Factors influencing the bioavailability of some selected elements in the agricultural soil of a geologically varied territory: The Campania region (Italy) case study

Bioavailability of some major and trace elements was evaluated in 1,993 topsoil samples collected across Campania region (Southern Italy). A main focus was made on Al, Ca, K, Mg, Cu, Tl since they are linked, for different reasons, to agriculture. Bioavailability was assessed by an extraction with ammonium nitrate and the data were compared with the pseudo-total concentration determined by Aqua Regia digestion. Geochemical maps of the pseudo-total and bioavailable concentrations were generated using a multifractal inverse distance weighted (MIDW) interpolation. In addition, the spatial distribution patterns of the percent bioavailability of elements, based on the ratio among bioavailable and the pseudo-total fractions, were also determined. The median value of the percent bioavailability showed the order Ca > K ≫ Mg ≃ Tl ≫ Cu ≫ Al and it represents a positive finding in terms of both agricultural productivity and environmental quality. Further, a multiple linear regression was finally applied to data to unveil any dependence of the bioavailable fraction on the pseudo-total content of elements. The grain size distribution and organic matter content of samples were later included to evaluate their possible role in promoting the environmental availability of elements. The pseudo-total concentrations of Al, Ca, K, and Mg alone resulted to be poorly able to predict the variability of the bioavailable fraction. The addition of the grain size distribution and organic matter content to the models expanded the predictive capability of Ca, K, and Mg whereas a marginal improvement was showed by Al, Cu, and Tl. This study represents a methodological contribution to a better understanding of the processes underlying the spatial variability of chemical elements in soil. Considering the positive outcomes obtained, further researches were planned to include more variables (e.g. soil pH, redox potential, content in Iron and Manganese oxides, etc.) in the predictive models.