Doctoral Dissertation Improvement Award: Historical Ecology of a Marginal Landscape

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Archaeological information provides a record of the ways humans interacted with their environments over centuries or millennia. One common way people interact with the environment is through agriculture. Agricultural productivity is influenced by environmental factors like soil quality or rainfall, which can constrain what kinds or how many crops can be grown in certain locations. However, people also use strategies like irrigation or mulching to overcome these limits. Within this broader context, this doctoral dissertation research investigates how people grow crops in less-than-ideal landscapes and how different natural constraints and human decision-making influence agricultural productivity over time. This project will produce data on soil quality, soil development, and human landscape modifications in a marginal (dry) setting, providing valuable results for other research on ancient and modern agriculture. In addition, project data will be shared with community members in Hawai‘i who are interested in developing a sustainable plan to promote long-term community and landscape resilience.The researchers will examine this topic from the perspective of historical ecology. Historical ecology is a theoretical framework that helps archaeologists study human-environmental interactions complexly, taking into account the influence both of human decision-making and natural factors (rather than assuming that one tends to dominate the other). The study area for this project is an ancient agricultural field system in Hawaii. It consists of an extensive network of field borders and a complex system of water management features (irrigation ditches, check dams) which were necessary in order to make agriculture possible in this dry landscape. The researchers will map the agricultural field system through an archaeological and remote sensing survey, collect soil samples in and outside of cultivated areas, and date archaeological features. Using these data, the researchers will test several potential models for which natural factors, human factors, or combination of factors contributed most to the study area’s agricultural productivity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.