Genotypic variation in metribuzin tolerance in narrow-leafed lupin (Lupinus angustifolius L.)
Bibliographic record
Abstract
Tolerance to metribuzin herbicide is an essential agronomic trait for narrow-leafed lupin (L. angustifolius L.) grown in Western Australia (WA), however, metribuzin causes up to 30% yield loss in cv. Tanjil. Tanjil is widely used as a parent in the WA lupin breeding programme to provide anthracnose resistance. Hence, identification of genotypes tolerant to metribuzin and incorporation of this tolerance into the disease-resistant cultivar is necessary for maintaining lupin production. This study identified tolerance to metribuzin among lupin cultivars and advanced breeding lines under both controlled temperature and natural winter conditions. Differences in dose responses between cultivars revealed that cv. Gungurru was tolerant and cv. Tanjil susceptible to metribuzin. Gungurru seedlings survived metribuzin applications of up to 1600 g/ha, whereas Tanjil seedlings exhibited zero survival at 800 g/ha. The rate of herbicide application that caused a 50% growth reduction (GR50, excluding dead plants) for Gungurru was 2 times greater than that for Tanjil. The level of tolerance in Gungurru is adequate to protect plants against metribuzin damage in the field. Large and consistent differences in tolerance between genotypes were identified among cultivars and advanced breeding lines across controlled temperatures (20°C during the day and 12°C at night) and in natural winter conditions. One breeding line (95L208–13–13) showed marginally better tolerance than Gungurru. A number of advanced breeding lines were as susceptible to metribuzin as Tanjil, indicating that it is very important to select for metribuzin tolerance concurrently with disease resistance in the breeding programme. Of the 6 measures of tolerance used in this study, leaf score proved to be the simplest and most effective measure and could be used for the selection of individual tolerant plants in segregating populations. Tolerance was independent of early vigour, suggesting that it is possible to combine both early vigour and tolerance into a cultivar for better weed management. In conclusion, breeding for metribuzin tolerance in lupin is feasible, and the screening method tested here was simple and consistent, which would assist a breeding programme in making rapid progress towards herbicide-tolerant plants.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".