Spatial Distribution of Forest Soil Base Elements (Ca, Mg and K): A Regression Kriging Prediction for Czechia

Abstract

Base cations have declined within European forests due to leaching, accelerated by atmospheric acid deposition. This study aims at predicting the spatial distribution of pseudototal content of Ca, Mg, and K for coniferous, broadleaved and mixed forest stands. A harmonised database of about 7000 samples from the top mineral layer of 0–30 cm from the entire forest areas of the Czech Republic was used. A regression kriging model was used for spatial prediction of the content of the elements. The influence of the covariates used for the prediction was assessed using generalized additive models for location scale and shape (GAMLSS). The variance explained by the model was best for Ca with the R2 of 0.32, the R2 for Mg was 0.30, and the R2 for K was 0.26. Model fitting assessed by the ratio of performance to inter-quartile distance (RPIQ) showed K as the best fit with a value of 1.12, followed by Mg with the value 0.87, and Ca with 0.25. Ca exhibited the best prediction fit for the GAMLSS, compared with K and Mg, based on their AIC matrix values. The predicted spatial distribution in this study provides information for policy and will provide information for the sustainable management of forests.