"Yaghi describes AI not as a silver bullet, but as an advanced form of statistical pattern recognition-tools that can identify trends in data that may be difficult or time-consuming for people to uncover on their own. The real opportunity, he says, depends heavily on what farms are already doing. Operations that are consistently collecting and digitizing high-quality data are better positioned to benefit, whether the goal is lowering per-cow costs in a dairy, improving financial analysis, or identifying operational efficiencies."
"A major focus of the discussion centres on large language models, including FCC's ag-specific model, Root, created in conjunction with RDAR. These tools are designed to provide faster access to trusted agricultural information-from dense crop guides to the hundreds of grants available to producers. While they are not intended to replace agronomists or financial advisors, Yaghi sees them as a first layer of support that can free up time and improve conversations with experts."
AI operates as advanced statistical pattern recognition that uncovers data trends that are hard or time-consuming for people to find. Farms that consistently collect and digitize high-quality data are better positioned to lower costs, improve financial analysis, and identify operational efficiencies. Large language models can deliver faster access to trusted agricultural information, including crop guides and grant listings. These models function as a first layer of support to free up expert time and enhance advisor conversations rather than replace agronomists or financial advisors. Adoption varies, with many using AI for administrative tasks and fewer for high-stakes decisions. Careful experimentation, validation with experienced advisors, interoperability, and clear data ownership are essential.
Read at Realagriculture
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