1+ government of canada gouvernement du canada canadian ... · resume des equalions de masse onl...

1+ Governmentof Canada

CanadianForestryService

Gouvernementdu Canada

Servicecanadien desforets

Development of biomassequations for British Columbiatree species

J.T. Standish, G.H. Manning and J.P. Demaerschalk

Information Report BC-X-264Pacific Forest Research Centre

Development of Biomass Equationsfor

British Columbia Tree Species

by

J.T. Standish'G.H. Manning*·

&J.P. Demaerschalk***

Talisman Land Resource Consultants300·842 Thurlow St.Vancouver, B.C. V6E 1W2

Pacific Forest Research Centre506 W. Burnside Rd.Victorino B.C. V8Z I M5

Faculty of ForestryUniversity of Brilish ColumbiaVancouver. B.C. V6T IW5

Canadian Forestry ServicePacific Foresl Research Centre

BC-X-264

1985

Canadian Forestry ServicePacific Forest Research Centre

506 West Burnside RoadVictoria, B.C.

V8Z I M5

© Minister ofSupply & Services Canada. 1985ISSN 0705-3274

ISBN 0-662-14013-3Cat. No. Fo46-171264E

J

Foreword

ENFOR is the acronym for the Canadian Government's E lergy from the FORest (ENergie dela FORet) program of research and developmentaimed at securing the knowledge and technicalcompetence to facilitate in the medium to longterm a greatly increased contribution from forestbiomass 10 our nation's primary energy production. This program is part of am uch larger federalgovernment initiative to promote the development and use of renewable energy as a means ofreducing dependence on petroleum and othernon-renewable energy sources.

The Canadian Forestry Service (CFS) administers the ENFOR Biomass Production programcomponent which deals with such rorest~oriented

subjects as inventory, harvesting technology, silviculture and environmental impacts. (The othercomponent, Biomass Conversion, deals with thetechnology of converting biomass to energy orfuels, and is administered by the Renewable

Energy Branch of the Department of Energy,Mines and Resources). Most Biomass Productionprojects, allhough developed by CFS scientists inthe light of E FOR program objectives, are carried out under contract by forestry consultantsand research specialists. Contractors are selectedin accordance with science procurement lender·ing procedures of the Department of Supply andServices. For further information on the ENFORBiomass Production program, contact...

ENFOR Secretar;atCanadian Forestry ServiceOttawa, OntarioKIA IG5

This report is based on ENFOR project P-142which was carried out under contract by TalismanLand Resource Consultants, Vancouver, B.C.(DSS File No. 07SB.KLOI7-9-0655).

Resume

Des equalions de masse onl 10110 elablies pour I155 arbres, de 22 essences comrnerciales, repartisdans loute la Colombie-Brilannique. sallf dans lapanie nord el les iles de la Reine-Charlotte. Lesarbres OIH ele echantillonnes suivant une methode destructive, et des equationde la formey = bo + b1D2H, ainsi Que des equations lineaires multiples basees sur D, H et Vet leurs interactions. ani ete construites. Les deux typesc!'equations donnel1l des estimations d'une precision et d'une exactitude raisollnable, el Ie choixde I'llne ou I'autre depend de I'application queI'on velll faire des equations de masse.

4

Abstract

Biomass equations were developed for 22 commercial species covering all of B.C. except thenorthern part of the province and the QueenCharlotte Islands, based on 1155 sample trees.Trees were destructively sampled and equationsof Ihe forl11 y = bo + b,D'll and l11ultiple linearequations based on 0, 1-1 and V and their interactions were developed. Both forms of equationgive reasonably accurate and precise predictions,and choice depends on the use to which the biomass equations will be put.

5

Contents

Foreword .

Abstract ..................•............................

Introduction

Meth~s............................. . .Sample Allocation and Sample Tree Selection .Tree Sampling Proceclures....... . .Olher Data Collected .Laboratory Processing .Data Processing... . .Regression Analysis .

Resulls and Discussion.. . .Comparison of Various Prediction Models and Use of V

as an Independent Variable .The Contribution ory as an Independent VariableFinal Regression Analysis .Additivity of Coefficients ..Precision . .Accuracy ....................•.•.... _..... ............•... ........•.•.....Degeneracy.. .. ..Extrapolalion .Time-Relaled Variability

Literature Cited .

Appendix AAppendix BAppendix C .Appendix D ..

Page

3

4

6

6677999

10

101213161717171717

18

42444548

6

Introduction

Love (1980) has indicated the potentially greatcontribution of forest biomass to Canada's futureenergy supply. In British Columbia logging residue has been identified as a potentially viableenergy source (McDaniels 1981). However, aprerequisite to realizing this potential is theavailability of an adequate forest biomass inventory (Bonner 1979; Dobbs 1981).

Many studies altempling to estimate biomass ofBritish Columbia tree species have been conducted (Stanek and State 1978) but in most casestheir scope has been local ralher than regional.Tree componenls have been defined in variousways for different studies, and various predictionequation models and units for coefficients(Imperial liS metric) have been used.

The objective of the study on which this paper isbased was 10 develop a generalized set of equa·lions which can be used in conjunction with theexisting provincial forest inventory to estimate

the quantity of forest biomass available in BritishColumbia. More specifically, the objective was todevelop whole-tree and tree component biomassrelationships for 22 native tree species and varieties (Appendix D), applicable to second-growthstands.

The regression equations were to be applicable,at least as a first approximation, on a regionalbasis. Independent variables were to be restrictedto those conventionally measured in forestinventories, namely diameter at breast height l

outside bark (Dbh) and height; and tree component relationships were Lo be conducive tosimple mathematical summation, as discussed byEvert and Alemdag (1979) and Kozak (1970).Although Dbh and height were the only measured variables used in developing the regressionequations l other tree, stand and site informationwas collected for each sample tree in order to provide a data base for future analyses.

Sample Allocation and Sample TreeSelection

Methods

due to budgetary constraints.

The initial allocation of samples to tree specieswas proportional to the provincial volume inventory, but was later modified so that each tree species had forty or more sample trees in order toproduce biomass relationships of the same orderof significance and confidence for each species.Ideally, sample allocation would have consistedof an equal number of randomly-selected observations of biomass over the range of interest foreach fixed value of the independent variables(Demaerschalk and Kozak 1974). Furthermore,samples should have reflected a wide range ofstand and site conditions and geographic variability so that the prediction equations would be applicable on a broad regional or provincial scale(Cunia 1979). However, technically rigoroussample tree selection and optimal sample allocation schemes, as discussed by Demaerschalk andKozak and Cunia (1979), were not practicable

Given the constraints, a more pragmatic approach was adopted as follows:

I) A target number of sample trees was designated for each species.

2) Provincial volume inventory data were examined to determine a range of Dbh and heightclasses for each species.

3) Sample trees were allocated equally to eachDbh and height class within species.

4) Where extra samples remained followingequal allocation to each class, the remainingsamples were allocated to the extreme classes.

5) The number of trees in each size class for aspecies were then allocated to variousgeographic areas.

6) As sampling progressed, the number of treesallocated to a size class for a species was,when necessary, reallocated, using the samegeneral approach as noted above, to reflectthe range of tree size actually occurring in accessible stands.

Geographic sampling areas were defined mainlyby administrative boundaries and access. Becauseof high costs, no sampling was conducted on theQueen Charlotte Islands, in the north coastalregion, in the Peace River region or in the northwestern part of the province north of Highway16. Another ENFOR biomass inventory projectin the Yukon Territory provides data applicableto some of northern British Columbia (Massie1983).

Additional sampling restrictions were thatsample trees were to be in the codominant crownclass, forked trees were avoided except in somedeliquescent hardwoods, and trees with dead orbroken tops were avoided. Open-growing or wolftrees which are not representative of a commongrowth habit for a species were avoided and treeswith decay indicators and trees infested with barkbeetles or defoliating insects were also avoided.Jt was necessary to include some sample trees inthe intermediate crown class for shade-tolerantspecies, so that smaller diameter classes were adequately represented.

Tree Saml,ling Procedures

Standards and procedures for sampling individualtrees are gi ven in Appendix C and closely followthose presented by Alemdag (1980). The biomass components defined for this study areshown in Figure I. They include bole wood andbark from ground level to a 2.5-cm bole diameter, three live branch size classes, and deadbranches and foliage. Cones were included withtheir appropriate branch size class for fresh massdeterminations but were excluded for oven-drymass. Only fresh mass was determined for deadbranches. Underground biomass was not sampled.

Trees were cut at a point 30 em above the pointof germination, then limbed and bucked at intervals of 2m or less. The total fresh mass of thebole and crown components was determined directly by weighing in the field, except for large

7

bole sections. These were measured for insideand outside bark volume, and mass was later calculated by applying mass-to-volume ratios determined from sample discs. The above-groundmass of the stump was determined in a similarmanner. Volume of stem sections was calculatedby the Smalian formula; volume calculation assumed a cylin(irical shape for stumps. Samplediscs were taken from each stem section to determine the ratios of fresh and oven-dry wood andbark mass.

Live branches (including the top) were sortedinto three basal diameter classes and weighed todetermine the total fresh mass in each class. Tworandomly selected branches from each of thebasal diameter classes were sub-sampled to determine the proportion of each of the three branchsize classes within a basal diameter class. Themass of dead branches was determined directlyby weighing in the field.

The fresh mass of bole sample discs and crownsubsamples was measured at the field base campwithin 24 hours of cutting to determine the ratiosof fresh wood mass to bark mass and the proportion of fresh mass in each of the three branch sizeclasses. For fresh mass, foliage was included withthe smallest branch size class but was later calculated separately based on dry mass ratios.

Each sample was labelled and stored at the basecamp until shipment to the laboratory in Vancouver (at one- to two-week intervals). Just priorto shipment, samples were sealed in plastic bags.Transit time to the laboratory was usually one ortwo days.

Other Data Collected

Diameter at breast height outside bark (Dbh),total tree height (H), crown width (CW) andcrown length (CL) were measured and recordedfor each sample tree. Dbh was measured to thenearest 0.1 cm and H, CW and CL to the nearest0.1 m. Crown width was recorded as the averageof two orthogonal axes measured at the base ofthe live crown. Age was recorded for each sampletree based on a ring count at a height of 30 cmand B.C. Ministry of Forests age correctiontables. Other information of interest which wasrecorded included the following:

8

-c

F

III

~

A - below ground componentsrools and slump

B - Stump above ground30 em above point01 germination

......-

C - Boleto a 2.5 em diameter topBole wood and barkare measured separately

o - Live Branchessize class 1 > 2.5 em

size class 2 0.5-2.5 emsize class 3 <0.5 em

E

E - Dead Branches

F - Foliage

G - Top< 2.5 em diameterincluded in appropriatebranch size classes

Figure I. Tree biomass components as defined for this study

Modified from Young. I-I.E., L. Stnmd llnd IC Alterbcrger. 1964. Preliminary fresh and dr~

weight tables for seven tree species Inl\laillC. l\:lainc Agr. Exp. $l<l .. Orono, l\lainc. Tech. Bull.12: 76 p. in Stanek and SWle. 1978.

Seclion F from ilraysh:tw. T.c. 1963. Key 10 the nalive trees of Canada. Can. Dept. ForestryBull. 125. Min. afForesls. Qllaw<l.

I) crown class (dominant, codominant or intermediate)

2) total (inside bark) wood volume

3) latitude and longitude of the sample location(to the nearest 15 seconds)

4) elevation (to the nearest 50 m a.s.l.)

5) biogeoclimatic zone (defined by Krajina inFarley (J 979))

6) stand density in cubic metres per hectare

7) crown closure (ocular estimate, to the nearest10%)

8) site position (e.g., upper slope, mid-slope,lower slope, etc.)

9) a general description of dominant understoryvegetation

The B.C. Ministry of Forests has developed asystem of biogeoclimatic zones which differsfrom the one originally developed by Krajina.Since maps were not available for the Ministry ofForests' system covering all areas of this study.Krajina's system was used. Those wishing to relate the sample lrees to other classifications ormaps can do so using the information on latitude, longitude and elevation recorded for eachsample tree.

Laboratory Processing

Upon arrival at the laboratory, samples werepromptly unpacked and laid out on a trestle tablefor preliminary drying at ambient temperature.Samples were then dried in large, force9·airdrying ovens equipped with constant temperaturecontrols. Drying times ranged from 24 to 48hours depending on the moisture content, sizeand type of sample. After removal from theovens, the samples were allowed to cool for 15minutes before weighing.

The immersion technique outlined in AppendixC was used to determine the volumes of oven-drywood and bank for sample discs from large bolesections.

9

Data Processing

Entries on the data form for each tree werechecked at the time of laboratory processing andagain when all sampling for the year had beencompleted. Errors, omissions or inconsistencieswere corrected, sometimes by reference to theoriginal field notes ancl samples. Following visualchecking, data were keypunched, verified andtransferred to computer tape. The data were thenprocessed by a computer program which reedited the data, produced a tabulated output ofall data and biomass measurements for each tree,and produced a computer file for statisticalanalyses. Complete data printouts were producedduring the winter following each field season.Data editing included calculation of various results for each biomass sample, sllch as the percentages of fresh wood, dry wood, bark, foliage,and the density. Inconsistent results or resultsoutside pre-determined ranges were nagged forfurther checking.

Regression Analysis

Preliminary analysis was conducted based on thedata sets collected during the 1980 field seasonfor ponderosa pine and western larch usingsimple and multiple linear and logarithmicmodels with Dbh, H, D2H, CW and CL as independent variables. During 1982, detailed preliminary analyses of large data sets for five specieswere conducted; simple and multiple linear aswell as nonwlinear models were tested using Dbh,H, volume(V) and their interactions as independent variables. Volume as used in the discussionof regression equations refers to the total insidebark wood volume predicted from B.C. Ministryof Forests metric volume equations (Forest Inventory Division 1976). Based on the results ofthe detailed analysis, final regression equationswere produced.

Both stepwise and elimination procedures wereused in computing multiple linear regressions.Equations were first fitted to all components.These equations were studied and a compromiseequation giving the best overall results for allcomponents was selected. This compromiseequation was then fitted for all components without eliminating any of the previously selectedvariables.

Many potential independem variables were essentially interchangeable with respect to theireffect when added to the model. In such cases,variables were selected to be consistent withinsimilar groups of tree species or varieties. Forexample, all Pill/ls species use V and D' and bothTS/lgaspecies use V, D'Vand DHV.

In order to use volume as an independent variable. the appropriate Ministry of Forests volumeequations must be used. In most cases this requires relating a sample tree 1O a volume equation

10

by forest invenlOry zone and age class. For example there are three volume equations for westernhemlock: two for the Coast and one for the Interior. The two Coastal equations are for ages up to

120 years and greater than 120 years; the Interiorequation covers all age classes. In other cases(such as with Interior Picea and, to some degree,Abies) species are aggregated in the volume equations. No volume equations are available formountain hemlock and shore pine. so the equations for western hemlock and lodgepole pine, respectively. were used.

Results and Discussion

A total of 1155 trees were sampled and 20730samples were processed. Generally 40 or moretrees were sampled for each species or variely;the actual number ranged from 39 for mountainhemlock to 105 for white spruce. Information onthe number of sample trees and the range, meanand median Dbh, height and age for each speciesor variety is shown at the top of each speciestable in Appendix A. Approximate sampling locations are shown in Figure 2.

Preliminary analysis conducted during 1981 ofthe data sets for ponderosa pine and westernlarch suggested that simple linear equationsusing D'H gave good predictions of bole wood,bark and total mass but often performed poorlyfor crown components. In order to confirm theseresults and to select regression models that suitthe objectives of the study, a more detailed analysis was conducted during 1982. Data were analyzed for five species: white spruce, lodgepolepine, subalpine fir, western hemlock and westernred cedar. These species give a representativecross section of variability in stem, branch andfoliage characteristics for the coniferous speciesin this study. Hardwood species were not analyzed because sufficient data were not availableat the time. Analyses were conducted on ovendry biomass data only.

Comparison of Varions Prediction Modelsand Use of V as an Independent Variable

The symbols nsed in the following discussion are

defined as follows:

D = Dbh obcmH = total height in mV = volume inside bark, predicted from B.C.

Ministry of Forests volume equations inm'

B = biomass component mass in kgn = number of samplesSEE ~ standard error of the estimateMSE = mean square errorRE ~ relative efficiency

Several biomass prediction models were testedand compared:

B = b,D'\-1 (I)

B = bo + b,D'H (2)

B = boo + biD + b,D' + b,H + b.D'H

+ b,DH' (3)

B = b,Db'H b, (4)

B = bo + b, D + b,D' + b, V + b.d'V

+ b,HV + b,D'HV + b,DHV (5)'

Given the strong relationship between volumeand biomass, the potential wealth of informationin tree volume data and equations available from

I Not all terms in this model are used for e:lch individual species. SeeTable 4 for independent variables selected in ellch casco

II

\\

•"

\

• Sampling area(approximate)

•"

• Nelson • "

\I. • . '• .-J !..- . .-1

o 200 kmL'_~~_--"

I

I

I

II,I

••••

• •••• •

• •

Prince George l

.~"\'"

••

• •• • •• • •

.:". .:~:'OOPS:{". .",,~-,.:..- .

Vancouver._. _ _ ._-

• ••

•••

Prince Rupert •

•

Figure 2. Approximate sampling areas

the B.C. Ministry of Forests should be used. Thevolume equations are based on a total of 34 641sample trees collected over a wide range of sizeclasses, stand conditions and geographic areas~

the number of sample trees for each equation applicable to this study ranges from 302 for yellowcedar to 4452 for white spruce (Forest InventoryOi vision 1976). The volume equations are usedto test the potential value of V as an independentvariable.

that it should provide better estimates of thecoefficients (b" b" and b,). Broadly speaking,the use of V can be thought of as analogous totwo-phase sampling where the volume sample re·presents the first phase and the biomass samplesare the subsample. Of course, the analogy is notstrictly correct because the trees sampled for biomass in this study are not a subset of the B.C.Ministry of Forests' volume samples.

Used in this way, V provides a point estimatethat is a nonlinear transformation, similar toO'H. In fact, the volume equations are derivedfrom the general model:

(6)

Even where no clear statistical improvement canbe seen from using V. it may give more accuratepredictions than D2H when biomass equationsare applied to an independent sample (if V is. infact, a better predictor of true volume). How·ever, this cannot be tested from analysis of thedata on hand.

and O'H is a special case of (6) where b, = I, b2

= 2 and b, = I. A potential advantage of V isSimple linear equations using D2H as the independent variable (models 1 and 2) are attractive

because of their simplicity. However, the rela~

tionship between biomass of components andD2 1-1 for the species studied is not linear and doesnot pass through the origin (i.e., an intercept isrequired). Plotting of residuals and comparisonof standard error and confidence Jimits indicatethat models I and 2 are not as precise as 3, 4 and5.

Multiple linear equations based on D, Handtheir interactions (model 3) are much more precise than models I and 2. All five independentvariables are not needed for every species; two orthree are often sufficient. However, all of thesame independent variables should be retainedfor all biomass components within a species sothat simple additivity of the coefficients is possible (Kozak 1970).

Non-linear regression equations such as model 4perform comparably or nearly as well as model 3for most species. However, calculations and interpretations are more complex, and regressioncoefficients for the components are not additive.

Using V as an independent variable together withD, H and/or various interaction terms (model 5)

12

results in a multiple linear equation which is, formany species and components, more precisethan any of the other models tested. As in model3, not all independent variables are needed for allspecies but, within a species, the same variablesare retained for all biomass components to allowsimple additivity.

Comparisons of standard errors of the estimatewere made for all five species and all oven-drybiomass components. On this basis, models 3, 4and 5 are superior to models 1 and 2; models 3and 4 are approximately comparable and model 5is the best. This is illustrated in Table 1 whichcompares the standard errors for several biomasscomponents for white spruce based on a sampleof 100 trees; comparisons for the other four species are similar.

The Contribution of Volume as anIndependent Variable

Comparing models 3 and 5, the gains from including V and its interactions, in addition 10 0and H, appear to be marginal for some compo-

Table I

Standard error of the estimate (oven-dry kg)for white spruce for different biomass modelsa

(11=100)

BiomassComponent Regression Model

2 3 4 5

Wood 33.519 332397 30.601 30.409 23.700

Bark 6.598 6.048 4.085 4.184 3903

Branch 15.404 17.786 8.957 9.835 8021Class 2

Foliage 19.396 17.394 13.323 13.224 10.487

TOTAL 71328 66092 45.364 46.664 36023

a From preliminary analysIs conducted in 1982.

13

Table 2

Relative efficiency of Model 5compared to model 33

Tree Species n Biomass Componentoven-dry

Foliage Total

Whi Ie spruce 100 1.61 1.59

Western red cedar 50 1.38 1.02

lodgepole pine 80 1.53 1.09

Western hemlock 48 1.15 1.10

Subalpine fir 74 1.75 1.60

a From preliminary analysis conducted in 1982.

nenlS in some species~ however, model 5 isgenerally superior. The degree of improvementassociated with the use ory depends on the variability within species, characteristics of the sampleand the accuracy orexisting volume equations.

The relative efficiency of model 5 to model 3 isgiven by:

RE = MSE(3)

MSE(5)

Table 2 shows the relati ve efficiency gain resulting from the inclusion of V and its interactions for oven-dry total and foliar biomass for thefive species tested.

Final Regression Anal)'sis

Following the preliminary analyses, two regression models were chosen: a simple linear equation with an intercept using D2H as the independent variable (model 2) and a multiple linearequation using V, D, H and interactions (model5). Both models (2 and 5) were computed for the22 tree species and varieties (Appendix D). forall components for both fresh and oven-dry biomass (only results for oven-dry biomass are

reported here). A detailed summary of the results, including equations, is presented in Appendix A. Coefficients of determination and 95%confidence limits (for average observations, atthe mean) expressed as a percentage or the meanfor oven-dry total and foliar mass of each speciesare summarized in Table 3. Independent variables selected for multiple regression are shown inTable 4.

In general, both simple linear and multiple linearmodels give good results for prediction of bolecomponents and total biomass. The multiplelinear equations show some improvement in precision over simple linear equations as indicatedby the relative efficiencies (RE) shown in Table5. The degree of improvement varies accordingto species and component but occurs most consistently in crown components. However, considerable improvements in precision are also shownin bole wood for western red cedar, ponderosapine and red alder and somewhat lesser gains forwestern hemlock, Pacific silver fir, western larchand black spruce. Some improvemenls are alsoevident in the bark component for both hemlockspecies and red alder.

White birch is the only species in which no gainis evident in any component from using model(5). Atthe other extreme, species such as Coastal

14

Table 3

Adjusted R2 and 95% confidence limitsa for the mean (el in %)for simple linear {2} and mulliple linear (5) models

for oven·dry 10lal and foliar biomass

Species Total Foliage

r' R' CL, CL. r' IF CL, CL,

Coastal Douglas-fir 099 0.99 5.6 3.8 0.56 0.83 24.3 14.8Sitka spruce 0.96 0.98 8.2 5.3 0.46 0.89 30.6 14.0Western red cedar 0.97 0.99 6.3 4.5 0.62 0.84 22.7 14.6Interior Douglas-fir 0.84 0.88 14.9 10.2 0.40 0.74 27.6 18.0

Ponderosa pine 0.98 0.98 5.8 5.6 0.40 0.53 29.0 25.4Western hemlock 098 0.99 7.8 5.2 0.57 0.85 23.4 13.8While spruce 0.96 0.98 5.7 4.3 0.44 0.68 17.7 13.4Engelmann spruce 0.98 0.98 5.6 53 0.37 0.49 20.7 18.6

Pacific silver fir 0.96 097 6.0 5.8 0.54 0.78 19.6 13.7Grand fir 0.96 096 6.3 6.2 0.46 0.64 23.0 18.7Western larch 0.88 0.94 12.6 9.1 0.19 0.47 35.9 29.5Western white pine 0.96 0.97 9.3 8.2 0.37 0.60 290 23.2

Lodgepole pine 0.93 0.96 6.0 4.2 0.24 058 20.4 15.3Mountain hemlock 0.99 0.99 3.4 3.6 063 0.74 24.8 20.4Black spruce 0.97 0.98 4.3 3.4 0.41 0.68 24.0 17.3Subalpine fir 0.95 0.97 7.0 5.2 0.56 0.75 19.5 14.8

Yellow cedar 0.99 0.98 6.2 6.7 0.59 0.67 18.3 16.4Black collonwood 0.95 0.95 7.7 7.6 0.55 0.56 25.5 25.5Red alder 0.99 0.99 5.5 4.3 0.02 0.65 27.6 17.2Trembling aspen 0.94 0.94 7.1 7.1 0.37 0.37 29.0 29.0

While birch 0.96 0.96 6.3 6.7 0.61 0.61 15.7 15.7Shore pine 0.98 0.98 4.8 3.9 0.71 0.77 17.3 15.3

a R' is rounded to the nearest 0.01 (except for total biomass for Coastal Douglas-lir which would round 10 1.00) and CLto the nearest 0.1%; CL in Kg is given in Appendix A.

Douglas-fir, Sitka spruce, western red cedar,western hemlock, white spruce, western larch,subalpine fir and red alder show generally goodgains. The remaining species show some reasonable gains in one or more components~ for example, Interior Douglas-fir shows considerable gainin the foliage component, lesser gains in branchclasses 2 and 3 and a somewhal smaller gain intotal biomass. Evaluation of the results for somespecies such as mountain hemlock and yellowcedar is more difficult because, although gainsare made for some components, RE is less for

total biomass.

Lack of improvement from using model 5 isprobably related 10 how closely Ihe biomass samples represent the R.C. Ministry of F·orests'volume samples. In the case of mountain hemlock, there is no specific volume equation available (western hemlock volume equations areused). All Abies species are lumped together inthe volume equations and the volume samplesare dominated by Pacific silver fir on the coastand subalpine fir in the interior; this may explain

Table 4

Independent variables selectedfor l11ulliple regression equations (model 5)

Species

Coastal Douglas·firSitka spruceWestern red cedarInterior Douglas-firPonderosa pineWestern hemlockWhile spruceEngelmann sprucePacific silver firGrand firWestern larchWeslern white pineLodgepole pineMountain hemlockBlack spruceSUb,llpine firYellow cedarBlack cottonwoodRed alderTrembling aspenWhite birchShore pine

Independent Variables

V, D'V,HVV,02

V, D', D'HVV, D2V, DHV, D'VV,02

V.02

V, D'V, D'V.HVV, D'V.0 2

V, DHV. D'VV. 0 2 , D 2 VV, D', DV, D'V.0 2

V.0 2

Y,0 2

V, D'V,0 2

15

The 95% confidence limits expressed as a percentage of the mean for multiple linear equationsare generally in the 5-6% range for total oven-drybiomass and 15-20% for foliage, Confidencelimits are more variable and offen much greaterfor the three branch classes. Branch class I is particularly variable because zero values occur ortenfor all species; confidence limils for oven-drybranch class I generally range from 20-30%.Higher values oflen occur but these representvery small portions of 10lal crown or total aboveground biomass. For example, the confidencelimits for black spruce are about 90% but thisrepresents, in absolute terms, an error of 0.8 kg(at the mean); a similar situation exists for western red cedar, Pacific silver fir and grand fir andbranch class 3 in ponderosa pine and blackcottonwood.

As a further test of the applicability of theequations, the data sets for white spruce (105trees) and lodgepole pine (98 trees) were eachdivided into (wo samples, regression equationswere computed for each sample and predictionswere compared with the data for the sampleswilhin each species. The results of these crossvalidations supported the magnitude and 95%confidence limits reported in Table 3 and Appendix A.

the lack of improvement in grand fir from usingthe multiple linear model with V. The lack ofimprovement for other species such as yellow cedarand white birch is less clearly explained.

Coefficients of determination for total oven-drybiomass are generally 0.95 or greater except forInterior Douglas-fir (r' = 0.84, R' = 0.88),western larch (r' = 0.88, R' = 0.94), lodgepolepine (r' = 0.93, R' = 0.96) and trembling aspen(r' = R' = 0.94); values for bole wood and barkare similar but often slightly less. Values forcrown components are less and extremely variable, ranging from about 0.26 for branch class 2in trembling aspen to 0.9 I for branch class 2 insubalpine fir. The extreme value of r' ~ R' =

0.00 for branch class 3 in ponderosa pine shownin Appendix A is a result of the scarcity of smallbranches on the sample trees; most samples hadno branches in that size class and branch class 3generally represents less than 1% of the total drymass of the crown.

A major criterion used to evaluate difTerent regression models and the use of V as an independent variable is precision, expressed by SEE,95% confidence limits and RE. Choice of a mostsuitable model or equation cannot be made simply on the basis of a single criterion. Other criteriaused to assess regression models in this studywere the following:

I) additivity of coefficients

2) precision - determined by plotting andexamining residuals

3) accuracy - R' and confidence limits (goodness offit) - indicated by adjusted R' values

4) degeneracy - holV well the equations predictbiomass near the extremes of the sampledtree sizes.

16

Table 5

Relative efficiency3 of model 5 compared 10 model 2b

Species Biomass Componcnl

Wood Bark Branches Branches Branches Foliage Total(I) (2) (3)

Coastal DOllglas-fi r 1.21 1.17 1.21 228 2.02 2.66 2.02Sitka spruce 1.34 1.17 1.80 3.84 1.99 4.75 2.34Western red cedar 1.77 1.17 2.43 1.69 1.35 2.46 1.99Interior Douglas-fir 1.12 1.10 0.98 1.51 1.56 2.31 1.37

Ponderosa pine 1.96 1.10 1.72 1.64 1.00 1.28 1.08Western hemlock 1.54 1.80 2.25 2.31 3.69 2.89 2.25While spruce 1.14 1.19 1.59 2.62 1.61 1.74 1.80Engelmann spruce 1.17 1.14 1.25 1.37 1.59 1.23 1.12

Pacific silver fir 1.44 1.02 0.98 1.88 1.51 2.13 1.08Grand fir 0.94 1.12 1.82 1.00 1.54 1.51 1.02Western larch 1.59 1.00 1.49 1.80 2.28 1.54 1.90Western white pine 0.98 1.08 1.35 1.21 1.06 1.61 1.30

Lodgepole pi ne 1.42 1.46 1.54 1.77 1.39 1.80 1.99Mountain hemlock 1.25 1.85 279 1.12 1.10 1.39 0.88Black spruce 1.56 1.04 1.37 1.85 1.61 1.82 1.61Subalpine fir 1.21 1.06 2.04 2.46 1.39 1.77 1.85

Yellow cedar 0.96 1.10 1.10 1.25 1.77 1.25 0.86Black cOllonwo9d 1.39 1.08 1.17 1.64 1.17 1.04 1.02Red alder 2.19 1.82 2.40 2.56 2.66 2.79 1.61Trembling aspen 0.98 1.04 1.25 1.02 1.80 1.00 1.06

While birch 0.90 0.94 1.00 0.96 0.98 1.00 0.90Shore pine 1.19 1.04 1.54 1.61 1.46 1.23 1.51

a RE., MSE, MSE = mean squnre error b based on oven.dry biomassdala.

~'ISE~simple linear = 2multiple linear = 5

Addith'ity of Coefficients

The requirement of addilivity effectively ruledout the use of logarithmic or non-linear regres·sion models. Adjusted, weighted linear modelsdescribed by Lavigne and van Nostrand (I981)were not calculaled because of the considerablevariability in the behaviour of most biomasscomponents. Also, since predictions of lotalmass, bole wood mass and bark mass, even forunweighted, simple linear equations, are ade-

qualely precise and reasonably accurate while estimates of crown components are relativelyimprecise, it seems that little would be gained byadjusting the regression coefficients for crowncomponents such as foliage. Therefore, themodels of choice were unweighted simple andmultiple linear models. All models suggested bythis study are additive since the same variableswere used for all components within a given species (or variety). However, recenl1y reported alternative methods of obtaining addilivily (Cunia

and Briggs 1984; Chiyenda and Kozak 1984)could be applied.

Precision

Examination of residuals shows that the assumptions of homogeneolls variance and normalityare not l11et by the simple linear equations or, inmany cases, by the multiple linear equations.Western larch, ponderosa pine and Engelmannspruce are examples of species showing considerable heterogeneity of variance (Appendix A)with either regression model. Therefore, theSEE and confidence limits reported should beregarded as approximations. However, cross validation of the data for white spruce and lodgepolepine indicates that the reported confidence limitsare realistic.

Relatively low R2 values found for many crowncomponents and species indicate a weak relationship to tree size (expressed by Dbh, H and V);similar results have been reported in other studies, for example, by Alemdag (J 982). The coefficient of determination is probably affected byspecies characteristics, stand and site conditions,and by characteristics of the sample for eachspecies.

In some cases, low R2 values renect the arbitrariness of branch size class definitions. For example, branch class 1 was often absent from sampletrees of all species and branch class 3 was almostalways absent in ponderosa pine.

Compared to stemwood and bark, crown components are greatly affected by relatively short-termchanges in stand and site conditions. The inclusion of independent variables renecling standdensity and age might improve the goodness offit and precision of estimates for crown components (Alemdag and Stiell 1982; Madgwick andKreh 1980).

Accuracy

Extensive plottings of the results and detailedanalysis did not indicate any significant bias orlack offit for most species.

17

Degeneracy

In general, both simple and multiple linear regression models give reasonable predictionsthroughout the range of the sample. Predictionsfrom the multiple linear equations for components of some very small trees near the lowerlimits of the sample can be zero or slightly negative depending on the dimensions of a particulartree, the characteristics of the sample, and thecharacteristics of the equations. The simplelinear equations behave similarly for somecomponents (commonly branch class I) forseveral species.

Extrapolation

Predictions for large trees well beyond the rangeof tree size for a sample are obviously inaccuratefor multiple linear equations for some components of all species and for total biomass of several species. This results from negative coefficientsfor some variables causing biomass estimates toreach a maximum at some point beyond the sample range and then decrease. An extreme example of this is the multiple linear equation for Sitkaspruce; a tree with a Dbh of 100 cm and a heightof 45 m has a negative biomass predicted. Thisproblem does not occur with the simple linearequations.

Even when obviously unrealistic predictions donot occur, extrapolation beyond the range of asample is not justified by regression theory. Suchpredictions may be reasonable approximations ofstem and total biomass but they should be regarded with caution. Statistics for the equations, suchas R2 or SEE, are not appl icable to suchextrapolations.

The sampling conducted in this study was aimedat a range of tree sizes which would be found insecond growth stands. Sampling was, of necessity, further constrained by budgetary and accesslimitations. However, with the possible exceptionof Sitka spruce and yellow cedar, the objectivesof the project have been achieved.

Time-Related Variability

Sampling was conducted during three consecutive field seasons over periods of about four to

six months so that seasonal nuctuations in treewater content and in dry biomass of crown components are largely represented in the error term(residual mean square) for the equations. However, given the potentially large degree of variability in water content (Kramer and Kozlowski1979; Hinckley el al. 1978; Waring and Running1978), predictions of fresh biomass may notalways be as reliable as indicated by the statisticspresented in this report. Fresh mass estimatesare further complicated because of the definitionof crown components for fresh mass; for example, reproductive structures such as strobili andcones are included with their associated branchsize class (usually class 3 which also includesfoliage).

Some of the variability in crown components,particularly foliage, is probably attributable tovariable rates of shoot growth and leaf expansion. Such variability may be especially importantwith respect to the relatively weak relationships

18

between tree size and biomass of some crowncomponents for species such as western larch,black cottonwood and trembling aspen which exhibit rapid expansion of short shoots followed bya gradual expansion of long shoots. A furthercomplication is that this growth habit changeswith tree age; shoot growth in mature trees ismainly by short shoots (Kramer and Kowzlowski1979).

With the exceptions of Coastal Douglas-fir, Sitkaspruce, black cOllonwood, red alder and shorepine, some old growth trees had to be sampled torepresent larger tree sizes. In older trees there isgenerally marc biomass in the bole and proportionally less in the crown (Kramer and Kowzlowski 1979; Ovington and Madgwick 1959;Whittaker and Wood well 1967). The inclusion ofa few relatively old, large trees in a sample mayfurther contribute to the non-linearity of treesize and crown componem relationships.

Literature Cited

Alemdag, I.S. 1980. Manual for data collection and processing for the development of forest biomass relationships. Can. For. Servo Pel. Natl. For. Ins1. Inr.Rep. PI-X-4.

- - -. 1982. Aboveground dry malter of jackpine, black spruce, white spruce and balsam firtrees at two localities in Onlario. For. Chron.58(1): 26-30.

Alemdag, I.S. and Stiell, W.M. 1982. Spacing and ageeffects on biomass production in red pine planlations. For. Chron. 58(5): 220-224.

Bonner, G.M. 1979. Development of forest biomassinventory methodology in Canada. pp. 736-744IN: Workshop proceedings: [oresl resourceinventories. Vol. II. Colorado State Univ. FortCollins, Col. 80523.

Chiyenda, S.S. and A. Kozak. 1984. Addilivily ofcomponent biomass equations when the underlying model is linear. Can. J. For. Res. 14(3):441-446.

Cunia, T. 1979. On tree biomass tables and regression:some slatistical commenlS. pp. 629-642 IN: Work-

shop proceedings: forest resource invenlories.Vol. II. Colorado State Univ. Fort Collins, Col.80523.

CUllia, T. and R.D. Briggs. 1984. Forcing addilivilY ofbiomass tables: some empirical results. Can. J.For. Res. 14(3): 376-384.

Demaerschalk, J.P. and A. Kozak. 1974. Suggestionsand criteria for more effective regression sampling. Can. J. For. Res. 4(3): 341-348.

Dobbs, R.C. 1981. The E FOR produclion program.pp. 1-16 IN: Proceedings: lhird bioenergy R&Dseminar. March 24-25, 1981, Otlawa, Can. Sponsored by: Energy Projecl Office, atl. Res. CouncilorCanada. Otlawa. Ont. Canada. 373 p.

Even, F. and I.S. Alemdag. 1979. Review leHer in For.Chron. 55(5): 209-210: Equalions predicling primary produclivily (biomass) of lrees shrubs andlesser vegelation based on current literalure. w.Stanek and D. State. Env. Can. Can. For. Serv.Victoria, B.C. Inr. Rep. BC-X-183.

Farley, A.L. 1979. Atlas of British Columbia. Univ. orB.C. Press. Vancouver, B.C.

Forest Inventory Division. 1976. Whole stem cubicmetre volume equations and tables: centimetrediameter class merchantable volume factors. B.C.For. Servo Victoria, B.C.

Hinckley, T.M., J.P. Lassoie and S.W. Running. 1978.Temporal and spatial variations in the water statllSof forest trees. For. Sci. Monograph 20. pp. 44-45.

Kozak, A. 1970. Methods of ensuring additivity of biomass components by regression analysis. For.Cl1ron. 46(5): 402-404.

Kramer, P.J. and T.T. Kozlowski. 1979. Physiology ofwoody plants. Academic Press, N.Y. pp. 65·78,479-480,546-551 & 597-602.

Lavigne, M.B. and R.S. van Nostrand. 1981. Biomassequations for six tree species in central Newfoundland. Env. Can. Can. For. Servo NewfoundlandFor. Res. Centre. SL Johns, Newfoundland. Inf.Rep. N-X-199.

Love, P. 1980. Biomass energy in Canada: its potentialcontribution to future energy supply. Min. ofSupply and Servo Can. Cat. No. M23-14/80-4-IE.Beauregard Press Limited.

Madgwick, I-I.A.I. and R.E. Kreh. 1980. Biomass estimation for Virginia pine trees and stands. For. Sci.26(1): 107-lll.

19

Massie, M. R.C. 1983. Development of biomass prediction equations for Yukon tree species. NawitkaRenewable Resource Consultants Ltd. for theCanadian Forestry Service.

McDaniels, T.L. 1981. Forest biomass energy in BritishColumbia: opportunities, impacts and constraints.pp. 181-182 IN: Proceedings; third bioenergy R &D seminar. March 24-25,1981, Ottawa, ant. Sponsored by: Energy Project Office, Natl. Res. Councilof Can. Ottawa, ant. Can.

Ovington, J.D. and I-LA.1. Madgwick. 1959. Thegrowth and composition of mature stands of birch.I. Dry matter production. Plant Soil 10: 271·283.

Stanek, W. and D. State. 1978. Equations, predictingprimary productivity (biomass) of trees, shrubsand lesser vegetation based on current literature.Env. Can. Can. For. Servo Victoria, B.C. Inf. Rep.BC-X-183.

Waring, R.H. and S.W. Running. 1978. Sapwood waterstorage: its contribution to transpiration and effectupon water conductance through the stems of oldgrowth Douglas-fir. Plant, Cell and Environ. I:131-140.

Whittaker, R.H. and G.M. Woodwell. 1967. Surfacearea relations of woody plants and forestcommunities. Amer. 1. Bot. 54(8): 931-939.

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British ColumbiaStandard Error of the Estimate for

Simple Linear Equationsa (Oven-Dry Massb)

Species Component

Wood Bark Branches Branches Branches Foliage Total(I) (2) (3)

Coastal Douglas-fir 35.65 9.36 19.73 7.47 3.27 10.08 48.75Sitka spruce 13.51 2.73 5.00 5.54 3.44 8.64 19.64Western red cedar 38.61 12.66 16.59 10.44 5.62 11.54 37.62Interior Douglas-fir 132.95 28.70 34.48 24.82 15.04 15.55 163.05

Ponderosa pine 99.41 15.75 38.21 30.88 1.38 23.54 88.94Western hemlock 32.88 14.26 10.90 9.29 5.07 8.60 59.75White spruce 32.93 690 7.74 9.50 9.15 13.18 50.82Englcmann spruce 31.20 5.61 10.64 13.14 8.54 14.80 54.28

Pacific silver fir 10.20 1.96 3.22 4.22 1.85 7.13 17.56Grand rir 38.09 12.75 5.73 8.42 4.11 16.94 47.65Western larch 79.81 8.24 2058 19.27 662 7.30 109.02Western white pine 18.55 3.72 10.28 18.11 3.28 10 15 46.04

Lodgepole pine 37.67 3.53 4.19 9.10 2.74 7.21 44.13Mountain hemlock 18.26 5.96 2.13 6.34 2.57 7.60 19.80Black spruce 14.40 2.04 328 4.89 3.64 7.39 14.15Subalpine fir 12.39 4.40 812 7.06 9.98 11.99 33.34

Yellow cedar 31.68 3.37 2.64 4.44 0.88 5.39 29.88Black cOllonwood 1680 4.55 3.30 9.41 1.16 3.93 23.36Red alder 3.47 1.05 3.98 4.06 0.82 1.66 9.32Trembling aspen 14.12 4.66 5.61 16.07 1.51 3.42 33.00

White birch 14.20 367 3.05 8.96 2.3 I 2.67 H23Shore pine 5.11 2.14 3.38 5.74 2.48 4.92 12.24

a B=bo+bJD211

b Rounded to the nearest 0.01 kg.

43

Standard Error orihe Estimate forMultiple Linear Equations (Oven-Dry Massa)

Species Biomass Component

Wood Bark Branches Branches Brunches Foliage Total(I) (2) (3)

Coastal Douglas-fir 39.21 10.13 21.80 11.26 4.94 16.46 69.27Si kla spruce 15.72 2.95 6.67 10.85 4.86 18.86 30.02Western red cedar 51.44 13.64 25.96 13.53 6.54 18.08 53.18Interior Douglas-fir 140.92 30 13 34.06 30.45 18.87 2359 191.13

Ponderosa pine 138.33 16.30 46.15 39.27 1.37 26.58 91.99Western hemlock 40.92 19.06 16.33 14.08 9.74 14.62 88.95White spruce 35.19 7.53 9.76 15.41 11.62 17.37 67.87Engelmann spruce 33.59 6.02 11.96 15.39 10.76 16.47 57.35

Pacific silver fir 12.29 1.99 3.19 5.80 2.27 10.43 18.24Grand fir 38.09 12.75 5.73 8.43 4.11 16.94 47.65Western larch 100.80 8.27 25.16 25.82 10.02 9.01 150.61Western white pine 18.53 3.92 12.03 20.24 3.42 13.02 53.04

Lodgepole pine 44.98 4.28 5.22 12.15 3.25 9.72 62.58Mountain hemlock 21.52 8.11 3.56 6.75 2.69 8.96 18.59Black spruce 17.97 2.09 3.83 6.66 4.62 9.95 17.94Subalpine fir 13.58 4.52 11.64 11.10 11.73 15.92 45.48

Yellow cedar 31.1 5 3.53 2.76 4.97 1.18 6.04 2780Black cellonwood 19.87 4.73 329 12.03 1.25 3.98 23.52Red alder 5.15 1.42 618 6.51 1.34 2.78 11.86Tremblingaspen 13.92 4.71 6.27 16.16 2.02 3.40 34.00

White birch 13.46 3.50 3.06 8.79 2.28 2.68 22.08Shore pine 5.58 2.18 4.20 7.29 2.69 5.46 15.05

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Appendix C

The rollowing lield, laboratory and recordingprocedures were employed in this study:

I. Sample trees were selected according to thecriteria established in discussion with thescientific 3Ulhority and B.C. Ministry orForests.

Basic tree data were recorded on the samplerorm as rollows:

I) Tree number: each tree is Hssigned aunique number

2) Card type: I3) SP GRp: Species group, coded as:

Hardwood 1Sort wood 2

4) SP: Species; coded as:

Douglas lir - coastal 0 ISitka spruce 02Western red cedar 03Douglas fir - interior 04Ponderosa pine 05Western hemlock 06White spruce 07Engelmann spruce 08Pacific silver fir 09Grandlir 10Western larch II\Vestern white pine 12Lodgepole pi ne 13Mountain hemlock 14Black spruce ISSubalpine lir 16Yellow cedar 17Black cOllonwood 18Red alder 19Trembling aspen 20White birch 21

5) MAP NO: B.C:M.F. Inventory SheetNo.

6) LAT: latitude (D.M.S)7) LONG: longitude (D.M.S.J8) DBH: diametcr breast height (1.3 m)

in em to nearest 0.1 CI11.

9) HT: total tree height (m) to nearest 0.1e111.

10) CW: average crO\\'ll \\ddth (111) to near-

est 0.1 cm.II) CL: crown length (m) to nearest 0.1

em.12) CT: cover type, coded as:

pure (0-25% other species)mixed (25-75% other species)mixed (75-99% other species)

13) CC: crown class, coded as:dominant Ico-dominam 2intermediate 3suppressed 4

II. Stem datal

After each sample tree was selected, felledand delimbecl, sample sections were CUL

from the stem at intervals of 2.0 m, startingat the base. The fresh weight of each stemsection was obtained as below:

A. Srct;olls w;tll > 30 em diomtter sma" end:

I. Volume was calculated by Smalian'sFormula (iband ob):

v = h (Ab + Au)12

2. A sample disc was cut from the smallend or the section. In extremely largepieces, a wedge was taken from this.Green weighl or this disc or segmentwas taken following removal of bark.The weight or bark was taken. Volume of the disc or segment, and barkwas determined as per T A1'1'1 standard TI8m-53. The green weightlvolume ratio was applied to thewhole section (with bark and withoutbark) and recorded as rollows:

B. Src/iol1s wi/II < 3D-cm diol1l('/er small end:

\\'eights of sections were taken directly.A sample disc was cut from the smallend or the section.

l Stcm isO.J em "bo\'c ground 103 2.5-el11 101' dialllctcr (o.b.)

C. Gel/eral

Fresh weights of wood and bark for eachdisc or segment were determined at afield lab wilhin 24 hrs of cUlling. Eachdisc was identified by tree number anddisc number and, at ihe end of eachweek, all discs were brought to the mainlab for drying and subsequent weighing.All green weighl dala for stem seclionsand discs was recorded as in (V).

III. Crown Data

Softwoods

The proportions of foliage, wood and barkin a softwood branch vary with the speciesand with the size and relative position of thebranch, as well as site and sland conditions.This variation is a source of potential bias inthe estimation of crown dry weights, inview of: I) the need for some kind of su bsampling system, 2) the low precision associated with small sample sizes, and 3) thepractical difficulties involved in constructing efficient, unbiased sampling frames forcrown weight estimation. The crown sampling methods used in lhis slUdy alLemptedto reduce bias by taking branch size in toaccounl.

All live branches were sorted into threegroups: I) lhose with basal diameler (d)greater than 2.5 cm (measured 3 cm fromthe base of lhe branch), 2) those with basaldiameter 0.5-2.5 cm, 3) those with basaldiameler less than 0.5 cm. The tOlal greenweight of each of group was taken andrecorded as indicated below. The IOtalweight of dead branches (all sizes) was alsolaken and recorded.

In addition, six sample branches were randomly selected from each lree 10 providedata for estimation of dry crown componentweighls. These branches were flagged priorto delimbing. Two branches were selectedfrom each of three size classes referred toearlier. The fresh weight of each of thesample branches were obtained in the fieldand lhe sample branches were bagged,

46

labelled and brought in 10 the main lab fordrying.

Hardwoods

Hardwood crowns were cut into sectionsless than 2 m long and then sOrled inlO thefollowing size classes according to lhe middiameter of the section: < 2 cm (includingall foliage), 2-6cm, 6-IOcl11 and> 10 cm.

The total weight of malerial in each oflhesesize classes was obtained in the field laboralOry and recorded as in (VI).

Two branches were randomly selected fromeach size class for estimation of the proportions of foliage and moisture. These sampleswere weighed in lhe field and broughl backto the main lab for drying. For each sizeclass only the total weighl of the sample wasrecorded.

IV. Lab Methods

All disc and branch samples were transported to the main Jab and dried in ovens at105°C for alleasl 24 hrs. Some of the largerdiscs required up 10 45 hrs depending onthe size of the disc and the type of oven(convection vs. fan-driven lypes).

Dry weighls of stem disc samples wererecorded. SOflwood foliage was separatedfrom twigs afler drying, either by hand orwith winnowing machine fitted with screensof appropbate size, and lhen weighed separately. Dry soflwood branch and foliageweighl data were recorded.

Foliage of hardwood branch samples wasseparaled by hand afler drying and lhenweighed. The IOlal dry weight of foliage andbranch was recorded.

V. Recording Stem Weight Data:

Each line (card) corresponds 10 one disc orsection.

1) TREE NO: repeat for each disc/section2) CARD TYPE: 2

I

3) D/S No.: disc/section no.4) SECTION WEIGHT: total fresh

weight of section (kg) to nearest .1 kg.The space to the left was used for recording weights of pieces of section, and forany conversions, e.g.

10 + 20 + 15 = 451bs = 20.4 kg5) DISC WEIG HTS: fresh and dry

weights of disc wood and bark (gm) tonearest .1 gm. Discs were taken at 2 111

intervals, starting at ground level. Eachdisc was labelled by tree no. and disc.no. e.g.

100-5 for disc 5 from tree 100.

VI Recording: Crown Weight Data:

One line (card) for each size class.

I) TREE 0.: repeat for each size class2) CARD TYPE: 33) SIZE CLASS: size of branch

Softwoods Code>2.5cm I0.5-2.5cm 2<0.5 cm 3Dead 4

47

Hardwoods:> 10 cm 16-IOcm 22-6 cm 3

4) TFW: Total fresh weight (kg) of materialin a given size class, to nearest .1 kg.Use space 10 left of intermediate calculations and con versions.

5) SAMPLE WEIGHTS: Green and ovendry sample weights were recorded asmeasured.

VII Weight Calculations:

Using the data recorded above, and the appropriately derived ralios for gw/odw,bark/wood, and foliage/wood, tree component weights are to be calculated andrecorded.

Appendix D

The 22 tree species and varieties selected for sampling are lisled below:

Coastal Douglas-fi rSitka spruceWestern red cedarInlerior Douglas-firPonderosa pineWeslern hemlock""hite spruceEngelmann sprucePacific silver firGrand firWeslern larchWestern while pineLodgepole pineMountain hemlockBlack spruceSubalpine firYellow cedarBlack cottonwoodRed alderTrembling aspenWhite birchShore pine

Pseudolsllgn mCJ/:iesii (Mirb.) FrancoPicea silchensis (Bong.) CarrT/lI(ja plicow 0011 nPselidofslIga mell:iesii (Mirb.) Franco var. glauco (BeissnJ FrancoPinus ponderosa Laws.TSI/ga IIeterophylla (Raf.) Sarg.Piceo glauco (Moench) VossPiceo euge/mollui; ParryAbies allfabills (Dougl.) ForbesAbies gral/dis (Doug!.) Lind!.Larix Geridell/alis ull.

Pinus momicola Doug!.Pinus COWorla Doug!. var. lati/olia Engelm.Tsuga mertensiano (Bong.) Carr.Piceallfarial/a (Mil!.) U.S.P.Abies lasiocarpa (Hook.) NULl.

Chamaecyparis lIoo/karens;s (D. Don.) SpachPopulus 'r;c/iocarpa Torr. & GrayAlnlls mbra Bong.Populus Iremuloides Michx.Bellda papyri/era Marsh.Piuus cOlI/oria Doug!. var. coulOria

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