Evaluation of on-farm crop management decisions on canola productivity
Bibliographic record
Abstract
Liu, C., Gan, Y. and Poppy, L. 2014. Evaluation of on-farm crop management decisions on canola productivity. Can. J. Plant Sci. 94: 131-139. This study determined key factors affecting canola productivity in western Canada and evaluated the differences among soil-climatic zones in canola crops responding to the key agronomic factors. A total of 68 canola farm fields were randomly selected in western Canada, and multiple correspondence analysis, coupled with multivariate predictive model with partial least squares projection and regressions, was used to analyze the data set. Canola produced in Alberta averaged 2500 kg ha-1, and was 23% greater than canola produced in southern Saskatchewan, 10% greater than canola produced in northern Saskatchewan, and 59% greater than canola produced in Manitoba. Canola produced on chem-fallow averaged 2557 kg ha-1, and was 17% greater than canola grown on cereal stubble, or 43% greater than canola grown on pea/lentil, corn stubble. Canola grown on canola stubble produced 54% of the seed yield as canola grown on cereal stubble, or 46% of the seed yield as canola grown on chem-fallow. Shallow and earlier seeding with narrow row spacing increased canola seed yields consistently. Canola receiving K fertilizer increased seed yield by an average of 25% compared with those receiving no K fertilizer. Straight combine resulted in 500 kg ha-1 or 24% more seed yield than conventional swath-combine method. Those key factors may serve as the first-hand information in the development of sound guidelines for less experienced canola producers in western Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".