Before looking at what "big data" is, we need to define what "farm data" is. When I look at the agriculture industry today, I see three different types of farm data that are being collected:
Agronomic Data. This is information derived about activities and conditions on farm fields. Examples include soil analysis, nutrient information, hybrid selection, plant populations, and yield data.
Machine Data. This information is associated with how equipment is functioning. Examples include fuel consumption, machine health indicators, diagnostic codes and engine performance.
Weather Data. This is information about precipitation, wind, temperature and other climate conditions.My prediction is that courts and legislative bodies will give each of these categories on the farm different levels of protection, as the law develops to address farm data privacy and ownership issues. Agronomic data is probably afforded the most protection under existing laws, since agronomic data is similar to a traditional "trade secret." Equipment manufacturers would also like to view machine data as a proprietary trade secret--but owned by the manufacturer--not the farmer. And weather data, I'm not sure anyone can claim to own that.
So with this understanding, what is agriculture's "big data"? Here is my definition:
Big data is the ability to aggregate information to discover trends and find patterns.Agronomic data becomes "big data" when multiple farmers upload their data to the same place, then that data is analyzed to discover, for example, that applying a certain amount of fertilizer at a certain time in the growing cycle of a certain hybrid provides the highest yield. A single farmer analyzing his own data might also discover this, but his isolated data can not tell him whether that formula will yield similar results in other fields. Big data proves it does (or does not).
Do you have a different definition of "big data"? Let me know.