Machine learning-based prediction of cadmium bioaccumulation capacity and associated analysis of driving factors in tobacco grown in Zunyi City, China

Tobacco grown in areas with high-geochemical backgrounds exhibits considerably different cadmium (Cd) bioaccumulation abilities due to regional disparities and environmental changes. However, the impact of key factors on the Cd bioaccumulation ability of tobacco grown in the karst regions with high selenium (Se) geochemical backgrounds is unclear. Herein, 365 paired rhizospheric soil-grown tobacco samples and 321 topsoil samples were collected from typical karst tobacco-growing soil in southwestern China and analyzed for Cd and Se. XGBoost was used to predict and evaluate the Cd bioaccumulation ability of tobacco and potential influencing factors. Results showed that regional geochemical characteristics, such as soil Cd and Se contents, soil type, and lithology, have the highest influence on the Cd bioaccumulation ability of tobacco, accounting for 46.5% of the overall variation. Moreover, soil Se contents in high-geochemical background areas considerably affect Cd bioaccumulation in tobacco, with a threshold for the mutual suppression effects of Cd and Se at a soil Se content of 0.8 mg/kg. According to the results of bivariate local indicators of spatial association analysis, tobacco cultivated in the central, northeast, and southeast regions of Zunyi City carries a lower risk of soil Cd contamination. This study provides new insights for managing tobacco cultivation in karst regions.

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions

Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also frequently contain high background levels of toxic metals, particularly cadmium (Cd), posing potential health risks. Understanding the bioaccumulation of Cd and Zn and the related drivers in a high geochemical background area can provide important insights for the safe development of Zn-enriched crops. Traditional models often struggle to accurately predict metal levels in crop systems grown on soils with high geochemical background. This study employed machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), to explore effective strategies for sustainable cultivation of Zn-enriched crops in karst regions, focusing on bioaccumulation factors (BAF). A total of 10,986 topsoil samples and 181 paired rhizosphere soil-crop samples, including early rice, late rice, and maize, were collected from a karst region in Guangxi. The SVM and XGBoost models demonstrated superior performance, achieving R2 values of 0.84 and 0.60 for estimating the BAFs of Zn and Cd, respectively. Key determinants of the BAFs were identified, including soil iron and manganese contents, pH level, and the interaction between Zn and Cd. By integrating these soil properties with machine learning, a framework for the safe cultivation of Zn-enriched crops was developed. This research contributes to the development of strategies for mitigating Zn deficiency in crops grown on Cd-contaminated soils.