The combine harvester, a staple of farmers’ fields since the late 1800s, does much more these days than just vacuum up corn, soybeans and other crops. It also beams back reams of data to its manufacturer.
GPS records the combine’s precise path through the field. Sensors tally the number of crops gathered per acre and the spacing between them. On a sister machine called a planter, algorithms adjust the distribution of seeds based on which parts of the soil have in past years performed best. Another machine, a sprayer, uses algorithms to scan for weeds and zap them with pesticides. All the while sensors record the wear and tear on the machines, so that when the farmer who operates them heads to the local distributor to look for a replacement part, it has already been ordered and is waiting for them.
Farming may be an earthy industry, but much of it now takes place in the cloud. Leading farm machine makers like Chicago-based John Deere & Co. DE +1.1% or Duluth’s AGCO AGCO +0.9% collect data from all around the world thanks to the ability of their bulky machines to extract a huge variety of metrics from farmers’ fields and store them online. The farmers who sit in the driver’s seats of these machines have access to the data that they themselves accumulate, but legal murk obfuscates the question of whether they actually own that data and only the machine manufacturer can see all the data from all the machines leased or sold.
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Still, farmers have yet to be fully won over. Many worry that by allowing the transfer of their data to manufacturers, it will inadvertently wind up in the hands of neighboring farmers with whom they compete for scarce land, who could then mine their closely guarded information about the number of acres they plow or the types of fertilizers and pesticides they use, thus gaining a competitive edge. Others fear that information about the type of seeds or fertilizer they use will wind up in the hands of the chemicals companies they buy from, allowing those companies to anticipate their product needs and charge them more, said Jonathan Coppess, a professor at the University of Illinois.
Sensitive to the suggestion that they are infringing on privacy, the largest equipment makers say they don’t share farmers’ data with third parties unless farmers give permission. (Farmers frequently agree to share data with, for example, their local distributors and dealers.)
It’s common to hear that farmers are, by nature, highly protective of their land and business, and that this predisposes them to worry about sharing data even when there are more potential benefits than drawbacks. Still, the concerns are at least partly the result of a lack of legal and regulatory standards around the collection of data from smart farming technologies, observers say. Contracts to buy or rent big machines are many pages long and the language unclear, especially since some of the underlying legal concepts regarding the sharing and collecting of agricultural data are still evolving.
As one 2019 paper puts it, “the lack of transparency and clarity around issues such as data ownership, portability, privacy, trust and liability in the commercial relationships governing smart farming are contributing to farmers’ reluctance to engage in the widespread sharing of their farm data that smart farming facilitates. At the heart of the concerns is the lack of trust between the farmers as data contributors, and those third parties who collect, aggregate and share their data.”
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Some farmers may still find themselves surprised to discover the amount of access Deere and others have to their data. Jacob Maurer is an agronomist with RDO Equipment Co., a Deere dealer, who helps farmers understand how to use their data to work their fields more efficiently. He explained that some farmers would be shocked to learn how much information about their fields he can access by simply tapping into Deere’s vast online stores of data and pulling up their details.
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Based on the mountains of data flowing in to their databases, equipment makers with sufficient sales of machines around the country may in theory actually be able to predict, at least to some small but meaningful extent, the prices of various crops by analyzing the data its machines are sending in — such as “yields” of crops per acre, the amount of fertilizer used, or the average number of seeds of a given crop planted in various regions, all of which would help to anticipate the supply of crops come harvest season.
Were the company then to sell that data to a commodities trader, say, it could likely reap a windfall. Normally, the markets must wait for highly-anticipated government surveys to run their course before having an indication of the future supply of crops. The agronomic data that machine makers collect could offer similar insights but far sooner.
Machine makers don’t deny the obvious value of the data they collect. As AGCO’s Crawford put it: “Anybody that trades grains would love to have their hands on this data.”
Experts occasionally wonder about what companies could do with the data. Mary Kay Thatcher, a former official with the American Farm Bureau, raised just such a concern in an interview with National Public Radio in 2014, when questions about data ownership were swirling after Monsanto began deploying a new “precision planting” tool that required it to have gobs of data.
“They could actually manipulate the market with it. You know, they only have to know the information about what’s actually happening with harvest minutes before somebody else knows it,” Thatcher said in the interview.
“Not saying they will. Just a concern.”
Source: Access To Big Data Turns Farm Machine Makers Into Tech Firms
Robin Edgar
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