Evidence map›Paper›PMID 38932930›Full record

ReviewFundamental research2023

Trends in grassland science: Based on the shift analysis of research themes since the early 1900s.

Guiling Zhao

Abstract readReview
In one paragraph

Review in Fundamental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Diversity Analysis of Global White Clover (International journal of molecular sciences · 2026
    Article
  2. Grassland Science in a New Era.Fundamental research · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Guiling ZhaoDivision of Environment and Ecology, Department of Life Sciences, National Natural Science Foundation of China, Beijing 100085, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Grasslands, one of the major terrestrial ecosystems, are essential for the maintenance of ecological and production functions; however, they are undergoing extensive degradation. The development and cutting-edge explorations in grassland science are critical to addressing challenges such as climate change and the increasing influence of human activities. To identify research trends in grassland science, latent Dirichlet allocation (LDA) topic modelling was used to conduct an automated content analysis on 123,829 papers available on Web of Science Core Collection from 1900 to 2020. Results from this analysis showed that grassland research has become increasing multidisciplinary, accompanied by a pronounced reduction in the relative frequency of traditional production-oriented research and an increase in the themes focusing on ecological functions and modern technologies. Changes in research activities have been uneven globally, with a significant increase in the number of publications in China and Brazil, which probably reflects an increased support from various governmental agencies in these countries. Additionally, in 2019, China surpassed the United States in terms of the total number of publications. Further, this study identified important topics and emerging challenges in grassland research, such as biodiversity conservation, climate changes, and genetic considerations. Comprehensive improvement of education, research, global cooperation, and funding strategies will be necessary to promote grassland science research on frontier themes and to effectively address the social and environmental challenges in the new era.

Indexed as

BibliometricsDevelopment strategiesGrassland scienceResearch themesTemporal trends

Identifiers

PMID38932930
PMCPMC11197771

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.