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农学学报

农学学报 ›› 2025, Vol. 15 ›› Issue (5): 48-61.doi: 10.11923/j.issn.2095-4050.cjas2024-0036

• 土壤肥料 资源环境 生态 • 上一篇    下一篇

基于文献计量分析的土壤质量评价最小数据集(MDS)研究热点分析及展望

侯意龙1(), 马睿岐1, 李征1, 石武良1, 李斌1, 张生武2, 曹宁1, 崔金虎1, 张玉斌1()   

  1. 1 吉林大学植物科学学院，长春 130062
    2 吉林省水利科学研究院，长春 130061
  • 收稿日期:2024-02-29 修回日期:2024-06-05 出版日期:2025-05-20 发布日期:2025-05-19
  • 通讯作者:
    张玉斌，男，1977年出生，山东莒南人，教授，博士，主要从事土壤退化与修复研究。主持科研项目10余项，发表论文60余篇。通信地址：130062 吉林省长春市西安大路5333号 吉林大学植物科学学院，Tel：0431-87835710，E-mail：。
  • 作者简介:

    侯意龙，男，2000年出生，河南漯河人，硕士研究生，主要从事土壤生态与土壤质量研究。E-mail：。

  • 基金资助:
    国家自然科学基金面上项目“黑土区玉米根际异质性与农田包气带磷运移规律的动态关联机制研究”(32272819); 吉林省科技厅重大科技专项“吉林省西部风蚀瘠薄黑土地保护与高效利用技术体系集成与示范、吉林省农产品绿色生产科技工程重大科技专项”(20220302003NC); 吉林省科技厅重大科技专项“吉林省西部风蚀瘠薄黑土地保护与高效利用技术体系集成与示范、吉林省农产品绿色生产科技工程重大科技专项”(20230302003NC)

Hotspots and Prospect of Minimum Data Set (MDS) in Soil Quality Assessment Based on Bibliometric Analysis

HOU Yilong1(), MA Ruiqi1, LI Zheng1, SHI Wuliang1, LI Bin1, ZHANG Shengwu2, CAO Ning1, CUI Jinhu1, ZHANG Yubin1()   

  1. 1 College of Plant Science, Jilin University, Changchun 130062
    2 Jilin Institute of Water Resources Science, Changchun 130061
  • Received:2024-02-29 Revised:2024-06-05 Online:2025-05-20 Published:2025-05-19

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摘要：

本研究采用文献计量学方法，总结当前土壤质量研究中最小数据集(MDS)选取的方法和指标，定量分析并指出土壤质量评价中最小数据集的热点和前沿，为中国土壤质量评价和农业绿色发展提供科学参考。通过检索1991—2022年CNKI和Web of Science相关文献，收集了文献中310个最小数据集进行筛选，借助CiteSpace和VOSviewer对年度发文量、国家/地区、机构、期刊进行共现分析，对关键词进行突现词和聚类分析。31年来该领域文献量逐步增加并仍处于快速发展阶段，中国是发文量最多的国家，期刊载文量最多的为《土壤通报》《生态学报》和Ecological Indicators；主要研究热点表现在“农业管理对土壤质量影响、土壤退化与修复、土壤质量对气候变化的响应与应对及最小数据集筛选方法与模型构建”等方面；前期MDS在土壤质量评价中选用较多的主要为物理、化学指标，但随着土壤健康的发展，生物学指标逐步增长。在未来一段时间内MDS发文量仍为快速增长阶段，发展中国家在全球起着重要节点作用；MDS核心指标为土壤有机质/碳(SOM/SOC)、pH、全氮、速效磷和容重；未来研究应注重在基于大数据平台构建不同尺度下静态评价与动态监测相结合的综合反映土壤功能的土壤健康质量评价框架体系，探讨气候变化背景下与土壤质量变化相对应的MDS及其指标体系，构建精准反映土壤质量变化规律的评价模型与最优最小数据集。

关键词: 土壤质量评价, 最小数据集, Citespace, 聚类分析, 评价指标, 土壤健康

Abstract:

To provide scientific reference for soil quality evaluation and agricultural green development in China, this study used bibliometric methods to conduct quantitatively analysis, screened the hotspots and frontiers of soil quality evaluation based on minimum data set (MDS), and summarized the current methods and indicators used to select the MDS in soil quality research. By searching relevant literatures on CNKI and Web of Science from 1991 to 2022, we collected and screened 310 MDS. CiteSpace and VOSviewer were used to conduct co-occurrence analysis of the annual number of publications, countries/regions, institutions, journals, and to perform burst words and clusters analysis on keywords. Over the past 31 years, the publications in this field have gradually increased and remain in a phase of rapid development. China is the country with the largest number of publications. The journals with the largest number of publications are Acta Ecologica Sinica, Chinese Journal of Soil Science, and Ecological Indicators, respectively. The main research hotspots were the impact of agricultural management on soil quality, soil degradation and remediation, soil quality response to climate change, MDS screening methods and model construction, respectively. In the early stage, MDS in soil quality evaluation mainly used physical and chemical indicators, but with the development of soil health, the use of biological indicators has gradually increased. So the number of publications will be still in a rapid growth stage in the next period of time, and developing countries will play an important role in the globe. The core indicators are SOM/SOC, pH, TN, AP and BD, respectively. In future, research on MDS should focus on the building of soil health quality evaluation framework system, which combines static evaluation and dynamic monitoring in different scales and comprehensively reflects soil functions based on big data. The MDS and evaluation system corresponding to soil quality change under the background of climate change should be discussed, and evaluation model and optimal MDS (OMDS) should be constructed to accurately reflect soil quality change rules.

Key words: soil quality evaluation, minimum data set (MDS), CiteSpace, cluster analysis, evaluation indicators, soil healthy