Organic and conventional management of mixtures of wheat and spring cereals
Bibliographic record
Abstract
Cereal mixtures may provide both organic and conventional producers with a more sustainable approach in reducing weed pressure, crop rotation flexibility, improved yield stability, buffering against pests and diseases, minimizing soil variability and increasing animal feed value. We examined the response of small grain mixtures containing wheat, oats, barley and triticale to varying degrees of natural competition and environmental stress at three locations in central Alberta, Canada. One modern and one heritage hard-red spring wheat cultivar, along with one cultivar each of oats, barley and triticale and eighteen two-way mixtures, were planted on organic and conventional land at seven location-years between 2003 and 2005. Average yields were 30% to 70% lower on organically managed sites. Monocrop barley yielded 43% and 16% higher than the site average at two organic locations. Our results suggest two main conclusions: (1) on conventionally managed land, wheat-barley mixtures exhibited potential for yield maintenance and weed suppression, and; (2) on organically managed land, competition with weeds had a large negative effect on yield ($>$30%). The 25:75 mixtures of wheat and oats, and all mixtures of Park (a heritage) wheat and Manny barley exhibited yield potential similar to or (up to 1.0 t ha$^{-1})$ greater than monocrop yield. Manny barley mixtures exhibited weed suppressive capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".