Unveiling and advancing grassland degradation research using a BERTopic modelling approach

作者：Tong Li, Lizhen Cui, Yu‐Wei Wu, Rajiv Pandey, Hongdou Liu, Junfu Dong, Weijin Wang, Zhihong Xu, Xiufang Song, Yanbin Hao, Xiaoyong Cui, Jianqing Du, Xuefu Zhang, Yanfen Wang · 发表于：Journal of Integrative Agriculture · 年份：2024 · DOI：10.1016/j.jia.2024.11.008 · 被引用次数：15 · 研究领域：Land Use and Ecosystem Services、Soil Carbon and Nitrogen Dynamics、Rangeland Management and Livestock Ecology

• First use of BERTopic to analyze grassland degradation research themes. • Combined use of BERTopic and scientometrics enhances topic modeling. • Pioneering computational tools in ecological research with BERTopic. Grassland degradation presents overwhelming challenges to biodiversity, ecosystem services, and the socio-economic sustainability of dependent communities. However, a comprehensive synthesis of global knowledge on the frontiers and key areas of grassland degradation research has <pg=>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 mo...