Equipping Urban Farms to Harvest Rainwater
At a hands-on training hosted on June 24 at UMD’s Terp Farm in Upper Marlboro, people came together to explore how to safely harvest, treat, and use rainwater in urban agriculture, protecting both crops and consumers.
The project, headed by Rachel Rosenberg Goldstein, Assistant Professor at the UMD School of Public Health, explores how to integrate rainwater harvesting into urban agriculture safely. Rachel and the project’s research partners gathered people in the farming, nonprofit and academic community for a training session on incorporating urban rainwater harvesting and water treatment on their farms.
Urban agriculture is expanding across Maryland, but climate change makes rainfall less predictable. Farmers are facing longer stretches of drought punctuated by heavy downpours, making water access both limited and costly, especially in cities.
This project partly seeks to answer how small urban farms can create their own rainwater collection systems so excess rainfall can be stored and used in times of drought. Rainwater collection can result in possible biohazards – water flowing from roofs can bring contaminants like bird droppings, debris and other unwanted substances into the collected water.
Concerns arise regarding the quality of the collected water when left untreated and used to water edible plants like vegetables and herbs and whether bacteria within untreated water could lead to foodborne illnesses. To offset this potential threat, Goldstein and her team are actively testing the success of different treatment and filtering methods versus using untreated water.
The training participants got a hands-on view of the existing rainwater collection systems and irrigation systems on the Terp Farm that were used in the project, as well as information on what was used in their water treatment and filtering processes. They also got to practice installing drip irrigation and watch a demonstration of a collection system from a rooftop.
Trainings like these help because “farmers like to build their own stuff and there are only so many contractors that work with small urban farms,” said Neith Little, a UMD Extension Agent specializing in urban agriculture who is partnering in the research on this project. This event marked a step in equipping Maryland’s urban farmers with practical tools and science-backed knowledge to manage and use rainwater sustainably.
Partners on this project include the University of Maryland School of Public Health, UMD Center for Food Safety and Security Systems, UMD Environmental Finance Center, UMD Extension, the Maryland Agricultural Experiment Station Upper Marlboro Facility, and Plantation Park Heights Urban Farm. The Hughes Center partially funds this study.
Can Machine Learning Help Grow Baby Oysters?
Join us Thursday, June 26, at noon for a webinar that explores how cutting-edge machine learning could boost the survival and success of baby oysters, known as spat, at hatcheries.
University of Maryland Center for Environmental Science Associate Research Professor Vyacheslav Lyubchich will review his study's initial findings that use advanced machine learning to investigate conditions that lead to hatchery inefficiencies and strategies to mitigate the impacts on production.
Oyster hatcheries are facilities where adult oysters are spawned, and spat (baby oysters) are grown and later sold for use in the aquaculture industry, fisheries augmentation, or restoration efforts. Despite highly skilled and experienced staff running hatcheries, there are periods of poor larval growth and uneven production levels, also termed “crashes.”
In most cases, the causes of crashes and their potential remedies are unidentified. These crashes disrupt oyster aquaculture production and the supply chain, from growers to consumers to conservation efforts. This project uses machine learning to identify key reasons why these crashes are occurring.
This research is funded by the Hughes Center for Agro-Ecology.
About The Presenter: Dr. Vyacheslav Lyubchich, Associate Research Professor of Statistics at the University of Maryland Center for Environmental Science, specializes in environmental statistics and time series analysis. He received his Ph.D. from Orenburg State University (2011) and held a Canadian postdoctoral fellowship at the University of Waterloo. Since 2015, he has been a research faculty member and a founding member of the Environmental Statistical Collaborative at UMCES, contributing expertise in data-driven environmental research.