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69
Chapter - 5
Assessment of biomass and carbon stocks in Tea agroforestry system
________________________________________________________________________
5.1 Introduction
The retained increase in atmospheric carbon dioxide (CO2) concentration is considered to
be hastened by human activities such as burning of fossil fuels and deforestation (IPCC
2007). Reduction in CO2 emission or sequestration through different carbon (C) sinks is
the probable option to mitigate climate change. The post-Kyoto Protocol to the United
Nations Framework Convention on Climate Change (UNFCCC) era drew substantial
attention in bracing the CO2 level in the atmosphere encouraging varied land use systems
as C sink. The woody perennial-based land use systems have relatively high capacities
for capturing and storing atmospheric CO2 in vegetation, soils, and biomass products
(Kumar & Nair 2011). Agroforestry systems (AFS) offer important opportunities of
creating synergies between both adaptation and mitigation actions with a technical
mitigation potential of 1.1–2.2 Pg C in terrestrial ecosystems over the next 50 years
(IPCC 2007). The accent of AFS have higher carbon content and can help attain net gains
in carbon than conventional lower biomass land uses like grasslands, crop fallows etc.
Agroforestry provides a unique opportunity to combine the twin objectives of climate
change adaptation and mitigation (Murthy et al. 2013). Although agroforestry systems
are not primarily designed for carbon sequestration, agroforestry systems can play a
major role in storing carbon in above and in belowground biomass and in soil (Sathaye et
al. 2001; Montagnini & Nair 2004; Nair et al. 2009).
In different AFS C stock and sequestration goes on both above and belowground
compartment, in the form of standing biomass, root biomass and enhancement of soil
organic carbon (SOC). Some studies on C storage in AFS and alternative land use
systems for India had estimated a sequestration potential of 68-228 Mg C ha-1
(Dixon et
al. 1994), 25 Mg C ha-1
over 96 M ha of land (Sathaye & Ravindranath 1998). But this
value varies in different regions depending on the biomass production (Pandey 2007).
70
Agrisilvicultural systems sequestrate C in tree biomass. Annual carbon sequestration
potential of planted tree species on abandoned agricultural land (3.9 t ha-1
yr-1
) and
degraded forest land (1.79 t ha-1
yr-1
) have been estimated. Leading carbon sequestrating
species was Alnus nepaliensis (0.256 Mg C ha-1
yr-1
) and Dalbergia sissoo (0.141 t C ha-1
yr-1
) intercropped with wheat and paddy in Central Himalaya, India (Maikhuri et al.
2000). Swamy et al. 2003) estimated C sequestration in a 6 year old Gmelina arborea
based agri-silvicultural system (31.37 Mg C ha-1
). C sequestration in monocropping of
trees and food crops exhibits 40 % and 84 % less than agri-silviculture indicating that
agroforestry systems have more potential to sequester carbon (Dhyani et al. 2009)
compared to 18.74 Mg C ha-1
insole wheat cultivation (Chauhan et al. 2010). In a system
comprising Albizia and mixed tree species like Mandarin accumulated 1.3 Mg biomass
ha-1
storing 6939 kg ha-1
in tree and crop biomass was reported (Sharma et al. 1995).
Agroforestry has the potential of restoration and maintenance of soil fertility, and
increase in productivity. Some of the agroforestry systems practiced in northeast India are
Agri-horticulture, Silvipastoral, Agri-silviculture, Silvi-horticulture, Pastoral-silviculture
and home gardens (Murthy et al. 2013).
Tea (Camellia sinensis (L.) O. Kuntze) is grown under a canopy of trees which provide
partial shade. It is grown widely in countries of Asia, Africa and the Near East and plays
a vital role for earnings and food security for a large fraction of population in these
countries. The Barak Valley of northeast India is well known for the high density of tea
gardens. In the valley tea agroforestry covers 32,312 hectare area of its total geographical
area of 6922 km2. (Tea Board of India 2007). The tea gardens are the man managed AFS
of eminent productivity. While much is known about the productivity and management of
tea little attention has been given to the plants overall biomass production and C
sequestration. There is limited information on C and nutrient study in tea AFS. The few
published studies are limited to where tea has been commonly studied in association with
shade tree species (Wijerante 1996, Dutta 2006, Kamau et al. 2008).The objectives of the
study were to (1) provide a useful snapshot of the carbon stock and sequestration in tea,
shade tree biomass and plantation floor litter in three plantations of different age and (2)
estimate the proportionate contribution towards biomass carbon storage by different
71
compartments and (3) give a glimpse of the potential of tea agroforestry system to offset
carbon emissions.
5.2 Results
5.2.1 Estimation of Tea biomass and carbon
Development of allometric equations
Allometric equations generated from a small sample of trees. These equations are used to
estimate biomass at landscape level. The scope of the allometric equations depends on the
empirical data used. Several allometric equations have been published for agroforestry
systems such as tea in Kenya (Kamau et al. 2008), coffee in Costa Rica (Segura et al.
2006, Hager 2012), Togo (Dossa 2008), Ethiopia (Negash 2013), Hawaii (Youkhana
2011), Cacao agroforestry in Costa Rica (Beer 1990), Cameroon (Saj et al. 2013),
agroforestry in Uganda (Tumwebaze et al. 2013), Poplar in India (Rizvi et al. 2008)
forest plantations (Basuki et al. 2009, Bastien-Henri et al. 2010) and various forest types
(Brown 1997, Henry et al 2011) among other vegetation types including shrubs (Murray
& Jacobson 1982, Navar et al. 2004) . Existing allometric equations for tea is based on
the age of the individuals rather than more simplified dendrometric parameters. Diameter
at breast height is commonly used for aboveground biomass (AGB) estimation because it
can easily be measured with high accuracy, repetitively and generally follows commonly
acknowledged forestry conventions (Husch et al. 2003). Even so, the relationship
between biomass and tree dimensions differs among species and may also be affected by
site characteristics and climatic conditions (Eamus et al. 2002). Management practices
like cutting and pruning can change biomass without changing diameter. As such,
allometric equations based on diameter can be refined by including height, wood density,
or crown area to improve accuracy (Ketterings et al. 2001, Chave et al. 2005). In the
vegetation type like tea agroforestry system, extensive management practices can
influence the growth and development of tea bushes. This leads us to the assumption that
biomass accumulation and allocation in different plant parts differs from other natural
and planted vegetative entities. Despite the acknowledged importance, there is little
knowledge about the amount of biomass accumulated in the Tea bushes contributing
72
towards climate change mitigation as carbon sink. We hypothesized that the total biomass
(above- and belowground) of tea bushes increases with stem diameter. This study aims to
(i) build biomass equations specific to dominant Tea (Camellia sinensis (L.) O. Kuntze)
of the 6922 km2 region in North East Indian agricultural landscapes, and (ii) determine
the biomass distribution in the above- and below-ground fractions based on variable
structural characteristics influenced by different management practices and climatic
conditions.
Relationship of dendrometric variables
The parameters taken into consideration for analyzing allometric relationship showed
significant relationship (Table 5.2.1). Diameter (5 cm above ground level) showed strong
relationship with height (R2
= 0.82), crown area (R2
= 0.96) and branch count whereas
height showed significant relationship with crown area (R2
= 0.90), branch count (R2
=
0.74) and wood density (R2
= 0.40). Besides diameter and height crown area shows
relationship with branch count (R2
= 0.73) (Table 5.2.2).
Table 5.2.1: Characteristics of the sampled Tea bushes used in the (Diameter at 5 cm
height, BEF = biomass expansion factor, R/S = root-to-shoot ratio)
Variables / Statistics Mean Range St. dev. CV (%) Number of
observations
Diameter (cm) 10.90 1.69 – 22.27 5.70 52.27 31
Height (m) 0.95 0.67 – 1.06 0.09 9.61 31
Crown area (m2) 0.54 0.03 – 1.11 0.28 50.76 31
Wood density (g/cm3) 0.55 0.41 – 0.75 0.07 13.08 31
Branch count 3.84 2 - 6 1.11 28.92 31
BEF 4.23 2.17 – 8.76 1.56 36.76 31
R/S 0.30 0.20 – 0.54 0.08 25.13 31
73
Table 5.2.2: Correlation matrix for measurement and biomass variables of Tea (D = diameter at 5 cm height, H = tea height, WD =
wood density, NB = no. of branches, CA = crown area, BEF = biomass expansion factor, R/S = root to shoot ratio. Correlations are
significant at 95% confidence interval. ** p < 0.01, * p < 0.05)
D H WD NB CA Stem Branches Leaves Roots Total BEF R/S
D 1
H 0.818** 1
WD 0.201 0.398* 1
NB 0.743** 0.739** 0.292 1
CA 0.958** 0.904** 0.276 0.731** 1
Stem 0.943** 0.729** 0.149 0.640** 0.878** 1
Branches 0.909** 0.770** 0.137 0.773** 0.865** 0.842** 1
Leaves 0.752** 0.772** 0.254 0.816** 0.788** 0.618** 0.815** 1
Roots 0.922** 0.728** 0.082 0.610** 0.870** 0.922** 0.893** 0.627** 1
Total 0.957** 0.789** 0.138 0.742** 0.908** 0.928** 0.978** 0.773** 0.958** 1
BEF -0.383* -0.411* -0.133 -0.210 -0.383* -0.472** -0.204 -0.168 -0.301 -0.296 1
R/S 0.074 0.020 -0.121 -0.147 0.080 0.129 -0.032 -0.236 0.313 0.080 0.028 1
74
0
5
10
15
20
25
0 5 10 15 20 25
Tea
bio
ma
ss (
kg
)
Diameter at 5 cm height (cm)
AGB
AGB, (R2
= 0.97) Stem
Stem, (R2
= 0.91) Branch
Branch, (R2
= 0.95) Leaf Leaf, (R
2
= 0.85)
(a)
0
5
10
15
20
25
0 5 10 15 20 25
Tea
bio
ma
ss (
kg
)
Diameter at 5 cm height (cm)
TB
TB, R2
= 0.97 AGB
AGB, R2
= 0.97 BGB
BGB, R2
= 0.95
(b)
Regression of diameter with biomass of different component and compartment of
Tea revealed that it has strong correlation with aboveground biomass (R2 = 0.97;
P < 0.0001), branch biomass (R2 = 0.95; P < 0.0001), stem biomass (R
2 = 0.91; P
< 0.0001) and moderate relationship with leaf biomass (R2 = 0.85, P < 0.0001)
(Figure 5.1a). Similarly the regression of root (BGB) and total biomass (TB) as a
function of diameter showed significance (p < 0.0001) with R2
values 0.95 and
0.97 respectively (Figure 5.1b).
Figure 5.1: (a) The relationship between diameter and the biomass of stem,
branches, leaves and aboveground biomass (AGB), and (b) relationship between
diameter and aboveground biomass (AGB), belowground biomass (BGB) and
total biomass (TB) in Tea
75
Parameters like height, crown area, wood density, branch count, biomass
expansion factor, root – shoot ratio reflects differences within different size
classes in Tea (Figure 5.2). ANOVA showed that mean values of height, crown
area, branch count, biomass expansion factor and root - to- shoot ratio differs
significantly in different size classes.
Figure 5.2: Parameter estimates (a) crown area, wood density, branch count, height and
(b) biomass expansion factor (BEF) and root-to-shoot ratio (BGB/AGB) of different size
class of Tea
Biomass equations
Observational allometric coefficients for estimating biomass of different
components based on diameter applying allometric power function equation is
presented in Table 5.2.3. Linear equivalent of the power equation (Eq. 1)
disclosed diameter as a significant (P < 0.0001) predictor variable for all
components (Figure 5.3). Eq. 1 estimated AGB with a small relative error (2.8 %).
Stem biomass exhibited comparatively higher overestimation (11.1%) than
branches (4.6%) leaves (6.2%). The diameter-based equation for root biomass
(BGB) and total biomass (TB) showed low RE, <5% (3.7% and 2.5 %
respectively) across the girth classes considered (Table 5. 2. 4). Diameter-based
equations for estimating AGB and TB showed underestimation between >15-45
cm girth size up to 27% and 22%. Stem biomass showed high and variable RE
across tree size whereas branch and leaf biomass presented moderate
0.000.050.100.150.200.250.300.350.400.45
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
BG
B/A
GB
BE
F
Girth class (cm)
BEF BGB/AGB(b)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.0
0.2
0.4
0.6
0.8
1.0
Bra
nch
cou
nt/
Hei
gh
t (m
)
Cro
wn
are
a (
m2),
Wood
den
sity
(g
cm
-3)
Girth class (cm)
Crown area Wood density
Branch count Height(a)
76
underestimation in >15-35 cm size range. BGB exhibited higher deviation in the
>15 25 cm size class followed by smaller RE values in other categories. Except
BGB and stem biomass all estimations showed higher RE values in >55 cm girth
class (Figure 5. 4). Height and crown area was a significant predictor variable for
biomass, but wood density was not a significant predictor variable for any of the
biomass components. Incorporation of height with diameter in the model (Eq. 2)
improved Adj. R2 (0.985), RMSE (0.16), AIC (-20.832), and RE (1.15 %)
compared to model with diameter alone (Eq. 1) for AGB where Adj. R2, RMSE ,
AIC and RE exhibited values 0.966, 0.238, 2.907 and 2.80 respectively and in the
model diameter with crown area (Adj. R2= 0.981, RMSE= 0.181, AIC= -13.292
and RE= 1.39). Different combinations with more than two variables (Eq. 6 – Eq.
12) improved Adj. R2, RMSE , AIC and RE among which height and crown area
with diameter (Eq. 8) performed well in terms of AIC (-21.984) in spite of slightly
higher RE and almost similar RMSE compared to models with four (Eq. 11) and
five ( Eq. 12)variables incorporated (Table 5.2.5a).
For belowground biomass estimation Eq. (2), (4), (9), (11) and (12) reflects
minute improvement in terms of adjusted coefficient of determination, RMSE, RE
but AIC suggests Eq. 9 (with diameter, height and crown area as compute
variables) as better model (Table 5.2.5b).
Regarding root (BGB) biomass estimation also diameter alone is a significant
predictor variable with high adjusted R2
(0.968). Incorporation of other supporting
predictor variables modified the adjusted coefficient of determination and
minimized estimated errors (Eq. 2 to Eq. 12). Akaike Information Criterion lifts
up Eq. 9 and Eq. 11 among the equations tested (Table 5.2.5c).
77
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
-3 -1 1 3
Sta
nd
ard
ized
res
idu
als
Pred(lnAGB(kg))
(b)
-3
-2
-1
0
1
2
3
4
0.5 1.5 2.5 3.5lnA
GB
(k
g)
lnDiameter(cm)
Observations
Model
Conf. interval (Mean 95%)
Conf. interval (Obs. 95%)
(a)
-3
-2
-1
0
1
2
3
-4 -2 0 2
Sta
nd
ard
ized
res
idu
als
Pred(lnStem(kg))
(d)
-5
-4
-3
-2
-1
0
1
2
3
0.5 1.5 2.5 3.5
lnS
tem
(kg
)
lnDiameter(cm)
Observations
Model
Conf. interval (Mean 95%)
Conf. interval (Obs. 95%)
(c)
-2
-1
0
1
-3 -1 1 3
Sta
nd
ard
ized
res
idu
als
Pred(lnBranch(kg))
(f)
-4
-3
-2
-1
0
1
2
3
4
0.5 1.5 2.5 3.5
lnB
ran
ch(k
g)
lnDiameter(cm)
Observations
Model
Conf. interval (Mean 95%)
Conf. interval (Obs. 95%)
(e)
-5
-4
-3
-2
-1
0
1
0.5 1.5 2.5 3.5
lnL
eaf(
kg
)
lnDiameter(cm)
Observations
Model
Conf. interval (Mean 95%)
Conf. interval (Obs. 95%)
(g)
-2
-1
0
1
2
-3.5 -2.5 -1.5 -0.5
Sta
nd
ard
ized
res
idu
als
Pred(lnLeaf(kg))
(h)
-4
-3
-2
-1
0
1
2
3
0.5 1.5 2.5 3.5
ln B
GB
(kg
)
lnDiameter(cm)
Observations
Model
Conf. interval (Mean 95%)
Conf. interval (Obs. 95%)
(i)
-2
-1
0
1
2
3
4
-4 -2 0 2
Sta
nd
ard
ized
res
idu
als
Pred(lnRoot (BGB)(kg))
(j)
78
-30
-20
-10
0
10
20
30
40
50
Rel
ati
ve
erro
r (%
)
Girth class (cm)
AGB(a)
-40
-20
0
20
40
60
80
100
Rel
ati
ve
erro
r (%
)
Girth class (cm)
stem(b)
-40
-20
0
20
40
60
Rel
ati
ve
erro
r (%
)
Girth class (cm)
branch(c)
-60
-40
-20
0
20
40
60
80R
ela
tiv
e er
ror
(%)
Girth class (cm)
leaf(d)
-40
-30
-20
-10
0
10
20
30
40
Rel
ati
ve
erro
r (%
)
Girth class (cm)
BGB(e)
-30
-20
-10
0
10
20
30
40
Rel
ati
ve
erro
r (%
)
Girth class (cm)
T B(f)
Figure 5.3: Observed and predicted values (with 95% confidence interval) using
diameter as predictor variable (Eq. 1) and standardized residuals vs. predicted
biomass values for aboveground biomass ((a) - (b)), stem biomass ((c)-(d)),
branch biomass (e) - (f)), leaf biomass ((g) – (h)), belowground (root) biomass (i),
and total biomass (j)
Figure 5.4: Relative error (%) for different girth class of Tea bush accompanying
the equations developed for estimation of (a) aboveground biomass (AGB), (b)
stem biomass, (c) branch biomass, (d) leaf biomass, (e) belowground (BGB)
biomass and (f) total biomass (TB) using diameter. Standard error of the average
relative error is indicated by the error bars
79
Table 5.2.3: Allometric power function equations (y = axb) for estimation of
aboveground biomass (AGB) and the biomass of the stem, branches, leaves, roots
(BGB) and total biomass. Allometric coefficients (a, b), coefficient of
determination (R2) and model bias (RE) are displayed
Table 5.2.4: Regression equations for estimation of aboveground biomass and the
biomass of the stem, branches, leaves, roots and total biomass of Tea bush.
Intercept coefficient (a), scaling exponent (b) standard error (SE), standard error
of the estimate (SEE), coefficient of determination (R2), adjusted coefficient of
determination (Adj.R2), model bias (RE) are presented
Component a (SE) b (SE) R
2
Adjusted
R2
SEE P value RE
(%)
Eq. (1) -3.051 (0.148) 1.878 (0.064) 0.967 0.966 0.238 < 0.001 2.80
Stem -4.819 (0.278) 2.033 (0.121) 0.907 0.904 0.448 < 0.001 11.11
Branches -3.718 (0.190) 1.964 (0.083) 0.951 0.95 0.306 < 0.001 4.63
Leaves -3.962 (0.225) 1.248 (0.098) 0.849 0.844 0.363 < 0.001 6.15
BGB -4.268 (0.179) 1.870 (0.078) 0.952 0.951 0.289 < 0.001 3.69
TB -2.789 (0.143) 1.877 (0.062) 0.969 0.968 0.230 < 0.001 2.54
Component a b R2 RE (%)
AGB 0.047 1.878 0.967 2.79
Stem 0.008 2.033 0.907 11.46
Branches 0.024 1.965 0.951 4.71
Leaves 0.019 1.248 0.849 6.03
BGB 0.014 1.870 0.952 3.57
TB 0.062 1.877 0.969 2.55
80
Table 5.2.5: Regression equations for biomass determination employing diameter alone (Eq. (1)) and diameter in combination with height (Eq.
(2)), wood density (Eq. (3)), crown area (Eq. (4)), number of branches (Eq. (5)) and diameter in different combinations with these parameters (Eqs.
(5) - (12)) as independent variable separately fitted in the model. The allometric coefficients (a, b, c, d, e, f), standard error (SE), adjusted
coefficient of determination (Adj. R2), root mean square error (RMSE), Akaike information criterion (AIC) and bias for each equation is
presented.***, ** and * indicates p-value <0.0001, 0.01 and 0.05 respectively at 95% confidence interval. (a) Aboveground biomass (AGB):
Equation a b c d e f Adj.R2 RMSE AIC RE (%)
Eq. (1) -3.051*** 1.878 *** 0.966 0.238 2.907 2.80
SE 0.143 0.062
Eq. (2) -1.426 *** 1.243 *** 4.369 *** 0.985 0.160 -20.832 1.15
SE 0.273 0.108 0.690
Eq. (3) -2.461 *** 1.821 *** 0.779 * 0.971 0.221 -0.714 2.19
SE 0.271 0.061 0.314
Eq. (4) -0.965* 1.151 *** 0.549 *** 0.981 0.181 -13.292 1.39
SE 0.433 0.153 0.110
Eq. (5) -3.164 *** 1.790 *** 0.237 0.966 0.239 3.972 2.82
SE 0.183 0.109 0.243
Eq. (6) -0.898 * 1.200 *** 0.375 0.492 *** 0.981 0.178 -13.219 1.40
SE 0.422 0.152 0.266 0.114
Eq. (7) -2.571 *** 1.746 *** 0.763 * 0.205 0.970 0.222 0.448 2.21
SE 0.293 0.102 0.310 0.222
Eq. (8) -1.245 *** 1.264 *** 4.021 *** 0.409 0.986 0.155 -21.984 1.12
SE 0.278 0.104 0.683 0.225
Eq. (9) -1.014 ** 1.117 ** 3.275 ** 0.215 0.985 0.157 -21.276 1.17
SE 0.369 0.131 0.954 0.135
Eq. (10) -1.504 *** 1.208 *** 4.311 *** 0.117 0.984 0.162 -19.348 1.14
SE 0.292 0.118 0.689 0.163
Eq. (11) -0.952 ** 1.162 *** 3.221 *** 0.344 0.169 0.986 0.154 -21.529 0.98
SE 0.358 0.130 0.921 0.225 0.134
Eq. (12) -1.020 ** 1.115 *** 3.103 ** 0.335 0.181 0.131 0.986 0.155 -20.276 0.93
SE 0.362 0.139 0.920 0.223 0.133 0.151
81
(b) Root biomass (BGB)
Equation a b c d e f Adj.R2 RMSE AIC RE (%)
Eq. (1) -4.268*** 1.870 *** 0.951 0.289 14.882 3.68
SE 0.173 0.075
Eq. (2) -2.620 *** 1.226 *** 4.428 *** 0.969 0.228 1.369 2.30
SE 0.391 0.155 0.987
Eq. (3) -3.896 *** 1.834 *** 0.490 0.951 0.287 15.470 3.48
SE 0.352 0.079 0.408
Eq. (4) -1.908** 1.048 *** 0.621 *** 0.969 0.230 1.756 2.28
SE 0.551 0.195 0.141
Eq. (5) -4.288 *** 1.854 *** 0.043 0.949 0.294 16.862 3.75
SE 0.225 0.135 0.299
Eq. (6) -1.912 ** 1.045 *** -0.024 0.624 *** 0.967 0.234 3.752 2.39
SE 0.555 0.200 0.349 0.150
Eq. (7) -3.908 *** 1.827 *** 0.488 0.021 0.949 0.292 17.465 3.59
SE 0.387 0.134 0.408 0.293
Eq. (8) -2.581 *** 1.231*** 4.352 *** 0.089 0.968 0.233 3.299 2.38
SE 0.418 0.156 1.027 0.338
Eq. (9) -1.947 *** 1.020 *** 2.640 0.352 0.971 0.221 0.140 2.11
SE 0.521 0.185 1.348 0.191
Eq. (10) -2.569 *** 1.250 *** 4.467 *** -0.079 0.968 0.233 3.257 2.21
SE 0.420 0.170 0.992 0.234
Eq. (11) -1.956 *** 1.014*** 2.648 -0.049 0.359 0.970 0.225 2.118 2.16
SE 0.524 0.190 1.349 0.330 0.196
Eq. (12) -1.935 *** 1.028 *** 2.683 * -0.046 0.355 -0.041 0.969 0.230 4.085 2.00
SE 0.537 0.206 1.362 0.330 0.197 0.224
82
(c)Total biomass (TB):
Equation a b c d e f Adj.R2 RMSE AIC RE (%)
Eq. (1) -2.789*** 1.877 *** 0.968 0.230 0.896 2.50
SE 1.384 0.060
Eq. (2) -1.145 *** 1.235*** 4.420 *** 0.987 0.145 -26.763 0.99
SE 0.249 0.984 0.627
Eq. (3) -2.251 *** 1.826 *** 0.711 * 0.972 0.217 -2.035 2.17
SE 0.266 0.060 0.307
Eq. (4) -0.623 1.123*** 0.570 *** 0.984 0.164 -19.267 1.26
SE 0.393 0.139 0.100
Eq. (5) -2.877 *** 1.808 *** 0.184 0.968 0.232 2.294 2.37
SE 0.178 0.106 0.236
Eq. (6) -0.574 1.158*** 0.276 0.528 *** 0.984 0.164 -18.523 1.02
SE 0.387 0.140 0.244 0.105
Eq. (7) -2.334 *** 1.768 *** 0.698 * 0.155 0.971 0.219 -0.534 1.99
SE 0.289 0.100 0.305 0.219
Eq. (8) -0.999 *** 1.252*** 4.139 *** 0.330 0.988 0.142 -27.210 0.99
SE 0.256 0.095 0.628 0.207
Eq. (9) -0.670 * 1.089*** 3.159 *** 0.248 * 0.988 0.139 -28.794 0.81
SE 0.326 0.116 0.845 0.120
Eq. (10) -1.186 *** 1.216*** 4.388 *** 0.063 0.987 0.148 -24.941 1.06
SE 0.267 0.108 0.630 0.148
Eq. (11) -0.626 1.122*** 3.120 *** 0.247 0.215 0.989 0.138 -28.250 0.79
SE 0.321 0.116 0.826 0.202 0.120
Eq. (12) -0.669 * 1.092 *** 3.045 *** 0.241 0.222 0.083 0.988 0.140 -26.619 0.80
SE 0.327 0.125 0.831 0.201 0.120 0.136
83
Stem 20.59
Branches 50.13
Leaves 6.24
Roots 23.04
Biomass estimates
The contribution of different components to the total tree biomass varied considerably.
AGB accounted for most of the total tree biomass (77.2 %), with the stems, branches and
leaves contributing 25.5, 64.1, and 10.4 % to AGB. Much of the tree biomass is held in
the branches, which constitutes half (50.13 %) of the total tree biomass, while stem and
leaves make up 20.59 and 6.24 % of the total tree biomass, respectively (Figure 5.5).
While the proportion of stem biomass on average, an increase with tree size, although the
trend was not continuous, the percentage of branch biomass was almost constant except a
considerably higher value in >25-35 cm size category. Proportion of leaf biomass in tea
bush decreased along girth size. The proportion of foliage declined from 12.7% in small
tea (diameter < 15cm) to 4.2 % in high biomass trees (diameter > 55 cm). (Figure 5.6)
The BGB of the harvested trees accounted for 22.8 % of the total tree biomass.
Figure 5.5: Biomass distribution in the analyzed compartments of Tea (in %)
84
0% 20% 40% 60% 80% 100%
>55
>45-55
>35-45
>25-35
>15-25
<15
Proportion of biomass
Gir
th c
lass
(cm
)
Root Stem Branch Leaf
Figure 5.6: Biomass allocation in root, stem, branch and leaves per tea bush in different girth
class
Carbon concentration in biomass
The carbon concentrations in different compartments of sampled Tea bushes were
analyzed. Among all compartments analyzed branches has the highest carbon
concentration (48.6 %), followed by stem (48.13 %), roots (47.53 %) and leaves (46.1
%). Carbon concentration statistics of tea samples are presented in Table 5.2.6). Carbon
concentration in different tea compartments exhibited significant difference (ANOVA, p
< 0.001). Multiple comparison analysis showed that Branches presented higher carbon
concentration than other compartments. Carbon concentration among different size
classes of tea did not show notable variation.
Table 5.2.6: Carbon concentration (in %) statistics of Tea. Different letters displayed
between two compartments indicate a significant difference (p < 0.01)
Compartment Range Mean Standard deviation
Leaves 44.75 – 46.72 46.10 b 0.48
Branches 47.70 – 49.17 48.60 a 0.38
Stem 46.45 – 49.15 48.13 b 0.69
Roots 46.18 – 48.83 47.53 b 0.72
85
Distribution of Tea biomass and carbon stock
Biomass stock in tea compartment exhibited the range of (14.08 – 43.39 Mg ha-1
) with
average value of (29.02 ± 7.04 Mg ha-1
). Among different age group of plantations 15-20
years age group stores maximum biomass (32.62 ± 7.76 Mg ha-1
) followed by 20-25
(32.09 ± 5.83 Mg ha-1
) and 25-30 years (30.62 ± 6.19 Mg ha -1
). Carbon stock in tea was
estimated 13.93 ± 3.38 Mg ha-1
. Tea carbon stock value varied between 6.76 Mg ha-1
and
20.83 Mg ha-1
. 15-20 years age group is the leading contributor towards carbon stock
(15.66 ± 3.73 Mg ha-1
) following 20-25 (15.40 ± 2.80 Mg ha-1
) and 25-30 (14.70 ± 2.97
Mg ha-1
) years age group of plantations respectively. Biomass and carbon stock increased
along with plantation age. Minimum stock was observed in 5-10 years age group and the
value gradually increased and attained maximum in 15-20 years age group which further
declined in subsequent age groups (20-25 and 25-30 years). Across the plantations
medium sized (> 15-25 cm) tea bushes were dominant followed by larger (> 25 cm) and
small sized (≤ 15 cm) tea bushes having 54, 23 and 22 % of occurrence. Basal area,
biomass and carbon stock was higher in larger sized tea bushes (Figure 5.7) Biomass and
carbon stock values in different age groups of plantations showed significant variation
(ANOVA, p < 0.01). LSD analysis pointed that tea carbon stock in 5-10 years is
remarkably less than rest of the age groups and 15-20 years age group contains
significantly higher carbon stock than 5-10 and 10-15 years age groups of plantations (p
< 0.05). 20-25 years age group stores notably higher carbon from 10-15 years age group
(p < 0.01). Aboveground and belowground compartment of tea contributes 77.4 % and
22.6 % towards biomass and carbon stock across the age group of plantations (Figure
5.8).
86
0
1000
2000
3000
4000
5000
6000
7000
8000
0
5
10
15
20
25
30
35
≤ 15 > 15-25 > 25
Den
sity
(S
tem
ha
-1)
Ba
sal
are
a (
m2 h
a-1
) ,
bio
ma
ss a
nd
C (
Mg
ha
-1)
Girth class (cm)
Biomass Carbon Basal area Density
Figure 5.7: Density, basal area, biomass and carbon stock allocation in different girth size
classes of tea in tea agroforestry system in Barak Valley, Assam
Figure 5.8: Biomass (a) and carbon (b) stock by tea compartment in different age groups
in tea agroforestry system in Barak Valley, Assam
5.1
6.2
7.4
7.2
6.9
17
.4
21
.1
25
.3
24
.9
23
.7
0.0
10.0
20.0
30.0
40.0
5-10 10-15 15-20 20-25 25-30
Bio
ma
ss (
Mg
ha
-1)
Age group (Years)
AGB
BGB
22.47
32.62
27.29
32.09 30.62
(a)
2.4
3.0
3.5
3.5
3.3
8.3
10
.1
12
.1
11
.9
11
.4
0.0
5.0
10.0
15.0
20.0
5-10 10-15 15-20 20-25 25-30
Ca
rbo
n (
Mg
ha
-1)
Age group (Years)
AGC
BGC
10.79 13.10
15.66 15.40 14.70
(b)
87
5.2.2 Shade tree biomass and carbon
Biomass and carbon estimation
Aboveground biomass (AGB) in shade trees was estimated using species specific volume
equation and regional volume equations published by Forest Survey of India (FSI 1996)
multiplying wood density (WD) and biomass expansion factor (BEF). Wood density for
different shade tree species was estimated from the samples collected from the trees in
the study site. Wood density statistics of shade tree species have been summarized in table 5.3.1.
Table 5.2.7:Wood density (g cm-3
) statistics of shade tree species in tea agroforestry
system. Different letters displayed between two species indicate a significant difference
(p < 0.01)
Distribution of shade tree biomass and carbon stock
Shade tree biomass in tea agroforestry system was estimated (78.12 ± 23.55 Mg ha-1
)
depicting range between (30.22 Mg ha -1
and 153.89 Mg ha-1
) across the plantations
studied. Carbon stock by this compartment exhibited value ranging from (15.11 Mg ha -1
to 76.94 Mg ha-1
) having mean value of 39.06 ± 11.78 Mg ha-1
. Biomass and carbon
stock displayed increasing trend from 5-10 years to 15-20 years age group and declined
in following age group (20-25 years) with subsequent increase in 25-30 years age group
of plantations (Figure 5.9).Across all the plantations girth wise small (10-50 cm),
medium (>50-90 cm) and larger (>90 cm) tree occupy 22, 64 and 14 % of population
sampled. Medium sized shade trees hold maximum basal area cover, biomass and carbon
stock followed by larger and small sized trees (Figure 5.10). Biomass and carbon stock in
shade tree compartment across different age groups of plantations showed significant
Shade tree species Range Mean Standard deviation CV (%)
Albizia lebbeck 0.38 - 0.71 0.59 b 0.05 9.23
Albizia odoratissima 0.48 - 0.73 0.61 b 0.06 9.02
Derris robusta 0.54 - 0.83 0.67 a 0.06 8.74
Albizia chinensis 0.36 - 0.54 0.42 b 0.06 14.57
Albizia procera 0.52 - 0.60 0.57 b 0.03 5.14
Senna siamea 0.54 - 0.61 0.56 b 0.02 4.41
Dalbergia sissoo 0.59 - 0.62 0.60 b 0.01 1.87
88
variation (ANOVA, p < 0.01). Multiple comparison analysis clears that biomass and
carbon stock in 5-10 years age group is remarkably less than other older age groups
concerned (LSD, p < 0.01). Biomass allocation in different shade tree showed leading
potency of Albizia odoratissima across different age groups followed by Albizia lebbeck
and Derris robusta. Biomass allocation in A. odoratissima from 5-10 years (22.78 ± 5.62
Mg ha-1
) showed increasing trend up to 15-30 years (74.39 ± 15.31 Mg ha-1
) age group
and gradually declined in subsequent age groups. Biomass stock by A. lebbeck increased
from 5-10 years (16.87 ± 3.73 Mg ha-1
) to 10-15 years (23.31 ± 16.45 Mg ha-1
) age
groups and gradual decline in the subsequent ages was observed. Biomass allocation in
Derris robusta initially declined from 5-10 years (13.40 ± 4.01 Mg ha-1
) to 10-15 years
age group (4.94 ± 3.16 Mg ha-1
) but gradually increased in the following age groups
(Figure 5.11). Biomass and carbon distribution in different shade tree species revealed
that A. odoratissima registers dominance over other species having 41.4 – 81 %
proportionate contribution followed by A. lebbeck and Derris robusta bearing
proportionate contribution of 13.3 – 30.7 % and 2.5 – 24.4 % across different age groups
of plantations ( Figure 5.12). Status of basal area, biomass and carbon among dominant
shade tree species in the dataset discloses that basal area, biomass and carbon stock of A.
odoratissima in different age groups differs significantly (ANOVA, p < 0.01). Post hoc
analysis showed that basal area, biomass and carbon stock in 5-10 years and 10-15 years
age group varies significantly from 15-20, 20-25 and 25-30 years age group of
plantations (p < 0.05). The parameter values gradually increased from 5-10 to 15-20
years age group and declined afterwards (Figure 5.13 a). Estimates of basal area, biomass
and carbon in different age groups by A. lebbeck initially increased from 5-10 to10-15
years age group and presented lower values in following age groups (Figure 5.13 b).
These values highlighted significant difference across age groups (ANOVA, p < 0.01).
Basal area, biomass and carbon stock in 5-10 and 10-15 years age group showed
statistically significant difference from 15-20, 20-25 and 25-30 years age group of
plantations. Basal area, biomass and carbon stock values in Derris robusta decreased
from 5-10 years to 15-20 years age group and gradually increased in the consecutive age
groups (Figure 5.13 c). Multiple comparison analysis pointed that basal area, biomass and
89
11
.3
16
.5
18
.9
16
.5
17
.3
43
.6
63
.4
72
.9
63
.4
66
.7
0
20
40
60
80
100
5-10 10-15 15-20 20-25 25-30
Bio
ma
ss (
Mg
ha
-1)
Age groups (Years)
AGB
BGB
54.97
91.80 79.90 79.87
84.06 (a)
5.7
8.2
9.5
8.2
8.7
21
.8 31
.7
36
.4
31
.7
33
.4
0
10
20
30
40
50
5-10 10-15 15-20 20-25 25-30
Ca
rbo
n (
Mg
ha
-1)
Age groups (Years)
AGC
BGC
27.48
39.95 45.90 39.93
42.03 (b)
0
30
60
90
120
150
0
10
20
30
40
50
10-50 > 50-90 > 90
Den
sity
(S
tem
ha
-1)
Ba
sal
are
a (
m2
ha
-1),
bio
ma
ss
an
d c
arb
on
(M
g h
a-1
)
Girth class (cm)
Biomass Carbon Basal area Density
carbon stock in 10-15 and 15-20 years age group exhibited significantly lower value
compared to 5-10, 20-25 and 25-30 years age group of plantations.
Figure 5.9: Biomass (a) and carbon (b) stock by shade tree compartments in different age
groups in tea agroforestry system in Barak Valley, Assam
Figure 5.10: Density, basal area, biomass and carbon stock allocation in different girth
size classes of shade trees in tea agroforestry system in Barak Valley, Assam
90
0
20
40
60
80
100
5-10 10-15 15-20 20-25 25-30
Bio
ma
ss (
Mg
ha
-1)
Age groups (Years)
A.odoratissima
A.lebbeck
Derris robusta
Albizia chinensis
Albizia procera
Senna siamea
Dalbergia sissoo
30.7
29.2
14.2
13.3
16.6
41.4
63.2
81.0
72.0
67.6
24.4
4.9
2.5
9.9
6.8 7.0
0% 20% 40% 60% 80% 100%
5-10
10-15
15-20
20-25
25-30
Proportionate distribution
Ag
e g
rou
p (
Yea
rs)
A.lebbeck
A.odoratissima
Derris robusta
Albizia chinensis
A.procera
Cassia siamea
Dalbergia sissoo
Figure 5.11: Biomass allocation in shade tree species in five different age groups of tea
agroforestry system
Figure 5.12: Proportionate distribution of biomass and carbon among different shade tree
species in tea agroforestry system
91
0
2
4
6
8
10
0
30
60
90
120
150
180
210
5-10 10-15 15-20 20-25 25-30
Ba
sal
are
a (
m2 h
a-1
)
Den
sity
(S
tem
ha
-1),
Bio
ma
ss a
nd
Ca
rbo
n (
Mg
ha
-1)
Age group (Years)
A. odoratissima
Biomass Carbon Density BA
(a)
0
1
2
3
4
0
20
40
60
80
100
120
5-10 10-15 15-20 20-25 25-30
Ba
sal
are
a (
m2 h
a-1
)
Den
sity
(S
tem
ha
-1),
Bio
ma
ss a
nd
Ca
rbo
n (
Mg
ha
-1)
Age group (Years)
A. lebbeck
Biomass Carbon Density BA
(b)
0.00
0.30
0.60
0.90
1.20
1.50
0
10
20
30
40
50
60
5-10 10-15 15-20 20-25 25-30
Ba
sal
are
a (
m2 h
a-1
)
Den
sity
(S
tem
ha
-1),
Bio
ma
ss a
nd
Ca
rbo
n (
Mg
ha
-1)
Age group (Years)
Derris robusta
Biomass Carbon Density BA
(c)
Figure 5.13: Density, basal area, biomass and carbon stock among dominant shade tree
species (a)-(c) in tea agroforestry system
92
Carbon stock potential of shade tree species
Carbon stock potential of the shade tree species in tea agroforestry was assessed on the
basis of carbon stock (kg) per tree. Across different age groups and size classes of shade
tree species carbon stock potential exhibited a wide range (7.22 – 1790.08 kg C / tree).
Analysis of carbon stock potential of dominant shade tree species (A. odoratissima, A.
lebbeck and D. robusta) depicted higher potential of D. robusta compared to A.
odoratissima and A. lebbeck. The ratio of carbon stock potential of these species (A.
lebbeck : A. odoratissima : D. robusta) was assessed as 1 : 1.03 : 1.14 from the dataset.
Nonlinear regression between tree girth and aboveground carbon stock (AGC) in dominant shade
trees resulted equations for estimation of carbon stock potential with high accuracy. Power
function equation y = aXb where y is the dependent variable and x is the independent
variable, and a, the coefficient and b the constant was used to predict carbon stock
potential using girth at 1.37 m (GBH) as predictor variable. Higher values of coefficient
of determination (R2) and minimal error (SSR) urges utility of the equations (Figure
5.14).
5.2.3 Litter carbon estimation
Litter carbon stock across all the sites presented range of (4.18 – 8.69 Mg ha-1
) with mean
value of (6.36 ± 0.84 Mg C ha-1
). Carbon stock in litter compartment gradually increased
from 5-10 years (5.77 ± 0.36 Mg ha-1
) to 15-20 years age group (7.21 ± 0.40 Mg ha-1
).
The value declined in 20-25 years age group (6.19 ± 0.82 Mg ha-1
) and enhanced (6.66 ±
1.04 Mg ha-1
) in the following age group (Figure 5.15). Litter carbon stock in different
age group of plantations varied statistically (ANOVA, p < 0.01). Multiple comparison
analysis elucidated that litter carbon stock in 15-20 years age group is significantly higher
than all other age groups of plantations. Litter carbon stock in 5-10 and 25-30 years age
group highlighted significant difference (LSD, p < 0.05). Leaf compartment of litter
carried comparatively higher proportion (53.8 %) than non-leaf compartment (46.2 %)
across the concerned age group of plantations (Figure 5.16). leaf and non-leaf litter
proportion presented the range of (51.7 – 56.5 % and 43.5 – 48.3 %) respectively.
93
Figure 5.14: Relation between tree girth (GBH) and carbon stock (AGC) in dominant
shade tree species (a)-(c) in tea agroforestry system. Equations resulted from nonlinear
regression between tree girth and AGC (R2: coefficient of determination; SSR: sum of
squares of residuals)
y = 0.008x2.296
R² = 0.990, SSR = 3.747
0
200
400
600
800
1000
0 50 100 150 200
AG
C (
kg
/tre
e)
GBH (cm)
A. lebbeck (b)
y = 0.007x2.358
R² = 0.997, SSR = 2.292
0
200
400
600
800
1000
1200
0 50 100 150 200A
GC
(k
g/t
ree)
GBH (cm)
A. odoratissima (a)
y = 0.006x2.413
R² = 0.999, SSR = 0.019
0
200
400
600
800
1000
1200
1400
0 50 100 150 200
AG
C (
kg
/tre
e)
GBH (cm)
Derris robusta (c)
94
(Mg
ha
-1
)
48
%
48
%
45
%
44
%
46
%
52
%
52
%
55
%
56
%
54
%
0
2
4
6
8
10
5-10 10-15 15-20 20-25 25-30
Lit
ter c
arb
on
sto
ck (
Mg
ha
-1)
Age group (Years)
Leaf Non-leaf
5.77
6.66 6.19
7.21
6.17
Figure 5.15: Litter carbon stock in different age groups of tea agroforestry system in
Barak Valley, Assam. Common letters displayed between two age groups indicate a
significant difference (p < 0.05) according to multiple comparison tests carried out
Figure 5.16: Litter carbon stock and proportionate contribution of litter components in tea
agroforestry system in Barak Valley, Assam
95
-20
-10
0
10
20
30
40
50
60
70
-20
-10
0
10
20
30
40
50
60
70
Ba
sal
are
a (
m2 h
a-1
)
carb
on
sto
ck (
Mg
ha
-1)
Age groups (Years)
Shade tree root Tea root Shade tree Tea Litter BA
10
20 5- 25-30 20-25 15-20 10-15
10
20
5.2.4 Biomass and carbon assessment in tea agroforestry system
Biomass stock in tea agroforestry system was estimated (121.39 ± 26.71 Mg ha-1
). The
estimate ranges from (64.37 Mg ha-1
to 192.53 Mg ha-1
) across different stands. Carbon
(C) stock in biomass depicted range of (31.11 Mg ha-1
to 95.04 Mg ha-1
) depicting mean
value of (59.39 ± 13.26 Mg ha-1
) across plantation sites. C stock measure presented
increasing trend from 5-10 years (44.04 ± 7.54 Mg ha-1
) to 15-20 years (68.77 ± 9.85 Mg
ha-1
) age group of plantations. C stock value dropped in 20-25 years (61.53 ± 8.75 Mg ha-
1) plantations and elevated in following age group of plantations. Basal area cover across
the plantations varied between 29.48 to 83.31m2 ha
-1with mean of 55.82 ± 13.13m
2ha
-1.
Basal area exhibited increasing trend from 5-10 years to 15-20 years age group and the
value marginally declined in the higher age groups. Aboveground and belowground
compartments shares 81.2 % and 18.8 % of total C stock. Among the aboveground
compartment Shade tree, tea and litter components hold 64.12 %, 22.44 % and 13.44 %
share of C stock. Belowground compartment showed 71.8 % and 28.2 % share of shade
tree and tea root components towards C stock (Figure 5.17). Combining three
compartments shade tree, tea and litter components contributed 65.6 %, 23.5 % and 10.9
% share towards C stock across all age groups of plantations (Figure 5.18).
Figure 5.17: Carbon stock by different compartments with basal area in different age
groups under tea agroforestry system in Barak Valley, Assam
96
16
%
13
%
13
%
12
.4%
13
%
23
.2%
21
%
22
%
23
.9%
22
.2%
60
.7%
66
%
65
.3%
63
.6%
64
.9%
0%
20%
40%
60%
80%
100%
5-10 10-15 15-20 20-25 25-30
Ca
rbo
n s
tock
pro
po
rtio
n
Age groups (Years)
Shade tree Tea Litter
Figure 5.18: Carbon stock proportion of different compartments in different age groups
under tea agroforestry system in Barak Valley, Assam
Across different age groups C stock in tea bush and shade tree compartments showed
difference in proportionate contribution by different size classes (Figure 5.19).
Proportionate C stock in smaller (≤ 15 cm) and medium sized (> 15-25 cm) tea bushes
gradually declined from plantations of younger to older age groups whereas larger girth
sized (> 25 cm) tea bushes contributed maximum proportion of C stock in the older
plantations (Figure 5.19 a). Medium girth sized (> 50-90 cm) shade trees exhibited
dominant proportionate contribution towards C stock in shade tree compartment across
different age groups of plantations. Contribution of smaller girth class (10-50 cm) was
less across the plantations with lower values in higher age groups. Proportionate C stock
in larger girth sized (> 90 cm) shade trees gradually increased with plantation age and
attained maximum proportion of shade tree C stock in 25-30 years plantations (Figure
5.19 b).
97
Figure 5.19: Carbon stock and proportionate distribution of tea (a) and shade tree
compartment (b) in five different age groups of tea agroforestry system
28% 7% 4% 3% 3%
66% 63% 42%
38% 28%
6% 30% 54% 59% 69%
0
5
10
15
20
5-10 10-15 15-20 20-25 25-30
Ca
rbo
n s
tock
(M
g h
a-1
)
Age groups (Years)
> 25 cm > 15-25 cm ≤ 15 cm
14.7 15.4 15.66
13.1
10.79
(a)
18% 6% 3% 3% 3%
78% 62% 54% 50% 46%
4%
32% 43% 47% 51%
0
10
20
30
40
50
5-10 10-15 15-20 20-25 25-30
Ca
rbo
n s
tock
(M
g h
a-1
)
Age groups (Years)
> 90 cm > 50-90 cm 10-50 cm(b)
42.03 39.93
45.9 39.95
27.48
98
5.3 Discussion
5.3.1 Allometric equations and biomass estimation
Diameter at breast height alone was the best independent variable for describing the
different biomass components, estimating stem, aboveground and total tree biomass with
about 95% accuracy. The results agree with previous reports (Brown et al. 1989, Basuki
et al. 2009, Baker et al. 2004) that dbh alone is a good predictor of biomass especially in
terms of the multiple tradeoffs between accuracy, cost and practicability of
measurements. BGB was overestimated by diameter based equations, confirming
previous reports that BGB is a major component of uncertainty in measuring total tree
biomass. This high and inconsistent RE could be attributed partly to uncertainties in
measuring diameter where stems tend to exhibit a much more fluted cross section. This is
even more pronounced with increasing tree size. The biomass of small trees was
generally overestimated, while the tendency to overestimate biomass dropped with
increasing tree size. This indicates that error in biomass estimation depends on the
average tree size (Kuyah et al. 2013). Other authors have reported the importance of tree
size in both formulation and use of allometric equations. Chave et al. (2004) reported that
biomass values of the smallest trees strongly affect values of allometric coefficients,
while Kuyah et al. (2012) showed that it is difficult to accurately estimate the biomass of
small trees which had been established under the dominance of Eucalyptus trees.
However, Wood density did not improve accuracy of estimating AGB due to the
extensive management through pruning canopy mass. This is due to much lower variation
in wood density of different aboveground components of the trees sampled; hence stem
wood density did not appear to affect the allometric relationship between diameter and
biomass resembling reports by Baker et al.(2004) and Basuki et al. (2009) who reported
that increasing dbh is not followed by an increase in wood density. Whereas the biomass
of stem, branches and BGB generally increased proportionally with tree size, the biomass
of leaves tended to decrease. The RS value determined in this study (0.30) is higher than
the IPCC default value of 0.24 ± 0.14 for tropical hardwood species (Cairns et al. 1997).
The RS mean (0.30 ± 0.08) reduce the influence of large outliers in the dataset arising
99
from pruning. Trees in the study site are more likely to emphasize in BGB as water and
nutrients are not considered limiting factor.
5.3.2 Distribution of biomass carbon stock
In extensively managed tea agroforestry system, shade tree density, tea bush density,
height, and crown shape of tea bushes are controlled. Managerial practices (tillage,
pruning, mulching and fertilization) adopted are also standardized across different
plantations. In the tea agroforestry system shade tree compartment plays vital role storing
maximum proportion of biomass carbon stock followed by tea bushes and litter.
Managerial practices maintain high tea density in the tea agroforestry system. This is
primarily due to the fact that tea plants are trimmed into a fixed frame that is low, broad,
heavily branched and capable of producing a large number of young shoots (Kamau et al.
2008). Biomass carbon stock density in all compartments across the plantation sites
revealed significant relationship with age of the plantations (Figure 5.20). Total carbon
stock density in tea agroforestry is significantly correlated with plantation age (y =
26.936 x0.276
, R2 = 0.35, p < 0.01, n = 100). Age group wise analysis showed that carbon
stock increased with increasing age up to 15-20 years age group but declined in 20-25
years group followed by slight increase in 25-30 years age group of plantations. The
reason for this may be that due to intensive management practices and lower shade tree
density to facilitate sparse shade for tea bushes compared to younger age groups. Infilling
of tea bushes and shade tree in plantations of higher age groups resulted increment in
carbon density in mature stands.
100
y = 15.73x0.311
R² = 0.24, p < 0.01
0
20
40
60
80
100
0 10 20 30
Bio
ma
ss c
arb
on
(M
g h
a-1
)
(a) Shade tree y = 6.668x0.255
R² = 0.25, p < 0.01
0
5
10
15
20
25
0 10 20 30
(b) Tea
y = 26.936x0.276
R² = 0.35, p , 0.01
0
20
40
60
80
100
0 10 20 30
Plantation age (Years)
(d) Total carbon y = 4.865x0.095
R² = 0.15, p < 0.01
0
2
4
6
8
10
0 10 20 30
Bio
ma
ss c
arb
on
(M
g h
a-1
)
Plantation age (Years)
(c) Litter
Figure 5.20: Relationships between biomass carbon stock and plantation age with respect
to tea agroforestry system in Barak Valley, Assam
5.3.3 Carbon stock potential of shade trees
Configuration of tea agroforestry in Barak Valley, Northeast India spotlights
multidimensional utility of shade trees starting from providing shade for tea compartment
to soil conservation and fertility management. Shade trees exhibited maximum
potentiality (65.6 % of total carbon estimated) in tea agroforestry system towards carbon
storage in the form of biomass. Among different shade tree species A. odoratissima
101
shows dominance over other species having 65 % proportionate contribution followed by
A. lebbeck (21 %) and Derris robusta (10 %) across different age groups of plantations.
D. robusta exhibited maximum carbon stock potential followed by A. odoratissima and
A. lebbeck . The ratio of carbon stock potential of these species (A. lebbeck : A.
odoratissima : D. robusta) was assessed as 1 : 1.03 : 1.14 from the dataset. Using GBH
(girth at 1.37 m) as predictor variable regression equations to estimate carbon stock for
individual trees have been proposed with high R2
values (0.990 – 0.999, p < 0.01). Higher
potency of biomass carbon stock in shade tree compartment recommends tea-shade tree
agroforestry system approach towards climate change mitigation. Along with age of the
plantation, the density of the shade tree and tea bushes decreased but basal cover showed
increasing trend. Due to management strategy the shade tree cover showed decline in 20-
25 years age group which is reflected in net carbon assimilation.
5.3.4 Carbon assessment in tea agroforestry system
Mean value of living biomass carbon (107.14 Mg C ha-1
) and litter biomass carbon (6.36
Mg C ha-1
) in the present study (Table 5.3.3) was higher than that of tea plantation
biomass carbon density (50.90 Mg C ha-1
) and litter carbon (4.91 Mg C ha-1
) stock for tea
plantations in China (Li et al. 2011). However the studied tea plantation is devoid of
shade trees. The estimate is comparable to carbon stock (81 Mg C ha-1
) in shaded coffee
AFS in south western Togo (Dossa et al. 2008) and higher than the biomass C stock
estimated (0.7 54 Mg C ha-1
) in traditional and improved agroforestry systems in the
West African Sahel (Takimoto et al. 2008). Biomass carbon density estimation in tea
plantations of Kenya exhibited range of 43 to72 Mg C ha-1
(Kamau et al. 2008).
Aboveground carbon stocks in tea agroforestry system (41.79 Mg C ha-1
) is comparable
to aboveground carbon stocks of tropical forests of Cachar District of Barak Valley,
Assam, Northeast India presenting carbon stock range of 16.24 to 130.82 Mg C ha-
1(Borah et al. 2013).
102
Table 5.3.1: Carbon stock (Mg ha-1
) in biomass of different compartments of tea
agroforestry system in five different age groups of plantation
Tea agroforestry bear remarkably higher carbon stock than carbon storage in silvipastoral
systems involving Acacia tortilis + Cenchrus ciliaris (6.82 Mg C ha–1
) and Acacia tortilis
+ Cenchrus setegerus (6.15 Mg C ha–1
) in arid northwestern India (Mangalassery et al.
2014). The estimate is comparable to carbon storage (39-102 Mg ha-1
) for agroforestry in
the humid tropics of South America (Albrecht & Kandji 2003). Aboveground carbon
stocks of cocoa agroecosystems with managerial practices have been reported as 16.8 and
15.9 Mg C ha-1
in Ghana (Isaac et al. 2005) and 49 Mg C ha-1
in shaded cocoa AFS in
Central America (Somarriba et al. 2013). Carbon content in tea in this study (59.39 Mg C
ha-1
) is much higher than C stock in Theobroma cacao plants (14.4 Mg C ha–1
) in cacao
AFS in Cameroon (Norgrove & Hauser 2013) and cocoa trees (9 Mg C ha–1
in
aboveground biomass) under cocoa AFS of Central America (Somarriba et al. 2013) and
comparable to the C estimation in cacao agroforestry (70 Mg C ha-1
) in Cameroon (Saj et
al. 2013) and presents higher value than the farmed Eucalyptus species (11.7 Mg C ha-1
)
in Kenya (Kuyah et al. 2013) and coffee-shade tree based agroforestry system (27.3 Mg
C ha-1
) in Guatemala (Powell & Delaney 1998). The reason for higher carbon stock may
Age group
(Years)
Compartment Shade tree Tea bush Litter Total
5-10
Aboveground 21.81 ± 5.54 8.35 ± 1.93
5.77 ± 0.17 44.03 ± 7.54 Belowground 5.67 ± 1.44 2.44 ± 0.56
Total 27.48 ± 6.97 10.79 ± 2.48
10-15
Aboveground 31.71 ± 8.13 10.14 ± 1.84
6.17 ± 0.18 59.22 ± 11.22 Belowground 8.24 ± 2.11 2.96 ± 0.54
Total 39.95 ± 10.24 13.10 ± 2.38
15-20
Aboveground 36.43 ± 6.66 12.13 ± 2.89
7.21 ± 0.18 68.77 ± 9.85 Belowground 9.47 ± 1.73 3.53 ± 0.84
Total 45.90 ± 8.34 15.66 ± 3.73
20-25
Aboveground 31.69 ± 6.84 11.93 ± 2.17
6.19 ± 0.18 61.53 ± 8.75 Belowground 8.24 ± 1.78 3.47 ± 0.63
Total 39.93 ± 8.62 15.40 ± 2.80
25-30
Aboveground 33.36 ± 11.86 11.39 ± 2.30
6.66 ± 0.27 63.39 ± 14.03 Belowground 8.67 ± 3.08 3.31 ± 0.67
Total 42.03 ± 14.94 14.70 ± 2.97
103
be that tea plantations maintain their biomass carbon density primarily by means of high
density (from 7,400 to 19,000 stems ha-1
) and massive proportionate contribution by
shade tree compartment in the system. Proper maintenance of shade trees, maintaining
high tea density, standardized fertilization practices will to some extent further increase
carbon storage in tea agroforestry system.
5.4 Conclusions
Reliable methods for estimating carbon in the trees of agricultural landscapes are required
if tea growers are to benefit from any carbon sequestered by their trees. Diameter-based
equations predicted biomass of most compartments with 95% accuracy, and with about
the same RE across trees of different size. Given that DBH is easy to measure with high
accuracy, the allometric equations provide a useful tool for estimating biomass and
carbon stocks of Tea for purposes such as bio-energy and carbon sequestration. These
equations can be best applied to Tea in North East Indian dominant tea agroforestry
systems in similar agro-ecological zones, provided that tree growth parameters fall within
similar ranges to those of the sampled population. The equations presented need to be
tested in other areas to determine their applicability in tea plantation systems across wide
range of geographic and agro-climatic conditions.
Tea agroforestry system store considerable amount of C in biomass components. Shade
trees are the major contributor for C stock in the system Shade tree species composition
and distribution of biomass C highlighted the key role of every component for C stock in
the system. Tea agroforestry developed as the process of conversion of natural forests for
economic benefit. Clearing mature forests to establish plantations typically leads to a
dramatic decrease in biomass carbon (Steffan - Dewenter et al. 2007). Managerial
practices can play an important role in agricultural ecosystem carbon storage (Li et al.
2011). Tea under the canopy of native shade tree species and sustainable managerial
practices are efficient for carbon storage which may compensate this loss along with
secondary environmental and economic benefit.