Keywords
community modeling, agricultural systems, database, european union
Start Date
1-7-2008 12:00 AM
Abstract
Integrated Assessment and Modelling (IAM) can be used to assess socioeconomic and environmental indicators, which generally require the linkage of models from different domains. To integrate a set of different models for an IAM, the data required by each of the models as inputs from a range of data sources also needs to be consistently integrated. This paper describes the process of development of a database integrating different data sources for an IAM project, and the human factors involved in the process of reaching consensus across peers with clashing requirements and needs. We adopted a structured process using a shared ontology as a means to one integrated relational database serving a set of models of a highly multi-disciplinary nature. The relational database covers data on agricultural systems, e.g. soil, climate, farm, agricultural management and agricultural policy data. The integrated database has been coupled to a range of quantitative models. The database schema and the shared ontology are distinct products that can be reused for or extended by other IAM projects requiring a similar set of data. It is recommended for any IAM project in which several models are coupled to adopt an explicit, collaborative and iterative process to specify an adequate data structure for storing data used in the project. For such a process to succeed it has to focus on the relevant domain knowledge captured across the data sources and this paper offers a proposal for such a process.
An European database for integrated assessment and modeling of agricultural systems
Integrated Assessment and Modelling (IAM) can be used to assess socioeconomic and environmental indicators, which generally require the linkage of models from different domains. To integrate a set of different models for an IAM, the data required by each of the models as inputs from a range of data sources also needs to be consistently integrated. This paper describes the process of development of a database integrating different data sources for an IAM project, and the human factors involved in the process of reaching consensus across peers with clashing requirements and needs. We adopted a structured process using a shared ontology as a means to one integrated relational database serving a set of models of a highly multi-disciplinary nature. The relational database covers data on agricultural systems, e.g. soil, climate, farm, agricultural management and agricultural policy data. The integrated database has been coupled to a range of quantitative models. The database schema and the shared ontology are distinct products that can be reused for or extended by other IAM projects requiring a similar set of data. It is recommended for any IAM project in which several models are coupled to adopt an explicit, collaborative and iterative process to specify an adequate data structure for storing data used in the project. For such a process to succeed it has to focus on the relevant domain knowledge captured across the data sources and this paper offers a proposal for such a process.
