The cumulative effects of local land-use and livelihood changes are a global force of environmental and socio-economic change. Land-use changes result from decisions of individual farmers, pastoralists, and housing consumers and developers (to name a few). Their decisions are influenced by not only local environmental, social, and economic conditions, but also by far-reaching forces such as economic globalization. The choice of a farmer in Brazil to grow soybeans, for example, can be influenced by the consumption of people in China.
Not all land-uses are created equal. Some have minimum impact on the environment, and some offer sustainable livelihoods for local farmers – finding land uses that accomplish both is difficult. Crafting policies to achieve this two-part goal must contend with both local and global considerations.
On January 29th, my colleagues and I published a paper in PLoS ONE, titled “Cross-site comparison of land-use decision-making and its consequences across land systems with a generalized agent-based model” that describes the development and application of an agent-based virtual laboratory for comparing land-use and livelihood decision-making processes of rural farmers across geographically distant locations and qualitatively different land-use systems. We use this modeling system across multiple study sites to understand the underlying motivations and rationale of land-use and livelihood decisions of our ‘farmer agents’ and the landscape and livelihood changes that result under various environmental, demographic, and economic scenarios.
Since the traditional mode of scientific experimentation is not feasible with real land-use systems – we are talking about people’s land and livelihoods here – we use simulation-based cross-site comparisons to teach us about what drives the choice of particular land uses and livelihood strategies under different conditions. We use the set of study sites as local examples to synthesis more broadly applicable knowledge of which factors are most important in what contexts.
To explore this question, our investigation had to happen at the decision-making level – a task to which agent-based models are well suited. We also needed a modeling framework that was sufficiently general that it could be applied across multiple locations, yet realistic enough that it could be grounded in real-world data. These needs gave rise to an innovative agent-based virtual laboratory approach that provides a powerful tool for model-based experimentation and synthesis.
Such a model synthesis system can generate the kind of high-level knowledge needed to inform regional policies designed to foster sustainable local land uses and livelihood strategies. Cross-site comparisons use each study site as an example of alternative conditions and/or potential future states, which can aid scenario analysis and the exploration of potential adaptive responses to changing conditions. Furthermore, insights gained from the application of the modeling system to one site can improve our understanding of other similar sites, and foster future research and policy efforts that are sensitive to both the global influence on and local realities of land-use and livelihood change.
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