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Contents lists available at ScienceDirect

Ecological Modelling

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

olicy relevance of three integrated assessment tools—A comparison withpecific reference to agricultural policies

andra Uthesa,∗, Katharina Frickea, Hannes Königa, Peter Zandera, Martin van Ittersumb,tefan Sieberc, Katharina Helminga, Annette Piorra, Klaus Müllera

Leibniz-Centre for Agricultural Landscape Research (ZALF), Eberswalder Strasse 84, D-15374 Müncheberg, GermanyWageningen University, Plant Production Systems Group, P.O. Box 430, 6700 AK Wageningen, The NetherlandsJoint Research Centre, Institute for Prospective Technological Studies European Commission (IPTS), Edificio EXPO – c/Inca Garcilaso, s/n – 41092 Seville, Spain

r t i c l e i n f o

rticle history:eceived 21 November 2008eceived in revised form 14 July 2009ccepted 14 August 2009vailable online xxx

eywords:olicy impact assessmentand use

a b s t r a c t

The Common Agricultural Policy (CAP), a system of market support instruments, direct income transfers,and rural development measures, has been put through an ongoing reform process in recent decades.This paper introduces three policy impact assessment tools (SIAT, SEAMLESS-IF, MEA-Scope tool) andanalyses how these tools have responded to a number of challenges for integrated assessment modellingas reported in the international literature. Significant progress has been made with regard to modellinglinkages whereas other challenges, particularly those related to issues of scale and uncertainty man-agement, require further efforts. It is also analysed which CAP instruments are represented and whatkinds of effects can be analysed at different scales. Market instruments and direct payments are com-

ommon agricultural policyntegrated assessment modelling

paratively well represented, while the ability to model rural development measures is mostly beyondthe scope of these tools. Because each tool has found a different solution for coping with the commonchallenges of integrated assessment modelling, the choice of one of the tools for a particular applicationdepends strongly on the policy questions being asked. The SIAT provides the big picture via its abilityto represent broad changes in policy instruments with EU-wide cross-sector impacts. The most compre-hensive analysis of agricultural policy instruments can be obtained with SEAMLESS-IF. The MEA-Scope

er tw

tool complements the oth

. Introduction

Any proposed action in the European Union (EU), whether it behe introduction of new policy measures or a change in existing pol-cy instruments, underlies an obligatory ex ante impact assessmentrocess (COM, 2005a, 2008b). Ex ante policy impact assessment

s expected to provide insights into the various intended andnintended consequences of the different policy options (Renda,006). The need for well-informed decision-making led to theevelopment of a variety of methods and modelling tools withifferent spatial, temporal and institutional scales (see Easterling,997; Bousquet and Le Page, 2004; Rossing et al., 2007; Oxley andpSimon, 2007; Schaldach and Priess, 2008). The wide selectionf available tools provides policy makers and researchers with the

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

pportunity to select the assessment tools that best fit the questionsosed (Thiel, 2009).

Due to their often parallel consequences on economic, social andnvironmental issues, impact assessment for many policy instru-

∗ Corresponding author. Tel.: +49 33432 82413; fax: +49 33432 82308.E-mail address: uthes@zalf.de (S. Uthes).

304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.08.010

o approaches with detailed regional profiles.© 2009 Elsevier B.V. All rights reserved.

ments requires integrated assessment (IA). Integrated assessmentattempts to provide a systematic way to integrate knowledge acrossdisciplines, scales, resolutions and degrees of certainty (cf. Scraseand Sheate, 2002).

The term ‘integrated assessment’ originated in the early 1970sand is rooted in population-environment research in Europe andcost–benefit analyses of environmental problems in the USA(Rotmans, 2004). In the 1980s and 1990s, when a number of inte-grated assessment models were developed (e.g., Alcamo et al.,1990; Rotmans et al., 1990; Hordijk, 1991; Prentice et al., 1993;Alcamo, 1994; Rotmans and de Vries, 1997; Boumans et al., 2002),IA became a rapidly evolving field. An overview of the early his-tory of IA is given by Dowlatabadi and Morgan (1993) and Rotmans(1998). Oxley and ApSimon (2007) provide an overview of bothpioneering and more recent IA tools.

Model building is only one option to perform integrated assess-ment (Parson, 1995). Rotmans (1998), for example, differentiates

ntegrated assessment tools—A comparison with specific reference to.010

between analytical and participatory IA tools. Integrated assess-ment overlaps with existing research areas such as risk analysis,technology assessment and policy analysis (cf. Rotmans, 1998) andis closely related to a number of other assessment types, includ-ing sustainability assessment, triple-bottom-line assessment,

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Table 1Characteristics of SENSOR, SEAMLESS, MEA-Scope.

SIAT SEAMLESS-IF MEA-Scope tool

Project homepage www.sensor-ip.org www.seamless-ip.orgwww.seamlessassociation.org

www.mea-scope.org

Coverage EU EU Selected EU regionsKey issues addressed Synthesising multi-sector simulations,

quick scan analysis, impact assessmenttool

Focus on agricultural sector, bridgebetween the macro and the microscale, flexibility of the tool because ofcomponent-based structure

Farm structural change, bridgebetween individual farms/fields typesand the regional scale, spatialheterogeneity

Land use sectors Agriculture, forestry, tourism,transport, energy, nature conservation

Agriculture Agriculture

Time horizon 2025 target year (=15 years) 10–15 years 10–15 yearsStand-alone model components

of the integrated toolsNEMESIS (macro econometric)CAPRI (agriculture, partial equilibrium)EFISCEN (forestry)DYNA-CLUE (land use change)

CAPRIa (agriculture, partialequilibrium)FSSIM (positive-mathematicalprogramming)APES (mechanistic) and others

AgriPoliS (agent-based)MODAM (linearprogramming + rule-basedenvironmental impacts)FASSET (linearprogramming + mechanisticenvironmental impacts)

Spatial resolution 1 km2 grid Agri-environmental zones (polygons) 0.01 km2 gridSmallest scale Region (NUTS2–3b) Representative farm typologies Spatially localised farm typologiesSize of regions Administrative regions (NUTS2–3) Administrative regions (NUTS2) Administrative regions (NUTS3) or

landscapes 22,000–125,000 ha

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manage the trade-off between breadth versus depth of scientificunderstanding, Easterling (1997) argues that to produce usableknowledge with integrated assessment models it is key in their

a The version of CAPRI used in SEAMLESS-IF was given the name SEAMCAP.b Nomenclature of Territorial Units for Statistics (NUTS) established by EUROSTA

tates, and NUTS2 and NUTS3 to regions, provinces and counties.

nvironmental impact assessment, and extended impact assess-ent (cf. Hacking and Guthrie, 2008). However, a clear distinction

etween these approaches can hardly be made. Hacking anduthrie (2008) suggest, therefore, that instead of attempting to

nterpret the often confusing terminology associated with relatedypes of analyses, assessment approaches should preferably berouped and compared based on their commonalities such as gen-ral focus and scope.

The objective of this paper is to analyse the policy relevancef three policy impact assessment tools. To this end, we focus onhe European Common Agricultural Policy (CAP). The CAP is a sys-em of agricultural subsidies and programmes including marketupport measures, direct income transfers, and rural developmenteasures (COM, 2006a).We chose the following three tool approaches for this com-

arison because they all consider CAP instruments within theirodelling frameworks, but each tool follows a very distinct

pproach. According to Easterling (1997) top-down integratedssessment models are capable of representing global scale phe-omena using coarse-resolution relationships that are assumed toapture the relevant causal mechanisms. Bottom-up approaches, inontrast, are characterised by the use of site-specific, often mecha-istic models to simulate ecosystem processes, that are aggregatedo regional scales.

The Sustainability Impact Assessment Tool (SIAT) was devel-ped with the objective of enabling compliance evaluation foruropean policy proposals with the European Guidelines for Sus-ainable Development (Sieber et al., 2008). SIAT follows a top-downpproach in the assessment of policy impacts with a specific focusn cross-sector effects (Helming et al., 2008a, b).

In contrast to SIAT, the SEAMLESS-integrated frameworkSEAMLESS-IF) focuses primarily on the agricultural sector andttempts to bridge the gap between the micro (field-farm-smallegion) and the macro (market, sector) scales. It therefore uses aulti-scale and component-based approach and allows both top-

own and bottom-up analyses (Van Ittersum et al., 2008).

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

The MEA-Scope tool is based on a bottom-up approach appliedo a number of case study regions and was motivated by the facthat most integrated assessment tools lack the ability to simultane-usly consider farm structural change, joint production, and spatialeterogeneity (Piorr et al., in press).

S0 refers to country level data, NUTS1 to the next subdivision of states or group of

Table 1 provides an overview of the three tool approaches.Details on the stand-alone model components can be found inAppendix A of this paper.

For the scope of this paper, we generally define policy rele-vance as the ability of tools acting at the science–policy interfaceto address the challenges of integrated assessment modellingas reported in the literature. From the specific perspective ofCAP decision-support, policy relevance is additionally defined asthe ability to represent the different policy instruments of theCAP,1 both with regard to distributional effects (between sectors,households, spatial scales) and effects on socio-economic and envi-ronmental issues.

Section 2 gives an overview of common challenges of integratedassessment tools as discussed in the international literature. Sec-tion 3 picks up the identified challenges (thematically grouped)and presents what has been done in the development process ofthe three tools to address these challenges (Table 2). Section 4gives a brief overview of the CAP and typical political questionsthat accompany its reform process. The second part of this sectionanalyses which policy instruments and policy effects of the CAP arerepresented in the three tools. Advantages and disadvantages of thethree tools with regard to CAP decision-support as well as futurechallenges are discussed in Section 5, providing the basis for ourconclusions in Section 6.

2. Challenges of integrated assessment modelling in theliterature

Major challenges in integrated assessment modelling (IAM)include the development of methods for linking knowledge acrossdomains or disciplines, particularly emphasising the roles ofimportant feedback mechanisms, non-linearities and uncertainties(Easterling, 1997; Rotmans, 1998; Jakeman and Letcher, 2003). To

ntegrated assessment tools—A comparison with specific reference to.010

development to integrate regional analysis into top-down approaches

1 It must be stated that CAP reform decision support is only one particular fieldof application within a wide scope of possible applications of the three tools.

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Table 2Challenges of integrated assessment modelling and how the tools address these challenges.

Challenges SIAT SEAMLESS-IF MEA-Scope tool

Link macro–micro level, integration ofmultiple scales(e.g., Easterling, 1997; Verburg et al.,2006a, 2006b; Van Ittersum et al.,2008)

Linking macro-econometric to partialequilibrium, sector models and landuse change modelling

Linking farm and partial-equilibriummodelling, coupling of farm modellingand mechanistic modelling

Linking agent-based, detailed linearprogramming and mechanisticmodelling through input/outputexchange

Land mobility between sectors,different land qualities (e.g., VanMeijl et al., 2006)

Common land balance between sectorand macro-econometric modelling

Land capability classification Land capability classification

Farm-level decision-making (e.g.,Easterling, 1997)

Simulation of representative farmtypes, farm decision-making based onpositive-mathematical programming

Simulation of regional farmpopulations, farm decision-makingbased on linear programming

Farm structural change (e.g., Happe etal., 2008; Zimmermann et al., 2009)

Markov chain estimation (forecasting) Agent-based simulation of land marketand investment decisions

Important feed-backs (e.g., Easterling,1997; Rotmans, 1998; Parker et al.,2002)

Iterative recalibration procedure toobtain a common land balance, factorproductivity changes from NEMESISare fed-back to CAPRI

Extrapolation of farm-level response torefine agricultural supply in partialequilibrium, prices are fed back to farmmodel

Changes in farm characteristics due todynamic development of farms inagent-based model are input to staticmodels (down-stream)

Scaling issues(up- and downscaling) (e.g.,Rotmans, 1998; Verburg et al.,2006a; Ewert et al., 2009)

Downscaling of macro-econometricresults with a land use change modelto 1 km2 grid

See aboveTypology building,Land classification, statisticalprocedure to allocate non-spatial farmtypes to land capability classes(polygons)

Iterative proportionate fitting torecreate total farm population, landcapability classification, recreation offarm localisation based on grasslandshare mentioned in the FADN (grid size0.01 km2)

IT-investment, overcome technicalhindrances, handling time delay(e.g., McIntosh et al., 2006; Ewert etal., 2009)

Data exchange between models over afile server(automated download, modelling, andresult upload)Client software/user interface forend-users, meta-model development

Software located on a server,ontology-based relational databases,Open MI interface to link components,Client software/user interface forend-users

Data exchange between models over afile server (partially automated),No online accessible software, resultpresentation on website

Balance between economic,environmental, social aspects (e.g.,Verburg et al., 2006a, 2006c; Oxleyand ApSimon, 2007; Van Ittersum etal., 2008; Schaldach and Priess, 2008)

Indicator framework includingeconomic, social and environmentalissues

Indicator framework includingeconomic, social and environmentalissues

Indicator framework includingeconomic, social and environmentalissues

Communication within the project(Parker et al., 2002)

Project structure formalised byEuropean commission regular projectmeetings, Internet website, deliverablereports

- Same as SIAT- Ontology development

- Same as SIAT

User/stakeholder involvement (e.g.,Jakeman and Letcher, 2003; Scrieciu,2007; Ewert et al., 2009)

Survey among end-users, Consultationwith European Commission officials,regional partners

Consultation with EuropeanCommission officials, and regionalauthorities

Demand-based analysis on preferencesfor sustainability dimensions,consultations with EuropeanCommission officials, regional partners

Result communication (Easterling,1997; Parker et al., 2002; Bousquetand Le Page, 2004; Verburg et al.,2006a)

User interface, Visualisation of results,maps, trade-offs, multi-criteriaapproach to identify key results

User interface, Visualisation of resultsin tables, maps, trade-offs

Internet presentation, Visualisation ofresults, maps, trade-offs

Data constraints/maintenance costs(Verburg et al., 2006c; Oxley andApSimon, 2007; Thiel, 2009)

Use of official data (e.g., Eurostat OECD) Use of official data (FADNdata) + additional data collection

Use of official data (FADNdata) + additional data collection oncrop and livestock production

Valuation procedures, uncertainty (e.g.,Rotmans, 1998; Van Asselt andRotmans, 1996, 2002; Janssen andvan Ittersum, 2007; Ewert et al.,2009; Gabbert et al., in press)

Sensitivity analyses, use of coherentdata sourcesIndividual models managed bydifferent institutes

Ex-post experiments, plausibilitychecks through expert involvement (inprocess), individual models managedby different institutes

Ex-post experiments, plausibilitychecks through expert involvement (inprocess), individual models managedby different institutes

Value-added of model linking Multi-sectoral analysis, land usechange between sectors (in CAPRIotherwise constant land),sustainability impact assessmentincluding macro-economic indicators

Refined agricultural supply inpartial-equilibrium model,representation of environmentaleffects, farm model receives pricesfrom partial equilibrium, technicalcoefficients and environmental effectsderived from mechanistic model

Dynamic farm development over time(size, investments), bridge fromagent-based to mechanistic model,associated environmental effects

Limitations for possible policyquestions

Coarse-resolution (1 km2 grid), coarserepresentation of policy instruments,

No economy-wide effects,Coarse representation of farm-leveld

No economy-wide effects, noprice-quantity effects, long simulation

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Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

imitations, to allow for consideration of enterprise-level decision-aking, and to develop mechanisms for incorporating the questions

f stakeholders and decision-makers.Scrase and Sheate (2002) and Verburg et al. (2006a) recom-

end stakeholders be involved not only in the use of the final

ecision-making time, no user interface, selected casestudies only

tool, but also that they participate actively in the development

ntegrated assessment tools—A comparison with specific reference to.010

process of the tools. Parker et al. (2002) see communication as acentral issue both internally among team members and externallywith decision-makers, stakeholders and other scientists. The dif-ficulty in communicating scientific knowledge to a policy audience,particularly if several knowledge domains are involved is acknowl-

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dged by Oxley and ApSimon (2007). Other challenges identifiedy these authors include the ability of IAM to capture behaviouralesponses to policy mechanisms, to handle delay times betweenolicy question and model answer, and to handle uncertainty

n data and interdependencies between effects (cf. Rotmans andan Asselt, 1996; Van Asselt and Rotmans, 1996, 2002; Rotmans,998).

With a more specific focus on agriculture, Van Ittersum et al.2008) identify four key challenges for the development of inte-rated assessment tools: (1) overcoming the gap between micro andacro level analysis,2 (2) overcoming the bias in integrated assess-ent towards either economic, social or environmental issues and

nstead performing a balanced evaluation of all three dimensions,3) improving the flexibility and re-use of the developed tools byhoosing a generic, modular and operational structure, and (4)vercoming hindrances in the technical linkage of the models (seelso Jansson et al., this issue). McIntosh et al. (2006) argue that thee-use of integrated assessment tools is particularly influenced byonceptual and software technologies but they also conclude thatew technologies and new techniques have to be translated intohe pre-existing knowledge of user communities before they cane effectively employed.

Van Meijl et al. (2006) add that the consideration of differ-nt land qualities and also the mobility of land between differentectors are essential for the plausibility of results produced by inte-rated assessment tools. Happe et al. (2008) identify that policympacts on farm structures and structural change including land

obility between farmers (land market), as well as distributionalmpacts have been neglected by most studies. As an additional con-ideration, the tools should have low maintenance costs (e.g., byaking use of existing data sources where possible), be well doc-

mented, have an appealing result presentation and visualisationhat is guided and adjusted to the interest of different users, and beignificant in terms of applications (cf. Janssen and van Ittersum,007).

Further challenges for IAM are imperfect or incomplete dataources (Reidsma et al., 2006; Schmit et al., 2006; Aalders anditkenhead, 2006). Information on the driving forces of landse change, such as population development or gross domes-ic product is often only available at a national level (Busch,006). Access to census data, which provide information on thepatial heterogeneity of farming activities is a possible sourceor validation, is often limited for reasons of data protectionSchmit et al., 2006). Moreover, the Farm Accountancy Dataetwork (FADN), the only source of EU-wide harmonised micro-conomic farm data has only an indirect spatial link and performssampling that, for example, excludes economically small farm

nterprises (Reidsma et al., 2006). Most economic data are notvailable at small spatial scales, or have only an indirect link topatial units, which hinders their potential combined use withechanistic models (Verburg et al., 2006a; Van Ittersum et al.,

008).Another issue acknowledged by several authors is that com-

lex tools that involve multiple disciplines and model componentsre more susceptible to error propagation and loss of disciplinarynowledge (e.g., Rotmans, 1998; Parker et al., 2002; Jakeman andetcher, 2003). Validation procedures are therefore important inrder to test the degree of correspondence between modelling

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

esults and the response of the studied system. The most intu-tive way to perform validation is to compare model results withbserved historic data (Verburg et al., 2006c). In practical terms,owever, it is often difficult to validate the result of integrated tools

2 Micro-level refers to field-, farm- or small-region models; macro-level refers toarket models or other sector models (Van Ittersum et al., 2008).

PRESSlling xxx (2009) xxx–xxx

in ex post assessments because existing monitoring or other datasources often do not provide a sufficient basis, can only be obtainedat high costs, or cannot be accessed at all due to data protectionreasons. On the other hand, Verburg et al. (2006c) emphasise thatvalidation against observed data should not be overstated sinceit bears the risk of overcalibrating the model to past processesthat might not necessarily be the processes driving future devel-opments.

3. What has been done with regard to the identifiedchallenges?

This section picks up the challenges of IAM from the previoussection and analyses what has been done in the development pro-cess of the three tools in addressing these challenges (summarizedin Table 2).

3.1. Model linkages, technical and scaling issues

The quantitative modelling chain of SIAT interlinks a macro-econometric model, a partial-equilibrium agricultural sectormodel, a forest sector model, and a land use change model throughiterative recalibration (Kuhlman, 2008). The value-added from thislinkage is land mobility between the different sectors, endogenoussimulation of research and development, and the ability to com-bine macroeconomic and environmental impacts (Jansson et al.,2008a,b). The technical linkage of the models is facilitated by anautomated file transfer over a server on the Internet (Jansson et al.,this issue). The integration of land use change modelling allowsfor downscaling of economic results into spatially explicit landuse changes at the grid level, which makes sustainability impactassessment possible (Verburg et al., 2006a). To reduce delay timesbetween policy questions and model response, SIAT has devel-oped a meta-model component, which is the actual policy impactassessment tool. The meta-model is based on estimated mathe-matical functions that express in a simplified way the responseof the modelling chain, and thus the correlations between policyvariables, land use changes and sustainability indicators (Kuhlman,2008). The use of response functions allows decision-makers toconduct policy scenario analyses within pre-calculated solutionspaces without having to run the whole model chain (Sieber et al.,2008).

SEAMLESS-IF currently comprises two main model chains (cf.Ewert et al., 2009). The first couples farm and partial-equilibriummodelling. The response of the farm model is used to derive the sup-ply elasticity coefficients through which the farm-level reaction isextrapolated to the whole EU as input for the partial-equilibriummodel (Pérez Domínguez et al., 2009). This linkage is beneficialbecause it refines agricultural supply in the partial-equilibriummodel, while the farm model, which is otherwise incapable oftaking into account price-quantity effects, receives equilibriumcommodity prices for the calculated production quantities. Asecond major linkage statically couples farm and mechanistic

ntegrated assessment tools—A comparison with specific reference to.010

3 General equilibrium models provide an economy-wide perspective includingmultiple sectors, multiple commodities and multiple countries or regions. The gen-eral idea of general equilibrium models, such as the GTAP model (Hertel et al.,2008) is to bring demand and supply on multiple commodity markets simulta-neously in equilibrium and thus to compute equilibrium prices and quantities.Mathematical form and variables to include in the behavioural equations is guidedby economic theory and, in contrast to macro-econometric models, not estimatedeconometrically. Advantages compared to macro-econometric modelling are fewer

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roviding the possibility of integrating economy-wide effects.owever, this link is at present not part of the core modellinghain (cf. Ewert et al., 2009). To account for structural changes inhe agricultural sector, an econometric approach based on Markovhain estimation has been developed to forecast future farm num-ers (Zimmermann et al., 2009). To overcome technical hindrances

n model linking, SEAMLESS-IF uses the Open Modelling InterfaceOpenMI) (cf. Ewert et al., 2009). A common ontology stand-ng for a ‘joint conceptualisation’ or ‘set of shared definitions’ ofrojects, experiments and scenarios is used for the developmentf databases, models and graphical user interfaces (Janssen et al.,009).

The MEA-Scope tool includes two model linkages. On top of theodelling chain stands an agent-based model (Happe et al., 2006a)

hat simulates the structural development of individual farms overime by modelling the interactions of the farms with a land mar-et and farm investment decisions. At distinct time steps theomputed structural characteristics (e.g., farm size, labour endow-ent, livestock heads) are passed to a static whole farm model

Zander, 2003) that allows for a detailed representation of cropping,ivestock, and fodder activities, and performs an environmentalmpact assessment that is based on expert rules. The input/outputoefficients from the whole farm model are input into a secondhole farm model, which performs a quantitative mechanistic

nvironmental impact assessment (cf. Hutchings et al., 2007). Theodel linking allows for a combined analysis of structural change

e.g., farm growth, farm exit), agricultural production patterns andelated environmental impacts in both qualitative and quantitativeerms.

.2. Farm-level decision-making

The consideration of farm decision-making in a tool approach isf relevance to model farm-level policies or to analyse farm-levelmpacts of changes at the macro level (cf. Zander et al., 2008).

SIAT has not incorporated a farm perspective and thus provideso insights into farm-level decision-making (Verburg et al., 2008).

SEAMLESS-IF includes the farm perspective by simulating theesponse of representative farm types, which are derived fromhe FADN, to price changes, technological development or policiesVan Ittersum et al., 2008). A farm type can represent hundreds ofndividual farms. A land capability classification accounts for land-ased differences in agricultural production potentials. Farm typesnd land capability classes were linked based on statistical methodso determine the relative importance of each farm type in each landapability class (Andersen et al., 2007). Results are up-scaled to theegional scale based on area-weighted aggregation. The optimisa-ion algorithm in the farm model is based on positive mathematicalrogramming (PMP).

The MEA-Scope tool also performs a land capability classifi-ation and makes use of FADN data. The goal is to create totalarm populations, so that the number of simulated farms equalshe total number of farms in reality. Each FADN farm is assignedweight that expresses how often it must be replicated to best

t the total regional characteristics. A spatial allocation procedurelaces the replicated FADN farms within a map of the regional land-cape based on available spatial information (total area, fraction ofrable and grassland). As a result farms physically occupy differ-

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

nt grid cells and are thus spatially heterogeneous (Kjeldsen et al.,006). Farms are not restricted to their initial production orienta-ion and land resources, but they can evolve over time as a resultf the endogenous land market and simulation of investment deci-

ata requirements, on the other hand the forecasting quality of these types of modelss usually less reliable.

PRESSlling xxx (2009) xxx–xxx 5

sions. The optimisation algorithms in the linked models are basedon linear programming (LP).

3.3. Balanced evaluation of policy impacts

All three tools provide impact indicators for all three sustainabil-ity categories (economic, environmental, and social). Policy impactsare derived from the output variables of the models of the tools bycomparing policy scenario results against the results of a counter-factual scenario (baseline, or reference).

Table 3 lists the indicators provided by the three tools that canbe publicly accessed. Some indicators constitute primary modeloutput and can be directly derived from the tools, while otherswere derived through further processing (e.g., with multi-criteriaapproaches or rule-based approaches).

A comparison of the indicators provided by SIAT, SEAMLESS-IFand the MEA-Scope tool must deal with the problem that the def-inition of indicators across these tools is not consistent; each tooluses its own list and definition of indicators. The assignment of indi-cators to the economic, social, and environmental categories wastherefore taken as used in the projects. The environmental dimen-sion was underpinned with subthemes (fertilisers, water etc.).

Disaggregation from higher scales to smaller scales is often only,if at all, possible through complex additional methodological stepswhile the opposite, aggregation from fine scale data to coarse scaledata, is usually easier (cf. Easterling, 1997). Therefore, the smallestscale at which results can be provided is indicated. While the MEA-Scope tool and SEAMLESS-IF terminate the analysis at the level ofindividual indicators, SIAT uses a multi-criteria approach to weighand aggregate indicator scores on thematic issues (Pérez-Soba etal., 2008).

3.3.1. Economic and social impactsMacroeconomic effects are represented in the SIAT approach.

Microeconomic impacts resulting from changes in agricultural pro-duction patterns (commodities produced, production intensity)and changes in technology and capital availability (e.g., land, labour,skills) as well as from structural changes in the agricultural sector(i.e., the change in the number of farms in different farm types)are represented in SEAMLESS-IF and the MEA-Scope tool. In addi-tion, SEAMLESS-IF provides detailed financial indicators on the CAPbudget and several farm-level indicators that can also be related todifferent land qualities.

Social effects (Table 3) in all three tools are mainly derived fromother simulated economic or environmental indicators, such asa proxy for quality and diversity of life in rural areas (all threetools), or ‘exposure to water pollution (N, P)’ (SIAT), which couldalso be interpreted as an indicator for the environment category.SIAT yet offers the greatest number of indicators in the socialcategory.

3.3.2. Environmental impactsSEAMLESS-IF and the MEA-Scope tool cover environmental

impacts related to the agricultural system (different intensities,technologies, or changes in cropping pattern), while environmen-tal effects in the SIAT are primarily a result of cross-sectoral landuse changes (e.g., between agriculture and forestry).

Environmental impacts of regional to global relevance are rep-resented in SIAT and SEAMLESS-IF, while environmental impacts oflocal or farm-level importance are represented in the MEA-Scopetool and SEAMLESS-IF.

ntegrated assessment tools—A comparison with specific reference to.010

SIAT makes use of expert rules to assess environmental impacts,while SEAMLESS-IF performs a quantitative assessment using amechanistic model (Donatelli et al., in press). The MEA-Scope toolperforms both qualitative and quantitative environmental impacts:a fuzzy-logic-based assessment module expresses the pressure of

Please cite this article in press as: Uthes, S., et al., Policy relevance of three integrated assessment tools—A comparison with specific reference toagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08.010

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Table 3Publicly available policy impact indicators provided by SIAT, SEAMLESS-IF, and MEA-Scope tool.

SIAT (meta model) Scale SEAMLESS-IF Scale MEA-Scope tool Scale

Economic Energy costs NUTS0 Agricultural income NUTS2 Profit per ha Single farmsGross domesticproduct

NUTS0 Direct CAP payments NUTS2 Rental prices per ha Single farms

Inflation rate –consumer price index

NUTS0 Export subsidy outlays NUTS2 Number of farms Regiona

Labour costs by sector NUTS0 First pillar CAP expenditure NUTS2 Change in farm size Single farmsLabour productivity NUTS0 Intervention stock costs NUTS2Net flow of energyproducts

NUTS0 Money metrics NUTS2

Net flow of tradedgoods by sector

NUTS0 Profits (accounting) of theagricultural processingindustry

NUTS2

Public expenditure NUTS0 Subsidies NUTS2Value-added by sector NUTSX Tariff revenues NUTS2

Terms of trade NUTS2Total agricultural inputs NUTS2Total agricultural outputs NUTS2Total costs NUTS2Total welfare NUTS2Value of farm production NUTS2Land shadow price Farm typeNet farm income Farm typePercent of debts in netfarm income

Farm type

Percent of subsidies in netfarm income

Farm type

EnvironmentalLand use Area of recently

abandoned arable landNUTSX % area with conservation

tillageFarm type Change in UAA Single farms

Area of irrigated arableland

NUTSX % low fertilised grassland Farm type Extensive area Single farms

Area of recentlyabandoned pastureland

NUTSX % non sprayed area Farm type Land abandonment Single farms

Area of arable land notirrigated

NUTSX % of area with catch crop Farm type Cropping pattern Single farms

Forest area NUTSX % of area of crops Farm type Livestock units per ha Single farmsArea of (semi-) naturalvegetation

NUTSX Crop diversity index Farm type

Area of pasture NUTSXArea of permanentcrops

NUTSX

Area of built-up land NUTSX

Fertilisers Ammonia emissionfrom agriculture

NUTSX Ammonia volatilisation Farm type Ammonia loss total,field

Single farms

Nitrogen oxideemissions

NUTSX Nitrate leaching Farm type N-Leaching potential Single farms

Nitrogen surplus NUTSX Nitrate surplus Farm type N-Balance Single farmsMineral nitrogen fertiliseruse

Farm type Soil N-Change Single farms

Indirect energy use bymineral fertiliser

Farm type Energy input Single farms

Phosphorus surplus NUTSX Farm typeMineral P, K use Farm type

Pesticide use NUTSX Pesticide consumption Farm type Pesticides in ground-and surface water

Single farms

Pesticide leaching Farm typePesticide runoff Farm typePesticide volatilisation Farm type

Water Water retentioncapacity of soil

NUTSX Water use by irrigation Farm type Groundwater recharge Single farms

Runoff Farm type Nutrients in surfacewater (N,P)

Single farms

Soil Soil erosion risk bywater

NUTSX Soil erosion Farm type Water erosion Single farms

Soil sealing NUTSX Soil fertility change Farm type Soil compaction Single farmsWind erosion risk NUTSX Soil organic matter change Farm typeSoil organic carboncontent

NUTSX

Carbon sequestrationin biomass, soil anddead organic matter

NUTSX

Greenhousegases (GHG)

Methane emission NUTSX Total CH4 emissions Farm type Greenhouse gases Single farms

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Table 3 (Continued )

SIAT (meta model) Scale SEAMLESS-IF Scale MEA-Scope tool Scale

Nitrous oxide emission NUTSX Total N2O emissions Farm typeCarbon dioxideemission

NUTSX

Renewable energyproduction – biomass(fossile energy demandarea, animal)

NUTSX

Global warmingpotential

NUTSX Global warming potential Farm type

Biodiversity Terrestrial habitat atrisk fromeutrophication

NUTS0 Crop diversity Farm type Field hares Single farms

Population trends offarmland birds

NUTS0 Skylarks Single farms

Dead wood NUTSX Hover flies Single farmsHigh nature valuefarmland

NUTSX Great Bustard Single farms

Spatial cohesion NUTSX Wild flora species Single farmsRed belly toad Single farms

Others Forest fire risk NUTSX

Social Continuity ofappreciated landscapeheritage

NUTSX Labour use Institutionalcompatibility

Farm type NUTS2 Labour use Single farms

Deviation of regionalincome

NUTS0

Deviation of regionalunemployment rates

NUTS0

Employment rate bysector, gender, and age

NUTSX

Exposure to airpollution

NUTSX

Exposure to fire risk NUTSXExposure to waterpollution (N, P)

NUTSX

Migration NUTSXSelf-sufficiency indexfor food (calories)

NUTS0

Self-sufficiency indexfor food (fat)

NUTS0

Self-sufficiency indexfor food (protein)

NUTS0

Recreational pressurefrom tourism

NUTS0

Unemployment rate by NUTSX

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a The largest region the MEA-Scope tool has been applied to is the NUTS3 region

gricultural management intensity for a number of abiotic andiotic indicators such as groundwater recharge potential, soil com-action, or the habitat potential for a number of indicator speciesSattler et al., 2006). In addition, a dynamic mechanistic modelimulates nitrogen-cycling (see Hutchings et al., 2007).

.4. Sectors and geographical coverage

The SIAT approach includes multiple sectors while SEAMLESS-IFnd the MEA-Scope tool focus on the agricultural system in detail.

SIAT and SEAMLESS-IF deliver results for the whole EU (Vanttersum et al., 2008; Helming et al., 2008b) and for administra-ive regions according to the Nomenclature of Territorial Units for

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

tatistics (NUTS)4.The MEA-Scope tool has been used in both administrative and

iophysical regions (Piorr et al., 2007).

4 NUTS0 refers to country level data, NUTS1 to the next subdivision of states orroup of states, and NUTS2 and NUTS3 to regions, provinces, and counties. SIATses a classification called NUTSX, which is a harmonised combination of NUTS2nd NUTS3 regions.

ignitz-Ruppin, Germany (www.mea-scope.org).

3.5. User involvement

Possible users of the three tools include so-called end-users andother users. End-users are the final or ultimate users of a finishedtool. Other users, in contrast, may use the tools at several stagesin the development process; they may also have the expertise todevelop the tool further. Stakeholders are individuals, groups andorganisations that are affected by decisions or actions and that havethe power to influence the outcomes of these decisions (Freeman,1984). Stakeholders can also be users, and vice versa, but they donot necessarily need to be.

In principle, all three tools were designed to address multipleusers, including decision-makers at different levels, scientists, andregional stakeholders. Primary end-users of the SIAT and MEA-Scope tool are EU policy-makers, whereas integrative modellersand policy experts (e.g., from the European Commission or regionalauthorities) are the targeted users of SEAMLESS-IF (Therond et al.,

ntegrated assessment tools—A comparison with specific reference to.010

2009).One condition for allowing the user to use the tools themselves

is the existence of an appropriate user interface. The user interfacesof SIAT and SEAMLESS-IF were therefore tailored to meet specificdemands presented by their end-users. End-users should be able

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attention to environmental issues and rural development over thepast decades (Van Huylenbroeck and Durand, 2003). In 2006, thetotal CAP budget added up to 51 billion Euros, of which 16.7% wereused to finance market intervention measures,6 68.1% for direct

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o use SIAT themselves after a brief training period, while the usef SEAMLESS-IF is always a joint effort of policy experts and inte-rative modellers. Users are guided through the scenario definition,he variation in policy variables of interest, and the scale of analysiswhole EU, regions). The results of the tools can be analysed directlyfter simulation and are presented in tables, graphs, and maps. Theser can choose which indicator to view and to some extent canontrol the spatial scale of the simulation. SEAMLESS-IF also allowshe user to decide which models to include in a particular analysis.he MEA-Scope tool has no special user interface for the integratedool and can therefore only be used by specialised modellers, whoo so following the instructions of those who are interested in theesults but are not able to use the tool (e.g., policymakers, stake-olders or non-modelling scientists).

Decision-makers, regional experts, other scientists, and stake-olders were particularly involved in the development of policycenarios (including their narrative description and translation intoodel parameters), the selection of indicators, the integration of

ractical knowledge, and the assessment of the validity of resultssee Morris et al., 2008; Therond et al., 2009; Piorr et al., in press;äcklund et al., 2010).

For example, to integrate stakeholder perspectives into thenalysis of policy impacts, the development of the SIAT was accom-anied by an extensive pan-European survey, which allowed for thelustering of sensitive areas based on socio-economic and environ-ental profiles, and which identified hot-spots requiring special

ttention in the modelling (Morris et al., 2008).Another example is the scenario development for the

EA-Scope tool. Results from brainstorming sessions with EUecision-makers on the future of the CAP, and from stakeholderurveys, which were conducted to identify regional peculiaritiesnd regional importance for economic, social, and environmentalroblems, were translated into the final policy scenarios to be mod-lled. User and end-user involvement also played a major role in theevelopment of SEAMLESS-IF and the definition of policy scenariosTherond et al., 2009) and in the validation of the results of all threeools. For example, experts from a number of representative regionsssisted in evaluating the results of SEAMLESS-IF. Modelling resultsf the MEA-Scope tool were evaluated interactively with the helpf experts from the MEA-Scope case study regions (see Piorr et al.,007).

.6. Dealing with data constraints

To overcome data constraints, all three tools made use of exist-ng, regularly updated data bases wherever possible, as providedor example by Eurostat, OECD databases, or the Farm Accoun-ancy Data network. To close data gaps, additional methodologiesere used, such as farm typology building, data extrapolationrocedures, and expert rules. However, some data, particularlygricultural management data, which varies greatly between theifferent European regions (Verburg et al., 2006b), cannot bebtained from official data sources. Therefore, SEAMLESS-IF andEA-Scope tool partially obtained agronomic and farm-level eco-

omic information from computer-based surveys among regionalxperts.

.7. Validation

Given the problems in the validation of IAMs as outlined in Sec-ion 2, Parker et al. (2002) suggested three evaluation criteria:

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

(i) Has the model been constructed of approved materials, i.e.,approved constituent hypotheses (in scientific terms)?

(ii) Does its behaviour approximate well that observed in respectof the real thing?

PRESSlling xxx (2009) xxx–xxx

(iii) Does it work, i.e., does it fulfil its designated task, or serve itsintended purpose?

Scientific acceptance and assessing whether the tools fulfil theirintended objectives can at least partially be ensured through peer-review process (cf. Parker et al., 2002). The existing publications (cf.Jansson et al., this issue; Van Ittersum et al., 2008; Ewert et al., 2009;Therond et al., 2009; Piorr et al., in press) suggest that the tools havescientific credibility, but since the presented results refer mostly toillustrative applications further testing and applications with peerreview are needed.

To meet the second criterion, several policy test cases wereused within the duration of the three projects, some of whichevaluated the tools’ performance, while others constituted alreadypractical applications of the tools based on the scenarios developedwith decision-makers. During the development of SEAMLESS-IF,policy test cases were used to evaluate the adoption of possi-ble WTO rules, and the introduction of environmental legislation.Policy scenarios simulated with the MEA-Scope tool include sev-eral options regarding implementation of the direct paymentscheme in the presence or absence of agri-environmental mea-sures (Happe et al., 2006a; Uthes et al., 2008; Piorr et al., in press).Among the policy scenarios simulated within the SIAT develop-ment process were the financial reform of the CAP in 2012 andpolicy cases with a focus on environmental issues, such as renew-able energy or biodiversity (Kuhlman, 2008). The target year ofthe SIAT policy cases is 2025 (Helming et al., 2008b). The sim-ulations with the MEA-Scope tool and the SEAMLESS-IF cover atime horizon of 10–15 years (Happe et al., 2006a; Van Ittersumet al., 2008). Ex post experiments5 were performed by SEAM-LESS and MEA-Scope to assess the forecasting quality of the farmmodels (Kanellopoulos et al., 2007; Damgaard, 2008). As a pos-sible way to keep disciplinary knowledge in integrated tools, allthree tools ensured that the individual components were managedand further developed by different research institutes with spe-cific expertise regarding these individual components. However,results of the integrated tools could only partially be validated,for example, by comparing results against historical data or byassessing the results interactively with decision-makers or regionalexperts.

4. Representation of CAP instruments

4.1. Possible policy questions regarding the CAP

The original motivation for the CAP in the early 1960s wasto gain market support to guarantee fair and stable commod-ity prices to European farmers and thus improve food securityafter the Second World War. In more recent years, criticism asto the CAP has been wide-ranging; critics have called attention toits trade-distorting anti-development effects, environmental prob-lems, and issues of equity, distribution and ethics (Tangermann,2005; Busch, 2006). The fields of conflict have induced a shift offocus away from market and income support policies to increased

ntegrated assessment tools—A comparison with specific reference to.010

5 Ex post experiments refer to starting the model runs at earlier periods via post-poning the base year and comparing whether the model results fit the observedfigures.

6 Market intervention measures of the CAP involve import tariffs, interventionpurchasing, stock disposal, subsidised exports, and production quotas. The directpayment scheme represents direct income transfers to European farmers that are

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ncome transfers, and 15.2% for rural development measures (COM,006a).

Changes to the CAP based on the Luxembourg Agreement in003 and the most recent reform package (the ‘Health Check’)pproved in November 2008 are under way. They include a changen the organisation of the direct payment scheme (payment decou-ling) and an increased phased transfer of subsidy from the directayment scheme to rural development measures (modulation)Bureau et al., 2007; COM, 2007; Henning, 2008). How the CAPill further develop in the future is difficult to say, as its devel-

pment is the result of a complex decision-making process7 witheveral influencing factors such as the budgetary pressures causedy the enlargement of the EU, a number of international commit-ents (e.g., WTO rules, Gothenburg agreement on biodiversity,

isbon strategy on sustainable growth), changing consumer pref-rences, and ongoing pressures from farming sector lobbies (Mannnd Wüstemann, 2008).

Decision-makers will be interested, for example, in the advan-ages and disadvantages of possible actions, their budgetarymplications, and distributional effects. Moreover, they will haveo answer questions that have been raised by important stake-olders throughout the policy development process (Thiel, 2009).ossible options for market support measures range from a com-lete abandonment of all such measures to changes in only selected

nstruments, such as further reductions of import tariffs for selectedommodities (Henning, 2008). Issues of interest related to themplementation of the direct payment scheme include furtherransforming the historic model towards flat rate payments, intro-ucing cut-off limits, and changing edibility criteria (Bureau et al.,007). The United Kingdom, for example, is even considering aomplete abandonment of market support and direct income trans-ers beyond 2013 with the remaining funds instead going to ruralevelopment measures.8 Policy questions related to rural devel-pment measures include issues of efficiency and distributionalonsequences, forecasting uptake of measures, quantification ofeadweight9 effects, and improving the effectiveness of the mea-ures, for example by targeting the measures to sensitive areasCOM, 2008a).

.2. Which CAP instruments are represented in the tools?

Table 4 summarizes which CAP instruments are representedn the three tools, and which policy effects can be anal-sed.

The meta-model component of SIAT allows for a variation ofotal market support and total direct income support with theption to re-invest remaining funds into research and development

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

olicies (R&D). These options provide the possibility to explore theonsequences of changes in the allocation of the EU agriculturaludget including impacts on non-agricultural sectors (see Table 4).

ndividual policy measures within the three groups of the CAP –

or the most part decoupled from production and made dependent on animal welfarend environmental standards (cross compliance) and still some set aside obligation.ural development measures include, for example, voluntary extensification mea-ures (so-called agri-environmental measures), cf. COM (2005b), investment aids,ducational measures, and community development programs (COM, 2006a).7 For details on the decision-making process and impact assessment at EU-level,

ee Thiel (2009).8 http://www.publications.parliament.uk/pa/cm200607/cmselect/cmenvfru/

46/546i.pdf.9 Deadweight costs express the extent to which the impact of a government

rogram is reduced because of its side-effects. For instance, creating a compen-ation program to encourage farmers to adopt environmentally friendly farmingractices has deadweight costs because farmers who have adopted such farmingractices anyway (for example because of ethical reasons or due to less favourableite conditions) would also benefit.

PRESSlling xxx (2009) xxx–xxx 9

market support, direct payments, re-invest into research and devel-opment – are not considered.

SEAMLESS-IF allows for differentiating market instruments ofthe CAP into individual measures. Price developments of singlecommodities or direct payments can be modelled at great detail(cf. Ewert et al., 2009). The farm model delivers the response atthe farm-level to changes in market prices and in the direct pay-ment scheme. In addition, a representation of farm-level policies ispossible, such as production quotas, different coupling degrees, orcross-compliance conditions (Therond et al., 2009). Environmentaleffects and impacts on productivity (primarily yields) are providedby the mechanistic cropping system model.

The farm models of the MEA-Scope tool support a detailed rep-resentation of different implementation options in the direct paymentscheme, (1) an assumed continuation of the Agenda, 2000 policy asa baseline, (2) decoupled single farm payments with and withoutpayment ceiling, and (3) a phasing-out of direct payments (Happe etal., 2006a; Uthes et al., 2008; Piorr et al., in press). Market measuresare not represented.

With regard to rural development measures, the third group ofmeasures within the CAP, both the SEAMLESS-IF and MEA-Scopetool have been used to analyse regional and farm-level impacts ofagri-environment schemes in selected case studies, such as grasslandextensification (Piorr et al., in press), or arable land extensifica-tion (Therond et al., 2009). Other CAP instruments of the ruraldevelopment policy, currently accounting for half of the total ruraldevelopment budget, include investment programmes, measureswith a focus on knowledge transfer such as vocational trainings,support innovation and quality in the food chain, or communitydevelopment programmes. None of the three tools has been usedto model individual policy measures from this list.

5. Discussion

The three policy impact assessment tools have in commonthat they all consider changes in the CAP a main cause of landuse changes (either with regard to use intensity or land mobilitybetween sectors) with associated impacts on social, economic andenvironmental indicators. However, none of the tools is capable toaddress all scientific and policy questions (see also Verburg et al.,2006a). The choice of one of the tools for a particular applicationin the CAP context and also what is considered an advantage of atool and what a disadvantage (see also Table 2) depends strongly onthe political questions being asked (see also Easterling, 1997). Forexample, the ability to represent land mobility between differentsectors is an important aspect to produce plausible future resultsin economic models in response to different assumptions regard-ing economic growth or population development (example: SIAT).However, not every change in the CAP, for example regarding thedirect payment scheme, will have significant impacts on other sec-tors, so that single-sector impact analysis will often be sufficient(examples: SEAMLESS-IF, MEA-Scope tool).

5.1. Advantages and disadvantages with regard to CAPdecision-support

The SIAT, which primarily targets policy makers, is able to repre-sent major relationships between groups of policy instruments andsustainability indicators for different economic sectors (i.e., breadthin analysis). By demonstrating the overall direction and magnitude

ntegrated assessment tools—A comparison with specific reference to.010

of change in relation to policy intervention, the results of SIAT pro-vide the big picture of budgetary changes at EU level. Results fromextreme scenarios such as abolishing all CAP support can stimulatediscussions about the future of the CAP. On the other hand, the toolfocuses on synthesising sector simulations and is less suitable for

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Table 4CAP instruments, policy effects and coverage of SIAT, SEAMLESS-IF, and MEA-Scope tool.

SIAT (meta model) SEAMLESS-IF MEA-Scope tool

CAP instrumentsDirect aid system

Total EU direct payment support x xSpecific changes (e.g., flat-rate system, progressive capping,

cut-off limits, cross compliance, etc.)x x

Market supportTotal EU expenditure x xSingle market instruments (tariffs, subsidies, intervention

purchasing, milk quota etc.)x

Rural development policy• Intensity-reducing agri-environmental measures x x• Organic farming (without conversion) x x• Habitat or landscape related measures (e.g., Natura 2000) x• Investment aids, early retirement program, vocational

training, village renewal and development, skills acquisitionetc.

Policy effectsMacro economic effects x (x)a

Micro economic effects x xCAP Budget x x

Environmental effects (of economic activities)Quantitative-mechanistic simulation x xRule-based or aggregation of input coefficients x x x

Distributional effectsBetween countries/administrative regions x xBetween sectors xBetween representative farm types x xBetween farm agents xBetween land capability classes2 (agricultural land) x x

Sector and geographical coverageRange of coverage of measures

Single-sector analysis (agriculture) x xMulti-sector analysis (agriculture, forestry, tourism, transport,

energy and nature conservation)x

Degree of disaggregation within a sector Low High High

Geographical coveragex

iutdbabrttobtnctlaf

irdbflb

European Union, administrative regionsEU case study regions

a Only if the GTAP model is included.

n-depth sector analyses. The current version facilitates the sim-lation of sector policy instruments with similar steering effectso be able to analyse regional differentiation. Because farm-levelecision-making is not part of the tool, the ability to perform distri-utional analyses is limited. Major advantages of the meta-modelpproach are a short response time, the integration of indicatorseyond those covered in the individual model components (e.g., fireisk), and the possibility to determine ‘sustainability choice spaces’hrough multiple simulations by iteratively shifted policy intensi-ies and expert-given weights for sustainability valuation. On thether hand, the introduction of new policy scenarios and the flexi-ility in using alternative model components is limited. Additionalesting of the plausibility of results gained with the meta-model isecessary, and the model linkages within the quantitative modelhain, though principally successful, require further efforts in ordero obtain a fully consistent solution for all variables involved in theinking (Jansson et al., this issue). The transparency and understand-bility of given background information is limited due to its broaderocus compared to specialised modelling systems.

A comprehensive analysis of different agricultural policynstruments is an application for SEAMLESS-IF; its detailed rep-

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

esentation of the agricultural sector interlinked with farm-levelecision-making and environmental effects allows for analysis ofoth budgetary and distributional effects. Major advantage is theexible and modular structure, which facilitates various possi-le applications of the tool. However, the tool is restricted to the

xx x

agricultural sector; thus, cross-sector effects are not represented(a compromise between breadth versus depth in analysis). Fur-ther improvements are necessary to achieve a balanced, equallydetailed, and EU-wide representation of market- and farm-levelimpacts. Other optional enhancements depend on the interests ofthe users and are possible given the modular structure. If economy-wide effects of policy changes are of interest in the assessment ofa policy change, the link from partial to general equilibrium mod-elling requires further investments.

The MEA-Scope tool has proved useful in evaluating farmstructural changes and environmental impacts of different imple-mentation options in the direct payment scheme in selected casestudy regions (i.e., depth in analysis). The transferability of theMEA-Scope approach to other regions is generally possible butrequires resource intensive data collection on agricultural man-agement, investments, and indicators because of the high levelof disaggregation of agricultural production. The technical repre-sentation of the model linkages is less advanced and thereforedoes not support an easy re-use. The farm-level representationis more detailed than in SEAMLESS-IF, while market-effects arenot represented at all. This can lead to overestimation of the total

ntegrated assessment tools—A comparison with specific reference to.010

farm production as price-quantity effects are not accounted for. Toachieve a greater geographical coverage would therefore requirea coupling to an equilibrium model. For policy questions with aspecific focus on farm structural change, farm management adap-tation, and environmental issues, the tool complements the other

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5.4. Future challenges

There are a number of other projects in which tools with similarpurposes were developed for example, EURURALIS,10 PRELUDE,11

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wo approaches by its ability to build up detailed regional profilesith local results, which may differ from overall trends. Piorr et al.

in press), for example, used the MEA-Scope tool to identify thatAP support elimination has significant impacts on farm exit and

andscape fragmentation in marginal areas.

.2. Shortcomings regarding the representation of CAPnstruments

Within SIAT and the MEA-Scope tool much emphasis was put onhe development of policy scenarios consistent with actual policyroposals. Capturing the relevant policy questions with the sce-arios is of particular importance for SIAT and MEA-Scope tool, ashe quantitative model components and their linkages were notesigned to be controlled by the end-users. Although the meta-odel of SIAT allows decision-makers to vary some major policy

ariables, the users are nonetheless restricted to predefined solu-ion spaces. It is therefore important that the solution spacesapture the options decision-makers wish to analyse as otherwisehe simulation of entirely new policy scenarios, which is gener-lly possible, requires new quantitative simulations and responseunction estimations. With the MEA-Scope tool the user is entirelyestricted to the pre-defined scenarios without the possibility ofhanging policy variables. Instead, new policy scenarios alwaysequire new model runs. SEAMLESS-IF puts more emphasis onstablishing the technical conditions for allowing the users of theool to define various policy experiments (without pre-defined pol-cy scenarios). Yet, also for this tool a collaboration of integrative

odellers and policy experts is needed.The comparison of the three tools has shown that market instru-

ents (SIAT, SEAMLESS-IF) and direct payments (all three tools)re relatively well represented. To answer specific political ques-ions regarding rural development measures, the developed impactssessment tools are less suitable, as none of the tools is able to pro-ide a comprehensive, EU-wide assessment for rural developmenteasures. Due to the individual character of most of the measureserged under the rural development policy of the CAP, they can

nly be represented in a very coarse way by making assumptionsegarding their impacts on overall technological change, while aetailed representation of theses policy measures is out of the scopef all three tools.

However, attention to the efficiency and distributional con-equences of rural development measures will increase in theuture, given the often unsatisfying results of these measures asdentified by the midterm and ex post evaluations (COM, 2005c).he existing applications of the tools for agri-environmental mea-ures, currently the most important group of measures within theural development policy, are only illustrative case studies. Resultsf these applications can inform decision-makers about on-farmdaptation strategies and can be of particular relevance to supportocal decision-makers with regard to contractual design or the cal-ulation of payment rates. Yet little attention has been given toolitical questions, the actual uptake of measures by farmers, or theccurrence of deadweight effects. In addition, the existing appli-ations do not allow for conclusions regarding budget allocationecisions at the EU level or provide insights into the differences inpatial vulnerability for different environmental problems. Furtheresearch should therefore explore whether alternative methods,uch as quantitative models based on spatial econometrics (or other

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

elevant econometric methods) or spatial micro simulation (Ballast al., 2006) could be appropriate approaches to establish causalelationships between socio-economic and environmental char-cteristics, EU expenditures, and the effects of rural developmenteasures.

PRESSlling xxx (2009) xxx–xxx 11

5.3. Will the tools be used for decision support?

The three tools have so far been primarily used for scientificpurposes. Whether they will also function within the setting of thepolitical arena is difficult to predict at this stage. The vast amountof literature on integrated assessment reflects the extent to whichnew tools are developed in this research field. As of yet, however,only a few tools have become effective resources that policymakersor practitioners utilise on a regular basis. Reasons for their limitedacceptance include, for example, too little, too much or wrong infor-mation provided by the tools, time and resource limitations, lack ofaccess, suboptimal functionality of the tools, and hurdles of com-munication and trust (Uran and Janssen, 2003). Another reason isthat the scales of assessment are often different from the levels ofdecision-making (Rotmans, 1998).

Based upon interviews with officials of the European Commis-sion, Thiel and König (2008) identified a number of characteristicsas important in making impact assessment tools effective aidsin political decision-making: plausibility, user-friendliness, trans-parency, timeliness, the use of existing data and monitoringsystems, a good scientific storyline, and an interactive communica-tion process with potential end-users and other interested parties.Considerable effort has been made in the projects behind the threetools to address all these aspects; however, this is certainly no guar-antee for actual use. Geertman and Stillwell (2009) argue that theuse of such tools is also influenced by what is considered trendyor “zeitgeisty” and what is not. There is, for example, evidencethat many tools are not extensively used after the initial nov-elty that provided the original motivation for developing them haspassed.

The original motivation for developing the three tools camefrom decision-makers at EU level. Officers from the European Com-mission, or their representatives, articulated needs for knowledgedevelopment with a particular focus on tool development. The con-cepts of SIAT, SEAMLESS-IF, and MEA-Scope tool were designed torespond to these needs; otherwise the three tools would not havebeen funded. Several years elapsed from the conception of the orig-inal research ideas behind the three tools and their developmentand initial applications. The long development process certainlyposes a significant risk that the tools may become outdated andirrelevant. To avoid the problems of a long development process,both users (including end-users) of the three tools and other inter-ested parties were actively involved at several stages of the tooldevelopment process.

However, instead of solely judging the three tools by their useor non-use in decision-making processes, they should also be seenas means by which the stakeholder interaction process can beimproved and as educational tools for learning about good prac-tices and mistakes. Rotmans (1998) recommended, for example,that IA models, scenario techniques, and participatory methodsbe used in a complementary, cyclic manner in order to make thetools more relevant to decision-makers and to increase their gen-eral acceptance (see also Scrieciu, 2007). This would certainly be agood practice for future applications of the three tools.

ntegrated assessment tools—A comparison with specific reference to.010

10 A scenario study on Europe’s Rural Areas to support policy discussion(http://www.eururalis.eu/).

11 PRospective Environmental analysis of Land Use Development in Europe(http://www.eea.europa.eu/multimedia/interactive/prelude-scenarios/prelude).

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cenar, 2020,12 TOP-MARD13 – to only mention a few. EURU-ALIS, for example, has in many aspects a focus similar to thatf SIAT. EURURALIS uses a modelling chain combining macroeco-omic modelling (LEITAP), biosphere-oriented modelling (IMAGE),nd land-use allocation modelling (CLUE-S) (Verboom et al., 2007).owever, there are several differences in the approaches usedy the various tools, only some of which can be discussed here.ne aspect is, for example, the use of macroeconomic or macroe-onometric models. Macroeconomic modelling (as in EURURALIS)odels require less data than do macroeconometric models, and

hey are easier to calibrate and therefore often used for scenarionalysis. However, due to their tautological behaviour, they areess suitable for forecasting (Hertel et al., 2008). Macroeconomet-ic models (as in SIAT) estimate future development trends frommpirical data. This can lead to better forecasting quality comparedo macroeconomic models, but it carries greater requirements forata and less flexibility. Further differences exist with respect tohe definition of policy scenarios, the level of detail at differentcales, and the modelling of various land use sectors. The represen-ation of individual land use sectors in SIAT is more detailed thann EURURALIS, for example, due to the integration of other sector

odels such as EFISCEN. Another differentiating aspect is the meta-odelling approach of SIAT. Biophysical aspects, covered by the

MAGE model in EURURALIS, in contrast, are less well representedn SIAT since a special biophysical model is not part of this tool.

The SEAMLESS-IF is a novelty in that no other existing toolntegrated field, farm and market models in a seamless way. How-ver, there are reasons why other IA tools have not included farmnd field level models; for example, the data requirements toeed and calibrate these types of models are very large. A projectith a research focus similar to that of the MEA-Scope tool is the

OP-MARD project. TOP-MARD seeks to analyse how the multi-unctions from agriculture affect the economic development anduality of life in rural areas (Bryden et al., 2008). To this end, TOP-ARD has developed a systems model called POMMARD using

he Stella software with the goal of capturing the dynamics, feed-acks and spatial dimensions of agricultural functions. The model

ncludes a regional social accounting matrix and has been appliedo eleven European study areas (Bryden et al., 2008). The functionsnd feedbacks incorporated in this model are estimated either fromxisting data or from new data gathered by special surveys of farm-rs or other agents. In contrast to MEA-Scope, POMMARD includeswo sectors, agriculture and tourism, but it is, on the other hand, lessetailed with regard to the representation of agricultural produc-ion systems and site characteristics. POMMARD, however, is freef the problems that are often associated with optimisation modelsthe MEA-Scope tool, for example) with respect to calibration andormative character.

Given the existing diversity of approaches, it would be inad-quate to say that one tool is generally superior over the others.he peculiarities of the different approaches may be advantagesr disadvantages depending on the focus of a particular decisionontext.

Table 2 shows that significant progress has been made withegard to integration and harmonisation of existing models andhe development of methods to close methodological gaps. Manytems, however, are still insufficiently solved and new challenges

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

ave emerged during the development process of the tools. Some ofhe most important tasks and challenges for the future with regardo the three tools are listed below.

12 Scenario study on agriculture and the rural world (http://ec.europa.eu/griculture/publi/reports/scenar2020/index en.htm).13 TOwards a Policy model of Multifunctional Agriculture and Rural Developmenthttp://www.topmard.org/).

PRESSlling xxx (2009) xxx–xxx

5.4.1. Continued model testingTime constraints and the fact that complex projects with multi-

ple partners and research tasks imply that many activities werecarried out simultaneously have not yet allowed for a compre-hensive testing of the tools. Model testing and sensitivity analysestherefore need much more attention in the future. In addition, pol-icy scenarios that can be simulated with all three tools, such asthe elimination of all CAP support, could be an interesting startingpoint for complementary analyses following the recommendationof Verburg et al. (2006a) who argue that it is often useful to use arange of modelling approaches to analyse different aspects of thesystem under study.

5.4.2. Focus on non-linearities and uncertaintiesThe so far addressed challenges focussed primarily on improved

integration of quantitative model components, whereas othermethodological challenges, such as the capturing of non-linearitiesand uncertainties, were given less attention. However, many chal-lenges related to methodology and data can often only be solved atthe level of the individual components. Therefore, in addition to afocus on improved integration, more core disciplinary research isalso required to solve methodological problems and achieve scien-tific progress in the disciplines involved in the integrated tools (seealso Jakeman and Letcher, 2003).

5.4.3. Enhanced integration of stakeholder-driven research andquantitative modelling

Several attempts were made to capture “softer” issues, such asinstitutional aspects influencing policy making, or the investiga-tion of societal preferences for economic, environmental, and socialgoods (Theesfeld et al., in press). Yet these results only accom-pany the integrated assessment, which is still primarily dominatedby quantitative biophysical and economic modelling, and requirefurther efforts for better integration.

5.4.4. Representation of social indicatorsA balanced representation of environmental, economic and

social impacts was identified as one challenge of IAM. The repre-sentation of social issues in the three tools therefore needs furtherattention as it is still behind those of economic and environmentalprocesses as can be concluded from the indicators in Table 3.

5.4.5. Handling of the toolsIntegrating multiple scales and disciplines with numerous peo-

ple involved also means that it becomes more difficult and costlyto control the development process and to keep the overview ofthe whole tool (see Ewert et al., 2009). Ewert et al. (2009) thereforesuggest that a new generation of “integrated scientists” with pro-found knowledge in multiple disciplines may possibly be requiredto manage the process of developing integrated assessment toolsand particularly the knowledge gained with them.

5.4.6. New application fieldsThe complementary use of SIAT and SEAMLESS-IF is tested14 in

a number of developing and transitioning countries, posing newchallenges for the application of the tools with more severe orentirely different environmental, socio-economic, and institutionalproblems (e.g., Nautiyal and Kächele, 2007; Zhen et al., in press)

ntegrated assessment tools—A comparison with specific reference to.010

than those of the European Union. In particular, the availability ofdata may often be more constrained than in the EU, putting sig-nificant limitations on the application of the mostly quantitativecomponents. Thus, the development of alternative tools becomes

14 Land Use Policies and Sustainable Development in Developing Countries(02/2007 to 07/2010). Homepage: www.lupis.eu.

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ecessary, to accommodate lower data demands and to offer moreoom for participatory use while still providing sound results.lso, considerations of fairness within the research communityave to be taken into account in the future, as resources are notqually distributed among research institutes. In particular, newU member states and developing countries will face difficultiesompeting with the long history of tool development in manynstitutes in the old EU member states. New actors entering thestablished research arena of Integrated Assessment are thus atisk of ending up as data providers. Open access solutions, particu-arly those promoted by the SEAMLESS team, are therefore stronglyncouraged.

A final evaluation of the three tools requires various applicationss recommended in Parker et al. (2002) or Jakeman and Letcher2003). Moreover, models are supposed to be simplified imagesf the reality which implies that model building and integrationannot be infinite (Easterling, 1997; Jakeman and Letcher, 2003;ansson et al., this issue; Ewert et al., 2009). Kok et al. (2007) put itlearly:

“. . .over-emphasising diversity might undermine the overarch-ing goal of strengthening and unifying the Integrated Assessmentcommunity. Restricting the number of approaches will increaseclarity on what type of scientific disciplines we seek to engageand which issues we wish to address. Integrated research [. . .] willonly continue to succeed when maintaining the balance betweendiversification and comprehensiveness”.

After all, we agree with Parker et al. (2002), who argue that therocess of integrated assessment and modelling, which makes dif-erent disciplines learn to work and appreciate each other, is asmportant as the product itself for any particular project. Essen-ially, that was one of the major objectives pursued by the Europeanommission when deciding to support these kinds of researchrojects – to support an improved integration and cooperationithin the European research area (cf. COM, 2006b).

. Conclusions

The aim of this paper was to compare three recently devel-ped impact assessment tools and to evaluate their policy relevanceased on their current status. We have explained why Euro-ean decision-making on agricultural support requires integratedssessment, surveyed the challenges for integrated assessment thatave been identified in the literature, and analysed how the toolsave progressed in addressing these challenges.

Although each tool underwent a different development process,he trade-offs influencing these processes, were similar:

Balancing breadth versus depth of scientific understanding;Representing complex processes versus delivering easy-to-communicate results;Using innovative state-of-the-art technological solutions whiledealing with limited financial or human resources and partialresistance from the scientific community;Providing a high level of detail while still being relevant in termsof the number of applications;Capturing global phenomena, local processes, and enterprise-level decision-making;Being generic, flexible, and extensible and still transparent andusable.

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

None of the three tools has found an ultimate solution toanaging these trade-offs. Having the joint objective of provid-

ng decision-support for different policy instruments at differentcales, each tool has found a different niche towards contribut-

PRESSlling xxx (2009) xxx–xxx 13

ing to better informed political decision-making. Which tool to usein a specific context depends strongly on the specific policy ques-tion, the policy instrument under question, and its scope and targetgroups. However, data and time constraints and a partial lack ofappropriate methods have so far prevented comprehensive testingof the developed tools, which requires further substantial efforts inthe future. In addition, to keep integrated assessment tools in linewith future political needs, it will be necessary to find ways to con-duct improved policy impact assessments for rural developmentmeasures. Whether this is a task for the three tools will have to betested.

The most important challenge for IA in general, however, willbe to further develop methods and approaches that are capableof translating model results into understandable outputs, so thatappropriate recommendations for action can be formulated.

Acknowledgements

This article was made possible through funding from theEU integrated projects SENSOR (Project Reference 003874), andSEAMLESS (Project Reference 010036), and the specific targetedresearch project MEA-Scope (Project Reference 501516). The opin-ions expressed herein are the sole responsibility of the authors. Theauthors would like to thank the anonymous reviewers for their use-ful suggestions on how to improve the paper. We also wish to thankPeter Verburg for helpful comments and assistance.

Appendix A. Stand-alone model components

The models that were linked in the three impact assessmenttools determine the possible fields of application of the integratedtools to a large extend. This section gives on overview of the stand-alone model components integrated in the three tools. Only broaddistinctions are explained, further details can be found in publi-cations dealing with the individual models and on the Internetpresentation of them.

The New Econometric Model for Environment and Sustainabledevelopment Implementation Strategies (NEMESIS) is a recursivedynamic, multi-sector, multi-national macro-econometric modelof the EU27 and seeks to explain production, consumption andprices in response to changes in exogenous variables, such as policyvariables or overall trends such as population growth (Brécard et al.,2006). The mathematical formulation is based on a set of non-linearequations representing the constrained optimizing behaviour ofproducers, consumers, factor suppliers, exporters, importers, tax-payers, savers, investors, or government. Equations include bothaccounting relationships (e.g., income equals expenditures, costequals sales) and behavioural equations (e.g., demand and supplyequations, price equations). Behavioural equations are economet-rically estimated from empirical data sets (Fougeyrollas et al.,2001; Jansson et al., this issue). NEMESIS provides an economy-wide perspective including the sectors agriculture (Arable andGrass Land), forestry, tourism, transport infrastructures, urban(Housing, Industrial and Commercial Buildings) and nature pro-tection (and unsuitable areas). The land claims for these sixsectors are endogenously calculated in NEMESIS with specific sub-models for tourism, transport infrastructures and urban land-use(http://www.nemesis-model.net/).

The European Forest Information Scenario Model (EFISCEN) is amatrix transition model for European forests (Nabuurs et al., 2000).

ntegrated assessment tools—A comparison with specific reference to.010

Based on national forest inventory data, matrices are set up, whichrepresent different forest types. Each matrix consists of age- andvolume-classes; the forest area is distributed over these classes,thereby representing the initial forest state. Transitions of areabetween classes represent natural processes such as growth, age-

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ng and mortality and also human actions such as wood harvesting.FISCEN projects stem wood volume, increment, age-class dis-ribution, removals, natural mortality and dead wood for everyve year time-step. With the help of biomass expansion factors,tem wood volume is converted into whole-tree biomass and sub-equently to whole tree carbon stocks. Information on litterfallates, felling residues and natural mortality is used as input to aoil model, which is dynamically linked to EFISCEN and deliversnformation on forest soil carbon stocks. The projections by EFIS-EN are driven by the market demand for round wood and byhanges in forest cover. Within SIAT, EFISCEN was therefore linkedo NEMESIS and DYNA-CLUE. NEMESIS translates major economicevelopments and policies into a demand for wood and DYNA-LUE projects changes in forest cover. Based on the results of thesewo models, EFISCEN checks whether the demand for wood cane satisfied and it projects the future forest resource developmenthttp://www.efi.int/portal/virtual library/databases/efiscen/).

The Conversion of Land Use and its Effects modelling frameworkCLUE) was developed to simulate land use change using relationsetween land use and its driving factors based on empirical anal-sis, neighbourhood analysis or scenario specific decision rulesn combination with dynamic modelling of competition betweenand use types (Verburg et al., 2002, 2009). The spatial allocationules are configured separately for each country to account forhe country-specific context and land use preferences. The landequirements for the different land use types to be allocated byhe model are specified at the national scale for each country withinurope separately. Changes in agricultural land area and urban areare based on the results of the combined simulations with the eco-omic model (NEMESIS) that is part of the modelling chain of SIAT.hanges in natural vegetation are the result of both net changes

n agricultural and built-up area and locally determined processesf re-growth of natural vegetation (Verburg and Overmars, 2009).fter abandonment of agricultural land re-growth of natural veg-tation is simulated as a function of the local growing conditionssoil and climate conditions), population and grazing pressure and

anagement. The possibilities to convert natural vegetation intogricultural land or residential/industrial land depend on the loca-ion and the type of natural area. Path-dependent dynamics ariserom the combination of top-down allocation of agricultural andrban demand and bottom-up simulation of the (re-)growth ofatural vegetation (http://www.cluemodel.nl/obtain.html).

CAPRI (Common Agricultural Policy Regionalized Impact Analysis)r SEAMCAP is an agricultural sector model of the EU (Heckelei andritz, 2001; Britz et al., 2006). The objective of the CAPRI model is tovaluate regional and aggregate impacts of the CAP and trade poli-ies on production, income, markets, trade, and the environment.APRI is a comparative static, partial-equilibrium model, solved by

terating supply and market modules. The supply side of CAPRI isased on profit maximising behaviour of around 300 regional pro-uction models at NUTS2 level combining Leontief technology forariable costs and a non-linear objective function based on positiveathematical programming. Prices are exogenous in the supplyodule and are provided by the market module that searches, in

n iterative procedure, for the set of prices that equilibrate sup-ly and demand on EU and international markets for all consideredgricultural outputs. The sub-module for marketable agriculturalutputs is a spatial, non-stochastic global multi-commodity modelor about 40 primary and processed agricultural products, coveringbout 40 countries or country blocks in 18 trading blocks. This sub-odule delivers prices used in the supply module and allows for

Please cite this article in press as: Uthes, S., et al., Policy relevance of three iagricultural policies. Ecol. Model. (2009), doi:10.1016/j.ecolmodel.2009.08

arket analysis at global, EU and national scale, including welfarenalysis (http://www.capri-model.org/).

The Farm system simulator (FSSIM) is a bio-economic farm modeleveloped to assess the response of the major farm types inhe EU (Louhichi et al., in press; Janssen et al., in press). FSSIM

PRESSlling xxx (2009) xxx–xxx

includes a data module for agricultural management, FSSIM-AM,which computes the technical coefficients and costs for rangesof current and alternative agricultural activities, and FSSIM-MP,the mathematical programming part, aims to capture resource,socio-economic and policy constraints and the farmer’s majorobjective. FSSIM-MP is a comparative, static mathematical pro-gramming model with a non-linear objective function representingexpected income and risk aversion towards price and yield varia-tions. Market prices are exogenously provided by CAPRI. To makethe regional models in CAPRI behave like the farm models in FSSIM,an extrapolation algorithm (EXPAMOD) has been developed thattransfers the data on supply elasticities derived through system-atic shocking of FSSIM to CAPRI (Pérez Domínguez et al., 2009). Thelinkage to APES allows for assessing productivity and externalities(www.seamlessassociation.org).

The Agricultural production and externalities simulator (APES)is a modular, deterministic simulation model targeted at esti-mating the biophysical behaviour of agricultural productionsystems in response to weather, soils and agro-managementoptions (Donatelli et al., in press). APES runs at a daily time-step in the communication among components and simulatesone-dimensional fluxes at field scale. The simulation approachesusually embody process knowledge about biophysical relation-ships. APES computes yields, both averages and variability acrossyears, as well as inputs such as irrigation water and externali-ties of crop rotations. APES itself consists of several componentsrepresenting land uses (crops, grassland, vineyards, orchardsand agro-forestry), soil water, carbon and nitrogen, soil erosion,pesticide fate, management activities and a component used to gen-erate/estimate synthetic weather (www.seamlessassociation.org;www.apesimulator.it).

The Agricultural Policy Simulator (AgriPoliS) is a spatial anddynamic agent-based model and simulates the future structuraldevelopment of farms based on economic considerations (Balmannand Happe, 2001; Happe et al., 2006b). Each farm is representedby an individually acting agent that acts and interacts withinan environment consisting of other farms, factor and productmarkets, and space. Farm activities encompass land use and pro-duction decisions, rental activities, labour allocation decisions, andinvestments. The entire system is embedded within the overalleconomic, political, and technological framework conditions. Dur-ing the simulation, a farm develops endogenously. It can changeits characteristics such as size, labour endowment, specialisationand production activities in response to changes in its environmentinfluenced by the technological and political settings. Thus, somefarms will thrive and continue farming from one period to the nextothers may exit depending on alternative options for using theirresources. Farms exit if their profits are below the opportunity costsor if the farm becomes illiquid. The spatial component of AgriPoliSis grid-based and considers each individual plot as a standardisedspatial entity (cell) of a specific size (1 ha). Cells can represent dif-ferent land characteristics. The plots can be owned or rented by thefarms. Depending on data availability, the spatial grid can be ini-tialised based on soil maps. The representation of farms in the casestudy region in the model is based on FADN data. Farms are allo-cated on the spatial grid based on farm characteristics. AgriPoliS isusually simulated for 15–20 time periods (www.mea-scope.eu).

The Multi-Objective Decision support tool for Agro-ecosystemManagement (MODAM) is a comparative static, mixed integer, lin-ear programming whole-farm model (including crop and livestockproduction) and has been developed for the analysis of relations

ntegrated assessment tools—A comparison with specific reference to.010

between economic and ecological objectives in agricultural landuse (Zander and Kächele, 1999; Zander, 2003). MODAM consists ofhierarchically linked modules, which are grouped into three mainsteps. Step 1 describes the farm or region with its production capac-ities and activities. In step 2, a partial evaluation of economic and

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cological effects is performed. In step 3, the economic behaviourf the farmer is simulated by a linear programming module, whichnsures that production factors are allocated according to their bestactor utilisation. The ecological evaluation in MODAM is indicator-ased and makes use of a fuzzy-logic approach (Sattler et al., 2006).he assessment develops rule-based algorithms and can be runith comparatively fewer data than process-orientated models

http://www.modam.eu/).The Farm ASSEssment Tool (FASSET) is a whole-farm model that

imulates production, economics and pollution (Berntsen et al.,003; Hutchings et al., 2007). The model consists of two parts, alanning module, and a simulation module. The planning mod-le is designed to carry out the optimal economic planning for theollowing year using linear programming. The simulation modulef FASSET is a dynamic, deterministic model, i.e., it can simulatehanges over time but will always give the same outputs whenresented with the same inputs. The model uses a daily time stepor most simulations. The sources of pollution described includeitrate leaching, ammonia emission and the emission/absorptionf greenhouse gasses. Other sources of pollution are only periph-rally described, e.g., phosphate is described in terms of field/farmalances and pesticides are only considered in terms of the amountsed (http://www.fasset.dk/).

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