Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics

The spatial modelling of soil properties provides us with essential and useful information relevant to soil fertility management and environmental protection. The study aims to investigate the ability of empirical Bayesian kriging and principal component analysis, multiple linear regressions with environmental covariates in the modelling of soil properties distribution. For this study, thirty (n = 30) soil samples were obtained at 0-30 cm depth and nine (9) soil-environmental covariates derived from the digital elevation model (Shutter Radar Topography Mission at 30 m resolution) in southeastern Nigeria. The summary statistics revealed high sand content (> 70%) which revealed that the soils of the humid tropics developed on the coastal plain parent material are coarse-textured. Pearson correlation matrix revealed a significant but weak correlation between soil properties and soil-environmental variables. Using empirical Bayesian kriging interpolation, the cross-validation results revealed an acceptable prediction for magnesium, potassium, phosphorus, pH and total nitrogen (R-2 > 0.5 with RMSE closer to 0). The principal component analysis reveals that principal component 1 to principal component 5 could interpret 78.1% of the total variability of soil properties. Modelling each soil property using multiple linear regression with the derived soil-environmental covariates, the study noted that only magnesium gave the best model fit with 50.9% of the soil-environmental covariates explaining its variability, while other soil properties presented unacceptable models. Therefore, to improve soil property prediction through multiple linear regression, more observation points are recommended to interpret better the performance of multiple linear regression over flat terrain system.