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Digital Mapping of Soil Particle‐Size Fractions for Nigeria
Author(s) -
Akpa Stephen I. C.,
Odeh Inakwu O. A.,
Bishop Thomas F. A.,
Hartemink Alfred E.
Publication year - 2014
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2014.05.0202
Subject(s) - loam , soil texture , silt , soil science , environmental science , texture (cosmology) , particle size , soil test , mathematics , hydrology (agriculture) , geology , soil water , geotechnical engineering , geomorphology , paleontology , artificial intelligence , computer science , image (mathematics)
There is a growing need for spatially continuous and quantitative soil information for environmental modeling and management, especially at the national scale. This study was aimed at predicting soil particle‐size fractions (PSF) for Nigeria using random forest model (RFM). Equal‐area quadratic splines were fitted to Nigerian legacy soil profile data to estimate PSFs at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) using the GlobalSoilMap project specification. We applied an additive log‐ratio (ALR) transformation of the PSFs. There was a better prediction performance (based on 33% model validation) in the upper depth intervals than the lower depth intervals (e.g., R 2 of 0.53; RMSE of 13.59 g kg −1 for clay at 0–5 cm and R 2 of 0.16; RMSE of 15.60 g kg −1 at 100–200 cm). Overall, the PSFs show marked variations across the entire Nigeria region with a higher sand content compared with silt and clay contents and increasing clay content with soil depth. The variation in soil texture (ST) shows a progressive transition from a coarse texture (sand) along the fringes of northern Nigeria (e.g., upper part of Maiduguri and Sokoto), to finer texture (loam to clay loam) toward the western part of the Niger Delta region in the south. The inclusion of depth as a predictor variable significantly improved the prediction accuracy of RFM especially at lower depth intervals. These results could be used for producing soil function maps for national agricultural planning and in assessments of environmental sustainability.