Past and Future Use of Wild Relatives in Crop Breeding
Bibliographic record
Abstract
Wild species related to agricultural crops (crop wild relatives, or CWR) can increase the adaptive capacity of agricultural systems around the world. They represent a large pool of genetic diversity from which to draw new allelic variation required in breeding programs. Crop wild relatives have been extremely valuable in adapting crop varieties to changing disease pressures, farming practices, market demands, and climatic conditions. Unfortunately, CWR are a threatened resource and measures need to be taken to protect them, both in the wild and in genebanks. Here, we review how wild species have contributed to the development of improved crop varieties and where efforts must be concentrated to harness their value in the future. Drawing on the results of an extensive literature search, a series of 14 expert consultation meetings, and in‐depth interview with experts on 24 crops, we document the role that CWR play in modern crop breeding. We discuss (i) their past and current use, (ii) advanced breeding methods and technologies that promise to facilitate the continued use of CWR, and (iii) what constraints continue to hinder increased use of CWR in breeding.
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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".