Scientific Approach

Icône de l'outil pédagogique Overall scientific approach

SEAMLESS-IF is a generic and flexible framework which is achieved through a modular set-up with stand-alone knowledge components linked and integrated through an advanced software infrastructure. The three main outputs of the project, i.e. the so-called triple I of SEAMLESS (Figure 1) are:

  1. Integrated framework for impact assessment;
  2. Infrastructure of software enabling model linkage;
  3. Individual stand-alone knowledge components (models, data and indicators). This includes existing and newly developed models simulating for instance crop growth, farm behaviour and agricultural markets.




Figure 1. Triple I concept of SEAMLESS with the three main outputs.


Development of SEAMLESS-IF via individual components linked through software infrastructure has clear advantages in terms of maintenance, extensibility, transparency and documentation. The three Is each have value for different types of users, such as the European Commission, international and national policy making agencies and the scientific community.

Icône de l'outil pédagogique Specific scientific approaches

1. Integrated framework for impact assessment:

SEAMLESS-IF is a framework that allows integrated assessments of agricultural systems at multiple scales (from field, farm, region to EU and global) through linking standalone components and provides analytical capabilities for environmental, economic, social and institutional aspects of agricultural systems (Figure 2). SEAMLESS-IF has been developed as a component-based system and is aimed to facilitate synthesis of scientific knowledge in the domain of agriculture and its environment beyond the specific setting of the project.


Figure 2. Integrated assessment procedure using SEAMLESS-IF, with pre-modelling, modelling and post-modelling phase.
2. Individual components (for more detail see module 5.1-5.10)
These consist of an extensive database, indicator systems and a large number of models (Figure 3). Key models in SEAMLESS-IF are APES, FSSIM, EXPAMOD and CAPRI. The models simulate different aspects of the system at different levels of organization and scale (from field with APES to EU with CAPRI).
  • APES (Agricultural Production and Externalities Simulator) is a modular simulation model for calculating agricultural production and the externalities.
  • FSSIM (Farm System Simulator) is a farm model for quantifying the integrated agricultural, environmental and socio-economic aspects of farming systems, partly using the output from APES.
  • EXPAMOD (Extrapolation Model) is used for up-scaling the outcomes from FSSIM to the European scale.
  • CAPRI (Common Agricultural Policy Regional Impact Analysis), an existing model but adapted to SEAMLESS-IF, is a comparative static equilibrium model providing information on price-supply relationships, solved by iterating supply (from EXPAMOD) and market modules, and applied to the agricultural sector of the European Union.
  • Other models that simulate landscape change and its visualization, economic change in developing countries, and change in employment in EU rural areas, and do Institutional Compatibility Assessments of policies (i.e. PICA) and Global Trade Analyses (i.e. GTAP model), are also linked to SEAMLESS-IF.




Figure 3. An overview of all quantitative models for integrated assessment in SEAMLESS. The vertical (grey) chain is the so-called backbone model chain. Source: Van Ittersum et al., 2008.


3. Infrastructure software
SeamFrame, the software architecture for SEAMLESS-IF, consists of the following components: modelling environment, project manager, processing environment and the domain manager. SeamFrame allows the linkage of standalone models and data bases such that they can be used in integrated assessments, and the end-user applications (e.g. graphical user interface, tool for delivering output). SeamFrame uses an ontology to structure domain knowledge and semantic meta-information about components of SEAMLESS-IF in order to facilitate retrieval and linkage of knowledge in the components (i.e. models, indicators and databases).

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