传统作物籽粒中Cd含量的预测主要依赖于基于土壤Cd含量(Cds)和pH值的多元线性回归模型,忽略外部环境因素与Cdg之间的因素间相互作用和非线性因果关系。在这项研究中,包括土壤特性在内的多类型环境因子的综合指标体系,地质学,气候,并建立了人为活动。基于树的集成的机器学习模型,支持向量回归,基于环境因子指标的人工神经网络预测水稻和小麦的Cdg比传统的基于土壤性质的线性回归模型的精度明显提高。其中,XGboost和随机森林的基于树的集成模型在预测水稻和小麦的Cdg方面表现出最高的准确性,测试数据集中的R2分别为0.349和0.546。这项研究发现,土壤性质,包括CD,pH值,和粘土,对水稻和小麦的Cdg有较大影响,综合贡献率分别为65.2%和29.7%。由于小麦采样区位于中国中部和北部,与南部的水稻采样区相比,它们受降水和温度的限制更大。地质和气候因素对小麦Cdg的影响较大,综合贡献率为49.9%,高于水稻相应的20.9%。此外,水稻和小麦的Cdg与Cds没有绝对的线性关系,过高的Cds会降低作物Cd积累的生物富集因子。同时,其他环境因素,如温度,降水,海拔对作物Cdg的增加有边际效应。本研究为优化传统土壤植物转移模型提供了一个新的框架,为实现作物中Cd含量的高精度预测提供了一步。
The traditional prediction of the Cd content in grains (Cdg) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cds) and pH, neglecting inter-factorial interactions and nonlinear causal links between external environmental factors and Cdg. In this study, a comprehensive index system of multi-type environmental factors including soil properties, geology, climate, and anthropogenic activity was constructed. The machine learning models of the tree-based ensemble, support vector regression, artificial neural network for predicting Cdg of rice and wheat based on the environmental factor indexes significantly improved the accuracy than the traditional models of linear regression based on soil properties. Among them, the tree-based ensemble models of XGboost and random forest exhibited highest accuracies for predicting Cdg of rice and wheat, with R2 in the test dataset of 0.349 and 0.546, respectively. This study found that soil properties, including Cds, pH, and clay, have greater impacts on Cdg of rice and wheat, with combined contribution rates accounting for 65.2 % and 29.7 % respectively. Since wheat sampling areas are located in central and northern
China, they are more constrained by precipitation and temperature than rice sampling areas in the south. Geologic and climate factors have a greater impact on Cdg of wheat, with a combined contribution rate of 49.9 %, which is higher than the corresponding rate of 20.9 % in rice. Furthermore, the Cdg of rice and wheat did not exhibit an absolute linear relationship with Cds, and excessively high Cds can reduce the bioconcentration factor of Cd accumulation in crops. Meanwhile, other environmental factors such as temperature, precipitation, elevation have marginal effects on the increase of Cdg of crops. This study provides a novel framework to optimize traditional soil plant transfer models, as well as offer a step towards realizing high precision prediction of Cd content in crops.