No abstract

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.

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R2; 0.29–0.69) outperformed both PTF‐1 (R2; 0.11–0.18) and PTF‐2 (R2; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R2; 0.61–0.86) performed comparably as PTF‐1 (R2; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R2; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).