Does yield loss due to weed competition differ between organic and conventional cropping systems?
Bibliographic record
Abstract
Summary High weed abundance in organic crops is thought to be a key factor contributing to the greater yield loss in organic as compared with conventional cropping systems. However, even with greater weed densities than conventional systems, some organic systems have yields comparable to conventional systems, suggesting that cropping systems might differ in yield loss due to weed competition. The diversity in soil nutrient resources due to diversity in crop rotations and variable inputs might enhance crop tolerance to weed competition. We assessed the long‐term effects of contrasting levels of crop rotations (low, medium and high diversity) on weed density, weed biomass and wheat yield loss in organic and no‐till conventional cropping systems using a microplot study within a long‐term cropping systems trial at Scott, Saskatchewan, Canada. Weed density and biomass were found to be four times higher in the organic systems than in the conventional systems. Under standard weed management practices, organic had 44% lower yield than the conventional system. Lower yields in organic, even without weed competition, suggest that the lower yields are due to low soil productivity rather than weed competition. No differences in yield loss were observed among the organic and conventional systems or among the diverse crop rotations. We conclude that the organic management practices and/or increased crop rotation diversity did not enhance yield or reduce yield loss due to weed competition, due to the factors associated with lower soil fertility.
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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 | 0.001 |
| 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".