Impact of Diverse Annual Forage Mixtures on Weed Control in a Semiarid Environment
Bibliographic record
Abstract
Intercropping with different crop species and different spatial patterns is suggested to lead to increased competition with weeds and reduced weed abundance and biomass. In this study, our objective was to explore the ability of multi-species annual forage crop mixtures to control weeds while providing productive forage. We utilized field and greenhouse trials to evaluate the impact of different crop mixtures and row spacing on weed control in the semi-arid Brown soil region of southwestern Saskatchewan, Canada. Seven different mixtures of up to eight annual forage crops were grown with row spacing of 15 cm or 30 cm in a replicated field trial. Weed abundance and biomass was significantly affected by crop species mixtures. Crop mixtures that contained radish and barley generally had higher weed suppression. Row spacing did not significantly impact weed abundance or biomass across the treatments. Results were similar over both years in spite of drastically different precipitation conditions. Forage production was significantly different between cropping mixtures in July in both years. The barley-radish mixture had the highest crop biomass in July, and this early crop production was linked to weed suppression because crop biomass had a significant effect on weed abundance and biomass in July, but not August. A greenhouse experiment was used to further evaluate the crops (i.e., barley and radish) that demonstrated the highest weed suppressive activity in the field trial. Crop identity and row spacing were both significant factors affecting weed and crop biomass production. Radish exhibited stronger control of common lamb’s quarters (Chenopodium album) compared to barley, but the binary mixture of the two species produced the highest crop biomass and equivalent weed control compared to the radish monoculture. This research suggests that cropping with multiple species (particularly forage radish) may be an effective way to control weeds in semiarid environments.
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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) | 0.001 | 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".