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A LOT OUT OF LITTLE: Achieving the sustainable development goals requires countries and regions to understand the diversity and dynamics of social and ecological needs in their territories.
However, data on this is not always available.
In a study accepted in Environmental Research Letters centre researchers Juan Rocha, Katja Malmborg, and Line Gordon together with colleagues, present a method for applying a social-ecological systems (SES) framework on larger scales and over longer time scales, in data scarce settings where solutions towards sustainable development are needed.
The SES framework has mainly been used in smaller local case studies. To target development interventions we need a tool that can be applied on larger scales and can facilitate decision-making in contexts where there isn’t much data, but an urgent need for action.
Juan Carlos Rocha, lead author
The authors apply their method to look at food security and poverty alleviation in the Volta River basin in Ghana and Burkina Faso. Focusing the analysis on food availability, they used crop production as a key determinant in the characterization.
They used publicly available data and national statistics, and found that they could identify a distinct set of social-ecological systems in the basin.
The different types of SES followed a north-south gradient, with the north being the most arid and the south a wetter climate. The SES types were strongly characterized by their crop productivity, but also by social variables such as urbanization, literacy, and migration.
Based on their results the authors were able to identify where high crop variability coincided with potential for water reservoirs to buffer variation.
Insights like these can inform policy decisions, and allow people to better manage their resources.
The authors conclude: “Identifying patterns of variables in space and time that characterize different SES is key for further developing theories of sustainability, testing when interventions work, and mapping how nations progress towards sustainable development goals.”
Identifying SES archetypes requires clustering, that is, classification of multiple elements by some measure of similarity. To test the optimal number of clusters, 30 different indexes were compared while testing the internal and stability validation of 9 different clustering techniques. To guide their choice of variables, the authors used Ostrom’ s SES framework.
They used publicly available datasets covering the second administrative level for Ghana and Burkina
Faso. From these, any data that in a meaningful way could be used as proxies of the Ostrom variables was matched.
The authors used crop production as the defining interaction in their SES characterization, and chose seven crops with minimum missing data using averages for the last seven years.
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