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’.

数字农业的无数潜在好处取决于提高准确性的承诺，它允许通过精确的数据驱动操作“事半功倍”。然而，精准农业提高准确性的基本主张几乎没有经过全面审查。本文借鉴大数据的社会科学研究，考察了数字农业的（不）准确性及其影响。根据对产量绘图各个组成部分的日常功能的检查，发现数字农业往往“精确不准确”，大数据的高容量和粒度被错误地等同于高精度。“超精确”数字技术的流行话语忽视了农民通过校准使这些技术更准确的基本努力，证实和解释。我们认为存在“精确陷阱”的危险。也就是说，对大数据精确性的夸大信念随着时间的推移会导致农场的制衡（模拟数据、农民观察等）受到侵蚀。“精确陷阱”的危险随着算法的不透明性、实时测量和建议向预测的转变以及农民远离田间作业而增加。此外，我们发现了一个新兴的“精准鸿沟”：由于专注于主要作物的农民之间的算法鸿沟不断扩大，一方面通过技术创新得到了很好的满足，农民种植其他作物的农民不得不凑合另一方面，不太先进或适用的算法。最后，