Competition Problems and Governance of Non-personal Agricultural Machine Data : Comparing Voluntary Initiatives in the US and EU

The arrival of digital data in agriculture opens the possibility to realise productivity gainsthrough precision farming. It also raises questions about the distribution of these gainsbetween farmers and agricultural service providers. Farmers’ control of the data is oftenperceived as a means to appropriate a larger share of these gains. We show how data-drivenagricultural business models lock farm data into machines and devices that reducecompetition in downstream agricultural services markets. Personal data protection regulationis not applicable to non-personal agricultural machine data. Voluntary data charters in theEU and US emulate GDPR-like principles to give farmers more control over their data but donot really change market-based outcomes due to their legal design. Third-party platforms area necessary intermediary because farmers cannot achieve the benefits from applications thatdepend on economies of scale and scope in data aggregation. The low marginal value ofindividual farm data in such applications puts farmers in a weak bargaining position. Neutralintermediaries that are not vertically integrated into agricultural machines, inputs or servicesmay circumvent monopolistic data lock-ins provided they can access the data. Unless theyfind a way to generate and monetise economies of scale and scope with their data, theirbusiness model may not be sustainable. Regulatory intervention that facilitates portability andinteroperability might be useful for farmers to overcome data lock-ins, but designing dataaccess rights is a complicated issue as many parties contribute data to the production processand may claim access rights. Minor changes in who gets access to which data under whichconditions may have significant effects on stakeholders. We conclude that digital agriculturestill has some way to go to reach equitable and efficient solutions to data access rights.Similar situations are likely to occur in other industries that rely on non-personal machinedata