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Soil–landscape variability: mapping and building detail information for soil management
Author(s) -
Iticha B.,
Takele C.
Publication year - 2018
Publication title -
soil use and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 81
eISSN - 1475-2743
pISSN - 0266-0032
DOI - 10.1111/sum.12404
Subject(s) - soil map , soil survey , environmental science , soil series , cation exchange capacity , soil carbon , soil science , soil water , vertisol , soil test , soil management , soil texture , soil ph , spatial variability , soil classification , mathematics , statistics
The study of soil–landscape relationships at a detailed scale (1:10 000) and its use for soil management was less common in developing countries. The study was conducted in western Ethiopia with the aim to explain the soil variability across landscapes, classify soils into mapping units and produce a map of these soils. This study was performed based on a discrete model of spatial variation. Five soil reference groups: Vertisols, Cambisols, Fluvisols, Luvisols and Leptosols were identified in the study site. Distribution of the soil reference groups was determined by landscape position. Variation in soil texture, colour, pH , exchangeable acidity, organic carbon, total nitrogen, available phosphorus (av. P), carbon to nitrogen ratio (C/N), exchangeable potassium (K), calcium (Ca), magnesium (Mg), sodium (Na) and cation exchange capacity ( CEC ) was observed within and among soil mapping units ( SMU s). Variability was considerably high for exchangeable Ca and CEC . Factor analysis result indicated that variation in soil properties within land unit was comparatively highest in Leptosols of SMU 9 (88.87%) and lowest in Vertisols of SMU 1 (60.82%). Moderate‐to‐fine scale mapping of soil properties helps to build detail information for soil management. Grouping fields into mapping units that require more or less similar management measure would be an important soil–landscape concept. As a result, mapping units could be used as cost‐effective means of treating variable field so as to optimize the forecasted benefits.