数字农科院2.0

Unveiling and advancing grassland degradation research using a BERTopic modelling approach

文献类型： 外文期刊

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

作者机构：

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

期刊名称： JOURNAL OF INTEGRATIVE AGRICULTURE

ISSN： 2095-3119

年卷期： 2025 年 24 卷 3 期

页码：

收录情况： SCIE（2025版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版） ; ; 农林核心（2024版）

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.

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