land cover change or land-use intensification: simulating land system change with a global-scale...

Land cover change or land-use intensification: simulatingland system change with a global-scale land changemodelSANNEKE VAN ASSELEN and PETER H. VERBURG

Institute for Environmental Studies, VU University Amsterdam, De Boelelaan 1087, Amsterdam, HV 1081, The Netherlands

Abstract

Land-use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo

model provides an innovative approach for global land-use change modeling to support integrated assessments.

Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by

their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by

the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land

system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural

management versus expansion of arable land, and urban versus rural settlements expansion based on land availabil-

ity in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allo-

cation of increased agricultural production by either management intensification or area expansion varies both

among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land

system approach allows the inclusion of different types of demand for goods and services from the land system as a

driving factor of land system change. Simulation results are compared to observed changes over the 1970–2000 period

and projections of other global and regional land change models.

Keywords: agricultural intensification, global model, integrated assessment models, land change, land systems, livestock

Received 22 January 2013 and accepted 15 July 2013

Introduction

Land-use change can have important impacts on the

environment. It may lead to a reduction in biodiversity,

soil, and water pollution through the use of fertilizers

and pesticides, soil sealing and compaction, and altered

hydrological, nutrient and atmospheric cycles (e.g.,

Foley et al., 2005). Land-use change also affects socio-

economic processes through attracting labor and stimu-

lating trade. The other way around, land-use decisions

are influenced by biophysical and socioeconomic pro-

cesses. For example, crop growth depends on local soil

and climatic conditions, and human decision-making

influences land use in response to (global) markets

(Verburg et al., 2011b). Land use is thus a central com-

ponent of biophysical, social, and economic systems

acting across various scales. Interactions between these

systems are commonly modeled using global integrated

assessment models, like IMAGE (Bouwman et al.,

2006), GCAM (Wise et al., 2009) or GLOBIOM (Havl�ık

et al., 2011). Because land use is both a cause and conse-

quence of biophysical and socioeconomic processes,

global land-use models are a fundamental component

of integrated assessment models. Especially in recent

years, such models are increasingly used to assist in

global environmental assessments, such as the IPCC

assessment (Smith et al., 2009), the Global Environmen-

tal Outlook (UNEP, 2007), the Millennium Ecosystem

Assessment (MEA, 2005), and the Global Biodiversity

Outlook (Pereira et al., 2010).

Many currently used global land-use models use a

spatial resolution of 0.5 9 0.5 degree, where pixels rep-

resent dominant land cover types (Bouwman et al.,

2006; Lotze-Campen et al., 2008; Havl�ık et al., 2011; Sou-

ty et al., 2012). Some recent models use a higher spatial

resolution of 5 arcmin, like the LandSHIFT model (Sch-

aldach et al., 2011). Others have used fractional land

cover data at a resolution of 0.5 9 0.5 degree for cli-

mate modeling (Hurtt et al., 2011). In current global

land-use models, land management is usually repre-

sented in a simplified and aggregated manner, for

example, by a single, uniform, management factor per

world region (Bouwman et al., 2006; Bondeau et al.,

2007). Such simplifications are likely to create biases in

the impact assessment results (Van Asselen & Verburg,

2012; Verburg et al., 2013). A more recent global land-

use model, the Nexus Land-Use model, describes inter-

actions between food demand, biomass energy, and

forest preservation, which drive food prices and land-

use changes (Souty et al., 2012). This model describes

agricultural intensification based on a nonlinearCorrespondence: Peter H. Verburg, tel. +31 20 59 83594, fax +31 20

59 89553, e-mail: [email protected]

© 2013 John Wiley & Sons Ltd 1

Global Change Biology (2013), doi: 10.1111/gcb.12331

relation with chemical inputs. However, this model

focuses largely on agricultural lands and uses forest

area as a rest category. The focus on agricultural areas

and/or the representation by dominant land cover

types in most models ignores the intricate functioning

of the complex mosaics of many of the world’s land-

scapes, as well as the interactions between arable and

livestock systems.

Letourneau et al. (2012) made a big step forward in

improving global land change modeling by using land-

use systems (LUS) defined at a 5 armin resolution as

modeling units. LUS represent human-environmental

interactions in mosaic landscapes and are typical com-

binations of land cover, land use (livestock, cropland,

pasture), population and accessibility. The LUS classifi-

cation was inspired by the ‘anthromes’ classification,

which linked land use and population in a single classi-

fication system (Ellis & Ramankutty, 2008). In the

model of Letourneau et al. (2012), drivers of land-use

change (local conditions, demand for agricultural com-

modities, and land-use intensity changes) are applied

in a sequential order to allocate outcomes of macro-

economic models at world region level to 5 min pixels

(Letourneau et al., 2012). In this model, the choice

between land cover change or intensification of agricul-

tural management to fulfill increasing demands for

agricultural commodities is not endogenous. The land-

use model uses the areas and management intensities

as determined by global economic models based on

world-level trends, predefined elasticities and the

application of economic rationale to highly aggregate

spatial units (Hertel, 2011). Novel approaches to endog-

enously simulate agricultural expansion and intensifi-

cation in land-use models are needed to better inform

the land sharing/land sparing debate (Phalan et al.,

2011; Tscharntke et al., 2012).

In this study, we aim to further improve the repre-

sentation of human-environment interactions in land

system change in an operational, global-scale land

change model. The objective is to describe land system

change at the interface of macro-economic demands

and the local physical and socioeconomic context. To

advance the representation of landscapes and farming

systems, we use a land systems classification, which

represents typical combinations of land cover, live-

stock, and land-use intensity (Van Asselen & Verburg,

2012). An important difference between the ‘anthromes’

classification (Ellis & Ramankutty, 2008) and the LUS

classification of Letourneau et al. (2012) is the sole use

of land-use variables to define the land-use systems

(land cover, livestock, and production intensity),

instead of factors like population density and market

accessibility as a proxy for land-use intensity. The

advantage of this approach is that now population and

accessibility can be used as independent driving factors

of land system change, allowing different land systems

to occur under the same population density as result of

differences in location factors and external demand.

The next section describes the model concept and

implementation. Section 3 describes a model simulation

based on the OECD Environmental Outlook scenario

and section 4 discusses the modeling approach, its

results and validation.

Materials and methods

Overall model structure

The CLUMondo model builds on concepts used in global and

regional spatial land change models, such as the Dyna-CLUE

model (Verburg & Overmars, 2009), the LandShift model

(Schaldach et al., 2011), the global model of Letourneau et al.

(2012), as well as regional models such as the Metronamica

model (Van Delden et al., 2005), and Geomod (Pontius et al.,

2001). In many of these spatial models, land change is driven

by changes in regional demands (top-down), and at the same

time influenced by local factors that either constrain or pro-

mote the conversion of land and account for land-use history,

leading to path dependence of land change trajectories (bot-

tom-up; Fig. 1). In many of such models regional, aggregate

land areas are derived from coarse-scale global or regional

economic models, such as the GTAP model (Van Meijl et al.,

2006; Hertel, 2011) or the CAPRI model for Europe (Britz et al.,

2010). Some models use a hierarchical approach of allocating

these areas, assuming a dominance of urban expansion while

having (semi) natural areas as the remaining land change type

(Pontius et al., 2001). Others allocate land cover areas in a

synchronous manner assuming competition between the dif-

ferent land cover types for locations (Verburg & Overmars,

2009). However, in all models, demand for land cover types is

one-to-one allocated to changes in the spatial distribution of

these land cover types. The CLUMondo model, in contrast,

simulates changes in land systems that are capable of provid-

ing various goods at the same time. Land systems have char-

acteristics regarding crop production, livestock numbers, and

built-up area. Therefore, the same production or area can be

fulfilled by multiple combinations of land systems and the

areas occupied by the different land systems are not straight-

forwardly determined by the regionally aggregated areas of

land cover types. The characteristics of the different land sys-

tems are calculated as average values per model region, based

on the initial land system map for the year 2000 that was

developed by Van Asselen & Verburg (2012). The land system

map combines data on land cover [tree and bare land cover

(Hansen et al., 2003); cropland area (Ramankutty et al., 2008);

built-up area (Schneider et al., 2009)], livestock density (Wint

& Robinson, 2007), and intensity of agricultural production

(Neumann et al., 2010) into a consistent land system map at

ca. 5 arcmin. The local suitability for all land systems is esti-

mated based on empirical relationships between the land

system and socioeconomic and biophysical factors (described

in Van Asselen & Verburg, 2012) and modified by changes in

© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331

2 S . VAN ASSELEN & P. H. VERBURG

land availability in the surrounding area. During an iterative

procedure, land systems compete with each other until the

demand for all demand types is fulfilled.

The set of driving factors (or location factors) of land change

differs by region (Lambin et al., 2001; Geist & Lambin, 2002;

Rudel et al., 2005; Van Asselen & Verburg, 2012). Therefore,

we have estimated econometric models of relations between

location factors and the current spatial distribution of land

systems for eight different world regions separately (Van

Asselen & Verburg, 2012; Fig. 2). These regions are further

subdivided into 24 regions for each of which the model

ensures that region-level results from macro-scale economic

models are spatially allocated. In its current configuration,

these regions correspond to the model regions used in the

IMAGE model (Bouwman et al., 2006). Using these regions

allows the implementation of the CLUMondo model into the

IMAGE model, which enables the interaction with the other

integrated assessment modules and the incorporation of feed-

backs in the modeling system.

Allocation procedure

The structure of the CLUMondo model is visualized in Fig. 3.

The model allocates at time (t) for each grid cell (i) the land

system (LS) with the highest transition potential (Ptrant,i,LS).

The transition potential is the sum of the local suitability (Ploc

t,i,LS), the conversion resistance (PresLS) and the competitive

advantage of a land system (Pcompt,LS):

Ptrant;i;LS ¼ Ploct;i;LS þ PresLS þ Pcompt;LS ð1ÞThe local suitability of a land system is determined based

on an econometric model that is parameterized by logistic

regression. In the model a set of biophysical and socio-

economic explanatory variables is used to predict the proba-

bility of occurrence of each land system in each pixel (Van

Asselen & Verburg, 2012). Logistic regressions are frequently

used as input to spatial land change allocation models (e.g.,

Geoghegan et al., 2001; Pontius et al., 2001; Serneels & Lambin,

2001; Braimoh & Onishi, 2007; Verburg & Overmars, 2009;

Letourneau et al., 2012).

The resistance factor is a measure for the costs of conversion

of a land system into another. For example, land systems with

high capital investment (e.g. urban settlements) are not easily

converted to other systems, and hence, have relatively high

conversion costs. Therefore, in case such a land system is pres-

ent at that location a high resistance factor is added to the

transition potential to illustrate the low conversion elasticity.

Land systems like extensively managed cropland or grassland

are easily converted (with low costs) to other systems, and,

therefore, have low values of the resistance factor. The values

Land System change

Land cover (%) Livestock (nr) Land use intensity

World-region level analysis

Crop production (tons)

Livestock (nr)

Built-up area (km2)

Ext

erna

l m

acro

-eco

nom

icm

odel

Allo

catio

nm

odul

e

Model settings

• Local suitability• Neighborhood influence• Conversion resistance

Loca

l con

ditio

nsan

d co

nver

sion

ru

les • Area restrictions

• Conversion restrictions• Competitive advantage

Fig. 1 Main concept of CLUMondo.

Fig. 2 Regions used to derive econometric models of location suitability (shaded areas with thick outline) and the model regions (num-

bers 1–24) used in this study.


A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 3

of the resistance factor are determined based on expert knowl-

edge and can be calibrated for a specific model run. The resis-

tance factor is only added to the transition potential for the

current land system type at location i at time t.

The relative competitive advantage of a land system is

determined in an iterative procedure in which the goods or

services provided by the allocated land systems are compared

to the aggregated quantities at the level of world regions as

derived from an external global economic model (Fig. 3). In

our simulations we used four demand types: crop production

(tons), land-based livestock [bovines, goats and sheep (nr)],

landless livestock [pigs and poultry (nr)], and built-up area

(km2). During each iteration (n), the services provided by the

allocated land systems, in terms of crop production, livestock

numbers, and built-up area, are compared to the imposed

regional-level aggregate quantities. An iteration factor (Iterd,n)

is calculated for each type of good or service (d) based on the

difference between externally determined aggregate quantity

(AggrSupd,n) and the allocated supply (Allocd,n):

Iterd;n ¼ Iterd;ðn�1Þ �Allocd;n�AggrSupd;n

AggrSupd;n

s � r ð2Þ

where s is a constant that determines the step size in the itera-

tion procedure, r is a random factor (1–100) that varies the step

size of each iterative step enabling a faster convergence of the

algorithm without influencing the final result, and s*r > 1 to

limit the maximum modification of Iter in one time step. At

the start of the simulation Iter is 0. During the iteration Iter

decreases if more is allocated than the imposed aggregate

quantity (Alloc > AggrSup) while Iter increases if allocation is

smaller than the imposed aggregate quantity. Next, the com-

petitive advantage of each land system LS at time t is calcu-

lated based on the values of Iter accounting for the

characteristics of the LS. Table 1 provides an example of the

land system lookup table used to determine which land

systems are assigned a competitive advantage. The lookup

table indicates the relative order of the land systems contribu-

tion to fulfilling a specific demand type. For example, exten-

sive cropland systems provide less crop production as

compared to intensively managed cropland systems, and

hence have a lower rank for fulfilling the demand for crop

production. If the regional aggregate quantity of crop produc-

tion imposed by the macro-economic model is higher than the

allocated area, systems that have a higher rank as compared

to the current land system at that location (i.e. that are more

intensively managed or having a larger arable area) obtain a

higher competitive advantage. This stimulates a shift to sys-

tems that have a higher crop production than the current sys-

tem, independent of how this is achieved, either by more

intensive management or by expansion of the cropland area.

At the same time, the lookup table ensures logical trajectories

of land change. For example, it does not promote the conver-

sion from an extensive cropland system with livestock to an

extensive system with no livestock, although the latter might

have a slightly higher average crop production in a specific

region. The land system lookup table may be defined differ-

ently by region, depending on the land system characteristics

in the specific regions and the likely trajectories of fulfilling

increasing (or decreasing) demands. The overall competitive

advantage for a land-use system (Pcompt,LS) is then calculated

by summing the iteration factors (Iterd,n) calculated for the dif-

ferent goods and services considered. Some land systems are

considered not to react to specific demands. For example,

(peri-)urban systems may have different levels of crop produc-

tion and livestock numbers, but they are not supposed to

change into each other in response to changes in demand for

crop production or livestock as these systems are governed by

the demand for built-up area. Therefore, they are excluded

from consideration by coding these systems ‘�1’ in the land

system lookup table. In this case, the value of the competitive

Explanatory location factors

Adapt competitiveadvantage

Calculate transition potential

(all LS)

Neighborhood

Allowedconversions

Determine new LS (highest potential)

Compare allocated areawith

external demand

World-region levelchanges

(per demand type)

Land Systemlookup table

if demand =allocated area

if demand =allocated area

Write output LSmap (t = t + 1)

Resistancefactor Iterative procedure

Location

Local suitability

Fig. 3 Model structure.



advantage is not changed (Iterd = 0). The value of the competi-

tive advantage at time t is modified according to the value of

Iter for that specific iteration step until all allocated goods and

services equal the aggregate quantities determined by the

macro-economic model.

During the allocation procedure each grid cell is assigned

the land system with the highest transition potential at that

location. However, it is first checked if the conversions are

possible. A conversion matrix is specified that indicates which

land system conversions are possible. Some conversions are

never allowed, such as the conversion of urban area into dense

forest. Other conversions are only allowed after a defined

number of time steps (e.g. conversion into forest needs time

for vegetation succession), or only within a certain areas. For

example, conversions to cropland are only allowed in areas

with a length of the growing period of minimally 45 days and

outside protected areas. Conversion to (peri-)urban area is

only allowed where the population density exceeds 10

people km�2. These settings can be specified by the model

users and be made dependent on the scenario.

Neighborhood influence

Cellular automata that allocate land change based on

neighboring land uses are common in land change models

(Van Delden et al., 2005; Clarke, 2008). Neighborhood effects

in land-use allocation can originate from imitation, econo-

mies of scale, and other centripetal and centrifugal forces

(Verburg et al., 2004). This applies for example to urban area:

urban expansion preferably occurs adjacent to existing cities

(Fujita et al., 1999). This effect cannot be captured by the

location suitability based on the logistic regressions

described above as these are independent of land change

in previous time steps. Therefore, the model allows adding

a neighborhood influence (Pneight,LS) to the transition

potential:

Table 1 Example of a land system lookup table for China, bgs, bovines, goats and sheep; pp, pigs and poultry. For each demand

type the first column presents a ranking of the land systems (based on the real values); the second column presents the average

value of the goods supplied by the land system within the region.

Land System

Demand type

Crop

production

(tons)

Livestock bgs

(nr)

Livestock pp

(nr)

Built-up

area (km2)

Cropland extensive, few livestock 4 8977 2 4658 2 9620 1 0.11

Cropland extensive, bgs 4 11047 3 10250 2 31630 1 0.12

Cropland extensive, pp 4 11110 2 3317 3 81968 1 0.11

Cropland med. intensive, few livestock 5 11695 2 3704 2 11899 1 0.36

Cropland med. intensive, bgs 5 13421 4 14282 2 79960 1 0.40

Cropland med. intensive, pp 5 16363 2 4387 4 102894 1 0.43

Cropland intensive, few livestock 6 24076 2 2076 2 7934 1 0.69

Cropland intensive, bgs 6 37740 5 23949 5 339985 1 1.24

Cropland intensive, pp 6 31785 2 4264 5 172779 1 0.67

Mosaic cropland & grassland, bgs 4 13563 4 13843 4 132327 1 0.52

Mosaic cropland & grassland, pp 4 16080 2 4005 4 122532 1 0.46

Mosaic cropland (ext.) & grassland, few livestock 2 3871 2 4736 2 8152 1 0.09

Mosaic cropland (med. int.) & grassland, few livestock 3 6504 2 4403 2 11890 1 0.25

Mosaic cropland (intensive) & grassland, few livestock 4 10984 2 3374 2 8815 1 0.47

Mosaic cropland & forest, pp 3 14548 2 3815 4 112431 1 0.33

Mosaic cropland (ext.) & open forest, few livestock 2 6104 2 3754 2 15727 1 0.09

Mosaic cropland (med. Int.) & forest, few livestock 3 6752 2 3511 2 13386 1 0.17

Mosaic cropland (intensive) & forest, few livestock 4 9774 2 3127 2 12680 1 0.32

Dense forest 1 1478 �1 2368 �1 28849 1 0.07

Open forest, few ls 1 1459 1 2302 1 8976 1 0.09

Open forest, pp �1 4576 �1 3073 3 89483 1 0.13

Mosaic grassland & forest 1 3043 1 3441 1 38943 1 0.14

Mosaic grassland & bare 1 381 1 2824 1 3294 1 0.08

Natural grassland 1 749 1 0 1 0 1 0.00

Grassland, few livestock 1 1610 1 2720 1 18250 1 0.13

Grassland, bgs �1 2059 4 14159 �1 37991 1 0.23

Bare �1 18 �1 4 �1 4 1 0.00

Bare, few livestock �1 430 1 2928 1 2948 1 0.04

Peri-urban & villages �1 22056 �1 9110 �1 184526 2 8.97

Urban �1 17796 �1 5010 �1 193283 3 37.60



Ptranst;i;LS ¼ Ploct;i;LS þ PresLS þ Pcompt;LS þ Pneight;i;LS ð3Þwhere:

Pneigh ¼ w � ðaþ fÞ ð4ÞIn Eqn (4) w is a weight factor with a value between 0 and 1

to scale the impact of the neighborhood composition on the

total transition potential, a a constant (default value 0; domain

�1 to 1) and f the fraction of the neighborhood occupied by

one or more selected land systems (e.g. urban systems).

This function can be used to increase the transition potential

for urban systems in case the neighborhood of a location con-

tains a high fraction of urban land use.

The neighborhood influence is also used to influence the

choice between expansion and intensification of cropland. It is

assumed that under conditions of high land availability in the

neighborhood, expansion of the cropland area is preferred. If

little suitable land for further cropland expansion is available,

intensification is induced. This is based on the generally

accepted notion that intensification is induced by increasing

pressure on limiting land resources (Boserup, 1965; Turner II,

1974). To determine the extent of available land for cropland

expansion, a cropland suitability map was derived from

Global Agro-Ecological Zones data (IIASA/FAO, 2012).

Unsuitable areas meet one of the following conditions: (i) clas-

sified as ‘very severe constraints’ regarding salt excess, nutri-

ent retention capacity, oxygen availability, rooting conditions

or toxicities; (ii) a median slope greater than 45 degrees, (iii) a

length of growing period less than 45 days on lands lacking

irrigation (irrigation area < 1%, from Siebert et al., 2006); and

(iv) classified as urban land system or protected area. Based

on this map, and the simulated land systems of the previous

year, the remaining available land area suitable for cropland

expansion in the neighborhood is calculated. The neighbor-

hood size can be varied by the user; by default a 3 9 3 kernel

is used. If there is much land available, the neighborhood

influence for extensive cropland systems is increased (positive

value of Pneigh), using the formula:

Pneigh ¼ w � ðaþ ðS� fcroplandÞÞ ð5Þwhere fcropland is the fraction cropland in the neighborhood and

S is the fraction of the neighborhood suitable for cropping. In

a similar way, the transition potential of intensive systems is

decreased by subtracting Pneigh from the total transition

potential.

In case abundant land is available for expansion of crop-

land, transitions to more intensively managed land-use sys-

tems are hindered, while expansion of more extensive systems

is favored; all proportional to the fraction of available land not

yet used for cropland. Vice versa, if little land is available, the

neighborhood influence of intensive systems (and the transi-

tion potential) is increased and the neighborhood influence of

extensive systems is decreased. Hence, in this situation inten-

sification will result. While the default value for constant a is

0, changing the constant a affects the threshold of available

cropland from which onward intensification is stimulated.

Using a = �0.5 stimulates intensification if more than 50% of

the available neighboring land is already occupied by crop-

land, using a = �0.6 stimulates intensification if more than

40% of the neighboring land is occupied by cropland (or is

unsuitable). An example of the calculation of the neighbor-

hood influence is given in Fig. 4. Calibration of the constant a

and weight w is needed to specify the role of land availability

in the overall land system allocation. Section 4 discusses a sen-

sitivity analysis for these parameters. It should be noted that

the role of other location factors (such as population pressure

and accessibility) is captured by the local suitability (Ploct, i,

LS); the neighborhood effect is only an additional driver that

can stimulate a transition from land expansion to intensifica-

tion upon decreasing land availability.

Location specific addition

A location-specific addition may be added to, or subtracted

from, the transition potential in case of specific scenarios (e.g.

representing a tax or subsidy) or conditions not well captured

by the local suitability models. For example, an aridity index

(AI = Precipitation/Potential evapotranspiration) is used to

lower the transition potential for cropland systems in very dry

regions (UNEP, 1997). In our application, the transition poten-

tial for (mosaic) cropland systems is lowered in areas with an

aridity index lower than 0.2 (Hyperarid and Arid regions;

UNEP, 1997).

Results

The model functioning is illustrated by a scenario run

for the period 2000–2040. In this study we have used a

0

X

1

1

1

Pneigh = w * (a + (S - (f= w * (–0.5 + ((8/9) – (4.5/9)) = w * –0.11

Extensive systems:Ptrans = Ploc + Pres + Pcomp + (w * –0.11)Intensive systems:Ptrans = Ploc + Pres + Pcomp – (w * –0.11)

0

X

1

0

0 0

0

1 0

0.5 0

0.5 0

Pneigh = w * (a + (S - (f cropland))Pneigh = w * (–0.5 + ((8/9) – (1.5/9))

= w * 0.22Extensive systems:Ptrans = Ploc + Pres + Pcomp + (w * 0.22)Intensive systems:Ptrans = Ploc + Pres + Pcomp – (w * 0.22)

Limited land availability

High land availability

(a)

(b)

cropland))

Fig. 4 Example of neighborhood influence calculations for a

3 9 3 kernel neighborhood. In situation (a) Eight of nine cells

are suitable for cropland (X = unsuitable), of which three cells

are not yet used for cropland (0), one grid cell is occupied for

50% by cropland and four grid cells are 100% occupied by crop-

land. Consequently, the total probability of extensive systems is

lowered and the transition potential for intensive systems is

increased. In situation (b) much more land is still available:

Eight from the nine cells are suitable for cropland and only 1.5

cells are currently used for cropland. Hence, the transition

potential for extensive systems increases while the transition

potential for intensive systems is decreased.



slightly adapted version of a scenario developed for the

OECD environmental outlook to 2050 (OECD, 2012).

For the OECD scenario the regional level quantities for

arable production, livestock, and built-up area are

determined by the GTAP model for arable production

and livestock and by the IMAGE urban demand mod-

ule for built-up area. Globally, the macro-level models

specify an increase of 64% in arable production, 57% in

livestock numbers and 112% in built-up area. The

macro-model provides aggregate changes for 24 world

regions. For a few regions, extreme fluctuations in crop

production were leveled out by linear interpolation

between year 2000 and 2040.

For this scenario the CLUMondo model produces

various outputs. The prime output is the change in land

system types. In Fig. 5 the global land system maps for

the years 2000 and 2040 are presented. Areas of large

changes are easily identified and consistent with the

GTAP model results for this scenario: increasing agri-

cultural production in Turkey, northern China, India,

western and southern Africa and Latin America. In

some areas, this shows as replacement of natural or

mosaic land systems by agriculturally dominated sys-

tems, in other locations as a conversion from extensive

or medium intensive systems to intensive agricultural

systems. In multiple locations, for example, the United

States, Brazil, Western Africa, India, and China, large

expansion of both urban and peri-urban areas is found,

consistent with the urbanization projections of Seto

et al. (2012). Figure 6 provides a more detailed view on

these results for two regions. Figure 6a provides a view

on part of Latin America comprising northern Argen-

tina, Uruguay, Paraguay, Bolivia, and the southern part

of Brazil. Strong urbanization and peri-urban develop-

ment is apparent, especially surrounding Rio de Janeiro

and Sao Paulo. Urban and peri-urban development

causes further losses of the Atlantic forest along the

coast. Southern Brazil and Uruguay face a loss of grass-

land/forest mosaic due to conversion into grassland

areas with higher grazing densities and some expan-

sion and intensification of arable farming. Larger areas

of intensification of arable farming are found in the

northern part of Argentina, Chaco province in particu-

lar. In western Bolivia, mosaics of grassland and forest

are converted into mosaics of grassland and cropland

including pig and poultry farming. Figure 6b zooms

into Central Africa and visualizes the loss of dense

(tropical) forest at the fringes of the core forest area,

mainly due to conversions of forest into both extensive

and intensive mosaic land systems with cropland and

forest. In more densely populated areas along the coast

of Nigeria intensification of cropland takes place, as

well as expansion of (peri-)urban and villages systems.

On the savannah, north of the tropical rainforest,

conversion into grassland systems with higher densities

of bovines, goats, and sheep occurs.

Based on the characteristics of the different land sys-

tems it is possible to calculate changes in indicators that

are closely linked to the land system. Here we calcu-

lated changes in crop production, number of bovines,

goats and sheep, number of pigs and poultry, built-up

area, cropland area, and tree cover. Depending on the

purpose of a study other land system characteristics

(for example pasture area, bare area, indicators of bio-

diversity based on land system characteristics) can be

calculated. Figure 7 shows the changes in built-up area.

Following expectations, built-up area especially

increases in areas already densely populated at the start

of the simulation (2000). When analyzed in more detail,

built-up area often increases around existing cities,

indicating urban expansion.

An analysis of changes in land systems also reveals

patterns of intensification of arable production versus

extensification of agricultural management. Similarly

patterns of expansion of the arable area versus agricul-

tural abandonment can be identified. Intensification

occurs when a cropland system is converted into a

more intensively managed cropland system. A shift

toward more extensively managed systems is noted as

extensification. Expansion occurs when a non cropland

system converts to a (mosaic) cropland system, or when

a mosaic cropland system converts to a cropland sys-

tem. Abandonment is, in this analysis, defined as a shift

from a cropland system to a mosaic cropland system or

a (semi)natural system, or as a shift from a mosaic crop-

land system to a (semi)natural system. Conversions

from (mosaic) cropland systems to (peri-)urban systems

or to dense livestock systems are not depicted as aban-

donment.

The absolute and relative increases in crop produc-

tion given as input to the CLUMondo model per model

region over the period 2000–2040 are presented in

Fig. 8. Figure 9 shows, based on the CLUMondo

results, how these increases in arable production are

allocated by showing the relative contribution of,

respectively, intensification and expansion to the total

increase in crop production. The contributions of inten-

sification and expansion are calculated according to

Eqn 6 and 7, respectively:

Cropint ¼ DY � Aend ð6Þ

Cropexp ¼ DA � Ystart ð7Þwhere, Crop is the total crop production that can be

attributed to intensification (int) or area expansion

(exp), Y is the average yield (tons km�2), and A is the

total cropland area. Both average yield and total crop-

land area are calculated at the start and end of a model



run (years 2000 and 2040). The model predicts large

areas with intensification processes in Eastern Europe,

Turkey, Western Russia, Western Africa, India, and

Eastern China (Fig. 10). Smaller areas of intensification

occur in North and Central America, along the east

coast of South America, South Africa, southeast Asia,

and Australia. Of all land classified as (mosaic) crop-

land at the start of the model simulation (year 2000)

Fig. 5 Land system maps for 2000 and 2040 (simulated).



26% has intensified after 40 time steps, while extensifi-

cation occurred in 3% of the cropland systems. Extensi-

fication as predicted by the model has different causes.

In West Russia, for example, the demand for crop pro-

duction decreases during the last 10 time steps causing

extensification. It may also be caused by a strong

increase in demand for livestock which can lead to a

shift to livestock dominated systems with less inten-

sive, associated, arable production. For example, in

southeast Asia a rapid increase in demand for pigs &

poultry causes a shift from sometimes intensively man-

aged cropland systems to less intensive systems with

more livestock. Another process leading to extensifica-

tion is the intensification of crop production in another

area that is more suitable for cropland. This occurs for

example in Europe where we see intensification in

Northwest Europe and extensification in south Europe.

These alternating processes within the same region

have happened also in the past leading to a segregation

of land-use functions (Mottet et al., 2006; Verburg et al.,

2010). Intensification is commonly found in the same

regions where also expansion of agricultural area is

found, although in general, expansion occurs in a more

widespread manner (Fig. 10). Abandonment has simi-

lar causes as described for extensification, therefore, the

two processes are often found in the same area. Crop-

land expansion often is associated with deforestation

(Fig. 11).

The increase and decrease in livestock numbers is

presented in Supplementary material Figure S2. In

most model regions the demand for livestock increased

during the simulated time period. In some regions, the

macro-economic simulation results for the OECD sce-

nario have predicted a very large increase in demand

for bovines, goats, and sheep. For example, in Western

Africa, Eastern Africa and the Middle East an increase

of 229%, 148%, and 125% respectively is predicted over

a time span of 40 years. Accordingly, a very high

(a)

(b)

Fig. 6 Detailed simulated land system maps for the year 2000 and 2040. Zoom regions and legend are indicated in Fig. 5.



increase in livestock numbers is seen in these regions

and large shifts in land systems are associated with

these increases. Also the predicted demand for pigs

and poultry is very high in some areas: a 241%

increase in Turkey and a 136% increase in North

Africa. In other regions, a decreasing demand for live-

stock is predicted. This causes a reduction in the num-

ber of bovines, goats, and sheep in Western Europe

and India. In Europe, this leads to a reduction in

(mosaic arable and) grassland systems with livestock,

while in India (mosaic) arable (and grassland) systems

with livestock are converted to arable systems with

few livestock. In some areas, the number of livestock

decreases in spite of increases in the region as a whole,

for example in parts of North America, Argentina, and

China. Sometimes this is caused by a shift to (mosaic)

cropland systems, driven by a high increase in

demand for crop production in combination with a rel-

atively high suitability for cropland systems that out-

competes the mixed systems. In other cases, a decrease

in livestock may be caused by an increase in livestock-

based land systems in another (more suitable) area

within the same model region.

Discussion

Land system approach

An important novelty of the model presented in this

article is the use of land systems as basic modeling

units. The high spatial resolution and land systems

classification allow a more detailed representation of

the composition of landscapes as compared to many

other global scale land-use representations, which often

use a relatively coarse resolution and/or pixels repre-

senting one dominant land cover (Van Asselen & Ver-

burg, 2012; Verburg et al., 2013). In this study, we focus

on land cover and agricultural characteristics, like crop

production, livestock, built-up area, cropland cover,

and tree cover. However, more characteristics may be

assigned to the land systems. This allows the use of the

land system concept for other studies like biodiversity

assessments and ecosystem services studies. In current

global scale biodiversity assessments, the biodiversity

values are directly linked to land cover types while

rough assumptions are made about the land-use inten-

sity as such information cannot be directly derived

Fig. 7 Global-scale simulated increases in built-up area. The two zoom areas show urban expansion.



from the global land-use models (Alkemade et al., 2009;

Pereira et al., 2010). The land systems classification

allows distinguishing intensive and extensively man-

aged systems in more detail and also accounts for

mosaic landscapes that often have high biodiversity

values (Overmars et al., 2013). Similar advantages hold

for other environmental impacts such as GHG emis-

sions and carbon sequestration.

In the current application, the model is configured to

respond to demands for crop and livestock production

Fig. 8 Total increase in crop production per model region for 2000–2040. In millions of tons and% of year 2000 production.

Fig. 9 Relative contribution (%) of intensification and expansion to the total increase in crop production per model region.



and built-up areas. However, the model can similarly be

configured to be driven by demands for additional eco-

system services that can be related to the land systems.

In this study, demand for crop production, livestock,

and built-up area drive land system change. Especially

in scenarios with contrasting demand directions the

model is sensitive to correlations between land system

properties. For example, if the demand for bovines,

goats, and sheep increases and the demand for pigs and

poultry decreases, the model may force conversion into

livestock systems with more bovines, goats, and sheep.

These systems are, however, also characterized by a rel-

(a)

(b)

Fig. 10 Global-scale intensification and extensification of agricultural management (A) and the expansion and abandonment of agricul-

tural area (B).



atively high number of pigs and poultry. The associated

increase in pigs and poultry associated with such con-

versions has to be compensated by other conversions in

the same model region to equal the decreasing demand

for pigs and poultry. Although such conversions are

conceptually possible, it is still to be investigated how

realistic such conversions are. In some situations, con-

centrations of intensive systems (regarding crop pro-

duction and number of livestock) may cause

abandonment and a reduction in livestock numbers in

other areas (Thornton, 2013). However, this may not

always be the case and the same demand could also be

fulfilled by changes within the land systems itself.

A specific land system does not have the same char-

acteristics worldwide, which is why we parameterized

the model for 24 world regions separately. Also, sys-

tems evolve, for example by the adoption of more live-

stock within a system through modern technologies.

The same holds for technological progress in crop pro-

duction, increasing yields at the same level of manage-

ment intensity. Such changes can be accommodated by

changing the characteristics of the land systems during

the simulation period, for example, accounting for tech-

nological progress in crop production can be incorpo-

rated by increasing the yield of cropland systems by,

for example, 1% per year. This will result in less land

system changes needed to fulfill the same demand. It is

also likely that new systems emerge in regions that

were not available previously. The specification and

parameterization of such systems can be part of the sce-

nario definition.

Model sensitivity to local suitability and resistancefactors

The logistic regression equations used to estimate the

local suitability for a land system generally show a good

fit with the observed data (Van Asselen & Verburg,

2012). However, sometimes the local suitability of a loca-

tion is high for multiple land systems (in other words,

the landscape is suitable for different systems). In such

circumstances, the land system associated with the high-

est transition potential in a pixel does not always corre-

spond to the initial land system map (example in

Fig. 12a and b). In the allocation procedure the land sys-

tem with the highest transition potential is chosen (Eqn

1). If the conversion resistance (Pres) was not included in

the allocation procedure themodel would fully reconfig-

ure the landscape toward the local suitability maps

(resulting in a pattern as indicated in Fig. 12c), that often

differs considerably from the initial land systems map.

Figure 12d shows that, after accounting for the conver-

sion resistance, and running the model one time step

with no change in demand, the resulting land system

map (Fig. 12d) does resemble the initial Land System

map (Fig. 12a) quite well. This small experiment indi-

cates the sensitivity to the conversion resistance para-

meter values. The higher the conversion resistance, the

Fig. 11 Global-scale simulated decrease in tree cover (2000–2040).



more stable is the spatial pattern. On the contrary, if all

conversion resistance values are very low, conversions

occur more easily and the influence of the local suitabili-

ties as estimated by the logistic regressions is larger.

In principle, an adequate parameter setting could be

found through model calibration if temporally consis-

tent series of global land system observations would be

available. Even for the land system characteristic that is

most easy to observe, land cover, no consistent

time-series data are available. For other variables, high-

resolution time series are completely absent (Verburg

et al., 2011a). For the moment, model calibration, there-

fore, relies on expert judgment or regional level time

series of land cover data.

Model sensitivity to neighborhood influence settings

The magnitude of the neighborhood influence is con-

trolled by the neighborhood weight (w) and the con-

stant (a) in Eqns (3), (4) and (5). For settlement systems

a higher neighborhood weight results in an increase in

the number of urban systems, and an associated

decrease in peri-urban & villages systems (Table 2). A

higher constant amplifies this effect.

For arable systems a similar effect is seen. Increasing

the neighborhood weight results in an increase of inten-

sively managed cropland systems, and a decrease in

the number of cells allocated to extensive cropland

(Table 3). An exception to this occurs in Eastern

(a) (b)

(c) (d)

Fig. 12 (a) Land System map (year 2000) for Eastern Europe, (b) Land System map based on highest local suitability, (c) Land System

maps after 1 time step with equal demand and conversion resistance factors = 0, and (d) Land System maps after 1 time step with equal

demand and normal resistance factors (legend in Fig. 5).



Europe, where the number of cells assigned to exten-

sive cropland increases with an increasing weight (first

row in Table 3). Such inconsistencies can be explained

by conversions to/from medium intensive cropland

systems, and to/from cropland systems with livestock.

Increased intensification following an increase in neigh-

borhood weight especially occurs in areas where land

availability is low (Fig. 13). Although the model

chooses for intensification or expansion in an endoge-

nous manner based on the local suitabilities and land

availability in the neighborhood, the sensitivity analysis

indicates that the weight and specification of this neigh-

borhood effect influences the partitioning into intensifi-

cation and expansion. Although the drivers of

intensification have been a topic of long debate (Bos-

erup, 1965; Turner II, 1974; Keys & McConnell, 2005)

and core to the discussions on land sparing vs. land

sharing (Phalan et al., 2011; Tscharntke et al., 2012),

there is little consistent empirical evidence of ways in

which trajectories of intensification replace cropland

expansion. Such information is urgently needed to fur-

ther parameterize land change models addressing this

issue.

Validation and comparison with other studies

Validating the model is difficult and, due to the absence

of consistent multi-temporal data, a common problem

for all global land-use models. Most models used in

Table 2 Effect of neighborhood influence settings on settlement systems for southeast Asia and Eastern Europe. For these model

runs, all four demand types increase with +0.5% per time step and the neighborhood settings for arable systems are a = 0.5 and

w = 0.3

Constant

Allocated area (number of cells)

No neighborhood

influence

Peri-urb. = 0.15

Urban = 0.45

Peri-urb. = 0.3*

Urban = 0.9

Peri-urb. = 0.6

Urban = 1.8

Southeast Asia

Peri-urban & villages 0.0 881 881 848 732

Urban 0.0 150 149 156 179


Urban 0.2 150 163 177 204


Urban 0.4 150 173 197 204

Eastern Europe


Urban 0.0 297 324 353 410


Urban 0.2 297 349 404 454


Urban 0.4 297 376 438 457

*Settings used in scenario calculations in this study.

Table 3 Effect of neighborhood influence settings on crop-

land systems, tested for southeast Asia and Eastern Europe.

For these model runs, all demand types increase with +0.5%per time step; the neighborhood settings for peri-urban & vil-

lages and urban systems are w = 0.3 and w = 0.9, respectively,

a = 0. Extensive cropland includes all extensive (mosaic) crop-

land systems with and without livestock, intensive cropland

includes all intensive (mosaic) cropland systems with and

without livestock

Constant

Allocated area (number of cells)

No

neighborhood

influence 0.15 0.3* 0.6

Southeast Asia

Ext. cropland 0.5 3158 2722 2401 2109

Int. cropland 0.5 1596 2062 2280 2653

Ext. cropland 0.6 3158 2535 2089 1637

Int. cropland 0.6 1596 2143 2400 2871

Ext. cropland 0.7 3158 2351 1816 1227

Int. cropland 0.7 1596 2220 2509 3097

Eastern Europe

Ext. cropland 0.5 1520 1405 1436 1479

Int. cropland 0.5 6316 6642 6790 7088

Ext. cropland 0.6 1520 1411 1385 1276

Int. cropland 0.6 6316 6673 6890 7342

Ext. cropland 0.7 1520 1337 1337 1151

Int. cropland 0.7 6316 6744 6965 7496

*Settings used in scenario calculations in this study.



important global assessments have not been validated,

including IMAGE, GLOBIOM, GCAM and LandShift

(Strengers et al., 2004; Thomson et al., 2010; Havl�ık

et al., 2011; Schaldach et al., 2011). Similar concerns

apply to land-use model applications for the entire Uni-

ted States and Europe (Verburg et al., 2010; Sleeter

et al., 2012). The main reason underlying the absence of

model validation for these models is the lack of consis-

tent, spatially explicit time series of land change data at

continental to global levels that can be used as indepen-

dent validation data (Verburg et al., 2011a). Instead of a

full validation often an analysis of the model sensitivity

to critical model settings is made (e.g., Alcamo et al.,

2011; Verburg et al., 2013). The CLUMondo model

requires relatively few variable settings and the model

sensitivity to those settings has been discussed above.

The important parameters determining location suit-

ability are estimated empirically based on current land

system distributions. This empirical estimation is pre-

ferred above the frequently used multi-criteria

approach, as experts are unlikely to be able to quantify

the regional differences of the determinants of land sys-

tems at a global scale. Also a more mathematical

approach in which location suitability is determined by

land rents (derived from potential production) has pro-

ven to be unrealistic given deviations from economic

rational decision making (Meyfroidt, 2012). The

assumption made in the CLUMondo model that the

relations between location factors and location suitabil-

ity are constant over the simulation period may, how-

ever, be violated as new technology and societal

changes can change such relations.

In the absence of data required for a full validation of

the model results, we have made a comparison of the

model results with land-use projections in other studies

as well as with observed data on specific conversions in

the recent past. Table 4 provides an overview of the

comparisons made. Overall, the CLUMondo results for

urbanization correspond well with the spatial patterns

derived from both a global projection and a land-use

model for the United States. Areas of deforestation for

the 2000–2005 period both show correspondence and

deviations from the modeling results. This may be

attributed to the different time period considered and

the different processes accounted for: a large part of the

forest cover changes in the gross forest cover loss data

can be attributed to naturally induced fire dynamics

which are not included in the CLUMondo model. Fig-

ure 14 shows a comparison between results of the

IMAGE model and the CLUMondo model for the same

scenario based on the same world-region level quanti-

ties of crop production, livestock and built-up area.

Although areas of agricultural expansion correspond,

there are also large differences, mostly due to the differ-

ent representations of land use and the spatial and the-

matic resolutions. Comparison of the rate of

agricultural expansion versus agricultural intensifica-

tion is only possible at the level of world regions or

countries based on FAO statistics of historic changes

and global economic model predictions. Especially

these figures show large deviations between the differ-

ent models, as well as between models and the histori-

cal numbers (supplementary material, Figure S1).

Deviations between numbers over the 1961–2000 period

and model predictions for 2000–2040 are obvious and

can be explained by the very different conditions

(increased land scarcity and differences in rates and

regional distributions of increases in agricultural pro-

duction). However, also between the global economic

model prediction and the CLUMondo prediction for

the same scenario large differences are found for a

number of regions. There is no regular pattern in the

deviations between the two models. In North America,

OECD Europe and China/Korea the CLUMondo attri-

butes higher fractions of production increases to area

expansion as compared to both the GTAP model and

the historical records, possibly due to overoptimistic

land availability estimates. Also in other regions the

production increases are, for a larger part, attributed to

area expansion in CLUMondo as compared to GTAP,

but in many cases for a smaller part than the historical

increases. The differences relate to the very different

ways of conceptualizing land system change and the

different ways of parameterizing land availability. In

the global economic models these processes are largely

dependent on the relative prices of inputs, outputs, esti-

mated land rents and estimated elasticities at the

world-region level (Van Meijl et al., 2006). In the CLU-

Mondo approach these changes are based on land sys-

tem changes that are determined by local suitabilities,

local land availability and overall demand for agricul-

tural production. Within CLUMondo trajectories of

intensification and extensification (as well as expansion

and agricultural abandonment) are happening within

the same region, making the net changes a poor indica-

tor of the variability within the regions. There is no

way to judge if one of the model predictions is better

than the other. The often higher attributions to agricul-

tural area expansion in CLUMondo can be explained

by the land availability constraints used. Estimates on

land availability in the literature are highly variable

and debated, given the various constraints beyond bio-

physical suitability (e.g. land tenure) (Fritz et al., 2012).

The differences in model results indicate that the vali-

dation and empirical underpinning of the processes of

induced intensification and land availability require

more attention and follow-up research. Consistent

gathering of sub-national statistics, the consistent



interpretation of time series of remote sensing archives,

and possibly crowdsourcing of land change and land

availability data will help to provide an empirical base

for validating and calibrating these critical aspects of

land change models.

Implementation in integrated assessment models

Compared to other global land-use change models

CLUMondo has three important differences: (i) it uses

land systems as basic modeling units, which better rep-

resent the composition and characteristics of the land-

scape; (ii) change is driven by changes in multiple

demands simultaneously; and (iii) the model uses a

region-specific approach to better account for regional

differences in driving factors and land system charac-

teristics. Furthermore, the model has multiple features

that can be used to influence land change depending on

regional processes and scenario conditions. For exam-

ple, intensification and urbanization can be influenced

through the development of land use in the neighbor-

hood of the location, certain conversions can be blocked

per region, and area specific restrictions can be applied.

Also, land system characteristics can be changed over

time to account for technological changes. The model

can also be used at regional level and with different

Table 4 Overview of comparison results between various studies reporting historic and projected land-use change and

CLUMondo results

Source Description of comparison Correspondences Differences

-Sleeter et al., 2012;

Land-use projections

for the United States

Patterns of urbanization in

the US for the IPCC SRES

scenarios 2000–2100 compared

to CLUMondo simulation

OECD baseline scenario

2000–2040

Large correspondences in

urban expansion patterns,

especially:

Mississippi alluvial and

southeastern coastal plain;

Piedmont plateau region; a

nd California

No large differences

detected

-Seto et al., 2012;

Urbanization projections

for the globe

Probabilities of urbanization for

the globe until 2030 compared

to CLUMondo urbanization

projections for 2000–2040

The coast of West Africa on

the Gulf of Guinea; the Kano

region in northern Nigeria;

greater Addis Ababa, Ethiopia;

spatial patterns in the United

States, India, and China

CLUMondo misses

strong urbanization

in Nile delta and

surrounding Lake

Victoria

-Renwick et al., 2013;

-Navarro & Pereira, 2012;

-Macdonald et al., 2000;

-CORINE land cover

data 1990–2006

(European Environmental

Agency)

Locations of historic, current,

and future agricultural

abandonment compared to

locations of projected land

abandonment in CLUMondo

Marginal and mountainous

areas in southern Europe

correspond (Spain, Italy).

Locations in Finland

correspond with projections

CLUMondo misses

abandonment in

mountain regions

in France and

Germany

-Hansen et al., 2010

Observed gross forest

cover change

Locations of observed forest

cover change during 2000–2005

compared to locations of projected

tree cover change in CLUMondo

Limited correspondence CLUMondo misses

deforestation on

Sumatra, has much

less forest cover loss,

more Cerrado than

Amazon forest losses,

somewhat different

patterns in the United

States and especially

more forest losses in

the Congo basin

-OECD, 2012

Scenario results OECD

scenario for

the GTAP model

-FAO statistics of

agricultural production

and agricultural areas

for 1970–2000

Fraction of agricultural production

increases attributed to expansion

of agricultural area in CLUMondo,

GTAP and in agricultural statistics

for 1970–2000 at world region level

(See figure S1, supplementary material)

Correspondence for a limited

number of regions (Japan,

India, Western Africa)

CLUMondo attribution

to area expansion in

many regions higher

than for GTAP. In

various regions

CLUMondo attribution

corresponds to

1970–2000 attribution



drivers of land change. All of this gives the model a

large flexibility.

In this study, we focused on the analysis of the

OECD environmental outlook scenario mainly on

changes in agricultural landscapes. However, land sys-

tems may also be characterized regarding for example,

biodiversity, or other ecosystem services. The model

outputs show that land system change trajectories are

variable between and within regions. Multiple, some-

times opposing, trajectories of change occur within the

same region. The scenario simulation also shows that

the increasing demand for livestock will drive major

changes in land systems worldwide, consistent with

regional studies of livestock induced land cover

change (Wassenaar et al., 2007). The intricate model

structure accounts for the complexity of land systems

Fig. 13 Results of sensitivity analysis: a higher neighborhood weight results in more intensification in areas where the land availability

is low (existing cropland areas). The figures show the eastern part of Ukraine, a = 0.6 (legend in Fig. 5).

Fig. 14 Comparison of the representation and results of the IMAGE land allocation module (2000–2050) and the CLUMondo model

(2000–2040) for the OECD environmental outlook scenario.



that are driven by different, competing demands from

society.

These scenario results indicate that the model may

help improve environmental assessments by account-

ing for the variety of locally specific land system change

trajectories and in finding novel ways to simulate

processes of expansion and intensification of human-

managed ecosystems.

Acknowledgements

This research reported in this article is funded by the Nether-lands Organization for Scientific Research (NWO; project IGLO)and the European Research Council under the EuropeanUnion’s Seventh Framework Programme (FP/2007-2013)/ERCGrant Agreement no. 311819. The work presented in this articlecontributes to the Global Land Project (http://www.globalland-project.org). The authors thank Elke Stehfest for her contribu-tions with respect to the integration of this land change modelin the IMAGE integrated assessment model and for making theOECD environmental outlook results of the GTAP/IMAGEmodels available.

References

Alcamo J, Schaldach R, Koch J, K€olking C, Lapola D, Priess J (2011) Evaluation of an

integrated land use change model including a scenario analysis of land use change

for continental Africa. Environmental Modelling & Software, 26, 1017–1027.

Alkemade R, van Oorschot M, Miles L, Nelleman C, Bakkenes M, ten Brink B (2009)

GLOBIO3: A framework to investigate options for reducing global terrestrial bio-

diversity loss. Ecosystems, 12, 374–390.

Bondeau A, Smith PC, Zaehle S et al. (2007) Modelling the role of agriculture for

the 20th century global terrestrial carbon balance. Global Change Biology, 13,

679–706.

Boserup E (1965) The Conditions of Agricultural Growth: The Economics of Agrarian

Change under Population Pressure. Allen & Unwin, Chicago, Aldine, London.

Bouwman AF, Kram T, Klein Goldewijk K (2006) Integrated Modelling of Global Envi-

ronmental Change. An Overview of IMAGE 2.4. Netherlands Environmental Assess-

ment Agency, Bilthoven.

Braimoh AK, Onishi T (2007) Spatial determinants of urban land use change in Lagos,

Nigeria. Land Use Policy, 24, 502–515.

Britz W, Verburg PH, Leip A (2010) Modelling of land cover and Agricultural change

in Europe: Combining the CLUE and CAPRI-Spat approaches. Agriculture, Ecosys-

tems & Environment, 142, 40–50.

Clarke KC (2008) Mapping and modeling land use change: an application of the

SLEUTH model. In: Landscape Analysis and Visualisation (eds Pettit C, Cartwright

W, Bishop I, Lowell K, Pullar D, Duncan D), pp. 353–366. Springer, Berlin

Heidelberg.

Ellis EC, Ramankutty N (2008) Putting people in the map: anthropogenic biomes of

the world. Frontiers in Ecology and the Environment, 6, 439–447.

Foley JA, DeFries R, Asner GP et al. (2005) Global consequences of land use. Science,

309, 570–574.

Fritz S, See L, van der Velde M et al. (2012) Downgrading recent estimates of land

available for biofuel production. Environmental Science & Technology, 47, 1688–1694.

Fujita M, Krugman P, Venables AJ (1999) The Spatial Economy: Cities, Regions and Inter-

national Trade. MIT press, Cambridge, Mass..

Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropi-

cal deforestation. BioScience, 52, 143–150.

Geoghegan J, Cortina Villar S, Klepeis P (2001) Modeling tropical deforestation in the

southern Yucat�an peninsular region: comparing survey and satellite data. Agricul-

ture, Ecosystems and Environment, 85, 25–46.

Hansen MC, Defries RS, Townshend JRC, Carroll M, Dimiceli C, Sohlberg RA (2003)

Global percent tree cover at a spatial resolution of 500 meters: first results of the

MODIS vegetation continuous fields algorithm. Earth Interactions, 7, 1–15.

Hansen MC, Stehman SV, Potapov PV (2010) Quantification of global gross forest

cover loss. Proceedings of the National Academy of Sciences, 107, 8650–8655.

Havl�ık P, Schneider UA, Schmid E et al. (2011) Global land-use implications of first

and second generation biofuel targets. Energy Policy, 39, 5690–5702.

Hertel TW (2011) The global supply and demand for land in 2050: a perfect storm?

American Journal of Agricultural Economics, 93, 259–275.

Hurtt GC, Chini LP, Frolking S et al. (2011) Harmonization of land-use scenarios for

the period 1500–2100: 600 years of global gridded annual land-use transitions,

wood harvest, and resulting secondary lands. Climate Change, 109, 117–161.

IIASA/FAO (2012) Global Agro-ecological Zones (GAEZ v3.0). IIASA, Laxenburg, Aus-

tria and FAO, Rome, Italy.

Keys E, McConnell W (2005) Global change and the intensification of agriculture in

the tropics. Global Environmental Change, Part A, 15, 320–337.

Lambin EF, Turner BL II, Geist HJ et al. (2001) The causes of land-use and land-

cover change: moving beyond the myths. Global Environmental Change, 11,

261–269.

Letourneau A, Verburg PH, Stehfest E (2012) A land-use systems approach to repre-

sent land-use dynamics at continental and global scales. Environmental Modelling &

Software, 33, 61–79.

Lotze-Campen H, M€uller C, Bondeau A, Rost S, Popp A, Lucht W (2008) Global food

demand, productivity growth, and the scarcity of land and water resources: a spa-

tially explicit mathematical programming approach. Agricultural Economics, 39,

325–338.

Macdonald D, Jr Crabtree, Wiesinger G et al. (2000) Agricultural abandonment in

mountain areas of Europe: Environmental consequences and policy response. Jour-

nal of Environmental Management, 59, 47–69.

MEA (2005) Ecosystems and Human Well-being: Synthesis. Millenium Ecosystem Assess-

ment. Island Press, Washington, DC.

Meyfroidt P (2012) Environmental cognitions, land change, ecological feedbacks: an

overview. Journal of Land Use Science, 8, 341–367.

Mottet A, Ladet S, Coqu�e N, Gibon A (2006) Agricultural land-use change and its

drivers in mountain landscapes: A case study in the Pyrenees. Agriculture, Ecosys-

tems & Environment, 114, 296–310.

Navarro L, Pereira H (2012) Rewilding abandoned landscapes in Europe. Ecosystems,

15, 900–912.

Neumann K, Verburg PH, Stehfest E, M€uller C (2010) The yield gap of global grain

production: A spatial analysis. Agricultural Systems, 103, 316–326.

OECD (2012) OECD Environmental Outlook to 2050: The Consequences of Inaction. OECD

Publishing. doi: 10.1787/9789264122246-en.

Overmars KP, Schulp CJE, Alkemade R, Verburg PH, Temme AJAM, Omtzigt N, Sch-

amin�ee JHJ (2013) Developing a methodology for a species-based and spatially

explicit indicator for biodiversity on agricultural land in the EU. Ecological Indica-

tors, doi: 10.1016/j.ecolind.2012.11.006.

Pereira HM, Leadley PW, Proenca V et al. (2010) Scenarios for global biodiversity in

the 21st century. Science, 330, 1496–1501.

Phalan B, Onial M, Balmford A, Green RE (2011) Reconciling food production and

biodiversity conservation: land sharing and land sparing compared. Science, 333,

1289–1291.

Pontius RG Jr, Cornell J, Hall C (2001) Modeling the spatial pattern of land-use

change with GEOMOD2: application and validation for Costa Rica. Agriculture,

Ecosystems & Environment, 85, 191–203.

Ramankutty N, Evan AT, Monfreda C, Foley JA (2008) Farming the planet: 1. Geo-

graphic distribution of global agricultural lands in the year 2000. Global Biogeo-

chemical Cycles, 22, GB1003. doi:10.1029/2007GB002952.

Renwick A, Jansson T, Verburg PH, Revoredo-Giha C, Britz W, Gocht A, Mccracken

D (2013) Policy reform and agricultural land abandonment in the EU. Land Use

Policy, 30, 446–457.

Rudel TK, Coomes OT, Moran E, Achard F, Angelsen A, Jianchu X, Lambin EF (2005)

Forest transitions: towards a global understanding of land use change. Global Envi-

ronmental Change, 15, 23–31.

Schaldach R, Alcamo J, Koch J, K€olking C, Lapola DM, Sch€ungel J, Priess JA (2011)

An integrated approach to modelling land-use change on continental and global

scales. Environmental Modelling & Software, 26, 1041–1051.

Schneider A, Friedl MA, Potere D (2009) A new map of global urban extent from

MODIS satellite data. Environmental Research Letters, 4, 044003.

Serneels S, Lambin EF (2001) Proximate causes of land-use change in Narok District,

Kenya: a spatial statistical model. Agriculture, Ecosystems and Environment, 85,

61–81.

Seto KC, G€uneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030

and direct impacts on biodiversity and carbon pools. Proceedings of the National

Academy of Sciences, Early Edition. doi: 10.1073/pnas.1211658109.

Siebert S, Hoogeveen J, Frenken K (2006) Irrigation in Africa, Europe and Latin America.

Update of the Digital Global Map of Irrigation Areas to Version 4. Frankfurt Hydrology



Paper 05, Institute of Physical Geography, University of Frankfurt, Frankfurt am

Main, Germany and Food and Agriculture Organization of the United Nations,

Rome, Italy.

Sleeter BM, Sohl TL, Bouchard MA et al. (2012) Scenarios of land use and land cover

change in the conterminous United States: Utilizing the special report on emission

scenarios at ecoregional scales. Global Environmental Change, 22, 896–914.

Smith JB, Schneider SH, Oppenheimer M et al. (2009) Assessing dangerous climate

change through an update of the intergovernmental panel on climate change

(IPCC): reasons for concern. Proceedings of the National Academy of Sciences of the

USA, 106, 4133–4137.

Souty F, Brunelle T, Dumas P et al. (2012) The Nexus Land-Use model version 1.0, an

approach articulating biophysical potentials and economic dynamics to model

competition for land-use. Geoscientific Model Development, 5, 1297–1322.

Strengers B, Leemans R, Eickhout B, De Vries B, Bouwman AF (2004) The land-use

projections and resulting emissions in the IPCC SRES scenarios as simulated by

the IMAGE 2.2 model. GeoJournal, 61, 381–393.

Thomson AM, Calvin KV, Chini LP et al. (2010) Climate mitigation and the future of

tropical landscapes. Proceedings of the National Academy of Sciences, 107, 19633–

19638.

Thornton PK (2013) Livestock production: recent trends, future prospects. Philosophi-

cal Transactions of the Royal Society B, 365, 2853–2867.

Tscharntke T, Clough Y, Wanger TC et al. (2012) Global food security, biodiversity

conservation and the future of agricultural intensification. Biological Conservation,

151, 53–59.

Turner II BL (1974) Prehistoric intensive agriculture in the Mayan Lowlands. Science,

185, 118–124.

UNEP (1997) World Atlas of Desertification, 2nd edn, (eds Middleton N, Thomas D).

Arnold, London.

UNEP (2007) Global environment outlook 4: environment for development. United Nations

Environment Programme, Nairobi.

Van Asselen S, Verburg PH (2012) A land system representation for global assesse-

ments and land-use modeling. Global Change Biology, 18, 3125–3148.

Van Delden H, Escudero JC, Uljee I, Engelen G (2005) METRONAMICA: A Dynamic

Spatial Land Use Model Applied to Vitoria-Gasteiz. Virtual Seminar of the MILES

Project. Contro de Estudios Ambientales, Vitoria-Gasteiz. Available at: http://

www.riks.nl/resources/papers/Metronamica_Vitoria.pdf (accessed 1 May 2013).

Van Meijl H, van Rheenen T, Tabeau A, Eickhout B (2006) The impact of different pol-

icy environments on Agricultural land use in Europe. Agriculture, Ecosystems &

Environment, 114, 21–38.

Verburg PH, Overmars K (2009) Combining top-down and bottom-up dynamics in

land use modeling: exploring the future of abandoned farmlands in Europe with

the Dyna-CLUE model. Landscape Ecology, 24, 1167–1181.

Verburg PH, Van Eck JRR, De Nijs TCM, Dijst MJ, Schot P (2004) Determinants of

land-use change patterns in the Netherlands. Environment and Planning B-Planning

& Design, 31, 125–150.

Verburg PH, van Berkel DB, van Doorn AM, van Eupen M, van den Heiligenberg

HARM (2010) Trajectories of land use change in Europe: a model-based explora-

tion of rural futures. Landscape Ecology, 25, 217–232.

Verburg PH, Neumann K, Nol L (2011a) Challenges in using land use and land cover

data for global change studies. Global Change Biology, 17, 974–989.

Verburg PH, Ellis EC, Letourneau A (2011b) A global assessment of market accessibil-

ity and market influence for global environmental change studies. Environmental

Research Letters, 6, 034019.

Verburg PH, Van Asselen S, Van der Zanden E, Stehfest E (2013) The representation

of landscapes in global scale assessments of environmental change. Landscape Ecol-

ogy, 28, 1067–1080. doi: 10.1007/s10980-012-9745-0.

Wassenaar T, Gerber P, Verburg PH, Rosales M, Ibrahim M, Steinfeld H (2007)

Projecting land use changes in the Neotropics: The geography of pasture

expansion into forest. Global Environmental Change, 17, 86–104.

Wint W, Robinson T (2007) Gridded Livestock of the World 2007. Food and Agriculture

Organizations of the United Nations, Rome.

Wise MA, Calvin KV, Thomson AM et al. (2009) Implications of limiting CO2 concen-

trations for land use and energy. Science, 324, 1183–1186.

Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Figure S1. Comparison of partitioning of agricultural pro-duction change between area expansion and managementintensity.Figure S2. Simulated changes in livestock.



land cover change or land-use intensification: simulating land system change with a global-scale...

Documents