Automated Net Return Mapping: Using Inexpensive Technology for Maximizing Profit of Small-Scale Farms

Project Overview

Project Type: Graduate Student
Funds awarded in 2019: $14,806.00
Projected End Date: 02/28/2021
Grant Recipient: University of Maine
Region: Northeast
State: Maine
Graduate Student:
Faculty Advisor:
Dr. Eric Gallandt
University of Maine


Not commodity specific


  • Farm Business Management: agricultural finance

    Proposal abstract:

    Managing the agricultural inputs of a functional small farm is often not as complicated as maintaining economic viability. It is imperative for farmers to monitor their net returns, which are directly affected by the costs and efficiency of labor, fuel, and a host of other fluctuating expenses. In this study, four methods of real-time on-farm asset tracking will be tested in order to develop an automated and inexpensive system for monitoring the financial characteristics of a small organic farm, with a primary focus on monitoring the efficiency of labor. Data acquired through tracking the movements of farm workers with GPS units, the placement of radio frequency identification (RFID) tags throughout the farm, and utilizing existing open-source farm input monitoring software will be integrated into an accessible and useful format that farmers will be able to make data-driven decisions for improved farm management. This system will allow the farmer to be able to track where inefficiencies exist within the operation. Through the use of a spatial analysis in GIS, this measure of the farm’s efficiency will then be used to create a visual representation of the farm, denoting all of the factors contributing to its net return. Combined with other data sources such as agricultural inputs, equipment (i.e. plastic, remay, etc), and returns (i.e. yield, sales, etc), the total net returns of the farm will be quantified.

    Project objectives from proposal:

    1. Evaluate the viability of three experimental methods of farm operational data collection relative to a reference method.
    2. Compile and integrate on-farm asset tracking data into accessible and helpful visual representation to aid farmers in monitoring their net returns.
    3. Develop a clear and replicable system for on-farm asset monitoring that small organic farmers can implement using inexpensive and non-hindering technology.

    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.