Σύγκριση μεθόδων

	Μοντελοποίηση της παιδογένεσης	Ψηφιακή Χαρτογράφηση Εδάφους
Πεδίο	Αγρονομία	Αγρονομία
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1941 (Jenny's factorial model); process-based numerical models from 1990s onward	Late 1990s – early 2000s (formalised ~2003)
Δημιουργός	Hans Jenny (foundational framework); later extended by multiple contributors including Simonson, Hoosbeek, and Bryant	Multiple contributors; foundational framework by Alex McBratney and colleagues
Τύπος	Quantitative process-based simulation model	Spatial prediction and mapping pipeline
Θεμελιώδης πηγή	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.	McBratney, A. B., Mendonca Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.
Εναλλακτικές ονομασίες	soil formation modeling, soil genesis simulation, pedogenic process modeling, quantitative pedology	DSM, predictive soil mapping, quantitative soil-landscape modelling, geostatistical soil mapping
Συναφείς	1	1
Σύνοψη	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.	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.