Comparer des méthodes

	Cartographie numérique des sols	Modélisation de la pédogenèse
Domaine	Agronomie	Agronomie
Famille	Process / pipeline	Process / pipeline
Année d'origine	Late 1990s – early 2000s (formalised ~2003)	1941 (Jenny's factorial model); process-based numerical models from 1990s onward
Auteur d'origine	Multiple contributors; foundational framework by Alex McBratney and colleagues	Hans Jenny (foundational framework); later extended by multiple contributors including Simonson, Hoosbeek, and Bryant
Type	Spatial prediction and mapping pipeline	Quantitative process-based simulation model
Source fondatrice	McBratney, A. B., Mendonca Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.	Minasny, B., Finke, P., Stockmann, U., Vanwalleghem, T., & McBratney, A. B. (2015). Resolving the integral connection between pedogenesis and landscape evolution. Earth-Science Reviews, 150, 102–120.
Alias	DSM, predictive soil mapping, quantitative soil-landscape modelling, geostatistical soil mapping	soil formation modeling, soil genesis simulation, pedogenic process modeling, quantitative pedology
Apparentées	1	1
Résumé	Digital Soil Mapping (DSM) is a quantitative, data-driven pipeline that predicts the spatial distribution of soil properties and classes across a landscape by statistically linking field observations to environmental covariates — terrain attributes, remote sensing imagery, climate surfaces, and geology layers. The approach replaces or augments traditional expert-drawn soil surveys with reproducible, spatially explicit models, and is applied in agronomy, land management, food security, and environmental assessment.	Pedogenesis modeling is a quantitative method used in agronomy and soil science to simulate the processes by which soils form and evolve over time. Rooted in Hans Jenny's 1941 factorial framework — soil as a function of climate, organisms, relief, parent material, and time — modern approaches translate these conceptual drivers into coupled numerical process equations, allowing researchers to reconstruct past soil states and project future soil properties under changing land use or climate scenarios.