Revisions to Land Conversion Emission 

Factors since the RFS2 Final Rule

Nancy L. Harris

report

December 2011
2 

ACKNOWLEDGEMENTS

This work was carried out in collaboration with EPA’s 

Office of Transportation and Air Quality under the 

direction of Aaron Levy, who provided useful comments 

and guidance during all stages of the work.

Cite as: Harris, N.L. 2011. Revisions to Land Conversion 

Emission Factors since the RFS2 Final Rule. Report 

submitted to EPA.

ACKNOWLEDGEMENTS

work was carried out in collaboration with EPA’s 

Office of Transportation and Air Quality under the 

direction of Aaron Levy, who provided useful comments 

and guidance during all stages of the work.

Harris, N.L. 2011. Revisions to Land Conversion 

Emission Factors since the RFS2 Final Rule. Report 

submitted to EPA.
 

TABLE OF CONTENTS

1

1 INTRODUCTION................................

1.1 EPA’s Renewable Fuel Standard

1.2 Summary of Changes since RF

2 HARVESTED WOOD PRODUCTS................................

2.1 Summary ................................

2.2 Initial Assessment of carbon storage in HWP

2.3 Updated Assessment of carbon 

3 OIL PALM CARBON STOCKS................................

3.1 Summary ................................

3.2 Initial Assessment of carbon storage in oil palm plantations

3.3 Updated Assessment of carbon storage in oil palm plantations

4 PEAT ASSUMPTIONS................................

4.1 Summary ................................

4.2 Initial Assessment of Peat Emissions

4.3 Updated Assessment of Peat Emissions

4.3.1 Estimation of peat depth burned

4.3.2 Estimation of peat bulk density and carbon content

4.3.3 Methane emissions from fire

5 FOREST CARBON STOCKS................................

5.1 Summary ................................

5.2 Initial Assessment of Forest Carbon Stocks

5.3 Updated Assessment of Forest Carbon Stocks

6 REFERENCES................................................................

................................................................................................................................

EPA’s Renewable Fuel Standard................................................................................................

Summary of Changes since RFS2................................................................................................

................................................................................................

................................................................................................................................

Initial Assessment of carbon storage in HWP ................................................................

Updated Assessment of carbon storage in HWP ................................................................

................................................................................................

................................................................................................................................

3.2 Initial Assessment of carbon storage in oil palm plantations ................................

Updated Assessment of carbon storage in oil palm plantations ................................

.........................................................................................................................

................................................................................................................................

Initial Assessment of Peat Emissions..............................................................................................

Updated Assessment of Peat Emissions................................................................

4.3.1 Estimation of peat depth burned...................................................Error! Bookmark not defined.

4.3.2 Estimation of peat bulk density and carbon content.....................Error! Bookmark not defined.

4.3.3 Methane emissions from fire.........................................................Error! Bookmark not defined.

................................................................................................

................................................................................................................................

Initial Assessment of Forest Carbon Stocks................................................................

Updated Assessment of Forest Carbon Stocks................................................................

................................................................................................

 3

..................................4

....................................... 4

...................................... 4

...........................................6

.......................................... 6

.................................................. 6

............................................. 7

............................................... 10

........................................ 10

......................................................... 10

................................................... 10

......................... 14

........................................ 14

.............................. 14

......................................................... 14

Error! Bookmark not defined.14

Error! Bookmark not defined.15

Error! Bookmark not defined.15

.................................................. 16

........................................ 16

.................................................... 16

............................................ 16

..................................... 17
4 

1.1 EPA’s Renewable Fuel Standard

The Renewable Fuel Standard (RFS) program was created under the Energy Policy Act (EPAct) of 2005 

and established the first renewable

changes to the RFS program as required by the Energy Indepe

and lays the foundation for achieving significant reductions of greenhouse gas emissions from the use of 

renewable fuels, for reducing imported petroleum, and encouraging the development and expansion of 

the U.S. renewable fuels sector.

Biofuel policies use the term “land use change effect” to refer to the sum of direct and indirect land use 

impacts of biofuel production. Greenhouse gas (GHG) emissions that occur when converting land are 

often considered the main impact, but other impacts include those on biodiversity, water availability, 

water and soil quality, and social impacts.

The US Environmental Protection Agency (US EPA) commissioned Winrock International to estimate 

greenhouse gas (GHG) emission and rever

indirect land use change analysis for various fuel options. EPA is currently in the process of updating the 

assumptions and data used in its analysis. T

the final RFS2 rule was released.

1.2 Summary of Changes since RFS2

After receiving several comments related to various aspects of our emission factor analysis

stakeholders and reviewers, we explored

assumptions:

1. Harvested Wood Products

harvested wood products after 30 years for 

concluded that the impact on emission factors continues to be 

1 INTRODUCTION

EPA’s Renewable Fuel Standard

Renewable Fuel Standard (RFS) program was created under the Energy Policy Act (EPAct) of 2005 

and established the first renewable fuel volume mandate in the United States. The RFS2

changes to the RFS program as required by the Energy Independence and Security Act of 2007 (EISA), 

lays the foundation for achieving significant reductions of greenhouse gas emissions from the use of 

renewable fuels, for reducing imported petroleum, and encouraging the development and expansion of 

Biofuel policies use the term “land use change effect” to refer to the sum of direct and indirect land use 

impacts of biofuel production. Greenhouse gas (GHG) emissions that occur when converting land are 

impact, but other impacts include those on biodiversity, water availability, 

water and soil quality, and social impacts.

The US Environmental Protection Agency (US EPA) commissioned Winrock International to estimate 

emission and reversion factors for various land use transitions as part of its 

indirect land use change analysis for various fuel options. EPA is currently in the process of updating the 

s and data used in its analysis. This report summarizes the main changes to t

Summary of Changes since RFS2

comments related to various aspects of our emission factor analysis

explored the following topics as part of a re-assessment of our 

Harvested Wood Products. We tested an alternate method for estimating 

harvested wood products after 30 years for the countries of Indonesia and Malaysia, and

that the impact on emission factors continues to be very small.

INTRODUCTION

Renewable Fuel Standard (RFS) program was created under the Energy Policy Act (EPAct) of 2005 

RFS2 final rule made 

ndence and Security Act of 2007 (EISA), 

lays the foundation for achieving significant reductions of greenhouse gas emissions from the use of 

renewable fuels, for reducing imported petroleum, and encouraging the development and expansion of 

Biofuel policies use the term “land use change effect” to refer to the sum of direct and indirect land use 

impacts of biofuel production. Greenhouse gas (GHG) emissions that occur when converting land are 

impact, but other impacts include those on biodiversity, water availability, 

The US Environmental Protection Agency (US EPA) commissioned Winrock International to estimate 

sion factors for various land use transitions as part of its 

indirect land use change analysis for various fuel options. EPA is currently in the process of updating the 

his report summarizes the main changes to the analysis since 

comments related to various aspects of our emission factor analysis from 

assessment of our 

method for estimating carbon stored in 

the countries of Indonesia and Malaysia, and
 

2. Revised carbon stock estimates

approach for estimating carbon stocks on oil palm plantations was changed from assuming one 

year of growth to using a time

3. Revised estimates of GHG emissions on peat soils

from peat drainage based on new literature.

4. Revised carbon stock estimates for forests thr

for Latin America and Africa were estimated in the final RFS2 rule using draft values 

from Saatchi et al. (2011). This work has now been published and covers

continental regions: Latin America, Africa and South and Southeast Asi

carbon stock values were included in our revisions.

were also updated based on 

developed by Gallaun et al. (2010

Revised carbon stock estimates on oil palm plantations in Indonesia and Malaysia

approach for estimating carbon stocks on oil palm plantations was changed from assuming one 

h to using a time-averaged approach. 

GHG emissions on peat soils. We updated our estimates of emissions 

from peat drainage based on new literature.

arbon stock estimates for forests throughout the tropics. Forest carbon stocks 

for Latin America and Africa were estimated in the final RFS2 rule using draft values 

from Saatchi et al. (2011). This work has now been published and covers

Latin America, Africa and South and Southeast Asia. Final published 

carbon stock values were included in our revisions. Forest carbon stocks for the EU 

were also updated based on EU-wide maps of above-ground biomass in forests

Gallaun et al. (2010).

 5

and Malaysia. The 

approach for estimating carbon stocks on oil palm plantations was changed from assuming one 

We updated our estimates of emissions 

Forest carbon stocks 

for Latin America and Africa were estimated in the final RFS2 rule using draft values 

from Saatchi et al. (2011). This work has now been published and covers three 

a. Final published 

Forest carbon stocks for the EU 

ground biomass in forests
6 

2.1 Summary

After re-assessing our analysis of carbon stored in harvested wood products after 30 years, we conclude 

that the impact of harvested wood products on

2.2 Initial Assessment of carbon 

For the final RFS2 rule, we addressed the potential significance of the harvested wood product pool and 

concluded that for most regions of the world, the amount of carbon stored in wood products long

was insignificant, especially if considering a ti

EPA’s lifecycle analysis). Therefore, carbon storage in harvested wood products was not incorporated 

into emission factors.

We originally reached this conclusion based on data presente

FAO forest products database (FAO 1995) 

source-sink balance. This database, spanning more than 30 yr (1961

recognized source of forest products data.

Based on the Winjum et al. methodology, t

(>5 yr) wood products was calculated by dividing the reported carbon in net production of industrial 

roundwood by the reported total carbon in commodity uses 

sawnwood, other industrial roundwood, woodbase panels and paper and paperboard. Of the eight 

countries highlighted by Winjum et al., this proportion ranged from 13% for Finland to 67% fo

Across the developing world, the proportion in long

Due to initial time constraints associated with the finalization of RFS2, we analyzed only the case study of 

Indonesia to assess the magnitude of carbon stored in H

had the highest reported timber extraction rate (50 m

would have the highest amount of carbon potentially stored in wood products that would influence the 

emission factor. Based on data reported in Winjum et al. (1998), 25% of extracted carbon ends up in 

long-lived (≥5 yrs) wood products. Using an assumed extraction rate of 50 m

2

assessing our analysis of carbon stored in harvested wood products after 30 years, we conclude 

harvested wood products on emission factors continues to be very small

Initial Assessment of carbon storage in HWP

For the final RFS2 rule, we addressed the potential significance of the harvested wood product pool and 

concluded that for most regions of the world, the amount of carbon stored in wood products long

considering a timeframe of 30 years (corresponding to the timeframe of 

EPA’s lifecycle analysis). Therefore, carbon storage in harvested wood products was not incorporated 

reached this conclusion based on data presented in Winjum et al. (1998), who used the 

FAO forest products database (FAO 1995) to estimate the influence of wood harvesting on a nation’s C 

sink balance. This database, spanning more than 30 yr (1961-1993), is an internationally 

f forest products data.

Based on the Winjum et al. methodology, the proportion of extracted timber that ends up in long

(>5 yr) wood products was calculated by dividing the reported carbon in net production of industrial 

tal carbon in commodity uses ≥5 yr for each of four product classes: 

sawnwood, other industrial roundwood, woodbase panels and paper and paperboard. Of the eight 

countries highlighted by Winjum et al., this proportion ranged from 13% for Finland to 67% fo

Across the developing world, the proportion in long-lived wood products was 53%. 

Due to initial time constraints associated with the finalization of RFS2, we analyzed only the case study of 

Indonesia to assess the magnitude of carbon stored in HWP. We chose Indonesia because this country 

had the highest reported timber extraction rate (50 m3ha-1) of the countries we reviewed and therefore 

would have the highest amount of carbon potentially stored in wood products that would influence the 

n factor. Based on data reported in Winjum et al. (1998), 25% of extracted carbon ends up in 

≥5 yrs) wood products. Using an assumed extraction rate of 50 m3ha-1for Indonesia (13.75 t 

2 HARVESTED WOOD PRODU

assessing our analysis of carbon stored in harvested wood products after 30 years, we conclude 

very small.

For the final RFS2 rule, we addressed the potential significance of the harvested wood product pool and 

concluded that for most regions of the world, the amount of carbon stored in wood products long-term 

responding to the timeframe of 

EPA’s lifecycle analysis). Therefore, carbon storage in harvested wood products was not incorporated 

d in Winjum et al. (1998), who used the 

to estimate the influence of wood harvesting on a nation’s C 

1993), is an internationally 

he proportion of extracted timber that ends up in long-lived 

(>5 yr) wood products was calculated by dividing the reported carbon in net production of industrial 

≥5 yr for each of four product classes: 

sawnwood, other industrial roundwood, woodbase panels and paper and paperboard. Of the eight 

countries highlighted by Winjum et al., this proportion ranged from 13% for Finland to 67% for India. 

Due to initial time constraints associated with the finalization of RFS2, we analyzed only the case study of 

WP. We chose Indonesia because this country 

) of the countries we reviewed and therefore 

would have the highest amount of carbon potentially stored in wood products that would influence the 

n factor. Based on data reported in Winjum et al. (1998), 25% of extracted carbon ends up in 

for Indonesia (13.75 t 

HARVESTED WOOD PRODUCTS
 

C ha-1), and assuming 25% of this is stored in HWP, the e

reduced by 11 t CO2 ha-1, or approximately 1

Furthermore, based on Winjum et al.’s analysis, we estimated the additional emissions that emanate 

from the oxidation (i.e. burning and decay) of 

year 30 (i.e., annual retirement rate of wood products)

additional oxidized fraction, the Indonesia value of carbon stored in wood products 

decreased further to 7 t CO2 ha-1.

Our initial approach used only the compiled summary tables for individual countries reported in Winjum 

et al. (1998); we did not attempt to re

how the values in the summary tables were calculated.

2.3 Updated Assessment of carbon storage in HWP

Our updated analysis uses basically 

analysis to be more comprehensive by including HW

the EPA analysis for which we had ready access to

study of Indonesia). Also, the Winjum et al. (1998) methodology and equations are explained in detail i

the approved Verified Carbon Standard (VCS) methodology module VMD0005: Estimation of carbon 

stocks in the long-term wood products pool (CP

in our updated assessment to provide better documentation on 

The carbon in each wood product class

paper and paperboard) can end up in one of four categories: 

(1) lost immediately to wood waste during production

(2) emitted to the atmosphere wit

(3) emitted to the atmosphere between 5 and 30 years after production; or 

(4) long-term wood products pool (stock remaining i

Carbon in long-term wood products after 30 years (timeframe 

[C in HWP] = [total C extracted] – [wood waste] 

Timber extraction rates (volume extracted; m

sources, ranging from in-country consultant data,

neighboring countries when FAO data failed to give realistic estimates.

volumes of each wood product class were obtained from FAO 

available at the time of analysis) and converted to carbon using biomass conversion factors from Winjum 

et al. (1998) (Table 1) and a carbon conversion factor of 0.5. The paper and paperboard class is repor

by FAO in units of biomass, therefore no biomass conversion factor was needed. After all product classes 

 

1

http://www.v-c-s.org/methodologies/find

), and assuming 25% of this is stored in HWP, the emission factor for forest conversion would be 

, or approximately 1-2% of the emission factor value. 

Furthermore, based on Winjum et al.’s analysis, we estimated the additional emissions that emanate 

g and decay) of in-use wood commodities each year between year 5 and 

year 30 (i.e., annual retirement rate of wood products). After accounting for emissions

, the Indonesia value of carbon stored in wood products after

Our initial approach used only the compiled summary tables for individual countries reported in Winjum 

et al. (1998); we did not attempt to re-create their methodology or re-do any of the steps involved in 

the values in the summary tables were calculated.

Updated Assessment of carbon storage in HWP

basically the same approach as Winjum et al., but we were able to revise our 

analysis to be more comprehensive by including HWP estimates for 95 of the 135 countries included in 

which we had ready access to timber extraction rates (rather than just the case 

study of Indonesia). Also, the Winjum et al. (1998) methodology and equations are explained in detail i

Carbon Standard (VCS) methodology module VMD0005: Estimation of carbon 

term wood products pool (CP-W), Sectoral Scope 141. We followed this methodology 

in our updated assessment to provide better documentation on how our estimates were derived.

product class (sawnwood, woodbase panels, other industrial roundwood, 

can end up in one of four categories: 

ood waste during production; 

atmosphere within five years of production; 

emitted to the atmosphere between 5 and 30 years after production; or 

term wood products pool (stock remaining in wood products after 30 years )

term wood products after 30 years (timeframe of EPA analysis) is therefore estimated 

[wood waste] - [short-lived products] – [additional oxidized fraction]

extracted; m3ha-1) were estimated based on a number of different data 

country consultant data, FAO data tables, or assumed to be the same as other 

neighboring countries when FAO data failed to give realistic estimates. In most cases, p

product class were obtained from FAO databases for the year 2008

and converted to carbon using biomass conversion factors from Winjum 

and a carbon conversion factor of 0.5. The paper and paperboard class is repor

O in units of biomass, therefore no biomass conversion factor was needed. After all product classes 

s.org/methodologies/find

 7

mission factor for forest conversion would be 

Furthermore, based on Winjum et al.’s analysis, we estimated the additional emissions that emanate 

use wood commodities each year between year 5 and 

from this 

after 30 years 

Our initial approach used only the compiled summary tables for individual countries reported in Winjum 

do any of the steps involved in 

, but we were able to revise our 

P estimates for 95 of the 135 countries included in 

timber extraction rates (rather than just the case 

study of Indonesia). Also, the Winjum et al. (1998) methodology and equations are explained in detail in 

Carbon Standard (VCS) methodology module VMD0005: Estimation of carbon 

. We followed this methodology 

how our estimates were derived.

(sawnwood, woodbase panels, other industrial roundwood, 

of EPA analysis) is therefore estimated as

[additional oxidized fraction].

) were estimated based on a number of different data 

, or assumed to be the same as other 

In most cases, production 

databases for the year 2008 (latest year 

and converted to carbon using biomass conversion factors from Winjum 

and a carbon conversion factor of 0.5. The paper and paperboard class is reported 

O in units of biomass, therefore no biomass conversion factor was needed. After all product classes 
8 

were represented in a consistent unit (carbon), proportions of the total 

class could be derived for each country

Table 1. Biomass conversion factors (Mg m

roundwood and commodity biomass from volumes reported in m

The fraction of extracted biomass tha

constant 19% for developed countries and 24% for developing countries (Winjum et al. 1998). 

The fraction of wood products with 

estimated by product class (Winjum et al. 1998):

 Sawnwood

 Woodbase panels

 Other industrial roundwood

 Paper and Paperboard

The additional oxidized fraction of wood products that will be emitted to the atmosphere between 

30 years after production is estimated by product class 

harvested (Winjum et al. 1998):

 Sawnwood

 Woodbase panels

 Other industrial roundwood

 Paper and Paperboard

After accounting for wood waste, carbon in short

fraction, the remainder is assumed to be the carbon stored in harvested wood products after 30 years. 

Using this methodology for Indonesia, with an assumed extraction rate of 50 m

carbon in HWP was estimated as 1.93 t C ha

were represented in a consistent unit (carbon), proportions of the total extracted carbon in each product 

h country.

. Biomass conversion factors (Mg m-3 green volume) by region and species group used to compute 

roundwood and commodity biomass from volumes reported in m3 by region. From Winjum et al. (1998).

The fraction of extracted biomass that ends up as wood waste during production is estimated as a 

constant 19% for developed countries and 24% for developing countries (Winjum et al. 1998). 

The fraction of wood products with short-term (<5 yr) uses after which they are retired and oxidized

estimated by product class (Winjum et al. 1998):

0.2

0.1

0.3

0.4

fraction of wood products that will be emitted to the atmosphere between 

is estimated by product class according to the biome where the wood was 

Boreal Temperate Tropical

0.12 0.22 0.4

0.22 0.40 0.64

0.40 0.64 0.88

0.12 0.22 0.93

After accounting for wood waste, carbon in short-lived products and carbon in the additional oxidized 

fraction, the remainder is assumed to be the carbon stored in harvested wood products after 30 years. 

ethodology for Indonesia, with an assumed extraction rate of 50 m3ha-1as before, the 

carbon in HWP was estimated as 1.93 t C ha-1, or 7 t CO2 ha-1. This is the same value as was estimated 

carbon in each product 

green volume) by region and species group used to compute 

by region. From Winjum et al. (1998).

during production is estimated as a 

constant 19% for developed countries and 24% for developing countries (Winjum et al. 1998). 

after which they are retired and oxidized is 

fraction of wood products that will be emitted to the atmosphere between 5 and 

according to the biome where the wood was 

lived products and carbon in the additional oxidized 

fraction, the remainder is assumed to be the carbon stored in harvested wood products after 30 years. 

as before, the 

. This is the same value as was estimated 
 

previously because the approach is the same (but now explained in 

calculated carbon stored in HWP for other countries using this approach. The maximum amount of 

carbon stored in HWP was 11 t CO2

CO2 ha-1for more than 70% of countries analyzed (

for most tropical developing countries are so low.

Table 2. Reductions in country-level emission factors after accounting for carbon stored in harvested wo

products longer than 30 years. 

Reduction in Emission Factor (t 

CO2 ha-1)

>10

5 – 10

4 – 5

3 – 4

2 – 3

1 – 2

< 1

Total Countries Analyzed

previously because the approach is the same (but now explained in more detail). However, we also 

calculated carbon stored in HWP for other countries using this approach. The maximum amount of 

ha-1for Malaysia, and the emission factor changed by less than 1 t 

f countries analyzed (Table 2), mainly because per-hectare extraction rates 

tropical developing countries are so low.

level emission factors after accounting for carbon stored in harvested wo

# Countries Countries

1 Malaysia

4 India, China, Indonesia, South Africa

1 Guatemala

3 Brazil, Belize, Nepal

8 Ghana, Cote d’Ivoire, Ecuador, Russia, 

Bangladesh, Honduras, Costa Rica, Gabon

10 Angola, Haiti, Guyana, Madagascar, Myanmar, 

Egypt, Philippines, Cameroon, Republic of 

Congo, Pakistan

68

95

 9

more detail). However, we also 

calculated carbon stored in HWP for other countries using this approach. The maximum amount of 

for Malaysia, and the emission factor changed by less than 1 t 

ctare extraction rates 

level emission factors after accounting for carbon stored in harvested wood 

India, China, Indonesia, South Africa

Ecuador, Russia, 

Bangladesh, Honduras, Costa Rica, Gabon

Angola, Haiti, Guyana, Madagascar, Myanmar, 

Egypt, Philippines, Cameroon, Republic of 
1

0

 

3.1 Summary

We increased our estimate of carbon stocks on oil palm plantations in Indonesia and Malaysia from 15 t 

CO2 ha-1to 128 t CO2 ha-1. This resulted from 

plantations from only one year of carbon accumulation

3.2 Initial Assessment of carbon storage in 

In the final RFS2 rule, conversion to perennial cropland was assumed to be sugarcane, except in the 

cases of Indonesia and Malaysia in which case the 

in oil palm plantations were estimated as the stocks present after one year of growth at 

based on Table 5.3 of the 2006 IPCC Guidelines for AFOLU, wh

plantations as 136 t biomass per hectare, or 

carbon stock value was divided by an assumed 15 year growth period to derive a linear growth rate of 4 t 

C ha-1yr-1(15 t CO2 ha-1yr-1).

3.3 Updated Assessment of carbon storage in oil palm plantations

Several comments were received on our estimates 

provide additional documentation to justi

palm, a request to change to a time

plantations, and a suggestion to include the soil organic carbon pool in our estimates of palm oil 

conversion based on the argument that 

carbon than the baseline land use (Imperata

3.3.1 Further Support for IPCC Carbon Stock Estimate for Oil Palm 

We compiled additional information to subs

plantations given in the 2006 IPCC Guidelines

were compiled by Niklas and Enquist (2004). 

3

our estimate of carbon stocks on oil palm plantations in Indonesia and Malaysia from 15 t 

This resulted from changing our estimate of carbon stocks on oil palm 

only one year of carbon accumulation to using a time-averaged approach

3.2 Initial Assessment of carbon storage in oil palm plantations

inal RFS2 rule, conversion to perennial cropland was assumed to be sugarcane, except in the 

ndonesia and Malaysia in which case the assumption was conversion to oil palm. 

were estimated as the stocks present after one year of growth at 

based on Table 5.3 of the 2006 IPCC Guidelines for AFOLU, which gives biomass stocks on oil palm 

biomass per hectare, or 68 t C ha-1assuming 1 t biomass = 0.5 t carbon

carbon stock value was divided by an assumed 15 year growth period to derive a linear growth rate of 4 t 

Updated Assessment of carbon storage in oil palm plantations

Several comments were received on our estimates of oil palm carbon stocks, including a request to 

to justify the use of the IPCC default carbon stock estimate for oil 

nge to a time-averaged approach to estimating carbon stocks on oil palm 

and a suggestion to include the soil organic carbon pool in our estimates of palm oil 

ument that palm oil plantations sequester higher amounts of soil organic 

Imperata grassland or primary/secondary forest).

IPCC Carbon Stock Estimate for Oil Palm 

information to substantiate the default estimate of carbon stocks in oil palm 

plantations given in the 2006 IPCC Guidelines. Biomass values for seeded plants throughout the world 

were compiled by Niklas and Enquist (2004). For oil palm (Elaeis guineensis), data were cited from 

3 OIL PALM CARBON STOCKS

our estimate of carbon stocks on oil palm plantations in Indonesia and Malaysia from 15 t 

tocks on oil palm 

averaged approach.

oil palm plantations

inal RFS2 rule, conversion to perennial cropland was assumed to be sugarcane, except in the 

oil palm. Carbon stocks 

were estimated as the stocks present after one year of growth at 15 t CO2 ha-1

ich gives biomass stocks on oil palm 

assuming 1 t biomass = 0.5 t carbon. The total 

carbon stock value was divided by an assumed 15 year growth period to derive a linear growth rate of 4 t 

Updated Assessment of carbon storage in oil palm plantations

of oil palm carbon stocks, including a request to 

fault carbon stock estimate for oil 

averaged approach to estimating carbon stocks on oil palm 

and a suggestion to include the soil organic carbon pool in our estimates of palm oil 

palm oil plantations sequester higher amounts of soil organic 

tantiate the default estimate of carbon stocks in oil palm 

. Biomass values for seeded plants throughout the world 

data were cited from 

STOCKS
 

Cannell (1982) but reported originally in Ng et al. (1968). Aboveground biomass values were measured in 

high-yielding, fertilized stands of palm oil plantations of various ages in Malaysia. Plantations were 

growing on marine clay with fine sandy loams. One or two palms were sampled from stands of various 

ages and stand values were obtained by multiplying mean palm values by the numbers of palms per 

hectare (148 stems/ha). Data from this study are summarized in Figure 1. The data show t

stocks of oil palm plantations increase through year 15, which was the oldest plantation in the dataset. 

We fitted a Chapman-Richards logistic growth curve to these data, and carbon stocks at age 15 are 

approximately 66 t C/ha, a value which is 

Other studies report slightly higher or lower estimates

estimates aboveground biomass for a 30 year palm oil planting as 

C/ha belowground, for a total carbon stock of 84.6 t C/ha. 

aboveground carbon stocks as approximately 40 t C/ha, for a 25 year oil palm planting

Kalimantan. Assuming a belowground biomass ratio of 33%, t

estimate of approximately 53 t C/ha.

Figure 1. Biomass carbon accumulation in palm oil plantations in Malaysia. Data originally from Ng et al. (1968).

3.3.2 Switching to a Time-Averaged 

Cannell (1982) but reported originally in Ng et al. (1968). Aboveground biomass values were measured in 

yielding, fertilized stands of palm oil plantations of various ages in Malaysia. Plantations were 

fine sandy loams. One or two palms were sampled from stands of various 

ages and stand values were obtained by multiplying mean palm values by the numbers of palms per 

Data from this study are summarized in Figure 1. The data show t

stocks of oil palm plantations increase through year 15, which was the oldest plantation in the dataset. 

Richards logistic growth curve to these data, and carbon stocks at age 15 are 

approximately 66 t C/ha, a value which is almost identical to the IPCC Tier 1 estimated value of 68 t C/ha. 

Other studies report slightly higher or lower estimates , such as that by Syahranudin (2005) 

estimates aboveground biomass for a 30 year palm oil planting as 62.8 t C/ha, with an additio

nd, for a total carbon stock of 84.6 t C/ha. Jiwan and Saharjo (2009) estimate 

approximately 40 t C/ha, for a 25 year oil palm planting

Assuming a belowground biomass ratio of 33%, this translates into a total carbon stock 

estimate of approximately 53 t C/ha.

. Biomass carbon accumulation in palm oil plantations in Malaysia. Data originally from Ng et al. (1968).

Averaged Approach

 1

1

Cannell (1982) but reported originally in Ng et al. (1968). Aboveground biomass values were measured in 

yielding, fertilized stands of palm oil plantations of various ages in Malaysia. Plantations were 

fine sandy loams. One or two palms were sampled from stands of various 

ages and stand values were obtained by multiplying mean palm values by the numbers of palms per 

Data from this study are summarized in Figure 1. The data show that carbon 

stocks of oil palm plantations increase through year 15, which was the oldest plantation in the dataset. 

Richards logistic growth curve to these data, and carbon stocks at age 15 are 

1 estimated value of 68 t C/ha. 

Syahranudin (2005) who 

, with an additional 21.8 t 

estimate 

in East 

his translates into a total carbon stock 

. Biomass carbon accumulation in palm oil plantations in Malaysia. Data originally from Ng et al. (1968).
1

2

 

Our original analysis accounted for only one year of growth when estimating carbon storage on palm oil 

plantations. We revised this approach to use a time

whereby average carbon stocks over the life o

Based on a review of the literature,

averaged value of 35 t C ha-1, or 128 t CO

t C ha-1is achieved by approximately Year 8

Germer and Sauerborn (2007): 35 t C/ha

In Figure 2, taken from Germer and Sauerborn (2007), biomass data from 51 oil palm fields were plotted. 

Integration of the fitted (nonlinear) 

aboveground biomass of 60 +/- 20 tonnes biomass

deviation is an estimation taking the large variation of the plotted data into acc

Sauerborn calculated a time-averaged oil palm root biomass of 20 +/

total ground cover biomass of 2.5 +/

maximum of 10 tonnes/ha and a linear biomass loss through 

closure at 5 years after planting). This yields an estimate of total above

82.5 +/- 26 t/ha. Considering an assumed carbon content of 40.4% for oil palm biomass (Syahrinudin 

2005) and of 50% for remaining vegetation, both together fix a time averaged quantity of 

C/ha within the economic lifespan of oil palm.

Figure 2. Aboveground oil palm biomass 

horizontal line indicates the time-averaged AGB as obtained by integration of the potential curve; the dashed 

horizontal line represents the time-averaged biomass of the linear curve. From Germer and Sauerborn (2007).

Our original analysis accounted for only one year of growth when estimating carbon storage on palm oil 

plantations. We revised this approach to use a time-averaged approach to carbon stock estimation, 

whereby average carbon stocks over the life of the plantation were used as the carbon stock value. 

of the literature, we modified the carbon stocks of palm oil plantations to a time

, or 128 t CO2 ha-1. This estimate is also in line with Figure 1 above, 

chieved by approximately Year 8. 

: 35 t C/ha

, taken from Germer and Sauerborn (2007), biomass data from 51 oil palm fields were plotted. 

(nonlinear) equation shown in Figure 2 returns a time averaged oil palm 

tonnes biomass/ha for 25 years after planting, where the standard 

deviation is an estimation taking the large variation of the plotted data into account. Germer and 

averaged oil palm root biomass of 20 +/- 5 tonnes/ha and a time

total ground cover biomass of 2.5 +/- 1.0 tonnes/ha (assuming a fast ground cover establishment with a 

maximum of 10 tonnes/ha and a linear biomass loss through increased shading to 1 tonne/ha at canopy 

This yields an estimate of total above- and belowground biomass of 

26 t/ha. Considering an assumed carbon content of 40.4% for oil palm biomass (Syahrinudin 

50% for remaining vegetation, both together fix a time averaged quantity of 

within the economic lifespan of oil palm.

Aboveground oil palm biomass – potential vs. linear estimation of time-averaged biomass. The thin solid 

averaged AGB as obtained by integration of the potential curve; the dashed 

averaged biomass of the linear curve. From Germer and Sauerborn (2007).

Our original analysis accounted for only one year of growth when estimating carbon storage on palm oil 

averaged approach to carbon stock estimation, 

f the plantation were used as the carbon stock value. 

we modified the carbon stocks of palm oil plantations to a time-

. This estimate is also in line with Figure 1 above, where 35 

, taken from Germer and Sauerborn (2007), biomass data from 51 oil palm fields were plotted. 

returns a time averaged oil palm 

/ha for 25 years after planting, where the standard 

ount. Germer and 

5 tonnes/ha and a time-averaged 

1.0 tonnes/ha (assuming a fast ground cover establishment with a 

increased shading to 1 tonne/ha at canopy 

and belowground biomass of 

26 t/ha. Considering an assumed carbon content of 40.4% for oil palm biomass (Syahrinudin 

50% for remaining vegetation, both together fix a time averaged quantity of 35.3 +/- 11.0 t 

d biomass. The thin solid 

averaged AGB as obtained by integration of the potential curve; the dashed 

averaged biomass of the linear curve. From Germer and Sauerborn (2007).
 

Henson (2007): 35.4 t C/ha

Henson (2007) has also quantified carbon sequestration of 

sequestration of an oil palm stand with a 25 year life was found to 

1

), with the oil palms accounting for 80% of the mean carbon storage by the system (and ground cover, 

litter, and palm products accounting

accumulated by the palms was 44 tonnes C/ha

stock over 30 years was calculated to be 

C/ha for oil palm and understorey determined

Morel (2009): 24.2 t C/ha (aboveground only)

Morel (2009), on the basis of field measurements at oil palm plantings in Sabah, calculated a weigh

average value of 24.2 t C/ha for aboveground biomass, assuming a 25 year planting period.

assumed belowground to aboveground biomass ratio of 0.

biomass ratio of 0.042 (from Germer and Sauerborn estimate

approximately 33 t C/ha. 

has also quantified carbon sequestration of oil palms in Malaysia. The mean 

sequestration of an oil palm stand with a 25 year life was found to be 2.09 t C ha-1yr-1(

accounting for 80% of the mean carbon storage by the system (and ground cover, 

litter, and palm products accounting for the remaining 20%). At the end of the cycle, total carbon 

44 tonnes C/ha and the.time-averaged in situ standing plantation carbon 

stock over 30 years was calculated to be 35.4 t C/ha, which is similar to the 25 year mean value of 35.3 t 

/ha for oil palm and understorey determined by Germer and Sauerborn (2007).

(aboveground only)

Morel (2009), on the basis of field measurements at oil palm plantings in Sabah, calculated a weigh

for aboveground biomass, assuming a 25 year planting period.

assumed belowground to aboveground biomass ratio of 0.33 and an understorey to aboveground 

(from Germer and Sauerborn estimates above), total estimated biomass would be 

 1

3

oil palms in Malaysia. The mean carbon 

(7.66 t CO2 ha-1yraccounting for 80% of the mean carbon storage by the system (and ground cover, 

otal carbon 

ding plantation carbon 

mean value of 35.3 t 

Morel (2009), on the basis of field measurements at oil palm plantings in Sabah, calculated a weighted 

for aboveground biomass, assuming a 25 year planting period. Using an 

and an understorey to aboveground 

d biomass would be 
1

4

 

4.1 Summary

In our revised assessment, new literature was used to estimate peat emissions from drainage

considered including GHG emissions from 

factor analysis, but decided to continue to exclude them d

Emissions from peat burning will be an area of continued research.

burning vegetation continue to be included in the emission factor analysis.

4.2 Initial Assessment of Peat Emissions

In addition to changes in aboveground

in GHG emissions from soil. These emissions are particularly important to include when land is cleared 

on peat soils due to their high organic matter c

clearing land for oil palm plantations on peat 

to a depth of 80 cm, which resulted in emissions of 73 t CO

relationship from Couwenberg et al. (2009) where every cm of peat drainage resulted in emissions of 

0.91 t CO2 ha-1yr-1. An assumed drainage depth of 80 cm therefore resulted in emissions of 0.9

t CO2 ha-1yr-1.

4.3 Updated Assessment of Peat Emissions

Estimates of emissions from peat drainage were revised based 

2011). Table 2, derived from Hooijer et al. (in press), presents estimates of a

different time periods, taking into account the nonlinear relationship between CO

since drainage. (Emissions are highest directly after conversion and then decrease through time.) Based 

on these new data, emissions from peat drainage over 30 years at a t

are estimated as 95 t CO2 ha-1yr-1, with an uncertainty 

maximum values of 54 to 115 t CO2

review on the state of the science of emissions from drained peatlands, with measurements included 

from both closed chamber flux studies as well as peat subsidence monitoring studies. 

ew literature was used to estimate peat emissions from drainage

GHG emissions from burning a portion of the peat layer into our updated emission 

factor analysis, but decided to continue to exclude them due to uncertainty in methods and data

Emissions from peat burning will be an area of continued research. GHG emissions associated with 

burning vegetation continue to be included in the emission factor analysis.

Initial Assessment of Peat Emissions

aboveground carbon stocks, emissions from land clearing also include increases 

in GHG emissions from soil. These emissions are particularly important to include when land is cleared 

on peat soils due to their high organic matter content. For the final RFS2 rule, GHG emissions from 

clearing land for oil palm plantations on peat originally included emissions associated with peat dra

, which resulted in emissions of 73 t CO2 ha-1yr-1based on a CO2 vs. drainage depth 

et al. (2009) where every cm of peat drainage resulted in emissions of 

. An assumed drainage depth of 80 cm therefore resulted in emissions of 0.9

d Assessment of Peat Emissions

Estimates of emissions from peat drainage were revised based on a new literature review

. Table 2, derived from Hooijer et al. (in press), presents estimates of annualized emissions over 

, taking into account the nonlinear relationship between CO2 emissions vs. time 

(Emissions are highest directly after conversion and then decrease through time.) Based 

missions from peat drainage over 30 years at a typical drainage depth of 60

, with an uncertainty range represented by the minimum and 

ha-1yr-1. This estimate is the result of a comprehensive literature 

review on the state of the science of emissions from drained peatlands, with measurements included 

studies as well as peat subsidence monitoring studies. 

4 PEAT ASSUMPTIONS

ew literature was used to estimate peat emissions from drainage. We 

into our updated emission 

ue to uncertainty in methods and data.

GHG emissions associated with 

carbon stocks, emissions from land clearing also include increases 

in GHG emissions from soil. These emissions are particularly important to include when land is cleared 

ontent. For the final RFS2 rule, GHG emissions from 

included emissions associated with peat drainage

vs. drainage depth 

et al. (2009) where every cm of peat drainage resulted in emissions of 

. An assumed drainage depth of 80 cm therefore resulted in emissions of 0.91 x 80 = 73 

on a new literature review (Page et al. 

nnualized emissions over 

emissions vs. time 

(Emissions are highest directly after conversion and then decrease through time.) Based 

ypical drainage depth of 60-85 cm 

represented by the minimum and 

This estimate is the result of a comprehensive literature 

review on the state of the science of emissions from drained peatlands, with measurements included 
 

Fire is often used when land is cleared for oil palm cultivation

vegetation are included in our assessment

not included in the updated emission factors for peatlands bec

to the most appropriate methods and data

Table 3. Annualized values for peat carbon

higher rates of emissions in the years immediately following drainage (values derived from Hooijer

2011).

Number of years

5

10

20

25

30

40

50

en used when land is cleared for oil palm cultivation. GHG emissions associated with burning 

vegetation are included in our assessment, but GHG emissions associated with burning peat soil 

not included in the updated emission factors for peatlands because there is still significant uncer

s and data for quantifying these emissions.

ed values for peat carbon losses from plantations over various time scales, accounting for 

in the years immediately following drainage (values derived from Hooijer

Carbon loss

(Mg CO2-eq ha-1yr-1)

178

121

106

100

95

90

86

 1

5

. GHG emissions associated with burning 

associated with burning peat soil were 

ause there is still significant uncertainty as 

from plantations over various time scales, accounting for 

in the years immediately following drainage (values derived from Hooijer et al., 
1

6

 

5.1 Summary

Forest carbon stock estimates for tropical countries were revised based on 

pantropical analysis by Saatchi et al. (2011). 

5.2 Initial Assessment of Forest Carbon Stocks 

For the RFS2 final rule, forest carbon stocks were estimated from several data sources, each derived 

using a different methodological approach. Over the course of the past two years, several efforts have 

been ongoing to improve forest carbon stock estimate

the increasing interest in REDD (reducing emissions from deforestation and forest degradation) 

mechanisms, but the accurate estimation of forest carbon stocks is equally as important for accurate 

estimation of emissions from the direct or indirect expansion of biofuels onto forest land. 

5.3 Updated Assessment of 

Two new analyses on forest carbon stock estimation were completed since the release of the final RFS2 

rule, one for three continental regions by Saatchi et al. (2011) and the other for the EU by Gallaun et al. 

(2010). Preliminary results for Latin America and Africa from Saatchi et al. (2011) were incorporated into 

the final RFS2 rule, but Asia results were not included d

(2011) analysis is now complete, and so the final map was used to calculate updated area

average forest carbon stocks for the entire area covered by the analysis (Latin America, sub

Africa and South and Southeast Asia; Figure 3

improvement over previous estimates because they incorporate data from more than 4,000 ground 

inventory plots, about 150,000 biomass values estimated from forest heig

lidar, and a suite of optical and radar satellite imagery products. Estimates are spatially refined at 1

grid cell resolution and are directly comparable across countries and regions. Another advantage is the 

uncertainty analysis performed on the map; pixel

Forest carbon stock estimates for tropical countries were revised based on the publication of a new 

pantropical analysis by Saatchi et al. (2011). 

Initial Assessment of Forest Carbon Stocks 

For the RFS2 final rule, forest carbon stocks were estimated from several data sources, each derived 

using a different methodological approach. Over the course of the past two years, several efforts have 

been ongoing to improve forest carbon stock estimates for large regions. This has been driven mainly by 

the increasing interest in REDD (reducing emissions from deforestation and forest degradation) 

mechanisms, but the accurate estimation of forest carbon stocks is equally as important for accurate 

on of emissions from the direct or indirect expansion of biofuels onto forest land. 

Updated Assessment of Forest Carbon Stocks

Two new analyses on forest carbon stock estimation were completed since the release of the final RFS2 

ree continental regions by Saatchi et al. (2011) and the other for the EU by Gallaun et al. 

(2010). Preliminary results for Latin America and Africa from Saatchi et al. (2011) were incorporated into 

the final RFS2 rule, but Asia results were not included due to timing considerations. The Saatchi et al. 

(2011) analysis is now complete, and so the final map was used to calculate updated area

average forest carbon stocks for the entire area covered by the analysis (Latin America, sub

uth and Southeast Asia; Figure 3). The Saatchi et al. results represent a significant 

improvement over previous estimates because they incorporate data from more than 4,000 ground 

inventory plots, about 150,000 biomass values estimated from forest heights measured by spaceborne 

lidar, and a suite of optical and radar satellite imagery products. Estimates are spatially refined at 1

grid cell resolution and are directly comparable across countries and regions. Another advantage is the 

sis performed on the map; pixel-level uncertainty ranged from 6 to 53%, but 

5 FOREST CARBON STOCKS

the publication of a new 

For the RFS2 final rule, forest carbon stocks were estimated from several data sources, each derived 

using a different methodological approach. Over the course of the past two years, several efforts have 

s for large regions. This has been driven mainly by 

the increasing interest in REDD (reducing emissions from deforestation and forest degradation) 

mechanisms, but the accurate estimation of forest carbon stocks is equally as important for accurate 

on of emissions from the direct or indirect expansion of biofuels onto forest land. 

Two new analyses on forest carbon stock estimation were completed since the release of the final RFS2 

ree continental regions by Saatchi et al. (2011) and the other for the EU by Gallaun et al. 

(2010). Preliminary results for Latin America and Africa from Saatchi et al. (2011) were incorporated into 

ue to timing considerations. The Saatchi et al. 

(2011) analysis is now complete, and so the final map was used to calculate updated area-weighted 

average forest carbon stocks for the entire area covered by the analysis (Latin America, sub-Saharan 

The Saatchi et al. results represent a significant 

improvement over previous estimates because they incorporate data from more than 4,000 ground 

hts measured by spaceborne 

lidar, and a suite of optical and radar satellite imagery products. Estimates are spatially refined at 1-km 

grid cell resolution and are directly comparable across countries and regions. Another advantage is the 

6 to 53%, but 

FOREST CARBON STOCKS
 

uncertainty for project-level (~10,000 ha) and national

1%, respectively.

In the final RFS2 rule, forest carbon stocks for the EU we

Nabuurs et al. (2003), Myneni et al. (2001) and FAO. Issues with this ‘patchwork’ approach were that the 

biomass estimates were not comparable across countries due to the differences in methodological 

approaches, and that estimates were not spatially derived (or, the spatial data were not provided to 

Winrock). Since the release of the final rule, Gallaun et al. (2010) developed EU

ground biomass in forests based on remote sensing and field mea

the classification, and comprehensive field measurement data from national forest inventories for nearly 

100,000 locations from 16 countries were also used to develop the final map. The map covers the whole 

European Union, the European Free Trade Association (EFTA) countries, the Balkans, Belarus, the 

Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey (Figure 3). Winrock obtained the GIS data 

for the biomass map from the corresponding author (H. Gallaun) and could 

weighted forest carbon stocks per country. For both data sources, we added belowground biomass to 

reported aboveground biomass values using an equation in Mokany et al. (2006). The data sources used 

per country for the estimation of forest carbon stocks in the updated emission factor analysis are 

presented in Figure 4.

level (~10,000 ha) and national-level assessments was constrained at 

In the final RFS2 rule, forest carbon stocks for the EU were estimated using a combination of data from 

Nabuurs et al. (2003), Myneni et al. (2001) and FAO. Issues with this ‘patchwork’ approach were that the 

biomass estimates were not comparable across countries due to the differences in methodological 

s, and that estimates were not spatially derived (or, the spatial data were not provided to 

Winrock). Since the release of the final rule, Gallaun et al. (2010) developed EU-wide maps of above

ground biomass in forests based on remote sensing and field measurements. MODIS data were used for 

the classification, and comprehensive field measurement data from national forest inventories for nearly 

100,000 locations from 16 countries were also used to develop the final map. The map covers the whole 

n, the European Free Trade Association (EFTA) countries, the Balkans, Belarus, the 

Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey (Figure 3). Winrock obtained the GIS data 

for the biomass map from the corresponding author (H. Gallaun) and could therefore calculate area

weighted forest carbon stocks per country. For both data sources, we added belowground biomass to 

reported aboveground biomass values using an equation in Mokany et al. (2006). The data sources used 

f forest carbon stocks in the updated emission factor analysis are 

 1

7

level assessments was constrained at 5% and 

re estimated using a combination of data from 

Nabuurs et al. (2003), Myneni et al. (2001) and FAO. Issues with this ‘patchwork’ approach were that the 

biomass estimates were not comparable across countries due to the differences in methodological 

s, and that estimates were not spatially derived (or, the spatial data were not provided to 

wide maps of abovesurements. MODIS data were used for 

the classification, and comprehensive field measurement data from national forest inventories for nearly 

100,000 locations from 16 countries were also used to develop the final map. The map covers the whole 

n, the European Free Trade Association (EFTA) countries, the Balkans, Belarus, the 

Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey (Figure 3). Winrock obtained the GIS data 

therefore calculate areaweighted forest carbon stocks per country. For both data sources, we added belowground biomass to 

reported aboveground biomass values using an equation in Mokany et al. (2006). The data sources used 

f forest carbon stocks in the updated emission factor analysis are 
1

8

 

Figure 3. Distribution of (A) above and belowground carbon stocks and (B) uncertainty circa the year 2000. From 

Saatchi et al. (2011).
 

Figure 4. Distribution of aboveground forest carbon stocks circa 2000. From Gallaun et al. (2010). Divide values in 

the legend by 10 for carbon estimates in units of t C ha-1.

 1

9

. Distribution of aboveground forest carbon stocks circa 2000. From Gallaun et al. (2010). Divide values in 
2

0

 

Figure 5. Updated data sources used for estimating forest carbon stocks in the emission factor analysis after the 

release of the final RFS2 rule. 
 

Blackard, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus

G.G., Nelson, M.D., Riemann, R., Reufenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, 

K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., Tymcio, R.P. 2008. Mapping 

U.S. forest biomass using nationwide forest inventory data and moderate resolution information. 

Remote Sensing of Environment

Cannell, M.G. 1982. World Forest Biomass and Primary Production Data. Academic Press. London. 391 

pp.

Couwenberg, J., Dommain, R., Joosten, H. 2009. Greenhouse gas fluxes from tropical peatlands in south

east Asia. Global Change Biology

FAO. 1995. FAO Yearbook: Forest products. FAO For. Serv. No. 28, FAO, Rome. 422 p.

Gallaun, H., Zanchi, G., Nabuurs, G.J., Hengeveld, G., Schardt, M., Verkerk, P.J. 2010. EU

growing stock and above-ground biomass in forests based on remote sensing and field 

measurements. Forest Ecology and Management

Germer, J. and Sauerborn, J. 2007. Estimation of the impact of oil palm plantation establishment on 

greenhouse gas balance. Springer Science + Business Media BV, 2007.

Henson, I.E. 2008. The carbon cost of palm oil production in Malaysia. 

Houghton, R.A., Butman, D., Bunn, A.G., Krankina, O., Schlesinger, P., Stone, T.A. 2007. Mapping Russian 

forest biomass with data from satellites and forest inventories. 

2: 045032.

Hooijer, A., Page, S. E., Jauhiainen, J., Lee, W. A., Idris, A., &

plantations on tropical peatland: reducing uncertainty and implications for emission reduction options. 

Biogeosciences Discussions. (in press).

6 REFERENCES

, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus, M.L., Jacobs, D.M., Lister, A.J., Moisen, 

G.G., Nelson, M.D., Riemann, R., Reufenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, 

K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., Tymcio, R.P. 2008. Mapping 

ss using nationwide forest inventory data and moderate resolution information. 

Remote Sensing of Environment 112: 1658-1677.

Cannell, M.G. 1982. World Forest Biomass and Primary Production Data. Academic Press. London. 391 

, R., Joosten, H. 2009. Greenhouse gas fluxes from tropical peatlands in south

Global Change Biology, doi: 10.1111/j.1365-2486.2009.02016.x

. FAO Yearbook: Forest products. FAO For. Serv. No. 28, FAO, Rome. 422 p.

G., Nabuurs, G.J., Hengeveld, G., Schardt, M., Verkerk, P.J. 2010. EU

ground biomass in forests based on remote sensing and field 

Forest Ecology and Management 260: 252-261.

2007. Estimation of the impact of oil palm plantation establishment on 

greenhouse gas balance. Springer Science + Business Media BV, 2007.

Henson, I.E. 2008. The carbon cost of palm oil production in Malaysia. The Planter 84: 988.

D., Bunn, A.G., Krankina, O., Schlesinger, P., Stone, T.A. 2007. Mapping Russian 

forest biomass with data from satellites and forest inventories. Environmental Research Letters

Page, S. E., Jauhiainen, J., Lee, W. A., Idris, A., &Anshari, G. (2011). Land subsidence and carbon loss in 

plantations on tropical peatland: reducing uncertainty and implications for emission reduction options. 

. (in press).

REFERENCES

 2

1

, M.L., Jacobs, D.M., Lister, A.J., Moisen, 

G.G., Nelson, M.D., Riemann, R., Reufenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, 

K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., Tymcio, R.P. 2008. Mapping 

ss using nationwide forest inventory data and moderate resolution information. 

Cannell, M.G. 1982. World Forest Biomass and Primary Production Data. Academic Press. London. 391 

, R., Joosten, H. 2009. Greenhouse gas fluxes from tropical peatlands in southG., Nabuurs, G.J., Hengeveld, G., Schardt, M., Verkerk, P.J. 2010. EU-wide maps of 

ground biomass in forests based on remote sensing and field 

2007. Estimation of the impact of oil palm plantation establishment on 

84: 988.

D., Bunn, A.G., Krankina, O., Schlesinger, P., Stone, T.A. 2007. Mapping Russian 

Environmental Research Letters

Land subsidence and carbon loss in 

plantations on tropical peatland: reducing uncertainty and implications for emission reduction options. 
2

2

 

Jiwan. N. and Saharjo, B.H. 2009. “The Above Ground Car

Mineral Soil ‘Case Study in Tanah Grogot, East Kalimantan, Indonesia.” July 2009.

Morel, A. 2009. Estimates of aboveground biomass forest and oil palm values for Sabah, Malaysia. 

University of Oxford, Environm

Muraleedharan, T.R., Radojevic, M., Waugh, A., Caruana, A. 2000. Emissions from the combustion of 

peat: an experimental study. 

Myneni, R.B., Dong, J., Tucker, C.J., Kaufmann

M.K. 2001. A large carbon sink in the woody biomass of Northern forests. 

14789.

Ng, S.K., Thamboo, S., and de Souza, P. 1968. Nutrient contents of oil palms in Malaya.

vegetative tissues. Malay. Agric. J.

Niklas, K.J. and Enquist, B.J. 2004. Biomass allocation and growth data of seeded plants. Data set. 

Available online [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distribut

Active Archive Center, Oak Ridge, Tennessee, U.S.A.

Page, S. E., Morrison, R., Malins, C., Hooijer, A., Rieley, J. O. & Jauhiainen, J. (2011). 

surface greenhouse gas emissions from oil palm plantations in Southeast Asia.

Council on Clean Transportation (ICCT). ICCT White Paper 15.

Ruesch, A. and Gibbs, H.K. 2008. New IPCC Tier

online from the Carbon Dioxide Information Analysis Center (

National Laboratory, Oak Ridge, Tennessee.

Saatchi, S.S., Harris, N.L., Brown, S., 

Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman

forest carbon stocks in tropical regions across three continents. 

10.1073/pnas.1019576108.

Syahrinudin (2005). The potential of oil palm and forest plantations for carbon sequestrati

degraded lands in Indonesia. Ecology and Development Series No. 28, 2005.

Ward, D.E., Hardy, C.C. 1984. Advances in the characterisation and control of emissions from prescribed 

fires. Proceedings of the 78

1984. San Francisco, CA. Paper no. 84

Winjum, J.K., Brown, S., Schlamadinger, B. 1998. Forest harvests and wood products: sources and sinks 

of atmospheric carbon dioxide. 

, B.H. 2009. “The Above Ground Carbon Stock of 25 years Oil Palm Planted in the 

Mineral Soil ‘Case Study in Tanah Grogot, East Kalimantan, Indonesia.” July 2009.

, A. 2009. Estimates of aboveground biomass forest and oil palm values for Sabah, Malaysia. 

University of Oxford, Environmental Exchange Institute, September 2009.

, T.R., Radojevic, M., Waugh, A., Caruana, A. 2000. Emissions from the combustion of 

peat: an experimental study. Atmospheric Environment 34: 3033-3035.

, R.B., Dong, J., Tucker, C.J., Kaufmann, R.K., Kauppi, P.E., Liski, J., Zhou, L., Alexeyev, V., Hughes, 

M.K. 2001. A large carbon sink in the woody biomass of Northern forests. PNAS

Ng, S.K., Thamboo, S., and de Souza, P. 1968. Nutrient contents of oil palms in Malaya.

Malay. Agric. J. 46, 332-390.

Niklas, K.J. and Enquist, B.J. 2004. Biomass allocation and growth data of seeded plants. Data set. 

Available online [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distribut

Active Archive Center, Oak Ridge, Tennessee, U.S.A.

Page, S. E., Morrison, R., Malins, C., Hooijer, A., Rieley, J. O. & Jauhiainen, J. (2011). Review of peat 

surface greenhouse gas emissions from oil palm plantations in Southeast Asia. 

ouncil on Clean Transportation (ICCT). ICCT White Paper 15.

, H.K. 2008. New IPCC Tier-1 global biomass carbon map for the year 2000. Available 

online from the Carbon Dioxide Information Analysis Center (http://cdiac.ornl.gov

National Laboratory, Oak Ridge, Tennessee.

Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., 

Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman, M. And Morel, A. 2011. Benchmark map of 

forest carbon stocks in tropical regions across three continents. PNAS doi: 

10.1073/pnas.1019576108.

Syahrinudin (2005). The potential of oil palm and forest plantations for carbon sequestrati

a. Ecology and Development Series No. 28, 2005.

Ward, D.E., Hardy, C.C. 1984. Advances in the characterisation and control of emissions from prescribed 

fires. Proceedings of the 78th Annual Meeting of the Air Pollution Control Association, 24

1984. San Francisco, CA. Paper no. 84-363.

Winjum, J.K., Brown, S., Schlamadinger, B. 1998. Forest harvests and wood products: sources and sinks 

of atmospheric carbon dioxide. Forest Science 44: 272-284.

bon Stock of 25 years Oil Palm Planted in the 

Mineral Soil ‘Case Study in Tanah Grogot, East Kalimantan, Indonesia.” July 2009.

, A. 2009. Estimates of aboveground biomass forest and oil palm values for Sabah, Malaysia. 

, T.R., Radojevic, M., Waugh, A., Caruana, A. 2000. Emissions from the combustion of 

, R.K., Kauppi, P.E., Liski, J., Zhou, L., Alexeyev, V., Hughes, 

PNAS 98: 14784-

II Nutrients in 

Niklas, K.J. and Enquist, B.J. 2004. Biomass allocation and growth data of seeded plants. Data set. 

Available online [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distribut ed 

Review of peat 

 International 

1 global biomass carbon map for the year 2000. Available 

http://cdiac.ornl.gov), Oak Ridge 

Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., 

, M. And Morel, A. 2011. Benchmark map of 

Syahrinudin (2005). The potential of oil palm and forest plantations for carbon sequestration on 

Ward, D.E., Hardy, C.C. 1984. Advances in the characterisation and control of emissions from prescribed 

Annual Meeting of the Air Pollution Control Association, 24-29 June 

Winjum, J.K., Brown, S., Schlamadinger, B. 1998. Forest harvests and wood products: sources and sinks 
 

Dr. Nancy Harris

Program Officer 2

Ecosystem Services 

office 703.302.6519

fax 703.302.6512

e-mail nharris@winrock.org

2121 Crystal Drive, Suite 500

Arlington, VA 22202, USA

www.winrock.org/ecosystems

 2

3