Preprints
https://doi.org/10.5194/egusphere-2025-4250
https://doi.org/10.5194/egusphere-2025-4250
12 Sep 2025
 | 12 Sep 2025
Status: this preprint is open for discussion and under review for SOIL (SOIL).

Why a mechanistic theory of soils is crucially important: Another line of supportive arguments exists, seldom invoked in soil science

Philippe C. Baveye

Abstract. In the last few decades, the sizable effort that has been devoted to the mechanistic, quantitative description of soil processes has been justified on the grounds that theories and models help us understand how soils function, and also predict how, e.g., they are likely to adjust in the future to environmental change. The argument, familiar to physicists, that theories uniquely determine what should be measured has rarely if ever been invoked in the soil science literature. On the contrary, to enable the classification and mapping of soil, enormous amounts of “theory-free” data have been and continue to be amassed by soil scientists. In this general context, the key objective of the present Forum article is to argue that the accumulation of more “theory-free” data, in particular to allow the application of artificial intelligence methods, is not sensible at this stage, and that the development of improved theories of soil processes is crucial, to provide guidance about the type of measurements that should be performed. Hopefully, this Forum article will stimulate a debate on this issue, and will lead to a much needed intensification of theoretical research and modelling in soil science.

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Philippe C. Baveye

Status: open (until 24 Oct 2025)

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Philippe C. Baveye
Philippe C. Baveye
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Short summary
The objective of this Forum article is to argue that one of the key justification for developing theories and models of soil processes is that these theories are needed to determine what it is relevant to measure in soils. Without these theories and models to guide us, in particular if we rely on machine-learning and artificial intelligence methods to make progress, we are navigating in the dark, and are likely to base decisions on more correlations that do not reflect true mechanisms.
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