Food security research programmes, such as Global Environmental Change and Food Systems (GECAFS) and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), need to consider extremely complex systems, with many agricultural, environmental, social and economic subsystems interacting with each other on a variety of scales and at a variety of levels on each. This poses considerable challenges in terms of representing the current state of knowledge, exploring how these systems might evolve in the future in response to external drivers and human input, and displaying the behaviour of the many variables involved in a way which is meaningful for stakeholders and policy advisers.
This paper aims to explore how a modelling approach based on System Dynamics can be used to:
- Represent influences and other relationships between the main agricultural and food system drivers and their consequences (i.e. outcomes) for the three areas of interest to CCAFS: food security, environment and livelihoods;
- Quantify these influences and outcomes as far as possible over time; and if these cannot be simulated directly, show how outputs from other models and tools could be incorporated;
- Represent dynamically the quantified outcomes on spider diagrams for CCAFS regional scenarios.
- Indicate how policy and technical interventions can be ‘applied’ to the system so as to show impacts in terms of changes to the spider diagrams.
The approach will be demonstrated using Simile, a visual modelling software developed specifically to meet the needs of ecosystem modelling. Simile supports System Dynamics modelling, like a number of other modelling packages (e.g. Stella, Vensim and Powersim), but has a number of additional capabilities which make it particularly suitable, including the ability to model object-based and disaggregated systems. The heart of the paper is a consideration of how two forms of analysis used within food security.
The paper concludes with the proposition that an approach based on extended System Dynamics has the potential to represent complex systems interactions in a formal standardised way; and that such representations can also form the basis for computable qualitative or quantitative models. It therefore supports a methodology which allows informal conceptual thinking by stakeholders and domain experts to be transformed smoothly into quantitative predictive models.
Muetzelfeldt, R. Extended System Dynamics modelling of theimpacts of food system drivers on food security,livelihoods and the environment. (2010) 27 pp.