Integrated agronomy for high yield and stable flax production in Canada
Bibliographic record
Abstract
Abstract Integrating agronomic practices can be useful in increasing flax ( Linum usitatissimum L.) yield under biotic or abiotic constraints. A study was conducted to determine the combined effect of seeding density, row spacing, fertilizer, and fungicide application on no‐till flax yields at three locations (7 site‐years) in Saskatchewan and Manitoba, Canada. The four treatments were plant density; low (190 plants m –2 ) vs. moderate (320 plants m –2 ), row spacing; narrow (20 cm) vs. wide‐row (40 cm), N rate; 65 vs. 130% of soil test recommendation, and foliar fungicide; pyraclostrobin + fluxapyroxad vs. no fungicide. No individual treatment parameter significantly affected yield; however, several combinations of treatments did. The combination of moderate density, narrow row spacing, 130% N, and fungicide application showed a 23% mean yield increase across all environments compared with the lowest‐yielding combination. Still, yield ranking differed across different growing environments. The overall high‐yielding combination was not productive under low‐yielding environments. The same high‐yielding combination, but with wide row spacing and the same combination with low density provided the most stable and moderate yield across all environments tested. Considering seed cost, yield advantage, and yield stability, the best combination was low density, narrow row spacing with 130% N and fungicide application. Among all practices, the combined application of 130% N and fungicide application significantly increased crop yield by 11% under all growing conditions. In the absence of negative interactions, producers can combine these four practices to increase flax yields depending on the cost.
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 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 | 0.001 | 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) | 0.001 | 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 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".