2022
Piloting a Data Science Extension Program to Advance Data-Driven Soybean Production
Contributor/Checkoff:
Category:
Sustainable Production
Keywords:
Extension
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
Jason Ward, North Carolina State University
Co-Principal Investigators:
Project Code:
2220-172-0123
Contributing Organization (Checkoff):
Institution Funded:
Brief Project Summary:
An Extension data science education program is proposed, that will assess the needs of soybean farmers and develop resources that will enable growers to access, use, and benefit from agricultural data.
Information And Results
Project Summary

Project Objectives

Project Deliverables

Progress Of Work

Final Project Results

The goal of the Data Science Extension program focused on equipping producers and Extension agents with hands-on training from agricultural data sources so that they could leverage the information available to them in making soybean production and management decisions. This goal was met by conducting a survey of soybean growers on their needs in handling and analyzing different kinds of on-farm data and by developing training materials and hands-on exercises on the topics of using satellite or UAV imagery to describe trends in field conditions, processing yield data, and collecting weather data from state or federal climate monitoring stations. Just under 50 completed surveys were received representing 10 states and these were analyzed by regions: Midwest, Northeast, and Southeast. The majority of responders in the Midwest and Southeast farmed 1000+ acres, and in the Northeast farm size was split between 250 and 1000 acres. The most important barriers to using data were limited time, finding the data, and knowing how to apply the data. Many respondents used specialists and consultants to assist them. A strong majority were interested in data science training and extension programs delivered by in-person hands-on events and informational webinars. The preferred topics were data interpretation, weather data, and satellite imagery analysis from free sources – which indicated that our planned topics aligned with what farmers were interested in learning. Multiple types of content were developed to help address the training gaps identified from the survey results. These are designed to be delivered in person through hands-on workshops or through self-paced on-line training courses. Three modules related to satellite data collection and analysis, and two modules each on yield data and weather data were developed. In partnership with the NC State Data Science Academy pilot courses have been scheduled for delivery to a select pool of NS State Extension Agents, pending AMS content approval, in the first quarter of 2024. Once vetted these training modules will be available for broad distribution for train-the trainer experiences or direct producer learning.

Benefit To Soybean Farmers

The content developed in this investment will eventually be publicly available. Teaching modules for data science for farmers, focused on how farmers can leverage public data sources, may be adopted by other extension programs.

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.