Their authors ignore key difficulties like algorithm inaccuracies, inflexibility, and farm inequalities---more like “imprecision farming”

This article critically examined the promise of superb precision and accuracy that widely features in accounts of digital agriculture. It showed that weak GPS reception, sensor errors, as well as spatially or temporally incomplete and self-learning algorithms that are still early in their learning process can all cause inaccuracies that are easily hidden by a high volume of data and sophisticated maps (with high granularity). As a result, digital agriculture is often ‘precisely inaccurate’ (McFarland and McFarland, 2015), giving a false sense of accuracy.

In the AgTech sector, the inadequate or inaccurate functioning of technologies, if acknowledged, is portrayed as a transitory problem as technologies will constantly become more powerful and precise. This view can also be encountered in the social science literature, where inaccuracies are labelled, for instance, as ‘teething problems’ (Jakku et al., 2019: 6). We do not contest that technologies will advance further. However, assuming that they are on a steady and steep curve towards evermore hyper-accuracy is problematic in several ways.

First, there is the danger that research will attribute blame for inaccuracy predominantly to the farmers who have to learn how to adopt, use, or ‘digi-grasp’ (Dufva and Dufva, 2018; Rijswijk et al., 2019: 11) digital technologies. AgTech representatives, but also academic studies, tend to highlight the farmers’ role in mis-adoption and ‘operator errors’. This reflects a de-contextualised view of technologies as abstract solutions, with implementation and use problems detached from them, and relegated to the ‘human factor’. This article, on the contrary, appreciates that an algorithm, or digital farming technology at large, cannot be grasped without examining ‘its wide socio-technical assemblage and its use’ (Kitchin, 2017: 23). Our examination of this assemblage and use in digital agriculture highlights farmers’ important role in preventing errors and shaping digital technologies’ accuracy, when calibrating technologies or corroborating algorithmic advice.

Second, to assume that inaccuracies of digital technologies are just a temporal blip hampers scrutinizing the more fundamental tensions and limits connected to digital agricultural technologies. If big data and algorithms indeed turn our world into a ‘black box society’ with hidden algorithms controlling ever more aspects of our lives, as critical scholars of algorithms like Pasquale (2015) assert, then we cannot afford to leave these technologies unexamined.

Third, even when the code of algorithms improves and future field trials with digital technologies show enhanced accuracy, this might not always translate into increased accuracy in diverse everyday farming operations. Following Burrell (2016) and Kitchin (2017) we contend that algorithms and their effects can only be grasped ‘in the wild’. Subsequently, the highly diverse and dynamic natural context in which they have to operate becomes just as important in determining accuracy as the code itself (and the other socio-technical aspects). It appears that the enormous variety of plant growth stages and ‘normal’ weather variation currently pose considerable challenges to technologies, varying from sensors to artificial intelligence. Climate change will highly increase this unpredictability, with more extreme weather, new plant diseases, and pests. This is likely to widen implementation gaps (Sumberg, 2012) of new technologies. Rising unpredictability and the subsequent lack of a stable, relevant baseline will make fine grained analysis of data a major challenge. This might mean that rapid technological advances only result in sluggish progress in terms of accurately measuring and influencing conditions in the field.

This article identified two major risks of inaccuracies in digital agriculture, which we termed the ‘precision trap’ and the ‘precision divide’. The ‘precision trap’, refers to a stark belief in the overall precision of digital technologies inspired by some seemingly precise output generated by big data, that hinders identifying (in time) the inaccuracies that may arise. Such a trap is likely under at least three conditions which are all likely to become more prevalent: increased opacity of digital technologies (particularly due to algorithmic opacity); increased remoteness of the farmers from the field; and a move from real-time measurement and advice to forecasting. These conditions impede corroboration by farmers of the data and advice generated by digital technologies. Also, there is the danger of an ‘accuracy trap’ (Rankin, 2020), namely a denouncing of all factors that cannot (yet) be measured with high accuracy. Farmers’ preoccupation with the nitty gritty of precisely measurable individual parameters and variable rate application might for instance distract them from deepening a holistic understanding of key determinants of cultivation, such as the soil, and the more difficult to measure environmental effects. A group of Dutch farmers for instance wondered ‘Does precision farming’s attention for the square meter not distract from what is much more important, namely improvement of the soil?’ (Tholhuijsen, 2020: A14).

The ‘precision divide’ refers to the unequally distributed precision benefits resulting from the growing ‘algorithmic divide’ (Yu, 2020) between farmers focusing on staple crops (mostly commercial and more industrial farms) and farmers cultivating other crops. Digital innovation focuses primarily on the first type of farmers, who consequently benefit from more advanced algorithms and relatively precise or ‘good enough’ data. At the same time, other farmers have to make do with less advanced or less applicable algorithms for their crops, which can be ‘a disaster’ as one farmer stated. Furthermore, the precision divide is likely to deepen and become multi-layered also within the group of farmers producing the staple crops targeted by the digital tools, as it is likely that higher-paying ’prime’ options will provide some users with more accurate and fine-grained advice, while others might need to content themselves with ’basic’ algorithmic advice. Earlier research on digital agriculture has identified a digital divide, namely unequal obstacles to access and use of digital technologies. The ‘precision divide’ goes beyond that by pointing out that even farmers who have managed to access and use new technologies may still experience major disadvantages depending on their crops and ability to pay for services. These farmers find an unequal share of digital farming’s inaccuracies on their ‘plate’.

Overall, turning digital farming into genuine precision farming requires substantial work from the farmer to tweak sensors, check yield maps, and corroborate algorithmic advice. For farmers with the right (staple) crops that may well result in ‘good enough’ data and quite precise operations. However, especially for farmers with crops that fall outside the gaze of the providers of digital technologies, digital farming might feel more like ‘imprecision farming’.