Chapter - 5. Assessment of biomass and carbon stocks in Tea agroforestry system

Transcription

1 Chapter - 5 Assessment of biomass and carbon stocks in Tea agroforestry system 5.1 Introduction The retained increase in atmospheric carbon dioxide (CO 2 ) concentration is considered to be hastened by human activities such as burning of fossil fuels and deforestation (IPCC 27). Reduction in CO 2 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 CO 2 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 CO 2 in vegetation, soils, and biomass products (Kumar & Nair 211). Agroforestry systems (AFS) offer important opportunities of creating synergies between both adaptation and mitigation actions with a technical mitigation potential of Pg C in terrestrial ecosystems over the next 5 years (IPCC 27). 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. 213). 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. 21; Montagnini & Nair 24; Nair et al. 29). 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 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 27). 69

2 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 (.256 Mg C ha -1 yr -1 ) and Dalbergia sissoo (.141 t C ha -1 yr -1 ) intercropped with wheat and paddy in Central Himalaya, India (Maikhuri et al. 2). Swamy et al. 23) 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 4 % and 84 % less than agri-silviculture indicating that agroforestry systems have more potential to sequester carbon (Dhyani et al. 29) compared to Mg C ha -1 insole wheat cultivation (Chauhan et al. 21). 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. 213). 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 km 2. (Tea Board of India 27). 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 26, Kamau et al. 28).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 7

3 compartments and (3) give a glimpse of the potential of tea agroforestry system to offset carbon emissions. 5.2 Results 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. 28), coffee in Costa Rica (Segura et al. 26, Hager 212), Togo (Dossa 28), Ethiopia (Negash 213), Hawaii (Youkhana 211), Cacao agroforestry in Costa Rica (Beer 199), Cameroon (Saj et al. 213), agroforestry in Uganda (Tumwebaze et al. 213), Poplar in India (Rizvi et al. 28) forest plantations (Basuki et al. 29, Bastien-Henri et al. 21) and various forest types (Brown 1997, Henry et al 211) among other vegetation types including shrubs (Murray & Jacobson 1982, Navar et al. 24). 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. 23). 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. 22). 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. 21, Chave et al. 25). 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 71

4 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 km 2 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 (R 2 =.82), crown area (R 2 =.96) and branch count whereas height showed significant relationship with crown area (R 2 =.9), branch count (R 2 =.74) and wood density (R 2 =.4). Besides diameter and height crown area shows relationship with branch count (R 2 =.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) Height (m) Crown area (m 2 ) Wood density (g/cm 3 ) Branch count BEF R/S

5 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 <.1, * p <.5) D H WD NB CA Stem Branches Leaves Roots Total BEF R/S D 1 H.818** 1 WD * 1 NB.743**.739** CA.958**.94** ** 1 Stem.943**.729** **.878** 1 Branches.99**.77** **.865**.842** 1 Leaves.752**.772** **.788**.618**.815** 1 Roots.922**.728**.82.61**.87**.922**.893**.627** 1 Total.957**.789** **.98**.928**.978**.773**.958** 1 BEF -.383* -.411* * -.472** R/S

6 Tea biomass (kg) Tea biomass (kg) Regression of diameter with biomass of different component and compartment of Tea revealed that it has strong correlation with aboveground biomass (R 2 =.97; P <.1), branch biomass (R 2 =.95; P <.1), stem biomass (R 2 =.91; P <.1) and moderate relationship with leaf biomass (R 2 =.85, P <.1) (Figure 5.1a). Similarly the regression of root (BGB) and total biomass (TB) as a function of diameter showed significance (p <.1) with R 2 values.95 and.97 respectively (Figure 5.1b). (a) AGB AGB, (R 2 =.97) Stem Stem, (R 2 =.91) Branch Branch, (R 2 =.95) Leaf Leaf, (R 2 =.85) 5 (b) TB TB, R 2 =.97 AGB AGB, R 2 =.97 BGB BGB, R 2 =.95 Diameter at 5 cm height (cm) Diameter at 5 cm height (cm) 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 74

7 Crown area (m 2 ), Wood density (g cm -3 ) Branch count/height (m) BEF BGB/AGB 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. (a) Crown area Branch count Wood density Height (b) BEF BGB/AGB Girth class (cm) Girth class (cm) 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 Linear equivalent of the power equation (Eq. 1) disclosed diameter as a significant (P <.1) 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 ). 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 75

8 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. R 2 (.985), RMSE (.16), AIC (-2.832), and RE (1.15 %) compared to model with diameter alone (Eq. 1) for AGB where Adj. R 2, RMSE, AIC and RE exhibited values.966,.238, 2.97 and 2.8 respectively and in the model diameter with crown area (Adj. R 2 =.981, RMSE=.181, AIC= and RE= 1.39). Different combinations with more than two variables (Eq. 6 Eq. 12) improved Adj. R 2, RMSE, AIC and RE among which height and crown area with diameter (Eq. 8) performed well in terms of AIC ( ) 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 R 2 (.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). 76

9 ln BGB(kg) Standardized residuals lnleaf(kg) Standardized residuals lnbranch(kg) Standardized residuals lnstem(kg) Standardized residuals lnagb (kg) Standardized residuals (a) (c) (e) (g) (i) Observations Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) lndiameter(cm) Observations Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) lndiameter(cm) Observations Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) lndiameter(cm) Observations Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) lndiameter(cm) Observations Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%) lndiameter(cm) (b) (d) (f) (h) Pred(lnAGB(kg)) Pred(lnStem(kg)) Pred(lnBranch(kg)) (j) Pred(lnLeaf(kg)) Pred(lnRoot (BGB)(kg))

10 Relative error (%) Relative error (%) Relative error (%) Relative error (%) Relative error (%) Relative error (%) 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) (a) Girth class (cm) AGB (b) Girth class (cm) stem (c) 6 branch (d) 8 leaf Girth class (cm) Girth class (cm) (e) Girth class (cm) BGB (f) Girth class (cm) T B 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 78

11 Table 5.2.3: Allometric power function equations (y = ax b ) 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 (R 2 ) and model bias (RE) are displayed Component a b R 2 RE (%) AGB Stem Branches Leaves BGB TB 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 (R 2 ), adjusted coefficient of determination (Adj.R 2 ), model bias (RE) are presented Component a (SE) b (SE) R 2 Adjusted RE R 2 SEE P value (%) Eq. (1) (.148) (.64) < Stem (.278) 2.33 (.121) < Branches (.19) (.83) < Leaves (.225) (.98) < BGB (.179) 1.87 (.78) < TB (.143) (.62) <

12 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. R 2 ), root mean square error (RMSE), Akaike information criterion (AIC) and bias for each equation is presented.***, ** and * indicates p-value <.1,.1 and.5 respectively at 95% confidence interval. (a) Aboveground biomass (AGB): Equation a b c d e f Adj.R 2 RMSE AIC RE (%) Eq. (1) -3.51*** *** SE Eq. (2) *** *** *** SE Eq. (3) *** ***.779 * SE Eq. (4) -.965* ***.549 *** SE Eq. (5) *** 1.79 *** SE Eq. (6) * 1.2 *** *** SE Eq. (7) *** ***.763 * SE Eq. (8) *** *** 4.21 *** SE Eq. (9) ** ** ** SE Eq. (1) *** 1.28 *** *** SE Eq. (11) ** *** *** SE Eq. (12) -1.2 ** *** 3.13 ** SE

13 (b) Root biomass (BGB) Equation a b c d e f Adj.R 2 RMSE AIC RE (%) Eq. (1) *** 1.87 *** SE Eq. (2) *** *** *** SE Eq. (3) *** *** SE Eq. (4) -1.98** 1.48 ***.621 *** SE Eq. (5) *** *** SE Eq. (6) ** 1.45 *** *** SE Eq. (7) *** *** SE Eq. (8) *** 1.231*** *** SE Eq. (9) *** 1.2 *** SE Eq. (1) *** 1.25 *** *** SE Eq. (11) *** 1.14*** SE Eq. (12) *** 1.28 *** * SE

14 (c)total biomass (TB): Equation a b c d e f Adj.R 2 RMSE AIC RE (%) Eq. (1) *** *** SE Eq. (2) *** 1.235*** 4.42 *** SE Eq. (3) *** ***.711 * SE Eq. (4) ***.57 *** SE Eq. (5) *** 1.88 *** SE Eq. (6) *** *** SE Eq. (7) *** ***.698 * SE Eq. (8) *** 1.252*** *** SE Eq. (9) -.67 * 1.89*** ***.248 * SE Eq. (1) *** 1.216*** *** SE Eq. (11) *** 3.12 *** SE Eq. (12) * 1.92 *** 3.45 *** SE

15 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 1.4 % to AGB. Much of the tree biomass is held in the branches, which constitutes half (5.13 %) of the total tree biomass, while stem and leaves make up 2.59 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. Roots 23.4 Stem 2.59 Leaves 6.24 Branches 5.13 Figure 5.5: Biomass distribution in the analyzed compartments of Tea (in %) 83

16 Girth class (cm) Root Stem Branch Leaf <15 >15-25 >25-35 >35-45 >45-55 >55 % 2% 4% 6% 8% 1% Proportion of biomass 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 <.1). 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 <.1) Compartment Range Mean Standard deviation Leaves b.48 Branches a.38 Stem b.69 Roots b.72 84

17 Distribution of Tea biomass and carbon stock Biomass stock in tea compartment exhibited the range of ( Mg ha -1 ) with average value of (29.2 ± 7.4 Mg ha -1 ). Among different age group of plantations 15-2 years age group stores maximum biomass (32.62 ± 7.76 Mg ha -1 ) followed by 2-25 (32.9 ± 5.83 Mg ha -1 ) and 25-3 years (3.62 ± 6.19 Mg ha -1 ). Carbon stock in tea was estimated ± 3.38 Mg ha -1. Tea carbon stock value varied between 6.76 Mg ha -1 and 2.83 Mg ha years age group is the leading contributor towards carbon stock (15.66 ± 3.73 Mg ha -1 ) following 2-25 (15.4 ± 2.8 Mg ha -1 ) and 25-3 (14.7 ± 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 5-1 years age group and the value gradually increased and attained maximum in 15-2 years age group which further declined in subsequent age groups (2-25 and 25-3 years). Across the plantations medium sized (> 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 <.1). LSD analysis pointed that tea carbon stock in 5-1 years is remarkably less than rest of the age groups and 15-2 years age group contains significantly higher carbon stock than 5-1 and 1-15 years age groups of plantations (p <.5) years age group stores notably higher carbon from 1-15 years age group (p <.1). 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). 85

18 Carbon (Mg ha -1 ) Biomass (Mg ha -1 ) Basal area (m 2 ha -1 ), biomass and C (Mg ha -1 ) Density (Stem 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 (a) AGB 1. BGB Age group (Years) (b) AGC 5. BGC Age group (Years) Figure 5.8: Biomass (a) and carbon (b) stock by tea compartment in different age groups in tea agroforestry system in Barak Valley, Assam 86

19 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 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 <.1) Shade tree species Range Mean Standard deviation CV (%) Albizia lebbeck b Albizia odoratissima b Derris robusta a Albizia chinensis b Albizia procera b Senna siamea b Dalbergia sissoo b Distribution of shade tree biomass and carbon stock Shade tree biomass in tea agroforestry system was estimated (78.12 ± Mg ha -1 ) depicting range between (3.22 Mg ha -1 and Mg ha -1 ) across the plantations studied. Carbon stock by this compartment exhibited value ranging from (15.11 Mg ha -1 to Mg ha -1 ) having mean value of 39.6 ± Mg ha -1. Biomass and carbon stock displayed increasing trend from 5-1 years to 15-2 years age group and declined in following age group (2-25 years) with subsequent increase in 25-3 years age group of plantations (Figure 5.9).Across all the plantations girth wise small (1-5 cm), medium (>5-9 cm) and larger (>9 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 5.1). Biomass and carbon stock in shade tree compartment across different age groups of plantations showed significant 87

20 variation (ANOVA, p <.1). Multiple comparison analysis clears that biomass and carbon stock in 5-1 years age group is remarkably less than other older age groups concerned (LSD, p <.1). 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 5-1 years (22.78 ± 5.62 Mg ha -1 ) showed increasing trend up to 15-3 years (74.39 ± Mg ha -1 ) age group and gradually declined in subsequent age groups. Biomass stock by A. lebbeck increased from 5-1 years (16.87 ± 3.73 Mg ha -1 ) to 1-15 years (23.31 ± Mg ha -1 ) age groups and gradual decline in the subsequent ages was observed. Biomass allocation in Derris robusta initially declined from 5-1 years (13.4 ± 4.1 Mg ha -1 ) to 1-15 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 % proportionate contribution followed by A. lebbeck and Derris robusta bearing proportionate contribution of % and % 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 <.1). Post hoc analysis showed that basal area, biomass and carbon stock in 5-1 years and 1-15 years age group varies significantly from 15-2, 2-25 and 25-3 years age group of plantations (p <.5). The parameter values gradually increased from 5-1 to 15-2 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 5-1 to1-15 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 <.1). Basal area, biomass and carbon stock in 5-1 and 1-15 years age group showed statistically significant difference from 15-2, 2-25 and 25-3 years age group of plantations. Basal area, biomass and carbon stock values in Derris robusta decreased from 5-1 years to 15-2 years age group and gradually increased in the consecutive age groups (Figure 5.13 c). Multiple comparison analysis pointed that basal area, biomass and 88

21 Basal area (m2 ha-1), biomass and carbon (Mg ha-1) Density (Stem ha-1) Carbon (Mg ha -1 ) Biomass (Mg ha -1 ) carbon stock in 1-15 and 15-2 years age group exhibited significantly lower value compared to 5-1, 2-25 and 25-3 years age group of plantations. (a) AGB BGB Age groups (Years) (b) AGC BGC Age groups (Years) 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 > 5-9 > 9 Girth class (cm) Biomass Carbon Basal area Density Figure 5.1: Density, basal area, biomass and carbon stock allocation in different girth size classes of shade trees in tea agroforestry system in Barak Valley, Assam 89

22 Age group (Years) 2.5 Biomass (Mg ha -1 ) A.odoratissima A.lebbeck Derris robusta Albizia chinensis Albizia procera Senna siamea Dalbergia sissoo Age groups (Years) Figure 5.11: Biomass allocation in shade tree species in five different age groups of tea agroforestry system Proportionate distribution % 2% 4% 6% 8% 1% A.lebbeck A.odoratissima Derris robusta Albizia chinensis A.procera Cassia siamea Dalbergia sissoo Figure 5.12: Proportionate distribution of biomass and carbon among different shade tree species in tea agroforestry system 9

23 Density (Stem ha -1 ), Biomass and Carbon (Mg ha -1 ) Basal area (m 2 ha -1 ) Density (Stem ha -1 ), Biomass and Carbon (Mg ha -1 ) Basal area (m 2 ha -1 ) Density (Stem ha -1 ), Biomass and Carbon (Mg ha -1 ) Basal area (m 2 ha -1 ) (a) 21 A. odoratissima Age group (Years) Biomass Carbon Density BA (b) 12 A. lebbeck Age group (Years) Biomass Carbon Density BA (c) 6 Derris robusta Age group (Years) Biomass Carbon Density BA Figure 5.13: Density, basal area, biomass and carbon stock among dominant shade tree species (a)-(c) in tea agroforestry system 91

24 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 ( 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 : 1.3 : 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 = ax b 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 (R 2 ) and minimal error (SSR) urges utility of the equations (Figure 5.14) Litter carbon estimation Litter carbon stock across all the sites presented range of ( Mg ha -1 ) with mean value of (6.36 ±.84 Mg C ha -1 ). Carbon stock in litter compartment gradually increased from 5-1 years (5.77 ±.36 Mg ha -1 ) to 15-2 years age group (7.21 ±.4 Mg ha -1 ). The value declined in 2-25 years age group (6.19 ±.82 Mg ha -1 ) and enhanced (6.66 ± 1.4 Mg ha -1 ) in the following age group (Figure 5.15). Litter carbon stock in different age group of plantations varied statistically (ANOVA, p <.1). Multiple comparison analysis elucidated that litter carbon stock in 15-2 years age group is significantly higher than all other age groups of plantations. Litter carbon stock in 5-1 and 25-3 years age group highlighted significant difference (LSD, p <.5). 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 ( % and %) respectively. 92

25 AGC (kg/tree) AGC (kg/tree) AGC (kg/tree) (a) (b) A. odoratissima A. lebbeck y =.7x R² =.997, SSR = GBH (cm) y =.8x R² =.99, SSR = GBH (cm) (c) Derris robusta y =.6x R² =.999, SSR = GBH (cm) 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 (R 2 : coefficient of determination; SSR: sum of squares of residuals) 93

26 Litter carbon stock (Mg ha -1 ) 48 % 48 % 45 % 44 % 46 % 52 % 52 % 56 % 55 % 54 % (Mg ha -1 ) 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 <.5) according to multiple comparison tests carried out 1 Leaf Non-leaf Age group (Years) Figure 5.16: Litter carbon stock and proportionate contribution of litter components in tea agroforestry system in Barak Valley, Assam 94

27 carbon stock (Mg ha -1 ) Basal area (m 2 ha -1 ) Biomass and carbon assessment in tea agroforestry system Biomass stock in tea agroforestry system was estimated ( ± Mg ha -1 ). The estimate ranges from (64.37 Mg ha -1 to Mg ha -1 ) across different stands. Carbon (C) stock in biomass depicted range of (31.11 Mg ha -1 to 95.4 Mg ha -1 ) depicting mean value of (59.39 ± Mg ha -1 ) across plantation sites. C stock measure presented increasing trend from 5-1 years (44.4 ± 7.54 Mg ha -1 ) to 15-2 years (68.77 ± 9.85 Mg ha -1 ) age group of plantations. C stock value dropped in 2-25 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 to 83.31m 2 ha -1 with mean of ± 13.13m 2 ha -1. Basal area exhibited increasing trend from 5-1 years to 15-2 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 %, % and % 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 1.9 % share towards C stock across all age groups of plantations (Figure 5.18). Shade tree root Tea root Shade tree Tea Litter BA Age groups (Years) Figure 5.17: Carbon stock by different compartments with basal area in different age groups under tea agroforestry system in Barak Valley, Assam 95

28 16% 13% 13% 12.4% 13% Carbon stock proportion 23.2% 21% 22% 23.9% 22.2% 6.7% 66% 65.3% 63.6% 64.9% 1% Shade tree Tea Litter 8% 6% 4% 2% % Age groups (Years) 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 (> 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 (> 5-9 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 (1-5 cm) was less across the plantations with lower values in higher age groups. Proportionate C stock in larger girth sized (> 9 cm) shade trees gradually increased with plantation age and attained maximum proportion of shade tree C stock in 25-3 years plantations (Figure 5.19 b). 96

29 Carbon stock (Mg ha -1 ) Carbon stock (Mg ha -1 ) (a) 2 > 25 cm > cm 15 cm % 3% 54% 59% 69% 5 66% 63% 42% 38% 28% 28% 7% 4% 3% 3% Age groups (Years) (b) > 9 cm > 5-9 cm 1-5 cm % 47% 51% 32% 4% 78% 62% 54% 5% 46% 18% 6% 3% 3% 3% Age groups (Years) 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 97

30 5.3 Discussion 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. 29, Baker et al. 24) 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. 213). Other authors have reported the importance of tree size in both formulation and use of allometric equations. Chave et al. (24) reported that biomass values of the smallest trees strongly affect values of allometric coefficients, while Kuyah et al. (212) 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.(24) and Basuki et al. (29) 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 (.3) is higher than the IPCC default value of.24 ±.14 for tropical hardwood species (Cairns et al. 1997). The RS mean (.3 ±.8) reduce the influence of large outliers in the dataset arising 98

31 from pruning. Trees in the study site are more likely to emphasize in BGB as water and nutrients are not considered limiting factor 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. 28). Biomass carbon stock density in all compartments across the plantation sites revealed significant relationship with age of the plantations (Figure 5.2). Total carbon stock density in tea agroforestry is significantly correlated with plantation age (y = x.276, R 2 =.35, p <.1, n = 1). Age group wise analysis showed that carbon stock increased with increasing age up to 15-2 years age group but declined in 2-25 years group followed by slight increase in 25-3 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. 99

32 Biomass carbon (Mg ha -1 ) Biomass carbon (Mg ha -1 ) 1 (a) Shade tree y = 15.73x.311 R² =.24, p <.1 25 (b) Tea y = 6.668x.255 R² =.25, p < (c) Litter y = 4.865x.95 R² =.15, p <.1 1 (d) Total carbon y = x.276 R² =.35, p, Plantation age (Years) Plantation age (Years) Figure 5.2: Relationships between biomass carbon stock and plantation age with respect to tea agroforestry system in Barak Valley, Assam 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 1

33 shows dominance over other species having 65 % proportionate contribution followed by A. lebbeck (21 %) and Derris robusta (1 %) 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 : 1.3 : 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 R 2 values ( , p <.1). 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 2-25 years age group which is reflected in net carbon assimilation Carbon assessment in tea agroforestry system Mean value of living biomass carbon (17.14 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 (5.9 Mg C ha -1 ) and litter carbon (4.91 Mg C ha -1 ) stock for tea plantations in China (Li et al. 211). 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. 28) and higher than the biomass C stock estimated (.7 54 Mg C ha -1 ) in traditional and improved agroforestry systems in the West African Sahel (Takimoto et al. 28). Biomass carbon density estimation in tea plantations of Kenya exhibited range of 43 to72 Mg C ha -1 (Kamau et al. 28). 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 to Mg C ha - 1 (Borah et al. 213). 11

34 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 Age group (Years) Compartment Shade tree Tea bush Litter Total Aboveground ± ± 1.93 Belowground 5.67 ± ±.56 Total ± ± 2.48 Aboveground ± ± 1.84 Belowground 8.24 ± ±.54 Total ± ± 2.38 Aboveground ± ± 2.89 Belowground 9.47 ± ±.84 Total 45.9 ± ± 3.73 Aboveground ± ± 2.17 Belowground 8.24 ± ±.63 Total ± ± 2.8 Aboveground ± ± 2.3 Belowground 8.67 ± ±.67 Total 42.3 ± ± ± ± ± ± ± ± ± ± ± ± 14.3 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. 214). The estimate is comparable to carbon storage (39-12 Mg ha -1 ) for agroforestry in the humid tropics of South America (Albrecht & Kandji 23). 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. 25) and 49 Mg C ha -1 in shaded cocoa AFS in Central America (Somarriba et al. 213). 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 213) and cocoa trees (9 Mg C ha 1 in aboveground biomass) under cocoa AFS of Central America (Somarriba et al. 213) and comparable to the C estimation in cacao agroforestry (7 Mg C ha -1 ) in Cameroon (Saj et al. 213) and presents higher value than the farmed Eucalyptus species (11.7 Mg C ha -1 ) in Kenya (Kuyah et al. 213) 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 12

35 be that tea plantations maintain their biomass carbon density primarily by means of high density (from 7,4 to 19, 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. 27). Managerial practices can play an important role in agricultural ecosystem carbon storage (Li et al. 211). 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. 13