Defining the Ag Data Pipelines
Apr 08, 2016
Keeping track of the ag data products on the market right now could make a farmer’s head swim. Which product does what? Who controls which data? More importantly, which products use which streams of data from the farm? For a long time it has been hard to classify the data streams coming off the farm, but thanks to organizations like AgGateway and the Ag Data Transparency Evaluator, these ag data “pipelines” are starting to take shape. This post explores the current definitions of the different ag data pipelines transferring information off the farm.
The Ag Data Transparency Evaluator asks companies to define in their contracts and policies which types of data a certain product collects from the farmer. The Transparency Evaluator forces companies to pick from five different categories, using AgGateway’s bullet-point definitions rather than creating new categories. These are:
- Agronomic Data. Agronomic data includes seed data, hybrid identification, planting data, yield data, pesticide and herbicide data, crop disease data, and nutrient information.
- Farm Management Data. Farm Management Data includes business operations data, such as financial and tax information, operating and loan information, HR data, farm labor contracts, supply chain data, transportation and storage data, commodity prices, and federal reporting and compliance data.
- Land Data. Land data includes conservation practices, tillage, soil and fertility information, watershed and drainage data, topographical information, and GIS and UAV imagery.
- Machine Data. Machine data includes telematics, fuel usage, machine load, equipment function, and remote sensing data such as radar, spectral, and Lidar.
- Weather Data. Information derived from weather stations, soil probes, and sensor data, including wind speed, direction, precipitation, temperature, humidity, barometric pressure, etc.
These five categories are not perfect. They will evolve and be refined over the next few years. There is some overlap. In the past, companies used definitions that were more vague and less helpful to the farmer, for instance, referring to ag data as “personal information” which is typically identifying information.
I have long advocated for treating different pipelines of ag data differently in contracts. Why? Because a farmer's expectation of privacy with each pipeline is different. Most farmers I know are very protective of their agronomic data, but have little concern over protecting the secrecy of weather data generated on their fields.
AGCO is a good example of a company making this distinction. Recently, AGCO explained that its FUSE Technologies take data from two of these pipelines, “agronomic data” and “machine data,” and treats them differently from a privacy perspective. That is an easy concept for a farmer to understand and makes sense.
These definitions may not be perfect, but we are moving in the right direction.