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.