Bibliometric Analysis of Soil Nutrient Research between 1992 and 2020
Bibliographic record
Abstract
Soil nutrient balance is related to the interaction mechanism between soil fertilizer, soil water, climate change, and plant capability. This paper provides a perspective from bibliometric analysis based on data from the Web of Science core collection with software tools, including Vosviewer, HistCite Pro, and Citespace, in order to reveal the evolution of research trends in soil nutrients. The results show that publication outputs have increased exponentially from 1992 to 2020. The synthetic parameter of the sum of normalized data (SND), calculated from the default indicators of the bibliometric software tools, was used to rank the overall contribution of journal/authors/institutions/countries. The results demonstrate that Agriculture Ecosystems & Environment, Soil Biology & Biochemistry and Science of the Total Environment are the leading journals in the soil nutrient field. The Chinese Academy of Sciences had the highest total citations and collaborated most closely with other organizations, followed by United States Department of Agriculture (USDA) Agricultural Research Service (ARS) and Agr& Agri Food Canada. In addition, USA, China, and UK are the top three research centers for this topic. Moreover, Ken E Giller, Qirong Shen, and Rattan Lal were the top three authors, while Andrew Sharpley ranked the first depending on citations per publication. In terms of co-occurrence of keyword analysis, the results indicate that nitrogen fertilizer, green manure, and soil population have gained close attention from scholars, while soil amendment of biochar have evolved as a hot topic in recent years. Perspectives on future studies are also given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.086 | 0.160 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".