Putting a value on big data
When it comes to agricultural big-data technologies, some experts see the inability of developers to articulate value – specifically savings and return on investment numbers – as one of the major barriers to adoption by farmers.
Determining savings and return on investment numbers, however, is not impossible.
“Data is raw material for information," says Dr. James Lowenberg-DeBoer, the Elizabeth Creek chair of agri-tech economics at Harper Adams University, an agricultural institution in the United Kingdom. "Information is what can be used to make decisions, but has value only if it influences decisions."
Knowing the return on investment with big-data technology could help lead to wider adoption in agriculture.
Speaking to big-data developers and researchers at a recent conference in Houston, Texas, Lowenberg-DeBoer says his work – as well as his own experience farming in Iowa – reveals how quickly technologies can be adopted if the economic value is clear. Precision-ag technologies like GPS and autosteer, for example, have a comparatively immediate and obvious economic return, and were widely adopted quickly.
Measuring value for big-data tools like multi-layered soil maps are more complicated. Articulating costs associated with the time to collect information, required equipment, software subscriptions, managing and archiving data, and analysis is the first step.
He gave examples of value-measurement.
If a new, better producing variety of corn were to become available, for example, the value would be the extra bushels minus the cost of information and extra production costs.
Comparison data showing what crop varieties work well in specific soil types in a specific area could help a farmer make more suitable agronomic choices, the value being the return from those extra bushels or the saved resources like time and money. Pooled regional data could even help farmers sell grain at a more appropriate time – in order to cut potential losses – should unforeseen growing-season events like flood or frost weaken crop quality and threaten lower commodity prices at later points in the year.
Open pooled data
A complicating factor, though, is the reliance on open pooled data. In the current landscape, most big-data systems are managed by private companies operating proprietary systems. This has many farmers concerned that farm data can be used to harm their business.
Lowenberg-DeBoer also says some farmers fear data-sharing means a loss of competitive advantage – if a neighbour, for example, purposely outbids someone for rented land because they know the other party experienced a poor growing year.
“This is accentuated by the feeling that much of the value of farm data is beyond the farm gate,” he says.
Still, Lowenberg-DeBoer believes there are data sharing business models that could work past these difficulties. This could include: development of data systems through public-private partnerships; compensation for the effort of generating data, as well as for its use by other parties; easing collection; and development of ways farmers can more easily use pooled data to make their own business decisions.
Experts say articulating the savings and return on investment of agricultural big-data technology is the first step of wider adoption by farmers. The gathered information increases in value when it influences decisions.
Article by: Matt McIntosh