Gene Editing-Assisted Development of Herbicide-Resistant Lentils
Bibliographic record
Abstract
Lentil ( Lens culinaris ) is a globally important legume crop, but its yield is increasingly constrained by weed competition and limited access to selective herbicides. This study explores how gene editing technologies, particularly the CRISPR/Cas system, can revolutionize the development of herbicide-resistant lentil varieties. We first discuss the potential mechanisms of herbicide resistance in plants, including target site resistance (TSR) and non-target site resistance (NTSR), and compare transgenic approaches with endogenous gene editing strategies. We then review advances in gene editing platforms, such as base editing and primer editing, and examine delivery systems for lentil. We focus specifically on recent advances in editing key genes, such as ALS ( acetolactate synthase ) and EPSPS ( 5-enolpyruvylshikimate-3-phosphate synthase ), followed by field evaluation of gene-edited lines. We also critically analyze regulatory frameworks, biosafety concerns, and public acceptance. A case study of imazethapyr-resistant lentil breeding in Canada showcases the practical applications and achievements of gene editing in lentil breeding. This study highlights the potential of combining gene editing with modern breeding tools to broaden the herbicide resistance spectrum, enhance sustainability, and ensure the sustainability of lentil production under changing agroecological conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".