Science Wednesday: Big Data in farming – have we worked it out yet?

With the Australian AgriFood Data Exchange moving ahead into implementing initial use cases, it’s timely to revisit this 2018 paper from Digiscape’s Social Dimensions project. Under the title Is big data for big farming or for everyone? Perceptions in the Australian grains industry, Aysha Fleming and colleagues analyzed interviews with stakeholders in the Australian Grains industry that to explored their perceptions of digital technologies and big data in agriculture.

From their 2016 interviews, the team discerned two largely-opposing discourses, which they summarized as follows:

	Big Data is for Big Agriculture	Big Data is for Everyone
Key language	Smarter farming, efficiency The gap between farmers is widening	Sharing, together
Norms	Individualism Revolution is an inevitable part of competition	If we all do our part we can all benefit Benefits will take time to accumulate
Values and assumptions	Economic values Information is valuable	Helping the struggling
Rules	Strive for maximum control of data	Everyone needs to be involved

I have some observations – and questions – about the state of play in early 2022:

• One of Fleming et al.‘s conclusions was that there was a “need for industry, research providers and government to consciously, transparently and inclusively choose a path for the future of big data in agriculture and subsequently to make appropriate changes in policy and finance.” Three years on from the paper, and five years after the fieldwork, has that choice been made? A clear “no” forms the background to Andrea Koch’s recent John Ralph competition-winning essay on farm data.
• “Big Data is for Big Agriculture vs Big Data is for Everyone” makes sense as a debate about the use of big data for on-farm operations, and the voluntary benchmarking use case being trialled by the Oz Ag Data Exchange project fits well within the “Big Data is for Everyone” discourse. But what about the Exchange’s other use cases: regulatory compliance, biosecurity and product traceability? Is there another discourse out there nowadays? Perhaps “Big Data is for the Customers”?
• Looking back at the paper, it is striking that few or no agtech providers (as opposed to researchers or funders) appear to have been interviewed. This reflects both the recent emergence of the Australian agtech sector but also, I fear, something of a two-way communication gap. Is big data for big farming or for everyone? Perceptions in the Australian grains industry now has 76 citations on Google Scholar – that’s a lot for a social sciences paper – but amongst them I could find only one paper studying an agtech SME. So, a question for those of my readers doing agtech: do either of these discourses match your farmer customers and/or your business model? If so, which one? If not, why not?