Institute of Karst Geology, CAGS, Key Laboratory of Karst, MNR & GZARDynamics, International Research Center on Karst under the Auspices of UNESCO, Guilin, Guangxi 541004, China; Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, Guangxi 531406, China.
School of Science, China University of Geosciences, Beijing 100083, China.
Sci Total Environ. 2024 Dec 1;954:176650. doi: 10.1016/j.scitotenv.2024.176650. Epub 2024 Oct 3.
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 R 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.
喀斯特土壤通常表现出较高的锌（Zn）水平，为种植富锌作物提供了机会。（与此同时）然而，这些土壤也经常含有高背景水平的有毒金属，特别是镉（Cd），这可能会带来健康风险。了解高地球化学背景地区中 Cd 和 Zn 的生物累积及其相关驱动因素，可为富锌作物的安全开发提供重要的见解。传统模型在预测高地球化学背景土壤上种植的作物系统中的金属水平时往往难以做到准确。本研究采用了机器学习模型，包括随机森林（RF）、支持向量机（SVM）和极端梯度提升（XGBoost），以探索在喀斯特地区可持续种植富锌作物的有效策略，重点是生物累积因子（BAF）。从广西的喀斯特地区共采集了 10986 个表层土壤样本和 181 对根际土壤-作物样本，包括早稻、晚稻和玉米。SVM 和 XGBoost 模型表现出较好的性能，对 Zn 和 Cd 的 BAF 估计的 R 值分别为 0.84 和 0.60。确定了 BAF 的关键决定因素，包括土壤铁和锰含量、pH 值以及 Zn 和 Cd 的相互作用。通过将这些土壤特性与机器学习相结合，开发了一种安全种植富锌作物的框架。这项研究有助于制定策略来减轻 Cd 污染土壤中作物的 Zn 缺乏症。
