Evidence of scientific research on organic plant breeding: A bibliometric study
Bibliographic record
Abstract
Abstract Use of varieties bred under organic conditions is essential in order to minimize the yield gap between organic and conventional agriculture. The aim of this study was to analyse research publications related to the topic ‘organic plant breeding’ from the Web of Science database using bibliometric science mapping and visualization tools. The number of analysed documents in the bibliographic dataset was 204 from the 53 sources. The overall trend in the organic plant breeding literature showed that the number of publications increased during the observed time‐span. We found that in total, 65 countries and 337 institutions are active in the field of organic plant breeding with a high degree of international collaboration. The top five countries according to the number of publications were the United States, Italy, Germany, France, and Canada, while the most active institutions were Wageningen University, Iowa State University, University of Alberta, University of Copenhagen, and University of Hohenheim. All keywords from the organic plant breeding research in the agronomy category were separated into seven clusters for different research topics. Although there is evident progress viewed through the increased trend in the number of publications, organic plant breeding needs further expansion and development. This is especially through the implementation of novel plant breeding techniques and methods aiming to improve traits that are highly specific to organic conditions.
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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 | 0.011 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.146 | 0.247 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".