Global research trends in Rhizodeposition-mediated soil carbon cycle: A bibliometric analysis
Applied Soil Ecology 202: 105531-105531
Article 2024 English
Authors
DC
Dongming Chen
FY
Fei Yan
XF
Xuemei Fang
Abstract
1 min read
Rhizodeposition plays a crucial role in the soil carbon (C) cycle, yet a comprehensive understanding of global research trends and directions related to rhizodeposition remains elusive. To provide a global perspective, this study employs bibliometric analysis to systematically review research on the Soil C cycle-rhizodeposition (SCC-Rhizo) publication characteristics, topic trends, and knowledge domains over the past decades. The SCC-Rhizo documents (2598) from 1966 to 2023 in the Web of Science Core Collection and Scopus were analyzed using VOSviewer, CiteSpace software and ‘bibliometrix’ package of R. A significant rise in annual publications and international scientific collaboration has been observed over the past years, indicating substantial growth potential and a dynamic research landscape. The study topics varied and flourished over time. The research scope expands from small-scale lab simulations to encompass large-scale ecosystem studies in diverse environments (i.e. grasslands, forests and paddy fields). The composition of rhizodeposition has been scrutinized from general compounds to specific materials (such as organic acids), while interactions between rhizodeposition and soil are currently being explored at the molecular level, which is expected to be a focal point of future research. Beyond bacteria, investigations now encompass fungi, microbial activity, and microbial communities. Isotope labeling and metagenomics sequencing are increasingly prevalent technology in SCC-Rhizo research. The results provide a global, objective perspective for understanding SCC-Rhizo research over the past decades. This research underscores the necessity for future studies to accurately quantify the rates and composition of rhizodeposition at the ecosystem level, explore the interactions with soil components, and develop predictive models.
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