Doctoral Dissertation Improvement Award: Sustainable Agricultural Practices

该项目的目标是审查过去农业系统的可持续性以及这些活动对当地环境的长期影响。随着人口急剧增加，社会努力增加粮食产量，仅仅扩大现有的农业系统将不足以满足需求。有必要研究旨在实现长期持久性和复原力的替代办法。这不仅包括技术创新，还包括导致农业系统持续使用的社会机制，从而实现可持续性。很少有农业研究有必要测试的可持续性假设的时间深度，但这是必不可少的负责任的管理农业景观。考古学非常适合通过研究一个系统的整个生命周期来解决这些问题，并能够研究农业成功和失败的具体实例。过去的人类找到了耕种许多不同景观的方法，随着干旱化和土壤质量下降，可用肥沃土地的数量减少，这一知识将变得越来越重要。本项目将调查围绕非传统农业系统的建设和培育的环境和社会条件。重点将放在这些方法的可行性，通过审查具体的当地条件，有助于过去的农业成功或失败的现代执行。一个长期的史前系统将被研究。通过整合土壤分析，地下地球物理学和遥感，这个数据集将允许这些古老的农业系统的多标量分析。这些数据将用于确定是什么促使了高地的建立，它们是在当地管理还是通过国家级组织管理，以及这些景观改造如何影响长期的土壤特性。这项研究的目的是区分代表古代人类活动的土壤条件与现代过程，这是考古研究所需的区别。这里使用的多传感器地球物理方法的集成有可能推进遥感方法，并提出了一种新的方法，低成本的热成像农业应用。该数据集将有助于了解干旱沿海环境中的粮食安全，并有助于了解未来如何在类似环境中有效耕作。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to examine the sustainability of past agricultural systems and the long-term impacts of these activities on the local environment. As population drastically increases and societies struggle to increase food production in turn, simply expanding existing agriculture systems will not be enough to meet demands. It is necessary to investigate alternative options that are designed for long-term persistence and resilience. This includes not only technological innovation, but the social mechanisms that cause the continued use of agricultural systems, leading to sustainability. Few agricultural studies have the time-depth necessary to test assumptions of sustainability, yet this is essential to the responsible management of agricultural landscapes. Archaeology is uniquely well-suited to addressing these questions through study of the entire lifespan of a system, and the ability to look at specific instances of agricultural success and failure. Past humans found ways to cultivate many diverse landscapes, and this knowledge will be increasingly vital as aridification and waning soil quality decrease the amount of available fertile land. This project will investigate the environmental and social conditions surrounding the construction and cultivation of non-traditional agricultural systems. Focus will be placed on the feasibility of these approaches for modern implementation by examining specific local conditions that contributed to past agricultural success or failure. One long term prehistoric system will be studied. Through the integration of soil analyses, subsurface geophysics, and remote sensing, this dataset will allow for multiscalar analyses of these ancient agricultural systems. This data will be used to determine what motivated the creation of raised fields, whether they were managed locally or through state-level organization, and how these landscape modifications affected long-term soil properties. This research is designed to differentiate soil conditions that represent ancient human activity from recent processes, a distinction needed in archaeological studies. The integration of multi-sensor geophysical methods used here has the potential to advance remote sensing approaches, and a new method of low-cost thermal imagery for agricultural applications is proposed. This dataset will contribute to knowledge of food security in arid coastal environments and help to understand how similar environments can be effectively cultivated in the future.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.