land cover change or land-use intensification: simulating land system change with a global-scale...
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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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
4 S . VAN ASSELEN & P. H. VERBURG
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
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 5
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
6 S . VAN ASSELEN & P. H. VERBURG
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
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 7
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).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
8 S . VAN ASSELEN & P. H. VERBURG
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 9
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
10 S . VAN ASSELEN & P. H. VERBURG
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 11
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).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
12 S . VAN ASSELEN & P. H. VERBURG
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).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 13
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).
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
14 S . VAN ASSELEN & P. H. VERBURG
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
Peri-urban & villages 0.2 881 812 742 604
Urban 0.2 150 163 177 204
Peri-urban & villages 0.4 881 764 640 603
Urban 0.4 150 173 197 204
Eastern Europe
Peri-urban & villages 0.0 1991 1888 1763 1525
Urban 0.0 297 324 353 410
Peri-urban & villages 0.2 1991 1783 1550 1341
Urban 0.2 297 349 404 454
Peri-urban & villages 0.4 1991 1669 1405 1326
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 15
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
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
16 S . VAN ASSELEN & P. H. VERBURG
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
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
A GLOBAL-SCALE LAND SYSTEM CHANGE MODEL 17
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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
18 S . VAN ASSELEN & P. H. VERBURG
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.
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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.
© 2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12331
20 S . VAN ASSELEN & P. H. VERBURG