Unearthing Urban Agriculture

PROJECT PROFILE: Satellites, Drones, and Urban Agriculture
Unearthing the Urban Agriculture–Environment Connection: Implications for UA Planning, Practice, and Policy

About

Objective: This project aims to employ Earth Science data and a mixed-methods approach, encompassing earth science, machine learning, and social science methodologies, to investigate the relationship between urban agriculture (UA) and built, natural, and social environments. The study will focus on two types of organized UA practices—urban farms and community gardens, excluding backyard gardens, indoor or rooftop farms, or other green spaces or parks that may produce food.

Justification: Urban farming, with its multifaceted nature, holds potential as a viable approach to promote sustainable and resilient cities and food systems. Although urban farms' yields vary across contexts, efficiently managed farms have demonstrated the potential to match or exceed conventional yields, highlighting their significant impact and effectiveness. Despite many UA benefits, a lack of comprehensive data on UA practices, crop types, and crop yields impedes efforts to quantify the environmental impacts of urban farming and hinders the development of policies for sustainable food production in urban areas.

Methodology and Significance: This research will harness high-resolution NASA Earth Science data and develop machine learning algorithms to construct three discrete UA models related to the built, natural, and social environments. The models will identify historic and existing UA projects, identify major UA crop types, and calculate UA crop yield. We will employ those models in six cities within New Jersey. Based on the model outputs, we will address additional social science questions. This research holds significance as it expands modeling efforts beyond the typical rural agricultural settings. By developing models in six distinct cities, this study aims to advance methodological innovation and enhance theoretical understanding of the research problems. Furthermore, the investigation of follow-up social science questions based on the outputs of the modeling process will yield valuable insights into UA research. These insights include a comprehensive understanding of diverse UA practices and trends across different city types, the identification of UA project dynamics within specific neighborhoods and cities, the exploration of disparities in UA practices between environmental justice and other communities, an analysis of the impact of natural and cultural factors on crop choices, an assessment of UA's potential to meet local food needs and generate economic benefits through sales, and an exploration of policies to optimize UA for improving food access and enhancing economic opportunities.

The team was awarded a $300,000 grant from the NASA Research Initiation Award (RIA) program, 2024-2026.