Linking Models for Assessing Agricultural Land Use Change
Abstract
The ex-ante assessment of the likely impacts of policy changes and technological innovations on agriculture can provide insight in 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 methodologically, 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 modelling techniques (e.g. optimization, simulation, estimation) and operate on different time and spatial scales. The methodological integration to ensure consistent linkage of simulated processes and scales required modellers 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, methodological and technical integration of models forced researchers to clarify the assumptions of their model interfaces, 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.
Download full text in pdf format
Published as:
S. Janssen,
I. N. Athanasiadis,
I. Bezlepkina,
R. Knapen,
H. Li,
I. P. Dominguez,
A. E. Rizzoli,
M. K. van Ittersum,
Linking Models for Assessing Agricultural Land Use Change,
Computers and Electronics in Agriculture, 76:148-160,
2011, doi:10.1016/j.compag.2010.10.011.
You might also enjoy (View all publications)
- Domain adaptation with transfer learning for pasture digital twins
- Causality and Explainability for Trustworthy Integrated Pest Management
- Breeding Programs Optimization with Reinforcement Learning