Since the end of the 19th century, the combine harvester has been an indispensable part of harvesting. Today, the imposing machines can do much more than just cut off and suck up grain, maize, soybeans and other crops: they also send large amounts of data to the manufacturer and in some cases to dealers, whose analysis together with other measured values from “Smart Farming” for transparent farmer.
Such an agricultural machine uses GPS to record its precise path through the field. Sensors count the plants harvested per hectare and the distance between them. In the meantime, algorithms in a sowing machine adjust the distribution of the seeds. They are based on which parts of the soil have produced the most yield in recent years. A sprayer uses other program routines to detect weeds and control them with pesticides.
Meanwhile, sensors measure the degree of wear and tear on the high-tech equipment. If the farmer who operates them goes to the local dealer to look for a spare part, in the best case scenario he has already ordered it and has it in stock based on the ongoing data analysis. Leading manufacturers such as AGCO, Claas, Fendt or John Deere collect the measured values from agricultural machines, some from all over the world, save them in the cloud and evaluate them using big data processes.
Collect data for Agriculture 4.0
The accumulation of all this data in the hands of the few large producers opens up opportunities for Agriculture 4.0. But there are also concerns that the corporations could misuse the information and undermine competition. The farmers themselves usually have access to the measured values that their machines produce. However, due to legal gray areas, manufacturers doubt whether this data also belongs to them. Only these receive an overview of the information from all devices they have sold or leased.
“We are on the cusp of a real revolution,” said Seth Crawford, AGCO Vice President. across from Forbes. “What held us back for a while was the inability to process data quickly enough.” In the beginning, knowledge could “only” be broken down to fields or fields as a whole, but not yet to the level of individual plants. With all the new technology and increasing computing speeds, one is now able to work much more in real time.
The machine manufacturers are turning more and more into technology companies. With this in mind, the management of John Deere created the position of Chief Technology Officer (CTO) last year. Farmers try to lure them, for example, with predictions that they could increase their net income by 20 percent in the next five years if they use their data intelligently.
However, many of the farmers fear that if their data is passed on to manufacturers, they will inadvertently end up in the hands of neighboring farmers with whom they are competing for scarce land. They could then evaluate their closely guarded information about the number of fields plowed or the types of fertilizers and pesticides used and thus gain a competitive advantage. Others fear that relevant analyzes will end up in the hands of chemical companies and seed manufacturers such as Bayer. This would allow them to anticipate their product needs and charge them higher prices.
On the basis of the mountains of measured values that flow into their databases, device manufacturers with sufficient machine sales are theoretically able to predict the prices of various crops and the main features of the coming harvest season at least to a certain meaningful extent. You could use the wheat yields per hectare, the amount of fertilizer used, or the average number of seeds laid out. Market manipulation and sales of the findings to commodity traders, for example, would be conceivable.
The lack of transparency and clarity on issues such as data ownership, portability, privacy, trust and liability in business relationships around smart farming contributes to this, according to researchersthat farmers are reluctant to get involved in sharing their farm data on a large scale. The focus of the concerns is the lack of trust between the farmers as data providers and third parties who aggregate and pass on their measured values. Mediation platforms such as Ag Data Transparent have hardly been able to dispel these worries: even in their contracts it is stated that farmers are allowed to “control” data if necessary.