Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping
Jonas Schmidinger; Viacheslav Barkov; Sebastian Vogel; Martin Atzmueller; G.B.M. Heuvelink · 2025 · Computers and Electronics in Agriculture
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Machine learning and geostatistics are two fundamentally different frameworks for the prediction and spatial mapping of soil properties. Geostatistics leverages the spatial structure of soil properties, whereas machine learning models capture the relationship between available environmental features and soil properties. We propose a hybrid framework that augments machine learning with spatial context through the engineering of ‘spatial lag’ features derived from ordinary kriging. We call this approach ‘kriging prior regression’ (KpR), as it reverses the logic of regression kriging by incorpera
Abstract by Jonas Schmidinger; Viacheslav Barkov; Sebastian Vogel; Martin Atzmueller; G.B.M. Heuvelink, Computers and Electronics in Agriculture (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.compag.2025.111352