Source apportionment of soil nitrogen and phosphorus based on robust residual kriging and auxiliary soil-type map in Jintan County, China

Abstract

Previous studies on the source apportionment of soil total nitrogen (TN) and total phosphorus (TP) mainly focused on specific spatial point sites and the closely related information such as soil-type map was seldom used. Moreover, traditional geostatistical models are usually sensitive to outliers. To address these problems, this study first proposed robust residual kriging (RRK) with soil-type data as auxiliary information to increase the spatial prediction accuracies for TN and TP in topsoil (0–20 cm) in Jintan County, China. Then, the spatial distribution patterns of the background concentrations of soil TN and TP were quantitatively assessed based on profile samples (80–100 cm) and the soil-type map. Last, the spatial distribution patterns of exogenous net inputs of TN and TP in topsoil and the corresponding contribution ratios were further calculated. Model comparisons showed that the relative improvement (RI) accuracies of RRK and residual kriging (RK) over the traditionally-used ordinary kriging (OK) were 23.91% and 15.22% for soil TN and 24.14% and 20.69% for soil TP, respectively. The results of the source apportionment indicated that (i) the exogenous net inputs of TN and TP had similar spatial distribution patterns, with the low inputs mainly in the west and high inputs mainly in the northeast and east of the county; (ii) high contribution ratio of exogenous net input of TN (≥67%) in topsoil was mainly located in the north, southeast, and middle of the study area, and the area with the contribution ratio over 70% is 294.16 km2, covering 30.16% of the area of the county; (iii) high contribution ratio of exogenous net input of TP (≥53%) in topsoil was mainly located in the north, northeast, and southeast of the county, and the area with contribution ratio over 70% is 24.35 km2, covering 2.50% of the area of the county. These results provided more accurate spatial decision support for effectively regulating TN and TP in topsoil.