Data as Currency in Ag: We Aren’t There Yet

Data as Currency in Ag: We Aren’t There Yet

A hot topic in the precision ag revolution is the idea ag data will become a form of currency. Many have emphatically stated the value of data, and growers are starting to ask, “Where’s the money for my data?”


On paper the concept of charging for data is great for the grower. A crop that is entirely renewable that they can charge repeatedly for seems like a silver bullet. So, why aren’t companies lining up to pay for field data?

The answer lies in the fact that paid precision services have yet to be adopted on a large scale. Most of the precision ag products have free tiers (in which the majority are signed up) and much smaller paid tiers. On one hand, growers expect to test data products and will only pay for them once they’re proven. On the other, to build a product that is accurate enough to perform on their field, it takes an immense amount of capital, time, and data to build. Where the currency idea fails at this stage is that predictive models could fail, and if the wrong dataset is purchased in the currency model, the loss of time and capital investment is compounded by paying up-front for data. The risk falls entirely to the technology provider.

The Way Forward

Ag tech, big tech, and growers need to first build and test the products they desire. The risk is shared in that the grower is giving the data to someone that they may not have a long relationship with, the technology providers then take on the risk of trying to make the models happen and the ag tech companies work with both parties for testing and deployment. It’s going to take collaboration, and right now it’s too early to place a monetary value on the data.


Still, that doesn’t mean the grower is going to be left high and dry when products get deployed. What’s missing in this process is that, for the data models to continue to work on a field, a constant stream of data is required. Historic data is only required the first step in developing prescriptive analytic models. Current data is as valuable, if not more valuable down the line. At that point, once the models for yield prediction, pest and disease, and seed prescription are created, tested, and proven, data will become the currency that drives what models continue to perform well. In the end, the grower will have the ultimate buying power and be in control, but if the science is going to advance, there should be a shared risk in the endeavor.

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