journal of applied ecology modelling environmental and ... · of the land-sharing and land-sparing...

Modelling environmental and socio-economic

trade-offs associated with land-sparing and

land-sharing approaches to oil palm expansion

Janice Ser Huay Lee1*, John Garcia-Ulloa1, Jaboury Ghazoul1, Krystof Obidzinski2 and

Lian Pin Koh1,3

1Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland; 2Center for International Forestry

Research, Bogor, Indonesia; and 3Environment Institute, and School of Earth and Environmental Sciences, University

of Adelaide, Adelaide, Australia

Summary

1. The effectiveness of land-sharing and land-sparing approaches has been widely debated.

Yet, few studies quantify the environmental and socio-economic outcomes of these

approaches within a real-world landscape. Indonesia’s plans to increase its palm oil produc-

tion present an opportunity to investigate the potential environmental and socio-economic

implications of the land-sharing and sparing approaches.

2. We developed a computer model to simulate the expansion of oil palm agriculture in

Sumatra, Indonesia, under four different scenarios distinguishable by the dominance of

scheme smallholders or industrial estates: business-as-usual, BAU (25 : 75, scheme smallhold-

ers:industrial estates); high-yielding industry dominated, ESTATE (10 : 90); low-yielding

smallholder dominated, SMALLHOLDER (40 : 60), high-yielding smallholder dominated,

HYBRID (40 : 60; but with improved smallholder yields).

3. Our results reveal several trade-offs associated with varying the proportion of scheme

smallholders and productivity of oil palm plantations. The ESTATE scenario (reflecting land-

sparing) resulted in lowest environmental costs in terms of forest conversion, greenhouse gas

emissions, biodiversity losses and nitrogen fertilizer usage. Additionally, infrastructural devel-

opment and tax revenues were highest under the land-sparing approach, though fewer jobs

were created. The SMALLHOLDER scenario (indicating land-sharing) resulted in highest

environmental costs in terms of forest conversion, carbon dioxide emissions and biodiversity

losses but involved more households in oil palm agriculture and thus created more employ-

ment opportunities. The HYBRID scenario ranked second best in terms of both minimizing

forest loss and job creation. However, the drawbacks of this approach included high nitrogen

fertilizer consumption, lower infrastructural development and lower tax revenues.

4. Synthesis and applications. From an environmental perspective, it is far more important to

implement spatial restrictions on oil palm expansion over forests since increasing the produc-

tivity of smallholdings and industrial estates among the four scenarios examined show mini-

mal differences to biodiversity loss and greenhouse gas emissions. The hybrid approach

shows that increasing the proportion of scheme smallholders need not come at a great envi-

ronmental cost for achieving Indonesia’s palm oil production target. From a policy perspec-

tive, this hybrid approach requires a change in legislation to increase the minimum land area

an industrial estate owner must allocate to scheme smallholders (40%), as well as increased

support to improve productivity in oil palm smallholdings.

Key-words: agribusiness, deforestation, Elaeis guineensis, farmer, livelihoods, trade-offs,

yield intensification

*Correspondence author. E-mail: [email protected]

© 2014 The Authors. Journal of Applied Ecology © 2014 British Ecological Society

Journal of Applied Ecology 2014 doi: 10.1111/1365-2664.12286

Introduction

Rising global demands for more food and biofuels are

driving agricultural production world-wide (WWI 2007;

Tilman et al. 2009; Cirera & Masset 2010; Kearney 2010;

Foley et al. 2011). Approximately 80% of recent agricul-

tural expansion in the tropics came at the expense of

intact and disturbed forests (Gibbs et al. 2010), raising

concerns for the fate of tropical biodiversity (Phalan et al.

2013). Some researchers advocate land-sharing, which

involves applying wildlife-friendly farming methods in

agricultural lands and integrating biodiversity conserva-

tion and agricultural production (Fischer et al. 2008;

Perfecto & Vandermeer 2008). Others argue that subopti-

mal agricultural yields resulting from this land-sharing

approach might drive agricultural expansion and convert

more natural vegetation to meet growing demands for

crops (Green et al. 2005). These critics, instead, propose a

contrasting strategy of land-sparing, which separates land

for conservation from land for agricultural production

and maximizing production on agricultural land so that

natural vegetation could be spared from agriculture

(Green et al. 2005; Phalan et al. 2011b). The effectiveness

of the land-sharing and land-sparing approaches to con-

servation in the face of rising agricultural demands has

been widely debated (Fischer et al. 2008, 2011; Tscharntke

et al. 2012). Yet, few studies have quantified the environ-

mental and socio-economic outcomes of these strategies in

relation to the biophysical and socio-economic contexts of

a real-world agricultural landscape.

Oil palm Elaeis guineensis expansion in Indonesia is a

striking example of how agricultural expansion has

impacted forests, livelihoods and biodiversity over the

last thirty years (Sheil et al. 2009). Oil palm has contrib-

uted significantly to Indonesia’s economic development

and provided an important source of employment (Zen,

Barlow & Gondowarsito 2006; Bahroeny 2009). How-

ever, its rapid expansion has also brought about environ-

mental problems (Koh et al. 2011; Carlson et al. 2013)

and social conflicts (Colchester et al. 2006). Indonesia

has plans to increase annual crude palm oil (CPO) out-

put by 69% to 40 Mt by 2020 (comparisons are made

relative to 2012 oil palm statistics) (Bahroeny 2009;

Suparno & Afrida 2009; Indonesian Ministry of Agricul-

ture 2011). This prospect of further oil palm expansion

in Indonesia presents an opportunity to examine the

potential environmental and socio-economic implications

of pursuing a land-sparing vs. land-sharing approach to

its development.

The oil palm industry in Indonesia comprises industrial

estates and oil palm smallholdings which broadly reflect

the land-sparing and land-sharing approaches to oil palm

cultivation, respectively. Industrial estates are large

commercial estates owned by either government or private

companies and are intensively managed to maximize

efficiency in plantation practices (e.g. fertilizing and

harvesting routines) (Corley & Tinker 2003). Industrial

estates are on average 5000–6000 ha in size, but can be as

large as 20 000 ha (Casson 2000; Elson 2009), and achieve

the highest CPO yields (3�60–3�77 t ha�1 y�1) among oil

palm producers in Indonesia (Indonesian Palm Oil Coun-

cil 2010). In contrast, smallholdings are between 2 and

50 ha in size with average CPO yields that are 35–40%

lower than industrial estates (Suharto 2009). Smallhold-

ings can be managed either independently by the farmer

(independent smallholders) or in association with an oil

palm company (scheme smallholders) (Zen, Barlow &

Gondowarsito 2006). In Indonesia, oil palm companies

are required by law to allocate at least 20% of their con-

cession area to affiliated scheme smallholders (OECD

2012).

Currently, approximately 40% of Indonesia’s oil palm

plantations are smallholdings and 60% are industrial

estates (Indonesian Ministry of Agriculture 2011). Within

the smallholder sector, there is an equal split between

independent and scheme smallholders (Colchester et al.

2011). Hence, the land occupied by independent small-

holdings, scheme smallholdings and industrial estates in

Indonesia is estimated to be in the ratio of 20 : 20 : 60.

This also implies that oil palm companies, on average,

allocate 25% of their concession area to scheme

smallholders.

An oil palm industry that is dominated by either indus-

trial estates or smallholdings could have different environ-

mental and livelihood implications. For example,

industrial estates have been shown to retain lower bird

diversity than smallholdings (Azhar et al. 2011). Also,

industrial estates typically use larger quantities of nitrogen

fertilizers per unit area to maintain high yields compared

to smallholders (Boer et al. 2012), which could increase

the risk of water pollution and other environmental

impacts (Sheil et al. 2009). Furthermore, more efficient

labour management in industrial estates allows them to

operate with fewer workers on a per unit area basis com-

pared to smallholdings, which might imply fewer employ-

ment opportunities, particularly for local communities

(Elson 2009). On the other hand, the development of

industrial estates is associated with greater infrastructure

development (e.g. roads), as well as higher tax revenues

for provincial and national governments, which might not

be as forthcoming from a smallholder-dominated oil palm

industry (Koh & Wilcove 2007; Gillespie 2011).

From a policy perspective, the national government has

the ability to influence the dominance of industrial estates

or smallholdings in the oil palm industry by legislating

the minimum proportion of concession area that an

industrial estate owner must allocate to its affiliated

scheme smallholders. We simulated oil palm expansion in

Sumatra, Indonesia, under four development scenarios

(Table 1) distinguishable by this particular requirement.

We did so to explore the environmental and socio-

economic implications of an oil palm industry dominated

© 2014 The Authors. Journal of Applied Ecology © 2014 British Ecological Society, Journal of Applied Ecology

2 J. S. H. Lee et al.

by industrial estates or smallholdings (i.e. land-sparing or

land-sharing approaches):

1. business-as-usual (BAU), whereby industrial estates

allocate at least 25% of their concession to scheme small-

holdings,

2. high-yielding land-sparing approach (ESTATE),

whereby this requirement was reduced to 10% of conces-

sion area,

3. low-yielding land-sharing approach (SMALL-

HOLDER), whereby this requirement was increased to

40% of concession area. This proportion of smallholder

affiliation is not unprecedented as the original smallholder

schemes (nucleus estate schemes) developed in Indonesia

in the late 1960s were based on a land allocation ratio of

60 : 40, 70 : 30 or 80 : 20 in favour of scheme smallhold-

ers (McCarthy 2010). In recent years, this land allocation

ratio has reversed (McCarthy, Vel & Afiff 2012), possibly

to increase tax and export revenues from industrial estates

(Gillespie 2011).

4. high-yielding land-sharing scenario (HYBRID),

whereby industrial estates allocate at least 40% of their

concession area to scheme smallholdings (same as the

SMALLHOLDER scenario) and the CPO yield gap

between industrial estates and smallholdings was reduced.

In this case, CPO yields from independent and scheme

smallholdings would be increased by 49% and 18%,

respectively (see Materials and methods), due primarily to

increased use of fertilizers, pesticides and higher quality

seeds, and improved labour efficiency (Zen, Barlow &

Gondowarsito 2006; Boer et al. 2012). The Indonesian

government and international organizations would

provide the institutional support for these improvements

(Drajat 2010; World Bank 2011; Yulisman 2011; Boer

et al. 2012). Indeed, increasing smallholder productivity is

one of the main aims of the Indonesian Sustainable Palm

Oil initiative (UNDP 2012), and has been identified by

World Bank as one of its key criteria for re-engagement

with the Indonesian oil palm industry (World Bank 2011).

It is important to note that these scenarios are distin-

guished by the dominance of industrial estates or scheme

smallholders. The proportion of independent smallholders

would remain at 20% of total oil palm area in Indonesia

across all scenarios since the expansion of independent

smallholdings is not regulated under any of Indonesia’s

laws, and the lack of baseline data on independent small-

holder oil palm expansion limits our ability to confidently

predict the growth of independent smallholdings. We

developed a computer model to simulate the expansion of

oil palm agriculture in Indonesia under these four devel-

opment scenarios, and compared the resultant environ-

mental (forest loss, biodiversity loss, net carbon

emissions, nitrogen fertilizer usage) and socio-economic

(employment, road development, tax revenues) outcomes.

Materials and methods

OIL PALM EXPANSION AND PRODUCTION TARGETS

In 2012, the estimated area of oil palm cultivation in Indonesia

was ~9�3 Mha (Indonesian Ministry of Agriculture 2011), of

which 71�7% (~6�7 Mha) was mature oil palm plantations pro-

ducing ~23�6 Mt y�1 of CPO. In order to meet the Indonesian

Ministry of Agriculture’s production target of 40 Mt y�1 of

CPO, Indonesia needs to produce an additional ~16�4 Mt y�1 of

CPO. If we assume that the production capacity of existing

immature oil palm plantations would contribute to future pro-

duction (i.e., ~9�3 Mt y�1 CPO from ~2�6 Mha of plantations at

2012 national average yield 3�55 t ha�1 y�1), Indonesia would

still need to produce an additional ~7�1 Mt y�1 of CPO in order

to reach its production target. Based on the percentage contribu-

tion of Sumatra to national CPO production (69�7%; see Appen-

dix S1, Supporting information) (Indonesian Ministry of

Agriculture 2010), we estimated that Sumatra would need to

increase its CPO production by ~4�9 Mt y�1. We included only

Sumatra in our analysis as Sumatra contributes to the bulk

(69�7%) of Indonesia’s oil palm production.

OIL PALM YIELDS OF SMALLHOLDINGS

We obtained data on average yields of FFB in independent small-

holdings, scheme smallholdings and industrial estates from two

meta-analytical papers (Zen, Barlow & Gondowarsito 2006; Boer

et al. 2012). Based on these data, we derived the average relative

yield of independent (~44%) and scheme (~76�6%) smallholdings

compared to industrial estates (see Appendix S1, Supporting

information). These values were used in our calculations of FFB

production in the BAU, SMALLHOLDER and ESTATE model

scenarios. The same studies also reported potential improvements

in FFB yields in smallholdings through better management and

production practices (Zen, Barlow & Gondowarsito 2006; Boer

Table 1. Proportion of land area under independent smallhold-

ings, scheme smallholdings and industrial estates under our four

model scenarios. Scheme smallholdings and Industrial estates

make up 80% of the land area across all scenarios while Indepen-

dent smallholdings make up 20% of the land area. Relative yields

for smallholdings were increased under the HYBRID scenario.

See Table S2 (Supporting information) for more information on

relative yields

Scenarios

BAU ESTATE

SMALL

HOLDER HYBRID

Proportion of land area

Independent

smallholdings

20 20 20 20

Scheme

smallholdings

20 8 32 32

Industrial

estates

60 72 48 48

Relative yields (%)

Independent

smallholdings

43�9 43�9 43�9 65�5

Scheme

smallholdings

76�6 76�6 76�6 90

Industrial

estates

100 100 100 100


Simulating oil palm expansion in Sumatra 3

et al. 2012). We derived that, under these improved conditions,

the relative yield of independent and scheme smallholdings would

increase to 65�5% (i.e., a 49% improvement) and 90% (18%

improvement), respectively (see Table S2, Supporting informa-

tion). These values for improved yields were used in our

HYBRID scenario.

SPATIAL DATA BASE

We generated a raster data base at 100-ha grid cell resolution

containing information on several key biophysical and infrastruc-

tural attributes for Sumatra using the following geographic infor-

mation systems (GIS) layers: land cover, above-ground biomass

carbon, peatlands, suitability for oil palm cultivation, existing

road networks, existing oil palm mills, existing shipping ports

and locations of capital cities and towns (see Appendix S1,

Supporting information). We excluded regions with missing

spatial data as well as unsuitable areas for oil palm expansion

such as urban areas, water bodies, islands, areas higher than

1000 m in altitude, montane land classes, areas under an oil

palm, timber or logging concession, areas zoned as settlements

and protected areas (see Appendix S1, Supporting information).

Variation in yield potential due to soil type and climate variables

was taken into account under our suitability map for oil palm

cultivation derived from the Global Agro-Ecological Zones

(Fischer et al. 2001). This data base was compiled into a data

frame in R using the ‘raster’ package for subsequent analyses

(R Development Core Team, 2008; Hijmans & Etten 2012).

SIMULATING OIL PALM EXPANSION

We simulated oil palm expansion in Sumatra by assigning ‘conver-

sion priorities’ to each grid cell based on the biophysical and in-

frastructural attributes described above. Furthermore, we

considered that different oil palm producers might respond differ-

ently to different attributes. We assumed that distance to oil palm

mills, roads, capital cities and main towns would have the strong-

est influence on a grid cell being converted to an independent

smallholding. Based on these attributes, each grid cell is assigned

a conversion priority index for it becoming an independent small-

holding (Pids; see Appendix S1, Supporting information).

On the other hand, an industrial estate developer who has the

resources to build new roads and oil palm mills might have a dif-

ferent set of criteria for deciding where to establish a new planta-

tion. Scheme smallholders would have similar criteria as they

typically rely on the estate developer for plantation management

and FFB processing (Vermeulen & Goad 2006). We assumed that

distance to shipping ports, contiguity of suitable land, and agri-

cultural suitability of the land for oil palm development would

have the highest weighting on a grid cell becoming an industrial

estate or scheme smallholder. Based on these attributes, each grid

cell is also assigned a second conversion priority index for it

becoming an industrial estate or scheme smallholding (Pcps; see

Appendix S1, Supporting information).

During the first part of the simulation, each grid cell was

labelled as an independent smallholding if Pids > Pcps; conversely,

if Pids < Pcps the grid cell would be labelled as either an industrial

estate or scheme smallholding, based on the ratio defining each

model scenario (e.g. 90 : 10 in ESTATE scenario). This part of

the simulation produced 159 097 grid cells labelled as indepen-

dent smallholdings, and 103 508 grid cells labelled as industrial

estates or scheme smallholdings. In order to maintain the propor-

tion of independent smallholdings at 20% of total oil palm

planted area in Sumatra, 133 641 grid cells were randomly

selected and excluded from subsequent analyses, leaving 25 456

grid cells representing independent smallholdings. It is important

to note that a different set of grid cells was excluded for each run

of the simulation.

During the second part of the simulation, the pool of 128 964

grid cells (25 456 independent smallholding cells and 103 508

industrial estates/scheme smallholding cells) was converted to oil

palm one cell at a time until FFB production reaches the produc-

tion target for Sumatra (21�7 Mt y�1). The order of conversion

of these cells into oil palm was quasi-random, weighted by a gen-

eral conversion priority index based on distance to roads, capital

cities and towns (Pgen; see Appendix S1, Supporting information).

Suitable regions for scheme smallholding and industrial estate

expansion were clustered in the middle of the island possibly due

to higher weighting given to the contiguity of suitable land under

our Pcps (Fig. 1). Our simulation model was coded in R (R Devel-

opment Core Team, 2008).

MEASURING OUTCOME VARIABLES

We performed a total of 10 000 runs for each model simulation

and obtained the average values for the following outcome vari-

ables: area of different land cover types converted to oil palm,

above-ground biomass and peat soil carbon losses to oil palm,

area of independent smallholdings, area of scheme smallholdings

and area of industrial estates.

Additionally, we calculated bird biodiversity outcomes based

on the projected areas of land cover converted to oil palm small-

holdings and industrial estates under each scenario using the

matrix calibrated species area model developed by Koh &

Ghazoul (2010a) (see Appendix S1, Supporting information). We

estimated net changes in carbon dioxide emissions by considering

(i) changes in biomass from land cover change (above-ground

biomass loss and peat soil carbon losses via oxidation as well as

carbon biomass gain in oil palm plantations) under a burning

and a non-burning scenario, and (ii) greenhouse gas emissions

from nitrogen fertilizer use (see Appendix S1, Supporting infor-

mation). We quantified nitrogen fertilizer usage, employment,

road development and tax revenue based on projected areas of

independent smallholdings, scheme smallholdings and industrial

estates in Sumatra upon reaching its oil palm production target

(see Appendix S1, Supporting information).

Results

Under the BAU scenario, our model predicted an expan-

sion of 1699 700 ha of oil palm plantations in Sumatra to

meet its FFB production target of 21�7 Mt y�1. The

ESTATE scenario (land-sparing) led to a reduction of this

area requirement by ~3�5% (i.e., predicted expansion of

1640 300 ha), while the SMALLHOLDER scenario (land-

sharing) led to an increase of ~4% in oil palm expansion

(i.e., 1763 500 ha) compared to the BAU scenario. The

HYBRID scenario (high-yield land-sharing) predicted an

expansion of 1645 200 ha of oil palm plantations, which

is just ~5000 ha higher than that of the ESTATE

scenario, and 3�2% lower than BAU.



Across all scenarios, oil palm expansion occurred pre-

dominantly over the following land cover categories

(Fig. 2): lowland mosaic (645 442–693 993 ha; 39�6%),

plantation or regrowth forests (564 447–606 797 ha;

34�4%) and lowland open (281 412–302 517 ha; 17�5%).

In comparison, the extent of oil palm expansion on low-

land forests and peatswamp forests was considerably

lower [lowland: 7�2% (117 291–126 126 ha); peatswamp:

1�4% (23 354–25 098 ha)]. The extent of oil palm expan-

sion over peatlands (which include both peatswamp for-

ests and non-forested land cover and which collectively

are an important reservoir of below-ground carbon)

amounted to ~9% (153 752–165 197 ha) across all four

scenarios.

The above model results on oil palm area requirements

were based on the mean relative yields of FFB derived for

independent and scheme smallholdings (Table S2, Support-

ing information). In a sensitivity analysis, we applied low

and high estimates of relative FFB yields for independent

and scheme smallholdings (Table S2, Supporting informa-

tion) to calculate the area of oil palm required to reach

Sumatra’s FFB production target (Table S8, Supporting

information). Under low estimates of relative yields for

independent and scheme smallholdings, the area of oil

palm predicted under the BAU scenario was 1760 300 ha,

and the ESTATE scenario accounted for the least amount

of land used (5�5% less than BAU). Under high estimates

of relative yields, the area of oil palm predicted under the

BAU scenario was 1643 100 ha, and the HYBRID

scenario accounted for the least amount of land used

(4�5% less than BAU) (Table S8, Supporting information).

Projected area of total oil palm expansion using low and

high relative yields for each scenario varied by 1–6%

compared to the use of mean estimates of relative yields

(Fig. S1, Supporting information).

The ESTATE scenario led to the lowest environmental

costs in terms of peatswamp and lowland forest loss and

peatland conversion (Table 2, Fig. 3a,b). The ESTATE

scenario also led to lower total nitrogen fertilizer usage

compared to the BAU and SMALLHOLDER scenarios

(Fig. 3c), even though on a per unit area basis nitrogen

fertilizer usage was second highest in the ESTATE sce-

nario (4�1 Nt ha�1). Net greenhouse gas emissions derived

from land cover change and nitrogen fertilizer usage was

lowest under the ESTATE scenario, followed by the

HYBRID scenario, BAU scenario and SMALLHOLDER

scenario (Fig. 3b). Oil palm expansion in Sumatra would

result in the loss of 0�27% of bird diversity under the

ESTATE scenario (equivalent to the local extirpation of

two bird species), which is only slightly lower than the

(a)

(b)

(c)

(d)

Fig. 1. Suitable regions determined by conversion priority indices for oil palm expansion under the (a) BAU, (b) ESTATE, (c) SMALL-

HOLDER and (d) HYBRID scenarios. The large figure of Sumatra reflects the BAU scenario.



loss of bird diversity under the BAU (0�28%) or SMALL-

HOLDER (0�29%) scenarios (Table 2).

Oil palm development under the ESTATE scenario gen-

erated a total of US$5�4 billion in tax revenues at the

national level, which was significantly higher than reve-

nues generated by the BAU (US$ 4�7 billion), SMALL-

HOLDER (US3�9 billion) and HYBRID (US 3�6 billion)

scenarios (Fig. 4a). A similar trend can be observed for

tax revenues at the provincial and district levels (Fig. 4b).

The ESTATE scenario also resulted in better infrastruc-

tural development in terms of road construction

(80 991 km) compared to the BAU (79 580 km),

SMALLHOLDER (78 781 km) and HYBRID

(73 343 km) scenarios (Fig. 4c). However, the ESTATE

scenario created the fewest jobs (245 102) compared to

the BAU (268 304), SMALLHOLDER (293 259) and

HYBRID (273 610) scenarios (Fig. 4d).

Discussion

Indonesia’s rapidly expanding oil palm industry is both

an important source of revenue for the economy and an

immediate threat to carbon-rich and biodiverse forests.

This has led to an urgent need to identify future

development pathways that could reconcile environmental

and socio-economic priorities of oil palm expansion in

the country (Sandker, Suwarno & Campbell 2007; Koh

& Ghazoul 2010b; Wicke et al. 2011; Sayer et al. 2012;

Carlson et al. 2013). Here, we modelled the environmen-

tal and socio-economic outcomes from a land-sparing vs.

land-sharing approach reflecting alternative policy

changes to the scheme smallholder initiative. From a bio-

diversity conservation perspective, our results show that

biodiversity losses derived from a land-sparing approach

are lower than a land-sharing approach, though the dif-

ferences are slight among all scenarios. This indicates

that the main determinant of biodiversity losses from oil

palm expansion depends not on who grows oil palm but

where expansion occurs. Therefore, spatial restrictions on

oil palm expansion over forests could be a priority for

conservation efforts, since differences in oil palm yields

appear to have little influence. However, it is important

to note here that our biodiversity outcomes were based

on species richness of bird taxa which could limit our

approach in two ways. First, birds represent only one

group of species, and being volant, are able to resist

land-use changes. Secondly, our use of species richness as

an indicator for biodiversity may not be the best

0

100 000

200 000

300 000

400 000

500 000

600 000

700 000

800 000

Mangrove Peatswamp forest Lowland forest Plantation/regrowth forest

Lowland mosaic Lowland open

Are

a co

nver

ted

to o

il pa

lm (h

a)

Land cover

BAUESTATESMALLHOLDERHYBRID

Fig. 2. Extent of land cover converted to

oil palm predicted by our model under the

BAU, ESTATE, SMALLHOLDER, and

HYBRID scenarios. Error bars represent

the sensitivity of land cover outcomes to

low (top error bar) and high (bottom error

bar) estimates of FFB yields drawn from

Table S2 (Supporting information). The

95% confidence intervals for land cover

outcomes are presented in Table S9

(Supporting information).

Table 2. Mean (� SD) values of outcome variables for oil palm plantations, peatland conversion and biodiversity losses averaged over

10 000 runs from our computer model simulation. Values in parentheses represent the 95% confidence interval range. Biodiversity loss is

represented as the percentage of bird species extinction (see Supporting Information for more details). Please refer to Table S8 (Support-

ing information) for oil palm expansion under low and high yield estimates

Oil palm plantations (ha)

Scenario

Oil palm

expansion

(ha)

Independent

smallholdings Scheme smallholdings Industrial estates

Peatland converted

(ha)

Biodiversity

losses (%)

BAU 1699 700 335 472 � 4879 341 068 � 4844 1023 158 � 5946 159 306 � 3593 0�286 � 5�991(335 376; 335 568) (340 973; 341 163) (1023 041; 1023 275) (159 236; 159 376) (0�169; 0�403)

ESTATE 1640 300 323 813 � 4766 131 596 � 3230 1184 890 � 5351 153 752 � 3590 0�277 � 5�987(323 720; 323 906) (131 533; 131 659) (1184 785; 1184 995) (153 682; 153 822) (0�160; 0�394)

SMALL

HOLDER

1763 500 348 002 � 4944 566 258 � 5755 849 238 � 6158 165 197 � 3718 0�296 � 6�021(347 905; 348 099) (566 145; 566 371) (849 117; 849 359) (165 124; 165 270) (0�178; 0�414)

HYBRID 1645 200 324 836 � 4764 528 154 � 5650 792 209 � 5996 154 172 � 3596 0�278 � 5�989(324 743; 324 929) (258 043; 528 265) (792 091; 792 327) (154 102; 154 242) (0�161; 0�395)



approach in evaluating land-sparing and land-sharing

strategies (Phalan et al. 2011a). Instead, species abun-

dance or population density within these landscapes

under different production systems (smallholdings and

industrial estates) could be considered (Phalan et al.

2011a). It is also important to note that our model may

overestimate biodiversity losses from smallholdings since

our simulation occurred in blocks of 100 ha units which

may introduce greater habitat fragmentation and resem-

ble a more homogenous landscape than we would expect

smallholdings to be. Concerning oil palm agriculture,

most studies propose a high-yielding land-sparing

approach to conserve biodiversity (e.g. Phalan et al.

(2009), Edwards et al. (2010), Phalan et al. (2011b)).

However, these studies (including ours) do not account

for possible expansion following intensification driven by

higher agricultural land rents (Angelsen 1999; Nantha &

Tisdell 2009) as illustrated by Phelps et al. (2013) in the

Congo Basin.

Based on the results of our model, the contribution of

forested land towards reaching Sumatra’s palm oil pro-

duction target is small (<10% of total expansion area),

suggesting that improving forest protection will not

greatly compromise palm oil production. Lowland forest

conversion predicted from our model (~120 000 ha) was

approximately 1�2–1�4 times lower compared to lowland

forest loss within Sumatra’s oil palm industry from 2000

to 2010 (~290 000 ha) (Lee et al. 2013a). The extent of

peatswamp forest conversion predicted from our model

was much lower (~24 000 ha; 14�2–15�4 times lower) com-

pared to peatswamp forest loss from 2000 to 2010

(~380 000 ha). The reduced extent of peatswamp forest

conversion may reflect (i) lower extents of forested peat-

lands in 2010 (1�8 Mha) as compared to 2000 (3�1 Mha)

in Sumatra, and/or (ii) the relative importance placed on

biophysical and geographical attributes as compared to

socio-political attributes in determining oil palm expan-

sion in our model (Casson 2000; Irawan, Tacconi & Ring

2013).

Non-forested peatlands constitute more than 80% of

projected peatland conversion to oil palm in our model.

While converting non-forested peatlands is certainly less

damaging than converting forested peatlands from a bio-

diversity conservation perspective, there could be severe

carbon emission impacts from the conversion of any peat-

lands into oil palm due to the drainage process associated

with cultivating oil palm on peat (Koh et al. 2011; Page

et al. 2011; Moore et al. 2013). We show that large

extents of non-forested peatlands are situated in favour-

able oil palm production regions and if not restored, are

likely to be converted into oil palm, accounting for 25–

34% of gross carbon emissions (84–125 Mt C; range

reflecting the minimum and maximum values from a no

burning and burning scenario) over a 25-year oil palm

plantation cycle. To reduce carbon emissions while reach-

ing Sumatra’s palm oil production target, a prohibition

on planting over peatlands is crucial.

Employment, road development and increased tax bene-

fits from the oil palm industry are often cited by local

and central authorities for expanding the oil palm sector

in Indonesia (Bahroeny 2009; Indonesian Ministry of

Economic Affairs 2011). We show within our land-sharing

scenarios (SMALLHOLDER, HYBRID), increasing the

proportion of land allocated to smallholders leads to a

higher number of jobs but results in lower road develop-

ment and tax revenues compared to an industrial expan-

sion of oil palm (ESTATE). The higher tax revenues

generated from the ESTATE scenario (Fig. 4a,b) indicate

a high economic incentive that might favour industrial

estate development over smallholdings. While increasing

employment and road development can spur greater rural

development, they might also impact rural population

dynamics by increasing the rate of migration as demon-

strated in Riau (Susanti & Burgers 2012; Budidarsono,

Susanti & Zoomers 2013). The impact of migration on

the long-term sustainability of oil palm development is

beyond the scope of our analysis but warrants future

research. Often, job creation, increased tax revenues and

road construction are seen as positive socioeconomic indi-

cators in developing countries. However, it is important

to note that labour conditions vary widely within oil palm

plantations (Li 2011; Skinner 2013), government revenues

have been mismanaged (Human Rights Watch 2013), and

roads could lead to further environmental degradation

(Chomitz & Gray 1996; Gaveau et al. 2009). Our results

provide only socio-economic figures but are unable to

paint the context of successful development of these indi-

cators, which rely crucially on effective governance.

Agricultural lands and secondary regrowth comprised

the bulk of land cover converted to oil palm under our

model simulations (Fig. 2). These largely agricultural

areas may come under existing ownership by local com-

munities and incur high social costs should a purely

industrial estate expansion occur. Allocating a higher pro-

portion of land for smallholder agriculture such as that

defined under our SMALLHOLDER and HYBRID sce-

nario may help mitigate social conflicts over land grab-

bing issues and spread the benefits of oil palm production

among local communities. Assuming that the average oil

palm smallholding is 2 ha (Lee et al. 2013b), the potential

number of households which can be supported by oil

palm agriculture would amount to 457 130 and 426 495

households under a SMALLHOLDER and HYBRID sce-

nario, respectively, approximately twice the number of

smallholder households involved under a ESTATE sce-

nario (227 704 households) (Appendix S1, Supporting

information). Linking smallholder oil palm development

with pro-poor policies such as involving landless farmers

could also contribute towards reducing the number of

landless farmer households, which was reported in 2003

to be ~1�7 million for the island of Sumatra (Badan Pusat

Statistik 2004; Zen, McCarthy & Gillespie 2008). How-

ever, success of Indonesian smallholder oil palm develop-

ment varies (Rist, Feintrenie & Levang 2010; McCarthy,



Gillespie & Zen 2012), and partnerships with oil palm

smallholders need to be transparent and established

under fair and equitable conditions [see Cramb (2013),

McCarthy (2010), Li (2011), and McCarthy, Gillespie &

Zen (2012) for varying outcomes of smallholder oil palm

development].

Our HYBRID scenario appears to be the best compro-

mise between environmental and socio-economic out-

comes for achieving Indonesia’s crude palm oil

production target. Less land is converted, and more small-

holder households were involved as well as employment

opportunities created under this HYBRID approach. The

drawbacks, however, include increased nitrogen fertilizer

consumption as well as lower road development and tax

revenues generated. In the last International Conference

on Oil Palm and the Environment 2012 meeting in Bali,

smallholder yield intensification was raised as an example

to increase palm oil production without resulting in rising

land demands (Widjaja 2012). Indeed initiatives such as

the Smallholder Acceleration Reducing Emissions from

Deforestation and Degradation Plus (REDD+) Program

(SHARP - http://www.sharp-partnership.org/) are finding

solutions for reduced deforestation and improved rural

livelihoods via increased yields from smallholder agricul-

tural production, including oil palm. While these initia-

tives are laudable for enhancing smallholder agriculture

and empowering smallholders, the link between deforesta-

tion and increased yields is complex (Angelsen 1999;

0

1

2

3

4

5

6

7

8

Tota

l Nit

roge

n fe

rtili

zer

usag

e (M

t)

0

20 000

40 000

60 000

80 000

100 000

120 000

140 000

160 000

Are

a of

fore

st lo

ss (h

a)

Peatswamp forest Lowland forest(a)

(b)

(c)

0

200

400

600

800

1000

1200

BAU ESTATE SMALLHOLDER HYBRID



Net

gre

enho

use

gas

emis

sion

s fr

om la

ndco

ver

chan

ge a

nd N

itro

gen

fert

ilize

r us

age

(Mt)

Burning No burning

Fig. 3. Environmental outcomes for (a)

forest cover loss, (b) net greenhouse gas

emissions and (c) total nitrogen fertilizer

usage predicted by our model under the

BAU, ESTATE, SMALLHOLDER and

HYBRID scenarios. Error bars represent

the sensitivity of environmental outcomes

to low (top error bar) and high (bottom

error bar) estimates of fresh fruit bunch

yields drawn from Table S2 (Supporting

information). The 95% confidence inter-

vals for environmental outcomes are pre-

sented in Table S10 (Supporting

information).



Fischer et al. 2011) and relies crucially on effective land-

use planning to avoid increased deforestation (Balmford,

Green & Phalan 2012). While more land is spared under

the HYBRID scenario, increased smallholder incomes

from oil palm production may lead to further land con-

version as wealthy smallholders acquire more land to

increase oil palm cultivation (McCarthy 2010). In Indone-

sia, other socio-political complexities of oil palm expan-

sion also exist, such as high economic incentives for local

authorities to allocate forested land for oil palm establish-

ment (Irawan, Tacconi & Ring 2013). These institutional

problems may continue to drive the expansion of high-

yielding oil palm plantations over forests as demonstrated

in Peru (Guti�errez-V�elez et al. 2011).

CONCLUSION

Using spatially explicit information on various important

attributes for oil palm expansion, we developed a com-

puter model to simulate different pathways of oil palm

expansion in Indonesia, and considered the environmental

and socio-economic outcomes of pursuing oil palm expan-

sion in Sumatra via a land-sparing, land-sharing, and a

hybrid approach. We demonstrate that the hybrid

approach generated the best compromise between envi-

ronmental and socio-economic outcomes from oil palm

expansion (Fischer et al. 2008). However, it is important

to note two limitations of our model: (i) we did not

account for complex socio-political determinants of oil

palm expansion, and (ii) we could not capture long-term

economic decisions towards land-use change from oil

palm smallholders who benefited from yield intensifica-

tion. Nevertheless, our model does show the value of

increasing oil palm land allocation to smallholders to

involve more local communities in oil palm production,

increase employment opportunities and potentially reduce

poverty in rural parts of Sumatra. We also demonstrate

that land spared from high-yielding oil palm expansion

led to lower biodiversity losses than low-yielding oil palm

expansion, though differences were very slight.

As Indonesia moves forward to reach its 40 Mt y�1

CPO production target, we show the value of investing in

high yields and increasing company–community partner-

ships to move towards a more equitable land-sharing and

sparing arrangement for future oil palm production. This,

however, needs to be coupled with effective land-use plan-

ning to avoid escalating deforestation from increasing oil

palm yields. We see the need for further research into the

impacts of migration resulting from oil palm development

as well as examining smallholder household dynamics to

provide insights as to whether our hybrid approach can

be a long-term sustainable solution for oil palm expansion

in Indonesia.

Acknowledgements

We thank B. Azhar for sharing data on bird diversity from oil palm plan-

tations, S. Fux and U. Borstnik for technical support, and Z. Burivalova

0

1000

2000

3000

4000

5000

6000

BAU ESTATE SMALLHOLDER HYBRID BAU ESTATE SMALLHOLDER HYBRID

BAU ESTATE SMALLHOLDER HYBRIDBAU ESTATE SMALLHOLDER HYBRID

Nat

iona

l rev

enue

(mill

ion

USD

)

National

0

5

10

15

20

25

Reve

nue

(mill

ion

USD

)

Provincial

District

0

50 000

100 000

150 000

200 000

250 000

300 000

350 000

Empl

oym

ent (

no. j

obs)

0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

90 000

Road

dev

elop

men

t (km

)

(a)

(d)(c)

(b)

Fig. 4. Socio-economic outcomes for (a) national tax revenues, (b) provincial and district tax revenues, (c) road development and (d)

employment predicted by our model under the BAU, ESTATE, SMALLHOLDER, and HYBRID scenarios. Error bars represent the

sensitivity of socio-economic outcomes to low (top error bar) and high (bottom error bar) estimates of fresh fruit bunch yields drawn

from Table S2 (Supporting information). The 95% confidence intervals for socio-economic outcomes are presented in Table S11

(Supporting information).



for helpful comments. J.S.H.L. is supported by the Swiss National Science

Foundation (SNF 0-12818-06). J.G.U. is supported by the Mercator Foun-

dation Switzerland and the Zurich-Basel Plant Science Center.

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Received 17 December 2013; accepted 7 May 2014

Handling Editor: Julia Jones

Supporting Information

Additional Supporting Information may be found in the online version

of this article.

Appendix S1. Supplementary methods and supporting informa-

tion.

Table S1. Projected CPO production from 2010 to 2014 from each

island.

Table S2. Reported yield (tFFB ha�1 y�1) of independent small-

holders (Ind), scheme smallholders (Sch), and industrial estates

(Est) from published literature.

Table S3. Yield factors for oil palm production based on Fischer

et al. (2001).

Table S4. Assigning values to conversion priority indices for each

attribute i) proximity to oil palm mills (Imill), ii) proximity to road

networks (Iroad), iii) proximity to shipping ports (Iport), iv)

proximity to cities and main towns (Itown), v) land contiguity for

oil palm (Icontiguity), and vi) oil palm yield suitability (Iyield), to

derive conversion priority indices of a grid cell being converted into

an independent smallholding (Pids) and an industrial estate or

scheme smallholding (Pcps).

Table S5. Nitrogen fertilizer usage in different oil palm plantations.

Low and high estimates are calculated using the standard devia-

tion.

Table S6. Road development under different oil palm plantations.

Low and high estimates are calculated using the standard deviation.



Table S7. Public revenues generated (USD ha�1) at different

government levels from oil palm plantation development without

logging.

Table S8. Projected area of oil palm expansion under mean, low

and high estimates of relative yields for independent and scheme

smallholdings.

Table S9. Mean (� SD) values of outcome variables for land cover

converted to oil palm averaged over 10 000 runs from our

computer model simulation.

Table S10. Mean (� SD) values of environmental outcomes

averaged over 10 000 runs from our computer model simulation.

Table S11. Mean (� SD) values of outcome variables for socio-

economic outcomes averaged over 10 000 runs from our computer

model simulation.

Fig. S1. Spatial data used to parameterize the model. Our input

parameters include (a) distance to oil palm mills, (b) distance to

shipping ports, (c) distance to roads, (d) distance to towns, (e) yield

suitability for oil palm cultivation and (f) land contiguity for large-

scale oil palm expansion.

Fig. S2. Percentage difference between projected area of total oil

palm expansion using mean estimates of relative yields and using

low and high estimates of relative yields derived under Table S2.



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