A few weeks ago, a colleague and I attended the International Conference of Precision Agriculture. During this annual joint conference of the International Society of Precision Agriculture (ISPA) and InfoAg, one theme constantly emerged: “It’s the data (management), stupid”
If this doesn’t make sense to you, let me explain…
During the four-day event, a variety of cutting edge topics in agriculture were discussed and explored, but perhaps none more than Big Data and how it impacts agriculture. Over the past decade there has been an explosion of data acquisition tools across vendors, and with these data acquisition tools come software packages to present the data in mapped, graphed, and organized forms of all flavors. However, with all the data available in a variety of formats it can make a PhD’s head spin. Which leads to perhaps the most interesting single theme presented succinctly by SST Software CEO Matt Waits; “It’s the data (management), stupid.”
The data management problems within the Ag space are driven by a variety of factors, but are particularly impacted by the data acquisition process.
What type of data can be studied to reduce input cost? Seed population, nitrogen levels, or some combination of other factors?
What type of data can be used to increase revenue? Harvesting speed, planting speed, or something else entirely?
Unfortunately, the data cannot be easily studied in a systematic way until the data acquisition process is matured. To speed the data acquisition process, the industry leaders will have to collaborate to develop an industry standard.
An industry standard around data acquisition can open competition to the data management and analysis space. With an industry standard of data acquisition, competitors’ tractors and implements can provide a baseline of data, much as we see with something as simple as CAN ISO standards. With the data in a standard format, storage and transfer of data become a much simpler problem to solve, and prime the pump for the data science space to solve the two basic problems all producers face; reducing input cost and increasing revenue.
What are your thoughts on the impact of Big Data in agriculture? How might an industry standard allow us to simplify common problems? Let us know what you think in the comments!