Editorial: Soybean molecular breeding and genetics
Bibliographic record
Abstract
Soybean molecular breeding and geneticsSoybean [Glycine max (L.) Merr.] is one of the most important supplies of plant protein and oil for human consumption as well as a major source of protein meal for animal feeds.Both the progress in technology and the increasing market demand have driven a sustainable increase of the world soybean crop both in acreage and yield since 1990s, especially in North and South America.On the other hand, soybean production still faces many challenges, such as the impacts of climate changes and environmental stresses, damages caused by diseases and pests, and demand of higher yield potential and improved quality for diverse end uses.Developing new cultivars with desired characteristics through genetic improvement is a key to the solution to these challenges.Molecular breeding integrated with novel technologies greatly helps the realization of the goal.This Research Topic provides a platform to present research progress and discuss potential approaches to facilitate further development.Twenty-four articles published involve a wide range of areas, mainly addressing important traits and breeding strategies, such as resistance to diseases, agronomic and yield traits, and nutritional profiles.In addition to the identification of quantitative trait loci (QTL) and candidate genes associated with target traits, molecular mechanisms underlying traits are also explored. Resistance to diseasesGrowing resistant cultivars is the most economically effective method of preventing yield loss, and resistance to diseases and/or pests is always one of the most important breeding objectives in soybean.Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is a devastating pathogen for soybean production worldwide (Wei et al.).Using a population of 392 F 2:8 recombinant inbred lines (RILs) derived from the cross Zheng 9525 × Handou 10, Wei et al. identified seven QTL with additive effects.Among these QTL, three on Chromosomes 7, 8, and 18 exhibited resistance to two races, SCN HG type 2.5.7 (race 1) and 1.2.5.7 (race 2).They assumed that the previously reported QTL on 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.029 |
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".