Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria

Background: Information addressing soil quality in developing countries often depends on results from small experimental plots, which are later extrapolated to vast areas of agricultural land. This approach often results in misinformation to end-users of land for sustainable soil nutrient management. The objective of this study was to estimate the spatial variability of soil quality index（SQI） at regional scale with predictive models using soil–environmental covariates.Methods: A total of 110 composite soil samples（0–30 cm depth） were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State, Nigeria, and selected soil physical and chemical properties were determined. We employed environmental covariates derived from a digital elevation model（DEM） and Sentinel-2 imageries for our modelling regime. We measured soil quality using two approaches [total data set（TDS） and minimum data set（MDS）]. Two scoring functions were also applied, linear（L） and non-linear（NL）, yielding four indices（MDS＿L, MDS＿NL, TDS＿L, and TDS＿NL）. Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis（PCA）. Random forest（RF）, support vector regression（SVR）, regression kriging（RK）, Cubist regression, and geographically weighted regression（GWR） were applied to predict SQI in unsampled locations.Results: The computed SQI via MDS＿L was classified into five classes: ≤ 0.38, 0.38–0.48, 0.48–0.58, 0.58–0.68, and ≥ 0.68, representing very low（class Ⅴ）, low（class Ⅳ）, moderate（class Ⅲ）, high（class Ⅱ） and very high（class Ⅰ） soil quality, respectively. GWR model was robust in predicting soil quality（R2 = 0.21, CCC = 0.39, RMSE = 0.15）, while RF was a model with inferior performance（R2 = 0.02, CCC = 0.32, RMSE = 0.15）. Soil quality was high in the southern region and low in the northern region. High soil quality class（> 49%） and moderate soil quality class（> 14%） dominate the study area in all predicted models used.Conclusions: Structural stability index, sand content, soil oganic carbon content, and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices, while land surface water index, Sentinel-2 near-infrared band, plane curvature, and clay index were the most important variables affecting soil quality variability. The MDS＿L and GWR are effective and useful models to identify the key soil properties for assessing soil quality, which can provide guidance for site-specific management of soils developed on diverse parent materials.