Integrating Cultural and Mechanical Methods for Additive Weed Control in Organic Systems
Bibliographic record
Abstract
Effective weed management strategies are limited in organic cropping systems because herbicide use is prohibited. Enhancing crop competitive ability by integrating both cultural and mechanical weed control methods is a key strategy in such instances, but the relative efficacy of different cultural and mechanical strategies and their interactions and additive effects when combined is not well known. The objective of this study was to determine the individual and additive effects of cultural and mechanical methods on weed suppression and crop yield under organic conditions. A study was performed in two organically managed oat ( Avena sativa L.) cropping systems in Saskatoon, SK, Canada, in 2008 and 2009. Three cultural practices, two crop genotypes (competitive and less competitive), high and standard crop densities (500 and 250 plants m –2 ), narrow and standard row spacings (11.5 and 23 cm), and two post‐emergence tillage levels (harrowing and nonharrowed control) were factorially applied in a randomized block design. Increasing crop density and harrowing increased grain yield by 11 and 13%, respectively. Competitive genotype and high crop density reduced weed biomass by 22 and 52%, respectively. Combining high crop density with post‐emergence harrowing increased the grain yield by 25%. The combined treatment effects on weed biomass were more profound, as the competitive genotype, increased seeding rate, and post‐emergence harrowing decreased weed biomass by 71% compared with standard practices. Integrating cultural and mechanical weed management practices was superior to the use of individual practices because they additively control weeds in an organic cropping system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".