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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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From the Session Room to the Field: Making Label Updates Stick All Season

October 20, 2026
Contributor Image

Jonathan Blatchford

Account Executive

From the Session Room to the Field: Making Label Updates Stick All Season 

By Jon Blatchford, Account Executive, Ever.Ag 

Last week I was in Anaheim for the CAPCA Annual Conference. I walked the expo floor and sat in on the Sponsored CE Sessions, the series many advisers still call the Label Update sessions.

If you haven’t been, the format is efficient: back-to-back, 10-minute technical presentations on label changes, new registrations, and how products fit into an IPM program. Sales pitches aren’t allowed. Many sessions qualify for California DPR continuing education in the “Laws” category, which licensed PCAs need as part of their 40-hour CE requirement. In one afternoon, an adviser can catch up on a season’s worth of changes. 

Sitting in those sessions, one question kept coming back to me: what happens to all of this on Monday morning? 

The gap between knowing and doing

A label change only matters if it shows up in the next recommendation an adviser writes. That’s usually where things break down. 

Consider the path a single label update has to travel inside a typical ag retail operation: 

  • A PCA hears about a revised rate, a newly registered crop, a changed pre-harvest interval or a new restriction. 
  • That adviser, and every other adviser on the team, has to apply it across dozens or hundreds of written recommendations for different growers, crops and fields. 
  • Each recommendation becomes a work order for an applicator, who needs the right product, rate and field instructions. 
  • Inventory, invoicing and regulatory records all need to reflect what was recommended and what was applied. 

In many operations, each step lives somewhere different: notes from the conference, a recommendation template on someone’s laptop, a dispatch whiteboard, a spreadsheet, an accounting system. Every handoff is another chance for an outdated rate or old restriction to slip through. 

In California, agricultural pest control recommendations must be in writing, and the adviser is accountable for them. A mistake creates a compliance problem, and it also costs grower trust. 

Label knowledge is a team asset, not an individual one 

The ag retailers who handle this best treat label updates like any other critical operational change. They don’t rely on memory. They build the change into the system everyone uses. 

That is the shift from “our advisers know the labels” to “our workflow reflects the labels.” Four principles stand out:

1. One source of truth for recommendations. When every adviser writes recommendations on the same platform, an update made once is available to everyone. Outdated templates stop circulating on individual laptops.

2. Recommendations that flow straight to the field. The fewer times a rate or instruction is retyped, the fewer chances it has to be wrong. A recommendation should become a work order without anyone re-entering it.

3. Records you can search, not just store. When a grower, an auditor or a county ag commissioner asks a question, you should be able to find who recommended what, where and when, and what was applied, in minutes rather than days.

4. Field and office working from the same data. What the adviser recommends, what the applicator applies, what leaves inventory and what the grower is billed should all match automatically.

How FieldAlytics helps close the gap 

FieldAlytics was built around this kind of workflow. Recommendations, dispatch and grower engagement are connected in one system instead of stitched together after the fact. 

Recommendations and records in one place. Advisers build recommendations in FieldAlytics using field history, soil data, imagery and scouting observations. Records are stored in one database, which makes state-by-state requirements easier to manage and complete field histories easier to pull. FieldAlytics also supports written recommendations that meet California PCA requirements. Product and label information, including rates, registered crops and pre-harvest intervals, is available as advisers write recs. Built-in checks flag a recommendation that goes beyond label limits before it leaves the adviser’s hands.  

Scouting that feeds the recommendation. With the mobile app, advisers can scout, take photos and check data layers from the truck. The observations that justify a recommendation stay attached to it.  

From recommendation to work order without retyping. FieldAlytics turns recommendations into work orders and dispatches them to applicators. Applicators get turn-by-turn directions, job details and grower contact information on their phone or tablet. More than 260,000 work orders run through the platform each year without spreadsheets or double entry.  

Growers see what you see. In the Engage app, growers can view their recommendations, field data and records directly. That transparency builds trust, especially when a label change affects what they can use and when. 

Where Merchant Ag fits 

For retailers that run Merchant Ag as their ERP, the integration with FieldAlytics carries the same consistency into the back office. Agronomic work orders, recommendations and field data connect directly to inventory, invoicing and grower statements. Merchant Ag also builds compliance workflows, including DOT paperwork, hazmat, regulatory reporting and state-specific reporting, into daily operations instead of side spreadsheets. California pesticide use reporting is supported as well.  

The result: what was recommended in the field, what was applied, what left the warehouse and what was billed all match. 

Questions worth asking your team after CAPCA 

If you or your advisers attended this year’s sessions, bring these questions back to the office: 

How quickly does a label change reach every recommendation template we use? Hours, days, or whenever someone remembers? 

How many times is a recommendation retyped between the adviser and the applicator? 

If we were audited tomorrow, how long would it take to pull every recommendation and application record for a given product on a given ranch? 

Do our growers see the same information our advisers do? If the answers make you uneasy, you’re not alone, and the problem can be fixed. 

The bottom line 

CAPCA’s label sessions do an excellent job of putting the right information in front of the right people. For ag retailers, the opportunity is making sure that knowledge leaves the session room with the advisers. When recommendations, field operations and business records live in one connected system, every label update learned in October can show up correctly in every recommendation written next season. 

Contributor Image

Jonathan Blatchford

Account Executive