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Paper short abstract

This paper argues that what counts as soil data depends on competing temporal regimes. Using the concept of soil clocks, it examines how algorithmic real-time data infrastructures clash with slower ecological rhythms of soil regeneration.

Paper long abstract

What counts as soil data in the Anthropocene may depend less on epistemic standards than on temporal assumptions embedded in data infrastructures. This paper explores how competing temporal regimes shape the production, interpretation, and political relevance of soil data. While agro-algorithmic systems rely on real-time sensing, predictive modelling, and continuous data refresh cycles, soil processes unfold through slower rhythms of decomposition, microbial activity, and regeneration. This temporal mismatch raises a central question: when is soil data considered timely, outdated, or actionable?

Building on STS approaches to infrastructure, classification, and boundary objects, the paper introduces the concept of soil clocks as an analytical device for understanding soil as a time-keeping system rather than merely a measurable resource. Soil clocks describe ecological rhythms through which soils signal conditions of readiness, stress, and recovery, often on seasonal or multi-year scales. By contrast, algorithmic systems operate within accelerated temporal regimes structured by alerts and optimisation cycles. From this perspective, data “shelf life” emerges relationally from the alignment—or misalignment—between algorithmic and ecological temporalities.

The paper develops a conceptual typology of temporal mismatches in soil data, drawing on digital agriculture platforms, policy documents, and monitoring frameworks as illustrative material. It argues that data infrastructures do not simply measure soil but actively re-time it, privileging certain forms of knowledge while marginalising others rooted in embodied practice and care. The paper contributes to STS debates on environmental data pluralism by showing how sustainability depends on synchronising algorithmic systems with ecological rhythms.