The myriad potential benefits of digital farming hinge on the promise of increased accuracy, which allows ‘doing more with less’ through precise, data-driven operations. Yet, precision farming's foundational claim of increased accuracy has hardly been the subject of comprehensive examination. Drawing on social science studies of big data, this article examines digital agriculture's (in)accuracies and their repercussions. Based on an examination of the daily functioning of the various components of yield mapping, it finds that digital farming is often ‘precisely inaccurate’, with the high volume and granularity of big data erroneously equated with high accuracy. The prevailing discourse of ‘ultra-precise’ digital technologies ignores farmers' essential efforts in making these technologies more accurate, via calibration, corroboration and interpretation. We suggest that there is the danger of a ‘precision trap’. Namely, an exaggerated belief in the precision of big data that over time leads to an erosion of checks and balances (analogue data, farmer observation et cetera) on farms. The danger of ‘precision traps’ increases with the opacity of algorithms, with shifts from real-time measurement and advice towards forecasting, and with farmers' increased remoteness from field operations. Furthermore, we identify an emerging ‘precision divide’: unequally distributed precision benefits resulting from the growing algorithmic divide between farmers focusing on staple crops, catered well by technological innovation on the one hand, and farmers cultivating other crops, who have to make do with much less advanced or applicable algorithms on the other. Consequently, for the latter farms digital farming may feel more like ‘imprecision farming’.