Description
Farmers are generating more data than ever before, but turning that information into timely, practical agronomic decisions remains a challenge. AGRIS Co-operative is looking to bridge that gap by integrating artificial intelligence (AI) into its digital agriculture strategy.
In this RealAgriculture interview, Bernard Tobin catches up with AGRIS Co-operative senior agronomist and agronomy strategy manager Dale Cowan to discuss how the organization is connecting farm equipment, field data and agronomic services to help growers make better crop management decisions throughout the growing season.
Cowan says AGRIS has shifted its focus from precision agriculture to digital agriculture, recognizing that technology now extends well beyond soil sampling and variable-rate applications.
The foundation is an integrated field management system that connects individual fields with customer accounts, machine data and agronomic records. Through Growmark's myFS Agronomy platform, AGRIS can bring together planting information, yield maps, soil test results, satellite imagery, weather data and scouting reports.
Farmers choose what information they share, with equipment data flowing into the system through platforms such as John Deere Operations Center and Climate FieldView.
Sharing planter data, for example, provides planting dates and hybrid information that can be used to model crop development and predict growth stages. AGRIS agronomists can then schedule scouting and crop protection activities around critical growth stages rather than relying on the calendar alone.
AGRIS is exploring how AI can take that integrated information a step further. Cowan and student intern Daniel Glasier have been testing a large language model that allows agronomists to ask questions of the database using everyday language.
In one example, the team analyzed 479 corn scouting reports and found that tar spot was mentioned in 79 of them. Combined with disease risk maps and field observations, the information helped agronomists assess disease pressure and determine where additional scouting might be needed.
Cowan sees potential to ask more complex questions after harvest, including which hybrids performed best under different soil test conditions or landscape positions.
AI could also help agronomists identify fields requiring immediate attention, prioritize scouting activities and access technical information without searching through manuals or multiple databases, Cowan notes.
For growers, that means faster access to recommendations based on their own field history, crop development and current conditions. For AGRIS agronomy staff, it means managing a growing volume of information, and creating valuable insights for growers, without becoming overwhelmed.
Cowan acknowledges that the technology is still in its early stages and that training agronomists to work effectively with digital tools will be essential. In the longer term, he says making agronomic information more accessible and actionable will allow farmers and their advisors to identify problems earlier, evaluate management decisions and improve crop performance.
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