Linganisha mbinu

	Uchoraji wa Udongo wa Dijitali	Mchoro wa unyevu wa udongo
Nyanja	Agronomia	Agronomia
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	Late 1990s – early 2000s (formalised ~2003)	1956-1980
Mwanzilishi	Multiple contributors; foundational framework by Alex McBratney and colleagues	Willard Robert Gardner, Rollin H. Brooks, Arthur T. Corey
Aina	Spatial prediction and mapping pipeline	Empirical soil water retention model
Chanzo asilia	McBratney, A. B., Mendonca Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.	Gardner, W. R. (1956). Representation of soil aggregate-size distribution by a logarithmic-normal distribution. Soil Science Society of America Journal, 20(2), 151-153.
Majina mbadala	DSM, predictive soil mapping, quantitative soil-landscape modelling, geostatistical soil mapping	Water Retention Curve, pF Curve, Characteristic Curve, SWRC
Zinazohusiana	1	3
Muhtasari	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.	The soil moisture curve (or soil water retention curve, SWRC) describes the relationship between soil water content and soil matric potential (water tension). It characterizes how tightly water is bound in pores of different sizes: large pores drain at low tensions (wet soils), while smaller pores retain water at high tensions (dry soils). Quantifying this relationship is essential for water balance modeling, unsaturated flow prediction, and assessing plant-available water.