宁夏固原原州区土壤及农作物硒地球化学特征及其研究意义
Geological Review（2023）
Abstract
硒(Se)是人类和动物必需微量元素之一,为科学预测作物硒含量,实现富硒土地资源合理开发利用.本研究系统采集并分析了 13042个表层土壤样品和313套玉米、164套马铃薯及其对应的根系土样品,研究了该区土壤Se含量分布特征及影响因素,分别建立了马铃薯、玉米可食部分Se含量的BP神经网络预测模型,对富Se农作物种植区进行了合理规划.结果表明:土壤Se含量均值是0.164 μg/g,空间分布不均匀,研究区内清水河平原地区出现富硒区且连片分布.研究区土壤硒元素含量主要受其成土母质控制,岩石经风化剥蚀、随河水迁移和农业灌溉,伴随着有机质含量增加,造成第四系冲洪积平原Se含量增加.研究区马铃薯、玉米籽实富Se率分别为82.32％和38.02％,且重金属含量不超标,具备开发富硒农产品的潜力.农作物籽实Se含量主要与根系土中Se、S、pH和有机质含量有关,通过作物籽实Se含量预测模型规划出研究区富Se马铃薯种植区面积为1050.11 km2,富Se玉米种植区面积19.19 km2.该认识可为当地富硒农产品种植区规划及作物种植调整提供依据.
MoreTranslated text
Key words
top soil,distribution characteristics,selenium-rich crop,Se-rich crop,prediction model,Guyuan Ningxia
求助PDF
上传PDF
View via Publisher
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