ORIGINAL ARTICLE

Wenhua Xiang • Shaohui Liu • Xiangwen Deng

Aihua Shen • Xiangdong Lei • Dalun Tian

Meifang Zhao • Changhui Peng

General allometric equations and biomass allocation of Pinus massonianatrees on a regional scale in southern China

Received: 8 August 2010 / Accepted: 1 March 2011 / Published online: 28 April 2011� The Ecological Society of Japan 2011

Abstract Applying allometric equations in combinationwith forest inventory data is an effective approach to usewhen qualifying forest biomass and carbon storage on aregional scale. The objectives of this study were to (1) de-velop general allometric tree component biomass equa-tions and (2) investigate tree biomass allocation patternsfor Pinus massoniana, a principal tree species native tosouthern China, by applying 197 samples across 20 sitelocations. The additive allometric equations utilized tocompute stem, branch, needle, root, aboveground, andtotal tree biomass were developed by nonlinear seeminglyunrelated regression. Results show that the relative pro-portion of stembiomass to tree biomass increasedwhile thecontribution of canopy biomass to tree biomass decreasedas trees continued to grow through time. Total root bio-mass was a large biomass pool in itself, and its relativeproportion to tree biomass exhibited a slight increase withtree growth. Although equations employing stem diameterat breast height (dbh) alone as a predictor could accuratelypredict stem, aboveground, root, and total tree biomass,

they were poorly fitted to predict the canopy biomasscomponent. The inclusion of the tree height (H) variableeither slightly improved or did not in any way increasemodel fitness. Validation results demonstrate that theseequations are suitable to estimate stem, aboveground, andtotal tree biomass across a broad range of P. massonianastands on a regional scale.

Keywords Pinus massoniana Æ Allometric equation ÆTree biomass carbon Æ Regional scale Æ Southern Chinasubtropical region

Introduction

Tree and stand biomass estimations have been recog-nized as one of the crucial steps in the assessment offorest carbon stocks in compliance with the KyotoProtocol on greenhouse gas reduction (Brown 2002;Korner 2005; Pilli et al. 2006). This is largely due to theimportance of forests and the forestry industry in miti-gating climate change by way of carbon sequestrationand accumulation. Pinus massoniana is a geographicallywidely distributed native tree species that spans an areain southern China from latitude 21�43¢N to 33�51¢N andfrom longitude 103�E to 120�E (Fig. 1) (He et al. 1996).Its multipurpose applications (firewood, pulp fiber, andtimber stock) and its ability to grow in poor site con-ditions as well as regenerate naturally as a secondarysuccession pioneer species following disturbances has ledto the tree species covering a total of 1.13 million ha offorestland in China (Xiang and Tian 2002). This largearea can greatly contribute to carbon sequestration andstorage on both a regional and a national scale. Rapidand easily implemented methods are therefore necessaryto reliably estimate tree and stand biomass as well ascarbon storage capacity.

Data from the National Forest Inventory of China(CNFI) can be applied to provide reliable estimates ormonitor regional forest carbon storage capacity anddynamics. The CNFI is a well-designed sampling system

W. Xiang (&) Æ S. Liu Æ X. Deng Æ D. Tian Æ M. Zhao Æ C. PengFaculty of Life Science and Technology,Central South University of Forestry and Technology,Changsha 410004 Hunan, ChinaE-mail: xiangwh2005@yahoo.comTel.: +86-0731-85623483Fax: +86-0731-85623483

S. LiuDepartment of Development Planning and Assets Management,China State Forestry Administration, Beijing 100714, China

A. ShenZhejiang Forestry Academy, Hangzhou 310023 Zhejiang, China

X. LeiInstitute of Forest Resource Information Techniques,Chinese Academy of Forestry, Beijing 100091, China

C. PengDepartment of Biological Sciences,Institute of Environment Sciences,University of Quebec at Montreal, Montreal,QC H3C 3P8, Canada

Ecol Res (2011) 26: 697–711DOI 10.1007/s11284-011-0829-0

that has been in existence since 1973. Its inventory in-cludes permanent systematically fixed plots that aresampled at 5-year intervals to collect stand characteris-tics data such as dominant tree species, average dbh, H,stand age, and site class (Lei et al. 2009). Being one ofthe five primary forest types classified by the CNFI insouthern China, P. massoniana stand characteristicshave been recorded. Several methods have been pro-posed to convert inventory measurements into biomassand carbon storage estimates (Somogyi et al. 2006).Tree-based allometric equations applied to stand-basedinventories is probably the most accurate technique toquantify forest carbon storage on local, regional, andnational scales (Williams et al. 2005).

Allometric biomass equations relate tree or standbiomass to one or more easily measured dimensionalvariables, such as dbh and H. In the past, an assortmentof P. massoniana site-specific allometric biomass equa-tions have been developed to evaluate stand productivity(Feng et al. 1982), compare aging biomass dynamics(Liu 1993; Ding and Wang 2001), and examine nutrientcycling (Xiang and Tian 2002) as well as to evaluate theeffects of stand density and site conditions on produc-tivity (Ding 2000). Published equations, however, revealat least three drawbacks. First, the equations in questionwere developed by way of locally measured biomass datathat averaged from six to ten tree samples in total. Thisscale is too small to be expanded to the point in whichthe equations could be applied to biomass on a regionalscale where different stand structures and the coexistenceof age class, site quality, and climatic conditions exist(Muukkonen 2007). Second, the equations were pri-marily selected to fit each tree fraction. Simultaneous fitsregarding related equations were not considered (Bor-ders 1989). Third, the equations were only developed forcertain provinces (such as Hunan, Guizhou, Fujian, and

Guangxi), and the range of dbh and H of tree sampleswas not provided within published literature. Hence, thedevelopment of generalized biomass equations that tra-verse geographical boundaries is imperative whenattempting to quantify regional biomass and carbonstorage capacity of P. massoniana stands. Certain studieshave suggested the possibility of generalizing allometricequations across regional boundaries (Montagu et al.2005; Williams et al. 2005). Due to the limitation of theavailable dataset and the lack of synthesis betweenpublished studies, consistent and generalized allometricbiomass equations relating to P. massoniana have not asyet been developed.

In general, most allometric equations were developedspecifically for aboveground biomass (Montagu et al.2005; Williams et al. 2005) and, consequently, a lack ofallometric data pertaining to belowground root biomasscontinues to be a problem for researchers (Peichl andArain 2007). However, tree root systems are a consid-erable part of total forest biomass (Kurz et al. 1996; Liet al. 2003; Ouimet et al. 2008) and may yet reveal bothan additional and a greater role in carbon storagecapacity (Peichl and Arain 2007). Moreover, estimatesof belowground root biomass primarily rely on the ratiobetween root and aboveground biomass (Cairns et al.1997), but certain studies (i.e., Ouimet et al. 2008; andPeichl and Arain 2007) have shown that root biomasscan be predicted from tree dimensions (such as dbh).The inclusion and improvement of root biomass esti-mates in allometric equations could therefore provide abetter understanding of biomass and carbon allocationand ultimately help to accurately assess forest carbonsequestration potential (Kurz et al. 1996; Cairns et al.1997; Li et al. 2003; Peichl and Arain 2007).

Theprimaryobjectivesof this studywere: (1) to examinevariations in allocation patterns of tree component

Fig. 1 Geographicaldistribution range (shaded area)of P. massoniana adapted fromHe et al. (1996), and locationsof study sites with the ordernumber corresponding toTable 1. Closed and open dotsdenote direct measurement sitelocations and the extracted datafrom published literature,respectively

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biomass, in particular the aboveground and root biomassratio (T/R) to tree size; (2) to develop general allometricequationsbywayof simultaneousfits for regional estimatesof biomass and carbon sequestration in P. massonianastands, including the root component; and (3) to validatethe performance of formulated general allometric equa-tions by testing them against published stand biomass datafrom existing literature and comparing them with previ-ously published equations.

Methods and data

Study sites and data sources

General allometric equations used to estimate P. mas-soniana tree component biomass were generated byapplying data from two sources (Table 1): field mea-surements and published data. A total of 161 datasamples were acquired by applying a destructive sam-pling method to field measurements across 11 site loca-tions. An additional 36 tree samples were taken fromliterature where the raw component biomass of indi-vidual trees and the corresponding dbh and H could beextracted. In total, the study obtained a total of 197 treesamples across 20 site locations. All sites were located inhumid subtropical monsoon climatic regions with anaverage annual temperature ranging from 14.8 to 21.1�Cand an annual rainfall between 1,020 mm and 1,750 mm(Table 1). Figure 1 shows the natural distribution rangeof P. massoniana and the location of the study sites.

Biomass measurement

The biomass of each tree component was measuredwithin closed P. massoniana forests by a direct harvest

technique that was applied in the same manner across allstudy sites. Trees were felled by hand sawing at thebuttress near the ground surface immediately aftersampling took place. After recording diameter at breastheight (1.3 m) (dbh), at ground level (D0) and totalheight (H), branches were stripped of stems and thestems were immediately cut at 1.3- and at 1-m intervalsthereafter up to the tips. The weight of logs (separatedstems) and canopy (branches and needles) fresh masswere measured in situ. The summation of log weight wasthe total amount of stem biomass. Five to ten random-ized samples of branches with needles intact were takento estimate the fraction of the branch and needle bio-mass. Needles were then removed from the branches,and the fresh mass of the needles and branches weremeasured separately. The calculated ratio of needle tobranch was used to determine the total fresh biomass offoliage and branches. Subsamples of needles and bran-ches as well as two to five disks per stem were brought tothe laboratory to determine moisture content. All sam-ples (including disks) were oven-dried at 80�C until aconstant mass was reached. The fresh mass of allaboveground components was converted to the dry massstate by way of their respective moisture content. Theaboveground biomass was determined by the sum oftotal stem, branch, and needle components.

The root biomass of individual trees was determinedthrough excavation where a cylinder extending from theground projection of the crown at a depth of 60 cm wasused as a means to ascertain the data. Although it isunderstood that a certain percentage of roots may growbeyond the diameter of the projected crown, roots thatoverlap with other tree roots could offset remote rootsoutside the projected area. Soil was carefully excavatedat 15-cm-depth intervals and sifted through a wire sieve(20-mm mesh) where roots were then separated intoplastic bags. Stumps and attached taproots were pulled

Table 1 Statistical climate data and forest type as well as total number of tree samples from each site and data sources used for this study

No. Site Longitude Latitude Mean annualtemperature (�C)

Annualrainfall (mm)

Forest type Number oftree samples

Data sources

1 Chuzhou E118�25¢ N32�27¢ 14.9 1,020 Plantation 2 Dong (2000)2 Yichang E111�05¢ N30�52¢ 15.0 1,160 Natural 10 Actually measured3 Enshi E109�25¢ N30�25¢ 16.3 1,500 Plantation 5 Ai et al. (1998)4 Bishan E106�12¢ N29�36¢ 17.8 1,042 Natural 13 Actually measured5 Zizhong E104�26¢ N29�48¢ 17.5 1,008 Natural 11 Actually measured6 Tiantai E121�02¢ N29�24¢ 17.8 1,319 Natural 5 Actually measured7 Shimen E111�22¢ N29�30¢ 16.7 1,367 Natural 19 Actually measured8 Yongshun E109�50¢ N29�00¢ 16.4 1,366 Natural 16 Actually measured9 Anhua E111�10¢ N28�20¢ 16.5 1,500 Natural 14 Actually measured10 Shupu E110�28¢ N27�54¢ 16.9 1,400 Natural 16 Actually measured11 Xiangtan E112�50’ N27�20¢ 17.7 1,350 Natural 32 Actually measured12 Huitong E109�45¢ N26�50¢ 16.5 1,332 Natural 7 Feng et al. (1982)13 Hengyang E112�24¢ N26�55¢ 17.9 1,237 Natural 3 Actually measured14 Hengnan E112�10¢ N26�50¢ 17.8 1,269 Natural 22 Actually measured15 Longli E106�59¢ N26�27¢ 14.8 1,089 Plantation 5 Ding and Wang (2001)16 Sanming E119�14¢ N26�52¢ 19.1 1,741 Plantation 3 Li (2007)17 Jianou E118�19¢ N27�03¢ 18.0 1,630 Plantation 6 Wu et al. (1999)18 Luzai E109�45¢ N24�30¢ 19.0 1,750 Plantation 3 Zhang et al. (2005)19 Wuxuan E105�05¢ N23�45¢ 21.1 1,418 Plantation 4 Liu (1993)20 Guiping E109�57¢ N23�23¢ 21.0 1,712 Plantation 1 Huang and Liang (1998)

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out. Any soil residue that remained on the roots wasremoved by washing and brushing. All retrieved rootswere weighted and divided into four categories: rootcrown (the main root component that starts below thecutting surface and ends at roots larger than 3 cm indiameter at the root tip), taproots (1.0–3.0 cm), primarylateral roots (0.5–1.0 cm), and small roots (<0.5 cm).Subsamples of each category were brought to the labo-ratory and dried to a constant mass. Moisture contentwas determined and used to calculate the dry mass foreach category, and the total root biomass was deter-mined by combining all categories.

Model formulation and statistical analysis

Regression models and parameter fitting methods arethe most important factors involved in estimatingoverall forest biomass (Navar et al. 2004). Power func-tions (Niklas 1994) were selected for this study toexamine the relationship between tree componentbiomass and independent variables. However, regressionequations of tree biomass are correlated and should becompatible with one another (Borders 1989) so that adesirable feature is primed and ready to use that enablesthe predictions of each component to determine the totalprediction of trees. Seemingly unrelated regression(SUR) was therefore applied to fit tree and individualcomponent biomass parameters in the form of a simul-taneous system in order to force the addition of a set ofbiomass functions (Borders 1989; Parresol 1999; Bi et al.2004). Heteroscedasticity always occurs in biomass data,and it is assumed that errors are multiplicative to derivelog-linear models. The logarithmic transformation has atendency to stabilize heteroscedastic variance. The fol-lowing biomass equations proposed by Bi et al. (2004)were used to fit the data:

lnðy1Þ ¼ b10 þ b11 lnðxÞ þ e1 ð1Þlnðy2Þ ¼ b20 þ b21 lnðxÞ þ e2 ð2Þlnðy3Þ ¼ b30 þ b31 lnðxÞ þ e3 ð3Þlnðy4Þ ¼ b40 þ b41 lnðxÞ þ e4 ð4Þlnðy5Þ ¼ lnðeb10xb11 þ eb20xb21 þ eb30xb31Þ þ e5 ð5Þlnðy6Þ ¼ lnðeb10xb11 þ eb20xb21 þ eb30xb31 þ eb40xb41Þþ e6 ð6Þ

where y1 to y6 represent stem, branch, needle, root,aboveground, and total tree biomass in kg, respectively;x represents independent variables; bij are the coeffi-cients; and ei are the error terms. Diameter at breastheight (dbh, cm), at ground level (D0, cm), total treeheight (H, m), and their respective combinations (suchas dbh2H or D0

2H) were tested as independent variables.The equations were fitted by means of PROC MODELstatements incorporated using SAS software (SASInstitute, Inc. 2002).

Using logarithmic forms of the equations theoreti-cally yields a systematic underestimation of the depen-dent variable yi when converting the estimated ln(yi)

back to the original untransformed scale yi. Thus, anadjustment to the biased regressions was made by mul-tiplying the correction factor (CF) that was calculatedby the following method (Chave et al. 2005):

CF ¼ exp RSE2=2� �

ð7Þ

where RSE is the standard error of residuals obtainedfrom the regression procedure.

To examine potential errors and to validate the per-formance of the fitted general allometric equationsdeveloped for this study, the predicted biomass of treecomponents at stand level was compared to availablepublished biomass data carried out at P. massonianastands, incorporating different ages and densities acrossthree provinces (Guizhou, Hunan, and Fujian) insouthern China (see Appendix 1). The predicted standbiomass of each component was estimated by multi-plying the average tree biomass calculated from itsrespective allometric equation and stand density. Theaverage percentage difference in tree component biomassacross the tested stands was used as the indictor of errorfor the biomass estimates. In addition, plotting equa-tions used in this study against previous equations is alsoan effective way to distinguish similarities or differencesin biomass predictions (Tanaka et al. 2009). Total treebiomass equations were selected for the comparisonsince this is typical approach employed when estimatingcarbon storage in forest systems. However, given thatmost published equations that have been applied toP. massoniana were individually fitted for each treefraction (such as stem, branch, needle, and root), onlythree dbh-based equations were used for comparisonundertaken for this study, and six dbh2H-based equa-tions were used to estimate total tree biomass as a resultof the availability within existing literature (see Table 2).

Results

Variation in tree biomass allocation

Total tree biomass (y6) ranged from 1.76 to 455 kgtree�1 across the data for all sampled trees (Appendix 2).Considerable variation was observed for tree biomassallocation among stem, branch, needle, and root pools(Fig. 2). As expected, stems (y1) were the primary treebiomass pool and accounted for 28.85 to 89.96% of totaltree biomass. For the canopy component, branch bio-mass (y2) contributed from 5–33.54% of total tree bio-mass, and needles (y3) contributed from 0.02–35.7% oftotal tree biomass. Belowground roots (y4) were also animportant component of total forest biomass and con-tributed from 3.37–32.43% of total tree biomass. Therelationship between the logarithmic transformations ofbelowground and aboveground tree biomass (y5)approximated to a linear regression with a slope of0.9986 and a coefficient (R2) of 0.9184 (Fig. 3).

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Tree size had an effect on tree biomass allocation.The relative proportion of stem to total tree biomassappeared to increase with an increase in tree size up to apoint of approximately 20 cm dbh and then slightlydeclined after that point (Fig. 2a). The allocation of treebiomass to branches showed a reverse changing patternof the stem component (Fig. 2a). Needle proportion totree biomass generally decreased along with an increase

in dbh (Fig. 2b). However, a minor increasing trend wasobserved within the relative proportion of root to treebiomass with an increase in dbh (Fig. 2c). Thus, bothneedle biomass and branch biomass in stem biomass,needle to root biomass decreased significantly with treediameter, but the correlation coefficients were very low(Fig. 4a, b, c). In contrast, no significant relationshipwas observed for the aboveground biomass to root ratio(T/R) with regards to tree diameter (Fig. 4d).

Allometric equations

Allometric equations showed a higher correlation whenusing dbh as an independent variable instead of D0 andH (Table 3). This indicates that stem dbh must be themost important variable in predicting the biomass of allcomponents. However, more variations existed for therelationships of needle and branch biomass against dbhcompared to other component biomass (Fig. 5). Corre-lation was high (R2 > 0.90) for stem, aboveground,root, and total tree biomass as a function of dbh, butwas relatively low (R2 = 0.70–0.85) for branch andneedle biomass (Table 3). The addition of tree height(H) improved the overall equations fit with stem,aboveground, and total tree biomass, but decreased thecorrelations for branch and needle biomass against dbhor D0. More accurate root biomass prediction wasachieved when using D0

2H as the predictive variable(Table 3).

Fig. 2 Variation in tree biomass component proportion (stem,branch, needle, and root) to total biomass with diameter at breastheight (dbh) for the P. massoniana tree samples

Fig. 3 Relationship between aboveground and root biomass forP. massoniana tree samples

Table 2 Predictive equations of total P. massoniana tree biomass available from published literature

Location Forest type dbh-based equation dbh2H-based equation Sources cited

Hubei Plantation 0.1056 (dbh2H)0.8247 Ai et al. (1998)Chongqing Natural 0.0977 (dbh)2.5206 0.0487 (dbh2H)0.9320 Zhang et al. (2006)Sichuan Natural 0.061166 (dbh2H)0.892101 Luo (1989)Guizhou Natural 0.177470 (dbh2H)0.739280 An et al. (1991)Fujian 1 Plantation 0.1309 (dbh)2.4367 0.1377 (dbh2H)0.8172 Wu et al. (1999)Fujian 2 Plantation 1.0152 (dbh)1.7965 Lin et al. (1993)Guangxi Plantation 0.0017684 (dbh2H)1.29963 Liu (1993)

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Validation and performance

The average percentage differences between predictedand observed biomass at a stand level indicate that theallometric equations performed satisfactorily as a func-tion of dbh and dbh2H when estimating stem, branch,aboveground, and the total tree biomass of stands butperformed poorly when predicting needle and root bio-mass (Fig. 6). Equations using dbh2H as predictor gen-erated lower errors than equations using dbh alone as apredictor (Fig. 6). Their differences, however, wereinsignificant (p > 0.90 after comparing all components).Stem biomass showed the lowest percentage differencewith an underestimation of 0.11% for dbh-based equa-tions and 0.04% for dbh2H-based equations. The high-est error was observed for needle biomass with anunderestimation of 31.36% for dbh-based equations andan underestimation 28.10% for dbh2H-based equations.Root biomass exhibited higher errors (an overestimationof 14.69% for dbh-based equations and an overestima-tion of 14.07% for dbh2H-based equations). An inter-mediate error with an approximate overestimation ofslightly less than 10% was found for total tree, above-ground, and branch biomass (Fig. 6).

Compared to previously published equations, theprediction of total tree biomass by means of equationsas a function of dbh in this study was lower than pub-lished equations from Chongqing (Zhang et al. 2006)and Fujian (Wu et al. 1999; Lin et al. 1993) with theexception of a similarity in predictions found within the0-cm to 12-cm dbh range (Fig. 7a). The prediction oftotal tree biomass by means of equations as a function ofdbh2H was more comparable to equations developedby Sichuan (Luo 1989) and Hubei (Ai et al. 1998).

Predictions were higher than equations developed byGuizhou (Ding and Wang 2001) but lower than equa-tions developed by Guangxi (Liu 1993), Chongqing(Zhang et al. 2006), and Fujian (Wu et al. 1999)(Fig. 7b).

Discussion

Biomass allocation and tree-component contribution

Information about biomass allocation and the contri-bution of individual tree components is of interest tolarge-scale applications such as forest carbon monitor-ing (Pajtık et al. 2008) and investigating the influences ofmanagerial practices (for example, thinning and har-vesting) on forest carbon dynamics (Fournier et al.2003). Regardless of the relative quantity of tree-com-ponent biomass, all components are important biomasscarbon storage pools whether individual trees or standsand must therefore be incorporated into large-scalebiomass accounting initiatives (Peichl and Arain 2007).

Data from this study supports the general allocationpattern of tree-component biomass reported by previousstudies (Ding and Wang 2001; Zhang et al. 2005) in thatan increase in stem biomass along with a decrease in theproportion of branch and needle biomass takes place astrees grow larger within P. massoniana stands. Otherconiferous pine forest studies (Helmisaari et al. 2002;Peichl and Arain 2007) have found a similar trend in treebiomass allocation. It stands to reason that the alloca-tion pattern could be the result of young trees allocatingmore resources to foliage than to woody components(Ryan et al. 1997). As trees continue to grow, biomass

Fig. 4 Change in biomassallocation ratios along withdiameter at ground height (D0)on a logarithmic (log10) scale

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accumulation in stems typically surpasses that of bran-ches and leaves. In this study, however, considerablevariation in the allocation of overall tree biomass to eachaboveground component was observed for various treesizes. This was especially true for trees of smaller size.The variation in the allocation of tree biomass may bedue to differences in stand age, density, and site quality,since foliage production and its corresponding bearingbranch parts are highly sensitive to light, water, andnutrient levels as well as other soil conditions (Bond-Lamberry et al. 2002; Lambers et al. 1998). Observationsin P. massoniana stands in Guizhou Province in south-western China found that an increase in stand density orsite quality enhanced the proportion of stem biomassbut decreased the proportion of branch and needlebiomass overall (Ding 2000, 2003).

The direct measurement of belowground root bio-mass can be laborious and time-consuming. As it was forother studies (Vanninen et al. 1996; Brown 2002), a closerelationship between aboveground and belowgroundbiomass was found in this study. In the current study,moreover, no significant relationship of aboveground toroot biomass ratio (T/R) with tree size is in accordancewith the findings from Tanaka et al. (2009), indicating arelative stable T/R value. The averaged ratio of root toaboveground biomass in this study (0.16) is within rangeof other reports on P. massoniana stands by Ding (2003)

(from 0.15 to 0.17) and Liu (1993) (from 0.12 to 0.17)but lower than those found in other studies (from 0.16 to0.32) that took place within temperate coniferous pineforest ecosystems (Peichl and Arain 2007; King et al.2007). The ratio or relationship between root andaboveground biomass found in this study can be appliedto estimate belowground biomass on a large-scale in theabsence of other stand data.

Applicability of general allometric equationsfor carbon monitoring

Allometric equations showed high correlations betweentree component biomass and dbh, supporting previousstudies (Jenkins et al. 2003; Montagu et al. 2005; Wil-liams et al. 2005) where the development of generalizableequations applicable to trees that grow across sitelocations is feasible. Correlations found in this studyindicate that equations using dbh as the predictive var-iable can offer a sound estimate of stem, total above-ground, root, and total tree biomass, but a poor estimateof branch and needle biomass. This is consistent withfindings from other studies in that a single dbh-basedallometric equation provides a reasonable prediction fortotal aboveground (Montagu et al. 2005; Pajtık et al.2008; Williams et al. 2005), root (Ouimet et al. 2008),

Table 3 Regression coefficients with standard errors (in parenthesis) and fit statistics for allometric equations that correlated stem,branch, needle, aboveground, belowground, and total tree biomass to the predictor variables

Independent variable Dependent variable (y) (kg) Intercept parameter Slope parameter Sample number Adjust R2 CF

dbh (cm) Stem b10 �2.9928 (0.0869) b11 2.5835 (0.0386) 197 0.9453 1.0526Branch b20 �3.4007 (0.1221) b21 2.0203 (0.0540) 197 0.8544 1.1061Needle b30 �2.4476 (0.1217) b31 1.4436 (0.0552) 197 0.7026 1.1464Aboveground 197 0.9549 1.0368Root b40 �4.2037 (0.1259) b41 2.4167 (0.0559) 197 0.9022 1.0887Total biomass 197 0.9577 1.0345

D0 (cm) Stem b10 �4.9905 (0.1900) b11 3.0859 (0.0781) 156 0.8723 1.1040Branch b20 �5.0459 (0.1980) b21 2.4360 (0.0817) 156 0.8465 1.0955Needle b30 �4.0153 (0.2123) b31 1.9353 (0.0879) 156 0.7442 1.1237Aboveground 156 0.9010 1.0691Root b40 �6.2674 (0.1890) b41 2.9555 (0.0778) 156 0.8929 1.0814Total biomass 156 0.9092 1.0631

H (m) Stem b10 �2.9468 (0.1515) b11 2.6207 (0.0693) 197 0.8793 1.1196Branch b20 �2.9540 (0.2065) b21 1.8529 (0.0941) 197 0.6470 1.2768Needle b30 �2.0898 (0.1732) b31 1.3049 (0.0803) 197 0.5125 1.1399Aboveground 197 0.8494 1.4354Root b40 �3.8583 (0.2055) b41 2.3072 (0.0938) 197 0.7568 1.3316Total biomass 197 0.8441 1.4299

dbh2H Stem b10 �3.2145 (0.0736) b11 0.9028 (0.0110) 197 0.9683 1.0301Branch b20 �3.4421 (0.1322) b21 0.6853 (0.0197) 197 0.8220 1.1311Needle b30 �2.4713 (0.1224) b31 0.4885 (0.0189) 197 0.6686 1.1644Aboveground 197 0.9654 1.0281Root b40 �4.3114 (0.1336) b41 0.8286 (0.0200) 197 0.8954 1.0952Total biomass 197 0.9658 1.0278

D02H Stem b10 �4.5479 (0.1202) b11 1.0179 (0.0172) 156 0.9496 1.0407

Branch b20 �4.3118 (0.1798) b21 0.7488 (0.0261) 156 0.8025 1.1244Needle b30 �3.4112 (0.1852) b31 0.5920 (0.0270) 156 0.6959 1.1487Aboveground 156 0.9520 1.0329Root b40 �5.5783 (0.1664) b41 0.9362 (0.0241) 156 0.9056 1.0714Total biomass 156 0.9562 1.0299

CF correction factor

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and total tree biomass (Peichl and Arain 2007). Theimproved predictability of total and aboveground bio-mass may also be attributable to the fact that most

biomass exists within the stem component that is, initself, highly correlated with dbh. Poor predictions forbranch and needle biomass may be the result of varia-tion in biomass allocation due to site location, stand age(King et al. 2007), and stand density (Ziania andMencuccini 2003). Other predictors such as diameter atcrown base could be used to improve the precision ofneedle and branch biomass estimation according to thepipe model theory (Lehtonen 2005).

Controversial evidence exists as to whether the addi-tion of H as a second predictor variable in conjunctionwith dbh or D0 can improve the overall predictivecapacity of the equations (Jenkins et al. 2003; Williamset al. 2005). The allometric equation in this study as afunction of dbh2H partly improved predictions for stem,aboveground, and total biomass but did not improvepredictions for branch, needle, and root biomass.The high correlation between dbh and H (R2 = 0.849,p < 0.0001) may explain the low gains yielded in pre-dictive capacity when the latter variable was includedinto the allometric models (Pajtık et al. 2008).

The validation through comparison between thepredicted tree component biomass at stand level and thepublished data obtained similar results in that both dbh-

-70-60-50-40-30-20-10

010203040506070

S B N A R TPer

cen

tag

e d

iffer

ence

in b

iom

ass

(%)

Fig. 6 Percentage differences in biomass between equations basedon dbh (shaded) and dbh2H (black) prediction and data frompublished literature for tree components at a stand level. S, B, N, A,R, and T represent stem, branch, needle, aboveground, root, andtotal tree biomass, respectively. Bars represent standard deviation(n = 19)

Fig. 5 Relationships betweentree-component biomass anddiameter at breast height (dbh)on a logarithmic (log10) scalefor P. massoniana tree samples:a stem (y1); b branch (y2);c needle (y3); d root (y4);e aboveground (y5); and f totaltree biomass (y6). Closed andopen diamonds denote datataken from directmeasurements and data takenfrom published literature. Lineswere fitted using equationsprovided in Table 3

704

based and dbh2H-based equations performed satisfac-tory for the estimation of stem, aboveground, and totalbiomass. Equations that applied dbh2H as a predictorvariable generated a marginal decrease in error com-pared to equations that applied dbh alone as a predictor.The validation effort also demonstrates that inaccuratepredictions may occur when general equations are ap-plied to specific stands, but overestimation and under-estimation generated by the general equations maycancel each other out when used for large geographicalareas (Muukkonen 2007; Ziania and Mencuccini 2003).The comparison that was carried out between all totaltree biomass equations suggests that while the allometricequations used in this study with dbh as an independentvariable provided lower overall estimations of tree bio-mass, the equations with dbh2H as an independentvariable was within the intermediate range of publishedequations and, therefore, would offer an appropriateestimation of total tree biomass on a regional scale.

It has been acknowledged among researchers that theinclusion of a height variable increases model precisionbut also increases end-user costs since an additionalvariable (H) must be measured. A balance betweencost and precision must be attained (Wang 2006). It is

important to point out that the inclusion of the H mea-surement in a biomass allometric equation may havelimited use in applications of biomass estimation andcarbon budget accounting on a regional scale since treeheight measurements are typically unavailable in forestinventory datasets due to the cost and the time requiredto collect the data in the field ( (Wang 2006)). Manyresearchers have reported that stem dbh is adequate forlocal or regional biomass estimations (Jenkins et al.2003). Since tree samples ranged from 2.5–32.5 cm indbh and from 2.6–24.0 m in H, caution must be takenwhen equations are applied to trees beyond the abovesize ranges.

Conclusions

Results from this study show that tree-component bio-mass allocation varies with tree size. Greater variationwas found in trees of smaller size. Mature trees main-tained a stable and proportional accumulated biomasswithin the stem component. As a result, differences inother biomass components seem relatively small. Besidesthe considerable contribution of stems, other compo-nents such as canopy and belowground biomass are alsoimportant biomass carbon pools and must be includedto accurately predict large-scale carbon storage inP. massoniana stands.

Performance validation suggests that allometricequations as a function of dbh and dbh2H are applicablefor the prediction of stem, aboveground, and total treebiomass in P. massoniana stands. Stem, aboveground,and total tree biomass components are highly correlated.Equations using dbh2H as a predictor variable generateda slight decrease in error compared to equations usingdbh alone as a predictor. The choice between allometricequations when estimating tree and forest biomass on aregional scale would depend upon the availability ofdimensional variables (dbh and H) within forestryinventory data.

Acknowledgments This study received grants from the ChinaNatural Science Foundation (No. 30771720), the IntroducingAdvanced Technology program (948 Program) (No. 2006-4-21),the New Century Excellent Talents program (No. NCET-06-0715), the program for Science and Technology InnovativeResearch Team in Higher Educational Institutions of HunanProvince, and the Furong Scholar program. The authors wouldlike to thank Mr. Yuanjun Xing for his assistance in producingthe map of P. massoniana natural distribution and the locationsof the study sites.

Appendix 1

See Table 4

(a)

0

100

200

300

400

500

600

700

dbh (cm)

Tota

l tre

e b

iom

ass

(kg

) Chongqing Fujian 1

Fujian2 model 1

(b)

0

100

200

300

400

500

600

700

800

900

1000

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

0 5000 10000 15000 20000 25000

dbh 2H

Tota

l tre

e b

iom

ass

(kg

) Hubei ChongqingSichuan GuizhouFujian 1 Guangximodel 2

Fig. 7 Comparison between the fitted equation based on dbh anddbh2H and the available published equations in relation to totalbiomass prediction. Equations from each location are provided inTable 2

705

Table

4Biomass

data

collectedfrom

publish

literature

forP.massonianatree

components

atastandlevel

insouthernChina

No.

County

Province

Stand

age(a)

Forest

type

dbh

(cm)

H(m

)Standdensity

(treeha�1)

Stem

(tha�1)

Branch

(tha�1)

Needle

(tha�1)

Aboveground

(tha�1)

Belowground

(tha�1)

Stand

(tha�1)

Cited

sources

1Longli

Guizhou

12

Plantation

10.50

7.95

850

13.19

4.98

2.56

20.73

3.05

23.78

Ding(2003)

2Longli

Guizhou

12

Plantation

10.86

8.00

1,750

28.46

7.63

4.15

40.24

6.60

46.84

Ding(2003)

3Longli

Guizhou

12

Plantation

9.48

8.75

2,725

36.72

7.67

4.45

48.84

8.27

57.11

Ding(2003)

4Longli

Guizhou

12

Plantation

7.67

8.50

6,435

57.37

10.82

5.82

74.01

12.89

86.90

Ding(2003)

5Longli

Guizhou

8Plantation

5.28

4.90

6,700

19.31

6.47

3.39

29.17

4.77

33.94

DingandWang(2001)

6Longli

Guizhou

18

Plantation

10.69

11.58

4,600

150.70

24.28

8.02

183.00

21.52

204.52

DingandWang(2001)

7Longli

Guizhou

22

Plantation

16.50

15.85

1,830

165.88

25.82

8.01

199.71

24.01

223.72

DingandWang(2001)

8Longli

Guizhou

30

Plantation

19.40

18.00

1,140

174.68

24.98

7.90

207.56

26.56

234.12

DingandWang(2001)

9Longli

Guizhou

22

Plantation

11.75

11.96

2,730

94.96

15.96

5.78

116.70

15.12

131.82

Ding(2000)

10

Longli

Guizhou

30

Plantation

16.70

14.80

1,365

123.56

22.18

6.50

152.24

21.50

173.74

Ding(2000)

11

Liling

Hunan

16

Natural

10.62

10.50

2,250

47.62

12.27

5.51

65.40

13.08

78.48

Chen

etal.(2001)

12

Liling

Hunan

16

Natural

9.18

8.30

2,680

35.15

7.99

3.51

46.65

9.96

56.61

Chen

etal.(2001)

13

Liling

Hunan

16

Natural

8.36

7.70

3,225

33.28

7.10

3.09

43.47

9.58

53.05

Chen

etal.(2001)

14

Huitong

Hunan

20

Natural

14.40

12.50

1,750

60.90

13.07

9.03

83.00

17.04

100.04

Fenget

al.(1982)

15

Jianou

Fujian

11

Plantation

8.01

7.15

3,525

42.05

10.80

6.80

59.65

11.40

71.05

Wuet

al.(1999)

16

Jianou

Fujian

14

Plantation

10.01

10.02

2,500

61.88

11.04

6.47

79.39

13.97

93.36

Wuet

al.(1999)

17

Jianou

Fujian

20

Plantation

14.62

15.30

1,450

107.86

11.90

5.08

124.84

18.49

143.33

Wuet

al.(1999)

18

Jianou

Fujian

24

Plantation

20.55

18.00

1,025

167.32

14.67

5.04

187.03

25.14

212.17

Wuet

al.(1999)

19

Jianou

Fujian

30

Plantation

23.24

19.06

875

189.47

15.33

4.86

209.66

26.53

236.19

Wuet

al.(1999)

706

Appendix 2

See Table 5

Table 5 Primary data of stand density, component biomass [including stem (y1), branch (y2), needle (y3), root (y4), aboveground (y5), andtotal tree biomass (y6)], diameter at breast height (dbh), at ground level (D0), and tree height (H) for 197 P. massoniana tree samples

County Province Stand density (tree ha�1) D0 (cm) dbh (cm) H (m) y1 (kg) y2 (kg) y3 (kg) y4 (kg) y5 (kg) y6 (kg)

Chuzhou Anhui 960 18.7 10.80 76.05 4.17 0.72 80.94 26.17 107.11Chuzhou Anhui 600 18.2 11.00 61.01 12.54 3.56 77.11 13.97 91.08Yichang Hubei 2,880 13.3 9.4 11.10 21.46 4.33 2.52 28.31 5.27 33.58Yichang Hubei 2,175 11.8 10.2 10.10 23.35 4.79 3.94 32.08 5.68 37.76Yichang Hubei 3,495 11.0 7.3 8.95 10.54 2.19 1.97 14.70 2.68 17.38Yichang Hubei 1,845 9.7 8.4 9.65 14.95 1.68 2.77 19.40 2.91 22.31Yichang Hubei 2,175 12.5 10.5 9.75 21.51 7.16 3.32 31.99 3.62 35.61Yichang Hubei 1,770 15.9 10.5 10.54 26.08 4.52 3.95 34.55 5.18 39.73Yichang Hubei 1,980 16.2 10.1 10.20 23.75 3.85 1.84 29.44 6.69 36.13Yichang Hubei 2,385 11.9 9.7 12.50 25.29 2.29 2.45 30.03 8.01 38.04Yichang Hubei 2,445 9.5 7.7 10.50 14.76 1.67 2.19 18.62 3.88 22.50Yichang Hubei 2,250 12.4 9.3 10.60 18.83 1.44 1.07 21.34 3.95 25.29Enshi Hubei 5.9 7.55 6.42 1.12 1.08 8.62 2.83 11.45Enshi Hubei 8.1 8.40 19.19 2.22 1.26 22.66 3.17 25.84Enshi Hubei 10.0 10.60 28.55 3.09 1.53 33.16 4.19 37.35Enshi Hubei 12.1 12.34 35.45 5.89 2.82 44.16 5.92 50.08Enshi Hubei 13.9 15.20 45.84 6.16 4.05 56.05 18.36 74.41Bishan Chongqing 1,125 16.5 13.3 15.20 57.84 6.73 15.53 80.10 8.88 88.98Bishan Chongqing 2,190 11.7 8.3 8.50 12.73 2.55 2.83 18.11 1.75 19.86Bishan Chongqing 1,080 20.5 15.6 15.00 76.37 16.15 9.84 102.36 29.95 132.31Bishan Chongqing 1,440 8.0 6.3 7.20 6.02 1.17 1.74 8.93 0.82 9.75Bishan Chongqing 1,440 5.7 3.8 3.40 1.47 0.62 1.34 3.43 0.32 3.75Bishan Chongqing 1,440 6.3 3.9 4.80 2.08 0.46 0.95 3.49 0.37 3.86Bishan Chongqing 1,665 6.5 4.7 5.70 3.09 0.49 0.98 4.56 0.49 5.05Bishan Chongqing 1,665 5.3 4.9 5.00 3.07 0.43 0.58 4.08 0.33 4.41Bishan Chongqing 2,610 9.7 7.9 9.30 10.62 1.49 1.73 13.84 1.40 15.24Bishan Chongqing 2,610 8.7 6.4 9.00 16.49 2.05 1.53 20.07 1.18 21.25Bishan Chongqing 2,610 11.4 8.8 7.70 10.22 2.00 3.16 15.38 1.65 17.03Bishan Chongqing 1,545 10.0 8.5 8.70 11.90 1.42 3.61 16.93 1.75 18.68Bishan Chongqing 1,545 13.2 10.2 8.80 16.08 3.88 3.36 23.32 2.83 26.15Zizhong Sichuan 1,470 12.1 7.4 11.40 17.16 1.37 1.04 19.57 3.07 22.64Zizhong Sichuan 1,470 15.3 10.4 13.50 38.10 2.15 4.57 44.82 7.27 52.09Zizhong Sichuan 1,470 18.3 13.7 14.10 60.77 6.77 6.14 73.68 12.02 85.70Zizhong Sichuan 1,485 21.5 15.7 16.30 62.89 8.24 5.45 76.58 12.62 89.20Zizhong Sichuan 750 24.8 21.3 20.20 157.47 12.63 8.22 178.32 27.27 205.59Zizhong Sichuan 1,110 18.7 14.4 16.40 62.32 6.22 4.22 72.76 10.77 83.53Zizhong Sichuan 1,500 16.9 13.1 15.70 62.21 6.02 4.26 72.49 11.67 84.16Zizhong Sichuan 1,125 18.2 13.6 14.50 51.42 3.11 7.42 61.95 10.22 72.17Zizhong Sichuan 1,560 13.7 11.2 11.60 33.48 4.89 6.25 44.62 8.74 53.36Zizhong Sichuan 645 23.5 23.3 20.90 219.86 18.01 12.32 250.19 46.70 296.89Zizhong Sichuan 945 20.3 17.7 18.90 100.31 10.33 3.92 114.56 16.15 130.71Tiantai Zhejiang 1,200 15.8 12.30 57.59 11.20 2.27 71.06 7.81 78.87Tiantai Zhejiang 1,267 30.6 19.80 360.59 13.57 12.93 387.09 38.61 425.70Tiantai Zhejiang 817 12.5 9.00 21.50 9.03 3.19 33.72 7.63 41.35Tiantai Zhejiang 1,517 18.4 12.70 80.74 3.13 2.86 86.73 3.03 89.76Tiantai Zhejiang 683 10.6 5.50 13.17 2.02 2.77 17.96 5.74 23.70Shimen Hunan 2,595 13.4 9.9 10.70 21.54 3.41 1.51 26.46 3.47 29.93Shimen Hunan 1,695 13.9 11.5 8.40 16.10 2.35 2.15 20.60 3.46 24.06Shimen Hunan 1,695 11.5 9.3 7.20 12.54 2.45 1.54 16.53 2.89 19.42Shimen Hunan 1,695 7.7 6.7 5.90 5.39 0.79 0.81 6.99 0.86 7.85Shimen Hunan 1,875 14.1 12.6 9.00 28.86 5.57 4.82 39.25 6.15 45.40Shimen Hunan 1,875 12.8 10.1 8.20 16.74 5.11 2.49 24.34 3.12 27.46Shimen Hunan 1,875 8.8 7.0 7.80 8.50 1.51 1.24 11.25 1.34 12.59Shimen Hunan 1,710 12.7 10.0 8.70 21.21 5.25 1.48 27.94 4.17 32.11Shimen Hunan 2,535 12.8 10.6 10.10 24.18 3.11 2.33 29.62 4.50 34.12Shimen Hunan 2,625 13.9 12.3 8.90 26.26 4.83 3.48 34.57 4.91 39.48Shimen Hunan 2,625 11.3 8.8 8.60 15.96 2.04 1.51 19.51 2.32 21.83Shimen Hunan 2,625 6.4 5.1 5.30 3.57 0.55 0.48 4.60 0.82 5.42Shimen Hunan 2,355 10.2 8.8 5.70 7.83 1.27 1.39 10.49 2.36 12.85Shimen Hunan 2,355 9.5 5.3 6.90 5.18 2.24 1.66 9.08 0.93 10.01

707

Table 5 continued

County Province Stand density (tree ha�1) D0 (cm) dbh (cm) H (m) y1 (kg) y2 (kg) y3 (kg) y4 (kg) y5 (kg) y6 (kg)

Shimen Hunan 2,355 8.6 5.9 5.30 3.91 1.20 1.22 6.33 0.86 7.19Shimen Hunan 990 11.9 10.3 6.70 12.85 5.78 3.67 22.30 5.23 27.53Shimen Hunan 990 10.7 8.7 6.50 9.40 1.93 1.86 13.19 2.35 15.54Shimen Hunan 990 8.5 6.8 5.80 4.96 2.15 2.98 10.09 1.54 11.63Shimen Hunan 1,020 18.4 15.8 13.40 71.39 7.83 3.93 83.15 9.73 92.88Yongshun Hunan 1,185 15.7 14.1 11.50 33.24 6.64 3.68 43.56 6.95 50.51Yongshun Hunan 840 19.9 16.0 11.30 40.66 13.23 8.24 62.13 14.39 76.52Yongshun Hunan 840 11.3 8.6 7.98 8.61 2.03 1.39 12.03 1.86 13.89Yongshun Hunan 2,175 16.1 12.6 8.90 20.63 6.34 4.00 30.97 4.89 35.86Yongshun Hunan 2,175 11.0 9.6 8.90 9.88 1.75 2.25 13.88 2.25 16.13Yongshun Hunan 2,175 8.5 7.1 7.50 6.47 1.59 1.31 9.37 0.91 10.28Yongshun Hunan 2,355 6.8 6.2 11.30 7.80 0.47 0.34 8.61 0.76 9.37Yongshun Hunan 2,355 10.5 9.3 12.30 18.17 2.72 1.69 22.58 2.44 25.02Yongshun Hunan 2,355 18.5 15.8 15.40 58.56 9.49 5.40 73.45 8.14 81.59Yongshun Hunan 4,260 16.5 12.0 10.75 26.42 8.03 4.89 39.34 6.92 46.26Yongshun Hunan 4,260 10.4 7.5 9.60 9.90 2.11 1.00 13.01 2.85 15.86Yongshun Hunan 4,260 6.1 4.8 6.69 2.99 0.32 0.31 3.62 0.81 4.43Yongshun Hunan 975 21.0 18.5 18.30 84.27 8.39 3.21 95.87 12.88 108.75Yongshun Hunan 2,025 15.2 12.4 10.50 27.28 8.84 7.40 43.52 4.85 48.37Yongshun Hunan 2,025 9.3 8.1 7.50 7.17 1.76 1.32 10.25 0.37 10.62Yongshun Hunan 2,025 5.7 4.2 5.40 1.97 0.48 0.30 2.75 0.43 3.18Anhua Hunan 495 21.3 18.8 17.90 123.02 19.85 8.81 151.68 12.57 164.25Anhua Hunan 960 15.4 15.1 16.80 69.14 14.92 5.71 89.77 11.62 101.39Anhua Hunan 1,605 17.4 9.4 13.15 36.63 8.01 4.76 49.40 8.06 57.46Anhua Hunan 1,605 11.5 10.0 11.15 18.94 2.08 1.62 22.64 2.34 24.98Anhua Hunan 4,035 9.1 7.0 10.70 9.21 0.74 0.69 10.64 0.61 11.25Anhua Hunan 4,035 4.5 4.0 7.78 2.00 0.10 0.19 2.29 0.26 2.55Anhua Hunan 1,305 13.4 12.2 16.40 36.73 3.36 2.46 42.55 5.29 47.84Anhua Hunan 1,200 20.8 18.4 24.00 209.83 17.20 8.68 235.71 32.72 268.43Anhua Hunan 1,200 18.4 12.6 21.80 71.68 3.65 2.06 77.39 9.05 86.44Anhua Hunan 1,215 22.2 16.5 15.40 65.33 13.83 6.55 85.71 17.56 103.27Anhua Hunan 1,980 13.6 10.3 11.50 21.27 2.49 2.60 26.36 1.41 27.77Anhua Hunan 2,430 16.2 14.6 14.40 47.86 7.10 3.93 58.89 8.41 67.30Anhua Hunan 2,430 12.5 8.7 12.50 15.03 2.65 1.13 18.81 2.16 20.97Anhua Hunan 2,430 10.2 6.9 9.70 7.96 1.16 0.41 9.53 1.52 11.05Shupu Hunan 2,730 13.4 9.6 7.25 11.75 1.18 1.83 14.76 4.12 18.88Shupu Hunan 2,730 7.8 5.7 5.30 3.69 1.10 0.89 5.68 0.62 6.30Shupu Hunan 7,005 9.9 6.0 5.54 4.59 1.28 1.35 7.22 0.97 8.19Shupu Hunan 7,005 7.0 3.8 4.45 1.83 0.58 0.36 2.77 0.49 3.26Shupu Hunan 8,400 10.8 7.9 8.80 10.27 1.86 0.98 13.11 1.52 14.63Shupu Hunan 8,400 6.0 4.6 5.95 2.22 0.30 0.43 2.95 0.64 3.59Shupu Hunan 5,280 13.3 7.7 6.45 6.66 3.16 1.94 11.76 1.77 13.53Shupu Hunan 5,280 6.3 4.1 4.94 2.13 0.45 0.32 2.90 0.78 3.68Shupu Hunan 7,305 9.1 6.5 5.52 4.26 1.32 0.94 6.52 1.15 7.67Shupu Hunan 7,305 6.9 4.1 4.35 1.83 0.67 0.28 2.78 0.48 3.26Shupu Hunan 7,305 5.1 2.7 4.00 1.03 0.20 0.12 1.35 0.35 1.70Shupu Hunan 4,920 10.1 7.3 5.98 6.08 1.53 1.21 8.82 2.94 11.76Shupu Hunan 4,920 7.7 4.8 5.00 2.81 1.00 0.91 4.72 0.88 5.60Shupu Hunan 2,040 13.5 11.0 10.85 24.46 3.73 1.42 29.61 4.11 33.72Shupu Hunan 6,405 9.1 6.4 5.00 4.05 1.74 1.52 7.31 1.25 8.56Shupu Hunan 6,405 7.8 4.0 4.20 1.70 0.88 0.57 3.15 0.80 3.95Xiangtan Hunan 2,910 13.3 9.2 5.45 10.61 4.08 2.09 16.78 2.79 19.57Xiangtan Hunan 4,440 10.4 6.3 4.99 6.40 1.96 2.15 10.51 1.66 12.17Xiangtan Hunan 4,440 7.5 4.3 3.99 2.94 0.89 0.49 4.32 0.73 5.05Xiangtan Hunan 2,070 11.9 9.0 5.65 11.01 2.75 2.86 16.62 3.24 19.86Xiangtan Hunan 2,070 7.7 4.7 3.90 1.57 1.11 0.61 3.29 0.65 3.94Xiangtan Hunan 3,270 7.4 4.6 3.80 2.94 0.44 0.50 3.88 0.68 4.56Xiangtan Hunan 3,270 12.1 8.3 5.37 8.43 1.58 2.69 12.70 2.22 14.92Xiangtan Hunan 1,320 12.9 9.0 5.62 10.04 1.68 3.00 14.72 4.58 19.30Xiangtan Hunan 1,320 6.1 4.0 4.50 2.55 0.30 0.48 3.33 0.58 3.91Xiangtan Hunan 3,045 9.8 7.7 4.74 6.27 5.03 1.83 13.13 1.87 15.00Xiangtan Hunan 3,045 7.8 4.8 4.02 2.89 1.00 0.78 4.67 0.71 5.38Xiangtan Hunan 1,215 22.3 14.4 9.20 41.05 11.03 9.21 61.29 9.62 70.91Xiangtan Hunan 1,215 6.6 3.2 2.97 1.43 0.64 0.65 2.72 0.36 3.08Xiangtan Hunan 765 26.0 20.7 12.90 86.59 21.59 8.71 116.89 32.47 149.36Xiangtan Hunan 765 11.2 8.4 6.63 8.01 2.43 1.97 12.41 1.37 13.78Xiangtan Hunan 7,050 10.5 7.7 7.94 8.27 1.79 2.01 12.07 2.03 14.10

708

Table 5 continued

County Province Stand density (tree ha�1) D0 (cm) dbh (cm) H (m) y1 (kg) y2 (kg) y3 (kg) y4 (kg) y5 (kg) y6 (kg)

Xiangtan Hunan 7,050 7.0 5.1 6.20 3.22 1.10 1.17 5.49 0.78 6.27Xiangtan Hunan 3,885 8.2 4.2 3.96 2.51 0.97 1.06 4.54 0.61 5.15Xiangtan Hunan 3,885 11.1 7.5 4.47 7.72 2.02 2.03 11.77 1.42 13.19Xiangtan Hunan 3,060 11.9 5.8 5.30 1.30 0.81 0.95 3.06 1.47 4.53Xiangtan Hunan 3,060 8.6 5.1 4.60 3.11 1.70 2.09 6.90 0.76 7.66Xiangtan Hunan 2,190 13.4 9.6 6.18 11.99 5.01 3.45 20.45 2.54 22.99Xiangtan Hunan 2,190 9.1 6.3 5.20 7.33 0.79 1.23 9.35 0.96 10.31Xiangtan Hunan 2,190 6.4 4.2 4.20 2.20 0.51 0.58 3.29 0.50 3.79Xiangtan Hunan 7,635 9.9 6.4 5.45 5.00 1.54 1.84 8.38 0.95 9.33Xiangtan Hunan 7,635 6.3 3.8 4.70 2.19 0.45 0.65 3.29 0.31 3.60Xiangtan Hunan 1,455 9.1 5.5 5.65 4.96 1.09 1.18 7.23 1.06 8.29Xiangtan Hunan 1,455 6.5 4.7 4.40 3.04 0.81 0.75 4.60 1.70 6.30Xiangtan Hunan 1,455 13.5 11.1 8.10 15.22 5.82 4.03 25.07 3.73 28.80Xiangtan Hunan 4,200 5.0 2.5 2.60 1.51 0.52 0.78 2.81 0.30 3.11Xiangtan Hunan 1,590 19.3 14.6 10.80 49.01 7.76 5.57 62.34 10.37 72.71Xiangtan Hunan 1,590 5.4 3.3 4.12 1.28 0.43 0.66 2.37 0.20 2.57Huitong Hunan 1,750 8.0 11.78 10.17 1.70 0.94 12.81 2.46 15.27Huitong Hunan 1,750 10.5 12.01 16.26 3.58 2.01 21.85 4.20 26.05Huitong Hunan 1,750 12.4 10.40 25.06 3.55 2.32 30.93 5.64 36.57Huitong Hunan 1,750 14.4 12.16 34.83 7.13 4.28 46.24 12.33 58.57Huitong Hunan 1,750 17.7 12.68 52.85 12.52 7.89 73.26 13.82 87.08Huitong Hunan 1,750 19.4 14.34 73.00 18.10 11.37 102.47 21.55 124.02Huitong Hunan 1,750 22.3 16.54 87.49 30.65 21.27 139.41 25.64 165.05Hengyang Hunan 1,095 19.7 17.8 16.80 125.04 15.83 8.53 149.40 22.63 172.03Hengyang Hunan 1,845 16.5 11.9 11.30 33.74 3.42 2.39 39.55 5.08 44.63Hengyang Hunan 1,440 18.4 13.5 12.50 56.89 10.40 6.50 73.79 5.77 79.56Hengnan Hunan 2,850 10.8 8.0 8.00 10.65 2.38 1.70 14.73 2.25 16.98Hengnan Hunan 2,850 5.4 3.9 3.90 1.35 0.24 0.45 2.04 0.41 2.45Hengnan Hunan 2,850 16.4 12.4 11.50 39.54 4.91 6.58 51.03 8.18 59.21Hengnan Hunan 2,235 10.8 8.3 6.30 8.18 0.77 1.23 10.18 1.29 11.47Hengnan Hunan 2,235 7.8 4.5 4.30 2.66 0.24 0.20 3.10 0.49 3.59Hengnan Hunan 2,625 6.2 3.4 3.30 1.08 0.41 0.91 2.40 0.28 2.68Hengnan Hunan 2,625 10.4 6.7 5.80 5.52 2.71 2.18 10.41 1.45 11.86Hengnan Hunan 3,000 7.3 4.8 4.70 2.38 0.99 1.26 4.63 0.45 5.08Hengnan Hunan 3,000 9.7 6.9 5.90 5.58 2.17 2.36 10.11 1.04 11.15Hengnan Hunan 3,000 14.2 12.1 8.20 24.33 2.78 6.03 33.14 4.45 37.59Hengnan Hunan 2,865 6.8 5.4 4.70 2.77 0.94 0.92 4.63 0.54 5.17Hengnan Hunan 3,090 14.2 10.2 8.10 17.53 2.94 5.36 25.83 4.28 30.11Hengnan Hunan 3,090 9.4 5.7 5.07 4.27 1.38 1.28 6.93 0.90 7.83Hengnan Hunan 1,560 15.4 12.3 11.10 28.60 5.21 4.87 38.68 5.46 44.14Hengnan Hunan 1,560 9.8 8.4 8.90 15.24 6.62 7.17 29.03 2.73 31.76Hengnan Hunan 1,830 19.0 14.5 12.60 61.23 12.36 5.85 79.44 11.35 90.79Hengnan Hunan 1,830 7.0 4.5 4.40 2.47 1.47 0.89 4.83 0.74 5.57Hengnan Hunan 4,080 11.4 9.6 6.50 10.71 2.37 1.21 14.29 2.37 16.66Hengnan Hunan 4,080 7.8 5.4 4.75 2.70 1.71 0.61 5.02 0.86 5.88Hengnan Hunan 4,080 5.5 3.6 3.48 1.38 0.50 0.55 2.43 0.38 2.81Hengnan Hunan 2,205 8.2 5.2 3.91 3.19 0.66 0.39 4.24 0.55 4.79Hengnan Hunan 1,485 13.6 12.0 9.07 18.46 3.09 2.75 24.30 5.36 29.66Longli Guizhou 6,700 5.3 4.90 2.93 0.93 0.52 4.38 0.74 5.12Longli Guizhou 6,425 8.0 9.00 9.23 2.06 0.92 12.21 1.93 14.14Longli Guizhou 4,600 10.7 11.58 33.89 5.61 1.86 41.36 4.79 46.15Longli Guizhou 1,830 16.5 15.85 82.48 12.42 3.85 98.75 12.27 111.02Longli Guizhou 1,140 19.4 18.00 120.11 17.43 5.55 143.09 17.92 161.01Sanming Fujian 1,630 14.5 11.50 74.49 7.22 2.55 84.26 14.27 98.53Sanming Fujian 570 28.1 20.12 255.70 32.23 3.54 291.47 66.75 358.23Sanming Fujian 570 26.6 20.59 248.49 38.07 3.58 290.14 61.54 351.68Jianou Fujian 4,075 4.8 4.14 2.77 1.33 1.16 5.27 1.08 6.35Jianou Fujian 3,525 8.0 7.15 11.93 2.86 1.93 16.72 3.23 19.95Jianou Fujian 2,500 10.0 10.02 24.75 4.02 2.59 31.36 5.59 36.94Jianou Fujian 1,450 14.6 15.30 74.39 8.21 3.50 86.10 12.75 98.85Jianou Fujian 1,025 20.6 18.00 163.24 14.31 4.92 182.47 24.53 207.00Jianou Fujian 875 23.2 19.06 216.54 17.52 5.55 239.61 30.32 269.93Luzai Guangxi 400 25.2 19.10 234.27 44.09 9.79 288.15 79.03 367.18Luzai Guangxi 1,425 16.1 15.40 68.95 17.11 5.72 91.78 5.75 97.53Luzai Guangxi 2,925 10.2 9.00 11.98 4.90 2.13 19.01 1.54 20.55Wuxuan Guangxi 16,020 5.2 5.40 2.30 0.90 0.40 3.60 0.60 4.20Wuxuan Guangxi 2,445 13.5 10.40 24.90 4.90 2.40 32.20 4.60 36.80

709

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Table 5 continued

County Province Stand density (tree ha�1) D0 (cm) dbh (cm) H (m) y1 (kg) y2 (kg) y3 (kg) y4 (kg) y5 (kg) y6 (kg)

Wuxuan Guangxi 1,725 17.6 13.80 72.70 9.90 1.60 84.20 9.90 94.10Wuxuan Guangxi 435 32.2 19.00 300.60 66.80 20.80 388.20 66.80 455.00Guiping Guangxi 3,000 6.8 5.30 6.15 2.63 1.32 10.10 2.19 12.29

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