Paper short abstract:

Our research examines digital agriculture’s (in)accuracies and their repercussions. We argue that over-reliance on big data and algorithms can lead to ‘precision traps’: Decision-making in and about agriculture that is governed by the needs of AI.

Paper long abstract:

An essential assumption of the ‘digital revolution’ in agriculture is that big-data-driven technologies provide more accurate and precise information to farmers, allowing them to spend less time gathering and interpreting data on soil, animal and crop health. Based on the claim that big data can more accurately represent and plan the various risks and workflows of a farming operation, farmers are promised impartial decision-support. In practice, however, the relations between farmers, farmworkers and digital technologies turn out to be more complex, often with unanticipated consequences. Our research examines digital agriculture’s (in)accuracies and their repercussions. We argue that over-reliance on big data and algorithms can lead to ‘precision traps’ and further cement dependency on particular machinery and inputs. Farmers’ experience, keen observation and understanding of the partialities of algorithm-derived data and advice remain crucial, as does their socio-economic agency in making autonomous, informed and selective choices between technologies and datasets.