Abstract

Intake of cadmium (Cd) via vegetable food poses a possible health risk. Cereals are one of the major sources of Cd, and the Cd concentration in the soil has a great effect on the levels in the grain. The aim of the study was to produce decision support for identification of areas suitable for low-Cd winter wheat production in the form of a detailed digital soil map covering an important agricultural region in southern Sweden. A two-step approach was used: (1) we increased the number of soil Cd observations by combining two sets of soil samples, one with laboratory Cd analyses (304 samples) and one with predicted Cd from a portable x-ray fluorescent (PXRF) sensor (2097 samples); and (2) a digital soil mapping (DSM) model (gradient boosting regression) was calibrated on all 2401 soil samples to create a soil Cd concentration map using a number of covariates, of which airborne gamma ray data was identified as the most important. In the first step, cross-validation of the PXRF model obtained a model efficiency (E) of 0.82 and mean absolute error (MAE) of 0.08 mg kg(-1). The DSM model had an E of 0.69 and MAE of 0.11 mg kg(-1). The map of predicted soil Cd concentrations were compared against 307 winter wheat (Triticum aestivum L.) grain samples with laboratory-analyzed Cd concentrations. Areas in the map with low soil Cd concentrations had a high frequency of lower grain Cd concentrations. The map thus seemed to have potential for finding areas suitable for production of low-Cd winter wheat; e.g., for baby food.

Keywords

cadmium; digital soil mapping; machine learning; winter wheat; PXRF

Published in

Agronomy
2023, volume: 13, number: 2, article number: 317
Publisher: MDPI

Authors' information