Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale.

ABSTRACT: Rice is a major dietary source of the toxic metal cadmium (Cd). Concentration of Cd in rice grain varies widely at the regional scale, and it is challenging to predict grain Cd concentration using soil properties. The lack of reliable predictive models hampers management of contaminated soils. Here, we conducted a three-year survey of 601 pairs of soil and rice samples at a regional scale. Approximately 78.3% of the soil samples exceeded the soil screening values for Cd in China, and 53.9% of rice grain samples exceeded the Chinese maximum permissible limit for Cd. Predictive models were developed using multiple linear regression and machine learning methods. The correlations between rice grain Cd and soil total Cd concentrations were poor (R ² < 0.17). Both linear regres

SUBMITTER: Huang BY 

PROVIDER: S-EPMC11489518 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature