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Model Comparison Guide
Amazon Nova Pro
Current default
$0.80 / $3.20 per 1M tokens

Amazon's mid-tier model. Good balance of quality and cost for RAG chatbots. Strong at following structured prompts and formatting rules.

Amazon Nova Lite
Lower cost
$0.06 / $0.24 per 1M tokens

Faster and cheaper than Nova Pro. Good for straightforward Q&A. Test whether the quality drop is noticeable for your visitors.

Amazon Nova Micro
Lowest cost
$0.035 / $0.14 per 1M tokens

Text-only, lowest latency in the Nova family. Best for simple factual lookups. May struggle with complex formatting instructions.

Claude Sonnet 4
Highest quality
$3.00 / $15.00 per 1M tokens

Anthropic's latest flagship. Excellent at following nuanced system prompts, inline linking, and natural conversational tone. Most expensive option but often the best output quality.

Claude Haiku 4.5
Fast & affordable
$1.00 / $5.00 per 1M tokens

Anthropic's fast, affordable model. Surprisingly capable for its price. Great candidate if Sonnet quality isn't needed for every query — compare to see the tradeoff.

Llama 3.3 70B
Open weights
$0.72 / $0.72 per 1M tokens

Meta's latest open-weight model on Bedrock. Strong general-purpose performance with competitive quality. Worth testing for cost predictability.

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Thinking in Ecosystems: How Central Valley Ag Is Digitally Rewiring the Cooperative Model

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Ever.Ag

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Ever.Ag

December 8, 2025

For Doug Clingman, Chief Technology Officer at Central Valley Ag Cooperative (CVA), technology in agriculture isn’t about apps or gadgets—it’s about ecosystems. “When we think of it as an ecosystem, that’s when the magic happens,” says Clingman. “That’s when we see the full picture of a grower’s operation—and the value we’re able to add.”

Serving row crop and livestock producers across Nebraska, Iowa, and Kansas, CVA operates across agronomy, grain, energy, and feed. That breadth means one-size-fits-all solutions won’t cut it. Clingman and his team are focused on connecting the dots—bringing together data from scouting, nutrition, prescriptions, and logistics to form a looped, decision-ready infrastructure that doesn’t just collect data, but turns it into action.

That mindset shows up in CVA’s drone spraying services, launched not for trendiness, but to solve real-world problems: tight labor, precise timing, and field-level accuracy. “It’s about treating the right acre at the right time—and doing it safer and smarter,” Clingman explains. Drone services are just one layer of a broader automation strategy that links field data to flight plans, analytics, and operational decisions.

Still, adoption varies. “Agriculture has always relied on tried-and-true methods,” says Clingman. “You don’t get many chances to win, so experimenting can feel risky.” To bridge that gap, CVA invests heavily in education—meeting early adopters where they are, highlighting wins, being transparent about setbacks, and continuously refining how they support growers. “Every day is education. It’s not just about selling tools—it’s about building trust.”

drone flying -thinking ecosystems

Automation, AI, and the Future of Decision-Making

Clingman is clear-eyed about the risks ahead. “Big data doesn’t mean much if you can’t make a decision from it,” he says. “We don’t want to be data-rich and decision-poor.” The most effective strategies, he believes, begin with questions—not spreadsheets. By focusing first on the problems growers need to solve, CVA can work backward toward the data that matters.

Looking ahead, logistics and automation are where Clingman sees transformational opportunity—particularly in rural America. With labor shortages intensifying, he believes the agriculture supply chain will need to take a page from Amazon’s playbook. “How do those packages get to my house so efficiently? That same thinking should apply to seed, grain, and input delivery.”

Of course, no conversation about ag tech is complete without touching on artificial intelligence. Clingman’s take? AI isn’t the end—it’s the means. “AI won’t replace people. But people who use AI will replace those who don’t,” he says. From automating manual tasks to powering better recommendations, Clingman sees AI as a productivity partner—one that should eventually be in every producer’s pocket.

But to get there, the cooperative has to lead by example. “We have to use these tools ourselves,” he says. “But it’s only when the grower uses them, too, that we unlock the real value.”

For Clingman, Ag Smarter is about stewardship and staying grounded in CVA’s mission. “We don’t own the technology—we’re just custodians of it,” he says. That means building systems that are quiet but powerful. Helpful, not overwhelming. And above all, grounded in lifelong learning. “We can’t forget how we got here. And we’ve got to keep growing.”

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Ever.Ag