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Climate outlooks and Agent Based Simulation of Adaptation in Africa (CLOUD)

Climate outlooks and Agent Based Simulation of Adaptation in Africa (CLOUD)

This is an archived project page.

Arguably the next grand challenge for climate research lies not with improvement of physical models, but in the improved assessment of the possible impacts of climate change on society. In order to make believable climate forecasts for the next 50-100 years we need accurate and robust dynamic models of the response of socio-economic systems to climate change, and the associated feedback into the climate system. With the recent rise in computing power, a new paradigm for modelling social systems is emerging:- agent based modelling is a technique arising from artificial intelligence, which attempts to give a direct representation to each person, or larger organization within the system being modelled, by allocating a set of behavioural rules for the conduct of each agent. Such a model forms a testbed for hypotheses about how climate change and its perception within society may affect both adaptation to change and the future climate, in much the same way that a global circulation model allows testing of hypotheses about the physics of the atmosphere and ocean.

Subsistence farmers are particularly vulnerable to fluctuations in climate, particularly rainfall. Seasonal, inter-annual and longer-term changes in availability of water all affect their ability to survive. One way in which they might improve their circumstances would be to have access to seasonal forecast information, so as to be able to anticipate the right crops to plant, both for food and for marketing. The village of Mangondi in Limpopo province, South Africa has a range of farmers whose livelihood comes both from subsistence farming of maize and the growing of crops for sale. Their market garden consists of 50 or so small plots (20 square metres) that have some (irregular) access to irrigation. A variety of vegetables such as butternut or cabbage may be grown, and sold either at the plots themselves, or at local markets. Their success therefore depends both on the availability of rain, particularly for growing maize, and the prices they can get at market, these being dependent on the climate also. Computer aided knowledge elicitation tools have been used during fieldwork in Mangondi to determine a set of strategies that the farmers can use, in the face of variable rainfall, to adapt their behaviour to the climate. An agent based model has been developed that captures these strategies and allows us to couple them to a crop model (CROPWAT) driven with rainfall and temperature derived from 140 year runs of the UK met. office couple climate model. The agents change their behaviour according to their memory of past climate, their interaction with other farmers, and their belief in a seasonal forecast. The projects studies how these factors influence the success of the farming community as the climate varies over annual, decadal and longer timescales, including the effect of changes in the accuracy of the seasonal forecasts.

The project specification can be found at:
www.geog.ox.ac.uk/research/projects/cloud/ .

Schematic Diagram showing the Main Components in the Model of Subsistence Farming
Schematic Diagram showing the Main Components in the Model of Subsistence Farming

Publications:

  • Bharwani, S., Bithell, M., Downing, T.E., New, M., Washington, R. and Ziervogel, G., "Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa" Phil Trans Roy. Soc., Accepted.
  • Ziervogel, G., Bithell, M., Washington, R. and Downing, T., 2005, "Agent-based social simulation: a method for assessing the impact of seasonal climate forecast applications among smallholder farmers", Agricultural systems 83, 1-26