| 水稻、小麦与土壤中重金属Cd含量的关系模拟研究 |
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| 引用本文： | 于灏,苏智杰,祝培甜,陈勇,杨侨,赵中秋.水稻、小麦与土壤中重金属Cd含量的关系模拟研究[J].地学前缘,2021,28(1):438-445. |
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| 作者姓名： | 于灏 苏智杰 祝培甜 陈勇 杨侨 赵中秋 |
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| 作者单位： | 中国地质大学(北京)土地科学技术学院,北京100083;自然资源部信息中心,北京100812;中国地质大学(北京)土地科学技术学院,北京100083;自然资源部土地整治重点实验室,北京100035 |
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| 基金项目： | 国土资源部公益性行业科研专项(201511082) |
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| 摘 要： | 土壤污染防治工作已成为提升耕地质量、保护国土生态安全的重要任务之一。为了科学预测我国大宗农作物(如水稻、小麦)与土壤重金属含量的关系,减少安全利用类农用地的大量农产品与土壤的协同监测,实现重金属污染农用地的安全利用,本研究以重金属Cd为例,选取对水稻、小麦Cd含量影响较大的土壤Cd含量、土壤pH值、土壤阳离子交换量(CEC)和土壤有机碳(OC)含量作为输入因子,水稻、小麦Cd含量作为输出因子,分别建立了多元回归模型与神经网络模型。结果表明:水稻、小麦Cd含量与土壤Cd含量呈现正相关关系;模拟出的水稻、小麦与土壤Cd的多元线性回归模型的预测能力分别为67.8%和83.8%;利用神经网络构建了水稻、小麦Cd含量预测模型,在训练集、验证集和测试集中都表现出很好的预测能力,R值均大于多元线性回归模型,且MSE(均方误差)值较小,神经网络对水稻、小麦Cd含量预测具有很好的适用性,模拟精度总体优于多元回归预测模型。研究结果可为污染农用地的安全利用评价及优化配置提供一定的理论依据和参考。
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| 关 键 词： | 土壤 水稻 小麦 镉 预测模型 神经网络 |
| 收稿时间： | 2020-03-13 |
Relationship between Cd contents in rice or wheat and soil:Insight from a simulation study |
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| YU Hao,SU Zhijie,ZHU Peitian,CHEN Yong,YANG Qiao,ZHAO Zhongqiu.Relationship between Cd contents in rice or wheat and soil:Insight from a simulation study[J].Earth Science Frontiers,2021,28(1):438-445. |
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| Authors: | YU Hao SU Zhijie ZHU Peitian CHEN Yong YANG Qiao ZHAO Zhongqiu |
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| Institution: | 1. School of Land Science and Technology, China University of Geosciences(Beijing), Beijing 100083, China2. Information Center of Ministry of Natural Resources, Beijing 100812, China3. Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Natural Resources, Beijing 100035, China; |
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| Abstract: | The prevention and control of heavy metal pollution in soil has become one of the important tasks in improving the quality of cultivated land and protecting the ecological security of the land. In order to scientifically predict the relationship between heavy metal contents in soil and staple crops (rice and wheat) in China and reduce the collaborative monitoring of a large number of agricultural products and soil form unpolluted agricultural land, as well as realize the safe use of heavy metal contaminated agricultural land, we studied heavy metal Cd as an example. We selected soil Cd content, pH value, cation exchange capacity (CEC) and organic carbon (OC) as inputs and the Cd contents of rice and wheat as outputs to build the multiple regression and neural network models through simulation. The results showed that the Cd content in rice or wheat was positively correlated with soil Cd content. The predictive abilities of the simulated multiple linear regression model for Cd in rice or wheat and soil were 67.8% and 83.8%, respectively. But the corresponding prediction model based on neural network had higher R-values in the training, validation and test sets than the multiple linear regression model, and the MSE value was small. Thus, the prediction accuracy of the neural network was better than that of the multivariate model for predicting Cd contents in rice and wheat. The research results can provide a theoretical basis and reference for the safety evaluation and optimal allocation of contaminated agricultural land. |
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| Keywords: | soil rice wheat cadmium prediction models neural network |
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