Developing Precision Oyster Farming Methods Using Environmental Data

Project Overview

Project Type: Farmer
Funds awarded in 2023: $21,268.00
Projected End Date: 12/31/2024
Grant Recipient: Ferda Farms LLC
Region: Northeast
State: Maine
Project Leader:
Max Burtis
Ferda Farms LLC


  • Animals: shellfish


  • Animal Production: aquaculture

    Proposal summary:

    Many oyster farmers struggle with farm management. It is not easy to predict oyster sales, schedule sorting, or protect oysters from mortality events without environmental data. Oyster growth rate is a function of temperature, food availability, and nutrient exchange. However, these parameters are not constant nor do they change linearly with time. Most farmers don’t have any way to monitor the environmental data on their farms, and thus have no way to accurately predict how their farm will respond in a dynamic environment. This project aims to predict oyster growth rate, mortality risk, and inform farm management decisions using environmental data by creating a tool that can be easily implemented by farmers. We will measure environmental, growth rate, and mortality data, build models and then use these models to create a tool for farmers to implement. We will then share this tool with our fellow oyster farmers and the broader aquaculture community. 

    Project objectives from proposal:

    This project seeks to…

    1. Collect environmental data using monitoring buoys at three growout sites on the New Meadows River. We anticipate an uninterrupted data set for each location with high temporal resolution throughout the growing season. 
    2. Collect oyster weight and mortality data from bags located adjacent to the data buoys. We anticipate collecting this data every two weeks at each site. Additionally, market oysters will be sampled once to calculate market volume and weight. 
    3. Model of oyster growth rate and mortality. We anticipate the results to be a multiple regression model with the response variable being oyster growth rate and the input parameters being temperature, dissolved oxygen, salinity, and current speed. 
    4. Use this model to make farming recommendations. We anticipate these recommendations to address density splitting, sales planning, and methods to limit mortality risk. 
    5. Package these results into a generalizable protocol. The anticipated result of this step will be both open source code and a web application that takes in a location of interest, environmental data from the closest data buoys then outputs predicted growth rates, and farm management recommendations such as a sorting or harvest schedule.
    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.