Advancements in lentil breeding: harnessing molecular markers and omics approaches for resistance to biotic and abiotic stresses
Bibliographic record
Abstract
Lentil (Lens culinaris Medik.), an essential cool-season legume crop, is widely cultivated in southern Asia as a sole winter crop following the rice harvest. It is highly valued for its rich nutritional profile, including abundant protein, folic acid, iron, and zinc. However, lentil production is severely threatened by various abiotic and biotic stresses. Key abiotic stresses include heat, drought, salinity, heavy metal toxicity, and iron deficiency. In contrast, biotic stresses comprise anthracnose, ascochyta blight, sclerotinia white mold, fusarium wilt, rust, and various viral, bacterial, and nematode diseases. To combat these challenges, plant breeders and geneticists have focused on identifying resistant germplasm, deciphering the genetic basis of resistance, and mapping associated resistance genes. Significant progress in lentil genomics, with efforts to establish a unified genetic map, has significantly enhanced breeding strategies. Presently, molecular breeding, specifically targeting anthracnose and ascochyta blight in Australia and Canada, has yielded promising results. Furthermore, the advent of molecular markers and genomics has revolutionized lentil breeding, enabling the precise development of disease-resistant and climate-resilient lentil varieties through marker-assisted selection. In addition, the integration of omics tools, such as genomics, transcriptomics, proteomics, and metabolomics, has provided deeper insights into the complex biological pathways underlying stress tolerance. These technologies allow for more comprehensive identification of candidate genes and biomarkers, further advancing lentil breeding efforts. This review highlights the integration of traditional and innovative breeding techniques to address emerging challenges, particularly in the context of climate change. By combining ancestral knowledge with modern molecular breeding tools, researchers are making substantial progress in developing robust lentil varieties with improved resistance to abiotic and biotic stresses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".