Measuring Agricultural 'success': Datasets and the Enabling of Normative Food Politics and Unsustainable Footways

Measuring Agricultural ‘Success’: Datasets and the enabling of normative food politics and unsustainable foodways

The collection and analysis of large datasets for measuring farm-level systems is a key site for the co-production of environmental knowledge and food politics. Every John Deere tractor now passively collects data on a host of farm-level environmental variables (e.g. soil quality) and feeds these data into an aggregated dataset so big it is thought to produce an unmediated and objective account of nature. Rather than seeing these data as raw, I take a science studies view that such datasets are necessarily partial, selective, and embedded with value commitments. Advancing in the idiom of co-production, I draw on interviews with 20 North American designers of agricultural data collection, storage and analysis artifacts to reveal the enactment of productivist goals and risks (e.g. yield maximization) through technical design. Theory from sociology of standards and food studies is used to support the claim that agricultural big data and its infrastructures measure aspects of nature while at once they reproduce unsustainable foodways and intervene into contested food politics.