Earth Informatics

SEAMLESS integrated project aims at developing an integrated framework that allows exante assessment of agricultural and environmental policies and technological innovations. The framework will have multi-scale capabilities ranging from field and farm to the EU25 and globe; it will be generic, modular and open and using state-of-the art software. The project is carried out by a consortium of 30 partners, led by Wageningen University (NL).

SEAMLESS integrated project aims at developing an integrated framework that allows exante assessment of agricultural and environmental policies and technological innovations. The framework will have multi-scale capabilities ranging from field and farm to the EU25 and globe; it will be generic, modular and open and using state-of-the art software. The project is carried out by a consortium of 30 partners, led by Wageningen University (NL).

As researchers we are often faced with the difficult and demanding task of preparing models, and their computer implementations, for decision making, or, more recently, for integrated assessment. Such assessment often involves large scale problems, where the decisions to be made can deeply affect the environment, the social context and the economic background of regions and even nations.

The ex-ante assessment of the likely impacts of policy changes and technological innovations on agriculture can provide insight into policy effects on land use and other resources and inform discussion on the desirability of such changes. Integrated assessment and modeling (IAM) is an approach that can be used for ex-ante assessment. It may combine several quantitative models representing different processes and scales into a framework for integrated assessment to allow for multi scale analysis of environmental, economic and social issues. IAM is a challenging task as models from different disciplines have a different representation of data, space and time. The aim of this paper is to describe our strategy to conceptually, semantically and technically integrate a chain of models from different domains to assess land use changes. The models that were linked are based on different modeling techniques (e.g. optimization, simulation, estimation) and operate on different time and spatial scales. The conceptual integration to ensure consistent linkage of simulated processes and scales required modelers representing the different models to clarify the data exchanged and interlinking of modeling methodologies across scales. For semantic integration, ontologies provided a way to rigorously define conceptualizations that can be easily shared between various disciplines. Finally, for technical integration, OpenMI was used and supplemented with the information from ontologies. In our case, explicitly tackling the challenge of semantic, conceptual and technical integration of models forced researchers to clarify the assumptions of their model interfaces, which helped to document the model linkage and to efficiently run models together. The linked models can now easily be used for integrated assessments of policy changes, technological innovations and societal and biophysical changes.

Agriculture Scenarios Indicators Model linking Scaling a b s t r a c t Agriculture is interrelated with the socio-economic and natural environment and faces increasingly the problem of managing its multiple functions in a sustainable way. Growing emphasis is on adequate policies that can support both agriculture and sustainable development. Integrated Assessment and Modelling (IAM) can provide insight into the potential impacts of policy changes. An increasing number of Integrated Assessment (IA) models are being developed, but these are mainly monolithic and are targeted to answer specific 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-ENVSCI-705; No of Pages 16 Please cite this article in press as: Ewert, F. et al., A methodology for enhanced flexibility of integrated assessment in agriculture, Environ. Sci. Policy (2009),

Integrated Assessment Modelling provides a systematic inter-disciplinary approach to support coherent ex-ante decision-making by a flexible integration of (reusable) models and datasets across scales. Within integrated assessment modelling a coherent and robust description of projects and scenarios is required to facilitate data preparation, model integration and the graphical user-interface development.

SEAMLESS integrated project aims at developing an integrated framework that allows exante assessment of agricultural and environmental policies and technological innovations. The framework will have multi-scale capabilities ranging from field and farm to the EU25 and globe; it will be generic, modular and open and using state-of-the art software. The project is carried out by a consortium of 30 partners, led by Wageningen University (NL).

Integrated modelling is one of the key aspects of Integrated Policy Assessment Tools, which are used to find out whether, why and how policies are developed and what the possible options for these policies are. They do this by determining the economic, social and environmental impacts for each option. Where modelling used to be a domain-centric undertaking, to support integrated

Evaluation of policies requires targeted and systematic approaches to assess their economical, environmental and social impacts at a wide range of scales. Intcgrated Assessment and Modelling (IAM) has been proposed as a method to ex-ante assess indicators for such policy impact assessment. In IAM these indicators have to be linked to model outputs and to user-relevant concepts, like scale and indicator framework and dimension.

Integrated policy assessments are tools to find out whether, why and how policies are developed and what the possible options for these policies are. Performing an integrated assessment involves determining the economic, social and environmental impacts of these options. In model based integrated policy assessments, there is a need for linking models and data from different domains.

Software systems that support integrated policy assessment need to work with models from different domains and provide a framework to link these models together. They need to store model results, intermediate and raw data, and have one or more user interfaces for input and presentation. Such software can be built with an inside-out focus, emphasising the model linking, but also from an outside-in perspective. Here the features and technical development is primarily driven by usability and business requirements.

For decision makers in the domains of agriculture and environment, for instance in government agencies, farming, and environmental NGOs it is beneficial to evaluate ex-post or to asses ex-ante the impacts of their choices. Modelling and modelling tools can be helpful by providing a simplified representation of reality, simulating potential contrasting pathways into the future and improving the understanding of interdisciplinary cause-and-effect relationships. To research these interdisciplinary relationships, models developed by different scientific disciplines and often operating at different scales can be integrated into model chains that cover processes across disciplines. Integration allows the single disciplinary models to complement each other and thereby provide comprehensive and balanced assessments across scales and disciplines. In order to assemble models into an operational model chain multiple levels of integration have to be taken into account. First and foremost method...

Policy makers are confronted with complex, socially relevant problems. To increase the quality of their policy and realize a broader support for and understanding of actions, the policy making process has become a participatory process where policy measures need to be assessed in an integrated context (Rotmans and van Asselt ,1996).