文献类型： 外文期刊

作者： Li, Tong ¹ ; Cui, Lizhen ² ; Wu, Yu ³ ; Pandey, Rajiv ⁴ ; Liu, Hongdou ⁵ ; Dong, Junfu ⁶ ; Wang, Weijin ¹ ; Xu, Zhihong ⁶ ; Song, Xiufang ⁷ ; Hao, Yanbin ¹ ; Cui, Xiaoyong ¹ ; Du, Jianqing ¹ ; Zhang, Xuefu ³ ; Wang, Yanfen ¹ ;

作者机构： 1.Univ Queensland, Sch Agr & Food Sustainabil, St Lucia 4072, Australia

2.Univ Chinese Acad Sci, Coll Life Sci, Beijing 100049, Peoples R China

3.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

4.Indian Council Forestry Res & Educ, Dehra Dun 248006, India

5.Griffith Univ, Ctr Planetary Hlth & Food Secur, Sch Environm & Sci, Brisbane 4111, Australia

6.Shandong Univ, Inst Marine Sci & Technol, Qingdao 264209, Peoples R China

7.Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China

8.Univ Chinese Acad Sci, Sch Econ & Management, Dept Informat Resources Management, Beijing, Peoples R China

9.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China

10.Univ Chinese Acad Sci, Beijing Yanshan Earth Crit Zone, Beijing 101408, Peoples R China

11.Chinese Acad Sci, Inst Tibetan Plateau Res, State Key Lab Tibetan Plateau Earth Syst Environm, Beijing 100101, Peoples R China

关键词： natural language processing; grassland degradation; knowledge synthesis; scientometrics; systematic review

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.4； 五年影响因子：4.8 ）

ISSN： 2095-3119

年卷期： 2025 年 24 卷 3 期

页码：

收录情况： SCI

摘要： Grassland degradation presents overwhelming challenges to biodiversity, ecosystem services, and the socioeconomic sustainability of dependent communities. However, a comprehensive synthesis of global knowledge on the frontiers and key areas of grassland degradation research has not been achieved due to the limitations of traditional scientometrics methods. The present synthesis of information employed BERTopic, an advanced natural language processing tool, to analyze the extensive ecological literature on grassland degradation. We compiled a dataset of 4,504 publications from the Web of Science core collection database and used it to evaluate the geographic distribution and temporal evolution of different grassland types and available knowledge on the subject. Our analysis identified key topics in the global grassland degradation research domain, including the effects of grassland degradation on ecosystem functions, grassland ecological restoration and biodiversity conservation, erosion processes and hydrological models in grasslands, and others. The BERTopic analysis significantly outperforms traditional methods in identifying complex and evolving topics in large datasets of literature. Compared to traditional scientometrics analysis, BERTopic provides a more comprehensive perspective on the research areas, revealing not only popular topics but also emerging research areas that traditional methods may overlook, although scientometrics offers more specificity and detail. Therefore, we argue for the simultaneous use of both approaches to achieve more systematic and comprehensive assessments of specific research areas. This study represents an emerging application of BERTopic algorithms in ecological research, particularly in the critical research focused on global grassland degradation. It also highlights the need for integrating advanced computational methods in ecological research in this era of data explosion. Tools like the BERTopic algorithm are essential for enhancing our understanding of complex environmental problems, and it marks an important stride towards more sophisticated, data-driven analysis in ecology.