Research status of seed improvement in underutilized crops: prospects for enhancing food security
Bibliographic record
Abstract
Abstract Planning future directions and adapting approaches for seed improvement in underutilized crops requires the assessment of research activities. We have examined the trends and research activities conducted on seed improvement of underutilized crops. We identified research hotspots based on keywords and prolific research titles mapped across three decades (1990 to 2021). Data were compiled using Google Scholar, Web of Science and Scopus databases, loaded into the bibliometric R-package and viewed via VOSviewers software. In research on seed improvement among underutilized crops (SUC), we have observed 7.2% annual growth in publication and increase in the studies and citations. There were strong research publications with strong research links from studies conducted in USA, Canada, India, Nigeria and China, while South Africa and Egypt were among the African countries with high research studies in SUC. Among underutilized crops with improvement among their seed traits are sorghum, quinoa, Bambara groundnut, amaranth, barley, tef, cowpea and millet. Some of the trending research areas are genetic diversity, seed performance, seed domestication, yield, crop management, water use efficiency, nutritional properties, molecular strategies and genetic analysis tools for seed improvement. There is gradual increase for international collaborations and funding in SUC studies. The current research emphases are on qualitative studies, appropriate methodological procedures and advanced breeding resources to help understand and promote seed improvement among underutilized crops.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".