A methodology for enhanced flexibility of integrated assessment in agriculture

environmental science & policy 12 (2009) 546–561

available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/envsci

A methodology for enhanced flexibility of integrated assessment in agriculture Frank Ewert a,b,*, Martin K. van Ittersum a, Irina Bezlepkina c,h, Olivier Therond d, Erling Andersen e, Hatem Belhouchette f, Christian Bockstaller g, Floor Brouwer h, Thomas Heckelei i, Sander Janssen a, Rob Knapen j, Marijke Kuiper h, Kamel Louhichi k, Johanna Alkan Olsson l, Nadine Turpin m, Jacques Wery f, Jan Erik Wien j, Joost Wolf a a

Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK, Wageningen, The Netherlands Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D - 53115 Bonn, Germany c Business Economics, Wageningen University, Hollandseweg 1, 6706 KL Wageningen, The Netherlands d INRA, UMR 1248 Agir, F-31326 Castanet Tolosan, France e Danish Centre for Forest, Landscape and Planning, University of Copenhagen, Rolighedsvej 23, Frederiksberg C, DK-1958, Denmark f UMR System #1123, SupAgro CIRAD INRA, 2 Place Viala 34060 Montpellier, France g UMR INPL-(ENSAIA)-INRA Agronomie et Environnement Nancy-Colmar, BP 20507 68021 Colmar Cedex, France h LEI, Wageningen UR P.O. Box 29703, 2502 LS The Hague, The Netherlands i Food and Resource Economics, University of Bonn, Nussallee 21, D-53115 Bonn, Germany j ALTERRA, Wageningen UR, P.O.Box 47, 6700 AA Wageningen, The Netherlands k CIHEAM-IAMM, 3191 route de Mende, 34090 Montpellier, France l Lund University Centre for Sustainability Studies, Lund University Box 117, 221 00 Lund, Sweden m Cemagref, UMR 1273 Me´tafort, 24 avenue des Landais, 63172 Aubie`re, France b

article info

abstract

Keywords:

Agriculture is interrelated with the socio-economic and natural environment and faces

Sustainability

increasingly the problem of managing its multiple functions in a sustainable way. Growing

Agriculture

emphasis is on adequate policies that can support both agriculture and sustainable devel-

Scenarios

opment. Integrated Assessment and Modelling (IAM) can provide insight into the potential

Indicators

impacts of policy changes. An increasing number of Integrated Assessment (IA) models are

Model linking

being developed, but these are mainly monolithic and are targeted to answer specific

Scaling

problems. Approaches that allow flexible IA for a range of issues and functions are scarce. Recently, a methodology for policy support in agriculture has been developed that attempts to overcome some of the limitations of earlier IA models. The proposed framework (SEAMLESS-IF) integrates relationships and processes across disciplines and scales and combines quantitative analysis with qualitative judgments and experiences. It builds on the concept of systems analysis and attempts to enable flexible coupling of models and tools. The present paper aims to describe progress in improving flexibility of IAM achieved with the methodology developed for SEAMLESS-IF. A brief literature review identifying limitations in the flexibility of IAM is followed by a description of the progress achieved with SEAMLESS-IF. Two example applications are used to illustrate relevant capabilities of SEAMLESS-IF. The examples refer to (i) the impacts on European agriculture of changes in world trade regulations and (ii) regional impacts of the EU Nitrates Directive in combina-

* Corresponding author at: Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D - 53115 Bonn, Germany. Tel.: +49 228 73 28 71; fax: +49 228 73 28 70. E-mail address: [email protected] (F. Ewert). 1462-9011/$ – see front matter # 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsci.2009.02.005

environmental science & policy 12 (2009) 546–561

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tion with agro-management changes. We show that improving the flexibility of IAM requires flexibility in model linking but also a generic set up of all IA steps. This includes problem and scenario definition, the selection and specification of indicators and the indicator framework, the structuring of the database, and the visualization of results. Very important is the flexibility to integrate, select and link models, data and indicators depending on the application. Technical coupling and reusability of model components is greatly improved through adequate software architecture (SEAMLESS-IF uses OpenMI). The use of ontology strongly supports conceptual consistency of model linkages. However, the scientific basis for linking models across disciplines and scales is still weak and requires specific attention in future research. We conclude that the proposed framework significantly advances flexibility in IAM and that it is a good basis to further improve integrated modelling for policy impact assessment in agriculture. # 2009 Elsevier Ltd. All rights reserved.

1.

Introduction

Agriculture operates on the interface between the socioeconomic and natural environment. It is increasingly regarded within the context of sustainability and sustainable development. The complex issues agriculture is facing, such as climate change, food and energy supply, globalisation of markets, population and economic growth and scarcity of natural resources, cannot be addressed through traditional disciplinary research. Policy can play an important role to balance the multiple functions of agriculture and support sustainable development. The effectiveness of policy design and implementation can be improved if the possible impacts of policies on agriculture and through agriculture on sustainable development are better understood. This calls for approaches that integrate knowledge across disciplines and scales. Integrated Assessment and Modelling (IAM) is increasingly seen as a way to address the complex issues of sustainability and sustainable development (Harris, 2002) and to support policy-making (Rotmans et al., 1990; Rotmans and van Asselt, 1996). There is also growing awareness among policymakers to support policy development using IAM. The European Commission (EC), has for instance recently introduced Impact Assessment of its policies as an essential step in the development and introduction of new policies (EC, 2005), for which IAM may play a supporting role. Much progress has been made in IAM and many models have become available (Parker et al., 2002; Tol, 2006) also for application to agriculture (Van Cauwenbergh et al., 2007; Van Ittersum et al., 2008a). However, most models are targeted at specific issues, e.g. assessment of land use change (Verburg et al., 2008), nitrogen emissions and leaching (Velthof et al., 2007) or food production (Fischer et al., 2005), or at specific scales ranging from farming systems to global assessments. Little has been done to develop Integrated Assessment (IA) models that are more generic and flexible and can be applied for a range of questions defined in interaction with policymakers. Recently, a framework for agricultural systems (SEAMLESS Integrated Framework) has been developed that allows exante assessment of agricultural and agri-environmental policies and technologies across a range of scales, from

field/farm to region and the European Union (Van Ittersum et al., 2008a). The framework is component-based and attempts to allow flexible (re-)use and linkage of models, database(s) and tools. The first available results allow a critical review of the underlying conceptual ideas and the methodology. The aim of this paper is to describe the progress in IA achieved with the methodology developed for SEAMLESS-IF. Specific focus is on the flexibility of SEAMLESS-IF to perform IA. Emphasis is on the whole process of IA and the flexible (re)use and linkage of models and tools across disciplines and scales. The first section briefly reviews progress in IA and identifies key challenges to improve flexibility of IA that SEAMLESS-IF tries to address. The next section evaluates the progress achieved with SEAMLESS-IF. Two example applications are used to illustrate relevant capabilities of SEAMLESSIF. Lastly, the achievements and limitations of the SEAMLESSIF methodology are discussed and challenges and priorities for future work are identified.

2. Integrated Assessment and Modelling (IAM) of complex systems in agriculture 2.1. A systems approach to structure complexity of agricultural systems The systems approach is the underlying paradigm of most IA models. Its application to agricultural systems has been significantly progressed by the work of C.T. De Wit (Leffelaar, 1999). Analysis of complex systems requires integration of knowledge from different disciplines and scales. Disciplines cover the whole range from social to natural science including institutional issues and refer to different spatial and temporal scales. Hierarchy theory offers a concept for the investigation of systems that operate on several spatio-temporal scales (Weston and Ruth, 1997). An example for a hierarchical system in agriculture is the organisation of food production with levels such as field, farm, region, country, continent and globe (Fig. 1). A system typically comprises elements, borders, relationships among elements and other systems. A detailed system description is often lacking in IAM. Integrated systems are

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environmental science & policy 12 (2009) 546–561

Fig. 1 – Schematic representation of a hierarchical (agricultural) system. Note that the system is larger than presented and selection for the most relevant parts of the systems has to be made depending on the study. In this case the effects of Policy changes on agriculture in the Midi Pyrenees is studied for intensive arable farms considering three crops (white circles).

complex and difficult to be described in detail and IA modelers are mainly concerned with the linking of models representing the parts of the system which are of interest to the user (Tol, 2006). The characterisation of agricultural systems also imposes considerable challenges. Processes in agriculture can refer to soil–crop–atmosphere interactions, issues of biodiversity, food production and supply chains, human nutrition, natural resource use and management, and can range from the organism (animal, plant) to the field, farm and up to the global level. Clearly, the characterisation of the system under study in terms of its spatial and temporal scales (extent and resolution), its boundaries and components will depend on the goal of the study. Thus, an a priory description of the agricultural system for which a flexible IAM framework should be applied is not possible. However, based on the systems concept a common generic structure can be used as a basic template to describe the system depending on the specific question. This structure can include elements of the system, relationships among elements, borders, inputs/outputs, organisational detail, scale (spatial and temporal) and goals. Another challenge is posed by the ambition to assess impacts on different (sub-) systems simultaneously, e.g. effects of policy change on farm income, regional food production and greenhouse gas (GHG) emission. An elaborated description of the entire system will be too complex and a focus on the most relevant (sub-)systems including feedback loops may be more practical (Ewert et al., 2006b) (Fig. 1). The description will then only refer to the parts of the system that will be modelled. Therefore, improving the flexibility of IA will require features that allow a flexible characterisation of systems (problems) to be analysed which most IA models do not provide.

tional, social and economic aspects (Harris, 2002). Typical for most IA models is that they are built to address one (or few) specific question. This is mainly done through the coupling of available models including data representing the different disciplines considered. These linkages can be hard-coded. The obtained monolithic IA model is then only applicable within the validity range of its model components. Prominent examples are IMAGE (Bouwman et al., 2006), DICE (Nordhaus, 1993) and RICE (Nordhaus and Yang, 1996) or RAINS (Amann et al., 1999). The flexibility to extend the range of possible applications is limited and it requires considerable effort and knowledge to integrate new models. This is due to both the conceptual and the technical part of the coupling. There are also examples for IA studies with loosely connected models, e.g. MIT (Prinn et al., 1999), Eururalis (Westhoek et al., 2006; Verburg et al., 2008), ATEAM (Schroter et al., 2005) or the tradeoff analysis model (Stoorvogel et al., 2004). Some authors have stressed the idea of modularity to overcome the monolithic character of traditional IA models (Argent, 2004; Leimbach and Jaeger, 2004), but little progress has been made in adopting this concept. IAM is most advanced for climate impact research (Weyant et al., 1996; Tol, 2006) and environmental pollutions (Amann et al., 1999). Another growing field for IAM is water resource management (Castelletti and Soncini Sessa, 2004; Letcher et al., 2007) though on a smaller spatial and temporal scale. Some efforts have been made for agriculture and examples are known at field/farm (Kokic et al., 2007), regional (Stoorvogel et al., 2004), continental (Ewert et al., 2005a; Rounsevell et al., 2005) or global scale (Rosegrant et al., 2001; Parry et al., 2004; Fischer et al., 2005), again mainly for climate change. However, IA models applicable at multiple scales a scarce.

2.3. 2.2. Characteristics of integrated assessment and modelling IAM can support the management of complex environmental systems in a balanced way considering biophysical, institu-

Assessing sustainability with the help of indicators

The goal of many IA studies is to inform about changes in sustainability and sustainable development as affected by policies. Despite the many definitions of sustainability and sustainable development that have become available (Robin-

environmental science & policy 12 (2009) 546–561

son, 2002) since the publication of the Brundtland report (Brundtland, 1987), the concept of sustainability is still vague. Central to most definitions is the need to maintain resilience in environmental and social systems by meeting a complex array of interacting environmental, social and economic conditions (Swart et al., 2004). The most common approach to assess impact of environmental or policy changes on sustainability relates to the use of indicators. Numerous impact indicators and indicator lists have been identified and their selection depends on many issues such as the goal of the assessment, the expertise of the scientist, the availability of data and models, etc. (Geniaux et al., 2005). In order to link the indicator selection to the conceptual understanding of sustainability, indicator frameworks have been developed. Examples are the DPSIR (OECD, 1993; United Nations, 2001), MESMIS (Lo´pez-Ridaura et al., 2002) or the SAFE (Van Cauwenbergh et al., 2007) framework. Some efforts have been made to link the definition of indicators to the systems concept. For instance, system’s performance has been evaluated on the basis of attributes (orientors or goal functions) for which indicators were derived (Bossel, 2002; Lo´pez-Ridaura et al., 2005; Alkan Olsson et al., 2009). However, almost all studies refer to a pre-defined list of indicators and a specific indicator framework. The issue of changing between indicator lists and frameworks depending on the question at stake is not supported.

2.4.

Scenario analysis

Scenario-based approaches in environmental and policy assessment studies are increasingly applied within IAM (Van Ittersum et al., 1998; Parker et al., 2002; Sharma and Norton, 2005; Rounsevell et al., 2006; Westhoek et al., 2006). In a policy decision context, scenarios allow policymakers to anticipate and assess uncertainty and risks involved in different options and to identify alternative courses of action (Malafant and Fordham, 1997). However, a wide variety of scenario concepts and uses exist (Van Notten et al., 2003; Bo¨rjeson et al., 2006; Therond et al., 2009). Scenarios can be descriptive or normative (Nijkamp and Blaas, 1994), predictive or explorative (Van Ittersum et al., 1998) and they can be assembled from a forecasting or a backcasting point of view. As noticed recently (Janssen et al., in review), the scenario concept can differ depending on the scientific discipline, the specific scenario study and the individual scientist. This makes it difficult to establish a clear presentation of the generic specificities, objectives and boundaries of scenarios and to identify a common understanding of the typical concept and features of a scenario and how it can be developed. Thus, a concept and procedure is required that allows scenario construction and analysis considering the specific understanding of scenarios by different disciplines. This should result in the formalization of the process of scenario definition and in transparency about the scenario assumptions.

2.5.

Participatory process

Interdisciplinary and policy-relevant research is the key characteristics of IAM (Tol and Vellinga, 1998). Many authors

549

stress the importance of the participatory process combining, interpreting and communicating knowledge from diverse scientific disciplines to allow a better understanding of complex phenomena (Rothman and Robinson, 1997). IAM represents a problem-focused area of research, i.e. mainly project based and undertaken depending on stakeholder needs or demands (Parker et al., 2002). Modelling is not seen anymore as a purely scientific activity that provides system descriptions and prescriptions for decision makers but as a participatory approach with strong emphasis on contextualization and communication (Sterk et al., 2009). However, most IA models do not provide explicit interfaces that support interactions with stakeholders. Few examples are available where explicit efforts have been made to present the assessment results in a user-friendly and understandable way (Schroter et al., 2005; Westhoek et al., 2006; Verburg et al., 2008). Interaction with stakeholders throughout the entire IA process is, however, not facilitated.

3.

Flexible IA in agriculture using SEAMLESS-IF

3.1.

Description of SEAMLESS-IF

The methodology developed for SEAMLESS-IF aims at an integrated assessment of policy impacts on sustainability (of agricultural systems) and sustainable development using scenario analysis following the systems approach. An important ambition has been to design a tool that is flexible and can be used to address a range of policy questions related to agriculture and sustainability. The proposed integrated framework SEAMLESS-IF comprises a set of methods and approaches to support the different steps of the IA process: -

Define problem and system Define scenarios Define and select indicators Model and assess changes of selected indicators for different scenarios - Visualise and evaluate results The conceptual linkage of these steps is described below (Section 3.3) together with their organisation into the three main modelling phases, pre-modelling, modelling and postmodelling within the developed modelling tool. Importantly, much emphasis has been placed on developing a modelling tool that supports both the modelling activities and the interaction with end-users and stakeholders through specific Graphical User Interfaces (GUI). The methodology of SEAMLESS-IF is described in more detail elsewhere (Ewert et al., 2005b, 2006a; Van Ittersum et al., 2008a) and in the different contributions of this special issue as for the scenario concept (Therond et al., 2009), the indicator framework (Alkan Olsson et al., 2009) and the database (Janssen et al., 2009), the most important models considered have been described earlier (Van Ittersum et al., 2008a). The present version of SEAMLESS-IF has three main models (Van Ittersum et al., 2008a). These are the agricultural sector model SEAMCAP, a version of the CAPRI model (Common Agricultural Policy Regionalized Impact) developed

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Table 1 – Summary description of two case studies, i.e. (1) Integrated assessment of a trade liberalisation proposal by the so-called G20 group of developing countries; (2) Evaluation of the impacts of the Nitrates directive at the regional scale. G20 proposal

Nitrates directive

Spatial scales  Extent

 Europe (EU25)

 Resolution

 Region, farm type

 Region (Midi Pyrenees)  Farm type, field

Temporal scale  Extent  Resolution

 2020  No time step

 2013  Day (only APES)

 Reduction export subsidies  Reduction tariffs

 Nitrates directive

Models

 Farming systems  Market model

 Cropping system  Farming system

Indicators

    

Policy

Agricultural income Consumer surplus Tariff revenues Total welfare Market prices

 Agro-management

 Farm income  Premium per farm  N leaching  Soil carbon content  Crop area distribution

 Farm income  Nitrogen leaching  Soil Carbon content

for the EU (Heckelei and Britz, 2001; Britz et al., 2006), the bioeconomic farm model (FSSIM) (Louhichi et al., in press) and the cropping system model APES (Agricultural Production and Externalities Simulator) (Donatelli et al., in press). Several additional models have been developed to scale data, e.g. from FSSIM to SEAMCAP through EXPAMOD (Pe´rez Domı´nguez et al., 2009) or farm data to the regional level and regional data to the EU level. In the next sections we focus specifically on the aspect of flexibility of the different IA steps and framework components developed.

3.2.

Description of applications

The methodology of SEAMLESS-IF is discussed on the basis of two examples which are summarised in Table 1. The first case study refers to the integrated assessment of a trade liberalisation proposal by the so-called G20 group of developing countries at the Doha round of the World Trade Organisation. In this application, the reduction in border protection on European agriculture, consumers of agricultural goods and the

Fig. 2 – Simple representation of assessment concept used in SEAMLESS-IF. Note that the dotted line represents a feedback mechanism that is not modelled but can be considered through user interaction outside the framework.

income from tariff cuts is assessed. The ‘‘G20 Proposal’’ results in reductions of import tariffs. According to this approach, tariffs lying within certain thresholds are cut differently. Furthermore, a maximum ad valorem tariff of 100% for developed countries and 150% for developing countries is recommended. In addition, a complete elimination of export subsidies is assumed. The scenarios are assessed using a combined cropping/farming system and market model. For more information about this case study, see related articles (Van Ittersum et al., 2008a; Pe´rez Domı´nguez et al., 2009). The second case study is targeted at the evaluation of the impacts of the Nitrates Directive and its interactions with the 2003 CAP reform (i.e. the Luxemburg agreements on Common Agricultural Policy Reform and other most probable changes in future legislation) at the regional scale. Specific incentives to promote agro-technological innovations in farming systems are considered (Therond et al., 2009). A set of detailed policy scenarios at regional scale is implemented to assess the impacts of the Nitrates Directive in combination with technological innovations on crop based farming systems in Midi-Pyre´ne´es, a region in France. The effects of improved nitrogen management are assessed through changes in application rate and timing. Cuts in premiums (3%) are considered when management is not in compliance with the agro-management regulations of the Nitrates Directive. These scenarios are assessed with the cropping and farming system models. A full description of the scenarios and the associated scenarios parameters is given elsewhere (Belhouchette et al., 2007; Therond et al., 2009).

3.3.

Conceptual basis of SEAMLESS-IF

Assessment of impacts in SEAMLESS-IF follows a general structure in which a certain policy and/or technology change is assessed with respect to its impact on different indicators (Fig. 2). Indicators are primarily assessed from model simulations. Importantly, possible responses by policymakers (feedbacks) to simulated changes are not endogenously modelled but can be explored through alternative scenarios (Fig. 2). Thus, the proposed concept is relatively simple mainly distinguishing between drivers and impacts. Concepts in other IA models and frameworks are often more detailed such as the DPSIR approach (OECD, 1993; United Nations, 2001) or variants of it used in frameworks like SENSOR (Helming et al., 2008) or SAFE (Van Cauwenbergh et al., 2007). Considering other concepts (e.g. DPSIR) within the modular structure of SEAMLESS-IF is possible but will require adjustment of the GUI and the indicator framework. Based on the concept described above, an assessment procedure has been defined for SEAMLESS-IF which is structured into a pre-modelling, modelling and post-modelling phase with the different IA steps relating to each phase (Ewert et al., 2005b; Therond et al., 2007; Van Ittersum et al., 2008a) (Table 2). Flexibility exists within this procedure as not all IA steps, particularly in the pre-modelling phase, need to be completed in order to perform an assessment. For example, a project in SEAMLESS-IF as defined and calibrated in a previous study can be used again in a new study after (few) changes have been made to represent the new policy assessed, the changed external conditions (e.g. fuel price) or the type of

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Table 2 – Integrated assessment phases and steps in SEAMLESS-IF. IA phase

IA step

Pre-modelling

 Problem description  Scenario definition (experiment designer)  Indicator selection (manager)

Modelling

 Composition of model chain  Scaling of model outputs

Post-modelling

 Visualization of results  Exporting, reporting of results

improved farm management (e.g. to reduce nitrate leaching or to promote soil conservation). Thus, project information from earlier studies of SEAMLESS-IF can be re-used. The user can make the required changes only at those steps of the assessment procedure that are relevant for the new problem. As SEAMLESS-IF is a computerised tool, the GUI is structured according to the assessment concept and procedure as described above. Importantly, the GUI has been developed to support modellers in performing IA and to ensure transparency and support interaction with stakeholders. We are not explicitly evaluating the flexibility of the GUI. Again, changes in the assessment concept and procedure can be considered through changes in the GUI design. This will not affect the individual model components and only to some extent the database and the indicator framework. SEAMLESS-IF distinguishes between two main user groups, the integrative modeller (IM) and the policy expert (PE). While the IM has the expertise to run the framework, the PE is primarily interested in the results. Accordingly, the requirements for the GUI are user-specific and are considered through user management classes with specific user rights for the different steps of the assessment procedure. The two user types primarily interact during the pre- and post-modelling phases of the assessment process. In principle, more user types can be identified and their interaction with the framework can be supported through specific graphical user interfaces.

3.4.

Problem description

The problem description is a general description of the problem/system related to the case study (e.g. impact of trade liberalization on the incomes in rural areas across the EU) according to the items in Table 1 (i.e. spatial and temporal scales, policies, models applied, indicators). Options are provided to specify the spatial and temporal extent and resolution which determine the appropriate model chain that is operational for the specific project. It is a specific strength of SEAMLESS-IF to allow impact analyses at various scales with different spatial extents (e.g. farm or region), while keeping the same resolution (i.e. the models’ precision) for the indicator assessment if required. The other items are described in the subsequent sections below. The description of the problem is done by the integrative modeller. Interaction with the policy expert is possible and recommended. For the two applications or case studies (Section 3.2) selected, spatial scales are defined as in Table 3 together with the corresponding model chains. Due to the

Table 3 – Spatial scales (extent and resolution) and model chains for the two case studies, i.e. (1) Integrated assessment of a trade liberalisation proposal by the socalled G20 group of developing countries; (2) Evaluation of the impacts of the Nitrates directive at the regional scale) and for a number of possible future applications considering models developed in SEAMLESS. Application

Spatial scale Extent

Resolution

a

Model chain

G20 proposal Nitrates directive (possible) (possible) (possible)

EU25 Region

Farm type Farm type

FSSIM-SEAMCAP APES-FSSIM

EU25 EU25 Global

AEnvZb NUTS2c Farmtype

(possible)

Landscape

AEnvZ

APES SEAMCAP GTAPd-SEAMCAPFSSIM SLEe-FSSIM-APES

a

EU25 are the 25 countries in the European Union. AEnvZ are Agri-environmental zones as based on climatic and soil zonations. c NUTS2 (Nomenclature of Territorial Units for Statistics) are administrative units used within the European Union. d GTAP is a global trade model (computable general equilibrium model) including global databases of production and trade, to analyse the interactions between EU policies and the rest of the world (Hertel, 1997); (Van Tongeren et al., 2001). The model is not integrated yet into SEAMLESS-IF. e SLE, the SEAMLESS Landscape Explorer is a model component developed to provide stakeholders the means for visualising changes in the landscape (Auclair and Griffon, 2007). The model is not integrated yet into SEAMLESS-IF. b

component-based structure of SEAMLESS-IF models can also be used for other applications than the two discussed here presuming the required models are available (Table 3). When more models are integrated in the future, the range of scales can be further extended.

3.5.

Scenario definition

A generic concept has been developed for scenario definition that allows implementation of scenarios for a range of policy issues and economic and environmental conditions. The scenarios (also indicated as experiments) give information about the context (e.g. farming conditions such as arable farming with present or improved agro-management in a certain region in France and main crop types), the outlook on the future (e.g. changes in temperature due to climate change and increasing CO2 concentration or the change in GDP in the studied country which are considered exogenous to the SEAMLESS-IF analyses but influence the future conditions), and the possibly implemented policy options (e.g. trade liberalization, nitrate regulation). The meaning of and the relationships between the terms context, outlook and policy options has been clarified in more detail in the ontology (Janssen et al., in review). A common ontology is a specification of a conceptualization that is shared between researchers. In the SEAMLESS-IF approach a project has one (and only one) problem description, but it can handle more scenarios (e.g. different policies) and indicator sets. The scenarios determine the composition of the model chain (Table 3) and the input

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parameters required for running each of the models (Janssen et al., in review). The approach is flexible as changes in policy options, context or outlook can be assessed independently. For the second case study (i.e. the implementation of the Nitrates directive) the scenarios are presented in more detail elsewhere (Therond et al., 2009) with their specific policy option, context and outlook.

3.6.

Indicator framework

Results from SEAMLESS-IF for a range of scenarios are assessed and compared through a set of indicators. An indicator framework has been developed for SEAMLESS-IF which distinguishes four classifiers to structure a broad range of indicators and to support the process of indicator selection and assessment (Van Ittersum et al., 2008a; Alkan Olsson et al., 2009). The indicator framework in SEAMLESS-IF with a selection of the indicators applied to the two case studies (Section 3.2), is presented in Table 4. The indicator framework forms a basis for the translation of the users’ policy questions into system characteristics that SEAMLESS-IF can assess. The framework will also facilitate a balanced selection of indicators across the dimensions of sustainability (see above), as well as their meaningful combination. Finally the framework creates a systematic and flexible approach for weighing and aggregation of indicators if desired by the users. Indicators can be added to a library and selected for an IAM project depending on demand, i.e. depending on the problem to be addressed. In this way the framework offers a large flexibility to select, add or remove indicators. Indicators considered in the two case studies (Table 4) represent only a limited selection of the total set of indicators. A more detailed description of the framework and the available indicators is provided elsewhere (Alkan Olsson et al., 2009).

3.7.

Modelling

3.7.1.

Models

A central part of SEAMLESS-IF is the modelling itself. Specific attention is given to the ability of the framework to support

flexible model linking so that models can be (re)used in different applications if required (Fig. 3). SEAMLESS-IF uses the Open Modelling Interface and Environment (OpenMI) (Gijsbers et al., 2003) to support the (technical) linking of models. The conceptually correct linking between models, the data base, the indicator framework and other components is ensured through the use of an ontology, in which the variables to be exchanged are defined (Wien et al., in press; Janssen et al., 2009). For example, the use of ontology ensures, among others, consistency between the output variables of one component with the input variables of another component. An advantage of the present approach is that once the ontology is defined, models can be linked relatively easily and data exchanges are properly organised. For the two case studies described above two model chains were composed (Fig. 4). In both cases the FSSIM model is used but in combination with different other models. In the application for the G20 proposal FSSIM is linked via EXPAMOD to SEAMCAP. The FSSIM model is run for sample regions and the results are extrapolated to all regions within EU25 in order to provide information on supply elasticities to the market model SEAMCAP. Information about changes in commodity prices as required by FSSIM is provided by SEAMCAP. In the second application, FSSIM is used in combination with APES to simulate farm performance for a selected region depending on policy changes, in this case with respect to the Nitrates Directive. Detailed information about cropping system responses (yield, externalities, etc.) are obtained from APES and are used for the generation of coefficients of production and agricultural activities which are inputs to FSSIM.

3.7.2.

Model parameterisation

An important part of scenario analysis is the proper implementation of scenarios for the models considered in a given application. Parameters will have to be selected and defined. An overview of the model parameters available when designing scenarios (or experiments) with their specific policy option, outlook and context is given in Table 5. As parameter editing is supported by the GUI, consideration of additional models will require the development of user interfaces to

Table 4 – Indicator framework in SEAMLESS-IF with the selection of indicators applied in the two case studies. Theme

Scale

Domain Sustainability of agriculture Environ.

Goal

Process

EU25 Region Farm

EU25 Region

EU25 Region Farm

Social

Environ.

Money Metric NO3 surplus NO3 leaching, Pesticide use

Pesticide use

Intervention stock costs CH4, N2O emissions, Global warming potential

Farm Means

Economic

Sustainable development

CH4, N2O emissions, Global warming potential, NH3 volatilization Premium, Farm income

Erosion

Indirect energy use Erosion

Economic

Social

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Fig. 3 – Schematic representation of the technical model and database linking in SEAMLESS-IF and the use of ontology. Note that the models are written in different languages and that the linking of models is through the component framework. Wrappers are the interfaces developed to link software components. Dotted line represents a relationship that will be considered in future versions.

allow parameter editing for these new models. This also applies if the original set of parameters in a model subjected to scenario changes is modified.

3.7.3.

Database

Closely related to the modelling activities is the development of a database for IAM. The models in SEAMLESS-IF require many different biophysical, farming system and socioeconomic data such as on weather, soil characteristics, agro-management activities, typologies (e.g. farm, regional), etc. (Janssen et al., 2009). Populating a database for IAM poses many challenges in terms of data availability as well as combining and processing data from many sources. Data is not always available at the level required for the models used. Also, IAM requires data from several different domains, typically with very different spatial concepts. Therefore, a spatial framework has been

designed specifically for SEAMLESS-IF to define the procedure and standards for data linking (Janssen et al., 2009). Finally, the use of available data is often restricted. A particular challenge has therefore been to process the data to a format that facilitates IAM in SEAMLESS-IF respecting the legal constraints for using the source data. Technically, the design of the database has to comply with the idea to flexibly add and link models, and retrieve and store model results. Through the use of ontology it is possible to generate the data base access layer (data base schema). This means whenever new components (models, indicators, etc.) require changes to the database structure it must be defined in and generated from the ontology. A large SEAMLESS database has already been compiled (Janssen et al., 2009) and integrated into SEAMLESS-IF. Models can be run for all policy questions that the integrative modeller can implement with the available model parameters.

Fig. 4 – Model chains considered in the two case studies (G20 proposal and Nitrates Directive implementation at the regional scale) with indicators derived from the different models. Boxes with up-scaling refer to models developed to up-scale environmental (e.g. nitrate leaching) and economic (e.g. farm income) indicators from the farm type to the regional and EUlevel. Up-scaling methods to EU-level are not yet implemented (dotted lines).

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Table 5 – Examples of parameters for different models available in SEAMLESS-IF to design scenarios (or experiments) with their specific relation to Outlook, Policy option and Context. Model

Parameter Outlook

Policy option

SEAMCAP

Relative demand shifts, Relative exchange rates, Inflation, Annual yield trend, Price index for non-agricultural goods, Biofuel demand

Value limits of export subsidies, Tariff rates, Tariff rate quotas, Tariff reduction rules, Maximum and minimum set-asides, Basic premiums, Modulation, Coupling degrees

FSSIM

Inflation, Yield trend, Relative fuel price, Relative product price shifts

Maximum and minimum set-asides, Basic premiums, Modulation, Coupling degrees, Farm type resource endowments, Product quotas, Maximums and minimums of subsidies, taxes, penalties of inputs, outputs, activities and externalities, Penalty of farm income

APES

3.7.4.

Context

Nutrient management, Water management, Tillage management

Scaling of data

The issue of up-scaling has received specific attention in SEAMLESS. However, only few crucial points shall be addressed in this paper mainly with respect to the flexibility of up-scaling. SEAMLESS-IF considers models at different scales (and disciplines). A particular challenge is the transfer of data between these models. In the considered example of the G20 application, data need to be extrapolated from the farming systems level to the market level. This is achieved by developing an extrapolation algorithm (EXPAMOD) that transfers the data on supply elasticities from FSSIM as demanded by SEAMCAP (Pe´rez Domı´nguez et al., 2009). These linkages have to ensure that the concepts, e.g. for yield or crop, are the same in both models. Conceptual mismatches between models are often overlooked when linking models across scales. There may also be situations where end-users expect information at a higher level where models are not available to estimate indicators. In these cases, indicators need to be upscaled from model outputs. In the first (G20) case study, information about the spatial allocation of farm types considered in the SEAMLESS database offers the possibility to up-scale model outputs to various spatial scales of interest for end-users (e.g. NUTS2 regions and agro-environmental zones). Up-scaling methods may be simple for some indicators using weights or area shares of the regions. However, more advanced methods are required when the underlying processes are non-linear as typical for many environmental indicators. In the second (regional scale) case study environmental (N-leaching) and economic (farm income) indicators are upscaled using specifically developed routines that are integrated into SEAMLESS-IF (Fig. 4). These routines are generic, use weights or area shares of the main farm types in a region but may also apply more advanced up-scaling methods, and are linkable to all economic and environmental indicators for which they are of relevance. In both case studies, the use of OpenMI and ontology supports the implementation and

(re-)use of these components for up-scaling (or down-scaling if required).

3.8.

Visualisation of results and uncertainty

Eventually the results of a simulation exercise need to be visualized and evaluated. Here it is required that results can be presented in different formats such as tables, maps and diagrams (line, bar, pie, spider, etc.). SEAMLESS-IF provides such features. The presentation of results refers primarily to the indicators. An example is shown for test cases 1, the effect of trade liberalisation on agricultural income in Europe as investigated in the G20 example (Fig. 5). However, modellers may also be interested in viewing some of the model variables (not considered as indicators) in order to understand the causal relationships that have determined the outcomes of the simulations. An attempt is made to support the visualisation of selected intermediate simulation results. In principle, all variables simulated can be visualised once they are considered in the ontology. In practice, visualisation is limited by the effort it takes to include model variables in the ontology. To ensure transparency of results there is an additional option to store all data simulated in specific (zip-compressed) files in the database. End-users are interested in the uncertainty of results. We consider that effective application of policy-relevant IAM can only be achieved, if uncertainty analysis effectively responds to the information needs of the model users and stakeholders of the problem (Gabbert et al., in review). Accordingly, a concept is developed and applied to determine uncertainty for two aims (Van Ittersum et al., 2008b): i) informing users of the model (or model chain) about critical model assumptions and parameters and their uncertainties, and ii) providing guidance to the model developers in improving the model or model chain. Uncertainty of results from uncertain parameters and model assumptions can be tested within the present design by creating different scenarios (or experiments) with different parameters (Table 5; e.g. different yield trends or biofuel demands). Automating the process of uncertainty analysis

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and 3.7 for the project and scenario definition and the model linking. However, although a high level of shared understanding was reached among partners in the project, the number of people overseeing and being able to use and understand the entire system is still rather small. In fact, the knowledge about SEAMLESS-IF is still much divided and ‘owned’ by specialist groups representing the different components which are required to use the framework. Only the component experts can perform a qualified evaluation of the behaviour of their components in response to policy change. It remains a subject for further discussion and development whether the use of a complex IA model such as SEAMLESS-IF will always require a group of experts with different disciplinary backgrounds or whether a new generation of experts will become available that will have the ‘integrated’ knowledge to perform IA with a tool like SEAMLESS-IF.

4.2. Fig. 5 – Relative change (%) in agricultural income across NUTS2 regions in Europe due to the price changes following from reductions of import tariffs and export subsidies (i.e. the G20 proposal case study). The differences in income change between regions are caused by the differences in original production structure and the land allocation responses to price changes.

and including specific uncertainty tools will be considered at a later stage of SEAMLESS-IF. The conceptual and technical design of SEAMLESS-IF allows extension of the framework for more advanced features of uncertainty analysis (e.g. sensitivity analysis), making use of the flexible approach for scenario definition (Section 3.5) and model linking (Section 3.7.1).

4.

Discussion

4.1.

Process of implementation and knowledge ownership

Reviewing the literature on integrated assessment (IA) we find that most IA models focus on environmental themes with little attention to agriculture in the specific context of land management practices. SEAMLESS-IF fills this gap. The particular novelty of the SEAMLESS-IF approach is that it goes beyond earlier IA models focussing on specific issues and scales, to combine disciplines and scales in a flexible way depending on the policy issue to be addressed. The developed concepts and procedure used in SEAMLESSIF is the result of many iterations integrating experiences and views of scientists from different disciplines. It also includes feedback from potential end users, i.e. policy experts from regions, member states and of the European Commission. As a result of the efforts spent in agreeing on the concepts and meaning of terms as used in SEAMLESS-IF, an advanced level of shared understanding among scientists has been reached. The use of ontology has been an important means to guide this process. Examples of its use are mentioned in Sections 3.4, 3.5

Complexity of IA models

The questions to be addressed with SEAMLESS-IF are typically complex. As SEAMLESS-IF is based on the systems approach, specific attention was given to the description of the system under study in order to structure the problem. It appeared most practical to provide features that allow users to identify the model chain relevant for a specific problem. Guidance is provided in selecting the relevant model chain by specifying the spatial and temporal extent and resolution of the analysis. Once the technical framework (of SEAMLESS-IF) is developed the number of models and data that can be integrated and linked is (technically) not limited. We think, there is a risk that the composition of models developed can become too complex for the user to oversee and understand. Model compositions may even become conceptually meaningless. Accordingly, attention should be given to the scientific quality of developed composite models. Guidelines for ‘good modelling practice’ as described for dynamic modelling (Sinclair and Seligman, 2000; Van Oijen, 2002) may be used as a basis to define such requirements for IAM. Consistency in conceptual linking is particularly challenging for models and data across scales and disciplines. The proposed framework offers a good technical basis to integrate scaling algorithms that ensure conceptual consistency of the data flow between models at different scales. Many up-(and down-)scaling methods exist (Ewert et al., 2006b) but knowledge about scaling in IAM is still infantile and often lacks scientific rigour. Much of the progress in IAM will depend on the development of appropriate scaling approaches.

4.3. Selection of indicators and availability of models and data In SEAMLESS-IF indicator framework(s) are used to structure impact indicators according to the conceptual understanding of sustainability and sustainable development. Importantly, indicators in SEAMLESS-IF refer to different scales and are derived from the models applicable at these scales. Thus, rather than scaling indicators, processes are scaled through

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Table 6 – Level of flexibility achieved in SEAMLESS-IF for the main IA steps and framework components. IA step/framework component

Characteristics

Level of flexibility

Available options

Assessment concept

User ! policy ! impact ! evaluation;

Limited

Changes require modification of the GUI

Assessment procedure

Pre-modelling ! modeling ! post-modelling phase

Limited

Changes require modification of the GUI

System description

Spatial and temporal extent and resolution

Flexible (within these characteristics)

Model chain can be generated from the specific spatial and temporal scales of the problem to be addressed

Problem and scenario description

Use of policy option, outlook, context, experiment and indicators

Flexible

Implementation of scenarios for model chains through policy option, outlook and context parameters of different models

Indicator framework

The present framework considers four classifiers (see text)

Limited

Implementation of new indicator frameworks requires GUI adjustments

Indicator library

Organizing indicators according to the indicator framework characteristics

Very flexible

Indicators can be added on demand

Database

Database of all model inputs and outputs including indicators and assessment results

Very flexible

Additional data and data types can be included; Structure (schema) is generated from ontology

Model linking

Linking of models available in SEAMLESS-IF and considered in the SEAMLESS-IF ontology

Very flexible

Integration of new models; Linking of models; Integration of new scaling procedures; Requires knowledge about ontology and wrapper

Visualization of results

Presentation and evaluation of results in form of tables, graphs, maps

Very flexible (within the options provided)

Refers only to indicators and model variables that are considered in the ontology

model chains from which indicators are derived. This allows capturing the consequences of policy options at different scales considering some of the mechanisms underlying indicator changes including interactions across scales. Our emphasis on multi-scale analysis (Ewert et al., 2006b) is different to many other IA models and allows a better understanding of scale interactions. Ideally, models required to calculate these indicators are then selected (and linked). However, in practice, there can be a mismatch between the indicators required and the models and data available. This commonly results in a selection of indicators driven by the available models and data. The indicator framework developed in SEAMLESS-IF and the process for indicator definition can facilitate the inventory of ‘demanded’ indicators and provided model outputs and data to calculate these indicators. This identifies the gaps where new models and data are needed. It may also clarify the relevance of certain indicators from a systems modelling perspective. Such activities will require interactions among scientists and stakeholders which SEAMLESS-IF can support.

4.4.

Flexibility of SEAMLESS-IF

The study has presented the options for a flexible performance of IA in agriculture using SEAMLESS-IF. As evident from this study, improvement of the flexibility of IAM requires consideration of the different steps of the IA process. Progress in the flexibility of the different IA steps and framework

components achieved with SEAMLESS-IF is summarised in Table 6. For some parts such as the general IA concept and procedure (pre-modelling, modelling and post-modelling) and the indicator framework, the flexibility is limited to the predefined options, which also determine the structure of the GUI. Accordingly, changes to the concept, the procedure and the indicator framework will require amendments of the GUI (e.g. restructuring screens of the pre-modelling phase and tables). The developed method of project and scenario description is a significant advancement of many earlier approaches in IA models using a single scenario concept. SEAMLESS-IF allows the combination of different scenario concepts implemented depending on the application and the available model chains. A large flexibility has been achieved with respect to the indicator library, the database and the integration and linking of models, database and indicators. Here the developed framework provides the largest flexibility and represents a considerable progress in IAM. The present version of SEAMLESS-IF allows the integration and linkage of new models, data and indicators, which will further extend the areas for which SEAMLESS-IF can be applied as more models, data and indicators become available. However, already with the set of models, data and indicators presently integrated, a range of issues can be addressed. As apparent from the available options to change parameters (see Table 5), the impacts of different policies (market level and CAP instruments and farm level options), outlooks and contexts can be explored for different regions across the EU.

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5.

Conclusions

SEAMLESS-IF goes beyond earlier developed IA models by explicitly considering the linking of models across scales and disciplines for the calculation of sustainability indicators in agriculture. Different to most IA models SEAMLESS-IF has also been designed to improve flexibility of IAM which we have specifically evaluated in this study. We show that improving the flexibility of IAM requires next to the flexibility in model linking a generic set up of all IA steps. This includes the problem and scenario definition, the selection and specification of indicators and the indicator framework, the structuring of the database, and the visualization of results. The flexibility to integrate, select and link models, data and indicators depending on the application is of key importance. Technical coupling and reusability of model components is greatly improved through adequate software architecture (SEAMLESS-IF uses OpenMI). The use of ontology strongly supports conceptual consistency of model linkages. However, the scientific basis for linking models across disciplines and scales is still weak and requires specific attention in future research. Also, the extent to which an IA framework and its components can be set up generically to improve flexibility utilizing the knowledge embedded in traditional model solutions for specific problems is not well understood. The proposed framework significantly advances flexibility in IAM and it is a good basis to further improve integrated modelling for policy impact assessment in agriculture.

Acknowledgements The work presented in this publication is funded by the SEAMLESS integrated project, EU 6th Framework Programme for Research Technological Development and Demonstration, Priority 1.1.6.3. Global Change and Ecosystems (European Commission, DG Research, Contract No. 010036-2). We gratefully acknowledge all SEAMLESS participants who contributed to the development of SEAMLESS-IF. The opinions expressed here can in no circumstance be considered as reflecting an official position of the European Commission.

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regarding agriculture-environment relationships, among them the environmental impact of pesticides. He is the co-developer of the INDIGO method based on 8 indicators in arable crops. He works also on methodological issues on indicator development (aggregation, validation, comparison, etc.) and led the development of calculation software of indicators (INDIGO, PHYTOCHOIX) and the testing of indicators among farmers.

Frank A. Ewert worked as senior scientist within the Plant Production Systems group at Wageningen University before he became professor of Crop Science at the Institute of Crop Science and Resource Conservation at the University of Bonn in 2008. His main expertise is on analysis and simulation modelling of agricultural systems with particular emphasis on climate change and policy impacts at regional and global scales. Recent activities focus on integrated assessment modelling and related approaches of upand down-scaling.

Floor M. Brouwer is Head of the Research Unit Management of Natural Resources at LEI - Wageningen UR. His main research supports policy assessments on the interaction between agriculture, environment and nature at European scale. On behalf of Wageningen UR, he is in charge of the coordination of research in the field of nature and economics to support the Netherlands Environmental Assessment Agency.

Martin K. van Ittersum is associate professor within the Plant Production Systems group at Wageningen University, The Netherlands. His research focus is on development of model-based methods for the assessment of current and future agricultural production systems, both at farm and regional scale. He has run a range of national and international research projects in Europe, Central America and south-east Asia.

Thomas Heckelei is professor for economic and agricultural policy at the Institute of Food and Resource Economics, University of Bonn. He received his PhD from Washington State University and holds a degree of habilitation from the University of Bonn. His main areas of research are the development of econometric methodologies and econometric estimation of behavioural models with an emphasis on in agricultural supply models. He also has extensive experience in agricultural policy modelling and the development of tools for policy impact assessment at European scale.

Irina Bezlepkina is a researcher at the Business Economics group at Wageningen University as well as at the Agricultural Economic Research Institute LEI-WUR with a PhD in Agricultural Economics from Wageningen University. Her main expertise is on microeconomic modelling. She is currently involved as research partner and task leader in ongoing EU projects on assessing responses of agricultural systems to (land use) policies. Recent activities focus on methods of integrated assessment and modelling. Her previous research has focused on effects of subsidies and debts in agriculture in transition economies. Olivier Therond is an agronomist within the INRA institute at Toulouse center. His main expertise is on procedure for integrated assessment, development of farm and spatial typologies and scale change procedure. Recent activities focus on evaluation of farm typology-bio-economical modelling chain. Erling Andersen is a senior researcher in agriculture and countryside planning at Forest & Landscape, University of Copenhagen. He has a PhD in countryside planning and a master degree in geography and communications. His research fields are in agricultural policies and the environment, agri-environmental policies, agri-environmental indicators, countryside planning and environmental impact assessment. Most recent research is on farm typologies for integrated analyses and on a European database integrating data on farming, biophysical endowments and socio-economics. Hatem Belhouchette is a researcher in the INRA-Montpellier (France). His principal areas of competence are sustainability analysis, water harvesting, cropping system and bio-economical modelling, focusing principally on environmental and economic sustainability analysis. His current work is to develop and use agronomic and bio-economic models and programs, where contradictory objectives are optimised. He has also participated in the evaluation of French, EU and Mediterranean sustainable water and natural resources projects that deal with water harvesting, conservation agriculture, nitrate leaching and different water quality management and use for agriculture irrigation. Christian Bockstaller has worked as a crop scientist at INRA Colmar since 1994 on the development of assessment methods

Sander Janssen is currently undertaking his PhD again at Wageningen University, as a joint project between the Business Economics group and the Plant Production Systems group. His PhD concerns integrated assessments, integration between disciplines and bio-economic farm models. Rob Knapen is a senior software engineer and architect at Alterra, Wageningen UR, The Netherlands. He has a background in Computer Science and extensive practical experience in Geographic Information Systems and Model Integration Frameworks. His focus is on agile software development methodologies, Object Oriented programming techniques and multi-tier software architectures. He is involved in several ongoing national and European projects based on the development of model integration frameworks (e.g. OpenMI) and applying them for integrated impact assessment tools. Marijke Kuiper is a senior researcher International Trade and Development at the Agricultural Economics Research Institute (LEI – Wageningen UR) in The Netherlands. Her main expertise is in modeling that ranges from bio-economic household modeling to village and global general equilibrium models. Recent research in the role of research partner and project leaders has focused on analyzing multilateral and bilateral trade agreements with special attention for the impact on developing countries and providing direct policy support to Dutch policymakers. Kamel Louhichi is a researcher and teacher at the Mediterranean Agronomic Institute of Montpellier (CIHEAM-IAMM). His research has been focused on quantitative methods for analysing agricultural and environmental policies. He has an extensive experience in mathematical programming model and in bioeconomic modelling approach, integrating bio-physical and economic models. He is also involved as research partner in several ongoing national and international projects on assessing the impact of agricultural policies, mainly the CAP and agricultural policies of the Mediterranean countries. Johanna Alkan Olsson is a Research Fellow at Lund University Centre for Sustainability Studies and the Department of Sociology of Law, Sweden. She has a PhD from the Interdisciplinary Depart-

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ment of Water and Environmental Studies at Linko¨ping University, Sweden. Her main research areas are the development of more participatory apporaches in water management and the analysis of the implementation of agro environmental policies and legal instruments. Nadine Turpin is senior scientist in the Joint Research Unit 1273 Me´tafort (‘‘Changes in Activities, Space and Forms of Organisation in Rural Areas’’) at Cemagref, Clermont-Ferrand, France. She specialised first in animal sciences (intensive productions) then in environmental economics. Her research activity is concentrated on multi-functionality issues of the rural sector and rural development. Jacques Wery is professor of agronomy and director of an INRACIRAD research unit on Tropical and Mediterranean Cropping Systems with emphasis on plurispecific systems (intercropping, relay cropping and agroforestry). He is President-elect of the European Society for Agronomy. His expertise is on integrated analysis of cropping systems at field and farm levels and the development of tools to assess or design innovative cropping systems. He has experience on a large set of grain or perennial

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cropping systems in Mediterranean and tropical countries, including grain legumes, cotton and vineyards. Jan Erik Wien is a team manager Knowledge Systems and project manager in the field of information management and information technology. He has a degree in agriculture and information technology from Wageningen University. He has worked at the Agricultural Economics Research Institute in economic and environmental modelling, ex ante policy evaluation and model integration. Currently, he works at the Centre for Geo-Information of Alterra and is a team manager and project manager of various information analysis and software development projects, e.g. frameworks for geo-knowledge systems, decision support tools and gaming for participatory planning processes. Joost Wolf is a scientist in the Plant Production Systems group at Wageningen University. He has a long experience on crop growth modelling, yield forecasting, climate change impacts on agricultural systems, and analyses of soil nutrient cycling, nutrient emissions and land use systems. He has been involved in many European projects dealing with climate change and policy impacts on agricultural systems at regional, national and continental scales.