Selectivity of Post-emergence Herbicides for the Chickpea
Bibliographic record
Abstract
Few herbicide options are available for controlling post-emergence weeds in the chickpea. The aim of this work therefore, was to study the selectivity of herbicides applied post-emergence for the chickpea ‘BRS Aleppo’. Two experiments were carried out, one in the greenhouse and the other in the field (winter-spring crop). A completely randomised experimental design was used for the screening experiment (greenhouse), with 15 treatments and four replications. Fourteen treatments with herbicides (g a.i. ha-1) were evaluated: bentazon (360 and 720), chlorimuron (10 and 20), clethodim (54 and 108), fluazifop (94 and 188), fomesafen (125 and 250), haloxyfop (30 and 60) and lactofen (90 and 180), in addition to the control with no application. From this experiment, herbicides that did not impair growth in the chickpea were selected for the field experiment based on plant height and shoot dry matter. During the field stage, a randomised block design was used, with 11 treatments and three replications. Ten treatments including the herbicides clethodim, fluazifop, fomesafen, haloxyfop and lactofen were evaluated in two rates, in addition to the control with no application. Based on the results of the two experiments, it was concluded that the ACCase inhibitors (clethodim, fluazifop and haloxyfop) caused no lesions or damage to the chickpea, while the latifolicides (fomesafen and lactofen) caused visual lesions which did not result in significant loss in yield. Bentazon (360 and 720 g a.i. ha-1) and chlorimuron-ethyl (10 and 20 a.i. g ha-1) were not selective, causing severe damage to the chickpea 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".