Weed suppression and crop production in annual intercrops
Bibliographic record
Abstract
Intercrops have been associated with greater yields and pest and weed control compared with sole crops. In this field experiment, we investigated agronomic performance and weed suppression by three crops—spring wheat (Triticum aestivum), canola (Brassica napus), and field pea (Pisum sativum)—alone and in all possible combinations at two sites in Manitoba, Canada, from 2001 to 2003. Crop treatments were planted at the same total density (144 seeds m−2). The effects of the different crop combinations on weed recruitment and biomass and crop production were studied in both the presence and absence of in-crop herbicides. The agronomic performance of intercrop and sole crop treatments varied greatly across site-years. Some intercrop treatments (e.g., wheat–canola and wheat–canola–pea) tended to produce greater weed suppression compared with sole component crops, indicating synergism among crops within intercrops with regard to weed suppression. Intercrop treatments resulted in land-equivalent ratios (LER) > 1 (i.e., overyielding) in both the presence and absence of in-crop herbicides. In the presence of herbicides, canola–pea was the most consistent intercrop treatment in terms of overyielding for grain (mean LER = 1.22), whereas in the absence of herbicides, wheat–canola–pea produced the most consistent overyielding frequency for dry matter production (mean LER = 1.28). In the presence of herbicides, overall grain yield stability was greatest for the wheat–canola–pea intercrop treatment. Results indicate that annual intercrops can enhance both weed suppression and crop production compared with sole crops.
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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".