A Framework for Evaluating New AgTech Products Before You Buy

The market is flooded with new agricultural technology products every season. Here is the structured evaluation process we use at Diaz Ag to separate genuine breakthroughs from expensive distractions.

The Problem with AgTech Marketing

Agricultural technology marketing has become increasingly sophisticated — and increasingly difficult to evaluate. Trial data is selectively presented, testimonials are curated, and the pressure to adopt the latest innovation can lead growers to make expensive commitments before the evidence is in.

At Diaz Ag, we have developed a structured evaluation framework that we apply to every new product or technology we consider for our clients. It's not complicated, but it is systematic — and it has saved our clients significant money and time.

Step 1: Mechanism Plausibility

Before looking at any trial data, we ask: is there a plausible biological, chemical, or physical mechanism by which this product could deliver the claimed benefit? If a product claims to increase yield by 30% through a mechanism that contradicts basic plant physiology, no amount of trial data will make it credible.

Step 2: Independent Trial Data

We look for trial data from sources independent of the manufacturer — university extension, third-party research organizations, or peer-reviewed publications. Manufacturer-funded trials are not worthless, but they should be weighted accordingly and examined for methodological rigor.

Key questions: What was the trial design? Were there adequate replications? Was the comparison to a realistic control? Were results consistent across locations and years?

Step 3: Relevance to Local Conditions

Trial data from Iowa corn fields has limited relevance to Arizona vegetable production. We specifically seek data from conditions similar to our clients' operations — similar soils, climate, crops, and management systems.

Step 4: On-Farm Trial

For products that pass the first three steps, we recommend a structured on-farm trial before broad adoption. This doesn't need to be elaborate — a replicated strip trial with proper documentation is sufficient. The goal is to generate data specific to your fields and management system.

Step 5: Economic Analysis

Finally, we run the numbers. Even a product that delivers a real agronomic benefit may not be economically justified at its price point. We calculate the break-even response rate and compare it to the realistic expected response based on trial data.

This framework won't catch every bad product or identify every good one — but it dramatically improves the odds of making sound technology adoption decisions.