quantifying global greenhouse gas emissions from land-use change for crop production

Quantifying global greenhouse gas emissions fromland-use change for crop productionHELEN C FLYNN* , L LOREN C M IL A I CANALS † , EMMA KELLER † , HENRY K ING † , SARAH

S IM † , A STLEY HAST INGS * , SH I FENG WANG* and PETE SMITH*

*Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3UU,

UK, †Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, UK

Abstract

Many assessments of product carbon footprint (PCF) for agricultural products omit emissions arising from land-use

change (LUC). In this study, we developed a framework based on IPCC national greenhouse gas inventory methodol-

ogies to assess the impacts of LUC from crop production using oil palm, soybean and oilseed rape as examples. Using

ecological zone, climate and soil types from the top 20 producing countries, calculated emissions for transitions from

natural vegetation to cropland on mineral soils under typical management ranged from �4.5 to 29.4 t CO2-

eq ha�1 yr�1 over 20 years for oil palm and 1.2–47.5 t CO2-eq ha�1 yr�1 over 20 years for soybeans. Oilseed rape

showed similar results to soybeans, but with lower maximum values because it is mainly grown in areas with lower

C stocks. GHG emissions from other land-use transitions were between 62% and 95% lower than those from natural

vegetation for the arable crops, while conversions to oil palm were a sink for C. LUC emissions were considered on a

national basis and also expressed per-tonne-of-oil-produced. Weighted global averages indicate that, depending on

the land-use transition, oil crop production on newly converted land contributes between �3.1 and 7.0 t CO2-eq t oil

production�1 yr�1 for palm oil, 11.9–50.6 t CO2-eq t oil production�1 yr�1 for soybean oil, and 7.7–31.4 t CO2-

eq t oil production�1 yr�1 for rapeseed oil. Assumptions made about crop and LUC distribution within countries

contributed up to 66% error around the global averages for natural vegetation conversions. Uncertainty around bio-

mass and soil C stocks were also examined. Finer resolution data and information (particularly on land management

and yield) could improve reliability of the estimates but the framework can be used in all global regions and repre-

sents an important step forward for including LUC emissions in PCFs.

Keywords: biomass carbon, carbon accounting, carbon footprinting, crop production, greenhouse gas emissions, land-use

change, soil carbon

Received 19 October 2011 and accepted 24 November 2011

Introduction

Land-use change (LUC) accounted for an estimated

5.9 ± 2.9 Gt CO2-eq yr�1 during the 1990s, represent-

ing 6–17% of total anthropogenic greenhouse gas

(GHG) emissions (IPCC, 2001). The body of research

into soil carbon changes in response to LUC is increas-

ing (Guo & Gifford, 2002; Smith, 2008; Don et al., 2011)

but large uncertainties remain, especially for tropical

regions (Don et al., 2011) where land conversion to agri-

culture continues to increase (IPCC, 2007). These uncer-

tainties and debates over appropriate methodologies to

use (e.g. Searchinger et al., 2008; and responses Fargi-

one et al., 2008; Wang & Haq, 2008) have resulted in

product carbon footprints (PCF) that tend to omit LUC

emissions (Garnett, 2008; Russell, 2010).

Current LUC emission methodologies differ in how

they apportion the amount of LUC per crop; from par-

tial and general equilibrium models used by the US

Environmental Protection Agency (EPA, 2010; see Bran-

dao, 2011 for a review) to the simple top-down method-

ology used by Audsley et al. (2009), which apportions

total global LUC emissions solely on the basis of the

crop’s land requirements. Despite this, most

approaches ultimately rely on the IPCC (2003) method-

ology for LUC to calculate GHG emissions per ha of

LUC; for example, PAS2050 (BSI, 2011), the EU Renew-

able Energy Directive (RED) (Directive 2009/28/EC)

(EU, 2009, 2010) and the recent European Commission

study into the indirect LUC impacts of RED (Hiederer

et al., 2010).

This article builds on the IPCC default LUC method-

ology using easily available additional input data. We

develop a framework for converting per ha emissions

from LUC to a per-tonne of product basis, particularly

when information on crop origin and growing condi-

tions is limited. Three oil crops (oil palm, oilseed rape

and soybean) are used as examples to demonstrate the

benefits, limitations and uncertainties of the method.Correspondence: Prof. Pete Smith, tel. + 44 0 1224 272 702,

fax + 44 0 1224 272 703, e-mail: [email protected]

© 2011 Blackwell Publishing Ltd 1

Global Change Biology (2012), doi: 10.1111/j.1365-2486.2011.02618.x

https://www.researchgate.net/publication/227675675_Impact_of_tropical_land-use_change_on_soil_organic_carbon_stocks_-_a_meta-analysis._Glob_Change_Biol?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

https://www.researchgate.net/publication/227675675_Impact_of_tropical_land-use_change_on_soil_organic_carbon_stocks_-_a_meta-analysis._Glob_Change_Biol?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

https://www.researchgate.net/publication/228550991_Corporate_Greenhouse_Gas_Inventories_for_the_Agricultural_Sector_Proposed_Accounting_and_Reporting_Steps?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

https://www.researchgate.net/publication/226551928_Land_use_change_and_soil_organic_carbon_dynamics._Nutr_Cycl_Agroecosyst?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

https://www.researchgate.net/publication/229471877_Soil_Carbon_Stocks_and_Land_Use_Change_A_Meta-Analysis?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

https://www.researchgate.net/publication/5593089_Use_of_U.S._Croplands_for_Biofuels_Increases_Greenhouse_Gases_Through_Emissions_from_Land-Use_Change?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

The methodology is intended to facilitate the inclusion

of LUC into existing PCF methods in a practical and

efficient manner and to enable differentiation at a crop

and country level. It includes biomass C losses from

vegetation clearance, and both C and N losses from

soil organic matter (SOM) mineralization, but not N

losses from fertilizer applications, which are already

routinely included in agricultural LCA. Here, we deal

only with the land-use/management issues which are

currently excluded from most current PCFs. The

approach is similar to that described by Hiederer et al.

(2010), but that study used the complex spatial data-

bases which are necessary to feed into agrieconomic

models and assess the impact of global agricultural

trends. Here, we have instead developed a simple

matrix that is more suited to the needs of users who

do not have the capacity or data (e.g. in developing

countries) to run such models. This matrix enables

land managers and companies to easily see how

climate, soil type, previous land use (LU) and crop

management affect LUC emissions, thereby supporting

product sourcing and management decisions. The

approach is thus fit for purpose and provides crop and

country specificity with more relevance to PCF meth-

ods than the simple defaults provided in the original

PAS 2050, without the costs and detailed data require-

ments of full, process-based spatial modelling.

Materials and methods

Land-use change emissions were taken to be the sum of three

components – change in soil C stocks, emissions of nitrous

oxide (N2O) from the mineralization of SOM, and change in

biomass C stocks, as per the IPCC Good Practice Guidance for

Land Use, Land Use Change and Forestry (GPG-LULUCF,

2003) tier 1 methodology. N2O was only included in the calcu-

lations where soils were losing SOM as soils do not act as a

sink for atmospheric N2O. Other N2O emissions from soils,

such as from nitrogen fertilizers, are associated with land-use

management practices rather than LUC. They were not

included in this analysis as they are already well understood

and routinely included in PCF methodologies. The methodol-

ogy differs from the default tier 1 IPCC methodology by using

biomass C stock data from the EU RED (2010) guidelines and

by spreading biomass C stock changes over 20 years (see

below).

Soil C stock

Changes in soil C stocks were calculated as follows:

(reference soil C stock � previous LU stock change factors)� (reference soil C stock � new LU stock change factors)¼ soil C stock loss over 20 years.

The emissions calculated for changes in soil organic

carbon (SOC) were allocated over 20 years, as explained

below. Reference soil C stock values were the default C levels

under native vegetation in the top 30 cm of the soil profile in

t ha�1, taken from IPCC (2006) for six different soil types

across nine climate zones. LU stock change factors modify C

stocks up or down, and account for the impact of management

(e.g. tillage regimes) and inputs (crop residue or manure addi-

tions) where relevant, as well as LU type (native or managed

forest and grassland, tropical shifting cultivation short or

mature fallow, set aside, annual crops and permanent crops),

and were also taken from IPCC (2006). The resulting C stock

changes were converted to CO2-eq ha�1 yr�1 for comparison,

with a negative value representing an increase in soil C, that

is, the soil acts as a C sink. The exception to this methodology

was organic soils; where they are mentioned, annual C losses

per-ha were not calculated from reference C stocks but

rather taken directly from IPCC (2006) and again converted to

CO2-eq.

N2O emissions from SOM loss

Where a decrease in the soil C stock occurred, the following

calculation was used to convert the loss of soil C (as a measure

of SOM loss) to N2O emissions (as per IPCC, 2003), assuming

a 15 : 1 C : N ratio and 1% emission factor for the proportion

of N lost as N2O (equivalent to losing 1 kg per 100 kg of N

added used for N fertilizer additions):

Loss of soil C over 20 years � ð1=15Þ � 0:01¼ N2O--N loss over 20 years;

where both soil C and N2O-N are in t ha�1 from the top 30 cm

of the soil profile.

This value was again converted to annual CO2-eq for com-

parison, using a global warming potential of 296 for N2O

(IPCC, 2001), which is the value used for national inventory

assessments (to ensure consistency in estimates at different

times), despite a more recent estimate of 310 (IPCC, 2007).

These emissions are directly proportional to soil C emissions,

making up 8% of total soil LUC emissions where SOM is lost

as a result of land conversion and are therefore not considered

separately in the results.

Biomass C stocks

Changes in biomass C stocks were calculated as follows:

Previous biomass C stock� new biomass C stock¼ loss of biomass C:

The results were also allocated over 20 years as with soil C

(see explanation below). Biomass C stock values representing

total above and below ground, living and dead matter C stock,

averaged over a production cycle where applicable, were

taken from the EU RED guidelines (2010). These are, in turn,

based on biomass dry matter stocks, C fraction, and root to

shoot ratio data given by the IPCC (2006). As data were not

available for all ecological zones some substitutions were

made; for temperate steppe, temperate grassland values were

used as the nearest vegetation type, and all tropical and sub-

tropical mountain systems were treated as tropical mountain

© 2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02618.x

2 H. C. FLYNN et al.

systems. For Australia, available biomass C values for each

vegetation class from other regions were averaged, and for

European subtropical dry forests, continental Asian values

were used. In desert regions, it was assumed some level of rec-

lamation would be necessary before crop production was pos-

sible so these ecological zones were excluded from the

analysis of conversions from natural vegetation. For soy and

oilseed rape, which are not covered in the EU RED report, the

IPCC (2006) default value of 5 t C ha�1 was used. As with soil

C stock changes, a positive value shows a loss of C on conver-

sion and a negative value indicates a C sink.

20-Year equilibrium rule

The IPCC methodology uses a 20-year period for soil C to

equilibrate to management or LUCs. As explained in the

Revised 1996 Guidelines (IPCC, 1996), this represents a com-

promise as systems vary in their response times. Tropical sys-

tems reach a new equilibrium faster than temperate ones, as

do systems where soil C is being degraded in comparison with

those where there is a build-up of soil C in response to land

abandonment or increased residue inputs. In general, while a

longer inventory period would mean systems were closer to

equilibrium, the most rapid changes in soil C occur during the

first 10–20 years following a significant change in manage-

ment practices or land use. Therefore, 20 years is deemed an

appropriate time period for the inclusion of most of the

change in soil C stocks resulting from land conversions to

agriculture and management-induced changes. At the same

time, it limits the historical LU data requirements and the

amount of bias introduced by the assumption of linear change

over longer inventory periods (IPCC, 1996).

Biomass C changes are inventoried in the year they occur as

the IPCC methodology is designed to calculate annual inven-

tories of GHG. In contrast, product assessment methods, such

as PCF or life cycle assessment (LCA), aim to ascribe the

impacts from LUC to the products obtained following a prin-

ciple of causality (whoever causes the LUC bears the burden).

Thus, when land is converted to provide agricultural prod-

ucts, the impacts from LUC should be allocated to such prod-

ucts even if this class of emissions occur mainly in the first

year. An allocation problem arises because there is usually no

certainty over how many years the land will be used for that

purpose. There is no scientific justification for choosing one

allocation period or another, but as both PAS 2050 (BSI, 2008,

2011) and EU RED (EU, 2009, 2010) suggest allocating all LUC

emissions (or fixation) over the 20 years following LUC,

including both those derived from SOC degradation (or build-

up) and biomass C stock loss (or gain), we have followed the

same allocation principle.

Choice of crops and conditions

Three oil crops were selected – oil palm and soybean as exam-

ples of perennial and annual crops, respectively, which are

currently implicated in high levels of LUC, potentially threat-

ening biodiversity across Asia and South America (Koh & Wil-

cove, 2007), and oilseed rape as a temperate annual crop with

similar food, biofuel and other uses for comparison. Further

crop types (cocoa, tea, sugar, sunflowers and arable fruit and

vegetables) are given in the Appendix S1. For each crop, com-

binations of climate, soil type and ecological zone were

selected to reflect conditions within the FAO top 20 producing

countries. All ecological zones except deserts were included

provided they covered at least 1% of the total area of suitable

ecological zones. Oil palm was assumed to be grown only in

tropical regions and oilseed rape in temperate ones (except for

in India, where temperate zones cover only 2% of the land

area), whereas soy was assumed to grow across both tropical

and temperate regions, and only boreal regions were

excluded. No further considerations of suitability for crop pro-

duction were taken into account. Previous land uses covered

were natural vegetation/forest (where referred to as forest,

shrubland and other nonforest ecological zones are excluded),

improved grassland, and for tropical forest zones, short fallow

shifting cultivation where vegetation has regenerated as far as

grassland, and mature fallow where scrub has developed,

with set aside grassland as a comparison for temperate

regions. A range of management options were tested for each

crop. Typical management was taken to be medium inputs

and full tillage for the two annual crops as this is the default

baseline for the IPCC methodology. The other two scenarios

were full tillage and low inputs (crop residues removed and

nutrients not replaced with fertilizer or N-fixing crops) (Sce-

nario A), and reduced tillage (because no-till is not suitable for

all crops or soil types) and high inputs (crop residues returned

and significant additional organic matter via use of green

manure, cover crops, etc. but not animal manure, as it is

unclear how widely this is used) (Scenario B). For oil palm,

no-till management was assumed for all scenarios (Wahid

et al., 2005) but input levels were the same as for annual crops

(low inputs for Scenario A and high inputs for Scenario B).

These management scenarios reflect a range of likely organic

matter inputs and disturbance affecting soil C stocks, not a full

range of management options which may affect yield levels.

Therefore, only per-ha emissions under typical management

were used to assess per-yield emissions.

Per-yield/product-based emissions

Per-ha emissions were converted to per-tonne-of-product

levels to reflect product-based climate impact. This was

performed at country scale using average emission levels

under typical management and crop oil yields. Two sets of

calculations were performed; one using emissions for conver-

sions from natural vegetation, and one using emissions for

conversions from agricultural or formerly agricultural (set

aside or fallow) land. For conversions from natural vegetation,

per-ha emission levels under typical management were aver-

aged for each country, using a proportional weighting based

on the coverage of each vegetation type analysed. Ecological

zones deemed climatically unsuitable for crop production

were discounted such that 100% coverage represented all suit-

able areas rather than 100% of the land area of the country.

Where multiple soil types occurred within the ecological

zones, these were assumed to be equally distributed, as were


GHG EMISSIONS FROM CROP PRODUCTION LUC 3

crops and different types of agricultural or previously agricul-

tural land. This is because a multilayered, high-resolution spa-

tial database would be required to account for these

distributions; the aim of this study was to produce a matrix

which could be easily manipulated in a simple spreadsheet

form. It means that these emissions have no spatial resolution

below country level, which is in keeping with the use of a sin-

gle yield value for each country, rather than multiple yield

values taking into account the effects of climate and soil type

within each country.

FAO cropping area and oil production data for 2008 were

used to calculate oil yields for oil palm and oilseed rape

(because yield values are only given for crops, not their oil).

For soy, rather than assume all cropping area is used for oil

production, FAO national production figures for soybeans

were used and converted to oil production assuming 1 t of soy-

beans produces 0.19 t of vegetable oil (Brandao, 2011). FAO

data were used for consistency as these are the most recent

data available which cover all the countries considered. How-

ever, these yield levels are not always those expected from

commercial growers, so it is recommended that grower data be

used for finer scale applications of the methodology. This

would also allow the effect of management scenarios on per-

yield emissions to be properly assessed, as discussed below.

Country-scale per-product emissions were then averaged

using a weighting according to the percentage of production

from each of the top 20 producing countries to give two esti-

mates of global impact, one based on the assumption that all

LUC occurs on previously natural vegetation, and one assum-

ing all new crop production occurs on land which is currently,

or has recently been, under agricultural management. Both of

these estimates assume that LUC is distributed according to

national production levels, that is, that a country which pro-

duces 20% of the crop also contributes 20% of the LUC emis-

sions. This is because the alternative is to use scenarios of

LUC distribution which need to be based on complex agrieco-

nomic modelling to be realistic. These final estimates, there-

fore, have a very high degree of associated uncertainty (see

below), and are presented here to illustrate the purpose of the

methodology rather than to provide an accurate estimate of

global LUC emissions.

Results and discussion

Area-based LUC emissions

Conversion of natural vegetation to oil palm under typ-

ical crop management on mineral soils results in the

loss of �4.5 to 29.4 t CO2-eq ha�1 yr�1 over 20 years,

with tropical shrubland conversions in Africa and Cen-

tral & South America acting as a C sink. The highest

emissions are from tropical rainforests in the countries

of insular Asia (Fig. 1a). This maximum emission level

is within the range calculated by Fargione et al. (2008)

for Malaysian and Indonesian rainforests not on peat

soils (equivalent to 35 ± 10 t CO2 ha�1 yr�1 if taken

over 20 years). Note that because conversions to oil

palm do not reduce soil C stocks under any of the con-

ditions tested, these emissions do not include any N2O.

Conversion of natural vegetation to soy cropping

under typical management emits between 1.2 and

47.5 t CO2-eq ha�1 yr�1; with the lowest emissions

from temperate dry steppe conversions in Europe and

South America and the highest from tropical rainforests

in the countries of insular Asia (Fig. 1b). Emissions

from Brazilian rainforest converted to soy were calcu-

lated as 41.6 t CO2 ha�1 yr�1, which is similar to previ-

ously published estimates for this land-use transition

(Fargione et al., 2008; Reijnders & Huijbregts, 2008b).

Conversion to oilseed rape production under typical

management results in LUC emissions of 1.2–39.2 t

CO2-eq ha�1 yr�1, with the lowest emissions from

warm temperate dry steppe conversions in Europe. The

highest emissions are from tropical rainforest conver-

sions in India, although this covers only 6% of the land

area, and warm temperate moist subtropical humid

forest conversions in the United States have the next

highest emissions of 27.2 t CO2-eq ha�1 yr�1 (Fig. 1c).

Emissions ranges for all the previous LU types selected

are given in Table 1 (note that for natural vegetation,

all classes are included on the emission maps [forest,

shrubland, natural grassland, etc.] but for simplicity,

only forests are included in the table).

Emissions are higher from the two annual crops

because postconversion biomass C stocks are much

lower than for oil palm which is a long-term tree crop,

and because continuous cultivation reduces soil C

stocks for all the scenarios investigated, whereas no-till

permanent crops are deemed to increase soil C stocks.

This second point is an area of some contention as the

IPCC acknowledge the methodology for tropical sys-

tems is based on fewer data points than that for temper-

ate ones (IPCC, 2006), and more recent studies suggest

it may overestimate the C sink strength of plantation

soils. For example, measurements of SOC concentra-

tions under plantations of different ages indicate high

levels of spatial variation and no constant directional

change over time (Smiley & Kroschel, 2008). Mean-

while, comparisons of soil C concentrations in top soil

under primary and secondary forest and plantations

have indicated LUC reduces soil C stocks at least at the

surface (van Noordwijk et al., 1997; Schroth et al., 2000,

2002). A meta-analysis of SOC concentrations under

different land uses in Brazil suggested that noninten-

sive cropping systems (including perennial crops and

plantations) had the same SOC stocks as natural vegeta-

tion in general, but that coarse-textured soils under this

management lost ca. 20% of their stored C (Zinn et al.,

2005). Reijnders & Huijbregts (2008a) used direct CO2

measurements made by Ishizuka et al. (2005) to infer a

loss of 1.2 t CO2-eq ha�1 yr�1 for South Asian forests



https://www.researchgate.net/publication/5593091_Land_Clearing_and_the_Biofuel_Carbon_Debt?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy


https://www.researchgate.net/publication/222406097_Huijbregts_M.A.J._Biogenic_greenhouse_gas_emissions_linked_to_the_life_cycles_of_bio-diesel_derived_from_European_rapeseed_and_Brazilian_soybeans._J._Clean._Prod._16(18)_1943-1948?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

https://www.researchgate.net/publication/225575464_Temporal_change_in_carbon_stocks_of_cocoaGliricidia_agroforests_in_Central_Sulawesi_Indonesia._Agrofor_Syst?el=1_x_8&enrichId=rgreq-6ae0a0ab-2cb9-4310-8b17-f93ea27daf1b&enrichSource=Y292ZXJQYWdlOzIzNTYzNTQ3NTtBUzo5OTkxMTMyODYwMDA3N0AxNDAwODMyMTI1NjAy

(a)

(b)

(c)

Fig. 1 Land-use change (LUC) emissions for conversion of natural vegetation on mineral soils to oil cropping under typical manage-

ment for top 20 producing countries. (a) Oil palm, (b) soybean and (c) oilseed rape. Strength of shading reflects level of emissions with

black representing 42 t CO2-eq h�1 yr�1 or more.



converted to oil palm plantations in the first 20 years

after conversion. Inclusion of N2O emissions from this

level of SOM loss would give total soil emissions of

1.4 t CO2-eq ha�1 yr�1, which in turn would increase

the LUC emissions calculated here for this region from

a range of 5.6–29.4 to 8.9–32.6 t CO2-eq ha�1 yr�1,

assuming conversions from natural forest to typical

management.

Previous land use

As shown in Table 1, previous land use can make a

big difference to resulting C losses when land is con-

verted. For oil palm, LUC emission levels do not over-

lap between categories of previous land use, even

when comparing across different world regions. Only

forest conversions to palm oil produce net LUC emis-

sions; conversions of all the former agricultural land

uses to palm oil produce net sinks of C. Conversion

of grassland which is fallow after shifting cultivation

provides the biggest sink of C, followed by conversion

of improved grassland with, on average, 65% of the C

sink strength of fallow grassland conversions (com-

paring emissions for different previous land uses from

the same region, soil type and climate zone). Conver-

sion of mature fallow scrubland provides the smallest

sink, with a sink strength of 41% that of converted

fallow grassland on average. Across all the conditions

tested, forest conversion to oil palm cropping loses an

average of 12.9 ± 9.0 t CO2-eq ha�1 yr�1 (mean ± SD),

while mature fallow conversions to palm oil are

a sink of �6.4 ± 1.5 t CO2-eq ha�1 yr�1. Conversions

of improved grasslands to palm oil are a sink

of �10.0 ± 0.1 t CO2-eq ha�1 yr�1, and conversions of

Table 1 Land-use change emission ranges (n = number of combinations of soil, climate and ecological zones) by region and for-

mer land use, assuming typical crop management

Region Previous land-use

Emissions range (t CO2-eq ha�1 yr�1) (n)

Oil palm Soybean Oilseed rape

Africa Natural forest 1.3 to 24.5 (6) 14.2 to 42.7 (9) n.a

Mature fallow �7.6 to –4.9 (5) 9.1 to 11.3 (4) n.a

Short fallow �16.4 to –13.6 (5) 0.3 to 2.5 (4) n.a

Set aside grassland* n.a 0.3 to 2.6 (3) n.a

Improved grassland �10.2 to –10.0 (5) 1.3 to 8.8 (8) n.a

C & S America Natural forest 3.7 to 23.5 (8) 17.8 to 41.6 (13) n.a

Mature fallow �7.1 to 3.7 (8) 10.3 to 12.9 (6) n.a


Set aside grassland* n.a 0.6 to 2.6 (3) n.a


Continental Asia Natural forest 2.3 to 21.0 (5) 16.5 to 39.2 (15) 16.5 to 39.2 (10)



Set aside grassland* n.a 0.6 to 2.8 (5) 0.6 to 5.0 (8)

Improved grassland �10.0 (3) 2.2 to 9.5 (11) 2.2 to 9.5 (8)

Insular Asia Natural forest 5.6 to 29.4 (6) 27.4 to 47.5 (5) n.a


Short fallow �18.4 to �14.2 (5) 0.3 to 2.5 (5) n.a


N America Natural forest n.a 17.3 to 28.1 (8) 17.3 to 27.2 (7)

Set aside grassland n.a 0.3 to 3.3 (6) 0.3 to 3.3 (6)

Improved grassland n.a 1.3 to 10.6 (7) 1.3 to 10.6 (6)

Europe Natural forest n.a 15.6 to 22.1 (9) 15.6 to 22.1 (11)

Set aside grassland n.a 0.6 to 3.3 (5) 0.6 to 3.3 (6)

Improved grassland n.a 2.2 to 10.6 (5) 2.2 to 10.6 (6)

Australia Natural forest n.a n.a 18.4 to 25.2 (3)

Set aside grassland n.a n.a 0.3 to 2.0 (3)

Improved grassland n.a n.a 1.3 to 6.0 (3)

*Temperate zones only except for oilseed rape where tropical regions of India are also included.



short fallow grasslands are a sink of

�15.7 ± 1.6 t CO2-eq ha�1 yr�1.

For the two annual crops, the picture is more com-

plex; all the conversion scenarios tested result in a net

loss of C and in general, forest conversions give the

highest emissions, then mature fallow scrubland

(where applicable) losing 62% less CO2-eq from LUC

on average (when compared with the same soil, cli-

mate and region), then improved grassland with LUC

emissions 73% lower on average than forest conver-

sions. The lowest emissions are from short fallow for

tropical regions (95% lower than forest conversions),

and set aside grassland for temperate regions (91%

lower than forest conversions). This fits with previous

work which showed conversion of abandoned agricul-

tural land greatly reduces carbon losses upon conver-

sion to biofuels (Fargione et al., 2008). There is some

overlap in LUC emissions from different previous LU

categories when compared across all the soil, climate

and regions tested, however; forest conversion to

annual arable cropping loses 24.8 ± 8.0 t CO2-eq

ha�1 yr�1, mature fallow 10.4 ± 1.3 t CO2-eq ha�1

yr�1, improved grassland 6.5 ± 2.8 t CO2-eq ha�1 yr�1,

set aside grassland 2.1 ± 1.2 t CO2-eq ha�1 yr�1, and

short fallow 1.6 ± 1.0 t CO2-eq ha�1 yr�1.

Crop management decisions

As shown in Fig. 2a, crop management decisions can

have a strong impact on loss of soil C and N2O from

SOM under annual arable crops, with scenario B man-

agement (reduced tillage and high inputs) significantly

reducing emissions upon conversion from natural veg-

etation. For oil palm, the relationship is even stronger

with scenario B management producing a more than

double strength of C sink, despite there being less dif-

ference between the two management strategies as both

feature no tillage. However, as noted above, this is

based on a methodology which may underestimate the

impact of conversions to plantations on soil C stocks

and assume they are more positive than more recent

field data may suggest (see, e.g. Hertel et al., 2009; Li

et al., 2011; Potvin et al., 2011).

When total LUC emissions are considered, the effect

of management strategies is far smaller for transitions

from natural vegetation with high standing above

ground C stocks (forests) because the biomass compo-

nent of LUC emissions generally outweighs the soil

component (Fig. 2b). This is also reflected in Reijnders

& Huijbregts (2008b), where tillage vs. no tillage man-

agement has little impact on land use and LUC emis-

sions associated with soybean production on former

Brazilian rainforest sites. However, for land-use transi-

tions from grasslands to arable crops, soil emissions

play a much bigger role in total LUC emissions, and

overall crop management can be considered a key fac-

tor when estimating GHG emissions from LUC (Kim

et al., 2009). Ideally, if yield levels were available for

different management scenarios, for example, at finer

spatial scales, it would be preferable to assess how

these management scenarios affect emissions per tonne

of oil production. This would, for example, allow land

managers to assess tradeoffs in terms of possible yield

loss if they switched to a reduced tillage regime, or sup-

plied a greater proportion of the crop’s N requirement

with organic matter rather than mineral N fertilizers.

However, without management-specific yields, these

assessments are not possible.

Soil type

Soil type does not, in general, have a clear impact on

LUC emission levels because initial soil C stocks are

also dependent on climate zone and prior land use and

management (see Fig. 3, which shows soil LUC emis-

sions from conversions to oilseed rape and soybean

cropping only, as oil palm is either C neutral or a sink

for C when only soil LUC emissions are taken into

Fig. 2 Land-use change (LUC) emissions from natural vegeta-

tion conversions to oilseed rape and soybean cropping under

two different management scenarios: (a) soil emissions only (b)

total LUC emissions. Closed diamonds: oil palm; open squares:

soybean; open triangles: oilseed rape. Linear regression lines

solid: palm oil; dashed: soybean; dotted: oilseed rape.




account). Soils in dry regions tend to lose less C than

those in wetter areas. Therefore, sandy soils, which

tend to occur in dry regions, tend to have lower LUC

emissions than spodic soils, which only occur in cool

temperate moist zones. However, the most common

soil types, high and low activity clays (HAC and LAC),

occur across a wider variety of climate zones, so it is

not possible to make any further generalizations about

relative emission levels based on soil type. The excep-

tion to this is organic soils, which always lose the most

C on conversion. These were not included in the analy-

sis as they tend to occur in very localized pockets and,

in the absence of fine resolution spatial data on cover-

age, would greatly skew the results. However, as the

IPCC considers that cropland on organic soils, such as

peat, loses 5–20 t C ha�1 yr�1, depending on climate

zone, converting these emissions to CO2-eq and includ-

ing N2O emissions from SOM loss, gives LUC emission

levels just for soils of 20–80 t CO2-eq ha�1 yr�1 (assum-

ing LUC from undrained peat to cropping, as opposed

to forest or grassland on already drained peat to

cropping). This means that growing palm oil on a peat

rainforest in Malaysia or Indonesia would lose

111 t CO2-eq ha�1 yr�1, a total of 2223 t CO2-eq ha�1

over 20 years, which is at the higher end of the range of

1294 ± 2158 t CO2 ha�1 calculated by Fargione et al.

(2008).

Emissions per tonne of production for top 20 producingcountries

Using FAO yield data and averaging emissions across

soil, climate and ecological zones, gives an emission

level per tonne of vegetable oil production by country,

assuming either conversion from natural vegetation

and typical management. These values can then be

averaged using a weighting based on country produc-

tion levels to give an estimated global contribution to

LUC emissions. These estimates assume LUC is distrib-

uted according to national production levels, that is,

that if a country grows 20% of global oil palm, it

contributes 20% of global LUC emissions. For palm oil

on mineral soils, national emissions vary from 1.0

to 105.6 t CO2-eq t oil production�1 (see Table 2),

although this highest value is for Guinea, with a subsis-

tence farming yield level. This gives a weighted global

average of 7.0 t CO2-eq t oil production�1 for oil pro-

duced on land converted from natural vegetation. In

comparison, assuming the previous land use is agricul-

tural (fallow after shifting cultivation or improved

grassland) gives a weighted global average of

�3.1 t CO2-eq t oil production�1, demonstrating that

these land-use transitions can act as a C sink, and can

offset emissions from conversion of forest land. Reijn-

ders & Huijbregts (2008a) calculated an approximate

value of 5.8 t CO2-eq t oil production�1 for South

Asian oil palm plantations on former rainforest on min-

eral soils, which is lower than those calculated here for

insular Asia, but only because they used a higher yield

value and divided emissions over 25 years, represent-

ing the lifespan of the plantation.

Land-use change emissions for soybean oil vary from

29.2 to 149.5 t CO2-eq t oil production�1 on land

converted from natural vegetation on a national basis,

giving a weighted global average emission of

50.6 t CO2-eq t oil production�1 (see Table 3). The

Fig. 3 Soil land-use change (LUC) change emissions by soil type for oilseed rape and soybean for all previous LU and management

scenarios. Climate zones open squares: cool temperate dry; closed squares, cool temperate moist; open triangles, tropical dry; closed tri-

angles, tropical moist; grey diamonds, tropical montane; closed diamonds, tropical wet; open circles, warm temperate dry; closed cir-

cles, warm temperate moist.



Table 2 Land-use change (LUC) emissions for natural vegetation to palm oil by country for top 20 producers and weighted global

average emissions based on FAO 2008 yield data

Country

Average LUC emissions

(t CO2-eq ha�1 yr�1)

Annual yield

(t oil ha�1)

LUC emissions

(t CO2-eq t oil�1)

Contribution to

weighted global

average

% t CO2-eq

Malaysia 27.9 4.5 6.1 43.4 2.7

Indonesia 26.9 3.4 8.0 41.3 3.3

Nigeria 11.5 4.2 2.8 3.3 0.1

Thailand 8.1 2.8 2.9 3.2 0.1

Colombia 16.3 4.7 3.5 1.9 0.1

Papua New Guinea 25.2 4.0 6.3 0.9 0.1

Ecuador 13.9 2.3 6.1 0.8 <0.1Ivory Coast 21.0 1.35 15.6 0.7 0.1

Honduras 16.1 2.9 5.5 0.7 <0.1China 4.6 4.7 1.0 0.6 <0.1Brazil 17.3 3.3 5.2 0.5 <0.1Costa Rica 17.1 3.7 4.6 0.5 <0.1Cameroon 17.7 3.1 5.6 0.5 <0.1Guatemala 15.0 3.7 4.1 0.5 <0.1DR Congo 21.9 1.0 21.0 0.4 0.1

Ghana 17.9 0.4 47.1 0.3 0.1

Venezuela 14.0 3.3 4.2 0.2 <0.1Philippines 25.1 3.7 6.7 0.2 <0.1Mexico 10.5 0.3 40.4 0.2 0.1

Guinea 17.0 0.2 105.6 0.1 0.1

Global weighted average LUC emissions (t CO2-eq t oil�1) 7.0

Table 3 Land-use change (LUC) emissions for natural vegetation to soybean oil by country for top 20 producers and weighted glo-

bal average emissions based on FAO 2008 yield data

Country



Annual yield

(t oil ha�1)

LUC emissions


Contribution to

weighted global

average

% t CO2-eq

United States 16.0 0.5 31.5 28.0 8.8

Brazil 34.8 0.5 65.1 19.4 12.6

Argentina 18.8 0.5 35.0 18.7 6.5

China 18.4 0.3 56.9 22.0 12.5

India 17.1 0.2 86.2 4.9 4.2

Paraguay 27.8 0.5 57.1 0.8 0.5

Canada 17.2 0.5 32.4 0.7 0.2

Bolivia 30.5 0.3 100.2 0.5 0.5

Uruguay 29.4 0.4 81.1 <0.1 <0.1Indonesia 44.5 0.2 178.4 1.6 2.8

Russian Federation 14.6 0.2 73.3 0.5 0.4

Ukraine 13.4 0.3 46.8 0.1 0.1

Nigeria 27.6 0.2 149.5 <0.1 <0.1Serbia 21.2 0.5 45.7 0.2 0.1

DPR Korea 20.1 0.2 91.9 0.1 0.1

South Africa 15.6 0.3 48.1 0.1 <0.1Vietnam 25.8 0.3 96.9 <0.1 <0.1Italy 17.8 0.6 29.2 1.0 0.3

Iran 15.6 0.4 35.2 0.6 0.2

Thailand 24.9 0.3 82.1 0.7 0.6




national emission levels for soybean oil have a greater

uncertainty around them than for the other two crops

because a single conversion factor was used for oil pro-

duction per t of soybeans to avoid the assumption that

all the crop area is used for oil production. Considering

only land-use transitions from improved grassland and

either shifting cultivation fallow for tropical regions,

or set aside grassland for temperate regions, reduces

this global average emission to 11.9 t CO2-eq t oil pro-

duction�1. For rapeseed oil, national LUC emissions

vary from 10.2 to 457.3 t CO2-eq t oil production�1,

giving a weighted global average of 31.4 t CO2-eq t oil

production�1 when natural vegetation is converted

(see Table 4). When land-use transitions from im-

proved or set aside grassland are considered, this

weighted global average drops to 7.7 t CO2-eq t oil

production�1.

Sources of uncertainty

Use of the IPCC default methodology (recommended

when country-specific data are unavailable) means

there is a high level of uncertainty associated with the

results of this study, but because a consistent data set

was used for all conditions, specific bias was avoided.

The variation in biomass C stock estimates in natural

vegetation differs depending on the ecological zone

and geographical region, while the default biomass C

stock of annual crops has a 75% error associated with it

(IPCC, 2006). Default reference soil C stocks have a

nominal estimate of 90% error associated with them

(IPCC, 2006). The soil stock change factors for land use,

tillage and inputs generally have error levels of 4–14%associated with them but tropical annual cropping,

shifting cultivation fallow, and all activities in tropical

montane regions have a much higher error of 46–61%(IPCC, 2006). Further uncertainty is added to per-

tonne-of-production emission levels by using FAO

cropping area and production data to calculate yields,

especially for soybean where crop yields were con-

verted to oil yields using a single factor for all countries

to avoid the assumption of all cropping area being used

for oil production.

These error levels are largely estimates based on

expert knowledge and not suitable for conversion to a

single error value for the results presented here. How-

ever, a sensitivity analysis to investigate the impact of

assuming equal crop distribution within countries, and

of using default soil and biomass C input data is

described below.

Table 4 Land-use change (LUC) emissions for natural vegetation to rapeseed oil by country for top 20 producers and weighted

global average emissions based on FAO 2008 yield data

Country



Annual yield

(t oil ha�1)

LUC emissions


Contribution to

weighted global

average

% t CO2-eq

Canada 17.2 0.3 62.5 11.2 7.0

China 18.0 0.7 26.3 28.3 7.4

India 17.1 0.3 55.0 11.3 6.2

Germany 20.6 2.0 10.2 17.3 1.8

France 18.2 1.1 17.3 9.4 1.6

Poland 20.2 1.0 21.2 4.6 1.0

Australia 12.1 0.1 86.6 1.5 1.3

UK 20.4 1.2 16.3 4.7 0.8

Czech Republic 21.2 0.8 27.3 1.7 0.5

Ukraine 13.4 0.1 221.9 0.5 1.2

Romania 16.7 0.2 67.5 0.6 0.4

United States 16.1 1.1 15.3 2.6 0.4

Hungary 20.9 0.2 116.1 0.3 0.3

Denmark 21.0 1.1 19.9 1.1 0.2

Russian Federation 14.6 0.2 61.5 0.9 0.6

Iran 8.7 0.8 11.1 0.9 0.1

Pakistan 10.7 0.9 12.1 2.2 0.3

Slovakia 19.3 0.5 40.6 0.5 0.2

Lithuania 21.5 <0.1 457.3 <0.1 0.2

Bulgaria 16.8 0.7 24.7 0.4 0.1




Crop distribution

The impact of assuming equal crop distribution within

countries was investigated by comparing global aver-

age LUC emissions per tonne of oil production calcu-

lated using the lowest and highest per ha LUC

emissions for each country. This gives a minimum and

maximum level of GHG impact for natural vegetation

conversions to crop production. For palm oil, this gives

a range of 2.4 to 8.0 t CO2-eq t oil production�1 for

conversions from natural vegetation, representing 34–114% of the value shown in Table 2. This reflects the

fact that much of the main producing countries is

covered by ecological zones with high biomass C

stocks, such as tropical rainforest, and therefore the

equal distribution scenario is much closer to the worst-

case scenario than the minimum emissions scenario.

For soybean, the range is 17.3–78.2 t CO2-eq t oil

production�1, representing 34–156% of the value shown

in Table 3. For oilseed rape, the range is 12.2–49.0 t CO2-eq t oil production�1, representing 39–156%of the value shown in Table 4. These values indicate

that assuming LUC is equally distributed within coun-

tries could be overestimating LUC emissions by as

much as 66% in some cases but is by no means the

worst-case scenario, especially for the annual crops.

The assumption that LUC is distributed across coun-

tries according to their current level of crop production

is the single biggest source of uncertainty in the global

LUC emissions per-product estimates. However, com-

plex agrieconomic modelling would be required to

improve on this, and the values are included here illus-

trate the possibilities based on the range of assumptions

outlined.

Biomass C input data

This study used regional defaults for broad classes of

vegetation but local knowledge of biomass C stocks

could greatly improve the estimates made using the

same methodology. For example, the National GHG

Inventory Report for Brazil (Brazil, 2010) gives a range

of biomass C stocks for 6 different forest classes within

the Amazon rainforest area (including submontane but

not montane forest or scrubland) which is covered by

a single ecological zone in this methodology. Substitut-

ing the minimum and maximum values of these

ranges for the default biomass C value, gives per ha

emissions of 19.2–56.1 t CO2-eq ha�1 yr�1 upon con-

version of Amazon rainforest to soybean production

using typical management, in comparison with

41.6 t CO2-eq ha�1 yr�1 calculated here. This single

change makes Brazil’s weighted average LUC

emissions vary between 44.6 and 78.4 t CO2-eq t oil

production�1 for natural vegetation conversions, giv-

ing a range of 46.4–53.0 t CO2-eq t oil production�1

for the global average. This represents 92–105% of the

average calculated here (shown in Table 3). The large

impact of these biomass C stocks is reflected in Hieder-

er et al. (2010) as they found the removal of biomass

contributed ca. 80% of the LUC emissions calculated

for a scenario, where most of the additional cropland

was assigned to Brazil.

Soil C input data

In the background material to the recent European

Commission study (Carre et al., 2010; Hiederer et al.,

2010), default soil C stocks under native vegetation

used by the IPCC methodology and this study are

updated using the latest complete global dataset of soil

parameters – the Harmonized World Soil Database.

Substituting these new values into the calculation of

LUC emissions for conversion from natural vegetation

to oilseed rape production under typical management

gives a range of per ha emissions from 0.7 to

38.0 t CO2-eq ha�1 yr�1 in comparison with the values

shown in Fig. 1c (the minimum emission level for forest

conversions as shown in Table 1 is 15.1 t CO2-

eq ha�1 yr�1). Table 5 shows how these changes reduce

the national average per ha emissions by 3–15% and the

global average LUC emissions for this LU transition by

9%. This indicates that improving soil C stock change

factors would probably have a greater impact in terms

of reducing the uncertainty around soil LUC emissions

than using different soil C stocks prior to conversion.

Implications for carbon footprinting

In 2009, at least 13 different methodologies for calculat-

ing carbon footprints were in use or development

(Plassmann et al., 2010), many with differing bound-

aries and assumptions to account for LUC impacts,

which can result in significantly disparate carbon foot-

print results with differing degrees of variability. The

PCFs which omit LUC impacts may fail to account for a

substantial portion of the product’s true contribution to

climate change. This is most pronounced for products

containing tropically produced agricultural materials

from developing countries, where recent deforestation

for oil and food crops is widespread (Fargione et al.,

2008; Gibbs et al., 2010), and has occurred within the

last 20 years. For example, the Ecoinvent database sug-

gests a value of ~1.7 t CO2-eq t palm oil�1 (Ecoinvent,

2007) over the whole life cycle of oil production exclud-

ing any considerations of LUC. Adding the global aver-

age emissions from LUC calculated in this study would

therefore increase the PCF of palm oil by more than



fivefold if all oil came from recently cleared forest land.

On the other hand, palm oil would become a net C

sink if all the plantations were previously agricultural

or fallow land. This also means that tropically pro-

duced agricultural raw materials will often have an

inequitable emissions burden compared to agricul-

tural materials from developed countries where LUC

has occurred well over 20 years ago (Brenton et al.,

2010; Cederberg et al., 2011). Moreover, limited data

from developing countries make accurate LUC

accounting more contentious, as illustrated by the

higher level of error associated with soil C changes

under agricultural management discussed above. The

first version of PAS 2050 (BSI, 2008) methodology

advocated that highest tier (IPCC, 2006) available data

be used where possible and if this is unknown, a

‘worst in class’ approach should be taken, represented

by the conversion of tropical forest to annual cropland

in Malaysia. In this instance PCFs can be unfairly

overestimated, by up to 1900% (Plassmann et al.,

2010). Recent revisions to PAS 2050 (BSI, 2011) recog-

nize this as ‘overly severe’ and replace the worst-case

default value with a tiered approach using land-use

factors based on country of sourcing or countries of

global production.

This has important implications for companies who

are increasingly seeking to quantify, and in many cases

communicate, the GHG impact of their products.

Nilsson et al. (2010) conducted an LCA comparing but-

ter and margarine and demonstrated the significance of

including LUC for palm oil when accounting for the

GHG emissions associated with margarine. The carbon

footprint of margarines containing a high proportion of

palm oil and palm kernel oil (ca. 50%) was found to

decrease by at least 25% if LUC was not considered

when compared to palm oil coming solely from former

tropical forest land. No LUC was considered for

the other oils used to produce margarine. However,

this study suggests that should LUC be taking place for

the other oils, then significant additional C emissions

could occur. Should a proportion of palm oil be sourced

from land already under agricultural management or

formerly so, then there may be no LUC emissions or

even negative emissions (i.e. sequestration) associated

with this palm oil (Table 1). LUC scenarios for butter,

which would largely be associated with land required

for feed production for cows, were also not considered

in Nilsson et al. (2010). This could result in a significant

underestimation of the PCF for butter where, for

example, soy-based feeds are used (Table 3; see also

Cederberg et al., 2011). Brenton et al. (2010) also high-

lighted the importance of using country-specific data

and the potential for huge inflation of the GHG

estimate when this was unknown due to the severe

Table 5 Land-use change (LUC) emissions for natural vegetation to rapeseed oil calculated using updated Harmonized World Soil

Database soil C stocks for comparison with Table 4

Country



% Change from

value in Table 4

LUC emissions


Contribution to

weighted global

average t CO2-eq

Canada 15.0 �13 54.8 6.1

China 16.9 �6 24.6 7.0

India 15.7 �8 50.7 5.7

Germany 18.1 �12 9.0 1.6

France 16.8 �8 16.0 1.5

Poland 18.1 �10 19.0 0.9

Australia 11.5 �5 82.9 1.2

UK 17.5 �14 14.0 0.7

Czech Republic 18.3 �14 23.5 0.4

Ukraine 11.4 �15 188.8 1.0

Romania 15.2 �9 61.5 0.3

United States 14.7 �9 13.9 0.4

Hungary 18.0 �14 100.0 0.3

Denmark 18.3 �13 17.4 0.2

Russian Federation 13.2 �10 55.6 0.5

Iran 8.1 �7 10.4 0.1

Pakistan 10.2 �5 11.6 0.3

Slovakia 17.6 �9 36.9 0.2

Lithuania 18.9 �12 400.8 0.2

Bulgaria 16.3 �3 24.0 0.1




default LUC values. They present the sensitivity of

carbon footprints to a number of parameters, including

loss of soil C from management practices and electricity

emission factor used, but none are more significant

than the factors relating to LUC (Brenton et al., 2010).

We use readily available methods and data sources

to develop a framework to estimate the LUC compo-

nents of PCFs. We illustrate the utility and limitations

of this framework by assessing the LUC emissions from

three oil crops globally. We show that the framework

can be used in all global regions, and also highlight

where finer resolution data and information (particu-

larly on land management and yield) could improve

reliability of the estimates. Frameworks operating at

higher tiers (region specific soil C change factors or pro-

cess-based models, and high-resolution, spatial data)

are desirable to reduce the uncertainties identified

using this approach, but the framework presented rep-

resents an important step forward for including LUC

emissions in PCFs.

Acknowledgements

Helen Flynn gratefully acknowledges funding from Unilever’sScience and Technology project CH-2010-0341. Pete Smith is aRoyal Society-Wolfson Research Merit Award holder. Theauthors would also like to thank the three anonymous review-ers, whose thorough comments have allowed us to greatlyimprove this manuscript.

References

Audsley E, Brander M, Chatterton J, Murphy-Bokern D, Webster C, Williams A

(2009) How low can we go? An Assessment of Greenhouse gas Emissions From the UK

Food System and the Scope to Reduce Them by 2050, FCRN-WWF-UK.

Brandao M (2011) Food, Feed, Fuel, Timber or Carbon Sink? Towards Sustainable Land use

Systems – A Consequential Life Cycle Approach. PhD Thesis, University of Surrey,

Guildford, UK.

Brazil (2010) Second National Communication of Brazil to the United Nations Framework

Convention on Climate Change. Ministry of Science and Technology, Brasilia.

Brenton P, Edwards-Jones G, Jensen MF (2010) Carbon Footprints and Food Systems. Do

Current Accounting Methodologies Disadvantage Developing Countries? The World

Bank, Washington, D.C.

BSI (2008) PAS2050: Specification for the Assessment of the Life Cycle Greenhouse gas Emis-

sions of Goods and Services. British Standards Institution, London.

BSI (2011) PAS2050: Specification for the Assessment of the Life Cycle Greenhouse gas Emis-

sions of Goods and Services. British Standards Institution, London.

Carre F, Hiederer R, Blujdea V, Koeble R (2010) Background Guide for the Calculation of

Land Carbon Stocks in the Biofuels Sustainability Scheme Drawing on the 2006 IPCC

Guidelines for National Greenhouse Gas Inventories. EUR 24573 EN. Office for Official

Publications of the European Communities, Luxembourg.

Cederberg C, Persson UM, Neouvius K, Molander S, Clift R (2011) Including carbon

emissions from deforestation in the carbon footprint of Brazilian beef. Environmen-

tal Science and Technology, 45, 1773–1779.

Don A, Schumacher J, Freibauer A (2011) Impact of tropical land-use change on soil

organic carbon stocks – a meta-analysis. Global Change Biology, 17, 1658–1670.

Ecoinvent (2007) Ecoinvent Data v2.0, Final Reports Ecoinvent 2007 No. 1–25. Swiss Cen-

tre for Life Cycle Inventories, Dubendorf, CD-ROM, Switzerland.

EPA (2010) Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard

Program; Final Rule. Federal Register Volume 75, No. 58, USA.

EU (2009) Directive 2009/28/EC of the European Parliament and of the Council of 23 April

2009 on the Promotion of the use of Energy From Renewable Sources and Amending and

Subsequently Repealing Directives 2001/77/EC and 2003/30/EC. European Commis-

sion, Brussels.

EU (2010) Commission Decision of 10 June 2010 on Guidelines for the Calculation of

Land Carbon Stocks for the Purpose of Annex V to Directive 2009/28/EC. European

Commission, Brussels.

Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P (2008) Land clearing and the bio-

fuel carbon debt. Science, 319, 1235–1238.

Garnett T (2008) Cooking up a Storm Food, Greenhouse gas Emissions and our Changing

Climate. Food Climate Research Network, Centre for Environmental Strategy, Uni-

versity of Surrey, UK.

Gibbs HK, Ruesch AS, Achard F, Clayton MK, Holmgren P, Ramankutty N, Foley JA

(2010) Tropical forests were the primary source of new agricultural land in the

1980s and 1990s. PNAS, 107, 16732–16737.

Guo LB, Gifford RM (2002) Soil carbon stocks and land use change: a meta analysis.

Global Change Biology, 8, 345–360.

Hertel D, Harteveld MA, Leuschner C (2009) Conversion of a tropical forest into agro-

forest alters the fine root-related carbon flux to the soil. Soil Biology and Biochemis-

try, 41, 481–490.

Hiederer R, Ramos F, Capitani C et al. (2010) Biofuels: A New Methodology to Estimate

GHG Emissions From Global Land Use Change. EUR 24483 EN. Office for Official

Publications of the European Communities, Luxembourg.

IPCC (1996) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Vol 3

(eds Houghton JT, Meira Filho LG, Lim B, Treanton K, Mamaty I, Bonduki Y, Gri-

ggs DJ, Callender BA), pp. 5.1–5.74. IPCC/OECD/IEA. UK meteorological Office,

Bracknell, United Kingdom.

IPCC (2001) Contribution of Working Group III to the Third Assessment Report of the Inter-

governmental Panel on Climate Change 2001 (eds Metz B, Davidson O, Swart R, Pan

J), pp. 171–343. Cambridge University Press, Cambridge, UK and New York, NY,

USA.

IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-

LULUCF) (eds Penman J, Gytarsky M, Hiraishi T, Krug T, Kruger D, Pipatti R,

Buendia L, Miwa K, Ngara T, Tanabe K, Wagner F), pp. 1.1–3.187. IGES, Hayama,

Japan.

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4

Agriculture, Forestry and Other Land Use (eds. Eggleston S, Buendia L, Miwa K,

Ngara T, Tanabe K), pp. 1.1–6.49. IGES, Hayama, Japan.

IPCC (2007) Contribution of Working Group III to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change 2007. (eds Metz B, Davidson OR, Bosch

PR, Dave R, Meyer LA), pp. 171–250. Cambridge University Press, Cambridge,

United Kingdom and New York, NY, USA.

Ishizuka S, Iswandi I, Nakajima Y et al. (2005) The variation of greenhouse gas emis-

sions from soils of various land-use/cover types in Jambi province Indonesia.

Nutrient Cycling in Agroecosystems, 71, 17–32.

Kim H, Kim S, Dale BE (2009) Biofuels, land use change, and greenhouse gas emis-

sions: some unexplored variables. Environmental Science & Technology, 43, 961–967.

Koh LP, Wilcove DS (2007) Cashing in palm oil for conservation. Nature, 448, 993–

994.

Li S, Wu X, Xue H et al. (2011) Quantifying carbon storage for tea plantations in

China. Agriculture, Ecosystems and Environment, 141, 390–398.

Nilsson K, Flysjo A, Davis J, Sim S, Unger N, Bell S (2010) Comparative life cycle

assessment of margarine and butter consumed in the UK, Germany and France.

International Journal of Life Cycle Assessment, 15, 916–926.

van Noordwijk M, Cerri CC, Woomer PL, Nugroho K, Bernoux M (1997) Soil carbon

dynamics in the humid tropical forest zone. Geoderma, 79, 187–225.

Plassmann K, Norton A, Attarzadeh N, Jensen MP, Brenton P, Edwards-Jones G

(2010) Methodological complexities of product carbon footprinting: a sensitivity

analysis of key variables in a developing country context. Environmental Science &

Policy, 13, 393–404.

Potvin C, Mancilla L, Buchmann N et al. (2011) An ecosystem approach to biodiver-

sity effects: Carbon pools in a tropical tree plantation. Forest Ecology and

Management, 261, 1614–1624.

Reijnders L, Huijbregts MAJ (2008a) Palm oil and the emission of carbon-based

greenhouse gases. Journal of Cleaner Production, 16, 477–482.

Reijnders L, Huijbregts MAJ (2008b) Biogenic greenhouse gas emissions linked to the

life cycles of biodiesel derived from European rapeseed and Brazilian soybeans.

Journal of Cleaner Production, 16, 1943–1948.

Russell S (2010) Corporate Greenhouse Gas Inventories for the Agricultural Sector: Proposed

Accounting and Reporting Steps. World Resources Institute, Washington, DC.

Schroth G, Rodrigues MRL, D’Angelo SA (2000) Spatial patterns of nitrogen minerali-

zation, fertilizer distribution and roots explain nitrate leaching from mature Ama-

zonian oil palm plantation. Soil Use and Management, 16, 222–229.



Schroth G, D’Angelo SA, Teixeira WG, Haag D, Lieberei R (2002) Conversion of sec-

ondary forest into agroforestry and monoculture plantations in Amazonia: conse-

quences for biomass, litter and soil carbon stocks after 7 years. Forest Ecology and

Management, 163, 131–150.

Searchinger, T, Heimlich et al. (2008) Use of US croplands for biofuels increases

greenhouse gases through emissions from land-use change. Science, 319, 1238–

1240.

Smiley GL, Kroschel J (2008) Temporal change in carbon stocks of cocoa-gliricidia ag-

roforests in Central Sulawesi, Indonesia. Agroforestry Systems, 73, 219–231.

Smith P (2008) Land use change and soil organic carbon dynamics. Nutrient Cycling in

Agroecosystems, 81, 169–178.

Wahid MB, Abdullah SNA, Henson IE (2005) Oil palm [Elaeis guineensis]: Achieve-

ments and potential. Plant Production Science, 8, 288–297.

Wang M, Haq Z (2008) Letter to Science in Response to 7 Feb 2008 Sciencexpress Arti-

cle. Available at: http://www.transportation.anl.gov/pdfs/letter_to_science_an-

ldoe_03_14_08.pdf (accessed 10 May 2011).

Zinn YL, Lal R, Resck DVS (2005) Changes in soil organic carbon stocks under agri-

culture in Brazil. Soil and Tillage Research, 84, 28–40.



quantifying global greenhouse gas emissions from land-use change for crop production

Documents