Global Warming and Tea Production—The Bibliometric View on a Newly Emerging Research Topic is a research paper published in Climate (2017). On theSindex it has a DataRank of 0.550. It has been cited 38 times.
In this study, we analyzed the newly emerging research field of climate change in combination with tea production. We adapted a valid search query to cover the relevant literature as completely as possible and to exclude irrelevant literature. The search resulted in a publication set of 14 key papers dealing with the implications of climate change for tea production as well as 71 papers citing at least one of the 14 key papers. The VOSviewer software was used for revealing the thematic content of the publication set based on the analysis of the keywords. The analysis illustrates the importance of climate change for tea production and mirrors the emerging discussion on climate change impacts and adaptation strategies. Questions regarding the historical context of research fields or specific research topics can be answered by using a bibliometric method called “Reference Publication Year Spectroscopy” (RPYS). The standard RPYS, as well as RPYS-CO, which is based on co-citations of a marker paper, were applied and the most important publication in the historical context of climate change in combination with tea production was identified: both RPYS analyses revealed a paper by M.A. Wijeratne working at the Tea Research Institute (TRI) in Sri Lanka as the starting point of the newly emerging research topic. Currently, the research topic is stimulated by research projects and publications of Selena Ahmed at the Montana State University (USA).
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.550
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.