxes)inaforestecosystemanditswoodproductsasdistinguishedinCO2FIX
V.2.
stand, can then be expressed as the sum of the where K is a constant to convert biomass to carbon
c
biomassesofeachcohort,i.e. content(kgCperkgbiomassdryweight).
(cid:1)
Cbt = Cbit (tCha−1) (2)
3.1.1. Biomassgrowth
In order to simulate Gbit the model uses as input
where Cbit is the carbon stored in the living biomass
ofcohorti(tCha−1). thegrowthrateofstemvolumes,whichcanbederived
from conventional yield tables. From the growth rate
For each new time step, Cbit is calculated as the
of stem volumes, growth rates for foliage, branches
balance between the original biomass, plus biomass
androotsarecalculated,usingtime-dependentalloca-
growth(Gbit),minustheturnoverofbranches,foliage
tioncoefficients.Hence,themodelusesstemvolume
androots(Tit),minustreemortalityduetosenescence growth in m3ha−1 per year as the main input, and
(Msbit), minus harvest (Hit) minus mortality due to
uses an allometric approach to derive net annual in-
logging(Mlit),i.e.
crement of the main biomass components from stem
volumegrowth.Thesegrowthratesarelatermodified
Cbit+1 =Cbit+Kc[Gbit−Msit−Tit−Hit−Mlit] bytheinteractionsofthecohortwithinitselfandwith
(tCha−1) (3) othercohorts.Toadjustfordifferencesinsitequality,
O.R.Maseraetal./EcologicalModelling164(2003)177–199 181
yieldtablesderivedforgood-,medium-,andpoor-site formulti-speciesandmulti-stratasituations(e.g.Beer
conditions may be used and other growth related pa- etal.,1990).
rametersmodifiedaccordingly(NabuursandMohren, Inthismodelasingleparameterisusedtosimulate
1995).Mathematically, the influence of the same cohort or the influence of
(cid:2) (cid:2) (cid:1) (cid:3)(cid:3) othercohortsonthegrowthofthecohortinquestion.
Gb = K Y 1+ (F ) ×Mg Mgit is defined as a function of total biomass of the
it v ist ijt it
stand.Themodelprovidestwobasicoptionsformod-
(tha−1peryear) (4) elingtheinteractionsbetweenandwithinthecohorts:
(a) Competition of a cohort as a function of relative
where K is a constant to convert volume yields into totalstandbiomass(i.e.totalabovegroundbiomassof
v
drybiomass(basicdensity,inkgdrybiomassperm3 all cohorts in a stand at any time “Bt” relative to the
of fresh stemwood volume); Y , the volume yield of maximum total stand biomass of all cohorts “B ”
ist max
stem wood for each cohort “i” in m3ha−1 per year, in tha−1). In this case, the interactions of this cohort
F , the biomass allocation coefficient of each living with all the cohorts combined, including the cohort
ijt
biomasscomponent“j”(foliage,branches,androots) inquestion,ismodeled.(b)Interactionsofthecohort
relativetostems,foreachcohort“i”attimet(kgper in question as a function of the relative biomass of
kg)andMgitisthedimensionlessgrowthmodifierdue each other cohort separately. Mathematically, we can
tointeractionsamongandwithincohorts. expressMgit eitheras
The model provides two alternative ways to define (cid:4) (cid:5)
B
t
stemgrowthofeachcohort:(a)asfunctionoftreeor Mg =f (5)
it B
standage(conventionalyieldtables),and(b)asafunc- max
tion of the cohort total and maximum aboveground or
biomass. The latter input option has been added be- (cid:6)
cause in tropical forests often diameter-dependent Mgit = Mgikt (6)
insteadofage-dependentgrowthoftreesisused.
In order to be able to model the carbon stored and where Mgikt is the dimensionless growth modifier
accumulatedinmulti-cohortstands,CO2FIXmodifies function of each cohort “i”, relative to each of the
thegrowthofeachcohortduetotreeinteractions.This othercohorts“k”and
(cid:4) (cid:5)
isbecausetreegrowthinacohortisinfluencedbythe B
Mg =f it (7)
presenceofothertrees.Themajortypeofinteractions ikt B
imax
is competition. For each cohort, the interactions can
be caused by the same cohort, or by other cohorts. where Bit and Bimax are the aboveground biomass of
Three types of interactions can be described: (a) no each cohort at time “t”, and the maximum above-
competition,(b)competition,and(c)synergiceffects. groundcohortbiomass,respectively.Thus,iftwoco-
There are various ways of modeling competition. horts are present, we have to include four possible
In gap models, “growth modifiers” are used for this growth modifiers, if three cohorts are present, then
purpose (Botkin et al., 1972). It is assumed that trees potentially nine growth modifiers might be defined,
growatamaximumrateunderoptimalconditions,but and so on. The maximum aboveground biomass of
that this growth can be affected by biotic and abiotic the stand—or of each of the cohorts—can be esti-
conditions of the environment. In growth and yield mated from inventory data coming from undisturbed
models, the growth modifier is usually defined as a or lightly disturbed forests in or around the site area
function of stand-basal area or as a function of other (typically forest stands with the highest basal area).
variableindicatingstockingdensityofthestand(Peng, Locally developed or published regression equations
2000;MonserudandSterba,1996).Themodifierval- thatconvertinventorydatatostandingbiomassshould
uesrangefrom1(i.e.growthisnotreduced)to0(no beusedforthispurpose(Brown,1997).Ifonlycom-
growth at all), to more than 1, when there are syner- mercialvolumedataareavailableforthewholeforest
giceffects(i.e.wheregrowthishigherinthemixture orthecohorts,standardizedbiomassexpansionfactors
thaninthecaseofeachcohortalone).Thisisrelevant canbeappliedtothesedata.Ifnoinventoryorvolume
182 O.R.Maseraetal./EcologicalModelling164(2003)177–199
dataareavailable,publisheddataofforestsundersim- 3.1.5. Mortalityduetologging(harvesting)damage
ilarecologicalconditionsshouldbeconsulted. Forest logging operations increase the mortality of
theremainingtrees.Thisdamagedependsverymuch
3.1.2. Treemortalityduetosenescence on the type of forest and the type of technology and
Mortality due to senescence can be estimated as a methods used in logging. In tropical forests, where
function of tree age or as a function of the relative traditionalloggingmethodsareused,mortalitydueto
biomass (standing biomass divided by the maximum loggingcanbeveryhigh,upto20%oftheremaining
standbiomass). basalarea(AlderandSilva,2000).Themortalitydue
(cid:4) (cid:5)
B tologgingisdirectlyrelatedtotheintensityoflogging,
Msit =f(age) or Msit =f B it (8) which can be expressed as the number of trees, basal
imax
area,volume,orbiomasslogged.
where,Msit isthecohortmortalityduetosenescence Also,theloggingmaycausemortalityseveralyears
at time t in years. In the first case, it is assumed that after the operation (Pinard and Putz, 1997). In many
all trees have a maximum age, and that the mortality cases,theinitialmortalityishighduringthefirstyears
(i.e. the probability of dying) increases when the age after the logging, and the mortality decreases gradu-
of the stand approaches the maximum age. In some ally,reaching0in10–20years,dependingonthefor-
situations,theremayalsobehighinitialmortality,for esttypeandtechnologyused(PinardandPutz,1997).
instance, of pioneer species in a natural succession In the CO2FIX V.2 model, we use a logging dam-
(Vanclay, 1989). If data of mortality related to age is age mortality coefficient (Klit) as a linear function
not available—a typical situation for tropical natural of time (years after logging) with three parameters:
forests,themortalitycanbemodeledasafunctionof (a) initial mortality (Moi), (b) duration of the dam-
relativecohortbiomass. age (π), and (c) intensity of the initial logging (Ioi).
Mathematically,
3.1.3. Turnover
Inadditiontotreemortality,anaccurateestimation Ml =B ×Kl (tha−1peryear) (10)
it it it
ofcarbondynamicsinlivingbiomassneedstoaccount
for the turnover of foliage, branches, and roots. This
where
turnover is also very important to adequately model
thecarbondynamicsofsoilorganicmatter.Wemodel
the turnover for each cohort (Tit) as the sum of the Klit =Moi−π×Ioi (11)
turnoversofeachcomponent“j”,whichinturnissim-
ply the existing biomass of the particular component Parametrizing the biomass module involves get-
“j” multiplied by a decay or turnover constant (KijT). ting information ranging from widely available yield
Mathematically, tables on stem volume increment, wood density of
(cid:1)
T = B K (tha−1peryear) (9) species, and general knowledge on harvesting and
it ij ijT
thinning practices, to more detailed information on
whereK rangesbetween1peryear(i.e.allthecom- stem growth allocation coefficients, competition and
ijT
ponent biomass is lost during the year) to 0 per year mortality.Amanualhasbeenpreparedandpublished
(noturnoveratall). on the Web to help users parametrizing the CO2FIX
model (Nabuurs et al., 2002, available free of charge
3.1.4. Harvesting at http://www.efi.fi/projects/casfor). We refer read-
If the particular forest ecosystem under analysis ers to this publication for a detailed discussion on
is managed, part or all of the tree biomass might how to obtain the model parameters and how to
be removed through thinnings, selective logging or find appropriate default values if needed. Five com-
clear-cutting. This harvested biomass is subtracted plete case studies describing common forest man-
from the existing biomass, and is allocated to the agement systems are also provided with the model,
products and soil modules (see the sections on soil which help users with the parametrization of similar
organicmatterandwoodproductsbelow). systems.
O.R.Maseraetal./EcologicalModelling164(2003)177–199 183
Fig.2.Flowchartofthesoilsubmodel.Theboxesrepresentcarboncompartments,thearrowscarbonfluxeswithassociatedacronymsof
parameterscontrollingthefluxes.
3.2. Carbonstoredinsoilorganicmatter ments are for soluble, holocellulose, and lignin-like
compoundsandfortwodifferenthumustypes.5
To calculate the fluxes and stocks of carbon in For climate information, YASSO requires mean
soil, a dynamic soil carbon module called YASSO annual temperature, plus precipitation, and potential
wasadaptedtotheCO2FIXmodel.Amongthemany evapotranspiration values during summer (from May
existing soil carbon models, this one was considered toSeptemberfortheNorthernHemisphere).Fortrop-
particularlysuitableforthispurpose.First,thesoilcar- ical circumstances the year-round precipitation, and
boninputscouldbederivedintherequiredformfrom the year-round potential evapotranspiration, may be
thevegetationmoduleoftheCO2FIXmodel.Second, used but the accuracy of this approach has not been
thesoilmodeldidnotrequireanyspecialinformation tested.
that would not be generally available. Third, it used In the current application in CO2FIX, YASSO re-
annual time step, like the rest of the CO2FIX model. ceiveslitterinput(tCha−1 peryear)fromlivingtrees
The current YASSO model has been developed for of each cohort from foliage, fine roots, branches,
non-waterloggedforestsoilsandtestedtocalculatede- coarse roots, and stems. Those inputs are quantified
compositionofvariouslittertypesappropriatelyfrom during the simulation in other parts of the CO2FIX
arctictundratotropicalrainforests(Liskietal.,2002). modelfromturnoverrates,naturalmortality,manage-
YASSO consists of three litter compartments de- mentmortality,andloggingslash(Eqs.(8)and(11)).
scribing physical fractionation of litter and five com- The user can initialize the module, i.e. determine
partments describing microbial decomposition and the contents of the soil carbon compartments in the
humification processes in the soil (Fig. 2). The litter
compartmentsareforstem,branchespluscoarseroot, 5 For a more detailed description of this model, write to
andfoliageplusfinerootlitter.Thefiveothercompart- Jari.Liski@efi.fiorseeKarjalainenetal.(2002).
184 O.R.Maseraetal./EcologicalModelling164(2003)177–199
beginning of the calculations either by manually Therobustnessofmodelingclimaticeffectsonde-
providing the carbon contents, just like giving the composition in CO2FIX has been tested using data
climate data, or by manually providing annual litter fromlitterbagexperiments(Liskietal.,2002).These
inputs of the vegetation type of the land-use before experiments have been carried out in a wide variety
afforestation,andthenlettingthemodelcalculatethe of environments ranging from arctic tundra to tropi-
associated equilibrium stocks. If no local litter fall cal rainforest across North and Central America, and
data specific for the site are available, then an indi- usingdifferentfoliarandfinerootlittertypes(Moore
cation of litter inputs may be derived from annual etal.,1999;Gholzetal.,2000).Theresultsshowthat,
NPP data for biomes. In case the latter option needs inmostconditions,CO2FIXestimatestheclimaticef-
to be applied, then the soil initialization becomes fectsondecompositionwithlittlesystematicerrorand
very uncertain because of large spatial variability in goodfitwithexperimentaldata(highR2values)(Liski
soiltypesandcarboncontents.Allfurthersoiloutput etal.,2002).Underverycontinentalclimate,however,
mustinthiscaseberegardedwithgreatcaution. such as in the middle of Canada, the model seems
In YASSO, litter entering the soil from the various to overestimate the climatic effects, because of a dif-
origins (see above, Eqs. (8) and (11)) is first divided ferent relationship between annual mean temperature
betweenthethreelittercompartmentsaccordingtothe andtemperatureofthegrowingseason.Thisproblem
litter type (non-woody, fine woody or coarse woody will be fixed by replacing annual mean temperature
litter). The fractionation rates of these litter compart- with the effective temperature sum (degree) days in
ments (ai) determine the proportions that leave each thenextversionofCO2FIX.
of these litter compartments each year. The matter
leaving the litter compartments goes to either solu- 3.3. Carbonstoredinwoodproducts
ble, holocellulose or lignin compartments according
to its chemical composition (ci). Each of these com- The products module is a carbon accounting sub-
partments, plus the two humus compartments have model that tracks the carbon from harvesting to final
a specific decomposition rate (ki) that determine the decay. It does this tracking through several interme-
fractions removed from the contents each year. Frac- diate processing and allocation steps. This module
tionsofmatter(px)leavingthesoluble,thecellulose, is based on a model developed by Karjalainen et al.
the lignin or the first humus compartments are trans- (1994) and has been used for modeling the carbon
ferred to the subsequent compartments as illustrated budgetintheFinnishforestsector(Karjalainenetal.,
inFig.2whiletherest(1−px)leavesthesystem. 1995, 1999; Pussinen et al., 1997). A more detailed
The decomposition rates of the soluble, cellulose andnewlyparameterizedversionofthemodelhasbeen
and lignin compartments as well as the transfer frac- applied to Finland (Liski et al., 2001), to Germany
tions between these compartments, have been deter- (Karjalainen et al., 2002) and to the European forest
minedusingmasslossdatafromSweden(Bergetal., sector(Karjalainenetal.,2001;Eggers,2001).
1991a,b). Also, the decomposition rates have been The outline of the wood product module is shown
settodependonannualtemperatureandprecipitation in Fig. 3. Harvested material from thinning and/or fi-
minus potential evapotranspiration between May and nal felling is raw material for manufacturing, and is
September according to an analysis of Berg et al.’s separatedintologwood,woodforpulpandpaper,and
(1993) data across Europe. The parameters related to slash.Slashcaneitherbeleftonthesiteandthusenter-
the humus compartments have been determined us- ingthesoilpool,orcanbeusedforproducingenergy.
ing data on the accumulation rate of carbon along a Manufacturingincludesvariouscategoriesofproduc-
soilchronosequenceontheFinnishcoast(Liskietal., tionlines,suchasmanufacturingofsawnwood,board
1998) and soil carbon contents at sites of different and panels, pulp and paper, and wood fuels. In the
productivities (Liski and Westman, 1995). The de- manufacturing process, part of the raw material ends
compositionratesofthehumuscompartmentsareset upasaprimaryproduct(e.g.sawnwoodandlaterfur-
less sensitive to temperature than those of the other niture), and part is allocated for secondary use (e.g.
compartments(Liskietal.,1999;GiardinaandRyan, by products from saw mill are used for manufactur-
2000). ing boards and pulp and paper). Also part of the raw
O.R.Maseraetal./EcologicalModelling164(2003)177–199 185
Fig.3.Outlineofthewoodproductmodule.Arrowsshowtransfersofcarbonbetweendifferentphasesoflifespan(fromharvesttofinal
allocation).Boxeswithboldlinesarestocksofcarbon.
material can be used to generate energy (wood fuels) for energy generation or are set aside to decompose
or set aside (no use). Manufactured products are dis- in the manufacturing phase (i.e. in mill-site dumps),
tributed to different usage categories (long-term use, whenwoodfuelsareburnedorwhenproductsareused
medium-termuse,short-termuse,woodfuels)accord- for energy production or decompose in landfills. The
ing to average usage time. This last, shows how long decompositionrateinmill-sitedumpsandinlandfills
(in years) products are assumed to be in use on aver- is based also on an exponential discard function as
age in each of the four broad categories. Every year above,butwithdifferentvaluesfortheparameter“a”.
thesameproportionofproductsthatareinusewithin Because data on products average usage time and
eachusagecategoryisdiscardedfromuse,according decomposition rates is not widely available, CO2FIX
totheexponentialdiscardfunction: V.2 gives the user the opportunity to choose default
Cpmt+1 =Cpmt×(1−am) (tCha−1) (12) ptraorpaimcaelterfsorefostrryevseyns-taegmesd., Aagltreorfnoarteisvterlyy,ourseprrsimcaaryn
where Cpm is the carbon stored in the wood usage choose to modify those default parameters based on
productcategory“m”attime“t”andamistheshareof own data. Default parameters are also provided for
theproductthatdecomposeseachyear.Iftheaverage pioneer,intermediateandclimaxspecies.Inaddition,
lifespanis40years,thenam equals1/40peryear,i.e. the model provides minimum and maximum values
2.5%oftheproductsarediscardedfromuseeachyear. foreachparameter,whichprovidesarangethathelps
Finally, products can be disposed to landfills, used the user to modify them based on own data. Default
for energy generation or recycled. Carbon is released parametersarebasedonavailablevaluesfromthelit-
to the atmosphere either when byproducts are used erature,andhavebeendescribedinLiskietal.(2001)
186 O.R.Maseraetal./EcologicalModelling164(2003)177–199
and Eggers (2001). Currently, the best knowledge is tion from companies selling wood products could be
onthemanufacturingphaseparameters.Otherparam- used.
eters such as the average usage time of products and
end-use of products, i.e. how much of the products
4. Applicationtoselectedcasestudies
are recycled, used for energy production or disposed
tolandfills,areknownlessaccurately.Parameterval-
Themodelwastestedandvalidatedinitiallyforfive
ues vary according to the infrastructure of the forest
representative case studies of temperate and tropical
industry (manufacturing parameters), and the struc-
forests: (a) even-aged Norway spruce of Central Eu-
ture of society and behavior of consumers (product
rope;(b)even-agedmixedDouglas-fir-beechforestof
life spans and end uses). In general, parameter val-
Atlantic Europe; (c) mixed pine–oak native forest of
ues are less accurate for tropical conditions, where
Central Mexico; (d) multi-strata agroforestry system
information is more difficult to gather. For specific
inCostaRica;and(e)tropicalrainforestinCostaRica.
case studies, local saw mills and manufacturers of
wood products might be able to provide data for pa- 4.1. Even-aged monoculture of Norway spruce in
rameters such as processing losses and allocation of regularrotationinCentralEurope
harvestedwoodtodifferentuses;fordataonlocaluse
of products such as life span, ultimate disposal and This is a Norway spruce (Picea abies) forest
recycling, knowledge of local conditions or informa- plantedonafertilesiteinthemiddlemountainregions
Table1
Mainparametersusedforsimulatingtheeven-agedsprucestandofCentralEurope
Spruce
Cohorts
Rotation(years) 95
Initialhumuscontentofthesoil(tCha−1)a 142
Basicwooddensity(kgm−3) 430
Carboncontent(%ofdryweight) 50
Turnoverrates
Foliage 0.3
Branches 0.04
Roots 0.08
Year
25 45 55 70 95
Fractionremovedduringthinningorharvest 0.2 0.2 0.2 0.2 1
Logwood Pulpwood Slash
Productallocationforthinningsandharvesting
Stem-thinning(averageforthethreethinnings) 0.3 0.6 0.1
Stem-finalharvesting 0.7 0.25 0.05
Branch 0.0 0.15 0.85
Foliage 0 0 1
Expectedlifetimeofproducts(years)
Long-termproducts 30
Medium-termproducts 15
Short-termproducts 1
Mill-sitedump 10
Landfill 145
a Includeshumus,fineandcoarselitter.