Yield and Weed Suppression of Crop Mixtures in Organic and Conventional Systems of the Western Canadian Prairie
Bibliographic record
Abstract
To investigate intercropping as a management strategy to increase crop productivity and weed suppression in organic systems, spring wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), canola (Brassica napus L.) and field pea (Pisum sativum L.) monocultures were compared with two‐, three‐, and four‐crop intercrops containing wheat at two organic and one conventional site in 2006 and 2007, central Alberta, Canada. We measured crop and weed biomass, grain yield, and crop competitiveness against weeds from a replacement design in a completely randomized block experiment. Pea and canola monocrops on organic sites yielded the least of all crop treatments. Conventional crop treatments generally yielded higher than organic treatments. Few land equivalent ratios (LERs) on organic sites were significantly >1.0. Some wheat intercrops without barley showed overyielding (LER > 1.0) potential. Most of the significant LERs were from three‐ and four‐crop intercrops. More than 50% of the intercrops on organic sites significantly suppressed weeds (based on relative weed biomass) and most of these intercrops had barley in the mixture. Barley as a sole crop and in intercrops suppressed weeds better than all other intercrops and sole crops. The wheat–canola intercrop exhibited the best weed suppression of the two‐crop intercrops on organic and conventional sites. The crop densities used in this study may have contributed to the extremely low pea and canola monocrop yields as well as low LERs. Due to this, our findings should be regarded as showing trends and potential from intercrops only. We therefore recommend further studies to establish ideal densities for the intercrops used.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".