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Allometric models for predicting above- and belowground
biomass ofLeucaena
-KX2 in a shaded coffee agroecosystemin Hawaii
Adel H. Youkhana Travis W. Idol
Received: 15 October 2010 / Accepted: 23 April 2011 / Published online: 4 May 2011
Springer Science+Business Media B.V. 2011
Abstract We developed site-specific allometric
models for Leucaena leucocephala 9 pallida var.
KX2 trees in a shaded coffee agroecosystem in
Hawaii to predict above- and belowground biomass
and the regrowth potential of pollarded trees. Models
were used to compare tree growth rates in an
experimental agroforestry system with different pol-
larding frequencies and additions of tree pruning
residues as mulch. For all allometric equations, a
simple power model (Y = aXb) provided the optimal
prediction of biomass or regrowth after pollard-ing. For aboveground biomass components (stem,
branches, leaves, and seed and pods), stem diameter
alone was the best predictor variable. Stump diameter
provided the best prediction of coarse root biomass
and aboveground regrowth after pollarding. Predic-
tions of biomass from generalized allometric models
often fell outside the 95% confidence intervals of our
site-specific models, especially as biomass increased.
The combination of pollarding trees once per year
plus the addition of tree mulch resulted in the greatest
aboveground regrowth rates as well as accumulation
of biomass and C in the stump plus coarse roots.
Although optimal prediction required the develop-
ment of site-specific allometric relationships, a sim-
ple power model using stem or stump diameter alone
can provide an accurate assessment of above- and
belowground tree biomass, as well as regrowth
potential under specific management scenarios.
Keywords Allometric models Leucaenaleucocephala-KX2 Nonlinear regression
Aboveground and belowground biomass Coarse root excavation Carbon sequestration
Introduction
Biomass equations form a basis for estimating
biomass and carbon (C) pools in forestry and
agroforestry systems (Albrecht and Kandji 2003;
Kenzo et al. 2009; Nair et al. 2009). Allometricmodels are based on correlations between morpho-
logical characters that can be measured in the field or
the laboratorysuch as diameter at breast height
(DBH) or tree height (H)and more complex size
measurementssuch as stem volume or tree bio-
massthat cannot be measured non-destructively. In
forestry, these models allow for the prediction of
ecologically or economically important pools, such as
biomass, C, nutrients, or merchantable wood yield
A. H. Youkhana (&) T. W. IdolDepartment of Natural Resources and Environmental
Management, College of Tropical Agriculture and Human
Resources, University of Hawaii at Manoa,
1910 East-West Road, Sherman Lab # 101, Honolulu,
HI 96822, USA
e-mail: [email protected]
T. W. Idol
e-mail: [email protected]
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Agroforest Syst (2011) 83:331345
DOI 10.1007/s10457-011-9403-6
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(Ares et al. 2002; Northup et al. 2005). Allometric
models have primarily been developed for applica-
tion in natural forests and forest plantations (Zianis
and Mencuccini 2004). In addition, several general-
ized allometric equations have been developed for
estimating biomass of tropical species (Chambers
et al. 2001; Chave et al. 2005; Overman et al. 1994;Santos-Martin et al. 2010). General models repre-
senting multiple species, land uses, and locations can
eliminate the investment needed to develop site- and
species-specific models (Lambert et al. 2005). Most
of these models use DBH, height, and sometimes
wood density as predictor variables. Litton and
Kauffman (2008) found that DBH alone was an
effective predictor of all categories of aboveground
biomass for Metrosideros polymorpha allometry in
Hawaii. Navar (2009) developed an individual allo-
metric equation using non-linear regression to fitparameters of the typical power equation for estimat-
ing biomass of 19 species in Mexico. Chave et al.
(2005) developed generalized equations for tropical
tree species in moist, wet, and dry forests using DBH,
height, and wood density. Sampaio and Silva (2005)
used basal diameter as well as DBH for shrubby
species in caatinga ecosystems of Brazil.
The advantage of generalized equations lies in
their broad applicability and the elimination of
destructive harvests for each species, variety, tree
size range, and/or location. However, using theseequations at a specific site can result in biased
estimates of biomass and C (Cairns et al. 2003). The
applicability of generalized models to new sites or
species, therefore, must be tested prior to application.
In particular, using generalized equations to estimate
biomass in agroforestry systems may be problematic
due to potential alterations in tree architecture and
biomass caused by the pruning and pollarding that are
typically used to manage shade levels and provide
farm products (Segura et al.2006). Pruning obviously
reduces total aboveground biomass and thus compli-cates assessments of biomass or C sequestration.
In addition, with pollarding or pruning there is a need
for models that predict regrowth rates of stems,
branches, leaves, and reproductive components, since
these separately provide important products and
services and interact with other components of the
system. Thus, agroforestry systems often require
development of models specific at least to the
management regime if not to the species or variety.
Allometric models to predict the biomass of coarse
roots are also needed because they contribute signif-
icantly to total individual tree and stand biomass and
C storage (Li et al. 2003; Santantonio et al. 1977).
Direct assessment of belowground coarse root bio-
mass is even more difficult than aboveground
biomass (Huxley 1999). There is large spatial heter-ogeneity in coarse root distribution and a limited
capacity to quantify the spatial distribution and
area- or volume-weighted biomass of roots given
current methods (Bengough et al. 2000). As a result,
modeling tree root biomass allometrically has gained
wide acceptance (Drexhage and Colin 2001). Scien-
tists have made numerous attempts to establish a
relationship between above- and belowground tree
attributes at the stand and individual tree level,
mostly using models of the relative allocation of dry
weight between roots and aboveground parts (Bolteet al. 2004; Kenzo et al. 2009; Santantonio et al.
1977). Similar to models of aboveground biomass,
these studies have shown that root and main stem
diameter are good predictors of individual root and
total coarse root biomass, respectively (Gower et al.
1996; Kurz et al. 1996; Li et al. 2003; Nadelhoffer
et al.1985). For individual trees, several studies have
developed relationships between coarse root biomass
and stem DBH, height (H), or both (Drexhage and
Colin 2001; Hoffmann and Usoltsev 2001; Laiho
and Finer 1996; Santantonio et al. 1977; Thies andCunningham 1996). Haynes and Gower (1995) used
basal stump diameter alone to predict coarse root
biomass. This is especially appropriate for agrofor-
estry systems in which trees are pollarded to manage
shade levels.
For pollarded trees in agroforestry systems,
regrowth potential is an important process to be able
to model and predict on an individual-tree basis and
at the stand level. Allometric equations have not been
developed specifically for this purpose, but it seems
logical that measures such as main stem diameterwould be appropriate for these predictions. This
would also allow for non-destructive assessments of
the influence of management practices such as mulch
addition, fertilization, tillage, etc. on this potential,
especially over repeated pollarding or pruning cycles.
The objectives of the present study were to (1)
develop allometric equations from destructive harvest
to predict live biomass of both aboveground and
belowground components of Leucaena-KX2 trees;
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(2) evaluate the potential of generalized allometric
models to predict the biomass of these trees; and (3)
use the best-fit equations to compare the regrowth
potential and biomass accumulation of trees under
different pollarding frequencies with and without
return of the pruning residues as mulch.
Materials and methods
Study site
The study was carried out at the University of
Hawaii, College of Tropical Agriculture and Human
Resources, Waimanalo Research Station on the
windward side of the island of Oahu (21 200 N,
158 200 W). The site is 20 m above sea level and is
classified as a humid tropical environment (Giam-belluca et al.1986). The soils are classified mainly as
Vertic Haplustolls, dominated by the Waimanalo
series. Further details of the study site can be found in
Youkhana and Idol (2009).
Leucaena variety KX2 was chosen for this study
due to the rapid growth of resprouts after pollarding
or coppicing, its high N fixation rate, resistance to
psyllid insects and self-sterility (Brewbaker 2008).
Plots were established in a seed orchard established in
2002 with trees planted in 8 rows of 20 trees per row,
planted on 2 9 2-m spacing. Eighteen plots wereestablished, each 4 9 6 m in size, encompassing six
leucaena trees in a 3 9 2 tree arrangement. The inner
6 rows and 18 trees per row were included in the
plots, leaving a border row of trees around the entire
orchard (Fig.1). Drip irrigation was applied to all
plots during dry periods to maintain plant survival
and growth.
On 20 September 2005, all trees in the plots were
pollarded at 1 m above ground level. This was chosen
because the trees had previously been pollarded at
approximately this height. The harvested biomass
was chipped in a mechanical tree chipper and
distributed uniformly back to the leucaena plots as
mulch. As shoots regrew after pollarding, the two
largest shoots initiated near the top of the stump and
on opposite sides from each other were retained;others were removed. Again, this was based on
the previous management regime. The boundaries
between plots were trenched to a depth of 1 m and
lined with plastic sheeting to prevent overlap of root
systems between adjacent plots. On 20 August 2006,
trees were pollarded again and assigned to one of
two pollarding frequency treatments: once every
6 months and once every 12 months. A second
treatment of mulch addition was crossed with
pollarding frequency. Coffee seedlings were planted
within the plots on 2 9 2-m spacing to establish acoffee agroforestry system in order to investigate the
effects of shade level and mulch additions on bean
productivity (yield) and quality. For mulch-addition
plots, tree pollarding residues were mechanically
chipped and added back to the plot of origin. For the
no-mulch plots, pollarding residues were not returned
to the plots. The treatments were imposed for 3 years
until September 2009.
Measurements of aboveground biomass
and carbon
For the development of allometric models of above-
ground tree biomass, 20 representative trees that
spanned a range of stump diameters (measured at
50 cm above the ground) were selected at the time of
initial pollarding in 2005 (Table 1). Stump diameter
(D) and basal diameter of the shoot emanating from
the stump were measured using a diameter tape. The
height (H) of each individual shoot was measured
Pt1 2 3 4
7 8 9 10 11 12
5 6
13 14 15 16 17 182 m 2 m
4 m
Coffea arabica var. Kona TypicaLeucaena leucocephalaKX2
6 m
Fig. 1 Layout of experimental plots in shade system ofLeucaena-KX2 and coffee
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using a Suunto Clinometer (Philip 1994). The trees
were pollarded and the biomass collected. Each
tree was divided into four biomass components:
orthotropic shoots emanating from the stump (stems),
plagiotropic shoots attached to the stems (branches),
leaves, and reproductive parts (seed and pods). Each
fraction was weighed, and representative sampleswere collected for dry matter determination (60C,
72 h until constant weight to the nearest 0.01 g).
Wood specific gravity (oven-dry weight over green
volume) was determined for 50 stem cross-section
samples using the water displacement method
(ASTM 1983); the average (0.94 g cm-3) was used
for prediction of biomass.
Biomass data for each individual component were
analyzed to derive predictive equations based on stem
D or H using a set of standard allometric models
(Husch et al. 2003) (Table2). These included thefollowing:
Y a bD2 1
Y a bDcD2 2
Y aDbHc 3
Y aDb 4
where Y is the dependent variable (e.g. biomass of
stems, branches, leaves, reproductive parts or total
shoot biomass in kg dry mass) and a, b and c areconstants. Stem D and H were log-transformed to
create linear models analogous to Eqs.3 and 4. All
regression equations were developed using the PROC
REG procedure in SAS (SAS Institute Inc.1990).
Models were selected according to goodness of fit
measures, including the sum of squares of the
residuals, the residual mean square (RMS), the
coefficient of determination (R2), the Akaike Infor-
mation Criterion (AIC), the bias-corrected AIC(AICc), and the Bayesian Information Criterion
(BIC) (Myung et al. 2010; Spiess and Neumeyer
2010). In addition, the residuals (observed minus
predicted values) were plotted against the predictor
variables to visually assess for bias in predictions.
The coefficient of determination for each model was
calculated as R2 = (1 - SSR)/corrected SST (Litton
and Kauffman 2008), where SSR is the sum of
squares of the residuals, and corrected SST is the total
sum of squares of deviations from the overall mean.
Models with higher R2; least bias for biomassprediction; and smallest SSR, RMSE, AIC, AICc
and BIC were selected for each biomass component.
Several generalized equations for predicting
above- and belowground biomass were evaluated
for their appropriateness for the managed Leucaena-
KX2 trees in this study. For shoot biomass, we used
equations from Brown (1997), Chave et al. (2005)
and Sampaio and Silva (2005), and previous equa-
tions developed for Leucaena-KX2 (Brewbaker
2008) (Table3). The Brown (1997) model requires
only stem D (cm) to predict total shoot biomass (kgdry weight); whereas, the Chave et al. (2005) models
require species-specific information on wood specific
gravity and either D alone or D and H specific to
climatic zones. The two models of Sampaio and Silva
(2005) require D alone or D and H combined to
predict total shoot biomass. We also used two
allometric equations developed for total shoot bio-
mass and forage annual dry matter yields ofLeuca-
ena-KX2 (Brewbaker2008). These models used tree
height (cm) alone as the predictor variable (Table 3).
The previous models used DBH to predict totalaboveground biomass. However because our trees
were pollarded, our models predicted total biomass of
the resprouting shoots based on basal stem D; this
may affect the model coefficients but should not
affect the goodness of fit, as resprouting stems
generally maintain the same architecture and thus
allometric relationships with biomass or volume as
the main stem (Fownes and Harrington1992; Tewari
et al. 2004).
Table 1 Diameter range of representative shoots and stumps
of Leucaena-KX2 trees for aboveground and belowground
study
Aboveground study Belowground study
Shoot D (cm) Stump D (cm) Stump D (cm)
Range # Trees Range # Trees Range # Trees
1.53 5 35 5 57 4
35 5 57 5 79 3
57 5 79 4 911 3
79 3 911 1 1113 3
911 2 1113 1 1315 2
1315 2 1517 3
1517 2 1720 2
Total 20 20 20
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Generalized models were evaluated by plotting thepredicted biomass and 95% confidence intervals from
our site-specific model with the predicted biomass
from each generalized model across the full range of
D and H of Leucaena-KX2 trees measured in this
study. Site-specific and generalized equations were
used to predict biomass from the 2006 pollarding
events to maintain independence from the data used
to develop the site-specific models. If the predicted
biomass of the generalized model fell outside of the
best-fit confidence intervals, especially at larger D orH, then it was rejected as being appropriate for
application to the trees in this study.
Stump and coarse root biomass
We used the methods of Kapeluck and Van Lear
(1995) to predict the biomass of coarse roots ([5 mm
diameter). Essentially, this is a two-step process in
which a regression relationship is developed first
Table 2 Goodness of fit comparisons of some standard allometric models explored in this study
Model a b R2
SSR RMS AIC AICc BIC
For shoot D
Stem biomass (kg)
Y = a ? bD2
0.19 2.03 0.91 15.22 1.04 145.53 143.73 146.52
Y = a ? bD ? cD2
0.03 2.10 0.97 12.65 0.72 154.89 153.09 155.89
Y = aDb
Hc
0.05 2.04 0.85 22.75 2.25 152.58 149.98 154.57
Y = aDb
0.16 2.28 0.98 11.84 0.68 138.74 136.95 139.74
Branches biomass (kg)
Y = a ? bD2 0.33 1.98 0.77 32.55 2.02 151.19 149.37 152.18
Y = a ? bD ? cD2 0.05 1.71 0.75 21.11 1.17 152.31 150.52 153.31
Y = aDb
Hc
0.06 1.57 0.72 25.24 1.95 150.75 148.15 152.74
Y = aDb
0.12 1.96 0.78 19.55 1.14 141.38 139.58 142.37
Leaves biomass (kg)
Y = a ? bD2
0.65 1.80 0.70 3.81 0.22 125.78 123.98 126.78
Y = a ? bD ? cD2
0.47 1.58 0.71 3.97 0.24 126.25 124.45 127.24
Y = aDbHc 0.32 1.48 0.66 4.33 0.56 122.17 119.57 124.16
Y = aDb
0.03 2.10 0.73 3.66 0.19 115.39 113.59 116.38
Reproductive parts biomass (kg)
Y = a ? bD2
0.44 1.42 0.49 11.58 7.05 139.98 138.17 140.96
Y = a ? bD ? cD2
0.60 1.55 0.51 14.65 7.33 148.75 146.95 149.74
Y = aDb
Hc
0.24 1.62 0.55 17.24 8.33 141.08 139.26 142.06
Y = aDb
0.09 1.69 0.57 10.13 0.56 134.01 132.21 135.01
Total biomass (kg)
Y = a ? bD2
0.18 2.03 0.90 32.5 1.83 159.19 157.38 160.18
Y = a ? bD ? cD2 0.38 1.92 0.97 30.97 1.74 165.12 163.32 166.12
Y=
aD
b
H
c
0.34 1.94 0.83 42.24 2.37 162.06 160.26 163.06Y = aD
b0.30 2.25 0.98 29.53 1.72 156.35 154.55 157.34
For stump D
Stump biomass (kg)
Y = a ? bD2
0.64 1.63 0.89 178.25 9.44 167.33 165.23 168.32
Y = a ? bD ? cD2
0.25 1.84 0.92 98.22 11.27 172.45 170.35 173.44
Y = aDb
0.09 1.96 0.93 22.17 1.23 160.46 158.25 161.45
a, b and c Constants; SSR sum of squares of the residuals; RMSresiduals mean square; R2
the coefficient of determination; AICthe
Akaike Information Criterion; AICc the bias-corrected (AIC); BIC the Bayesian Information Criterion
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between individual root diameter and root mass and
then between stump D and predicted root mass.
The simple power model (Eq.4) is used for both
relationships. For this study, five Leucaena-KX2
trees from the border rows were randomly selected to
be excavated for measurement of total coarse root
mass on 6 December 2007. Before excavating the
root system, the selected trees were marked and
measurements were made of stump D, shoot D, and
tree H (Table1). Roots were excavated using handtools and a bulldozer with an excavator bucket to lift
the stump. Coarse roots were excavated down to a
diameter of 5 mm (Kapeluck and Van Lear 1995).
After excavation, the roots and stump were washed
with water and a stiff brush to remove soil. The
diameter of all coarse roots was measured using a
diameter tape or calipers where they were attached at
the stump. After measurement, roots were cut from
the stump and oven-dried for at least 72 h at 70C to
a constant weight. Proximal diameters of 126 lateral
roots from the five excavated trees were regressedagainst their respective biomass weights to develop
the regression relationship.
An additional 20 trees from the border rows were
selected that spanned a range of stump D from 5 to
20 cm. Tree dimensions were measured as before prior
to excavation. The coarse root system was excavated
using a bulldozer but without the requirement of
collecting roots down to a set diameter. After washing,
the diameter of all coarse roots at the stump was
measured. Roots were cut from the stump, and the
stumps were collected and dried to measure stump
biomass.
Stump D at 50 cm above the ground was also used
to predict stump biomass using Eqs.1, 2, and 4,
where Y is stump biomass in kg dry mass and D is
stump D. For our purposes, the stump was consideredto include the aboveground shoot below the pollard-
ing height and the belowground portion to which all
lateral roots were attached. The best-fit model was
chosen based on goodness of fit measures.
Coarse root and stump samples from various
diameter ranges were analyzed for C content using
a combustion furnace elemental analyzer by the
University of Hawaii Agricultural Diagnostic Service
Center (ADSC) in order to compare C sequestration
rates among the treatments.
Shoot regrowth
Stump D was used to predict annual shoot regrowth,
i.e. the regrowth of both shoots retained on the stump
after pollarding, using Eq.4. Separate equations were
developed for each treatment combination and during
each year of the study (20062009). For the 6-month
pollarding treatment, stump D was used to predict
annual growth by combining biomass growth over
two successive pollarding intervals.
Statistical analysis
Because the influence of the pollarding and mulch
treatments were expected to increase over time, we
analyzed these effects on the allometric relationship
between stump D and shoot regrowth via repeated
measures multivariate analysis of variance (MA-
NOVA), using the GLM procedure in SAS (Max-
well and Delaney 2004; von Ende 1993). Best-fit
regression equations were created within each plot
(n = 6 trees/plot) during each year to predict shootbiomass regrowth using stump D. The coefficients
(a and b) for each plot within a treatment were
averaged to generate treatment means and vari-
ances for comparison (n = 4). Where there were
significant interactions among treatments or with
time, specific contrasts were constructed to ana-
lyze differences. Bonferroni adjustments were made
to the critical P-value to account for multiple
comparisons.
Table 3 Above-and-belowground generalized models used in
this study
Equation Source
Aboveground biomass
Y = 5.37 9 10-59 H
2.714Brewbaker (2008)
Y = 1.66 9 10-4
9 H2.365
Brewbaker (2008)Y = exp (-2.134 ?
2.530 9 ln(D))
Brown (1997)
Y = 0.0612 9 (D 9 H)1.5811 Sampaio and Silva (2005)
Y = 0.173 9 D2.295
Sampaio and Silva (2005)
Y = 0.0509 9 p D2
H Chave-moist (2005)
Y = 0.0776 9 (p*D2
H)0.94
Chave-wet (2005)
Coarse root biomass
Y = 0.06 9 D2.00
Kapeluck and Van Lear
(1995)
For aboveground biomass, D stem diameter; for belowground
biomass,D stump diameter
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The effects of treatment and time on shoot
regrowth and biomass of the stump and coarse roots
at the plot level (Mg ha-1) were analyzed using
functional data analysis procedures (Ramsay and
Silverman 2005). Shoot regrowth and stump ?
coarse root biomass and C content for each treatment
combination were regressed against time to develop alinear relationship (n = 4 plots per treatment). Sig-
nificant regression equations (non-zero slopes) indi-
cated a significant time effect. Significantly different
slopes among treatments indicated a significant
treatment effect. To compare analytical procedures,
the 2009 data on shoot regrowth and stump ? coarse
root biomass and C content were compared among
treatment combinations via one-way ANOVA with
Tukeys honest significant difference as the means
separation test.
Results
Biomass prediction
The simple power model using only stem D as a
single independent variable was found to be the
best predictor of total aboveground biomass of
individual shoots (Table2). Stem and total shoot
biomass were both well-predicted (R2 = 0.98),
while branches, leaves, and reproductive part (seedpods) were less well-predicted (Fig.2ae). Error
variance increased for larger-diameter stems but
showed no systematic bias (Fig.2fj). Logarithmic
transformation of the data did not reduce this
increase in error variance with stem D or improve
the fit for most components.
There was a strong relationship between shoot H
and D (Fig.3), but shoot H alone was not as good a
predictor of total shoot biomass (R2 = 0.79) (Fig. 4).
Coarse root basal diameter was an effective
predictor of individual coarse root biomass. As withother biomass components, larger diameter roots
exhibited greater error variance, but there was no
evidence of bias. Logarithmic transformation did
reduce this increase in error variance and so was
selected for the final model (Fig. 5).
Stump D was an effective predictor of stump
biomass (Fig.6a) and total coarse root biomass
(Fig.6b). As with stem D, stumps with larger D
showed greater error variance (Fig. 6c, d).
Evaluation of generalized models
All of the generalized models evaluated were highly
significant (P\ 0.01), and several had R2[ 0.90
(Table4). None of them, however, provided a better
fit than the site-specific models. In addition, biomass
predicted from all of the generalized models tendedto fall outside of the confidence intervals from the
best-fit models, especially at larger D or H (Fig. 7).
The two generalized tropical tree models by Chave
et al. (2005) underestimated aboveground biomass
relative to the site-specific model at all tree diameters
(Fig.7c) but did not show an increasing deviation as
stem D increased.
The previously published models for Leucaena-
KX2 (Brewbaker 2008) also showed divergence
from our site-specific model using H as the
predictor variable (Fig.8). Model (1) provided abetter fit but underestimated biomass at moderate
heights (47 m) when applied to the data in our
study.
Shoot regrowth
There were no significant time or treatment effects
of pollarding or mulching on the prediction of
individual shoot biomass (kg stem-1) based on stem
D. On the other hand, there was a significant declinefrom 2006 to 2009 in the exponential parameter for
stump D as a predictor variable for shoot regrowth
in the no-mulch treatments (Table5), indicating a
decline in shoot regrowth potential. There were no
differences in the relationship due to pollarding
frequency.
Total shoot regrowth at the plot level (Mg ha-1
year-1) increased significantly over time for the
treatment combination pollarding once-mulch addi-
tion. For the other treatment combinations, shoot
regrowth declined significantly over time in the orderpollarding once-no mulch, pollarding twice-mulch,
pollarding twice-no mulch (Fig.9; Table6). The
one-way ANOVA comparing treatments during 2009
showed the same significant differences (data not
shown).
Stump plus coarse root biomass and C sequestra-
tion on a plot-level basis increased significantly over
time for all treatments (Table6). The pollard once-
mulch addition treatment was significantly greater
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than the pollard twice-no mulch treatment, adding
approx. 2.4 Mg ha-1 of biomass (1.1 Mg ha-1 of C)
from 2006 to 2009 (Fig. 10). The one-way ANOVA
comparing treatments during 2009 showed the same
significant difference (data not shown).
Discussion
In general, stem and stump diameter were good
predictors of biomass and regrowth after pollarding
forLeucaena-KX2 using a simple power relationship.
A
y = 0.16 D2.28
R2
= 0.98
S
temb
iomass(kg)
0
5
10
15
20
25F
Stemb
iomassresiduals
-4
-2
0
2
4
B
y = 0.12 D1.96
R2
= 0.78
Branchesbiomass(kg)
0
2
4
6
8
G
Branchesbiomassresiduals
-4
-2
0
2
4
C
y = 0.03 D 2.10
R2= 0.73
Leafbiomass(kg)
0
1
2
3
4
H
Leafbiomassresidu
als
-4
-2
0
2
4
D
y = 0.09 D1.69
R2= 0.57
Reproduc
tivebiomass(kg)
0
2
4
6
I
Reproductiveresiduals
-4
-2
0
2
4
E
y = 0.30 D2.25
R2
= 0.98
Shoot D (cm)
Totalshootbiomass(kg)
0
10
20
30
J
0 2 4 6 8 0 2 4 6 8
Totalshootbiomassresiduals
-4
-2
0
2
4
Fig. 2 Allometric models
for predicting: a stem,
b branches, c leaves,
d reproductive component
(pods and seeds), ande total
shoot biomass (kg) from
shoot diameter (cm) in
individuals ofLeucaena
-KX2, and fj biomass
residuals (observed minus
predicted values). P \ 0.01
for all models
338 Agroforest Syst (2011) 83:331345
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For individual stems, basal stem D was quite accuratein predicting biomass of the main stem, but less so for
branches, leaves, or seed pods. The main stem
represents the major proportion of aboveground
biomass (Kaonga and Bayliss-Smith 2009; Santos-
Martin et al.2010; Senelwa and Sims1998; Van et al.
2000), so stem D was sufficient for predicting total
shoot biomass. Repeated pollarding and pollarding
frequency did not alter the allometric relationship
between stem D and stem biomass. Other studies
have found similar results (Fownes and Harrington
1992; Tewari et al. 2004), which suggests that
pollarding does not alter the morphology, and thus
allometry, of resprouting stems.
Many authors have confirmed that using stem D
alone (DBH or basal stem D) is a reliable predictorof total aboveground biomass for a variety of
species and ecosystems (Bartelink 1998; Fownes
and Harrington 1992; Litton and Kauffman 2008;
Sampaio and Silva 2005; Segura and Kanninen
2005). However, Harrington (1979) and Claesson
et al. (2001) argue that allometric equations for
biomass estimation based on only one response
variable are of questionable accuracy. In our study,
we also used tree height (H) as a second predictor
variable, since it is often measured in forest inven-
tories and has been used in allometric models topredict biomass either singly or in combination with
D. Our results showed that H alone was not as good a
predictor of total stem biomass, and the combination
of D and H did not improve the prediction compared
with D alone. This has also been shown in other
studies for a range of species (Onyekwelu2004; Salis
et al.2006; Tumwebaze2008). It is not the number of
predictor variables built into the model but rather the
H = 15.18 (1-exp(-0.08D))
R2
= 0.80
Shoot D (cm)
2 4 6 8
Sho
otH
(m)
0
2
4
6
8
10
Fig. 3 Diameter versus height relationship for Leucaena-KX2
shoots that were harvested to develop the allometric model for
predicting total biomass of stem
A y = 0.64x1.80
R2= 0.79
H (m)
Totalbiomass(kg)
0
10
20
30
B
0 2 4 6 8 0 2 4 6 8
Totalbiomass(kg)residuals
-10
-5
0
5
10Fig. 4 a Allometric model
for predicting total biomass(kg) from stem height
(m) for Leucaena-KX2 and
b biomass residual of stem
height
B
lnCoarserootbiomassresiduals
-4
-2
0
2
4A R
2= 0.88
ln Coarse root D (cm)
-2 -1 0 1 2 3-2 -1 0 1 2 3
lnCoarserootbiomass(kg)
-8
-6
-4
-2
0
2Fig. 5 a Predicting
biomass (kg) from coarse
root diameter (cm) of
Leucaena-KX2 in Hawaii
using log transformed linear
model and b biomass
residuals of log transformed
coarse root diameter
Agroforest Syst (2011) 83:331345 339
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significance of the predictive strength of the individ-
ual variables that counts. For most studies, predic-
tions based on stem D result in less error than H alone
(Otieno et al.1991), and the combination of D and H
does not necessarily reduce this error (e.g. Litton and
Kauffman2008).
Similar to stem D, coarse root basal diameter was an
effective predictor of individual coarse root biomass.
In addition, stump D was an accurate predictor of
total coarse root biomass. These relations provide an
effective means to estimate biomass, and thus
belowground carbon pools, using common inventory
variables.
The simple exponential (power) equation proved
superior to other more complex equations for predicting
biomass and regrowth rates (Table4). This has been the
most frequently used equation to estimate biomass
based on stem D, representing plants from different
A
y = 0.09 D 1.96
R2 = 0.93
Stu
mpbiomass(kg)
0
5
10
15
C
Stum
pbiomassresiduals
-2
0
2
B
y = 0.06 D2.00
R2
= 0.91
Stump D (cm)
Totalcoarserootbiomass(kg)
0
5
10
15
D
0 5 10 15 0 5 10 15Totalcoarserootbiomassresiduals
-2
0
2
Fig. 6 Allometric models
for predicting biomass (kg)
for a stump, b total coarse
root, and c and d biomass
residuals (observed minus
predicted values). P \ 0.01
for all models
Table 4 Goodness of fit
comparisons ofLeucaena-
KX2 model and other
generalized models for
predict aboveground and
belowground biomass (kg)
D Basal stem diameter
Models R2 SSR RMS P value
Individual shoot biomass
Leucaena KX2-Hawaii
Y = 0.30 9 D2.25 0.97 7.01 0.37 \0.01
Y = 0.64 9 H1.80 0.80 30.69 1.90 \0.01
Brewbaker (2008)
Y = 5.37 9 10-5
9 H2.714
0.82 43.90 2.17 \0.01Y = 1.66 9 10-
49 H
2.3650.75 54.22 4.24 \0.01
Brown (1997)
Y = exp (-2.134 ? 2.530 9 ln(D)) 0.86 53.85 2.99 \0.01
Chave et al. (2005):
Y = 0.0509 9 p D2
H (moist) 0.92 16.18 0.80 \0.01
Y = 0.0776 9 (p*D2
H)0.94
(wet) 0.85 42.07 2.45 \0.01
Sampaio and Silva (2005):
Y = 0.0612 9 (D 9 H)1.5811
0.86 251.23 17.49 \0.01
Y = 0.173 9 D2.295
0.91 184.42 6.36 \0.01
340 Agroforest Syst (2011) 83:331345
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environments and sizes (Zianis and Mencuccini2004).Although much attention has been placed on developing
generalized allometric models for tropical trees (Brown
1997; Zianis and Mencuccini2004; Chave et al.2005;
Pilli et al. 2006), using generalized equations can lead to
a bias in estimating biomass for a particular species
(Clark et al.2001; Cairns et al.2003; Chave et al. 2005;
Litton and Kauffman 2008; Pilli et al. 2006), Ulti-
mately, this is more problematic than simply poorer
accuracy, as bias results in unreliable estimates within
certain tree size ranges and not just wider confidence
intervals across the entire range. Major sources of
uncertainties include (i) differences in site indices,
including soil and climatic conditions; (ii) unquantified
diameter and height ranges (Specht and West 2003);(iii) narrow ranges of diameter and height (Burrows
et al.2000); and (iv) differences in age classes and tree
density (Chen et al. 1998; Lin et al. 2001). Thus,
generalized models are subject to the same limitations
that constrain the application of site-and species-
specific models.
Simply adding additional variables, such as tree
height and wood density, to account for more sources
of variation, may not overcome these potential
A
0
10
20
30
40
Leucaena
Site specific modelUper 95% Conf. Int. of site modelLower 95% Conf. Int. of site modelBrown (moist) 1997
B
Totalsh
ootbiomass(kg)
0
10
20
30
40
C
Shoot D (cm)
0 2 4 6 80
10
20
30
40
Sampaio & Silva (1) 2005Sampaio & Silva (2) 2005
Chave (wet) 2005Chave (moist) 2005
-KX2 Waimanalo
Fig. 7 Comparison of predicted biomass in generalized
models using: a D alone, b D alone and both D and H, and
c D, H, and P (wood specific gravity) as predictor variables
with predicted biomass and values of 95% confidence intervals
ofLeucaena-KX2 site-specific model
H (m)
0 2 4 6 8
Totalshootbiomass(kg)
0
10
20
30
40
Leucaena-KX2 Waimanalo
Site specific model
Brewbaker (1) 2008
Brewbaker (2) 2008
Lower 95% Conf. Int.of site model
Upper 95% Conf. Int. of site model
Fig. 8 Comparison of predicted values of individual shoot
biomass (kg) in Brewbaker (2008) models (1) and (2) with
predicted biomass and values of 95% confidence intervals of
Leucaena-KX2 site-specific models, uses shoot height (H) as
predictor variable
Table 5 Comparison of coefficients from best-fit allometric
models (Y = aDb
) developed during 2006 and 2009 for pre-
diction of total shoot biomass regrowth (kg) ofLeucaena-KX2
(Y) based on stump diameter (D)
Year a (SE) b (SE) R2
No mulch
2006 0.25 (0.17) 2.00 (0.20) 0.93
2009 0.68* (0.34) 1.49* (1.55) 0.91
Mulch
2006 0.26 (0.08) 2.02 (0.14) 0.912009 0.31
(0.23) 2.06
(2.55) 0.90
* Indicates a significant difference between 2006 and 2009 at
P\ 0.025
Indicates a significant difference between mulch and
no-mulch treatments at P \0.025
Agroforest Syst (2011) 83:331345 341
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limitations. In our study, the models of Chave et al.
(2005) showed a consistent underestimate of shoot
biomass, despite the use of stem D, H, and wood
density. Unlike many of the other generalized
models, however, there was no increasing divergence
with increasing stem D, suggesting at least a
reasonable level of precision in its prediction of
biomass. Across a wide range of species or site
conditions, therefore, such models may be beneficial.
For forest plantations or agroforestry systems, how-ever, the effort required to develop site-specific
models is recouped in the ability to quickly and
reliably predict biomass using simple inventory
measures.
The 2-step procedure for predicting coarse root
biomass by Kapeluck and Van Lear (1995) also proved
useful in this study, despite the potential problems
introduced by pollarding. One potential concern with
this method is the use of an allometric equation to
predict individual root biomass in order to develop the
allometric relationship between stump D and totalcoarse root biomass. This additional step is introduced
to save time and effort in developing a site-specific
allometric. If individual root biomass were poorly
predicted from root diameter or if there were evidence
of bias in root biomass prediction, such concerns would
be valid. In our case, there was no evidence of bias in
the prediction of individual root biomass, and stump D
was able to predict total coarse root biomass with
reasonable accuracy and precision.
Year
2006 2007 2008 2009
Totalshootregrowth(Mgha-1)
0
4
8
12
Pollard once + Mulch
Pollard twice + Mulch
Pollard once + No mulch
Pollard twice + No mulch
Fig. 9 Effect of pollarding frequency (once and twice) and
mulching (mulch, no mulch) on total shoot regrowth
(Mg ha-1
year-1
) from 2005 to 2009
Table 6 Comparison of time and treatment effects on shoot
growth and stump ? root biomass and C sequestration
Treatment Slope SE R2 P-value
Shoot growth
Pollard once ? no mulch -0.46ab 0.07 0.80 \0.01
Pollard twice ? no mulch -1.21c 0.06 0.97 \0.01
Pollard once ? mulch 0.79a 0.15 0.74 \0.01
Pollard twice ? mulch -0.48b 0.11 0.64 \0.01
(Stump ? root) biomass
Pollard once ? no mulch 0.41ab 0.15 0.43 0.02
Pollard twice ? no mulch 0.24b 0.07 0.55 \0.01
Pollard once ? mulch 0.84a 0.13 0.82 \0.01
Pollard twice ? mulch 0.46ab 0.11 0.64 \0.01
(Stump ? root) C sequestration
Pollard once ? no mulch 0.19ab 0.07 0.43 0.02
Pollard twice ? no mulch 0.11b 0.31 0.55 \0.01
Pollard once ? mulch 0.38a 0.06 0.82 \0.01
Pollard twice ? mulch 0.21ab 0.05 0.64 \0.01
Slope coefficient for the time regression, standard error (SE),
coefficient of determination (R2
), and significance of the slope
term (P-value)
Means followed by the same letter do not differ significantly
according to Tukeys honest significant different test
A
0
2
4
6
8
B
Year
2006 2007 2008 2009
Mgha-1
0
1
2
3
4
Pollard once + Mulch
Pollard twice + Mulch
Pollard once + No mulchPollard twice + No mulch
Fig. 10 Effect of pollarding frequency (once and twice)
and mulching (mulch, no mulch) on stump ? coarse root:
a biomass and b carbon sequestration from 2006 to 2009
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Both pollarding frequency and mulch additions
had significant effects on stem regrowth rates.
Pollarding twice per year led to a significant decline
in shoot regrowth over time, regardless of mulch
addition. The negative effect of more frequent
pollarding has been reported previously (Duguma
et al. 1988). After pollarding, plants must use storedreserves to initiate new growth and then replace those
lost reserves with new assimilates to maintain
regrowth potential (Lovett and Tobiessen 1993).
Lower stem biomass production with shorter harvest-
ing cycles may also be influenced by the season of
cutting. Seasonal variations in solar radiation and
average temperature are known to be highly corre-
lated with leaf yield, leaf N and wood yield of
Leucaena (Evensen1985). During the winter season
in Hawaii, days are measurably shorter (10.5 h day
length minimum vs. 13.5 h maximum), averagetemperatures are *4C cooler, and rainfall and thus
cloud cover are generally higher. This may result in
slower regrowth of shoots pollarded just before or
during winter and therefore a delay in the replenish-
ment of stored reserves, reducing annual biomass
production.
Mulch addition had a positive effect on shoot
regrowth. Mulch from these pollarded trees provides
significant quantities of N and organic matter to the
soil on an annual basis (Youkhana and Idol2009) and
thus can be used as an organic amendment for soilimprovement. Repeated removal of this nutrient-rich
organic matter would likely result in nutrient limita-
tion to the pollarded trees. As with any harvested
crop, ifLeucaena were to be used in a cut-and-carry
system for animal fodder, green manure or mulch
production, restoration of nutrient losses in harvested
biomass is a key for sustainability of shoot regrowth
and harvestable yields.
Finally, the increase in plot-level stump ? coarse
root biomass and C sequestration from 2006 to 2009
under even the best conditions (pollard once ?
mulch) were modest (*1.1 Mg ha-1 of C) compared
to most tropical forest plantations or unmanaged
stands. Predicted coarse root biomass of individual
trees was *15% of shoot regrowth potential on an
annual basis, slightly lower than the 20% average
area-based (Mg ha-1) root:shoot ratio predicted for a
large dataset of upland forests from Cairns et al.
(1997). The low C sequestration rate is thus an
inevitable trade-off of managing shade levels through
pollarding. However, a previous study showed that
returning mulch to the soil surface increased C
sequestration as soil organic matter by 10.8 Mg ha-1
over the same time period (Youkhana and Idol2009).
Unfortunately, current programs to provide compen-
sation or recognition of C sequestration in forestry
and agroforestry systems focus exclusively on theaboveground biomass components (Idol et al. 2011).
Conclusion
In a coffea-Leucaena agroecosystem, aboveground
biomass and shoot regrowth after pollarding can be
reliably predicted using stem and stump diameter,
respectively, using a simple exponential relationship.
Similarly, basal root diameter and stump diameter
can reliably predict individual and total coarse rootdiameter using the same power function. For this
system, biomass predictions from generalized models
are inadequate, as they result in over- or under-
estimates of biomass, especially as tree size
increases. Repeated pollarding did not alter the
allometric relationship between stem D and shoot
biomass, but shoot regrowth potential and stump ?
coarse root biomass growth over time were both
affected by pollarding frequency and mulch addition.
The allometric equations developed in this study
should be sufficient for modeling of overall produc-tion and C sequestration of this multipurpose tree
under standard management practices.
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