浙江农业学报 ›› 2023, Vol. 35 ›› Issue (11): 2688-2697.DOI: 10.3969/j.issn.1004-1524.20230785

• 环境科学 • 上一篇    下一篇

基于Web of Science数据库的土壤质量评价及其微生物指标研究趋势分析

崔玲宇1(), 喻曼2,*(), 乔宇颖2, 苏瑶2, 王云龙2, 沈阿林2   

  1. 1.浙江农林大学 环境与资源学院,浙江 杭州 311300
    2.浙江省农业科学院 环境资源与土壤肥料研究所,浙江 杭州 310021
  • 收稿日期:2023-06-21 出版日期:2023-11-25 发布日期:2023-12-04
  • 作者简介:崔玲宇(2000—),女,内蒙古通辽人,硕士研究生,从事土壤生态与健康研究。E-mail:1765841685@qq.com
  • 通讯作者: * 喻曼,E-mail:yuman@zaas.ac.cn
  • 基金资助:
    现代农业产业技术体系(CARS-03-28)

Research trend of soil quality assessment and microbiological indicators based on Web of Science database

CUI Lingyu1(), YU Man2,*(), QIAO Yuying2, SU Yao2, WANG Yunlong2, SHEN Alin2   

  1. 1. College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, China
    2. Environmental Resources and Soil Fertilizer Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
  • Received:2023-06-21 Online:2023-11-25 Published:2023-12-04

摘要:

为全面客观分析土壤质量评价领域的研究动态和发展趋势,利用知识图谱工具HistCite Pro 2.1、VOSviewer 1.6.19和CiteSpace 6.1.R6软件,基于Web of Science核心合集数据库,就近10年(2012—2022年)土壤质量评价领域的发文量、高被引文章、研究热点和研究趋势等进行计量分析。结果显示,近10年土壤质量评价领域的发文量呈上升趋势。关键词聚类网络划分出土壤健康评价、土壤质量评价、微生物指标3类。应用机器学习算法评价土壤质量、筛选微生物指标构建评价最小数据集是当前土壤质量评价领域的两大研究热点。将机器学习模型应用于不同土壤类型、种植系统和管理措施下评价土壤质量,挖掘土壤核心功能微生物和优势菌种作为微生物评价指标是未来的研究趋势。

关键词: 土壤质量评价, 土壤健康评价, 计量分析, 微生物指标, 机器学习

Abstract:

To clarify the dynamics and trend of soil quality assessment comprehensively and objectively, a bibliometric analysis of the number of published literautres, highly cited articles, research hotspots and trends was conducted based on the Web of Science database pertaining to soil quality assessment in the past 10 years (2012-2022). Knowledge mapping tools, such as softwares of HistCite Pro2.1, VOSviewer 1.6.19 and CiteSpace 6.1.R6 were used. It was shown that there was a consistent increase in the number of published literatures related to soil quality assessment during the past 10 years. The keywords clustering network was divided into three groups, namely, soil health assessment, soil quality evaluation, and microbial indicators. The research hotspots included indicator screening methods and the application of microbial indicators in soil quality/health assessment. Future research trends involved exploring the core functional microorganisms and dominant strains in soil as microbial evaluation indicators and applying machine learning models to evaluate soil quality under different soil types, cropping systems and management measures.

Key words: soil quality assessment, soil health evaluation, bibliometric analysis, microbial indicators, machine learning

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