Evaluating the spatial and vertical distribution of agriculturally important nutrients — nitrogen, phosphorous and boron — in North West Iran

Abstract Soil legacy data is ubiquitous and usually contains routine soil analysis information. In Iran, like most places, legacy soil data constitutes genetic horizon soil information recorded from excavated soil profiles. Describing and sampling from each genetic horizon is assumed to be heterogeneous from site to site. Digital soil mapping (DSM) using observed data is valuable because it provides a means to exploit the available information together with leveraging commonly available information by way of environmental covariates. It creates a much more detailed view of soil at the landscape scale. The purpose of this paper is to model and map the spatial distribution of nitrogen, phosphorous and boron at four standardized depths: 0–15, 15–30, 30–60, 60–100 cm, in an area of 7300 ha in the north west of Iran, and compare different model types. To circumvent the issue of heterogeneous soil depth observations from site to site, mass-preserving soil depth function splines were used to harmonise the soil profile observed data to the aforementioned standard depths. This facilitated the spatial modelling of each of the target variables for each standard depth with the aim of creating digital soil maps. Twenty-three covariates were extracted from a publically available digital elevation model (DEM) as well as freely available Landsat 8 ETM+ imagery. The DEM-derivative covariates used in this study were divided into three main categories: i) Morphometry; ii) hydrology; and iii) lighting visibility. Both Random Forest and Cubist were assessed as candidate models for predicting each target variable. The results showed that Cubist was the most accurate method. Terrain attributes play an important role in estimating N, P, and B, while optical images do not have significant role. The most important findings of this paper in terms of environmental hazards are that the inundated regions in the west part of the study area are susceptible to boron contamination, providing future guidance for remediation.