United States

Department

of Agriculture

Forest Service

Intermountain

Forest and Range

Experiment Station

Ogden, UT 84401

Research Paper

INT-332

November 1984

Volume and Biomass

For Curlleaf

Cercocarpus

in Nevada

David C. Chojnacky
THE AUTHOR 

DAVID C. CHOJNACKY is a research forester, Forest 

Survey, Intermountain Forest and Range Experiment Station. 

He holds a B.S. degree in mathematics from the University 

of Idaho, Moscow, an M.S. degree in watershed management 

from the University of Arizona, Tucson, and is a Ph.D. 

candidate in forest biometry at Colorado State University, 

Fort Collins. He has been with the Forest Service since 

1979. Before coming to Ogden, he held a position with the 

Forest Research Lab at Oregon State University, Corvallis. 

ACKNOWLEDGMENTS 

Data for this study were collected in cooperation with the 

Ely District of the Bureau of Land Management. Special 

thanks to John McGlothlin, formerly Ely, NV, District Forester 

(now with Burns, OR, BLM District), who helped locate 

study sites and provided field assistance. 

RESEARCH SUMMARY 

Volume and biomass equations were developed for curlleaf cercocarpus (Cercocarpus ledifolius Nutt .) in 

the Egan and Schell Mountains near Ely, NV. Fifty-two 

trees were sampled to measure cubic foot volume. Diameter 

at root-collar (DRC) was used to develop a simple linear 

volume prediction equation for individual trees. Volume 

predictions can be converted to biomass by wood density 

factors reported from the study. A ratio equation was developed to predict volume and biomass for various utilization 

standards. A method was developed for obtaining bark 

volume and biomass. 

The jackknife technique was used to assess the reliability 

of the equations. This technique allowed computation of 

confidence intervals for predictions from several equations 

used in a series. The 95 percent confidence intervals (expressed as a percentage of the predicted value) were generally less than 20 percent of predicted volume and biomass 

for curlleaf cercocarpus trees within 2-inch diameter classes. 
Volume and Biomass For 

Curlleaf Cercocarpus 

in Nevada 

David C. Chojnacky 

INTRODUCTION 

The Resources Planning Act (RPA) of 1974 calls for 

assessment of all forest and rangelands, including 

shrubby woodlands. Curllcaf ccrcocarpus (Crrcm,arp~r.s 

1ed~i)liu.s Nutt., with the former common name of 

curl leaf mountain-mahogany), a woodland trce found 

mainly in Nevada and Utah, is included. In addition to 

its wood fiber value, it is an important browse plant 

for wildlife and has root nodulcs that fix nitrogen. This 

study sought to develop volume and biomass prediction 

equations for ccrcocarpus to provide foresters with tools 

ncedcd to assess woodlands. 

In anticipation of changing inventory and management 

needs for ccrcocarpus, I developed equations and 

mcthods to obtain thc following: 

1. Gross volumc and biomass of wood and bark to 

a 1.5-inch minimun~ branch diamctcr (mbd'), with 

capability to vary mbd. 

2. Gross volumc and biomass of wood to a 1.25-inch 

mbd, with thc capability to vary mbd. 

3. Volume and biomass of bark to a 1.5-inch mbd. 

Volumc was expressed in cubic feet and biomass in 

pounds according to current USDA Forcst Scrvicc Forcst 

Survey standards. 

R. 0. Mccuwig (personal communications, 1980) did 

some preliminary work on biomass estimation of ccrcocarpus in the Swcctwater Mountains of Nevada. 

He identified crown diamctcr, the number of stems 

grcatcr than 3 inches found at breast height, and basal 

diamctcr 6 inchcs abovc ground line as potential variablcs 

to predict biomass. Weaver ( 1977) developed sevcral 

biomass equations for cercocarpus and other shrubs in 

Montana. He related crown arca to total biomass in a 

single cquation, applicable to sagebrush, ccrcocarpus, 

dogwood, nincbark, bittcrbrush, sumac, buffalobcrry. 

and hucklcbcrry: 

logl0BM = -1.95 + 1.26 logI,,A 

where: 

BM = total abovcground biomass (kg) 

A = 3.34 MXR . MNR 

MXR = maximum plant radius (cm) 

MNR = minimum plant radius (cm). 

Rcscarch has indicated that diameter, height, numbcr 

of stems, and crown arca should be considered as potcntial predictor variablcs of ccrcocarpus volunx. For 

this study, these and scvcral other variablcs were mcasured on trees. 

PREVIOUS WORK 

Numcrous ecological studics have been conductcd on 

cercocarpus, but few studies include n~cthods to estimatc 

wood volumc or biomass. Whittaker and Niering (1975) 

evaluated biomass of hairy cercocarpus (C. hr-e\'i$lorlls) 

in the Santa Catalina ~ounhins of Arizona. They dcvelopcd a stcm-wood volun~e cquation from 15 trees 

(with avcragc diamcter 3.26 cm): 

where: 

V = volumc of stcrn wood (cd) 

D = basal diamctcr (cm) at 10 cm above ground 

line 

H = total trce hcight (n~). 

'The mbd ih mcasul-cd inhick thc' bark lor woocl only. In all other c;~scs it 

is rncahurcd oi~(~icIe the bark. 

DATA COLLECTION METHOD 

Data were collcctcd at thrcc locations in thc Egan 

Mountains and onc location in thc Schell Mountains 

ncar Ely, NV (fig. I). Trees wcrc selcctcd arbitrarily 

by '-inch diameter classes. Diameter mcasurcrncnt was 

made at the root-collar (DRC) at ground linc. At each 

location, one or two trccs in each diamcter class wcrc 

sampled, totaling 52 trccs in diamcter classcs ranging 

from 2 to 22 inchcs. Tree hcights ranged from 5 to 2 I ft 

Thc following variablcs wcrc mcasured for potcntial 

volumc and biomass prediction: 

1. DRC to ncarcst 0.1 inch 

2. Diamctcr at breast height (d.b.h.) to ncarcst 0. I 

inch 

3. Total trec height to ncarcst foot 

4. Crown diameter maximum and minimum to ncarcst 

foot 

5. Number of stems at 20 pcrccnt of tree hcight 

6. Numbcr of stcms grcatcr than 3 inchcs in diamctcr 

at 4.5 ft 
NEV 

ORENO 

*CARS 

SCHELL 

MTNS. 

0. CITY ,J' 

EGAN MTNS. 

TONOPAH 

A STUDY LOCATIONS 

Figure I. -Study locations were in the Schell and 

Egan Mountains. 

If thc trcc stem forked at the the point of mcasurcmcnt, 

I computcd an equivalent diameter (Meeuwig and Budy 

Di = DRC of the ith stem. 

n = number of stems at measurcmcnt point (DRC 

or d.b.h.). 

Stems smallcr than 1.5 inchcs in diameter were not 

included in the DRC and d.b.h. measurements. 

The trces were cut into segments from 1 to 5 ft long 

to measure diamctcr and lcngth, depending on stem 

forks and tapcr of the segrncnts. Although the intcnt 

was to keep segment taper less than 15 to 20 percent, 

averagc tapcr was 23 percent on the 1,122 segments measured. 

I made outsidc bark diameter measurements (to the 

nearest 0.1 inch) at cach segment's large end, midpoint, 

and small end. Thcsc werc used to compute volume. 

I recorded percent of internal defect for each segmcnt 

and identified segments containing all dead wood. Limbs 

scvercd from the trees wcrc also included as dead wood. 

These werc fairly common, apparently due to a heavy 

wet snowfall thc spring prior to sampling. 

Thrcc scgmcnts from cach trcc wcrc subsariiplcd for 

a cross-sectional disk 2 inchcs thick. Thcsc disks wcrc 

from the top, midscction, and butt of cach trcc and 

wcrc uscd to dctcrminc specific gravity and insidc bark 

diamctcr for biomass computation. 

DATA ANALYSIS AND RESULTS 

I uscd thc scgmcnt data to coniputc volumc and 

biomass for thc trces, developed mcthods to prcdict 

volumc and biomass, and asscsscd thc reliability of the 

prcdiction mcthods. 

Computation Volume and Biomass 

To dcvclop prediction modcls, volumc and biomass 

of the wood and bark wcrc rcquircd for cach trcc (for 

variablc nibd's). Volunic computations wcrc done scparatcly for wood and bark cornbincd, and for wood 

alonc. The volumc coniputations wcrc convcrtcd to 

biomass using wood dcnsity factors. Dcfcctivc and 

dcad wood was also cxaniincd. 

Wood and bark volunic, to a 1.5-inch mbd, was 

computcd for cach trcc using Ncwton's log formula 

(Husch and others 1972, p. 122) on cach scgrncnt, and 

then scgmcnt volumcs wcrc sumnicd. In some cascs 

thc scgmcnt rncasurcmcnts wcrc inadcquatc for computing tree volumc to an nibd largcr than 1.5 inchcs. 

Thcsc cascs rcquircd an cstimatc of scgmcnt Icngth. I 

uscd a gcomctric schcrnc to cstimatc an unknown scgmcnt lcngth whcn thc dcsircd mbd fcll somcwhcrc 

bctwccn the scgmcnt's largc and small diamctcr mcasurcmcnts. I calculated thc unknown scgmcnt length 

from the known scgmcnt length and known diamctcrs, 

assuming segment shapc to bc a cone:' 

mbd-SD 

whcrc: 

L = known scgmcnt lcngth 

X = unknown scgmcnt lcngth 

SD = small diamctcr of a scglncnt 

LD = largc diamctcr of a scgmcnt. 

In this fashion wood and bark volumcs to variablc 

nibd's of 2, 3, 4, 5, 6, 7, and 8 inches wcrc computcd 

using Ncwton's formula and thc lcngth approximation 

(X) whcn ncccssary. 

Wood volumc was computcd by first converting the 

outsidc bark segmcnt dianictcr mcasurcmcnts to insidc 

bark diamctcr. I uscd a rcgrcssion relationship dcvclopcd 

from. a subsamplc of scgmcnts (fig. 2): 

IBD = -0.1501 + 0.93613(OBD) 

whcrc: 

IBD = insidc bark diamctcr (inchcs) 

OBD = outside bark dianictcr (inchcs). 

The same proccdure used for wood and bark volumc 

computation was also uscd for computing wood volumc. 

I also cornputcd volumcs to variable rnbd's from 2 to 

8 inchcs. 

A comparison of mean volumes comp~itcd for I. I22 scgments using Newton's 

formula versus a cone frustrum formula ahowed a 0.14 percent difference: 

heiicc. the conic assumption is reasonable. 
0 2 U 6 8 10 12 1U 16 

OUTSIDE BARK DIAMETER (INCHES) 

Figure 2. -Inside bark diameter plotted against outside bark 

diameter with the regression equation overlaid (R*= 0.99 7). 

Biomass of thc trcc segnicnts was computed using 

the physical relationship between volumc, mass, and 

density. I dctcrmincd specific gravities (the density of 

a material divided by the density of water, 62.4 ~b/ft-~) 

instead of densities for 36 disks, three sampled from 

each of 12 trecs. Spccific gravity was dctermined on 

thc basis of grccn vol~~nie and ovendry wcight. Proccdurcs for irregularly shapcd blocks werc used (Hcinricks 

and Lasscn 1970). Specific gravity did vary within a 

tree. However, I grouped the data as in table 1 for 

dctcrmining bioniass bccausc no reasonable way to 

apply niorc than one specific gravity value per trcc 

was dcsigncd into the study. 

Summary of thc defective and dead wood showed no 

rot in live branches, but did reveal sonic dead material 

(table 2). For fuelwood management, dead cercocarpus 

limbs are just as valuablc as live material because the 

wood is usually sound; hence, there is probably no 

need for net volume reductions. 

Predicting Volume and Biomass 

Capabilitics to predict gross cubic foot volunie and 

pounds of woody bioniass to variablc mbd's were developed. I also dcvcloped scparate equations for wood 

and bark combined, and for wood only. Becausc thc 

regression proccdurc was thc same for wood and bark 

as for wood only, 1 omitted discussion about equations 

predicting just wood. An explanation follows for the 

devclopnicnt of a volume equation, a variable mbd 

equation, a biomass conversion, and a bark method. 

I selected a simplc lincar cquation predicting volurnc 

froni DRC. Combinations of the variables height, crown 

diameter, and numbers of stems werc examined for 

predicting volunic, but none proved to be much bcttcr 

than the model based on DRC. Diameter at breast height 

(equivalcnt d.b.h. for multiple stems) appeared to be 

as good or better a predictor of volume as was DRC, 

but was not considered because d.b.h. measurements 

arc incompatible with current woodland inventory procedures. 

Bccausc the relationship of volume to DRC is nonlinear, the volume equation was dcvelopcd using a 

natural log transformation and simple lincar regression 

(fig. 3 and table 3). I applied a positive correction of 

5 pcrccnt for predicting the rnean volumc instead of 

median volume that results froni application of log 

transforniations (Flewelling and Picnaar 198 1 ). 

I dcvcloped anothcr equation to predict volunic to 

variable minimum branch diamctcrs. Burkhart (1977) 

showed that for loblolly pine a ratio of volume to a 

desired upper stem diamctcr divided by total volume is 

a function of d.b.h. and upper stem diamctcr. In this 

study a ratio of mbd volunie divided by total volumc 

was rcgrcsscd against the log transformations of DRC 

and nibd (tablc 3 and fig. 4). 

Biomass equations wcrc not developed because the 

best mcasurc of trcc wcight obtainable within the study 

design was a conversion of volume to biomass using a 

constant wood density factor for all trecs. Volume 

equations in table 3 can be convcrtcd to biomass equations by usc of thc appropriate wood density factor in 

table 1. 

Table 1.-Density and specific gravity for curlleaf cercocarpus wood and bark 

95 percent 

Component Density Mean confidence' Range Sample2 

interval size 

L b/ft3 ------------em------------ Specific gravity ------- ------------- 

Bark and wood 43.68 0.70 1- 0.025 0.55-0.89 36 

Wood 50.54 .81 2 .022 .70- .94 36 

Bark 22.46 .36 2 .019 .29- .48 36 -- 

'The average specific gravity for curlleaf cercocarpus is expected to lie within the confidence interval unless a 1-in-20 chance has occurred in 

selecting the samples. The confidence intervals were based on the t-statistic, 11 degrees of freedom, and variances jackknifed by deleting one 

tree at a time. 

'Samples are from 12 trees, but each tree was sampled at the top, midpoint, and butt. 
Table 2.-Dead volume expressed as a percent of gross wood and bark volume to a 1.5-inch minimum branch diameter (mbd) 

and wood volume to a 1.25-inch mbd 

Diameter 

class 

(DRC in 

inches) 

Percent 

dead wood 

and bark 

Percent Percent 

dead sampling Sample 

wood error' size 

'The sampling error is for wood and bark values (it is roughly the same for wood). Sampling error is expressed as a percentage of the average 

percent dead and implies the true value for percent dead lies within the sampling error unless a 1-in-20 chance has occurred in sample selection. 

The large sampling errors are due to the small sample sizes and due to data that are distributed both discretely and continuously. 

Table 3.-Equations for predicting curlleaf cercocarpus wood 

fiber products 

Product Equation Equation # R* 

Cubic foot V = 0.00356(~~~)*-~*~ (1) 0.96 

volume of 

wood and 

bark to a 

1.5-inch mbd 

Cubic foot V = 0.00258(~~~)~-~~~ (2) .96 

volume of 

wood to a 

1.25-inch mbd 

Mbd ratio R = 0.6281 + 0.23875 ~'(DRC) (3) .78 

for wood - 0.47745 In(mbd) 

and bark 

Mbd ratio R = 0.521 0 + 0.25729 In(DRC) (4) .75 

for wood - 0.461 92 In(mbd) 

'In is the natural log function. 

0 5 10 15 20 25 

DRC (INCHES) 

Figure 3. - Wood and bark volume data plotted against DRC, 

with the volume equation overlaid. ' 
x = 2-inch mbd 

= 4-inch mbd 

* = 6-inch rnbd 

A = 8-inch mbd 

0.0 1""""'I""""'I""""'I""' 

0 5 10 15 20 

DRC [INCHES) 

Figure 4. -The means within 2-inch DRC classes for the ratio Figure 5. -Bark volume data plotted against DRC with the 

of mbd volume to total volume are plotted for several mbd classes. prediction of bark overlaid-eq. (1) minus eq. (2) from table 3. 

The corresponding prediction equation is overlaid. 

Bark volumc and biomass can bc obtaincd by subtracting prcdictions for a wood equation from a wood 

and bark cquation (fig. 5). This nicthod only givcs 

bark cstimatcs to a 1.5-inch mbd bccausc thc subtraction 

nicthod is incompatible with volunic prcdictions for 

other mbd's. 

In summary, thc cquations in tablc 3 and thc wood 

density factors in tablc 1 can be used to cstimatc volumc 

and biomass of ccrcocarpus wood and bark. Tablc 4 

provides a guide and an examplc for computing these. 
Table 4.-A guide to use the equations in table 3 and wood 

density factors in table 1 for the estimation of curlleaf 

cercocarpus wood volume and biomass 

Product to Wood and/or bark Wood 

be estimated 

Volume 

Volume to a 

variable rnbd 

Biomass 

Biomass to a 

variable rnbd 

Bark volume 

Bark biomass 

Bark volume 

and biomass to 

a variable rnbd 

Eq.(l) x wood and 

bark density 

none 

Eq.(2) x wood density 

none 

none 

none 

An example for computing wood and bark biomass of a tree to a 

4-inch mbd: 

Given: DRC - 18 inches 

rnbd = 4 inches 

wood and bark density - 43.68 lblft (from table 1) 

Wanted: Pounds of wood and bark biomass for a 4-inch rnbd 

Step 1 -Compute wood and bark volume for a 1.5-inch rnbd 

Volume - 0.00356(~~C)~ 92 

- 16.48 ft3 (1) 

Step 2-Compute wood and bark 4-inch rnbd ratio 

Ratio = 0.6281 + 0.23875 In(DRC) 

-0.47745 In(mbd) 

0.66 (3) 

Step 3-Compute biomass result 

Result = Eq.(l) x Eq.(3) x wood and bark density 

- 16.48 x 0.66 x 43.68 

= 475 Ib of wood and bark for a 4-inch mbd. 

Reliability of the Equations 

Determining reliability for equations developed in 

logarithmic units and for systems of cquations can be 

difficult. The R' and standard error of regression arc in 

log units and havc little meaning for assessing cubic 

foot volume prcdictions. However, a technique called 

jackknifing is useful for estimating variances in complex 

situations (Mostcllcr and Tukcy 1977; Schrcuder and 

Brink 1'383). The jackknife method involves computing 

a statistic (in this case regression cocfficicnts) from a 

data set numerous times, each time deleting a different 

observation(s). Each computation is called a pscudovalue. whcn subtracted from the statistic using all data 

points: 

y';:, = (k)yLtIl - (k- 1 )y(-,,, j= I ,2,. . . , k 

where: 

y'!', = a pseudo-value 

k = number of observations 

y;,,~ = the statistic bascd on all observations 

y,,, = the statistic bascd on k- 1 observations with 

observation j deleted. 

A variancc is easily calculated from the pseudo-values: 

where: - y:': = the arithmetic mean of y"',. 

Once variances arc computed, confidence limits can 

be calculated using the t-statistic: - 

Y'" 2 tcYl2 h. 1 S/V~ . 

Using the jackknife theory presented above, I computed confidcncc limits by ?-inch diameter class (DRC) 

for prcdictions from three sets of equations: (1 ) wood 

and bark volume to I .S-inch mbd, (2) wood volun~e to 

a 3-inch mbd, and (3) wood and bark biomass to a 

_?-inch mbd. These represent prcdictions for one, two, 

and three sets of cquations, I-cspectivcly. The confidcncc 

limits wcrc constructcd by diameter class because the 

variancc of an estimate increases with tree size, which 

makes it inappropriate to use an average variancc for 

all tree sizes. 

Each jackknifed variancc was based on 52 pscudovalues. Each pseudo-value required development of 

regression cquations with one tree missing. The sanic 

tree was deleted for all regressions whcn a pseudo-value 

was based on more than one cquation. 

Table 5 contains prcdictions, percent of actual volumes, and the confidcncc intcrvals. Confidence intcrvals 

were constructcd from jackknifing and expressed as a 

percent of a prediction. The percent of the actual volunie 

lies within the confidence intcrvals most of the time. 

Also, the confidcncc intcrvals were similar whether 

one, two, or thrcc cquations wcrc used, indicating that 

errors may not increase by using two or three cquations 

in a series. 

The small differences between the weighted averages 

of the actual and predicted values in the last row of 

table 5 indicate that the cquation or systems of equations 

arc best when averaging over all tree sizes. Some individual size classes havc quite a large difference between 

predicted and actual estimates, but these average out 

whcn considering totals. 
Table 5.- A comparison of actual curlleaf cercocarpus volume and biomass values with predicted values and with 95 percent confidence intervals (C.I.) fr

Wood volume in Wood volume in W

cubic feet to cubic feet to 

a 1.25-inch mbd a 3-inch mbd 

Diameter Average ~rees' Predicted Percent 95 per- Predicted Percent 95 per- Predict

class DRC value actual cent value actual cent value 

(DRC) value c.L2 value c.L2 

1.5- 2.9 

3- 4.9 

5- 6.9 

7- 8.9 

9-1 0.9 

11 -1 2.9 

13-1 4.9 

15-1 6.9 

17-1 8.9 

19-20.9 

>21 

Weighted averages 

over all 

diameter classes 

'Number of trees averaged for actual volume values. 

The confidence intervals are expressed as a percent of the predicted value. They imply the true value lies within the interval unless a 1 -in-20 chance has occurred in selecting th
DISCUSSION 

The volumc and biomass estimation methods given 

in this study should predict within about 20 percent of 

the actual values for individual trccs. getter results can 

be expected for averages from a sarnplc of trecs. However, these inferences rely on the assumption that future 

samples of ccrcocarpus trecs will have forms similar to 

those in this study. This is because the modeling tcchniqucs were based on empirical data manipulation with 

little biological reasoning to warrant extrapolation to 

other areas. This study, however, could provide insights 

useful in hypothesis building for future ccrcocarpus 

volumc modeling I-csenrch done from a more biological 

perspective. 

Observation of the equations in table 3 indicates 

ccrcocarpus volumes may be proportional to DRC cubed. 

Other people have supported a hypothcsis that a function 

of DRC is proportional to volume or biomass for pinyonjuniper and othcr woodland trees (Gholz 1980; Tausch 

1980; Weaver and Lund 1982; Felkct- and others 1983). 

Perhaps the best use of this study could be an application of the modeling techniques to a subsample of 

trccs in a multistage or niultiphasc woodland inventory. 

DRC is the only variable that would need to be measured 

in the main large sample. Developing new equations 

for the subsa~nplc of trecs woi~ld require considerable 

effort in destructive sampling, but a visual sampling 

method (Born and Chqjnacky, in preparation) would 

alleviate this constraint. Such an application would rely 

heavily on well-established sampling theory and would 

shun uncertain equation extrapolations. 

REFERENCES 

Born, J. D.; Chqjnacky. D. C. Woodland tree volume 

estimation: a visual technique. Ogden, UT: U .S. 

Ikpartnicnt of Agricultul-e. Forest Service, Intermountain Forest and Rangc Experiment Station; lin 

preparation 1. 

Rurkhart, H. E. Cubic-foot volumc of loblolly pine to 

any nicrchantablc top limit. Southern Journal ot' 

Applied Forcstry. 1 : 7-9; 1977. 

Felker, P.; Cmncll, G. H.; Clark, P. R.; Osborn, J. 

F.; Nash, P. BionIass production of PI-o.sopi.s species 

(mcscluitc), Lcirc~crc~tltr and othcr Icguniinous trccs 

grown under heatldrought stress. Forcst Science. 

29(3): 592-606; 1983. 

Flewelling, J. W.; Pienaar, L. V. Multiplicative regression with lognormal errors. Forcst Science. 27(2): 

281-289; 1981. 

Gholz, H . L. Structure and productivity of J~rtlipct-irs 

oc.c.iciet7lcrli.s in central Oregon. American Midland 

Naturalist. 1 O3(2): 25 1-16 1 ; l9XO. 

Heinricks, J. F.; Lassen, L. E. Improved technique for 

determining the volume of irregularly shaped wood 

blocks. Forest Products Journal. 20(4): 24; 1970. 

Husch, B.; Miller, C. 1.; Beers, T. W. Forcst mensuI-ation New York: Ronald Press; 1972. 410 p. 

Mecuwig, R. 0.; Budy, J. D. Point and line-intersect 

sampling in pinyon-juniper woodlands. Gcncral 

Technical Report INT-104. Ogden, UT: U.S. Department of Agriculti~rc, Forest Service, Intermountain 

Forest and Rangc Experiment Station; 1981. 3 p. 

Mostcllcr, F.; Tukey, J. W. Data analysis and regression. 

Reading, MA: Addison-Wesley; 1977. 588 p. 

Schreudcr, H. T.; Brink. G. E. The jackknife-a useful 

statistical tool. In: Procccdings, renewable resource 

inventories for monitoring changes and trends international conference; 1983 August 15- 19; Col-vallis. 

OR. Corvallis, OR: Orcgon State University, College 

of Forcstry: 1983: 53 1-535. 

Tausch, R. Allomctric analysis of plant growth in woodland communities. Logan, UT: Utah State Univcrsity; 

1980. 143 p. Ph.D. dissertation. 

Wcavcr, T. Area-riiass relationships for common Montana shrubs. Procccdings of Montana Academy of 

Science. 37: 54-58; 1977. 

Wcavcr, T.; Lund, R. Dianictcr-weight relationships 

for juniper from wet and dry sites. Great Basin 

Naturalist. 42( 1 ): 73-76; 1982. 

Whittaker, R. H.; Niering, W. A. Vegetation of the 

Santa Catalina Mountains, Arizona: volume, biomass, 

production and diversity along the elevation gradient. 

Ecology. 56(4): 77 1-790; 1975. 
Chojnacky, David C. Volume and biomass for curlleaf cercocarpus in Nevada. Research 

Paper INT-332 Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 1984. 8 p. 

Volume and biomass equations were developed for curlleaf cercocarpus (Cercocarpus 

ledifolius Nutt.) in the Egan and Schell Mountains near Ely, NV. The equations predict 

cubic foot volume of wood and bark for variable minimum branch diameters. Wood 

density factors are given to convert volume predictions to pounds of fiber biomass. The 

reliability of the equations was assessed using the jackknife technique to construct 

confidence intervals. 

KEYWORDS: Cercocarpus, woodland, jackknife, specific gravity 
The Intermountain Station, headquartered in Ogden, 

Utah, is one of eight regional experiment stations charged 

with providing scientific knowledge to help resource 

managers meet human needs and protect forest and range 

ecosystems. 

The Intermountain Station includes the States of 

Montana, Idaho, Utah, Nevada, and western Wyoming. 

About 23 1 million acres, or 85 percent, of the land area in the 

Station territory are classified as forest and rangeland. These 

lands include grasslands, deserts, shru blands, alpine areas, 

and well-stocked forests. They supply fiber for forest industries; minerals for energy and industrial development; and 

water for domestic and industrial consumption. They also 

provide recreation opportunities for millions of visitors each 

year. 

Field programs and research work units of the Station 

are maintained in: 

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State University) 

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University) 

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University of Montana) 

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Provo, Utah (in cooperation with Brigham Young 

University) 

Reno, Nevada (in cooperation with the University 

of Nevada) 

.'. U.S. GOVERNMENT PRINTING OFFICE: 1984-576-040110,518 REGION NO. 8