Weed Dynamics in No-Till Rainfed Crops in Chaouia, Semi-Arid Morocco
Bibliographic record
Abstract
Three on-farm research-managed trials were conducted in Settat province, Chaouia, semi-arid Morocco, from 2012-13 to 2014-15, to investigate the dynamics of germinable soil seedbank, weed density, and community composition of weeds in 3 crop rotations: continuous durum wheat, barley + pea/durum wheat/durum wheat, and canola/durum wheat/durum wheat. Initial germinable weed seedbank density estimated in September 2012, before no-till planting in November 2012 was 1890 seeds m-2. After two growing seasons, seedbank reductions were 23% in continuous durum wheat, 68% in canola/durum wheat/durum wheat, and 72% in barley + pea/durum wheat/durum wheat. In continuous durum wheat, weed densities before no-till planting were 273, 46, and 59 plants m-2 in November 2012, December 2013, and November 2014, respectively. In herbicide-free barley + pea/durum wheat/durum wheat, weed densities before no-till planting durum wheat were 128 and 42 plants m-2 in December 2013 and November 2014, respectively. In canola/durum wheat/durum wheat, weed densities before no-till planting wheat were only 20 and 25 plants m-2 in December 2013 and November 2014, respectively. This study demonstrated the combined merits of pre-plant glyphosate, herbicide use in wheat, herbicide-free barley + pea haying, and durum wheat rotation with either canola or barley + pea to manage weeds in no-till systems in semi-arid Morocco.
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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 | 0.001 | 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) | 0.000 | 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".