Conventional and Molecular Breeding Tools for Accelerating Genetic Gain in Faba Bean (Vicia Faba L.)
Bibliographic record
Abstract
Faba bean is a cool-season grain legume crop, which is grown worldwide for food and feed. Despite a decrease in area under faba bean in the past, the interest in growing faba bean is increasing globally due to its high seed protein content and its excellent ecological service. The crop is, however, exposed to diverse biotic and abiotic stresses causing unstable, low grain yield. Although, sources of resistance to main diseases, such as ascochyta blight (Ascochyta fabaeSpeg.), rust (Uromyces viciae-fabae(Pers.) Schroet.), chocolate spot (Botrytis fabaeSard.) and gall disease (Physioderma viciae), have been identified, their resistance is only partial and cannot prevent grain yield losses without agronomical practices. Tightly associated DNA markers for host plant resistance genes are needed to enhance the level of resistance. Less progress has been made for abiotic stresses. Different breeding methods are proposed, but until now line breeding, based on the pedigree method, is the dominant practice in breeding programs. Nonetheless, the low seed multiplication coefficient and the requirement for growing under insect-proof enclosures to avoid outcrossing hampers breeding, along with the lack of tools such as double haploid system and cytoplasmic male sterility. This reduces breeding population size and speed of breeding hence the chances of capturing rare combinations of favorable alleles. Availability and use of the DNA markers such as vicine-convicine (vc−) and herbicide tolerance in breeding programs have encouraged breeders and given confidence in marker assisted selection. Closely linked QTL for several biotic and abiotic stress tolerance are available and their verification and conversion in breeder friendly platform will enhance the selection process. Recently, genomic selection and speed breeding techniques together with genomics have come within reach to accelerate the genetic gains in faba bean. Advancements in genomic resources with other breeding tools, methods and platforms will enable to accelerate the breeding process for enhancing genetic gain in this species.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".