WeChat Mini Program
Old Version Features

恩施富硒土壤区土壤硒镉与其理化性质关系研究

Southwest China Journal of Agricultural Sciences(2019)

Cited 7|Views12
Abstract
[目的]为了研究湖北省恩施州富硒高镉土壤区土壤pH、有机质、CEC和土壤硒镉关系,揭示土壤理化性质对土壤硒镉含量的影响.[方法]以恩施州目前已经所做土地质量地球化学调查的区域,圈出富硒高镉土壤区,选择在区内采样分析的土壤数据,整理出土壤理化性质和对应的土壤硒镉元素及有效态硒镉数据,分析它们的关系及理化性质对土壤硒镉的影响.[结果]在恩施富硒高镉土壤区内,①土壤硒含量和土壤有机碳含量呈显著的正相关.第一等级有机质土壤中硒平均含量达到9.688 mg/kg,分别是第二、三、四等级的有机质土壤硒平均含量的5.32、8.8和4.67倍.②农作物根系土镉含量和土壤有机质含量呈显著的正相关,有机质第一等级的土壤中镉均值含量达到19.604 mg/kg,分别是第二、三、四等级有机质土壤镉含量的6.84、9.51和8.25倍.③土壤有效硒含量和土壤酸碱度呈显著的正相关.随着土壤的酸碱度等级上升,土壤有效态Se的含量显著上升(P=0.02968 <0.05),碱性土壤区间中有效态硒均值达到26.296 ng/kg,分别是中性、微酸、酸性、强酸性土壤区间的1.51、2.86、4.62、3.63倍.④农作物根系土壤酸碱度和土壤镉含量相关性不显著.⑤土壤有效态硒含量和土壤阳离子的线性关系不显著.⑥土壤镉含量和土壤阳离子交换量的线性关系不显著.[结论]研究认为恩施富硒高镉土壤区土壤有机质、土壤酸碱度显著影响土壤硒镉含量,有效态硒在碱性土壤中显著增多,农作物富硒种植可以适当增加土壤碱性,土壤阳离子交换量CEC对土壤硒镉含量的影响有限.
More
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined