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Spatiotemporal modelling of cropland soil pH dynamics in Southern China using a process-guided machine learning
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DOI：10.1016/j.geoderma.2026.117951.png)
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Abstract
En 中文
• A process-guided meta-model integrating VSD+ simulations with machine learning enables long-term spatiotemporal reconstruction of cropland soil pH dynamics. • Cropland soil in southern China experienced persistent acidification, with divergent trajectories between upland and paddy soils. • Progressive depletion of soil buffering capacity resulted in a shift from base cation exchange toward aluminum-iron buffering under sustained acid inputs.
Keywords:
Cropland soil acidification
Process-guided machine learning
VSD+ model
Ferralsols
Acrisols
Spatiotemporal prediction
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