How to Build Robust, Credible Data for an Agricultural Innovation

Turning a great concept into a successful product in the marketplace requires a lot of work. For agriculture products, this development process is based on the generation of solid, credible data. That is true for all stages, from proof of concept going through product formulation and registration, all the way to product positioning. But how do you build robust and credible data for an agricultural innovation?

What is Credible Product Data?

Credibility of your data is the first criteria for successful product development.

“To produce credible data, you must know what questions you’re seeking to answer,” says Gloverson Moro, Ph.D., AgriThority® Chief Technology Officer. Moro says there are 5 main questions to be answered depending on the stage of the development process:

  1. Does the concept work?
  2. Can it be registered?
  3. Can it be scaled up?
  4. How is it used?
  5. How resilient is it?

Questions 1, 4 and 5 are directly related to the performance of the product. “The experiments should be designed to answer those questions,” Moro says. “Focus is key. Replicated trials following solid protocols that are designed to answer specific questions and implemented in sufficient numbers will produce credible data.”

With past credibility and effectiveness challenges for biologicals especially, it becomes even more necessary to generate enough credible data to show consistent and efficacious results. According to the CropLife Biologicals Surveys, there has been, and continues to be, a lack of trust especially in biostimulants for product performance. In the 2024 survey, 41% of respondents still say trust in product performance is a barrier to adoption.

What is Robust Agriculture Product Data?

In addition to credible, the data should also be robust. Well-designed experiments should be conducted in enough numbers to generate trust. The number of trials depends on the phase of development.

For proof-of-concept (Question 1) the number of trials can be just one of very few but conducted under very controlled conditions aimed at maximizing the effects. Trials to define rates and modes of application (Question 4) are not many but usually have many treatments and are conducted in a subsample of the final market.

Finally, in the final stage, that of resilience, the goal is to test the limits of product performance. Typically, this phase has few treatments but many locations and ideally seasons/years.

“Don’t skip over trials to determine resiliency of a product,” Moro says. “You need to challenge your product to know it works in most conditions. You want growers to have a positive experience with your product, so you need to take steps to ensure that will happen before going to growers. As important as knowing where a product does work, it is important to know in what conditions it does not work.”

Don’t Overlook Best Management Practices for New Ag Innovations

Credible and robust data must then be translated into meaningful Best Management Practices. You must carefully analyze all your body of data and mine it for insights, and then use those insights to develop Best Management Practices (BMPs) as well as position the product into the market. With the right amount of robust and credible data, you can determine the BMPs with a high degree of confidence. Those BMPs can help ensure retailers and growers use your product in a consistent way that would elicit the best results and increase the potential for repeat business.

There is a lot of data out there, but not a lot can claim the necessary credibility and robustness. To stand out in the sea of data, that is a requirement for your agriculture innovation. That data will be the foundation for product positioning and BMPs to help ensure commercial success.

Solid Best Management Practices can drive product performance, allow better grower experience, and build the necessary trust to drive adoption. Without the critical piece of adoption, no amount of innovation in agriculture can take root and make a difference. The success and commercial viability of biological crop inputs on farm rely heavily on product development, which includes producing credible data before going to market.