future european agricultural landscapes—what can we learn from existing quantitative land use...

Future European agricultural landscapes—What can we learn from

existing quantitative land use scenario studies?

Gerald Busch *

Center for Environmental Systems Research, University of Kassel, Kurt-Wolters-Str. 3, Germany

Available online 18 January 2006

Abstract

The structure of agricultural production and spatial patterns of agricultural land use in Europe are expected to face major changes over the

next decades due to changes in global trade, technology, demography and policies. This paper presents a set of 25 scenarios comprising

information on quantitative land use changes in Europe. The scenarios have been selected from studies with different foci, operating on both

different spatial scales and different time horizons. Given the diversity of quantitative scenarios this review illustrates the scenario design and

its quantification, and evaluates the results of land use/cover changes on a European level. Major gaps of current scenario exercises and

suggestions for improvement are topics of the discussion section (Section 4).

The focus of this review is on the comparison of selected driving forces and on agricultural land use/cover change in ‘‘Western Europe’’

(i.e. EU-15 plus Switzerland and Norway for some studies). Results show large differences in future land use/cover changes ranging from

moderate decreases (15%) to large increases (30%) depending on the assumptions about global trade, increase in agricultural productivity and

biofuel production. Domestic demand is a minor factor of land use/cover change since population is only changing slightly, and the

consumption level is stable and decoupled from economic growth. Scenarios show that the rate and direction of land cover change differ over

time. Considerable shifts towards grassland abandonment in many scenarios reflect the changes in agricultural management. Increasing

biofuel production as a result of both increasing energy demand and pro-active climate policies takes up considerable areas in many scenarios

and prevents substantial abandonment of agricultural land.

Although comparable quantitative results concerning European agricultural land use/cover change are only available on a very aggregated

level, the results are important to be dealt with when discussing future challenges of rural areas.

# 2005 Elsevier B.V. All rights reserved.

Keywords: European land use scenarios; Agricultural land use/cover; Land cover change

www.elsevier.com/locate/agee

Agriculture, Ecosystems and Environment 114 (2006) 121–140

1. Introduction

Agriculture has shaped many European landscapes over

centuries. This has given rise to unique semi-natural

environments with a rich variety of habitats and species

dependent on the continuation of farming. Agricultural land

use still dominates the European landscape since it covers

about 45% by area of the EU-25 states. However, the extent

of agricultural land is declining and the value added to the

* Present address: Bureau for Applied Landscape Ecology and Scenario

Analysis, Am Weißen Steine 4, 37085 Gottingen, Germany.

Tel.: +49 1212 315 916666; fax: +49 1212 624 124124.

E-mail address: [email protected].

0167-8809/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2005.11.007

overall annual gross domestic product (GDP) of the EU-25

countries is merely 2% (EUROSTAT, 2005).

In the last 50 years, a considerable change of agricultural

production has taken place. Technological progress and the

aim to establish nationally and internationally competitive

agricultural production have produced a marked intensifica-

tion and specialization in agriculture supported by the

Common Agricultural Policy (CAP), a strong European

policy framework with various incentives and subsidies

indirectly affecting not only agricultural land use but the

rural landscape (Commission of the European Communities,

2000; European Environment Agency, 2003; European

Communities, 2004). Agriculture plays a key role in the

management of natural resources in rural areas and

G. Busch / Agriculture, Ecosystems and Environment 114 (2006) 121–140122

agricultural policy is increasingly perceived by regional

stakeholders and politicians as an integrative part of rural

development complementing other sectoral policies (Bal-

dock et al., 2002; European Communities, 2004) and taking

into account the multiple functions of rural areas (e.g.

recreation, ecosystem services, agricultural production,

economic infrastructure). Thus, increasing migration away

from rural areas, a decrease of agricultural employees, aging

of farmers and abandonment of both farm holdings and

agricultural land have been a reason for major concerns.

With the implementation of both the Agenda 2000 in

1999 (European Commission, 1999) and the Lisbon strategy

in 2001 (European Commission Internet document, 2005),

two important steps were taken to integrate agricultural

policy with rural development issues in the European Union.

The relaunched Lisbon strategy in 2005 aims at combining

and modifying existing programs, such as the LEADER + -

initiative (Liaisons Entre Actions de Developpement de

l’Economie Rurale) or the INTERREG-III and SAPARD

programs (Special Accession Programme for Agriculture &

Rural Development) to foster rural development (i.e.

diversification of production, innovation, increasing R&D

measures) which in turn is a key element of restructuring the

agricultural sector (Baldock et al., 2002; European Com-

munities, 2003, 2004).

Over the next decades many regions in Europe will have

to face major demographic changes, structural changes in

agricultural production and the diversification of the

regional economy (European Commission, 2004). How

will European agricultural land use evolve in time and what

are the major uncertainties in future agricultural production?

Changes in agricultural land use and land cover reflect

economic causes, policy measures as well as spatial

planning objectives and show a wide range of impacts,

including biophysical and socio-economic changes and

feedbacks between land use and its drivers. Scenario

generation is an appropriate tool to develop plausible visions

of future pathways of European land use. Scenarios can help

to understand how critical uncertainties will play out and

what new factors will come into play and can, thus, result in

surprising and innovative insights (Davis, 2002). Literature

review on European land use scenarios revealed that a large

number of different scenario exercises exist (Busch et al.,

2004), but only a limited number of studies address driving

forces and land use/cover changes quantitatively—which is

the entry point of this review paper. Thus, comprehensive

and instructive studies, such as the ‘‘VISIONS’’ exercise

(Rotmans et al., 2001) or the ‘‘Scenarios Europe 2010’’

(Bertrand et al., 2001) could not be considered because they

focus on qualitative information about European land use.

Though quantitative and comprehensive, the ‘‘Ground for

Choices’’ study (Wetenschappelijke Raad voor het Reger-

ingsbeleid, 1992) as well as the ‘‘European Mid-Term

Review’’ exercise (Commission of the European Commu-

nities, 2002) were not considered either. The ‘‘Ground for

Choices’’ study is outdated because the scenarios that had

been developed for this study refer to the European

agricultural situation in 1990 and more than half of the

scenario period from 1992 until 2015 has elapsed. The

CAPRI modeling (Britz and Heckelei, 1997) for the

‘‘European Mid-Term Review’’ shows a very short time

horizon (2002–2009) and was assessed to be a projection of

current policies rather than a scenario building exercise.

Seven environmental studies comprising quantitative

scenarios on agricultural land use/cover change in Europe

have been selected by searching publicly available

information. This review focuses on agricultural land use

and land cover changes, and evaluates the different

assumptions (i.e. the direct and indirect driving forces

behind the scenarios) made in these scenarios. Five of the

seven studies reviewed are global in their scope and only two

studies comprise both the national and regional level in their

analysis of driving forces.

In consequence, the aim of this paper is two-fold: (1) to

illustrate the level of information which can be derived from

currently available quantitative land use scenarios and (2) to

evaluate if the scenario design and its quantification

adequately elaborate on problems of European agriculture

being currently discussed (European Environment Agency,

2003; European Commission, 2004, 2005; European

Communities, 2004). The goal of this review is to discuss

scenario results of seven studies with respect to quantitative

changes in agricultural land us/cover change on a European

level. The comparison of agricultural land use and land

cover change in this study covers ‘‘Western Europe’’ (i.e.

EU-15 countries plus Norway and Switzerland for some

studies).

2. Material and methods

2.1. Overview of the review approach

To evaluate the quantification of scenario conditions a

scheme as presented in Fig. 1 is followed. The main

qualitative scenario characteristics of the seven studies

selected are introduced (a), and then the quantification of

population growth and Gross Domestic Product as the two

most important exogenous drivers of land use/cover change

are briefly described (b). The major commonalities and

differences of the models being used for quantification of

land use/cover change simulation are discussed (c). Based

on a selected set of driving forces the outcome of land use/

cover change modeling concerning both changes in arable

land and pasture area (d) is discussed in the results section.

2.2. Scenario study selection

Twenty-four studies have been reviewed based on

publicly available information (i.e. scientific papers,

published reports, the Internet and technical documents)

in order to select quantitative land use scenarios being

G. Busch / Agriculture, Ecosystems and Environment 114 (2006) 121–140 123

Fig. 1. Schematic representation of the review approach. Sources: Raskin et al. (1998), IMAGE-Team (2001), Organisation for Economic Co-Operation and

Development (2001), Kemp-Benedict et al. (2002), Raskin et al. (2002), Gallopın and Raskin (2002), United Nations Environment Programme (2002), Potting

and Bakkes (2004), EURuralis (2004), Schroter (2004), Klijn et al. (2005), van Meijl et al. (2006) and Rounsevell et al. (2005, 2006).

spatially explicit, covering at least the EU-15 states and

having a time horizon of more than 15 years ahead.

Table 1 shows the seven studies that were selected. These

seven studies comprise 25 scenarios with a time horizon

ranging from 2020 to 2100. Due to different finalization of

the scenario studies, the reference year (base year) varies

between 1995 and 2002. Further, the scenario studies cover

different spatial scales. The first five studies shown in

Table 1 are global in their scope and address driving forces

on a global scale and for different world regions (e.g.

Northern America and Western Europe). The two latter

studies focus on Europe taking into account global level

information but comprising both the national and regional

level in their analysis of driving forces.

With a focus on environment, poverty reduction and

human values, the scenarios developed by the Global

Scenarios Group (GSG Futures) are based on various

exercises carried out at the Stockholm Environment

Institute-Boston (Raskin et al., 1998, 2002; Gallopın and

Table 1

Studies included in the review

Base year Time horizon No. of scenarios Global Spatia

World

Global Scenarios Group Futures/Stockholm Environment Institute Boston (GSG

1995 2050 4 X X

Special Report on Emission Scenarios/National Institute for Public Health and t

1995 2100 4 X X

Global Environmental Outlook 3/National Institute for Public Health and the En

2002 2032 4 X X

Global Environment Outlook 3/Stockholm Environment Institute Bostona (GEO-

2002 2032 4 X X

Environmental Outlook/OECD (OECD)

1995 2020 1 X X

Advanced Terrestrial Ecosystem Analysis and Modeling/University of Louvain-l

2000 2080 4 X X

EURuralis/Wageningen University/National Institute for Public Health and the E

2000 2030 4 X X

a The two studies use the same storylines, but different tools for quantificatio

Raskin, 2002). The GEO-3 scenarios are part of the third

Global Environmental Outlook coordinated by UNEP and

described in the GEO-3 report (United Nations Environment

Programme, 2002). The four scenarios have an environmental

focus considering the social and economic spheres. Building

to a large extent on the GSG-Futures scenarios, regional

policies are more elaborated in the SEI-Futures scenarios. In

both studies, the four scenarios address fundamentally

different societal visions which are characterized by (a)

essential continuity with current patterns, (b) fundamental but

undesirable societal change and (c) fundamental and

favorable societal transformation, respectively.

The IPCC Special Report on Emission Scenarios

(SRES) focuses on greenhouse gas emissions assuming

that policies to mitigate emissions are not implemented

(Intergovernmental Panel on Climate Change, 2000). The

IMAGE-SRES scenarios referred to in this study represent

an elaboration of the IPCC-SRES scenarios (IMAGE-

Team, 2001). The four scenarios build on the gradient of

l scale Focus

regions National Sub-national

Futures)

Environment, society

he Environment (SRES)

Climate

vironment (GEO-3/RIVM)a

Environment

3/SEI)

Environment

Environment, economy

a-Neuve (ATEAM)

X X Environment

nvironment (EURuralis)

X X Rural environment

n and were thus considered separately.


two major dimensions: whether the emphasis is on

economic wealth (A) or on sustainability and equity (B)

and whether the world is globally oriented (1) or regionally

focused (2).

The OECD Environmental Outlook (Organisation for

Economic Co-Operation and Development, 2001) reflects an

economy-based vision of environmental impacts. The

scenario of the OECD Environmental Outlook relies on

the ‘‘reference’’ variant of the ‘‘Bending the Curve’’

scenarios, developed by the Global Scenarios Group (Raskin

et al., 1998; Gallopın and Raskin, 2002), which in turn

addresses the vision of essential continuity with current

patterns.

The focus of the ATEAM study lied on the assessment of

European Ecosystem vulnerability. Within an integrative

assessment approach four scenarios for socio-economic

development and land use change were developed. Based on

the four global SRES scenarios, a European specific

interpretation of major driving forces was elaborated taking

into account European and national policy measures.

The main goal of the EURuralis exercise was to develop

different visions about the future of the EU-25 rural areas

taking into account possible effects on the environment, the

economy and the socio-cultural sphere. Drawing from the

SRES and GEO-3 scenario approaches, the EURuralis

project developed four scenarios which build on the gradient

of two major dimensions: (1) ranging from a world which is

facing increasing globalization to regionally oriented

economies and cultural blocks and (2) ranging from low

government regulation to ambitious governance with high

regulation (EURuralis, 2004).

2.3. Qualitative scenario information and scenario

classification

All scenarios reviewed refer to key elements acting as

driving forces, such as demography, culture and society,

economic development, technology, policy and governance

and environment. The qualitative information derived from

this study review is visualized in Fig. 2, in order to address

the major differences and commonalities of the scenarios.

The scenarios considerably differ in the aspects of solidarity,

environment and policy regulation. Further, the scenarios

diverge into globally oriented and regionally focused

pathways. Population growth, technological innovation

and economic growth show different but positive dynamics.

Many of the driving forces show similar patterns of change,

which is not surprising since all 25 scenarios build on two

scenario exercises: the SRES scenarios and the GSG

scenarios, which were then elaborated and interpreted.

Based on the information shown in Fig. 2, a gradient of

two major dimensions is constructed: (1) whether the

emphasis is on self-interest or on solidarity and (2) whether

the world is globally oriented or regionally focused. As a

result of this matrix approach, four scenario categories are

designated (1) Global Markets, (2) Global Society, (3)

Continental Barriers, and (4) Regional Sustainability. The

results shown for selected driving forces and land use/cover

change in Section 3 refer to these four scenario categories

(see Fig. 3).

2.4. Quantification of population and economic growth

as major driving forces

Demographic and economic assumptions play a funda-

mental role in driving demand for agricultural products and

thus in changes of agricultural land use/cover. An overview

on data used to quantify population growth and gross

domestic product is given in Table 2. Both indicators were

used as exogenous drivers in all scenario studies. Concern-

ing population data, four studies referred directly or

indirectly to projections developed by the United Nations.

These projections comprised, however, different variants

and each study made use of its own downscaling or regional

aggregation procedure (Center for International Earth

Science Information Network, 2002). The other three

studies, which used population data from the Phoenix

model (Hilderink, 2000) took the macroeconomic data from

the WorldScan model, an economic multi-region, multi-

sector, applied general equilibrium (AGE) model (Nether-

lands Bureau for Economic Policy Analysis, 1999). The SEI

studies made use of Worldbank projections (Raskin et al.,

1996) and developed own interpretations based on these

projections (Raskin et al., 1998). The OECD utilized data

from its own organization for the Environmental Outlook

study, which were based on the work by Burniaux (2000).

2.5. Modeling agricultural land use/cover change

Given the qualitative information on important driving

forces, socio-economic boundary conditions and policy

decisions from the scenarios, land use models were used to

project how much land is utilized where and for what

purpose. Since the scenario studies had different foci, and

operated on both different spatial scales and different time

horizons, distinct techniques were applied to compute land

use/cover change. Quantification of land use/cover change,

however, followed a general scheme: land use/cover change

is a result of changing demands of agricultural products,

production technology and biophysical suitability. Food

demand, economic growth, international trade and policies

drive the demand of agricultural products. Demand for

agricultural production leads to land use requirements. The

land use requirements result in land use/cover changes

depending on production technology, biophysical suitability

and spatial restrictions of land resources.

2.5.1. Modeling approaches used for quantification

Both the GSG Futures scenarios study and the GEO-3

study carried out by the Stockholm Environment Institute

used the PoleStar model for quantification (Kemp-Benedict

et al., 2002). PoleStar is a so-called accounting model


Fig. 2. Major global driving forces and their qualitative patterns of change.

(Potting and Bakkes, 2004) combining exogenous eco-

nomic, resource and environmental information on a global

and regional level (Fig. 4).

To calculate food supply and demand and the resulting

land use/cover changes, PoleStar started with human

dietary and industrial demands for agricultural products.

These demands for agricultural production were translated

into requirements for land, water and nutrient inputs.

International trade of food products was based on current

patterns of food trade. Changes in production technology

were addressed by interpretation of current trends or

borrowing data from other studies (Food and Agriculture

Organization, 2003). Land allocation was computed on a

continental level. Land use competition and land

conversion was addressed by using statistical information

on potential agricultural land and conversion rules,

respectively.

The SRES scenarios we refer to in this review were

computed with IMAGE. In contrast to PoleStar, IMAGE is a

dynamic integrated assessment modeling framework for

global change (Alcamo et al., 1998; IMAGE-Team, 2001).

IMAGE endogenously computed the demand for land,


Fig. 3. Classification of scenarios.

agricultural productivity and land use competition, and

provided spatially explicit data for 17 world regions

simulating land use/cover change on a half-by-half degree

grid. An iterative optimization between food demand and

land use requirements was calculated taking into account

income, biophysical suitability of land and technology.

Trade was introduced by exogenously prescribing self-

sufficiency ratios for each of the 17 world regions. Land

use/cover change was computed pixel-based taking into

account neighborhood to other cells and applying spatial

allocation rules including a hierarchy of land use types

(Strengers, 2001).

As shown in Fig. 4, the IMAGE model was used for an

alternative quantification of the GEO-3 scenarios (Potting

and Bakkes, 2004). Thus, it allowed comparing quantifica-

Table 2

Sources used for projections of population and economic development

GDP

GSG Raskin et al. (1996, 2002)

SRES Worldscan (Netherlands Bureau for Economic Policy

Analysis, 1999)

GEO-3/RIVM Worldscan (Netherlands Bureau for Economic Policy

Analysis, 1999)

GEO-3/SEI Raskin et al. (1996, 2002)

OECD Burniaux (2000)

ATEAM Center for International Earth Science

Information Network (2002)

EURuralis Worldscan (Netherlands Bureau for Economic

Policy Analysis, 2003)

tion results from different models within one scenario

framework.

For the OECD Environmental Outlook a soft-link between

two models was established. The JOBS model (Organisation

for Economic Co-Operation and Development, 2001), a

neoclassical general equilibrium model, was used to calculate

the sectoral demands, prices and commodity production (e.g.

crops, livestock and forest products) on a global level and for

different world regions. In JOBS, computation of agricultural

commodity demand and production was iteratively processed

in order to reach equilibrium between supply and demand.

International trade was described as substitutability between

domestic products and foreign products. Exogenous assump-

tions concerning overall agricultural productivity growth,

agricultural management factors and land supply were used

Population

United Nations (2000), Raskin et al. (2002)

Phoenix (Hilderink, 2000)

Phoenix (Hilderink, 2000)

United Nations (2000), Raskin et al. (2002)

United Nations (2000)

Center for International Earth Science

Information Network (2002)

Phoenix (Hilderink, 2000; Netherlands Bureau for Economic

Policy Analysis, 2003)


Fig. 4. Modeling tools used and spatial scales addressed for quantification of land/use cover change.

(Organisation for Economic Co-Operation and Development,

2001). The PoleStar System then used the economic and

demographic variables as input to calculate the quantitative

land use/cover changes.

The ATEAM study utilized the European agricultural

demand figures from the IMAGE 2.2 model as basic input

for agricultural demand–supply modeling to take care of

both domestic demand of agricultural goods and global trade

patterns including agricultural demand from other world

regions. Depending on the European-specific interpretation

of the SRES scenarios, the IMAGE numbers on agricultural

demand were modified and accompanied by own calcula-

tions of major driving forces (e.g. population growth and

economic growth). The Louvain-la-Neuve land cover model

(LLN-LCM) computed land use/cover change spatially

explicit on a 10 min � 10 min grid scale. Productivity

changes were taken into account exogenously (Ewert et al.,

2006), and spatial allocation rules were applied to compute

land use patterns (Rounsevell et al., 2005, 2006) and

included European policy options.

EURuralis used the most complex approach to simulate

land cover changes on different spatial scales by linking an

economic model to a biophysical model (van Meijl et al.,

2006). Changes in agricultural land were computed by using

combined results of the general economic equilibrium

model GTAP (Hertel, 1997) and the integrated assessment

model IMAGE. GTAP computed changes in agricultural

production, and demand for agricultural land at national to

global level taking into account international trade. GTAP

calculated productivity increase, management factors and

land supply endogenously. Further the implementation of an

endogenous quota mechanism improved the reflection of

European agricultural policies. The IMAGE model used

information on yields, feed efficiency rates and demand for

land from GTAP to calculate agricultural productivity and

demand for land. Through re-iteration the land projections

converged and produced a harmonization of both biophy-

sical and economic land use processes (Klijn et al., 2005;

van Meijl et al., 2006). The information on land use/cover

change at national level was used by the CLUE-S model to

downscale land use demands to land use patterns at a

1 km � 1 km grid scale resolution (Verburg et al., 2006).

2.5.2. Geographical coverage of the models

For the comparison of land use scenarios on a European

level, it is important to define a common geographical

coverage. Due to different study foci and separate models

used for quantification the geographical coverage of world

regions is, unfortunately, not consistently addressed. Europe

does not exist as one single world region but is addressed by

sub-regions, which are, again, defined differently. Each study,

however, includes the EU-15 countries in its regional

classification. The differences due to classification addressing

four additional countries in some studies are assessed not to be

notably important for this review because the agricultural area

in these countries is very small. This review refers to the

regional classification given as ‘‘Western Europe’’.

2.5.3. Drivers of land use/cover change

Major driving forces of land use/cover change as

addressed by the models are shown in Fig. 5. The first

part of Fig. 5 depicts the drivers of demand for agricultural

production. Demand for agricultural production is a

combination of domestic demand and demand from other

world regions. All models addressed this issue by computing

variables, such as population growth, diets, caloric intake,


Fig. 5. Drivers of agricultural land use/cover change.


economic development, policies and international trade.

Major differences occurred in the consideration of demo-

graphic and economic variables. Changes in age structure

and migration were only addressed in the ATEAM and the

EURuralis study. Economic effects of tariffs and export

subsidies were only considered in the OECD and the

EURuralis studies. Further, the GSG and the GEO-3/SEI

studies do not address biofuel crop demand.

The second section of Fig. 5 addresses agricultural

production and its drivers. All models computed both crop

and animal production but with different detail. All models

considered major factors of technology and management but

the number of crops and animals simulated, varied

considerably. In the IMAGE-GTAP framework productivity

of 11 crop types was simulated endogenously. PoleStar only

addressed three major crop categories (wheat, rice, other

crops). The LLN land cover model used exogenously

calculated productivity of wheat as a proxy for food crop

productivity. In addition to food crops, grass and fodder

crops, and three biofuel crop types were considered.

Calculation of animal production is most advanced in the

IMAGE-GTAP modeling framework in terms of animal

types being considered.

In the IMAGE-GTAP modeling framework the imple-

mentation of policy measures on a European or sub-

European level was most elaborated. Economic indicators

were only computed by JOBS and GTAP. Spatial restric-

tions, biophysical constraints and land use transition

pathways were not addressed by PoleStar. Land use/cover

classification varied considerably between only five classes

in the LLN land cover model and 21 classes in the IMAGE

calculations. Comparison of land use/cover changes is

hampered because different classifications are used.

2.6. Driving force selection for quantitative comparison

To discuss land use/cover change results of the different

studies, eight driving forces have been selected. The criteria

for the driving force selection have been data availability and

Table 3

Driving forces used for the quantitative comparison

Driving force

Demand

Income

Population

Caloric intake

Production technology

Fertilizer input

Crop yield

Grass and fodder production

Trade and production

Crop demand

Crop production

Animal demand

Animal production

representation of important factors of land use/cover change,

which are: (1) food demand, (2) production technology and

(3) production and trade.

Table 3 shows the eight driving forces selected for

comparison. The ratio of agricultural demand to agricultural

production shows how much of a commodity is consumed

regionally, and thus indicates the export-orientation of

agricultural production.

3. Quantitative comparison of driving forces and

agricultural land cover change

The quantitative comparison is based on the scenario

classification given in Section 2.3 and the selection of

driving forces described in Section 2.6. Since the base years

of the studies vary, the geographical coverage is not entirely

consistent, and the time horizon being considered ranges

from 2020 to 2050, the data are normalized to the base year.

The figures presented in the following sections show relative

percent changes. The changes of the driving forces are

calculated as average annual percentage changes and are

presented as a comparison of two driving forces in each

figure. The diagonal in the driving force figures represent an

equivalent growth rate (1:1 line) of both indicators.

Three categories of agricultural land use/cover changes are

compared: (1) cropland, (2) pasture and (3) abandoned land.

All figures depict the scenario studies in the same order,

which is: (1) GSG, (2) SRES, (3) GEO-3/SEI, (4) GEO-3/

RIVM, (5) OECD, (6) ATEAM and (7) EURuralis. Note,

that not all studies are represented in each scenario category.

3.1. Drivers of land use requirements

3.1.1. Demand

With the selected set of driving forces (population

growth, income, crop demand, animal demand, and caloric

intake), changes in European demand for agricultural

production are illustrated. Starting with the two important

Explanation

Growth in gross domestic product per capita

Total population growth

Intake of food calories per capita

Synthetic fertilizer input per hectare of cropland

Crop yield per hectare

Green fodder and legume production

Regional demand for food and feed crops

Total crop production (food and feed)

Regional demand for animal products

Total animal production


Fig. 6. Average annual percentage change in agricultural demand of selected indicators.

exogenous variables population growth and economic

growth, all scenarios assume a continued economic growth

in Europe and only small population changes in ‘‘Western

Europe’’ (Fig. 6). Economic growth shows a higher

variability and ranges from slight increases in the ‘‘Regional

Sustainability’’ category to moderate increases in the

‘‘Global Markets’’ category. Caloric intake, however, is

decoupled from economic growth in all scenarios since the

current level of food consumption and caloric intake in

‘‘Western Europe’’ is high.

Most scenarios show both an increasing crop demand and

an increasing animal demand. Increasing crop demand being

considerably higher than population growth reflects increas-

ing resource consumption in form of food demand. Small


changes in caloric intake, however, indicate that the increase

in animal and crop demand is only partially caused by

raising domestic demand and a shift in diets. For most

scenarios, the increase in demand for animal products is of

the same magnitude as the increase in population, showing

that diets are fairly stable.

Within the ‘‘Global Society’’ category, the GSG ‘‘Great

Transition’’ and the GEO-3/SEI ‘‘Sustainability First’’

scenarios reflect a special pathway showing a considerable

decline in crop demand and demand for animal products

(Fig. 6d and f). Decreasing per capita food demand (Fig. 6k),

and a shift in diets towards less meat consumption (Fig. 6m)

reflect changing consumer preferences in the ‘‘Regional

Sustainability’’ category. In contrast, ‘‘Security First’’,

shows both a considerable increase in caloric intake

(Fig. 6h) and a shift in diets towards more meat consumption

(Fig. 6i).

3.1.2. Production technology

Eight variables are used to highlight changes in

agricultural production technology that is influencing land

use/cover change (Fig. 7). Increasing yields, increasing

production and intensification of agricultural production

dominate the overall picture of agricultural production

technology (Figs. 7 and 8). In many scenarios, decreasing

green fodder production accompanied by increasing animal

production indicate both further intensification of livestock

production as well as a shift in species towards poultry and

pork which heavily rely on grain feed.

High rates of innovation are an attribute of the ‘‘Global

Markets’’ scenarios category and are expressed as high

increases in crop yields which mainly stem from better

management and biotechnological development rather than

from increased fertilizer input (Fig. 7a). The assumptions of

increase in crop productivity differ considerably and the

widespread combination of crop yields and fertilizer

application indicate significant differences in agricultural

management. In both the ‘‘Global Society’’ category and the

‘‘Regional Sustainability’’ category, a strong focus on

technological improvements and better management is

reflected by increasing crop productivity while fertilizer

input decreases in all scenarios. In contrast to the scenarios

of the ‘‘Global Markets’’, the shift in agricultural manage-

ment results from strong environmental regulation. Animal

production (Figs. 7e and 8e), however, shows a similar

pattern of intensification and shift in production as described

for the ‘‘Global Markets’’ scenarios. Cropland area

decreases due to intensified crop production and increasing

crop yields combined with stable or decreasing demand for

cop products (Fig. 7m). Pasture area declines since livestock

production is intensified and a shift towards grain-based

fodder consumption is assumed. Note that total animal

production is decreasing in the ‘‘Regional Communities’’

scenario of the EURuralis study, but pasture areas are

maintained due to extensive grazing management (Fig. 8k)

supported by tariffs on agricultural imports (van Meijl et al.,

2006). In the ‘‘Continental Markets’’ category, the two GSG

scenarios show a distinct management of agricultural

production. A considerable, fertilizer-based intensification

of crop production is accompanied by an expansion of

grazing management and a lower shift towards pork and

poultry production, resulting in an extension of green fodder

production (Fig. 7g and h).

3.1.3. Production and trade

Both the relation of crop and animal production to crop

and animal demand are used to illustrate the trade

orientation of agricultural production (Fig. 8). In the three

scenario categories ‘‘Global Markets’’, ‘‘Global Society’’

and ‘‘Continental Barriers’’ crop and animal production by

far exceeds the domestic demand in most scenarios

reflecting global food demand stemming from other world

regions. This development is most pronounced in the

scenario categories representing global and open markets.

In three scenarios of the ‘‘Global Society’’ category,

animal production only serves the domestic demand

(Fig. 8f). In the EURuralis scenario, trade liberalization

and abolished domestic support payments (van Meijl et al.,

2006) cause considerably reduced crop production (Fig. 8e).

In contrast the export-orientation of crop production in the

SEI scenarios is even higher than in the scenarios of the

‘‘Global Markets’’ category since environmental policies in

developing regions prevent uncontrolled expansion of

agricultural land and thus trigger additional crop imports

from other regions (Kemp-Benedict et al., 2002; Gallopın

and Raskin, 2002). In the ‘‘Continental Barriers’’ scenario

category demand for food imports from other world regions

is assumed to increase since world population is consider-

ably increasing and regional barriers hamper technological

diffusion. Subsidies and domestic support are assumed to be

typical policies and thus support European farmers to

produce for the world market. Fig. 8h and i illustrate this

export-oriented agricultural production of many scenarios.

Three of six scenarios, however, follow a more regionally

oriented pathway in crop production showing a nearly

balanced situation of domestic crop demand and crop

production (Fig. 8h). In contrast to the other scenario

categories agricultural production in the ‘‘Regional Sustain-

ability’’ category only meets the domestic demand. Since

there is no export-oriented production, crop production

increases only slightly and animal products demand is even

assumed to decrease (Fig. 8l and m).

3.2. Land use/cover change

3.2.1. Cropland change

Assumptions on production technology, domestic

demand and international trade determine the extension

of agricultural land. In three of four scenario categories, the

various combinations of these three elements result in both

considerable decrease and substantial increase of cropland

area (Fig. 9). In the ‘‘Regional Sustainability’’ scenario


Fig. 7. Average annual percentage change in production technology of selected indicators.

category, decreasing cropland area is the only outcome

caused by less food exports combined with decreasing

domestic demand and increasing productivity.

In the ‘‘Global Markets’’ category, the SRES and the

ATEAM scenarios denote the extremes in cropland area

change (Fig. 9a). The SRES scenario shows, surprisingly,

only a marginal increase in crop productivity but an even

higher gain in total crop production, and thus, results in a

substantial expansion of cropland area to 2050. The increase

in cropland area does not only relate to export-oriented food

crop production but also results from extended biofuel

production (see Fig. 10) because of very high energy

demand in an economically oriented future (IMAGE-Team,

2001). The ATEAM scenario represents the opposite

combination, i.e. crop yield increase outweighs by far the

rise in crop production, which in turn causes a considerable

decline in cropland area to 2050. This pathway is enforced

by crop allocation on optimal location, which is an

interpretation of the global and economic scenario orienta-

tion (Rounsevell et al., 2005). A similar effect appears in the


Fig. 8. Average annual percentage change in agricultural production and trade of selected indicators.

scenario of the EURuralis study. Increase in crop yields

and crop production nearly balances (Fig. 7a and c) but

cropland declines. This is caused by abandonment of less

productive land due to abolished subsidies and reduced

domestic support payments ((Klijn et al., 2005; van Meijl

et al., 2006).

In the ‘‘Global Society’’ scenario category, the diver-

gence of decreases and increases in cropland area is smaller

than shown for the scenarios of the ‘‘Global Markets’’

category. The IPCC-SRES ‘‘B1’’ scenario and the GEO-3/

RIVM ‘‘Sustainability First’’ scenario show an increase of

cropland area (Fig. 9b) due to highest crop production

combined with lowest increase in crop yields (Fig. 7d and f).

The two scenarios with moderate to high increases in yields

but reduced growth in crop production (ATEAM ‘‘B1’’ and

EURuralis ‘‘Global Co-operation’’) show a decrease in crop


Fig. 9. Change in cropland area.

area. Both the GSG ‘‘Great Transition’’ scenario and the

GEO-3/SEI ‘‘Sustainability First’’ scenario assume a

transition from a growth-oriented to a sustainability-oriented

world. In the ‘‘Continental Barriers’’ category, again,

different combinations of crop yield increase, domestic

demand and export-oriented production result in both a

considerable decrease of cropland and moderate to high

growth (Fig. 9c). In the ‘‘Regional Sustainability’’ category

increasing crop yields combined with decreasing crop

production result in a continuous decline of cropland area, as

shown for the ATEAM scenario and the EURuralis scenario

in Fig. 9d. The SRES scenario indicates a transition from

cropland decline towards a slight increase in cropland area

reflecting agricultural dis-intensification.

3.2.2. Pasture change

Diverging pathways of pastureland are characteristic for

all scenario categories. In the two global-oriented cate-

gories, the development of pastureland varies between slight

and considerable decreases in area. In the two regional-

oriented categories, the divergences ranges between slight

increases and considerable decreases in pasture area. The

rate at which pastureland decreases and its trend reflect both

the different assumptions on intensification in livestock

production (e.g. increasing productivity, less grazing) and a

shift from grass-based production systems towards fodder

crops. This shift in turn results from two changes in animal

production (1) change in feed composition and (2)

preference change in meat consumption from ‘‘red meat’’

(beef) towards ‘‘white meat’’ stemming from pork and

poultry production (Sere and Steinfeld, 1996).

In the ‘‘Global Markets’’ category, the change in feed

composition is only marginal since the shift from green

fodder to feed crops is assumed to be less than 3% in all

scenarios (IMAGE-Team, 2001; Kemp-Benedict et al.,

2002; Bouwman et al., 2005). Hence, a combination of a

considerable increase in livestock productivity, a shift in

consumer preferences towards pork and poultry meat

consumption, and changes in agricultural management

towards stable-based livestock production cause a decrease

in pastureland. In the ‘‘Global Society’’ category, minor

decreases in the ATEAM ‘‘B1’’ scenario and the EURuralis

‘‘Global Co-operation’’ scenario reflect assumptions on

grassland preservation as part of both environmental policy

goals and restrictive planning. In the ‘‘Continental Barriers’’

category the extensification of grassland production with a

shift towards pasture-based fodder production cause a slight

increase of pasture area in the EURuralis scenario and the

GSG scenarios. In the ‘‘Regional Sustainability’’ scenario

category, only the EURuralis scenario shows slightly

increasing pasture area resulting from preservation of

grazing management.


Fig. 10. Change in pasture area.

3.2.3. Extension of agricultural land and biofuel

production

Fig. 11 summarizes the development of extension in

agricultural land and biofuel production. Expansion of

agricultural land is mainly caused by international food

demand and increasing biofuel crop production. The

expansion of agricultural land is most pronounced in the

global-oriented scenarios with large food exports. Abandon-

ment is a result of increasing agricultural productivity and a

shift in agricultural production systems. The decrease of

agricultural land is most pronounced in scenarios with lower

food exports and less biofuel production.

In the ‘‘Global Markets’’ category, increase in biofuel

production is not a result of pro-active environmental

policies but a consequence of a fast increasing high energy

demand and an energy mix given by external models

containing renewables (IMAGE-Team, 2001). In the

‘‘Global Society’’ category, increase in biofuel area is

reduced in the ATEAM and SRES scenario due to lower total

energy demand. In contrast, the implementation of climate

mitigation policies enhances biofuel production in the GEO-

3/RIVM scenario. Increasing biofuel production in the

‘‘Continental Barriers’’ category does not reflect a higher

environmental awareness but is part of the security-oriented

self-reliance policy expressed in the general scenario

characteristics (see Section 2.3). Note that in the SRES

and in the GEO-3/RIVM scenario biofuel production takes

place on former pasture areas or on other land converted to

agricultural land. The ATEAM scenario is the only scenario

showing abandonment of agricultural land stemming from

both cropland areas and pastureland. Without the substantial

production of biofuels, agricultural land would diminish by

about 40% in area. In the ‘‘Regional Sustainability’’ scenario

category, biofuels are promoted as a regional and more

sustainable energy carrier since a major focus in this

scenario category is on environmental and socio-economic

sustainability. In the ‘‘Regional Communities’’ scenario,

biofuel crops are produced on abandoned cropland whereas

in the ATEAM ‘‘B2’’ and the SRES ‘‘B2’’ scenarios biofuel

crops grow on former pasture areas. All three scenarios show

a pronounced increase in area for biofuel production

compared to the base year situation.

4. Discussion

4.1. Storylines and scenario building

A large set of quantitative scenarios is available that

describe changes in European agricultural land. All 25

scenarios reviewed, however, referred to two global scenario

sets explored by the IPCC and the SEI. This is not a


Fig. 11. Change in agricultural land and biofuel production.


drawback since these scenario sets reflected the major

uncertainties (globalization versus regionalization, solidar-

ity versus self-interest, economic-orientation versus envir-

onment orientation), and described a broad range of future

pathways. All scenario studies reviewed followed a top-

down approach taking into account actors and factors on a

global and European level. The ATEAM and the EURuralis

further reflected actors and factors on a national and sub-

national level and integrated sectoral perspectives in order to

address land use/cover changes in rural areas. Even in

entirely liberalized markets and a globalized economy,

farmers who act on a local level take proximate decisions.

The top-down approaches lack a feedback between

decisions taking place on a local/regional level and the

European level. To keep track of important decision levels

top-down and bottom-up approaches have to complement

one another. Both narrative storylines and quantitative

modeling have to address this issue. Thus, narrative

storylines have to refer to regionally specific spatial

processes. Such a scenario building process should make

use of two elements: (1) iteration between qualitative and

quantitative information and (2) participatory approaches.

Computation of regional spatial patterns could be used to

check the consistency of qualitative scenario information.

Re-iteration could help to harmonize qualitative and

quantitative scenario information. Participatory scenario

approaches would help to reflect the regional complexity of

European land use processes in the scenario building process

(European Commission, 2004). The PRELUDE exercise

carried out by the European Environment Agency was a first

attempt to develop participatory land use scenarios for

Europe and to combine the qualitative information and the

model quantification in an iterative way (Mc Glade, 2004).

Insights from this study could be a starting point for further

integration between existing scenario information and more

region-specific participatory approaches.

4.2. Quantification of land use/cover change

Given the qualitative information on major uncertainties

of the scenario storylines, the quantification of the important

driving forces revealed that technological development,

agricultural management and demand from other world

regions emerge as key factors of changes in European

agricultural land use/cover change. Changes in domestic

food demand were assumed to be of minor importance since

both population and caloric intake were not significantly

increasing. Agricultural land use continues to be a dynamic

process with multiple plausible pathways in all scenarios.

Non-linear land use/cover changes indicate that it is

important to examine different time scales to understand

the future consequences of changing trends. Major changes

in land use/cover are caused by intensification of agricultural

land use, abandonment and conversion of both natural

vegetation and pasture to cropland. These diverging

pathways imply major challenges for regional and European

policies since massive changes in socio-economic infra-

structure in rural areas could be expected and substantial

environmental impacts would have to be addressed. The

information provided by current scenario exercises could help

to stimulate the policy discussion of future rural development.

Substantial increases in agricultural productivity and a focus

on optimal location as assumed in some scenarios pose the

question what kind of rural landscapes we want to maintain in

Europe. Could land simply be abandoned and become natural

vegetation after decades or do we want to maintain patchy

landscapes with high recreation and aesthetic value? What are

possible strategies to cope with structural changes in

agricultural production assuming a highly industrialized

agriculture and shift in consumer preferences as indicated by

some scenarios? Do we want to create multifunctional

landscape all over Europe or could we benefit from functional

landscapes in both environmental and economic terms? How

will be dealt with the trade-off between agricultural expansion

due to agricultural demand from other world regions and

environmental protection? Will the economic return of

agricultural production justify substantial changes of land-

scape composition? Is biofuel production an appropriate

alternative to abandonment or can it even justify conversion of

natural vegetation?

Current scenario exercises provide stimulating informa-

tion on European land use/cover change but there are some

limitations and drawbacks that need to be highlighted. Five of

the seven studies reviewed are global in their perspective.

They address land use change only on a European level. These

global studies have a broad environmental perspective, but

land use changes are commonly viewed from a sectoral

perspective addressing changes in agriculture, forestry and

urban areas separately. A comparison of the different studies

is hampered due to land cover classification inconsistencies,

various spatial definitions of Europe, and limited spatial

resolution of quantitative results. In consequence, it was only

possible to compare relative changes in land use/cover

distribution on a European level lacking the interpretation of

changing land use/cover patterns.

Within the same scenario category the assumptions on

major driving forces and land use/cover change varied

considerably. Population growth and income as important

exogenous variables differed notably since data from various

sources were applied. On the one hand, this is positive since it

highlights the uncertainties of the scenario exercises – a very

important information scenarios can provide – on the other

hand a comparison of scenario results is hampered because of

too many variables. Current scenario exercises lack a

transparent approach how qualitative scenario information

is translated into numbers. As an example, the interpretation

of the same qualitative information resulted in a notably

different quantification for the GEO-3 scenarios (GEO-3/

RIVM and GEO-3/SEI), which in turn led to significantly

different land use/cover changes. A transparent methodology

and documentation would be of great added value for the

interpretation of quantitative modeling results.


International trade negotiations and European-specific

agricultural policies were only marginally and indirectly

addressed in the globally focused studies. Market intervention

through European agricultural policies was adequately

addressed only in the EURuralis study because policy

measures, such as domestic support, and tariffs were explicitly

implemented. The ATEAM study reflected European policy

intervention in the agricultural sector only through broad

assumptions about productivity changes, changes in over-

supply and different spatial distribution of agricultural area.

Current agricultural practices in Europe differ strongly

both between regions and within regions reflecting regional

socio-economic and biophysical conditions (European

Environment Agency, 2003; European Communities, 2003;

European Commission, 2005). Land use changes reflect the

decision making process by those who control land resources

(Verburg et al., 2006). In consequence, simulation of land use

change has to address the spatial scale and the decision

making level of the main actors. Depending on the scenario

philosophy, this decision making level could vary from the

global to the local level or be a combination of different levels.

In all scenarios, the proximate actors of both agricultural land

use and rural development are local farmers and regional

institutions. Hence, the quantification of driving forces on a

national or even a European level does not adequately reflect

this issue. Regional information has to be implemented in

both the scenario building process and its quantification.

Apart from the spatial scale or level of information, the

studies lack important information on land use intensity and

rural development. Transition into organic farming systems

or changes in farming structure need to be addressed since

both have an important impact on agricultural landscapes.

Aging of farmers and migration from rural to urban areas are

important factors (European Environment Agency, 2003;

European Communities, 2003; European Commission,

2005), which need to be addressed in assessments of

changing rural landscapes. Agricultural land use (change) is

increasingly perceived as multi-level, multi-actor and multi-

domain process (Knickel and Kok, 2003; European

Commission, 2004). Thus, modeling of agricultural change

needs to consider different levels, actors and domains.

4.3. Modeling framework

Scenario quantification in the different studies was

carried out with a limited set of modeling tools for a very

simple reason: only very few models can generate

quantitative long-term projections of future land use/cover

changes at regional to global levels. In consequence,

different scenarios borrow from the same quantitative data

computed by the same models and diminish their qualitative

diversity. Hence, scenario development and interpretation

may be hampered by model limitations.

The discussion of the quantitative results turns out that

changes in European agricultural land use/cover changes are

only adequately addressed when taking into account

different levels of information and being spatially explicit

on a high-resolution grid. In the ATEAM and the EURuralis

study these goals were, at least partly, achieved by (soft-)

linking different models. A potential problem of both

approaches is repeated computing of input- and output

information from different models without cross-checking

the underlying processes. The ATEAM and the EURuralis

study applied land use information from the IMAGE model

for downscaling procedures to create spatial patterns of land

use/cover changes. The ATEAM study utilized the European

agricultural demand figures from the IMAGE 2.2 model as

input information for land use/cover change modeling

(Rounsevell et al., 2005, 2006). The information on

agricultural demand in turn was calculated by IMAGE

using, e.g. biophysical suitability and crop yields (IMAGE-

Team, 2001). In the ATEAM study crop yields and

management factors were adopted from other external

sources to compute land use/cover changes. In the

EURuralis project CLUE-S used information on spatial

extension of land use changes from IMAGE to create spatial

patterns of land use/cover change (Verburg et al., 2006).

Variables, such as soil texture, temperature, precipitation

and slope were used to compute biophysical suitability in the

downscaling process. IMAGE computed agricultural areas

with similar variables but from different sources and with a

distinct spatial resolution. Hence, in both studies agricultural

land was re-allocated without checking possible contra-

dictions between modeling approaches.

Another potential problem appearing in these kinds of

downscaling procedures is a gap between level of

information and spatial resolution. Some driving force

information is only available at national or European level

but the spatial output information on land use/cover change

in case of the EURuralis study is 1 km � 1 km. A validation

based on time slices of European land use data would help to

answer the question if the current level of input information

is appropriate to generate patterns of land use/cover change

on a high-resolution grid.

5. Conclusion

The currently available quantitative scenario information

on European land use/cover change indicates that both

European agriculture and rural areas might undergo a process

of radical change in the next decades. Providing various

pathways of future development the scenario studies could

stimulate policy discussion with respect to possible implica-

tions of future agricultural demand, agricultural productivity

and impacts on rural areas. The ATEAM and the EURuralis

studies for the first time linked continental level information

with downscaling procedures and thus allowed to analyze

regional patterns of land use/cover change.

The current approaches of top-down modeling should be

accompanied by region-specific bottom-up modeling to

strengthen the value of quantitative scenario studies in


supporting policy discussion. The different spatial patterns

resulting from various trade-offs between land use types are of

particular interest for regional environmental assessments and

can only be addressed by quantitative modeling with a high

spatial resolution. European-specific land use scenarios with a

strong spatial and regionally varied perspective would be a

major step ahead in addressing this issue. To reach this goal of

comprehensive regional land use scenarios, more detail need

to be taken into account with respect to multiple functions of

landscapes, different intensities in land use, land use-relevant

policies, regional land use competition and the impacts of

urban and infrastructure development on rural landscapes.

Acknowledgements

I would like to thank Eric Kemp-Benedict, Bas Eickhout

and Isabelle Reginster for their data support. I would like to

thank Mark Rounsevell and Joseph Alcamo for interesting

discussions about land use scenarios. Finally, I would like to

thank the PRELUDE project team for providing inspiring

insight into participatory scenario approaches.

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