Editorial: Advances in pea breeding and genomics
Bibliographic record
Abstract
Advances in pea breeding and genomicsPea is an important food legume crop in temperate regions of the world.World dry pea production averaged 13.5 million tons/year (2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022), while vegetable pea production averaged 19.2 million tons/year over that period (FAOSTAT).Dry pea production has been relatively stable while vegetable pea production has been increasing.The leading producers of dry pea are Canada, Russia, China, and India, while the leading producers of vegetable pea are China and India.Dry pea is used for whole seeds, dehulled seeds, flour, and in fractionated forms including protein concentrates and isolates and starch concentrates and isolates (Warkentin et al., 2015).Pea has become a leading crop type in the growing plantbased protein industries.Vegetable pea is used in the fresh form as shelled peas, snap peas, and snow peas.As a nitrogen-fixing grain legume crop, pea fits well into cereal-based crop rotations (Rubiales et al., 2019).As pea production typically does not require nitrogen fertilizer, it is a highly beneficial cropping option for addressing the climate change objective of reducing greenhouse gas emissions.This Research Topic, 'Advances in pea breeding and genomics', includes five papers that address important aspects of pea production and utilization.Weeden et al. explored genetic diversity in Pisum fulvum L., a key wild relative of Pisum sativum L., the cultivated pea.Their study of 90 P. fulvum accessions showed remarkable sequence diversity at the STAYGREEN (SGR) locus encoding Mendel's cotyledon color gene.Fifty-seven alleles were identified based on the sequence of the third intron of SGR.The SGR genotype was more effective than morphological traits in distinguishing the accessions.The accessions were classified as Group A (mainly northern Israel) and Group B (mainly the arid regions of southern Israel).P. fulvum accessions have been explored by breeders, potentially as donors of adaptive or disease resistance traits; for example, improved resistance to the scochyta complex (Jha et al., 2016, Jha et al, 2017) and drought tolerance (Naim-Feil et al., 2017) was identified in several P. fulvum accessions.Three of the papers in this Research Topic described progress in mapping key traits of interest in pea breeding.Yan et al. reported on the discovery of a key QTL associated with resistance to two bruchid (Callosobruchus) species.Bruchid beetles can cause substantial damage to pea and faba bean during the cropping season and especially in storage in Frontiers in Plant Science frontiersin.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.035 | 0.028 |
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".