Desertification and land degradation: origins, processes and solutions

Table of Contents

1. Authors and acknowledgements
2. Introduction
   1. Aims
   2. Definitions and key concepts
   3. Synthesis of previous and ongoing projects
   4. Key issues and outline
3. Evolution of desertification in the Mediterranean
   1. Climatic fluctuation
   2. Human influence
4. Primary drivers of desertification
   1. Biophysical and socio-economic causes
   2. Pathways of desertification
   3. Driving forces in the Mediterranean
   4. Synthesis of causes for DESIRE hotspots
   5. Conclusions
5. Processes and consequences of desertification in the Mediterranean
   1. Socio-economic and political factors
   2. Biophysical processes
   3. Conclusions
6. Indicators: monitoring desertification
   1. History of monitoring
   2. Indicators
   3. Monitoring and mapping techniques
   4. Concluding remarks
7. Modelling desertification
   1. Definition of modelling
   2. Model types
   3. Biophysical modelling
   4. Socio-economic and participatory modelling
   5. Gaps and progression in modelling
8. Solutions
   1. Biophysical solutions
   2. Political and socio-economic solutions
9. References

Socio-economic and participatory modelling

Increasingly sophisticated models are being used to represent the kinds of highly complex environmental, economic and social systems found in drylands susceptible to desertification. Modelling has primarily been used by natural scientists as a means of capturing and predicting aspects of these systems, usually within disciplinary boundaries (e.g. hydrology, soil or atmospheric models). Economists also have a fairly long tradition of modelling components of socio-ecological systems, especially human-environment interactions (Bergh and Straaten, 1997, Clark, 1976).

For example, regional economic models (based on input-output analysis) can provide quantitative information about production and consumption in a dryland economy, for example quantifying economic outputs from agriculture and effects on water consumption, pollution or soil degradation. Such models can be used to analyse how different future scenarios (e.g. changes in lifestyles, growth or decline of certain economic sectors, social or economic policies, or changes in availability of natural resources) might affect land management within the production-consumption cycle (Duchin and Hubacek 2003; Duchin and Lange 1994).

More recently, sophisticated social models such as agent-based models (ABMs) have begun being used in environmental disciplines to describe and predict the way people ('social agents' or 'stakeholders') are likely to behave in response to different stimuli given various decision-rules (Gilbert and Troitzsch, 1999, Janssen, 2002). However, these models tend to treat the environment as a static system (Matthews, 2006). In order to better approximate feedbacks and more accurately represent the complexity of real-life systems, dynamic models can be integrated from different disciplines. In this way it is possible to predict how people may respond to environmental change, and how their responses in turn are likely to influence their environment. Accurately representing human behaviour in ABMs requires inputs from the people who live and interact with the systems (e.g. landscapes) one is trying to model. This involves deriving "rules of behaviour" from the actual experiences, opinions and perceptions of real-life social agents.

Researchers are increasingly taking inputs from stakeholders beyond the construction of social models, collaborating with them to build and integrate models in what is known as "mediated modelling" (van den Belt, 2004). This offers a number of advantages, as social agents are often intimately acquainted with a level of complexity and detail that is rarely represented in computational models. Participatory modelling has a relatively long history. Since 1969 a decision making process has been evolving to address the twin challenges of learning and management in complex systems. This process, known as "adaptive management", has been refined in a series of on-the-ground applications in problems of forestry, fisheries, national parks, and river systems (Holling, 1978, Walters, 1986, Gunderson and Holling, 1995, Gunderson and Holling, 2002, Walker et al., 2002, Sendzimir et al., 2007, Magnuszewski et al., 2005).