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TECHBOOKS Journal: MITI MS Code: 219 DISK 20-10-2005 15:0 Pages: 15

Mitigation and Adaptation Strategies for Global Change (2005)DOI: 10.1007/s11027-005-9011-8 C© Springer 2005

BASELINE CARBON STOCKS ASSESSMENT AND PROJECTION OFFUTURE CARBON BENEFITS OF A CARBON SEQUESTRATION

PROJECT IN EAST TIMOR

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2

3

R.D. LASCO1,∗ and M.M. CARDINOZA241World Agroforestry Centre (ICRAF), Philippines, 2/F College of Forestry and Natural Resources

Administration Building, P.O. Box 35024, University of the Philippines at Los Banos 4031 College,Laguna, Philippines; 2CARE International, SKB, Vila Verde, Dili, East Timor

567

(∗Author for correspondence: Tel: +63 49 5362925; Fax: +63 49 5364521;8E-mail: r.lasco@cgiar.org)9

(Received 26 April 2005; accepted in final form 23 September 2005)10

Abstract. Climate change is one of the most pressing environmental problems humanity is facingtoday. Forest ecosystems serve as a source or sink of greenhouse gases, primarily CO2. With sup-port from the Canadian Climate Change Fund, the Community-based Natural Resource Managementfor Carbon Sequestration project in East Timor (CBNRM-ET) was implemented to “maintain car-bon (C) stocks and increase C sequestration through the development of community-based resourcemanagement systems that will simultaneously improve livelihood security”. Project sites were inthe Laclubar and Remexio Sub-districts of the Laclo watershed. The objective of this study wasto quantify baseline C stocks and sequestration benefits of project components (reforestation withfast-growing species, primarily Casuarina equisetifolia, and agroforestry involving integration ofParaserianthes falcataria). Field measurements show that mature stands (≥30 years) of P. falcatariaand C. equisetifolia contain up to 200 Mg C ha−1 in above ground biomass, indicating the vast poten-tial of project sites to sequester carbon. Baseline C stocks in above ground biomass were very lowin both Laclubar (6.2 Mg C ha−1 for reforestation sites and 5.2 Mg C ha−1 for agroforestry sites andRemexio (3.0 Mg C ha−1 for reforestation and 2.5 Mg C ha−1 for agroforestry). Baseline soil organicC levels were much higher reaching up to 160 Mg C ha−1 in Laclubar and 70 Mg C ha−1 in Remexio.For the next 25 years, it is projected that 137 671 Mg C and 84 621 Mg C will be sequestered underhigh- and low C stock scenarios, respectively.

1112131415161718192021222324252627

Keywords: agroforestry, carbon sequestration, East Timor, soil organic carbon, tropical plantations28

1. Introduction29

Climate change is one of the primary concerns of humanity today. The IPCC Third30

Assessment Report (TAR) concludes that there is strong evidence that human ac-31

tivities have affected the world’s climate (IPCC WG 1 2001). The rise in global32

temperatures has been attributed to emissions of greenhouse gases (GHG), notably33

CO2.34

In the last few decades there have been massive deforestation and lan-35

duse/cover change in the tropics. Deforestation rates in tropical Asia were esti-36

mated to be 3.9 M ha yr−1 from 1981 to1990 (Brown 1993). In Southeast Asia,37

the 1990 annual deforestation rate was about 2.6 M ha yr−1 (Trexler and Haugen38

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R.D. LASCO AND M.M. CARDINOZA

1994). A recent review showed that natural forests in South East Asia typically 39

contain a high carbon (C) density, more than 200 Mg C ha−1 in above ground 40

biomass (Lasco 2002). However, logging activities could reduce C stocks by 41

at least 50% while deforestation could result in C density much lower than 42

40 Mg C ha−1. 43

Amongst the world’s forests, tropical forests have the largest potential to miti- 44

gate climate change through conservation of existing C pools (e.g. reduced impact 45

logging), expansion of C sinks (e.g. reforestation, agroforestry), and substitution 46

of wood products for fossil fuels (IPCC WG III 2001; Watson et al. 2000; Brown 47

et al. 1996). In tropical Asia, it has been estimated that forestation, agroforestry, re- 48

generation and avoided deforestation activities have the potential to sequester 7.50, 49

2.03, 3.8–7.7, and 3.3–5.8 Pg C respectively between 1995–2050 (Brown et al. 50

1996). 51

As the newest independent nation in the world, East Timor is beginning to 52

grapple with the management of its natural resources. In common with much of 53

Southeast Asia, its forest resources have been degraded in the last few decades. 54

East Timor’s forest cover rapidly declined from 1972–1999 when about 200,000 55

ha or 30% of dense and sparse forest cover were deforested (Sundland et al. 56

2001). 57

The current state of forest lands offers much potential for mitigation. Existing 58

dense and sparse forests could be protected to avoid GHG emissions, principally 59

of CO2. Sparse canopy forests and open areas could be restocked and reforested, 60

respectively, to expand existing C stocks. 61

With support from the Canadian Climate Change Fund, the Community-based 62

Natural Resource Management for Carbon Sequestration project in East Timor 63

(CBNRM-ET) was implemented to stabilize the concentration of GHG in the atmo- 64

sphere. The goal of the project was to “maintain C stocks and increase C sequestra- 65

tion through the development of community-based resource management systems 66

that will simultaneously improve livelihood security” (CARE 2002). The focus of 67

land development was on agroforestry and reforestation activities. The agroforestry 68

component involves integration of Paraseriantes falcataria (L.) Nielsen in upland 69

farms. It was envisioned that after a few years, a multistorey system will be cre- 70

ated with fruit trees/palms in the upper canopy and agricultural crops in the lower 71

canopy. On the other hand, reforestation involves planting of fast growing species 72

primarily Casuarina equisetifolia L. At the end of the project, it was expected that 73

1,645 ha would have been developed. 74

One of the key components of the project is the measurement and moni- 75

toring of the C benefits of the project. This study was designed to fulfil this 76

need. 77

The study’s primary objective was to quantify the C benefits of the CBNRM 78

project in East Timor. Specifically, the study aimed to: Measure the baseline C 79

stocks of the project sites, and estimate the rate of C sequestration of the project 80

for 25 years. 81

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CARBON SEQUESTRATION PROJECT IN EAST TIMOR

2. Methods82

2.1. DESCRIPTION OF STUDY AREA83

East Timor, with a total land area of 14,874 km2, is located on the eastern end84

of the island of Timor between latitude 8◦17′−10◦22′S and longitude 123◦25′–85

127◦19′E (Martins 2001). The topography is mountainous and sloping with 44% of86

the land having slopes greater than 40%. Its climate is hot and humid with rainfall87

dominated by the passage of the northwest and southeast monsoons. The northern88

coast receives an average of only 500–1000 mm yr−1 of rainfall while the eastern89

and southern half gets 1500–2000 mm yr−1. The central mountainous part of the90

country receives much higher rainfall averaging 2500–3000 mm yr−1.91

Terrestrial vegetation also varies corresponding to differences in rainfall pattern.92

The northern part is dominated by plant species such as Eucalyptus alba Reinw.93

Ex Blume and Tamarindus indicus L. (Nunes 2001). In the east and the south,94

canarium, Pterocarpus indicus Willd., charia (Toona sureni (Blume) Merr.), and95

Tectona grandis L.f. predominate. The mountain areas and the uplands are domi-96

nated by Eucalyptus urophylla S.T. Blake and several species of ferns.97

Recent satellite image analysis show that most of the country are now with-98

out forest cover (Table I). Of the remaining forests, dense forests comprise about99

200,000 ha, only 16% of the country’s total land area, while sparse forests cover100

a little less than 20% (Sundland et al. 2001). From 1972 to 1999, open areas with101

no forest increased from 49% to 65% of total land area. During this period, about102

200,000 ha of forests were lost.103

The anthropogenic drivers of deforestation are varied, including (Sundland et al.1042001): fires, hunting, fuelwood collection, conversion to agriculture, logging and105human settlements. About 75% of the estimated population of 787,000 reside in106rural areas (ETTA et al. 2001). Many of them are subsistence farmers who cultivate107previously forested lands. It is estimated that more than 90% of forest area is108impacted in some way by human activities (Martins 2001).109

TABLE I

Change in forest cover and open areas in East Timor from 1972 to 1999

Area (Ha)

Forest type 1972 % 1999 %

Dense forest 321,542 25 207,654 16

Sparse forest 324,558 26 246,196 19

Open 624,546 49 816,796 64

Total 1,270,646 100 1,270,646 100

Source. Sundland et al. 2001

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The Indonesian system of land tenure still applies. Forested lands are typically 110

state-owned while land management based on traditional systems are owned com- 111

munally and individually (Pedersen and Arneberg 1999). However, there is no clear 112

demarcation of which lands belong to the state and which lands are communally 113

owned. There are communal laws or agreements (called tara bandus) which gov- 114

ern the management and protection of common property natural resources (CARE 115

2002). For example, there are regulations regarding control of burning, control of 116

free grazing, and felling of trees. 117

The CBNRM-ET project sites are located in the Laclubar Sub-district (Manatuto 118

District) and Remexio Sub-district (Aileu District). These sub-districts are located 119

in the upper reaches of the Laclo watershed, the longest in the country. They cover 120

about 54,000 ha which is 30% of the total watershed area. 121

2.2. APPROACH TO C BENEFITS CALCULATION 122

The basic approach in calculating C benefits to be generated by the project is to 123

estimate the C sequestration of the project case and subtract from it the C sequestered 124

under a baseline or reference case. 125

The study compared two scenarios: a baseline scenario and a project develop- 126

ment scenario for a 25-year period. The former is a projection of the condition 127

of the project sites under a business-as-usual scenario while the latter takes into 128

account proposed project activities to rehabilitate and develop the area, primarily 129

reforestation and agroforestry. 130

Field measurements were conducted to determine C stocks of land use types 131

representing stages in the baseline- and project development scenarios. Two types 132

of field measurements were conducted from June to November 2003. First, plots 133

were established in mature tree- and agroforestry farms (≥30 years) to determine the 134

C stocks in above-ground biomass. Second, plots were also established to determine 135

the baseline C stocks of the project site. 136

2.3. FIELD MEASUREMENTS 137

2.3.1. Measurement of Baseline Carbon Stocks 138

The methods described here are based on the manuals on C measurements published 139

by the ASB (Alternative to Slash and Burn) Project of the International Center for 140

Research in Agroforestry (ICRAF) now the World Agroforestry Centre (Hairiah 141

et al. 2001) and Winrock International (MacDicken 1997). They also benefited 142

from the IPCC good practice guidance for land use, land use change and forestry 143

(LULUCF) greenhouse gas inventory (IPCC 2003). 144

Based on observations at the project sites in Remexio and Laclubar, four strata 145

were delineated from the location characteristics and site development strategy: 146

• Stratum 1: Laclubar Reforestation (Lac REFO) 147

• Stratum 2: Laclubar Agroforestry (Lac AF) 148

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• Stratum 3: Remexio Reforestation (Rem REFO)149

• Stratum 4: Remexio Agroforestry (Rem AF)150

A total of fifty-seven 40 m × 5 m plots were randomly established to measure151

the baseline C stocks of the project:152

Stratum Number of plots

Lac REFO 13

Lac AF 15

Rem REFO 15

Rem AF 14

TOTAL 57153

(a) Understorey Vegetation154

Sampling frames within the 40 m × 5 m plots were located randomly in each155

quarter of the length of the central rope. All vegetation with <5 cm diameter-156

at-breast height (dbh) were harvested within a 1 m × 1 m (1 m2) quadrat. The157

total fresh sample was weighed, mixed well and a composite fresh sub-sample158

(∼300 g) taken for subsequent oven drying. The average of four samples for159

understorey vegetation was used as replicate for each plot.160

(b) Litter/necromass161

In the same quadrats for understorey vegetation, all litter was collected in162

two randomly chosen 0.50 m × 0.50 m quadrats (0.25 m2). All undecomposed163

(green or brown) material was collected in a plastic bag. Oven dry weight of164

samples was determined as above.165

(c) Soils166

The same sampling quadrat used for litter sampling was used for soil sampling.167

After removing the litter layer, soil samples were taken at the 10–20 cm soil168

depth to represent the 0–30 cm layer (MacDicken 1997; PCARR 1980). All169

the sub-samples per plot were combined to form a 1-kg composite sample. The170

composite samples were air dried and then sieved. The samples were sent to171

the Soils Analytical Services Laboratory of the University of the Philippines172

at Los Banos for soil organic matter (SOM) analysis using the Walkley Black173

method. Soil organic C (SOC) was calculated by dividing SOM with 1.724174

(Young 1997).175

To determine bulk density, a sampling spot close to the sample sites for destruc-176

tive samples was chosen avoiding any place with possible soil compaction due to177

other sampling activities. After removing the litter layer, a fabricated metal ring178

sampler (diameter = 6.8 cm; height = 5 cm) was inserted gently and directly into179

the soil surface to sample the 10–20 cm depth. The soil from around the ring was180

excavated and soil beneath the ring bottom was cut. Excess soil from above the ring181

was removed using a knife.182

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The fresh weight (W1) of the samples was obtained after which they were oven- 183

dried and weighed again (W2). The bulk density (BD, g cm−3) was calculated by 184

W2/V where V = soil volume. 185

2.3.2. Determination of Potential Biomass and Carbon Density 186

Sampling plots measuring 20 m × 100 m (2000 m2) were laid out in representa- 187

tive mature Paraserianthes falcataria and Casuarina equisitofolia (the dominant 188

species) agroforestry- and tree farms: five in Laclubar and three in Remexio. Only 189

trees were sampled in these plots. Tree biomass contains more than 90% of above- 190

ground C stocks in forests. For practical reasons, destructive sampling was not 191

conducted. Instead, the biomass was estimated through the use of allometric equa- 192

tions relating tree diameter and wood density to biomass. 193

In each 20 m × 100 m plot, all trees ≥30 cm dbh including dead standing trees 194

were sampled (there were no trees <30 cm dbh in the mature stands). For each tree, 195

the following information were obtained: 196

• species name (local and preferably scientific name) 197

• diameter at 1.3 m above the soil surface (dbh) 198

The biomass (in kg tree−1) were calculated for each tree using the following 199

allometric equations: 200

For P. falcataria and other broadleaf species using two different allometric 201

relationships: 202

(a) Brown (1997) 203

kg tree−1 = 0.092∗dbh2.60

(b) Ketterings et al. (2001) 204

kg tree−1 = 0.11∗ρ∗dbh2.62

where ρ is wood density 205

For C. equisetifolia (Brown 1997): 206

kg tree−1 = exp(−1.17 + 2.119 ln dbh)

A C content of 45% was used in calculating C stocks on the basis of data from 207

the Philippines (Lasco and Pulhin 2000). 208

2.4. PROJECTION OF C BENEFITS FROM THE PROJECT 209

2.4.1. Assumptions 210

(a) Baseline case 211

Based on visits to the project site and interviews with project staff, reforestation 212

was deemed most appropriate for grassland areas that have been in that state 213

for decades. Maintenance of this state is believed to be due to regular fires that 214

prevent plant succession from proceeding. It can thus be assumed that without 215

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CARBON SEQUESTRATION PROJECT IN EAST TIMOR

TABLE II

Area of land for development by the CBNRM-ETproject based on actual and planned accomplishments

Area (ha)

Component Year 1 Year 2 Year 3 Total

Reforestation 125 500 200 825

Agroforestry 222 420 178 820

Total 347 920 378 1645

the project these lands will continue to remain grassland areas. The baseline C216

stocks are thus assumed to be constant over time.217

Similarly, the target of agroforestry development was upland farms which218

are mainly planted to annuals such as corn. As for grassland areas, it can be219

assumed that the C stocks are relatively constant over time.220

The baseline method we used was consistent with the first approach of the221

approved modalities and guidelines for A/R CDM project by the UNFCCC222

which involves the use of “existing or historical, as applicable, changes in223

carbon stocks in the carbon pools within the project boundary” (UNFCCC224

2004).225

(b) Project case226

Under the project case, a total of 1,645 ha will be developed through agro-227

forestry and reforestation. The following pace of development was assumed228

based on on-going and planned project accomplishments (Table II).229

(c) C stocks and rate of C sequestration230

For the reforestation component, the following values were used in the calcu-231

lation of C benefits:232

• Biomass accumulation rate for Year 1–15 at a High Scenario assumption of233

14.3 Mg ha−1 yr−1 and 5.6 Mg ha−1 yr−1 in Laclubar and Remexio, respec-234

tively. These are based on measurements of mature sample plots discussed235

in the previous section. For Laclubar, the rate was based on the allometric236

equation of Brown (1997). For Remexio, the equations of Brown (1997)237

and Ketterings et al. (2001) gave almost the same rate. These values are238

within the range of IPCC default values for tree plantations in the tropics239

(Houghton et al. 1997).240

• Biomass accumulation rates for Year 1–15 at a Low Scenario rate of241

8.8 Mg ha−1 yr−1 for Laclubar (based on the allometric equation by242

Ketterings et al. 2001). For Remexio, it was assumed based on expert’s243

judgment that the low rate is 2/3 of the high rate (3.8 Mg ha−1 yr−1) just244

like in Laclubar.245

• Biomass accumulation rate declines by 10% in Year 16–25 (based on246

Brown et al. 1986). This is of course an approximation since the growth247

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R.D. LASCO AND M.M. CARDINOZA

curve follows a logistic growth function. However, in the absence of a long 248

term monitoring data in East Timor, we used the global data set for forest 249

plantations to approximate the decline in growth rate after 15 years. This 250

method is similar to the default approach used by the Intergovernmental 251

Panel on Climate Change (IPCC) for national greenhouse gas inventories 252

(Houghton et al. 1997; IPCC 2003). 253For the agroforestry component the following assumptions were used: 254

• Biomass accumulation: 20% of reforestation at Years 1–5; 40% at Years 255

6–10; 70% at years 11–15, and 100% at Years 16–25. During years 1–5 the 256

fruit tree stand density is 20% of a reforested land, hence it was assumed that 257

biomass accumulation would be in the same order of magnitude. However, 258

as the fruit trees mature the canopy will eventually cover the whole are so 259

that from year 16 onwards, it was assumed that the biomass accumulation 260

would be the same as a reforested land. 261

• Includes overstorey trees only 262

Note that SOC is not included in this analysis. In the long term, a forest cover 263

tends to promote higher SOC. However, initially some soil C may be released in 264

grasslands as a result of cultivation during site preparation. In upland farms, the 265

integration of trees would most likely lead to increasing SOC over time (Polgase 266

et al. 2000; IPCC 2003; Silver et al. 2004). There is however little basis for predicting 267

the magnitude of this change. 268

2.4.2. Calculation of C Benefits 269

Change in C stocks over the time was calculated for each land development type 270

(reforestation and agroforestry). The net change in each development type was 271

aggregated to estimate the total C stocks for the whole project. The net C benefits 272

were determined by subtracting C stocks in the baseline scenario from those in the 273

project scenario. 274

Total C benefits = �(Cp − Cb)i

where Cp = net C increments with project for year i, Cb = net C increments at 275

baseline for year i. 276

Because of the absence of comprehensive data on the rate of C sequestration 277

of trees in East Timor, two scenarios were included in this analysis: a high C 278

sequestration scenario and a low C sequestration scenario. 279

3. Results and Discussion 280

3.1. BASELINE CARBON STOCKS 281

• Baseline carbon density in aboveground biomass (AGB) ranged from 5.6 to 282

13.8 Mg ha−1 which is but a tiny fraction of the total (Table III). This is because 283

most of the baseline vegetation was composed of grasses and annual crops. 284

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TABLE III

Mean biomass and C density under baseline conditions in the project site

Aboveground Biomass (AGB)(Mg ha−1)

Stratum Understorey Litter Total

Total AGBCarbon(Mg C ha−1)

SOC(MgC ha−1)

C Density(MgC ha−1) s.e.∗

Lac REFO 11.4 2.4 13.8 6.2 159.7 165.9 4.7

Lac AF 9.4 2.0 11.4 5.2 157.5 162.7 4.2

Rem REFO 2.8 3.9 6.7 3.0 60.1 63.1 2.6

Rem AF 2.1 3.5 5.6 2.5 70.7 73.2 2.4

∗s.e. = Standard error.

Total carbon density ranged from 63.1 to 165.9 Mg ha−1, most of it in the soils.285

The low standard error of the mean values suggest that the baseline carbon286

density is fairly similar across project areas.287

Our findings are consistent with similar ecosystems in Indonesia and the Philip-288

pines (Lasco 2002). As expected in grassland areas, most of the C stocks are in289

the soil. The low C density of existing land cover is due to decades of farming and290

grazing. As a result, plant succession cannot proceed and grasses and crops are291

perpetuated.292

Statistical analysis of results using the t-test (Table IV), revealed the following:293

• There are no significant differences in the C stocks within each site indicating294

minimal variability.295

• C stocks in aboveground biomass and soil were higher in Laclubar than in296

Remexio. This could be attributed to the favorable climatic conditions in the297

former, especially higher rainfall.298Overall, total baseline C stocks of the project sites amount to 222,000 Mg C,299

most of which are in the soil (Table V).300

3.2. BIOMASS AND C DENSITY OF AGROFORESTRY AND TREE FARMS301

The mean tree biomass densities of sample plots in Laclubar Sub-district were302

312 Mg ha−1 and 540 Mg ha−1 using the allometric equations by Ketterings et al.303

(2001) and Brown (1997), respectively (Table VI). For Remexio Sub-district, mean304

tree biomass densities were 279–256 Mg ha−1, respectively. The moderately high305

standard error of the mean indicates that there is a large variation in the biomass306

density of trees in East Timor. This could be due to the limited number of sample307

plots used in this study.308

Our findings are consistent with data obtained in other countries in the region.309

Field measurements in the Philippines showed that old tree plantations (>50 years)310

have biomass densities greater than 500 Mg ha−1 using the allometric equation of311

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TABLE IV

t-test (2-tailed; different variances) for aboveground, SOCand total carbon stocks in the project sites

Comparison Probability Resulta

AGB Lac: REFO vs. AF 0.28 ns

SOC Lac: REFO vs. AF 0.93 ns

ns

AGB Rem: REFO vs. AF 0.42 ns

SOC Rem: REFO vs. AF 0.43 ns

ns

Total Lac: REFO vs. AF 0.89 ns

Total Rem: REFO vs. AF 0.45 ns

REFO: Lac vs. Rem, AGB C <0.01 ∗∗

REFO: Lac vs. Rem, SOC <0.01 ∗∗

REFO: Lac vs. Rem,Total C <0.01–05 ∗∗

AF: Lac vs. Rem, AGB C <0.01 ∗∗

AF: Lac vs. Rem, SOC <0.01 ∗∗

AF: Lac vs. Rem, Total C <0.01 ∗∗

ans: not significant. ∗∗ Highly significant at 0.01.

TABLE V

Total baseline C stocks in aboveground biomass and the soil

Biomass density (Mg ha−1) Total carbon stock (Mg ha−1)

Site/component Area (ha) Understorey Litter AGB AGB SOC Total

Lac REFO 413 4,723 995 5,718 2,573 65,957 79,966

Lac AF 410 3,870 823 4,693 2,112 64,577 76,074

Rem REFO 413 1,149 1,620 2,769 1,246 24,801 31,586

Rem AF 410 875 1,424 2,299 1,035 28,996 34,628

TOTAL 1,646 10,617 4,862 15,479 6,966 184,330 222,254

Brown (1997) (Lasco and Pulhin 2003). These biomass densities are quite high, 312

almost approaching those of natural tropical forests in Southeast Asia (Lasco 2002). 313

For example, the IPCC estimate for biomass density in Philippine old-growth forests 314

is 370–520 Mg ha−1 (Houghton et al. 1997). In Indonesia, natural forests can have 315

biomass density as high as 700 Mg ha−1 (Hairiah and Sitompul 2000). These data 316

suggests that given time tree plantations could have biomass densities comparable 317

to natural forests. 318

For falcataria (a broadleaf species), the Brown equation gave consistently 319

higher biomass estimates than that of Ketterings. On the other hand, for casuarina 320

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CARBON SEQUESTRATION PROJECT IN EAST TIMOR

TABLE VI

Tree biomass and carbon density (Mg C ha−1) of sampling plots in Laclubar and Remexio

Tree biomass Carbon density(Mg ha−1) (Mg ha−1)

Ketterings Brown Ketterings Brown Ave.Plot No. et al. (2001) (1997) et al. (2001) (1997) age Species

LACLUBAR

1 455 1041 205 469 40 Pareserianthes falcataria

2 342 763 154 343 40 P. falcataria

3 302 700 136 315 35 P. falcataria

4 214 94 96 42 30 Casuarina equisetifolia

5 247 103 111 46 30 C. equisetifolia

Mean 312 540 140 243

s.e. 42 189 19 85

REMEXIO

1 205 475 92 214 45 P. falcataria

2 412 156 185 70 60 C. equisetifolia

3 219 136 99 61 40 C. equisetifolia

Mean 279 256 125 115

s.e. 103 160 46 72

(a conifer), the reverse is true. Overall, the equation by Ketterings gives lower321

estimates because most of the species are falcataria.322

Agroforestry and tree farms in Laclubar have a mean C density of 140 Mg C ha−1323

with a range of 96–205 Mg C ha−1. In Remexio, the mean C density is324

125 Mg C ha−1 which is lower . The degree of variability among the farms could325

be attributed to many factors such as variations in age, species composition, site326

conditions and management treatments.327

The data obtained in this study are generally higher than findings from tree328

plantations in other Southeast Asian countries (Lasco 2002). This could be attributed329

to the older age of plantations measured in this study compared to other studies330

in the region. In fact, plantations of comparable age in Bogor, Indonesia contain331

similar amounts of C (Lasco 2001). In addition, the C stocks in this study are lower332

than those of natural forests in the region, which is what one might expect. Thus,333

overall, the findings seem to be credible.334

3.3. PROJECTED C BENEFITS335

At the end of 25 years, the AGB C density of each project component will range336

from 28 to 147 Mg C ha−1 (Figure 1), much higher than the baseline C density337

of less than 7 Mg C ha−1. This is due to the integration of trees in farms and tree338

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R.D. LASCO AND M.M. CARDINOZA

Figure 1. Above-ground biomass C density of project components at the end of 25 years (HS = HighScenario; LS = Low Scenario).

Figure 2. Total net C sequestration of the project sites through time under high (HS) and low (LS)scenarios.

planting in open areas. The projected C density is still lower than the C density of 339

mature farms as presented in a previous section (Table VI), indicating that more 340

growth is possible. 341

As expected, the Laclubar site is projected to sequester more C than Remexio 342

owing to its favorable climatic condition (Figure 2). In terms of project components, 343

the reforestation component will sequester more C than agroforestry farms . This 344

is because there are more trees planted per unit area in the former. 345

Overall, the total C that will be sequestered by the project for 25 years will 346

be 137,671 Mg C and 84,621 Mg C under the high and low scenarios, respectively 347

(Figure 3). 348

The main source of error in the study is the biomass and carbon density estimate 349

of the reforestation and agroforestry areas. The baseline (grassland) carbon density 350

has a low standard error of the mean suggesting fairly homogenous carbon density 351

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CARBON SEQUESTRATION PROJECT IN EAST TIMOR

Figure 3. Total net C sequestered by each project component at the end of 25 years (HS = HighScenario; LS = Low Scenario).

across the various project sites. However, the relatively higher standard error of352

the mean of tree biomass in plantations indicates that there is wide variation in the353

growth of trees and carbon sequestration ability.354

Other possible sources of error that we did not quantify include measurement355

error and sampling error. It is possible that errors had been committed in measuring356

data in the field since this is the first time such as study had been conducted in East357

Timor. In addition, the sampling plots were limited in number and may not have358

been representative of all the project areas.359

4. Conclusions and Recommendations360

The CBNRM-ET project is a pioneering effort in East Timor. It is the very first361

C sequestration project ever implemented in the country. The foregoing analysis362

showed that the baseline condition in the project sites has a very low C density.363

Project development activities, specifically reforestation and agroforestry, will be364

able to enhance C density by up to 20 times the baseline C density. Overall, it is365

projected that the total C that will be sequestered by the project for 25 years will366

be 137,671 Mg C and 84,621 Mg C under the high and low scenarios, respectively.367

In the future, C stocks monitoring will be quite a challenge considering that368

the project will end in the year 2005 when the tree C is hardly measurable. It is369

recommended that the project explore how monitoring of C will be continued after370

the project ends.371

More importantly, the trees planted may be destroyed once the project staff leave372

the project sites for a variety of reasons. It must be ensured that local community373

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R.D. LASCO AND M.M. CARDINOZA

partners see the value of the trees planted and that they continue maintaining them. 374

Failure to do so will negate the C benefits projected in this study. 375

Acknowledgments 376

The authors gratefully acknowledge the assistance of CARE CBNRM project staff 377

in East Timor in data gathering. Funds for the CBNRM project are provided by the 378

Canadian International Development Agency (CIDA). We also express our great 379

appreciation to an anonymous reviewer whose comments significantly improved 380

the manuscript. However, any error and the views expressed in this paper are the 381

sole responsibility of the authors. 382

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